repo_name
stringclasses 6
values | pr_number
int64 99
20.3k
| pr_title
stringlengths 8
158
| pr_description
stringlengths 0
6.54k
| author
stringlengths 4
18
| date_created
unknown | date_merged
unknown | previous_commit
stringlengths 40
40
| pr_commit
stringlengths 40
40
| query
stringlengths 37
6.57k
| filepath
stringlengths 8
153
| before_content
stringlengths 0
876M
| after_content
stringlengths 0
876M
| label
int64 -1
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./js/lykke.js | 'use strict';
// ---------------------------------------------------------------------------
const Exchange = require ('./base/Exchange');
// ---------------------------------------------------------------------------
module.exports = class lykke extends Exchange {
describe () {
return this.deepExtend (super.describe (), {
'id': 'lykke',
'name': 'Lykke',
'countries': [ 'CH' ],
'version': 'v1',
'rateLimit': 200,
'has': {
'CORS': false,
'fetchOHLCV': false,
'fetchOpenOrders': true,
'fetchClosedOrders': true,
'fetchOrder': true,
'fetchOrders': true,
'fetchTrades': true,
'fetchMyTrades': true,
'createOrder': true,
'cancelOrder': true,
'cancelAllOrders': true,
'fetchBalance': true,
'fetchMarkets': true,
'fetchOrderBook': true,
'fetchTicker': true,
},
'timeframes': {
'1m': 'Minute',
'5m': 'Min5',
'15m': 'Min15',
'30m': 'Min30',
'1h': 'Hour',
'4h': 'Hour4',
'6h': 'Hour6',
'12h': 'Hour12',
'1d': 'Day',
'1w': 'Week',
'1M': 'Month',
},
'requiredCredentials': {
'apiKey': true,
'secret': false,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/34487620-3139a7b0-efe6-11e7-90f5-e520cef74451.jpg',
'api': {
'mobile': 'https://public-api.lykke.com/api',
'public': 'https://hft-api.lykke.com/api',
'private': 'https://hft-api.lykke.com/api',
},
'test': {
'mobile': 'https://public-api.lykke.com/api',
'public': 'https://hft-service-dev.lykkex.net/api',
'private': 'https://hft-service-dev.lykkex.net/api',
},
'www': 'https://www.lykke.com',
'doc': [
'https://hft-api.lykke.com/swagger/ui/',
'https://www.lykke.com/lykke_api',
],
'fees': 'https://www.lykke.com/trading-conditions',
},
'api': {
'mobile': {
'get': [
'AssetPairs/rate',
'AssetPairs/rate/{assetPairId}',
'AssetPairs/dictionary/{market}',
'Assets/dictionary',
'Candles/history/{market}/available',
'Candles/history/{market}/{assetPair}/{period}/{type}/{from}/{to}',
'Company/ownershipStructure',
'Company/registrationsCount',
'IsAlive',
'Market',
'Market/{market}',
'Market/capitalization/{market}',
'OrderBook',
'OrderBook/{assetPairId}',
'Trades/{AssetPairId}',
'Trades/Last/{assetPair}/{n}',
],
'post': [
'AssetPairs/rate/history',
'AssetPairs/rate/history/{assetPairId}',
],
},
'public': {
'get': [
'AssetPairs',
'AssetPairs/{id}',
'IsAlive',
'OrderBooks',
'OrderBooks/{AssetPairId}',
],
},
'private': {
'get': [
'Orders',
'Orders/{id}',
'Wallets',
'History/trades',
],
'post': [
'Orders/limit',
'Orders/market',
'Orders/{id}/Cancel',
'Orders/v2/market',
'Orders/v2/limit',
'Orders/stoplimit',
'Orders/bulk',
],
'delete': [
'Orders',
'Orders/{id}',
],
},
},
'fees': {
'trading': {
'tierBased': false,
'percentage': true,
'maker': 0.0, // as of 7 Feb 2018, see https://github.com/ccxt/ccxt/issues/1863
'taker': 0.0, // https://www.lykke.com/cp/wallet-fees-and-limits
},
'funding': {
'tierBased': false,
'percentage': false,
'withdraw': {
'BTC': 0.001,
},
'deposit': {
'BTC': 0,
},
},
},
'commonCurrencies': {
'XPD': 'Lykke XPD',
},
});
}
parseTrade (trade, market) {
//
// public fetchTrades
//
// {
// "id": "d5983ab8-e9ec-48c9-bdd0-1b18f8e80a71",
// "assetPairId": "BTCUSD",
// "dateTime": "2019-05-15T06:52:02.147Z",
// "volume": 0.00019681,
// "index": 0,
// "price": 8023.333,
// "action": "Buy"
// }
//
// private fetchMyTrades
// {
// Id: '3500b83c-9963-4349-b3ee-b3e503073cea',
// OrderId: '83b50feb-8615-4dc6-b606-8a4168ecd708',
// DateTime: '2020-05-19T11:17:39.31+00:00',
// Timestamp: '2020-05-19T11:17:39.31+00:00',
// State: null,
// Amount: -0.004,
// BaseVolume: -0.004,
// QuotingVolume: 39.3898,
// Asset: 'BTC',
// BaseAssetId: 'BTC',
// QuotingAssetId: 'USD',
// AssetPair: 'BTCUSD',
// AssetPairId: 'BTCUSD',
// Price: 9847.427,
// Fee: { Amount: null, Type: 'Unknown', FeeAssetId: null }
// },
const marketId = this.safeString (trade, 'AssetPairId');
const symbol = this.safeSymbol (marketId, market);
const id = this.safeString2 (trade, 'id', 'Id');
const orderId = this.safeString (trade, 'OrderId');
const timestamp = this.parse8601 (this.safeString2 (trade, 'dateTime', 'DateTime'));
const price = this.safeFloat2 (trade, 'price', 'Price');
let amount = this.safeFloat2 (trade, 'volume', 'Amount');
let side = this.safeStringLower (trade, 'action');
if (side === undefined) {
if (amount < 0) {
side = 'sell';
} else {
side = 'buy';
}
}
amount = Math.abs (amount);
const cost = price * amount;
const fee = {
'cost': 0, // There are no fees for trading. https://www.lykke.com/wallet-fees-and-limits/
'currency': market['quote'],
};
return {
'id': id,
'info': trade,
'timestamp': timestamp,
'datetime': this.iso8601 (timestamp),
'symbol': symbol,
'type': undefined,
'order': orderId,
'side': side,
'takerOrMaker': undefined,
'price': price,
'amount': amount,
'cost': cost,
'fee': fee,
};
}
async fetchTrades (symbol, since = undefined, limit = undefined, params = {}) {
await this.loadMarkets ();
const market = this.market (symbol);
if (limit === undefined) {
limit = 100;
}
const request = {
'AssetPairId': market['id'],
'skip': 0,
'take': limit,
};
const response = await this.mobileGetTradesAssetPairId (this.extend (request, params));
return this.parseTrades (response, market, since, limit);
}
async fetchMyTrades (symbol = undefined, since = undefined, limit = undefined, params = {}) {
await this.loadMarkets ();
const request = {};
let market = undefined;
if (limit !== undefined) {
request['take'] = limit; // How many maximum items have to be returned, max 1000 default 100.
}
if (symbol !== undefined) {
market = this.market (symbol);
request['assetPairId'] = market['id'];
}
const response = await this.privateGetHistoryTrades (this.extend (request, params));
return this.parseTrades (response, market, since, limit);
}
async fetchBalance (params = {}) {
await this.loadMarkets ();
const response = await this.privateGetWallets (params);
const result = { 'info': response };
for (let i = 0; i < response.length; i++) {
const balance = response[i];
const currencyId = this.safeString (balance, 'AssetId');
const code = this.safeCurrencyCode (currencyId);
const account = this.account ();
account['total'] = this.safeFloat (balance, 'Balance');
account['used'] = this.safeFloat (balance, 'Reserved');
result[code] = account;
}
return this.parseBalance (result);
}
async cancelOrder (id, symbol = undefined, params = {}) {
const request = { 'id': id };
return await this.privateDeleteOrdersId (this.extend (request, params));
}
async cancelAllOrders (symbol = undefined, params = {}) {
await this.loadMarkets ();
const request = {};
let market = undefined;
if (symbol !== undefined) {
market = this.market (symbol);
request['assetPairId'] = market['id'];
}
return await this.privateDeleteOrders (this.extend (request, params));
}
async createOrder (symbol, type, side, amount, price = undefined, params = {}) {
await this.loadMarkets ();
const market = this.market (symbol);
const query = {
'AssetPairId': market['id'],
'OrderAction': this.capitalize (side),
'Volume': amount,
'Asset': market['baseId'],
};
if (type === 'limit') {
query['Price'] = price;
}
const method = 'privatePostOrdersV2' + this.capitalize (type);
const result = await this[method] (this.extend (query, params));
//
// market
//
// {
// "Price": 0
// }
//
// limit
//
// {
// "Id":"xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
// }
//
const id = this.safeString (result, 'Id');
price = this.safeFloat (result, 'Price');
return {
'id': id,
'info': result,
'clientOrderId': undefined,
'timestamp': undefined,
'datetime': undefined,
'lastTradeTimestamp': undefined,
'symbol': symbol,
'type': type,
'side': side,
'price': price,
'amount': amount,
'cost': undefined,
'average': undefined,
'filled': undefined,
'remaining': undefined,
'status': undefined,
'fee': undefined,
'trades': undefined,
};
}
async fetchMarkets (params = {}) {
const markets = await this.publicGetAssetPairs ();
//
// [ { Id: "AEBTC",
// Name: "AE/BTC",
// Accuracy: 6,
// InvertedAccuracy: 8,
// BaseAssetId: "6f75280b-a005-4016-a3d8-03dc644e8912",
// QuotingAssetId: "BTC",
// MinVolume: 0.4,
// MinInvertedVolume: 0.0001 },
// { Id: "AEETH",
// Name: "AE/ETH",
// Accuracy: 6,
// InvertedAccuracy: 8,
// BaseAssetId: "6f75280b-a005-4016-a3d8-03dc644e8912",
// QuotingAssetId: "ETH",
// MinVolume: 0.4,
// MinInvertedVolume: 0.001 } ]
//
const result = [];
for (let i = 0; i < markets.length; i++) {
const market = markets[i];
const id = this.safeString (market, 'Id');
const name = this.safeString (market, 'Name');
const [ baseId, quoteId ] = name.split ('/');
const base = this.safeCurrencyCode (baseId);
const quote = this.safeCurrencyCode (quoteId);
const symbol = base + '/' + quote;
const precision = {
'price': this.safeInteger (market, 'Accuracy'),
'amount': this.safeInteger (market, 'InvertedAccuracy'),
};
result.push ({
'id': id,
'symbol': symbol,
'base': base,
'quote': quote,
'active': true,
'info': market,
'precision': precision,
'limits': {
'amount': {
'min': Math.pow (10, -precision['amount']),
'max': Math.pow (10, precision['amount']),
},
'price': {
'min': Math.pow (10, -precision['price']),
'max': Math.pow (10, precision['price']),
},
'cost': {
'min': undefined,
'max': undefined,
},
},
'baseId': undefined,
'quoteId': undefined,
});
}
return result;
}
parseTicker (ticker, market = undefined) {
const timestamp = this.milliseconds ();
let symbol = undefined;
if (market) {
symbol = market['symbol'];
}
const close = this.safeFloat (ticker, 'lastPrice');
return {
'symbol': symbol,
'timestamp': timestamp,
'datetime': this.iso8601 (timestamp),
'high': undefined,
'low': undefined,
'bid': this.safeFloat (ticker, 'bid'),
'bidVolume': undefined,
'ask': this.safeFloat (ticker, 'ask'),
'askVolume': undefined,
'vwap': undefined,
'open': undefined,
'close': close,
'last': close,
'previousClose': undefined,
'change': undefined,
'percentage': undefined,
'average': undefined,
'baseVolume': undefined,
'quoteVolume': this.safeFloat (ticker, 'volume24H'),
'info': ticker,
};
}
async fetchTicker (symbol, params = {}) {
await this.loadMarkets ();
const market = this.market (symbol);
const request = {
'market': market['id'],
};
const ticker = await this.mobileGetMarketMarket (this.extend (request, params));
return this.parseTicker (ticker, market);
}
parseOrderStatus (status) {
const statuses = {
'Open': 'open',
'Pending': 'open',
'InOrderBook': 'open',
'Processing': 'open',
'Matched': 'closed',
'Cancelled': 'canceled',
'Rejected': 'rejected',
'Replaced': 'canceled',
'Placed': 'open',
};
return this.safeString (statuses, status, status);
}
parseOrder (order, market = undefined) {
//
// {
// "Id": "string",
// "Status": "Unknown",
// "AssetPairId": "string",
// "Volume": 0,
// "Price": 0,
// "RemainingVolume": 0,
// "LastMatchTime": "2020-03-26T20:58:50.710Z",
// "CreatedAt": "2020-03-26T20:58:50.710Z",
// "Type": "Unknown",
// "LowerLimitPrice": 0,
// "LowerPrice": 0,
// "UpperLimitPrice": 0,
// "UpperPrice": 0
// }
//
const status = this.parseOrderStatus (this.safeString (order, 'Status'));
const marketId = this.safeString (order, 'AssetPairId');
const symbol = this.safeSymbol (marketId, market);
const lastTradeTimestamp = this.parse8601 (this.safeString (order, 'LastMatchTime'));
let timestamp = undefined;
if (('Registered' in order) && (order['Registered'])) {
timestamp = this.parse8601 (order['Registered']);
} else if (('CreatedAt' in order) && (order['CreatedAt'])) {
timestamp = this.parse8601 (order['CreatedAt']);
}
const price = this.safeFloat (order, 'Price');
let side = undefined;
let amount = this.safeFloat (order, 'Volume');
if (amount < 0) {
side = 'sell';
amount = Math.abs (amount);
} else {
side = 'buy';
}
const remaining = Math.abs (this.safeFloat (order, 'RemainingVolume'));
const filled = amount - remaining;
const cost = filled * price;
const id = this.safeString (order, 'Id');
return {
'info': order,
'id': id,
'clientOrderId': undefined,
'timestamp': timestamp,
'datetime': this.iso8601 (timestamp),
'lastTradeTimestamp': lastTradeTimestamp,
'symbol': symbol,
'type': undefined,
'timeInForce': undefined,
'postOnly': undefined,
'side': side,
'price': price,
'stopPrice': undefined,
'cost': cost,
'average': undefined,
'amount': amount,
'filled': filled,
'remaining': remaining,
'status': status,
'fee': undefined,
'trades': undefined,
};
}
async fetchOrder (id, symbol = undefined, params = {}) {
await this.loadMarkets ();
const request = {
'id': id,
};
const response = await this.privateGetOrdersId (this.extend (request, params));
return this.parseOrder (response);
}
async fetchOrders (symbol = undefined, since = undefined, limit = undefined, params = {}) {
await this.loadMarkets ();
const response = await this.privateGetOrders (params);
let market = undefined;
if (symbol !== undefined) {
market = this.market (symbol);
}
return this.parseOrders (response, market, since, limit);
}
async fetchOpenOrders (symbol = undefined, since = undefined, limit = undefined, params = {}) {
const request = {
'status': 'InOrderBook',
};
return await this.fetchOrders (symbol, since, limit, this.extend (request, params));
}
async fetchClosedOrders (symbol = undefined, since = undefined, limit = undefined, params = {}) {
const request = {
'status': 'Matched',
};
return await this.fetchOrders (symbol, since, limit, this.extend (request, params));
}
async fetchOrderBook (symbol, limit = undefined, params = {}) {
await this.loadMarkets ();
const response = await this.publicGetOrderBooksAssetPairId (this.extend ({
'AssetPairId': this.marketId (symbol),
}, params));
const orderbook = {
'timestamp': undefined,
'bids': [],
'asks': [],
};
let timestamp = undefined;
for (let i = 0; i < response.length; i++) {
const side = response[i];
if (side['IsBuy']) {
orderbook['bids'] = this.arrayConcat (orderbook['bids'], side['Prices']);
} else {
orderbook['asks'] = this.arrayConcat (orderbook['asks'], side['Prices']);
}
const sideTimestamp = this.parse8601 (side['Timestamp']);
timestamp = (timestamp === undefined) ? sideTimestamp : Math.max (timestamp, sideTimestamp);
}
return this.parseOrderBook (orderbook, timestamp, 'bids', 'asks', 'Price', 'Volume');
}
parseBidAsk (bidask, priceKey = 0, amountKey = 1) {
const price = this.safeFloat (bidask, priceKey);
let amount = this.safeFloat (bidask, amountKey);
if (amount < 0) {
amount = -amount;
}
return [ price, amount ];
}
sign (path, api = 'public', method = 'GET', params = {}, headers = undefined, body = undefined) {
let url = this.urls['api'][api] + '/' + this.implodeParams (path, params);
const query = this.omit (params, this.extractParams (path));
if (api === 'mobile') {
if (Object.keys (query).length) {
url += '?' + this.urlencode (query);
}
} else if (api === 'public') {
if (Object.keys (query).length) {
url += '?' + this.urlencode (query);
}
} else if (api === 'private') {
if ((method === 'GET') || (method === 'DELETE')) {
if (Object.keys (query).length) {
url += '?' + this.urlencode (query);
}
}
this.checkRequiredCredentials ();
headers = {
'api-key': this.apiKey,
'Accept': 'application/json',
'Content-Type': 'application/json',
};
if (method === 'POST') {
if (Object.keys (params).length) {
body = this.json (params);
}
}
}
return { 'url': url, 'method': method, 'body': body, 'headers': headers };
}
};
| 'use strict';
// ---------------------------------------------------------------------------
const Exchange = require ('./base/Exchange');
// ---------------------------------------------------------------------------
module.exports = class lykke extends Exchange {
describe () {
return this.deepExtend (super.describe (), {
'id': 'lykke',
'name': 'Lykke',
'countries': [ 'CH' ],
'version': 'v1',
'rateLimit': 200,
'has': {
'CORS': false,
'fetchOHLCV': false,
'fetchOpenOrders': true,
'fetchClosedOrders': true,
'fetchOrder': true,
'fetchOrders': true,
'fetchTrades': true,
'fetchMyTrades': true,
'createOrder': true,
'cancelOrder': true,
'cancelAllOrders': true,
'fetchBalance': true,
'fetchMarkets': true,
'fetchOrderBook': true,
'fetchTicker': true,
},
'timeframes': {
'1m': 'Minute',
'5m': 'Min5',
'15m': 'Min15',
'30m': 'Min30',
'1h': 'Hour',
'4h': 'Hour4',
'6h': 'Hour6',
'12h': 'Hour12',
'1d': 'Day',
'1w': 'Week',
'1M': 'Month',
},
'requiredCredentials': {
'apiKey': true,
'secret': false,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/34487620-3139a7b0-efe6-11e7-90f5-e520cef74451.jpg',
'api': {
'mobile': 'https://public-api.lykke.com/api',
'public': 'https://hft-api.lykke.com/api',
'private': 'https://hft-api.lykke.com/api',
},
'test': {
'mobile': 'https://public-api.lykke.com/api',
'public': 'https://hft-service-dev.lykkex.net/api',
'private': 'https://hft-service-dev.lykkex.net/api',
},
'www': 'https://www.lykke.com',
'doc': [
'https://hft-api.lykke.com/swagger/ui/',
'https://www.lykke.com/lykke_api',
],
'fees': 'https://www.lykke.com/trading-conditions',
},
'api': {
'mobile': {
'get': [
'AssetPairs/rate',
'AssetPairs/rate/{assetPairId}',
'AssetPairs/dictionary/{market}',
'Assets/dictionary',
'Candles/history/{market}/available',
'Candles/history/{market}/{assetPair}/{period}/{type}/{from}/{to}',
'Company/ownershipStructure',
'Company/registrationsCount',
'IsAlive',
'Market',
'Market/{market}',
'Market/capitalization/{market}',
'OrderBook',
'OrderBook/{assetPairId}',
'Trades/{AssetPairId}',
'Trades/Last/{assetPair}/{n}',
],
'post': [
'AssetPairs/rate/history',
'AssetPairs/rate/history/{assetPairId}',
],
},
'public': {
'get': [
'AssetPairs',
'AssetPairs/{id}',
'IsAlive',
'OrderBooks',
'OrderBooks/{AssetPairId}',
],
},
'private': {
'get': [
'Orders',
'Orders/{id}',
'Wallets',
'History/trades',
],
'post': [
'Orders/limit',
'Orders/market',
'Orders/{id}/Cancel',
'Orders/v2/market',
'Orders/v2/limit',
'Orders/stoplimit',
'Orders/bulk',
],
'delete': [
'Orders',
'Orders/{id}',
],
},
},
'fees': {
'trading': {
'tierBased': false,
'percentage': true,
'maker': 0.0, // as of 7 Feb 2018, see https://github.com/ccxt/ccxt/issues/1863
'taker': 0.0, // https://www.lykke.com/cp/wallet-fees-and-limits
},
'funding': {
'tierBased': false,
'percentage': false,
'withdraw': {
'BTC': 0.001,
},
'deposit': {
'BTC': 0,
},
},
},
'commonCurrencies': {
'XPD': 'Lykke XPD',
},
});
}
parseTrade (trade, market) {
//
// public fetchTrades
//
// {
// "id": "d5983ab8-e9ec-48c9-bdd0-1b18f8e80a71",
// "assetPairId": "BTCUSD",
// "dateTime": "2019-05-15T06:52:02.147Z",
// "volume": 0.00019681,
// "index": 0,
// "price": 8023.333,
// "action": "Buy"
// }
//
// private fetchMyTrades
// {
// Id: '3500b83c-9963-4349-b3ee-b3e503073cea',
// OrderId: '83b50feb-8615-4dc6-b606-8a4168ecd708',
// DateTime: '2020-05-19T11:17:39.31+00:00',
// Timestamp: '2020-05-19T11:17:39.31+00:00',
// State: null,
// Amount: -0.004,
// BaseVolume: -0.004,
// QuotingVolume: 39.3898,
// Asset: 'BTC',
// BaseAssetId: 'BTC',
// QuotingAssetId: 'USD',
// AssetPair: 'BTCUSD',
// AssetPairId: 'BTCUSD',
// Price: 9847.427,
// Fee: { Amount: null, Type: 'Unknown', FeeAssetId: null }
// },
const marketId = this.safeString (trade, 'AssetPairId');
const symbol = this.safeSymbol (marketId, market);
const id = this.safeString2 (trade, 'id', 'Id');
const orderId = this.safeString (trade, 'OrderId');
const timestamp = this.parse8601 (this.safeString2 (trade, 'dateTime', 'DateTime'));
const price = this.safeFloat2 (trade, 'price', 'Price');
let amount = this.safeFloat2 (trade, 'volume', 'Amount');
let side = this.safeStringLower (trade, 'action');
if (side === undefined) {
if (amount < 0) {
side = 'sell';
} else {
side = 'buy';
}
}
amount = Math.abs (amount);
const cost = price * amount;
const fee = {
'cost': 0, // There are no fees for trading. https://www.lykke.com/wallet-fees-and-limits/
'currency': market['quote'],
};
return {
'id': id,
'info': trade,
'timestamp': timestamp,
'datetime': this.iso8601 (timestamp),
'symbol': symbol,
'type': undefined,
'order': orderId,
'side': side,
'takerOrMaker': undefined,
'price': price,
'amount': amount,
'cost': cost,
'fee': fee,
};
}
async fetchTrades (symbol, since = undefined, limit = undefined, params = {}) {
await this.loadMarkets ();
const market = this.market (symbol);
if (limit === undefined) {
limit = 100;
}
const request = {
'AssetPairId': market['id'],
'skip': 0,
'take': limit,
};
const response = await this.mobileGetTradesAssetPairId (this.extend (request, params));
return this.parseTrades (response, market, since, limit);
}
async fetchMyTrades (symbol = undefined, since = undefined, limit = undefined, params = {}) {
await this.loadMarkets ();
const request = {};
let market = undefined;
if (limit !== undefined) {
request['take'] = limit; // How many maximum items have to be returned, max 1000 default 100.
}
if (symbol !== undefined) {
market = this.market (symbol);
request['assetPairId'] = market['id'];
}
const response = await this.privateGetHistoryTrades (this.extend (request, params));
return this.parseTrades (response, market, since, limit);
}
async fetchBalance (params = {}) {
await this.loadMarkets ();
const response = await this.privateGetWallets (params);
const result = { 'info': response };
for (let i = 0; i < response.length; i++) {
const balance = response[i];
const currencyId = this.safeString (balance, 'AssetId');
const code = this.safeCurrencyCode (currencyId);
const account = this.account ();
account['total'] = this.safeFloat (balance, 'Balance');
account['used'] = this.safeFloat (balance, 'Reserved');
result[code] = account;
}
return this.parseBalance (result);
}
async cancelOrder (id, symbol = undefined, params = {}) {
const request = { 'id': id };
return await this.privateDeleteOrdersId (this.extend (request, params));
}
async cancelAllOrders (symbol = undefined, params = {}) {
await this.loadMarkets ();
const request = {};
let market = undefined;
if (symbol !== undefined) {
market = this.market (symbol);
request['assetPairId'] = market['id'];
}
return await this.privateDeleteOrders (this.extend (request, params));
}
async createOrder (symbol, type, side, amount, price = undefined, params = {}) {
await this.loadMarkets ();
const market = this.market (symbol);
const query = {
'AssetPairId': market['id'],
'OrderAction': this.capitalize (side),
'Volume': amount,
'Asset': market['baseId'],
};
if (type === 'limit') {
query['Price'] = price;
}
const method = 'privatePostOrdersV2' + this.capitalize (type);
const result = await this[method] (this.extend (query, params));
//
// market
//
// {
// "Price": 0
// }
//
// limit
//
// {
// "Id":"xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
// }
//
const id = this.safeString (result, 'Id');
price = this.safeFloat (result, 'Price');
return {
'id': id,
'info': result,
'clientOrderId': undefined,
'timestamp': undefined,
'datetime': undefined,
'lastTradeTimestamp': undefined,
'symbol': symbol,
'type': type,
'side': side,
'price': price,
'amount': amount,
'cost': undefined,
'average': undefined,
'filled': undefined,
'remaining': undefined,
'status': undefined,
'fee': undefined,
'trades': undefined,
};
}
async fetchMarkets (params = {}) {
const markets = await this.publicGetAssetPairs ();
//
// [ { Id: "AEBTC",
// Name: "AE/BTC",
// Accuracy: 6,
// InvertedAccuracy: 8,
// BaseAssetId: "6f75280b-a005-4016-a3d8-03dc644e8912",
// QuotingAssetId: "BTC",
// MinVolume: 0.4,
// MinInvertedVolume: 0.0001 },
// { Id: "AEETH",
// Name: "AE/ETH",
// Accuracy: 6,
// InvertedAccuracy: 8,
// BaseAssetId: "6f75280b-a005-4016-a3d8-03dc644e8912",
// QuotingAssetId: "ETH",
// MinVolume: 0.4,
// MinInvertedVolume: 0.001 } ]
//
const result = [];
for (let i = 0; i < markets.length; i++) {
const market = markets[i];
const id = this.safeString (market, 'Id');
const name = this.safeString (market, 'Name');
const [ baseId, quoteId ] = name.split ('/');
const base = this.safeCurrencyCode (baseId);
const quote = this.safeCurrencyCode (quoteId);
const symbol = base + '/' + quote;
const precision = {
'price': this.safeInteger (market, 'Accuracy'),
'amount': this.safeInteger (market, 'InvertedAccuracy'),
};
result.push ({
'id': id,
'symbol': symbol,
'base': base,
'quote': quote,
'active': true,
'info': market,
'precision': precision,
'limits': {
'amount': {
'min': Math.pow (10, -precision['amount']),
'max': Math.pow (10, precision['amount']),
},
'price': {
'min': Math.pow (10, -precision['price']),
'max': Math.pow (10, precision['price']),
},
'cost': {
'min': undefined,
'max': undefined,
},
},
'baseId': undefined,
'quoteId': undefined,
});
}
return result;
}
parseTicker (ticker, market = undefined) {
const timestamp = this.milliseconds ();
let symbol = undefined;
if (market) {
symbol = market['symbol'];
}
const close = this.safeFloat (ticker, 'lastPrice');
return {
'symbol': symbol,
'timestamp': timestamp,
'datetime': this.iso8601 (timestamp),
'high': undefined,
'low': undefined,
'bid': this.safeFloat (ticker, 'bid'),
'bidVolume': undefined,
'ask': this.safeFloat (ticker, 'ask'),
'askVolume': undefined,
'vwap': undefined,
'open': undefined,
'close': close,
'last': close,
'previousClose': undefined,
'change': undefined,
'percentage': undefined,
'average': undefined,
'baseVolume': undefined,
'quoteVolume': this.safeFloat (ticker, 'volume24H'),
'info': ticker,
};
}
async fetchTicker (symbol, params = {}) {
await this.loadMarkets ();
const market = this.market (symbol);
const request = {
'market': market['id'],
};
const ticker = await this.mobileGetMarketMarket (this.extend (request, params));
return this.parseTicker (ticker, market);
}
parseOrderStatus (status) {
const statuses = {
'Open': 'open',
'Pending': 'open',
'InOrderBook': 'open',
'Processing': 'open',
'Matched': 'closed',
'Cancelled': 'canceled',
'Rejected': 'rejected',
'Replaced': 'canceled',
'Placed': 'open',
};
return this.safeString (statuses, status, status);
}
parseOrder (order, market = undefined) {
//
// {
// "Id": "string",
// "Status": "Unknown",
// "AssetPairId": "string",
// "Volume": 0,
// "Price": 0,
// "RemainingVolume": 0,
// "LastMatchTime": "2020-03-26T20:58:50.710Z",
// "CreatedAt": "2020-03-26T20:58:50.710Z",
// "Type": "Unknown",
// "LowerLimitPrice": 0,
// "LowerPrice": 0,
// "UpperLimitPrice": 0,
// "UpperPrice": 0
// }
//
const status = this.parseOrderStatus (this.safeString (order, 'Status'));
const marketId = this.safeString (order, 'AssetPairId');
const symbol = this.safeSymbol (marketId, market);
const lastTradeTimestamp = this.parse8601 (this.safeString (order, 'LastMatchTime'));
let timestamp = undefined;
if (('Registered' in order) && (order['Registered'])) {
timestamp = this.parse8601 (order['Registered']);
} else if (('CreatedAt' in order) && (order['CreatedAt'])) {
timestamp = this.parse8601 (order['CreatedAt']);
}
const price = this.safeFloat (order, 'Price');
let side = undefined;
let amount = this.safeFloat (order, 'Volume');
if (amount < 0) {
side = 'sell';
amount = Math.abs (amount);
} else {
side = 'buy';
}
const remaining = Math.abs (this.safeFloat (order, 'RemainingVolume'));
const filled = amount - remaining;
const cost = filled * price;
const id = this.safeString (order, 'Id');
return {
'info': order,
'id': id,
'clientOrderId': undefined,
'timestamp': timestamp,
'datetime': this.iso8601 (timestamp),
'lastTradeTimestamp': lastTradeTimestamp,
'symbol': symbol,
'type': undefined,
'timeInForce': undefined,
'postOnly': undefined,
'side': side,
'price': price,
'stopPrice': undefined,
'cost': cost,
'average': undefined,
'amount': amount,
'filled': filled,
'remaining': remaining,
'status': status,
'fee': undefined,
'trades': undefined,
};
}
async fetchOrder (id, symbol = undefined, params = {}) {
await this.loadMarkets ();
const request = {
'id': id,
};
const response = await this.privateGetOrdersId (this.extend (request, params));
return this.parseOrder (response);
}
async fetchOrders (symbol = undefined, since = undefined, limit = undefined, params = {}) {
await this.loadMarkets ();
const response = await this.privateGetOrders (params);
let market = undefined;
if (symbol !== undefined) {
market = this.market (symbol);
}
return this.parseOrders (response, market, since, limit);
}
async fetchOpenOrders (symbol = undefined, since = undefined, limit = undefined, params = {}) {
const request = {
'status': 'InOrderBook',
};
return await this.fetchOrders (symbol, since, limit, this.extend (request, params));
}
async fetchClosedOrders (symbol = undefined, since = undefined, limit = undefined, params = {}) {
const request = {
'status': 'Matched',
};
return await this.fetchOrders (symbol, since, limit, this.extend (request, params));
}
async fetchOrderBook (symbol, limit = undefined, params = {}) {
await this.loadMarkets ();
const response = await this.publicGetOrderBooksAssetPairId (this.extend ({
'AssetPairId': this.marketId (symbol),
}, params));
const orderbook = {
'timestamp': undefined,
'bids': [],
'asks': [],
};
let timestamp = undefined;
for (let i = 0; i < response.length; i++) {
const side = response[i];
if (side['IsBuy']) {
orderbook['bids'] = this.arrayConcat (orderbook['bids'], side['Prices']);
} else {
orderbook['asks'] = this.arrayConcat (orderbook['asks'], side['Prices']);
}
const sideTimestamp = this.parse8601 (side['Timestamp']);
timestamp = (timestamp === undefined) ? sideTimestamp : Math.max (timestamp, sideTimestamp);
}
return this.parseOrderBook (orderbook, timestamp, 'bids', 'asks', 'Price', 'Volume');
}
parseBidAsk (bidask, priceKey = 0, amountKey = 1) {
const price = this.safeFloat (bidask, priceKey);
let amount = this.safeFloat (bidask, amountKey);
if (amount < 0) {
amount = -amount;
}
return [ price, amount ];
}
sign (path, api = 'public', method = 'GET', params = {}, headers = undefined, body = undefined) {
let url = this.urls['api'][api] + '/' + this.implodeParams (path, params);
const query = this.omit (params, this.extractParams (path));
if (api === 'mobile') {
if (Object.keys (query).length) {
url += '?' + this.urlencode (query);
}
} else if (api === 'public') {
if (Object.keys (query).length) {
url += '?' + this.urlencode (query);
}
} else if (api === 'private') {
if ((method === 'GET') || (method === 'DELETE')) {
if (Object.keys (query).length) {
url += '?' + this.urlencode (query);
}
}
this.checkRequiredCredentials ();
headers = {
'api-key': this.apiKey,
'Accept': 'application/json',
'Content-Type': 'application/json',
};
if (method === 'POST') {
if (Object.keys (params).length) {
body = this.json (params);
}
}
}
return { 'url': url, 'method': method, 'body': body, 'headers': headers };
}
};
| -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./js/static_dependencies/node-rsa/formats/pkcs1.js | var ber = require('../asn1/index').Ber;
var _ = require('../utils')._;
var utils = require('../utils');
const PRIVATE_OPENING_BOUNDARY = '-----BEGIN RSA PRIVATE KEY-----';
const PRIVATE_CLOSING_BOUNDARY = '-----END RSA PRIVATE KEY-----';
const PUBLIC_OPENING_BOUNDARY = '-----BEGIN RSA PUBLIC KEY-----';
const PUBLIC_CLOSING_BOUNDARY = '-----END RSA PUBLIC KEY-----';
module.exports = {
privateExport: function (key, options) {
options = options || {};
var n = key.n.toBuffer();
var d = key.d.toBuffer();
var p = key.p.toBuffer();
var q = key.q.toBuffer();
var dmp1 = key.dmp1.toBuffer();
var dmq1 = key.dmq1.toBuffer();
var coeff = key.coeff.toBuffer();
var length = n.length + d.length + p.length + q.length + dmp1.length + dmq1.length + coeff.length + 512; // magic
var writer = new ber.Writer({size: length});
writer.startSequence();
writer.writeInt(0);
writer.writeBuffer(n, 2);
writer.writeInt(key.e);
writer.writeBuffer(d, 2);
writer.writeBuffer(p, 2);
writer.writeBuffer(q, 2);
writer.writeBuffer(dmp1, 2);
writer.writeBuffer(dmq1, 2);
writer.writeBuffer(coeff, 2);
writer.endSequence();
if (options.type === 'der') {
return writer.buffer;
} else {
return PRIVATE_OPENING_BOUNDARY + '\n' + utils.linebrk(writer.buffer.toString('base64'), 64) + '\n' + PRIVATE_CLOSING_BOUNDARY;
}
},
privateImport: function (key, data, options) {
options = options || {};
var buffer;
if (options.type !== 'der') {
if (Buffer.isBuffer(data)) {
data = data.toString('utf8');
}
if (_.isString(data)) {
var pem = utils.trimSurroundingText(data, PRIVATE_OPENING_BOUNDARY, PRIVATE_CLOSING_BOUNDARY)
.replace(/\s+|\n\r|\n|\r$/gm, '');
buffer = Buffer.from(pem, 'base64');
} else {
throw Error('Unsupported key format');
}
} else if (Buffer.isBuffer(data)) {
buffer = data;
} else {
throw Error('Unsupported key format');
}
var reader = new ber.Reader(buffer);
reader.readSequence();
reader.readString(2, true); // just zero
key.setPrivate(
reader.readString(2, true), // modulus
reader.readString(2, true), // publicExponent
reader.readString(2, true), // privateExponent
reader.readString(2, true), // prime1
reader.readString(2, true), // prime2
reader.readString(2, true), // exponent1 -- d mod (p1)
reader.readString(2, true), // exponent2 -- d mod (q-1)
reader.readString(2, true) // coefficient -- (inverse of q) mod p
);
},
publicExport: function (key, options) {
options = options || {};
var n = key.n.toBuffer();
var length = n.length + 512; // magic
var bodyWriter = new ber.Writer({size: length});
bodyWriter.startSequence();
bodyWriter.writeBuffer(n, 2);
bodyWriter.writeInt(key.e);
bodyWriter.endSequence();
if (options.type === 'der') {
return bodyWriter.buffer;
} else {
return PUBLIC_OPENING_BOUNDARY + '\n' + utils.linebrk(bodyWriter.buffer.toString('base64'), 64) + '\n' + PUBLIC_CLOSING_BOUNDARY;
}
},
publicImport: function (key, data, options) {
options = options || {};
var buffer;
if (options.type !== 'der') {
if (Buffer.isBuffer(data)) {
data = data.toString('utf8');
}
if (_.isString(data)) {
var pem = utils.trimSurroundingText(data, PUBLIC_OPENING_BOUNDARY, PUBLIC_CLOSING_BOUNDARY)
.replace(/\s+|\n\r|\n|\r$/gm, '');
buffer = Buffer.from(pem, 'base64');
}
} else if (Buffer.isBuffer(data)) {
buffer = data;
} else {
throw Error('Unsupported key format');
}
var body = new ber.Reader(buffer);
body.readSequence();
key.setPublic(
body.readString(0x02, true), // modulus
body.readString(0x02, true) // publicExponent
);
},
/**
* Trying autodetect and import key
* @param key
* @param data
*/
autoImport: function (key, data) {
// [\S\s]* matches zero or more of any character
if (/^[\S\s]*-----BEGIN RSA PRIVATE KEY-----\s*(?=(([A-Za-z0-9+/=]+\s*)+))\1-----END RSA PRIVATE KEY-----[\S\s]*$/g.test(data)) {
module.exports.privateImport(key, data);
return true;
}
if (/^[\S\s]*-----BEGIN RSA PUBLIC KEY-----\s*(?=(([A-Za-z0-9+/=]+\s*)+))\1-----END RSA PUBLIC KEY-----[\S\s]*$/g.test(data)) {
module.exports.publicImport(key, data);
return true;
}
return false;
}
};
| var ber = require('../asn1/index').Ber;
var _ = require('../utils')._;
var utils = require('../utils');
const PRIVATE_OPENING_BOUNDARY = '-----BEGIN RSA PRIVATE KEY-----';
const PRIVATE_CLOSING_BOUNDARY = '-----END RSA PRIVATE KEY-----';
const PUBLIC_OPENING_BOUNDARY = '-----BEGIN RSA PUBLIC KEY-----';
const PUBLIC_CLOSING_BOUNDARY = '-----END RSA PUBLIC KEY-----';
module.exports = {
privateExport: function (key, options) {
options = options || {};
var n = key.n.toBuffer();
var d = key.d.toBuffer();
var p = key.p.toBuffer();
var q = key.q.toBuffer();
var dmp1 = key.dmp1.toBuffer();
var dmq1 = key.dmq1.toBuffer();
var coeff = key.coeff.toBuffer();
var length = n.length + d.length + p.length + q.length + dmp1.length + dmq1.length + coeff.length + 512; // magic
var writer = new ber.Writer({size: length});
writer.startSequence();
writer.writeInt(0);
writer.writeBuffer(n, 2);
writer.writeInt(key.e);
writer.writeBuffer(d, 2);
writer.writeBuffer(p, 2);
writer.writeBuffer(q, 2);
writer.writeBuffer(dmp1, 2);
writer.writeBuffer(dmq1, 2);
writer.writeBuffer(coeff, 2);
writer.endSequence();
if (options.type === 'der') {
return writer.buffer;
} else {
return PRIVATE_OPENING_BOUNDARY + '\n' + utils.linebrk(writer.buffer.toString('base64'), 64) + '\n' + PRIVATE_CLOSING_BOUNDARY;
}
},
privateImport: function (key, data, options) {
options = options || {};
var buffer;
if (options.type !== 'der') {
if (Buffer.isBuffer(data)) {
data = data.toString('utf8');
}
if (_.isString(data)) {
var pem = utils.trimSurroundingText(data, PRIVATE_OPENING_BOUNDARY, PRIVATE_CLOSING_BOUNDARY)
.replace(/\s+|\n\r|\n|\r$/gm, '');
buffer = Buffer.from(pem, 'base64');
} else {
throw Error('Unsupported key format');
}
} else if (Buffer.isBuffer(data)) {
buffer = data;
} else {
throw Error('Unsupported key format');
}
var reader = new ber.Reader(buffer);
reader.readSequence();
reader.readString(2, true); // just zero
key.setPrivate(
reader.readString(2, true), // modulus
reader.readString(2, true), // publicExponent
reader.readString(2, true), // privateExponent
reader.readString(2, true), // prime1
reader.readString(2, true), // prime2
reader.readString(2, true), // exponent1 -- d mod (p1)
reader.readString(2, true), // exponent2 -- d mod (q-1)
reader.readString(2, true) // coefficient -- (inverse of q) mod p
);
},
publicExport: function (key, options) {
options = options || {};
var n = key.n.toBuffer();
var length = n.length + 512; // magic
var bodyWriter = new ber.Writer({size: length});
bodyWriter.startSequence();
bodyWriter.writeBuffer(n, 2);
bodyWriter.writeInt(key.e);
bodyWriter.endSequence();
if (options.type === 'der') {
return bodyWriter.buffer;
} else {
return PUBLIC_OPENING_BOUNDARY + '\n' + utils.linebrk(bodyWriter.buffer.toString('base64'), 64) + '\n' + PUBLIC_CLOSING_BOUNDARY;
}
},
publicImport: function (key, data, options) {
options = options || {};
var buffer;
if (options.type !== 'der') {
if (Buffer.isBuffer(data)) {
data = data.toString('utf8');
}
if (_.isString(data)) {
var pem = utils.trimSurroundingText(data, PUBLIC_OPENING_BOUNDARY, PUBLIC_CLOSING_BOUNDARY)
.replace(/\s+|\n\r|\n|\r$/gm, '');
buffer = Buffer.from(pem, 'base64');
}
} else if (Buffer.isBuffer(data)) {
buffer = data;
} else {
throw Error('Unsupported key format');
}
var body = new ber.Reader(buffer);
body.readSequence();
key.setPublic(
body.readString(0x02, true), // modulus
body.readString(0x02, true) // publicExponent
);
},
/**
* Trying autodetect and import key
* @param key
* @param data
*/
autoImport: function (key, data) {
// [\S\s]* matches zero or more of any character
if (/^[\S\s]*-----BEGIN RSA PRIVATE KEY-----\s*(?=(([A-Za-z0-9+/=]+\s*)+))\1-----END RSA PRIVATE KEY-----[\S\s]*$/g.test(data)) {
module.exports.privateImport(key, data);
return true;
}
if (/^[\S\s]*-----BEGIN RSA PUBLIC KEY-----\s*(?=(([A-Za-z0-9+/=]+\s*)+))\1-----END RSA PUBLIC KEY-----[\S\s]*$/g.test(data)) {
module.exports.publicImport(key, data);
return true;
}
return false;
}
};
| -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./php/bitcoincom.php | <?php
namespace ccxt;
// PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
// https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
use Exception; // a common import
class bitcoincom extends hitbtc {
public function describe() {
return $this->deep_extend(parent::describe (), array(
'id' => 'bitcoincom',
'name' => 'bitcoin.com',
'countries' => array( 'KN' ),
'urls' => array(
'logo' => 'https://user-images.githubusercontent.com/1294454/97296144-514fa300-1861-11eb-952b-3d55d492200b.jpg',
'api' => array(
'public' => 'https://api.exchange.bitcoin.com',
'private' => 'https://api.exchange.bitcoin.com',
),
'www' => 'https://exchange.bitcoin.com',
'doc' => 'https://api.exchange.bitcoin.com/api/2/explore',
'fees' => 'https://exchange.bitcoin.com/fees-and-limits',
'referral' => 'https://exchange.bitcoin.com/referral/da948b21d6c92d69',
),
'fees' => array(
'trading' => array(
'maker' => 0.15 / 100,
'taker' => 0.2 / 100,
),
),
));
}
}
| <?php
namespace ccxt;
// PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
// https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
use Exception; // a common import
class bitcoincom extends hitbtc {
public function describe() {
return $this->deep_extend(parent::describe (), array(
'id' => 'bitcoincom',
'name' => 'bitcoin.com',
'countries' => array( 'KN' ),
'urls' => array(
'logo' => 'https://user-images.githubusercontent.com/1294454/97296144-514fa300-1861-11eb-952b-3d55d492200b.jpg',
'api' => array(
'public' => 'https://api.exchange.bitcoin.com',
'private' => 'https://api.exchange.bitcoin.com',
),
'www' => 'https://exchange.bitcoin.com',
'doc' => 'https://api.exchange.bitcoin.com/api/2/explore',
'fees' => 'https://exchange.bitcoin.com/fees-and-limits',
'referral' => 'https://exchange.bitcoin.com/referral/da948b21d6c92d69',
),
'fees' => array(
'trading' => array(
'maker' => 0.15 / 100,
'taker' => 0.2 / 100,
),
),
));
}
}
| -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./python/ccxt/vbtc.py | # -*- coding: utf-8 -*-
# PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
# https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
from ccxt.foxbit import foxbit
class vbtc(foxbit):
def describe(self):
return self.deep_extend(super(vbtc, self).describe(), {
'id': 'vbtc',
'name': 'VBTC',
'countries': ['VN'],
'has': {
'CORS': False,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/27991481-1f53d1d8-6481-11e7-884e-21d17e7939db.jpg',
'api': {
'public': 'https://api.blinktrade.com/api',
'private': 'https://api.blinktrade.com/tapi',
},
'www': 'https://vbtc.exchange',
'doc': 'https://blinktrade.com/docs',
},
'options': {
'brokerId': '3', # https://blinktrade.com/docs/#brokers
},
})
| # -*- coding: utf-8 -*-
# PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
# https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
from ccxt.foxbit import foxbit
class vbtc(foxbit):
def describe(self):
return self.deep_extend(super(vbtc, self).describe(), {
'id': 'vbtc',
'name': 'VBTC',
'countries': ['VN'],
'has': {
'CORS': False,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/27991481-1f53d1d8-6481-11e7-884e-21d17e7939db.jpg',
'api': {
'public': 'https://api.blinktrade.com/api',
'private': 'https://api.blinktrade.com/tapi',
},
'www': 'https://vbtc.exchange',
'doc': 'https://blinktrade.com/docs',
},
'options': {
'brokerId': '3', # https://blinktrade.com/docs/#brokers
},
})
| -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./examples/js/binance-fetch-all-deposits.js | "use strict";
const ccxt = require ('../../ccxt.js')
;(async function main () {
const exchange = new ccxt.binance ({
'apiKey': 'YOUR_API_KEY',
'secret': 'YOUR_SECRET',
'enableRateLimit': true,
})
await exchange.loadMarkets ()
// exchange.verbose = true // uncomment for debugging
const ninetyDays = 90 * 24 * 60 * 60 * 1000;
let startTime = exchange.parse8601 ('2018-01-01T00:00:00')
const now = exchange.milliseconds ()
const currencyCode = undefined // any currency
let allTransactions = []
while (startTime < now) {
const endTime = startTime + ninetyDays
const transactions = await exchange.fetchDeposits (currencyCode, startTime, undefined, {
'endTime': endTime,
})
if (transactions.length) {
const lastTransaction = transactions[transactions.length - 1]
startTime = lastTransaction['timestamp'] + 1
allTransactions = allTransactions.concat (transactions)
} else {
startTime = endTime;
}
}
console.log ('Fetched', allTransactions.length, 'transactions')
for (let i = 0; i < allTransactions.length; i++) {
const transaction = allTransactions[i]
console.log (i, transaction['datetime'], transaction['txid'], transaction['currency'], transaction['amount'])
}
}) () | "use strict";
const ccxt = require ('../../ccxt.js')
;(async function main () {
const exchange = new ccxt.binance ({
'apiKey': 'YOUR_API_KEY',
'secret': 'YOUR_SECRET',
'enableRateLimit': true,
})
await exchange.loadMarkets ()
// exchange.verbose = true // uncomment for debugging
const ninetyDays = 90 * 24 * 60 * 60 * 1000;
let startTime = exchange.parse8601 ('2018-01-01T00:00:00')
const now = exchange.milliseconds ()
const currencyCode = undefined // any currency
let allTransactions = []
while (startTime < now) {
const endTime = startTime + ninetyDays
const transactions = await exchange.fetchDeposits (currencyCode, startTime, undefined, {
'endTime': endTime,
})
if (transactions.length) {
const lastTransaction = transactions[transactions.length - 1]
startTime = lastTransaction['timestamp'] + 1
allTransactions = allTransactions.concat (transactions)
} else {
startTime = endTime;
}
}
console.log ('Fetched', allTransactions.length, 'transactions')
for (let i = 0; i < allTransactions.length; i++) {
const transaction = allTransactions[i]
console.log (i, transaction['datetime'], transaction['txid'], transaction['currency'], transaction['amount'])
}
}) () | -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./doc/.gitignore | _build
| _build
| -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./python/ccxt/async_support/bcex.py | # -*- coding: utf-8 -*-
# PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
# https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
from ccxt.async_support.base.exchange import Exchange
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import AuthenticationError
from ccxt.base.errors import ArgumentsRequired
from ccxt.base.errors import InsufficientFunds
from ccxt.base.errors import InvalidOrder
from ccxt.base.errors import OrderNotFound
class bcex(Exchange):
def describe(self):
return self.deep_extend(super(bcex, self).describe(), {
'id': 'bcex',
'name': 'BCEX',
'countries': ['CN', 'HK'],
'version': '1',
'has': {
'cancelOrder': True,
'createOrder': True,
'fetchBalance': True,
'fetchClosedOrders': 'emulated',
'fetchMarkets': True,
'fetchMyTrades': True,
'fetchOpenOrders': True,
'fetchOrder': True,
'fetchOrders': True,
'fetchOrderBook': True,
'fetchTicker': True,
'fetchTickers': False,
'fetchTrades': True,
'fetchTradingLimits': True,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/51840849/77231516-851c6900-6bac-11ea-8fd6-ee5c23eddbd4.jpg',
'api': 'https://www.bcex.top',
'www': 'https://www.bcex.top',
'doc': 'https://github.com/BCEX-TECHNOLOGY-LIMITED/API_Docs/wiki/Interface',
'fees': 'https://bcex.udesk.cn/hc/articles/57085',
'referral': 'https://www.bcex.top/register?invite_code=758978&lang=en',
},
'status': {
'status': 'error',
'updated': None,
'eta': None,
'url': None,
},
'api': {
'public': {
'get': [
'Api_Market/getPriceList', # tickers
'Api_Order/ticker', # last ohlcv candle(ticker)
'Api_Order/depth', # orderbook
'Api_Market/getCoinTrade', # ticker
'Api_Order/marketOrder', # trades...
],
'post': [
'Api_Market/getPriceList', # tickers
'Api_Order/ticker', # last ohlcv candle(ticker)
'Api_Order/depth', # orderbook
'Api_Market/getCoinTrade', # ticker
'Api_Order/marketOrder', # trades...
],
},
'private': {
'post': [
'Api_Order/cancel',
'Api_Order/coinTrust', # limit order
'Api_Order/orderList', # open / all orders(my trades?)
'Api_Order/orderInfo',
'Api_Order/tradeList', # open / all orders
'Api_Order/trustList', # ?
'Api_User/userBalance',
],
},
},
'fees': {
'trading': {
'tierBased': False,
'percentage': True,
'maker': 0.1 / 100,
'taker': 0.2 / 100,
},
'funding': {
'tierBased': False,
'percentage': False,
'withdraw': {
'ckusd': 0.0,
'other': 0.05 / 100,
},
'deposit': {},
},
},
'exceptions': {
'该币不存在,非法操作': ExchangeError, # {code: 1, msg: "该币不存在,非法操作"} - returned when a required symbol parameter is missing in the request(also, maybe on other types of errors as well)
'公钥不合法': AuthenticationError, # {code: 1, msg: '公钥不合法'} - wrong public key
'您的可用余额不足': InsufficientFunds, # {code: 1, msg: '您的可用余额不足'} - your available balance is insufficient
'您的btc不足': InsufficientFunds, # {code: 1, msg: '您的btc不足'} - your btc is insufficient
'参数非法': InvalidOrder, # {'code': 1, 'msg': '参数非法'} - 'Parameter illegal'
'订单信息不存在': OrderNotFound, # {'code': 1, 'msg': '订单信息不存在'} - 'Order information does not exist'
},
'commonCurrencies': {
'UNI': 'UNI COIN',
'PNT': 'Penta',
},
'options': {
'limits': {
# hardcoding is deprecated, using these predefined values is not recommended, use loadTradingLimits instead
'AFC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 6, 'max': 120000}}},
'AFC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 6, 'max': 120000}}},
'AFT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 15, 'max': 300000}}},
'AICC/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 5, 'max': 50000}}},
'AIDOC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 100000}}},
'AISI/ETH': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.001, 'max': 500}}},
'AIT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 20, 'max': 400000}}},
'ANS/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.1, 'max': 500}}},
'ANS/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.1, 'max': 1000}}},
'ARC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 60, 'max': 600000}}},
'AXF/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 100, 'max': 1000000}}},
'BASH/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 250, 'max': 3000000}}},
'BATT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 60, 'max': 1500000}}},
'BCD/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.3, 'max': 7000}}},
'BHPC/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 70000}}},
'BHPC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 60000}}},
'BOPO/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 2000000}}},
'BOPO/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 100, 'max': 10000000}}},
'BTC/CKUSD': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.001, 'max': 10}}},
'BTC/CNET': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.0005, 'max': 5}}},
'BTC/USDT': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.0002, 'max': 4}}},
'BTE/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 25, 'max': 250000}}},
'BU/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 20, 'max': 400000}}},
'CIC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 3000, 'max': 30000000}}},
'CIT/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 4, 'max': 40000}}},
'CIT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 4, 'max': 40000}}},
'CMT/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 2500000}}},
'CNET/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 12, 'max': 120000}}},
'CNMC/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 4, 'max': 50000}}},
'CTC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 5, 'max': 550000}}},
'CZR/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 12, 'max': 500000}}},
'DCON/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 8, 'max': 300000}}},
'DCT/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 40000}}},
'DCT/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 2, 'max': 2000}}},
'DOGE/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 3000, 'max': 14000000}}},
'DOGE/CKUSD': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 500, 'max': 2000000}}},
'DRCT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 16, 'max': 190000}}},
'ELA/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.02, 'max': 500}}},
'ELF/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.1, 'max': 100000}}},
'ELF/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 0.01, 'max': 100000}}},
'EOS/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.5, 'max': 5000}}},
'EOS/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 2.5, 'max': 30000}}},
'EOS/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.18, 'max': 1800}}},
'ETC/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.2, 'max': 2500}}},
'ETC/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.2, 'max': 2500}}},
'ETF/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 7, 'max': 150000}}},
'ETH/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.015, 'max': 100}}},
'ETH/CKUSD': {'precision': {'amount': 4, 'price': 4}, 'limits': {'amount': {'min': 0.005, 'max': 100}}},
'ETH/USDT': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.005, 'max': 100}}},
'FCT/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.24, 'max': 1000}}},
'FCT/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.24, 'max': 1000}}},
'GAME/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 1, 'max': 10000}}},
'GOOC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 200, 'max': 2000000}}},
'GP/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 600, 'max': 6000000}}},
'HSC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 1000, 'max': 20000000}}},
'IFISH/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 300, 'max': 8000000}}},
'IIC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 50, 'max': 4000000}}},
'IMOS/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 15, 'max': 300000}}},
'JC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 300, 'max': 3000000}}},
'LBTC/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.1, 'max': 3000}}},
'LEC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 500, 'max': 5000000}}},
'LKY/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 10, 'max': 70000}}},
'LKY/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 100000}}},
'LMC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 25, 'max': 250000}}},
'LSK/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.3, 'max': 3000}}},
'LTC/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.01, 'max': 500}}},
'LTC/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.01, 'max': 500}}},
'LTC/USDT': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.02, 'max': 450}}},
'MC/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 10000, 'max': 100000000}}},
'MCC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 30, 'max': 350000}}},
'MOC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 25, 'max': 600000}}},
'MRYC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 300, 'max': 3000000}}},
'MT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 200, 'max': 6000000}}},
'MXI/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 5000, 'max': 60000000}}},
'NAI/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 100000}}},
'NAS/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.2, 'max': 15000}}},
'NAS/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.5, 'max': 5000}}},
'NEWOS/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 65, 'max': 700000}}},
'NKN/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 3, 'max': 350000}}},
'NTK/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 30000}}},
'ONT/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 0.2, 'max': 2000}}},
'ONT/ETH': {'precision': {'amount': 3, 'price': 8}, 'limits': {'amount': {'min': 0.01, 'max': 1000}}},
'PNT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 80, 'max': 800000}}},
'PST/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 5, 'max': 100000}}},
'PTT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 450, 'max': 10000000}}},
'QTUM/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.4, 'max': 2800}}},
'QTUM/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.1, 'max': 1000}}},
'RATING/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 500, 'max': 10000000}}},
'RHC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 1000, 'max': 10000000}}},
'SDA/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 20, 'max': 500000}}},
'SDD/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 10, 'max': 100000}}},
'SHC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 250, 'max': 2500000}}},
'SHE/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 5000000}}},
'SMC/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 1000, 'max': 10000000}}},
'SOP/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 50, 'max': 1000000}}},
'TAC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 35, 'max': 800000}}},
'TIP/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 7, 'max': 200000}}},
'TKT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 40, 'max': 400000}}},
'TLC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 500, 'max': 10000000}}},
'TNC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 110000}}},
'TUB/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 200, 'max': 8000000}}},
'UC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 3000000}}},
'UDB/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 2000, 'max': 40000000}}},
'UIC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 150000}}},
'VAAC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 250000}}},
'VPN/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 200, 'max': 2000000}}},
'VSC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 30, 'max': 650000}}},
'WAVES/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 0.15, 'max': 1500}}},
'WDNA/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 250000}}},
'WIC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 3, 'max': 30000}}},
'XAS/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 2.5, 'max': 25000}}},
'XLM/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 300000}}},
'XLM/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 1, 'max': 300000}}},
'XLM/USDT': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 150000}}},
'XRP/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 24, 'max': 100000}}},
'XRP/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 5, 'max': 50000}}},
'YBCT/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 15, 'max': 200000}}},
'YBCT/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 10, 'max': 200000}}},
'YBY/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 25000, 'max': 250000000}}},
'ZEC/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.02, 'max': 100}}},
'ZEC/CKUSD': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.02, 'max': 100}}},
},
},
})
async def fetch_trading_limits(self, symbols=None, params={}):
# self method should not be called directly, use loadTradingLimits() instead
# by default it will try load withdrawal fees of all currencies(with separate requests, sequentially)
# however if you define symbols = ['ETH/BTC', 'LTC/BTC'] in args it will only load those
await self.load_markets()
if symbols is None:
symbols = self.symbols
result = {}
for i in range(0, len(symbols)):
symbol = symbols[i]
result[symbol] = await self.fetch_trading_limits_by_id(self.market_id(symbol), params)
return result
async def fetch_trading_limits_by_id(self, id, params={}):
request = {
'symbol': id,
}
response = await self.publicPostApiOrderTicker(self.extend(request, params))
#
# { code: 0,
# msg: "获取牌价信息成功",
# data: { high: 0.03721392,
# low: 0.03335362,
# buy: "0.03525757",
# sell: "0.03531160",
# last: 0.0352634,
# vol: "184742.4176",
# min_trade: "0.01500000",
# max_trade: "100.00000000",
# number_float: "4",
# price_float: "8" }}}
#
return self.parse_trading_limits(self.safe_value(response, 'data', {}))
def parse_trading_limits(self, limits, symbol=None, params={}):
#
# { high: 0.03721392,
# low: 0.03335362,
# buy: "0.03525757",
# sell: "0.03531160",
# last: 0.0352634,
# vol: "184742.4176",
# min_trade: "0.01500000",
# max_trade: "100.00000000",
# number_float: "4",
# price_float: "8" }
#
return {
'info': limits,
'precision': {
'amount': self.safe_integer(limits, 'number_float'),
'price': self.safe_integer(limits, 'price_float'),
},
'limits': {
'amount': {
'min': self.safe_float(limits, 'min_trade'),
'max': self.safe_float(limits, 'max_trade'),
},
},
}
async def fetch_markets(self, params={}):
response = await self.publicGetApiMarketGetPriceList(params)
result = []
keys = list(response.keys())
for i in range(0, len(keys)):
currentMarketId = keys[i]
currentMarkets = response[currentMarketId]
for j in range(0, len(currentMarkets)):
market = currentMarkets[j]
baseId = self.safe_string(market, 'coin_from')
quoteId = self.safe_string(market, 'coin_to')
base = baseId.upper()
quote = quoteId.upper()
base = self.safe_currency_code(base)
quote = self.safe_currency_code(quote)
id = baseId + '2' + quoteId
symbol = base + '/' + quote
active = True
defaults = self.safe_value(self.options['limits'], symbol, {})
result.append(self.extend({
'id': id,
'symbol': symbol,
'base': base,
'quote': quote,
'baseId': baseId,
'quoteId': quoteId,
'active': active,
# overrided by defaults from self.options['limits']
'precision': {
'amount': None,
'price': None,
},
# overrided by defaults from self.options['limits']
'limits': {
'amount': {'min': None, 'max': None},
'price': {'min': None, 'max': None},
'cost': {'min': None, 'max': None},
},
'info': market,
}, defaults))
return result
def parse_trade(self, trade, market=None):
symbol = None
if market is not None:
symbol = market['symbol']
timestamp = self.safe_timestamp_2(trade, 'date', 'created')
id = self.safe_string(trade, 'tid')
orderId = self.safe_string(trade, 'order_id')
amount = self.safe_float_2(trade, 'number', 'amount')
price = self.safe_float(trade, 'price')
cost = None
if price is not None:
if amount is not None:
cost = amount * price
side = self.safe_string(trade, 'side')
if side == 'sale':
side = 'sell'
return {
'info': trade,
'id': id,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'symbol': symbol,
'type': None,
'side': side,
'price': price,
'amount': amount,
'cost': cost,
'order': orderId,
'fee': None,
'takerOrMaker': None,
}
async def fetch_trades(self, symbol, since=None, limit=None, params={}):
await self.load_markets()
request = {
'symbol': self.market_id(symbol),
}
if limit is not None:
request['limit'] = limit
market = self.market(symbol)
response = await self.publicPostApiOrderMarketOrder(self.extend(request, params))
return self.parse_trades(response['data'], market, since, limit)
async def fetch_balance(self, params={}):
await self.load_markets()
response = await self.privatePostApiUserUserBalance(params)
data = self.safe_value(response, 'data')
keys = list(data.keys())
result = {}
for i in range(0, len(keys)):
key = keys[i]
amount = self.safe_float(data, key)
parts = key.split('_')
currencyId = parts[0]
lockOrOver = parts[1]
code = self.safe_currency_code(currencyId)
if not (code in result):
result[code] = self.account()
if lockOrOver == 'lock':
result[code]['used'] = float(amount)
else:
result[code]['free'] = float(amount)
keys = list(result.keys())
for i in range(0, len(keys)):
key = keys[i]
total = self.sum(result[key]['used'], result[key]['free'])
result[key]['total'] = total
result['info'] = data
return self.parse_balance(result)
async def fetch_ticker(self, symbol, params={}):
await self.load_markets()
market = self.markets[symbol]
request = {
'part': market['quoteId'],
'coin': market['baseId'],
}
response = await self.publicPostApiMarketGetCoinTrade(self.extend(request, params))
timestamp = self.milliseconds()
return {
'symbol': symbol,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'high': self.safe_float(response, 'max'),
'low': self.safe_float(response, 'min'),
'bid': self.safe_float(response, 'buy'),
'bidVolume': None,
'ask': self.safe_float(response, 'sale'),
'askVolume': None,
'vwap': None,
'open': None,
'close': self.safe_float(response, 'price'),
'last': self.safe_float(response, 'price'),
'previousClose': None,
'change': None,
'percentage': self.safe_float(response, 'change_24h'),
'average': None,
'baseVolume': self.safe_float(response, 'volume_24h'),
'quoteVolume': None,
'info': response,
}
async def fetch_order_book(self, symbol, limit=None, params={}):
await self.load_markets()
marketId = self.market_id(symbol)
request = {
'symbol': marketId,
}
response = await self.publicPostApiOrderDepth(self.extend(request, params))
data = self.safe_value(response, 'data')
timestamp = self.safe_timestamp(data, 'date')
return self.parse_order_book(data, timestamp)
async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}):
await self.load_markets()
market = self.market(symbol)
request = {
'symbol': market['id'],
}
response = await self.privatePostApiOrderOrderList(self.extend(request, params))
return self.parse_trades(response['data'], market, since, limit)
def parse_order_status(self, status):
statuses = {
'0': 'open',
'1': 'open', # partially filled
'2': 'closed',
'3': 'canceled',
}
return self.safe_string(statuses, status, status)
async def fetch_order(self, id, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOrder requires a `symbol` argument')
await self.load_markets()
request = {
'symbol': self.market_id(symbol),
'trust_id': id,
}
response = await self.privatePostApiOrderOrderInfo(self.extend(request, params))
order = self.safe_value(response, 'data')
timestamp = self.safe_timestamp(order, 'created')
status = self.parse_order_status(self.safe_string(order, 'status'))
side = self.safe_string(order, 'flag')
if side == 'sale':
side = 'sell'
# Can't use parseOrder because the data format is different btw endpoint for fetchOrder and fetchOrders
return {
'info': order,
'id': id,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'symbol': symbol,
'type': None,
'side': side,
'price': self.safe_float(order, 'price'),
'cost': None,
'average': self.safe_float(order, 'avg_price'),
'amount': self.safe_float(order, 'number'),
'filled': self.safe_float(order, 'numberdeal'),
'remaining': self.safe_float(order, 'numberover'),
'status': status,
'fee': None,
'clientOrderId': None,
'trades': None,
}
def parse_order(self, order, market=None):
id = self.safe_string(order, 'id')
timestamp = self.safe_timestamp(order, 'datetime')
symbol = None
if market is not None:
symbol = market['symbol']
type = None
side = self.safe_string(order, 'type')
if side == 'sale':
side = 'sell'
price = self.safe_float(order, 'price')
average = self.safe_float(order, 'avg_price')
amount = self.safe_float(order, 'amount')
remaining = self.safe_float(order, 'amount_outstanding')
filled = amount - remaining
status = self.parse_order_status(self.safe_string(order, 'status'))
cost = filled * price
fee = None
result = {
'info': order,
'id': id,
'clientOrderId': None,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'symbol': symbol,
'type': type,
'timeInForce': None,
'postOnly': None,
'side': side,
'price': price,
'stopPrice': None,
'cost': cost,
'average': average,
'amount': amount,
'filled': filled,
'remaining': remaining,
'status': status,
'fee': fee,
'trades': None,
}
return result
async def fetch_orders_by_type(self, type, symbol=None, since=None, limit=None, params={}):
await self.load_markets()
request = {
'type': type,
}
market = None
if symbol is not None:
market = self.market(symbol)
request['symbol'] = market['id']
response = await self.privatePostApiOrderTradeList(self.extend(request, params))
if 'data' in response:
return self.parse_orders(response['data'], market, since, limit)
return []
async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}):
return self.fetch_orders_by_type('open', symbol, since, limit, params)
async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}):
orders = await self.fetch_orders(symbol, since, limit, params)
return self.filter_by(orders, 'status', 'closed')
async def fetch_orders(self, symbol=None, since=None, limit=None, params={}):
return self.fetch_orders_by_type('all', symbol, since, limit, params)
async def create_order(self, symbol, type, side, amount, price=None, params={}):
await self.load_markets()
request = {
'symbol': self.market_id(symbol),
'type': side,
'price': self.price_to_precision(symbol, price),
'number': self.amount_to_precision(symbol, amount),
}
response = await self.privatePostApiOrderCoinTrust(self.extend(request, params))
data = self.safe_value(response, 'data', {})
id = self.safe_string(data, 'order_id')
return {
'info': response,
'id': id,
}
async def cancel_order(self, id, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' cancelOrder requires a `symbol` argument')
await self.load_markets()
request = {}
if symbol is not None:
request['symbol'] = self.market_id(symbol)
if id is not None:
request['order_id'] = id
return await self.privatePostApiOrderCancel(self.extend(request, params))
def sign(self, path, api='public', method='GET', params={}, headers=None, body=None):
url = self.urls['api'] + '/' + self.implode_params(path, params)
query = self.omit(params, self.extract_params(path))
if api == 'public':
if query:
url += '?' + self.urlencode(query)
else:
self.check_required_credentials()
payload = self.urlencode({'api_key': self.apiKey})
if query:
payload += '&' + self.urlencode(self.keysort(query))
auth = payload + '&secret_key=' + self.secret
signature = self.hash(self.encode(auth))
body = payload + '&sign=' + signature
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
return {'url': url, 'method': method, 'body': body, 'headers': headers}
def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody):
if response is None:
return # fallback to default error handler
errorCode = self.safe_value(response, 'code')
if errorCode is not None:
if errorCode != 0:
#
# {code: 1, msg: "该币不存在,非法操作"} - returned when a required symbol parameter is missing in the request(also, maybe on other types of errors as well)
# {code: 1, msg: '公钥不合法'} - wrong public key
# {code: 1, msg: '价格输入有误，请检查你的数值精度'} - 'The price input is incorrect, please check your numerical accuracy'
# {code: 1, msg: '单笔最小交易数量不能小于0.00100000,请您重新挂单'} -
# 'The minimum number of single transactions cannot be less than 0.00100000. Please re-post the order'
#
message = self.safe_string(response, 'msg')
feedback = self.id + ' ' + message
self.throw_exactly_matched_exception(self.exceptions, message, feedback)
if message.find('请您重新挂单') >= 0: # minimum limit
raise InvalidOrder(feedback)
else:
raise ExchangeError(feedback)
| # -*- coding: utf-8 -*-
# PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
# https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
from ccxt.async_support.base.exchange import Exchange
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import AuthenticationError
from ccxt.base.errors import ArgumentsRequired
from ccxt.base.errors import InsufficientFunds
from ccxt.base.errors import InvalidOrder
from ccxt.base.errors import OrderNotFound
class bcex(Exchange):
def describe(self):
return self.deep_extend(super(bcex, self).describe(), {
'id': 'bcex',
'name': 'BCEX',
'countries': ['CN', 'HK'],
'version': '1',
'has': {
'cancelOrder': True,
'createOrder': True,
'fetchBalance': True,
'fetchClosedOrders': 'emulated',
'fetchMarkets': True,
'fetchMyTrades': True,
'fetchOpenOrders': True,
'fetchOrder': True,
'fetchOrders': True,
'fetchOrderBook': True,
'fetchTicker': True,
'fetchTickers': False,
'fetchTrades': True,
'fetchTradingLimits': True,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/51840849/77231516-851c6900-6bac-11ea-8fd6-ee5c23eddbd4.jpg',
'api': 'https://www.bcex.top',
'www': 'https://www.bcex.top',
'doc': 'https://github.com/BCEX-TECHNOLOGY-LIMITED/API_Docs/wiki/Interface',
'fees': 'https://bcex.udesk.cn/hc/articles/57085',
'referral': 'https://www.bcex.top/register?invite_code=758978&lang=en',
},
'status': {
'status': 'error',
'updated': None,
'eta': None,
'url': None,
},
'api': {
'public': {
'get': [
'Api_Market/getPriceList', # tickers
'Api_Order/ticker', # last ohlcv candle(ticker)
'Api_Order/depth', # orderbook
'Api_Market/getCoinTrade', # ticker
'Api_Order/marketOrder', # trades...
],
'post': [
'Api_Market/getPriceList', # tickers
'Api_Order/ticker', # last ohlcv candle(ticker)
'Api_Order/depth', # orderbook
'Api_Market/getCoinTrade', # ticker
'Api_Order/marketOrder', # trades...
],
},
'private': {
'post': [
'Api_Order/cancel',
'Api_Order/coinTrust', # limit order
'Api_Order/orderList', # open / all orders(my trades?)
'Api_Order/orderInfo',
'Api_Order/tradeList', # open / all orders
'Api_Order/trustList', # ?
'Api_User/userBalance',
],
},
},
'fees': {
'trading': {
'tierBased': False,
'percentage': True,
'maker': 0.1 / 100,
'taker': 0.2 / 100,
},
'funding': {
'tierBased': False,
'percentage': False,
'withdraw': {
'ckusd': 0.0,
'other': 0.05 / 100,
},
'deposit': {},
},
},
'exceptions': {
'该币不存在,非法操作': ExchangeError, # {code: 1, msg: "该币不存在,非法操作"} - returned when a required symbol parameter is missing in the request(also, maybe on other types of errors as well)
'公钥不合法': AuthenticationError, # {code: 1, msg: '公钥不合法'} - wrong public key
'您的可用余额不足': InsufficientFunds, # {code: 1, msg: '您的可用余额不足'} - your available balance is insufficient
'您的btc不足': InsufficientFunds, # {code: 1, msg: '您的btc不足'} - your btc is insufficient
'参数非法': InvalidOrder, # {'code': 1, 'msg': '参数非法'} - 'Parameter illegal'
'订单信息不存在': OrderNotFound, # {'code': 1, 'msg': '订单信息不存在'} - 'Order information does not exist'
},
'commonCurrencies': {
'UNI': 'UNI COIN',
'PNT': 'Penta',
},
'options': {
'limits': {
# hardcoding is deprecated, using these predefined values is not recommended, use loadTradingLimits instead
'AFC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 6, 'max': 120000}}},
'AFC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 6, 'max': 120000}}},
'AFT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 15, 'max': 300000}}},
'AICC/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 5, 'max': 50000}}},
'AIDOC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 100000}}},
'AISI/ETH': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.001, 'max': 500}}},
'AIT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 20, 'max': 400000}}},
'ANS/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.1, 'max': 500}}},
'ANS/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.1, 'max': 1000}}},
'ARC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 60, 'max': 600000}}},
'AXF/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 100, 'max': 1000000}}},
'BASH/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 250, 'max': 3000000}}},
'BATT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 60, 'max': 1500000}}},
'BCD/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.3, 'max': 7000}}},
'BHPC/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 70000}}},
'BHPC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 60000}}},
'BOPO/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 2000000}}},
'BOPO/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 100, 'max': 10000000}}},
'BTC/CKUSD': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.001, 'max': 10}}},
'BTC/CNET': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.0005, 'max': 5}}},
'BTC/USDT': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.0002, 'max': 4}}},
'BTE/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 25, 'max': 250000}}},
'BU/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 20, 'max': 400000}}},
'CIC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 3000, 'max': 30000000}}},
'CIT/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 4, 'max': 40000}}},
'CIT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 4, 'max': 40000}}},
'CMT/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 2500000}}},
'CNET/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 12, 'max': 120000}}},
'CNMC/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 4, 'max': 50000}}},
'CTC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 5, 'max': 550000}}},
'CZR/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 12, 'max': 500000}}},
'DCON/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 8, 'max': 300000}}},
'DCT/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 40000}}},
'DCT/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 2, 'max': 2000}}},
'DOGE/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 3000, 'max': 14000000}}},
'DOGE/CKUSD': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 500, 'max': 2000000}}},
'DRCT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 16, 'max': 190000}}},
'ELA/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.02, 'max': 500}}},
'ELF/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.1, 'max': 100000}}},
'ELF/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 0.01, 'max': 100000}}},
'EOS/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.5, 'max': 5000}}},
'EOS/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 2.5, 'max': 30000}}},
'EOS/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.18, 'max': 1800}}},
'ETC/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.2, 'max': 2500}}},
'ETC/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.2, 'max': 2500}}},
'ETF/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 7, 'max': 150000}}},
'ETH/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.015, 'max': 100}}},
'ETH/CKUSD': {'precision': {'amount': 4, 'price': 4}, 'limits': {'amount': {'min': 0.005, 'max': 100}}},
'ETH/USDT': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.005, 'max': 100}}},
'FCT/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.24, 'max': 1000}}},
'FCT/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.24, 'max': 1000}}},
'GAME/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 1, 'max': 10000}}},
'GOOC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 200, 'max': 2000000}}},
'GP/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 600, 'max': 6000000}}},
'HSC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 1000, 'max': 20000000}}},
'IFISH/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 300, 'max': 8000000}}},
'IIC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 50, 'max': 4000000}}},
'IMOS/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 15, 'max': 300000}}},
'JC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 300, 'max': 3000000}}},
'LBTC/BTC': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 0.1, 'max': 3000}}},
'LEC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 500, 'max': 5000000}}},
'LKY/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 10, 'max': 70000}}},
'LKY/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 100000}}},
'LMC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 25, 'max': 250000}}},
'LSK/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.3, 'max': 3000}}},
'LTC/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.01, 'max': 500}}},
'LTC/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.01, 'max': 500}}},
'LTC/USDT': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.02, 'max': 450}}},
'MC/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 10000, 'max': 100000000}}},
'MCC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 30, 'max': 350000}}},
'MOC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 25, 'max': 600000}}},
'MRYC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 300, 'max': 3000000}}},
'MT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 200, 'max': 6000000}}},
'MXI/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 5000, 'max': 60000000}}},
'NAI/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 100000}}},
'NAS/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.2, 'max': 15000}}},
'NAS/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.5, 'max': 5000}}},
'NEWOS/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 65, 'max': 700000}}},
'NKN/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 3, 'max': 350000}}},
'NTK/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 2, 'max': 30000}}},
'ONT/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 0.2, 'max': 2000}}},
'ONT/ETH': {'precision': {'amount': 3, 'price': 8}, 'limits': {'amount': {'min': 0.01, 'max': 1000}}},
'PNT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 80, 'max': 800000}}},
'PST/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 5, 'max': 100000}}},
'PTT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 450, 'max': 10000000}}},
'QTUM/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.4, 'max': 2800}}},
'QTUM/CKUSD': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 0.1, 'max': 1000}}},
'RATING/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 500, 'max': 10000000}}},
'RHC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 1000, 'max': 10000000}}},
'SDA/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 20, 'max': 500000}}},
'SDD/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 10, 'max': 100000}}},
'SHC/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 250, 'max': 2500000}}},
'SHE/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 5000000}}},
'SMC/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 1000, 'max': 10000000}}},
'SOP/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 50, 'max': 1000000}}},
'TAC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 35, 'max': 800000}}},
'TIP/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 7, 'max': 200000}}},
'TKT/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 40, 'max': 400000}}},
'TLC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 500, 'max': 10000000}}},
'TNC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 110000}}},
'TUB/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 200, 'max': 8000000}}},
'UC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 3000000}}},
'UDB/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 2000, 'max': 40000000}}},
'UIC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 150000}}},
'VAAC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 250000}}},
'VPN/CNET': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 200, 'max': 2000000}}},
'VSC/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 30, 'max': 650000}}},
'WAVES/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 0.15, 'max': 1500}}},
'WDNA/ETH': {'precision': {'amount': 2, 'price': 8}, 'limits': {'amount': {'min': 100, 'max': 250000}}},
'WIC/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 3, 'max': 30000}}},
'XAS/CNET': {'precision': {'amount': 2, 'price': 2}, 'limits': {'amount': {'min': 2.5, 'max': 25000}}},
'XLM/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 10, 'max': 300000}}},
'XLM/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 1, 'max': 300000}}},
'XLM/USDT': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 5, 'max': 150000}}},
'XRP/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 24, 'max': 100000}}},
'XRP/CKUSD': {'precision': {'amount': 2, 'price': 3}, 'limits': {'amount': {'min': 5, 'max': 50000}}},
'YBCT/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 15, 'max': 200000}}},
'YBCT/CKUSD': {'precision': {'amount': 2, 'price': 4}, 'limits': {'amount': {'min': 10, 'max': 200000}}},
'YBY/CNET': {'precision': {'amount': 2, 'price': 6}, 'limits': {'amount': {'min': 25000, 'max': 250000000}}},
'ZEC/BTC': {'precision': {'amount': 4, 'price': 8}, 'limits': {'amount': {'min': 0.02, 'max': 100}}},
'ZEC/CKUSD': {'precision': {'amount': 4, 'price': 2}, 'limits': {'amount': {'min': 0.02, 'max': 100}}},
},
},
})
async def fetch_trading_limits(self, symbols=None, params={}):
# self method should not be called directly, use loadTradingLimits() instead
# by default it will try load withdrawal fees of all currencies(with separate requests, sequentially)
# however if you define symbols = ['ETH/BTC', 'LTC/BTC'] in args it will only load those
await self.load_markets()
if symbols is None:
symbols = self.symbols
result = {}
for i in range(0, len(symbols)):
symbol = symbols[i]
result[symbol] = await self.fetch_trading_limits_by_id(self.market_id(symbol), params)
return result
async def fetch_trading_limits_by_id(self, id, params={}):
request = {
'symbol': id,
}
response = await self.publicPostApiOrderTicker(self.extend(request, params))
#
# { code: 0,
# msg: "获取牌价信息成功",
# data: { high: 0.03721392,
# low: 0.03335362,
# buy: "0.03525757",
# sell: "0.03531160",
# last: 0.0352634,
# vol: "184742.4176",
# min_trade: "0.01500000",
# max_trade: "100.00000000",
# number_float: "4",
# price_float: "8" }}}
#
return self.parse_trading_limits(self.safe_value(response, 'data', {}))
def parse_trading_limits(self, limits, symbol=None, params={}):
#
# { high: 0.03721392,
# low: 0.03335362,
# buy: "0.03525757",
# sell: "0.03531160",
# last: 0.0352634,
# vol: "184742.4176",
# min_trade: "0.01500000",
# max_trade: "100.00000000",
# number_float: "4",
# price_float: "8" }
#
return {
'info': limits,
'precision': {
'amount': self.safe_integer(limits, 'number_float'),
'price': self.safe_integer(limits, 'price_float'),
},
'limits': {
'amount': {
'min': self.safe_float(limits, 'min_trade'),
'max': self.safe_float(limits, 'max_trade'),
},
},
}
async def fetch_markets(self, params={}):
response = await self.publicGetApiMarketGetPriceList(params)
result = []
keys = list(response.keys())
for i in range(0, len(keys)):
currentMarketId = keys[i]
currentMarkets = response[currentMarketId]
for j in range(0, len(currentMarkets)):
market = currentMarkets[j]
baseId = self.safe_string(market, 'coin_from')
quoteId = self.safe_string(market, 'coin_to')
base = baseId.upper()
quote = quoteId.upper()
base = self.safe_currency_code(base)
quote = self.safe_currency_code(quote)
id = baseId + '2' + quoteId
symbol = base + '/' + quote
active = True
defaults = self.safe_value(self.options['limits'], symbol, {})
result.append(self.extend({
'id': id,
'symbol': symbol,
'base': base,
'quote': quote,
'baseId': baseId,
'quoteId': quoteId,
'active': active,
# overrided by defaults from self.options['limits']
'precision': {
'amount': None,
'price': None,
},
# overrided by defaults from self.options['limits']
'limits': {
'amount': {'min': None, 'max': None},
'price': {'min': None, 'max': None},
'cost': {'min': None, 'max': None},
},
'info': market,
}, defaults))
return result
def parse_trade(self, trade, market=None):
symbol = None
if market is not None:
symbol = market['symbol']
timestamp = self.safe_timestamp_2(trade, 'date', 'created')
id = self.safe_string(trade, 'tid')
orderId = self.safe_string(trade, 'order_id')
amount = self.safe_float_2(trade, 'number', 'amount')
price = self.safe_float(trade, 'price')
cost = None
if price is not None:
if amount is not None:
cost = amount * price
side = self.safe_string(trade, 'side')
if side == 'sale':
side = 'sell'
return {
'info': trade,
'id': id,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'symbol': symbol,
'type': None,
'side': side,
'price': price,
'amount': amount,
'cost': cost,
'order': orderId,
'fee': None,
'takerOrMaker': None,
}
async def fetch_trades(self, symbol, since=None, limit=None, params={}):
await self.load_markets()
request = {
'symbol': self.market_id(symbol),
}
if limit is not None:
request['limit'] = limit
market = self.market(symbol)
response = await self.publicPostApiOrderMarketOrder(self.extend(request, params))
return self.parse_trades(response['data'], market, since, limit)
async def fetch_balance(self, params={}):
await self.load_markets()
response = await self.privatePostApiUserUserBalance(params)
data = self.safe_value(response, 'data')
keys = list(data.keys())
result = {}
for i in range(0, len(keys)):
key = keys[i]
amount = self.safe_float(data, key)
parts = key.split('_')
currencyId = parts[0]
lockOrOver = parts[1]
code = self.safe_currency_code(currencyId)
if not (code in result):
result[code] = self.account()
if lockOrOver == 'lock':
result[code]['used'] = float(amount)
else:
result[code]['free'] = float(amount)
keys = list(result.keys())
for i in range(0, len(keys)):
key = keys[i]
total = self.sum(result[key]['used'], result[key]['free'])
result[key]['total'] = total
result['info'] = data
return self.parse_balance(result)
async def fetch_ticker(self, symbol, params={}):
await self.load_markets()
market = self.markets[symbol]
request = {
'part': market['quoteId'],
'coin': market['baseId'],
}
response = await self.publicPostApiMarketGetCoinTrade(self.extend(request, params))
timestamp = self.milliseconds()
return {
'symbol': symbol,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'high': self.safe_float(response, 'max'),
'low': self.safe_float(response, 'min'),
'bid': self.safe_float(response, 'buy'),
'bidVolume': None,
'ask': self.safe_float(response, 'sale'),
'askVolume': None,
'vwap': None,
'open': None,
'close': self.safe_float(response, 'price'),
'last': self.safe_float(response, 'price'),
'previousClose': None,
'change': None,
'percentage': self.safe_float(response, 'change_24h'),
'average': None,
'baseVolume': self.safe_float(response, 'volume_24h'),
'quoteVolume': None,
'info': response,
}
async def fetch_order_book(self, symbol, limit=None, params={}):
await self.load_markets()
marketId = self.market_id(symbol)
request = {
'symbol': marketId,
}
response = await self.publicPostApiOrderDepth(self.extend(request, params))
data = self.safe_value(response, 'data')
timestamp = self.safe_timestamp(data, 'date')
return self.parse_order_book(data, timestamp)
async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}):
await self.load_markets()
market = self.market(symbol)
request = {
'symbol': market['id'],
}
response = await self.privatePostApiOrderOrderList(self.extend(request, params))
return self.parse_trades(response['data'], market, since, limit)
def parse_order_status(self, status):
statuses = {
'0': 'open',
'1': 'open', # partially filled
'2': 'closed',
'3': 'canceled',
}
return self.safe_string(statuses, status, status)
async def fetch_order(self, id, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOrder requires a `symbol` argument')
await self.load_markets()
request = {
'symbol': self.market_id(symbol),
'trust_id': id,
}
response = await self.privatePostApiOrderOrderInfo(self.extend(request, params))
order = self.safe_value(response, 'data')
timestamp = self.safe_timestamp(order, 'created')
status = self.parse_order_status(self.safe_string(order, 'status'))
side = self.safe_string(order, 'flag')
if side == 'sale':
side = 'sell'
# Can't use parseOrder because the data format is different btw endpoint for fetchOrder and fetchOrders
return {
'info': order,
'id': id,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'symbol': symbol,
'type': None,
'side': side,
'price': self.safe_float(order, 'price'),
'cost': None,
'average': self.safe_float(order, 'avg_price'),
'amount': self.safe_float(order, 'number'),
'filled': self.safe_float(order, 'numberdeal'),
'remaining': self.safe_float(order, 'numberover'),
'status': status,
'fee': None,
'clientOrderId': None,
'trades': None,
}
def parse_order(self, order, market=None):
id = self.safe_string(order, 'id')
timestamp = self.safe_timestamp(order, 'datetime')
symbol = None
if market is not None:
symbol = market['symbol']
type = None
side = self.safe_string(order, 'type')
if side == 'sale':
side = 'sell'
price = self.safe_float(order, 'price')
average = self.safe_float(order, 'avg_price')
amount = self.safe_float(order, 'amount')
remaining = self.safe_float(order, 'amount_outstanding')
filled = amount - remaining
status = self.parse_order_status(self.safe_string(order, 'status'))
cost = filled * price
fee = None
result = {
'info': order,
'id': id,
'clientOrderId': None,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'symbol': symbol,
'type': type,
'timeInForce': None,
'postOnly': None,
'side': side,
'price': price,
'stopPrice': None,
'cost': cost,
'average': average,
'amount': amount,
'filled': filled,
'remaining': remaining,
'status': status,
'fee': fee,
'trades': None,
}
return result
async def fetch_orders_by_type(self, type, symbol=None, since=None, limit=None, params={}):
await self.load_markets()
request = {
'type': type,
}
market = None
if symbol is not None:
market = self.market(symbol)
request['symbol'] = market['id']
response = await self.privatePostApiOrderTradeList(self.extend(request, params))
if 'data' in response:
return self.parse_orders(response['data'], market, since, limit)
return []
async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}):
return self.fetch_orders_by_type('open', symbol, since, limit, params)
async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}):
orders = await self.fetch_orders(symbol, since, limit, params)
return self.filter_by(orders, 'status', 'closed')
async def fetch_orders(self, symbol=None, since=None, limit=None, params={}):
return self.fetch_orders_by_type('all', symbol, since, limit, params)
async def create_order(self, symbol, type, side, amount, price=None, params={}):
await self.load_markets()
request = {
'symbol': self.market_id(symbol),
'type': side,
'price': self.price_to_precision(symbol, price),
'number': self.amount_to_precision(symbol, amount),
}
response = await self.privatePostApiOrderCoinTrust(self.extend(request, params))
data = self.safe_value(response, 'data', {})
id = self.safe_string(data, 'order_id')
return {
'info': response,
'id': id,
}
async def cancel_order(self, id, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' cancelOrder requires a `symbol` argument')
await self.load_markets()
request = {}
if symbol is not None:
request['symbol'] = self.market_id(symbol)
if id is not None:
request['order_id'] = id
return await self.privatePostApiOrderCancel(self.extend(request, params))
def sign(self, path, api='public', method='GET', params={}, headers=None, body=None):
url = self.urls['api'] + '/' + self.implode_params(path, params)
query = self.omit(params, self.extract_params(path))
if api == 'public':
if query:
url += '?' + self.urlencode(query)
else:
self.check_required_credentials()
payload = self.urlencode({'api_key': self.apiKey})
if query:
payload += '&' + self.urlencode(self.keysort(query))
auth = payload + '&secret_key=' + self.secret
signature = self.hash(self.encode(auth))
body = payload + '&sign=' + signature
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
return {'url': url, 'method': method, 'body': body, 'headers': headers}
def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody):
if response is None:
return # fallback to default error handler
errorCode = self.safe_value(response, 'code')
if errorCode is not None:
if errorCode != 0:
#
# {code: 1, msg: "该币不存在,非法操作"} - returned when a required symbol parameter is missing in the request(also, maybe on other types of errors as well)
# {code: 1, msg: '公钥不合法'} - wrong public key
# {code: 1, msg: '价格输入有误，请检查你的数值精度'} - 'The price input is incorrect, please check your numerical accuracy'
# {code: 1, msg: '单笔最小交易数量不能小于0.00100000,请您重新挂单'} -
# 'The minimum number of single transactions cannot be less than 0.00100000. Please re-post the order'
#
message = self.safe_string(response, 'msg')
feedback = self.id + ' ' + message
self.throw_exactly_matched_exception(self.exceptions, message, feedback)
if message.find('请您重新挂单') >= 0: # minimum limit
raise InvalidOrder(feedback)
else:
raise ExchangeError(feedback)
| -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./examples/py/async-okex-fetch-margin-balance-with-options.py | # -*- coding: utf-8 -*-
import asyncio
import os
import sys
from pprint import pprint
root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(root + '/python')
import ccxt.async_support as ccxt # noqa: E402
async def loop(exchange):
while True:
try:
balance = await exchange.fetch_balance()
pprint(balance)
except Exception as e:
print('fetch_balance() failed')
print(e)
async def main():
exchange = ccxt.okex({
'enableRateLimit': True,
'apiKey': 'YOUR_API_KEY',
'secret': 'YOUR_SECRET',
# okex requires this: https://github.com/ccxt/ccxt/wiki/Manual#authentication
'password': 'YOUR_API_PASSWORD',
# to always default to 'margin' balance type
'options': {
'fetchBalance': 'margin',
},
})
await loop(exchange)
await exchange.close()
if __name__ == '__main__':
asyncio.get_event_loop().run_until_complete(main())
| # -*- coding: utf-8 -*-
import asyncio
import os
import sys
from pprint import pprint
root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(root + '/python')
import ccxt.async_support as ccxt # noqa: E402
async def loop(exchange):
while True:
try:
balance = await exchange.fetch_balance()
pprint(balance)
except Exception as e:
print('fetch_balance() failed')
print(e)
async def main():
exchange = ccxt.okex({
'enableRateLimit': True,
'apiKey': 'YOUR_API_KEY',
'secret': 'YOUR_SECRET',
# okex requires this: https://github.com/ccxt/ccxt/wiki/Manual#authentication
'password': 'YOUR_API_PASSWORD',
# to always default to 'margin' balance type
'options': {
'fetchBalance': 'margin',
},
})
await loop(exchange)
await exchange.close()
if __name__ == '__main__':
asyncio.get_event_loop().run_until_complete(main())
| -1 |
ccxt/ccxt | 8,318 | SBTC mapping | https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ndubel | "2021-01-21T10:35:23Z" | "2021-01-21T20:04:45Z" | 2614db0ebd43f3cf9e1222bde6cefbabb955f681 | 05f5feeaaac3d213bc1314ba47b814db9ac30852 | SBTC mapping. https://coinmarketcap.com/currencies/sbtc/markets/
https://coinmarketcap.com/currencies/super-bitcoin/
https://www.coingecko.com/en/coins/siambitcoin | ./python/ccxt/yobit.py | # -*- coding: utf-8 -*-
# PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
# https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
from ccxt.base.exchange import Exchange
# -----------------------------------------------------------------------------
try:
basestring # Python 3
except NameError:
basestring = str # Python 2
import hashlib
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import AuthenticationError
from ccxt.base.errors import ArgumentsRequired
from ccxt.base.errors import InsufficientFunds
from ccxt.base.errors import InvalidOrder
from ccxt.base.errors import OrderNotFound
from ccxt.base.errors import DDoSProtection
from ccxt.base.errors import RateLimitExceeded
from ccxt.base.errors import ExchangeNotAvailable
from ccxt.base.errors import InvalidNonce
class yobit(Exchange):
def describe(self):
return self.deep_extend(super(yobit, self).describe(), {
'id': 'yobit',
'name': 'YoBit',
'countries': ['RU'],
'rateLimit': 3000, # responses are cached every 2 seconds
'version': '3',
'has': {
'cancelOrder': True,
'CORS': False,
'createDepositAddress': True,
'createMarketOrder': False,
'createOrder': True,
'fetchBalance': True,
'fetchDepositAddress': True,
'fetchDeposits': False,
'fetchMarkets': True,
'fetchMyTrades': True,
'fetchOpenOrders': True,
'fetchOrder': True,
'fetchOrderBook': True,
'fetchOrderBooks': True,
'fetchTicker': True,
'fetchTickers': True,
'fetchTrades': True,
'fetchTransactions': False,
'fetchWithdrawals': False,
'withdraw': True,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/27766910-cdcbfdae-5eea-11e7-9859-03fea873272d.jpg',
'api': {
'public': 'https://yobit.net/api',
'private': 'https://yobit.net/tapi',
},
'www': 'https://www.yobit.net',
'doc': 'https://www.yobit.net/en/api/',
'fees': 'https://www.yobit.net/en/fees/',
},
'api': {
'public': {
'get': [
'depth/{pair}',
'info',
'ticker/{pair}',
'trades/{pair}',
],
},
'private': {
'post': [
'ActiveOrders',
'CancelOrder',
'GetDepositAddress',
'getInfo',
'OrderInfo',
'Trade',
'TradeHistory',
'WithdrawCoinsToAddress',
],
},
},
'fees': {
'trading': {
'maker': 0.002,
'taker': 0.002,
},
'funding': {
'withdraw': {},
},
},
'commonCurrencies': {
'AIR': 'AirCoin',
'ANI': 'ANICoin',
'ANT': 'AntsCoin', # what is self, a coin for ants?
'ATMCHA': 'ATM',
'ASN': 'Ascension',
'AST': 'Astral',
'ATM': 'Autumncoin',
'BCC': 'BCH',
'BCS': 'BitcoinStake',
'BITS': 'Bitstar',
'BLN': 'Bulleon',
'BOT': 'BOTcoin',
'BON': 'BONES',
'BPC': 'BitcoinPremium',
'BTS': 'Bitshares2',
'CAT': 'BitClave',
'CBC': 'CryptoBossCoin',
'CMT': 'CometCoin',
'COV': 'Coven Coin',
'COVX': 'COV',
'CPC': 'Capricoin',
'CREDIT': 'Creditbit',
'CS': 'CryptoSpots',
'DCT': 'Discount',
'DFT': 'DraftCoin',
'DGD': 'DarkGoldCoin',
'DIRT': 'DIRTY',
'DROP': 'FaucetCoin',
'DSH': 'DASH',
'EKO': 'EkoCoin',
'ENTER': 'ENTRC',
'EPC': 'ExperienceCoin',
'ESC': 'EdwardSnowden',
'EUROPE': 'EUROP',
'EXT': 'LifeExtension',
'FUNK': 'FUNKCoin',
'GCC': 'GlobalCryptocurrency',
'GEN': 'Genstake',
'GENE': 'Genesiscoin',
'GOLD': 'GoldMint',
'GOT': 'Giotto Coin',
'HTML5': 'HTML',
'HYPERX': 'HYPER',
'ICN': 'iCoin',
'INSANE': 'INSN',
'JNT': 'JointCoin',
'JPC': 'JupiterCoin',
'KNC': 'KingN Coin',
'LBTCX': 'LiteBitcoin',
'LIZI': 'LiZi',
'LOC': 'LocoCoin',
'LOCX': 'LOC',
'LUNYR': 'LUN',
'LUN': 'LunarCoin', # they just change the ticker if it is already taken
'MDT': 'Midnight',
'NAV': 'NavajoCoin',
'NBT': 'NiceBytes',
'OMG': 'OMGame',
'PAC': '$PAC',
'PLAY': 'PlayCoin',
'PIVX': 'Darknet',
'PRS': 'PRE',
'PUTIN': 'PutinCoin',
'STK': 'StakeCoin',
'SUB': 'Subscriptio',
'PAY': 'EPAY',
'PLC': 'Platin Coin',
'RCN': 'RCoin',
'REP': 'Republicoin',
'RUR': 'RUB',
'SBTC': 'Super Bitcoin',
'TTC': 'TittieCoin',
'UNI': 'Universe',
'UST': 'Uservice',
'VOL': 'VolumeCoin',
'XIN': 'XINCoin',
'XRA': 'Ratecoin',
},
'options': {
# 'fetchTickersMaxLength': 2048,
'fetchOrdersRequiresSymbol': True,
'fetchTickersMaxLength': 512,
},
'exceptions': {
'exact': {
'803': InvalidOrder, # "Count could not be less than 0.001."(selling below minAmount)
'804': InvalidOrder, # "Count could not be more than 10000."(buying above maxAmount)
'805': InvalidOrder, # "price could not be less than X."(minPrice violation on buy & sell)
'806': InvalidOrder, # "price could not be more than X."(maxPrice violation on buy & sell)
'807': InvalidOrder, # "cost could not be less than X."(minCost violation on buy & sell)
'831': InsufficientFunds, # "Not enougth X to create buy order."(buying with balance.quote < order.cost)
'832': InsufficientFunds, # "Not enougth X to create sell order."(selling with balance.base < order.amount)
'833': OrderNotFound, # "Order with id X was not found."(cancelling non-existent, closed and cancelled order)
},
'broad': {
'Invalid pair name': ExchangeError, # {"success":0,"error":"Invalid pair name: btc_eth"}
'invalid api key': AuthenticationError,
'invalid sign': AuthenticationError,
'api key dont have trade permission': AuthenticationError,
'invalid parameter': InvalidOrder,
'invalid order': InvalidOrder,
'The given order has already been cancelled': InvalidOrder,
'Requests too often': DDoSProtection,
'not available': ExchangeNotAvailable,
'data unavailable': ExchangeNotAvailable,
'external service unavailable': ExchangeNotAvailable,
'Total transaction amount': InvalidOrder, # {"success": 0, "error": "Total transaction amount is less than minimal total: 0.00010000"}
'The given order has already been closed and cannot be cancelled': InvalidOrder,
'Insufficient funds': InsufficientFunds,
'invalid key': AuthenticationError,
'invalid nonce': InvalidNonce, # {"success":0,"error":"invalid nonce(has already been used)"}'
'Total order amount is less than minimal amount': InvalidOrder,
'Rate Limited': RateLimitExceeded,
},
},
'orders': {}, # orders cache / emulation
})
def fetch_balance(self, params={}):
self.load_markets()
response = self.privatePostGetInfo(params)
#
# {
# "success":1,
# "return":{
# "funds":{
# "ltc":22,
# "nvc":423.998,
# "ppc":10,
# },
# "funds_incl_orders":{
# "ltc":32,
# "nvc":523.998,
# "ppc":20,
# },
# "rights":{
# "info":1,
# "trade":0,
# "withdraw":0
# },
# "transaction_count":0,
# "open_orders":1,
# "server_time":1418654530
# }
# }
#
balances = self.safe_value(response, 'return', {})
result = {'info': response}
free = self.safe_value(balances, 'funds', {})
total = self.safe_value(balances, 'funds_incl_orders', {})
currencyIds = list(self.extend(free, total).keys())
for i in range(0, len(currencyIds)):
currencyId = currencyIds[i]
code = self.safe_currency_code(currencyId)
account = self.account()
account['free'] = self.safe_float(free, currencyId)
account['total'] = self.safe_float(total, currencyId)
result[code] = account
return self.parse_balance(result)
def fetch_markets(self, params={}):
response = self.publicGetInfo(params)
markets = self.safe_value(response, 'pairs')
keys = list(markets.keys())
result = []
for i in range(0, len(keys)):
id = keys[i]
market = markets[id]
baseId, quoteId = id.split('_')
base = baseId.upper()
quote = quoteId.upper()
base = self.safe_currency_code(base)
quote = self.safe_currency_code(quote)
symbol = base + '/' + quote
precision = {
'amount': self.safe_integer(market, 'decimal_places'),
'price': self.safe_integer(market, 'decimal_places'),
}
amountLimits = {
'min': self.safe_float(market, 'min_amount'),
'max': self.safe_float(market, 'max_amount'),
}
priceLimits = {
'min': self.safe_float(market, 'min_price'),
'max': self.safe_float(market, 'max_price'),
}
costLimits = {
'min': self.safe_float(market, 'min_total'),
}
limits = {
'amount': amountLimits,
'price': priceLimits,
'cost': costLimits,
}
hidden = self.safe_integer(market, 'hidden')
active = (hidden == 0)
result.append({
'id': id,
'symbol': symbol,
'base': base,
'quote': quote,
'baseId': baseId,
'quoteId': quoteId,
'active': active,
'taker': market['fee'] / 100,
'precision': precision,
'limits': limits,
'info': market,
})
return result
def fetch_order_book(self, symbol, limit=None, params={}):
self.load_markets()
market = self.market(symbol)
request = {
'pair': market['id'],
}
if limit is not None:
request['limit'] = limit # default = 150, max = 2000
response = self.publicGetDepthPair(self.extend(request, params))
market_id_in_reponse = (market['id'] in response)
if not market_id_in_reponse:
raise ExchangeError(self.id + ' ' + market['symbol'] + ' order book is empty or not available')
orderbook = response[market['id']]
return self.parse_order_book(orderbook)
def fetch_order_books(self, symbols=None, limit=None, params={}):
self.load_markets()
ids = None
if symbols is None:
ids = '-'.join(self.ids)
# max URL length is 2083 symbols, including http schema, hostname, tld, etc...
if len(ids) > 2048:
numIds = len(self.ids)
raise ExchangeError(self.id + ' has ' + str(numIds) + ' symbols exceeding max URL length, you are required to specify a list of symbols in the first argument to fetchOrderBooks')
else:
ids = self.market_ids(symbols)
ids = '-'.join(ids)
request = {
'pair': ids,
# 'ignore_invalid': True,
}
if limit is not None:
request['limit'] = limit
response = self.publicGetDepthPair(self.extend(request, params))
result = {}
ids = list(response.keys())
for i in range(0, len(ids)):
id = ids[i]
symbol = self.safe_symbol(id)
result[symbol] = self.parse_order_book(response[id])
return result
def parse_ticker(self, ticker, market=None):
#
# { high: 0.03497582,
# low: 0.03248474,
# avg: 0.03373028,
# vol: 120.11485715062999,
# vol_cur: 3572.24914074,
# last: 0.0337611,
# buy: 0.0337442,
# sell: 0.03377798,
# updated: 1537522009 }
#
timestamp = self.safe_timestamp(ticker, 'updated')
symbol = None
if market is not None:
symbol = market['symbol']
last = self.safe_float(ticker, 'last')
return {
'symbol': symbol,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'high': self.safe_float(ticker, 'high'),
'low': self.safe_float(ticker, 'low'),
'bid': self.safe_float(ticker, 'buy'),
'bidVolume': None,
'ask': self.safe_float(ticker, 'sell'),
'askVolume': None,
'vwap': None,
'open': None,
'close': last,
'last': last,
'previousClose': None,
'change': None,
'percentage': None,
'average': self.safe_float(ticker, 'avg'),
'baseVolume': self.safe_float(ticker, 'vol_cur'),
'quoteVolume': self.safe_float(ticker, 'vol'),
'info': ticker,
}
def fetch_tickers(self, symbols=None, params={}):
self.load_markets()
ids = self.ids
if symbols is None:
numIds = len(ids)
ids = '-'.join(ids)
maxLength = self.safe_integer(self.options, 'fetchTickersMaxLength', 2048)
# max URL length is 2048 symbols, including http schema, hostname, tld, etc...
if len(ids) > self.options['fetchTickersMaxLength']:
raise ArgumentsRequired(self.id + ' has ' + str(numIds) + ' markets exceeding max URL length for self endpoint(' + str(maxLength) + ' characters), please, specify a list of symbols of interest in the first argument to fetchTickers')
else:
ids = self.market_ids(symbols)
ids = '-'.join(ids)
request = {
'pair': ids,
}
tickers = self.publicGetTickerPair(self.extend(request, params))
result = {}
keys = list(tickers.keys())
for k in range(0, len(keys)):
id = keys[k]
ticker = tickers[id]
market = self.safe_market(id)
symbol = market['symbol']
result[symbol] = self.parse_ticker(ticker, market)
return self.filter_by_array(result, 'symbol', symbols)
def fetch_ticker(self, symbol, params={}):
tickers = self.fetch_tickers([symbol], params)
return tickers[symbol]
def parse_trade(self, trade, market=None):
timestamp = self.safe_timestamp(trade, 'timestamp')
side = self.safe_string(trade, 'type')
if side == 'ask':
side = 'sell'
elif side == 'bid':
side = 'buy'
price = self.safe_float_2(trade, 'rate', 'price')
id = self.safe_string_2(trade, 'trade_id', 'tid')
order = self.safe_string(trade, 'order_id')
marketId = self.safe_string(trade, 'pair')
symbol = self.safe_symbol(marketId, market)
amount = self.safe_float(trade, 'amount')
type = 'limit' # all trades are still limit trades
fee = None
feeCost = self.safe_float(trade, 'commission')
if feeCost is not None:
feeCurrencyId = self.safe_string(trade, 'commissionCurrency')
feeCurrencyCode = self.safe_currency_code(feeCurrencyId)
fee = {
'cost': feeCost,
'currency': feeCurrencyCode,
}
isYourOrder = self.safe_value(trade, 'is_your_order')
if isYourOrder is not None:
if fee is None:
fee = self.calculate_fee(symbol, type, side, amount, price, 'taker')
cost = None
if amount is not None:
if price is not None:
cost = amount * price
return {
'id': id,
'order': order,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'symbol': symbol,
'type': type,
'side': side,
'takerOrMaker': None,
'price': price,
'amount': amount,
'cost': cost,
'fee': fee,
'info': trade,
}
def fetch_trades(self, symbol, since=None, limit=None, params={}):
self.load_markets()
market = self.market(symbol)
request = {
'pair': market['id'],
}
if limit is not None:
request['limit'] = limit
response = self.publicGetTradesPair(self.extend(request, params))
if isinstance(response, list):
numElements = len(response)
if numElements == 0:
return []
return self.parse_trades(response[market['id']], market, since, limit)
def create_order(self, symbol, type, side, amount, price=None, params={}):
if type == 'market':
raise ExchangeError(self.id + ' allows limit orders only')
self.load_markets()
market = self.market(symbol)
request = {
'pair': market['id'],
'type': side,
'amount': self.amount_to_precision(symbol, amount),
'rate': self.price_to_precision(symbol, price),
}
price = float(price)
amount = float(amount)
response = self.privatePostTrade(self.extend(request, params))
id = None
status = 'open'
filled = 0.0
remaining = amount
if 'return' in response:
id = self.safe_string(response['return'], 'order_id')
if id == '0':
id = self.safe_string(response['return'], 'init_order_id')
status = 'closed'
filled = self.safe_float(response['return'], 'received', 0.0)
remaining = self.safe_float(response['return'], 'remains', amount)
timestamp = self.milliseconds()
return {
'id': id,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'status': status,
'symbol': symbol,
'type': type,
'side': side,
'price': price,
'cost': price * filled,
'amount': amount,
'remaining': remaining,
'filled': filled,
'fee': None,
# 'trades': self.parse_trades(order['trades'], market),
'info': response,
'clientOrderId': None,
'average': None,
'trades': None,
}
def cancel_order(self, id, symbol=None, params={}):
self.load_markets()
request = {
'order_id': int(id),
}
return self.privatePostCancelOrder(self.extend(request, params))
def parse_order_status(self, status):
statuses = {
'0': 'open',
'1': 'closed',
'2': 'canceled',
'3': 'open', # or partially-filled and canceled? https://github.com/ccxt/ccxt/issues/1594
}
return self.safe_string(statuses, status, status)
def parse_order(self, order, market=None):
id = self.safe_string(order, 'id')
status = self.parse_order_status(self.safe_string(order, 'status'))
timestamp = self.safe_timestamp(order, 'timestamp_created')
marketId = self.safe_string(order, 'pair')
symbol = self.safe_symbol(marketId, market)
remaining = self.safe_float(order, 'amount')
amount = self.safe_float(order, 'start_amount')
price = self.safe_float(order, 'rate')
filled = None
cost = None
if amount is not None:
if remaining is not None:
filled = max(0, amount - remaining)
cost = price * filled
fee = None
type = 'limit'
side = self.safe_string(order, 'type')
result = {
'info': order,
'id': id,
'clientOrderId': None,
'symbol': symbol,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'type': type,
'timeInForce': None,
'postOnly': None,
'side': side,
'price': price,
'stopPrice': None,
'cost': cost,
'amount': amount,
'remaining': remaining,
'filled': filled,
'status': status,
'fee': fee,
'average': None,
'trades': None,
}
return result
def fetch_order(self, id, symbol=None, params={}):
self.load_markets()
request = {
'order_id': int(id),
}
response = self.privatePostOrderInfo(self.extend(request, params))
id = str(id)
orders = self.safe_value(response, 'return', {})
return self.parse_order(self.extend({'id': id}, orders[id]))
def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOpenOrders() requires a symbol argument')
self.load_markets()
request = {}
market = None
if symbol is not None:
market = self.market(symbol)
request['pair'] = market['id']
response = self.privatePostActiveOrders(self.extend(request, params))
orders = self.safe_value(response, 'return', [])
return self.parse_orders(orders, market, since, limit)
def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchMyTrades requires a `symbol` argument')
self.load_markets()
market = self.market(symbol)
# some derived classes use camelcase notation for request fields
request = {
# 'from': 123456789, # trade ID, from which the display starts numerical 0(test result: liqui ignores self field)
# 'count': 1000, # the number of trades for display numerical, default = 1000
# 'from_id': trade ID, from which the display starts numerical 0
# 'end_id': trade ID on which the display ends numerical ∞
# 'order': 'ASC', # sorting, default = DESC(test result: liqui ignores self field, most recent trade always goes last)
# 'since': 1234567890, # UTC start time, default = 0(test result: liqui ignores self field)
# 'end': 1234567890, # UTC end time, default = ∞(test result: liqui ignores self field)
'pair': market['id'],
}
if limit is not None:
request['count'] = int(limit)
if since is not None:
request['since'] = int(since / 1000)
response = self.privatePostTradeHistory(self.extend(request, params))
trades = self.safe_value(response, 'return', {})
ids = list(trades.keys())
result = []
for i in range(0, len(ids)):
id = ids[i]
trade = self.parse_trade(self.extend(trades[id], {
'trade_id': id,
}), market)
result.append(trade)
return self.filter_by_symbol_since_limit(result, symbol, since, limit)
def create_deposit_address(self, code, params={}):
request = {
'need_new': 1,
}
response = self.fetch_deposit_address(code, self.extend(request, params))
address = self.safe_string(response, 'address')
self.check_address(address)
return {
'currency': code,
'address': address,
'tag': None,
'info': response['info'],
}
def fetch_deposit_address(self, code, params={}):
self.load_markets()
currency = self.currency(code)
request = {
'coinName': currency['id'],
'need_new': 0,
}
response = self.privatePostGetDepositAddress(self.extend(request, params))
address = self.safe_string(response['return'], 'address')
self.check_address(address)
return {
'currency': code,
'address': address,
'tag': None,
'info': response,
}
def withdraw(self, code, amount, address, tag=None, params={}):
self.check_address(address)
self.load_markets()
currency = self.currency(code)
request = {
'coinName': currency['id'],
'amount': amount,
'address': address,
}
# no docs on the tag, yet...
if tag is not None:
raise ExchangeError(self.id + ' withdraw() does not support the tag argument yet due to a lack of docs on withdrawing with tag/memo on behalf of the exchange.')
response = self.privatePostWithdrawCoinsToAddress(self.extend(request, params))
return {
'info': response,
'id': None,
}
def calculate_fee(self, symbol, type, side, amount, price, takerOrMaker='taker', params={}):
market = self.markets[symbol]
key = 'quote'
rate = market[takerOrMaker]
cost = float(self.cost_to_precision(symbol, amount * rate))
if side == 'sell':
cost *= price
else:
key = 'base'
return {
'type': takerOrMaker,
'currency': market[key],
'rate': rate,
'cost': cost,
}
def sign(self, path, api='public', method='GET', params={}, headers=None, body=None):
url = self.urls['api'][api]
query = self.omit(params, self.extract_params(path))
if api == 'private':
self.check_required_credentials()
nonce = self.nonce()
body = self.urlencode(self.extend({
'nonce': nonce,
'method': path,
}, query))
signature = self.hmac(self.encode(body), self.encode(self.secret), hashlib.sha512)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
'Key': self.apiKey,
'Sign': signature,
}
elif api == 'public':
url += '/' + self.version + '/' + self.implode_params(path, params)
if query:
url += '?' + self.urlencode(query)
else:
url += '/' + self.implode_params(path, params)
if method == 'GET':
if query:
url += '?' + self.urlencode(query)
else:
if query:
body = self.json(query)
headers = {
'Content-Type': 'application/json',
}
return {'url': url, 'method': method, 'body': body, 'headers': headers}
def handle_errors(self, httpCode, reason, url, method, headers, body, response, requestHeaders, requestBody):
if response is None:
return # fallback to default error handler
if 'success' in response:
#
# 1 - Liqui only returns the integer 'success' key from their private API
#
# {"success": 1, ...} httpCode == 200
# {"success": 0, ...} httpCode == 200
#
# 2 - However, exchanges derived from Liqui, can return non-integers
#
# It can be a numeric string
# {"sucesss": "1", ...}
# {"sucesss": "0", ...}, httpCode >= 200(can be 403, 502, etc)
#
# Or just a string
# {"success": "true", ...}
# {"success": "false", ...}, httpCode >= 200
#
# Or a boolean
# {"success": True, ...}
# {"success": False, ...}, httpCode >= 200
#
# 3 - Oversimplified, Python PEP8 forbids comparison operator(==) of different types
#
# 4 - We do not want to copy-paste and duplicate the code of self handler to other exchanges derived from Liqui
#
# To cover points 1, 2, 3 and 4 combined self handler should work like self:
#
success = self.safe_value(response, 'success', False)
if isinstance(success, basestring):
if (success == 'true') or (success == '1'):
success = True
else:
success = False
if not success:
code = self.safe_string(response, 'code')
message = self.safe_string(response, 'error')
feedback = self.id + ' ' + body
self.throw_exactly_matched_exception(self.exceptions['exact'], code, feedback)
self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback)
self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback)
raise ExchangeError(feedback) # unknown message
| # -*- coding: utf-8 -*-
# PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
# https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
from ccxt.base.exchange import Exchange
# -----------------------------------------------------------------------------
try:
basestring # Python 3
except NameError:
basestring = str # Python 2
import hashlib
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import AuthenticationError
from ccxt.base.errors import ArgumentsRequired
from ccxt.base.errors import InsufficientFunds
from ccxt.base.errors import InvalidOrder
from ccxt.base.errors import OrderNotFound
from ccxt.base.errors import DDoSProtection
from ccxt.base.errors import RateLimitExceeded
from ccxt.base.errors import ExchangeNotAvailable
from ccxt.base.errors import InvalidNonce
class yobit(Exchange):
def describe(self):
return self.deep_extend(super(yobit, self).describe(), {
'id': 'yobit',
'name': 'YoBit',
'countries': ['RU'],
'rateLimit': 3000, # responses are cached every 2 seconds
'version': '3',
'has': {
'cancelOrder': True,
'CORS': False,
'createDepositAddress': True,
'createMarketOrder': False,
'createOrder': True,
'fetchBalance': True,
'fetchDepositAddress': True,
'fetchDeposits': False,
'fetchMarkets': True,
'fetchMyTrades': True,
'fetchOpenOrders': True,
'fetchOrder': True,
'fetchOrderBook': True,
'fetchOrderBooks': True,
'fetchTicker': True,
'fetchTickers': True,
'fetchTrades': True,
'fetchTransactions': False,
'fetchWithdrawals': False,
'withdraw': True,
},
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/27766910-cdcbfdae-5eea-11e7-9859-03fea873272d.jpg',
'api': {
'public': 'https://yobit.net/api',
'private': 'https://yobit.net/tapi',
},
'www': 'https://www.yobit.net',
'doc': 'https://www.yobit.net/en/api/',
'fees': 'https://www.yobit.net/en/fees/',
},
'api': {
'public': {
'get': [
'depth/{pair}',
'info',
'ticker/{pair}',
'trades/{pair}',
],
},
'private': {
'post': [
'ActiveOrders',
'CancelOrder',
'GetDepositAddress',
'getInfo',
'OrderInfo',
'Trade',
'TradeHistory',
'WithdrawCoinsToAddress',
],
},
},
'fees': {
'trading': {
'maker': 0.002,
'taker': 0.002,
},
'funding': {
'withdraw': {},
},
},
'commonCurrencies': {
'AIR': 'AirCoin',
'ANI': 'ANICoin',
'ANT': 'AntsCoin', # what is self, a coin for ants?
'ATMCHA': 'ATM',
'ASN': 'Ascension',
'AST': 'Astral',
'ATM': 'Autumncoin',
'BCC': 'BCH',
'BCS': 'BitcoinStake',
'BITS': 'Bitstar',
'BLN': 'Bulleon',
'BOT': 'BOTcoin',
'BON': 'BONES',
'BPC': 'BitcoinPremium',
'BTS': 'Bitshares2',
'CAT': 'BitClave',
'CBC': 'CryptoBossCoin',
'CMT': 'CometCoin',
'COV': 'Coven Coin',
'COVX': 'COV',
'CPC': 'Capricoin',
'CREDIT': 'Creditbit',
'CS': 'CryptoSpots',
'DCT': 'Discount',
'DFT': 'DraftCoin',
'DGD': 'DarkGoldCoin',
'DIRT': 'DIRTY',
'DROP': 'FaucetCoin',
'DSH': 'DASH',
'EKO': 'EkoCoin',
'ENTER': 'ENTRC',
'EPC': 'ExperienceCoin',
'ESC': 'EdwardSnowden',
'EUROPE': 'EUROP',
'EXT': 'LifeExtension',
'FUNK': 'FUNKCoin',
'GCC': 'GlobalCryptocurrency',
'GEN': 'Genstake',
'GENE': 'Genesiscoin',
'GOLD': 'GoldMint',
'GOT': 'Giotto Coin',
'HTML5': 'HTML',
'HYPERX': 'HYPER',
'ICN': 'iCoin',
'INSANE': 'INSN',
'JNT': 'JointCoin',
'JPC': 'JupiterCoin',
'KNC': 'KingN Coin',
'LBTCX': 'LiteBitcoin',
'LIZI': 'LiZi',
'LOC': 'LocoCoin',
'LOCX': 'LOC',
'LUNYR': 'LUN',
'LUN': 'LunarCoin', # they just change the ticker if it is already taken
'MDT': 'Midnight',
'NAV': 'NavajoCoin',
'NBT': 'NiceBytes',
'OMG': 'OMGame',
'PAC': '$PAC',
'PLAY': 'PlayCoin',
'PIVX': 'Darknet',
'PRS': 'PRE',
'PUTIN': 'PutinCoin',
'STK': 'StakeCoin',
'SUB': 'Subscriptio',
'PAY': 'EPAY',
'PLC': 'Platin Coin',
'RCN': 'RCoin',
'REP': 'Republicoin',
'RUR': 'RUB',
'SBTC': 'Super Bitcoin',
'TTC': 'TittieCoin',
'UNI': 'Universe',
'UST': 'Uservice',
'VOL': 'VolumeCoin',
'XIN': 'XINCoin',
'XRA': 'Ratecoin',
},
'options': {
# 'fetchTickersMaxLength': 2048,
'fetchOrdersRequiresSymbol': True,
'fetchTickersMaxLength': 512,
},
'exceptions': {
'exact': {
'803': InvalidOrder, # "Count could not be less than 0.001."(selling below minAmount)
'804': InvalidOrder, # "Count could not be more than 10000."(buying above maxAmount)
'805': InvalidOrder, # "price could not be less than X."(minPrice violation on buy & sell)
'806': InvalidOrder, # "price could not be more than X."(maxPrice violation on buy & sell)
'807': InvalidOrder, # "cost could not be less than X."(minCost violation on buy & sell)
'831': InsufficientFunds, # "Not enougth X to create buy order."(buying with balance.quote < order.cost)
'832': InsufficientFunds, # "Not enougth X to create sell order."(selling with balance.base < order.amount)
'833': OrderNotFound, # "Order with id X was not found."(cancelling non-existent, closed and cancelled order)
},
'broad': {
'Invalid pair name': ExchangeError, # {"success":0,"error":"Invalid pair name: btc_eth"}
'invalid api key': AuthenticationError,
'invalid sign': AuthenticationError,
'api key dont have trade permission': AuthenticationError,
'invalid parameter': InvalidOrder,
'invalid order': InvalidOrder,
'The given order has already been cancelled': InvalidOrder,
'Requests too often': DDoSProtection,
'not available': ExchangeNotAvailable,
'data unavailable': ExchangeNotAvailable,
'external service unavailable': ExchangeNotAvailable,
'Total transaction amount': InvalidOrder, # {"success": 0, "error": "Total transaction amount is less than minimal total: 0.00010000"}
'The given order has already been closed and cannot be cancelled': InvalidOrder,
'Insufficient funds': InsufficientFunds,
'invalid key': AuthenticationError,
'invalid nonce': InvalidNonce, # {"success":0,"error":"invalid nonce(has already been used)"}'
'Total order amount is less than minimal amount': InvalidOrder,
'Rate Limited': RateLimitExceeded,
},
},
'orders': {}, # orders cache / emulation
})
def fetch_balance(self, params={}):
self.load_markets()
response = self.privatePostGetInfo(params)
#
# {
# "success":1,
# "return":{
# "funds":{
# "ltc":22,
# "nvc":423.998,
# "ppc":10,
# },
# "funds_incl_orders":{
# "ltc":32,
# "nvc":523.998,
# "ppc":20,
# },
# "rights":{
# "info":1,
# "trade":0,
# "withdraw":0
# },
# "transaction_count":0,
# "open_orders":1,
# "server_time":1418654530
# }
# }
#
balances = self.safe_value(response, 'return', {})
result = {'info': response}
free = self.safe_value(balances, 'funds', {})
total = self.safe_value(balances, 'funds_incl_orders', {})
currencyIds = list(self.extend(free, total).keys())
for i in range(0, len(currencyIds)):
currencyId = currencyIds[i]
code = self.safe_currency_code(currencyId)
account = self.account()
account['free'] = self.safe_float(free, currencyId)
account['total'] = self.safe_float(total, currencyId)
result[code] = account
return self.parse_balance(result)
def fetch_markets(self, params={}):
response = self.publicGetInfo(params)
markets = self.safe_value(response, 'pairs')
keys = list(markets.keys())
result = []
for i in range(0, len(keys)):
id = keys[i]
market = markets[id]
baseId, quoteId = id.split('_')
base = baseId.upper()
quote = quoteId.upper()
base = self.safe_currency_code(base)
quote = self.safe_currency_code(quote)
symbol = base + '/' + quote
precision = {
'amount': self.safe_integer(market, 'decimal_places'),
'price': self.safe_integer(market, 'decimal_places'),
}
amountLimits = {
'min': self.safe_float(market, 'min_amount'),
'max': self.safe_float(market, 'max_amount'),
}
priceLimits = {
'min': self.safe_float(market, 'min_price'),
'max': self.safe_float(market, 'max_price'),
}
costLimits = {
'min': self.safe_float(market, 'min_total'),
}
limits = {
'amount': amountLimits,
'price': priceLimits,
'cost': costLimits,
}
hidden = self.safe_integer(market, 'hidden')
active = (hidden == 0)
result.append({
'id': id,
'symbol': symbol,
'base': base,
'quote': quote,
'baseId': baseId,
'quoteId': quoteId,
'active': active,
'taker': market['fee'] / 100,
'precision': precision,
'limits': limits,
'info': market,
})
return result
def fetch_order_book(self, symbol, limit=None, params={}):
self.load_markets()
market = self.market(symbol)
request = {
'pair': market['id'],
}
if limit is not None:
request['limit'] = limit # default = 150, max = 2000
response = self.publicGetDepthPair(self.extend(request, params))
market_id_in_reponse = (market['id'] in response)
if not market_id_in_reponse:
raise ExchangeError(self.id + ' ' + market['symbol'] + ' order book is empty or not available')
orderbook = response[market['id']]
return self.parse_order_book(orderbook)
def fetch_order_books(self, symbols=None, limit=None, params={}):
self.load_markets()
ids = None
if symbols is None:
ids = '-'.join(self.ids)
# max URL length is 2083 symbols, including http schema, hostname, tld, etc...
if len(ids) > 2048:
numIds = len(self.ids)
raise ExchangeError(self.id + ' has ' + str(numIds) + ' symbols exceeding max URL length, you are required to specify a list of symbols in the first argument to fetchOrderBooks')
else:
ids = self.market_ids(symbols)
ids = '-'.join(ids)
request = {
'pair': ids,
# 'ignore_invalid': True,
}
if limit is not None:
request['limit'] = limit
response = self.publicGetDepthPair(self.extend(request, params))
result = {}
ids = list(response.keys())
for i in range(0, len(ids)):
id = ids[i]
symbol = self.safe_symbol(id)
result[symbol] = self.parse_order_book(response[id])
return result
def parse_ticker(self, ticker, market=None):
#
# { high: 0.03497582,
# low: 0.03248474,
# avg: 0.03373028,
# vol: 120.11485715062999,
# vol_cur: 3572.24914074,
# last: 0.0337611,
# buy: 0.0337442,
# sell: 0.03377798,
# updated: 1537522009 }
#
timestamp = self.safe_timestamp(ticker, 'updated')
symbol = None
if market is not None:
symbol = market['symbol']
last = self.safe_float(ticker, 'last')
return {
'symbol': symbol,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'high': self.safe_float(ticker, 'high'),
'low': self.safe_float(ticker, 'low'),
'bid': self.safe_float(ticker, 'buy'),
'bidVolume': None,
'ask': self.safe_float(ticker, 'sell'),
'askVolume': None,
'vwap': None,
'open': None,
'close': last,
'last': last,
'previousClose': None,
'change': None,
'percentage': None,
'average': self.safe_float(ticker, 'avg'),
'baseVolume': self.safe_float(ticker, 'vol_cur'),
'quoteVolume': self.safe_float(ticker, 'vol'),
'info': ticker,
}
def fetch_tickers(self, symbols=None, params={}):
self.load_markets()
ids = self.ids
if symbols is None:
numIds = len(ids)
ids = '-'.join(ids)
maxLength = self.safe_integer(self.options, 'fetchTickersMaxLength', 2048)
# max URL length is 2048 symbols, including http schema, hostname, tld, etc...
if len(ids) > self.options['fetchTickersMaxLength']:
raise ArgumentsRequired(self.id + ' has ' + str(numIds) + ' markets exceeding max URL length for self endpoint(' + str(maxLength) + ' characters), please, specify a list of symbols of interest in the first argument to fetchTickers')
else:
ids = self.market_ids(symbols)
ids = '-'.join(ids)
request = {
'pair': ids,
}
tickers = self.publicGetTickerPair(self.extend(request, params))
result = {}
keys = list(tickers.keys())
for k in range(0, len(keys)):
id = keys[k]
ticker = tickers[id]
market = self.safe_market(id)
symbol = market['symbol']
result[symbol] = self.parse_ticker(ticker, market)
return self.filter_by_array(result, 'symbol', symbols)
def fetch_ticker(self, symbol, params={}):
tickers = self.fetch_tickers([symbol], params)
return tickers[symbol]
def parse_trade(self, trade, market=None):
timestamp = self.safe_timestamp(trade, 'timestamp')
side = self.safe_string(trade, 'type')
if side == 'ask':
side = 'sell'
elif side == 'bid':
side = 'buy'
price = self.safe_float_2(trade, 'rate', 'price')
id = self.safe_string_2(trade, 'trade_id', 'tid')
order = self.safe_string(trade, 'order_id')
marketId = self.safe_string(trade, 'pair')
symbol = self.safe_symbol(marketId, market)
amount = self.safe_float(trade, 'amount')
type = 'limit' # all trades are still limit trades
fee = None
feeCost = self.safe_float(trade, 'commission')
if feeCost is not None:
feeCurrencyId = self.safe_string(trade, 'commissionCurrency')
feeCurrencyCode = self.safe_currency_code(feeCurrencyId)
fee = {
'cost': feeCost,
'currency': feeCurrencyCode,
}
isYourOrder = self.safe_value(trade, 'is_your_order')
if isYourOrder is not None:
if fee is None:
fee = self.calculate_fee(symbol, type, side, amount, price, 'taker')
cost = None
if amount is not None:
if price is not None:
cost = amount * price
return {
'id': id,
'order': order,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'symbol': symbol,
'type': type,
'side': side,
'takerOrMaker': None,
'price': price,
'amount': amount,
'cost': cost,
'fee': fee,
'info': trade,
}
def fetch_trades(self, symbol, since=None, limit=None, params={}):
self.load_markets()
market = self.market(symbol)
request = {
'pair': market['id'],
}
if limit is not None:
request['limit'] = limit
response = self.publicGetTradesPair(self.extend(request, params))
if isinstance(response, list):
numElements = len(response)
if numElements == 0:
return []
return self.parse_trades(response[market['id']], market, since, limit)
def create_order(self, symbol, type, side, amount, price=None, params={}):
if type == 'market':
raise ExchangeError(self.id + ' allows limit orders only')
self.load_markets()
market = self.market(symbol)
request = {
'pair': market['id'],
'type': side,
'amount': self.amount_to_precision(symbol, amount),
'rate': self.price_to_precision(symbol, price),
}
price = float(price)
amount = float(amount)
response = self.privatePostTrade(self.extend(request, params))
id = None
status = 'open'
filled = 0.0
remaining = amount
if 'return' in response:
id = self.safe_string(response['return'], 'order_id')
if id == '0':
id = self.safe_string(response['return'], 'init_order_id')
status = 'closed'
filled = self.safe_float(response['return'], 'received', 0.0)
remaining = self.safe_float(response['return'], 'remains', amount)
timestamp = self.milliseconds()
return {
'id': id,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'status': status,
'symbol': symbol,
'type': type,
'side': side,
'price': price,
'cost': price * filled,
'amount': amount,
'remaining': remaining,
'filled': filled,
'fee': None,
# 'trades': self.parse_trades(order['trades'], market),
'info': response,
'clientOrderId': None,
'average': None,
'trades': None,
}
def cancel_order(self, id, symbol=None, params={}):
self.load_markets()
request = {
'order_id': int(id),
}
return self.privatePostCancelOrder(self.extend(request, params))
def parse_order_status(self, status):
statuses = {
'0': 'open',
'1': 'closed',
'2': 'canceled',
'3': 'open', # or partially-filled and canceled? https://github.com/ccxt/ccxt/issues/1594
}
return self.safe_string(statuses, status, status)
def parse_order(self, order, market=None):
id = self.safe_string(order, 'id')
status = self.parse_order_status(self.safe_string(order, 'status'))
timestamp = self.safe_timestamp(order, 'timestamp_created')
marketId = self.safe_string(order, 'pair')
symbol = self.safe_symbol(marketId, market)
remaining = self.safe_float(order, 'amount')
amount = self.safe_float(order, 'start_amount')
price = self.safe_float(order, 'rate')
filled = None
cost = None
if amount is not None:
if remaining is not None:
filled = max(0, amount - remaining)
cost = price * filled
fee = None
type = 'limit'
side = self.safe_string(order, 'type')
result = {
'info': order,
'id': id,
'clientOrderId': None,
'symbol': symbol,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'type': type,
'timeInForce': None,
'postOnly': None,
'side': side,
'price': price,
'stopPrice': None,
'cost': cost,
'amount': amount,
'remaining': remaining,
'filled': filled,
'status': status,
'fee': fee,
'average': None,
'trades': None,
}
return result
def fetch_order(self, id, symbol=None, params={}):
self.load_markets()
request = {
'order_id': int(id),
}
response = self.privatePostOrderInfo(self.extend(request, params))
id = str(id)
orders = self.safe_value(response, 'return', {})
return self.parse_order(self.extend({'id': id}, orders[id]))
def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOpenOrders() requires a symbol argument')
self.load_markets()
request = {}
market = None
if symbol is not None:
market = self.market(symbol)
request['pair'] = market['id']
response = self.privatePostActiveOrders(self.extend(request, params))
orders = self.safe_value(response, 'return', [])
return self.parse_orders(orders, market, since, limit)
def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchMyTrades requires a `symbol` argument')
self.load_markets()
market = self.market(symbol)
# some derived classes use camelcase notation for request fields
request = {
# 'from': 123456789, # trade ID, from which the display starts numerical 0(test result: liqui ignores self field)
# 'count': 1000, # the number of trades for display numerical, default = 1000
# 'from_id': trade ID, from which the display starts numerical 0
# 'end_id': trade ID on which the display ends numerical ∞
# 'order': 'ASC', # sorting, default = DESC(test result: liqui ignores self field, most recent trade always goes last)
# 'since': 1234567890, # UTC start time, default = 0(test result: liqui ignores self field)
# 'end': 1234567890, # UTC end time, default = ∞(test result: liqui ignores self field)
'pair': market['id'],
}
if limit is not None:
request['count'] = int(limit)
if since is not None:
request['since'] = int(since / 1000)
response = self.privatePostTradeHistory(self.extend(request, params))
trades = self.safe_value(response, 'return', {})
ids = list(trades.keys())
result = []
for i in range(0, len(ids)):
id = ids[i]
trade = self.parse_trade(self.extend(trades[id], {
'trade_id': id,
}), market)
result.append(trade)
return self.filter_by_symbol_since_limit(result, symbol, since, limit)
def create_deposit_address(self, code, params={}):
request = {
'need_new': 1,
}
response = self.fetch_deposit_address(code, self.extend(request, params))
address = self.safe_string(response, 'address')
self.check_address(address)
return {
'currency': code,
'address': address,
'tag': None,
'info': response['info'],
}
def fetch_deposit_address(self, code, params={}):
self.load_markets()
currency = self.currency(code)
request = {
'coinName': currency['id'],
'need_new': 0,
}
response = self.privatePostGetDepositAddress(self.extend(request, params))
address = self.safe_string(response['return'], 'address')
self.check_address(address)
return {
'currency': code,
'address': address,
'tag': None,
'info': response,
}
def withdraw(self, code, amount, address, tag=None, params={}):
self.check_address(address)
self.load_markets()
currency = self.currency(code)
request = {
'coinName': currency['id'],
'amount': amount,
'address': address,
}
# no docs on the tag, yet...
if tag is not None:
raise ExchangeError(self.id + ' withdraw() does not support the tag argument yet due to a lack of docs on withdrawing with tag/memo on behalf of the exchange.')
response = self.privatePostWithdrawCoinsToAddress(self.extend(request, params))
return {
'info': response,
'id': None,
}
def calculate_fee(self, symbol, type, side, amount, price, takerOrMaker='taker', params={}):
market = self.markets[symbol]
key = 'quote'
rate = market[takerOrMaker]
cost = float(self.cost_to_precision(symbol, amount * rate))
if side == 'sell':
cost *= price
else:
key = 'base'
return {
'type': takerOrMaker,
'currency': market[key],
'rate': rate,
'cost': cost,
}
def sign(self, path, api='public', method='GET', params={}, headers=None, body=None):
url = self.urls['api'][api]
query = self.omit(params, self.extract_params(path))
if api == 'private':
self.check_required_credentials()
nonce = self.nonce()
body = self.urlencode(self.extend({
'nonce': nonce,
'method': path,
}, query))
signature = self.hmac(self.encode(body), self.encode(self.secret), hashlib.sha512)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
'Key': self.apiKey,
'Sign': signature,
}
elif api == 'public':
url += '/' + self.version + '/' + self.implode_params(path, params)
if query:
url += '?' + self.urlencode(query)
else:
url += '/' + self.implode_params(path, params)
if method == 'GET':
if query:
url += '?' + self.urlencode(query)
else:
if query:
body = self.json(query)
headers = {
'Content-Type': 'application/json',
}
return {'url': url, 'method': method, 'body': body, 'headers': headers}
def handle_errors(self, httpCode, reason, url, method, headers, body, response, requestHeaders, requestBody):
if response is None:
return # fallback to default error handler
if 'success' in response:
#
# 1 - Liqui only returns the integer 'success' key from their private API
#
# {"success": 1, ...} httpCode == 200
# {"success": 0, ...} httpCode == 200
#
# 2 - However, exchanges derived from Liqui, can return non-integers
#
# It can be a numeric string
# {"sucesss": "1", ...}
# {"sucesss": "0", ...}, httpCode >= 200(can be 403, 502, etc)
#
# Or just a string
# {"success": "true", ...}
# {"success": "false", ...}, httpCode >= 200
#
# Or a boolean
# {"success": True, ...}
# {"success": False, ...}, httpCode >= 200
#
# 3 - Oversimplified, Python PEP8 forbids comparison operator(==) of different types
#
# 4 - We do not want to copy-paste and duplicate the code of self handler to other exchanges derived from Liqui
#
# To cover points 1, 2, 3 and 4 combined self handler should work like self:
#
success = self.safe_value(response, 'success', False)
if isinstance(success, basestring):
if (success == 'true') or (success == '1'):
success = True
else:
success = False
if not success:
code = self.safe_string(response, 'code')
message = self.safe_string(response, 'error')
feedback = self.id + ' ' + body
self.throw_exactly_matched_exception(self.exceptions['exact'], code, feedback)
self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback)
self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback)
raise ExchangeError(feedback) # unknown message
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/bert/__init__.py | -1 |
||
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py | # coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Wav2Vec2Conformer model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/wav2vec2-conformer-rel-pos-large": (
"https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large/resolve/main/config.json"
),
}
class Wav2Vec2ConformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Wav2Vec2ConformerModel`]. It is used to
instantiate an Wav2Vec2Conformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Conformer
[facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large)
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*):
Vocabulary size of the Wav2Vec2Conformer model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`Wav2Vec2ConformerModel`]. Vocabulary size of the
model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward
method of [`Wav2Vec2ConformerModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`Wav2Vec2ConformerForCTC`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for quantized feature encoder states.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
num_codevectors_per_group (`int`, *optional*, defaults to 320):
Number of entries in each quantization codebook (group).
num_codevector_groups (`int`, *optional*, defaults to 2):
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for the output of the feature encoder that's used by the quantizer.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the quantized feature vectors.
proj_codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`Wav2Vec2ConformerForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`Wav2Vec2ConformerForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`Wav2Vec2ConformerForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Wav2Vec2Conformer Encoder. Can be very
useful for warm-starting Wav2Vec2Conformer for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
position_embeddings_type (`str`, *optional*, defaults to `"relative"`):
Can be specified to `relative` or `rotary` for relative or rotary position embeddings respectively. If left
`None` no relative position embedding is applied.
rotary_embedding_base (`int`, *optional*, defaults to 10000):
If `"rotary"` position embeddings are used, defines the size of the embedding base.
max_source_positions (`int`, *optional*, defaults to 5000):
if `"relative"` position embeddings are used, defines the maximum source input positions.
conv_depthwise_kernel_size (`int`, defaults to 31):
Kernel size of convolutional depthwise 1D layer in Conformer blocks.
conformer_conv_dropout (`float`, defaults to 0.1):
The dropout probability for all convolutional layers in Conformer blocks.
Example:
```python
>>> from transformers import Wav2Vec2ConformerConfig, Wav2Vec2ConformerModel
>>> # Initializing a Wav2Vec2Conformer facebook/wav2vec2-conformer-rel-pos-large style configuration
>>> configuration = Wav2Vec2ConformerConfig()
>>> # Initializing a model (with random weights) from the facebook/wav2vec2-conformer-rel-pos-large style configuration
>>> model = Wav2Vec2ConformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "wav2vec2-conformer"
def __init__(
self,
vocab_size=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
feat_quantizer_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
num_codevectors_per_group=320,
num_codevector_groups=2,
contrastive_logits_temperature=0.1,
num_negatives=100,
codevector_dim=256,
proj_codevector_dim=256,
diversity_loss_weight=0.1,
ctc_loss_reduction="sum",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
tdnn_dim=(512, 512, 512, 512, 1500),
tdnn_kernel=(5, 3, 3, 1, 1),
tdnn_dilation=(1, 2, 3, 1, 1),
xvector_output_dim=512,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
add_adapter=False,
adapter_kernel_size=3,
adapter_stride=2,
num_adapter_layers=3,
output_hidden_size=None,
position_embeddings_type="relative",
rotary_embedding_base=10000,
max_source_positions=5000,
conv_depthwise_kernel_size=31,
conformer_conv_dropout=0.1,
**kwargs
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.use_weighted_layer_sum = use_weighted_layer_sum
self.max_source_positions = max_source_positions
self.position_embeddings_type = position_embeddings_type
self.rotary_embedding_base = rotary_embedding_base
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# Conformer-block related
self.conv_depthwise_kernel_size = conv_depthwise_kernel_size
self.conformer_conv_dropout = conformer_conv_dropout
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
self.mask_feature_min_masks = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
self.num_codevectors_per_group = num_codevectors_per_group
self.num_codevector_groups = num_codevector_groups
self.contrastive_logits_temperature = contrastive_logits_temperature
self.feat_quantizer_dropout = feat_quantizer_dropout
self.num_negatives = num_negatives
self.codevector_dim = codevector_dim
self.proj_codevector_dim = proj_codevector_dim
self.diversity_loss_weight = diversity_loss_weight
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
self.tdnn_dim = list(tdnn_dim)
self.tdnn_kernel = list(tdnn_kernel)
self.tdnn_dilation = list(tdnn_dilation)
self.xvector_output_dim = xvector_output_dim
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
| # coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Wav2Vec2Conformer model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/wav2vec2-conformer-rel-pos-large": (
"https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large/resolve/main/config.json"
),
}
class Wav2Vec2ConformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Wav2Vec2ConformerModel`]. It is used to
instantiate an Wav2Vec2Conformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Conformer
[facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large)
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*):
Vocabulary size of the Wav2Vec2Conformer model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`Wav2Vec2ConformerModel`]. Vocabulary size of the
model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward
method of [`Wav2Vec2ConformerModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`Wav2Vec2ConformerForCTC`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for quantized feature encoder states.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
num_codevectors_per_group (`int`, *optional*, defaults to 320):
Number of entries in each quantization codebook (group).
num_codevector_groups (`int`, *optional*, defaults to 2):
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for the output of the feature encoder that's used by the quantizer.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the quantized feature vectors.
proj_codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`Wav2Vec2ConformerForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`Wav2Vec2ConformerForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`Wav2Vec2ConformerForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Wav2Vec2Conformer Encoder. Can be very
useful for warm-starting Wav2Vec2Conformer for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
position_embeddings_type (`str`, *optional*, defaults to `"relative"`):
Can be specified to `relative` or `rotary` for relative or rotary position embeddings respectively. If left
`None` no relative position embedding is applied.
rotary_embedding_base (`int`, *optional*, defaults to 10000):
If `"rotary"` position embeddings are used, defines the size of the embedding base.
max_source_positions (`int`, *optional*, defaults to 5000):
if `"relative"` position embeddings are used, defines the maximum source input positions.
conv_depthwise_kernel_size (`int`, defaults to 31):
Kernel size of convolutional depthwise 1D layer in Conformer blocks.
conformer_conv_dropout (`float`, defaults to 0.1):
The dropout probability for all convolutional layers in Conformer blocks.
Example:
```python
>>> from transformers import Wav2Vec2ConformerConfig, Wav2Vec2ConformerModel
>>> # Initializing a Wav2Vec2Conformer facebook/wav2vec2-conformer-rel-pos-large style configuration
>>> configuration = Wav2Vec2ConformerConfig()
>>> # Initializing a model (with random weights) from the facebook/wav2vec2-conformer-rel-pos-large style configuration
>>> model = Wav2Vec2ConformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "wav2vec2-conformer"
def __init__(
self,
vocab_size=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
feat_quantizer_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
num_codevectors_per_group=320,
num_codevector_groups=2,
contrastive_logits_temperature=0.1,
num_negatives=100,
codevector_dim=256,
proj_codevector_dim=256,
diversity_loss_weight=0.1,
ctc_loss_reduction="sum",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
tdnn_dim=(512, 512, 512, 512, 1500),
tdnn_kernel=(5, 3, 3, 1, 1),
tdnn_dilation=(1, 2, 3, 1, 1),
xvector_output_dim=512,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
add_adapter=False,
adapter_kernel_size=3,
adapter_stride=2,
num_adapter_layers=3,
output_hidden_size=None,
position_embeddings_type="relative",
rotary_embedding_base=10000,
max_source_positions=5000,
conv_depthwise_kernel_size=31,
conformer_conv_dropout=0.1,
**kwargs
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.use_weighted_layer_sum = use_weighted_layer_sum
self.max_source_positions = max_source_positions
self.position_embeddings_type = position_embeddings_type
self.rotary_embedding_base = rotary_embedding_base
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# Conformer-block related
self.conv_depthwise_kernel_size = conv_depthwise_kernel_size
self.conformer_conv_dropout = conformer_conv_dropout
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
self.mask_feature_min_masks = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
self.num_codevectors_per_group = num_codevectors_per_group
self.num_codevector_groups = num_codevector_groups
self.contrastive_logits_temperature = contrastive_logits_temperature
self.feat_quantizer_dropout = feat_quantizer_dropout
self.num_negatives = num_negatives
self.codevector_dim = codevector_dim
self.proj_codevector_dim = proj_codevector_dim
self.diversity_loss_weight = diversity_loss_weight
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
self.tdnn_dim = list(tdnn_dim)
self.tdnn_kernel = list(tdnn_kernel)
self.tdnn_dilation = list(tdnn_dilation)
self.xvector_output_dim = xvector_output_dim
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/roformer/tokenization_utils.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization utils for RoFormer."""
from typing import List
from tokenizers import NormalizedString, PreTokenizedString, normalizers
class JiebaPreTokenizer:
def __init__(self, vocab) -> None:
self.vocab = vocab
self.normalizers = normalizers.BertNormalizer(
clean_text=False,
handle_chinese_chars=True,
strip_accents=False,
lowercase=False,
)
try:
import rjieba
except ImportError:
raise ImportError(
"You need to install rjieba to use RoFormerTokenizer. "
"See https://pypi.org/project/rjieba/ for installation."
)
self.jieba = rjieba
def jieba_split(self, i: int, normalized_string: NormalizedString) -> List[NormalizedString]:
splits = []
# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False):
if token in self.vocab:
splits.append(normalized_string[start:end])
else:
token_list = self.normalizers.normalize_str(token).split()
for token in token_list:
if token:
end = start + len(token)
splits.append(normalized_string[start:end])
start = end
# this code test_alignement_methods can't pass but fast (300ms)
# for token in self.jieba.cut(str(normalized_string), False):
# if token in self.vocab:
# splits.append(NormalizedString(token))
# else:
# token_list = self.normalizers.normalize_str(token).split()
# for token in token_list:
# if token:
# splits.append(NormalizedString(token))
return splits
def pre_tokenize(self, pretok: PreTokenizedString):
pretok.split(self.jieba_split)
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization utils for RoFormer."""
from typing import List
from tokenizers import NormalizedString, PreTokenizedString, normalizers
class JiebaPreTokenizer:
def __init__(self, vocab) -> None:
self.vocab = vocab
self.normalizers = normalizers.BertNormalizer(
clean_text=False,
handle_chinese_chars=True,
strip_accents=False,
lowercase=False,
)
try:
import rjieba
except ImportError:
raise ImportError(
"You need to install rjieba to use RoFormerTokenizer. "
"See https://pypi.org/project/rjieba/ for installation."
)
self.jieba = rjieba
def jieba_split(self, i: int, normalized_string: NormalizedString) -> List[NormalizedString]:
splits = []
# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False):
if token in self.vocab:
splits.append(normalized_string[start:end])
else:
token_list = self.normalizers.normalize_str(token).split()
for token in token_list:
if token:
end = start + len(token)
splits.append(normalized_string[start:end])
start = end
# this code test_alignement_methods can't pass but fast (300ms)
# for token in self.jieba.cut(str(normalized_string), False):
# if token in self.vocab:
# splits.append(NormalizedString(token))
# else:
# token_list = self.normalizers.normalize_str(token).split()
# for token in token_list:
# if token:
# splits.append(NormalizedString(token))
return splits
def pre_tokenize(self, pretok: PreTokenizedString):
pretok.split(self.jieba_split)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/pytorch/audio-classification/run_audio_classification.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import numpy as np
from datasets import DatasetDict, load_dataset
import evaluate
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
logger = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.25.0.dev0")
require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def random_subsample(wav: np.ndarray, max_length: float, sample_rate: int = 16000):
"""Randomly sample chunks of `max_length` seconds from the input audio"""
sample_length = int(round(sample_rate * max_length))
if len(wav) <= sample_length:
return wav
random_offset = randint(0, len(wav) - sample_length - 1)
return wav[random_offset : random_offset + sample_length]
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
dataset_name: Optional[str] = field(default=None, metadata={"help": "Name of a dataset from the datasets package"})
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(
default=None, metadata={"help": "A file containing the training audio paths and labels."}
)
eval_file: Optional[str] = field(
default=None, metadata={"help": "A file containing the validation audio paths and labels."}
)
train_split_name: str = field(
default="train",
metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
},
)
eval_split_name: str = field(
default="validation",
metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
)
},
)
audio_column_name: str = field(
default="audio",
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
)
label_column_name: str = field(
default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"}
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
max_length_seconds: float = field(
default=20,
metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."},
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default="facebook/wav2vec2-base",
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
feature_extractor_name: Optional[str] = field(
default=None, metadata={"help": "Name or path of preprocessor config."}
)
freeze_feature_encoder: bool = field(
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
)
attention_mask: bool = field(
default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."}
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
freeze_feature_extractor: Optional[bool] = field(
default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
)
ignore_mismatched_sizes: bool = field(
default=False,
metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
)
def __post_init__(self):
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"The argument `--freeze_feature_extractor` is deprecated and "
"will be removed in a future version. Use `--freeze_feature_encoder`"
"instead. Setting `freeze_feature_encoder==True`.",
FutureWarning,
)
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"The argument `--freeze_feature_extractor` is deprecated and "
"should not be used in combination with `--freeze_feature_encoder`."
"Only make use of `--freeze_feature_encoder`."
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_audio_classification", model_args, data_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to train from scratch."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Initialize our dataset and prepare it for the audio classification task.
raw_datasets = DatasetDict()
raw_datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.train_split_name,
use_auth_token=True if model_args.use_auth_token else None,
)
raw_datasets["eval"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.eval_split_name,
use_auth_token=True if model_args.use_auth_token else None,
)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--audio_column_name` to the correct audio column - one of "
f"{', '.join(raw_datasets['train'].column_names)}."
)
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--label_column_name` to the correct text column - one of "
f"{', '.join(raw_datasets['train'].column_names)}."
)
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
feature_extractor = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path,
return_attention_mask=model_args.attention_mask,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
raw_datasets = raw_datasets.cast_column(
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
)
def train_transforms(batch):
"""Apply train_transforms across a batch."""
output_batch = {"input_values": []}
for audio in batch[data_args.audio_column_name]:
wav = random_subsample(
audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate
)
output_batch["input_values"].append(wav)
output_batch["labels"] = [label for label in batch[data_args.label_column_name]]
return output_batch
def val_transforms(batch):
"""Apply val_transforms across a batch."""
output_batch = {"input_values": []}
for audio in batch[data_args.audio_column_name]:
wav = audio["array"]
output_batch["input_values"].append(wav)
output_batch["labels"] = [label for label in batch[data_args.label_column_name]]
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
labels = raw_datasets["train"].features[data_args.label_column_name].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
label2id[label] = str(i)
id2label[str(i)] = label
# Load the accuracy metric from the datasets package
metric = evaluate.load("accuracy")
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(eval_pred):
"""Computes accuracy on a batch of predictions"""
predictions = np.argmax(eval_pred.predictions, axis=1)
return metric.compute(predictions=predictions, references=eval_pred.label_ids)
config = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path,
num_labels=len(labels),
label2id=label2id,
id2label=id2label,
finetuning_task="audio-classification",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
raw_datasets["train"] = (
raw_datasets["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
)
# Set the training transforms
raw_datasets["train"].set_transform(train_transforms, output_all_columns=False)
if training_args.do_eval:
if data_args.max_eval_samples is not None:
raw_datasets["eval"] = (
raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
)
# Set the validation transforms
raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False)
# Initialize our trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=raw_datasets["train"] if training_args.do_train else None,
eval_dataset=raw_datasets["eval"] if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=feature_extractor,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Write model card and (optionally) push to hub
kwargs = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
if __name__ == "__main__":
main()
| #!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import numpy as np
from datasets import DatasetDict, load_dataset
import evaluate
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
logger = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.25.0.dev0")
require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def random_subsample(wav: np.ndarray, max_length: float, sample_rate: int = 16000):
"""Randomly sample chunks of `max_length` seconds from the input audio"""
sample_length = int(round(sample_rate * max_length))
if len(wav) <= sample_length:
return wav
random_offset = randint(0, len(wav) - sample_length - 1)
return wav[random_offset : random_offset + sample_length]
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
dataset_name: Optional[str] = field(default=None, metadata={"help": "Name of a dataset from the datasets package"})
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(
default=None, metadata={"help": "A file containing the training audio paths and labels."}
)
eval_file: Optional[str] = field(
default=None, metadata={"help": "A file containing the validation audio paths and labels."}
)
train_split_name: str = field(
default="train",
metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
},
)
eval_split_name: str = field(
default="validation",
metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
)
},
)
audio_column_name: str = field(
default="audio",
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
)
label_column_name: str = field(
default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"}
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
max_length_seconds: float = field(
default=20,
metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."},
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default="facebook/wav2vec2-base",
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
feature_extractor_name: Optional[str] = field(
default=None, metadata={"help": "Name or path of preprocessor config."}
)
freeze_feature_encoder: bool = field(
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
)
attention_mask: bool = field(
default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."}
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
freeze_feature_extractor: Optional[bool] = field(
default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
)
ignore_mismatched_sizes: bool = field(
default=False,
metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
)
def __post_init__(self):
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"The argument `--freeze_feature_extractor` is deprecated and "
"will be removed in a future version. Use `--freeze_feature_encoder`"
"instead. Setting `freeze_feature_encoder==True`.",
FutureWarning,
)
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"The argument `--freeze_feature_extractor` is deprecated and "
"should not be used in combination with `--freeze_feature_encoder`."
"Only make use of `--freeze_feature_encoder`."
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_audio_classification", model_args, data_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to train from scratch."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Initialize our dataset and prepare it for the audio classification task.
raw_datasets = DatasetDict()
raw_datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.train_split_name,
use_auth_token=True if model_args.use_auth_token else None,
)
raw_datasets["eval"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.eval_split_name,
use_auth_token=True if model_args.use_auth_token else None,
)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--audio_column_name` to the correct audio column - one of "
f"{', '.join(raw_datasets['train'].column_names)}."
)
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--label_column_name` to the correct text column - one of "
f"{', '.join(raw_datasets['train'].column_names)}."
)
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
feature_extractor = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path,
return_attention_mask=model_args.attention_mask,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
raw_datasets = raw_datasets.cast_column(
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
)
def train_transforms(batch):
"""Apply train_transforms across a batch."""
output_batch = {"input_values": []}
for audio in batch[data_args.audio_column_name]:
wav = random_subsample(
audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate
)
output_batch["input_values"].append(wav)
output_batch["labels"] = [label for label in batch[data_args.label_column_name]]
return output_batch
def val_transforms(batch):
"""Apply val_transforms across a batch."""
output_batch = {"input_values": []}
for audio in batch[data_args.audio_column_name]:
wav = audio["array"]
output_batch["input_values"].append(wav)
output_batch["labels"] = [label for label in batch[data_args.label_column_name]]
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
labels = raw_datasets["train"].features[data_args.label_column_name].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
label2id[label] = str(i)
id2label[str(i)] = label
# Load the accuracy metric from the datasets package
metric = evaluate.load("accuracy")
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(eval_pred):
"""Computes accuracy on a batch of predictions"""
predictions = np.argmax(eval_pred.predictions, axis=1)
return metric.compute(predictions=predictions, references=eval_pred.label_ids)
config = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path,
num_labels=len(labels),
label2id=label2id,
id2label=id2label,
finetuning_task="audio-classification",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
raw_datasets["train"] = (
raw_datasets["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
)
# Set the training transforms
raw_datasets["train"].set_transform(train_transforms, output_all_columns=False)
if training_args.do_eval:
if data_args.max_eval_samples is not None:
raw_datasets["eval"] = (
raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
)
# Set the validation transforms
raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False)
# Initialize our trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=raw_datasets["train"] if training_args.do_train else None,
eval_dataset=raw_datasets["eval"] if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=feature_extractor,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Write model card and (optionally) push to hub
kwargs = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
if __name__ == "__main__":
main()
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/research_projects/information-gain-filtration/run_clm_igf.py | # Copyright 2022 - Intel Corp. All rights reserved.
# Authors: Mayank Kumar Raunak, Javier Turek, Nicole Beckage
"""
Implementation of a new method for fine-tuning transformer models that we call
Information Gain Filtration 'IGF' on WikiText data set and compared the results
with the standard fine-tuning method
Steps followed in the code:
1) Generate a objective dataset of pairs (X, IG(X)). IG(X)--Informativeness of context 'X'.
Our IG (information gain) model is learning to predict the ‘informativeness’ of a particular
context. Informativeness is the change in metric between the model’s accuracy on an
objective set before and after seeing that context. For casual language modeling, the
metric is perplexity.
2) A secondary learner is trained to infer a function approximation for IG using the dataset
created in (1).
3) The learner created in (2) is used to inform the fine-tuning process and filter out low informative samples.
Last, a plot is generated to compare the performance of IGF to standard fine-tuning without any filtering
"""
# Prerequisite libraries:
import argparse
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
import joblib
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpt2,
recopy_gpt2,
set_seed,
train_secondary_learner,
)
from transformers import GPT2LMHeadModel
def generate_n_pairs(
context_len=32,
max_steps=10,
size_objective_set=100,
min_len=1026,
trim=True,
data_file="data/tokenized_stories_train_wikitext103.jbl",
igf_data_file="igf_context_pairs.jbl",
):
"""
Collecting *n* pairs for training the secondary learner
Args:
context_len: The maximum total input sequence length after tokenization. Sequences longer
than this will be truncated, sequences shorter will be padded
max_steps: To calculate training epochs of secondary learner
size_objective_set: size of objective data set used to create (X,IG(X)) pairs which is the training data for secondary learner
min_len: The minimum length of the article to be used as objective set
trim: If True truncate the context if it exceeds context length
data_file: Tokenized data set split for training and evaluation of model
igf_data_file: file to store (I,IG(X)) paired data set to train secondary learner
Returns:
Data stored in igf_data_file
"""
# generates same data everytime
set_seed(3)
# generate train_data and objective_set
train_data, objective_set = generate_datasets(
context_len, data_file, number=size_objective_set, min_len=1026, trim=True
)
# keeps model same across runs
set_seed(4)
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load pretrained model
model = load_gpt2("gpt2").to(device)
print("computing perplexity on objective set")
orig_perp = compute_perplexity(model, objective_set, context_len).item()
print("perplexity on objective set:", orig_perp)
# collect igf pairs and save to file demo.jbl
collect_objective_set(model, orig_perp, context_len, train_data, objective_set, max_steps, device, igf_data_file)
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def training_secondary_learner(
secondary_learner_train_data,
secondary_learner_max_epochs=15,
secondary_learner_batch_size=128,
eval_freq=100,
igf_model_path="igf_model.pt",
):
"""
Train the secondary learner
Args:
secondary_learner_train_data: Data set with (X,IG(X)) pairs to train secondary learner where IG(X) - measure of informativeness and X- context
secondary_learner_max_epochs: Number of epochs to train secondary learner
secondary_learner_batch_size: Batch size to train secondary learner
eval_freq (object): secondary model evaluation can be triggered at eval_freq
igf_model_path: path to store trained secondary learner
Returns:
Trained secondary learner
"""
set_seed(42)
# Load pre-trained model
model = GPT2LMHeadModel.from_pretrained("gpt2")
# Initialize secondary learner to use embedding weights of model
secondary_learner = SecondaryLearner(model)
# Train secondary learner
secondary_learner = train_secondary_learner(
secondary_learner,
secondary_learner_train_data,
max_epochs=secondary_learner_max_epochs,
batch_size=secondary_learner_batch_size,
eval_freq=100,
igf_model_path=igf_model_path,
)
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def finetune(
model,
train_dataset,
test_dataset,
context_len=32,
max_steps=1000,
batch_size=16,
threshold=1.0,
recopy_model=recopy_gpt2,
secondary_learner=None,
eval_interval=10,
finetuned_model_name="gpt2_finetuned.pt",
):
"""
fine-tune with IGF if secondary_learner is not None, else standard fine-tuning
Args:
model: pre-trained GPT-2 model
train_dataset: Data set to train GPT-2 model
test_dataset: Evaluate GPT-2 model
context_len: The maximum total input sequence length after tokenization. Sequences longer
than this will be truncated, sequences shorter will be padded
max_steps: To calculate training epochs
batch_size: Batch size to train GPT-2 model
threshold: The threshold value used by secondary learner to filter the train_data and allow only"
informative data as input to the model
recopy_model: Reset the model to the original pretrained GPT-2 weights after each iteration
secondary_learner: Selection of IGF as fine-tuning method if not None
eval_interval: number of batches after which decay the selectivity of our secondary learner filter from
1 standard deviation above average to 1 below average
fine-tuned_model_name: name of the final final-tuned GPT-2 model
Returns:
Fine-tuned GPT-2 model
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler)
num_train_epochs = max_steps // (len(train_dataset)) + 1
global_step = 0
context = torch.zeros((1, context_len), dtype=torch.long, device=device)
model, lm_optimizer, lm_scheduler = recopy_model(model, device, max_steps)
model.train()
if secondary_learner is not None:
secondary_learner.to(device)
secondary_learner.eval()
contexts = []
examples = 0
observed_qs = []
test_perps = []
# Compute the performance of the transformer model at the beginning
real_perp = compute_perplexity(model, test_dataset, context_len)
test_perps.append(real_perp)
print("Test perplexity, step", global_step, ":", real_perp)
for epoch in range(int(num_train_epochs)):
for step, example in enumerate(train_dataloader):
torch.cuda.empty_cache()
start = random.randint(0, example.size(2) - context_len - 1)
context[0, :] = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
outputs = model(context, labels=context)
do_backprop = True
if secondary_learner is not None:
predicted_q = secondary_learner.forward(
torch.tensor(context, dtype=torch.long, device=device).unsqueeze(0)
)[0].item()
observed_qs.append(float(predicted_q))
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
threshold = -1
if predicted_q < threshold:
do_backprop = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu()))
lm_loss = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
examples = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters(), 3.0)
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
real_perp = compute_perplexity(model, test_dataset, context_len)
test_perps.append(real_perp)
print("Test perplexity, step", global_step, ":", real_perp)
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict(), finetuned_model_name)
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def main():
parser = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task")
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain data files for WikiText.",
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--data_file",
type=str,
default=None,
help=(
"A jbl file containing tokenized data which can be split as objective dataset, "
"train_dataset and test_dataset."
),
)
parser.add_argument(
"--igf_data_file",
type=str,
default=None,
help="A jbl file containing the context and information gain pairs to train secondary learner.",
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the final fine-tuned model is stored.",
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--context_len",
default=32,
type=int,
help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
),
)
parser.add_argument(
"--size_objective_set",
default=100,
type=int,
help="number of articles that are long enough to be used as our objective set",
)
parser.add_argument(
"--eval_freq", default=100, type=int, help="secondary model evaluation is triggered at eval_freq"
)
parser.add_argument("--max_steps", default=1000, type=int, help="To calculate training epochs")
parser.add_argument(
"--secondary_learner_batch_size",
default=128,
type=int,
help="batch size of training data for secondary learner",
)
parser.add_argument(
"--batch_size", default=16, type=int, help="batch size of training data of language model(gpt2) "
)
parser.add_argument(
"--eval_interval",
default=10,
type=int,
help=(
"decay the selectivity of our secondary learner filter from"
"1 standard deviation above average to 1 below average after 10 batches"
),
)
parser.add_argument(
"--number", default=100, type=int, help="The number of examples split to be used as objective_set/test_data"
)
parser.add_argument(
"--min_len", default=1026, type=int, help="The minimum length of the article to be used as objective set"
)
parser.add_argument(
"--secondary_learner_max_epochs", default=15, type=int, help="number of epochs to train secondary learner"
)
parser.add_argument("--trim", default=True, type=bool, help="truncate the example if it exceeds context length")
parser.add_argument(
"--threshold",
default=1.0,
type=float,
help=(
"The threshold value used by secondary learner to filter the train_data and allow only"
" informative data as input to the model"
),
)
parser.add_argument("--finetuned_model_name", default="gpt2_finetuned.pt", type=str, help="finetuned_model_name")
parser.add_argument(
"--recopy_model",
default=recopy_gpt2,
type=str,
help="Reset the model to the original pretrained GPT-2 weights after each iteration",
)
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32,
max_steps=10,
size_objective_set=100,
min_len=1026,
trim=True,
data_file="data/tokenized_stories_train_wikitext103.jbl",
igf_data_file="igf_context_pairs.jbl",
)
# Load train data for secondary learner
secondary_learner_train_data = joblib.load("data/IGF_values.jbl")
# Train secondary learner
secondary_learner = training_secondary_learner(
secondary_learner_train_data,
secondary_learner_max_epochs=15,
secondary_learner_batch_size=128,
eval_freq=100,
igf_model_path="igf_model.pt",
)
# load pretrained gpt2 model
model = GPT2LMHeadModel.from_pretrained("gpt2")
set_seed(42)
# Generate train and test data to train and evaluate gpt2 model
train_dataset, test_dataset = generate_datasets(
context_len=32, file="data/tokenized_stories_train_wikitext103.jbl", number=100, min_len=1026, trim=True
)
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
model,
train_dataset,
test_dataset,
context_len=32,
max_steps=1000,
batch_size=16,
threshold=1.0,
recopy_model=recopy_gpt2,
secondary_learner=secondary_learner,
eval_interval=10,
finetuned_model_name="gpt2_finetuned.pt",
)
if __name__ == "__main__":
main()
| # Copyright 2022 - Intel Corp. All rights reserved.
# Authors: Mayank Kumar Raunak, Javier Turek, Nicole Beckage
"""
Implementation of a new method for fine-tuning transformer models that we call
Information Gain Filtration 'IGF' on WikiText data set and compared the results
with the standard fine-tuning method
Steps followed in the code:
1) Generate a objective dataset of pairs (X, IG(X)). IG(X)--Informativeness of context 'X'.
Our IG (information gain) model is learning to predict the ‘informativeness’ of a particular
context. Informativeness is the change in metric between the model’s accuracy on an
objective set before and after seeing that context. For casual language modeling, the
metric is perplexity.
2) A secondary learner is trained to infer a function approximation for IG using the dataset
created in (1).
3) The learner created in (2) is used to inform the fine-tuning process and filter out low informative samples.
Last, a plot is generated to compare the performance of IGF to standard fine-tuning without any filtering
"""
# Prerequisite libraries:
import argparse
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
import joblib
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpt2,
recopy_gpt2,
set_seed,
train_secondary_learner,
)
from transformers import GPT2LMHeadModel
def generate_n_pairs(
context_len=32,
max_steps=10,
size_objective_set=100,
min_len=1026,
trim=True,
data_file="data/tokenized_stories_train_wikitext103.jbl",
igf_data_file="igf_context_pairs.jbl",
):
"""
Collecting *n* pairs for training the secondary learner
Args:
context_len: The maximum total input sequence length after tokenization. Sequences longer
than this will be truncated, sequences shorter will be padded
max_steps: To calculate training epochs of secondary learner
size_objective_set: size of objective data set used to create (X,IG(X)) pairs which is the training data for secondary learner
min_len: The minimum length of the article to be used as objective set
trim: If True truncate the context if it exceeds context length
data_file: Tokenized data set split for training and evaluation of model
igf_data_file: file to store (I,IG(X)) paired data set to train secondary learner
Returns:
Data stored in igf_data_file
"""
# generates same data everytime
set_seed(3)
# generate train_data and objective_set
train_data, objective_set = generate_datasets(
context_len, data_file, number=size_objective_set, min_len=1026, trim=True
)
# keeps model same across runs
set_seed(4)
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load pretrained model
model = load_gpt2("gpt2").to(device)
print("computing perplexity on objective set")
orig_perp = compute_perplexity(model, objective_set, context_len).item()
print("perplexity on objective set:", orig_perp)
# collect igf pairs and save to file demo.jbl
collect_objective_set(model, orig_perp, context_len, train_data, objective_set, max_steps, device, igf_data_file)
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def training_secondary_learner(
secondary_learner_train_data,
secondary_learner_max_epochs=15,
secondary_learner_batch_size=128,
eval_freq=100,
igf_model_path="igf_model.pt",
):
"""
Train the secondary learner
Args:
secondary_learner_train_data: Data set with (X,IG(X)) pairs to train secondary learner where IG(X) - measure of informativeness and X- context
secondary_learner_max_epochs: Number of epochs to train secondary learner
secondary_learner_batch_size: Batch size to train secondary learner
eval_freq (object): secondary model evaluation can be triggered at eval_freq
igf_model_path: path to store trained secondary learner
Returns:
Trained secondary learner
"""
set_seed(42)
# Load pre-trained model
model = GPT2LMHeadModel.from_pretrained("gpt2")
# Initialize secondary learner to use embedding weights of model
secondary_learner = SecondaryLearner(model)
# Train secondary learner
secondary_learner = train_secondary_learner(
secondary_learner,
secondary_learner_train_data,
max_epochs=secondary_learner_max_epochs,
batch_size=secondary_learner_batch_size,
eval_freq=100,
igf_model_path=igf_model_path,
)
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def finetune(
model,
train_dataset,
test_dataset,
context_len=32,
max_steps=1000,
batch_size=16,
threshold=1.0,
recopy_model=recopy_gpt2,
secondary_learner=None,
eval_interval=10,
finetuned_model_name="gpt2_finetuned.pt",
):
"""
fine-tune with IGF if secondary_learner is not None, else standard fine-tuning
Args:
model: pre-trained GPT-2 model
train_dataset: Data set to train GPT-2 model
test_dataset: Evaluate GPT-2 model
context_len: The maximum total input sequence length after tokenization. Sequences longer
than this will be truncated, sequences shorter will be padded
max_steps: To calculate training epochs
batch_size: Batch size to train GPT-2 model
threshold: The threshold value used by secondary learner to filter the train_data and allow only"
informative data as input to the model
recopy_model: Reset the model to the original pretrained GPT-2 weights after each iteration
secondary_learner: Selection of IGF as fine-tuning method if not None
eval_interval: number of batches after which decay the selectivity of our secondary learner filter from
1 standard deviation above average to 1 below average
fine-tuned_model_name: name of the final final-tuned GPT-2 model
Returns:
Fine-tuned GPT-2 model
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler)
num_train_epochs = max_steps // (len(train_dataset)) + 1
global_step = 0
context = torch.zeros((1, context_len), dtype=torch.long, device=device)
model, lm_optimizer, lm_scheduler = recopy_model(model, device, max_steps)
model.train()
if secondary_learner is not None:
secondary_learner.to(device)
secondary_learner.eval()
contexts = []
examples = 0
observed_qs = []
test_perps = []
# Compute the performance of the transformer model at the beginning
real_perp = compute_perplexity(model, test_dataset, context_len)
test_perps.append(real_perp)
print("Test perplexity, step", global_step, ":", real_perp)
for epoch in range(int(num_train_epochs)):
for step, example in enumerate(train_dataloader):
torch.cuda.empty_cache()
start = random.randint(0, example.size(2) - context_len - 1)
context[0, :] = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
outputs = model(context, labels=context)
do_backprop = True
if secondary_learner is not None:
predicted_q = secondary_learner.forward(
torch.tensor(context, dtype=torch.long, device=device).unsqueeze(0)
)[0].item()
observed_qs.append(float(predicted_q))
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
threshold = -1
if predicted_q < threshold:
do_backprop = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu()))
lm_loss = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
examples = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters(), 3.0)
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
real_perp = compute_perplexity(model, test_dataset, context_len)
test_perps.append(real_perp)
print("Test perplexity, step", global_step, ":", real_perp)
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict(), finetuned_model_name)
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def main():
parser = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task")
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain data files for WikiText.",
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--data_file",
type=str,
default=None,
help=(
"A jbl file containing tokenized data which can be split as objective dataset, "
"train_dataset and test_dataset."
),
)
parser.add_argument(
"--igf_data_file",
type=str,
default=None,
help="A jbl file containing the context and information gain pairs to train secondary learner.",
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the final fine-tuned model is stored.",
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--context_len",
default=32,
type=int,
help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
),
)
parser.add_argument(
"--size_objective_set",
default=100,
type=int,
help="number of articles that are long enough to be used as our objective set",
)
parser.add_argument(
"--eval_freq", default=100, type=int, help="secondary model evaluation is triggered at eval_freq"
)
parser.add_argument("--max_steps", default=1000, type=int, help="To calculate training epochs")
parser.add_argument(
"--secondary_learner_batch_size",
default=128,
type=int,
help="batch size of training data for secondary learner",
)
parser.add_argument(
"--batch_size", default=16, type=int, help="batch size of training data of language model(gpt2) "
)
parser.add_argument(
"--eval_interval",
default=10,
type=int,
help=(
"decay the selectivity of our secondary learner filter from"
"1 standard deviation above average to 1 below average after 10 batches"
),
)
parser.add_argument(
"--number", default=100, type=int, help="The number of examples split to be used as objective_set/test_data"
)
parser.add_argument(
"--min_len", default=1026, type=int, help="The minimum length of the article to be used as objective set"
)
parser.add_argument(
"--secondary_learner_max_epochs", default=15, type=int, help="number of epochs to train secondary learner"
)
parser.add_argument("--trim", default=True, type=bool, help="truncate the example if it exceeds context length")
parser.add_argument(
"--threshold",
default=1.0,
type=float,
help=(
"The threshold value used by secondary learner to filter the train_data and allow only"
" informative data as input to the model"
),
)
parser.add_argument("--finetuned_model_name", default="gpt2_finetuned.pt", type=str, help="finetuned_model_name")
parser.add_argument(
"--recopy_model",
default=recopy_gpt2,
type=str,
help="Reset the model to the original pretrained GPT-2 weights after each iteration",
)
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32,
max_steps=10,
size_objective_set=100,
min_len=1026,
trim=True,
data_file="data/tokenized_stories_train_wikitext103.jbl",
igf_data_file="igf_context_pairs.jbl",
)
# Load train data for secondary learner
secondary_learner_train_data = joblib.load("data/IGF_values.jbl")
# Train secondary learner
secondary_learner = training_secondary_learner(
secondary_learner_train_data,
secondary_learner_max_epochs=15,
secondary_learner_batch_size=128,
eval_freq=100,
igf_model_path="igf_model.pt",
)
# load pretrained gpt2 model
model = GPT2LMHeadModel.from_pretrained("gpt2")
set_seed(42)
# Generate train and test data to train and evaluate gpt2 model
train_dataset, test_dataset = generate_datasets(
context_len=32, file="data/tokenized_stories_train_wikitext103.jbl", number=100, min_len=1026, trim=True
)
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
model,
train_dataset,
test_dataset,
context_len=32,
max_steps=1000,
batch_size=16,
threshold=1.0,
recopy_model=recopy_gpt2,
secondary_learner=secondary_learner,
eval_interval=10,
finetuned_model_name="gpt2_finetuned.pt",
)
if __name__ == "__main__":
main()
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/mctct/modeling_mctct.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch M-CTC-T model."""
import math
import random
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import BaseModelOutput, CausalLMOutput
from ...modeling_utils import (
PreTrainedModel,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
from ...utils import logging
from .configuration_mctct import MCTCTConfig
logger = logging.get_logger(__name__)
_HIDDEN_STATES_START_POSITION = 1
_CONFIG_FOR_DOC = "MCTCTConfig"
_PROCESSOR_FOR_DOC = "MCTCTProcessor"
# Base docstring
_CHECKPOINT_FOR_DOC = "speechbrain/m-ctc-t-large"
_EXPECTED_OUTPUT_SHAPE = [1, 195, 1536]
# CTC docstring
_CTC_EXPECTED_OUTPUT = '"Mr. Quilter is the apostle of the middle classes, and we\'re glad to welcome his gospel."'
_CTC_EXPECTED_LOSS = 1885.65
MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"speechbrain/m-ctc-t-large",
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
]
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class MCTCTConv1dSubsampler(nn.Module):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config):
super().__init__()
self.config = config
self.glu_dim = config.conv_glu_dim
self.dropout = nn.Dropout(config.conv_dropout)
self.num_layers = config.num_conv_layers
self.in_channels = config.input_feat_per_channel * config.input_channels
if self.num_layers > 1:
if config.conv_channels is None:
raise ValueError(
"Need to specify `conv_channels` configuration in `MCTCTConfig` to use multiple convolution"
" layers."
)
self.mid_channels = config.conv_channels
else:
self.mid_channels = None
self.out_channels = config.hidden_size * 2 # considering GLU halving
self.kernel_size = config.conv_kernel
self.stride = config.conv_stride
# NOTE: MCTCT by construction only uses one convolution kernel. I've made this flexible to allow for
# multiple layers of convolutions, but not sure if this model definition should just restrict it
# to one layer. This becomes especially relevant when considering the padding like line 1 of forward().
self.conv_layers = nn.ModuleList(
nn.Conv1d(
self.in_channels if i == 0 else self.mid_channels[i],
self.mid_channels[i] if i < self.num_layers - 1 else self.out_channels,
kernel_size=k,
stride=self.stride[i],
padding="valid",
)
for i, k in enumerate(self.kernel_size)
)
def forward(self, input_features):
# NOTE: in reference to the NOTE in __init__, right now it just calculates padding as if
# there will be just one conv layer.
padding = sum([size // 2 for size in self.kernel_size]) # (7, 7) -> (3, 3)
input_features = torch.nn.functional.pad(input_features, (0, 0, padding, padding), "constant", 0)
hidden_states = input_features.transpose(1, 2).contiguous() # -> Batch x Frame x Time
for conv in self.conv_layers:
hidden_states = conv(hidden_states)
hidden_states = nn.functional.glu(hidden_states, dim=self.glu_dim)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.transpose(1, 2).contiguous() # -> Batch x Time x Frame
return hidden_states
class MCTCTEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
# self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.LayerNorm = MCTCTLayerNorm()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
persistent=False,
)
def forward(
self, input_features=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
input_shape = input_features.size() if input_features is not None else inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_features)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class MCTCTSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = config.attention_head_dim
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def reshape_fortran(self, x, shape):
if len(x.shape) > 0:
x = x.permute(*reversed(range(len(x.shape))))
return x.reshape(*reversed(shape)).permute(*reversed(range(len(shape))))
def relative_position_embedding_rotate(self, scores):
# NOTE: should re-evaluate whether this re-implementation was truly necessary
# or the reason why my complete re-haul worked was due to some other part
# of the code. Adding this and the reshape fortrain code seems very undesirable.
scores = scores.permute(0, 2, 3, 1) # e.g. [10, 1839, 14, 4]
batch, hidden_state, seq_len, heads = scores.shape
# e.g. [10, 1853, 14, 4]
scores = torch.cat((scores, torch.zeros((batch, seq_len, seq_len, heads), device=scores.device)), dim=1)
# e.g. [10, 25942, 1, 4]
scores = self.reshape_fortran(scores, [batch, (hidden_state + seq_len) * seq_len, 1, heads])
# e.g. [10, 25928, 1, 4]
scores = scores[:, : (seq_len + hidden_state - 1) * seq_len]
# e.g. [10, 1852, 14, 4]
scores = self.reshape_fortran(scores, [batch, hidden_state + seq_len - 1, seq_len, heads])
halfpoint = hidden_state // 2
scores = scores[:, halfpoint : halfpoint + seq_len].transpose(1, 2) # e.g. [10, 14, 14, 4]
return scores.permute(0, 3, 1, 2)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
mixed_query_layer = mixed_query_layer / math.sqrt(self.attention_head_size)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
# relative key position embeddings
positional_embedding = self.distance_embedding.weight
relative_position_scores = torch.einsum("lh, bche -> bcle", positional_embedding, query_layer.transpose(2, 3))
relative_position_scores = self.relative_position_embedding_rotate(relative_position_scores)
attention_scores = attention_scores + relative_position_scores
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in MCTCTModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).flatten(start_dim=-2)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class MCTCTLayerNorm(nn.Module):
def __init__(self):
super().__init__()
self.singleton_weight = nn.Parameter(torch.ones(1))
self.singleton_bias = nn.Parameter(torch.zeros(1))
def forward(self, hidden_states):
return (hidden_states * self.singleton_weight) + self.singleton_bias
class MCTCTSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MCTCTAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = MCTCTSelfAttention(config)
self.output = MCTCTSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class MCTCTIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class MCTCTOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MCTCTLayer(nn.Module):
def __init__(self, config: MCTCTConfig):
super().__init__()
self.seq_len_dim = 1
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.intermediate = MCTCTIntermediate(config)
self.attention = MCTCTAttention(config)
self.is_decoder = config.is_decoder
self.output = MCTCTOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs = self.attention(
hidden_states, attention_mask, head_mask, output_attentions=output_attentions
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class MCTCTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MCTCTConfig
base_model_prefix = "mctct"
main_input_name = "input_features"
_keys_to_ignore_on_load_missing = ["position_ids"]
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
std = self.config.initializer_range
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, MCTCTLayerNorm):
module.singleton_weight.data.fill_(1.0)
module.singleton_bias.data.zero_()
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers
"""
dilation = 1
for _, kernel_sz, stride in zip(
range(self.config.num_conv_layers), self.config.conv_kernel, self.config.conv_stride
):
padding = kernel_sz // 2
input_lengths = input_lengths + 2 * padding - dilation * (kernel_sz - 1) - 1
input_lengths = torch.div(input_lengths, stride, rounding_mode="trunc") + 1
return input_lengths
def _get_feature_vector_attention_mask(self, feature_vector_length, attention_mask):
# generate creates 3D attention mask, because of the shape of input_features
# convert it to 2D if thats the case
if len(attention_mask.shape) > 2:
attention_mask = attention_mask[:, :, -1]
# subsampled_lengths = attention_mask.sum(-1)
subsampled_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
bsz = attention_mask.size()[0]
attention_mask = torch.zeros(
(bsz, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values
# before the output lengths indices are attended to
attention_mask[(torch.arange(bsz, device=attention_mask.device), subsampled_lengths - 1)] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).long()
return attention_mask
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (MCTCTEncoder)):
module.gradient_checkpointing = value
MCTCT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MCTCTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MCTCT_INPUTS_DOCSTRING = r"""
Args:
input_features (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`Wav2Vec2CTCTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
class MCTCTEncoder(MCTCTPreTrainedModel):
def __init__(self, config: MCTCTConfig):
super().__init__(config)
self.hidden_dropout_prob = config.hidden_dropout_prob
self.layer_norm = MCTCTLayerNorm()
self.conv = MCTCTConv1dSubsampler(config)
self.layers = nn.ModuleList([MCTCTLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
input_features: torch.Tensor,
attention_mask: torch.Tensor,
head_mask: torch.Tensor,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
input_features = self.layer_norm(input_features)
inputs_embeds = self.conv(input_features)
# subsample attention mask if necessary
if attention_mask is not None:
attention_mask = self._get_feature_vector_attention_mask(inputs_embeds.shape[1], attention_mask)
hidden_states = nn.functional.dropout(inputs_embeds, p=self.hidden_dropout_prob, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, "
f"but it is for {head_mask.size()[0]}."
)
deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
if not skip_the_layer or deepspeed_zero3_is_enabled:
# under deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
@add_start_docstrings(
"The bare M-CTC-T Model transformer outputting raw hidden-states without any specific head on top.",
MCTCT_START_DOCSTRING,
)
class MCTCTModel(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = MCTCTEncoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_features: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_features is None:
raise ValueError("You have to specify input_features.")
encoder_outputs = self.encoder(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""MCTCT Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
MCTCT_START_DOCSTRING,
)
class MCTCTForCTC(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mctct = MCTCTModel(config)
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
"does not define the vocabulary size of the language model head. Please "
"instantiate the model as follows: `MCTCTForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
"or define `vocab_size` of your model's configuration."
)
output_hidden_size = config.hidden_size
self.ctc_head = nn.Linear(output_hidden_size, config.vocab_size)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_CTC_EXPECTED_OUTPUT,
expected_loss=_CTC_EXPECTED_LOSS,
)
def forward(
self,
input_features: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mctct(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.ctc_head(hidden_states)
loss = None
if labels is not None:
if labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
# retrieve loss input_lengths from attention_mask
attention_mask = (
attention_mask
if attention_mask is not None
else torch.ones(input_features.shape[:-1], dtype=torch.long)
)
input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch M-CTC-T model."""
import math
import random
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import BaseModelOutput, CausalLMOutput
from ...modeling_utils import (
PreTrainedModel,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
from ...utils import logging
from .configuration_mctct import MCTCTConfig
logger = logging.get_logger(__name__)
_HIDDEN_STATES_START_POSITION = 1
_CONFIG_FOR_DOC = "MCTCTConfig"
_PROCESSOR_FOR_DOC = "MCTCTProcessor"
# Base docstring
_CHECKPOINT_FOR_DOC = "speechbrain/m-ctc-t-large"
_EXPECTED_OUTPUT_SHAPE = [1, 195, 1536]
# CTC docstring
_CTC_EXPECTED_OUTPUT = '"Mr. Quilter is the apostle of the middle classes, and we\'re glad to welcome his gospel."'
_CTC_EXPECTED_LOSS = 1885.65
MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"speechbrain/m-ctc-t-large",
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
]
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class MCTCTConv1dSubsampler(nn.Module):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config):
super().__init__()
self.config = config
self.glu_dim = config.conv_glu_dim
self.dropout = nn.Dropout(config.conv_dropout)
self.num_layers = config.num_conv_layers
self.in_channels = config.input_feat_per_channel * config.input_channels
if self.num_layers > 1:
if config.conv_channels is None:
raise ValueError(
"Need to specify `conv_channels` configuration in `MCTCTConfig` to use multiple convolution"
" layers."
)
self.mid_channels = config.conv_channels
else:
self.mid_channels = None
self.out_channels = config.hidden_size * 2 # considering GLU halving
self.kernel_size = config.conv_kernel
self.stride = config.conv_stride
# NOTE: MCTCT by construction only uses one convolution kernel. I've made this flexible to allow for
# multiple layers of convolutions, but not sure if this model definition should just restrict it
# to one layer. This becomes especially relevant when considering the padding like line 1 of forward().
self.conv_layers = nn.ModuleList(
nn.Conv1d(
self.in_channels if i == 0 else self.mid_channels[i],
self.mid_channels[i] if i < self.num_layers - 1 else self.out_channels,
kernel_size=k,
stride=self.stride[i],
padding="valid",
)
for i, k in enumerate(self.kernel_size)
)
def forward(self, input_features):
# NOTE: in reference to the NOTE in __init__, right now it just calculates padding as if
# there will be just one conv layer.
padding = sum([size // 2 for size in self.kernel_size]) # (7, 7) -> (3, 3)
input_features = torch.nn.functional.pad(input_features, (0, 0, padding, padding), "constant", 0)
hidden_states = input_features.transpose(1, 2).contiguous() # -> Batch x Frame x Time
for conv in self.conv_layers:
hidden_states = conv(hidden_states)
hidden_states = nn.functional.glu(hidden_states, dim=self.glu_dim)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.transpose(1, 2).contiguous() # -> Batch x Time x Frame
return hidden_states
class MCTCTEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
# self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.LayerNorm = MCTCTLayerNorm()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
persistent=False,
)
def forward(
self, input_features=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
input_shape = input_features.size() if input_features is not None else inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_features)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class MCTCTSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = config.attention_head_dim
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def reshape_fortran(self, x, shape):
if len(x.shape) > 0:
x = x.permute(*reversed(range(len(x.shape))))
return x.reshape(*reversed(shape)).permute(*reversed(range(len(shape))))
def relative_position_embedding_rotate(self, scores):
# NOTE: should re-evaluate whether this re-implementation was truly necessary
# or the reason why my complete re-haul worked was due to some other part
# of the code. Adding this and the reshape fortrain code seems very undesirable.
scores = scores.permute(0, 2, 3, 1) # e.g. [10, 1839, 14, 4]
batch, hidden_state, seq_len, heads = scores.shape
# e.g. [10, 1853, 14, 4]
scores = torch.cat((scores, torch.zeros((batch, seq_len, seq_len, heads), device=scores.device)), dim=1)
# e.g. [10, 25942, 1, 4]
scores = self.reshape_fortran(scores, [batch, (hidden_state + seq_len) * seq_len, 1, heads])
# e.g. [10, 25928, 1, 4]
scores = scores[:, : (seq_len + hidden_state - 1) * seq_len]
# e.g. [10, 1852, 14, 4]
scores = self.reshape_fortran(scores, [batch, hidden_state + seq_len - 1, seq_len, heads])
halfpoint = hidden_state // 2
scores = scores[:, halfpoint : halfpoint + seq_len].transpose(1, 2) # e.g. [10, 14, 14, 4]
return scores.permute(0, 3, 1, 2)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
mixed_query_layer = mixed_query_layer / math.sqrt(self.attention_head_size)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
# relative key position embeddings
positional_embedding = self.distance_embedding.weight
relative_position_scores = torch.einsum("lh, bche -> bcle", positional_embedding, query_layer.transpose(2, 3))
relative_position_scores = self.relative_position_embedding_rotate(relative_position_scores)
attention_scores = attention_scores + relative_position_scores
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in MCTCTModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).flatten(start_dim=-2)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class MCTCTLayerNorm(nn.Module):
def __init__(self):
super().__init__()
self.singleton_weight = nn.Parameter(torch.ones(1))
self.singleton_bias = nn.Parameter(torch.zeros(1))
def forward(self, hidden_states):
return (hidden_states * self.singleton_weight) + self.singleton_bias
class MCTCTSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MCTCTAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = MCTCTSelfAttention(config)
self.output = MCTCTSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class MCTCTIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class MCTCTOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MCTCTLayer(nn.Module):
def __init__(self, config: MCTCTConfig):
super().__init__()
self.seq_len_dim = 1
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.intermediate = MCTCTIntermediate(config)
self.attention = MCTCTAttention(config)
self.is_decoder = config.is_decoder
self.output = MCTCTOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs = self.attention(
hidden_states, attention_mask, head_mask, output_attentions=output_attentions
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class MCTCTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MCTCTConfig
base_model_prefix = "mctct"
main_input_name = "input_features"
_keys_to_ignore_on_load_missing = ["position_ids"]
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
std = self.config.initializer_range
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, MCTCTLayerNorm):
module.singleton_weight.data.fill_(1.0)
module.singleton_bias.data.zero_()
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers
"""
dilation = 1
for _, kernel_sz, stride in zip(
range(self.config.num_conv_layers), self.config.conv_kernel, self.config.conv_stride
):
padding = kernel_sz // 2
input_lengths = input_lengths + 2 * padding - dilation * (kernel_sz - 1) - 1
input_lengths = torch.div(input_lengths, stride, rounding_mode="trunc") + 1
return input_lengths
def _get_feature_vector_attention_mask(self, feature_vector_length, attention_mask):
# generate creates 3D attention mask, because of the shape of input_features
# convert it to 2D if thats the case
if len(attention_mask.shape) > 2:
attention_mask = attention_mask[:, :, -1]
# subsampled_lengths = attention_mask.sum(-1)
subsampled_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
bsz = attention_mask.size()[0]
attention_mask = torch.zeros(
(bsz, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values
# before the output lengths indices are attended to
attention_mask[(torch.arange(bsz, device=attention_mask.device), subsampled_lengths - 1)] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).long()
return attention_mask
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (MCTCTEncoder)):
module.gradient_checkpointing = value
MCTCT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MCTCTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MCTCT_INPUTS_DOCSTRING = r"""
Args:
input_features (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`Wav2Vec2CTCTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
class MCTCTEncoder(MCTCTPreTrainedModel):
def __init__(self, config: MCTCTConfig):
super().__init__(config)
self.hidden_dropout_prob = config.hidden_dropout_prob
self.layer_norm = MCTCTLayerNorm()
self.conv = MCTCTConv1dSubsampler(config)
self.layers = nn.ModuleList([MCTCTLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
input_features: torch.Tensor,
attention_mask: torch.Tensor,
head_mask: torch.Tensor,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
input_features = self.layer_norm(input_features)
inputs_embeds = self.conv(input_features)
# subsample attention mask if necessary
if attention_mask is not None:
attention_mask = self._get_feature_vector_attention_mask(inputs_embeds.shape[1], attention_mask)
hidden_states = nn.functional.dropout(inputs_embeds, p=self.hidden_dropout_prob, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, "
f"but it is for {head_mask.size()[0]}."
)
deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
if not skip_the_layer or deepspeed_zero3_is_enabled:
# under deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
@add_start_docstrings(
"The bare M-CTC-T Model transformer outputting raw hidden-states without any specific head on top.",
MCTCT_START_DOCSTRING,
)
class MCTCTModel(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = MCTCTEncoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_features: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_features is None:
raise ValueError("You have to specify input_features.")
encoder_outputs = self.encoder(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""MCTCT Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
MCTCT_START_DOCSTRING,
)
class MCTCTForCTC(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mctct = MCTCTModel(config)
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
"does not define the vocabulary size of the language model head. Please "
"instantiate the model as follows: `MCTCTForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
"or define `vocab_size` of your model's configuration."
)
output_hidden_size = config.hidden_size
self.ctc_head = nn.Linear(output_hidden_size, config.vocab_size)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_CTC_EXPECTED_OUTPUT,
expected_loss=_CTC_EXPECTED_LOSS,
)
def forward(
self,
input_features: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mctct(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.ctc_head(hidden_states)
loss = None
if labels is not None:
if labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
# retrieve loss input_lengths from attention_mask
attention_mask = (
attention_mask
if attention_mask is not None
else torch.ones(input_features.shape[:-1], dtype=torch.long)
)
input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/research_projects/seq2seq-distillation/make_student.py | import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
logger = logging.get_logger(__name__)
def copy_layers(src_layers: nn.ModuleList, dest_layers: nn.ModuleList, layers_to_copy: List[int]) -> None:
layers_to_copy = nn.ModuleList([src_layers[i] for i in layers_to_copy])
assert len(dest_layers) == len(layers_to_copy), f"{len(dest_layers)} != {len(layers_to_copy)}"
dest_layers.load_state_dict(layers_to_copy.state_dict())
LAYERS_TO_COPY = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
LAYERS_TO_SUPERVISE = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def pick_layers_to_copy(n_student, n_teacher):
try:
val = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
f" {n_student}"
)
return list(range(n_student))
def get_layers_to_supervise(n_student, n_teacher) -> List[int]:
"""Used or the --supervise_forward kwarg"""
if n_student > n_teacher:
raise ValueError(f"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}")
elif n_teacher == n_student:
return list(range(n_teacher))
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def create_student_by_copying_alternating_layers(
teacher: Union[str, PreTrainedModel],
save_path: Union[str, Path] = "student",
e: Union[int, None] = None,
d: Union[int, None] = None,
copy_first_teacher_layers=False,
e_layers_to_copy=None,
d_layers_to_copy=None,
**extra_config_kwargs
) -> Tuple[PreTrainedModel, List[int], List[int]]:
"""Make a student by copying alternating layers from a teacher, save it to save_path.
Args:
teacher: str or PreTrainedModel if str, this will call AutoModelForSeq2SeqLM.from_pretrained(teacher) before
copying layers
save_path: where to save the student, defaults to student directory.
e: how many Encoder layers should the student have, default is fully copy of teacher
d: how many Decoder layers should the student have, default is fully copy of teacher
copy_first_teacher_layers: [bool] dont copy alternating layers, just the first e/d.
**extra_config_kwargs: extra kwargs to pass to the student, by default the teacher config is used.
Returns:
student: new, smaller model. (Also saves it to save_path)
e_layers_to_copy: list of which teacher encoder layers were used
d_layers_to_copy: list of which teacher decoder layers were used
"""
_msg = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(teacher, str):
AutoTokenizer.from_pretrained(teacher).save_pretrained(save_path) # purely for convenience
teacher = AutoModelForSeq2SeqLM.from_pretrained(teacher).eval()
else:
assert isinstance(teacher, PreTrainedModel), f"teacher must be a model or string got type {type(teacher)}"
init_kwargs = teacher.config.to_diff_dict()
try:
teacher_e, teacher_d = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
e = teacher_e
if d is None:
d = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
except AttributeError: # T5
if hasattr(teacher.config, "num_encoder_layers"):
teacher_e, teacher_d = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
teacher_e, teacher_d = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
e = teacher_e
if d is None:
d = teacher_d
if hasattr(teacher.config, "num_encoder_layers"):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d})
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d})
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(extra_config_kwargs)
# Copy weights
student_cfg = teacher.config_class(**init_kwargs)
student = AutoModelForSeq2SeqLM.from_config(student_cfg)
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
info = student.load_state_dict(teacher.state_dict(), strict=False)
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
e_layers_to_copy, d_layers_to_copy = list(range(e)), list(range(d))
logger.info(
f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
f" {save_path}"
)
student.save_pretrained(save_path)
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
e_layers_to_copy: List[int] = pick_layers_to_copy(e, teacher_e)
if d_layers_to_copy is None:
d_layers_to_copy: List[int] = pick_layers_to_copy(d, teacher_d)
try:
if hasattr(
teacher, "prophetnet"
): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers, student.prophetnet.encoder.layers, e_layers_to_copy)
copy_layers(teacher.prophetnet.decoder.layers, student.prophetnet.decoder.layers, d_layers_to_copy)
else:
copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, e_layers_to_copy)
copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, d_layers_to_copy)
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block, student.encoder.block, e_layers_to_copy)
copy_layers(teacher.decoder.block, student.decoder.block, d_layers_to_copy)
logger.info(
f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"
)
student.config.init_metadata = dict(
teacher_type=teacher.config.model_type,
copied_encoder_layers=e_layers_to_copy,
copied_decoder_layers=d_layers_to_copy,
)
student.save_pretrained(save_path)
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
logger = logging.get_logger(__name__)
def copy_layers(src_layers: nn.ModuleList, dest_layers: nn.ModuleList, layers_to_copy: List[int]) -> None:
layers_to_copy = nn.ModuleList([src_layers[i] for i in layers_to_copy])
assert len(dest_layers) == len(layers_to_copy), f"{len(dest_layers)} != {len(layers_to_copy)}"
dest_layers.load_state_dict(layers_to_copy.state_dict())
LAYERS_TO_COPY = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
LAYERS_TO_SUPERVISE = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def pick_layers_to_copy(n_student, n_teacher):
try:
val = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
f" {n_student}"
)
return list(range(n_student))
def get_layers_to_supervise(n_student, n_teacher) -> List[int]:
"""Used or the --supervise_forward kwarg"""
if n_student > n_teacher:
raise ValueError(f"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}")
elif n_teacher == n_student:
return list(range(n_teacher))
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def create_student_by_copying_alternating_layers(
teacher: Union[str, PreTrainedModel],
save_path: Union[str, Path] = "student",
e: Union[int, None] = None,
d: Union[int, None] = None,
copy_first_teacher_layers=False,
e_layers_to_copy=None,
d_layers_to_copy=None,
**extra_config_kwargs
) -> Tuple[PreTrainedModel, List[int], List[int]]:
"""Make a student by copying alternating layers from a teacher, save it to save_path.
Args:
teacher: str or PreTrainedModel if str, this will call AutoModelForSeq2SeqLM.from_pretrained(teacher) before
copying layers
save_path: where to save the student, defaults to student directory.
e: how many Encoder layers should the student have, default is fully copy of teacher
d: how many Decoder layers should the student have, default is fully copy of teacher
copy_first_teacher_layers: [bool] dont copy alternating layers, just the first e/d.
**extra_config_kwargs: extra kwargs to pass to the student, by default the teacher config is used.
Returns:
student: new, smaller model. (Also saves it to save_path)
e_layers_to_copy: list of which teacher encoder layers were used
d_layers_to_copy: list of which teacher decoder layers were used
"""
_msg = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(teacher, str):
AutoTokenizer.from_pretrained(teacher).save_pretrained(save_path) # purely for convenience
teacher = AutoModelForSeq2SeqLM.from_pretrained(teacher).eval()
else:
assert isinstance(teacher, PreTrainedModel), f"teacher must be a model or string got type {type(teacher)}"
init_kwargs = teacher.config.to_diff_dict()
try:
teacher_e, teacher_d = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
e = teacher_e
if d is None:
d = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
except AttributeError: # T5
if hasattr(teacher.config, "num_encoder_layers"):
teacher_e, teacher_d = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
teacher_e, teacher_d = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
e = teacher_e
if d is None:
d = teacher_d
if hasattr(teacher.config, "num_encoder_layers"):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d})
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d})
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(extra_config_kwargs)
# Copy weights
student_cfg = teacher.config_class(**init_kwargs)
student = AutoModelForSeq2SeqLM.from_config(student_cfg)
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
info = student.load_state_dict(teacher.state_dict(), strict=False)
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
e_layers_to_copy, d_layers_to_copy = list(range(e)), list(range(d))
logger.info(
f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
f" {save_path}"
)
student.save_pretrained(save_path)
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
e_layers_to_copy: List[int] = pick_layers_to_copy(e, teacher_e)
if d_layers_to_copy is None:
d_layers_to_copy: List[int] = pick_layers_to_copy(d, teacher_d)
try:
if hasattr(
teacher, "prophetnet"
): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers, student.prophetnet.encoder.layers, e_layers_to_copy)
copy_layers(teacher.prophetnet.decoder.layers, student.prophetnet.decoder.layers, d_layers_to_copy)
else:
copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, e_layers_to_copy)
copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, d_layers_to_copy)
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block, student.encoder.block, e_layers_to_copy)
copy_layers(teacher.decoder.block, student.decoder.block, d_layers_to_copy)
logger.info(
f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"
)
student.config.init_metadata = dict(
teacher_type=teacher.config.model_type,
copied_encoder_layers=e_layers_to_copy,
copied_decoder_layers=d_layers_to_copy,
)
student.save_pretrained(save_path)
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/auto/modeling_tf_auto.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Auto Model class."""
import warnings
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
logger = logging.get_logger(__name__)
TF_MODEL_MAPPING_NAMES = OrderedDict(
[
# Base model mapping
("albert", "TFAlbertModel"),
("bart", "TFBartModel"),
("bert", "TFBertModel"),
("blenderbot", "TFBlenderbotModel"),
("blenderbot-small", "TFBlenderbotSmallModel"),
("camembert", "TFCamembertModel"),
("clip", "TFCLIPModel"),
("convbert", "TFConvBertModel"),
("convnext", "TFConvNextModel"),
("ctrl", "TFCTRLModel"),
("cvt", "TFCvtModel"),
("data2vec-vision", "TFData2VecVisionModel"),
("deberta", "TFDebertaModel"),
("deberta-v2", "TFDebertaV2Model"),
("deit", "TFDeiTModel"),
("distilbert", "TFDistilBertModel"),
("dpr", "TFDPRQuestionEncoder"),
("electra", "TFElectraModel"),
("esm", "TFEsmModel"),
("flaubert", "TFFlaubertModel"),
("funnel", ("TFFunnelModel", "TFFunnelBaseModel")),
("gpt2", "TFGPT2Model"),
("gptj", "TFGPTJModel"),
("groupvit", "TFGroupViTModel"),
("hubert", "TFHubertModel"),
("layoutlm", "TFLayoutLMModel"),
("layoutlmv3", "TFLayoutLMv3Model"),
("led", "TFLEDModel"),
("longformer", "TFLongformerModel"),
("lxmert", "TFLxmertModel"),
("marian", "TFMarianModel"),
("mbart", "TFMBartModel"),
("mobilebert", "TFMobileBertModel"),
("mobilevit", "TFMobileViTModel"),
("mpnet", "TFMPNetModel"),
("mt5", "TFMT5Model"),
("openai-gpt", "TFOpenAIGPTModel"),
("opt", "TFOPTModel"),
("pegasus", "TFPegasusModel"),
("regnet", "TFRegNetModel"),
("rembert", "TFRemBertModel"),
("resnet", "TFResNetModel"),
("roberta", "TFRobertaModel"),
("roformer", "TFRoFormerModel"),
("segformer", "TFSegformerModel"),
("speech_to_text", "TFSpeech2TextModel"),
("swin", "TFSwinModel"),
("t5", "TFT5Model"),
("tapas", "TFTapasModel"),
("transfo-xl", "TFTransfoXLModel"),
("vit", "TFViTModel"),
("vit_mae", "TFViTMAEModel"),
("wav2vec2", "TFWav2Vec2Model"),
("whisper", "TFWhisperModel"),
("xglm", "TFXGLMModel"),
("xlm", "TFXLMModel"),
("xlm-roberta", "TFXLMRobertaModel"),
("xlnet", "TFXLNetModel"),
]
)
TF_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
[
# Model for pre-training mapping
("albert", "TFAlbertForPreTraining"),
("bart", "TFBartForConditionalGeneration"),
("bert", "TFBertForPreTraining"),
("camembert", "TFCamembertForMaskedLM"),
("ctrl", "TFCTRLLMHeadModel"),
("distilbert", "TFDistilBertForMaskedLM"),
("electra", "TFElectraForPreTraining"),
("flaubert", "TFFlaubertWithLMHeadModel"),
("funnel", "TFFunnelForPreTraining"),
("gpt2", "TFGPT2LMHeadModel"),
("layoutlm", "TFLayoutLMForMaskedLM"),
("lxmert", "TFLxmertForPreTraining"),
("mobilebert", "TFMobileBertForPreTraining"),
("mpnet", "TFMPNetForMaskedLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("roberta", "TFRobertaForMaskedLM"),
("t5", "TFT5ForConditionalGeneration"),
("tapas", "TFTapasForMaskedLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("vit_mae", "TFViTMAEForPreTraining"),
("xlm", "TFXLMWithLMHeadModel"),
("xlm-roberta", "TFXLMRobertaForMaskedLM"),
("xlnet", "TFXLNetLMHeadModel"),
]
)
TF_MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
[
# Model with LM heads mapping
("albert", "TFAlbertForMaskedLM"),
("bart", "TFBartForConditionalGeneration"),
("bert", "TFBertForMaskedLM"),
("camembert", "TFCamembertForMaskedLM"),
("convbert", "TFConvBertForMaskedLM"),
("ctrl", "TFCTRLLMHeadModel"),
("distilbert", "TFDistilBertForMaskedLM"),
("electra", "TFElectraForMaskedLM"),
("esm", "TFEsmForMaskedLM"),
("flaubert", "TFFlaubertWithLMHeadModel"),
("funnel", "TFFunnelForMaskedLM"),
("gpt2", "TFGPT2LMHeadModel"),
("gptj", "TFGPTJForCausalLM"),
("layoutlm", "TFLayoutLMForMaskedLM"),
("led", "TFLEDForConditionalGeneration"),
("longformer", "TFLongformerForMaskedLM"),
("marian", "TFMarianMTModel"),
("mobilebert", "TFMobileBertForMaskedLM"),
("mpnet", "TFMPNetForMaskedLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("rembert", "TFRemBertForMaskedLM"),
("roberta", "TFRobertaForMaskedLM"),
("roformer", "TFRoFormerForMaskedLM"),
("speech_to_text", "TFSpeech2TextForConditionalGeneration"),
("t5", "TFT5ForConditionalGeneration"),
("tapas", "TFTapasForMaskedLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("whisper", "TFWhisperForConditionalGeneration"),
("xlm", "TFXLMWithLMHeadModel"),
("xlm-roberta", "TFXLMRobertaForMaskedLM"),
("xlnet", "TFXLNetLMHeadModel"),
]
)
TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
[
# Model for Causal LM mapping
("bert", "TFBertLMHeadModel"),
("camembert", "TFCamembertForCausalLM"),
("ctrl", "TFCTRLLMHeadModel"),
("gpt2", "TFGPT2LMHeadModel"),
("gptj", "TFGPTJForCausalLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("opt", "TFOPTForCausalLM"),
("rembert", "TFRemBertForCausalLM"),
("roberta", "TFRobertaForCausalLM"),
("roformer", "TFRoFormerForCausalLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("xglm", "TFXGLMForCausalLM"),
("xlm", "TFXLMWithLMHeadModel"),
("xlnet", "TFXLNetLMHeadModel"),
]
)
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
[
("deit", "TFDeiTForMaskedImageModeling"),
("swin", "TFSwinForMaskedImageModeling"),
]
)
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Image-classsification
("convnext", "TFConvNextForImageClassification"),
("cvt", "TFCvtForImageClassification"),
("data2vec-vision", "TFData2VecVisionForImageClassification"),
("deit", ("TFDeiTForImageClassification", "TFDeiTForImageClassificationWithTeacher")),
("mobilevit", "TFMobileViTForImageClassification"),
("regnet", "TFRegNetForImageClassification"),
("resnet", "TFResNetForImageClassification"),
("segformer", "TFSegformerForImageClassification"),
("swin", "TFSwinForImageClassification"),
("vit", "TFViTForImageClassification"),
]
)
TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = OrderedDict(
[
# Model for Semantic Segmentation mapping
("data2vec-vision", "TFData2VecVisionForSemanticSegmentation"),
("mobilevit", "TFMobileViTForSemanticSegmentation"),
("segformer", "TFSegformerForSemanticSegmentation"),
]
)
TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
[
("vision-encoder-decoder", "TFVisionEncoderDecoderModel"),
]
)
TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
[
# Model for Masked LM mapping
("albert", "TFAlbertForMaskedLM"),
("bert", "TFBertForMaskedLM"),
("camembert", "TFCamembertForMaskedLM"),
("convbert", "TFConvBertForMaskedLM"),
("deberta", "TFDebertaForMaskedLM"),
("deberta-v2", "TFDebertaV2ForMaskedLM"),
("distilbert", "TFDistilBertForMaskedLM"),
("electra", "TFElectraForMaskedLM"),
("esm", "TFEsmForMaskedLM"),
("flaubert", "TFFlaubertWithLMHeadModel"),
("funnel", "TFFunnelForMaskedLM"),
("layoutlm", "TFLayoutLMForMaskedLM"),
("longformer", "TFLongformerForMaskedLM"),
("mobilebert", "TFMobileBertForMaskedLM"),
("mpnet", "TFMPNetForMaskedLM"),
("rembert", "TFRemBertForMaskedLM"),
("roberta", "TFRobertaForMaskedLM"),
("roformer", "TFRoFormerForMaskedLM"),
("tapas", "TFTapasForMaskedLM"),
("xlm", "TFXLMWithLMHeadModel"),
("xlm-roberta", "TFXLMRobertaForMaskedLM"),
]
)
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "TFBartForConditionalGeneration"),
("blenderbot", "TFBlenderbotForConditionalGeneration"),
("blenderbot-small", "TFBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "TFEncoderDecoderModel"),
("led", "TFLEDForConditionalGeneration"),
("marian", "TFMarianMTModel"),
("mbart", "TFMBartForConditionalGeneration"),
("mt5", "TFMT5ForConditionalGeneration"),
("pegasus", "TFPegasusForConditionalGeneration"),
("t5", "TFT5ForConditionalGeneration"),
]
)
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict(
[
("speech_to_text", "TFSpeech2TextForConditionalGeneration"),
("whisper", "TFWhisperForConditionalGeneration"),
]
)
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "TFAlbertForSequenceClassification"),
("bert", "TFBertForSequenceClassification"),
("camembert", "TFCamembertForSequenceClassification"),
("convbert", "TFConvBertForSequenceClassification"),
("ctrl", "TFCTRLForSequenceClassification"),
("deberta", "TFDebertaForSequenceClassification"),
("deberta-v2", "TFDebertaV2ForSequenceClassification"),
("distilbert", "TFDistilBertForSequenceClassification"),
("electra", "TFElectraForSequenceClassification"),
("esm", "TFEsmForSequenceClassification"),
("flaubert", "TFFlaubertForSequenceClassification"),
("funnel", "TFFunnelForSequenceClassification"),
("gpt2", "TFGPT2ForSequenceClassification"),
("gptj", "TFGPTJForSequenceClassification"),
("layoutlm", "TFLayoutLMForSequenceClassification"),
("layoutlmv3", "TFLayoutLMv3ForSequenceClassification"),
("longformer", "TFLongformerForSequenceClassification"),
("mobilebert", "TFMobileBertForSequenceClassification"),
("mpnet", "TFMPNetForSequenceClassification"),
("openai-gpt", "TFOpenAIGPTForSequenceClassification"),
("rembert", "TFRemBertForSequenceClassification"),
("roberta", "TFRobertaForSequenceClassification"),
("roformer", "TFRoFormerForSequenceClassification"),
("tapas", "TFTapasForSequenceClassification"),
("transfo-xl", "TFTransfoXLForSequenceClassification"),
("xlm", "TFXLMForSequenceClassification"),
("xlm-roberta", "TFXLMRobertaForSequenceClassification"),
("xlnet", "TFXLNetForSequenceClassification"),
]
)
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
# Model for Question Answering mapping
("albert", "TFAlbertForQuestionAnswering"),
("bert", "TFBertForQuestionAnswering"),
("camembert", "TFCamembertForQuestionAnswering"),
("convbert", "TFConvBertForQuestionAnswering"),
("deberta", "TFDebertaForQuestionAnswering"),
("deberta-v2", "TFDebertaV2ForQuestionAnswering"),
("distilbert", "TFDistilBertForQuestionAnswering"),
("electra", "TFElectraForQuestionAnswering"),
("flaubert", "TFFlaubertForQuestionAnsweringSimple"),
("funnel", "TFFunnelForQuestionAnswering"),
("gptj", "TFGPTJForQuestionAnswering"),
("layoutlmv3", "TFLayoutLMv3ForQuestionAnswering"),
("longformer", "TFLongformerForQuestionAnswering"),
("mobilebert", "TFMobileBertForQuestionAnswering"),
("mpnet", "TFMPNetForQuestionAnswering"),
("rembert", "TFRemBertForQuestionAnswering"),
("roberta", "TFRobertaForQuestionAnswering"),
("roformer", "TFRoFormerForQuestionAnswering"),
("xlm", "TFXLMForQuestionAnsweringSimple"),
("xlm-roberta", "TFXLMRobertaForQuestionAnswering"),
("xlnet", "TFXLNetForQuestionAnsweringSimple"),
]
)
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
("layoutlm", "TFLayoutLMForQuestionAnswering"),
]
)
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
# Model for Table Question Answering mapping
("tapas", "TFTapasForQuestionAnswering"),
]
)
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Token Classification mapping
("albert", "TFAlbertForTokenClassification"),
("bert", "TFBertForTokenClassification"),
("camembert", "TFCamembertForTokenClassification"),
("convbert", "TFConvBertForTokenClassification"),
("deberta", "TFDebertaForTokenClassification"),
("deberta-v2", "TFDebertaV2ForTokenClassification"),
("distilbert", "TFDistilBertForTokenClassification"),
("electra", "TFElectraForTokenClassification"),
("esm", "TFEsmForTokenClassification"),
("flaubert", "TFFlaubertForTokenClassification"),
("funnel", "TFFunnelForTokenClassification"),
("layoutlm", "TFLayoutLMForTokenClassification"),
("layoutlmv3", "TFLayoutLMv3ForTokenClassification"),
("longformer", "TFLongformerForTokenClassification"),
("mobilebert", "TFMobileBertForTokenClassification"),
("mpnet", "TFMPNetForTokenClassification"),
("rembert", "TFRemBertForTokenClassification"),
("roberta", "TFRobertaForTokenClassification"),
("roformer", "TFRoFormerForTokenClassification"),
("xlm", "TFXLMForTokenClassification"),
("xlm-roberta", "TFXLMRobertaForTokenClassification"),
("xlnet", "TFXLNetForTokenClassification"),
]
)
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "TFAlbertForMultipleChoice"),
("bert", "TFBertForMultipleChoice"),
("camembert", "TFCamembertForMultipleChoice"),
("convbert", "TFConvBertForMultipleChoice"),
("distilbert", "TFDistilBertForMultipleChoice"),
("electra", "TFElectraForMultipleChoice"),
("flaubert", "TFFlaubertForMultipleChoice"),
("funnel", "TFFunnelForMultipleChoice"),
("longformer", "TFLongformerForMultipleChoice"),
("mobilebert", "TFMobileBertForMultipleChoice"),
("mpnet", "TFMPNetForMultipleChoice"),
("rembert", "TFRemBertForMultipleChoice"),
("roberta", "TFRobertaForMultipleChoice"),
("roformer", "TFRoFormerForMultipleChoice"),
("xlm", "TFXLMForMultipleChoice"),
("xlm-roberta", "TFXLMRobertaForMultipleChoice"),
("xlnet", "TFXLNetForMultipleChoice"),
]
)
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
[
("bert", "TFBertForNextSentencePrediction"),
("mobilebert", "TFMobileBertForNextSentencePrediction"),
]
)
TF_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_MAPPING_NAMES)
TF_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
TF_MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_WITH_LM_HEAD_MAPPING_NAMES)
TF_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES
)
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES
)
TF_MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
TF_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES
)
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES
)
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
class TFAutoModel(_BaseAutoModelClass):
_model_mapping = TF_MODEL_MAPPING
TFAutoModel = auto_class_update(TFAutoModel)
class TFAutoModelForPreTraining(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_PRETRAINING_MAPPING
TFAutoModelForPreTraining = auto_class_update(TFAutoModelForPreTraining, head_doc="pretraining")
# Private on purpose, the public class will add the deprecation warnings.
class _TFAutoModelWithLMHead(_BaseAutoModelClass):
_model_mapping = TF_MODEL_WITH_LM_HEAD_MAPPING
_TFAutoModelWithLMHead = auto_class_update(_TFAutoModelWithLMHead, head_doc="language modeling")
class TFAutoModelForCausalLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING
TFAutoModelForCausalLM = auto_class_update(TFAutoModelForCausalLM, head_doc="causal language modeling")
class TFAutoModelForMaskedImageModeling(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING
TFAutoModelForMaskedImageModeling = auto_class_update(
TFAutoModelForMaskedImageModeling, head_doc="masked image modeling"
)
class TFAutoModelForImageClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
TFAutoModelForImageClassification = auto_class_update(
TFAutoModelForImageClassification, head_doc="image classification"
)
class TFAutoModelForSemanticSegmentation(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
TF_AutoModelForSemanticSegmentation = auto_class_update(
TFAutoModelForSemanticSegmentation, head_doc="semantic segmentation"
)
class TFAutoModelForVision2Seq(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_VISION_2_SEQ_MAPPING
TFAutoModelForVision2Seq = auto_class_update(TFAutoModelForVision2Seq, head_doc="vision-to-text modeling")
class TFAutoModelForMaskedLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING
TFAutoModelForMaskedLM = auto_class_update(TFAutoModelForMaskedLM, head_doc="masked language modeling")
class TFAutoModelForSeq2SeqLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
TFAutoModelForSeq2SeqLM = auto_class_update(
TFAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class TFAutoModelForSequenceClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
TFAutoModelForSequenceClassification = auto_class_update(
TFAutoModelForSequenceClassification, head_doc="sequence classification"
)
class TFAutoModelForQuestionAnswering(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING
TFAutoModelForQuestionAnswering = auto_class_update(TFAutoModelForQuestionAnswering, head_doc="question answering")
class TFAutoModelForDocumentQuestionAnswering(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
TFAutoModelForDocumentQuestionAnswering = auto_class_update(
TFAutoModelForDocumentQuestionAnswering,
head_doc="document question answering",
checkpoint_for_example='impira/layoutlm-document-qa", revision="52e01b3',
)
class TFAutoModelForTableQuestionAnswering(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING
TFAutoModelForTableQuestionAnswering = auto_class_update(
TFAutoModelForTableQuestionAnswering,
head_doc="table question answering",
checkpoint_for_example="google/tapas-base-finetuned-wtq",
)
class TFAutoModelForTokenClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
TFAutoModelForTokenClassification = auto_class_update(
TFAutoModelForTokenClassification, head_doc="token classification"
)
class TFAutoModelForMultipleChoice(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
TFAutoModelForMultipleChoice = auto_class_update(TFAutoModelForMultipleChoice, head_doc="multiple choice")
class TFAutoModelForNextSentencePrediction(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
TFAutoModelForNextSentencePrediction = auto_class_update(
TFAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class TFAutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
TFAutoModelForSpeechSeq2Seq = auto_class_update(
TFAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling"
)
class TFAutoModelWithLMHead(_TFAutoModelWithLMHead):
@classmethod
def from_config(cls, config):
warnings.warn(
"The class `TFAutoModelWithLMHead` is deprecated and will be removed in a future version. Please use"
" `TFAutoModelForCausalLM` for causal language models, `TFAutoModelForMaskedLM` for masked language models"
" and `TFAutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
return super().from_config(config)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
warnings.warn(
"The class `TFAutoModelWithLMHead` is deprecated and will be removed in a future version. Please use"
" `TFAutoModelForCausalLM` for causal language models, `TFAutoModelForMaskedLM` for masked language models"
" and `TFAutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
| # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Auto Model class."""
import warnings
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
logger = logging.get_logger(__name__)
TF_MODEL_MAPPING_NAMES = OrderedDict(
[
# Base model mapping
("albert", "TFAlbertModel"),
("bart", "TFBartModel"),
("bert", "TFBertModel"),
("blenderbot", "TFBlenderbotModel"),
("blenderbot-small", "TFBlenderbotSmallModel"),
("camembert", "TFCamembertModel"),
("clip", "TFCLIPModel"),
("convbert", "TFConvBertModel"),
("convnext", "TFConvNextModel"),
("ctrl", "TFCTRLModel"),
("cvt", "TFCvtModel"),
("data2vec-vision", "TFData2VecVisionModel"),
("deberta", "TFDebertaModel"),
("deberta-v2", "TFDebertaV2Model"),
("deit", "TFDeiTModel"),
("distilbert", "TFDistilBertModel"),
("dpr", "TFDPRQuestionEncoder"),
("electra", "TFElectraModel"),
("esm", "TFEsmModel"),
("flaubert", "TFFlaubertModel"),
("funnel", ("TFFunnelModel", "TFFunnelBaseModel")),
("gpt2", "TFGPT2Model"),
("gptj", "TFGPTJModel"),
("groupvit", "TFGroupViTModel"),
("hubert", "TFHubertModel"),
("layoutlm", "TFLayoutLMModel"),
("layoutlmv3", "TFLayoutLMv3Model"),
("led", "TFLEDModel"),
("longformer", "TFLongformerModel"),
("lxmert", "TFLxmertModel"),
("marian", "TFMarianModel"),
("mbart", "TFMBartModel"),
("mobilebert", "TFMobileBertModel"),
("mobilevit", "TFMobileViTModel"),
("mpnet", "TFMPNetModel"),
("mt5", "TFMT5Model"),
("openai-gpt", "TFOpenAIGPTModel"),
("opt", "TFOPTModel"),
("pegasus", "TFPegasusModel"),
("regnet", "TFRegNetModel"),
("rembert", "TFRemBertModel"),
("resnet", "TFResNetModel"),
("roberta", "TFRobertaModel"),
("roformer", "TFRoFormerModel"),
("segformer", "TFSegformerModel"),
("speech_to_text", "TFSpeech2TextModel"),
("swin", "TFSwinModel"),
("t5", "TFT5Model"),
("tapas", "TFTapasModel"),
("transfo-xl", "TFTransfoXLModel"),
("vit", "TFViTModel"),
("vit_mae", "TFViTMAEModel"),
("wav2vec2", "TFWav2Vec2Model"),
("whisper", "TFWhisperModel"),
("xglm", "TFXGLMModel"),
("xlm", "TFXLMModel"),
("xlm-roberta", "TFXLMRobertaModel"),
("xlnet", "TFXLNetModel"),
]
)
TF_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
[
# Model for pre-training mapping
("albert", "TFAlbertForPreTraining"),
("bart", "TFBartForConditionalGeneration"),
("bert", "TFBertForPreTraining"),
("camembert", "TFCamembertForMaskedLM"),
("ctrl", "TFCTRLLMHeadModel"),
("distilbert", "TFDistilBertForMaskedLM"),
("electra", "TFElectraForPreTraining"),
("flaubert", "TFFlaubertWithLMHeadModel"),
("funnel", "TFFunnelForPreTraining"),
("gpt2", "TFGPT2LMHeadModel"),
("layoutlm", "TFLayoutLMForMaskedLM"),
("lxmert", "TFLxmertForPreTraining"),
("mobilebert", "TFMobileBertForPreTraining"),
("mpnet", "TFMPNetForMaskedLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("roberta", "TFRobertaForMaskedLM"),
("t5", "TFT5ForConditionalGeneration"),
("tapas", "TFTapasForMaskedLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("vit_mae", "TFViTMAEForPreTraining"),
("xlm", "TFXLMWithLMHeadModel"),
("xlm-roberta", "TFXLMRobertaForMaskedLM"),
("xlnet", "TFXLNetLMHeadModel"),
]
)
TF_MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
[
# Model with LM heads mapping
("albert", "TFAlbertForMaskedLM"),
("bart", "TFBartForConditionalGeneration"),
("bert", "TFBertForMaskedLM"),
("camembert", "TFCamembertForMaskedLM"),
("convbert", "TFConvBertForMaskedLM"),
("ctrl", "TFCTRLLMHeadModel"),
("distilbert", "TFDistilBertForMaskedLM"),
("electra", "TFElectraForMaskedLM"),
("esm", "TFEsmForMaskedLM"),
("flaubert", "TFFlaubertWithLMHeadModel"),
("funnel", "TFFunnelForMaskedLM"),
("gpt2", "TFGPT2LMHeadModel"),
("gptj", "TFGPTJForCausalLM"),
("layoutlm", "TFLayoutLMForMaskedLM"),
("led", "TFLEDForConditionalGeneration"),
("longformer", "TFLongformerForMaskedLM"),
("marian", "TFMarianMTModel"),
("mobilebert", "TFMobileBertForMaskedLM"),
("mpnet", "TFMPNetForMaskedLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("rembert", "TFRemBertForMaskedLM"),
("roberta", "TFRobertaForMaskedLM"),
("roformer", "TFRoFormerForMaskedLM"),
("speech_to_text", "TFSpeech2TextForConditionalGeneration"),
("t5", "TFT5ForConditionalGeneration"),
("tapas", "TFTapasForMaskedLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("whisper", "TFWhisperForConditionalGeneration"),
("xlm", "TFXLMWithLMHeadModel"),
("xlm-roberta", "TFXLMRobertaForMaskedLM"),
("xlnet", "TFXLNetLMHeadModel"),
]
)
TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
[
# Model for Causal LM mapping
("bert", "TFBertLMHeadModel"),
("camembert", "TFCamembertForCausalLM"),
("ctrl", "TFCTRLLMHeadModel"),
("gpt2", "TFGPT2LMHeadModel"),
("gptj", "TFGPTJForCausalLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("opt", "TFOPTForCausalLM"),
("rembert", "TFRemBertForCausalLM"),
("roberta", "TFRobertaForCausalLM"),
("roformer", "TFRoFormerForCausalLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("xglm", "TFXGLMForCausalLM"),
("xlm", "TFXLMWithLMHeadModel"),
("xlnet", "TFXLNetLMHeadModel"),
]
)
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
[
("deit", "TFDeiTForMaskedImageModeling"),
("swin", "TFSwinForMaskedImageModeling"),
]
)
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Image-classsification
("convnext", "TFConvNextForImageClassification"),
("cvt", "TFCvtForImageClassification"),
("data2vec-vision", "TFData2VecVisionForImageClassification"),
("deit", ("TFDeiTForImageClassification", "TFDeiTForImageClassificationWithTeacher")),
("mobilevit", "TFMobileViTForImageClassification"),
("regnet", "TFRegNetForImageClassification"),
("resnet", "TFResNetForImageClassification"),
("segformer", "TFSegformerForImageClassification"),
("swin", "TFSwinForImageClassification"),
("vit", "TFViTForImageClassification"),
]
)
TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = OrderedDict(
[
# Model for Semantic Segmentation mapping
("data2vec-vision", "TFData2VecVisionForSemanticSegmentation"),
("mobilevit", "TFMobileViTForSemanticSegmentation"),
("segformer", "TFSegformerForSemanticSegmentation"),
]
)
TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
[
("vision-encoder-decoder", "TFVisionEncoderDecoderModel"),
]
)
TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
[
# Model for Masked LM mapping
("albert", "TFAlbertForMaskedLM"),
("bert", "TFBertForMaskedLM"),
("camembert", "TFCamembertForMaskedLM"),
("convbert", "TFConvBertForMaskedLM"),
("deberta", "TFDebertaForMaskedLM"),
("deberta-v2", "TFDebertaV2ForMaskedLM"),
("distilbert", "TFDistilBertForMaskedLM"),
("electra", "TFElectraForMaskedLM"),
("esm", "TFEsmForMaskedLM"),
("flaubert", "TFFlaubertWithLMHeadModel"),
("funnel", "TFFunnelForMaskedLM"),
("layoutlm", "TFLayoutLMForMaskedLM"),
("longformer", "TFLongformerForMaskedLM"),
("mobilebert", "TFMobileBertForMaskedLM"),
("mpnet", "TFMPNetForMaskedLM"),
("rembert", "TFRemBertForMaskedLM"),
("roberta", "TFRobertaForMaskedLM"),
("roformer", "TFRoFormerForMaskedLM"),
("tapas", "TFTapasForMaskedLM"),
("xlm", "TFXLMWithLMHeadModel"),
("xlm-roberta", "TFXLMRobertaForMaskedLM"),
]
)
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "TFBartForConditionalGeneration"),
("blenderbot", "TFBlenderbotForConditionalGeneration"),
("blenderbot-small", "TFBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "TFEncoderDecoderModel"),
("led", "TFLEDForConditionalGeneration"),
("marian", "TFMarianMTModel"),
("mbart", "TFMBartForConditionalGeneration"),
("mt5", "TFMT5ForConditionalGeneration"),
("pegasus", "TFPegasusForConditionalGeneration"),
("t5", "TFT5ForConditionalGeneration"),
]
)
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict(
[
("speech_to_text", "TFSpeech2TextForConditionalGeneration"),
("whisper", "TFWhisperForConditionalGeneration"),
]
)
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "TFAlbertForSequenceClassification"),
("bert", "TFBertForSequenceClassification"),
("camembert", "TFCamembertForSequenceClassification"),
("convbert", "TFConvBertForSequenceClassification"),
("ctrl", "TFCTRLForSequenceClassification"),
("deberta", "TFDebertaForSequenceClassification"),
("deberta-v2", "TFDebertaV2ForSequenceClassification"),
("distilbert", "TFDistilBertForSequenceClassification"),
("electra", "TFElectraForSequenceClassification"),
("esm", "TFEsmForSequenceClassification"),
("flaubert", "TFFlaubertForSequenceClassification"),
("funnel", "TFFunnelForSequenceClassification"),
("gpt2", "TFGPT2ForSequenceClassification"),
("gptj", "TFGPTJForSequenceClassification"),
("layoutlm", "TFLayoutLMForSequenceClassification"),
("layoutlmv3", "TFLayoutLMv3ForSequenceClassification"),
("longformer", "TFLongformerForSequenceClassification"),
("mobilebert", "TFMobileBertForSequenceClassification"),
("mpnet", "TFMPNetForSequenceClassification"),
("openai-gpt", "TFOpenAIGPTForSequenceClassification"),
("rembert", "TFRemBertForSequenceClassification"),
("roberta", "TFRobertaForSequenceClassification"),
("roformer", "TFRoFormerForSequenceClassification"),
("tapas", "TFTapasForSequenceClassification"),
("transfo-xl", "TFTransfoXLForSequenceClassification"),
("xlm", "TFXLMForSequenceClassification"),
("xlm-roberta", "TFXLMRobertaForSequenceClassification"),
("xlnet", "TFXLNetForSequenceClassification"),
]
)
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
# Model for Question Answering mapping
("albert", "TFAlbertForQuestionAnswering"),
("bert", "TFBertForQuestionAnswering"),
("camembert", "TFCamembertForQuestionAnswering"),
("convbert", "TFConvBertForQuestionAnswering"),
("deberta", "TFDebertaForQuestionAnswering"),
("deberta-v2", "TFDebertaV2ForQuestionAnswering"),
("distilbert", "TFDistilBertForQuestionAnswering"),
("electra", "TFElectraForQuestionAnswering"),
("flaubert", "TFFlaubertForQuestionAnsweringSimple"),
("funnel", "TFFunnelForQuestionAnswering"),
("gptj", "TFGPTJForQuestionAnswering"),
("layoutlmv3", "TFLayoutLMv3ForQuestionAnswering"),
("longformer", "TFLongformerForQuestionAnswering"),
("mobilebert", "TFMobileBertForQuestionAnswering"),
("mpnet", "TFMPNetForQuestionAnswering"),
("rembert", "TFRemBertForQuestionAnswering"),
("roberta", "TFRobertaForQuestionAnswering"),
("roformer", "TFRoFormerForQuestionAnswering"),
("xlm", "TFXLMForQuestionAnsweringSimple"),
("xlm-roberta", "TFXLMRobertaForQuestionAnswering"),
("xlnet", "TFXLNetForQuestionAnsweringSimple"),
]
)
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
("layoutlm", "TFLayoutLMForQuestionAnswering"),
]
)
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
# Model for Table Question Answering mapping
("tapas", "TFTapasForQuestionAnswering"),
]
)
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Token Classification mapping
("albert", "TFAlbertForTokenClassification"),
("bert", "TFBertForTokenClassification"),
("camembert", "TFCamembertForTokenClassification"),
("convbert", "TFConvBertForTokenClassification"),
("deberta", "TFDebertaForTokenClassification"),
("deberta-v2", "TFDebertaV2ForTokenClassification"),
("distilbert", "TFDistilBertForTokenClassification"),
("electra", "TFElectraForTokenClassification"),
("esm", "TFEsmForTokenClassification"),
("flaubert", "TFFlaubertForTokenClassification"),
("funnel", "TFFunnelForTokenClassification"),
("layoutlm", "TFLayoutLMForTokenClassification"),
("layoutlmv3", "TFLayoutLMv3ForTokenClassification"),
("longformer", "TFLongformerForTokenClassification"),
("mobilebert", "TFMobileBertForTokenClassification"),
("mpnet", "TFMPNetForTokenClassification"),
("rembert", "TFRemBertForTokenClassification"),
("roberta", "TFRobertaForTokenClassification"),
("roformer", "TFRoFormerForTokenClassification"),
("xlm", "TFXLMForTokenClassification"),
("xlm-roberta", "TFXLMRobertaForTokenClassification"),
("xlnet", "TFXLNetForTokenClassification"),
]
)
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "TFAlbertForMultipleChoice"),
("bert", "TFBertForMultipleChoice"),
("camembert", "TFCamembertForMultipleChoice"),
("convbert", "TFConvBertForMultipleChoice"),
("distilbert", "TFDistilBertForMultipleChoice"),
("electra", "TFElectraForMultipleChoice"),
("flaubert", "TFFlaubertForMultipleChoice"),
("funnel", "TFFunnelForMultipleChoice"),
("longformer", "TFLongformerForMultipleChoice"),
("mobilebert", "TFMobileBertForMultipleChoice"),
("mpnet", "TFMPNetForMultipleChoice"),
("rembert", "TFRemBertForMultipleChoice"),
("roberta", "TFRobertaForMultipleChoice"),
("roformer", "TFRoFormerForMultipleChoice"),
("xlm", "TFXLMForMultipleChoice"),
("xlm-roberta", "TFXLMRobertaForMultipleChoice"),
("xlnet", "TFXLNetForMultipleChoice"),
]
)
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
[
("bert", "TFBertForNextSentencePrediction"),
("mobilebert", "TFMobileBertForNextSentencePrediction"),
]
)
TF_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_MAPPING_NAMES)
TF_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
TF_MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_WITH_LM_HEAD_MAPPING_NAMES)
TF_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES
)
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES
)
TF_MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
TF_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES
)
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES
)
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
class TFAutoModel(_BaseAutoModelClass):
_model_mapping = TF_MODEL_MAPPING
TFAutoModel = auto_class_update(TFAutoModel)
class TFAutoModelForPreTraining(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_PRETRAINING_MAPPING
TFAutoModelForPreTraining = auto_class_update(TFAutoModelForPreTraining, head_doc="pretraining")
# Private on purpose, the public class will add the deprecation warnings.
class _TFAutoModelWithLMHead(_BaseAutoModelClass):
_model_mapping = TF_MODEL_WITH_LM_HEAD_MAPPING
_TFAutoModelWithLMHead = auto_class_update(_TFAutoModelWithLMHead, head_doc="language modeling")
class TFAutoModelForCausalLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING
TFAutoModelForCausalLM = auto_class_update(TFAutoModelForCausalLM, head_doc="causal language modeling")
class TFAutoModelForMaskedImageModeling(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING
TFAutoModelForMaskedImageModeling = auto_class_update(
TFAutoModelForMaskedImageModeling, head_doc="masked image modeling"
)
class TFAutoModelForImageClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
TFAutoModelForImageClassification = auto_class_update(
TFAutoModelForImageClassification, head_doc="image classification"
)
class TFAutoModelForSemanticSegmentation(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
TF_AutoModelForSemanticSegmentation = auto_class_update(
TFAutoModelForSemanticSegmentation, head_doc="semantic segmentation"
)
class TFAutoModelForVision2Seq(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_VISION_2_SEQ_MAPPING
TFAutoModelForVision2Seq = auto_class_update(TFAutoModelForVision2Seq, head_doc="vision-to-text modeling")
class TFAutoModelForMaskedLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING
TFAutoModelForMaskedLM = auto_class_update(TFAutoModelForMaskedLM, head_doc="masked language modeling")
class TFAutoModelForSeq2SeqLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
TFAutoModelForSeq2SeqLM = auto_class_update(
TFAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class TFAutoModelForSequenceClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
TFAutoModelForSequenceClassification = auto_class_update(
TFAutoModelForSequenceClassification, head_doc="sequence classification"
)
class TFAutoModelForQuestionAnswering(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING
TFAutoModelForQuestionAnswering = auto_class_update(TFAutoModelForQuestionAnswering, head_doc="question answering")
class TFAutoModelForDocumentQuestionAnswering(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
TFAutoModelForDocumentQuestionAnswering = auto_class_update(
TFAutoModelForDocumentQuestionAnswering,
head_doc="document question answering",
checkpoint_for_example='impira/layoutlm-document-qa", revision="52e01b3',
)
class TFAutoModelForTableQuestionAnswering(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING
TFAutoModelForTableQuestionAnswering = auto_class_update(
TFAutoModelForTableQuestionAnswering,
head_doc="table question answering",
checkpoint_for_example="google/tapas-base-finetuned-wtq",
)
class TFAutoModelForTokenClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
TFAutoModelForTokenClassification = auto_class_update(
TFAutoModelForTokenClassification, head_doc="token classification"
)
class TFAutoModelForMultipleChoice(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
TFAutoModelForMultipleChoice = auto_class_update(TFAutoModelForMultipleChoice, head_doc="multiple choice")
class TFAutoModelForNextSentencePrediction(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
TFAutoModelForNextSentencePrediction = auto_class_update(
TFAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class TFAutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
TFAutoModelForSpeechSeq2Seq = auto_class_update(
TFAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling"
)
class TFAutoModelWithLMHead(_TFAutoModelWithLMHead):
@classmethod
def from_config(cls, config):
warnings.warn(
"The class `TFAutoModelWithLMHead` is deprecated and will be removed in a future version. Please use"
" `TFAutoModelForCausalLM` for causal language models, `TFAutoModelForMaskedLM` for masked language models"
" and `TFAutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
return super().from_config(config)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
warnings.warn(
"The class `TFAutoModelWithLMHead` is deprecated and will be removed in a future version. Please use"
" `TFAutoModelForCausalLM` for causal language models, `TFAutoModelForMaskedLM` for masked language models"
" and `TFAutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/distilbert/tokenization_distilbert.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for DistilBERT."""
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class DistilBertTokenizer(BertTokenizer):
r"""
Construct a DistilBERT tokenizer.
[`DistilBertTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting
and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
model_input_names = ["input_ids", "attention_mask"]
| # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for DistilBERT."""
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class DistilBertTokenizer(BertTokenizer):
r"""
Construct a DistilBERT tokenizer.
[`DistilBertTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting
and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
model_input_names = ["input_ids", "attention_mask"]
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./utils/test_module/custom_tokenization_fast.py | from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class CustomTokenizerFast(BertTokenizerFast):
slow_tokenizer_class = CustomTokenizer
pass
| from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class CustomTokenizerFast(BertTokenizerFast):
slow_tokenizer_class = CustomTokenizer
pass
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/tapex/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...file_utils import _LazyModule
_import_structure = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...file_utils import _LazyModule
_import_structure = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/data2vec/configuration_data2vec_audio.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Data2VecText configuration"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class Data2VecAudioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Data2VecAudioModel`]. It is used to instantiate
an Data2VecAudio model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Data2VecAudio
[facebook/data2vec-audio-base-960h](https://huggingface.co/facebook/data2vec-audio-base-960h) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the Data2VecAudio model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`Data2VecAudioModel`] or [`TFData2VecAudioModel`]. Vocabulary size
of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the
forward method of [`Data2VecAudioModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`Data2VecAudioForCTC`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`Data2VecAudioForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`Data2VecAudioForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`Data2VecAudioForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Data2VecAudio Encoder. Can be very useful
for warm-starting Data2VecAudio for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
Example:
```python
>>> from transformers import Data2VecAudioConfig, Data2VecAudioModel
>>> # Initializing a Data2VecAudio facebook/data2vec-audio-base-960h style configuration
>>> configuration = Data2VecAudioConfig()
>>> # Initializing a model (with random weights) from the facebook/data2vec-audio-base-960h style configuration
>>> model = Data2VecAudioModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "data2vec-audio"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embedding_groups=16,
conv_pos_kernel_size=19,
num_conv_pos_embeddings=5,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
ctc_loss_reduction="sum",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
tdnn_dim=(512, 512, 512, 512, 1500),
tdnn_kernel=(5, 3, 3, 1, 1),
tdnn_dilation=(1, 2, 3, 1, 1),
xvector_output_dim=512,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
add_adapter=False,
adapter_kernel_size=3,
adapter_stride=2,
num_adapter_layers=3,
output_hidden_size=None,
**kwargs
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.conv_pos_kernel_size = conv_pos_kernel_size
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.use_weighted_layer_sum = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
self.mask_feature_min_masks = mask_feature_min_masks
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
self.tdnn_dim = list(tdnn_dim)
self.tdnn_kernel = list(tdnn_kernel)
self.tdnn_dilation = list(tdnn_dilation)
self.xvector_output_dim = xvector_output_dim
@property
def inputs_to_logits_ratio(self):
return math.prod(self.conv_stride)
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Data2VecText configuration"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class Data2VecAudioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Data2VecAudioModel`]. It is used to instantiate
an Data2VecAudio model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Data2VecAudio
[facebook/data2vec-audio-base-960h](https://huggingface.co/facebook/data2vec-audio-base-960h) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the Data2VecAudio model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`Data2VecAudioModel`] or [`TFData2VecAudioModel`]. Vocabulary size
of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the
forward method of [`Data2VecAudioModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`Data2VecAudioForCTC`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`Data2VecAudioForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`Data2VecAudioForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`Data2VecAudioForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Data2VecAudio Encoder. Can be very useful
for warm-starting Data2VecAudio for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
Example:
```python
>>> from transformers import Data2VecAudioConfig, Data2VecAudioModel
>>> # Initializing a Data2VecAudio facebook/data2vec-audio-base-960h style configuration
>>> configuration = Data2VecAudioConfig()
>>> # Initializing a model (with random weights) from the facebook/data2vec-audio-base-960h style configuration
>>> model = Data2VecAudioModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "data2vec-audio"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embedding_groups=16,
conv_pos_kernel_size=19,
num_conv_pos_embeddings=5,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
ctc_loss_reduction="sum",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
tdnn_dim=(512, 512, 512, 512, 1500),
tdnn_kernel=(5, 3, 3, 1, 1),
tdnn_dilation=(1, 2, 3, 1, 1),
xvector_output_dim=512,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
add_adapter=False,
adapter_kernel_size=3,
adapter_stride=2,
num_adapter_layers=3,
output_hidden_size=None,
**kwargs
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.conv_pos_kernel_size = conv_pos_kernel_size
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.use_weighted_layer_sum = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
self.mask_feature_min_masks = mask_feature_min_masks
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
self.tdnn_dim = list(tdnn_dim)
self.tdnn_kernel = list(tdnn_kernel)
self.tdnn_dilation = list(tdnn_dilation)
self.xvector_output_dim = xvector_output_dim
@property
def inputs_to_logits_ratio(self):
return math.prod(self.conv_stride)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/gpt2/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
"tokenization_gpt2": ["GPT2Tokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_gpt2"] = [
"GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPT2DoubleHeadsModel",
"GPT2ForSequenceClassification",
"GPT2ForTokenClassification",
"GPT2LMHeadModel",
"GPT2Model",
"GPT2PreTrainedModel",
"load_tf_weights_in_gpt2",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_gpt2"] = [
"TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGPT2DoubleHeadsModel",
"TFGPT2ForSequenceClassification",
"TFGPT2LMHeadModel",
"TFGPT2MainLayer",
"TFGPT2Model",
"TFGPT2PreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
from .tokenization_gpt2 import GPT2Tokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt2_fast import GPT2TokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt2 import (
GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
GPT2DoubleHeadsModel,
GPT2ForSequenceClassification,
GPT2ForTokenClassification,
GPT2LMHeadModel,
GPT2Model,
GPT2PreTrainedModel,
load_tf_weights_in_gpt2,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_gpt2 import (
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGPT2DoubleHeadsModel,
TFGPT2ForSequenceClassification,
TFGPT2LMHeadModel,
TFGPT2MainLayer,
TFGPT2Model,
TFGPT2PreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
"tokenization_gpt2": ["GPT2Tokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_gpt2"] = [
"GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPT2DoubleHeadsModel",
"GPT2ForSequenceClassification",
"GPT2ForTokenClassification",
"GPT2LMHeadModel",
"GPT2Model",
"GPT2PreTrainedModel",
"load_tf_weights_in_gpt2",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_gpt2"] = [
"TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGPT2DoubleHeadsModel",
"TFGPT2ForSequenceClassification",
"TFGPT2LMHeadModel",
"TFGPT2MainLayer",
"TFGPT2Model",
"TFGPT2PreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
from .tokenization_gpt2 import GPT2Tokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt2_fast import GPT2TokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt2 import (
GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
GPT2DoubleHeadsModel,
GPT2ForSequenceClassification,
GPT2ForTokenClassification,
GPT2LMHeadModel,
GPT2Model,
GPT2PreTrainedModel,
load_tf_weights_in_gpt2,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_gpt2 import (
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGPT2DoubleHeadsModel,
TFGPT2ForSequenceClassification,
TFGPT2LMHeadModel,
TFGPT2MainLayer,
TFGPT2Model,
TFGPT2PreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for Conditional DETR."""
import pathlib
import warnings
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_transforms import center_to_corners_format, corners_to_center_format, rgb_to_id
from ...image_utils import ImageFeatureExtractionMixin
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
from torch import nn
logger = logging.get_logger(__name__)
ImageInput = Union[Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]]
# Copied from transformers.models.detr.feature_extraction_detr.masks_to_boxes
def masks_to_boxes(masks):
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
"""
if masks.size == 0:
return np.zeros((0, 4))
h, w = masks.shape[-2:]
y = np.arange(0, h, dtype=np.float32)
x = np.arange(0, w, dtype=np.float32)
# see https://github.com/pytorch/pytorch/issues/50276
y, x = np.meshgrid(y, x, indexing="ij")
x_mask = masks * np.expand_dims(x, axis=0)
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
x_min = x.filled(fill_value=1e8)
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
y_mask = masks * np.expand_dims(y, axis=0)
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
y_min = y.filled(fill_value=1e8)
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
return np.stack([x_min, y_min, x_max, y_max], 1)
# Copied from transformers.models.detr.feature_extraction_detr.binary_mask_to_rle
def binary_mask_to_rle(mask):
"""
Args:
Converts given binary mask of shape (height, width) to the run-length encoding (RLE) format.
mask (`torch.Tensor` or `numpy.array`):
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
segment_id or class_id.
Returns:
`List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
format.
"""
if is_torch_tensor(mask):
mask = mask.numpy()
pixels = mask.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return [x for x in runs]
# Copied from transformers.models.detr.feature_extraction_detr.convert_segmentation_to_rle
def convert_segmentation_to_rle(segmentation):
"""
Converts given segmentation map of shape (height, width) to the run-length encoding (RLE) format.
Args:
segmentation (`torch.Tensor` or `numpy.array`):
A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
Returns:
`List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
"""
segment_ids = torch.unique(segmentation)
run_length_encodings = []
for idx in segment_ids:
mask = torch.where(segmentation == idx, 1, 0)
rle = binary_mask_to_rle(mask)
run_length_encodings.append(rle)
return run_length_encodings
# Copied from transformers.models.detr.feature_extraction_detr.remove_low_and_no_objects
def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
"""
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
`labels`.
Args:
masks (`torch.Tensor`):
A tensor of shape `(num_queries, height, width)`.
scores (`torch.Tensor`):
A tensor of shape `(num_queries)`.
labels (`torch.Tensor`):
A tensor of shape `(num_queries)`.
object_mask_threshold (`float`):
A number between 0 and 1 used to binarize the masks.
Raises:
`ValueError`: Raised when the first dimension doesn't match in all input tensors.
Returns:
`Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
< `object_mask_threshold`.
"""
if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
raise ValueError("mask, scores and labels must have the same shape!")
to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
return masks[to_keep], scores[to_keep], labels[to_keep]
# Copied from transformers.models.detr.feature_extraction_detr.check_segment_validity
def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
# Get the mask associated with the k class
mask_k = mask_labels == k
mask_k_area = mask_k.sum()
# Compute the area of all the stuff in query k
original_area = (mask_probs[k] >= mask_threshold).sum()
mask_exists = mask_k_area > 0 and original_area > 0
# Eliminate disconnected tiny segments
if mask_exists:
area_ratio = mask_k_area / original_area
if not area_ratio.item() > overlap_mask_area_threshold:
mask_exists = False
return mask_exists, mask_k
# Copied from transformers.models.detr.feature_extraction_detr.compute_segments
def compute_segments(
mask_probs,
pred_scores,
pred_labels,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
segments: List[Dict] = []
if target_size is not None:
mask_probs = nn.functional.interpolate(
mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
)[0]
current_segment_id = 0
# Weigh each mask by its prediction score
mask_probs *= pred_scores.view(-1, 1, 1)
mask_labels = mask_probs.argmax(0) # [height, width]
# Keep track of instances of each class
stuff_memory_list: Dict[str, int] = {}
for k in range(pred_labels.shape[0]):
pred_class = pred_labels[k].item()
should_fuse = pred_class in label_ids_to_fuse
# Check if mask exists and large enough to be a segment
mask_exists, mask_k = check_segment_validity(
mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
)
if mask_exists:
if pred_class in stuff_memory_list:
current_segment_id = stuff_memory_list[pred_class]
else:
current_segment_id += 1
# Add current object segment to final segmentation map
segmentation[mask_k] = current_segment_id
segment_score = round(pred_scores[k].item(), 6)
segments.append(
{
"id": current_segment_id,
"label_id": pred_class,
"was_fused": should_fuse,
"score": segment_score,
}
)
if should_fuse:
stuff_memory_list[pred_class] = current_segment_id
return segmentation, segments
class ConditionalDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
r"""
Constructs a Conditional DETR feature extractor.
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input to a certain `size`.
size (`int`, *optional*, defaults to 800):
Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
height / width, size)`.
max_size (`int`, *optional*, defaults to `1333`):
The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input with mean and standard deviation.
image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
ImageNet std.
"""
model_input_names = ["pixel_values", "pixel_mask"]
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.__init__
def __init__(
self,
format="coco_detection",
do_resize=True,
size=800,
max_size=1333,
do_normalize=True,
image_mean=None,
image_std=None,
**kwargs
):
super().__init__(**kwargs)
self.format = self._is_valid_format(format)
self.do_resize = do_resize
self.size = size
self.max_size = max_size
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else [0.485, 0.456, 0.406] # ImageNet mean
self.image_std = image_std if image_std is not None else [0.229, 0.224, 0.225] # ImageNet std
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._is_valid_format
def _is_valid_format(self, format):
if format not in ["coco_detection", "coco_panoptic"]:
raise ValueError(f"Format {format} not supported")
return format
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare
def prepare(self, image, target, return_segmentation_masks=False, masks_path=None):
if self.format == "coco_detection":
image, target = self.prepare_coco_detection(image, target, return_segmentation_masks)
return image, target
elif self.format == "coco_panoptic":
image, target = self.prepare_coco_panoptic(image, target, masks_path)
return image, target
else:
raise ValueError(f"Format {self.format} not supported")
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, segmentations, height, width):
try:
from pycocotools import mask as coco_mask
except ImportError:
raise ImportError("Pycocotools is not installed in your environment.")
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = np.asarray(mask, dtype=np.uint8)
mask = np.any(mask, axis=2)
masks.append(mask)
if masks:
masks = np.stack(masks, axis=0)
else:
masks = np.zeros((0, height, width), dtype=np.uint8)
return masks
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_detection with DETR->ConditionalDETR
def prepare_coco_detection(self, image, target, return_segmentation_masks=False):
"""
Convert the target in COCO format into the format expected by ConditionalDETR.
"""
w, h = image.size
image_id = target["image_id"]
image_id = np.asarray([image_id], dtype=np.int64)
# get all COCO annotations for the given image
anno = target["annotations"]
anno = [obj for obj in anno if "iscrowd" not in obj or obj["iscrowd"] == 0]
boxes = [obj["bbox"] for obj in anno]
# guard against no boxes via resizing
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2]
boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=w)
boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=h)
classes = [obj["category_id"] for obj in anno]
classes = np.asarray(classes, dtype=np.int64)
if return_segmentation_masks:
segmentations = [obj["segmentation"] for obj in anno]
masks = self.convert_coco_poly_to_mask(segmentations, h, w)
keypoints = None
if anno and "keypoints" in anno[0]:
keypoints = [obj["keypoints"] for obj in anno]
keypoints = np.asarray(keypoints, dtype=np.float32)
num_keypoints = keypoints.shape[0]
if num_keypoints:
keypoints = keypoints.reshape((-1, 3))
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
boxes = boxes[keep]
classes = classes[keep]
if return_segmentation_masks:
masks = masks[keep]
if keypoints is not None:
keypoints = keypoints[keep]
target = {}
target["boxes"] = boxes
target["class_labels"] = classes
if return_segmentation_masks:
target["masks"] = masks
target["image_id"] = image_id
if keypoints is not None:
target["keypoints"] = keypoints
# for conversion to coco api
area = np.asarray([obj["area"] for obj in anno], dtype=np.float32)
iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno], dtype=np.int64)
target["area"] = area[keep]
target["iscrowd"] = iscrowd[keep]
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
return image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_panoptic
def prepare_coco_panoptic(self, image, target, masks_path, return_masks=True):
w, h = image.size
ann_info = target.copy()
ann_path = pathlib.Path(masks_path) / ann_info["file_name"]
if "segments_info" in ann_info:
masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
masks = rgb_to_id(masks)
ids = np.array([ann["id"] for ann in ann_info["segments_info"]])
masks = masks == ids[:, None, None]
masks = np.asarray(masks, dtype=np.uint8)
labels = np.asarray([ann["category_id"] for ann in ann_info["segments_info"]], dtype=np.int64)
target = {}
target["image_id"] = np.asarray(
[ann_info["image_id"] if "image_id" in ann_info else ann_info["id"]], dtype=np.int64
)
if return_masks:
target["masks"] = masks
target["class_labels"] = labels
target["boxes"] = masks_to_boxes(masks)
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
if "segments_info" in ann_info:
target["iscrowd"] = np.asarray([ann["iscrowd"] for ann in ann_info["segments_info"]], dtype=np.int64)
target["area"] = np.asarray([ann["area"] for ann in ann_info["segments_info"]], dtype=np.float32)
return image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._resize
def _resize(self, image, size, target=None, max_size=None):
"""
Resize the image to the given size. Size can be min_size (scalar) or (w, h) tuple. If size is an int, smaller
edge of the image will be matched to this number.
If given, also resize the target accordingly.
"""
if not isinstance(image, Image.Image):
image = self.to_pil_image(image)
def get_size_with_aspect_ratio(image_size, size, max_size=None):
w, h = image_size
if max_size is not None:
min_original_size = float(min((w, h)))
max_original_size = float(max((w, h)))
if max_original_size / min_original_size * size > max_size:
size = int(round(max_size * min_original_size / max_original_size))
if (w <= h and w == size) or (h <= w and h == size):
return (h, w)
if w < h:
ow = size
oh = int(size * h / w)
else:
oh = size
ow = int(size * w / h)
return (oh, ow)
def get_size(image_size, size, max_size=None):
if isinstance(size, (list, tuple)):
return size
else:
# size returned must be (w, h) since we use PIL to resize images
# so we revert the tuple
return get_size_with_aspect_ratio(image_size, size, max_size)[::-1]
size = get_size(image.size, size, max_size)
rescaled_image = self.resize(image, size=size)
if target is None:
return rescaled_image, None
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
ratio_width, ratio_height = ratios
target = target.copy()
if "boxes" in target:
boxes = target["boxes"]
scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
target["boxes"] = scaled_boxes
if "area" in target:
area = target["area"]
scaled_area = area * (ratio_width * ratio_height)
target["area"] = scaled_area
w, h = size
target["size"] = np.asarray([h, w], dtype=np.int64)
if "masks" in target:
# use PyTorch as current workaround
# TODO replace by self.resize
masks = torch.from_numpy(target["masks"][:, None]).float()
interpolated_masks = nn.functional.interpolate(masks, size=(h, w), mode="nearest")[:, 0] > 0.5
target["masks"] = interpolated_masks.numpy()
return rescaled_image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._normalize
def _normalize(self, image, mean, std, target=None):
"""
Normalize the image with a certain mean and std.
If given, also normalize the target bounding boxes based on the size of the image.
"""
image = self.normalize(image, mean=mean, std=std)
if target is None:
return image, None
target = target.copy()
h, w = image.shape[-2:]
if "boxes" in target:
boxes = target["boxes"]
boxes = corners_to_center_format(boxes)
boxes = boxes / np.asarray([w, h, w, h], dtype=np.float32)
target["boxes"] = boxes
return image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.__call__ with Detr->ConditionalDetr,DETR->ConditionalDETR
def __call__(
self,
images: ImageInput,
annotations: Union[List[Dict], List[List[Dict]]] = None,
return_segmentation_masks: Optional[bool] = False,
masks_path: Optional[pathlib.Path] = None,
pad_and_return_pixel_mask: Optional[bool] = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s) and optional annotations. Images are by default
padded up to the largest image in a batch, and a pixel mask is created that indicates which pixels are
real/which are padding.
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
annotations (`Dict`, `List[Dict]`, *optional*):
The corresponding annotations in COCO format.
In case [`ConditionalDetrFeatureExtractor`] was initialized with `format = "coco_detection"`, the
annotations for each image should have the following format: {'image_id': int, 'annotations':
[annotation]}, with the annotations being a list of COCO object annotations.
In case [`ConditionalDetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`, the
annotations for each image should have the following format: {'image_id': int, 'file_name': str,
'segments_info': [segment_info]} with segments_info being a list of COCO panoptic annotations.
return_segmentation_masks (`Dict`, `List[Dict]`, *optional*, defaults to `False`):
Whether to also include instance segmentation masks as part of the labels in case `format =
"coco_detection"`.
masks_path (`pathlib.Path`, *optional*):
Path to the directory containing the PNG files that store the class-agnostic image segmentations. Only
relevant in case [`ConditionalDetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`.
pad_and_return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether or not to pad images up to the largest image in a batch and create a pixel mask.
If left to the default, will return a pixel mask that is:
- 1 for pixels that are real (i.e. **not masked**),
- 0 for pixels that are padding (i.e. **masked**).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **pixel_values** -- Pixel values to be fed to a model.
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
*"pixel_mask"* is in `self.model_input_names`).
- **labels** -- Optional labels to be fed to a model (when `annotations` are provided)
"""
# Input type checking for clearer error
valid_images = False
valid_annotations = False
valid_masks_path = False
# Check that images has a valid type
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
valid_images = True
elif isinstance(images, (list, tuple)):
if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
valid_images = True
if not valid_images:
raise ValueError(
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
)
is_batched = bool(
isinstance(images, (list, tuple))
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
)
# Check that annotations has a valid type
if annotations is not None:
if not is_batched:
if self.format == "coco_detection":
if isinstance(annotations, dict) and "image_id" in annotations and "annotations" in annotations:
if isinstance(annotations["annotations"], (list, tuple)):
# an image can have no annotations
if len(annotations["annotations"]) == 0 or isinstance(annotations["annotations"][0], dict):
valid_annotations = True
elif self.format == "coco_panoptic":
if isinstance(annotations, dict) and "image_id" in annotations and "segments_info" in annotations:
if isinstance(annotations["segments_info"], (list, tuple)):
# an image can have no segments (?)
if len(annotations["segments_info"]) == 0 or isinstance(
annotations["segments_info"][0], dict
):
valid_annotations = True
else:
if isinstance(annotations, (list, tuple)):
if len(images) != len(annotations):
raise ValueError("There must be as many annotations as there are images")
if isinstance(annotations[0], Dict):
if self.format == "coco_detection":
if isinstance(annotations[0]["annotations"], (list, tuple)):
valid_annotations = True
elif self.format == "coco_panoptic":
if isinstance(annotations[0]["segments_info"], (list, tuple)):
valid_annotations = True
if not valid_annotations:
raise ValueError(
"""
Annotations must of type `Dict` (single image) or `List[Dict]` (batch of images). In case of object
detection, each dictionary should contain the keys 'image_id' and 'annotations', with the latter
being a list of annotations in COCO format. In case of panoptic segmentation, each dictionary
should contain the keys 'file_name', 'image_id' and 'segments_info', with the latter being a list
of annotations in COCO format.
"""
)
# Check that masks_path has a valid type
if masks_path is not None:
if self.format == "coco_panoptic":
if isinstance(masks_path, pathlib.Path):
valid_masks_path = True
if not valid_masks_path:
raise ValueError(
"The path to the directory containing the mask PNG files should be provided as a"
" `pathlib.Path` object."
)
if not is_batched:
images = [images]
if annotations is not None:
annotations = [annotations]
# Create a copy of the list to avoid editing it in place
images = [image for image in images]
if annotations is not None:
annotations = [annotation for annotation in annotations]
# prepare (COCO annotations as a list of Dict -> ConditionalDETR target as a single Dict per image)
if annotations is not None:
for idx, (image, target) in enumerate(zip(images, annotations)):
if not isinstance(image, Image.Image):
image = self.to_pil_image(image)
image, target = self.prepare(image, target, return_segmentation_masks, masks_path)
images[idx] = image
annotations[idx] = target
# transformations (resizing + normalization)
if self.do_resize and self.size is not None:
if annotations is not None:
for idx, (image, target) in enumerate(zip(images, annotations)):
image, target = self._resize(image=image, target=target, size=self.size, max_size=self.max_size)
images[idx] = image
annotations[idx] = target
else:
for idx, image in enumerate(images):
images[idx] = self._resize(image=image, target=None, size=self.size, max_size=self.max_size)[0]
if self.do_normalize:
if annotations is not None:
for idx, (image, target) in enumerate(zip(images, annotations)):
image, target = self._normalize(
image=image, mean=self.image_mean, std=self.image_std, target=target
)
images[idx] = image
annotations[idx] = target
else:
images = [
self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
]
else:
images = [np.array(image) for image in images]
if pad_and_return_pixel_mask:
# pad images up to largest image in batch and create pixel_mask
max_size = self._max_by_axis([list(image.shape) for image in images])
c, h, w = max_size
padded_images = []
pixel_mask = []
for image in images:
# create padded image
padded_image = np.zeros((c, h, w), dtype=np.float32)
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
padded_images.append(padded_image)
# create pixel mask
mask = np.zeros((h, w), dtype=np.int64)
mask[: image.shape[1], : image.shape[2]] = True
pixel_mask.append(mask)
images = padded_images
# return as BatchFeature
data = {}
data["pixel_values"] = images
if pad_and_return_pixel_mask:
data["pixel_mask"] = pixel_mask
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
if annotations is not None:
# Convert to TensorType
tensor_type = return_tensors
if not isinstance(tensor_type, TensorType):
tensor_type = TensorType(tensor_type)
if not tensor_type == TensorType.PYTORCH:
raise ValueError("Only PyTorch is supported for the moment.")
else:
if not is_torch_available():
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
encoded_inputs["labels"] = [
{k: torch.from_numpy(v) for k, v in target.items()} for target in annotations
]
return encoded_inputs
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._max_by_axis
def _max_by_axis(self, the_list):
# type: (List[List[int]]) -> List[int]
maxes = the_list[0]
for sublist in the_list[1:]:
for index, item in enumerate(sublist):
maxes[index] = max(maxes[index], item)
return maxes
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.pad_and_create_pixel_mask
def pad_and_create_pixel_mask(
self, pixel_values_list: List["torch.Tensor"], return_tensors: Optional[Union[str, TensorType]] = None
):
"""
Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
Args:
pixel_values_list (`List[torch.Tensor]`):
List of images (pixel values) to be padded. Each image should be a tensor of shape (C, H, W).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **pixel_values** -- Pixel values to be fed to a model.
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
*"pixel_mask"* is in `self.model_input_names`).
"""
max_size = self._max_by_axis([list(image.shape) for image in pixel_values_list])
c, h, w = max_size
padded_images = []
pixel_mask = []
for image in pixel_values_list:
# create padded image
padded_image = np.zeros((c, h, w), dtype=np.float32)
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
padded_images.append(padded_image)
# create pixel mask
mask = np.zeros((h, w), dtype=np.int64)
mask[: image.shape[1], : image.shape[2]] = True
pixel_mask.append(mask)
# return as BatchFeature
data = {"pixel_values": padded_images, "pixel_mask": pixel_mask}
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
return encoded_inputs
def post_process(self, outputs, target_sizes):
"""
Args:
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
outputs ([`ConditionalDetrObjectDetectionOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
image size (before any data augmentation). For visualization, this should be the image size after data
augment, but before padding.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
warnings.warn(
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_object_detection`",
FutureWarning,
)
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
if len(out_logits) != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
if target_sizes.shape[1] != 2:
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 300, dim=1)
scores = topk_values
topk_boxes = topk_indexes // out_logits.shape[2]
labels = topk_indexes % out_logits.shape[2]
boxes = center_to_corners_format(out_bbox)
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
# and from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
boxes = boxes * scale_fct[:, None, :]
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
return results
# Copied from transformers.models.deformable_detr.feature_extraction_deformable_detr.DeformableDetrFeatureExtractor.post_process_object_detection with DeformableDetr->ConditionalDetr
def post_process_object_detection(
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
):
"""
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
Args:
outputs ([`DetrObjectDetectionOutput`]):
Raw outputs of the model.
threshold (`float`, *optional*):
Score threshold to keep object detection predictions.
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*, defaults to `None`):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
(height, width) of each image in the batch. If left to None, predictions will not be resized.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
if target_sizes is not None:
if len(out_logits) != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
scores = topk_values
topk_boxes = topk_indexes // out_logits.shape[2]
labels = topk_indexes % out_logits.shape[2]
boxes = center_to_corners_format(out_bbox)
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
# and from relative [0, 1] to absolute [0, height] coordinates
if isinstance(target_sizes, List):
img_h = torch.Tensor([i[0] for i in target_sizes])
img_w = torch.Tensor([i[1] for i in target_sizes])
else:
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :]
results = []
for s, l, b in zip(scores, labels, boxes):
score = s[s > threshold]
label = l[s > threshold]
box = b[s > threshold]
results.append({"scores": score, "labels": label, "boxes": box})
return results
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process_semantic_segmentation with Detr->ConditionalDetr
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
"""
Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple[int, int]]`, *optional*, defaults to `None`):
A list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the
batch. If left to None, predictions will not be resized.
Returns:
`List[torch.Tensor]`:
A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
`torch.Tensor` correspond to a semantic class id.
"""
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
# Remove the null class `[..., :-1]`
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Semantic segmentation logits of shape (batch_size, num_classes, height, width)
segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
batch_size = class_queries_logits.shape[0]
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if batch_size != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
semantic_segmentation = []
for idx in range(batch_size):
resized_logits = nn.functional.interpolate(
segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
)
semantic_map = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(semantic_map)
else:
semantic_segmentation = segmentation.argmax(dim=1)
semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process_instance_segmentation with Detr->ConditionalDetr
def post_process_instance_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
target_sizes: Optional[List[Tuple[int, int]]] = None,
return_coco_annotation: Optional[bool] = False,
) -> List[Dict]:
"""
Converts the output of [`ConditionalDetrForSegmentation`] into instance segmentation predictions. Only supports
PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.
return_coco_annotation (`bool`, *optional*):
Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE)
format.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or
`List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
`True`. Set to `None` if no mask if found above `threshold`.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- An integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
continue
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_labels_item,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
label_ids_to_fuse=[],
target_size=target_size,
)
# Return segmentation map in run-length encoding (RLE) format
if return_coco_annotation:
segmentation = convert_segmentation_to_rle(segmentation)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process_panoptic_segmentation with Detr->ConditionalDetr
def post_process_panoptic_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_sizes: Optional[List[Tuple[int, int]]] = None,
) -> List[Dict]:
"""
Converts the output of [`ConditionalDetrForSegmentation`] into image panoptic segmentation predictions. Only
supports PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
The outputs from [`ConditionalDetrForSegmentation`].
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
label_ids_to_fuse (`Set[int]`, *optional*):
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction in batch. If left to None, predictions will not be
resized.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id` or
`None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized to
the corresponding `target_sizes` entry.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- an integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
Multiple instances of the same class / label were fused and assigned a single `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
if label_ids_to_fuse is None:
warnings.warn("`label_ids_to_fuse` unset. No instance will be fused.")
label_ids_to_fuse = set()
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
continue
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_labels_item,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
label_ids_to_fuse=label_ids_to_fuse,
target_size=target_size,
)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for Conditional DETR."""
import pathlib
import warnings
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_transforms import center_to_corners_format, corners_to_center_format, rgb_to_id
from ...image_utils import ImageFeatureExtractionMixin
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
from torch import nn
logger = logging.get_logger(__name__)
ImageInput = Union[Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]]
# Copied from transformers.models.detr.feature_extraction_detr.masks_to_boxes
def masks_to_boxes(masks):
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
"""
if masks.size == 0:
return np.zeros((0, 4))
h, w = masks.shape[-2:]
y = np.arange(0, h, dtype=np.float32)
x = np.arange(0, w, dtype=np.float32)
# see https://github.com/pytorch/pytorch/issues/50276
y, x = np.meshgrid(y, x, indexing="ij")
x_mask = masks * np.expand_dims(x, axis=0)
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
x_min = x.filled(fill_value=1e8)
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
y_mask = masks * np.expand_dims(y, axis=0)
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
y_min = y.filled(fill_value=1e8)
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
return np.stack([x_min, y_min, x_max, y_max], 1)
# Copied from transformers.models.detr.feature_extraction_detr.binary_mask_to_rle
def binary_mask_to_rle(mask):
"""
Args:
Converts given binary mask of shape (height, width) to the run-length encoding (RLE) format.
mask (`torch.Tensor` or `numpy.array`):
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
segment_id or class_id.
Returns:
`List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
format.
"""
if is_torch_tensor(mask):
mask = mask.numpy()
pixels = mask.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return [x for x in runs]
# Copied from transformers.models.detr.feature_extraction_detr.convert_segmentation_to_rle
def convert_segmentation_to_rle(segmentation):
"""
Converts given segmentation map of shape (height, width) to the run-length encoding (RLE) format.
Args:
segmentation (`torch.Tensor` or `numpy.array`):
A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
Returns:
`List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
"""
segment_ids = torch.unique(segmentation)
run_length_encodings = []
for idx in segment_ids:
mask = torch.where(segmentation == idx, 1, 0)
rle = binary_mask_to_rle(mask)
run_length_encodings.append(rle)
return run_length_encodings
# Copied from transformers.models.detr.feature_extraction_detr.remove_low_and_no_objects
def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
"""
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
`labels`.
Args:
masks (`torch.Tensor`):
A tensor of shape `(num_queries, height, width)`.
scores (`torch.Tensor`):
A tensor of shape `(num_queries)`.
labels (`torch.Tensor`):
A tensor of shape `(num_queries)`.
object_mask_threshold (`float`):
A number between 0 and 1 used to binarize the masks.
Raises:
`ValueError`: Raised when the first dimension doesn't match in all input tensors.
Returns:
`Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
< `object_mask_threshold`.
"""
if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
raise ValueError("mask, scores and labels must have the same shape!")
to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
return masks[to_keep], scores[to_keep], labels[to_keep]
# Copied from transformers.models.detr.feature_extraction_detr.check_segment_validity
def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
# Get the mask associated with the k class
mask_k = mask_labels == k
mask_k_area = mask_k.sum()
# Compute the area of all the stuff in query k
original_area = (mask_probs[k] >= mask_threshold).sum()
mask_exists = mask_k_area > 0 and original_area > 0
# Eliminate disconnected tiny segments
if mask_exists:
area_ratio = mask_k_area / original_area
if not area_ratio.item() > overlap_mask_area_threshold:
mask_exists = False
return mask_exists, mask_k
# Copied from transformers.models.detr.feature_extraction_detr.compute_segments
def compute_segments(
mask_probs,
pred_scores,
pred_labels,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
segments: List[Dict] = []
if target_size is not None:
mask_probs = nn.functional.interpolate(
mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
)[0]
current_segment_id = 0
# Weigh each mask by its prediction score
mask_probs *= pred_scores.view(-1, 1, 1)
mask_labels = mask_probs.argmax(0) # [height, width]
# Keep track of instances of each class
stuff_memory_list: Dict[str, int] = {}
for k in range(pred_labels.shape[0]):
pred_class = pred_labels[k].item()
should_fuse = pred_class in label_ids_to_fuse
# Check if mask exists and large enough to be a segment
mask_exists, mask_k = check_segment_validity(
mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
)
if mask_exists:
if pred_class in stuff_memory_list:
current_segment_id = stuff_memory_list[pred_class]
else:
current_segment_id += 1
# Add current object segment to final segmentation map
segmentation[mask_k] = current_segment_id
segment_score = round(pred_scores[k].item(), 6)
segments.append(
{
"id": current_segment_id,
"label_id": pred_class,
"was_fused": should_fuse,
"score": segment_score,
}
)
if should_fuse:
stuff_memory_list[pred_class] = current_segment_id
return segmentation, segments
class ConditionalDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
r"""
Constructs a Conditional DETR feature extractor.
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input to a certain `size`.
size (`int`, *optional*, defaults to 800):
Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
height / width, size)`.
max_size (`int`, *optional*, defaults to `1333`):
The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input with mean and standard deviation.
image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
ImageNet std.
"""
model_input_names = ["pixel_values", "pixel_mask"]
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.__init__
def __init__(
self,
format="coco_detection",
do_resize=True,
size=800,
max_size=1333,
do_normalize=True,
image_mean=None,
image_std=None,
**kwargs
):
super().__init__(**kwargs)
self.format = self._is_valid_format(format)
self.do_resize = do_resize
self.size = size
self.max_size = max_size
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else [0.485, 0.456, 0.406] # ImageNet mean
self.image_std = image_std if image_std is not None else [0.229, 0.224, 0.225] # ImageNet std
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._is_valid_format
def _is_valid_format(self, format):
if format not in ["coco_detection", "coco_panoptic"]:
raise ValueError(f"Format {format} not supported")
return format
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare
def prepare(self, image, target, return_segmentation_masks=False, masks_path=None):
if self.format == "coco_detection":
image, target = self.prepare_coco_detection(image, target, return_segmentation_masks)
return image, target
elif self.format == "coco_panoptic":
image, target = self.prepare_coco_panoptic(image, target, masks_path)
return image, target
else:
raise ValueError(f"Format {self.format} not supported")
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(self, segmentations, height, width):
try:
from pycocotools import mask as coco_mask
except ImportError:
raise ImportError("Pycocotools is not installed in your environment.")
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = np.asarray(mask, dtype=np.uint8)
mask = np.any(mask, axis=2)
masks.append(mask)
if masks:
masks = np.stack(masks, axis=0)
else:
masks = np.zeros((0, height, width), dtype=np.uint8)
return masks
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_detection with DETR->ConditionalDETR
def prepare_coco_detection(self, image, target, return_segmentation_masks=False):
"""
Convert the target in COCO format into the format expected by ConditionalDETR.
"""
w, h = image.size
image_id = target["image_id"]
image_id = np.asarray([image_id], dtype=np.int64)
# get all COCO annotations for the given image
anno = target["annotations"]
anno = [obj for obj in anno if "iscrowd" not in obj or obj["iscrowd"] == 0]
boxes = [obj["bbox"] for obj in anno]
# guard against no boxes via resizing
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2]
boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=w)
boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=h)
classes = [obj["category_id"] for obj in anno]
classes = np.asarray(classes, dtype=np.int64)
if return_segmentation_masks:
segmentations = [obj["segmentation"] for obj in anno]
masks = self.convert_coco_poly_to_mask(segmentations, h, w)
keypoints = None
if anno and "keypoints" in anno[0]:
keypoints = [obj["keypoints"] for obj in anno]
keypoints = np.asarray(keypoints, dtype=np.float32)
num_keypoints = keypoints.shape[0]
if num_keypoints:
keypoints = keypoints.reshape((-1, 3))
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
boxes = boxes[keep]
classes = classes[keep]
if return_segmentation_masks:
masks = masks[keep]
if keypoints is not None:
keypoints = keypoints[keep]
target = {}
target["boxes"] = boxes
target["class_labels"] = classes
if return_segmentation_masks:
target["masks"] = masks
target["image_id"] = image_id
if keypoints is not None:
target["keypoints"] = keypoints
# for conversion to coco api
area = np.asarray([obj["area"] for obj in anno], dtype=np.float32)
iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno], dtype=np.int64)
target["area"] = area[keep]
target["iscrowd"] = iscrowd[keep]
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
return image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.prepare_coco_panoptic
def prepare_coco_panoptic(self, image, target, masks_path, return_masks=True):
w, h = image.size
ann_info = target.copy()
ann_path = pathlib.Path(masks_path) / ann_info["file_name"]
if "segments_info" in ann_info:
masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
masks = rgb_to_id(masks)
ids = np.array([ann["id"] for ann in ann_info["segments_info"]])
masks = masks == ids[:, None, None]
masks = np.asarray(masks, dtype=np.uint8)
labels = np.asarray([ann["category_id"] for ann in ann_info["segments_info"]], dtype=np.int64)
target = {}
target["image_id"] = np.asarray(
[ann_info["image_id"] if "image_id" in ann_info else ann_info["id"]], dtype=np.int64
)
if return_masks:
target["masks"] = masks
target["class_labels"] = labels
target["boxes"] = masks_to_boxes(masks)
target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
if "segments_info" in ann_info:
target["iscrowd"] = np.asarray([ann["iscrowd"] for ann in ann_info["segments_info"]], dtype=np.int64)
target["area"] = np.asarray([ann["area"] for ann in ann_info["segments_info"]], dtype=np.float32)
return image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._resize
def _resize(self, image, size, target=None, max_size=None):
"""
Resize the image to the given size. Size can be min_size (scalar) or (w, h) tuple. If size is an int, smaller
edge of the image will be matched to this number.
If given, also resize the target accordingly.
"""
if not isinstance(image, Image.Image):
image = self.to_pil_image(image)
def get_size_with_aspect_ratio(image_size, size, max_size=None):
w, h = image_size
if max_size is not None:
min_original_size = float(min((w, h)))
max_original_size = float(max((w, h)))
if max_original_size / min_original_size * size > max_size:
size = int(round(max_size * min_original_size / max_original_size))
if (w <= h and w == size) or (h <= w and h == size):
return (h, w)
if w < h:
ow = size
oh = int(size * h / w)
else:
oh = size
ow = int(size * w / h)
return (oh, ow)
def get_size(image_size, size, max_size=None):
if isinstance(size, (list, tuple)):
return size
else:
# size returned must be (w, h) since we use PIL to resize images
# so we revert the tuple
return get_size_with_aspect_ratio(image_size, size, max_size)[::-1]
size = get_size(image.size, size, max_size)
rescaled_image = self.resize(image, size=size)
if target is None:
return rescaled_image, None
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
ratio_width, ratio_height = ratios
target = target.copy()
if "boxes" in target:
boxes = target["boxes"]
scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
target["boxes"] = scaled_boxes
if "area" in target:
area = target["area"]
scaled_area = area * (ratio_width * ratio_height)
target["area"] = scaled_area
w, h = size
target["size"] = np.asarray([h, w], dtype=np.int64)
if "masks" in target:
# use PyTorch as current workaround
# TODO replace by self.resize
masks = torch.from_numpy(target["masks"][:, None]).float()
interpolated_masks = nn.functional.interpolate(masks, size=(h, w), mode="nearest")[:, 0] > 0.5
target["masks"] = interpolated_masks.numpy()
return rescaled_image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._normalize
def _normalize(self, image, mean, std, target=None):
"""
Normalize the image with a certain mean and std.
If given, also normalize the target bounding boxes based on the size of the image.
"""
image = self.normalize(image, mean=mean, std=std)
if target is None:
return image, None
target = target.copy()
h, w = image.shape[-2:]
if "boxes" in target:
boxes = target["boxes"]
boxes = corners_to_center_format(boxes)
boxes = boxes / np.asarray([w, h, w, h], dtype=np.float32)
target["boxes"] = boxes
return image, target
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.__call__ with Detr->ConditionalDetr,DETR->ConditionalDETR
def __call__(
self,
images: ImageInput,
annotations: Union[List[Dict], List[List[Dict]]] = None,
return_segmentation_masks: Optional[bool] = False,
masks_path: Optional[pathlib.Path] = None,
pad_and_return_pixel_mask: Optional[bool] = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s) and optional annotations. Images are by default
padded up to the largest image in a batch, and a pixel mask is created that indicates which pixels are
real/which are padding.
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
annotations (`Dict`, `List[Dict]`, *optional*):
The corresponding annotations in COCO format.
In case [`ConditionalDetrFeatureExtractor`] was initialized with `format = "coco_detection"`, the
annotations for each image should have the following format: {'image_id': int, 'annotations':
[annotation]}, with the annotations being a list of COCO object annotations.
In case [`ConditionalDetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`, the
annotations for each image should have the following format: {'image_id': int, 'file_name': str,
'segments_info': [segment_info]} with segments_info being a list of COCO panoptic annotations.
return_segmentation_masks (`Dict`, `List[Dict]`, *optional*, defaults to `False`):
Whether to also include instance segmentation masks as part of the labels in case `format =
"coco_detection"`.
masks_path (`pathlib.Path`, *optional*):
Path to the directory containing the PNG files that store the class-agnostic image segmentations. Only
relevant in case [`ConditionalDetrFeatureExtractor`] was initialized with `format = "coco_panoptic"`.
pad_and_return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether or not to pad images up to the largest image in a batch and create a pixel mask.
If left to the default, will return a pixel mask that is:
- 1 for pixels that are real (i.e. **not masked**),
- 0 for pixels that are padding (i.e. **masked**).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **pixel_values** -- Pixel values to be fed to a model.
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
*"pixel_mask"* is in `self.model_input_names`).
- **labels** -- Optional labels to be fed to a model (when `annotations` are provided)
"""
# Input type checking for clearer error
valid_images = False
valid_annotations = False
valid_masks_path = False
# Check that images has a valid type
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
valid_images = True
elif isinstance(images, (list, tuple)):
if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
valid_images = True
if not valid_images:
raise ValueError(
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
)
is_batched = bool(
isinstance(images, (list, tuple))
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
)
# Check that annotations has a valid type
if annotations is not None:
if not is_batched:
if self.format == "coco_detection":
if isinstance(annotations, dict) and "image_id" in annotations and "annotations" in annotations:
if isinstance(annotations["annotations"], (list, tuple)):
# an image can have no annotations
if len(annotations["annotations"]) == 0 or isinstance(annotations["annotations"][0], dict):
valid_annotations = True
elif self.format == "coco_panoptic":
if isinstance(annotations, dict) and "image_id" in annotations and "segments_info" in annotations:
if isinstance(annotations["segments_info"], (list, tuple)):
# an image can have no segments (?)
if len(annotations["segments_info"]) == 0 or isinstance(
annotations["segments_info"][0], dict
):
valid_annotations = True
else:
if isinstance(annotations, (list, tuple)):
if len(images) != len(annotations):
raise ValueError("There must be as many annotations as there are images")
if isinstance(annotations[0], Dict):
if self.format == "coco_detection":
if isinstance(annotations[0]["annotations"], (list, tuple)):
valid_annotations = True
elif self.format == "coco_panoptic":
if isinstance(annotations[0]["segments_info"], (list, tuple)):
valid_annotations = True
if not valid_annotations:
raise ValueError(
"""
Annotations must of type `Dict` (single image) or `List[Dict]` (batch of images). In case of object
detection, each dictionary should contain the keys 'image_id' and 'annotations', with the latter
being a list of annotations in COCO format. In case of panoptic segmentation, each dictionary
should contain the keys 'file_name', 'image_id' and 'segments_info', with the latter being a list
of annotations in COCO format.
"""
)
# Check that masks_path has a valid type
if masks_path is not None:
if self.format == "coco_panoptic":
if isinstance(masks_path, pathlib.Path):
valid_masks_path = True
if not valid_masks_path:
raise ValueError(
"The path to the directory containing the mask PNG files should be provided as a"
" `pathlib.Path` object."
)
if not is_batched:
images = [images]
if annotations is not None:
annotations = [annotations]
# Create a copy of the list to avoid editing it in place
images = [image for image in images]
if annotations is not None:
annotations = [annotation for annotation in annotations]
# prepare (COCO annotations as a list of Dict -> ConditionalDETR target as a single Dict per image)
if annotations is not None:
for idx, (image, target) in enumerate(zip(images, annotations)):
if not isinstance(image, Image.Image):
image = self.to_pil_image(image)
image, target = self.prepare(image, target, return_segmentation_masks, masks_path)
images[idx] = image
annotations[idx] = target
# transformations (resizing + normalization)
if self.do_resize and self.size is not None:
if annotations is not None:
for idx, (image, target) in enumerate(zip(images, annotations)):
image, target = self._resize(image=image, target=target, size=self.size, max_size=self.max_size)
images[idx] = image
annotations[idx] = target
else:
for idx, image in enumerate(images):
images[idx] = self._resize(image=image, target=None, size=self.size, max_size=self.max_size)[0]
if self.do_normalize:
if annotations is not None:
for idx, (image, target) in enumerate(zip(images, annotations)):
image, target = self._normalize(
image=image, mean=self.image_mean, std=self.image_std, target=target
)
images[idx] = image
annotations[idx] = target
else:
images = [
self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
]
else:
images = [np.array(image) for image in images]
if pad_and_return_pixel_mask:
# pad images up to largest image in batch and create pixel_mask
max_size = self._max_by_axis([list(image.shape) for image in images])
c, h, w = max_size
padded_images = []
pixel_mask = []
for image in images:
# create padded image
padded_image = np.zeros((c, h, w), dtype=np.float32)
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
padded_images.append(padded_image)
# create pixel mask
mask = np.zeros((h, w), dtype=np.int64)
mask[: image.shape[1], : image.shape[2]] = True
pixel_mask.append(mask)
images = padded_images
# return as BatchFeature
data = {}
data["pixel_values"] = images
if pad_and_return_pixel_mask:
data["pixel_mask"] = pixel_mask
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
if annotations is not None:
# Convert to TensorType
tensor_type = return_tensors
if not isinstance(tensor_type, TensorType):
tensor_type = TensorType(tensor_type)
if not tensor_type == TensorType.PYTORCH:
raise ValueError("Only PyTorch is supported for the moment.")
else:
if not is_torch_available():
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
encoded_inputs["labels"] = [
{k: torch.from_numpy(v) for k, v in target.items()} for target in annotations
]
return encoded_inputs
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._max_by_axis
def _max_by_axis(self, the_list):
# type: (List[List[int]]) -> List[int]
maxes = the_list[0]
for sublist in the_list[1:]:
for index, item in enumerate(sublist):
maxes[index] = max(maxes[index], item)
return maxes
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.pad_and_create_pixel_mask
def pad_and_create_pixel_mask(
self, pixel_values_list: List["torch.Tensor"], return_tensors: Optional[Union[str, TensorType]] = None
):
"""
Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
Args:
pixel_values_list (`List[torch.Tensor]`):
List of images (pixel values) to be padded. Each image should be a tensor of shape (C, H, W).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **pixel_values** -- Pixel values to be fed to a model.
- **pixel_mask** -- Pixel mask to be fed to a model (when `pad_and_return_pixel_mask=True` or if
*"pixel_mask"* is in `self.model_input_names`).
"""
max_size = self._max_by_axis([list(image.shape) for image in pixel_values_list])
c, h, w = max_size
padded_images = []
pixel_mask = []
for image in pixel_values_list:
# create padded image
padded_image = np.zeros((c, h, w), dtype=np.float32)
padded_image[: image.shape[0], : image.shape[1], : image.shape[2]] = np.copy(image)
padded_images.append(padded_image)
# create pixel mask
mask = np.zeros((h, w), dtype=np.int64)
mask[: image.shape[1], : image.shape[2]] = True
pixel_mask.append(mask)
# return as BatchFeature
data = {"pixel_values": padded_images, "pixel_mask": pixel_mask}
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
return encoded_inputs
def post_process(self, outputs, target_sizes):
"""
Args:
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
outputs ([`ConditionalDetrObjectDetectionOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
image size (before any data augmentation). For visualization, this should be the image size after data
augment, but before padding.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
warnings.warn(
"`post_process` is deprecated and will be removed in v5 of Transformers, please use"
" `post_process_object_detection`",
FutureWarning,
)
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
if len(out_logits) != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
if target_sizes.shape[1] != 2:
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 300, dim=1)
scores = topk_values
topk_boxes = topk_indexes // out_logits.shape[2]
labels = topk_indexes % out_logits.shape[2]
boxes = center_to_corners_format(out_bbox)
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
# and from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
boxes = boxes * scale_fct[:, None, :]
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
return results
# Copied from transformers.models.deformable_detr.feature_extraction_deformable_detr.DeformableDetrFeatureExtractor.post_process_object_detection with DeformableDetr->ConditionalDetr
def post_process_object_detection(
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
):
"""
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
Args:
outputs ([`DetrObjectDetectionOutput`]):
Raw outputs of the model.
threshold (`float`, *optional*):
Score threshold to keep object detection predictions.
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*, defaults to `None`):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
(height, width) of each image in the batch. If left to None, predictions will not be resized.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
"""
out_logits, out_bbox = outputs.logits, outputs.pred_boxes
if target_sizes is not None:
if len(out_logits) != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
scores = topk_values
topk_boxes = topk_indexes // out_logits.shape[2]
labels = topk_indexes % out_logits.shape[2]
boxes = center_to_corners_format(out_bbox)
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
# and from relative [0, 1] to absolute [0, height] coordinates
if isinstance(target_sizes, List):
img_h = torch.Tensor([i[0] for i in target_sizes])
img_w = torch.Tensor([i[1] for i in target_sizes])
else:
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :]
results = []
for s, l, b in zip(scores, labels, boxes):
score = s[s > threshold]
label = l[s > threshold]
box = b[s > threshold]
results.append({"scores": score, "labels": label, "boxes": box})
return results
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process_semantic_segmentation with Detr->ConditionalDetr
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
"""
Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple[int, int]]`, *optional*, defaults to `None`):
A list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the
batch. If left to None, predictions will not be resized.
Returns:
`List[torch.Tensor]`:
A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
`torch.Tensor` correspond to a semantic class id.
"""
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
# Remove the null class `[..., :-1]`
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Semantic segmentation logits of shape (batch_size, num_classes, height, width)
segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
batch_size = class_queries_logits.shape[0]
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if batch_size != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
semantic_segmentation = []
for idx in range(batch_size):
resized_logits = nn.functional.interpolate(
segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
)
semantic_map = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(semantic_map)
else:
semantic_segmentation = segmentation.argmax(dim=1)
semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process_instance_segmentation with Detr->ConditionalDetr
def post_process_instance_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
target_sizes: Optional[List[Tuple[int, int]]] = None,
return_coco_annotation: Optional[bool] = False,
) -> List[Dict]:
"""
Converts the output of [`ConditionalDetrForSegmentation`] into instance segmentation predictions. Only supports
PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.
return_coco_annotation (`bool`, *optional*):
Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE)
format.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or
`List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
`True`. Set to `None` if no mask if found above `threshold`.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- An integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
continue
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_labels_item,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
label_ids_to_fuse=[],
target_size=target_size,
)
# Return segmentation map in run-length encoding (RLE) format
if return_coco_annotation:
segmentation = convert_segmentation_to_rle(segmentation)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
# Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor.post_process_panoptic_segmentation with Detr->ConditionalDetr
def post_process_panoptic_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_sizes: Optional[List[Tuple[int, int]]] = None,
) -> List[Dict]:
"""
Converts the output of [`ConditionalDetrForSegmentation`] into image panoptic segmentation predictions. Only
supports PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
The outputs from [`ConditionalDetrForSegmentation`].
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
label_ids_to_fuse (`Set[int]`, *optional*):
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction in batch. If left to None, predictions will not be
resized.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id` or
`None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized to
the corresponding `target_sizes` entry.
- **segments_info** -- A dictionary that contains additional information on each segment.
- **id** -- an integer representing the `segment_id`.
- **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
- **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
Multiple instances of the same class / label were fused and assigned a single `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
"""
if label_ids_to_fuse is None:
warnings.warn("`label_ids_to_fuse` unset. No instance will be fused.")
label_ids_to_fuse = set()
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width]
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
# Loop over items in batch size
results: List[Dict[str, TensorType]] = []
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
continue
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_labels_item,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
label_ids_to_fuse=label_ids_to_fuse,
target_size=target_size,
)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/t5/configuration_t5.py | # coding=utf-8
# Copyright 2020, The T5 Authors and HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" T5 model configuration"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeq2SeqConfigWithPast
from ...utils import logging
logger = logging.get_logger(__name__)
T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class T5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
instantiate a T5 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the T5
[t5-small](https://huggingface.co/t5-small) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Arguments:
vocab_size (`int`, *optional*, defaults to 32128):
Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
d_model (`int`, *optional*, defaults to 512):
Size of the encoder layers and the pooler layer.
d_kv (`int`, *optional*, defaults to 64):
Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
num_heads`.
d_ff (`int`, *optional*, defaults to 2048):
Size of the intermediate feed forward layer in each `T5Block`.
num_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_decoder_layers (`int`, *optional*):
Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
num_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer.
relative_attention_max_distance (`int`, *optional*, defaults to 128):
The maximum distance of the longer sequences for the bucket separation.
dropout_rate (`float`, *optional*, defaults to 0.1):
The ratio for all dropout layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers.
initializer_factor (`float`, *optional*, defaults to 1):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
`"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
"""
model_type = "t5"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__(
self,
vocab_size=32128,
d_model=512,
d_kv=64,
d_ff=2048,
num_layers=6,
num_decoder_layers=None,
num_heads=8,
relative_attention_num_buckets=32,
relative_attention_max_distance=128,
dropout_rate=0.1,
layer_norm_epsilon=1e-6,
initializer_factor=1.0,
feed_forward_proj="relu",
is_encoder_decoder=True,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
**kwargs
):
self.vocab_size = vocab_size
self.d_model = d_model
self.d_kv = d_kv
self.d_ff = d_ff
self.num_layers = num_layers
self.num_decoder_layers = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
self.num_heads = num_heads
self.relative_attention_num_buckets = relative_attention_num_buckets
self.relative_attention_max_distance = relative_attention_max_distance
self.dropout_rate = dropout_rate
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_factor = initializer_factor
self.feed_forward_proj = feed_forward_proj
self.use_cache = use_cache
act_info = self.feed_forward_proj.split("-")
self.dense_act_fn = act_info[-1]
self.is_gated_act = act_info[0] == "gated"
if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
raise ValueError(
f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'"
)
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
self.dense_act_fn = "gelu_new"
super().__init__(
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
**kwargs,
)
class T5OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence"
common_inputs["decoder_input_ids"] = {0: "batch"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
return common_inputs
@property
def default_onnx_opset(self) -> int:
return 13
| # coding=utf-8
# Copyright 2020, The T5 Authors and HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" T5 model configuration"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeq2SeqConfigWithPast
from ...utils import logging
logger = logging.get_logger(__name__)
T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class T5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
instantiate a T5 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the T5
[t5-small](https://huggingface.co/t5-small) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Arguments:
vocab_size (`int`, *optional*, defaults to 32128):
Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
d_model (`int`, *optional*, defaults to 512):
Size of the encoder layers and the pooler layer.
d_kv (`int`, *optional*, defaults to 64):
Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
num_heads`.
d_ff (`int`, *optional*, defaults to 2048):
Size of the intermediate feed forward layer in each `T5Block`.
num_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_decoder_layers (`int`, *optional*):
Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
num_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer.
relative_attention_max_distance (`int`, *optional*, defaults to 128):
The maximum distance of the longer sequences for the bucket separation.
dropout_rate (`float`, *optional*, defaults to 0.1):
The ratio for all dropout layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers.
initializer_factor (`float`, *optional*, defaults to 1):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
`"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
"""
model_type = "t5"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__(
self,
vocab_size=32128,
d_model=512,
d_kv=64,
d_ff=2048,
num_layers=6,
num_decoder_layers=None,
num_heads=8,
relative_attention_num_buckets=32,
relative_attention_max_distance=128,
dropout_rate=0.1,
layer_norm_epsilon=1e-6,
initializer_factor=1.0,
feed_forward_proj="relu",
is_encoder_decoder=True,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
**kwargs
):
self.vocab_size = vocab_size
self.d_model = d_model
self.d_kv = d_kv
self.d_ff = d_ff
self.num_layers = num_layers
self.num_decoder_layers = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
self.num_heads = num_heads
self.relative_attention_num_buckets = relative_attention_num_buckets
self.relative_attention_max_distance = relative_attention_max_distance
self.dropout_rate = dropout_rate
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_factor = initializer_factor
self.feed_forward_proj = feed_forward_proj
self.use_cache = use_cache
act_info = self.feed_forward_proj.split("-")
self.dense_act_fn = act_info[-1]
self.is_gated_act = act_info[0] == "gated"
if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
raise ValueError(
f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'"
)
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
self.dense_act_fn = "gelu_new"
super().__init__(
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
**kwargs,
)
class T5OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence"
common_inputs["decoder_input_ids"] = {0: "batch"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
return common_inputs
@property
def default_onnx_opset(self) -> int:
return 13
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/speech_to_text_2/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_import_structure = {
"configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
"processing_speech_to_text_2": ["Speech2Text2Processor"],
"tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_speech_to_text_2"] = [
"SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2Text2ForCausalLM",
"Speech2Text2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_speech_to_text_2 import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2Text2Config
from .processing_speech_to_text_2 import Speech2Text2Processor
from .tokenization_speech_to_text_2 import Speech2Text2Tokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text_2 import (
SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
Speech2Text2ForCausalLM,
Speech2Text2PreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_import_structure = {
"configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
"processing_speech_to_text_2": ["Speech2Text2Processor"],
"tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_speech_to_text_2"] = [
"SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2Text2ForCausalLM",
"Speech2Text2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_speech_to_text_2 import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2Text2Config
from .processing_speech_to_text_2 import Speech2Text2Processor
from .tokenization_speech_to_text_2 import Speech2Text2Tokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text_2 import (
SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
Speech2Text2ForCausalLM,
Speech2Text2PreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/deformable_detr/convert_deformable_detr_to_pytorch.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Deformable DETR checkpoints."""
import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import cached_download, hf_hub_url
from transformers import DeformableDetrConfig, DeformableDetrFeatureExtractor, DeformableDetrForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def rename_key(orig_key):
if "backbone.0.body" in orig_key:
orig_key = orig_key.replace("backbone.0.body", "backbone.conv_encoder.model")
if "transformer" in orig_key:
orig_key = orig_key.replace("transformer.", "")
if "norm1" in orig_key:
if "encoder" in orig_key:
orig_key = orig_key.replace("norm1", "self_attn_layer_norm")
else:
orig_key = orig_key.replace("norm1", "encoder_attn_layer_norm")
if "norm2" in orig_key:
if "encoder" in orig_key:
orig_key = orig_key.replace("norm2", "final_layer_norm")
else:
orig_key = orig_key.replace("norm2", "self_attn_layer_norm")
if "norm3" in orig_key:
orig_key = orig_key.replace("norm3", "final_layer_norm")
if "linear1" in orig_key:
orig_key = orig_key.replace("linear1", "fc1")
if "linear2" in orig_key:
orig_key = orig_key.replace("linear2", "fc2")
if "query_embed" in orig_key:
orig_key = orig_key.replace("query_embed", "query_position_embeddings")
if "cross_attn" in orig_key:
orig_key = orig_key.replace("cross_attn", "encoder_attn")
return orig_key
def read_in_q_k_v(state_dict):
# transformer decoder self-attention layers
for i in range(6):
# read in weights + bias of input projection layer of self-attention
in_proj_weight = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_deformable_detr_checkpoint(
checkpoint_path,
single_scale,
dilation,
with_box_refine,
two_stage,
pytorch_dump_folder_path,
push_to_hub,
):
"""
Copy/paste/tweak model's weights to our Deformable DETR structure.
"""
# load default config
config = DeformableDetrConfig()
# set config attributes
if single_scale:
config.num_feature_levels = 1
config.dilation = dilation
config.with_box_refine = with_box_refine
config.two_stage = two_stage
# set labels
config.num_labels = 91
repo_id = "huggingface/label-files"
filename = "coco-detection-id2label.json"
id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
# load feature extractor
feature_extractor = DeformableDetrFeatureExtractor(format="coco_detection")
# prepare image
img = prepare_img()
encoding = feature_extractor(images=img, return_tensors="pt")
pixel_values = encoding["pixel_values"]
logger.info("Converting model...")
# load original state dict
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
# rename keys
for key in state_dict.copy().keys():
val = state_dict.pop(key)
state_dict[rename_key(key)] = val
# query, key and value matrices need special treatment
read_in_q_k_v(state_dict)
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
prefix = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_embed") and not key.startswith("bbox_embed"):
val = state_dict.pop(key)
state_dict[prefix + key] = val
# finally, create HuggingFace model and load state dict
model = DeformableDetrForObjectDetection(config)
model.load_state_dict(state_dict)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# verify our conversion
outputs = model(pixel_values.to(device))
expected_logits = torch.tensor(
[[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]
)
expected_boxes = torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]])
if single_scale:
expected_logits = torch.tensor(
[[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]
)
expected_boxes = torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]])
if single_scale and dilation:
expected_logits = torch.tensor(
[[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]
)
expected_boxes = torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]])
if with_box_refine:
expected_logits = torch.tensor(
[[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]
)
expected_boxes = torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]])
if with_box_refine and two_stage:
expected_logits = torch.tensor(
[[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]
)
expected_boxes = torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]])
print("Logits:", outputs.logits[0, :3, :3])
assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4)
assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4)
print("Everything ok!")
# Save model and feature extractor
logger.info(f"Saving PyTorch model and feature extractor to {pytorch_dump_folder_path}...")
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
feature_extractor.save_pretrained(pytorch_dump_folder_path)
# Push to hub
if push_to_hub:
model_name = "deformable-detr"
model_name += "-single-scale" if single_scale else ""
model_name += "-dc5" if dilation else ""
model_name += "-with-box-refine" if with_box_refine else ""
model_name += "-two-stage" if two_stage else ""
print("Pushing model to hub...")
model.push_to_hub(repo_path_or_name=model_name, organization="nielsr", commit_message="Add model")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
type=str,
default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
)
parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to output PyTorch model.",
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_deformable_detr_checkpoint(
args.checkpoint_path,
args.single_scale,
args.dilation,
args.with_box_refine,
args.two_stage,
args.pytorch_dump_folder_path,
args.push_to_hub,
)
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Deformable DETR checkpoints."""
import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import cached_download, hf_hub_url
from transformers import DeformableDetrConfig, DeformableDetrFeatureExtractor, DeformableDetrForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def rename_key(orig_key):
if "backbone.0.body" in orig_key:
orig_key = orig_key.replace("backbone.0.body", "backbone.conv_encoder.model")
if "transformer" in orig_key:
orig_key = orig_key.replace("transformer.", "")
if "norm1" in orig_key:
if "encoder" in orig_key:
orig_key = orig_key.replace("norm1", "self_attn_layer_norm")
else:
orig_key = orig_key.replace("norm1", "encoder_attn_layer_norm")
if "norm2" in orig_key:
if "encoder" in orig_key:
orig_key = orig_key.replace("norm2", "final_layer_norm")
else:
orig_key = orig_key.replace("norm2", "self_attn_layer_norm")
if "norm3" in orig_key:
orig_key = orig_key.replace("norm3", "final_layer_norm")
if "linear1" in orig_key:
orig_key = orig_key.replace("linear1", "fc1")
if "linear2" in orig_key:
orig_key = orig_key.replace("linear2", "fc2")
if "query_embed" in orig_key:
orig_key = orig_key.replace("query_embed", "query_position_embeddings")
if "cross_attn" in orig_key:
orig_key = orig_key.replace("cross_attn", "encoder_attn")
return orig_key
def read_in_q_k_v(state_dict):
# transformer decoder self-attention layers
for i in range(6):
# read in weights + bias of input projection layer of self-attention
in_proj_weight = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_deformable_detr_checkpoint(
checkpoint_path,
single_scale,
dilation,
with_box_refine,
two_stage,
pytorch_dump_folder_path,
push_to_hub,
):
"""
Copy/paste/tweak model's weights to our Deformable DETR structure.
"""
# load default config
config = DeformableDetrConfig()
# set config attributes
if single_scale:
config.num_feature_levels = 1
config.dilation = dilation
config.with_box_refine = with_box_refine
config.two_stage = two_stage
# set labels
config.num_labels = 91
repo_id = "huggingface/label-files"
filename = "coco-detection-id2label.json"
id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
# load feature extractor
feature_extractor = DeformableDetrFeatureExtractor(format="coco_detection")
# prepare image
img = prepare_img()
encoding = feature_extractor(images=img, return_tensors="pt")
pixel_values = encoding["pixel_values"]
logger.info("Converting model...")
# load original state dict
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
# rename keys
for key in state_dict.copy().keys():
val = state_dict.pop(key)
state_dict[rename_key(key)] = val
# query, key and value matrices need special treatment
read_in_q_k_v(state_dict)
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
prefix = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_embed") and not key.startswith("bbox_embed"):
val = state_dict.pop(key)
state_dict[prefix + key] = val
# finally, create HuggingFace model and load state dict
model = DeformableDetrForObjectDetection(config)
model.load_state_dict(state_dict)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# verify our conversion
outputs = model(pixel_values.to(device))
expected_logits = torch.tensor(
[[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]
)
expected_boxes = torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]])
if single_scale:
expected_logits = torch.tensor(
[[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]
)
expected_boxes = torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]])
if single_scale and dilation:
expected_logits = torch.tensor(
[[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]
)
expected_boxes = torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]])
if with_box_refine:
expected_logits = torch.tensor(
[[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]
)
expected_boxes = torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]])
if with_box_refine and two_stage:
expected_logits = torch.tensor(
[[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]
)
expected_boxes = torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]])
print("Logits:", outputs.logits[0, :3, :3])
assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4)
assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4)
print("Everything ok!")
# Save model and feature extractor
logger.info(f"Saving PyTorch model and feature extractor to {pytorch_dump_folder_path}...")
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
feature_extractor.save_pretrained(pytorch_dump_folder_path)
# Push to hub
if push_to_hub:
model_name = "deformable-detr"
model_name += "-single-scale" if single_scale else ""
model_name += "-dc5" if dilation else ""
model_name += "-with-box-refine" if with_box_refine else ""
model_name += "-two-stage" if two_stage else ""
print("Pushing model to hub...")
model.push_to_hub(repo_path_or_name=model_name, organization="nielsr", commit_message="Add model")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
type=str,
default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
)
parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to output PyTorch model.",
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_deformable_detr_checkpoint(
args.checkpoint_path,
args.single_scale,
args.dilation,
args.with_box_refine,
args.two_stage,
args.pytorch_dump_folder_path,
args.push_to_hub,
)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/segformer/feature_extraction_segformer.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for SegFormer."""
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
logger = logging.get_logger(__name__)
SegformerFeatureExtractor = SegformerImageProcessor
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for SegFormer."""
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
logger = logging.get_logger(__name__)
SegformerFeatureExtractor = SegformerImageProcessor
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/modeling_outputs.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from .utils import ModelOutput
@dataclass
class BaseModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithNoAttention(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPooling(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPoolingAndNoAttention(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state after a pooling operation on the spatial dimensions.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithCrossAttentions(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MoEModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary
loss and the z_loss for Mixture of Experts models.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
router_probs: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MoEModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding) as well as
Mixture of Expert's router hidden states terms, to train a MoE model.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary
loss and the z_loss for Mixture of Experts models.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
router_probs: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqMoEModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
modules.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class CausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class CausalLMOutputWithPast(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class CausalLMOutputWithCrossAttentions(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if `config.is_decoder = True`.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class SequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MaskedLMOutput(ModelOutput):
"""
Base class for masked language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked language modeling (MLM) loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqMoEOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the encoder model, useful to compute the auxiliary loss and z_loss for Mixture of Experts
models.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
encoder_z_loss: torch.FloatTensor = None
decoder_z_loss: torch.FloatTensor = None
encoder_aux_loss: torch.FloatTensor = None
decoder_aux_loss: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class NextSentencePredictorOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided):
Next sequence prediction (classification) loss.
logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class SequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice models.
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
Classification scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class TokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class QuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-end scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-end scores (before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class SemanticSegmenterOutput(ModelOutput):
"""
Base class for outputs of semantic segmentation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class ImageClassifierOutput(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
(also called feature maps) of the model at the output of each stage.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class ImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
called feature maps) of the model at the output of each stage.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class DepthEstimatorOutput(ModelOutput):
"""
Base class for outputs of depth estimation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
predicted_depth (`torch.FloatTensor` of shape `(batch_size, height, width)`):
Predicted depth for each pixel.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
predicted_depth: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Wav2Vec2BaseModelOutput(ModelOutput):
"""
Base class for models that have been trained with the Wav2Vec2 loss objective.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
extract_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`):
Sequence of extracted feature vectors of the last convolutional layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
extract_features: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class XVectorOutput(ModelOutput):
"""
Output type of [`Wav2Vec2ForXVector`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
Classification hidden states before AMSoftmax.
embeddings (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
Utterance embeddings used for vector similarity-based retrieval.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
embeddings: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BackboneOutput(ModelOutput):
"""
Base class for outputs of backbones.
Args:
feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, width)`):
Feature maps of the stages.
"""
feature_maps: Tuple[torch.FloatTensor] = None
| # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from .utils import ModelOutput
@dataclass
class BaseModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithNoAttention(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPooling(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPoolingAndNoAttention(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state after a pooling operation on the spatial dimensions.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithCrossAttentions(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BaseModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MoEModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary
loss and the z_loss for Mixture of Experts models.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
router_probs: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MoEModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding) as well as
Mixture of Expert's router hidden states terms, to train a MoE model.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary
loss and the z_loss for Mixture of Experts models.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
router_probs: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqMoEModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
modules.
"""
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class CausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class CausalLMOutputWithPast(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class CausalLMOutputWithCrossAttentions(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if `config.is_decoder = True`.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class SequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MaskedLMOutput(ModelOutput):
"""
Base class for masked language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked language modeling (MLM) loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqMoEOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
Router logits of the encoder model, useful to compute the auxiliary loss and z_loss for Mixture of Experts
models.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
encoder_z_loss: torch.FloatTensor = None
decoder_z_loss: torch.FloatTensor = None
encoder_aux_loss: torch.FloatTensor = None
decoder_aux_loss: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class NextSentencePredictorOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided):
Next sequence prediction (classification) loss.
logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class SequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class MultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice models.
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
Classification scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class TokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class QuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-end scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-end scores (before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class SemanticSegmenterOutput(ModelOutput):
"""
Base class for outputs of semantic segmentation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class ImageClassifierOutput(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
(also called feature maps) of the model at the output of each stage.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class ImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
called feature maps) of the model at the output of each stage.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class DepthEstimatorOutput(ModelOutput):
"""
Base class for outputs of depth estimation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
predicted_depth (`torch.FloatTensor` of shape `(batch_size, height, width)`):
Predicted depth for each pixel.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
predicted_depth: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Wav2Vec2BaseModelOutput(ModelOutput):
"""
Base class for models that have been trained with the Wav2Vec2 loss objective.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
extract_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`):
Sequence of extracted feature vectors of the last convolutional layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: torch.FloatTensor = None
extract_features: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class XVectorOutput(ModelOutput):
"""
Output type of [`Wav2Vec2ForXVector`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
Classification hidden states before AMSoftmax.
embeddings (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
Utterance embeddings used for vector similarity-based retrieval.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
embeddings: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BackboneOutput(ModelOutput):
"""
Base class for outputs of backbones.
Args:
feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, width)`):
Feature maps of the stages.
"""
feature_maps: Tuple[torch.FloatTensor] = None
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/markuplm/configuration_markuplm.py | # coding=utf-8
# Copyright 2021, The Microsoft Research Asia MarkupLM Team authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MarkupLM model configuration"""
from transformers.utils import logging
from ...configuration_utils import PretrainedConfig
logger = logging.get_logger(__name__)
MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class MarkupLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the MarkupLM
[microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture.
Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
documentation from [`BertConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`MarkupLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
gradient_checkpointing (`bool`, *optional*, defaults to `False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the tree id unit embedding might ever use. Typically set this to something large
just in case (e.g., 1024).
max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256):
The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large
just in case (e.g., 256).
max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something
large just in case (e.g., 1024).
tag_pad_id (`int`, *optional*, defaults to 216):
The id of the padding token in the xpath tags.
subs_pad_id (`int`, *optional*, defaults to 1001):
The id of the padding token in the xpath subscripts.
xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32):
The hidden size of each tree id unit. One complete tree index will have
(50*xpath_tag_unit_hidden_size)-dim.
max_depth (`int`, *optional*, defaults to 50):
The maximum depth in xpath.
Examples:
```python
>>> from transformers import MarkupLMModel, MarkupLMConfig
>>> # Initializing a MarkupLM microsoft/markuplm-base style configuration
>>> configuration = MarkupLMConfig()
>>> # Initializing a model from the microsoft/markuplm-base style configuration
>>> model = MarkupLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "markuplm"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
bos_token_id=0,
eos_token_id=2,
gradient_checkpointing=False,
max_xpath_tag_unit_embeddings=256,
max_xpath_subs_unit_embeddings=1024,
tag_pad_id=216,
subs_pad_id=1001,
xpath_unit_hidden_size=32,
max_depth=50,
position_embedding_type="absolute",
use_cache=True,
classifier_dropout=None,
**kwargs
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
gradient_checkpointing=gradient_checkpointing,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.classifier_dropout = classifier_dropout
# additional properties
self.max_depth = max_depth
self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
self.tag_pad_id = tag_pad_id
self.subs_pad_id = subs_pad_id
self.xpath_unit_hidden_size = xpath_unit_hidden_size
| # coding=utf-8
# Copyright 2021, The Microsoft Research Asia MarkupLM Team authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MarkupLM model configuration"""
from transformers.utils import logging
from ...configuration_utils import PretrainedConfig
logger = logging.get_logger(__name__)
MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class MarkupLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the MarkupLM
[microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture.
Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
documentation from [`BertConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`MarkupLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
gradient_checkpointing (`bool`, *optional*, defaults to `False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the tree id unit embedding might ever use. Typically set this to something large
just in case (e.g., 1024).
max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256):
The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large
just in case (e.g., 256).
max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something
large just in case (e.g., 1024).
tag_pad_id (`int`, *optional*, defaults to 216):
The id of the padding token in the xpath tags.
subs_pad_id (`int`, *optional*, defaults to 1001):
The id of the padding token in the xpath subscripts.
xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32):
The hidden size of each tree id unit. One complete tree index will have
(50*xpath_tag_unit_hidden_size)-dim.
max_depth (`int`, *optional*, defaults to 50):
The maximum depth in xpath.
Examples:
```python
>>> from transformers import MarkupLMModel, MarkupLMConfig
>>> # Initializing a MarkupLM microsoft/markuplm-base style configuration
>>> configuration = MarkupLMConfig()
>>> # Initializing a model from the microsoft/markuplm-base style configuration
>>> model = MarkupLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "markuplm"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
bos_token_id=0,
eos_token_id=2,
gradient_checkpointing=False,
max_xpath_tag_unit_embeddings=256,
max_xpath_subs_unit_embeddings=1024,
tag_pad_id=216,
subs_pad_id=1001,
xpath_unit_hidden_size=32,
max_depth=50,
position_embedding_type="absolute",
use_cache=True,
classifier_dropout=None,
**kwargs
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
gradient_checkpointing=gradient_checkpointing,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.classifier_dropout = classifier_dropout
# additional properties
self.max_depth = max_depth
self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
self.tag_pad_id = tag_pad_id
self.subs_pad_id = subs_pad_id
self.xpath_unit_hidden_size = xpath_unit_hidden_size
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/marian/tokenization_marian.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"source_spm": "source.spm",
"target_spm": "target.spm",
"vocab": "vocab.json",
"target_vocab_file": "target_vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"source_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
},
"target_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
},
"vocab": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
},
"tokenizer_config_file": {
"Helsinki-NLP/opus-mt-en-de": (
"https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
)
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"Helsinki-NLP/opus-mt-en-de": 512}
PRETRAINED_INIT_CONFIGURATION = {}
# Example URL https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json
class MarianTokenizer(PreTrainedTokenizer):
r"""
Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
source_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary for the source language.
target_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary for the target language.
source_lang (`str`, *optional*):
A string representing the source language.
target_lang (`str`, *optional*):
A string representing the target language.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
model_max_length (`int`, *optional*, defaults to 512):
The maximum sentence length the model accepts.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<eop>", "<eod>"]`):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Examples:
```python
>>> from transformers import MarianTokenizer
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
>>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional
>>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True)
# keys [input_ids, attention_mask, labels].
>>> outputs = model(**inputs) # should work
```"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
language_code_re = re.compile(">>.+<<") # type: re.Pattern
def __init__(
self,
source_spm,
target_spm,
vocab,
target_vocab_file=None,
source_lang=None,
target_lang=None,
unk_token="<unk>",
eos_token="</s>",
pad_token="<pad>",
model_max_length=512,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
separate_vocabs=False,
**kwargs
) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
# bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id
source_lang=source_lang,
target_lang=target_lang,
unk_token=unk_token,
eos_token=eos_token,
pad_token=pad_token,
model_max_length=model_max_length,
sp_model_kwargs=self.sp_model_kwargs,
target_vocab_file=target_vocab_file,
separate_vocabs=separate_vocabs,
**kwargs,
)
assert Path(source_spm).exists(), f"cannot find spm source {source_spm}"
self.separate_vocabs = separate_vocabs
self.encoder = load_json(vocab)
if self.unk_token not in self.encoder:
raise KeyError("<unk> token must be in vocab")
assert self.pad_token in self.encoder
if separate_vocabs:
self.target_encoder = load_json(target_vocab_file)
self.decoder = {v: k for k, v in self.target_encoder.items()}
self.supported_language_codes = []
else:
self.decoder = {v: k for k, v in self.encoder.items()}
self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]
self.source_lang = source_lang
self.target_lang = target_lang
self.spm_files = [source_spm, target_spm]
# load SentencePiece model for pre-processing
self.spm_source = load_spm(source_spm, self.sp_model_kwargs)
self.spm_target = load_spm(target_spm, self.sp_model_kwargs)
self.current_spm = self.spm_source
self.current_encoder = self.encoder
# Multilingual target side: default to using first supported language code.
self._setup_normalizer()
def _setup_normalizer(self):
try:
from sacremoses import MosesPunctNormalizer
self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize
except (ImportError, FileNotFoundError):
warnings.warn("Recommended: pip install sacremoses.")
self.punc_normalizer = lambda x: x
def normalize(self, x: str) -> str:
"""Cover moses empty string edge case. They return empty list for '' input!"""
return self.punc_normalizer(x) if x else ""
def _convert_token_to_id(self, token):
return self.current_encoder.get(token, self.current_encoder[self.unk_token])
def remove_language_code(self, text: str):
"""Remove language codes like >>fr<< before sentencepiece"""
match = self.language_code_re.match(text)
code: list = [match.group(0)] if match else []
return code, self.language_code_re.sub("", text)
def _tokenize(self, text: str) -> List[str]:
code, text = self.remove_language_code(text)
pieces = self.current_spm.encode(text, out_type=str)
return code + pieces
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the decoder."""
return self.decoder.get(index, self.unk_token)
def batch_decode(self, sequences, **kwargs):
"""
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not to clean up the tokenization spaces.
use_source_tokenizer (`bool`, *optional*, defaults to `False`):
Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
problems).
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`List[str]`: The list of decoded sentences.
"""
return super().batch_decode(sequences, **kwargs)
def decode(self, token_ids, **kwargs):
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not to clean up the tokenization spaces.
use_source_tokenizer (`bool`, *optional*, defaults to `False`):
Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
problems).
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
return super().decode(token_ids, **kwargs)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += sp_model.decode_pieces(current_sub_tokens) + token + " "
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += sp_model.decode_pieces(current_sub_tokens)
return out_string.strip()
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""Build model inputs from a sequence by appending eos_token_id."""
if token_ids_1 is None:
return token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
def _switch_to_input_mode(self):
self.current_spm = self.spm_source
self.current_encoder = self.encoder
def _switch_to_target_mode(self):
self.current_spm = self.spm_target
if self.separate_vocabs:
self.current_encoder = self.target_encoder
@property
def vocab_size(self) -> int:
return len(self.encoder)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
saved_files = []
if self.separate_vocabs:
out_src_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"],
)
out_tgt_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"],
)
save_json(self.encoder, out_src_vocab_file)
save_json(self.target_encoder, out_tgt_vocab_file)
saved_files.append(out_src_vocab_file)
saved_files.append(out_tgt_vocab_file)
else:
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]
)
save_json(self.encoder, out_vocab_file)
saved_files.append(out_vocab_file)
for spm_save_filename, spm_orig_path, spm_model in zip(
[VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]],
self.spm_files,
[self.spm_source, self.spm_target],
):
spm_save_path = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + spm_save_filename
)
if os.path.abspath(spm_orig_path) != os.path.abspath(spm_save_path) and os.path.isfile(spm_orig_path):
copyfile(spm_orig_path, spm_save_path)
saved_files.append(spm_save_path)
elif not os.path.isfile(spm_orig_path):
with open(spm_save_path, "wb") as fi:
content_spiece_model = spm_model.serialized_model_proto()
fi.write(content_spiece_model)
saved_files.append(spm_save_path)
return tuple(saved_files)
def get_vocab(self) -> Dict:
return self.get_src_vocab()
def get_src_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def get_tgt_vocab(self):
return dict(self.target_encoder, **self.added_tokens_decoder)
def __getstate__(self) -> Dict:
state = self.__dict__.copy()
state.update(
{k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}
)
return state
def __setstate__(self, d: Dict) -> None:
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.spm_source, self.spm_target = (load_spm(f, self.sp_model_kwargs) for f in self.spm_files)
self.current_spm = self.spm_source
self._setup_normalizer()
def num_special_tokens_to_add(self, *args, **kwargs):
"""Just EOS"""
return 1
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
if already_has_special_tokens:
return self._special_token_mask(token_ids_0)
elif token_ids_1 is None:
return self._special_token_mask(token_ids_0) + [1]
else:
return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
spm.Load(path)
return spm
def save_json(data, path: str) -> None:
with open(path, "w") as f:
json.dump(data, f, indent=2)
def load_json(path: str) -> Union[Dict, List]:
with open(path, "r") as f:
return json.load(f)
| # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"source_spm": "source.spm",
"target_spm": "target.spm",
"vocab": "vocab.json",
"target_vocab_file": "target_vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"source_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
},
"target_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
},
"vocab": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
},
"tokenizer_config_file": {
"Helsinki-NLP/opus-mt-en-de": (
"https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
)
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"Helsinki-NLP/opus-mt-en-de": 512}
PRETRAINED_INIT_CONFIGURATION = {}
# Example URL https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json
class MarianTokenizer(PreTrainedTokenizer):
r"""
Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
source_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary for the source language.
target_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary for the target language.
source_lang (`str`, *optional*):
A string representing the source language.
target_lang (`str`, *optional*):
A string representing the target language.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
model_max_length (`int`, *optional*, defaults to 512):
The maximum sentence length the model accepts.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<eop>", "<eod>"]`):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Examples:
```python
>>> from transformers import MarianTokenizer
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
>>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional
>>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True)
# keys [input_ids, attention_mask, labels].
>>> outputs = model(**inputs) # should work
```"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
language_code_re = re.compile(">>.+<<") # type: re.Pattern
def __init__(
self,
source_spm,
target_spm,
vocab,
target_vocab_file=None,
source_lang=None,
target_lang=None,
unk_token="<unk>",
eos_token="</s>",
pad_token="<pad>",
model_max_length=512,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
separate_vocabs=False,
**kwargs
) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
# bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id
source_lang=source_lang,
target_lang=target_lang,
unk_token=unk_token,
eos_token=eos_token,
pad_token=pad_token,
model_max_length=model_max_length,
sp_model_kwargs=self.sp_model_kwargs,
target_vocab_file=target_vocab_file,
separate_vocabs=separate_vocabs,
**kwargs,
)
assert Path(source_spm).exists(), f"cannot find spm source {source_spm}"
self.separate_vocabs = separate_vocabs
self.encoder = load_json(vocab)
if self.unk_token not in self.encoder:
raise KeyError("<unk> token must be in vocab")
assert self.pad_token in self.encoder
if separate_vocabs:
self.target_encoder = load_json(target_vocab_file)
self.decoder = {v: k for k, v in self.target_encoder.items()}
self.supported_language_codes = []
else:
self.decoder = {v: k for k, v in self.encoder.items()}
self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]
self.source_lang = source_lang
self.target_lang = target_lang
self.spm_files = [source_spm, target_spm]
# load SentencePiece model for pre-processing
self.spm_source = load_spm(source_spm, self.sp_model_kwargs)
self.spm_target = load_spm(target_spm, self.sp_model_kwargs)
self.current_spm = self.spm_source
self.current_encoder = self.encoder
# Multilingual target side: default to using first supported language code.
self._setup_normalizer()
def _setup_normalizer(self):
try:
from sacremoses import MosesPunctNormalizer
self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize
except (ImportError, FileNotFoundError):
warnings.warn("Recommended: pip install sacremoses.")
self.punc_normalizer = lambda x: x
def normalize(self, x: str) -> str:
"""Cover moses empty string edge case. They return empty list for '' input!"""
return self.punc_normalizer(x) if x else ""
def _convert_token_to_id(self, token):
return self.current_encoder.get(token, self.current_encoder[self.unk_token])
def remove_language_code(self, text: str):
"""Remove language codes like >>fr<< before sentencepiece"""
match = self.language_code_re.match(text)
code: list = [match.group(0)] if match else []
return code, self.language_code_re.sub("", text)
def _tokenize(self, text: str) -> List[str]:
code, text = self.remove_language_code(text)
pieces = self.current_spm.encode(text, out_type=str)
return code + pieces
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the decoder."""
return self.decoder.get(index, self.unk_token)
def batch_decode(self, sequences, **kwargs):
"""
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not to clean up the tokenization spaces.
use_source_tokenizer (`bool`, *optional*, defaults to `False`):
Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
problems).
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`List[str]`: The list of decoded sentences.
"""
return super().batch_decode(sequences, **kwargs)
def decode(self, token_ids, **kwargs):
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not to clean up the tokenization spaces.
use_source_tokenizer (`bool`, *optional*, defaults to `False`):
Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
problems).
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
return super().decode(token_ids, **kwargs)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += sp_model.decode_pieces(current_sub_tokens) + token + " "
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += sp_model.decode_pieces(current_sub_tokens)
return out_string.strip()
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""Build model inputs from a sequence by appending eos_token_id."""
if token_ids_1 is None:
return token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
def _switch_to_input_mode(self):
self.current_spm = self.spm_source
self.current_encoder = self.encoder
def _switch_to_target_mode(self):
self.current_spm = self.spm_target
if self.separate_vocabs:
self.current_encoder = self.target_encoder
@property
def vocab_size(self) -> int:
return len(self.encoder)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
saved_files = []
if self.separate_vocabs:
out_src_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"],
)
out_tgt_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"],
)
save_json(self.encoder, out_src_vocab_file)
save_json(self.target_encoder, out_tgt_vocab_file)
saved_files.append(out_src_vocab_file)
saved_files.append(out_tgt_vocab_file)
else:
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]
)
save_json(self.encoder, out_vocab_file)
saved_files.append(out_vocab_file)
for spm_save_filename, spm_orig_path, spm_model in zip(
[VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]],
self.spm_files,
[self.spm_source, self.spm_target],
):
spm_save_path = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + spm_save_filename
)
if os.path.abspath(spm_orig_path) != os.path.abspath(spm_save_path) and os.path.isfile(spm_orig_path):
copyfile(spm_orig_path, spm_save_path)
saved_files.append(spm_save_path)
elif not os.path.isfile(spm_orig_path):
with open(spm_save_path, "wb") as fi:
content_spiece_model = spm_model.serialized_model_proto()
fi.write(content_spiece_model)
saved_files.append(spm_save_path)
return tuple(saved_files)
def get_vocab(self) -> Dict:
return self.get_src_vocab()
def get_src_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def get_tgt_vocab(self):
return dict(self.target_encoder, **self.added_tokens_decoder)
def __getstate__(self) -> Dict:
state = self.__dict__.copy()
state.update(
{k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}
)
return state
def __setstate__(self, d: Dict) -> None:
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.spm_source, self.spm_target = (load_spm(f, self.sp_model_kwargs) for f in self.spm_files)
self.current_spm = self.spm_source
self._setup_normalizer()
def num_special_tokens_to_add(self, *args, **kwargs):
"""Just EOS"""
return 1
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
if already_has_special_tokens:
return self._special_token_mask(token_ids_0)
elif token_ids_1 is None:
return self._special_token_mask(token_ids_0) + [1]
else:
return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
spm.Load(path)
return spm
def save_json(data, path: str) -> None:
with open(path, "w") as f:
json.dump(data, f, indent=2)
def load_json(path: str) -> Union[Dict, List]:
with open(path, "r") as f:
return json.load(f)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/trocr/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_import_structure = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
"processing_trocr": ["TrOCRProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_trocr"] = [
"TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrOCRForCausalLM",
"TrOCRPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_import_structure = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
"processing_trocr": ["TrOCRProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_trocr"] = [
"TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrOCRForCausalLM",
"TrOCRPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./utils/get_modified_files.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
fork_point_sha = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
modified_files = subprocess.check_output(f"git diff --name-only {fork_point_sha}".split()).decode("utf-8").split()
joined_dirs = "|".join(sys.argv[1:])
regex = re.compile(rf"^({joined_dirs}).*?\.py$")
relevant_modified_files = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
fork_point_sha = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
modified_files = subprocess.check_output(f"git diff --name-only {fork_point_sha}".split()).decode("utf-8").split()
joined_dirs = "|".join(sys.argv[1:])
regex = re.compile(rf"^({joined_dirs}).*?\.py$")
relevant_modified_files = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/splinter/__init__.py | -1 |
||
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/mvp/configuration_mvp.py | # coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MVP model configuration"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MVP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class MvpConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the MVP [RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp)
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50267):
Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MvpModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
use_prompt (`bool`, *optional*, defaults to `False`):
Whether or not to use prompt.
prompt_length (`int`, *optional*, defaults to 100):
The length of prompt.
prompt_mid_dim (`int`, *optional*, defaults to 800):
Dimensionality of the "intermediate" layer in prompt.
Example:
```python
>>> from transformers import MvpModel, MvpConfig
>>> # Initializing a MVP RUCAIBox/mvp style configuration
>>> configuration = MvpConfig()
>>> # Initializing a model from the RUCAIBox/mvp style configuration
>>> model = MvpModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mvp"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=50267,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
activation_function="gelu",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
use_cache=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
is_encoder_decoder=True,
decoder_start_token_id=2,
forced_eos_token_id=2,
use_prompt=False,
prompt_length=100,
prompt_mid_dim=800,
**kwargs
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.use_prompt = use_prompt
self.prompt_length = prompt_length
self.prompt_mid_dim = prompt_mid_dim
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
)
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
self.forced_bos_token_id = self.bos_token_id
warnings.warn(
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
"The config can simply be saved and uploaded again to be fixed."
)
| # coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MVP model configuration"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MVP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class MvpConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the MVP [RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp)
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50267):
Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MvpModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
use_prompt (`bool`, *optional*, defaults to `False`):
Whether or not to use prompt.
prompt_length (`int`, *optional*, defaults to 100):
The length of prompt.
prompt_mid_dim (`int`, *optional*, defaults to 800):
Dimensionality of the "intermediate" layer in prompt.
Example:
```python
>>> from transformers import MvpModel, MvpConfig
>>> # Initializing a MVP RUCAIBox/mvp style configuration
>>> configuration = MvpConfig()
>>> # Initializing a model from the RUCAIBox/mvp style configuration
>>> model = MvpModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mvp"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=50267,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
activation_function="gelu",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
use_cache=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
is_encoder_decoder=True,
decoder_start_token_id=2,
forced_eos_token_id=2,
use_prompt=False,
prompt_length=100,
prompt_mid_dim=800,
**kwargs
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.use_prompt = use_prompt
self.prompt_length = prompt_length
self.prompt_mid_dim = prompt_mid_dim
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
)
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
self.forced_bos_token_id = self.bos_token_id
warnings.warn(
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
"The config can simply be saved and uploaded again to be fixed."
)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/barthez/tokenization_barthez.py | # coding=utf-8
# Copyright 2020 Ecole Polytechnique and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
""" Tokenization classes for the BARThez model."""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"moussaKam/mbarthez": 1024,
"moussaKam/barthez": 1024,
"moussaKam/barthez-orangesum-title": 1024,
}
SPIECE_UNDERLINE = "▁"
class BarthezTokenizer(PreTrainedTokenizer):
"""
Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a BARThez tokenizer. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs
) -> None:
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(vocab_file))
self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) - 1
self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BARThez sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
@property
def vocab_size(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text: str) -> List[str]:
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
spm_id = self.sp_model.PieceToId(token)
return spm_id if spm_id else self.unk_token_id
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
| # coding=utf-8
# Copyright 2020 Ecole Polytechnique and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
""" Tokenization classes for the BARThez model."""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"moussaKam/mbarthez": 1024,
"moussaKam/barthez": 1024,
"moussaKam/barthez-orangesum-title": 1024,
}
SPIECE_UNDERLINE = "▁"
class BarthezTokenizer(PreTrainedTokenizer):
"""
Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a BARThez tokenizer. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs
) -> None:
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(vocab_file))
self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) - 1
self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BARThez sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
@property
def vocab_size(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text: str) -> List[str]:
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
spm_id = self.sp_model.PieceToId(token)
return spm_id if spm_id else self.unk_token_id
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/test_feature_extraction_common.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo, set_access_token
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
from transformers.utils import is_torch_available, is_vision_available
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
if is_torch_available():
import numpy as np
import torch
if is_vision_available():
from PIL import Image
SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
One can specify whether the images are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
image_inputs = []
for i in range(feature_extract_tester.batch_size):
if equal_resolution:
width = height = feature_extract_tester.max_resolution
else:
# To avoid getting image width/height 0
min_resolution = feature_extract_tester.min_resolution
if getattr(feature_extract_tester, "size_divisor", None):
# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
min_resolution = max(feature_extract_tester.size_divisor, min_resolution)
width, height = np.random.choice(np.arange(min_resolution, feature_extract_tester.max_resolution), 2)
image_inputs.append(
np.random.randint(
255,
size=(
feature_extract_tester.num_channels,
width,
height,
),
dtype=np.uint8,
)
)
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
if torchify:
image_inputs = [torch.from_numpy(image) for image in image_inputs]
return image_inputs
def prepare_video(feature_extract_tester, width=10, height=10, numpify=False, torchify=False):
"""This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
video = []
for i in range(feature_extract_tester.num_frames):
video.append(np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
if torchify:
video = [torch.from_numpy(frame) for frame in video]
return video
def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
One can specify whether the videos are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
video_inputs = []
for i in range(feature_extract_tester.batch_size):
if equal_resolution:
width = height = feature_extract_tester.max_resolution
else:
width, height = np.random.choice(
np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
)
video = prepare_video(
feature_extract_tester=feature_extract_tester,
width=width,
height=height,
numpify=numpify,
torchify=torchify,
)
video_inputs.append(video)
return video_inputs
class FeatureExtractionSavingTestMixin:
def test_feat_extract_to_json_string(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
obj = json.loads(feat_extract.to_json_string())
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key], value)
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
def test_init_without_params(self):
feat_extract = self.feature_extraction_class()
self.assertIsNotNone(feat_extract)
class FeatureExtractorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.request", return_value=response_mock) as mock_head:
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = Wav2Vec2FeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"
)
@is_staging_test
class FeatureExtractorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
set_access_token(TOKEN)
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-feature-extractor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-feature-extractor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-feature-extractor")
except HTTPError:
pass
def test_push_to_hub(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="test-feature-extractor", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_in_organization(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("valid_org/test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="valid_org/test-feature-extractor-org", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_dynamic_feature_extractor(self):
CustomFeatureExtractor.register_for_auto_class()
feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-dynamic-feature-extractor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map,
{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
)
new_feature_extractor = AutoFeatureExtractor.from_pretrained(
f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
)
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")
| # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo, set_access_token
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
from transformers.utils import is_torch_available, is_vision_available
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
if is_torch_available():
import numpy as np
import torch
if is_vision_available():
from PIL import Image
SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
One can specify whether the images are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
image_inputs = []
for i in range(feature_extract_tester.batch_size):
if equal_resolution:
width = height = feature_extract_tester.max_resolution
else:
# To avoid getting image width/height 0
min_resolution = feature_extract_tester.min_resolution
if getattr(feature_extract_tester, "size_divisor", None):
# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
min_resolution = max(feature_extract_tester.size_divisor, min_resolution)
width, height = np.random.choice(np.arange(min_resolution, feature_extract_tester.max_resolution), 2)
image_inputs.append(
np.random.randint(
255,
size=(
feature_extract_tester.num_channels,
width,
height,
),
dtype=np.uint8,
)
)
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
if torchify:
image_inputs = [torch.from_numpy(image) for image in image_inputs]
return image_inputs
def prepare_video(feature_extract_tester, width=10, height=10, numpify=False, torchify=False):
"""This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
video = []
for i in range(feature_extract_tester.num_frames):
video.append(np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
if torchify:
video = [torch.from_numpy(frame) for frame in video]
return video
def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
One can specify whether the videos are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
video_inputs = []
for i in range(feature_extract_tester.batch_size):
if equal_resolution:
width = height = feature_extract_tester.max_resolution
else:
width, height = np.random.choice(
np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
)
video = prepare_video(
feature_extract_tester=feature_extract_tester,
width=width,
height=height,
numpify=numpify,
torchify=torchify,
)
video_inputs.append(video)
return video_inputs
class FeatureExtractionSavingTestMixin:
def test_feat_extract_to_json_string(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
obj = json.loads(feat_extract.to_json_string())
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key], value)
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
def test_init_without_params(self):
feat_extract = self.feature_extraction_class()
self.assertIsNotNone(feat_extract)
class FeatureExtractorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.request", return_value=response_mock) as mock_head:
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = Wav2Vec2FeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"
)
@is_staging_test
class FeatureExtractorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
set_access_token(TOKEN)
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-feature-extractor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-feature-extractor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-feature-extractor")
except HTTPError:
pass
def test_push_to_hub(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="test-feature-extractor", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_in_organization(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("valid_org/test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="valid_org/test-feature-extractor-org", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_dynamic_feature_extractor(self):
CustomFeatureExtractor.register_for_auto_class()
feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-dynamic-feature-extractor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map,
{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
)
new_feature_extractor = AutoFeatureExtractor.from_pretrained(
f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
)
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/convnext/test_modeling_tf_convnext.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow ConvNext model. """
import inspect
import unittest
from typing import List, Tuple
from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers import TFConvNextForImageClassification, TFConvNextModel
if is_vision_available():
from PIL import Image
from transformers import ConvNextFeatureExtractor
class TFConvNextModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
num_channels=3,
num_stages=4,
hidden_sizes=[10, 20, 30, 40],
depths=[2, 2, 3, 2],
is_training=True,
use_labels=True,
intermediate_size=37,
hidden_act="gelu",
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_stages = num_stages
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ConvNextConfig(
num_channels=self.num_channels,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
num_stages=self.num_stages,
hidden_act=self.hidden_act,
is_decoder=False,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFConvNextModel(config=config)
result = model(pixel_values, training=False)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = TFConvNextForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFConvNextModelTest(TFModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
test_pruning = False
test_onnx = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = TFConvNextModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=ConvNextConfig,
has_text_modality=False,
hidden_size=37,
)
@unittest.skip(reason="ConvNext does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_keras_fit(self):
super().test_keras_fit()
@unittest.skip(reason="ConvNext does not support input and output embeddings")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="Model doesn't have attention layers")
def test_attention_outputs(self):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_stages = self.model_tester.num_stages
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
# Since ConvNext does not have any attention we need to rewrite this test.
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(tuple_object, dict_object)),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class TFConvNextModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return (
ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
)
@slow
def test_inference_image_classification_head(self):
model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
feature_extractor = self.default_feature_extractor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-0.0260, -0.4739, 0.1911])
tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow ConvNext model. """
import inspect
import unittest
from typing import List, Tuple
from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers import TFConvNextForImageClassification, TFConvNextModel
if is_vision_available():
from PIL import Image
from transformers import ConvNextFeatureExtractor
class TFConvNextModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
num_channels=3,
num_stages=4,
hidden_sizes=[10, 20, 30, 40],
depths=[2, 2, 3, 2],
is_training=True,
use_labels=True,
intermediate_size=37,
hidden_act="gelu",
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_stages = num_stages
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ConvNextConfig(
num_channels=self.num_channels,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
num_stages=self.num_stages,
hidden_act=self.hidden_act,
is_decoder=False,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFConvNextModel(config=config)
result = model(pixel_values, training=False)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = TFConvNextForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFConvNextModelTest(TFModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
test_pruning = False
test_onnx = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = TFConvNextModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=ConvNextConfig,
has_text_modality=False,
hidden_size=37,
)
@unittest.skip(reason="ConvNext does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_keras_fit(self):
super().test_keras_fit()
@unittest.skip(reason="ConvNext does not support input and output embeddings")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="Model doesn't have attention layers")
def test_attention_outputs(self):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_stages = self.model_tester.num_stages
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
# Since ConvNext does not have any attention we need to rewrite this test.
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(tuple_object, dict_object)),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class TFConvNextModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return (
ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
)
@slow
def test_inference_image_classification_head(self):
model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
feature_extractor = self.default_feature_extractor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-0.0260, -0.4739, 0.1911])
tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/__init__.py | -1 |
||
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/data2vec/test_modeling_data2vec_vision.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Data2VecVision model. """
import inspect
import unittest
from transformers import Data2VecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
Data2VecVisionForImageClassification,
Data2VecVisionForSemanticSegmentation,
Data2VecVisionModel,
)
from transformers.models.data2vec.modeling_data2vec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import BeitFeatureExtractor
class Data2VecVisionModelTester:
def __init__(
self,
parent,
vocab_size=100,
batch_size=13,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=4,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
scope=None,
out_indices=[0, 1, 2, 3],
):
self.parent = parent
self.vocab_size = 100
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
self.out_indices = out_indices
self.num_labels = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
pixel_labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels, pixel_labels
def get_config(self):
return Data2VecVisionConfig(
vocab_size=self.vocab_size,
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
out_indices=self.out_indices,
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
model = Data2VecVisionModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
num_patches = (self.image_size // self.patch_size) ** 2
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.type_sequence_label_size
model = Data2VecVisionForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = Data2VecVisionForSemanticSegmentation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
)
result = model(pixel_values, labels=pixel_labels)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels, pixel_labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class Data2VecVisionModelTest(ModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation)
if is_torch_available()
else ()
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = Data2VecVisionModelTester(self)
self.config_tester = ConfigTester(
self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.config_tester.run_common_tests()
def test_inputs_embeds(self):
# Data2VecVision does not use inputs_embeds
pass
@require_torch_multi_gpu
@unittest.skip(
reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_segmentation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs)
def test_training(self):
if not self.model_tester.is_training:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in [*get_values(MODEL_MAPPING)]:
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
config.use_cache = False
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in [*get_values(MODEL_MAPPING)] or not model_class.supports_gradient_checkpointing:
continue
# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
# this can then be incorporated into _prepare_for_class in test_modeling_common.py
elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, height, width], device=torch_device
).long()
model = model_class(config)
model.gradient_checkpointing_enable()
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None):
# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = Data2VecVisionModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class Data2VecVisionModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return (
BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k")
if is_vision_available()
else None
)
@slow
def test_inference_image_classification_head_imagenet_1k(self):
model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to(
torch_device
)
feature_extractor = self.default_feature_extractor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device)
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]]
self.assertEqual(logits[0].topk(2).indices.cpu().tolist(), expected_top2)
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Data2VecVision model. """
import inspect
import unittest
from transformers import Data2VecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
Data2VecVisionForImageClassification,
Data2VecVisionForSemanticSegmentation,
Data2VecVisionModel,
)
from transformers.models.data2vec.modeling_data2vec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import BeitFeatureExtractor
class Data2VecVisionModelTester:
def __init__(
self,
parent,
vocab_size=100,
batch_size=13,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=4,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
scope=None,
out_indices=[0, 1, 2, 3],
):
self.parent = parent
self.vocab_size = 100
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
self.out_indices = out_indices
self.num_labels = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
pixel_labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels, pixel_labels
def get_config(self):
return Data2VecVisionConfig(
vocab_size=self.vocab_size,
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
out_indices=self.out_indices,
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
model = Data2VecVisionModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
num_patches = (self.image_size // self.patch_size) ** 2
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.type_sequence_label_size
model = Data2VecVisionForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = Data2VecVisionForSemanticSegmentation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
)
result = model(pixel_values, labels=pixel_labels)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels, pixel_labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class Data2VecVisionModelTest(ModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation)
if is_torch_available()
else ()
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = Data2VecVisionModelTester(self)
self.config_tester = ConfigTester(
self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.config_tester.run_common_tests()
def test_inputs_embeds(self):
# Data2VecVision does not use inputs_embeds
pass
@require_torch_multi_gpu
@unittest.skip(
reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_segmentation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs)
def test_training(self):
if not self.model_tester.is_training:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in [*get_values(MODEL_MAPPING)]:
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
config.use_cache = False
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in [*get_values(MODEL_MAPPING)] or not model_class.supports_gradient_checkpointing:
continue
# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
# this can then be incorporated into _prepare_for_class in test_modeling_common.py
elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, height, width], device=torch_device
).long()
model = model_class(config)
model.gradient_checkpointing_enable()
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None):
# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = Data2VecVisionModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class Data2VecVisionModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return (
BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k")
if is_vision_available()
else None
)
@slow
def test_inference_image_classification_head_imagenet_1k(self):
model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to(
torch_device
)
feature_extractor = self.default_feature_extractor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device)
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]]
self.assertEqual(logits[0].topk(2).indices.cpu().tolist(), expected_top2)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/funnel/test_modeling_funnel.py | # coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
)
class FunnelModelTester:
"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
block_sizes=[1, 1, 2],
num_decoder_layers=1,
d_model=32,
n_head=4,
d_head=8,
d_inner=37,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=3,
initializer_std=0.02, # Set to a smaller value, so we can keep the small error threshold (1e-5) in the test
num_labels=3,
num_choices=4,
scope=None,
base=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.block_sizes = block_sizes
self.num_decoder_layers = num_decoder_layers
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = 2
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.initializer_std = initializer_std
# Used in the tests to check the size of the first attention layer
self.num_attention_heads = n_head
# Used in the tests to check the size of the first hidden state
self.hidden_size = self.d_model
# Used in the tests to check the number of output hidden states/attentions
self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
self.expected_num_hidden_layers = self.num_hidden_layers + 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
)
def get_config(self):
return FunnelConfig(
vocab_size=self.vocab_size,
block_sizes=self.block_sizes,
num_decoder_layers=self.num_decoder_layers,
d_model=self.d_model,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_inner,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
activation_dropout=self.activation_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_std=self.initializer_std,
)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
def create_and_check_base_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelBaseModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_choices = self.num_choices
model = FunnelForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(
FunnelModel,
FunnelForMaskedLM,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForTokenClassification,
)
if is_torch_available()
else ()
)
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = FunnelModelTester(self)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
)
def setUp(self):
self.model_tester = FunnelModelTester(self, base=True)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_base_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
# overwrite from test_modeling_common
def test_training(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class.__name__ == "FunnelBaseModel":
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
@require_sentencepiece
@require_tokenizers
class FunnelModelIntegrationTest(unittest.TestCase):
def test_inference_tiny_model(self):
batch_size = 13
sequence_length = 7
input_ids = torch.arange(0, batch_size * sequence_length).long().reshape(batch_size, sequence_length)
lengths = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
token_type_ids = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])
model = FunnelModel.from_pretrained("sgugger/funnel-random-tiny")
output = model(input_ids, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2344.8352)
expected_output_mean = torch.tensor(0.8052)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
attention_mask = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2343.8425)
expected_output_mean = torch.tensor(0.8049)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
@slow
def test_inference_model(self):
tokenizer = FunnelTokenizer.from_pretrained("huggingface/funnel-small")
model = FunnelModel.from_pretrained("huggingface/funnel-small")
inputs = tokenizer("Hello! I am the Funnel Transformer model.", return_tensors="pt")
output = model(**inputs)[0]
expected_output_sum = torch.tensor(235.7246)
expected_output_mean = torch.tensor(0.0256)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
| # coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
)
class FunnelModelTester:
"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
block_sizes=[1, 1, 2],
num_decoder_layers=1,
d_model=32,
n_head=4,
d_head=8,
d_inner=37,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=3,
initializer_std=0.02, # Set to a smaller value, so we can keep the small error threshold (1e-5) in the test
num_labels=3,
num_choices=4,
scope=None,
base=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.block_sizes = block_sizes
self.num_decoder_layers = num_decoder_layers
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = 2
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.initializer_std = initializer_std
# Used in the tests to check the size of the first attention layer
self.num_attention_heads = n_head
# Used in the tests to check the size of the first hidden state
self.hidden_size = self.d_model
# Used in the tests to check the number of output hidden states/attentions
self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
self.expected_num_hidden_layers = self.num_hidden_layers + 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
)
def get_config(self):
return FunnelConfig(
vocab_size=self.vocab_size,
block_sizes=self.block_sizes,
num_decoder_layers=self.num_decoder_layers,
d_model=self.d_model,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_inner,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
activation_dropout=self.activation_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_std=self.initializer_std,
)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
def create_and_check_base_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelBaseModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_choices = self.num_choices
model = FunnelForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(
FunnelModel,
FunnelForMaskedLM,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForTokenClassification,
)
if is_torch_available()
else ()
)
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = FunnelModelTester(self)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
)
def setUp(self):
self.model_tester = FunnelModelTester(self, base=True)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_base_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
# overwrite from test_modeling_common
def test_training(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class.__name__ == "FunnelBaseModel":
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
@require_sentencepiece
@require_tokenizers
class FunnelModelIntegrationTest(unittest.TestCase):
def test_inference_tiny_model(self):
batch_size = 13
sequence_length = 7
input_ids = torch.arange(0, batch_size * sequence_length).long().reshape(batch_size, sequence_length)
lengths = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
token_type_ids = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])
model = FunnelModel.from_pretrained("sgugger/funnel-random-tiny")
output = model(input_ids, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2344.8352)
expected_output_mean = torch.tensor(0.8052)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
attention_mask = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2343.8425)
expected_output_mean = torch.tensor(0.8049)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
@slow
def test_inference_model(self):
tokenizer = FunnelTokenizer.from_pretrained("huggingface/funnel-small")
model = FunnelModel.from_pretrained("huggingface/funnel-small")
inputs = tokenizer("Hello! I am the Funnel Transformer model.", return_tensors="pt")
output = model(**inputs)[0]
expected_output_sum = torch.tensor(235.7246)
expected_output_mean = torch.tensor(0.0256)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/opt/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_opt"] = [
"OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OPTForCausalLM",
"OPTModel",
"OPTPreTrainedModel",
"OPTForSequenceClassification",
"OPTForQuestionAnswering",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_opt"] = ["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_opt"] = [
"FlaxOPTForCausalLM",
"FlaxOPTModel",
"FlaxOPTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_opt"] = [
"OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OPTForCausalLM",
"OPTModel",
"OPTPreTrainedModel",
"OPTForSequenceClassification",
"OPTForQuestionAnswering",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_opt"] = ["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_opt"] = [
"FlaxOPTForCausalLM",
"FlaxOPTModel",
"FlaxOPTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/speech_to_text/test_modeling_speech_to_text.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Speech2Text model. """
import copy
import inspect
import os
import tempfile
import unittest
from transformers import Speech2TextConfig
from transformers.testing_utils import (
is_torch_available,
require_sentencepiece,
require_tokenizers,
require_torch,
require_torchaudio,
slow,
torch_device,
)
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
if is_torch_available():
import torch
from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor
from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder
def prepare_speech_to_text_inputs_dict(
config,
input_features,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_features.ne(0)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
# "input_ids": input_features,
"input_features": input_features,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_torch
class Speech2TextModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
num_conv_layers=2,
conv_kernel_sizes=(5, 5),
conv_channels=32,
input_feat_per_channel=24,
input_channels=1,
hidden_act="relu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=20,
max_target_positions=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.num_conv_layers = num_conv_layers
self.conv_kernel_sizes = conv_kernel_sizes
self.conv_channels = conv_channels
self.input_feat_per_channel = input_feat_per_channel
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_features = floats_tensor(
[self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
)
attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)
config = self.get_config()
inputs_dict = prepare_speech_to_text_inputs_dict(
config,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
return config, inputs_dict
def get_config(self):
return Speech2TextConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
num_conv_layers=self.num_conv_layers,
conv_kernel_sizes=self.conv_kernel_sizes,
conv_channels=self.conv_channels,
input_feat_per_channel=self.input_feat_per_channel,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
def create_and_check_model_forward(self, config, inputs_dict):
model = Speech2TextModel(config=config).to(torch_device).eval()
input_features = inputs_dict["input_features"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
# first forward pass
last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16))
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = Speech2TextModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["decoder_input_ids"]
attention_mask = inputs_dict["decoder_attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = Speech2TextModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = Speech2TextEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(
inputs_dict["input_features"], attention_mask=inputs_dict["attention_mask"]
)[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = Speech2TextDecoder.from_pretrained(tmpdirname).to(torch_device)
encoder_attention_mask = encoder._get_feature_vector_attention_mask(
encoder_last_hidden_state.shape[1], inputs_dict["attention_mask"]
)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=encoder_attention_mask,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (Speech2TextModel, Speech2TextForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (Speech2TextForConditionalGeneration,) if is_torch_available() else ()
is_encoder_decoder = True
fx_compatible = True
test_pruning = False
test_missing_keys = False
input_name = "input_features"
def setUp(self):
self.model_tester = Speech2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Speech2TextConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_model_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# not implemented currently
def test_inputs_embeds(self):
pass
# training is not supported yet
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_features = input_dict["input_features"]
attention_mask = input_dict["attention_mask"]
model = Speech2TextForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
input_features = input_features.half()
model.half()
model.generate(input_features, attention_mask=attention_mask)
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"input_features",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
else:
seq_length = self.model_tester.seq_length
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[subsampled_seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
subsampled_encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
def test_resize_tokens_embeddings(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# make sure that decoder_input_ids are resized
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
def test_generate_without_input_ids(self):
pass
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = input_ids[:, :, 0]
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + model._get_decoder_start_token_id()
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, seq_length = input_ids.shape[:2]
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# Attentions
# encoder
self._check_encoder_attention_for_generate(
output.encoder_attentions, batch_size, config, subsampled_seq_length
)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
input_features = inputs["input_features"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
traced_model = torch.jit.trace(
model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
)
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
models_equal = True
for layer_name, p1 in model_state_dict.items():
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
@require_torch
@require_torchaudio
@require_sentencepiece
@require_tokenizers
@slow
class Speech2TextModelIntegrationTests(unittest.TestCase):
@cached_property
def default_processor(self):
return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
def _load_datasamples(self, num_samples):
from datasets import load_dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_generation_librispeech(self):
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
model.to(torch_device)
processor = self.default_processor
input_speech = self._load_datasamples(1)
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
generated_ids = model.generate(input_features)
generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
EXPECTED_TRANSCRIPTIONS = [
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
]
self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)
def test_generation_librispeech_batched(self):
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
model.to(torch_device)
processor = self.default_processor
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_features = inputs.input_features.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
generated_ids = model.generate(input_features, attention_mask=attention_mask)
generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)
EXPECTED_TRANSCRIPTIONS = [
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel",
"nor is mister cultar's manner less interesting than his matter",
"he tells us that at this festive season of the year with christmas and roast beef looming before us"
" similes drawn from eating and its results occur most readily to the mind",
"he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it"
" but little of rocky ithaca",
]
self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Speech2Text model. """
import copy
import inspect
import os
import tempfile
import unittest
from transformers import Speech2TextConfig
from transformers.testing_utils import (
is_torch_available,
require_sentencepiece,
require_tokenizers,
require_torch,
require_torchaudio,
slow,
torch_device,
)
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
if is_torch_available():
import torch
from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor
from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder
def prepare_speech_to_text_inputs_dict(
config,
input_features,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_features.ne(0)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
# "input_ids": input_features,
"input_features": input_features,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_torch
class Speech2TextModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
num_conv_layers=2,
conv_kernel_sizes=(5, 5),
conv_channels=32,
input_feat_per_channel=24,
input_channels=1,
hidden_act="relu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=20,
max_target_positions=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.num_conv_layers = num_conv_layers
self.conv_kernel_sizes = conv_kernel_sizes
self.conv_channels = conv_channels
self.input_feat_per_channel = input_feat_per_channel
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_features = floats_tensor(
[self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
)
attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)
config = self.get_config()
inputs_dict = prepare_speech_to_text_inputs_dict(
config,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
return config, inputs_dict
def get_config(self):
return Speech2TextConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
num_conv_layers=self.num_conv_layers,
conv_kernel_sizes=self.conv_kernel_sizes,
conv_channels=self.conv_channels,
input_feat_per_channel=self.input_feat_per_channel,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
def create_and_check_model_forward(self, config, inputs_dict):
model = Speech2TextModel(config=config).to(torch_device).eval()
input_features = inputs_dict["input_features"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
# first forward pass
last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16))
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = Speech2TextModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["decoder_input_ids"]
attention_mask = inputs_dict["decoder_attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = Speech2TextModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = Speech2TextEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(
inputs_dict["input_features"], attention_mask=inputs_dict["attention_mask"]
)[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = Speech2TextDecoder.from_pretrained(tmpdirname).to(torch_device)
encoder_attention_mask = encoder._get_feature_vector_attention_mask(
encoder_last_hidden_state.shape[1], inputs_dict["attention_mask"]
)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=encoder_attention_mask,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (Speech2TextModel, Speech2TextForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (Speech2TextForConditionalGeneration,) if is_torch_available() else ()
is_encoder_decoder = True
fx_compatible = True
test_pruning = False
test_missing_keys = False
input_name = "input_features"
def setUp(self):
self.model_tester = Speech2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Speech2TextConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_model_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# not implemented currently
def test_inputs_embeds(self):
pass
# training is not supported yet
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_features = input_dict["input_features"]
attention_mask = input_dict["attention_mask"]
model = Speech2TextForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
input_features = input_features.half()
model.half()
model.generate(input_features, attention_mask=attention_mask)
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"input_features",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
else:
seq_length = self.model_tester.seq_length
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[subsampled_seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
subsampled_encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
def test_resize_tokens_embeddings(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# make sure that decoder_input_ids are resized
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
def test_generate_without_input_ids(self):
pass
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = input_ids[:, :, 0]
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + model._get_decoder_start_token_id()
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, seq_length = input_ids.shape[:2]
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# Attentions
# encoder
self._check_encoder_attention_for_generate(
output.encoder_attentions, batch_size, config, subsampled_seq_length
)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
input_features = inputs["input_features"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
traced_model = torch.jit.trace(
model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
)
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
models_equal = True
for layer_name, p1 in model_state_dict.items():
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
@require_torch
@require_torchaudio
@require_sentencepiece
@require_tokenizers
@slow
class Speech2TextModelIntegrationTests(unittest.TestCase):
@cached_property
def default_processor(self):
return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
def _load_datasamples(self, num_samples):
from datasets import load_dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_generation_librispeech(self):
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
model.to(torch_device)
processor = self.default_processor
input_speech = self._load_datasamples(1)
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
generated_ids = model.generate(input_features)
generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
EXPECTED_TRANSCRIPTIONS = [
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
]
self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)
def test_generation_librispeech_batched(self):
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
model.to(torch_device)
processor = self.default_processor
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_features = inputs.input_features.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
generated_ids = model.generate(input_features, attention_mask=attention_mask)
generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)
EXPECTED_TRANSCRIPTIONS = [
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel",
"nor is mister cultar's manner less interesting than his matter",
"he tells us that at this festive season of the year with christmas and roast beef looming before us"
" similes drawn from eating and its results occur most readily to the mind",
"he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it"
" but little of rocky ithaca",
]
self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/sagemaker/test_multi_node_data_parallel.py | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
},
{
"framework": "pytorch",
"script": "run_ddp.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
},
{
"framework": "tensorflow",
"script": "run_tf_dist.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
},
]
)
class MultiNodeTest(unittest.TestCase):
def setUp(self):
if self.framework == "pytorch":
subprocess.run(
f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
encoding="utf-8",
check=True,
)
assert hasattr(self, "env")
def create_estimator(self, instance_count):
job_name = f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
# distributed data settings
distribution = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None
# creates estimator
return HuggingFace(
entry_point=self.script,
source_dir=self.env.test_path,
role=self.env.role,
image_uri=self.env.image_uri,
base_job_name=job_name,
instance_count=instance_count,
instance_type=self.instance_type,
debugger_hook_config=False,
hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path},
metric_definitions=self.env.metric_definitions,
distribution=distribution,
py_version="py36",
)
def save_results_as_csv(self, job_name):
TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")
# @parameterized.expand([(2,), (4,),])
@parameterized.expand([(2,)])
def test_script(self, instance_count):
# create estimator
estimator = self.create_estimator(instance_count)
# run training
estimator.fit()
# result dataframe
result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()
# extract kpis
eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
# get train time from SageMaker job, this includes starting, preprocessing, stopping
train_runtime = (
Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
assert all(t <= self.results["eval_loss"] for t in eval_loss)
# dump tests result into json file to share in PR
with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)
| import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
},
{
"framework": "pytorch",
"script": "run_ddp.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
},
{
"framework": "tensorflow",
"script": "run_tf_dist.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.p3.16xlarge",
"results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
},
]
)
class MultiNodeTest(unittest.TestCase):
def setUp(self):
if self.framework == "pytorch":
subprocess.run(
f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
encoding="utf-8",
check=True,
)
assert hasattr(self, "env")
def create_estimator(self, instance_count):
job_name = f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
# distributed data settings
distribution = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None
# creates estimator
return HuggingFace(
entry_point=self.script,
source_dir=self.env.test_path,
role=self.env.role,
image_uri=self.env.image_uri,
base_job_name=job_name,
instance_count=instance_count,
instance_type=self.instance_type,
debugger_hook_config=False,
hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path},
metric_definitions=self.env.metric_definitions,
distribution=distribution,
py_version="py36",
)
def save_results_as_csv(self, job_name):
TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")
# @parameterized.expand([(2,), (4,),])
@parameterized.expand([(2,)])
def test_script(self, instance_count):
# create estimator
estimator = self.create_estimator(instance_count)
# run training
estimator.fit()
# result dataframe
result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()
# extract kpis
eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
# get train time from SageMaker job, this includes starting, preprocessing, stopping
train_runtime = (
Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
assert all(t <= self.results["eval_loss"] for t in eval_loss)
# dump tests result into json file to share in PR
with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py | # coding=utf-8
# Copyright 2019 Facebook AI Research and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 XLM-RoBERTa model."""
from ...utils import add_start_docstrings, logging
from ..roberta.modeling_tf_roberta import (
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaModel,
)
from .configuration_xlm_roberta import XLMRobertaConfig
logger = logging.get_logger(__name__)
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
# See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]
XLM_ROBERTA_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`XLMRobertaConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaModel(TFRobertaModel):
"""
This class overrides [`TFRobertaModel`]. Please check the superclass for the appropriate documentation alongside
usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"XLM-RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.",
XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaForCausalLM(TFRobertaForCausalLM):
"""
This class overrides [`TFRobertaForCausalLM`]. Please check the superclass for the appropriate documentation
alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""XLM-RoBERTa Model with a `language modeling` head on top.""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForMaskedLM(TFRobertaForMaskedLM):
"""
This class overrides [`TFRobertaForMaskedLM`]. Please check the superclass for the appropriate documentation
alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForSequenceClassification(TFRobertaForSequenceClassification):
"""
This class overrides [`TFRobertaForSequenceClassification`]. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
XLM-RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForTokenClassification(TFRobertaForTokenClassification):
"""
This class overrides [`TFRobertaForTokenClassification`]. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
XLM-RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForQuestionAnswering(TFRobertaForQuestionAnswering):
"""
This class overrides [`TFRobertaForQuestionAnsweringSimple`]. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForMultipleChoice(TFRobertaForMultipleChoice):
"""
This class overrides [`TFRobertaForMultipleChoice`]. Please check the superclass for the appropriate documentation
alongside usage examples.
"""
config_class = XLMRobertaConfig
| # coding=utf-8
# Copyright 2019 Facebook AI Research and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 XLM-RoBERTa model."""
from ...utils import add_start_docstrings, logging
from ..roberta.modeling_tf_roberta import (
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaModel,
)
from .configuration_xlm_roberta import XLMRobertaConfig
logger = logging.get_logger(__name__)
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
# See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]
XLM_ROBERTA_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`XLMRobertaConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaModel(TFRobertaModel):
"""
This class overrides [`TFRobertaModel`]. Please check the superclass for the appropriate documentation alongside
usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"XLM-RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.",
XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaForCausalLM(TFRobertaForCausalLM):
"""
This class overrides [`TFRobertaForCausalLM`]. Please check the superclass for the appropriate documentation
alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""XLM-RoBERTa Model with a `language modeling` head on top.""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForMaskedLM(TFRobertaForMaskedLM):
"""
This class overrides [`TFRobertaForMaskedLM`]. Please check the superclass for the appropriate documentation
alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForSequenceClassification(TFRobertaForSequenceClassification):
"""
This class overrides [`TFRobertaForSequenceClassification`]. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
XLM-RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForTokenClassification(TFRobertaForTokenClassification):
"""
This class overrides [`TFRobertaForTokenClassification`]. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
XLM-RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForQuestionAnswering(TFRobertaForQuestionAnswering):
"""
This class overrides [`TFRobertaForQuestionAnsweringSimple`]. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = XLMRobertaConfig
@add_start_docstrings(
"""
Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
XLM_ROBERTA_START_DOCSTRING,
)
class TFXLMRobertaForMultipleChoice(TFRobertaForMultipleChoice):
"""
This class overrides [`TFRobertaForMultipleChoice`]. Please check the superclass for the appropriate documentation
alongside usage examples.
"""
config_class = XLMRobertaConfig
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/perceiver/tokenization_perceiver.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for Perceiver."""
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
class PerceiverTokenizer(PreTrainedTokenizer):
"""
Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
bos_token (`str`, *optional*, defaults to `"[BOS]"`):
The BOS token (reserved in the vocab, but not actually used).
eos_token (`str`, *optional*, defaults to `"[EOS]"`):
The end of sequence token (reserved in the vocab, but not actually used).
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The MASK token, useful for masked language modeling.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The CLS token (reserved in the vocab, but not actually used).
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from two sequences.
"""
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
pad_token="[PAD]",
bos_token="[BOS]",
eos_token="[EOS]",
mask_token="[MASK]",
cls_token="[CLS]",
sep_token="[SEP]",
model_max_length=2048,
**kwargs
) -> None:
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
super().__init__(
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
mask_token=mask_token,
cls_token=cls_token,
sep_token=sep_token,
model_max_length=model_max_length,
**kwargs,
)
self._utf_vocab_size = 2**8 # utf is 8 bits
# define special tokens dict
self.special_tokens_encoder: Dict[str, int] = {
self.pad_token: 0,
self.bos_token: 1,
self.eos_token: 2,
self.mask_token: 3,
self.cls_token: 4,
self.sep_token: 5,
}
self._num_special_tokens = len(self.special_tokens_encoder)
self.special_tokens_decoder: Dict[int, str] = {v: k for k, v in self.special_tokens_encoder.items()}
def get_vocab(self) -> Dict[str, int]:
vocab = self.special_tokens_encoder.copy()
vocab.update(self.added_tokens_encoder)
for i in range(self._utf_vocab_size):
token = chr(i)
vocab[token] = i + len(self.special_tokens_encoder)
return vocab
@property
def vocab_size(self):
return self._utf_vocab_size + self._num_special_tokens
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
# normal case: some special tokens
if token_ids_1 is None:
return [1] + [0] * len(token_ids_0) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks. A sequence has the
following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
else:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.sep_token_id]
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
tokens = [chr(i) for i in text.encode("utf-8")]
return tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token in self.special_tokens_encoder:
token_id = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
token_id = self.added_tokens_encoder[token]
elif len(token) != 1:
token_id = self.unk_token_id
else:
token_id = ord(token) + self._num_special_tokens
return token_id
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index in self.special_tokens_decoder:
token = self.special_tokens_decoder[index]
elif index in self.added_tokens_decoder:
token = self.added_tokens_decoder[index]
else:
token = chr(index - self._num_special_tokens)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
bstring = b""
for token in tokens:
if token in self.special_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
tok_string = token.encode("utf-8")
elif token in self.added_tokens_encoder:
tok_string = token.encode("utf-8")
else:
tok_string = bytes([ord(token)])
bstring += tok_string
string = bstring.decode("utf-8", errors="replace")
return string
# PerceiverTokenizer has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
return ()
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for Perceiver."""
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
class PerceiverTokenizer(PreTrainedTokenizer):
"""
Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
bos_token (`str`, *optional*, defaults to `"[BOS]"`):
The BOS token (reserved in the vocab, but not actually used).
eos_token (`str`, *optional*, defaults to `"[EOS]"`):
The end of sequence token (reserved in the vocab, but not actually used).
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The MASK token, useful for masked language modeling.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The CLS token (reserved in the vocab, but not actually used).
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from two sequences.
"""
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
pad_token="[PAD]",
bos_token="[BOS]",
eos_token="[EOS]",
mask_token="[MASK]",
cls_token="[CLS]",
sep_token="[SEP]",
model_max_length=2048,
**kwargs
) -> None:
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
super().__init__(
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
mask_token=mask_token,
cls_token=cls_token,
sep_token=sep_token,
model_max_length=model_max_length,
**kwargs,
)
self._utf_vocab_size = 2**8 # utf is 8 bits
# define special tokens dict
self.special_tokens_encoder: Dict[str, int] = {
self.pad_token: 0,
self.bos_token: 1,
self.eos_token: 2,
self.mask_token: 3,
self.cls_token: 4,
self.sep_token: 5,
}
self._num_special_tokens = len(self.special_tokens_encoder)
self.special_tokens_decoder: Dict[int, str] = {v: k for k, v in self.special_tokens_encoder.items()}
def get_vocab(self) -> Dict[str, int]:
vocab = self.special_tokens_encoder.copy()
vocab.update(self.added_tokens_encoder)
for i in range(self._utf_vocab_size):
token = chr(i)
vocab[token] = i + len(self.special_tokens_encoder)
return vocab
@property
def vocab_size(self):
return self._utf_vocab_size + self._num_special_tokens
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
# normal case: some special tokens
if token_ids_1 is None:
return [1] + [0] * len(token_ids_0) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks. A sequence has the
following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
else:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.sep_token_id]
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
tokens = [chr(i) for i in text.encode("utf-8")]
return tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token in self.special_tokens_encoder:
token_id = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
token_id = self.added_tokens_encoder[token]
elif len(token) != 1:
token_id = self.unk_token_id
else:
token_id = ord(token) + self._num_special_tokens
return token_id
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index in self.special_tokens_decoder:
token = self.special_tokens_decoder[index]
elif index in self.added_tokens_decoder:
token = self.added_tokens_decoder[index]
else:
token = chr(index - self._num_special_tokens)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
bstring = b""
for token in tokens:
if token in self.special_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
tok_string = token.encode("utf-8")
elif token in self.added_tokens_encoder:
tok_string = token.encode("utf-8")
else:
tok_string = bytes([ord(token)])
bstring += tok_string
string = bstring.decode("utf-8", errors="replace")
return string
# PerceiverTokenizer has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
return ()
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/flax/question-answering/utils_qa.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Post-processing utilities for question answering.
"""
import collections
import json
import logging
import os
from typing import Optional, Tuple
import numpy as np
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
def postprocess_qa_predictions(
examples,
features,
predictions: Tuple[np.ndarray, np.ndarray],
version_2_with_negative: bool = False,
n_best_size: int = 20,
max_answer_length: int = 30,
null_score_diff_threshold: float = 0.0,
output_dir: Optional[str] = None,
prefix: Optional[str] = None,
log_level: Optional[int] = logging.WARNING,
):
"""
Post-processes the predictions of a question-answering model to convert them to answers that are substrings of the
original contexts. This is the base postprocessing functions for models that only return start and end logits.
Args:
examples: The non-preprocessed dataset (see the main script for more information).
features: The processed dataset (see the main script for more information).
predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
first dimension must match the number of elements of :obj:`features`.
version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the underlying dataset contains examples with no answers.
n_best_size (:obj:`int`, `optional`, defaults to 20):
The total number of n-best predictions to generate when looking for an answer.
max_answer_length (:obj:`int`, `optional`, defaults to 30):
The maximum length of an answer that can be generated. This is needed because the start and end predictions
are not conditioned on one another.
null_score_diff_threshold (:obj:`float`, `optional`, defaults to 0):
The threshold used to select the null answer: if the best answer has a score that is less than the score of
the null answer minus this threshold, the null answer is selected for this example (note that the score of
the null answer for an example giving several features is the minimum of the scores for the null answer on
each feature: all features must be aligned on the fact they `want` to predict a null answer).
Only useful when :obj:`version_2_with_negative` is :obj:`True`.
output_dir (:obj:`str`, `optional`):
If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
:obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
answers, are saved in `output_dir`.
prefix (:obj:`str`, `optional`):
If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
``logging`` log level (e.g., ``logging.WARNING``)
"""
if len(predictions) != 2:
raise ValueError("`predictions` should be a tuple with two elements (start_logits, end_logits).")
all_start_logits, all_end_logits = predictions
if len(predictions[0]) != len(features):
raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
if version_2_with_negative:
scores_diff_json = collections.OrderedDict()
# Logging.
logger.setLevel(log_level)
logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_prediction = None
prelim_predictions = []
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
# available in the current feature.
token_is_max_context = features[feature_index].get("token_is_max_context", None)
# Update minimum null prediction.
feature_null_score = start_logits[0] + end_logits[0]
if min_null_prediction is None or min_null_prediction["score"] > feature_null_score:
min_null_prediction = {
"offsets": (0, 0),
"score": feature_null_score,
"start_logit": start_logits[0],
"end_logit": end_logits[0],
}
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or len(offset_mapping[start_index]) < 2
or offset_mapping[end_index] is None
or len(offset_mapping[end_index]) < 2
):
continue
# Don't consider answers with a length that is either < 0 or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
# Don't consider answer that don't have the maximum context available (if such information is
# provided).
if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
continue
prelim_predictions.append(
{
"offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
"score": start_logits[start_index] + end_logits[end_index],
"start_logit": start_logits[start_index],
"end_logit": end_logits[end_index],
}
)
if version_2_with_negative and min_null_prediction is not None:
# Add the minimum null prediction
prelim_predictions.append(min_null_prediction)
null_score = min_null_prediction["score"]
# Only keep the best `n_best_size` predictions.
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
# Add back the minimum null prediction if it was removed because of its low score.
if (
version_2_with_negative
and min_null_prediction is not None
and not any(p["offsets"] == (0, 0) for p in predictions)
):
predictions.append(min_null_prediction)
# Use the offsets to gather the answer text in the original context.
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
if len(predictions) == 0 or (len(predictions) == 1 and predictions[0]["text"] == ""):
predictions.insert(0, {"text": "empty", "start_logit": 0.0, "end_logit": 0.0, "score": 0.0})
# Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
# the LogSumExp trick).
scores = np.array([pred.pop("score") for pred in predictions])
exp_scores = np.exp(scores - np.max(scores))
probs = exp_scores / exp_scores.sum()
# Include the probabilities in our predictions.
for prob, pred in zip(probs, predictions):
pred["probability"] = prob
# Pick the best prediction. If the null answer is not possible, this is easy.
if not version_2_with_negative:
all_predictions[example["id"]] = predictions[0]["text"]
else:
# Otherwise we first need to find the best non-empty prediction.
i = 0
while predictions[i]["text"] == "":
i += 1
best_non_null_pred = predictions[i]
# Then we compare to the null prediction using the threshold.
score_diff = null_score - best_non_null_pred["start_logit"] - best_non_null_pred["end_logit"]
scores_diff_json[example["id"]] = float(score_diff) # To be JSON-serializable.
if score_diff > null_score_diff_threshold:
all_predictions[example["id"]] = ""
else:
all_predictions[example["id"]] = best_non_null_pred["text"]
# Make `predictions` JSON-serializable by casting np.float back to float.
all_nbest_json[example["id"]] = [
{k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
for pred in predictions
]
# If we have an output_dir, let's save all those dicts.
if output_dir is not None:
if not os.path.isdir(output_dir):
raise EnvironmentError(f"{output_dir} is not a directory.")
prediction_file = os.path.join(
output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
)
nbest_file = os.path.join(
output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
)
if version_2_with_negative:
null_odds_file = os.path.join(
output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
)
logger.info(f"Saving predictions to {prediction_file}.")
with open(prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
logger.info(f"Saving nbest_preds to {nbest_file}.")
with open(nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
logger.info(f"Saving null_odds to {null_odds_file}.")
with open(null_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
def postprocess_qa_predictions_with_beam_search(
examples,
features,
predictions: Tuple[np.ndarray, np.ndarray],
version_2_with_negative: bool = False,
n_best_size: int = 20,
max_answer_length: int = 30,
start_n_top: int = 5,
end_n_top: int = 5,
output_dir: Optional[str] = None,
prefix: Optional[str] = None,
log_level: Optional[int] = logging.WARNING,
):
"""
Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the
original contexts. This is the postprocessing functions for models that return start and end logits, indices, as well as
cls token predictions.
Args:
examples: The non-preprocessed dataset (see the main script for more information).
features: The processed dataset (see the main script for more information).
predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
first dimension must match the number of elements of :obj:`features`.
version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the underlying dataset contains examples with no answers.
n_best_size (:obj:`int`, `optional`, defaults to 20):
The total number of n-best predictions to generate when looking for an answer.
max_answer_length (:obj:`int`, `optional`, defaults to 30):
The maximum length of an answer that can be generated. This is needed because the start and end predictions
are not conditioned on one another.
start_n_top (:obj:`int`, `optional`, defaults to 5):
The number of top start logits too keep when searching for the :obj:`n_best_size` predictions.
end_n_top (:obj:`int`, `optional`, defaults to 5):
The number of top end logits too keep when searching for the :obj:`n_best_size` predictions.
output_dir (:obj:`str`, `optional`):
If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
:obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
answers, are saved in `output_dir`.
prefix (:obj:`str`, `optional`):
If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
``logging`` log level (e.g., ``logging.WARNING``)
"""
if len(predictions) != 5:
raise ValueError("`predictions` should be a tuple with five elements.")
start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = predictions
if len(predictions[0]) != len(features):
raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict() if version_2_with_negative else None
# Logging.
logger.setLevel(log_level)
logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None
prelim_predictions = []
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_log_prob = start_top_log_probs[feature_index]
start_indexes = start_top_index[feature_index]
end_log_prob = end_top_log_probs[feature_index]
end_indexes = end_top_index[feature_index]
feature_null_score = cls_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
# available in the current feature.
token_is_max_context = features[feature_index].get("token_is_max_context", None)
# Update minimum null prediction
if min_null_score is None or feature_null_score < min_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_start_top`/`n_end_top` greater start and end logits.
for i in range(start_n_top):
for j in range(end_n_top):
start_index = int(start_indexes[i])
j_index = i * end_n_top + j
end_index = int(end_indexes[j_index])
# Don't consider out-of-scope answers (last part of the test should be unnecessary because of the
# p_mask but let's not take any risk)
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or len(offset_mapping[start_index]) < 2
or offset_mapping[end_index] is None
or len(offset_mapping[end_index]) < 2
):
continue
# Don't consider answers with a length negative or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
# Don't consider answer that don't have the maximum context available (if such information is
# provided).
if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
continue
prelim_predictions.append(
{
"offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
"score": start_log_prob[i] + end_log_prob[j_index],
"start_log_prob": start_log_prob[i],
"end_log_prob": end_log_prob[j_index],
}
)
# Only keep the best `n_best_size` predictions.
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
# Use the offsets to gather the answer text in the original context.
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
if len(predictions) == 0:
# Without predictions min_null_score is going to be None and None will cause an exception later
min_null_score = -2e-6
predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": min_null_score})
# Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
# the LogSumExp trick).
scores = np.array([pred.pop("score") for pred in predictions])
exp_scores = np.exp(scores - np.max(scores))
probs = exp_scores / exp_scores.sum()
# Include the probabilities in our predictions.
for prob, pred in zip(probs, predictions):
pred["probability"] = prob
# Pick the best prediction and set the probability for the null answer.
all_predictions[example["id"]] = predictions[0]["text"]
if version_2_with_negative:
scores_diff_json[example["id"]] = float(min_null_score)
# Make `predictions` JSON-serializable by casting np.float back to float.
all_nbest_json[example["id"]] = [
{k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
for pred in predictions
]
# If we have an output_dir, let's save all those dicts.
if output_dir is not None:
if not os.path.isdir(output_dir):
raise EnvironmentError(f"{output_dir} is not a directory.")
prediction_file = os.path.join(
output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
)
nbest_file = os.path.join(
output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
)
if version_2_with_negative:
null_odds_file = os.path.join(
output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
)
logger.info(f"Saving predictions to {prediction_file}.")
with open(prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
logger.info(f"Saving nbest_preds to {nbest_file}.")
with open(nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
logger.info(f"Saving null_odds to {null_odds_file}.")
with open(null_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions, scores_diff_json
| # coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Post-processing utilities for question answering.
"""
import collections
import json
import logging
import os
from typing import Optional, Tuple
import numpy as np
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
def postprocess_qa_predictions(
examples,
features,
predictions: Tuple[np.ndarray, np.ndarray],
version_2_with_negative: bool = False,
n_best_size: int = 20,
max_answer_length: int = 30,
null_score_diff_threshold: float = 0.0,
output_dir: Optional[str] = None,
prefix: Optional[str] = None,
log_level: Optional[int] = logging.WARNING,
):
"""
Post-processes the predictions of a question-answering model to convert them to answers that are substrings of the
original contexts. This is the base postprocessing functions for models that only return start and end logits.
Args:
examples: The non-preprocessed dataset (see the main script for more information).
features: The processed dataset (see the main script for more information).
predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
first dimension must match the number of elements of :obj:`features`.
version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the underlying dataset contains examples with no answers.
n_best_size (:obj:`int`, `optional`, defaults to 20):
The total number of n-best predictions to generate when looking for an answer.
max_answer_length (:obj:`int`, `optional`, defaults to 30):
The maximum length of an answer that can be generated. This is needed because the start and end predictions
are not conditioned on one another.
null_score_diff_threshold (:obj:`float`, `optional`, defaults to 0):
The threshold used to select the null answer: if the best answer has a score that is less than the score of
the null answer minus this threshold, the null answer is selected for this example (note that the score of
the null answer for an example giving several features is the minimum of the scores for the null answer on
each feature: all features must be aligned on the fact they `want` to predict a null answer).
Only useful when :obj:`version_2_with_negative` is :obj:`True`.
output_dir (:obj:`str`, `optional`):
If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
:obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
answers, are saved in `output_dir`.
prefix (:obj:`str`, `optional`):
If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
``logging`` log level (e.g., ``logging.WARNING``)
"""
if len(predictions) != 2:
raise ValueError("`predictions` should be a tuple with two elements (start_logits, end_logits).")
all_start_logits, all_end_logits = predictions
if len(predictions[0]) != len(features):
raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
if version_2_with_negative:
scores_diff_json = collections.OrderedDict()
# Logging.
logger.setLevel(log_level)
logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_prediction = None
prelim_predictions = []
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
# available in the current feature.
token_is_max_context = features[feature_index].get("token_is_max_context", None)
# Update minimum null prediction.
feature_null_score = start_logits[0] + end_logits[0]
if min_null_prediction is None or min_null_prediction["score"] > feature_null_score:
min_null_prediction = {
"offsets": (0, 0),
"score": feature_null_score,
"start_logit": start_logits[0],
"end_logit": end_logits[0],
}
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or len(offset_mapping[start_index]) < 2
or offset_mapping[end_index] is None
or len(offset_mapping[end_index]) < 2
):
continue
# Don't consider answers with a length that is either < 0 or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
# Don't consider answer that don't have the maximum context available (if such information is
# provided).
if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
continue
prelim_predictions.append(
{
"offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
"score": start_logits[start_index] + end_logits[end_index],
"start_logit": start_logits[start_index],
"end_logit": end_logits[end_index],
}
)
if version_2_with_negative and min_null_prediction is not None:
# Add the minimum null prediction
prelim_predictions.append(min_null_prediction)
null_score = min_null_prediction["score"]
# Only keep the best `n_best_size` predictions.
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
# Add back the minimum null prediction if it was removed because of its low score.
if (
version_2_with_negative
and min_null_prediction is not None
and not any(p["offsets"] == (0, 0) for p in predictions)
):
predictions.append(min_null_prediction)
# Use the offsets to gather the answer text in the original context.
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
if len(predictions) == 0 or (len(predictions) == 1 and predictions[0]["text"] == ""):
predictions.insert(0, {"text": "empty", "start_logit": 0.0, "end_logit": 0.0, "score": 0.0})
# Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
# the LogSumExp trick).
scores = np.array([pred.pop("score") for pred in predictions])
exp_scores = np.exp(scores - np.max(scores))
probs = exp_scores / exp_scores.sum()
# Include the probabilities in our predictions.
for prob, pred in zip(probs, predictions):
pred["probability"] = prob
# Pick the best prediction. If the null answer is not possible, this is easy.
if not version_2_with_negative:
all_predictions[example["id"]] = predictions[0]["text"]
else:
# Otherwise we first need to find the best non-empty prediction.
i = 0
while predictions[i]["text"] == "":
i += 1
best_non_null_pred = predictions[i]
# Then we compare to the null prediction using the threshold.
score_diff = null_score - best_non_null_pred["start_logit"] - best_non_null_pred["end_logit"]
scores_diff_json[example["id"]] = float(score_diff) # To be JSON-serializable.
if score_diff > null_score_diff_threshold:
all_predictions[example["id"]] = ""
else:
all_predictions[example["id"]] = best_non_null_pred["text"]
# Make `predictions` JSON-serializable by casting np.float back to float.
all_nbest_json[example["id"]] = [
{k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
for pred in predictions
]
# If we have an output_dir, let's save all those dicts.
if output_dir is not None:
if not os.path.isdir(output_dir):
raise EnvironmentError(f"{output_dir} is not a directory.")
prediction_file = os.path.join(
output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
)
nbest_file = os.path.join(
output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
)
if version_2_with_negative:
null_odds_file = os.path.join(
output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
)
logger.info(f"Saving predictions to {prediction_file}.")
with open(prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
logger.info(f"Saving nbest_preds to {nbest_file}.")
with open(nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
logger.info(f"Saving null_odds to {null_odds_file}.")
with open(null_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
def postprocess_qa_predictions_with_beam_search(
examples,
features,
predictions: Tuple[np.ndarray, np.ndarray],
version_2_with_negative: bool = False,
n_best_size: int = 20,
max_answer_length: int = 30,
start_n_top: int = 5,
end_n_top: int = 5,
output_dir: Optional[str] = None,
prefix: Optional[str] = None,
log_level: Optional[int] = logging.WARNING,
):
"""
Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the
original contexts. This is the postprocessing functions for models that return start and end logits, indices, as well as
cls token predictions.
Args:
examples: The non-preprocessed dataset (see the main script for more information).
features: The processed dataset (see the main script for more information).
predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
first dimension must match the number of elements of :obj:`features`.
version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the underlying dataset contains examples with no answers.
n_best_size (:obj:`int`, `optional`, defaults to 20):
The total number of n-best predictions to generate when looking for an answer.
max_answer_length (:obj:`int`, `optional`, defaults to 30):
The maximum length of an answer that can be generated. This is needed because the start and end predictions
are not conditioned on one another.
start_n_top (:obj:`int`, `optional`, defaults to 5):
The number of top start logits too keep when searching for the :obj:`n_best_size` predictions.
end_n_top (:obj:`int`, `optional`, defaults to 5):
The number of top end logits too keep when searching for the :obj:`n_best_size` predictions.
output_dir (:obj:`str`, `optional`):
If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
:obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
answers, are saved in `output_dir`.
prefix (:obj:`str`, `optional`):
If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
``logging`` log level (e.g., ``logging.WARNING``)
"""
if len(predictions) != 5:
raise ValueError("`predictions` should be a tuple with five elements.")
start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = predictions
if len(predictions[0]) != len(features):
raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict() if version_2_with_negative else None
# Logging.
logger.setLevel(log_level)
logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None
prelim_predictions = []
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_log_prob = start_top_log_probs[feature_index]
start_indexes = start_top_index[feature_index]
end_log_prob = end_top_log_probs[feature_index]
end_indexes = end_top_index[feature_index]
feature_null_score = cls_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
# available in the current feature.
token_is_max_context = features[feature_index].get("token_is_max_context", None)
# Update minimum null prediction
if min_null_score is None or feature_null_score < min_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_start_top`/`n_end_top` greater start and end logits.
for i in range(start_n_top):
for j in range(end_n_top):
start_index = int(start_indexes[i])
j_index = i * end_n_top + j
end_index = int(end_indexes[j_index])
# Don't consider out-of-scope answers (last part of the test should be unnecessary because of the
# p_mask but let's not take any risk)
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or len(offset_mapping[start_index]) < 2
or offset_mapping[end_index] is None
or len(offset_mapping[end_index]) < 2
):
continue
# Don't consider answers with a length negative or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
# Don't consider answer that don't have the maximum context available (if such information is
# provided).
if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
continue
prelim_predictions.append(
{
"offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
"score": start_log_prob[i] + end_log_prob[j_index],
"start_log_prob": start_log_prob[i],
"end_log_prob": end_log_prob[j_index],
}
)
# Only keep the best `n_best_size` predictions.
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
# Use the offsets to gather the answer text in the original context.
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
if len(predictions) == 0:
# Without predictions min_null_score is going to be None and None will cause an exception later
min_null_score = -2e-6
predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": min_null_score})
# Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
# the LogSumExp trick).
scores = np.array([pred.pop("score") for pred in predictions])
exp_scores = np.exp(scores - np.max(scores))
probs = exp_scores / exp_scores.sum()
# Include the probabilities in our predictions.
for prob, pred in zip(probs, predictions):
pred["probability"] = prob
# Pick the best prediction and set the probability for the null answer.
all_predictions[example["id"]] = predictions[0]["text"]
if version_2_with_negative:
scores_diff_json[example["id"]] = float(min_null_score)
# Make `predictions` JSON-serializable by casting np.float back to float.
all_nbest_json[example["id"]] = [
{k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
for pred in predictions
]
# If we have an output_dir, let's save all those dicts.
if output_dir is not None:
if not os.path.isdir(output_dir):
raise EnvironmentError(f"{output_dir} is not a directory.")
prediction_file = os.path.join(
output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
)
nbest_file = os.path.join(
output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
)
if version_2_with_negative:
null_odds_file = os.path.join(
output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
)
logger.info(f"Saving predictions to {prediction_file}.")
with open(prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
logger.info(f"Saving nbest_preds to {nbest_file}.")
with open(nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
logger.info(f"Saving null_odds to {null_odds_file}.")
with open(null_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions, scores_diff_json
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/deberta/__init__.py | -1 |
||
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./docs/source/en/model_doc/audio-spectrogram-transformer.mdx | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Audio Spectrogram Transformer
## Overview
The Audio Spectrogram Transformer model was proposed in [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
The Audio Spectrogram Transformer applies a [Vision Transformer](vit) to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results
for audio classification.
The abstract from the paper is the following:
*In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.*
Tips:
- When fine-tuning the Audio Spectrogram Transformer (AST) on your own dataset, it's recommended to take care of the input normalization (to make
sure the input has mean of 0 and std of 0.5). [`ASTFeatureExtractor`] takes care of this. Note that it uses the AudioSet
mean and std by default. You can check [`ast/src/get_norm_stats.py`](https://github.com/YuanGongND/ast/blob/master/src/get_norm_stats.py) to see how
the authors compute the stats for a downstream dataset.
- Note that the AST needs a low learning rate (the authors use a 10 times smaller learning rate compared to their CNN model proposed in the
[PSLA paper](https://arxiv.org/abs/2102.01243)) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/audio_spectogram_transformer_architecture.png"
alt="drawing" width="600"/>
<small> Audio pectrogram Transformer architecture. Taken from the <a href="https://arxiv.org/abs/2104.01778">original paper</a>.</small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/YuanGongND/ast).
## ASTConfig
[[autodoc]] ASTConfig
## ASTFeatureExtractor
[[autodoc]] ASTFeatureExtractor
- __call__
## ASTModel
[[autodoc]] ASTModel
- forward
## ASTForAudioClassification
[[autodoc]] ASTForAudioClassification
- forward | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Audio Spectrogram Transformer
## Overview
The Audio Spectrogram Transformer model was proposed in [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
The Audio Spectrogram Transformer applies a [Vision Transformer](vit) to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results
for audio classification.
The abstract from the paper is the following:
*In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.*
Tips:
- When fine-tuning the Audio Spectrogram Transformer (AST) on your own dataset, it's recommended to take care of the input normalization (to make
sure the input has mean of 0 and std of 0.5). [`ASTFeatureExtractor`] takes care of this. Note that it uses the AudioSet
mean and std by default. You can check [`ast/src/get_norm_stats.py`](https://github.com/YuanGongND/ast/blob/master/src/get_norm_stats.py) to see how
the authors compute the stats for a downstream dataset.
- Note that the AST needs a low learning rate (the authors use a 10 times smaller learning rate compared to their CNN model proposed in the
[PSLA paper](https://arxiv.org/abs/2102.01243)) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/audio_spectogram_transformer_architecture.png"
alt="drawing" width="600"/>
<small> Audio pectrogram Transformer architecture. Taken from the <a href="https://arxiv.org/abs/2104.01778">original paper</a>.</small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/YuanGongND/ast).
## ASTConfig
[[autodoc]] ASTConfig
## ASTFeatureExtractor
[[autodoc]] ASTFeatureExtractor
- __call__
## ASTModel
[[autodoc]] ASTModel
- forward
## ASTForAudioClassification
[[autodoc]] ASTForAudioClassification
- forward | -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/pytorch/text-classification/run_glue_no_trainer.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning a 🤗 Transformers model for sequence classification on GLUE."""
import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
PretrainedConfig,
SchedulerType,
default_data_collator,
get_scheduler,
)
from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.25.0.dev0")
logger = get_logger(__name__)
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
def parse_args():
parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
parser.add_argument(
"--task_name",
type=str,
default=None,
help="The name of the glue task to train on.",
choices=list(task_to_keys.keys()),
)
parser.add_argument(
"--train_file", type=str, default=None, help="A csv or a json file containing the training data."
)
parser.add_argument(
"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
)
parser.add_argument(
"--max_length",
type=int,
default=128,
help=(
"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
" sequences shorter will be padded if `--pad_to_max_lengh` is passed."
),
)
parser.add_argument(
"--pad_to_max_length",
action="store_true",
help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument(
"--use_slow_tokenizer",
action="store_true",
help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--checkpointing_steps",
type=str,
default=None,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
parser.add_argument(
"--with_tracking",
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument(
"--ignore_mismatched_sizes",
action="store_true",
help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
)
args = parser.parse_args()
# Sanity checks
if args.task_name is None and args.train_file is None and args.validation_file is None:
raise ValueError("Need either a task name or a training/validation file.")
else:
if args.train_file is not None:
extension = args.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if args.validation_file is not None:
extension = args.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
return args
def main():
args = parse_args()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_glue_no_trainer", args)
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
# in the environment
accelerator = (
Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator()
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
# label if at least two columns are provided.
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if args.task_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset("glue", args.task_name)
else:
# Loading the dataset from local csv or json file.
data_files = {}
if args.train_file is not None:
data_files["train"] = args.train_file
if args.validation_file is not None:
data_files["validation"] = args.validation_file
extension = (args.train_file if args.train_file is not None else args.validation_file).split(".")[-1]
raw_datasets = load_dataset(extension, data_files=data_files)
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
if args.task_name is not None:
is_regression = args.task_name == "stsb"
if not is_regression:
label_list = raw_datasets["train"].features["label"].names
num_labels = len(label_list)
else:
num_labels = 1
else:
# Trying to have good defaults here, don't hesitate to tweak to your needs.
is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
if is_regression:
num_labels = 1
else:
# A useful fast method:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
label_list = raw_datasets["train"].unique("label")
label_list.sort() # Let's sort it for determinism
num_labels = len(label_list)
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
ignore_mismatched_sizes=args.ignore_mismatched_sizes,
)
# Preprocessing the datasets
if args.task_name is not None:
sentence1_key, sentence2_key = task_to_keys[args.task_name]
else:
# Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
sentence1_key, sentence2_key = "sentence1", "sentence2"
else:
if len(non_label_column_names) >= 2:
sentence1_key, sentence2_key = non_label_column_names[:2]
else:
sentence1_key, sentence2_key = non_label_column_names[0], None
# Some models have set the order of the labels to use, so let's make sure we do use it.
label_to_id = None
if (
model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
and args.task_name is not None
and not is_regression
):
# Some have all caps in their config, some don't.
label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
logger.info(
f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
"Using it!"
)
label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
else:
logger.warning(
"Your model seems to have been trained with labels, but they don't match the dataset: ",
f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
"\nIgnoring the model labels as a result.",
)
elif args.task_name is None and not is_regression:
label_to_id = {v: i for i, v in enumerate(label_list)}
if label_to_id is not None:
model.config.label2id = label_to_id
model.config.id2label = {id: label for label, id in config.label2id.items()}
elif args.task_name is not None and not is_regression:
model.config.label2id = {l: i for i, l in enumerate(label_list)}
model.config.id2label = {id: label for label, id in config.label2id.items()}
padding = "max_length" if args.pad_to_max_length else False
def preprocess_function(examples):
# Tokenize the texts
texts = (
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True)
if "label" in examples:
if label_to_id is not None:
# Map labels to IDs (not necessary for GLUE tasks)
result["labels"] = [label_to_id[l] for l in examples["label"]]
else:
# In all cases, rename the column to labels because the model will expect that.
result["labels"] = examples["label"]
return result
with accelerator.main_process_first():
processed_datasets = raw_datasets.map(
preprocess_function,
batched=True,
remove_columns=raw_datasets["train"].column_names,
desc="Running tokenizer on dataset",
)
train_dataset = processed_datasets["train"]
eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"]
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
# DataLoaders creation:
if args.pad_to_max_length:
# If padding was already done ot max length, we use the default data collator that will just convert everything
# to tensors.
data_collator = default_data_collator
else:
# Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None))
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
# Optimizer
# Split weights in two groups, one with weight decay and the other not.
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.max_train_steps,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states
checkpointing_steps = args.checkpointing_steps
if checkpointing_steps is not None and checkpointing_steps.isdigit():
checkpointing_steps = int(checkpointing_steps)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("glue_no_trainer", experiment_config)
# Get the metric function
if args.task_name is not None:
metric = evaluate.load("glue", args.task_name)
else:
metric = evaluate.load("accuracy")
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
completed_steps = 0
starting_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
accelerator.load_state(args.resume_from_checkpoint)
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
dirs.sort(key=os.path.getctime)
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
training_difference = os.path.splitext(path)[0]
if "epoch" in training_difference:
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
resume_step = None
else:
resume_step = int(training_difference.replace("step_", ""))
starting_epoch = resume_step // len(train_dataloader)
resume_step -= starting_epoch * len(train_dataloader)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
for step, batch in enumerate(train_dataloader):
# We need to skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == starting_epoch:
if resume_step is not None and step < resume_step:
completed_steps += 1
continue
outputs = model(**batch)
loss = outputs.loss
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
loss = loss / args.gradient_accumulation_steps
accelerator.backward(loss)
if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
completed_steps += 1
if isinstance(checkpointing_steps, int):
if completed_steps % checkpointing_steps == 0:
output_dir = f"step_{completed_steps }"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if completed_steps >= args.max_train_steps:
break
model.eval()
samples_seen = 0
for step, batch in enumerate(eval_dataloader):
with torch.no_grad():
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze()
predictions, references = accelerator.gather((predictions, batch["labels"]))
# If we are in a multiprocess environment, the last batch has duplicates
if accelerator.num_processes > 1:
if step == len(eval_dataloader) - 1:
predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
references = references[: len(eval_dataloader.dataset) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=predictions,
references=references,
)
eval_metric = metric.compute()
logger.info(f"epoch {epoch}: {eval_metric}")
if args.with_tracking:
accelerator.log(
{
"accuracy" if args.task_name is not None else "glue": eval_metric,
"train_loss": total_loss.item() / len(train_dataloader),
"epoch": epoch,
"step": completed_steps,
},
step=completed_steps,
)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
)
if args.checkpointing_steps == "epoch":
output_dir = f"epoch_{epoch}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.with_tracking:
accelerator.end_training()
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
if args.task_name == "mnli":
# Final evaluation on mismatched validation set
eval_dataset = processed_datasets["validation_mismatched"]
eval_dataloader = DataLoader(
eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
eval_dataloader = accelerator.prepare(eval_dataloader)
model.eval()
for step, batch in enumerate(eval_dataloader):
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1)
metric.add_batch(
predictions=accelerator.gather(predictions),
references=accelerator.gather(batch["labels"]),
)
eval_metric = metric.compute()
logger.info(f"mnli-mm: {eval_metric}")
if args.output_dir is not None:
all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
json.dump(all_results, f)
if __name__ == "__main__":
main()
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning a 🤗 Transformers model for sequence classification on GLUE."""
import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
PretrainedConfig,
SchedulerType,
default_data_collator,
get_scheduler,
)
from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.25.0.dev0")
logger = get_logger(__name__)
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
def parse_args():
parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
parser.add_argument(
"--task_name",
type=str,
default=None,
help="The name of the glue task to train on.",
choices=list(task_to_keys.keys()),
)
parser.add_argument(
"--train_file", type=str, default=None, help="A csv or a json file containing the training data."
)
parser.add_argument(
"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
)
parser.add_argument(
"--max_length",
type=int,
default=128,
help=(
"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
" sequences shorter will be padded if `--pad_to_max_lengh` is passed."
),
)
parser.add_argument(
"--pad_to_max_length",
action="store_true",
help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument(
"--use_slow_tokenizer",
action="store_true",
help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--checkpointing_steps",
type=str,
default=None,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
parser.add_argument(
"--with_tracking",
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument(
"--ignore_mismatched_sizes",
action="store_true",
help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
)
args = parser.parse_args()
# Sanity checks
if args.task_name is None and args.train_file is None and args.validation_file is None:
raise ValueError("Need either a task name or a training/validation file.")
else:
if args.train_file is not None:
extension = args.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if args.validation_file is not None:
extension = args.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
return args
def main():
args = parse_args()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_glue_no_trainer", args)
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
# in the environment
accelerator = (
Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator()
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
# label if at least two columns are provided.
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if args.task_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset("glue", args.task_name)
else:
# Loading the dataset from local csv or json file.
data_files = {}
if args.train_file is not None:
data_files["train"] = args.train_file
if args.validation_file is not None:
data_files["validation"] = args.validation_file
extension = (args.train_file if args.train_file is not None else args.validation_file).split(".")[-1]
raw_datasets = load_dataset(extension, data_files=data_files)
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
if args.task_name is not None:
is_regression = args.task_name == "stsb"
if not is_regression:
label_list = raw_datasets["train"].features["label"].names
num_labels = len(label_list)
else:
num_labels = 1
else:
# Trying to have good defaults here, don't hesitate to tweak to your needs.
is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
if is_regression:
num_labels = 1
else:
# A useful fast method:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
label_list = raw_datasets["train"].unique("label")
label_list.sort() # Let's sort it for determinism
num_labels = len(label_list)
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
ignore_mismatched_sizes=args.ignore_mismatched_sizes,
)
# Preprocessing the datasets
if args.task_name is not None:
sentence1_key, sentence2_key = task_to_keys[args.task_name]
else:
# Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
sentence1_key, sentence2_key = "sentence1", "sentence2"
else:
if len(non_label_column_names) >= 2:
sentence1_key, sentence2_key = non_label_column_names[:2]
else:
sentence1_key, sentence2_key = non_label_column_names[0], None
# Some models have set the order of the labels to use, so let's make sure we do use it.
label_to_id = None
if (
model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
and args.task_name is not None
and not is_regression
):
# Some have all caps in their config, some don't.
label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
logger.info(
f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
"Using it!"
)
label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
else:
logger.warning(
"Your model seems to have been trained with labels, but they don't match the dataset: ",
f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
"\nIgnoring the model labels as a result.",
)
elif args.task_name is None and not is_regression:
label_to_id = {v: i for i, v in enumerate(label_list)}
if label_to_id is not None:
model.config.label2id = label_to_id
model.config.id2label = {id: label for label, id in config.label2id.items()}
elif args.task_name is not None and not is_regression:
model.config.label2id = {l: i for i, l in enumerate(label_list)}
model.config.id2label = {id: label for label, id in config.label2id.items()}
padding = "max_length" if args.pad_to_max_length else False
def preprocess_function(examples):
# Tokenize the texts
texts = (
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True)
if "label" in examples:
if label_to_id is not None:
# Map labels to IDs (not necessary for GLUE tasks)
result["labels"] = [label_to_id[l] for l in examples["label"]]
else:
# In all cases, rename the column to labels because the model will expect that.
result["labels"] = examples["label"]
return result
with accelerator.main_process_first():
processed_datasets = raw_datasets.map(
preprocess_function,
batched=True,
remove_columns=raw_datasets["train"].column_names,
desc="Running tokenizer on dataset",
)
train_dataset = processed_datasets["train"]
eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"]
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
# DataLoaders creation:
if args.pad_to_max_length:
# If padding was already done ot max length, we use the default data collator that will just convert everything
# to tensors.
data_collator = default_data_collator
else:
# Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None))
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
# Optimizer
# Split weights in two groups, one with weight decay and the other not.
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.max_train_steps,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states
checkpointing_steps = args.checkpointing_steps
if checkpointing_steps is not None and checkpointing_steps.isdigit():
checkpointing_steps = int(checkpointing_steps)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("glue_no_trainer", experiment_config)
# Get the metric function
if args.task_name is not None:
metric = evaluate.load("glue", args.task_name)
else:
metric = evaluate.load("accuracy")
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
completed_steps = 0
starting_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
accelerator.load_state(args.resume_from_checkpoint)
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
dirs.sort(key=os.path.getctime)
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
training_difference = os.path.splitext(path)[0]
if "epoch" in training_difference:
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
resume_step = None
else:
resume_step = int(training_difference.replace("step_", ""))
starting_epoch = resume_step // len(train_dataloader)
resume_step -= starting_epoch * len(train_dataloader)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
for step, batch in enumerate(train_dataloader):
# We need to skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == starting_epoch:
if resume_step is not None and step < resume_step:
completed_steps += 1
continue
outputs = model(**batch)
loss = outputs.loss
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
loss = loss / args.gradient_accumulation_steps
accelerator.backward(loss)
if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
completed_steps += 1
if isinstance(checkpointing_steps, int):
if completed_steps % checkpointing_steps == 0:
output_dir = f"step_{completed_steps }"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if completed_steps >= args.max_train_steps:
break
model.eval()
samples_seen = 0
for step, batch in enumerate(eval_dataloader):
with torch.no_grad():
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze()
predictions, references = accelerator.gather((predictions, batch["labels"]))
# If we are in a multiprocess environment, the last batch has duplicates
if accelerator.num_processes > 1:
if step == len(eval_dataloader) - 1:
predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
references = references[: len(eval_dataloader.dataset) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=predictions,
references=references,
)
eval_metric = metric.compute()
logger.info(f"epoch {epoch}: {eval_metric}")
if args.with_tracking:
accelerator.log(
{
"accuracy" if args.task_name is not None else "glue": eval_metric,
"train_loss": total_loss.item() / len(train_dataloader),
"epoch": epoch,
"step": completed_steps,
},
step=completed_steps,
)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
)
if args.checkpointing_steps == "epoch":
output_dir = f"epoch_{epoch}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.with_tracking:
accelerator.end_training()
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
if args.task_name == "mnli":
# Final evaluation on mismatched validation set
eval_dataset = processed_datasets["validation_mismatched"]
eval_dataloader = DataLoader(
eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
eval_dataloader = accelerator.prepare(eval_dataloader)
model.eval()
for step, batch in enumerate(eval_dataloader):
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1)
metric.add_batch(
predictions=accelerator.gather(predictions),
references=accelerator.gather(batch["labels"]),
)
eval_metric = metric.compute()
logger.info(f"mnli-mm: {eval_metric}")
if args.output_dir is not None:
all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
json.dump(all_results, f)
if __name__ == "__main__":
main()
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/research_projects/distillation/train.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Training the distilled model.
Supported architectures include: BERT -> DistilBERT, RoBERTa -> DistilRoBERTa, GPT2 -> DistilGPT2.
"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPT2Config,
GPT2LMHeadModel,
GPT2Tokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
MODEL_CLASSES = {
"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
"gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
}
def sanity_checks(args):
"""
A bunch of args sanity checks to perform even starting...
"""
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts)
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config)
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights)
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def freeze_pos_embeddings(student, args):
if args.student_type == "roberta":
student.roberta.embeddings.position_embeddings.weight.requires_grad = False
elif args.student_type == "gpt2":
student.transformer.wpe.weight.requires_grad = False
def freeze_token_type_embeddings(student, args):
if args.student_type == "roberta":
student.roberta.embeddings.token_type_embeddings.weight.requires_grad = False
def main():
parser = argparse.ArgumentParser(description="Training")
parser.add_argument("--force", action="store_true", help="Overwrite dump_path if it already exists.")
parser.add_argument(
"--dump_path", type=str, required=True, help="The output directory (log, checkpoints, parameters, etc.)"
)
parser.add_argument(
"--data_file",
type=str,
required=True,
help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.",
)
parser.add_argument(
"--student_type",
type=str,
choices=["distilbert", "roberta", "gpt2"],
required=True,
help="The student type (DistilBERT, RoBERTa).",
)
parser.add_argument("--student_config", type=str, required=True, help="Path to the student configuration.")
parser.add_argument(
"--student_pretrained_weights", default=None, type=str, help="Load student initialization checkpoint."
)
parser.add_argument(
"--teacher_type", choices=["bert", "roberta", "gpt2"], required=True, help="Teacher type (BERT, RoBERTa)."
)
parser.add_argument("--teacher_name", type=str, required=True, help="The teacher model.")
parser.add_argument("--temperature", default=2.0, type=float, help="Temperature for the softmax temperature.")
parser.add_argument(
"--alpha_ce", default=0.5, type=float, help="Linear weight for the distillation loss. Must be >=0."
)
parser.add_argument(
"--alpha_mlm",
default=0.0,
type=float,
help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.",
)
parser.add_argument("--alpha_clm", default=0.5, type=float, help="Linear weight for the CLM loss. Must be >=0.")
parser.add_argument("--alpha_mse", default=0.0, type=float, help="Linear weight of the MSE loss. Must be >=0.")
parser.add_argument(
"--alpha_cos", default=0.0, type=float, help="Linear weight of the cosine embedding loss. Must be >=0."
)
parser.add_argument(
"--mlm", action="store_true", help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."
)
parser.add_argument(
"--mlm_mask_prop",
default=0.15,
type=float,
help="Proportion of tokens for which we need to make a prediction.",
)
parser.add_argument("--word_mask", default=0.8, type=float, help="Proportion of tokens to mask out.")
parser.add_argument("--word_keep", default=0.1, type=float, help="Proportion of tokens to keep.")
parser.add_argument("--word_rand", default=0.1, type=float, help="Proportion of tokens to randomly replace.")
parser.add_argument(
"--mlm_smoothing",
default=0.7,
type=float,
help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",
)
parser.add_argument("--token_counts", type=str, help="The token counts in the data_file for MLM.")
parser.add_argument(
"--restrict_ce_to_mask",
action="store_true",
help="If true, compute the distillation loss only the [MLM] prediction distribution.",
)
parser.add_argument(
"--freeze_pos_embs",
action="store_true",
help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.",
)
parser.add_argument(
"--freeze_token_type_embds",
action="store_true",
help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.",
)
parser.add_argument("--n_epoch", type=int, default=3, help="Number of pass on the whole dataset.")
parser.add_argument("--batch_size", type=int, default=5, help="Batch size (for each process).")
parser.add_argument(
"--group_by_size",
action="store_false",
help="If true, group sequences that have similar length into the same batch. Default is true.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=50,
help="Gradient accumulation for larger training batches.",
)
parser.add_argument("--warmup_prop", default=0.05, type=float, help="Linear warmup proportion.")
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--learning_rate", default=5e-4, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=5.0, type=float, help="Max gradient norm.")
parser.add_argument("--initializer_range", default=0.02, type=float, help="Random initialization range.")
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
),
)
parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
parser.add_argument("--seed", type=int, default=56, help="Random seed")
parser.add_argument("--log_interval", type=int, default=500, help="Tensorboard logging interval.")
parser.add_argument("--checkpoint_interval", type=int, default=4000, help="Checkpoint interval.")
args = parser.parse_args()
sanity_checks(args)
# ARGS #
init_gpu_params(args)
set_seed(args)
if args.is_master:
if os.path.exists(args.dump_path):
if not args.force:
raise ValueError(
f"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"
" itUse `--force` if you want to overwrite it"
)
else:
shutil.rmtree(args.dump_path)
if not os.path.exists(args.dump_path):
os.makedirs(args.dump_path)
logger.info(f"Experiment will be dumped and logged in {args.dump_path}")
# SAVE PARAMS #
logger.info(f"Param: {args}")
with open(os.path.join(args.dump_path, "parameters.json"), "w") as f:
json.dump(vars(args), f, indent=4)
git_log(args.dump_path)
student_config_class, student_model_class, _ = MODEL_CLASSES[args.student_type]
teacher_config_class, teacher_model_class, teacher_tokenizer_class = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
tokenizer = teacher_tokenizer_class.from_pretrained(args.teacher_name)
special_tok_ids = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
idx = tokenizer.all_special_tokens.index(tok_symbol)
special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
logger.info(f"Special tokens {special_tok_ids}")
args.special_tok_ids = special_tok_ids
args.max_model_input_size = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f"Loading data from {args.data_file}")
with open(args.data_file, "rb") as fp:
data = pickle.load(fp)
if args.mlm:
logger.info(f"Loading token counts from {args.token_counts} (already pre-computed)")
with open(args.token_counts, "rb") as fp:
counts = pickle.load(fp)
token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
token_probs[idx] = 0.0 # do not predict special tokens
token_probs = torch.from_numpy(token_probs)
else:
token_probs = None
train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
logger.info("Data loader created.")
# STUDENT #
logger.info(f"Loading student config from {args.student_config}")
stu_architecture_config = student_config_class.from_pretrained(args.student_config)
stu_architecture_config.output_hidden_states = True
if args.student_pretrained_weights is not None:
logger.info(f"Loading pretrained weights from {args.student_pretrained_weights}")
student = student_model_class.from_pretrained(args.student_pretrained_weights, config=stu_architecture_config)
else:
student = student_model_class(stu_architecture_config)
if args.n_gpu > 0:
student.to(f"cuda:{args.local_rank}")
logger.info("Student loaded.")
# TEACHER #
teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)
if args.n_gpu > 0:
teacher.to(f"cuda:{args.local_rank}")
logger.info(f"Teacher loaded from {args.teacher_name}.")
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(student, args)
if args.freeze_token_type_embds:
freeze_token_type_embeddings(student, args)
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
distiller = Distiller(
params=args, dataset=train_lm_seq_dataset, token_probs=token_probs, student=student, teacher=teacher
)
distiller.train()
logger.info("Let's go get some drinks.")
if __name__ == "__main__":
main()
| # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Training the distilled model.
Supported architectures include: BERT -> DistilBERT, RoBERTa -> DistilRoBERTa, GPT2 -> DistilGPT2.
"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPT2Config,
GPT2LMHeadModel,
GPT2Tokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
MODEL_CLASSES = {
"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
"gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
}
def sanity_checks(args):
"""
A bunch of args sanity checks to perform even starting...
"""
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts)
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config)
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights)
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def freeze_pos_embeddings(student, args):
if args.student_type == "roberta":
student.roberta.embeddings.position_embeddings.weight.requires_grad = False
elif args.student_type == "gpt2":
student.transformer.wpe.weight.requires_grad = False
def freeze_token_type_embeddings(student, args):
if args.student_type == "roberta":
student.roberta.embeddings.token_type_embeddings.weight.requires_grad = False
def main():
parser = argparse.ArgumentParser(description="Training")
parser.add_argument("--force", action="store_true", help="Overwrite dump_path if it already exists.")
parser.add_argument(
"--dump_path", type=str, required=True, help="The output directory (log, checkpoints, parameters, etc.)"
)
parser.add_argument(
"--data_file",
type=str,
required=True,
help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.",
)
parser.add_argument(
"--student_type",
type=str,
choices=["distilbert", "roberta", "gpt2"],
required=True,
help="The student type (DistilBERT, RoBERTa).",
)
parser.add_argument("--student_config", type=str, required=True, help="Path to the student configuration.")
parser.add_argument(
"--student_pretrained_weights", default=None, type=str, help="Load student initialization checkpoint."
)
parser.add_argument(
"--teacher_type", choices=["bert", "roberta", "gpt2"], required=True, help="Teacher type (BERT, RoBERTa)."
)
parser.add_argument("--teacher_name", type=str, required=True, help="The teacher model.")
parser.add_argument("--temperature", default=2.0, type=float, help="Temperature for the softmax temperature.")
parser.add_argument(
"--alpha_ce", default=0.5, type=float, help="Linear weight for the distillation loss. Must be >=0."
)
parser.add_argument(
"--alpha_mlm",
default=0.0,
type=float,
help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.",
)
parser.add_argument("--alpha_clm", default=0.5, type=float, help="Linear weight for the CLM loss. Must be >=0.")
parser.add_argument("--alpha_mse", default=0.0, type=float, help="Linear weight of the MSE loss. Must be >=0.")
parser.add_argument(
"--alpha_cos", default=0.0, type=float, help="Linear weight of the cosine embedding loss. Must be >=0."
)
parser.add_argument(
"--mlm", action="store_true", help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."
)
parser.add_argument(
"--mlm_mask_prop",
default=0.15,
type=float,
help="Proportion of tokens for which we need to make a prediction.",
)
parser.add_argument("--word_mask", default=0.8, type=float, help="Proportion of tokens to mask out.")
parser.add_argument("--word_keep", default=0.1, type=float, help="Proportion of tokens to keep.")
parser.add_argument("--word_rand", default=0.1, type=float, help="Proportion of tokens to randomly replace.")
parser.add_argument(
"--mlm_smoothing",
default=0.7,
type=float,
help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",
)
parser.add_argument("--token_counts", type=str, help="The token counts in the data_file for MLM.")
parser.add_argument(
"--restrict_ce_to_mask",
action="store_true",
help="If true, compute the distillation loss only the [MLM] prediction distribution.",
)
parser.add_argument(
"--freeze_pos_embs",
action="store_true",
help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.",
)
parser.add_argument(
"--freeze_token_type_embds",
action="store_true",
help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.",
)
parser.add_argument("--n_epoch", type=int, default=3, help="Number of pass on the whole dataset.")
parser.add_argument("--batch_size", type=int, default=5, help="Batch size (for each process).")
parser.add_argument(
"--group_by_size",
action="store_false",
help="If true, group sequences that have similar length into the same batch. Default is true.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=50,
help="Gradient accumulation for larger training batches.",
)
parser.add_argument("--warmup_prop", default=0.05, type=float, help="Linear warmup proportion.")
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--learning_rate", default=5e-4, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=5.0, type=float, help="Max gradient norm.")
parser.add_argument("--initializer_range", default=0.02, type=float, help="Random initialization range.")
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
),
)
parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
parser.add_argument("--seed", type=int, default=56, help="Random seed")
parser.add_argument("--log_interval", type=int, default=500, help="Tensorboard logging interval.")
parser.add_argument("--checkpoint_interval", type=int, default=4000, help="Checkpoint interval.")
args = parser.parse_args()
sanity_checks(args)
# ARGS #
init_gpu_params(args)
set_seed(args)
if args.is_master:
if os.path.exists(args.dump_path):
if not args.force:
raise ValueError(
f"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"
" itUse `--force` if you want to overwrite it"
)
else:
shutil.rmtree(args.dump_path)
if not os.path.exists(args.dump_path):
os.makedirs(args.dump_path)
logger.info(f"Experiment will be dumped and logged in {args.dump_path}")
# SAVE PARAMS #
logger.info(f"Param: {args}")
with open(os.path.join(args.dump_path, "parameters.json"), "w") as f:
json.dump(vars(args), f, indent=4)
git_log(args.dump_path)
student_config_class, student_model_class, _ = MODEL_CLASSES[args.student_type]
teacher_config_class, teacher_model_class, teacher_tokenizer_class = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
tokenizer = teacher_tokenizer_class.from_pretrained(args.teacher_name)
special_tok_ids = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
idx = tokenizer.all_special_tokens.index(tok_symbol)
special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
logger.info(f"Special tokens {special_tok_ids}")
args.special_tok_ids = special_tok_ids
args.max_model_input_size = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f"Loading data from {args.data_file}")
with open(args.data_file, "rb") as fp:
data = pickle.load(fp)
if args.mlm:
logger.info(f"Loading token counts from {args.token_counts} (already pre-computed)")
with open(args.token_counts, "rb") as fp:
counts = pickle.load(fp)
token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
token_probs[idx] = 0.0 # do not predict special tokens
token_probs = torch.from_numpy(token_probs)
else:
token_probs = None
train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
logger.info("Data loader created.")
# STUDENT #
logger.info(f"Loading student config from {args.student_config}")
stu_architecture_config = student_config_class.from_pretrained(args.student_config)
stu_architecture_config.output_hidden_states = True
if args.student_pretrained_weights is not None:
logger.info(f"Loading pretrained weights from {args.student_pretrained_weights}")
student = student_model_class.from_pretrained(args.student_pretrained_weights, config=stu_architecture_config)
else:
student = student_model_class(stu_architecture_config)
if args.n_gpu > 0:
student.to(f"cuda:{args.local_rank}")
logger.info("Student loaded.")
# TEACHER #
teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)
if args.n_gpu > 0:
teacher.to(f"cuda:{args.local_rank}")
logger.info(f"Teacher loaded from {args.teacher_name}.")
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(student, args)
if args.freeze_token_type_embds:
freeze_token_type_embeddings(student, args)
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
distiller = Distiller(
params=args, dataset=train_lm_seq_dataset, token_probs=token_probs, student=student, teacher=teacher
)
distiller.train()
logger.info("Let's go get some drinks.")
if __name__ == "__main__":
main()
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/squeezebert/configuration_squeezebert.py | # coding=utf-8
# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" SqueezeBERT model configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
),
}
class SqueezeBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SqueezeBertModel`]. It is used to instantiate a
SqueezeBERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the SqueezeBERT
[squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the SqueezeBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`SqueezeBertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
pad_token_id (`int`, *optional*, defaults to 0):
The ID of the token in the word embedding to use as padding.
embedding_size (`int`, *optional*, defaults to 768):
The dimension of the word embedding vectors.
q_groups (`int`, *optional*, defaults to 4):
The number of groups in Q layer.
k_groups (`int`, *optional*, defaults to 4):
The number of groups in K layer.
v_groups (`int`, *optional*, defaults to 4):
The number of groups in V layer.
post_attention_groups (`int`, *optional*, defaults to 1):
The number of groups in the first feed forward network layer.
intermediate_groups (`int`, *optional*, defaults to 4):
The number of groups in the second feed forward network layer.
output_groups (`int`, *optional*, defaults to 4):
The number of groups in the third feed forward network layer.
Examples:
```python
>>> from transformers import SqueezeBertConfig, SqueezeBertModel
>>> # Initializing a SqueezeBERT configuration
>>> configuration = SqueezeBertConfig()
>>> # Initializing a model (with random weights) from the configuration above
>>> model = SqueezeBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained
checkpoints.
"""
pretrained_config_archive_map = SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "squeezebert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
embedding_size=768,
q_groups=4,
k_groups=4,
v_groups=4,
post_attention_groups=1,
intermediate_groups=4,
output_groups=4,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.embedding_size = embedding_size
self.q_groups = q_groups
self.k_groups = k_groups
self.v_groups = v_groups
self.post_attention_groups = post_attention_groups
self.intermediate_groups = intermediate_groups
self.output_groups = output_groups
# # Copied from transformers.models.bert.configuration_bert.BertOnxxConfig with Bert->SqueezeBert
class SqueezeBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
]
)
| # coding=utf-8
# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" SqueezeBERT model configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
),
}
class SqueezeBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SqueezeBertModel`]. It is used to instantiate a
SqueezeBERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the SqueezeBERT
[squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the SqueezeBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`SqueezeBertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
pad_token_id (`int`, *optional*, defaults to 0):
The ID of the token in the word embedding to use as padding.
embedding_size (`int`, *optional*, defaults to 768):
The dimension of the word embedding vectors.
q_groups (`int`, *optional*, defaults to 4):
The number of groups in Q layer.
k_groups (`int`, *optional*, defaults to 4):
The number of groups in K layer.
v_groups (`int`, *optional*, defaults to 4):
The number of groups in V layer.
post_attention_groups (`int`, *optional*, defaults to 1):
The number of groups in the first feed forward network layer.
intermediate_groups (`int`, *optional*, defaults to 4):
The number of groups in the second feed forward network layer.
output_groups (`int`, *optional*, defaults to 4):
The number of groups in the third feed forward network layer.
Examples:
```python
>>> from transformers import SqueezeBertConfig, SqueezeBertModel
>>> # Initializing a SqueezeBERT configuration
>>> configuration = SqueezeBertConfig()
>>> # Initializing a model (with random weights) from the configuration above
>>> model = SqueezeBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained
checkpoints.
"""
pretrained_config_archive_map = SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "squeezebert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
embedding_size=768,
q_groups=4,
k_groups=4,
v_groups=4,
post_attention_groups=1,
intermediate_groups=4,
output_groups=4,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.embedding_size = embedding_size
self.q_groups = q_groups
self.k_groups = k_groups
self.v_groups = v_groups
self.post_attention_groups = post_attention_groups
self.intermediate_groups = intermediate_groups
self.output_groups = output_groups
# # Copied from transformers.models.bert.configuration_bert.BertOnxxConfig with Bert->SqueezeBert
class SqueezeBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
]
)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/research_projects/lxmert/demo.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# %pip install-r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PyTorch version 1.6.0 available.\n"
]
}
],
"source": [
"from IPython.display import clear_output, Image, display\n",
"import PIL.Image\n",
"import io\n",
"import json\n",
"import torch\n",
"import numpy as np\n",
"from processing_image import Preprocess\n",
"from visualizing_image import SingleImageViz\n",
"from modeling_frcnn import GeneralizedRCNN\n",
"from utils import Config\n",
"import utils\n",
"from transformers import LxmertForQuestionAnswering, LxmertTokenizer\n",
"import wget\n",
"import pickle\n",
"import os\n",
"\n",
"\n",
"# URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/images/input.jpg\",\n",
"URL = \"https://vqa.cloudcv.org/media/test2014/COCO_test2014_000000262567.jpg\"\n",
"OBJ_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/objects_vocab.txt\"\n",
"ATTR_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/attributes_vocab.txt\"\n",
"GQA_URL = \"https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_label2ans.json\"\n",
"VQA_URL = \"https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_label2ans.json\"\n",
"\n",
"\n",
"# for visualizing output\n",
"def showarray(a, fmt=\"jpeg\"):\n",
" a = np.uint8(np.clip(a, 0, 255))\n",
" f = io.BytesIO()\n",
" PIL.Image.fromarray(a).save(f, fmt)\n",
" display(Image(data=f.getvalue()))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# load object, attribute, and answer labels\n",
"\n",
"objids = utils.get_data(OBJ_URL)\n",
"attrids = utils.get_data(ATTR_URL)\n",
"gqa_answers = utils.get_data(GQA_URL)\n",
"vqa_answers = utils.get_data(VQA_URL)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading configuration file cache\n",
"loading weights file https://cdn.huggingface.co/unc-nlp/frcnn-vg-finetuned/pytorch_model.bin from cache at /home/eltoto/.cache/torch/transformers/57f6df6abe353be2773f2700159c65615babf39ab5b48114d2b49267672ae10f.77b59256a4cf8343ae0f923246a81489fc8d82f98d082edc2d2037c977c0d9d0\n",
"All model checkpoint weights were used when initializing GeneralizedRCNN.\n",
"\n",
"All the weights of GeneralizedRCNN were initialized from the model checkpoint at unc-nlp/frcnn-vg-finetuned.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use GeneralizedRCNN for predictions without further training.\n"
]
}
],
"source": [
"# load models and model components\n",
"frcnn_cfg = Config.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\")\n",
"\n",
"frcnn = GeneralizedRCNN.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\", config=frcnn_cfg)\n",
"\n",
"image_preprocess = Preprocess(frcnn_cfg)\n",
"\n",
"lxmert_tokenizer = LxmertTokenizer.from_pretrained(\"unc-nlp/lxmert-base-uncased\")\n",
"lxmert_gqa = LxmertForQuestionAnswering.from_pretrained(\"unc-nlp/lxmert-gqa-uncased\")\n",
"lxmert_vqa = LxmertForQuestionAnswering.from_pretrained(\"unc-nlp/lxmert-vqa-uncased\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# image viz\n",
"frcnn_visualizer = SingleImageViz(URL, id2obj=objids, id2attr=attrids)\n",
"# run frcnn\n",
"images, sizes, scales_yx = image_preprocess(URL)\n",
"output_dict = frcnn(\n",
" images,\n",
" sizes,\n",
" scales_yx=scales_yx,\n",
" padding=\"max_detections\",\n",
" max_detections=frcnn_cfg.max_detections,\n",
" return_tensors=\"pt\",\n",
")\n",
"# add boxes and labels to the image\n",
"\n",
"frcnn_visualizer.draw_boxes(\n",
" output_dict.get(\"boxes\"),\n",
" output_dict.pop(\"obj_ids\"),\n",
" output_dict.pop(\"obj_probs\"),\n",
" output_dict.pop(\"attr_ids\"),\n",
" output_dict.pop(\"attr_probs\"),\n",
")\n",
"showarray(frcnn_visualizer._get_buffer())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Question: ['Where is the cat?']\n",
"prediction from LXMERT GQA: desk\n",
"prediction from LXMERT VQA: desk\n",
"Question: ['What is near the disk?']\n",
"prediction from LXMERT GQA: can\n",
"prediction from LXMERT VQA: cat\n",
"Question: ['What is the color of the table?']\n",
"prediction from LXMERT GQA: brown\n",
"prediction from LXMERT VQA: brown\n",
"Question: ['What is the color of the cat?']\n",
"prediction from LXMERT GQA: black\n",
"prediction from LXMERT VQA: black and white\n",
"Question: ['What is the shape of the monitor?']\n",
"prediction from LXMERT GQA: square\n",
"prediction from LXMERT VQA: rectangle\n"
]
}
],
"source": [
"test_questions_for_url1 = [\n",
" \"Where is this scene?\",\n",
" \"what is the man riding?\",\n",
" \"What is the man wearing?\",\n",
" \"What is the color of the horse?\",\n",
"]\n",
"test_questions_for_url2 = [\n",
" \"Where is the cat?\",\n",
" \"What is near the disk?\",\n",
" \"What is the color of the table?\",\n",
" \"What is the color of the cat?\",\n",
" \"What is the shape of the monitor?\",\n",
"]\n",
"\n",
"# Very important that the boxes are normalized\n",
"normalized_boxes = output_dict.get(\"normalized_boxes\")\n",
"features = output_dict.get(\"roi_features\")\n",
"\n",
"for test_question in test_questions_for_url2:\n",
" # run lxmert\n",
" test_question = [test_question]\n",
"\n",
" inputs = lxmert_tokenizer(\n",
" test_question,\n",
" padding=\"max_length\",\n",
" max_length=20,\n",
" truncation=True,\n",
" return_token_type_ids=True,\n",
" return_attention_mask=True,\n",
" add_special_tokens=True,\n",
" return_tensors=\"pt\",\n",
" )\n",
"\n",
" # run lxmert(s)\n",
" output_gqa = lxmert_gqa(\n",
" input_ids=inputs.input_ids,\n",
" attention_mask=inputs.attention_mask,\n",
" visual_feats=features,\n",
" visual_pos=normalized_boxes,\n",
" token_type_ids=inputs.token_type_ids,\n",
" output_attentions=False,\n",
" )\n",
" output_vqa = lxmert_vqa(\n",
" input_ids=inputs.input_ids,\n",
" attention_mask=inputs.attention_mask,\n",
" visual_feats=features,\n",
" visual_pos=normalized_boxes,\n",
" token_type_ids=inputs.token_type_ids,\n",
" output_attentions=False,\n",
" )\n",
" # get prediction\n",
" pred_vqa = output_vqa[\"question_answering_score\"].argmax(-1)\n",
" pred_gqa = output_gqa[\"question_answering_score\"].argmax(-1)\n",
" print(\"Question:\", test_question)\n",
" print(\"prediction from LXMERT GQA:\", gqa_answers[pred_gqa])\n",
" print(\"prediction from LXMERT VQA:\", vqa_answers[pred_vqa])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# %pip install-r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PyTorch version 1.6.0 available.\n"
]
}
],
"source": [
"from IPython.display import clear_output, Image, display\n",
"import PIL.Image\n",
"import io\n",
"import json\n",
"import torch\n",
"import numpy as np\n",
"from processing_image import Preprocess\n",
"from visualizing_image import SingleImageViz\n",
"from modeling_frcnn import GeneralizedRCNN\n",
"from utils import Config\n",
"import utils\n",
"from transformers import LxmertForQuestionAnswering, LxmertTokenizer\n",
"import wget\n",
"import pickle\n",
"import os\n",
"\n",
"\n",
"# URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/images/input.jpg\",\n",
"URL = \"https://vqa.cloudcv.org/media/test2014/COCO_test2014_000000262567.jpg\"\n",
"OBJ_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/objects_vocab.txt\"\n",
"ATTR_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/attributes_vocab.txt\"\n",
"GQA_URL = \"https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_label2ans.json\"\n",
"VQA_URL = \"https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_label2ans.json\"\n",
"\n",
"\n",
"# for visualizing output\n",
"def showarray(a, fmt=\"jpeg\"):\n",
" a = np.uint8(np.clip(a, 0, 255))\n",
" f = io.BytesIO()\n",
" PIL.Image.fromarray(a).save(f, fmt)\n",
" display(Image(data=f.getvalue()))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# load object, attribute, and answer labels\n",
"\n",
"objids = utils.get_data(OBJ_URL)\n",
"attrids = utils.get_data(ATTR_URL)\n",
"gqa_answers = utils.get_data(GQA_URL)\n",
"vqa_answers = utils.get_data(VQA_URL)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading configuration file cache\n",
"loading weights file https://cdn.huggingface.co/unc-nlp/frcnn-vg-finetuned/pytorch_model.bin from cache at /home/eltoto/.cache/torch/transformers/57f6df6abe353be2773f2700159c65615babf39ab5b48114d2b49267672ae10f.77b59256a4cf8343ae0f923246a81489fc8d82f98d082edc2d2037c977c0d9d0\n",
"All model checkpoint weights were used when initializing GeneralizedRCNN.\n",
"\n",
"All the weights of GeneralizedRCNN were initialized from the model checkpoint at unc-nlp/frcnn-vg-finetuned.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use GeneralizedRCNN for predictions without further training.\n"
]
}
],
"source": [
"# load models and model components\n",
"frcnn_cfg = Config.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\")\n",
"\n",
"frcnn = GeneralizedRCNN.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\", config=frcnn_cfg)\n",
"\n",
"image_preprocess = Preprocess(frcnn_cfg)\n",
"\n",
"lxmert_tokenizer = LxmertTokenizer.from_pretrained(\"unc-nlp/lxmert-base-uncased\")\n",
"lxmert_gqa = LxmertForQuestionAnswering.from_pretrained(\"unc-nlp/lxmert-gqa-uncased\")\n",
"lxmert_vqa = LxmertForQuestionAnswering.from_pretrained(\"unc-nlp/lxmert-vqa-uncased\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAGPAlgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDA1q3ik8VajNKu9V8pQvHUoDn9KbHZWxCgwpl84+UcVpz6Ne3/AIvvjbywqrxoxEhPZVHTBrTi8HaoRgXFp/303/xNdrnCPKpLov636r7iDn1srXA/cJnbn7op4srXk+RHjGcbR6/SumTwPqpx/pFn0x99un/fNWI/Auq4P+kWfTA+dv8A4miNam3Zr+vvCx55qOmW0944WJQ4ij2YAAGWbP6CmTaZZxwtttFO+ZfLyQMDZnk4zjOfyrtrr4da1Lq0Zi1CziZ4tpGGYEcnutOPwr19txbWLNt3qrHB9RxweTyKzVak3Ll31X9a+noZxfM3Z7M4w6RaQy4URqxRkYIwIPBBwDyP1rF162gJ8qNcDbGm44z2H4cV6efhVr7bd2sWZK9G2tn8TjJrG8R/CnWbXRrm7a/tZ2Tb8q7gT8wHGRinKUJSSpx3f9ItK2rZxV9Z211HeWwREFrMFQiILsX5sjI5bgZ59Kj0SCGOZEEgNvJliDApLEYBUknK9uR612a/Dnxnf21tOYrXBAkBDoN+R1YZ54P61Inwy8ax7vKgs4wc4Csnyk9SCTkH8at1YKrzdvLz/pDtocbZWkUcUiuIzAFZ5N0I3PnPBbqGyDwPSs+30W1lklhDF5hB5qKFwM4BxnPpn/PFehR/DHxtHbrbiK0MSqVCsY269TknOaU/CvxfBOsltDarIqIolEik8KOOTjqPSo56b5ey3/ry6BY4+LQbSy1OCaLcVS5gWMk9Tvwx/MfrTU0WwuLwTWv2iMLcPHJj72euQR0Fdmfhl43aKOMRWo8tw6sJFzuBBzyfUUifC7xnG+5be0ALmQr5i4Lnq33s5/Stfb0dktN/61FZnHS6HYywafAyGKTY2WBHzAFyeuME46k8cCqF5pun2tutwkUchZthi88OF685XFdrefDnxRp1nF9qn0+zgSX928txGgDcnaGZvqcfWqLeENSlGJtV0CRePlN7AoyO/wArConUhKOi1/4C/rzuO2pjixt/tX9lJCgtmt9+4qN24jOc9fbHSo9KsrVXlmWK1jVcIJTlwrZHBDZ5PqB61vHwrrBi8v8AtzRfvbt32+Dd1zjO7pnnFOXwrqaODHqnh9F43Ri9g2t06gv7VXtYcydvw/rYLGNNaJb37SRW0EYZsyFkBCqAMtznaDntz0ra8N+HbC8068uDEHHnFo9wOSCAcde2G/KsKe3137ZcQxXdgV8xhhWikVyuckE5zj2NaWhXmvabBFBA1lLtle4CqyHzA3BBAP3eD0x1NKVWN9G1v/Wn9XGovqdUfDOkCBYHgiVhctGHCZJOF6nPTNNt/DWlN5az2se3E3CpyCq565BP/wBb3rGtL7Wp0hBv7EML5V+aWLJZ/X5hwNvt160y31nW5r6KGO601mV5Dh54wrBh8wY7uAAD0IqfbO+7/EfKbUPhHT50V47QkOSIyIyRx/eOeP1qC10DSmaR5LJGWNC+3JGeg9fes6bWNUtkXfJpE0UoLwsJ1IQZwQPnB69mz+tQpf6vp/lzvqemyeZHu8hpozvU8YOMY/MGl7V3Vm/xDlNe60DSlMTpZIqyRh9uScHJHr7VNceH9HaDdb2NsVULuKs+4HHOcnHX0rFm1HVriKe5W90tkRFXy0nQeSCRjGTz6dW6n601ta1i4hWK3/s+PewUtDIpMhHblj+QxR7WWvvfmHKdTZ+HNFl1qxENhbNbm8jQlGfOCw4OT9eleof8IP4a/wCgTB+bf414lZaxrM9zE8Eun2YgdZ/3Tod7KwH8THOM9B+VdMPHmugzJJr0KyoBhBDEeSQPm446+h5wO9N4iqn7s2vmw5E+qPR/+EI8Nf8AQIg/Nv8AGj/hCPDX/QIg/Nv8a89uvGPiW1vmtG1y2eQEgeXHGQ4Hdfl5FVf+Fha75vl/8JBaB87dpWHOfT7tH1mv/wA/H97H7Nd0emf8IP4a/wCgRB+bf41h+HPDOjyatrkTWS7IrgKih2G0Zb39q5q98Z67ZR7/APhKtLmAfy38kxHY/ocqPfkZHB5rN0DxHrTTXt1H4l0yB7u4ZY0laPdMy5Jx8pA+91OAa1hiq3JNOo+nV9xOmr7o9Z/4RPQ/+fEf9/H/AMaX/hE9D/58R/38f/GvNoPG2tXFs0kfizTDKqM5gIQPtXJJzs29BnGa7bSvEcV5p9mz+ItKe4khRnXz4927aCeB+NZqtXf/AC8f3spUovqv6+Rp/wDCJ6H/AM+A/wC/j/41Q1zwtosegai62QDLaykHzH4O0+9XP7S/6jOn/wDf1ar31yL2wubQ61YDz4mjyJFJ+YEdPxqufEfzv73/AJBKlFJvmX9fI4a08GaJd28Oy02verG9ufNb5Quzzu/TLMef7tRad4W0a9JDWFvHFctKbcmWYyhVzjbjK4H+11rTj8KyRCIL4vtovKDKilgCgOcgfNxnJ/Onw+GZreEww+NLaOIncUR8Ln1wGrJqr3f4k04xlFO6M3+z7X+y9vlcf2Js+8en2nOPzrMsLZdN8Pareaarw3qtEoliYh1QhycHqAWCA/l3rov+ETO3b/wl9nt2eXjcMbc52/e6Z5x61DL4Yk0+0ubi08X2sUqwuQYWCseOmQ1EYVW0r/n/AJF8i7ox7+MQ6tKsCBHEmSkYxtkPLAAdMNnitG/vZbnR7O7X7R5sNwV864m8xy2AflOB8ox05wTUeheGGl0aCT/hK7WLe5kKFhkMGOGPzdeTz71qTeGp55o5pvGtvJLGco7yZZT7EtxRUoTjNp/1+AKKt8SMXxBI82oxSysXke0t2ZmPJJiTJq/ZaPaXWmRziEmS4hMMQDH/AI+BvOevOQijHT56t3Xhqe+2fa/GlvcbM7fOk37c9cZbjoKZH4VaJY1j8YWiCN/MQKwG1uPmHzcHgc+1R7GX9X/yHyL+ZGdql/Y6RCLT+z1u4W1MQAGVlAG0KzDHOTjI7c9Kh0+8gsNRtgtgJXN3qNqWeRvmWKJSvAx13Efj9Kp6j4YM/iSLS/8AhI4ZRJALhHTkK4Ylm+912q3PWqz6DPNNpc0HiBUkvbhriEnqjYUMy/N95mBHHUqKwd1JoOUu6XeaXd6daXt3BaW6Xc0iSIXuGaFVxny9iMC2DnDnnI+tZWg6441C72Wf+kRWUs9vh8lmC5BAx125I7jFaU+jalZztMviS8inura4kuPMUwu5RSQXUOefc1zd9ozaffWssOspFKLeGVXT5GUlAcgg+/WhJvZisaPiDX5xaaPd3Fk5ubq0MkrM5y37x1Uk45JUKfpiuhvdVeXS7yxijlkuYLK1d7Zvlt4QTH88b92O4ZyB95uTisLVdCvozql5Prrsl46pBM5P+lITkYO75htAz17CnaTpNzcLBpVz4q8tlnMZsZzIfLC9lXoDnIxxjHvRZ2vcfKb91fT6fpVlLb2lvc2tjqiBTBeRuZBhcv8AKSeT26gYz0rU/wCFlN/0BW/8CP8A7CuT0LwvdQnTpI9QaeGC9MrNGh8mErjDuCehAzzjj16U7RbaW7vzZuY2NxE8cZYdHxlSOOOQB+NS03e0hqKOth+Ik1xPHBFoZaSRgij7TjJJwOq0i/EWV5vK/sYBs4+a6CgfiVxVMw263thd2qQqtxfQxRhVHyrG5DH6keWT9TVW0T7dPaSzQWwIvniISIAMm0EA8c4Pc889am0v5h8qNVviNKiozaG6q4ypM5AYZIyPk55BH4Uz/hZLf9AY/wDgR/8AYVm26S3NvoS3SxGyWNkkkEC/6wPJtUtgdfl4yM5z3zWfr0DRpagRSxT/AD72ltFtw4424VSRxzzxnimou9uYOVeR0/8Awn919m+0/wDCPTeRnHm+adufTOzFFv49vLxmW18OzzsoyRFKWI/JKxhHc+ULrd/xL/7LMW7+HzNhG36+Zzj8axdO064n1O3guSY4mxJISuCIgNxbp/dBNCjKz94OVeR2h8bag872y+GrkzoMtEHbco9xsyK09F1yTWbOSc2LQFJTGU37ugB9B61x1nJNqa6pNJDNc+dPG32W1ba4GWwc4PyqOMY7jpiuo0JbtzqjJcRMDfyHKjg8L9f5mhQlJ257DUY36fibJmf/AJ4tTTK//PFqQx3v/PZPy/8ArUwx3n/PVPy/+tVfV5/8/fwX+RfJDuvvf+Q4yt/zyamGVv8AnkaQx3n/AD1T8v8A61MKXf8Az1T8v/rUvq8/+fv4L/IOSHdfe/8AIguXYzQZQ/e/wqYu39w1WuVuPOhzIpO7jj6VKUuf+eifl/8AWrGnQnzz/edu3b0OajCHtamq3XV9vQUu39w0wsf7ppClz/z0X8v/AK1MK3H/AD0X8q2+rz/5+/l/kdPJDuvvf+QpY/3aYTntSFZ/761GRMP4l/Kj6vP/AJ+/l/kHJDuvvf8AkK1RsPelKy/31/Ko2En94Uvq8/8An7+X+Q+SHdfe/wDIfEv75eaglQeY/wAw6mpIdwuUDEHrTJR+8b6mnOjUjFLn/BHLJKFfZP3V37shMa/3xRQV+YfWiuDEVKtJpKX4I6acYzWxl6af+Kru/wDriP8A2WuvgPSuOsDjxXd/9cR/7LXXWx6V7WI+KP8AhX5HIjTi6VbjqnFV2PoKwAhb/kLwf7h/rWkBWc//ACF4P9w/1rSA4rGjvP1/RHNQ+Kf+L9ELWR4pH/FNXn0X/wBCFbNZHir/AJFq8+i/+hiu3Dfxoeq/M3exd0kf8Sex/wCveP8A9BFXRVPSf+QPZf8AXvH/AOgirtRV+OXq/wA2NBS0UuKzGGKMUUtAHnvxgGfCNoMdb9P/AECSvNG061VV3Wah2bCgSt+p/CvU/ivaT3nha1jt03uL1GIyBxscd/rXF6v4K12DRbuSa1crHEzbmmjyuB7GuuE6cKSc+77/AKee/lsLW5z32KzJVFsxvLFSDIcAgZ60R2Vk5QmzCq+QD5hzkdf5Gku/BNnYyXobVZpFsbgQzlbUZYtnBQb+funOSMds1D/whXkTyx3l28SrdtawvHFvDsMEseRtXDL6nnpXP/aWGlHRP/yb8Pw+RXIzLtZreB5ikZDpMXjHULuIyM/QUjw2EsrjHyJGscWSwHH075zWnaeASyQx3M7R3s8ssKxLCGUOhx8zbhgE9CAT7VY07wfBJZxyajFLtbTZJo/Jt03RuJmXnDLvOBkEnoQOwqpZhhI1OZRvZ+euvTv+o3CWxkGS3a4MoTINzHN8uT90e/1qpbxW0F0ZHGFcOhIzwGBUn9a1k8BySWy7ZGF3JC88MLQrtKLnAZt3ysQpIABHTJqHU/BclhDbiItcXEsMUzx+UqpGHQMRuLZJBOOmCOc9qn65h5LljH3vn9/p2/UXKyhLb2jQwW4ZHRNx3fMBk9h39OtSKIRZvDJceahTasJ3HYc9eeBjnpW8ng6I20dgdLT7Q+nvdG8835llCs4QLnbtwoXpnJzmsrSPDdwl8BdafYNG4I3XbuI075PlHd7d+tSsZCpFe78P3tf8Fpvv+Actioy26WDQNMzxnBjg3NhTnOeeB36etLaNa2scy42OuHjxk/Pgr+H3s/hW+3hOCDxJdW4srBrFtpilvpJQqggE7fLO7BzwWHTGar2/hAw+OIYUs1fT11FVCz7CWi8wcMO/FKpj4zjLljo/e6LS3+fS4KBnyPbM8nl5QNCyADPVm3H+eKrt9neB04JeBIxnP8JBx+ldNYeBo4tdtLiMx3dqLsxTxSQqqg4JGBk7lODjODx0FZ1p8Pbq70qW5VZ1mjjeTabceVheSPMDcHAz93HbNaf2jhV9nTS979X+H/DBySM92tJLm5Y+WUuDuYtuAxnocc9SOnpUEqxNfrdbFYptYFA20hQPU57d63LfwMtpfWCzuZbkywvLB5KmMKxBKli3JweRtx71Brfgeazu5MIvnPK7fZ0VQIkz8uTnqR2A4GOe1OOOw82ocvR2311/qz2/AOV7mZLHZeRLHG6us0yuSQw24zjP/fR6UWElnaqYt6IEkLOGQsxDAfcOPlOOO1dHa+C7U21pYy6aGnu7WSZrvzSGicb9ihQdpX5RnIJ+Y4IxXO6Nocn2+4WXSI75lPlrFI5A3Z4PysCehGAe9ZxxSqUXGMdV6d3rfz212t5hy2Yy1NvDbyqZWWJ1IaFWbDkjj29Oa7TwBpdhda/YieHzIihG0sRz5ZJ6fh+VUZvBtnZz6jdDTEuEhWBUtHmPlrK65cblYMQpDAc+nJ79J4K8EPD4wmeGGVNPjWOTPmKTGJImYL6nBOPwprMee6px1aXZaqzfnfVfeDhbc9G/4RrQf+fFf+/j/wCNIfDuhINy2QDDkHzH6/nUl5olvaRLKkkpO8Dk9jUdof3Df7xrKOLr+0UJ6X877EziuRtAvh/RJhvlslZz1PmN/jU0fhXQX6WK/wDfx/8AGnxn5BV61NdDrVLv3n95nRS9nH0RS/4RHQv+fAf9/H/xqrqnhTRE0m8dbEBlgcg+Y/XafeukHSqmrf8AIGvv+veT/wBBNVTrVOePvPddfNGlkc14Z8LaLP4dtJJLIM7BsnzG/vH3rW/4RHQv+fAf9/H/AMaXwn/yLFl9G/8AQzWzWmJrVPbT957vr5gkrGJ/wiOhf8+A/wC/j/40f8IjoX/PgP8Av4/+NbdFYe2qfzP7x2Rw2q/Drw/qWrws8U8X7rbtil46k55z61EfhL4a9b3/AL+j/wCJrsZP+QnF/uH+tWzWFOrNud29/wDI0klZehwR+E3hv1vP+/o/+JpD8JvDfre/9/R/8TXdmm1pzy7kWOF/4VN4b9bz/v6P/iaxPE/w50TStNintXuw7TKh3SA8EH29q9UrmvHI/wCJJD/18r/Jq2oScqsU2J7FY/D7Sf8An4vf++0/+Jo/4V9pP/Pxe/8Afaf/ABNdbiiuco5L/hX2k/8APxe/99p/8TSf8K+0n/n4vf8AvtP/AImutpKAOS/4V9pP/Pxe/wDfaf8AxNJ/wr/Sv+fi9/77X/4mutpDQByX/Cv9K/5+L3/vtf8A4mk/4V/pX/Pxe/8Afa//ABNdZRQBx8ngHSgf+Pi8/wC+1/8Aia1tJ0i30W0e2tnkdGcyEyEE5IA7Aelakn3qiNADDTTTjTDQAw0w080w0gKd1/r7f/e/wqY1Dd/663/3v8KmNYU/4k/Vfkc9H+LU9V+RGajNSNUbVsdIwjFMIp5phFAEZqNgKlNRsKQEcfN2n0NMlH7xvqafH/x+J9D/AFpJRl2+proqfBEVT+Mv8K/NlfHzCin4+YUV42O+KJ10NmYFtNHF4ruvMkRP3I+8cf3a6i2vrXvcw/8AfwV5X4ltluPGcxc4jS2Qt/IVDFp9qw3EYUttU88/rXvVYczi/Jfkec6lrr/M9thv7Pj/AEuD/v4Kux6hZf8AP5b/APf0f414emmW3AZMMSRjJ7fjUiaZaHGE4Oe57fjUexf9WJ9sv6ue0NqFn/a0Dfa4MBDz5g9/etIajY/8/tt/39X/ABrwQ6Zam4UiP5dpPU/41ONNs8cxdvU/41nSoWcvN36GNOXLzPu/8j3b+0bH/n9tv+/q/wCNZPii/sm8OXird27EhcASg/xD3ryD+zLTH+p/U/41U1TT7VNOmKxYIx3PqK6KUHTqRk11RsqnNome76VqNiukWQN5bgiBMgyr/dHvV3+0rD/n9tv+/q/414Fb6bafZogYQTsXJ3H0+tSjTbP/AJ4j/vo/41NSC9o7vdv8wjNuN0j3n+0rD/n9tv8Av6v+NH9pWH/P7bf9/V/xrwcabZ5/1I/76P8AjR/Ztn/zxH/fR/xqHTX42Gqj7dLnvP8AaVh/z+23/f1f8aP7SsP+f22/7+r/AI14N/Zlp/zxH/fR/wAaU6daFi3kjOfU4p+xdrk+21t/mep+N76zk0WFY7uBz9pU4WQHs1aHiK9tZ/D2oxRXMMkj27hUSQEscdAB1rw3V9OtvsybI9sjSgDGeSe3XirU+nWqRMyxYI9zTxFN+wt2uOFW8kjT1XWJreK4ku7VlN9MJX2Qnhhk4GT0+Y1VPjdjNJI+nNLuk85VeE4R8YyPm9hwcjiszUNLtJbOIeT8zsq7txypPGevrXIw+Ur5aESeikkDP4Vz/VKPKrr+rL9LFKrNt2O5j8X3VvNBMbGSRopGlQshySxyd3/1qSPxpdRCFRpRkSOFodr7gGVmLHOOep7Y6CubawtvMZmREEUId0Z22qxIGCRz3+varOl28cc9yGjQAhGUIxK4IPIzzWtTB0lq0N1pSlZG1/wm18sOxNIjDqjRpKS25EbOVHbueTk89apXXinUbqRXbT1UrGkYxu6KoUdvQVL5UP8Ac/U0GKL+7+pqI0KMXdLX+vML1H2E/wCEz1gWvkjT4N4jMQmKMXCHqvXHcjpnHGarxeKdRick6TaSLtC7XSXGR34YHJ+uPapzFF/d/WozHH/d/WkqFFXst/67jvU8hD4y1d7iWa4060nL7QqvDIBGFGAF2sOMYHOelVD4r1/+1F1BgrSrMJtphO0kHOMA9KsMkY/h/WomVP7tJUKK2XS3y+8L1PIkfxxr3nwSxW1vF5UplZI4X2yORjLZY/kMD2qOLxrrsNksH2eF5FgktxO0T7wjhgf4tuRuJBxnpnI4qFgoPSomx6VP1bD7W/r7x81TyJG8X60yW5e2ja5gKYuSjh2CYwGAbaeABnGcd6qX/iXXNQhVJy+9HZllVSHCnnZnuoPTOSPXFOYioy1UqNFO6W39dxXqeRJF4s1uHTha+WHlSN4orpkbzY0bO5Rzt7tyQSMnBFVtH8R3+k30lzDYQyu0JhbzPN6k8vlXBD44yCB6DvSsxqtGx8yXnvWsMNRlTqadr/f6i5ql+hbi8R30FxcPHp0X2W4RVks284xcHIIJfeDn/a7kdOKj/tvV7i9urya5nieZgxCEooA6AD0A4FIjZBJNMmOYXPtWcKNNPmjv/XmJymrXSPqSKQz+FLKUnJMUZJ/Cqdqf3Df7xp+jv5vgPT39bdD+tR2v+pb/AHjWE/48Pn+hpL4Jf13LafdFXrQ9qz0PFXrM10S+JkUf4UfRGiOlVNW/5A19/wBe8n/oJq2OlVNW/wCQNff9e8n/AKCaqn8cfVfmiyj4T/5Fiy+jf+hmtqsbwn/yLFl9G/8AQzWzWmJ/jT9X+YlsJRS0VgMpSj/iZxf7h/rVo1Vl/wCQnF/uH+tWjWFLefr+iNJ7L0GmmmnEU2tiBK5nxz/yBIf+vlf5NXT1zHjn/kCQ/wDXyv8AJq3w38aPqJ7HS0UtFYFDaQ9KdTSKAENNp1JQA00lONYHiy+v7DTYX05mWd5wnyoHJG1jjBB9KTdlcDWbqajNeb/8JN4kZUcTSlXVnVvs64ZVzuI+XkDBye2Kc2v+Jo1heeSeKKYgJI9qoVs+hK81PM+wHoZphrhdR1nWrCKV/wC0/M2Xktrj7Ogzs2/N077ulJb6p4hubRJ11KFXlV3hhZF3yqudxX5cdj1Izg4zRzPsB3BphrgLLxBrl/M6LqCRrGhkkkkjUKijqThSfToD1qz/AGhrwnkV9Vt0gSJZjcsg8so3CkYTdyTjGM9fSjmfYZ1N1/roP97/AAqZq8+v9c1y31JLOScvOHAQRxo27OCpXA5yCCPrSjX/ABAwQhpyJGZUIt1+Yr1A+XkjvWMLqcnbe35GNONqk33a/I701Gxrjr/U9fsoY5xNPJbNDFIZxbKEUuobbnGMjOKUXfiOazuru3N08NsyK4a0Af5lLZwARgAcnPRlPeteZ9jY6w0w1w8uua9FbR3Mv2hLeT7krW6hW+hxg1YsNS1jUITL/aMFvH5giRp1ADueijCn8zgDjJo5n2A60mmk1xUeua3LqAsFlb7SZPK2GNBhs4OTjilutbv4Z44odUhvHc4H2eLOD6fMgz+GRS5n2GdhHj7WmPQ0kgPmN9TWbpMt5b+JzY6pMk5FqJR5WMAnGOQBngkelb7yWW5swydfX/69a1aj5Irlf9fMmetVf4V+bM0jkUVoK1i8iqIXyxA6/wD16K8fHVPeV00dVF2TPLtYtLy58cSraxCUNbqrKWAB79yParMOgawDgWKYzkDzF4P/AH1WhF/yPr/9cf8A2UV1kZwwr6HEScHCz+yjgjGMm7rqchF4a1xtp+xA4Of9an/xVWofCmudPsIwAf8Alqnp/vV3Ns3StOE8VjGrJO9xypxatY8xPhTXft0afYRkofl81Pf/AGquDwdr5/5h4/7/ACf/ABVegk/8TaD/AHD/AFrWWs6Vabctev6IwpRi3O62f+R4BFqDzuI4rC6kfBO1ApOAMno3oKo6nqQfT5R9lnGccnZ6j/aru/Ddrb20dhstDLJcWk87XG5sqQJFwBnGBtAORnJqrrOn6XD4ckWWWESvZLOrgTGQyEBguAuzbn5fbrntXSpLnS8zp5YrZHL2N+8sMEUVhdSSFAAECknj03U/+1R/z53H/jn/AMVXb6VaWYutOnsIoktVcRFyZBKpMZIEgbjPB+7xxWPq9tFZ3KW8MZ2KgInJz54P8Y7AegH481Mql5Xf9fgJQglaxgf2sP8Anzuf/HP/AIqj+1R/z53P/jn/AMVXcTtIYbm0Of7Pj02KWNf4Q5VDuH+0XJBPuRVPw3b5u/tavAZopEWJJJUQ5J5bDEZwAencipc0/wDhylGK2Ryn9rD/AJ87j/xz/wCKo/tYf8+dz/45/wDFV2UNzNp3iW5RvtTRyXJBS1nChzuyAcAhhg/d96oxW0TeJTaXKRiNrloWCEhVJJUEewPP4VTqt73JVOC2Rxmr6rm0TFpOGEgILbeDz0w1W5tTDxEC0uBn/c/+KrqvEei2Nv4fknki2yW6JDJlz/x8Eocnnsrvx0+Srtxo9jcXM9l5BtUgvobbz95JkVn2knJxnA3DGBilVqc1K3r/AFsNQgndI4K61Jm02REtZxIE+Vjs4Pr96uXt3urMQXX2A7SGVJHB2uR1IOeoyOnTivYrfTLLUIYy+ntbASyxG3V2zKFjLBeSTuyApx/eHArlk02x1PT9EE9nHYwJHqE4gLSmOQpt6EbpNvGTjJ4bGOxGpZLy/rsDhDXTc4lryRSGW0UI6bXjzlWHB/vZ6gd6sWOpGOSV54mXcFVVTGABkY610U1n4fW2vb6K3t7xbfTFn8mF7hIUmNykfyl9rlSrcgnuQD0I57X7S0tNZgFvH5FtcW1vceWGLeX5kaswBPJAJOM84qpVL6MuUYqV7F3+2IP7sv5D/Gk/tiD+5J+Q/wAa2fE8txNbeJ7e7z9ksNQjj05SPlhXc4VY/RTGM4HXANZfhK+v7FWu5L2W30O2lEl1Gp+W5bj9zt6OWAxg5AGSajmj2DQhOrwf3JPyH+NXbcG6t1mTAVs4DdeuK1/C0oXTdJtxLLCb+4uTHaQx5guhgKEuGzwAR6NgHOF61yunP/xL4h9f5mmpQ6r8Rq3Y1GtZD/En51G1pL/eT86rM+aiZqvmp/y/j/wB+72LTWUp/iT86iawm/vJ+dVmaoi1Lmp/y/j/AMAV49i02nzH+KP86jOmzf3o/wA6qk0w0c1P+X8f+AK8exaOmT/3o/zP+FVYNPmeaYBo/lbB5+tRmoI/vyfWt6Uqfs6nu9F18/QTcbrQ0DpU/Z4/++j/AIU06bNtMZePJHrVPGeKe67I2ArKMqf8v4/8Aio42WnVH0R4e1WGPwJYWzrIZEgCkqMjIP1p1vqESLsKvkn0p/gV/N+GNgfSFh+tTWn+pP1/pXLOVP28Pd6Pr6eRcnHklp/Wo06nCjFSkhI9v/r1btdZtl6pL/3yP8aF6VfsetdDlTv8P4/8Aii4+yjp0QDXrXH+rm/75H+NVdU1y1bSL1RHNkwOPuj+6fet4dKq6sP+JNff9e8n/oJq6cqfPH3eq6+a8i2422Of8L63bR+HLRGSYkBuij+8fetj+3rX/nnP/wB8j/GovCf/ACLFl9G/9DNbVXiZU/bT93q+vn6Ci422Mn+3rX/nnP8A98j/ABo/t61/55z/APfI/wAa1aSsOal/L+P/AACrx7GE+sW5vo5Qku0Lj7oz396n/t21/wCec3/fI/xq1L/yE4v9w/1qyawoyp3n7vXv5LyLm42WnQyjrlr/AM85v++R/jSf25a/885v++R/jWoaK35qX8v4/wDAIvHsZf8Ablr/AM85v++R/jXN+NNXt5tGhVUlBFwp5Ueje9dvXMeOf+QJD/18r/Jq2w8qftY+7+P/AABScbbGn/blt/zzm/75H+NH9uWv/POb/vkf41p0lYc1L+X8f+AO8exmf23bf885v++R/jSHW7b+5N/3yP8AGtQ0ho5qX8v4/wDAHePYy/7btv8AnnN/3yP8aT+2rb/nnN/3yP8AGtM0lPmpfy/j/wAALx7GZ/bVt/cm/wC+R/jXP+K9cijs7GeJJN8F7HLyBzgMfWuzxUM9pb3cfl3NvFMgO4LKgYA+uDWVadNQbUfx/wCAF49jy7UPEWkCwvra0ldhAohsf3ZG5H2+Yeen3Dwcffpk+saHDpc8NtMhMjQOgCzGQ7T82/d8uRk/dH416C2i6V/0DLP/AL8L/hUZ0bS/+gbZ/wDfhf8ACnePYWh5lrWtWN1bzrBIzltTuJwNhGY2C7W59cHjrVuw1GxEOmXklxsmsIXj8goxMpLOykEDGMvg5I6d67XUdH0xbGUjTrQHjpAvqPanQaPphtoidOtPuD/lgvp9Kcork5l3G1pc890ae2srl5JLiFXltmWN3jZ0icnGHXHzDaD0DD5h7ir13qNhfNd2rXkaedBCDc+UwiMkfYKBlVweML1HQCu1Oj6Z/wBA2z/78L/hTDpGmf8AQOtP+/C/4VmSeZa1fJPrMU9ozbIVijjcjBby0VQ2Pfbmulvdf0pkvVt5DhIme0Gwj95LvEg6cYEvU/8APMe1btzpOmiaDGn2nLf88V9vapTpGm/9A60/78r/AIVlCV5yXp+RlTlec12t+RxU2r20jzKbgmM6RHaqCGx5gVMr0/vA89KL6/sb6DVIVu0jMrWssbOj4fy4mVl4UkHLcZwOOtdkdJ03/oHWn/flf8KYdJ03tp9r/wB+V/wrS5scnrOs2d3Z3j2v2JDdrGGj2z+cu0ggckxjGMZHbsM1n6fNYz6VHZXd2LUwXf2gMUZt6lQGA2g/MNoxnA56iu5Ok6cOmn2n/flf8KadK04f8w+1/wC/K/4UXCxxdrq8EXir+25CoSW7ldodpZkV884xtP3jxnt9Ksy63awSWT3Nw+q3UHnE3UbNGyhgoQBnXJK4YjI4LDHSuoOlad/z4Wv/AH5X/Cm/2Vp3/Pha/wDflf8ACi4WMjSrzT7zxPbNYLMFj01I38x92CoUY+6Onc9633++31NQ2tlaW9+rQW0MTFSMpGFOPwqeT75+tbVF7kSZq1Zf4V+bEh4uIv8AfH86KWH/AI+Yv98fzorwcz+OPp+p1UtmcdF/yPj/APXH/wBlFdWtcpD/AMj4/wD1x/8AZRXVrX0WL3h/hRx0936mhav0rVhPSsSBsNWtbtwK5kWybP8AxNof9w/1rXQ1jA/8TWH/AHD/AFrYjPFZUd5+v6I5aHxT/wAX6I4TRPBd5/Zzxxa/PFF5hzGkZCk4HON9R694Mu7LwzdomvT/AGcAZhEZCnLDtvxXZ6D/AMeT/wDXU/yFR+K/+RZvPov/AKGK9JRX1lR6XX6HTU0bOdtPBl/eafYzzeI7l2EKshdGYplR0JenN8O5HjSNtaYomdimAkLnrgb+K67Sf+QNY/8AXvH/AOgirtc9RWm15v8ANiucQfAFy1sLU69KbdTkRGE7QfXG/FNb4dyvKJW1t2kGMOYCSMdOd/bAruaWoA4uLwNfQeZ5PiK4j805k2RsNx98PzVcfDYhgw1cgg5yLfp/4/Xe0HPagDyrxf4LuLfSA8mtSzCa6VnVoz8zYb5j83J68+9a+o+A7l7ALLr00scQGxHiJC89hv4rX8d/8gOD/r5T/wBBat7UP+PKT8P5iniNMNzLf3gW5xq+Bbu6EEs3iCeSRFGxnjLFfoS/FeQ+LrDVLDxbcwz61dzS28u+GV3bcpIHK5bg8Dp6CvpK34giP+yP5V4n8XLT7P4tjnAwLiEHPqRx/SohrFBfU4vU01G5jjlutYurh7iIJK0rsxdQQwUktyMgHB7jNZ0tjLcMrTXjyMqKilwThQMADJ6AAACte/b/AESz/wCuf9BVEPxW1RJSsvL8jeskp2Xl+SEmXULmG3in1a6litv9QjuzCL/dBPy/hVi2vdcshKLXxDqMAlkMsgindd7nqxw3JOBz1qHfS76gzGwtqVtbzW8GsXcUE5JmjSRlWQnruAbB/Glt4/IgWLdu255xjvRu4ppagZKWpjNUZamlqBDmNRk0E0wmkICaYaU0lAhpqvH/AKyT61YNQR/fk+tdFL+FU9F+Ynuh4OCDTj86N25pv0pwOIz9ayhuyKmy9UfQvw0fzPhnbD+75i/yq7af6n8azPhQ/mfDlR/dlkH6CtO0/wBRj3/pXLP+PD5/oXL4Jf13LI/Sr9j96s9TV6xPz10S+JkUf4UfRGuvSqurf8ga+/695P8A0E1aXpVXVv8AkDX3/XvJ/wCgmqp/HH1X5oroUvCf/IsWX0b/ANDNbVYvhP8A5Fiy+jf+hmtqtMT/ABp+r/MFsJQaKSsBlOT/AJCcX+4f61aqrL/yE4v9w/1qzWFHefr+iNJ7L0ENJS0lbECVzPjn/kCQf9fK/wAmrp65jxz/AMgSH/r5X+TVvhv40fUT2OlopaSsBiGkzSmkNACUlKaSgBKDR3pD978Kyr/w5DW5UaozUjVG1aAUdS/48Jfw/mKdB/x7Rf7g/lTdS/48Jfw/mKWD/j2i/wBwfyrV/wAFev6F/ZHGmGnmmViSVLr/AF0H+9/hUpqG6/10H+9/hUxrCn/En6r8jno/xanqvyGHvUbU8jmmGtjpGn60w08mmGgBhxTTinHGaacUANT/AI/E+lK/32+tJH/x+J9D/Wlfh2+tdFT4Iiqfxl/hX5sIf+PiL/eH86KIOLiP/fH86K8DM/jj6fqdFLZnhdje+IJZbjU5bp0kW18xGEi7jyAMrnIBBOOmanOueMERWN1J8xAwHBYE9MgHIz71Auq2T288v2s5lsliEJZdqsNoOOc/w+g/Grt3r1pMu+O6A8yRGKgRALhgeo+bt3r15SqN6r+vvPKjKrfb+v66j7fXPFG+dZ9SYGOB5F8uVW5XscE/lULeLfGNvII31CVXwDt3DIz688H2qX+3rCC7ScTrJIsUgLMUGScYGFOOx96yL29tWvvOhuFKthgGcfKfTr0+tEHK/vL+vvLpuo5e8tDo7zxB4stQ0ya3JJLAwjlXGNpOeh3HIzkcgVLpnirxjfkqNZulfOBtQMoHqx3ggfgaxrvUtOK3TQ3ILXkiswLLhBkk459T3xUdjd6bbzGaS6JaJsqqlfnHbndx+tTHmUHpr/X9ehEVP2b7+nkv1/A29J8SeMJreZYdYuUZGbO1AYwQO7buOnoaoXXjPxne2U8UmpSyRBdzqW7Aj3qtp2oaavl3ElwqNG7MYgV2nJyBnOcduhqKyv7KK6HnzR+RIGjkAcfdYY/rW3PJTckv6+80fPeT3NiPxb48tokhGoTKI1RQoYcAj5R1qRvGHj9JEQ6nKWckLtkDDI6jIPaqr+IbN1t5fOj8wTh5PnHKgkr/AOhGmpq+n25jjF2JFLuWZnXIDKV4wT6//WFZ89TrFX/rz/4chSrW1j/X3/8ADlz/AITHx95oj/tSQll3AiQFceu7djH41Pc+LfHEbwJFq0xZoBJIWkG1TkjrnGOn51mLq1girb/awV8p0Mpdd2SQR3x29akGtWCShBdJt8hU3koxyGJ6E471LlUvpH+vv/ATlVvov6/roWl8X/EBi4GpyjyyFYtIoAJ6clu+Kjj8beO5Ltbb+1pllL7MM3Q5xzzVC61i2eK4CXSbmkjw25ASFVh/Ccdx0qOTVbRdbN2k8RQTB8FxyM5qlKdndL+vmXF1GnddPxLviDxR4qexiL61Lcwebj5htIcD03Hsf/rCr0HjHxddW53a9MZdrN5TKCvAJwTuz0HpXO6ldae1nFaQ3QKNceYzFkJAxgfxY/Mj8KtW99p0ensv2wRyuGEjDYxI7KPmyPyond0+Vrv/AEw9/l63v/VzaXxX4ya1Drr0om8nzhEAMBeuM7s5xz0x71L4zs/EU+gaLq+qTPKs8KFXL5+8ob+8cda5+31m2t9NK/a1eVozEFOwbQevzZyRz045r1LxXLY3vwd0ry7u3aaC0t2CLKpP+rXtmrw8+W/NFP1v/mhwjNt8x5Fdx3q29sXkbaU+X5u3HvVXZdf3z+f/ANetXUpF+x2HzDBj9fYVnCZe7L+dddStBS+BdO/b1OutStPr0/JEe26/vn8//r0uy6/vn8//AK9S+an98fnR5yd2X86j28P+fcfx/wAzL2b8yHbdf3z+f/16Cl1/fP5//XqbzU/vr+dJ5y/3h+dHtof8+4/j/mP2b8yHZc/3z+f/ANemE3COmSTk9zVrzE/vL+dQzOhki+YYz61Mq0LaQX4/5kuDXcdvn/uL+dJum/uL+dPLqP41/Ok8xP7y/nWn1mP/AD7j+P8AmVyPzGbpv7i0hab+4Kk8xP7y/nSb0/vD86PrMf8An3H8f8w5H5keZf7oqKMyb5MKOvNWN6f3l/OoI3UPJyOvrXRSxEfZz/dx2Xfv6kuDuhxeRBkqMVKeIz9aimdTGPmHX1p5dfKY7h19azqOEqUZqKTfNt5W8yJp7eaPe/g62/wFMv8AduHH/jorYtP9R+Ncv8G9StIPCl7DPdwRkXBIDyBTyvvXQW19ZqmxrqAMW4BkGTXlzf7+Hz/Q1knyS0/rU0B7VdsM+YayDqNkjYa8t1I7GVQf51bsdU08SHN9bD6zL/jW8n7zIoxfso6dEdKvSqurf8ga+/695P8A0E01NX03H/IRtP8Av8v+NVtV1bTm0e9Av7Uk28gAEy8/Kferpv34+q/NFcrsN8J/8ixZfRv/AENq2q5rwrqmnp4as1e/tVYBsgzKD94+9bH9r6Z/0EbT/v8AL/jV4l/vp+r/ADBRdti5SGqn9r6Z/wBBG0/7/L/jSf2vpv8A0EbT/v8AL/jWNx8r7BJ/yFIv9w/1q1WXJqdgdQjkF7bbAuC3mrgdfepzq+m/9BC0/wC/y/41hR3n6/ojSadlp0LZpKqHVtN/6CFp/wB/l/xpP7X03/oIWn/f5f8AGtiOV9i3XM+Of+QJD/18r/Jq2v7X03/oIWn/AH+X/GsLxpNHPoFvJFIkiNcrhkOQeG71vhv40fUmSaR1NNpeKaTWIC5ptBNQT3EdvGXc4ApATGkNVIL+KdcowNTeaP7woGS5ppP7z8KaJFPQg03fmY/Ssq/8NjW5AxqM04mmE1oBT1L/AI8Jfw/mKWD/AI9Yv9wfypupf8eMv4fzFLB/x7Rf7g/lWr/hL1/Qv7Ip60005qjJrEkq3P8AroP97/CpjUNz/roP97/CpSawp/xJ/L8jno/xanqvyGseKYaeaYeK2OkYaaTz0p5NRmgBD9KaRSkU2gBqf8fkefT/ABok++31oT/j7j+h/rRJ/rGHvW9T4Iiqfxl/hX5sWD/j4j/3x/OilhwLiP8A3h/OivBzP44+n6nRS2Z8+6bpNpvCzDfJ5O5l5AGSMc5681q/2FYDrb/XLN/jTNCs9QvZYY0tkZ3hI37gCQDj1x2rox4f1Y/8uYz3PmL/AI17VeM4yV+yKjPDuNkvz/q5hLoen5I+z547s3+NKmiabuwbcH/gbf410I8Pav2tAP8Atov+NOXw5q5b/j0/8iJ/jWcb31FVlScbQ3MNNC00yqv2bg8n52/xqZvD+lg/8e2OM8O3+Nb8fhnWTcIos+SP+eqe/vWing7XX6WR6YOJo+f1qI3aaXcUK1F1XLovL+umn9XOOtPD2mOFLW2csRje3p9adqPh3S47GRo7TDAdfMbj9a6zTvB2uzwl0szgOeksfXHuak1fwnrtrpU872ICptyTKh/iH+1XWoS9rZLW/wDkTOULxu9Ounn+OhyMGgaT9nTfZ/MEyx8xuwHvUp8PaSuQ1jg8YAlc5zx6111l4J12WyhlFgSJIV/5bR8jH+9U/wDwguv4ObFiTxkzx8f+PVjKM02mac+HvotPn/X+fQ4r/hHtJHymxG/OMea+P500aBpIB3Wag7sDMrAD8c13H/CC6/j/AI8WznO7z48/zoHgTXwOLFs5znz485/OptManQSs/wBf6t+JxI8P6Q23ZYgkg/8ALZu3404+HtI8gyCz/hyAZH/xrth4H8QBgxsCSARzNH/8VSjwNr4j8v8As/K4xzNH/wDFU0pdTOU6N1ZdV93X9DzfVtA063t12W/JYc729/ertx4e0pB+7tcbTyfMb/Gui8ReD9bstPjlmsjgzKuWmQ9jxwa07vwProgaRtNAIwc+bH/8VTqpqjf1HGpS9peWunbr3scUPD2lM0X+icHg/vG5OM+tes3Pg7SJ/h6BFZhZf7PRlbex52D3rnU8Ca60cb/2aOgbPmx88f71eo2Fo8ehWlnOu2RbVInXOcEKARRTTtqZ1qkWo+z001Pl7UYV+xWCleRHj9BWb5Mf939a6PxJbfZrpbfGPLZ1/IisPYc1vVS5/u/JFVpy59+i/JEHlR/3f1p3lR/3f1qXZS+WaysjLnl3IPJj/u/rSiGL0x+NTbDUdw3kQNJtzt7dO9FkCnJ9Q8mLHC/rUEsUYePC9T61B/aR/wCeB/76/wDrUn2uSaQbLfOwFyN3Ydal2sNqo9F+Zd8mM/w/rSmCPqF/WqsV9JLIEjtiWPQbv/rU6W+kgfbJbYJGQRICCPUEcGneOxXLVtzdCbyY/wC7+tVry1MwhhhTMkkgVRnuc1N505haZYIyiruOJ1JA+nXvUE11c24hulgQiKRX4kDfgQORmtKMoe0jfuOVOty63FbR4zZw+TJFLK87L5iOdoAUE5zjGOTnFV10dmfKzwtDsL+cC20AHB4xnPI7d6sx6qbe1imtLNokinOd0pLEsuDyAMcDrTH1J28q8cXzIGaNd14TIrcEkHbwOfSu9VqXK9e39bGPs59vMamlLLYsYijus+0zbiECbcknPQfhmoLKzjuJ2jdz5SKzuy9Sqgk4z64qd9fkYSr9jHlTSBpELcOoXGDx14zn15qva3D24kvEg3Qq3lNGzclXVhjOPQHn6Up16doWemtyeSb2Op8P6Fp97ZNOPPCN0VnBKnJBGQBnpnp3q6uiWUv70iUMmOd3HX6VleH9cmihkhtNKmkhTAADkkck5Y7ff26VoSaxeRKI/wCxpyRzlJNwJ/Ba53VXtb8y2f36baadynTm4N20JpNBsJt0rCYMSB9/j+VEPhqxc8+b1x9+oW1bUPs6v/Y8oUdvM5/LGafHrV/Egb+xpmD9AHyR9QBkU/rEXNJvS3Tvv/wAjRqKF7dC6PC2nY/5bf8AfdMn8MactvIwMoIQnJfgcfStoWviA/8AMs6n/wCA7/8AxNR3NtrotZjJ4a1MJsbcTA4wMc87a1VWlzaSf4/5bfiHJMxbLwvYm0jMok385w/HWrB8L6aSSBKM9g/Sr1hFrs1jHJH4c1F0OdrJA5GMn/Zqz9l8Qf8AQs6n/wCA7/8AxNXKrS5mm3v5/wCX9dRckzI/4RbTv+m3/fdJ/wAItp3/AE2/77rY+y+IP+hZ1P8A8B3/APiaT7L4g/6FnU//AAHf/wCJrD20eZWf9W9O4+SRjHw9YqwhHm7G5Pzc0v8Awi+nf9Nv++//AK1abWuvfaVz4b1PdjgeQ/8A8TUhtfEH/Qtan/34f/4mohWXNN82l/8AL+vuKlCWhU0rwlpk+s2MLibbJcRqcP2LAelepf8ACsPDvpd/9/v/AK1cFpaa7b6vZTN4a1ILHOjnMLgYDA9dtemf8JJqX/QvXf5n/wCJq62KcZ/upWX9eQRpTa/4P/BMfUPhtoFvp11NH9q3xwu65l7gEjtXP3nhixTwTp9yJbje8+CN4x1f29q63UvEOoPpd2p0C7UGBwWJPHynn7tcpeaxeN4KsITpE4RZ8iTJweX46UUMZV9rG8nb+vIt0JOD/wA1/mdifBGmY/195/38H+FNPgrTB/y3vP8Av4P8KU+JNRH/ADL93+Z/+JqJ/EuoDOdBuh7kn/4ms/rtb+d/18iPq8u34r/MyvEml6H4Z0aXUruS/dFYIqowJZjnA+7x0rw+/wDEd/PftcxyMsecLAWyNvoff3rZ8deNbzxTqTQqzQ6dA2I4A2dzDqzeprjWI7mh42t/O/6+Q/q8u34r/M34fGEbJmG0mVx1zMMZ/Klj8XT+cDPbs0XcJLhv5VzQAV+CMGn4B70vrtb+d/18g+ry7fiv8z13S206/so7y2ublo3H3S4yp7g8V2nh/RrCT/SI5bgsyYILg46e1eEeG9WOm3jQyORbzcH/AGT2Ney+E78rdFM8eWT+orKviq7pyTkzNRSZqDwfpxH+uu/+/g/woPg7Tv8Antd/99j/AArahl3oGHcZqbPArf63X/mYuVHK33hGwSzkYTXWRjq49fpTovCOntbxkzXWSgP3x6fSt3Uf+PGX8P5ilg/49ov9wfyrR4qv7JPme/6F8q5djBPhDT/+e11/32P8KYfCVh/z2uv++x/hXRNUbVj9br/zsXKjnJPDVlCyIstwRIcHLj/ClPhOw/563P8A32P8K2Ln/XQcfxf4VMawpYquqk3zPdfkc9GK9rU9V+Rz58J2A/5bXP8A32P8KafClh/z2uf++x/hW+3SmE5rf63X/nZ08qOa0m1Sx8TXdtEzsiQDBc5PO010JPHWsW2/5HC+/wCuA/8AZa2feni25TTe9l+QR2EzTTnFKcfjSE1ylDE/4/E+n+NLIMO31pE/4/E+n+NK5+dvrW9T4Ik1P4y/wr82EGftEf8AvD+dFLD/AMfEX+8P50V4OZfHH0/U6KWzPL/BX/H/AGn/AFwf/wBCNd70bFcH4J/5CFp/1wf/ANCNd64wc19Jj/4kfRHHR6+pKpqZTVdDxUymuG6WrNi7bP8A6ZF/u/410MDdK5i3fF5GSeAK6C3mQYy6/nWVCcbz1W/6I5KKfNP/ABfoibQD/ob/APXQ/wAhR4p/5Fm8+i/+hiotCljS0cM6j94ep9hTvE8sb+GrxUkVmIXAByfvCvUjUh9bWq+JdV5eZ0VFqzT0j/kDWP8A17x/+girtZ+kzxLo9kGlQEW8YILDj5RV37RD/wA9o/8AvoVzVJw55ard9V3ZKRJS1F9oh/57R/8AfQo+0Qf89o/++hUc8e6+9DsSilqL7RD/AM9o/wDvoUfaIP8AntH/AN9Clzx7r70FjnfHf/ICg/6+k/k1b+of8eMv4fzFc744kSXRYVjdXP2lThTns1b1/cQtZSASxnp0YeoqsTOH1Xdfa6oIr3izB/x6xf7g/lStxg+lQwXEItYgZo/uD+IelOa4hI/10f8A30KinOPKtVsuq7A0eAfEG08nxRcoBhfMcj865PyOelegfEtAPEEcwIKybxkeuRXGhQDXRVacrry/I1rfH935IqC3p32aryqvWpViBHAqDEzTbmqWpQhLCQsOAVz+YrofI46VU1C2V7KRXXKnGR+IpPYqGs0c/JukvEJulFvvJi2uPlHbA/h7CnvMEuISk+2V4ZELGYE57ZYYFaaaRaeSh8nkqP4j/jVO7022SeACLgt/ePtWTp6HW8Y4t6de7/r07GTbHbdSCVxudHTeWyMkEZz9e9Jcp+6ghDozRIxYhwQMknAPf8PWtSW0skbb5ZLeikk1A1pD2tW/Fj/jV8utzBVvccLf1e5Q3LFpxVWBeZ/mAPIVen5k/pSy7otMljklhwdpQRlSzHPfHOMZ6+1WjawjrbsPxNUroWqYXy2z16n/ABq6UVzq7tqONV7JdLf1+ZNZySLYSJBcJFKZlPzOFJXBzyacZLeSUCOZYk+1yMCpAIGFwfYEjrVUvaf88n/P/wCvUINtub923Xjn/wCvUxhFxk7/ANXNlWqKKjy7f18jTmmi8yJhMouTC6h3mVyrZ4yw4zjOD9OaZbzqkFwLuZHmMqbXLhwDtbDHHUDj/PFZrNb9o2/P/wCvT99t5RHltnPXP/16fs4+zXvdxyxNXm5uT+np9/mdP4LLtJqSu298qSQ27Jye/et6PgN9TWb8NtU8Pabe351ixubiN0XyxCeQQTnPzCurGt+DkZt2lX/3iRg9v+/lYSt7SPzMrzVNx5d7/wBfgjHJqSBv3lara34NzzpGoA+h/wD3lTQ674Kil/eaPqPHUf5kq3a+5UHP2aTg9rff/Wx7dGflFV9W/wCQNff9e8n/AKCa4lPi5oCgf6Hqf/fpP/i6h1H4saFLpt1CLPUw8kLKN0SAcqcfx1tCUVNa9V+aOf2U7bHX+E/+RYsvo3/oZrZry3QPinodlodtbyWuol0DZKxpjlif7/vWl/wt7QP+fPU/+/Sf/F1WInF1pNPq/wAwVKdtjv8ANITXn5+Lugf8+ep/9+k/+LqxYfE7R9RnaGG2v1ZV3fPGuMZA7MfWseZDVGbdkjq5WH9pxf7h/rVgvXIy+MLA3qP5NzgL/cHv71HN4pt3kMga7SM9AFH8s1z05pOXr+iNp4erp7vQ68v700ye9cd/wk9r/wA9b3/vgf8AxVV5fFtmhw0l8PrH/wDZVr7VE/Vav8p1mrS/8Se+/wCveT/0E1xd+/8AxQGmj/p4/q9LeeKbV7CcB7pgYm4KjB4+tYWoa5D/AMItZw7ZRmVXAwMD73v71th6sVWi2N4eqoW5ep6jLNgE1geJdUGn+HtRus4Mdu+D7kYH6ms+bxhZkHEVx/3yP8a4T4ieJlvNA+x2/mp50g3luMqOcdfUCs+ePcX1at/KzzEEk7ic7uT9TUM4weh/CpYlYoMkU7y8rhiPejnj3D6tW/lZmyTtkALgZqa3dyfnx7U+UxohRVHmZ64BGKas8hbb5cQB9EGab2Ippqok+4NMoYgmvU/hpqr3d1tkJJSMruP8XIryRgTIR0yfpXpvgJ/J1VYgQQsB5HTqtZVv4bE/iZ7FZSZiUe1Xwaw9OkBhj57CthGz1rdmaItR/wCPGT8P5inQf8e0X+4P5UzUD/oMv4fzFPg/49Yv9wfyrR/wl6/oafZBqYae1MNYCKlz/roP97/Cpj0qK5/10H+9/hUp61hT/iT+X5GFH+LU9V+Qw0z6U9v0pnetjoMG2/5HC9x/zwX/ANlraPSsW248YX3/AFwX/wBlra78HiujE/FH/CvyFESmmlJzSdq5yhif8fkf0/xpX++31NIn/H4n0/xpz/fbnvW9T4Ik1P4y/wAK/NhDjz4/94fzooh/4+I/94fzorwcz+OPp+p0UtmeYeCf+Qhaf9cH/wDQjXfydDXn3go/6faf9cH/APQjXY6xqP8AZmnPdeX5u0gbd23OTjrX0uOi5VYpdl+pyUE5Npdy4j1OrAjrXF/8JZcC2F1/ZZEJcxhvP/iABIxjPQihfHBXJbTyFAyf33/2NediKM3RkvLyOn2cl/SO5jdRcJk8Y5ratvs7EA4J+tebweO0+zx3h0cG3LeUGN0PmYjP92pT42laVfs+jS/MQFUTEnPt8tRSwaTleC38vI56NKpzTv37+S8z0vRYIZbVi65O89z6CneI7aKHw7dyRptcBcHJ/vCuA0/4h3elhrW40GZZQ27a8pRhx6FKl134jXVz4cui3h+aO3O0NOZiVX5h1OzFelHC0vrKfIrXXReRtUpVLu35npWl2Vu+k2btHlmgQk5PXaKu/YLX/nl+pryyD4rz6bpNmZ/DtzHB5SKk0jsqPhR0JSp7f4wNdLut9I8wf7Nzn/2SsKmEhzv3Fu+3cn2VT+memfYLX/nl+ppf7Ptf+eQ/M150PitdDr4fc/8Abwf/AIinf8LYn7+HJP8AwJP/AMRUfVIfyr8A9nP+mv8AM9C+wWv/ADy/8eNch8RryfQfD9vc6Y/k3El2sRO0PlSrHGGz3ArFuPjG8EkcZ8NXDvIGIWO4ycKMk/c6Ac/hXK+KPi9baqtlFNos0Atr5JXbzg5AUMCAMDnnPXtThQoqaUor8BWknq/xGa7qniyPS4Xnv8O9wEEX2ZAemQfu+9W5NX8Y7jC95l8Z8sWy7iPpsrmpPH+mpFZq0U0xjuJHfg4UMhUEdCTk57dODSzePE8qJEsI0jkjdYyRKY3GRkZI3Hkduh+taVKdBw5eWP3IE9ToLvxH4nsLFZZdT/fGUQpbLaqXYkZAA25zVWDVvG+qyIsupLaLnPlmNAwA5O4heOlY1n420awNwq212zTTA+a3LRDYASuegByMdcHrmorTx7bwXf8ApFhMsLq6GRWz1UjIBA9aXs6KsuWP3Id2S+MLvXGiglur6K4TeSjxxgA5yD/CD1B61i3cOoWlnFPJqdr5kkayrbhG37W6c7Nv/j1WdX8TWt9ZQWKQSDGXMpBA3FicKMZPGOuOc0HUDDp1xZX91eTK9uqw2zxH903ysGG4/LxnkdQfQ1cJRiuWy/AHqyvfLfaaqiTVbV5yqMYI0bcoZQwySgXoR0NNtrvWp/L8lnk8xtibIQdzeg45PI4qaXUopNJntbu+vLwtsFuHTPk7TyQSeOOMDjn2qXRdZs9NsrhW+1ecp8y2YIPlfayHv6MD9VFX7SN+lvkK3oRRXGuzNGkRkdpM7AsIJbHXHHNRSNr1w8lqkc0sy/eiSDLDHqAM1sXev6XNLcxQC6igltjFG3lj5C0vmMDz05K/TFV7/WtOutOntFkuo3ZLdfPZPv8Alggg45x8wx/ujpRKcbNXX4DirNMoWR1u7YRReawTAkKwbvLHTLccfjTLux1ma/nijMslvaSsklyIPlUA4ycA46ZrdGt6dqF1CsP9orL9tWdfLjBeY7VUcDPzZUnH+0eafJ4m0+6vrVrexZ5oLuSaMSLJnBIOVCMATgHIYdPxpOcLWbX4EySOdFveRWzTxwXktuF3GZYcJjJGc46ZBH4UlksmoSyKG8mOJDJLNI3yooIGTgE9SBwDya2Itf0hXsVaO98uCzmgZRjAL+Zxj/gQ5+npVOwvtDhgvLeS3u1S5iCFiQcEOrDp2+Wn7SN1t+A7ehRvLc2d39nlvShKq6SbdyOrDIIPXBB9KgvNMMIluXurbUEjhVgIC+AS2Pm+VTwMnj861tQ1DQLl4gLa5ZYIEiUscFsf/XJ69qqale6eyRLb314vlbVhUFiI1y2TkkY69B71EpqWisaUpKEuZ7fIo2Vmkrq7i0a3lmEQwZcqcZwvGc98nIqG7aOLTIYfKjJEsqiTJzwR74/SugtLrTpZ4o5r+9m3MdwjUgY2n1cc5xxW1oOhafqAvgfN8gzcIynk9ckbvpWaVrttdDo9vT9nyRWrur+76/p/kcTYtJHBYrB0muSkwHRx8vyt7YJ/WqVpcR2V6Zo1Z0QtjY2DjBGQe1eoan4WtbSwzbboQ7gPhCoYYPB55pg0XTV1WBYbhFRFPCrjBOeg3fSqbjy3bjrcidde4k7OLXVfhr835/ect4eWaW+eaKeUtPbBh9obey4YgDPccH866BYbtlDRzIAPv5HU/l6YraTStNilZ1uQsrcMfKGT/wCPUkWmxQlo2upDvbIPlev/AAKuZVIxqxu47PsXXrqVKUFbXXdf5vV/lpczGju2UiOSNcgbTjnPfPFIEvVckSRFCc9OQPyrcXTYAxVbl2I/6Y//AGVWIdDE+dsz8f8ATH/69XCLl7sZRubQxFOpJPlvfXeP9fqc4EvwmBNEW9WHT0xxUV2t79jmCzJ/qznI9ucceua6w+HW/wCez/8Afn/69VL7QCljcEztxGx/1XsfeumGHrc6em/deXmDT5JRUVqu8dLX89fn8uhy2nJenTYds0Y5J5HbP0qxImolm23EYXPAI7flWrpOjbtKgb7QRkH/AJZ+596mk0jH/L0f+/f/ANerqUKsastFu+3+ZHLOdKEbbW6x/DXru/MwpPtny7JUA/izyT/47W94SbU/7YkNrcxRH7Mcl+c/Mv8As1Uk0ls8XRz/ANc//r1Z0mN9KvHuDIZd0ZTG3HUg56n0rOFGpBp2jp6f5mtRVJxkrb26x6dd/wCu7Oxd9a/ivYDL2baMAf8AfNQh9a3krew+b/E23gj8qzhq7G3eUoeDj71WNP1J7iVfLgaRj1VTzgfhWdOVXml7sfi/u9l5nPPD1NNF96/zIdd8Sav4aht/Mu0nurptsNvDGC7Y6nkduPzrF1fxpqi6jaafcOZIroDE/lAKrEcr06jvW/qkEk+qR6pJpEk8tpCRboSQQ5Iyc49BXPeIZdT8U2sdvb6VJp7W7B4WdNzbz948Ada7Oat/LH7o/wCZH1ar2X3r/MnuLnV4bO4iN3EU8tsjA6Y+lZupXF7/AMI1YhbhAhZeMd8H2qXXZWs7YiRTGrRlAX4ycVi6neovh/T13rzg4z7UUZVfbwTjH7l/mE6FRQ1S+9f5nUT3uqlTuvYsfQf4V5/46uJ5ILb7Y/mgyYG3jBwfpXRS6rCf+W8f/fQrmPEv2e/hgBu41KSZzuB7Uuat/LH7o/5h9Wq9l96/zObSSFFA2sKHnhEirtbJIzSyWcABxqEQz9P8ab9ig84N9viJGOOP8acpVbP3Y/dH/McMPV5lovvX+Y2WWESldjZJA601XhLgBHz9Klexhkm/4/YwxIwvfP50ySAx3LlpC3l4HHGSRmspTqRpXcY29EVUpVIVuZpW5vIY5gDEMjZ69a7X4fz+drEgXOVgYc/Va4V3BkYkZOa7j4UKJPFDoeAbdjj15WsK2Ik6TXKtuyOSUveZ6XpiazJaxOl3AAVGMr/9atiK31/tfW//AHz/APY1HpsYigVOy1swngYrpeJl/LH/AMBRmpGRe22viyctfW5XjIC+/wDu06G31/7PHi+t8bRgbfb/AHa1705sJfoP5inw/wDHvEP9gfyqniZeyT5Y79l2Neb3TAvf7dsrWS4kvICqYyFQZ5OP7tbFrI0tlBI5yzxqzH3Iqtr3/IFufov/AKEKnsf+Qdbf9ck/kKzqy56Ck0k7taK3RCewy5/10H+9/hUx6YqG5H76D/e/wqY15tP+JP1X5HLR/i1PVfkRtzTT7089eaYeK2OkwLYf8Vhff9cF/wDZa2j04rFtv+Rwvv8ArgP/AGWto10Yn4o/4V+QoiU0+1O6dqb+tc5QxP8Aj8j+n+NK/Ejc96RP+PxPof60r/fPrmt6nwRJqfxl/hX5sWH/AI+I/wDfH86KIc/aI8/3x/OivBzL44+n6nRS2Z5X4NOL61/64P8A+hGut1QW80cMV2R9naeISbjgY3jqewrjvCLbby2/64v/AOhGuh1hEu7NrdywVyM7TzxzX1WIV8RBeS/JmOBV6qS7/oRrEWt7CPV7SK13Xk37tYljDYjGzKggctgdRkd+9Qtbq13Gn9lXbXBgkDE6bGh6rtcQ5Ktjkds5HpWHPpVmjECWb/vof4VZC+H7VvsUE9zc3QjLvtZQo/HHvXm1ayjBtrY05/I1YLewuNOubKYW0k0F0ohQxLHE0vlhgGUcAjONvTcMHis/RtRJ1WS1t4XlulinjD7NiLLsYKueADuwOOhrE8PRWmrW01xqFldXCpIT5Nu6qAvHQEc11l+3htPCNxqGktqDXceI47SVQrBj0428j3FKOJpyk1fVM5o14uT6WZi6vZX/APZ2nW888kNzBDI80EXEiR7iRnuABk/Q1p31rpFx4f8AtRtR5UWnxiO9di37wAZQEnGS2cjrkk1xsvh+SS+EkguGygycd8n2q5LoEUdk7kTgjHX6/SuiNWP1jkT3aXQ6JOKu7r70drBdfYfDLC6g+yqEjxI6sDMc9FycHrnKjtVfTG0K5jn1BoITNYMJ2fy8FwQQAf7w3bOD61iWnh63a3iJNxygPUen0q4vhy1x964/Mf4VlKvFTeu1/wBSVKCad196Na4uW0u6ms7eNNRngtPNhTYDJKzSgjA6NiJgQOehqaPVLaV7hLW1EmpRpC0tosYdo9wJfCc8g7c/3cnpWKfDNo3G64OeOSP8KytU8AQRzY066lSQDLqwyN3oCBR9Yg/6Q5VILqvvR1Fy1lN4hgjtzEu37Yk7feES/Z+vHQbtwz7VlHS9KLXzXNtGqWzqu24SSUShif3h8vJAIHBGB8w5PfNt/DM7SQJqEt3psgDK06rlZFYYbbjB5GQQeuat3fhpbDypbHXdT3xfu4zGmzYnJIBDZHPP41zcrrVmoSSvZb/16HPKUZTbjJfeMGk6LqEot7WGMwRkTTS+WQfIIfcw3AH5GUDOATuFLpVtY6np+lQTaWuL150Rtx/cKZRwvuoOTnOQtZ7WYij1WeJ76Rrn/RRcztgupILnb3yVxknpnjvV2HwzfR2zQLq95DbsCpgjJ2kHqCM4P5VlOlNxTc9Omvlq9H3ehNub7S+/y9SI6dZeXBavpiln017p7wZ+R1VjjH3cZUKcjOT17VLJo0MXh26luLKzt7m3SCUlPMZ/nZV+fcNnR8/Ke2Klj8IzizNoNYvFtmOTCM7CfUrnFSSeFLqS1Fs+t3rW4XYImJKBcg4xnGMgHHsKVp3uprf+uv8AwCuXW/Mvv/4JHqMOk6dd6rJHoK7LG8VFR2Y+YC5G/r0GMDHHzDOal1rVbGDxOLS40+NAPIV96KSoKJwT7A/pUEfhXUYrq9vdL1G7n1WOaLzAoPmNG6lmcnOSMgAn35rUufCUc2p6mz3d3e3cV6YpEt7AXTeUFXacNICFPIyM42jkU6VKV1Lmvpbr2X6pv5j9naWr6f5FfVtcVrLUDeWSZtL1beFZIh8vD7lX2AVf09al0PWbZpbawKxWjtPi4g8jInQ7e65GAM53EADmqzaFfXukzTi81CKKyEr2891bbo9qMcKJd5Mb8ABR1IHNN8RaReWFncJYarPNbuyf2goBUo7KNuV3fdIx83c8HHApPDydLkuv6Vv+D6/eQ6a5eXQdYa1KNNup/s5OlJvRIVgBEzEHt0wMgknp9SKydOni1yWfTY7WH7RNCxgxCo+dfm/UKw/GtDQdM1m60nTo9N1a7WGO9dLuONmCQRnYQ7/NgIfn64GQfWuctdC1e+8QQw6H9qV7ieRLOdFaMMF+8Qw6YU5OOgq1SnaVmk3/AF/w/ctK17WuztUSy0/WdKlt7MJHdX8MVsrRgMqqSshz1znaa5GO20/WJ7C7bTvswlubiCSFZGwwSJXVuvB+bnGAcDgU7U7HVNJ1C203brEcNvGVt5ntpIpeXBaWNCQRyOOhwBnBp99omo2+u2GnQzyW1oJH+zz+XhXOGHy8/MSir35yAacMJVtfnu9uvn/wPmZ+zfcei2erzeHbGayhjU6a0zTK0m5ghmOw4LcMy84XOTx2FYWtDTAbRtOEEksgZZY7VZjGGB42+aA3IPI56e9dgmkTwPp9jFe3UYntprlLaS22FJE7CIMQGILdOePesCwt9R1vUYv7QuLl5XhnR5ZlLNBKNwEfJypPGBxy3StYYapTmtdNdNfN/r+BUabT3Ll48ogudMlidNMj0WOdY2XCrIUQ78dm80lc9eorM8MwzWOkapfMbuxiCwqLu2hzMNzE/LyvykDk7h/D16VPqularbaJbabJNeNbRWTXM0JVtsMgJIBGeufyBzila21DTtFu9VTUriF5XjhgmiYiR4F2DduByR8wGOcbSOKlYaSi4adH93fzfX9RKFk0atwE0iTUr5YL2Ce41JIQ2nkRvsZMr8xXuScqAMkY4xUWn67caBHrdpdWzX0C3zQrexyhHSUbuR1yCATgjFTPpl1Y3d7dJql5FFcvbx29ysexbje68Blcl/l3Zz0wR3rP0MXUvg2d7HTku7gaoq7DbrIdnlnnaeD7nqM9e9a4bDzoykr3uvyt/lpqOnDleo661/W7nShdbbxrQSlfMEnAYAHnHThhzVSfXZdYuQLixlvXx0PzMf610y2cMn9mwaUyNYw6jeJISvmKBjhf9rIwB65FU59IUXlpcxWHm3rWE0iWs9osHmSK+BuhU4ztJ4HXaOOa7XzuCjpZX79dzZ2fQ5SXTby4MtxZWd1GkUixyJ99lZgSOM5HCn24rWj8T3h1iHy2cBSim3kbliD0Hoe1b9jYTanFfpqVskMwmtH+ypH5aI4jlAR1GMDkHHHUA4GSOf0ee8fx20t1araSl2XZIu0xvsYJngAfNt9K53Tjzxk1/TB2tqa+oeILsaq0c9hcW9w4BELuQxGOwIz2qSbVL+C1W4n026S3HBlywUn0ztxWVrsGpwwaXCY5jfRSTyeUEJdYsIRkdQMrIfoc1oae+pnTLuaeyEEUtq7LeMr+gIQc7SSRjpkZ9qJxpuorx79+wQkkopLb/ImTxJE1s0w0W4lhRgrSPdPtBPQHaAAa0Lq4tHtZv9BljJQ8pcMe3vmqUzRDwtdxQ3tvPDF5J2bZAzOd24nK4yTwOeiilvrqM20wjSUAoSAyHPToauM6UJxvFO9uvn6myqU0nzP8SO0lgS0iUzFDz/rGx39aluImhK+azx7xlSTgN9PWudkluWtIxHFIwGeiH1qFbnUmtjbmGYx5yAUPyn2qsTWo+2neC3fV9/USxNNJLT7zoWhB581vruqJrUHnzjz7/wD16x431IKCqS49ChqVJb8cmGTHptNc/tqP8i/8Cf8AmaLFUvL71/mbiQAaTNFklWcEv2HStPQtTh8N/Zb+Rg8Y3xlS23O4N3+tc/Ff3S+HrpjbyFlmGF2HJGVp1hZ2+r6Hq11K8NlLBdWqLNd7wEV0l3DCgk5Kp2OMdhmsaVWk5StBaPu+3qOpXpSSSa+86a9+JdxcSCKyNlECcAud5/nVefxprGn3piuZrHIAJjMW04I+v41yEeiXTaxPpUt3YW19FP8AZ1imL/vXzgBWVSoBPdiBz1o1OwFr4WstVN1CL6aWaOS0dJC/yMq4XCbQRkk5bpjHORXR7aN/gVvV/wCZi6lNdPxOh8Ra/ZeItKaNvKjn3CTcsm4DAx07Vg6vbW50nTc3EYITAzj0HvRotha3Flpr6mLpZdVvGtLc2+AsAXYPMcEEsN0g+UFeFPPSsjStOXUfE8Om6wTDboZVkZJVh+ZUYgb3BVcsoGSO9a0qtNVYy5Fp5sTrR5bJfiXTptow4vIvyH+NUrnSLUjnUIUx3wP8alutKtLDxDbW95pepW9jKgYKl7FO0mSQGSVYwhXPoD0NZ2v6X/Z3iDU9PgWZobW7lhjZxliquVBJAxnAqfa0l9hfexuvFqzS+8l/sGybG/WreM56MB/8VR/YVgr7hrtqxByFAHP/AI9VuX4f607MYzbybb2KyXazfvGkAKuvy/c+dMnr868VPN4J+0aNo8tlPZpeS21zI8bPJuumimlBKfKQPkQY3bc9snNZ1ornfK7ISnBSWn4lCLRbJrqOU6zbq+4HyyBn6feq3d6Xaf6Q/wDa0G7cp8vjPTHrUmneDor2C4uL66tdP8rSo76B185g5NwseZMI56EghcclD03VTl8J332aS4Fxaeabf7WLPL+c0IH3x8u3GAWwWDY5xihP9y436/odDrw10+1fcYNEsHAY65bKTyRgcf8Aj1dr8PdKsrHVpblNXt3KQMOMDuPeuD0XR7Wax1LVdTivHtbMxIIbZhG8ryE4+ZlYAAKxPB6Ad81dbRxo3im4tEMstt5KSxOy4YxyKki57Z2sM+9YVItwa5vyOV1IX2/E9l8LarDqUEiJfQXLxnkxkdPzrp45kVwu4bj2zXjGh+GLIy6Rc2ry2a6hci3MMl9DdsVIUh/3arsPONjDOTVuO4tETS9ag03VIbV9Qa2dJgJJFKbG3DAGQQxGMcFSMmtNf5vyJ54dl956/eOTYyfKe386njLfZojsP3B/KuUsvFmn6gr2cdwXZvuFo2XP5gV1ttNGbSMeYmQg6sPSrcl7Fe91fbsP21Ll6ff/AMEzNeY/2Lc/Kei/+hCp7An+zrX5f+WSfyFQa86NpFwqupJ28A5/iFT2MiDT7YF1BES8E+wqpSX1Ze99p9uyH7albp9//BEuSfOg4/i/wqZs+lQzsrTQbWBw3Y/SpzXDSTc52fb8jChOLqVGl1XXyIzk0w09hTDW9n3Ormj/AC/mYNsP+Kxvuf8Algv/ALLW0QO5rEtv+Rwvv+uA/wDZa2j+tdGJT5o6/Zj+RMZR7BgetJtHqKbkGkJxzXNyvuVzR/lBAPtic9v8aV1Bc/NzmmJ/x+Rn2/xpW/1p/wB6uiqn7OOvQmbj7dafZX5sdF8tzGDz8w/nRSx/8fSf74orwsxd5QfkbwSTaR5B4WcLc25P/PFv/QjW1f3QBPzcDk9q5PTNRi06JLiV9qrEw+uWPAqGO/udUvWeYlINp2RZ/U19ZW/3qn6L9THLleuvX9CK91Wa+do7U7YujS+v0qz4etES+kwOsRyT1PIqjGoUAAAAdAKswXFxbzRi1DtNKwiVUXczE9AB9a8LEJzpuKNYwSVx+j3Fxpco8iTCHOVwOcitCG6n+YGTOT12j/CqZ03UFvFtBFG0xVnPl3MLKgX7xdgxCY77iKmj03VnuLiIW+026JJK7zRLGqN91t5baVPqDiudujzc7S19CHSoyd3BX9ESw67dXt4sMSKrFCfmfjgEn+H0FVbjxLNJaOjRZBx/EPX6Vo6XZ6rDpNxcTzGC0g0551jWWLJZmAVnTk4YNwxA7YNUNPmju9Bupry/kt9LgjjhdY0WQmV8kYjO3n5WbdnOB36V3Qnh1U9pFbNbLr0sYOjT/lX3I0NM8Q3Ny0FrHAoYqAC0mBwP932p6+Kpym42uF/vF+P5Vn315d6dr8dpe6iB5EwUFWDRqhXh1XHygqQQPeo9WWCW1sdRTVZ5tMaVrfH2RUeEqATiPfhuGHO4E98cVE/YN83Le/l31F7Gn/KvuR0jeJbmCwFzHpAYbQxkkuAxAJwG2AAgZ7nIqmPF2tX42QCCEpzuMkcZP4uOfwrO1horGxtNRtdUmmF/alBFNapE6opCKcBm4JRvT7tH9jalDYabqWniOXzrNriUTNC3KySBtiNywCopOAcZ7VLeGsny6PTbsH1el/KvuReTU9a1D7RFLFI8kZ2N5kqrtY54GQBnjoPSsOTVtTgfbJPM0IODEwUEH0ztrT1C2u7maGCzvEZp4o76R7maKJdzjBIZtoxnGB15qmLbW5ddu5WiERguCLh53jhRS2Rjc5C5IzgDr2qqLw0J86S/DzWwlQp3+Bfcia/8UefYxxJZ7FVwRiT2PtWg3i2Qn/jzYe3m/wD1qq6lbXP9hWUttIZW+wyCYkxqqRrMykhuAAdnckktgdQKjsbqTUUu7nUNRCabZtHKNkKylQW2qqpxjOeRkDgnnFOfsJUkraJ/r+ti4U6cZXUV9y/yL48WS/8APm//AH9/+tU8niS4jtYZzaHbKWCgTHI24zn5fesjUrrULLV4baa/S7l3LLbTbFOUkQMhxjgHcpx25rZ1OK90gWa3t9cPcK7KZZbIBM9ykhyXweM4HtWap4XRcu+39XLuv5V/4DH/ACMrUdQhvHFzJa3AdwN22YY4AH932qibi2/597n/AL/D/wCIrpzNLaa/AiX7XUdzaLJ5jwqjNySOmeQV657Cr11DbSS7zFG7siF/lBJOBnJ9a1pUaTgnFaDlNX2X3L/I5K4tmtYVlms7hUJA4uVJUkZAYBcqcdjiktoftYPkW0pOcBWu0VmPoAVyfwrtrlICtw/7phNIGGMZ6k5P/wBemWsKxXImH2dQp2ljsyPp/wDWrX6vTvaxHP5L7l/kcULZ5LQ3K27rECwzJeRoSR1wCAT1HSqgtVmsRceXPsPuCOuOu2vRINoDBzF5BZjhtu7nv656V2tmqf8ACqREFGQDkYGOZjSdGmlexVOSc1ot+yPCJtIL2o3w3IRwMcYz39Kbf+GPK/s/ybe9YXEeVBxndnnHy+4r6P1MrJ4Gibk7IIjyeQflHFUdTIFz4TkPTMYyDz/BXQ6VNYdSS1v+iJqSsnZLfsjwaLwbc3lncMllqL3Fu4VgFzwex+XrUMHgq/uIlmSwvzC3/LXZ8oHrnbX1em2OZ/70pJyp64GOfypm1DA1uAMgDI/h/KsuSHYnm8l9yPl6b4f6pJO5t9I1Vo/4SY85GOv3azofDJMpWS3uywJAQEAkjPH3fUGvrctsjwM4A9TXlPimytbfXILiKILJLyzZPP36unThKpGNt2jWk0+a6Wz6Hl6fDvXriaRF0fUWeMgONn3c9P4ap2vgfVLsXMkOm3pjtz+9YD7vXr8vsa+rI3HmTPtA+brzziuT8PMv2jxESoGZuQfcvV06VN05trZL8zLn1Wi+5HgMPhCW7tTcQQXLwq4QuGGAxGcfd9K0I/AV61wUezvhIAx4IyNuc8bfY17NrljZaf4YgS0tIoA90rNsQAE7TVi6m2a/ayYUE20hzjrkPVexp+xjK2/N+FiZ1Gnol06I8hHw8D2BmNlqbcgCeN12gnsV2fTvTP8AhXmpaTPG80dx5LSqrPj3+lerxata2ul7JriKJjNnaxA7CuL8U+LlvtQg02GQPGLhTvTofmHSuOnyuKujXERipSSS6/kcXq+mQ2uqyxG4kUDGN2PQe1RTWltFDE3nv8y5+8v+FN8VvdS6vMgLOqkY3HOPlFVLyOU2tn93cI+/0FRVjFVFp1f5GFNRcIadP0GyRqchLpse5FSyXt3IjA6gvIPG0c1mtFL/ABsMe1SoQsbbSucelbU4U3ON4rdfmhyo03vFfcWIrq8jiUC+UL/d2ilN9d5yNRA9ii1V83bECwBHsKgnAddyNwe1XiqdL28/cW7/ADJWHpNX5V9yNaK/us836t/wFatRXs7Hi8Rh3G0VzkD7Wwe/pVohoysqHaR1I/rXP7Kl/IhrD0v5V9yN1ri6OjXKx3eWMow+0Hb04rHl1O+XRtR05kaR7m5gm87pt8pZFxjHOfM9eNvfNX7R1bRblgcDzh/7LWHNduZZN2OGIBxUUqdP3mopa/oi3Rpxs1Fbdjo7b4mT2Wp3d3/Z91G818LtRbXph3AADy5CEy6cZwNvU1g6j4q/tHSmtZrOZLhLqae3mSYBUEjKWV1KHdjbwQV69DWNO3zZzyarElia25V2FyR7HT6L4y/s+0tYbzT2vJLG5a6s3E3lhHO3IcbTvTKKcAqevPNZkeqwPcRyahb3dzlna4CXCxmQn7pU+WdpBOTndn2rKVvmA96lVd9xtzWtCEZVYxsN048l7HQS+K4Li/00Pp9yul2ERjjt0ulErZZn3NKYyM7m7IBgY461Dr+vrrGu3WoWdrcWi3MjTPFLMsxDsxJwQi8c8DBPuasaXFZQ/LLEr7ThiRyKxrtBFqcqIMKGOMCseWPYHTjbY7WH4h3totnt03d9n042py5G+X5Ak33eCvlQ8d9nXnjAsvGVxaT6ITal/wCzYJoCPMx5vmNIc/d+XHmYxznHvURG4BSe1Zs6RpOoHXcK3xUFCrKKWxoqcXI3rbxkVlitbmwlktm01dNlSKcI7YmEodWKMFOQowQeM+vFm88e30ul/wBlSPqyPHbfZVWHUTHAyYwN8Oz5jtODhgDjkdc8kONUj/66L/Spr1c6vIc9x/6DUqEfYOduv6G/sI6v+9b8y7pHiP7BBe2N9ayXdldBN8cUoidWQ5VlYqwHVhyp4Y/WtXT9Yn1/xFd3UtlKXcJ5UUL/ACRwoAuwjaSflCANkYweDnjlBsEjZPeuq8Bf8hqcD/n2b+a1z1ElBuxyunFS2NS3uo7qwhsdO0q9i08XS3MzvP5ssrKCAqsI1CgBm7E5I54rX8Q6zf3OlHyYNQhS0DSrLdStPJvwOS5UDACjAxgc+tYWhaibe2CSSrHFGOpOAKh17xQl9YS2Vpu2ycPM3AI7gCteWO1iFCPVFTwtq97L4ksElnLI0uCNo54PtX0baW8BhjJTOVHc+lfNvhK2H9u2s7Z2KxKn1ODX0XY3Ae1iIPO0fyq3RpeyXurft5E/V6XL8K+4Zr9tFDpFw8aYIC4Of9oVJZW0L2FuxTJMSknJ9BS66wfQLn1AX/0IVLYD/iXWv/XJP5CqlQpfV0+VfE+nkh/V6PL8K+5ALaFSGCYI6cmnmpGqJuKwjGMfhVi4U4w0irEZPFRtUjd6iJFM0MG2/wCRwvf+uA/9lraNYtt/yOF7n/ngv/stbRPSujE/FH/CvyJiN/Ck47ilNI3Nc5Q1P+PyP6f405v9afrTI8/a48+n+NOb/Wn/AHq3rfw4+n+Ypfx4/wCFf+lMdH/x9p/vCiiP/j7QY/jFFfP5hvD0OmO8vU8Mn0dWtNF+zndPeWskz+fPHHGm2V04ZyoAwo6nrUlnoesfbbuBLTY9rGjzPJPEqIj42tvLbSpyMEHHI5qa213R4xpUd9aSSfZLCWDe1uk4jkaZnVxG7BXADYw2OT7Cto6zpOu6frcrJc29oljZ27PFbxq4ZZPvCNWCY/2QRj8K+mlO9RO5y4e6acbmInhy9GlapeTtFbzafPFC8E08SFt6O2QWcZ4QYAzu3fLnBqJItT8P3Om6zPY7o4bmJzH5ybxuBKhlBLJuGcFgKnvPEOlajaanZSQ3UMEgsxauqLIx+zRPEokG4Y3BskgnHoanl13SdQvbieK0me+1WaATxzxr5UGHVmKNuJbJXA4XAJHNcsowlFxbWvkV73n95m79O0OW7jJ1B7K9ga1ndo4vMgbcrrhVkIJyg6lcjPSrT6vY3uktYQvcRpcww2Nm0iDc5jfeWkwflBZ8ADdj8M0/xRNpMV3ren6ZaO1xcak0kpmhVVgVGcbUIYlgS3UheABg9ar6Rq2iWltp66havPd6dctPGttArR3AO0iORiQQAy9QG4YjHesvq1Jvmcrv+vL5DvIeutaRcm+leLUlv77To7F40iVo0ZfLG4HcC2fLHGBjPeodPs7aK0vNEvVvRBcrHeo8UAMyPHvUKYyw5Ku3f0NVrLRU1G5jeaW7SKQuZ2ht0Ijb+HaC43e4+XHvW5qPh7R5Le1je6uoLa0tvLa4S2Rpp3MhbLLvAAAbaBuJwBz2rSOEpp8qlu18rbWJbZgaxcafqOvT3cxureN5418oxLvWELtOfm++AAAOh5OR0rQ8RQ2180EelG8+y28v2WG1kt0jWInn7wkbexPJYgZ+mAH66ulG9thBDLsjtIo1d413yALjc2D1P49uTXRnxRodtKI5NIlDpYeWw+zxn/TAABJ1+78o/M0vYQi01Lbp/SCzOY1+0/tO6gubKN0s47VY4klZQypHlegJ5IXccZ5Y81tWUtglpplxtuFvdLsGiWNmQRSl3kIO4sCAPMyeOenHUwQa5pEc2ms9pOy29hNbyjyE+eRvN2t97nHmJyefl9hUz6tou2S0ubW6hD2ENuZI7WN2SRWViwG8A5AI6g80pYanKKhfRf8ADBaRAl3ZAJPcW7DZp8MEcr28VwY2U/M3lO2Cp6bj09Kj1fV9I1qPUTcvdWtm1xBLG8MKO4cRbCpQMq4O0kEEYx05wLt1rtlf6OYYLWaOVbRbd1i0i3YHChd5m++uQMn3zg1y+m3Glm0u9I1CO7P2iWKWOW1t1klR03DAUsMhg5zz1APOKmGEpe05r2YK5f1DW7BdAt/DV4lz9mtlY+dEo3CXzJHRgNw3qVcAg4xnI961g+l2el3MF1cXX2XUcJvS3XfG0bBg+3fhhyRjI6+2DX19oNS1O5lgs7qCeSVRDZi2HEQUgZIOd2Av8POScjvNrd5ptzeWdslvPaW1tbxwtm3USZ6yOV3DJLFiMnpgZqnhqSp2T3d/ne9xpPmL0rWl/wCLrZ4XmEcUNsLVGjHKRxKAXO7g/KDwDkk9K7TU20u8hvfkuWa9uVuZhKeIyN3C/Nz9488cDFc5DdaBLrenz2H9oELbpBIJLKNM7Itu4YkbJYgk9Me9b32vTnJDJeYB4Atk/wDiqqODoOzc7W9f8u+oWm9vzRf0fTtB1LxTpcMaXEohiNuyzJ5YwFcg5Vyepr0E+ENAEoX7APmBJzPJ+nzV5XaX1pb3ck0L3scynKmOBQR+IathNcu54ZZ0vtXIgALtgfKCcdPM55I6V0Rw9GmuVVPwf+RVSnLm/wCCjvF8HeHyzIbAADHBnkz/ADpB4R0Bo2Y6eNyk4HnPkf8Aj1cB/wAJJMH3DUNYyep29f8Ax+kHiOUZUX+sBD229f8Ax+q9lS/5+L7mR7Kf9NHoLeEfD4jDrYjPGT50nH/j1Gu2ttpnhWe0s0EUAxhdxbq4PU8964RNYv5LV51n117dD80iwkop9zvwKp3uq6hqdjLbW02t3U5AIjSIueozwGP8qTpUrfxF9zKhTmpJ2/FHot+wHgsRkgL9lhIX8Vqlfvvk8LgkErIuB6cR1w8t9qc+nCwj/t2S5WNVa3EBLDGONu7PH0pqS3+oXulQxXWpxyQyKrLKhUqxwOBuPp7VtONL6uo+0W/Z9kKdOTXz7o9mMpa1LhxvAILkcj1pZJgFSRWABYbiBya4ldH11iwGo6js6Yw2P50v9ka43y/2hqWQfRv8ay9nS/5+L7mP2FTt+K/zOzupTHbSN7V514qAN/YktjIHH/fVXLvStcEW19T1Ebj33c/rXJeKtN1S2urVZru7ZioKlwc/xe9XSp01Whad9V0ZrSo1FzNro+q7ep7Lsiit5Faf5m3cY9a5TRjFHL4h/e9J1xkdeXrI1HT9atbf97ql/GGOMvu/xrlLgzwi4WPUZGMpBdicZPPvz1qb06dOaUrt26Pv5mSw1Rvb8V/mdn411uwt9Dgi+0JvWVW25x2avPtd8ePeTIbNSixx+UTnk9c4/OsDWI22ZkvhId38XJFUnto2b/j9i9d6gcfrTb/2eH/b36EVKE0/muq/zLWqSvJMHLt90E7jiqdtOralaAsc+cnT6ip9RtYZLlWk1FEAQfKce/vT9KsIJdQt2ivbdwJV7ZPUf7VedR+GJ1YqjPmn8+q7eoa5Iia5cZOMlev+6Kde2yS21q6vyE45znpV3W9KjfWp3+1IpOMqw/2R702exi8m3Buo48JgHsenvSrfxY+r/I5aVGfJT9O67eph+WFbBYg+pHFQypBySoDY4I5FbyWMTDi/ib2wP8aqNoMCiV2ul6Egdv51vS+OPqvzRvKhO3T71/mZqkCFcyDGOBiqshBzhh9RXSW3hiC8s0k+27c54A6c1Fc+E44eftqge4/+vWmKf7+fq/zFGhUcVt96/wAzll+/1GK04ZBsxlT6CrI0O2U83sePoP8AGnx6Tbwtn7fDn3H/ANesB/V6n9Nf5lmzjQaROpO3dMPp/DXL3wCSuNwxuPT612UdjE+g3BW8jYCUcgfT3rnn060JZjqcGQT97HBz9aijtL/F+iHOjNtLy7r/ADOfkACjBJz6jFQZ5NbsunWkgwdWg+px/jUI0a0xk6tAB9B/jWlyPq9Ty+9f5mOv3xircZ8uQnHPvWnDoVuVZ01GJyq7sAD/ABqFrCAIH/tCIsTgpxkfrW2Gf7+JUqE1Tf8Amv8AMbNfMWDxbUkHX3qOWZboq5QrIBhsdD71eTSbMj/kKQH8B/jUq6XaqMHUoPwx/jWIvq9T+mv8yJBmYcHpTLu0kaRXONuR0ra07SoJ7hSb5BngLgc/rVvU9Ot7M7Hv03HkIQB/Wt8d/Hnbua06EnNN/mv8zlEgUX0ZxzvX+lNv4caq7e4/lWzFZ2pnRzfRBtw+U4z/ADov7GA3Ekn22Pdx8uB6fWsY831Z+q/JnfKlo/8AEu3n5mI9jKtuLhkj8thuG6RQxGcZC5yRnvitWw0/UdPvZDhrd1UxuYpQWBz0IU5GccZ64qNHtJrD7NLcb3KbUDxL+6Oc5D53Y68dOa6JIYbbXdQlikdpZZt7K4G1drZ455yfpitpxhytt/19xlKlTs3e23b/AC19Djri2vYTGksY+dti/vVIDehIOFPscVcstGllkcXi7IxC0ibJFcPgeoyMZrUEEMs8YL+fCJfMMItI05wQMlfvYz3rViWaJoXjictFG6gmBUBJ6fKOKtcl9xRpUb3ctPkYkFnfpdKqO8ZjUHPmhdgPQdeD7da3be51qOIsNUvI0QlT+/IwR261EkRh8791IiSMJCWiWTa3II+bqOevWorm6k8nDo+4ys+5lABBAA6fSh8qhuRKnTULp3evb/I02vNSmsJDLrt8IiQpHmM3OfTdVlZ9ahRY/wC274bQFwJnwMenNc8mow/ZHinLBdwYGPBJ9RgkVuW+ofaEW4SIsWOcdQKG17JepDScI6q/Xb5/pYstLrIZV/t+/wCQSf3z8Y6/xe1Ng1LVIy5OrXsqHgFpmBBBII60hu/uf6OwABDYz3z/AI1RS5BkkjjjcqjHJYYJJJzUy5HF2/rb/gmlWFNwly26fp/wb+ZryahqaqGW/uyNoY/v2/xpr3+pGQqmoXXQHm4I6j61U+1RnHyuH8vbgjjp1p3nR7mYqckLg7QegwetJqmTKnQva9r26laC71M6xczC7uQQoUuZiCTxx1rQfUNSChlv7vGwMSZm4/Wsz7TFNqU6MHXDM6kAHIYLwfyFWXmWSFYyCNo4I9auqod+iM/Z0EpJO/b1u/LT9Swmo6gys7aldqoIGRKxOT+PtTJNS1KORkOoXXBx/rm/xqvG6eW0cm4AkHKjPI//AF0jSgzGXHIbIUjjFY2jyohxpumtdf8Ah7/pYttfahFLCW1K6y2c4lb5f1rS0S8vG8SW0Ml7cSxtkkPISDlCemaw3mSRok2bSCSSMnr9TWtoLxv4ntGjLHgg7hjohHr7VpWUeTQqrGlzXhbaNu/W+/8AVz0BP+PqP/fFFEf/AB9p/viivncw3h6CjvL1PD7+2gEseIYx+7H8I9TTbS3gMh/cxn5f7orr9N8InWbqz866EUUsZ+6MtwW/wrq5PBOi6RapJHE80pcKXlbPHPavp5Qvi16/5meVTjzRT7/oeT2mmSX8nl2diZ39Iot38q6rTvhhq14ym6toLKI9TIBux9K9igghtoxHBEkSDoqLgVKK4VBCdZ9EcNb/AAs0RVRLovLGvPloAgY/7RHJ+mcV0dl4V8P2EYS30WwUDu0Cs35kZrXpaoycmzlvCuk6dJpcpewtWPnsMtCp7D2qz4j0jTE8P3bJp1orALgiFQfvD2pPCkqppM2eT57cD6CrXiSVW8PXQ5yQv/oQrrh/vS9V+gp9Tl7rSbXaHsdJs7i9Fra5ja2R8RlX3MFI9QoLdvUZq1Jo+lf2jqLRaZFNMt6VeKHT47nEWBjgkbQTu+Yc8Dkd7S+G7PWrm2muZZ1ZbKJR5bADGAe4PrVkeAdK/wCfi9/77X/4muep8cvV/mzSorNei/I5v+x7KXSLn7NpUdtDH5zedPZRusgDHA83qj4woA6n60eItK05IpHsrC1YZT7U5hXfG20bQBj5VPqOpyD2FdMPAGlf8/F7/wB9r/8AE07/AIV/pP8Az8Xv/fa//E1BmcXY6RHd22jtZ2ULeRqDPdkRr8iHy9rP/s4D8njr61yviLw79r1CO50vTDKJZ3WKKOIgSqMtgYxnA7DnmvUL/wACaXHd2Sie8w0mDl19R/s1U8UeBdLt9LRlnuyWmCkM69MH/ZqsMuetyre/6BH4jzrX9JuZ5NFhg8PQLdJAWfSYYpNwUSMfnG4yfNnpnIHTioPGFrHHqFmGsrW0k+xx77SJMfZyCw2tkkk4Abk5+YA9Km8XeEYtBvXti8xTeDGxI+ZTn2/Ck1DQLdEuWiluN8Vr5qgFTubzY0x09HP6U5xfsVLzNINc9vJndWCaATaF7XT4ZLm3XVfliVSsMYQSKPQZ8/j/AGBUGlahY3emW12sFo2mSQTSXt0Ih+5lDPgFv4CAE2rxnPfNeXHwxqE1zKghBdGCNvkjX5iOFyerf7I59qjg0C7aW3jELBrjPlj5BnBIOfTBB649ax9oV7Jdz1Yy2cenTXX2e3GkGwSWK9Ea5achcjf1Lbyy7M8AdO9aN3c21vYaqz28UGlAwCC5SIDzIjIvzBh9/jBJ5weOOleZXfh24h063uLeSRoWt0mcFo8jPUhcZ2j1x+NVJtGv7e0F05dYsKxwULKG+6So5APYkc01VT6FyprqeuNLpw1nT4HsJvKl1KKKCRrFI4XQk/KHDHzQeDnnp15rmr3XrG48P2t+Y4o2N1LBmKILlQsbAHHXG48nn1ri20TUle3QMXa4lEMflyxMN56KSOFPPQ4p8Wh6m8/llzhWVWxNFwTn5f8Af4Py9fan7RdvxEqUT0jT9UiltNMvot502CznW6kA+SNsyblf0ZgVwO+RiuXF1PeL5VtpV1eu0YmW38l/3se4DICkMRnuD2rnZNPkfxHLpVvdygLcvCrybeFDEZOB6DNVtVtUgsIru2v5rm2lZowXiVGDrtJBHPZlI570e0XYapxuen3N4t3FqFo9lcTzGwtlfTLE4lT7vygkMfkwM5DHnnpkQS+I7ex8YLvWVzFNASF5K4UfKTzlh0PuDXn8ej+dYRL/AGhL9smtWuY4PKBUooYkFuzYQkDGOnNLpy2406e7h1K6SW2iWR1ktE2btwUKG355J/u9AfSm6i5Nuoeyj1Pdf+Fi2SE4s71wfwqP/hYlsrFhYXhz2LV4fBqus3as8EjOBIkZwEzufO0Y98Gr1iNZuNTe1ujImPtEfy7MmWKJn29DnkLz0wetHtor7P4/8A2VPDvo/vZ63P8AEC2mIJ0y7OOg8zpXH+LvFo1G8tXFlLGI1Aw0hJP3v8a4ea41mKR0mTfi3adWiliK7RxuDAEMAQcgHPFN1e11a3v4bVJba6d4UlBWWH5AY1clsE7VG77zYBxkVdOvFVIvl2a6lxhQSlZPZnqWr+Km1KEL9mMRHQtISa5WQSysSBEc+prhL+/1KymMV2FD4DcbGDA8ggjII9xWtcpcWMF0Ir8Pc2JRbuHyFUIW4+Vud2G4PA56ZqHVi94fj/wDFKh0X5k2taZM0avJ5f3gMA/Wql3oF7M/kK0MeRlcE4qSwV9RtBLc3jRK1wlvGsdqsrF2BwSMjA9+T7Vbso0vdSaxuNSmiuIzIGZLNJI1VASW3FwcYB7Vu8RD2UY8j05uvexEoUpP5oim8Ganqcysk1tsC4wWwc1qaX8NL23uIZ3mhDRyK2B3wc9ayLJrmeJpmZmcOVBAA4wP8au2vie+0+6gtWnnYSSqpDNkcmsaThGKTjr6ixE6UuaSj36+Ru3/AIdkGpzPcFGV8cBvYVWm8MXd6mLfywsfBDDrWJqniCWPxBcBn6Feozj5RWy3jB9LsFdbna0oB29c/wCc1NZw9pHTq+vkc1KVPkh7vTv5ehWt9CkjcxlIi44IOaffaTILJyEVMISQD14rS0TxFBrYkY2yROozvJ5Jqa8gN/ZS+XvQBGLMe/Hat6bp88fd6rr5o6Oenb4fx/4Bzul2l0ttE8RTByCMnnmr11a3UseAIs/7X/6qsaZoKy6fCwkZWYHPPXk1ox6escDC4CnH3cCrxMqftp+71fXzCEqdl7v4/wDAOJudCudwfdFnOTzgfypn9g3Eh3kxn0AY/wCFb1/pcRUlVwc8VlSQrbw+WnGDg/Wsean/AC/j/wAAfNS/l/H/AIBag0ySPQriJvKGZB0J9q5Q+HXeWVXliRixKtuOME9xiujZlj0GZu5lH9K8/kuvJvZX2BmDtgntzWdGUPf0+138kE5U9Pd6dzWPg3UZJ1SNoGLHC4Y8/pXWa38L103wnazmZv7VDHzs5MTAngD0IFcfpfi270/UoLgEhYzyFPOK63VfiJf69G1na6hJ5BHzQz8b/bNa81P+X8SVKl/L+P8AwDjz4c1H5T58AXgH5z+XSo5tAu4PneSEgnHDH/CvRrDWr+98MvpV7otlPbbSSXADZ/vggckVVtPC+k2enxX7L5jzSBdrDITr/gK1w8qfto2X4jlKn7N+7+P/AADjoPCepzNiERyH0Usf6VbbwhqtuwFwkaZ7MWH9K+h7GKztF8m3hijC8jYoGRWH400VNaslKKDNGcof6VlzU/5fxFzUv5fx/wCAeceF9G3T+ZMsbxZ2kZzg1d8XeGZrmMTW8w2oN2G4IqXw/bvaRPuQoS/ysfuk+lJrXiJpIJbWeHZKMq6np7EVtjZU1Wndde5rBwc0uX8f+AcNHoV8LiKQvBguCAXOev0o1DSL1buSQvDsGP4jnoPapfLilmjYRgHcOfxqG/tF+0yOOvH8qxVWH1Z6dV18j0PZU7PT7Xf1LVho1xcXZZni8pk2nk5/lXpnhKzM2qEMwBMZWQBiehHIry+Fdm4/3QX4613/AMP5Vtr37ajbmEZEi+2RzUYmUOSWn4+hw4n2arS93r3PQ7T7HYQLDawGNAP4VGT9T3qU3kfo35U+0vIL62W4t5A8bDqD09jS3EQngeIkgOpUkdqrmp/y/j/wDFSpfy/j/wAAy7zVLN4XjEo3nHH41Pb3sL2yAHeAoBAwRXF6h4c1Czl3KDJCDneh/mKTSbPVYrtGhilDbsliMDHvWrdP2S069/Irmp8vw/j/AMA2PEel6Zc6bNMLbyphjDxjb3HUDg03wybjQDGkp8yxnUEMOCpI9K2dbJ/sWfPXC5/76FWbNUm0u3jcAq0Kgj8BWjlD6stPtPr5IV6bXw/j/wAAuSXsR5Cvg+1YOmXKC/1M4bmX092q7pOqI88+lySAzW5wpPcVX0s/8TDVP+u39WrFShZ+7+JpTlT5J+726+foPluFN4jYOAv+NPNwno35Usv/AB/R/wC7/jTyeaz5qf8AL+P/AACqsqdo+707+vkc/bTL/wAJdenB/wBQP/Za2TOvoaybY/8AFX3v/XAf+y1tZ9a6MTKnzR937K6+RzxlT/l/H/gEJnXrg0hmX0NTZwODTDjmufmp/wAv4/8AAK5qX8v4/wDAGwsHu0POKe3+tP8AvUsR/eimsf3p/wB6nVkpQVlsc7mpYjRWsl+bHp/x9x4/viikT/j7j/3xRXg5hvD0OuO8vUxfC3+t0r/rlJ/N66fW/wDjyT/roP5GuZ8Lf6zSv+uUn83rptb/AOPJP+ug/ka+rf8AvS9f8zkyv+JH1NQU6kFVbjVdOs5fKur+1gkxnZLMqnH0JrgAuClxWd/wkGi/9Bew/wDAlP8AGl/4SDRv+gvYf+BKf40AZfhaEnTJSvP78jH4Cr3iSNU8O3ZA5wv/AKEKyvC2t6TDpkiy6nZIxnY4adAeg96vavqek6hpU9rFrOmq8gGC1ymOCD6+1dKko4lSe11+gS6lnResP/XpH/6Ctbgrzy2v7uC8McfibR1VIwqkzJjAAAHStBdT1AnjxVon/f6P/Cqlh4yk2qkd33/yNK0rtadF+R2opwrjlvtQPXxdoQ/7eI/8Kf8A2ndR8/8ACXaEx9poz/7LS+qr/n5H8f8AIx5vI6W8sHvmiKSNG0ZJBAz6f4Vh+JtLuYNMieW8kcGZRtbPoeetVxrupfw+K9CUe80f+FY3iTU7+fT4/N8UaROPOB2xzJxweeBV4fAUnXjKU1v3a/RCVnLYk+J2kpZ6JDeSXH2qRJgu1x0BBPvXFX+uDRrszJHulktjHGMZGfMjbnuBhTyOc4+ta/j3xEraKLSTxFpl8xkB8q0KuRweSQtczfXBa4Ux6ha42D+Me9KWCpRoJKa37v8AyFTTVRO3chttb0+C3e1iEsUImM8byWkNwykqAV+f02jDAjPcVLDqDroOoXtxG5kkmdbSZgF3NKCJen+yvbgE+9QefL/0EbT/AL6X/Cgzy/8AQRtP++h/hXJ9Th/z8j97/wAjtXp/X3kz6xYrYwXCC5NylkbQRlV2ElWUtnOeAx4x1x0qC41uxkS6nSOdrm8ijiljYARoFKEkNnJzsGBgYz3q3Jcz/wBmxA6pZkBz8u9cjr7VAv2yRAyXUDKehXBB/SksJD/n5H73/kU1/X9MnbxRpUMlt5EEoii1KC72rbRR7I03ZTKnLnkYLHn2741lrFiLQQXy3AEV19pjMKhi+QAVOSMdBzzjnirskV6RzPF+X/1qqPFd/wDPRPy/+tR9Uh/z8j97/wAgtfp/X3kH9vRR+KJdUSBmhe5eXYxAYoxOR3wcGmX2paa1lb6fb/afssckkzyyRLvLsFAAUNjA2DnPcn2qQxXefvr+X/1qimiuRGxZhj6UpYWCV+eP3v8AyLjTu7Wev9dy1YeJo7DSNiyzyXAhkhSIwIFQPkcSZ345ztwBmsx9WhTQ47CKJvNluPNnY4AIUYQD1xucn6irPl3HlDJG3A7VBNDP5kOSOTxxWrwkFQU+db932XkTUhZdf6+ZPoOvwaTcXLzwO6vD+7CY4lVg0bc9gy8+xNXZPFlo89i4t5gIrKaObgZaeSExlhz0OEyevXiqJgucdR+X/wBaoZUuEGeCf92s1hIN/wASP3v/ACBrlX9f5liLxHaQ6dDbtBIXSyntzgDG53LDv055qRPFWmx3n2sRSlriyjtLlXto5BHsWMBlDEh8mMHBC9evesOWOdyco5/4CaYYpBCR5bZJ6bTW0MDDniudatdX/kYwm5N+Sf4E+ta3Ff3kZjzNFHGI0L28cGACTgImQBknvWne+JdPu47+W2gnF3qTI1yJFUJHhtzbSDlssAeQMD1rCFrMWDeW/wCRpqxzK5/dyDPX5TR9Sh/z8j97/wAjHnfY63S/EFnpls8KXF9bgXIkEtvGu6dMcI43DA79WHJ4NQxatbxnU2jgaKa7GyIKBtjjLbmH14VenQmqUP2m4tkClioOMlcY/SupgtdRS8izdW4YqeWUHHX2rb+zqfs1L2i1v1fS3kTKo9rGZpd40Fi7CFmxIfmH0FINRgnv7dfsg3mVRuIHqPatHUY9TRSWubeQHqFUZ/lXPw293HfwOj4bzVPsOa5/7Ppt39ovvl/kKaXK9C3rfkrq1xuto3bK8nqflHtXOapO0wjAQIsWRgHI7f4Vr6yLo6pceY6s2RllXr8o9qxZhM67SjnPbbT+oU1K6qR++X+RFJJQjp0Lfh7WBaXwEuRGcDg16tFqMI06YKdwaJxuXt8pxXiDwyAnEEvH+ya6TwlNqFxeNZwTlcg7o5T1HetY4eMZJupHT1/yNebyPWdCjE2m2sgB+6wP/fRq1dW+6LyySN2RmrehWRttIihcgumc46ckmn3cO6VQOxzWGIkpVZSWzbKjsctEfPkuLaTHmwnp7CsDW7cwXa4+7KuR9RWne3CW3jSRgcCUcj8Kj8RoZRGVx+7YHP1rAowbn/kA3HtKD/6DXnVxue6kABJLngD3r1YWYuNIu0xzvz/Krvwx8JaDqGq3TXyGa8iYkxzcKOeoHeoo7S/xfoh1Onocd4c+F2v67MjNam3txgs8hxkHpiu/h/Z+TyFabW2SUnO2ODt9c9a9mstOtLGFY7ePao9WJ/nV0dK0bIPIp/hh/YmkzvYapcsiQsWW5w2cA9MYxXGahKbTR7WKSZMs+dufdq9z8YRXD+FdU+ycT/ZpNuBk/dNfJVx9vmvfs8iytL6EEmtcLrWiW/4T9T2221lzcAo/8OPrW3DdXToWLhl6gEVwWmeH9c0fTopdUiESkgYJ+YfWt2616HTLAu7huOOeTXO20wSTRS/tO3g0iZJCNxl2kfUcH8xXI62/m3aOWVn8oCTBzk//AKsVmT3UtzI3zHBOetSBTsOTniujHS/fzXmdVOnaaYkKneh/2h/OnXw+eT8P6U6AfNGff+tF/wANJx6VjH/dn6r8md72f+L/ADKxYp5jcfcGK6TwROz390kWdqQlwPbI4/KuUkkBjki/i+8PpW74EnaDWblwM/6MxP8A30uaWJ+GR5uJX76Xqa+j6vf6d5c1lcr0/eRNyCOx9x1H4V29h45s5VCX8T20ndl+ZD/hXkcLXNvfRLbAmFF3pnoUOMg/Q8j8a0ZNUcOwGCAcAgda0aORM9im1KzvLFzb3UUmcfdb3qzbE+SmWGNo714paamr3aKVwTnkDHanv4gdJXQT3C4YjiQitGv3S9f0Lv7p63rzqNFuMso4Hf8A2hWbN4psNM0yBEk8+4ES4jTscdzXmFxrJniZGlmfPZpCajXVI0UKBjArRr/Zl/if5IL+6bQ1O6j1QagJGEu/c20dR6V3vh2+jv3vrmM8SOGI9OteUHVC27GcAZJ56V1fgKaf+1JjGSLdo/nz/Ee1Yr4WXT+Cfy/M72U/6dH/ALp/rUhNV3ObxP8Ad/xqb61kXV2h6fqzEtv+Rvvf+uA/9lraP61iWx/4q69/64j/ANlrZOetdOJ+KP8AhX5GEQPrSHNHNNOfU5rnGSRf65aacmY/WlhP75e1MbiY88bv61T+Axh/vHyX5kkf/H2n++KKRP8Aj8jP+2KK8TMN4eh3R3l6mP4W/wBbpX/XKT+b10+t/wDHkn/XQfyNcz4W/wBbpX/XKT+b102t/wDHkn/XQfyNfVv/AHpev+ZyZX/Ej6moK878XQ2z6/eySjdIsESqDGGAzu9T7de1eiDnpXDeINF1bVPFFzHY2gljaCMsS6qQVz6kf3q4o9QOXuNJszcyu4ESGTYoVc8/TIwKjXQ7fescmFldyiKMkEg45OeOa6oeEvEe52l02Ngzb8GVMA+o+amr4c12M/8AHgskgYsrGVCVJ64+aq0EcVY6Rbpp0UrEvJMC4XbgKMkYJz7VqQaNZFIUkgBaYE7tzfLyQMc47VZ0jw/r95patHYKUhJjUiRMnHPduvNXV8MeLRBmSwgt0XO2SWZNy564+YD881UtJO4Pc5qG0sY79hJGhQL/ABs2B09Dmr8tjZJcqsVrHIHUYyz4JPpyDWdeC30i4LXkaXJA2YjkD7269m69uPSqU/iTWJplksdMji2DCmZs49MDI+vWs+dLdm0qc3ay6I2bvT7MXEmyFVjU4+8ccdTyaZfx6NHfXKJIrSJMEeKNT8m5to6HHU47VxtxaazeHN3LI+f4Q4A/Q1oz3eozXDzixtYpZZlmlaPjzCDkA5bp9MZ70vaoXsX1LV1PZn7QYSYYIZfI85oi5Z+ei7vu4GcnB56VQ/4R+7u7oQ3OoAYmkichflRlxt79GLAUkLaghmD2UE0c0nmmOQ/KG5wRhge57/Wmy3GsLb3aMik3UqyO5I3Bgd3HPHOPyFXRkpVEkX7NrZEMmiwR2bzT3TQrHFHK4WHc3znAUDI5xg9utPurDy9WSz8wNuKBXxjIbBBx9CKNWm1O4W7kmt4l+1sgcKRhdvQLzx0qe/s9SlvVmeAJIEQDa442gAd/ak2vZXW1yeV89mLfW1m1rdPb2/km1uFizvJ8xTu5OT1+Xtgc9Kj0qK1uJPIntFaMAtNcF2BjT1GDjj3ByeKnuTqN0NrWMCq0nmyhDjzW9W+b3PTHU0sP2yGze1OlW0kbybzudgT6AlXGQO2ay51e9y+R22GWMFrPpzCW1ULHG7Pc72BU87eM45OBjHNXdKVf7MhyPX/0I0z7PfpoEMD6ZbvFvYh2kIJY55OHAJHbIqSwjkt7GOOQbXGcjOe5rKU9NGawhZ6omkCgcDNVZMjvVh2461UlJyeay5pdzoUY9iJmbsar3Dt5Lc1Ix55zVeckwtzScpdzWEY8y0H72MA54wKhndvMt+eh/wAKX/liOewqCckPD7Gulyl9VWv2n+SM60Y2en9XLUlwyZO7isue/mL4D8fQVJdy7Iz3NUY03Zd84qabe7ZyYlq9kh32q5PPmYH0FONzNtLF8YPBwKjHUscYHSnrH+5y/O411UW3Vj11RlSaTd3a6Y5Ly6m+VG4XvgU5jeDlpAR7AVoRRL5fljaOD2pohY+YRjgDPvWHtJeZXsY/zr8f8h8OpoLZULN5gYfeGK6H+0YWuI2JBXb0FcpPFsRX9aUNJu2vJ5fON1dbqP2ENH9r9DKdFX+Nbrv/AJHTXV/CynY2PrWZDJLLc22dwUzKM46/NWVM6W4LRz+bnqKZZar5d3AA5RPNU5b7q89a5FOV+ppOiuV++vx/yNPxFbX8WqTshUxAqAARn7orAknvgcFmBB4+UV2ssEeoyvc/2vZHJ+7uHpii38GXF/eLAt9bvIeRk8gVblO+39fec9P2agk5rbz/APkTiY7i6adA7kgsB0HrViwupNO8SLdRkgowP14GRXXap4Mm0va13d2yYPyk8ZNZEmlWbT731WzSQdi4zQ3Jx1X9feX+7351+P8A8ieyaVqcdzZRXEePLdcn2NS3dwoaNwQVbqa810meSyVooNcsxE4wRvBGfWrU91eqiIuu2g2nI5FQ2+35f5lqMH9tfdL/AORKGp6jDcaqLgHMnmAfQZrSub2OdGGcnIrCubCG4uBM+q2KuG3EqwGTTfsfBA1q0H/AhU3fb8v8zTlp/wA6+6X/AMidTYyQjSrt2OP3g5P4VEuoJZ6mmr6U8SXcCASx5z5i9Dmsqz0xxpcwXWLVozJyd3fiqcGkRpcM66vaBsn+Os6bklLTr5dl5lSjSdvf6dpf/ImjrnxN8RLfi7sLp44nUDyuqqe9el/Dn4jp4nVNMuIpft0Ue55T0f1NeP3GhxucHVLTbnOA3erWhLJ4d1D7ZYaxZxykYbkcj8a05n2/L/MydOHSa+6X/wAifSWrYOiX/wD17yf+gmvPrzTbNPBOn3i28YuJJwGk28kAv3qtZ+Lb3VNNuLc6/p+8wsGUlQcEc/w0tzaay/hGyQ6ham0E2UYAYJy3fH171th7qrF2/r7yZOmoNOa/8m/+RPQryzjnjIdFZAOhFfPfxI0qbTvEjny1S3k5jCHj8uxr2trbxSet7b/98f8A2NcR420e5v7Vv7T1GzWVTlCcA/yFYuMu39feOnOkn8a/H/5E8mgXJq0R8h+lW7XSY5EJOo2qYOMM1WTo6EYOq2f/AH1WuMUnXm7dfL/M7PaUoy1mvx/+RMuD70f1H86ZqJw0n4VuQ6NaLs3arb7gegwf61X1XSrRUlcapAzDHyjGe3vSUGsO15r8mWsVSaaTfxLo/PyOTumKTK46gfnXQ+DFD6zckdBaOw/Nar/2PZzqHk1aCI4+6wH+NdB4N0ezgv7pk1e3lPkFcKBwMj3rPERfLL+uxx4utD20l59n/kctJct5cYC4BjFVTubqK0xotkljCp1+2+ZmbPHPT/aqP+xrL/oYLf8AT/4qt+RnB9Yp+f3P/Ih04H+0IvlPft7GorgH7VNwfvnt71qadpFmt/ERr0DHnjjng/7VEukWhuZf+J/bj5zxxxz/AL1auL9kl5/oX9Yp8vX7n/kZA3bhwfyppZsnr19K2F0e03D/AIn8B9uP/iqa2j2m4/8AFQW/X/Z/+Kq3F/V0v7z/ACQvrFPl6/c/8jJaV1t5sbjlQP1r1HwMnl2uSMHavb61xlh4etLq4ii/tuCQvIoCgDnHPrXpWiW0UV3eosqgK4UfrWKi+VmtKvDkn6Lo+/obDH/S0/3f8amz71H5SC4Q+cvA6VN5aD/lqtZckiquJp2jvt2fn5GDbf8AI23v/XEf+y1tE8nrWbb20Q8S3cguULGIAp3H3fetbyl/56rXRiItyj/hX5GCxFPz+5/5EXJ780hNS+Wn/PVaTyk/57LXPyMf1in5/c/8hsP+tA701v8AXH/e/rUqIqOG81TjtUJIMpI/vU5K0LMVKanXuuy/MfHn7ZH/AL4ooT/j9j/31orw8w3h6HoR3l6md4RgeaTR9veGT+b11uu2Qi0+Nnf/AJagfoa43wLeNPNYQRtiSBHBwOcHcf611/iGNl09JZ2wPNHzSNgdD619a1/tS9Tjyx2qxXmbYltYuIl3kf3Rn9axoLiVvFN4UATMI9/7tZeq/ETwvpBMf21r6cf8srRS/wD490/WuHvPiPqs19NeaRZx2RlXZmfDso45x0zxXDzRVxqnOR7E0bFDJM52DktI2FH9K5nVPiF4X0gtE2oC7nX/AJY2nznPocdK8c1K/wBT1l92r6pcXX+wznYPw6VXjjhhXEaKBWTq9jaOHX2mdenj7WNLs2stJtbeMO5k8+YbmXOBgDp29K5vUtR1TWHL6vq11c5/5Z7yif8AfK4FJNLslH+7VORgW5NGIk/aSN4QitbFz9zBpkSxRqAHOAB9ahExPanyN/xLIv8AfP8AWqgb3rnudVXdeiLJfPWgNzUAfJqRWpGRMG9qq3l5An7tnwwIyMH0qcHnrQtzFa6PrcwFytwTDGJIZxGQGB4+6TjI5GeRxxW1CbhVjKO9wKWoX9vLbqqPk7wcbTWreatZNOCs2RtH8B/wqtr1nY/2leXd+bpklv8A7Oi27BSuFBLHIOfvDA4zzyKkbQLSO/ttPuZZ2ubq4kt4pImASMq+wFgQS2W7AjA9arnfsOXzMH/FTGf2paf89f8Ax0/4Uv8Aaln/AM9f/HT/AIVl39rZWem2LL9oe7uYPOZi42J87LjGMnhfXj37aumQweXo1k1tC8eopIZ5HjBcHeyDa3VdoUNxjrzmsLOxqp6l2XWrBtJhjE/zhySNje/tVA6paH/lr/46f8KybS4W2WCVioXdhmaBZsDnna3BP1rRvp7W11mC4hRYrWe2RjI1lFJu4wXERO1cspGM8c4qVFobqJitqVr/AM9f/HT/AIVXe+tj0k/8dP8AhUOupbx643lxPHaOI5FVQFLIyKdwHIXOc47Zx2rVl8KW0IbzLic+VNK8m1hzbqJNrDjqfKb/AL6Xiiw1MyjeQdpD+RqCe5iaMgOc/SrTaPaC3NuJLn7cLEXvmbh5WCofZtxn7p6568Yp1x4ftXku7C2muFvLN4klkkYGOTe6odoABXDMO5yM9KXKXGq0yj9piEPL9h2NQT3ETPDh+/oa6GLTLK90qXTLGS5j36xb27yXDBs/JMNwwBjv8vOMDnmqn/CPaZLfWS/anjjfz/MijvYbiQBIi6uCnABIxg+nXnjfm/cqHm3+CIqTcl/XcxZTE7ctkD261BKynCpnHrVnWLW3gtLG7sjcLFdIx8uZw7KysVPzADIPB6VqSxQrC+l/Z4RENHF4J/LHmeb5Yk3b/vYydmM49s1MboxqWluc7y0qjGEWrJK/ZwM/Nmq+hX4tb5RPKEiYYd2tI7kj6LIQPxzmuolvNN07UNWtmRbNp7iFrV5LGO6CRlWJyrn5QdyHjJGMY4rooznComjDlg0Z1nFJPC8kakgPjOfxq9DaTq5LRnaRg8iqsVtc2U19ZzSYnguXR/LOF3A4OMdqvNIy5RnbBxg56Vj7/Ror911T/ApXNlcGPaIzw3qKbe2NwYGxDuYdORUl5JIGx5jjJGPmNE0suwjzHyPeuxqr7CGq+138jKbo32e67GYNHvZEDGILnryK2/Dng+5vJlWZAsO7Dlhkgeoqbwva3et6gljDMGmByQ7HpX0XoXh2003T44XgiZwOSVB5+priSqd0bN0V0f4Hnlp8PvD9pdW1wtwDtX94u04LevSte80+wsrv7fa3C5GFKhCMjv2r0FrGzxxawf8AfsVVutPgkhcJawZI/uCn7/kTel2f4Hn/AIsFlqPhqfy9jyou+MFD1rw26064kunbyB25BFe1+J9TTS9Km0+SzVZGUgSbQDzXjsrSm5f97J/30ambqKO6OijGk3s/wEt9NmUcx4/EVYaynP8AB+op0RlA++/5mpAZD/y0b8zXN7/kdsVT6JlU2E//ADz/AFFINOn5/d/qKtEyY5d/++jQN+Pvvj6mj3/Id4dmXLO1kTRZ0K4Yyg4z9KzPsU6ysQnf1FbVtu/sS4O458wYJPPash3k3sd79fU1NPn5Zev6Im9Pm2exMLaU9U/UU9bFjyY/5VWEkm7iRv8AvqniaVeTIf8Avqq98b9n2ZYOmjy3YxgttOOla8evavbeGbbTUbMUcu9VODjr/jWE93IInG9h8p71Vku5P7PjHmPnd/e+tdGF9p7aNmtzOqqbpu6Z6ZqPxB1u7t1iUpHxhigwTXB6jPqF9MTK8kme7Nk1DNdyY4dufeqwlkY58x/++jWLlUfUIwpR2T/AlgtpWThe/qKcRtJUjpTrBn81QWOCTxn2pZR++f6murFyf1qafcUox5VJDF5kX6iq+oj5pPwqymN6/UVBqAG6T8Kpf7u/VfkzWn/D/wC3l+RkXQ+cfStjwYWXWLjHQ2zZH4rWbOivg7wOK1PCo8jVJWUeaTAw2r25HNZ4iEmpNfmjOvRlKcmvzRzFx8kdvGeNsQ4+pNQZHrV6SFXYEzLwAOKj+zx/89hW9n/Vji+qVPL71/mLpJH9pw8+v8jUc7hb6fPTzG/nVzToUS/iYSBiCeB9DUF1BGbuYmZQS549Oa1al7Jev6D+qz5bfqv8wT/WCmsrM7ALnntSxKRKo+1Agdqs2wKTZ8/POcA1q4v6ul/ef5Ih4edrfqv8zc8FWpk8RWquP9UrSsD+AFelaX/x+Xx7eZ/U1yugSeTILhbQvIV27wOSPyrqNJEnnXMkkTR+YwIDD61z2ai7lRpuEJc3l1Xc18/6Sn0qxmqoP+kJ9KnrEVXaHp+rMe2P/FWXn/XEf+y1tZrEtuPFt5z/AMsR/Ja2s4rpxPxR/wAK/IwiO64pp60hz2pDn0rnGGcNg0L99frSbgRSqRvAI5zSew47olT/AI/Y/wDfWikQ/wCmxgj+Nf50V4+Ybw9DeO79TzOwuNTsb2G80y7FvIE2bs/XPH41JcQ6vqN00upapJdEjgPJwPwArjLJiLVOeh/rWvazFm5P8NfUKpGWIjdfj6meXU5qpFRa37eRoR6S0IxGIR+P/wBantp8+0fPH+f/ANasQy+9PaT/AEdTnvXEpU9fd/H/AIA3Gpp734f8E1P7NnJ+/H/30f8ACkOm3H/PSL8//rVjeYfWjec8moc6f8v4/wDANFGp/N+H/BN6606dpRh4/u+v/wBaq50u4P8AHH+Z/wAKp6g2J15/gH8zVXfx96tK8qftZXj+P/ACMalvi/D/AIJ0D6bOdOiTfHkMT976+1VhpU/9+P8AM/4VWkb/AIlEBz/Gf61UDe9YuVP+X8f+AbVY1br3ui6f8E1l0ucfxx/99f8A1qkGmTf3o/8Avr/61ZKvj+Knh896Oan/AC/j/wAAy5an834f8E1hpsw/jj/76P8AhVK90XUXhnjingWCdkLqTySoOO3uaiDcdakuT/oMf+//AI1th3TdeEeXd9/XyKjTqST97p2/4I+S28QwvLOt7aGSaQSOXRWG8dGAKYVh6jBpHXWbLMMd5FhsvuIDMpbhirFcqT3IIqlqDf6OvP8AGP61eDAdDmio4exjyrdv8DKFOXPeUr2M+bT764SFZZ4mEMflx9sLknHT1JqxAmr2tmbWG6hER3YyoLLuGG2sVyuR1wRmrO/1o3gmuW7NuVCR2Go2mnW9xG9ljmMB4lcEZJ5BUg/U5PSoc6sbprh57WR2QJiSFHQKOgClSoA9hW1Mw/sG25/5aH/2as3eM9eKlNspwRl3djf3lw9xczxySv1Yk9uB26YHSrEs2tP5u++QiW3W1fgcxLjC9Pbr1/M1YZ896hLj1qtRcqKzPq32D7H9qi8nZ5f3Rv2Zzt37d23PbOKivJ9XnsvIlu4yi7clVCs23hdzBctjtknFW2bjrUE7fuWyaltmkIRckJc3+t3Vuqy3kYAlW4zGioTIAcOSqglvmPPU9+gqvNe6o1zDN5tskih13R28aZ3rtbO1RkkE8nmrSO2xeR0HaoJ2Jmh6fe/wrenWpqHLOF/nb9CKkUr/ANdSnPBdzW0EDyoYoNwjXH3cnJ7c806SbVX0/wDs83KfZ9uz7i79md2zfjdtzztzj2rSDc9KQqTnir9vQ/59v/wL/gD9lFmXaSahaXLPF9i+ZVVg9rG6/KMA4KEZ9+p7mpv7Q1aCeWfz7eSeWQSNJNAkjBuxUsp2n6Yq0I8HIWqV4P3e4Kc7h/KurC1MNOrGLpvX+9/wDGrS5VdFi2kkRJHncvK0m9mzkknHJPrU88+8BhmqCFwrqQ2HHHHQ9qky/wBnCFW4as/bYf8Akf8A4F/wDL2T7DribzYlH8QbIp80u9SV71SlRwnKkEHvVqOPzIypV1BI7V2OtQ9hD3H9r7Xp5GU6avquqGafeX+k6xFf6fMYpVwCR3HcV774f+KFnfQJDcWF5HOqgHADBjjrnIry3RfDtlPIrvJqLueoghA/Ug1674V0/T7UCO03CUff3zF3/H0rjjVoP7D/APAv+AbTjGK2NYeNNNwN0N0D6bB/jTT4y0/nEV3/AN+x/jW5KluTGJHUvn5cnkmkZ1UtGCCw64PShzoL7D/8C/4BkuXseSfEXUbTVoIpIIpgydS6gZH515iQvmlueTXsPxM1MC1isoJVeXdmQJztHoa8v8pyf9Wx98VE62H5fgf/AIF/wDroQfYpq6Ds30qQSp/darflP/zzP5UeTLwfKbH0rH2uF/kf/gX/AADq5ZFQyp2B/Km+auMYNXDFL/zzP5UnkTHpE35Ue2wv8j/8C/4AOMh8N1GukzxlWyZARx9Ky3dST1roYYpRolwChz5g7fSslrafJPlHFTTrYXll7j3/AJvJeRLi+b5FEuvvTC/XrV421wf+WRqFrWfvGar2uF/kf/gX/AHyyKsj5RwM8g1WfP2VF96vy204hc7ONpqnJbS/YY328bvX61vhauG9tG0Hv/N/wCakX7N6E+4e5pysB61KLWfOCo/Opfsky9uvvWHtsL/I/wDwL/gD5JdhbAfvVz6n+VLL/rHx2Y0+2ikW4XIH5+1RzRymV8AdT3rCtWVWvKptcpwl7PbqIg/eL65FQ3/WT8P6VKiSCRen3h3qG/Vt0h4xxW6kvq79V+TNKcX7Pb7S/Izpl+UfStjwYp/tqb/r3b+a1Q+zGSMfMBxWz4QtCmsSnfn/AEdv5rXJVkuRnPOL1OPZc1ERV42f+2fyqNrUd2P5VspIzcWGlj/iZw/U/wAjVa8X/TJ/+ujfzrR023C6jEdx7/yNQXVuv2uYknl2/nXQ5fuV6v8AIlxdihCP3y1oWkJaYY9ahSBRIOTW3pFsG5zk5rdO+GX+J/kjnqxsrnY6G22NR0wK6uBvl965TTVKBa6W3bt1rBmKLuf9IT6VOD6VWzmdT7VYB5qToq7Q9P1ZkW3/ACNV5/1xH/stbWaw7bjxXef9cR/7LW1k4rpxPxR/wr8jCI7JI+lNPFHQZFJnrXMMOlC/eX60n86FPI+ooew1uiwhzeR9OHWimJ/x/wAZ/wBtf6UV4+Ybw9DeO8vU8Q06ENYxHA5z1+prRggZX+XaOPSqWmg/2fEcev8AM1pQM28g56V7dOpL60o+f+Z24PDwcoPVXts/Ir/Zs9An5U8wERgYXj2pUZuwNSEkr3rljVlZ6LbsRKhHTV/eQC3OeQn5UvksOML9cVOMntSrnJGKj20uy+4v6vDu/vGzROZBuKk49KYIDn+D8qvMIX5dyD04FAS3/wCejf5/CuuupSqNpx/AmnQjyrV/eRPBL9jj5Tbu4GPr7VEbdh/zz/KtNhbiyj/eNt3cH86gxbZ/1rflWXLN9Y/gbVKEbrV7LqUfs7Kf4Pypwhk7BPyq4fs2MeYaX/Rto/emjln3j96I9hDu/vKqxyg/wflS3SOLNMlfvdh9atZtRj94aivDD9lTDnG//GtcNCf1indx36NdmXGjFKWr27leexknQISgGc5xSDTrkjH2pvzNXw9vj/WmnLJAD/rG/KoU68VyqUbfIj6rTbu2/vZltYXAP/Hy3606PT7g/wDL0361ouYOpkb8qYrwZyJW/Kn7Wv8AzR/8l/yD6pS8/vYsmlXo06JjfZjLnC5PB5qqNMuMf8fWPzrceWP+yYCzHZvOD+dVhJBtOGNR7XEfzQ/8l/yL+qUu8vvZlnTbgHBuj+tL/Z1w3/L3/OtAyQ45kP5U4NCB/rT+VL22I/mh/wCS/wCQfU6Xn97M3+zLj/n6/nUV1p06WzsbkkDHHPrWsJIc/wCtb8qhvjELOTEjE8fzpOrXtrKP/kv+RcMJSUk7v72VINMu3gjK3JAKjjn0qvd6ddJc2oNxks+Ac9Olblq0Qtov3hzsHf2qrqDRfa7Ihv8Alpz+Ype1r94/+S/5GdbC01Fu7+99yMaVeY/4/P1NNbTLsHBvf1Na3mQ5yHpxaI85P5UvbYj+aH/kv+Rr9Upd5fezJ/sy725F6xHtmqeoafPHbqz3LMC4GOfeukV4wOCaz9adTZpj/noP5GujCVa7rwTcd/7vn5GdbC0lTbvL72QDSrsj/j9P5mm/2Rdk/wDH2fzNbW5dvX9KQSoBgs35Vz+2xHeH/kv+Rp9Tpd5feznNQ02eOAF7kuCwGOfera6Nc7wzXhB9ec1Y1dk+yJhmJ8wdvY1faRMcsfyrqqVMQsNTalHVy/l8vIzjhaPtHe+lurIY9KmCfvdVnC/3FB5/WrcBvYIfKh1m6hj7JFkAf+PUxXRh94n8KTcg53NXIquJW0o/+S/5G8sPSlun+JPFJf283nQ6tdedjHmsx3fnu4FV5JdVMrMNcustyTuPP60eYn9400SRg/eP5U3Wxb+3H/yX/ISw1BdPzKEtndMzM2ozNnk5zz+tMWwnZci/lXPYZ4/WtGSSIqcE9KZG8YiByeBRzYhw+KN7/wB3/IOSmppeXn/mUTp06nnUZfyP+NPbT7kJn+0psfj/AI1aaSI8lzQZoQuPM/OoviP5o/8Akv8Akaezp+f4/wCZRGnTkH/iYzfr/jSDT5zx/aEw/A/41bM0J/5a/lTPPhGf31UniP5o/wDkv+RPLT8/vf8AmNOnXI0uZv7RmIDD5ecHp71QNnPt5vpf1/xrZE0LaNORLkbx/Sso3VkBg3QB+tOnKu4tc0d/7vZeRjy0+d+nd/5kH2Gb/n9l/X/GoJLSbP8Ax+Sf5/GrpvdPUHN4v51Cb3TM5N4Pz/8ArVoniP5o/wDkv+Q3Gn/Tf+ZSltZRC+buQ/Kf89abbWLS2ke64YjOdpGe/wBanuL3SzE4F6CdpwPfH0plnqOlx2qLJeBWGcj8fpVqWIW0o/8Akv8AkTy0uv5/8EvYGelB4GMVWOraOD/x/D8j/hTH1nRv+f8AP/fJ/wAK5vqlTuvvLdWHcuQn9+tQSsfNfnuait9V0h7lFjvGZznA2n0+lRy6xoyyuGu3DBiCAh6/lSWFnz2utu4OrDk36kwPzr9RUF+RmT8P6UxdW0Z5UC3UhJYADYf8KjvtQ0vzJI/Pk83jjacfyrsWGmsO9VuuvkyoVYez36r8hUcCNfXFbfhM51eX/r3b+YrnRqmjKgVp5gwGDhD1/Kt3whqGlS6xMIppi32duqnplfauarhZqm3dfejkqVYaq5zzNxUDuBTmv9HP/LxP/wB8n/CmG80c/wDLef8A75P+FbrCz7r70ZurHuWNObOoxcev8jVe6b/Spv8Afb+dPg1HSreZZUmlJXplT/hWXPqSyXUrBDsZyQc9s1pOny0lG636PyM3Uj3LiNlxW7ohJIyOM8VzlvIZHOAAB711OixgFc+lbJWwy/xP8kc9WaklY6yzHT3rct+grHswNorYh+6PcVzsyRbB/eqfarIORVRT++X6VYB680joq7R9P1Zl2x/4qq87/uR/7LWxnjNYtv8A8jVeEf8APEf+y1sg/nXTifij/hX5HPEcTz703PAGMUGgmucYnSlQ/OPrTc+1Kv31+opPYcdydP8Aj+j/AN9f6UU2P/j/AI/99f6UV4+Ybw9DeO79T54ju5o4wizMFHQA1JDO11d28NxMzRtKoIz15ro7CHTksdEhRVkkuobmaZZbOM7iqSAfvCSwwVGABg9eDxXPNpTW8Vu6XTvqPkC9FusGVWMAuCXz12jdjbjHevZcuZt2NIRjSmtW7FR4YPJmnF1KI0kEagx8kkE/3unHrThp80sJaKSbIjVwGhI3EsF455HPX2qS6s52jurZI9ksDJLcRLE2I8kLwST3cA5A5PFSXM6W+oahazyCOUxmGSTyWXdIrjIIyTjAOen09aoRvNKW2v5Dc6T/AK9fP0KVvbXRuJIQsxYIcq0XI9wOfzpPKuixEW+QKu44TkD39OlaEktuXniZRm1jWPdIJNp5w2cc+gFOS5i1G8McJxi4EqtsfLDGT07jnr610xw8O/5EScWrJlCOOVrhbcS/viwUoVAwatT28sAj/wBJjZHzhgo7deoqNLpF1y5naHIjkdi6gk7c4z6dDTRJE/kWkLq+0yyM6o20ZX3Gf4ahwjyy7q//AAPv1FzQ11JEikmj3x3cTYUuUK4OB1PTH61OInEOfPg3iMSFeNwX16YxVe1G2yObpzbNFJmFUcFjjv24JB61OGieJZVz509uIUjMR3EBcHnpjCk1ahTcVff1/H/gfgCmt79Bsbs3KywOCQvQdT0HA61JDE8kjqXjGN2AyDOVGSP5VBpkHkCYvGfmVZIAEb5pB939SatNKjXUXllmkmilx+7bmUrg4/SlCnSaUpP5BCSsrsi8uUSvG8iq6jOwxrkfUY460lzY3kUojY5ZhkAR9fpxz1FQJcIyjeXeZbJt42nrvLgfkRViW4i8x3ZVKXlsD86vhCAuc4wccds1SpU9Ne3YIyjZ3ZFc7g75mjVlOGTao2/hio7y1khjkf7QjyCXY4jA+QnoCOAPwplzN54luPLj2KEQSIrhWIxx83NXLwRquobAu551dg6uPLJOTuP1I6VmqcLSXb+vnroP2id9Sl9kkFuZnuliQPs/eIwycZ4wDxT7FGnjRRPH5khOEOd2B6YGPzNOsWnj1NbWSdIT9p2yW6pIfN5xjGMHPTnFP0rKMHhuyLZnYPEEcE4BOBjg8dz+VONKD5br11/Hf+uzBThffQs2U0LWcAuJEIy+QxPXDYzjnrikeLy5JbnCMiQ+YsaOdj/MFzyc9T+lR6fpxmt9PwrM97K0NqrwMRO2cckNx8xwMZ9xU0Wn6pNAb1Yj5As2ZYvIbY2C5aPOc8eVI2c5+U1xuLvoEK1JRSk/w/P0NvwrHa3fivR7eVcQXk0CvHvIJVnUFc5z6+9aUls1q0cranZPavM0DzJOxWGQclWyoPTuMg4OCawtJjm0XxNaatfNIBp2oxRGAQFRvjKsUyT8uCCD1PfBqfTfFEFvrtrZ2uk+RBBeS3Nyj3HmF5NjL8h2jaF5K5DEE5JOKpQXU5qsoym2m7fd+p1kGnh3Ux6paPbNbtci6WR/L2K21jjG7hsDG3v6Uj2cFxYyuNb08Q+YIUmedtkjnB2jgnoR1AAyM4rFvPGttqL6XqMlvqyraieBHTVGNxuyjBvNKk9GIxjH0q9b+OY54NQCre2iyP8AaFj0+9kgmZ1RVJZgpVshQWJAOQTxmnyR7GaSvu/vf+Zf0y3hm1OPTbm+SGWLcJ0EgLR7FJcYz1AU/lVXX1s44tKv7GaQ29zI6ASyhyjJtz8wABGGU9BXL2+sC0u49bS33z/aX83zpWczK6ncGYnkkMQT15q/dahbahHpVpbWcsFhbK5SJpw0hdwPmL7MHkJxtHC46nNJxXYrlg95P7/+CdPaRWd1o11cG4eKS3jLGQ30ZDNkYURY34OfvZIqLVoIrLSY7myF1cDyYXluE1CN1jZwCQ0SruUZOASayo7mHTrOX7PY/wCnS2727TPc7owGG1mCbAc4J6sQM9KhS+hi0u5tbKzaK4uolhnlmufMG0MrHYoQbclR1LUcnkXyU/5n/XzLmmQ6hqiK8E6qpuI7dt8rDYXDEMePu4Vsn26VoxaLcSx3cOoSopSK52b5mAjeLALnHbJPrnB46Z5vSby80W31OOPy5vttq0Kbnx5LngSDjkhSwx/te1P1TxldyxSyT2axuNPNiQsuQXblpenUszNj3xmqhFqSaWpE4wW0n9//AATqPJuYhc5vIZYltFulnEr7TGZFTcvAOcnBDD14zirOo2P2fVLm3t9ZiNvbqHllkkceUOAN3yjJJIwFBrztPF0z6c1p5ABOmDT95k7i4E2/G3224/HPatSLxvLHqNxe28V3CbyFY7sW995b7l24aJgmU+70O8cmi3katR/mZc8QJercQ2H2kySSSRmNkkJVg4yrD2IIq3rUEMVrfSWF5fF9PultpjNKCJd24b1AA2jKHg56jmuI1nXru71U3qXV1uUrsN1P58gwOMuVGenoKt6t4ujv4p4rewa2+2XS3V8VuA3mMN3yx5T5F+djg7jkjnjFXLmdNJrTUEoX+JnReHGtNRvFtL86n/HLJPBehFjiVSzHaY2yQAe4zwKs6VpA1KPTYftGptdaoZRA8c37uEqSAHGMt0ycFcAg81wdp4gmsbPVYYIn33sYhSVnBMMe8Mw4Xknaozxxnjni1ovjSbQrJ0hN6bskspF5ttw2PlZognzMvUfNjIGQayt5DfL0kzqtLMkmmRO5Z2OcknJPzGp2V/7h/OuBtNdvIrVI0ucAZwNgPf6VIfEF+G5uCo7koo/pWbUux1KGGa1m/wADtHV+fkaoSj4+5J+Rrkk1++d1xdDlgPur/hT7jWtSSVgJmb6Rr/hT9/l2D2eF/wCfj/A0tQjuzbSrDHPu3gjapzWM1hq0pH7i+Y9vlar+l6tqVxcrGbjBJ/iRf8K3o7rVIpAUu0EnZto/wrmqVeSXvI78NhKVWm/ZzelzO0LTtQW0kWSyuQQ/8cTZ/WtNtOvP+fOX/v0f8KuWmrakrOLm9JzypjRP8KsHW7kH/j6nP/AY60VWm1ucksLWT0K8Wn3Q0adTay7vMHHln29q4C80DU2vp9mnXTDzDyIm9a9KOr37afLJHdSBFYA7lTOePasaTV9Y8xmW8UA+qLn+VTGpCMXbv+iEsLUlK0mtjhz4d1X/AKBl1/35b/Ck/wCEc1X/AKBlz/35b/Cu1bWNaA5vl/74X/Cmf2zrWM/bVx/uL/hT9ui/qb7o44eHNUHP9mXPH/TE/wCFKPDmqvyNNuSD/wBMj/hXXPrWtCJm+2r0P8C/4U2DW9ZeBW+2rj3Rf8KPbIPqjta6/r5HKf8ACNarn/kHXH/fs/4Uv/CN6r/0D5/+/Z/wrrW1nWgMm+H/AHwv+FRHXNZ6/bR/37X/AApqqiXhGuq/r5GFp3h7U476J2sZwATzsPofaobjw7qbXUpFjNguSPkPr9K6e11zWGuU3XmRz/yzX0+lRza5rImfF5xuP/LNfX6VKqLnKeGfJa63/roc5b+HdTW4jY2cvDg/cPr9KnvNA1E30kgtJscf8s29K2E17WTIv+mHlh/Av+FLPr2srMym8/8AHF/wrp9p+5a8/wBGXCg4091uvy9DnW8Nai3zfZpeeceU1b/gzQr631mZpLaVQbdhkxsO60v/AAkWqAYNyeP9hf8ACtnwrrmoT6rKklyWUQMcbFHce1c1Sa5Hc5J0t2efnw7qPe0n/wC/LUn/AAj2o97O4H/bFv8ACt7/AISTVf8An8P/AHwv+FNPiXVf+fw/98L/AIVtzEOmYf8Awj2of8+lz/35b/Cj+wL8f8ulz/35b/Cto+JdW/5/D/3wv+FN/wCEm1bH/H4f++F/wp3J5DPttIv1uEH2K5wO/kt/hXYaTp94qgG0mHuYzWDZeJNXlnJN4cD/AKZr/hXXabrGotGpa4J4/ur/AIV26/Vl6v8AJHPI1reCSJR5iMhPTcMVqQgcZNJdM0lvZO5yxTJ+uBQg6VzMETqf3qmrCn0qsp/eD6VYU8UjertH0/VmXbH/AIqm7/64j/2WtjisW2P/ABVN5/1xH/stbNdOJ+KP+GP5HPEcOCOaT2ozjGaQ56VzjDJxSo3zrn1FJn1oTh1+tJ7DW5Mn/H/H/vr/ADopEP8AxMI/99f5iivHzDeHobx3l6nz42sX1lNYhYYc2cMkce4E5Em7OeevzHH4VJa63evbfZhbWxmFsbYXWD5vk/3PvbenGcZxxnFUtRBNwP8AdFJYfJOxP9w/zFe0mubU6XSaxfL0uT3HiS9nhaP7NarPN5az3CKfMnCEFQ3zY6qpOAMkDOauT21zf3sl5NBGJbl3mfGMZZixxk9MmsWzh8y9j3cqDk4rrpbiBjCUVwqrgZHPetKKi7+jOFxm9yC8lurq2MJ02zjZiplljQB5dowM84HvtAyeTUVubq2+2fZ7OGJbpdhVTnyxuB+XLE9sZOTgn1qybuPnh/yqNrtB0DflUqSWzJ5al72H2BfTo5kbRbG681dpactkLxwNrjHTr196r2MdxYX63UdjbuyhgElUMnzAjpnnrVm5vFRwMN09KZHeIHDEN+VErJsFGppoX5I7trdTHpNlDEYmhCIowN3Vslid3uSaaYdZ0+xtU/sawfAYwTyKpcK3UcNg9T1GRngirMmqwmwjAWTO70+tX9av1GnaUcN80Pp7LU6GtWE01bsjCtLzWLRLQDRtNka0EnltIgJO8knd82Gx2z0wKjs11izisSukWLm0cukkqqWYEgkN82COMdM4zzVs6xawgYV2b3AqvLr+4khG/Gn7ply1DHb+0LPUY5/sVruC7GidVKOu3aQRnuP8Rit42Orahp6zLoFgkRi8iMxtjYvXjL5znucn1rEW93zPPKpMh6Hrge1dXpOqm38NoZQ/MhKDH1rWiozrQi+rt+ZpSpTaaa6EWo2OppElhL4Y0wMsaplZDnAOc8S4ye5xzTLWLVry+eOTw9pjYeOS4OMeYFIAz8+MeoXGe+aunxAr3r3DiQs+c5UH+tPtddW3vp5mjYmSPGNv09/apiou/kSo1He6OX1SW60vxCNVks7VrmC7Fx5ciKY2YNuwVUj5fYY4rPm8STrcWxh0nTIFi80+VFCdsryJsZmyxOcdACFU9AOaueLrlLvWWljLqjxq6owxz0NYwUK24f6wj7x/h+lZOSuONKXI3bsb1hrOo6J4bsGWxtJntrl5LOeYEtayHOWTDAE5UH5gQCMgZqPSPEmtafp2n28MFqbawvTeK06n52Ixsbn5kwX4GPvtzzSyXcEXhm0hWPzJFlJy4+Ufe96xJ5ZLg7mYuVIPHQf4URkKdKStZHRabrN7d3c9olsk8n2t9Rklc4JlOM9+n60ukarrN/4lfV44Ihtme6faoCqeScZPI5xg9RxTPCkfmaxPI4wGiY7R9RWlocsUdtPEPNLTyRxgKowF3Bj+gpxa5mc8YTdSSt2/U7nQ9A1u9sYWh0WzS0Uu0UEEm2PL9W+aQsT079gOgxW9D4e8Q29u0cOi26StGYjOJV3lSMHq+3kEjOM1raV4s0uyso4Vt7vCrjiMf41of8Jzpn/PC8/79j/4qtLxL9nPscu+ga8NK+wHQrbaG3+b5/z7sYz/AKzHT2xXVW6yW2j2kEo2yR26Iy5zghQCKrT+OdNxxBd/9+x/jWPd+NLBs4huv++B/jRzIPZT7CarLnPNc0bho5jtNLqHia0kJxHP+Kj/ABrCk1u3L52S/wDfI/xqHJFKlPsdVFPuGc151rN2b6x1ubOR9sCL7Bdo/pXQjxDbxwudk3Cn+Een1rjoJQ/hS/kIOWudx/Eqa1oSXtYeqMMRTkoq66r8yhHCpTJz+FTxRBTzuU9uaSK6hOAUcAdxU4u4QclH/EVldHUqcuxHdPKIlBldgD0ODVe53t95nbJqW5uUZFwrDnJyOtNlmQnofyrqqtfV6frL9CVTk5PQo+WVHAwfrSB5s/eJ/CrBlRVwoP4imtMAMBSBj0rkuinTl2K7PNgkuRjnoKYskwGfNP4jIqUyBlwQenpSAqFy4OBQ2hKnPsOChDbTLGoaR8EgcDB9KgvizXcnJI47+1WLaZTOA6ny3YDaP4fQim3uEvJRjnj+VO65Rezle1ix4eONSjDZxk117FfPXGelcVpUwhvUY569q6H7cpG/5sDivNxavNNHv5VeMJJ9n+RqsyK5/nULOgJJ61nfbl7lvypjXsfq/wCQrFQZs5HRRODoc+Cf9YP6VlNMoP6GprS5R9CuDlv9aO30rGku4gzD5uvpVU4+4/8AE/yRztvnfoX3mXHFRGcdc9aom7h9G/KkF1B3D/kK0UQbZclnXyX4HIPeo7eUfZkGMf8A66rvcxFWAD5IxT4HAt045/8Ar0coXdiy8w9/yphmx0/lTScjoM+tRsTnpimkiG2WbaYm7jGPX+VV7iVvOk/3jT7T/j7j/H+VV7g/v5P94/zoS9/5BJvk+YRSN5yem4fzp94x+0vz6fyqKL/XR4/vD+dOvP8Aj6f8P5V2L/d36r8mH/Ll+q/IryOw71u+DWJ1mYk/8u7fzWufkJArd8F5/tib/r3b+a1y1P4bOSZzZJppPag59abzW5mwPSmOcKTSkGmMCSq56mqSM5uyNHTI8IWx1rtNNX92n0FctZR7Y8D0rrtNH7pCPQV3P/dl/if5I5vsnWzjNnY/9c/6Ckj5wD0zT7kYsrL/AK5/0FRJ93JrkY0TrzIB7VYBFVlOZBUwoN620fT9WZlsf+Kou/8AriP/AGWtkfWsS2/5Gi7/AOuI/wDZa2c85rpxPxR/wr8jmiOznvRnIpvSjJzXOULuyPelU/vFx6imbsGnLjzF9M0nsC3RMh/4mMY/6aL/AEopE/5CMf8A10X+lFePmG8PQ6I7y9T531Bf34/3RTdO/wCPlv8AcP8AMVNfr+9B/wBkVDp//Hy3+4f5ivcj8fz/AMzp/wCY/wCf6Gn4J09r7XVwdqpjLeldprlmsOuJGgx8mTz7Gs74SWf2nWJQegYV6g+lWlx8RIYJFXy/s+SCM/wmumlH3fkzy0/efoeXSW0mSAjH6DNVpLabH+pk/wC+TXpGsaeuk6lLBJbP5LHMUoXhh6exriNX1bUba4zb22FBI2bCeK5ybmNdo6yAhGPHpUCSuTgDmr11q8+BGbORs85VTgn8qbDDf3XI0y5wehCU57saLjMw0iHc2PnP9am8R3D/ANlaUsIyTAe+M8LxSS6Drl7pkMFtZMkgfJErBcDnmti/8GancabpkUlxBE0UWH5Lc4Xpx7VKN6+69F+RwUMs6hnuxGgx8qKQT+NVJ/Mlb5JHwei16PZfDnzMbpJ5h3baEUfrmup0rwZpukkSCESz+rchatJs5m0cBoHge5uFS61SRoIcBhEPvMPf0rZ1Ro9kCIu2AOAi+wFdbqSSSKsCkh5HEage/X9M1zniG08y9MUSnbGyoMewrWgrYin/AIv0Zvh3dy9P8gltla8uLm4QLCn7z6+grHhuHutXmduPkyB7cVd1O5UP9gW4V0RvmfeDuNYV7qcWkvczrh5SgSJV5y3HP0ohCXvaPbs/8iIJ2ZmeNJIZr2JYzmW2gIbHbJ4H865d7meDGyQKpHT1/CpT5s9vLNLuaWWbLE98D/69QSblJIjLHoBtzWDhPmWj+5/5GkU/Zy+X6m1cyb/CFiZfmJuG69Or1mRTmVtsjjCnCoeFH4VqTRbvBlmjAeb5zHHpy1RaeIZrY+dYwtIhH3sgsKUKc7PR/c/8hTTuvRHT+FI1FyzKVZfKI+XqORXb/Dm2gm1KeLA3Q7ZkHUdNv/s1cXpNnpaXpeyE0NwYiHgD715I5B4xWt4Ia70Lw3repG5T7WGijCmQZ2EjIHr71Uac+d6Pp0ZzL+LLTov1Pf4NvlAqQR6g0skm0Vg+Hb+0t/D9tHJeQAgEjdKM4PPPPqTV2bUrExFvttuT2Hmr/jWvJPs/uf8AkVZjbu4yDXP30/B5o1HW7WIfLPDJ/uyCsS41S2kGRcRc9t4qHCfZ/c/8i1Fle9ferY6j+VYkgJbIq9Lewb8iaPj/AGhWRrt8tnYStbujOeAQ2doI61Hs59n9z/yKsxupT+TpF22eRGa4+0B/4Q+7zn/XL1/4DWldXpuPCkzs+ZGUKQepOfSs61D/APCH3YP/AD3Xgf8AAa1oQkqsLp7rozDEp8q9UNhMeA289fTrT12s5BY896rxgbQcgY7mpo5VRic7m9xwKjkn/K/uf+R0KI28ZDFwMndk7u1TuFfndj29Kgu2R41bCg7ux61KXQA/Lye4NdNSnP6vT0e8uj8vIFpJlKRirHnC471GWcjJPB6CrU21QWGG9P8A69QCR9xAAY471y+zn2f3P/IdiNxxubJX+FQeWP8AhULAuA8hA9FHYVbmjErGWM5LYymeU9h6iqjqRng8eoo9nPs/uf8AkJpjY8eagTIUsOtTXSLPK6ceag+T/aGORTI0JlQkEfMOlOuSyXTFQcjnOO+Kfs58uz+5/wCQrO5DpzgX0Z6DdXQeYNpPTHvWHjbqEbKMBiGPHQ4rVyDG33etefiqU+ZaP7n/AJHt5U7RkvJ/kSGYHim+aM9P1qLeuMcUxpEHSs1Rn/K/uf8AkaORuWkg/sO4x2lHf6VjSSDzD06mrFlqklupt0RCjtuORz0/+tUjeIrpGKCKEgcfcP8AjQqNaMXaF7vz7LyMXJc/yM/zfm60hce9Xx4ku8/6mH/vg/40v/CR3f8Azyg/75P+NLkxH/Pv8/8AIfMu5nq496UMM96v/wDCR3n/ADxg/wC+T/jTh4ivP+eUH/fJ/wAaOTEf8+/z/wAguu5nEgnHNNLAds1p/wDCQ3f/ADzg/wC+T/jSHxFef88oP++T/jT5MR/z7/P/ACBtFKzYfak49f5VXnJNxJx/Ef51sW2vXktyiNFAFOein/Gmya/epK6iO3wGIHyn/GpUa/N8H5/5DdnBepkx5E0eR/EKW9OLqTj0/lWpH4gvTKmUgxuGflP+NOuvEF2twwWOAj/dPp9a6lHEewa9n1X5PyHp7J+v6HOsd3AyK6DwZu/tiYf9O7fzWoT4hvAP9VD/AN8H/Gtvwlrt3Pq0qNFEAIGOQp9R71zVI1+R3h/X3HLJK25whBB6U09O1bp8R3v/ADxg/wC+D/jTT4kvv+eEH/fB/wAa25cR/wA+/wCvuIaj3ME9aWFd9yo9K2j4kv8A/nhB/wB+z/jVqHXb0SHMUGMD+A/41pGOI/59/wBfcY1VG1r/AIDbZMQk11WnL+6j9NorNh1u4MR+WDPptP8AjXRWOoTPChKx5IHQV0zddYdJwt7z6+S8jG0bb/gbd1xaWQ9Y/wCgqBT3q5d3Ti2szheU9PYVAt0+BwvPPSuVyq/y/j/wBpR7gpw4qwCM5qNbhyw4X8qmE7Y6ClzVf5fx/wCAbVVG0den+Zj2x/4qe7/65D/2WtkH8qzbe8kbxDcwkJtWMEHHP8NannN6CunETrXjeC2XXy9DBKHf8BtJnjg08SsSelHmtnoK5+ar/L+P/AHaHf8AAYeQaWP76/UU7zj6ChJm8xQQME0nOrb4fx/4AJQutfwJU/5CMf8A10X+lFKspW/jHGN6/wBKK87FwqT5bq2nc1vGMmfPl+P3g/3ag0//AI+X/wBw/wAxVq+XLD/dqrYD/SmH+wf5ivbj8fz/AMzp/wCY/wCf6HpnwPhD395IeisP5Cuztr5H+KwkdtqyRMqn+78pxVP4W+GH0nw685BN5dAuyjsMcD8qy9QdoPGynBVkT8Rwa7Kbs7PseTHWT9Gex+UjxhJgrg+oBBrOutC0u4z59oRno68VQ0TxDBcxGG5lVSB/EeG/+vXRROpAaNjtI71nKCZlscZoXhe0ntnlEpBEhUAgEdBXQRaOkOFfy2x6Ej+tR+HCy6dJt4/fHoPYVrbiDzQ4q4Ns56XTLOXUpkdXChQcBiPSrnk28SIscS4AwM81DMxfV5yR/COB+FSykhEI9KhHRX3XovyGkb2CHgHpiq8sDR++KeZcjBFXIv8ASYAWUh14Pv71RznMyFRr1uSPlijeQ/XBrKvVRftElwm55BuAzjFdNcaaV1Pz2X92Ewff2rltVk8/VJdxG0cfpWU5OLi1vf8AzOihtL0/VHKzWdvHIXkQKmC7HJ6VwOuXYuLx5IgUjP3F9BXVeKdSDym0iPAA8zH6CuI1JS+zGM5710Rr1bP3nt3HCTsyuLi4a3OGwA/JxUgaZnG1yB9BUEcnlo8bx/Kw4x2PrV6CASSqvmPjvsXP86xderzL3n95pFv2cvl+ptvAn/CKWk0jsGMrZb2+alsNNuL1EffHbwN/y2bnj0A7mtV9PM3hezjWFUjablpnGQMn3/zmrkls1uiLsVY1XqMAf/qqqdetZ3m/vZM5O69EW9HsbSC5b7ODxEVMjnLPyOT2H0GK7P4faTotz4fvptQjR0+0bT5rlRgZ9DXIaNxcNsZWUxn8OlZeSq7XZnI6jOQDihYiqpv3n06mELurL0X6npWva/4T0VNsdgbhwPlVJGx/OuD1Lx6zEfZdGtYEPA3O7Ef+PViS3LEup+UcZzzWRcvu3fMeu4n2qnia38z+9m1mupoXHizUWc4W3Xnsh/xqkfE+o558k/8AAP8A69ZrEM2M8fyqMEDOMmp+s1v5n97Dmfc24vFsytiaxt3H1Yf1rUg8UaTcoI7uxeEnurFhXHFc549qUIxc/LzS+sVv5397GpM7HWHsZNEkNiyM2QAFPI/CqdsJF8IXZIOfPXr/AMBrDhQ55OK3wp/4RS8wODMpGf8AgNaUa9V1YJye66nPiW+Veq/MonZgDJz2x0oCAOPmBU9fam42Kd5G30FRRsNrDOAOc1H1it/O/vZ0czHzOnl7Qo+961YjKOGJXoOxqlKq7Bh+M5PHNSq0YLFQ3THWumpiKv1eHvPeXX0EpPmZLL5ZQ4BVuvB61TMhAwDk9/8ACn5YnndjPBIqKQYbjv0Fc31it/O/vY3J9x25mHA5Hemlp0P+sGPRgDTHc8DawA9RimlGO3jb9eKX1it/O/vYczZYjmJljHlpncASCR/WlupWF0wVUA9eSelQwgCZfnBG4cCluwpumDdM5z+FP6xVtfmf3sLsgR3F0qly2WrUJ/cN9ax12m7TbnG4VqhsW7/WuLEYitde+/vZ62WvSV+z/IjZsDmmGTA60jfWonPWksTX/nf3smTsT28xN0gz6/yqGa4IncZ/iNJan/S0/H+VVpz/AKRJ/vH+dbKvW5b87+9nO5e8Ti4bHX9KeJ84yapZIpQ1L29b+d/ex87Lwm96eJuKzxIacJDS9vW/nf3saqF8S8etIZRVQS0eZS9vX/nf3srnNOxkzexj6/yqOeQ/aJcf3j/OotOkzfx+vP8AI1HcP/pMv++f51n7etz353t3Zpzfu16k8L7pkB/vD+dOvH2XTgdOP5VWgb9/HjP3h/On37f6ZJ+H8q61XrewfvvddX2ZV17FvzX5DDK3XPFdD4LcnWZuf+Xdv5rXMFq6PwUf+JzN/wBezfzWuWtXrOm05v72cspHOGR/WmmVv71ITTCa2WJrfzv72S2P818jLY59K10XIU+3NYafNMg98mugtFBXn1rWOIrW+N/ezmqSfMa1jbxsnK559a6S1ARAFGABwKw7BOnYZreg47dsVM6s56SbZF2zbvCfsdljr5f9BUSHPX0qS7x9lsv+ufX8BUMbc5zWbGiZT8wqdTxVdT834VMvOMUjattD0/Vmbb/8jPd4/wCeQ/8AZa2c+lYluf8Aiprv/rkP/Za2Sea6cT8Uf8K/I54iggUtNzR6D8q5yhQ2RjvSp/rV+opuaEP7xP8AeFD2BbosD/kIR/8AXRf6UU0EnUU/66L/AEorgxH2fQuXxM8JvCMj/dqPR4ll1IknhV6evIp96MsP92maYy2955hOOMHP1FenH49Tuv8A7f8AP9D6Q8KXaIYkBwykfKeKx9Xsra/+I/lTjCmHkr1HBrQ0W8jvrCGeCxaYFQUkQ8fmBWBqltq9x4nku7SN45VjAw4JPT6e9ejGjJO0tNO6/wAzz44aonrbXzX+Zoar4bl06bdbzebH1AIw1dBoOpH7Ekdz1XgOOR+PpXISXHiadVt5ZAJFHy5TBI/75qpDceIbS4LGVUJ+8DHwfw20lh5rS6+9B9Tqd196/wAz0Tw2QdLk5yPObn8BWo5GwmvMtFv9eitW8idQpc8Bfp/s1sLfeKJRhZEPt5Y/+IoeHk9mvvF9TqPW6+9f5m4P+QrN/uD+lSXjiKJSRntiuT3+J11CQ5G/aMjy/p/s0+8PibZFukRs84EfT/x2o+rS7r7zathZ3W2y6rt6nVWcHn4bBwa1wkNqmWwTjpXCQXXiqCEIpUD/AK5f/YUNdeKmOXYH/tn/APY1X1WXdfejH6nU7r71/mdRfzNJGxGF4OK8r1m7eGSYoMyscKPT3ra1DUfEdtEWmlQA8DKdf/Ha466GpTMzyTx5Y8/L/wDWrGrhZXjqt+/qb0sLUipbbd15eZy2oRFGJY5YnLH1NZFzEJUAZc4NdHfWcrH95PGKy5bJCpBuoxjHSr+ryinqtu6COFqJPb71/mY6QxqfugGui0awWRt7sAOwPeobPR1mk3/aFdV6gD/69bunwqtsVIBG4gcdelYewmpL/Nf5lxwtTkktOnVf5mvIj/2JaxsCuZCDx/vVVijZGwAdnQq/9KvyPt0aCM7iokOHJ+vFZu4gfNJhskkZqoYepZ7bvqv8xVMLVuttl1X+ZJHdSWc4eONMjgbu/wDnFFx4iuc7VSEdz8p/xqnJtwAJlBBLHJqpIY9rZnQHdnPtSlhZPVpfev8AMxeXuTvKK+9f5libxDeAjEUH4of8arP4ivVXmKDr/cPT86qPBHJkm6U57/5NQiBM4+1KW9ABUfU32X3r/MX9nL+Vfev8y23iS7VR+6t8kZ4Q/wCNIviS9JOYrfj/AGD/AI1UNkvQXUQOOhH/ANenC2hUY+1Rn8sfzo+pvsvvX+Yv7N/ur71/mWT4nve8Nv8A98H/ABqRfEl22cRW/v8AIf8AGqAtoFGftERye/8A+ulNvDwPtUY78Y/xpfU32X3r/Mf9mr+Vfev8zSXxJeY/1VuSOg2H/Gqmpa7d3tk1vIkQRiM7VIPHPrUJt4sAC6T1/wA809II8ZW4QnHHtWtHDypzU+Vaea/zD+zu0V96/wAyi06v/C349KaJBj8egq79mU5xdIB3/wA5pRaI3/Lyp+gFbeyh/J/5MjVYSr5fev8AMz5pUKAbSDmhZ0DDcGI9AKt3VsgUOZ064AP/AOun/ZVz/wAfcY9en+NdFSnD2EPc6y+0vIlYWrzNafev8ykLobictkn0oNxG3BViR3Aq75EK8tcREjjp/wDXoMMZU4uohkdgP8a5/Zw/k/8AJkP6pW8vvX+ZneeoPcgdjUJk3kkkmtD7HCAcXcfT8v1pq2cPH+lxnHbjn9afsofyf+TIX1St5fev8yrGwM0WARhhT72QfaXH0/pVlLRPPVjdoeRwf/1064s43uWb7Sg9sf8A16Xs4fyf+TIPq1W9tPvX+ZlKds+4duauidvschx/F/hTktIUuA32uPOOh/8A11c8qIwMBLH169v51y4ilC69zqvtI9LAYeslLVbPqu3qZolBQHBzio3kJ7V01lp0UllE3nRtgYJzjmntY2i/euIR9XFbKhS/59/+To43RxPdfev8zlbd3+2RcDGT/Kqk7yfaZen3z/OuqubazBjMd3AXDcBWBPSsSWzhM8hN7ECWPGOnP1pulC1uT/yZErD127N/iv8AMzd8vtSbpfUVofYoP+f6H8v/AK9H2GH/AJ/ovyH+NL2UP+ff/k6K+q1u6+9f5mful9RRul/vCtD7DD/z/RfkP8aPsMH/AD/RfkP8aPZQ/wCff/k6D6rW7r71/mZ+6X+8KTdL/frR+wwf8/0X5D/Gj7DB/wA/0X5D/Gj2UP8An3/5Og+q1u6+9f5kWltL/aMOX9f5Goroy/a5sOf9Y3861NPsolv42F7Gx54AHofeorixhNzKft8Yy54445+tZKlD2r9zp/Mu5o8NW9mlfr3Xb1M6B5RcREucBx/On37u97IyswBxj8hVyOzgSVH+3RnawOOP8arX7K97IysCpxyOe1by9jClaUOv83kTOlUhRak+vl29SniTu5rp/Aob+3JssT/ozfzWudx710vgYAa3N/17N/Na4a86Hs5Wg/v/AOActpdzldjHuaNh9alwKQgAGteeh/I//Av+AJqXcktfkkye1btrcooBIbGfSsS0B3iuksgdgGa056H8j/8AAv8AgGOvc0rTUYUXlZPwA/xrUj1m3A+7N/3yP8ajsuIwe5rTjbA4pc9D+R/+Bf8AAKSfcu3+rwR2OnsUlw0WRhR6D3qkmvWo/wCWc3/fI/xravGxaWP/AFy/oKrRt8ppKdDrB/f/AMAEn3Ka6/a/885+n90f41KviG0A/wBXP/3yP8avrwalXoaOeh/I/wDwL/gG1VStHXp/mZGm3KXWvXE6BgjRDG4c/wAIre6GmE5INLmorVFUldK2iX3GSVh+aQnNJ70p+lZABPFKh/eoO2RTc/LihD+9X6ik9hx3ROMf2lH6+Yv9KKZnGpxkf89F/pRXDiPs+hpL4meFXmPNH+7UC4zT7xv3w/3RUSNzXqxxdf2KjzO1jevFfXX6mhYave6Y2badlXumflP4V3GheK9DlkWTXBdWyyDb5kR3KG9+OnFebFqnl/5BkX++f612RzDEfale39djz3CLPoe18LeHtctFuNO1CS5TqGimVsfkMiq114OtrY/vGuX994B/lzXz3baje6dJ5lndSwP6xsRW5bfE7xZZDb/arzr/AHZxuFP6/Ue02iPZHrGk6Bp11A/mSTq4cjAYDjj2q8vhjT1b93Nc59Aw/wAK5Hwx8R9PjuFsNaUW8jHdHcqPlyeMH06V6ctyHt1nhMcsRGVkjIINOWKxEdVN2/ryCy6mPZ+EobjUnRvtIXaCSZAOOPatDUvBtrPHDH9onAjG0EOM/wAvakgvLh9QkfzTGpUZJ5OOKtXmpNGsSpkK4OXbrUrF13Z87NK8UmrdkZT+FNKto8yXVyAByzSDn9Kxrq10iPKwTXLn+8ZBj+VaFxI8jlncufU1kzWqysxThs/hV/XMR/O/6+Rz2Rm3GkQTuzNPM4/hG4cfpWdPpEPl+XukwDnr/wDWrWmhkhIBOM9CKEzMSZV5H61jWxdduPvvf/M3opWn6fqjjNS0yKJCwL59zXOPBvGE3ZZsD+tdrrhEpZV4UcVR0myiUmWQDAUhRnn603jMRf43/XyJjFDbLSLVbQMJmyRz6VHaWUUiBQ75y2R2AAFbMqKH3P8ALGi/jVXTQBbuSj8khcDk+tRLF1+ZPnfX+tjphFezl8v1LEmkW40mCUvIBvJO4jGOfb6Vj3FrBvIDSEeu4Vupq81vAsMaoF67XGTj86qy+ILlWb5YCOg+Q8/rUrG4tX95/f8A8AJRpu3+RgyWsW7hmZR1yRxVKRE3EKzfietbz+Jrv5lCW599hH9ahPia8H/LK3I9Np5P50njsX/M/v8A+AL2dLv+Bguo4wTSeX3y1bv/AAlF1n/UW5PfCn/Gr1hrOo3M8eyzjkiJ+fYhzj160fXsX/M/v/4A/Z0u/wCBy0dpLKsjqjBIxlmPatLTdCiv7a4uXufKghAG5v4mPQCuyutUfTftqXCxeQ3EY2/MMHqeaqQ69PdQNJa26Jawgnlc59+tL6/iv5n9/wDwBclLv+Byd3o5S8jtrUPNJ5YaTbztPXn04qi9usLbXV9w/vcV3lhf6xcwbzBarG5O3k8+xPTNWY5JZhiSGKNz0dV35P5in9fxX8z+/wD4AuSn3/A858uPb3B7CkMYjxhiSfTtXXaje6rYTgNajyyTtbyic/kazj4hvs/6u2+uw/rzR9exf8z+/wD4A+Sl3/Aw2AwcMc9xSccfM2K3P+Eou+AsNuwHqh/xpqeKLwsf3NsG/wBw/wCNH17F/wAz+/8A4AclPv8AgYrRlhkBjn14pwUB8Ek+yjAFa6eKb85zFb/Taf8AGmnxTej/AJZ23/fB/wAayqV69W3tHe3n/wAAaVNbP8DFlYbxz+NKCgQl8sSfujpW0fEmo4B8m1G48Daf8aQ+KLtflMVqzdyEPH61jeXYdod/wMR5Fb/lmAPbtTMKOcnHpW8fE90oyYrcnPGFP+NR/wDCUXZ6Jbg/7h/xp3l2BqHf8DJhY+bGScfOOv1pt85F5IN3cd/atdPE98ZQrxWxDEDhD3/GnT+J72CRo1it9q/3kPP607y5dibQvv8Agc/GwF2prQL/AOiOff1+lXI/FV606gw24B9UP+NWW1+6MZm8uAMvAG04/nXJWburo9TActpWfR/kc25Vs/KD9arttz91fyrpW8UXoGfKtv8Avg/41A3iq+HSG2/74P8AjVJvsc8uXuY1jj7bH8oHXoPao5yPtEv++f510Fv4mvZ51jaK3APXCH0+tMk8U3qSsgitsKSBlD/jWt5cuxlaN9zn8ik3Vv8A/CWX3/PK2/74P+NH/CV33/PG1/74P+NTeXYdo9/wMAmjNb3/AAll9/zytv8Avg/40Dxbfn/lja/98H/Gi8uwWj3/AAMHNFb/APwll/8A88bb/vg/40f8JZfZ/wBTa/8AfB/xpXl2C0e5maYf+JjD+P8AI1DdH/S5v+ujfzrobLxPez3kcbRW4DZyQh9PrUU/iq9S4kQRW2FYjlD6/WoTlz7dDVqPs1r1/Q53mit//hLL/wD542v/AHwf8aT/AISy/wD+eNr/AN8H/GtLy7GVo9zCwa6bwMP+J3P/ANezfzWq3/CWX/8Azxtv++D/AI10Xg7xLeXOryo8VuALdj8qH1X3rOs5ezegrR7nAc01umK3/wDhLb7/AJ423/fB/wAaT/hLb4nHk23/AHwf8a1TlfYmShbf8DMtF+b3rpbFehqO28TXrEfurb/vg/41u2mu3bAZih/BT/jV3l2MuWHf8CS14A9KvxtkUQ61O3VIv++T/jVtNWnxnbF+R/xpXl2HaHf8DQvT/oVj/wBc/wCgqBCM49av3moSraWJ2p80eTx7CoI9RlOPlT8qLy7AlDv+ABqlU0LeSMwcqmfpUy3smOiflReXY3qqFo69O3mxg6/zp+eKeLyT0X8qd9rfphfyovLsY2h3/Aj7UZ6VKt254wufpQbt/RfyovLsFod/wISaEP71P94VKbx/RfyphvZB2T8qG5dgSgtb/gPyP7UQf9NF/pRUEMhkv4nbGTIvT6iiuPEqzivIG7ts8KvT+/X/AHRUUbc/hU13DJJKGRcjaO4qOO3lU5KfqK6oyXs7XO6tRqvFuSi7X7DM1bkP/Esi/wB8/wBarfZ5f7n6irqRo1mkUpZSCTxWynHXU4vq1b+R/czMbpVaQVstZ25H+sk/z+FRHT7Y/wDLST9P8KV49194/q1b+R/cypqqlrxAOpQfzNerfB/xMIzN4dvX/dyfvLZmPRu6/jxXByadaTzCUzuCF24xVuws7exuUuYbqQSxncrY6VvCcVJ3as/Mh4Ws18D+5nvUsezUZQvZRx6dKjv1JhhZey/4VxNv46dwDJ5bS7QCSGrSuvGKNBb+UI3bZ84KsMHinzRXVFVcJXk01F7LozTf96mU69xVac+RCQgy546ViN4lYsGVIwc9s1G/iBnDfLGpPcA8fSn7SHdfeZfUcR/Ky7BM5nLzEFcHqOgqrN5m9wjlgf4u2KpnVIQwaRRIQMAHOPyqOfVUuBjcIweyA1jVqR9136nRRwVf3ly7r/IhuIVlO0N0OP8AePpTTbq1yxRcBVJ9qGnhYAeawxjpmgzxH/l4cc9h/wDWpfWKfcpZdiF9kkmYOY432qFG5t3f0rOtC6wM6yA5c9TV83UO52D8vjccHmqlnp32qzYNu27z0IB7VDrQutTeOBrqDXL2KkpLO2Byo556VF/Z11cYWKPAxkM3ANbkOlwwsWEG85z8zVNNBPNwWKp/dXFDrw7mTwGJ/lMFfDUpG6eYKBzlanj0jTkljDSb3Y4Ck9TWiNOG3afMb6vSrYxxtGyQImw5BAGan20O4v7PxH8oWnhqCzuPOeNxIjY2uOMfT0pLW7t7e+aPTl+yurZzyVcnqKtXj3d2CGuJRkAAgjIxTLW3aBQqgkg5Z+NzH3PWn7WHcP7PxP8AKYOstcXmqXXlqGMJ6N90e59q6VfDs1r4etrV5DIb2Tc0qDG0kdB7cVWS0jj2I9ukqq2/a/IZvVh3P1rbtdb1CGF0jCYMu8f7IxjA9qPa0+4v7PxP8pR1SCHS9HTS7dibyOECGIf8tWYfe/OuX03Vf7LnNpfbnnXPmOHG1PpxzW7qNrLfapHflnikjxsCNwPpWLdeEIJZzMZJxk5I3g0OrT7h/Z+J/lLn9swai4gMs5izzJvALe3Sqd/4ajG6S3eRUb5iXbdj+VaNhpdpZ9LKOU9mkJJX6c1bMDbCgUkN1DHP86XtYdx/UMT/ACnFvo7ouY3EmDyAefwqCW3niTmEqnXOOa7eezjnQK1tErgcOgANVf7KdcYkfA9SDR7aHcP7PxP8pwxDo3KsPrTCwD8fdHt1rvG0pZBiRQynsQKrHw1Z7TtjZW7MG5FP20O4/qGI/lOLdnZtzAqO1IM54XLV2H/CLxBtwlmzjHJB/pTH8LxsuDLKBnPBXml7WHcf1DEfynJFQW5c59hxTShz1Bx+Ga6v/hEoP+es35r/AIUHwlCcDzpvzWn7aHcX9n4j+U5WIHz48g/eHI6dakvSBcucZ6dfpXTJ4ThR1YTTfKc4yMUs/hSKeRnaWUE+hFP29O24LLsS38Jx6yYnRm4HrV4zKbOQg8A/4VunwfDkfvpeP92pE8JBx9nR5CH5zlf89q5qs4Sasd2Ewlakpcyto/yOQeUdjUDOCa7c+AG/vzf99JTf+FfN/wA9Jv8AvpKtSRyOjN9V95yNi4+2R9e/8qjncfaJf98/zrtofAbwyrIHlJHYstRSeBS8jsXmBJJ+8tU6kVGxKw829196OK3e1G6uy/4QP/ppN/30tH/CBD/npN/30tT7SJX1afdfejjM0ZA4rs/+ED/6aT/99LR/wgQ/vzf99LR7SIvq0+6+9HGbs0m73Fdp/wAIF/00m/76Wj/hAh/z0m/NKPaRD6tPuvvRy+lv/wATGH6n+RqC7b/S5+n+sb+ddpbeCTbXCShpiV7Fl9KpT+Ela4kY+fksT99fWlFqU212KlSkqaTa37o5ItSbzXVf8Ignrcf99rR/wiCes/8A32ta2MfZPuvvOVDmuo8CsTrc+f8An2b+a07/AIRBP+m//fa10HhDwyltq0r/AL7mBl5ZfUVnWX7ti9k11X3nmu406Plq7f8A4Vzef88J/wDv7H/jUkfw7vAf9RP/AN/Y/wDGtFKPcmVGT6r70c5ZIMrXR2gxHmtG28CXiEfuJf8Av6n+Nasfg++VMeQ//fxP8afPHuT7CXdfejJhP/16tRsBx+NaieFb4f8ALB/+/if41Kvhi+DZ+zt/38T/ABo5o9x+wfdfeiS+b/QtOz/zy/oKgjPpWrfaRcG1s4/LO5EwRuXjgVXj0m6AH7o/99L/AI0nUguoKg31X3jEOVqRTxUy6XeAYEP/AI8P8aeumXgP+p/8eH+NL2kO46ytyrsv8yIHpT+pqYabef8APH/x4f40/wDs67z/AKn/AMeH+NHtIdzArZwM+9KT1HrVj+zrvH+q/wDHh/jSHTbz/nl/48P8aPaQ7oLFbOCKjZqt/wBm3n/PH/x4f40w6bebv9T/AOPD/Gj2kO4EFq3+mQD/AKaL/OirEGnXaXUTNDhVcEncOmfrRXHiZJtWZSP/2Q==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# image viz\n",
"frcnn_visualizer = SingleImageViz(URL, id2obj=objids, id2attr=attrids)\n",
"# run frcnn\n",
"images, sizes, scales_yx = image_preprocess(URL)\n",
"output_dict = frcnn(\n",
" images,\n",
" sizes,\n",
" scales_yx=scales_yx,\n",
" padding=\"max_detections\",\n",
" max_detections=frcnn_cfg.max_detections,\n",
" return_tensors=\"pt\",\n",
")\n",
"# add boxes and labels to the image\n",
"\n",
"frcnn_visualizer.draw_boxes(\n",
" output_dict.get(\"boxes\"),\n",
" output_dict.pop(\"obj_ids\"),\n",
" output_dict.pop(\"obj_probs\"),\n",
" output_dict.pop(\"attr_ids\"),\n",
" output_dict.pop(\"attr_probs\"),\n",
")\n",
"showarray(frcnn_visualizer._get_buffer())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Question: ['Where is the cat?']\n",
"prediction from LXMERT GQA: desk\n",
"prediction from LXMERT VQA: desk\n",
"Question: ['What is near the disk?']\n",
"prediction from LXMERT GQA: can\n",
"prediction from LXMERT VQA: cat\n",
"Question: ['What is the color of the table?']\n",
"prediction from LXMERT GQA: brown\n",
"prediction from LXMERT VQA: brown\n",
"Question: ['What is the color of the cat?']\n",
"prediction from LXMERT GQA: black\n",
"prediction from LXMERT VQA: black and white\n",
"Question: ['What is the shape of the monitor?']\n",
"prediction from LXMERT GQA: square\n",
"prediction from LXMERT VQA: rectangle\n"
]
}
],
"source": [
"test_questions_for_url1 = [\n",
" \"Where is this scene?\",\n",
" \"what is the man riding?\",\n",
" \"What is the man wearing?\",\n",
" \"What is the color of the horse?\",\n",
"]\n",
"test_questions_for_url2 = [\n",
" \"Where is the cat?\",\n",
" \"What is near the disk?\",\n",
" \"What is the color of the table?\",\n",
" \"What is the color of the cat?\",\n",
" \"What is the shape of the monitor?\",\n",
"]\n",
"\n",
"# Very important that the boxes are normalized\n",
"normalized_boxes = output_dict.get(\"normalized_boxes\")\n",
"features = output_dict.get(\"roi_features\")\n",
"\n",
"for test_question in test_questions_for_url2:\n",
" # run lxmert\n",
" test_question = [test_question]\n",
"\n",
" inputs = lxmert_tokenizer(\n",
" test_question,\n",
" padding=\"max_length\",\n",
" max_length=20,\n",
" truncation=True,\n",
" return_token_type_ids=True,\n",
" return_attention_mask=True,\n",
" add_special_tokens=True,\n",
" return_tensors=\"pt\",\n",
" )\n",
"\n",
" # run lxmert(s)\n",
" output_gqa = lxmert_gqa(\n",
" input_ids=inputs.input_ids,\n",
" attention_mask=inputs.attention_mask,\n",
" visual_feats=features,\n",
" visual_pos=normalized_boxes,\n",
" token_type_ids=inputs.token_type_ids,\n",
" output_attentions=False,\n",
" )\n",
" output_vqa = lxmert_vqa(\n",
" input_ids=inputs.input_ids,\n",
" attention_mask=inputs.attention_mask,\n",
" visual_feats=features,\n",
" visual_pos=normalized_boxes,\n",
" token_type_ids=inputs.token_type_ids,\n",
" output_attentions=False,\n",
" )\n",
" # get prediction\n",
" pred_vqa = output_vqa[\"question_answering_score\"].argmax(-1)\n",
" pred_gqa = output_gqa[\"question_answering_score\"].argmax(-1)\n",
" print(\"Question:\", test_question)\n",
" print(\"prediction from LXMERT GQA:\", gqa_answers[pred_gqa])\n",
" print(\"prediction from LXMERT VQA:\", vqa_answers[pred_vqa])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | # coding=utf-8
# Copyright 2021 Google Research The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch BigBirdPegasus model."""
import copy
import math
import random
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
Seq2SeqQuestionAnsweringModelOutput,
Seq2SeqSequenceClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_bigbird_pegasus import BigBirdPegasusConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/bigbird-pegasus-large-arxiv"
_CONFIG_FOR_DOC = "BigBirdPegasusConfig"
_TOKENIZER_FOR_DOC = "PegasusTokenizerFast"
# Base model docstring
_EXPECTED_OUTPUT_SHAPE = [1, 7, 1024]
# SequenceClassification docstring
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "hf-internal-testing/tiny-random-bigbird_pegasus"
_SEQ_CLASS_EXPECTED_LOSS = 0.69
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
# QuestionAsnwering docstring
_CHECKPOINT_FOR_QA = "hf-internal-testing/tiny-random-bigbird_pegasus"
_QA_EXPECTED_LOSS = 3.96
_QA_EXPECTED_OUTPUT = "''"
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/bigbird-pegasus-large-arxiv",
"google/bigbird-pegasus-large-pubmed",
"google/bigbird-pegasus-large-bigpatent",
# See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
]
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
# Copied from transformers.models.big_bird.modeling_big_bird.BigBirdSelfAttention with BigBird->BigBirdPegasus
class BigBirdPegasusSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BigBirdPegasusModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.big_bird.modeling_big_bird.BigBirdBlockSparseAttention with BigBird->BigBirdPegasus
class BigBirdPegasusBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.max_seqlen = config.max_position_embeddings
self.seed = seed
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.num_random_blocks = config.num_random_blocks
self.block_size = config.block_size
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions=None,
):
# Currently this `class` can't be used in decoder.
batch_size, seqlen, _ = hidden_states.size()
to_seq_length = from_seq_length = seqlen
from_block_size = to_block_size = self.block_size
if from_seq_length % from_block_size != 0:
raise ValueError("Query sided sequence length must be multiple of block size")
if to_seq_length % to_block_size != 0:
raise ValueError("Key/Value sided sequence length must be multiple of block size")
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
context_layer, attention_probs = self.bigbird_block_sparse_attention(
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
self.num_attention_heads,
self.num_random_blocks,
self.attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_length,
to_seq_length,
seed=self.seed,
plan_from_length=None,
plan_num_rand_blocks=None,
output_attentions=output_attentions,
)
context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
@staticmethod
def torch_bmm_nd(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication"""
# faster replacement of torch.einsum ("bhqk,bhkd->bhqd")
return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:])).view(
inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 1])
)
@staticmethod
def torch_bmm_nd_transpose(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication with transpose"""
# faster replacement of torch.einsum (bhqd,bhkd->bhqk)
return torch.bmm(
inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:]).transpose(1, 2)
).view(inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 2]))
def bigbird_block_sparse_attention(
self,
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
n_heads,
n_rand_blocks,
attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_len,
to_seq_len,
seed,
plan_from_length,
plan_num_rand_blocks,
output_attentions,
):
# BigBirdPegasus block-sparse attention as suggested in paper
# ITC:
# global tokens: 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# ETC:
# global tokens: extra_globals_tokens + 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# Note:
# 1) Currently, ETC is not supported.
# 2) Window size is fixed to 3 blocks & it can be changed only by
# changing `block_size`.
# 3) Number of global blocks are fixed (2 blocks here) & global tokens can be
# controlled only by `block_size`.
# attention is calculated separately for q[0], q[1], q[2:-2], q[-2], q[-1] in order to use special trick of shifting tokens (for calculating sliding attention)
# hence following code can be divided into 5 parts.
if from_seq_len // from_block_size != to_seq_len // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
rsqrt_d = 1 / math.sqrt(attention_head_size)
bsz = batch_size
attn_mask_penalty = -10000.0
# generate random attention and corresponding masks
np.random.seed(seed)
if from_seq_len in [1024, 3072, 4096]: # old plans used in paper
rand_attn = [
self._bigbird_block_rand_mask(
self.max_seqlen, self.max_seqlen, from_block_size, to_block_size, n_rand_blocks, last_idx=1024
)[: (from_seq_len // from_block_size - 2)]
for _ in range(n_heads)
]
else:
if plan_from_length is None:
plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(
from_seq_len, from_block_size, n_rand_blocks
)
rand_attn = self._bigbird_block_rand_mask_with_head(
from_seq_length=from_seq_len,
to_seq_length=to_seq_len,
from_block_size=from_block_size,
to_block_size=to_block_size,
num_heads=n_heads,
plan_from_length=plan_from_length,
plan_num_rand_blocks=plan_num_rand_blocks,
)
rand_attn = np.stack(rand_attn, axis=0)
rand_attn = torch.tensor(rand_attn, device=query_layer.device, dtype=torch.long)
rand_attn.unsqueeze_(0)
rand_attn = torch.cat([rand_attn for _ in range(batch_size)], dim=0)
rand_mask = self._create_rand_mask_from_inputs(
from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size
)
blocked_query_matrix = query_layer.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1)
blocked_key_matrix = key_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
blocked_value_matrix = value_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
# preparing block for randn attn
gathered_key = self.torch_gather_b2(blocked_key_matrix, rand_attn)
gathered_key = gathered_key.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
gathered_value = self.torch_gather_b2(blocked_value_matrix, rand_attn)
gathered_value = gathered_value.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
# 1st PART
# 1st block (global block) attention scores
# q[0] x (k[0], k[1], k[2], k[3], k[4] .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
first_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 0], key_layer, ndim=4)
first_product = first_product * rsqrt_d
first_product += (1.0 - to_mask) * attn_mask_penalty
first_attn_weights = nn.functional.softmax(
first_product, dim=-1
) # [bsz, n_heads, from_block_size, to_seq_len]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
first_context_layer = self.torch_bmm_nd(first_attn_weights, value_layer, ndim=4)
first_context_layer.unsqueeze_(2)
# 2nd PART
# 2nd block attention scores
# q[1] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> 2nd, 3rd blocks
# global key blocks -> 1st block
second_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, 1],
blocked_key_matrix[:, :, 2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
second_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, 1],
blocked_value_matrix[:, :, 2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 1], second_key_mat, ndim=4)
second_seq_pad = torch.cat(
[
to_mask[:, :, :, : 3 * to_block_size],
to_mask[:, :, :, -to_block_size:],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, 0],
],
dim=3,
)
second_product = second_product * rsqrt_d
second_product += (1.0 - torch.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
second_attn_weights = nn.functional.softmax(
second_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_context_layer = self.torch_bmm_nd(second_attn_weights, second_value_mat, ndim=4)
second_context_layer.unsqueeze_(2)
# 3rd PART
# Middle blocks attention scores
# q[-2:2] x (sliding_keys, random_keys, global_keys)
# sliding attn is calculated using special trick of shifting tokens as discussed in paper
# random keys are generated by taking random indices as per `rand_attn`
# global keys -> 1st & last block
exp_blocked_key_matrix = torch.cat(
[blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], dim=3
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
exp_blocked_value_matrix = torch.cat(
[blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]],
dim=3,
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
middle_query_matrix = blocked_query_matrix[:, :, 2:-2]
# sliding attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [b, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
inner_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, exp_blocked_key_matrix, ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, 3*to_block_size]
inner_band_product = inner_band_product * rsqrt_d
# randn attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
rand_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, gathered_key[:, :, 1:-1], ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
rand_band_product = rand_band_product * rsqrt_d
# Including 1st block (since it's global)
first_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
first_band_product = first_band_product * rsqrt_d
# Including last block (since it's global)
last_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
last_band_product = last_band_product * rsqrt_d
# masking padded tokens
inner_band_product += (1.0 - band_mask) * attn_mask_penalty
first_band_product += (1.0 - to_mask[:, :, :, :to_block_size].unsqueeze(3)) * attn_mask_penalty
last_band_product += (1.0 - to_mask[:, :, :, -to_block_size:].unsqueeze(3)) * attn_mask_penalty
rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty
# completing attention scores matrix for all q[-2:2]
band_product = torch.cat(
[first_band_product, inner_band_product, rand_band_product, last_band_product], dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# safely doing softmax since attention matrix is completed
attn_weights = nn.functional.softmax(
band_product, dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# contribution of sliding keys
# [bsz, n_heads, m//from_block_size-4, from_block_size, 3*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
context_layer = self.torch_bmm_nd(
attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix, ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of random keys
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
context_layer += self.torch_bmm_nd(
attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size], gathered_value[:, :, 1:-1], ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of global keys
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# 4th PART
# last 2nd token attention scores
# q[-2] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> last 3 blocks
# global key block -> 1st block
# random key block -> based on indices stored in `randn_attn`
second_last_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, -3],
blocked_key_matrix[:, :, -2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+n_random_blocks)*to_block_size, -1]
second_last_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, -3],
blocked_value_matrix[:, :, -2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+r)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -2], second_last_key_mat, ndim=4)
second_last_seq_pad = torch.cat(
[
to_mask[:, :, :, :to_block_size],
to_mask[:, :, :, -3 * to_block_size :],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_last_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, -1],
],
dim=3,
)
second_last_product = second_last_product * rsqrt_d
second_last_product += (1.0 - torch.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty
second_last_attn_weights = nn.functional.softmax(
second_last_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_last_context_layer = self.torch_bmm_nd(second_last_attn_weights, second_last_value_mat, ndim=4)
second_last_context_layer.unsqueeze_(2)
# 5th PART
# last block (global) attention scores
# q[-1] x (k[0], k[1], k[2], k[3], .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -1], key_layer, ndim=4)
last_product = last_product * rsqrt_d
last_product += (1.0 - to_mask) * attn_mask_penalty
last_attn_weights = nn.functional.softmax(last_product, dim=-1) # [bsz, n_heads, from_block_size, n]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
last_context_layer = self.torch_bmm_nd(last_attn_weights, value_layer, ndim=4)
last_context_layer.unsqueeze_(2)
# combining representations of all tokens
context_layer = torch.cat(
[first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer],
dim=2,
)
context_layer = context_layer.view((bsz, n_heads, from_seq_len, -1)) * from_mask
context_layer = torch.transpose(context_layer, 1, 2)
# this is just for visualizing; forward pass doesn't depend on following code
if output_attentions:
# TODO(PVP): need to verify if below code is correct
attention_probs = torch.zeros(
bsz, n_heads, from_seq_len, to_seq_len, dtype=torch.float, device=context_layer.device
)
# 1st query block
# corresponding to `first_context_layer`
attention_probs[:, :, :from_block_size, :] = first_attn_weights # all keys global
# 2nd query block
# corresponding to `second_context_layer`
attention_probs[:, :, from_block_size : 2 * from_block_size, : 3 * to_block_size] = second_attn_weights[
:, :, :, : 3 * to_block_size
] # 1st three key blocks (global + sliding)
attention_probs[:, :, from_block_size : 2 * from_block_size, -to_block_size:] = second_attn_weights[
:, :, :, 3 * to_block_size : 4 * to_block_size
] # last key block (global)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, 1, :, i2[0]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Middle query blocks
# corresponding to `context_layer`
# sliding keys
for q_idx in range(from_seq_len // from_block_size - 4):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)[:, :, 2:-2, :, 1:-1, :]
right_slice = attn_weights[:, :, q_idx, :, to_block_size : 4 * to_block_size]
attn_probs_view[:, :, q_idx, :, q_idx : q_idx + 3, :] = right_slice.view(
bsz, n_heads, from_block_size, 3, to_block_size
) # inner_band_product
# global keys (corresponding to 1st key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[
:, :, :, :, :to_block_size
].view(
bsz, n_heads, -1, to_block_size
) # first_band_product
# global keys (corresponding to last key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[
:, :, :, :, -to_block_size:
].view(
bsz, n_heads, -1, to_block_size
) # last_band_product
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
for q_idx in range(1, len(i2) - 1):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[q_idx - 1, :, 4 * to_block_size : -to_block_size]
attn_probs_view[p1, p2, q_idx + 1, :, i2[q_idx]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Second-last query block
# corresponding to `second_last_context_layer`
attention_probs[:, :, -2 * from_block_size : -from_block_size, :to_block_size] = second_last_attn_weights[
:, :, :, :to_block_size
] # 1st key block (global)
attention_probs[
:, :, -2 * from_block_size : -from_block_size, -3 * to_block_size :
] = second_last_attn_weights[
:, :, :, to_block_size : 4 * to_block_size
] # last three blocks (global + sliding)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_last_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, -2, :, i2[-1]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# last query block
# corresponding to `last_context_layer`
attention_probs[:, :, -from_block_size:, :] = last_attn_weights # all keys global
else:
attention_probs = None
return context_layer, attention_probs
@staticmethod
def torch_gather_b2(params, indices):
# this operation is equivalent to tf.gather when batch_dims=2
if params.shape[:2] != indices.shape[:2]:
raise ValueError(
"Make sure that the first two dimensions of params and indices are identical, but"
f" they are params: {params.shape[:2]} vs. indices: {indices.shape[:2]}"
)
num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
num_indices_to_pick_from = params.shape[2]
indices_shift = (
torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
// num_indices_to_gather
* num_indices_to_pick_from
)
flattened_indices = indices.view(-1) + indices_shift
flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])
out_flattened = flattened_params.index_select(0, flattened_indices)
out = out_flattened.reshape(params.shape[:2] + (num_indices_to_gather,) + params.shape[3:])
return out
@staticmethod
def _create_rand_mask_from_inputs(
from_blocked_mask,
to_blocked_mask,
rand_attn,
num_attention_heads,
num_rand_blocks,
batch_size,
from_seq_length,
from_block_size,
):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
rand_attn: [batch_size, num_attention_heads,
from_seq_length//from_block_size-2, num_rand_blocks]
num_attention_heads: int. Number of attention heads.
num_rand_blocks: int. Number of random chunks per row.
batch_size: int. Batch size for computation.
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
Returns:
float Tensor of shape [batch_size, num_attention_heads, from_seq_length//from_block_size-2,
from_block_size, num_rand_blocks*to_block_size].
"""
num_windows = from_seq_length // from_block_size - 2
rand_mask = torch.stack([p1[i1.flatten()] for p1, i1 in zip(to_blocked_mask, rand_attn)])
rand_mask = rand_mask.view(batch_size, num_attention_heads, num_windows, num_rand_blocks * from_block_size)
rand_mask = torch.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
return rand_mask
@staticmethod
def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
"""
Gives the plan of where to put random attention.
Args:
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
num_rand_blocks: int. Number of random chunks per row.
Returns:
plan_from_length: ending location of from block plan_num_rand_blocks: number of random ending location for
each block
"""
plan_from_length = []
plan_num_rand_blocks = []
if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(0)
elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks // 2)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
else:
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks)
return plan_from_length, plan_num_rand_blocks
@staticmethod
def _bigbird_block_rand_mask(
from_seq_length, to_seq_length, from_block_size, to_block_size, num_rand_blocks, last_idx=-1
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_rand_blocks: int. Number of random chunks per row.
last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence,
if positive then num_rand_blocks blocks chosen only up to last_idx.
Returns:
adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks
"""
# using this method when from_seq_length in [1024, 3072, 4096]
if from_seq_length // from_block_size != to_seq_length // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
rand_attn = np.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32)
middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32)
last = to_seq_length // to_block_size - 1
if last_idx > (2 * to_block_size):
last = (last_idx // to_block_size) - 1
r = num_rand_blocks # shorthand
for i in range(1, from_seq_length // from_block_size - 1):
start = i - 2
end = i
if i == 1:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r]
elif i == 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r]
elif i == from_seq_length // from_block_size - 3:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -3: should have been sliced till last-3
elif i == from_seq_length // from_block_size - 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -4: should have been sliced till last-4
else:
if start > last:
start = last
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
elif (end + 1) == last:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
else:
rand_attn[i - 1, :] = np.random.permutation(
np.concatenate((middle_seq[:start], middle_seq[end + 1 : last]))
)[:r]
return rand_attn
def _bigbird_block_rand_mask_with_head(
self,
from_seq_length,
to_seq_length,
from_block_size,
to_block_size,
num_heads,
plan_from_length,
plan_num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_top=1,
global_block_bottom=1,
global_block_left=1,
global_block_right=1,
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_heads: int. total number of heads.
plan_from_length: list. plan from length where num_random_blocks are chosen from.
plan_num_rand_blocks: list. number of rand blocks within the plan.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_top: int. number of blocks at the top.
global_block_bottom: int. number of blocks at the bottom.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
adjacency list of size num_head where each element is of size from_seq_length//from_block_size-2 by
num_rand_blocks
"""
# using this method when from_seq_length not in [1024, 3072, 4096]
if from_seq_length // from_block_size != to_seq_length // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
if from_seq_length not in plan_from_length:
raise ValueError("Error from sequence length not in plan!")
# Total number of blocks in the mmask
num_blocks = from_seq_length // from_block_size
# Number of blocks per plan
plan_block_length = np.array(plan_from_length) // from_block_size
# till when to follow plan
max_plan_idx = plan_from_length.index(from_seq_length)
# Random Attention adjacency list
rand_attn = [
np.zeros((num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=np.int32)
for i in range(num_heads)
]
# We will go iteratively over the plan blocks and pick random number of
# Attention blocks from the legally allowed blocks
for plan_idx in range(max_plan_idx + 1):
rnd_r_cnt = 0
if plan_idx > 0:
# set the row for all from_blocks starting from 0 to
# plan_block_length[plan_idx-1]
# column indx start fromm plan_block_length[plan_idx-1] and ends at
# plan_block_length[plan_idx]
if plan_num_rand_blocks[plan_idx] > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=plan_block_length[plan_idx - 1],
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for pl_id in range(plan_idx):
if plan_num_rand_blocks[pl_id] == 0:
continue
for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]):
rnd_r_cnt = 0
to_start_block_id = 0
if pl_id > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id]))
to_start_block_id = plan_block_length[pl_id - 1]
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1]))
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[pl_id],
num_rand_blocks=plan_num_rand_blocks[pl_id],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
if plan_num_rand_blocks[plan_idx] == 0:
continue
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
from_start_block_id = global_block_top
to_start_block_id = 0
if plan_idx > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
from_start_block_id = plan_block_length[plan_idx - 1]
to_start_block_id = plan_block_length[plan_idx - 1]
for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for nh in range(num_heads):
rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
return rand_attn
@staticmethod
def _get_single_block_row_attention(
block_id,
to_start_block_id,
to_end_block_id,
num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_left=1,
global_block_right=1,
):
"""
For a single row block get random row attention.
Args:
block_id: int. block id of row.
to_start_block_id: int. random attention column start id.
to_end_block_id: int. random attention column end id.
num_rand_blocks: int. number of random blocks to be selected.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
row containing the random attention vector of size num_rand_blocks.
"""
# list of to_blocks from which to choose random attention
to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32)
# permute the blocks
perm_block = np.random.permutation(to_block_list)
# illegal blocks for the current block id, using window
illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1))
# Add blocks at the start and at the end
illegal_blocks.extend(list(range(global_block_left)))
illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))
# The second from_block cannot choose random attention on second last to_block
if block_id == 1:
illegal_blocks.append(to_end_block_id - 2)
# The second last from_block cannot choose random attention on second to_block
if block_id == to_end_block_id - 2:
illegal_blocks.append(1)
selected_random_blokcs = []
for i in range(to_end_block_id - to_start_block_id):
if perm_block[i] not in illegal_blocks:
selected_random_blokcs.append(perm_block[i])
if len(selected_random_blokcs) == num_rand_blocks:
break
return np.array(selected_random_blokcs, dtype=np.int32)
class BigBirdPegasusEncoderAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.config = config
self.seed = seed
self.attention_type = config.attention_type
if self.attention_type == "original_full":
self.self = BigBirdPegasusSelfAttention(config)
elif self.attention_type == "block_sparse":
self.self = BigBirdPegasusBlockSparseAttention(config, seed)
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.config.attention_type}"
)
self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
if value == "original_full":
# copy all weights to new full attention class
attn_weights = BigBirdPegasusSelfAttention(self.config)
else:
# copy all weights to new sparse attention class
attn_weights = BigBirdPegasusBlockSparseAttention(self.config, self.seed)
attn_weights.query = self.self.query
attn_weights.value = self.self.value
attn_weights.key = self.self.key
self.self = attn_weights
self.attention_type = value
if not self.training:
self.self.eval()
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
past_key_value=None,
output_attentions=False,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
):
# Expand dims to enable multiplication in the self-attention module
head_mask = head_mask.reshape(1, -1, 1, 1) if head_mask is not None else None
if self.config.attention_type == "original_full":
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
)
else:
self_outputs = self.self(
hidden_states, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, output_attentions
)
attention_output = self.output(self_outputs[0])
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BigBirdPegasusDecoder
class BigBirdPegasusDecoderAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class BigBirdPegasusEncoderLayer(nn.Module):
def __init__(self, config: BigBirdPegasusConfig, seed=None):
super().__init__()
self.attention_type = config.attention_type
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusEncoderAttention(config, seed=seed)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
self_attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=layer_head_mask,
output_attentions=output_attentions,
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=from_blocked_mask,
to_blocked_mask=to_blocked_mask,
)
hidden_states = self_attention_outputs[0]
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attention_outputs[1],)
return outputs
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
self.self_attn.set_attention_type(value)
class BigBirdPegasusDecoderLayer(nn.Module):
def __init__(self, config: BigBirdPegasusConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusDecoderAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = BigBirdPegasusDecoderAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape *(seq_len, batch, embed_dim)*
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
*(encoder_attention_heads,)*.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size *(decoder_attention_heads,)*.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->BigBirdPegasus
class BigBirdPegasusClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
class BigBirdPegasusPreTrainedModel(PreTrainedModel):
config_class = BigBirdPegasusConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (BigBirdPegasusDecoder, BigBirdPegasusEncoder)):
module.gradient_checkpointing = value
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
BIGBIRD_PEGASUS_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`BigBirdPegasusConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
BIGBIRD_PEGASUS_GENERATION_EXAMPLE = r"""
Summarization example:
```python
>>> from transformers import PegasusTokenizer, BigBirdPegasusForConditionalGeneration
>>> model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> tokenizer = PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> ARTICLE_TO_SUMMARIZE = (
... "The dominant sequence transduction models are based on complex recurrent or convolutional neural "
... "networks in an encoder-decoder configuration. The best performing models also connect the encoder "
... "and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, "
... "based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. "
... "Experiments on two machine translation tasks show these models to be superior in quality "
... "while being more parallelizable and requiring significantly less time to train."
... )
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=4096, return_tensors="pt", truncation=True)
>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=15)
>>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
'dominant sequence models are based on recurrent or convolutional neural networks .'
```
"""
BIGBIRD_PEGASUS_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the `input_ids` to the right, following the paper.
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
[`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in
[the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
`(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
can choose to directly pass an embedded representation. This is useful if you want more control over how to
convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
BIGBIRD_PEGASUS_STANDALONE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`ProphetNetTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BigBirdPegasusEncoderLayer`].
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.attention_type = config.attention_type
self.block_size = config.block_size
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([BigBirdPegasusEncoderLayer(config, seed=i) for i in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=hidden_states.device)
attention_mask = attention_mask.long()
# in order to use block_sparse attention, sequence_length has to be at least
# bigger than all global attentions: 2 * block_size
# + sliding tokens: 3 * block_size
# + random tokens: 2 * num_random_blocks * block_size
max_tokens_to_attend = (5 + 2 * self.config.num_random_blocks) * self.config.block_size
if self.attention_type == "block_sparse" and input_shape[1] <= max_tokens_to_attend:
# change attention_type from block_sparse to original_full
sequence_length = input_shape[1]
logger.warning(
"Attention type 'block_sparse' is not possible if sequence_length: "
f"{sequence_length} <= num global tokens: 2 * config.block_size "
"+ min. num sliding tokens: 3 * config.block_size "
"+ config.num_random_blocks * config.block_size "
"+ additional buffer: config.num_random_blocks * config.block_size "
f"= {max_tokens_to_attend} with config.block_size "
f"= {self.config.block_size}, config.num_random_blocks "
f"= {self.config.num_random_blocks}. "
"Changing attention type to 'original_full'..."
)
self.set_attention_type("original_full")
if self.attention_type == "block_sparse":
padding_len, hidden_states, attention_mask = self._pad_to_block_size(hidden_states, attention_mask)
else:
padding_len = 0
# expand attention_mask
if self.attention_type == "original_full":
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
blocked_encoder_mask = band_mask = from_mask = to_mask = None
elif self.attention_type == "block_sparse":
blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(
attention_mask, self.block_size
)
attention_mask = None
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.attention_type}"
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
band_mask,
from_mask,
to_mask,
blocked_encoder_mask,
blocked_encoder_mask,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=blocked_encoder_mask,
to_blocked_mask=blocked_encoder_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layernorm_embedding(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if padding_len > 0:
# unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1)
hidden_states = hidden_states[:, :-padding_len]
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
self.encoder_o = hidden_states
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
for layer in self.layers:
layer.set_attention_type(value)
@staticmethod # Copied from transformers.models.big_bird.modeling_big_bird.BigBirdModel.create_masks_for_block_sparse_attn
def create_masks_for_block_sparse_attn(attention_mask: torch.Tensor, block_size: int):
batch_size, seq_length = attention_mask.size()
if seq_length % block_size != 0:
raise ValueError(
f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block"
f" size is {block_size}."
)
def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
Returns:
float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size,
3*to_block_size].
"""
exp_blocked_to_pad = torch.cat(
[to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], dim=2
)
band_mask = torch.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
band_mask.unsqueeze_(1)
return band_mask
blocked_encoder_mask = attention_mask.view(batch_size, seq_length // block_size, block_size)
band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)
from_mask = attention_mask.view(batch_size, 1, seq_length, 1)
to_mask = attention_mask.view(batch_size, 1, 1, seq_length)
return blocked_encoder_mask, band_mask, from_mask, to_mask
def _pad_to_block_size(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor):
"""A helper function to pad tokens and mask to work with implementation of BigBird block-sparse attention."""
# padding
block_size = self.config.block_size
batch_size, seq_len = hidden_states.shape[:2]
padding_len = (block_size - seq_len % block_size) % block_size
if padding_len > 0:
logger.info(
f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
f"`config.block_size`: {block_size}"
)
pad_id = self.config.pad_token_id
device = hidden_states.device
input_ids_padding = torch.ones((batch_size, padding_len), dtype=torch.long, device=device) * pad_id
inputs_embeds_padding = self.embed_tokens(input_ids_padding)
hidden_states = torch.cat([hidden_states, inputs_embeds_padding], dim=-2)
attention_mask = nn.functional.pad(
attention_mask, (0, padding_len), value=0
) # no attention on the padding tokens
return padding_len, hidden_states, attention_mask
class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BigBirdPegasusDecoderLayer`]
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([BigBirdPegasusDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(inputs_embeds.device)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
control over how to convert `input_ids` indices into associated vectors than the model's internal
embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, use_cache)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layernorm_embedding(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare BigBirdPegasus Model outputting raw hidden-states without any specific head on top.",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartModel with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
self.encoder = BigBirdPegasusEncoder(config, self.shared)
self.decoder = BigBirdPegasusDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqModelOutput]:
# different to other models, BigBirdPegasus automatically creates decoder_input_ids from
# input_ids if no decoder_input_ids are provided
if decoder_input_ids is None and decoder_inputs_embeds is None:
if input_ids is None:
raise ValueError(
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
"passed, `input_ids` cannot be `None`. Please pass either "
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
)
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The BigBirdPegasus Model with a language modeling head. Can be used for summarization.",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartForConditionalGeneration with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
r"final_logits_bias",
r"lm_head.weight",
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
]
def __init__(self, config: BigBirdPegasusConfig):
super().__init__(config)
self.model = BigBirdPegasusModel(config)
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens)
self._resize_final_logits_bias(new_num_tokens)
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BIGBIRD_PEGASUS_GENERATION_EXAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0])
lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs
):
# cut decoder_input_ids if past is used
if past is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
)
return reordered_past
@add_start_docstrings(
"""
BigBirdPegasus model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
""",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = BigBirdPegasusModel(config)
self.classification_head = BigBirdPegasusClassificationHead(
config.d_model,
config.d_model,
config.num_labels,
config.classifier_dropout,
)
self.model._init_weights(self.classification_head.dense)
self.model._init_weights(self.classification_head.out_proj)
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
if input_ids is None and inputs_embeds is not None:
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0] # last hidden state
eos_mask = input_ids.eq(self.config.eos_token_id)
if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same number of <eos> tokens.")
sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
:, -1, :
]
logits = self.classification_head(sentence_representation)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.config.num_labels == 1:
self.config.problem_type = "regression"
elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.config.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return Seq2SeqSequenceClassifierOutput(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"""
BigBirdPegasus Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartForQuestionAnswering with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = BigBirdPegasusModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.model._init_weights(self.qa_outputs)
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_QA,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_loss=_QA_EXPECTED_LOSS,
expected_output=_QA_EXPECTED_OUTPUT,
)
def forward(
self,
input_ids: torch.Tensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if start_positions is not None and end_positions is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (
start_logits,
end_logits,
) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return Seq2SeqQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
# Copied from transformers.models.pegasus.modeling_pegasus.PegasusDecoderWrapper with Pegasus->BigBirdPegasus
class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = BigBirdPegasusDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = BigBirdPegasusDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import PegasusTokenizer, BigBirdPegasusForCausalLM
>>> tokenizer = PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> model = BigBirdPegasusForCausalLM.from_pretrained(
... "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
... )
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past:
input_ids = input_ids[:, -1:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
| # coding=utf-8
# Copyright 2021 Google Research The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch BigBirdPegasus model."""
import copy
import math
import random
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
Seq2SeqQuestionAnsweringModelOutput,
Seq2SeqSequenceClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_bigbird_pegasus import BigBirdPegasusConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/bigbird-pegasus-large-arxiv"
_CONFIG_FOR_DOC = "BigBirdPegasusConfig"
_TOKENIZER_FOR_DOC = "PegasusTokenizerFast"
# Base model docstring
_EXPECTED_OUTPUT_SHAPE = [1, 7, 1024]
# SequenceClassification docstring
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "hf-internal-testing/tiny-random-bigbird_pegasus"
_SEQ_CLASS_EXPECTED_LOSS = 0.69
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
# QuestionAsnwering docstring
_CHECKPOINT_FOR_QA = "hf-internal-testing/tiny-random-bigbird_pegasus"
_QA_EXPECTED_LOSS = 3.96
_QA_EXPECTED_OUTPUT = "''"
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/bigbird-pegasus-large-arxiv",
"google/bigbird-pegasus-large-pubmed",
"google/bigbird-pegasus-large-bigpatent",
# See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
]
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
# Copied from transformers.models.big_bird.modeling_big_bird.BigBirdSelfAttention with BigBird->BigBirdPegasus
class BigBirdPegasusSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BigBirdPegasusModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.big_bird.modeling_big_bird.BigBirdBlockSparseAttention with BigBird->BigBirdPegasus
class BigBirdPegasusBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.max_seqlen = config.max_position_embeddings
self.seed = seed
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.num_random_blocks = config.num_random_blocks
self.block_size = config.block_size
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions=None,
):
# Currently this `class` can't be used in decoder.
batch_size, seqlen, _ = hidden_states.size()
to_seq_length = from_seq_length = seqlen
from_block_size = to_block_size = self.block_size
if from_seq_length % from_block_size != 0:
raise ValueError("Query sided sequence length must be multiple of block size")
if to_seq_length % to_block_size != 0:
raise ValueError("Key/Value sided sequence length must be multiple of block size")
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
context_layer, attention_probs = self.bigbird_block_sparse_attention(
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
self.num_attention_heads,
self.num_random_blocks,
self.attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_length,
to_seq_length,
seed=self.seed,
plan_from_length=None,
plan_num_rand_blocks=None,
output_attentions=output_attentions,
)
context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
@staticmethod
def torch_bmm_nd(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication"""
# faster replacement of torch.einsum ("bhqk,bhkd->bhqd")
return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:])).view(
inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 1])
)
@staticmethod
def torch_bmm_nd_transpose(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication with transpose"""
# faster replacement of torch.einsum (bhqd,bhkd->bhqk)
return torch.bmm(
inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:]).transpose(1, 2)
).view(inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 2]))
def bigbird_block_sparse_attention(
self,
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
n_heads,
n_rand_blocks,
attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_len,
to_seq_len,
seed,
plan_from_length,
plan_num_rand_blocks,
output_attentions,
):
# BigBirdPegasus block-sparse attention as suggested in paper
# ITC:
# global tokens: 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# ETC:
# global tokens: extra_globals_tokens + 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# Note:
# 1) Currently, ETC is not supported.
# 2) Window size is fixed to 3 blocks & it can be changed only by
# changing `block_size`.
# 3) Number of global blocks are fixed (2 blocks here) & global tokens can be
# controlled only by `block_size`.
# attention is calculated separately for q[0], q[1], q[2:-2], q[-2], q[-1] in order to use special trick of shifting tokens (for calculating sliding attention)
# hence following code can be divided into 5 parts.
if from_seq_len // from_block_size != to_seq_len // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
rsqrt_d = 1 / math.sqrt(attention_head_size)
bsz = batch_size
attn_mask_penalty = -10000.0
# generate random attention and corresponding masks
np.random.seed(seed)
if from_seq_len in [1024, 3072, 4096]: # old plans used in paper
rand_attn = [
self._bigbird_block_rand_mask(
self.max_seqlen, self.max_seqlen, from_block_size, to_block_size, n_rand_blocks, last_idx=1024
)[: (from_seq_len // from_block_size - 2)]
for _ in range(n_heads)
]
else:
if plan_from_length is None:
plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(
from_seq_len, from_block_size, n_rand_blocks
)
rand_attn = self._bigbird_block_rand_mask_with_head(
from_seq_length=from_seq_len,
to_seq_length=to_seq_len,
from_block_size=from_block_size,
to_block_size=to_block_size,
num_heads=n_heads,
plan_from_length=plan_from_length,
plan_num_rand_blocks=plan_num_rand_blocks,
)
rand_attn = np.stack(rand_attn, axis=0)
rand_attn = torch.tensor(rand_attn, device=query_layer.device, dtype=torch.long)
rand_attn.unsqueeze_(0)
rand_attn = torch.cat([rand_attn for _ in range(batch_size)], dim=0)
rand_mask = self._create_rand_mask_from_inputs(
from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size
)
blocked_query_matrix = query_layer.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1)
blocked_key_matrix = key_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
blocked_value_matrix = value_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
# preparing block for randn attn
gathered_key = self.torch_gather_b2(blocked_key_matrix, rand_attn)
gathered_key = gathered_key.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
gathered_value = self.torch_gather_b2(blocked_value_matrix, rand_attn)
gathered_value = gathered_value.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
# 1st PART
# 1st block (global block) attention scores
# q[0] x (k[0], k[1], k[2], k[3], k[4] .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
first_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 0], key_layer, ndim=4)
first_product = first_product * rsqrt_d
first_product += (1.0 - to_mask) * attn_mask_penalty
first_attn_weights = nn.functional.softmax(
first_product, dim=-1
) # [bsz, n_heads, from_block_size, to_seq_len]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
first_context_layer = self.torch_bmm_nd(first_attn_weights, value_layer, ndim=4)
first_context_layer.unsqueeze_(2)
# 2nd PART
# 2nd block attention scores
# q[1] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> 2nd, 3rd blocks
# global key blocks -> 1st block
second_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, 1],
blocked_key_matrix[:, :, 2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
second_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, 1],
blocked_value_matrix[:, :, 2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 1], second_key_mat, ndim=4)
second_seq_pad = torch.cat(
[
to_mask[:, :, :, : 3 * to_block_size],
to_mask[:, :, :, -to_block_size:],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, 0],
],
dim=3,
)
second_product = second_product * rsqrt_d
second_product += (1.0 - torch.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
second_attn_weights = nn.functional.softmax(
second_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_context_layer = self.torch_bmm_nd(second_attn_weights, second_value_mat, ndim=4)
second_context_layer.unsqueeze_(2)
# 3rd PART
# Middle blocks attention scores
# q[-2:2] x (sliding_keys, random_keys, global_keys)
# sliding attn is calculated using special trick of shifting tokens as discussed in paper
# random keys are generated by taking random indices as per `rand_attn`
# global keys -> 1st & last block
exp_blocked_key_matrix = torch.cat(
[blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], dim=3
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
exp_blocked_value_matrix = torch.cat(
[blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]],
dim=3,
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
middle_query_matrix = blocked_query_matrix[:, :, 2:-2]
# sliding attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [b, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
inner_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, exp_blocked_key_matrix, ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, 3*to_block_size]
inner_band_product = inner_band_product * rsqrt_d
# randn attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
rand_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, gathered_key[:, :, 1:-1], ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
rand_band_product = rand_band_product * rsqrt_d
# Including 1st block (since it's global)
first_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
first_band_product = first_band_product * rsqrt_d
# Including last block (since it's global)
last_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
last_band_product = last_band_product * rsqrt_d
# masking padded tokens
inner_band_product += (1.0 - band_mask) * attn_mask_penalty
first_band_product += (1.0 - to_mask[:, :, :, :to_block_size].unsqueeze(3)) * attn_mask_penalty
last_band_product += (1.0 - to_mask[:, :, :, -to_block_size:].unsqueeze(3)) * attn_mask_penalty
rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty
# completing attention scores matrix for all q[-2:2]
band_product = torch.cat(
[first_band_product, inner_band_product, rand_band_product, last_band_product], dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# safely doing softmax since attention matrix is completed
attn_weights = nn.functional.softmax(
band_product, dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# contribution of sliding keys
# [bsz, n_heads, m//from_block_size-4, from_block_size, 3*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
context_layer = self.torch_bmm_nd(
attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix, ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of random keys
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
context_layer += self.torch_bmm_nd(
attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size], gathered_value[:, :, 1:-1], ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of global keys
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# 4th PART
# last 2nd token attention scores
# q[-2] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> last 3 blocks
# global key block -> 1st block
# random key block -> based on indices stored in `randn_attn`
second_last_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, -3],
blocked_key_matrix[:, :, -2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+n_random_blocks)*to_block_size, -1]
second_last_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, -3],
blocked_value_matrix[:, :, -2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+r)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -2], second_last_key_mat, ndim=4)
second_last_seq_pad = torch.cat(
[
to_mask[:, :, :, :to_block_size],
to_mask[:, :, :, -3 * to_block_size :],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_last_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, -1],
],
dim=3,
)
second_last_product = second_last_product * rsqrt_d
second_last_product += (1.0 - torch.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty
second_last_attn_weights = nn.functional.softmax(
second_last_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_last_context_layer = self.torch_bmm_nd(second_last_attn_weights, second_last_value_mat, ndim=4)
second_last_context_layer.unsqueeze_(2)
# 5th PART
# last block (global) attention scores
# q[-1] x (k[0], k[1], k[2], k[3], .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -1], key_layer, ndim=4)
last_product = last_product * rsqrt_d
last_product += (1.0 - to_mask) * attn_mask_penalty
last_attn_weights = nn.functional.softmax(last_product, dim=-1) # [bsz, n_heads, from_block_size, n]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
last_context_layer = self.torch_bmm_nd(last_attn_weights, value_layer, ndim=4)
last_context_layer.unsqueeze_(2)
# combining representations of all tokens
context_layer = torch.cat(
[first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer],
dim=2,
)
context_layer = context_layer.view((bsz, n_heads, from_seq_len, -1)) * from_mask
context_layer = torch.transpose(context_layer, 1, 2)
# this is just for visualizing; forward pass doesn't depend on following code
if output_attentions:
# TODO(PVP): need to verify if below code is correct
attention_probs = torch.zeros(
bsz, n_heads, from_seq_len, to_seq_len, dtype=torch.float, device=context_layer.device
)
# 1st query block
# corresponding to `first_context_layer`
attention_probs[:, :, :from_block_size, :] = first_attn_weights # all keys global
# 2nd query block
# corresponding to `second_context_layer`
attention_probs[:, :, from_block_size : 2 * from_block_size, : 3 * to_block_size] = second_attn_weights[
:, :, :, : 3 * to_block_size
] # 1st three key blocks (global + sliding)
attention_probs[:, :, from_block_size : 2 * from_block_size, -to_block_size:] = second_attn_weights[
:, :, :, 3 * to_block_size : 4 * to_block_size
] # last key block (global)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, 1, :, i2[0]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Middle query blocks
# corresponding to `context_layer`
# sliding keys
for q_idx in range(from_seq_len // from_block_size - 4):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)[:, :, 2:-2, :, 1:-1, :]
right_slice = attn_weights[:, :, q_idx, :, to_block_size : 4 * to_block_size]
attn_probs_view[:, :, q_idx, :, q_idx : q_idx + 3, :] = right_slice.view(
bsz, n_heads, from_block_size, 3, to_block_size
) # inner_band_product
# global keys (corresponding to 1st key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[
:, :, :, :, :to_block_size
].view(
bsz, n_heads, -1, to_block_size
) # first_band_product
# global keys (corresponding to last key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[
:, :, :, :, -to_block_size:
].view(
bsz, n_heads, -1, to_block_size
) # last_band_product
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
for q_idx in range(1, len(i2) - 1):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[q_idx - 1, :, 4 * to_block_size : -to_block_size]
attn_probs_view[p1, p2, q_idx + 1, :, i2[q_idx]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Second-last query block
# corresponding to `second_last_context_layer`
attention_probs[:, :, -2 * from_block_size : -from_block_size, :to_block_size] = second_last_attn_weights[
:, :, :, :to_block_size
] # 1st key block (global)
attention_probs[
:, :, -2 * from_block_size : -from_block_size, -3 * to_block_size :
] = second_last_attn_weights[
:, :, :, to_block_size : 4 * to_block_size
] # last three blocks (global + sliding)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_last_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, -2, :, i2[-1]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# last query block
# corresponding to `last_context_layer`
attention_probs[:, :, -from_block_size:, :] = last_attn_weights # all keys global
else:
attention_probs = None
return context_layer, attention_probs
@staticmethod
def torch_gather_b2(params, indices):
# this operation is equivalent to tf.gather when batch_dims=2
if params.shape[:2] != indices.shape[:2]:
raise ValueError(
"Make sure that the first two dimensions of params and indices are identical, but"
f" they are params: {params.shape[:2]} vs. indices: {indices.shape[:2]}"
)
num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
num_indices_to_pick_from = params.shape[2]
indices_shift = (
torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
// num_indices_to_gather
* num_indices_to_pick_from
)
flattened_indices = indices.view(-1) + indices_shift
flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])
out_flattened = flattened_params.index_select(0, flattened_indices)
out = out_flattened.reshape(params.shape[:2] + (num_indices_to_gather,) + params.shape[3:])
return out
@staticmethod
def _create_rand_mask_from_inputs(
from_blocked_mask,
to_blocked_mask,
rand_attn,
num_attention_heads,
num_rand_blocks,
batch_size,
from_seq_length,
from_block_size,
):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
rand_attn: [batch_size, num_attention_heads,
from_seq_length//from_block_size-2, num_rand_blocks]
num_attention_heads: int. Number of attention heads.
num_rand_blocks: int. Number of random chunks per row.
batch_size: int. Batch size for computation.
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
Returns:
float Tensor of shape [batch_size, num_attention_heads, from_seq_length//from_block_size-2,
from_block_size, num_rand_blocks*to_block_size].
"""
num_windows = from_seq_length // from_block_size - 2
rand_mask = torch.stack([p1[i1.flatten()] for p1, i1 in zip(to_blocked_mask, rand_attn)])
rand_mask = rand_mask.view(batch_size, num_attention_heads, num_windows, num_rand_blocks * from_block_size)
rand_mask = torch.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
return rand_mask
@staticmethod
def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
"""
Gives the plan of where to put random attention.
Args:
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
num_rand_blocks: int. Number of random chunks per row.
Returns:
plan_from_length: ending location of from block plan_num_rand_blocks: number of random ending location for
each block
"""
plan_from_length = []
plan_num_rand_blocks = []
if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(0)
elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks // 2)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
else:
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks)
return plan_from_length, plan_num_rand_blocks
@staticmethod
def _bigbird_block_rand_mask(
from_seq_length, to_seq_length, from_block_size, to_block_size, num_rand_blocks, last_idx=-1
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_rand_blocks: int. Number of random chunks per row.
last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence,
if positive then num_rand_blocks blocks chosen only up to last_idx.
Returns:
adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks
"""
# using this method when from_seq_length in [1024, 3072, 4096]
if from_seq_length // from_block_size != to_seq_length // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
rand_attn = np.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32)
middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32)
last = to_seq_length // to_block_size - 1
if last_idx > (2 * to_block_size):
last = (last_idx // to_block_size) - 1
r = num_rand_blocks # shorthand
for i in range(1, from_seq_length // from_block_size - 1):
start = i - 2
end = i
if i == 1:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r]
elif i == 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r]
elif i == from_seq_length // from_block_size - 3:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -3: should have been sliced till last-3
elif i == from_seq_length // from_block_size - 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -4: should have been sliced till last-4
else:
if start > last:
start = last
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
elif (end + 1) == last:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
else:
rand_attn[i - 1, :] = np.random.permutation(
np.concatenate((middle_seq[:start], middle_seq[end + 1 : last]))
)[:r]
return rand_attn
def _bigbird_block_rand_mask_with_head(
self,
from_seq_length,
to_seq_length,
from_block_size,
to_block_size,
num_heads,
plan_from_length,
plan_num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_top=1,
global_block_bottom=1,
global_block_left=1,
global_block_right=1,
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_heads: int. total number of heads.
plan_from_length: list. plan from length where num_random_blocks are chosen from.
plan_num_rand_blocks: list. number of rand blocks within the plan.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_top: int. number of blocks at the top.
global_block_bottom: int. number of blocks at the bottom.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
adjacency list of size num_head where each element is of size from_seq_length//from_block_size-2 by
num_rand_blocks
"""
# using this method when from_seq_length not in [1024, 3072, 4096]
if from_seq_length // from_block_size != to_seq_length // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
if from_seq_length not in plan_from_length:
raise ValueError("Error from sequence length not in plan!")
# Total number of blocks in the mmask
num_blocks = from_seq_length // from_block_size
# Number of blocks per plan
plan_block_length = np.array(plan_from_length) // from_block_size
# till when to follow plan
max_plan_idx = plan_from_length.index(from_seq_length)
# Random Attention adjacency list
rand_attn = [
np.zeros((num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=np.int32)
for i in range(num_heads)
]
# We will go iteratively over the plan blocks and pick random number of
# Attention blocks from the legally allowed blocks
for plan_idx in range(max_plan_idx + 1):
rnd_r_cnt = 0
if plan_idx > 0:
# set the row for all from_blocks starting from 0 to
# plan_block_length[plan_idx-1]
# column indx start fromm plan_block_length[plan_idx-1] and ends at
# plan_block_length[plan_idx]
if plan_num_rand_blocks[plan_idx] > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=plan_block_length[plan_idx - 1],
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for pl_id in range(plan_idx):
if plan_num_rand_blocks[pl_id] == 0:
continue
for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]):
rnd_r_cnt = 0
to_start_block_id = 0
if pl_id > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id]))
to_start_block_id = plan_block_length[pl_id - 1]
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1]))
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[pl_id],
num_rand_blocks=plan_num_rand_blocks[pl_id],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
if plan_num_rand_blocks[plan_idx] == 0:
continue
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
from_start_block_id = global_block_top
to_start_block_id = 0
if plan_idx > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
from_start_block_id = plan_block_length[plan_idx - 1]
to_start_block_id = plan_block_length[plan_idx - 1]
for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for nh in range(num_heads):
rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
return rand_attn
@staticmethod
def _get_single_block_row_attention(
block_id,
to_start_block_id,
to_end_block_id,
num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_left=1,
global_block_right=1,
):
"""
For a single row block get random row attention.
Args:
block_id: int. block id of row.
to_start_block_id: int. random attention column start id.
to_end_block_id: int. random attention column end id.
num_rand_blocks: int. number of random blocks to be selected.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
row containing the random attention vector of size num_rand_blocks.
"""
# list of to_blocks from which to choose random attention
to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32)
# permute the blocks
perm_block = np.random.permutation(to_block_list)
# illegal blocks for the current block id, using window
illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1))
# Add blocks at the start and at the end
illegal_blocks.extend(list(range(global_block_left)))
illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))
# The second from_block cannot choose random attention on second last to_block
if block_id == 1:
illegal_blocks.append(to_end_block_id - 2)
# The second last from_block cannot choose random attention on second to_block
if block_id == to_end_block_id - 2:
illegal_blocks.append(1)
selected_random_blokcs = []
for i in range(to_end_block_id - to_start_block_id):
if perm_block[i] not in illegal_blocks:
selected_random_blokcs.append(perm_block[i])
if len(selected_random_blokcs) == num_rand_blocks:
break
return np.array(selected_random_blokcs, dtype=np.int32)
class BigBirdPegasusEncoderAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.config = config
self.seed = seed
self.attention_type = config.attention_type
if self.attention_type == "original_full":
self.self = BigBirdPegasusSelfAttention(config)
elif self.attention_type == "block_sparse":
self.self = BigBirdPegasusBlockSparseAttention(config, seed)
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.config.attention_type}"
)
self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
if value == "original_full":
# copy all weights to new full attention class
attn_weights = BigBirdPegasusSelfAttention(self.config)
else:
# copy all weights to new sparse attention class
attn_weights = BigBirdPegasusBlockSparseAttention(self.config, self.seed)
attn_weights.query = self.self.query
attn_weights.value = self.self.value
attn_weights.key = self.self.key
self.self = attn_weights
self.attention_type = value
if not self.training:
self.self.eval()
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
past_key_value=None,
output_attentions=False,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
):
# Expand dims to enable multiplication in the self-attention module
head_mask = head_mask.reshape(1, -1, 1, 1) if head_mask is not None else None
if self.config.attention_type == "original_full":
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
)
else:
self_outputs = self.self(
hidden_states, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, output_attentions
)
attention_output = self.output(self_outputs[0])
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BigBirdPegasusDecoder
class BigBirdPegasusDecoderAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class BigBirdPegasusEncoderLayer(nn.Module):
def __init__(self, config: BigBirdPegasusConfig, seed=None):
super().__init__()
self.attention_type = config.attention_type
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusEncoderAttention(config, seed=seed)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
self_attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=layer_head_mask,
output_attentions=output_attentions,
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=from_blocked_mask,
to_blocked_mask=to_blocked_mask,
)
hidden_states = self_attention_outputs[0]
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attention_outputs[1],)
return outputs
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
self.self_attn.set_attention_type(value)
class BigBirdPegasusDecoderLayer(nn.Module):
def __init__(self, config: BigBirdPegasusConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusDecoderAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = BigBirdPegasusDecoderAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape *(seq_len, batch, embed_dim)*
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
*(encoder_attention_heads,)*.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size *(decoder_attention_heads,)*.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->BigBirdPegasus
class BigBirdPegasusClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
class BigBirdPegasusPreTrainedModel(PreTrainedModel):
config_class = BigBirdPegasusConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (BigBirdPegasusDecoder, BigBirdPegasusEncoder)):
module.gradient_checkpointing = value
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
BIGBIRD_PEGASUS_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`BigBirdPegasusConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
BIGBIRD_PEGASUS_GENERATION_EXAMPLE = r"""
Summarization example:
```python
>>> from transformers import PegasusTokenizer, BigBirdPegasusForConditionalGeneration
>>> model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> tokenizer = PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> ARTICLE_TO_SUMMARIZE = (
... "The dominant sequence transduction models are based on complex recurrent or convolutional neural "
... "networks in an encoder-decoder configuration. The best performing models also connect the encoder "
... "and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, "
... "based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. "
... "Experiments on two machine translation tasks show these models to be superior in quality "
... "while being more parallelizable and requiring significantly less time to train."
... )
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=4096, return_tensors="pt", truncation=True)
>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=15)
>>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
'dominant sequence models are based on recurrent or convolutional neural networks .'
```
"""
BIGBIRD_PEGASUS_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the `input_ids` to the right, following the paper.
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
[`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in
[the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
`(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
can choose to directly pass an embedded representation. This is useful if you want more control over how to
convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
BIGBIRD_PEGASUS_STANDALONE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`ProphetNetTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BigBirdPegasusEncoderLayer`].
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.attention_type = config.attention_type
self.block_size = config.block_size
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([BigBirdPegasusEncoderLayer(config, seed=i) for i in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=hidden_states.device)
attention_mask = attention_mask.long()
# in order to use block_sparse attention, sequence_length has to be at least
# bigger than all global attentions: 2 * block_size
# + sliding tokens: 3 * block_size
# + random tokens: 2 * num_random_blocks * block_size
max_tokens_to_attend = (5 + 2 * self.config.num_random_blocks) * self.config.block_size
if self.attention_type == "block_sparse" and input_shape[1] <= max_tokens_to_attend:
# change attention_type from block_sparse to original_full
sequence_length = input_shape[1]
logger.warning(
"Attention type 'block_sparse' is not possible if sequence_length: "
f"{sequence_length} <= num global tokens: 2 * config.block_size "
"+ min. num sliding tokens: 3 * config.block_size "
"+ config.num_random_blocks * config.block_size "
"+ additional buffer: config.num_random_blocks * config.block_size "
f"= {max_tokens_to_attend} with config.block_size "
f"= {self.config.block_size}, config.num_random_blocks "
f"= {self.config.num_random_blocks}. "
"Changing attention type to 'original_full'..."
)
self.set_attention_type("original_full")
if self.attention_type == "block_sparse":
padding_len, hidden_states, attention_mask = self._pad_to_block_size(hidden_states, attention_mask)
else:
padding_len = 0
# expand attention_mask
if self.attention_type == "original_full":
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
blocked_encoder_mask = band_mask = from_mask = to_mask = None
elif self.attention_type == "block_sparse":
blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(
attention_mask, self.block_size
)
attention_mask = None
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.attention_type}"
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
band_mask,
from_mask,
to_mask,
blocked_encoder_mask,
blocked_encoder_mask,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=blocked_encoder_mask,
to_blocked_mask=blocked_encoder_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layernorm_embedding(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if padding_len > 0:
# unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1)
hidden_states = hidden_states[:, :-padding_len]
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
self.encoder_o = hidden_states
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
for layer in self.layers:
layer.set_attention_type(value)
@staticmethod # Copied from transformers.models.big_bird.modeling_big_bird.BigBirdModel.create_masks_for_block_sparse_attn
def create_masks_for_block_sparse_attn(attention_mask: torch.Tensor, block_size: int):
batch_size, seq_length = attention_mask.size()
if seq_length % block_size != 0:
raise ValueError(
f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block"
f" size is {block_size}."
)
def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
Returns:
float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size,
3*to_block_size].
"""
exp_blocked_to_pad = torch.cat(
[to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], dim=2
)
band_mask = torch.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
band_mask.unsqueeze_(1)
return band_mask
blocked_encoder_mask = attention_mask.view(batch_size, seq_length // block_size, block_size)
band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)
from_mask = attention_mask.view(batch_size, 1, seq_length, 1)
to_mask = attention_mask.view(batch_size, 1, 1, seq_length)
return blocked_encoder_mask, band_mask, from_mask, to_mask
def _pad_to_block_size(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor):
"""A helper function to pad tokens and mask to work with implementation of BigBird block-sparse attention."""
# padding
block_size = self.config.block_size
batch_size, seq_len = hidden_states.shape[:2]
padding_len = (block_size - seq_len % block_size) % block_size
if padding_len > 0:
logger.info(
f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
f"`config.block_size`: {block_size}"
)
pad_id = self.config.pad_token_id
device = hidden_states.device
input_ids_padding = torch.ones((batch_size, padding_len), dtype=torch.long, device=device) * pad_id
inputs_embeds_padding = self.embed_tokens(input_ids_padding)
hidden_states = torch.cat([hidden_states, inputs_embeds_padding], dim=-2)
attention_mask = nn.functional.pad(
attention_mask, (0, padding_len), value=0
) # no attention on the padding tokens
return padding_len, hidden_states, attention_mask
class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BigBirdPegasusDecoderLayer`]
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([BigBirdPegasusDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(inputs_embeds.device)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
control over how to convert `input_ids` indices into associated vectors than the model's internal
embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, use_cache)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layernorm_embedding(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare BigBirdPegasus Model outputting raw hidden-states without any specific head on top.",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartModel with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
self.encoder = BigBirdPegasusEncoder(config, self.shared)
self.decoder = BigBirdPegasusDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqModelOutput]:
# different to other models, BigBirdPegasus automatically creates decoder_input_ids from
# input_ids if no decoder_input_ids are provided
if decoder_input_ids is None and decoder_inputs_embeds is None:
if input_ids is None:
raise ValueError(
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
"passed, `input_ids` cannot be `None`. Please pass either "
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
)
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The BigBirdPegasus Model with a language modeling head. Can be used for summarization.",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartForConditionalGeneration with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
r"final_logits_bias",
r"lm_head.weight",
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
]
def __init__(self, config: BigBirdPegasusConfig):
super().__init__(config)
self.model = BigBirdPegasusModel(config)
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens)
self._resize_final_logits_bias(new_num_tokens)
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BIGBIRD_PEGASUS_GENERATION_EXAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0])
lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs
):
# cut decoder_input_ids if past is used
if past is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
)
return reordered_past
@add_start_docstrings(
"""
BigBirdPegasus model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
""",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = BigBirdPegasusModel(config)
self.classification_head = BigBirdPegasusClassificationHead(
config.d_model,
config.d_model,
config.num_labels,
config.classifier_dropout,
)
self.model._init_weights(self.classification_head.dense)
self.model._init_weights(self.classification_head.out_proj)
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
if input_ids is None and inputs_embeds is not None:
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0] # last hidden state
eos_mask = input_ids.eq(self.config.eos_token_id)
if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same number of <eos> tokens.")
sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
:, -1, :
]
logits = self.classification_head(sentence_representation)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.config.num_labels == 1:
self.config.problem_type = "regression"
elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.config.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return Seq2SeqSequenceClassifierOutput(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"""
BigBirdPegasus Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
BIGBIRD_PEGASUS_START_DOCSTRING,
)
# Copied from transformers.models.bart.modeling_bart.BartForQuestionAnswering with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = BigBirdPegasusModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.model._init_weights(self.qa_outputs)
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_QA,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_loss=_QA_EXPECTED_LOSS,
expected_output=_QA_EXPECTED_OUTPUT,
)
def forward(
self,
input_ids: torch.Tensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if start_positions is not None and end_positions is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (
start_logits,
end_logits,
) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return Seq2SeqQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
# Copied from transformers.models.pegasus.modeling_pegasus.PegasusDecoderWrapper with Pegasus->BigBirdPegasus
class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = BigBirdPegasusDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
_keys_to_ignore_on_load_missing = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = BigBirdPegasusDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import PegasusTokenizer, BigBirdPegasusForCausalLM
>>> tokenizer = PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> model = BigBirdPegasusForCausalLM.from_pretrained(
... "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
... )
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past:
input_ids = input_ids[:, -1:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/legacy/run_openai_gpt.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" OpenAI GPT model fine-tuning script.
Adapted from https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/train.py
It self adapted from https://github.com/openai/finetune-transformer-lm/blob/master/train.py
This script with default values fine-tunes and evaluate a pretrained OpenAI GPT on the RocStories dataset:
python run_openai_gpt.py \
--model_name openai-gpt \
--do_train \
--do_eval \
--train_dataset "$ROC_STORIES_DIR/cloze_test_val__spring2016 - cloze_test_ALL_val.csv" \
--eval_dataset "$ROC_STORIES_DIR/cloze_test_test__spring2016 - cloze_test_ALL_test.csv" \
--output_dir ../log \
--train_batch_size 16 \
"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
logger = logging.getLogger(__name__)
def accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def load_rocstories_dataset(dataset_path):
"""Output a list of tuples(story, 1st continuation, 2nd continuation, label)"""
with open(dataset_path, encoding="utf_8") as f:
f = csv.reader(f)
output = []
next(f) # skip the first line
for line in tqdm(f):
output.append((" ".join(line[1:5]), line[5], line[6], int(line[-1]) - 1))
return output
def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
"""Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation:
input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
"""
tensor_datasets = []
for dataset in encoded_datasets:
n_batch = len(dataset)
input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)
mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64)
lm_labels = np.full((n_batch, 2, input_len), fill_value=-100, dtype=np.int64)
mc_labels = np.zeros((n_batch,), dtype=np.int64)
for (
i,
(story, cont1, cont2, mc_label),
) in enumerate(dataset):
with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
input_ids[i, 0, : len(with_cont1)] = with_cont1
input_ids[i, 1, : len(with_cont2)] = with_cont2
mc_token_ids[i, 0] = len(with_cont1) - 1
mc_token_ids[i, 1] = len(with_cont2) - 1
lm_labels[i, 0, : len(with_cont1)] = with_cont1
lm_labels[i, 1, : len(with_cont2)] = with_cont2
mc_labels[i] = mc_label
all_inputs = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs))
return tensor_datasets
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="openai-gpt", help="pretrained model name")
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--train_dataset", type=str, default="")
parser.add_argument("--eval_dataset", type=str, default="")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--num_train_epochs", type=int, default=3)
parser.add_argument("--train_batch_size", type=int, default=8)
parser.add_argument("--eval_batch_size", type=int, default=16)
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", type=int, default=1)
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--learning_rate", type=float, default=6.25e-5)
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--lr_schedule", type=str, default="warmup_linear")
parser.add_argument("--weight_decay", type=float, default=0.01)
parser.add_argument("--lm_coef", type=float, default=0.9)
parser.add_argument("--n_valid", type=int, default=374)
parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
args = parser.parse_args()
print(args)
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(device, n_gpu))
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
special_tokens = ["_start_", "_delimiter_", "_classify_"]
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name)
tokenizer.add_tokens(special_tokens)
special_tokens_ids = tokenizer.convert_tokens_to_ids(special_tokens)
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name)
model.resize_token_embeddings(len(tokenizer))
model.to(device)
# Load and encode the datasets
def tokenize_and_encode(obj):
"""Tokenize and encode a nested object"""
if isinstance(obj, str):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
elif isinstance(obj, int):
return obj
return list(tokenize_and_encode(o) for o in obj)
logger.info("Encoding dataset...")
train_dataset = load_rocstories_dataset(args.train_dataset)
eval_dataset = load_rocstories_dataset(args.eval_dataset)
datasets = (train_dataset, eval_dataset)
encoded_datasets = tokenize_and_encode(datasets)
# Compute the max input length for the Transformer
max_length = model.config.n_positions // 2 - 2
input_length = max(
len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3
for dataset in encoded_datasets
for story, cont1, cont2, _ in dataset
)
input_length = min(input_length, model.config.n_positions) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
tensor_datasets = pre_process_datasets(encoded_datasets, input_length, max_length, *special_tokens_ids)
train_tensor_dataset, eval_tensor_dataset = tensor_datasets[0], tensor_datasets[1]
train_data = TensorDataset(*train_tensor_dataset)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
eval_data = TensorDataset(*eval_tensor_dataset)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
param_optimizer = list(model.named_parameters())
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
if args.do_train:
nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_steps = 0
tqdm_bar = tqdm(train_dataloader, desc="Training")
for step, batch in enumerate(tqdm_bar):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
losses = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
loss = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
exp_average_loss = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0])
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, "module") else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.output_dir)
model.to(device)
if args.do_eval:
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch in tqdm(eval_dataloader, desc="Evaluating"):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
with torch.no_grad():
_, mc_loss, _, mc_logits = model(
input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels
)
mc_logits = mc_logits.detach().cpu().numpy()
mc_labels = mc_labels.to("cpu").numpy()
tmp_eval_accuracy = accuracy(mc_logits, mc_labels)
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
train_loss = tr_loss / nb_tr_steps if args.do_train else None
result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if __name__ == "__main__":
main()
| #!/usr/bin/env python
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" OpenAI GPT model fine-tuning script.
Adapted from https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/train.py
It self adapted from https://github.com/openai/finetune-transformer-lm/blob/master/train.py
This script with default values fine-tunes and evaluate a pretrained OpenAI GPT on the RocStories dataset:
python run_openai_gpt.py \
--model_name openai-gpt \
--do_train \
--do_eval \
--train_dataset "$ROC_STORIES_DIR/cloze_test_val__spring2016 - cloze_test_ALL_val.csv" \
--eval_dataset "$ROC_STORIES_DIR/cloze_test_test__spring2016 - cloze_test_ALL_test.csv" \
--output_dir ../log \
--train_batch_size 16 \
"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
logger = logging.getLogger(__name__)
def accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def load_rocstories_dataset(dataset_path):
"""Output a list of tuples(story, 1st continuation, 2nd continuation, label)"""
with open(dataset_path, encoding="utf_8") as f:
f = csv.reader(f)
output = []
next(f) # skip the first line
for line in tqdm(f):
output.append((" ".join(line[1:5]), line[5], line[6], int(line[-1]) - 1))
return output
def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
"""Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation:
input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
"""
tensor_datasets = []
for dataset in encoded_datasets:
n_batch = len(dataset)
input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)
mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64)
lm_labels = np.full((n_batch, 2, input_len), fill_value=-100, dtype=np.int64)
mc_labels = np.zeros((n_batch,), dtype=np.int64)
for (
i,
(story, cont1, cont2, mc_label),
) in enumerate(dataset):
with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
input_ids[i, 0, : len(with_cont1)] = with_cont1
input_ids[i, 1, : len(with_cont2)] = with_cont2
mc_token_ids[i, 0] = len(with_cont1) - 1
mc_token_ids[i, 1] = len(with_cont2) - 1
lm_labels[i, 0, : len(with_cont1)] = with_cont1
lm_labels[i, 1, : len(with_cont2)] = with_cont2
mc_labels[i] = mc_label
all_inputs = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs))
return tensor_datasets
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="openai-gpt", help="pretrained model name")
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--train_dataset", type=str, default="")
parser.add_argument("--eval_dataset", type=str, default="")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--num_train_epochs", type=int, default=3)
parser.add_argument("--train_batch_size", type=int, default=8)
parser.add_argument("--eval_batch_size", type=int, default=16)
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", type=int, default=1)
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--learning_rate", type=float, default=6.25e-5)
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--lr_schedule", type=str, default="warmup_linear")
parser.add_argument("--weight_decay", type=float, default=0.01)
parser.add_argument("--lm_coef", type=float, default=0.9)
parser.add_argument("--n_valid", type=int, default=374)
parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
args = parser.parse_args()
print(args)
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(device, n_gpu))
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
special_tokens = ["_start_", "_delimiter_", "_classify_"]
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name)
tokenizer.add_tokens(special_tokens)
special_tokens_ids = tokenizer.convert_tokens_to_ids(special_tokens)
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name)
model.resize_token_embeddings(len(tokenizer))
model.to(device)
# Load and encode the datasets
def tokenize_and_encode(obj):
"""Tokenize and encode a nested object"""
if isinstance(obj, str):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
elif isinstance(obj, int):
return obj
return list(tokenize_and_encode(o) for o in obj)
logger.info("Encoding dataset...")
train_dataset = load_rocstories_dataset(args.train_dataset)
eval_dataset = load_rocstories_dataset(args.eval_dataset)
datasets = (train_dataset, eval_dataset)
encoded_datasets = tokenize_and_encode(datasets)
# Compute the max input length for the Transformer
max_length = model.config.n_positions // 2 - 2
input_length = max(
len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3
for dataset in encoded_datasets
for story, cont1, cont2, _ in dataset
)
input_length = min(input_length, model.config.n_positions) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
tensor_datasets = pre_process_datasets(encoded_datasets, input_length, max_length, *special_tokens_ids)
train_tensor_dataset, eval_tensor_dataset = tensor_datasets[0], tensor_datasets[1]
train_data = TensorDataset(*train_tensor_dataset)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
eval_data = TensorDataset(*eval_tensor_dataset)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
param_optimizer = list(model.named_parameters())
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
if args.do_train:
nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_steps = 0
tqdm_bar = tqdm(train_dataloader, desc="Training")
for step, batch in enumerate(tqdm_bar):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
losses = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
loss = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
exp_average_loss = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0])
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, "module") else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.output_dir)
model.to(device)
if args.do_eval:
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch in tqdm(eval_dataloader, desc="Evaluating"):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
with torch.no_grad():
_, mc_loss, _, mc_logits = model(
input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels
)
mc_logits = mc_logits.detach().cpu().numpy()
mc_labels = mc_labels.to("cpu").numpy()
tmp_eval_accuracy = accuracy(mc_logits, mc_labels)
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
train_loss = tr_loss / nb_tr_steps if args.do_train else None
result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if __name__ == "__main__":
main()
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert MobileNetV2 checkpoints from the tensorflow/models library."""
import argparse
import json
import re
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import (
MobileNetV2Config,
MobileNetV2ForImageClassification,
MobileNetV2ForSemanticSegmentation,
MobileNetV2ImageProcessor,
load_tf_weights_in_mobilenet_v2,
)
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_mobilenet_v2_config(model_name):
config = MobileNetV2Config(layer_norm_eps=0.001)
if "quant" in model_name:
raise ValueError("Quantized models are not supported.")
matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name)
if matches:
config.depth_multiplier = float(matches[1])
config.image_size = int(matches[2])
if model_name.startswith("deeplabv3_"):
config.output_stride = 8
config.num_labels = 21
filename = "pascal-voc-id2label.json"
else:
# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
# of the usual 1000. The first class (index 0) is "background".
config.num_labels = 1001
filename = "imagenet-1k-id2label.json"
repo_id = "huggingface/label-files"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
if config.num_labels == 1001:
id2label = {int(k) + 1: v for k, v in id2label.items()}
id2label[0] = "background"
else:
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
return config
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
"""
Copy/paste/tweak model's weights to our MobileNetV2 structure.
"""
config = get_mobilenet_v2_config(model_name)
# Load 🤗 model
if model_name.startswith("deeplabv3_"):
model = MobileNetV2ForSemanticSegmentation(config).eval()
else:
model = MobileNetV2ForImageClassification(config).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path)
# Check outputs on an image, prepared by MobileNetV2ImageProcessor
feature_extractor = MobileNetV2ImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size},
size={"shortest_edge": config.image_size + 32},
)
encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
outputs = model(**encoding)
logits = outputs.logits
if model_name.startswith("deeplabv3_"):
assert logits.shape == (1, 21, 65, 65)
if model_name == "deeplabv3_mobilenet_v2_1.0_513":
expected_logits = torch.tensor(
[
[[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
[[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
[[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
]
)
else:
raise ValueError(f"Unknown model name: {model_name}")
assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4)
else:
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v2_1.4_224":
expected_logits = torch.tensor([0.0181, -1.0015, 0.4688])
elif model_name == "mobilenet_v2_1.0_224":
expected_logits = torch.tensor([0.2445, -1.1993, 0.1905])
elif model_name == "mobilenet_v2_0.75_160":
expected_logits = torch.tensor([0.2482, 0.4136, 0.6669])
elif model_name == "mobilenet_v2_0.35_96":
expected_logits = torch.tensor([0.1451, -0.4624, 0.7192])
else:
expected_logits = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving feature extractor to {pytorch_dump_folder_path}")
feature_extractor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
print("Pushing to the hub...")
repo_id = "google/" + model_name
feature_extractor.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="mobilenet_v2_1.0_224",
type=str,
help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
)
parser.add_argument(
"--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert MobileNetV2 checkpoints from the tensorflow/models library."""
import argparse
import json
import re
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import (
MobileNetV2Config,
MobileNetV2ForImageClassification,
MobileNetV2ForSemanticSegmentation,
MobileNetV2ImageProcessor,
load_tf_weights_in_mobilenet_v2,
)
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_mobilenet_v2_config(model_name):
config = MobileNetV2Config(layer_norm_eps=0.001)
if "quant" in model_name:
raise ValueError("Quantized models are not supported.")
matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name)
if matches:
config.depth_multiplier = float(matches[1])
config.image_size = int(matches[2])
if model_name.startswith("deeplabv3_"):
config.output_stride = 8
config.num_labels = 21
filename = "pascal-voc-id2label.json"
else:
# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
# of the usual 1000. The first class (index 0) is "background".
config.num_labels = 1001
filename = "imagenet-1k-id2label.json"
repo_id = "huggingface/label-files"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
if config.num_labels == 1001:
id2label = {int(k) + 1: v for k, v in id2label.items()}
id2label[0] = "background"
else:
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
return config
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
"""
Copy/paste/tweak model's weights to our MobileNetV2 structure.
"""
config = get_mobilenet_v2_config(model_name)
# Load 🤗 model
if model_name.startswith("deeplabv3_"):
model = MobileNetV2ForSemanticSegmentation(config).eval()
else:
model = MobileNetV2ForImageClassification(config).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path)
# Check outputs on an image, prepared by MobileNetV2ImageProcessor
feature_extractor = MobileNetV2ImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size},
size={"shortest_edge": config.image_size + 32},
)
encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
outputs = model(**encoding)
logits = outputs.logits
if model_name.startswith("deeplabv3_"):
assert logits.shape == (1, 21, 65, 65)
if model_name == "deeplabv3_mobilenet_v2_1.0_513":
expected_logits = torch.tensor(
[
[[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
[[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
[[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
]
)
else:
raise ValueError(f"Unknown model name: {model_name}")
assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4)
else:
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v2_1.4_224":
expected_logits = torch.tensor([0.0181, -1.0015, 0.4688])
elif model_name == "mobilenet_v2_1.0_224":
expected_logits = torch.tensor([0.2445, -1.1993, 0.1905])
elif model_name == "mobilenet_v2_0.75_160":
expected_logits = torch.tensor([0.2482, 0.4136, 0.6669])
elif model_name == "mobilenet_v2_0.35_96":
expected_logits = torch.tensor([0.1451, -0.4624, 0.7192])
else:
expected_logits = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving feature extractor to {pytorch_dump_folder_path}")
feature_extractor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
print("Pushing to the hub...")
repo_id = "google/" + model_name
feature_extractor.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="mobilenet_v2_1.0_224",
type=str,
help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
)
parser.add_argument(
"--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/mbart/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart"] = ["MBartTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart_fast"] = ["MBartTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mbart"] = [
"MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"MBartForCausalLM",
"MBartForConditionalGeneration",
"MBartForQuestionAnswering",
"MBartForSequenceClassification",
"MBartModel",
"MBartPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mbart"] = [
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMBartPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_mbart"] = [
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart"] = ["MBartTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_mbart_fast"] = ["MBartTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mbart"] = [
"MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"MBartForCausalLM",
"MBartForConditionalGeneration",
"MBartForQuestionAnswering",
"MBartForSequenceClassification",
"MBartModel",
"MBartPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mbart"] = [
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMBartPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_mbart"] = [
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/clipseg/processing_clipseg.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Image/Text processor class for CLIPSeg
"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class CLIPSegProcessor(ProcessorMixin):
r"""
Constructs a CLIPSeg processor which wraps a CLIPSeg feature extractor and a CLIP tokenizer into a single
processor.
[`CLIPSegProcessor`] offers all the functionalities of [`ViTFeatureExtractor`] and [`CLIPTokenizerFast`]. See the
[`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information.
Args:
feature_extractor ([`ViTFeatureExtractor`]):
The feature extractor is a required input.
tokenizer ([`CLIPTokenizerFast`]):
The tokenizer is a required input.
"""
feature_extractor_class = "ViTFeatureExtractor"
tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
ViTFeatureExtractor's [`~ViTFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
doctsring of the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
if text is not None:
encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
if images is not None:
image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
if text is not None and images is not None:
encoding["pixel_values"] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Image/Text processor class for CLIPSeg
"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class CLIPSegProcessor(ProcessorMixin):
r"""
Constructs a CLIPSeg processor which wraps a CLIPSeg feature extractor and a CLIP tokenizer into a single
processor.
[`CLIPSegProcessor`] offers all the functionalities of [`ViTFeatureExtractor`] and [`CLIPTokenizerFast`]. See the
[`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information.
Args:
feature_extractor ([`ViTFeatureExtractor`]):
The feature extractor is a required input.
tokenizer ([`CLIPTokenizerFast`]):
The tokenizer is a required input.
"""
feature_extractor_class = "ViTFeatureExtractor"
tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
ViTFeatureExtractor's [`~ViTFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
doctsring of the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
if text is not None:
encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
if images is not None:
image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
if text is not None and images is not None:
encoding["pixel_values"] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/xlnet/test_modeling_xlnet.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
from transformers import XLNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
)
from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST
class XLNetModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
mem_len=10,
clamp_len=-1,
reuse_len=15,
is_training=True,
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
hidden_size=32,
num_attention_heads=4,
d_inner=128,
num_hidden_layers=5,
type_sequence_label_size=2,
untie_r=True,
bi_data=False,
same_length=False,
initializer_range=0.05,
seed=1,
type_vocab_size=2,
bos_token_id=1,
eos_token_id=2,
pad_token_id=5,
num_choices=4,
):
self.parent = parent
self.batch_size = 14
self.seq_length = 7
self.mem_len = 10
# self.key_len = seq_length + mem_len
self.clamp_len = -1
self.reuse_len = 15
self.is_training = True
self.use_labels = True
self.vocab_size = 99
self.cutoffs = [10, 50, 80]
self.hidden_size = 32
self.num_attention_heads = 4
self.d_inner = 128
self.num_hidden_layers = 5
self.type_sequence_label_size = 2
self.untie_r = True
self.bi_data = False
self.same_length = False
self.initializer_range = 0.05
self.seed = 1
self.type_vocab_size = 2
self.bos_token_id = 1
self.eos_token_id = 2
self.pad_token_id = 5
self.num_choices = 4
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
input_mask = random_attention_mask([self.batch_size, self.seq_length])
input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
perm_mask = torch.zeros(
self.batch_size,
self.seq_length + 1,
self.seq_length + 1,
dtype=torch.float,
device=torch_device,
)
perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = torch.zeros(
self.batch_size,
1,
self.seq_length + 1,
dtype=torch.float,
device=torch_device,
)
target_mapping[:, 0, -1] = 1.0 # predict last token
sequence_labels = None
lm_labels = None
is_impossible_labels = None
token_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = self.get_config()
return (
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
)
def get_config(self):
return XLNetConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
n_head=self.num_attention_heads,
d_inner=self.d_inner,
n_layer=self.num_hidden_layers,
untie_r=self.untie_r,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
same_length=self.same_length,
reuse_len=self.reuse_len,
bi_data=self.bi_data,
initializer_range=self.initializer_range,
num_labels=self.type_sequence_label_size,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
eos_token_id=self.eos_token_id,
)
def set_seed(self):
random.seed(self.seed)
torch.manual_seed(self.seed)
def create_and_check_xlnet_base_model(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1, input_mask=input_mask)
result = model(input_ids_1, attention_mask=input_mask)
result = model(input_ids_1, token_type_ids=segment_ids)
result = model(input_ids_1)
config.mem_len = 0
model = XLNetModel(config)
model.to(torch_device)
model.eval()
base_model_output = model(input_ids_1)
self.parent.assertEqual(len(base_model_output), 2)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_use_mems_train(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForSequenceClassification(config)
model.to(torch_device)
model.train()
train_size = input_ids_1.shape[0]
batch_size = 4
for i in range(train_size // batch_size + 1):
input_ids = input_ids_1[i : (i + 1) * batch_size]
labels = sequence_labels[i : (i + 1) * batch_size]
outputs = model(input_ids=input_ids, labels=labels, return_dict=True)
self.parent.assertIsNone(outputs.mems)
self.parent.assertIsNotNone(outputs.loss)
def create_and_check_xlnet_model_use_mems(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
causal_mask = torch.ones(
input_ids_1.shape[0],
input_ids_1.shape[1],
input_ids_1.shape[1],
dtype=torch.float,
device=torch_device,
)
causal_mask = torch.triu(causal_mask, diagonal=0)
outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask)
outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask)
outputs_conf = model(input_ids_1)
self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)
output, mems = outputs_cache.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)
# causal mask
causal_mask = torch.ones(
input_ids_1.shape[0],
input_ids_1.shape[1] + 1,
input_ids_1.shape[1] + 1,
dtype=torch.float,
device=torch_device,
)
causal_mask = torch.triu(causal_mask, diagonal=0)
single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)
# second forward pass
output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_xlnet_base_model_with_att_output(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetModel(config)
model.to(torch_device)
model.eval()
attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]
self.parent.assertEqual(len(attentions), config.n_layer)
self.parent.assertIsInstance(attentions[0], tuple)
self.parent.assertEqual(len(attentions[0]), 2)
self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)
def create_and_check_xlnet_lm_head(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetLMHeadModel(config)
model.to(torch_device)
model.eval()
result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems)
_ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)
self.parent.assertEqual(result1.loss.shape, ())
self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in result1.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
self.parent.assertEqual(result2.loss.shape, ())
self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in result2.mems],
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_xlnet_qa(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForQuestionAnswering(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask,
)
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
)
total_loss, mems = result_with_labels.to_tuple()
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
total_loss, mems = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape, ())
self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(
result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(
result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_xlnet_token_classif(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
result = model(input_ids_1, labels=token_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_xlnet_sequence_classif(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
result = model(input_ids_1, labels=sequence_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids_1}
return config, inputs_dict
@require_torch
class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(
XLNetModel,
XLNetLMHeadModel,
XLNetForTokenClassification,
XLNetForSequenceClassification,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForMultipleChoice,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (
(XLNetLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
fx_compatible = False
test_pruning = False
# XLNet has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "XLNetForQuestionAnswering":
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = XLNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlnet_base_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
def test_xlnet_base_model_use_mems(self):
# checking that in auto-regressive mode, `use_mems` gives the same results
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs)
def test_seq_classification_use_mems_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_use_mems_train(*config_and_inputs)
def test_xlnet_base_model_with_att_output(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)
def test_xlnet_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
def test_xlnet_sequence_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
def test_xlnet_token_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)
def test_xlnet_qa(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# xlnet cannot keep gradients in attentions or hidden states
return
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
# check hidden size
for i, layer_hidden_states in enumerate(iter_hidden_states):
# every 2nd tensor is from extra stream
if i % 2 != 0:
seq_len = 1
else:
# for first item dummy PAD token is appended so need one more
seq_len = (min_length + 1) if idx == 0 else min_length
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
self.assertEqual(layer_hidden_states.shape, expected_shape)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, attentions_item in enumerate(attentions):
for iter_attentions in attentions_item:
tgt_len = min_length
# for first item dummy PAD token is appended so need one more
if idx == 0:
tgt_len += 1
src_len = min_length + idx + 1
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions],
[expected_shape] * len(iter_attentions),
)
@slow
def test_model_from_pretrained(self):
for model_name in XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class XLNetModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_xlnet_base_cased(self):
model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
model.to(torch_device)
# fmt: off
input_ids = torch.tensor(
[
[
67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3,
]
],
dtype=torch.long,
device=torch_device,
)
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family
# (except for Alexei and Maria) are discovered.
# The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
# remainder of the story. 1883 Western Siberia,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic.
# Rasputin has a vision and denounces one of the men as a horse thief. Although his
# father initially slaps him for making such an accusation, Rasputin watches as the
# man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
# the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
# with people, even a bishop, begging for his blessing. """
# fmt: off
expected_output_ids = [
67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, 19, 12943, 4354, 153, 27, 442, 22, 2771, 4901, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771,
]
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria)
# are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich,
# narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin
# is asked by his father and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially slaps
# him for making such an accusation, Rasputin watches as the man is chased outside and beaten.
# Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing.
# <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary.
# He is asked to perform a ritual of the Virgin Mary. He is asked to perform
output_ids = model.generate(input_ids, max_length=200, do_sample=False)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
| # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
from transformers import XLNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
)
from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST
class XLNetModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
mem_len=10,
clamp_len=-1,
reuse_len=15,
is_training=True,
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
hidden_size=32,
num_attention_heads=4,
d_inner=128,
num_hidden_layers=5,
type_sequence_label_size=2,
untie_r=True,
bi_data=False,
same_length=False,
initializer_range=0.05,
seed=1,
type_vocab_size=2,
bos_token_id=1,
eos_token_id=2,
pad_token_id=5,
num_choices=4,
):
self.parent = parent
self.batch_size = 14
self.seq_length = 7
self.mem_len = 10
# self.key_len = seq_length + mem_len
self.clamp_len = -1
self.reuse_len = 15
self.is_training = True
self.use_labels = True
self.vocab_size = 99
self.cutoffs = [10, 50, 80]
self.hidden_size = 32
self.num_attention_heads = 4
self.d_inner = 128
self.num_hidden_layers = 5
self.type_sequence_label_size = 2
self.untie_r = True
self.bi_data = False
self.same_length = False
self.initializer_range = 0.05
self.seed = 1
self.type_vocab_size = 2
self.bos_token_id = 1
self.eos_token_id = 2
self.pad_token_id = 5
self.num_choices = 4
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
input_mask = random_attention_mask([self.batch_size, self.seq_length])
input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
perm_mask = torch.zeros(
self.batch_size,
self.seq_length + 1,
self.seq_length + 1,
dtype=torch.float,
device=torch_device,
)
perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = torch.zeros(
self.batch_size,
1,
self.seq_length + 1,
dtype=torch.float,
device=torch_device,
)
target_mapping[:, 0, -1] = 1.0 # predict last token
sequence_labels = None
lm_labels = None
is_impossible_labels = None
token_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = self.get_config()
return (
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
)
def get_config(self):
return XLNetConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
n_head=self.num_attention_heads,
d_inner=self.d_inner,
n_layer=self.num_hidden_layers,
untie_r=self.untie_r,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
same_length=self.same_length,
reuse_len=self.reuse_len,
bi_data=self.bi_data,
initializer_range=self.initializer_range,
num_labels=self.type_sequence_label_size,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
eos_token_id=self.eos_token_id,
)
def set_seed(self):
random.seed(self.seed)
torch.manual_seed(self.seed)
def create_and_check_xlnet_base_model(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1, input_mask=input_mask)
result = model(input_ids_1, attention_mask=input_mask)
result = model(input_ids_1, token_type_ids=segment_ids)
result = model(input_ids_1)
config.mem_len = 0
model = XLNetModel(config)
model.to(torch_device)
model.eval()
base_model_output = model(input_ids_1)
self.parent.assertEqual(len(base_model_output), 2)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_use_mems_train(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForSequenceClassification(config)
model.to(torch_device)
model.train()
train_size = input_ids_1.shape[0]
batch_size = 4
for i in range(train_size // batch_size + 1):
input_ids = input_ids_1[i : (i + 1) * batch_size]
labels = sequence_labels[i : (i + 1) * batch_size]
outputs = model(input_ids=input_ids, labels=labels, return_dict=True)
self.parent.assertIsNone(outputs.mems)
self.parent.assertIsNotNone(outputs.loss)
def create_and_check_xlnet_model_use_mems(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
causal_mask = torch.ones(
input_ids_1.shape[0],
input_ids_1.shape[1],
input_ids_1.shape[1],
dtype=torch.float,
device=torch_device,
)
causal_mask = torch.triu(causal_mask, diagonal=0)
outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask)
outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask)
outputs_conf = model(input_ids_1)
self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)
output, mems = outputs_cache.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)
# causal mask
causal_mask = torch.ones(
input_ids_1.shape[0],
input_ids_1.shape[1] + 1,
input_ids_1.shape[1] + 1,
dtype=torch.float,
device=torch_device,
)
causal_mask = torch.triu(causal_mask, diagonal=0)
single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)
# second forward pass
output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_xlnet_base_model_with_att_output(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetModel(config)
model.to(torch_device)
model.eval()
attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]
self.parent.assertEqual(len(attentions), config.n_layer)
self.parent.assertIsInstance(attentions[0], tuple)
self.parent.assertEqual(len(attentions[0]), 2)
self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)
def create_and_check_xlnet_lm_head(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetLMHeadModel(config)
model.to(torch_device)
model.eval()
result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems)
_ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)
self.parent.assertEqual(result1.loss.shape, ())
self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in result1.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
self.parent.assertEqual(result2.loss.shape, ())
self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in result2.mems],
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_xlnet_qa(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForQuestionAnswering(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask,
)
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
)
total_loss, mems = result_with_labels.to_tuple()
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
total_loss, mems = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape, ())
self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(
result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(
result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_xlnet_token_classif(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
result = model(input_ids_1, labels=token_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_xlnet_sequence_classif(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
result = model(input_ids_1, labels=sequence_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids_1}
return config, inputs_dict
@require_torch
class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(
XLNetModel,
XLNetLMHeadModel,
XLNetForTokenClassification,
XLNetForSequenceClassification,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForMultipleChoice,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (
(XLNetLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
fx_compatible = False
test_pruning = False
# XLNet has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "XLNetForQuestionAnswering":
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = XLNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlnet_base_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
def test_xlnet_base_model_use_mems(self):
# checking that in auto-regressive mode, `use_mems` gives the same results
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs)
def test_seq_classification_use_mems_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_use_mems_train(*config_and_inputs)
def test_xlnet_base_model_with_att_output(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)
def test_xlnet_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
def test_xlnet_sequence_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
def test_xlnet_token_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)
def test_xlnet_qa(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# xlnet cannot keep gradients in attentions or hidden states
return
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
# check hidden size
for i, layer_hidden_states in enumerate(iter_hidden_states):
# every 2nd tensor is from extra stream
if i % 2 != 0:
seq_len = 1
else:
# for first item dummy PAD token is appended so need one more
seq_len = (min_length + 1) if idx == 0 else min_length
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
self.assertEqual(layer_hidden_states.shape, expected_shape)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, attentions_item in enumerate(attentions):
for iter_attentions in attentions_item:
tgt_len = min_length
# for first item dummy PAD token is appended so need one more
if idx == 0:
tgt_len += 1
src_len = min_length + idx + 1
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions],
[expected_shape] * len(iter_attentions),
)
@slow
def test_model_from_pretrained(self):
for model_name in XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class XLNetModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_xlnet_base_cased(self):
model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
model.to(torch_device)
# fmt: off
input_ids = torch.tensor(
[
[
67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3,
]
],
dtype=torch.long,
device=torch_device,
)
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family
# (except for Alexei and Maria) are discovered.
# The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
# remainder of the story. 1883 Western Siberia,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic.
# Rasputin has a vision and denounces one of the men as a horse thief. Although his
# father initially slaps him for making such an accusation, Rasputin watches as the
# man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
# the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
# with people, even a bishop, begging for his blessing. """
# fmt: off
expected_output_ids = [
67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, 19, 12943, 4354, 153, 27, 442, 22, 2771, 4901, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771,
]
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria)
# are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich,
# narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin
# is asked by his father and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially slaps
# him for making such an accusation, Rasputin watches as the man is chased outside and beaten.
# Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing.
# <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary.
# He is asked to perform a ritual of the Virgin Mary. He is asked to perform
output_ids = model.generate(input_ids, max_length=200, do_sample=False)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/xlm/test_modeling_xlm.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class XLMModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_lengths=True,
use_token_type_ids=True,
use_labels=True,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=False,
n_langs=2,
vocab_size=99,
n_special=0,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=2,
num_choices=4,
summary_type="last",
use_proj=True,
scope=None,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_lengths = use_input_lengths
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.gelu_activation = gelu_activation
self.sinusoidal_embeddings = sinusoidal_embeddings
self.causal = causal
self.asm = asm
self.n_langs = n_langs
self.vocab_size = vocab_size
self.n_special = n_special
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.summary_type = summary_type
self.use_proj = use_proj
self.scope = scope
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = random_attention_mask([self.batch_size, self.seq_length])
input_lengths = None
if self.use_input_lengths:
input_lengths = (
ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
) # small variation of seq_length
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
sequence_labels = None
token_labels = None
is_impossible_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def get_config(self):
return XLMConfig(
vocab_size=self.vocab_size,
n_special=self.n_special,
emb_dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
gelu_activation=self.gelu_activation,
sinusoidal_embeddings=self.sinusoidal_embeddings,
asm=self.asm,
causal=self.causal,
n_langs=self.n_langs,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
summary_type=self.summary_type,
use_proj=self.use_proj,
num_labels=self.num_labels,
bos_token_id=self.bos_token_id,
)
def create_and_check_xlm_model(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
result = model(input_ids, langs=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_xlm_lm_head(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMWithLMHeadModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_xlm_simple_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnsweringSimple(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
result = outputs
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_xlm_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnswering(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
result_with_labels = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask,
)
result_with_labels = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
)
(total_loss,) = result_with_labels.to_tuple()
result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
(total_loss,) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape, ())
self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(
result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(
result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
def create_and_check_xlm_sequence_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
result = model(input_ids, labels=sequence_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def create_and_check_xlm_token_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_labels = self.num_labels
model = XLMForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_xlm_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_choices = self.num_choices
model = XLMForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
# XLM has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = XLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
def test_xlm_simple_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)
def test_xlm_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
def test_xlm_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
def test_xlm_token_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*config_and_inputs)
def test_xlm_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
# adds PAD dummy token
tgt_len = min_length + idx + 1
src_len = min_length + idx + 1
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
# adds PAD dummy token
seq_len = min_length + idx + 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
pass
@slow
def test_model_from_pretrained(self):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class XLMModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_xlm_mlm_en_2048(self):
model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
model.to(torch_device)
input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device) # the president
expected_output_ids = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].cpu().numpy().tolist(), expected_output_ids)
| # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class XLMModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_lengths=True,
use_token_type_ids=True,
use_labels=True,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=False,
n_langs=2,
vocab_size=99,
n_special=0,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=2,
num_choices=4,
summary_type="last",
use_proj=True,
scope=None,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_lengths = use_input_lengths
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.gelu_activation = gelu_activation
self.sinusoidal_embeddings = sinusoidal_embeddings
self.causal = causal
self.asm = asm
self.n_langs = n_langs
self.vocab_size = vocab_size
self.n_special = n_special
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.summary_type = summary_type
self.use_proj = use_proj
self.scope = scope
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = random_attention_mask([self.batch_size, self.seq_length])
input_lengths = None
if self.use_input_lengths:
input_lengths = (
ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
) # small variation of seq_length
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
sequence_labels = None
token_labels = None
is_impossible_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def get_config(self):
return XLMConfig(
vocab_size=self.vocab_size,
n_special=self.n_special,
emb_dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
gelu_activation=self.gelu_activation,
sinusoidal_embeddings=self.sinusoidal_embeddings,
asm=self.asm,
causal=self.causal,
n_langs=self.n_langs,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
summary_type=self.summary_type,
use_proj=self.use_proj,
num_labels=self.num_labels,
bos_token_id=self.bos_token_id,
)
def create_and_check_xlm_model(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
result = model(input_ids, langs=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_xlm_lm_head(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMWithLMHeadModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_xlm_simple_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnsweringSimple(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
result = outputs
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_xlm_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnswering(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
result_with_labels = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask,
)
result_with_labels = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
)
(total_loss,) = result_with_labels.to_tuple()
result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
(total_loss,) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape, ())
self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(
result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(
result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
def create_and_check_xlm_sequence_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
result = model(input_ids, labels=sequence_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def create_and_check_xlm_token_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_labels = self.num_labels
model = XLMForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_xlm_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_choices = self.num_choices
model = XLMForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
# XLM has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = XLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
def test_xlm_simple_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)
def test_xlm_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
def test_xlm_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
def test_xlm_token_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*config_and_inputs)
def test_xlm_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
# adds PAD dummy token
tgt_len = min_length + idx + 1
src_len = min_length + idx + 1
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
# adds PAD dummy token
seq_len = min_length + idx + 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
pass
@slow
def test_model_from_pretrained(self):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class XLMModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_xlm_mlm_en_2048(self):
model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
model.to(torch_device)
input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device) # the president
expected_output_ids = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].cpu().numpy().tolist(), expected_output_ids)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./docs/source/en/tasks/token_classification.mdx | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Token classification
[[open-in-colab]]
<Youtube id="wVHdVlPScxA"/>
Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization.
This guide will show you how to:
1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
2. Use your finetuned model for inference.
<Tip>
See the token classification [task page](https://huggingface.co/tasks/token-classification) for more information about other forms of token classification and their associated models, datasets, and metrics.
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load WNUT 17 dataset
Start by loading the WNUT 17 dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> wnut = load_dataset("wnut_17")
```
Then take a look at an example:
```py
>>> wnut["train"][0]
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
}
```
Each number in `ner_tags` represents an entity. Convert the numbers to their label names to find out what the entities are:
```py
>>> label_list = wnut["train"].features[f"ner_tags"].feature.names
>>> label_list
[
"O",
"B-corporation",
"I-corporation",
"B-creative-work",
"I-creative-work",
"B-group",
"I-group",
"B-location",
"I-location",
"B-person",
"I-person",
"B-product",
"I-product",
]
```
The letter that prefixes each `ner_tag` indicates the token position of the entity:
- `B-` indicates the beginning of an entity.
- `I-` indicates a token is contained inside the same entity (for example, the `State` token is a part of an entity like
`Empire State Building`).
- `0` indicates the token doesn't correspond to any entity.
## Preprocess
<Youtube id="iY2AZYdZAr0"/>
The next step is to load a DistilBERT tokenizer to preprocess the `tokens` field:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
```
As you saw in the example `tokens` field above, it looks like the input has already been tokenized. But the input actually hasn't been tokenized yet and you'll need to set `is_split_into_words=True` to tokenize the words into subwords. For example:
```py
>>> example = wnut["train"][0]
>>> tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
>>> tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
>>> tokens
['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
```
However, this adds some special tokens `[CLS]` and `[SEP]` and the subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may now be split into two subwords. You'll need to realign the tokens and labels by:
1. Mapping all tokens to their corresponding word with the [`word_ids`](https://huggingface.co/docs/tokenizers/python/latest/api/reference.html#tokenizers.Encoding.word_ids) method.
2. Assigning the label `-100` to the special tokens `[CLS]` and `[SEP]` so they're ignored by the PyTorch loss function.
3. Only labeling the first token of a given word. Assign `-100` to other subtokens from the same word.
Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT's maximum input length:
```py
>>> def tokenize_and_align_labels(examples):
... tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
... labels = []
... for i, label in enumerate(examples[f"ner_tags"]):
... word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
... previous_word_idx = None
... label_ids = []
... for word_idx in word_ids: # Set the special tokens to -100.
... if word_idx is None:
... label_ids.append(-100)
... elif word_idx != previous_word_idx: # Only label the first token of a given word.
... label_ids.append(label[word_idx])
... else:
... label_ids.append(-100)
... previous_word_idx = word_idx
... labels.append(label_ids)
... tokenized_inputs["labels"] = labels
... return tokenized_inputs
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] function. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
```py
>>> tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
```
Now create a batch of examples using [`DataCollatorWithPadding`]. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximium length.
<frameworkcontent>
<pt>
```py
>>> from transformers import DataCollatorForTokenClassification
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
```
</pt>
<tf>
```py
>>> from transformers import DataCollatorForTokenClassification
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")
```
</tf>
</frameworkcontent>
## Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [seqeval](https://huggingface.co/spaces/evaluate-metric/seqeval) framework (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric). Seqeval actually produces several scores: precision, recall, F1, and accuracy.
```py
>>> import evaluate
>>> seqeval = evaluate.load("seqeval")
```
Get the NER labels first, and then create a function that passes your true predictions and true labels to [`~evaluate.EvaluationModule.compute`] to calculate the scores:
```py
>>> import numpy as np
>>> labels = [label_list[i] for i in example[f"ner_tags"]]
>>> def compute_metrics(p):
... predictions, labels = p
... predictions = np.argmax(predictions, axis=2)
... true_predictions = [
... [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
... for prediction, label in zip(predictions, labels)
... ]
... true_labels = [
... [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
... for prediction, label in zip(predictions, labels)
... ]
... results = seqeval.compute(predictions=true_predictions, references=true_labels)
... return {
... "precision": results["overall_precision"],
... "recall": results["overall_recall"],
... "f1": results["overall_f1"],
... "accuracy": results["overall_accuracy"],
... }
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training.
## Train
Before you start training your model, create a map of the expected ids to their labels with `id2label` and `label2id`:
```py
>>> id2label = {
... 0: "O",
... 1: "B-corporation",
... 2: "I-corporation",
... 3: "B-creative-work",
... 4: "I-creative-work",
... 5: "B-group",
... 6: "I-group",
... 7: "B-location",
... 8: "I-location",
... 9: "B-person",
... 10: "I-person",
... 11: "B-product",
... 12: "I-product",
... }
>>> label2id = {
... "O": 0,
... "B-corporation": 1,
... "I-corporation": 2,
... "B-creative-work": 3,
... "I-creative-work": 4,
... "B-group": 5,
... "I-group": 6,
... "B-location": 7,
... "I-location": 8,
... "B-person": 9,
... "I-person": 10,
... "B-product": 11,
... "I-product": 12,
... }
```
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load DistilBERT with [`AutoModelForTokenClassification`] along with the number of expected labels, and the label mappings:
```py
>>> from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
>>> model = AutoModelForTokenClassification.from_pretrained(
... "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the seqeval scores and save the training checkpoint.
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_wnut_model",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... num_train_epochs=2,
... weight_decay=0.01,
... evaluation_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_wnut["train"],
... eval_dataset=tokenized_wnut["test"],
... tokenizer=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
</Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_train_epochs = 3
>>> num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs
>>> optimizer, lr_schedule = create_optimizer(
... init_lr=2e-5,
... num_train_steps=num_train_steps,
... weight_decay_rate=0.01,
... num_warmup_steps=0,
... )
```
Then you can load DistilBERT with [`TFAutoModelForTokenClassification`] along with the number of expected labels, and the label mappings:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained(
... "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_wnut["train"],
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_wnut["validation"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer)
```
The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](./main_classes/keras_callbacks).
Pass your `compute_metrics` function to [`~transformers.KerasMetricCallback`]:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
```
Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_wnut_model",
... tokenizer=tokenizer,
... )
```
Then bundle your callbacks together:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for token classification, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).
</Tip>
## Inference
Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:
```py
>>> text = "The Golden State Warriors are an American professional basketball team based in San Francisco."
```
The simplest way to try out your finetuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for NER with your model, and pass your text to it:
```py
>>> from transformers import pipeline
>>> classifier = pipeline("ner", model="stevhliu/my_awesome_wnut_model")
>>> classifier(text)
[{'entity': 'B-location',
'score': 0.42658573,
'index': 2,
'word': 'golden',
'start': 4,
'end': 10},
{'entity': 'I-location',
'score': 0.35856336,
'index': 3,
'word': 'state',
'start': 11,
'end': 16},
{'entity': 'B-group',
'score': 0.3064001,
'index': 4,
'word': 'warriors',
'start': 17,
'end': 25},
{'entity': 'B-location',
'score': 0.65523505,
'index': 13,
'word': 'san',
'start': 80,
'end': 83},
{'entity': 'B-location',
'score': 0.4668663,
'index': 14,
'word': 'francisco',
'start': 84,
'end': 93}]
```
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Tokenize the text and return PyTorch tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="pt")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import AutoModelForTokenClassification
>>> model = AutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predictions = torch.argmax(logits, dim=2)
>>> predicted_token_class = [model.config.id2label[t.item()] for t in predictions[0]]
>>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</pt>
<tf>
Tokenize the text and return TensorFlow tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="tf")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
>>> predicted_token_class = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</tf>
</frameworkcontent> | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Token classification
[[open-in-colab]]
<Youtube id="wVHdVlPScxA"/>
Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization.
This guide will show you how to:
1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
2. Use your finetuned model for inference.
<Tip>
See the token classification [task page](https://huggingface.co/tasks/token-classification) for more information about other forms of token classification and their associated models, datasets, and metrics.
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load WNUT 17 dataset
Start by loading the WNUT 17 dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> wnut = load_dataset("wnut_17")
```
Then take a look at an example:
```py
>>> wnut["train"][0]
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
}
```
Each number in `ner_tags` represents an entity. Convert the numbers to their label names to find out what the entities are:
```py
>>> label_list = wnut["train"].features[f"ner_tags"].feature.names
>>> label_list
[
"O",
"B-corporation",
"I-corporation",
"B-creative-work",
"I-creative-work",
"B-group",
"I-group",
"B-location",
"I-location",
"B-person",
"I-person",
"B-product",
"I-product",
]
```
The letter that prefixes each `ner_tag` indicates the token position of the entity:
- `B-` indicates the beginning of an entity.
- `I-` indicates a token is contained inside the same entity (for example, the `State` token is a part of an entity like
`Empire State Building`).
- `0` indicates the token doesn't correspond to any entity.
## Preprocess
<Youtube id="iY2AZYdZAr0"/>
The next step is to load a DistilBERT tokenizer to preprocess the `tokens` field:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
```
As you saw in the example `tokens` field above, it looks like the input has already been tokenized. But the input actually hasn't been tokenized yet and you'll need to set `is_split_into_words=True` to tokenize the words into subwords. For example:
```py
>>> example = wnut["train"][0]
>>> tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
>>> tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
>>> tokens
['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
```
However, this adds some special tokens `[CLS]` and `[SEP]` and the subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may now be split into two subwords. You'll need to realign the tokens and labels by:
1. Mapping all tokens to their corresponding word with the [`word_ids`](https://huggingface.co/docs/tokenizers/python/latest/api/reference.html#tokenizers.Encoding.word_ids) method.
2. Assigning the label `-100` to the special tokens `[CLS]` and `[SEP]` so they're ignored by the PyTorch loss function.
3. Only labeling the first token of a given word. Assign `-100` to other subtokens from the same word.
Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT's maximum input length:
```py
>>> def tokenize_and_align_labels(examples):
... tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
... labels = []
... for i, label in enumerate(examples[f"ner_tags"]):
... word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
... previous_word_idx = None
... label_ids = []
... for word_idx in word_ids: # Set the special tokens to -100.
... if word_idx is None:
... label_ids.append(-100)
... elif word_idx != previous_word_idx: # Only label the first token of a given word.
... label_ids.append(label[word_idx])
... else:
... label_ids.append(-100)
... previous_word_idx = word_idx
... labels.append(label_ids)
... tokenized_inputs["labels"] = labels
... return tokenized_inputs
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] function. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
```py
>>> tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
```
Now create a batch of examples using [`DataCollatorWithPadding`]. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximium length.
<frameworkcontent>
<pt>
```py
>>> from transformers import DataCollatorForTokenClassification
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
```
</pt>
<tf>
```py
>>> from transformers import DataCollatorForTokenClassification
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")
```
</tf>
</frameworkcontent>
## Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [seqeval](https://huggingface.co/spaces/evaluate-metric/seqeval) framework (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric). Seqeval actually produces several scores: precision, recall, F1, and accuracy.
```py
>>> import evaluate
>>> seqeval = evaluate.load("seqeval")
```
Get the NER labels first, and then create a function that passes your true predictions and true labels to [`~evaluate.EvaluationModule.compute`] to calculate the scores:
```py
>>> import numpy as np
>>> labels = [label_list[i] for i in example[f"ner_tags"]]
>>> def compute_metrics(p):
... predictions, labels = p
... predictions = np.argmax(predictions, axis=2)
... true_predictions = [
... [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
... for prediction, label in zip(predictions, labels)
... ]
... true_labels = [
... [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
... for prediction, label in zip(predictions, labels)
... ]
... results = seqeval.compute(predictions=true_predictions, references=true_labels)
... return {
... "precision": results["overall_precision"],
... "recall": results["overall_recall"],
... "f1": results["overall_f1"],
... "accuracy": results["overall_accuracy"],
... }
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training.
## Train
Before you start training your model, create a map of the expected ids to their labels with `id2label` and `label2id`:
```py
>>> id2label = {
... 0: "O",
... 1: "B-corporation",
... 2: "I-corporation",
... 3: "B-creative-work",
... 4: "I-creative-work",
... 5: "B-group",
... 6: "I-group",
... 7: "B-location",
... 8: "I-location",
... 9: "B-person",
... 10: "I-person",
... 11: "B-product",
... 12: "I-product",
... }
>>> label2id = {
... "O": 0,
... "B-corporation": 1,
... "I-corporation": 2,
... "B-creative-work": 3,
... "I-creative-work": 4,
... "B-group": 5,
... "I-group": 6,
... "B-location": 7,
... "I-location": 8,
... "B-person": 9,
... "I-person": 10,
... "B-product": 11,
... "I-product": 12,
... }
```
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load DistilBERT with [`AutoModelForTokenClassification`] along with the number of expected labels, and the label mappings:
```py
>>> from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
>>> model = AutoModelForTokenClassification.from_pretrained(
... "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the seqeval scores and save the training checkpoint.
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_wnut_model",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... num_train_epochs=2,
... weight_decay=0.01,
... evaluation_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_wnut["train"],
... eval_dataset=tokenized_wnut["test"],
... tokenizer=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
</Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_train_epochs = 3
>>> num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs
>>> optimizer, lr_schedule = create_optimizer(
... init_lr=2e-5,
... num_train_steps=num_train_steps,
... weight_decay_rate=0.01,
... num_warmup_steps=0,
... )
```
Then you can load DistilBERT with [`TFAutoModelForTokenClassification`] along with the number of expected labels, and the label mappings:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained(
... "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_wnut["train"],
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_wnut["validation"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer)
```
The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](./main_classes/keras_callbacks).
Pass your `compute_metrics` function to [`~transformers.KerasMetricCallback`]:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
```
Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_wnut_model",
... tokenizer=tokenizer,
... )
```
Then bundle your callbacks together:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for token classification, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).
</Tip>
## Inference
Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:
```py
>>> text = "The Golden State Warriors are an American professional basketball team based in San Francisco."
```
The simplest way to try out your finetuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for NER with your model, and pass your text to it:
```py
>>> from transformers import pipeline
>>> classifier = pipeline("ner", model="stevhliu/my_awesome_wnut_model")
>>> classifier(text)
[{'entity': 'B-location',
'score': 0.42658573,
'index': 2,
'word': 'golden',
'start': 4,
'end': 10},
{'entity': 'I-location',
'score': 0.35856336,
'index': 3,
'word': 'state',
'start': 11,
'end': 16},
{'entity': 'B-group',
'score': 0.3064001,
'index': 4,
'word': 'warriors',
'start': 17,
'end': 25},
{'entity': 'B-location',
'score': 0.65523505,
'index': 13,
'word': 'san',
'start': 80,
'end': 83},
{'entity': 'B-location',
'score': 0.4668663,
'index': 14,
'word': 'francisco',
'start': 84,
'end': 93}]
```
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Tokenize the text and return PyTorch tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="pt")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import AutoModelForTokenClassification
>>> model = AutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predictions = torch.argmax(logits, dim=2)
>>> predicted_token_class = [model.config.id2label[t.item()] for t in predictions[0]]
>>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</pt>
<tf>
Tokenize the text and return TensorFlow tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="tf")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
>>> predicted_token_class = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</tf>
</frameworkcontent> | -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/speech_encoder_decoder/modeling_flax_speech_encoder_decoder.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Classes to support Flax Speech-Encoder-Decoder architectures"""
import os
from typing import Optional, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput
from ...modeling_flax_utils import FlaxPreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_flax_auto import FlaxAutoModel, FlaxAutoModelForCausalLM
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "SpeechEncoderDecoderConfig"
SPEECH_ENCODER_DECODER_START_DOCSTRING = r"""
This class can be used to initialize a speech-sequence-to-text-sequence model with any pretrained speech
autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is
loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via
[`~AutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder
and should be fine-tuned on a downstream generative task, like summarization.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
Additionally, in [Large-Scale Self- and Semi-Supervised Learning for Speech
Translation](https://arxiv.org/abs/2104.06678) it is shown how leveraging large pretrained speech models for speech
translation yields a significant performance improvement.
After such an Speech-Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other
models (see the examples for more information).
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Parameters:
config ([`SpeechEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
Args:
inputs (`jnp.ndarray` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*):
Float values of input raw speech waveform or speech features. Values can be obtained by loading a *.flac*
or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile
library (*pip install soundfile*). To prepare the array into *inputs*, either the [`Wav2Vec2Processor`] or
[`Speech2TextProcessor`] should be used for padding and conversion into a tensor of type
*torch.FloatTensor*.
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
For sequence to sequence training, `decoder_input_ids` should be provided. `decoder_input_ids` should be
created outside of the model by shifting the `labels` to the right, replacing -100 by the `pad_token_id`
and prepending them with the `decoder_start_token_id`.
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.decoder.max_position_embeddings - 1]`.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple.
"""
SPEECH_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r"""
Args:
inputs (`jnp.ndarray` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*):
Float values of input raw speech waveform or speech features. Values can be obtained by loading a *.flac*
or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile
library (*pip install soundfile*). To prepare the array into *inputs*, either the [`Wav2Vec2Processor`] or
[`Speech2TextProcessor`] should be used for padding and conversion into a tensor of type
*torch.FloatTensor*.
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple.
"""
SPEECH_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
For sequence to sequence training, `decoder_input_ids` should be provided. `decoder_input_ids` should be
created outside of the model by shifting the `labels` to the right, replacing -100 by the `pad_token_id`
and prepending them with the `decoder_start_token_id`.
encoder_outputs (`tuple(tuple(jnp.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.decoder.max_position_embeddings - 1]`.
past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a
plain tuple.
"""
class FlaxSpeechEncoderDecoderModule(nn.Module):
config: SpeechEncoderDecoderConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
encoder_config = self.config.encoder
decoder_config = self.config.decoder
# Copied from `modeling_hybrid_clip.py` with modifications.
from ...models.auto.modeling_flax_auto import FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_MAPPING
encoder_module = FLAX_MODEL_MAPPING[encoder_config.__class__].module_class
decoder_module = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING[decoder_config.__class__].module_class
self.encoder = encoder_module(encoder_config, dtype=self.dtype)
self.decoder = decoder_module(decoder_config, dtype=self.dtype)
# encoder outputs might need to be projected to different dimension for decoder
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
self.enc_to_dec_proj = nn.Dense(
self.decoder.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.decoder.config.initializer_range),
dtype=self.dtype,
)
else:
self.enc_to_dec_proj = None
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
"""
Computes the output length of the convolutional layers
"""
add_adapter = self.config.encoder.add_adapter if add_adapter is None else add_adapter
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return (input_length - kernel_size) // stride + 1
for kernel_size, stride in zip(self.config.encoder.conv_kernel, self.config.encoder.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
if add_adapter:
for _ in range(self.config.encoder.num_adapter_layers):
input_lengths = _conv_out_length(input_lengths, 1, self.config.encoder.adapter_stride)
return input_lengths
def _get_encoder_module(self):
return self.encoder
def _get_projection_module(self):
return self.enc_to_dec_proj
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
inputs,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
encoder_outputs=None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
freeze_feature_encoder: bool = False,
):
if encoder_outputs is None:
encoder_outputs = self.encoder(
inputs,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
freeze_feature_encoder=freeze_feature_encoder,
)
encoder_hidden_states = encoder_outputs[0]
# optionally project encoder_hidden_states
if self.enc_to_dec_proj is not None:
encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
# compute correct encoder attention mask
if attention_mask is not None:
encoder_attention_mask = self.encoder._get_feature_vector_attention_mask(
encoder_hidden_states.shape[1], attention_mask
)
else:
encoder_attention_mask = None
# flax script modeling_flax_wav2vec2.py
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqLMOutput(
logits=decoder_outputs.logits,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_hidden_states,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(SPEECH_ENCODER_DECODER_START_DOCSTRING)
class FlaxSpeechEncoderDecoderModel(FlaxPreTrainedModel):
r"""
[`FlaxSpeechEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture
with the module (flax.nn.Module) of one of the base model classes of the library as encoder module and another one
as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method for the
encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder.
"""
config_class = SpeechEncoderDecoderConfig
base_model_prefix: str = "speech_encoder_decoder"
module_class = FlaxSpeechEncoderDecoderModule
def __init__(
self,
config: SpeechEncoderDecoderConfig,
input_shape: Optional[Tuple] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs
):
if not _do_init:
raise ValueError(
"`FlaxSpeechEncoderDecoderModel` cannot be created without initializing, `_do_init` must be `True`."
)
if config.decoder.cross_attention_hidden_size is not None:
# Raise ValueError or option to project enc to dec hidden_size (eg EncAdapterLayer)
if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
raise ValueError(
"If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
" `config.encoder.hidden_size`."
)
# make sure input & output embeddings are not tied
config.tie_word_embeddings = False
module = self.module_class(config=config, dtype=dtype, **kwargs)
if input_shape is None:
# speech encoders almost always downsample the sequence length dimension
encoder_input_length = 1024
decoder_input_length = module._get_feat_extract_output_lengths(encoder_input_length)
input_shape = ((1, encoder_input_length), (1, decoder_input_length))
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
encoder_input_shape, decoder_input_shape = input_shape
# init input DeviceArrays
inputs = jnp.zeros(encoder_input_shape, dtype="f4")
attention_mask = jnp.ones_like(inputs, dtype="i4")
decoder_input_ids = jnp.zeros(decoder_input_shape, dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
batch_size, sequence_length = inputs.shape
decoder_batch_size, decoder_sequence_length = decoder_input_ids.shape
if not decoder_batch_size == batch_size:
raise ValueError(
f"The inputs of encoder and decoder should have the same batch size, but got {batch_size} for encoder"
f" and {decoder_batch_size} for decoder."
)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_sequence_length)[None, :], (decoder_batch_size, decoder_sequence_length)
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
inputs,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter)
@add_start_docstrings(SPEECH_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=_CONFIG_FOR_DOC)
def encode(
self,
inputs: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
freeze_feature_encoder: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel
>>> # initialize a wav2vec2-2-bart from pretrained wav2vec2 and bart models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
... "facebook/wav2vec2-large-lv60", "facebook/bart-large"
... )
>>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)
>>> encoder_outputs = model.encode(inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
if attention_mask is None:
attention_mask = jnp.ones_like(inputs, dtype="i4")
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, inputs, attention_mask, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(inputs, attention_mask, **kwargs)
outputs = self.module.apply(
{"params": params or self.params},
inputs=jnp.array(inputs, dtype="f4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
freeze_feature_encoder=freeze_feature_encoder,
rngs=rngs,
method=_encoder_forward,
)
if return_dict:
outputs = FlaxBaseModelOutput(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
return outputs
@add_start_docstrings(SPEECH_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel
>>> import jax.numpy as jnp
>>> # initialize a wav2vec2-2-bart from pretrained wav2vec2 and bart models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
... "facebook/wav2vec2-large-lv60", "facebook/bart-large"
... )
>>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)
>>> encoder_outputs = model.encode(inputs)
>>> decoder_start_token_id = model.config.decoder.bos_token_id
>>> decoder_input_ids = jnp.ones((inputs.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
params = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxBartAttention module
if past_key_values:
params["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(
module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs
):
projection_module = module._get_projection_module()
decoder_module = module._get_decoder_module()
# optionally project encoder_hidden_states
if projection_module is not None:
encoder_hidden_states = projection_module(encoder_hidden_states)
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
encoder_hidden_states=encoder_hidden_states,
**kwargs,
)
outputs = self.module.apply(
params,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
@add_start_docstrings_to_model_forward(SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def __call__(
self,
inputs: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
freeze_feature_encoder: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Examples:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel, BartTokenizer
>>> # load a fine-tuned wav2vec2-2-bart model
>>> model = FlaxSpeechEncoderDecoderModel.from_pretrained("patrickvonplaten/wav2vec2-2-bart-large")
>>> # load output tokenizer
>>> tokenizer_output = BartTokenizer.from_pretrained("facebook/bart-large")
>>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)
>>> # use bart's special bos, pad and eos tokens
>>> model.config.decoder_start_token_id = model.decoder.config.bos_token_id
>>> model.config.pad_token_id = model.decoder.config.pad_token_id
>>> model.config.eos_token_id = model.decoder.config.eos_token_id
>>> outputs = model.generate(inputs)
# Assert something? More interesting input? dtype correct?
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(inputs, dtype="i4")
# prepare decoder inputs
if decoder_input_ids is None:
raise ValueError(
"`decoder_input_ids` cannot be `None`. For sequence to sequence training, `decoder_position_ids` must"
" be specified as an input argument."
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
if decoder_position_ids is None:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
inputs=jnp.array(inputs, dtype="f4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
freeze_feature_encoder=freeze_feature_encoder,
rngs=rngs,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jnp.DeviceArray] = None,
decoder_attention_mask: Optional[jnp.DeviceArray] = None,
encoder_outputs=None,
**kwargs
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if decoder_attention_mask is not None:
decoder_position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
)
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": decoder_position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
@classmethod
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
*model_args,
**kwargs
) -> FlaxPreTrainedModel:
r"""
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Params:
encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*):
Information necessary to initiate the encoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
user or organization name, like `dbmdz/bert-base-german-cased`.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
decoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*, defaults to `None`):
Information necessary to initiate the decoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
user or organization name, like `dbmdz/bert-base-german-cased`.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
model_args (remaining positional arguments, *optional*):
All remaning positional arguments will be passed to the underlying model's `__init__` method.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
- To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
- To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a `config` is provided or automatically loaded.
Example:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel
>>> # initialize a wav2vec2-2-bart from pretrained wav2vec2 and bart models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
... "facebook/wav2vec2-large-lv60", "facebook/bart-large"
... )
>>> # saving model after fine-tuning
>>> model.save_pretrained("./wav2vec2-2-bart-large")
>>> # load fine-tuned model
>>> model = FlaxSpeechEncoderDecoderModel.from_pretrained("./wav2vec2-2-bart-large")
```"""
kwargs_encoder = {
argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
}
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
# remove encoder, decoder kwargs from kwargs
for key in kwargs_encoder.keys():
del kwargs["encoder_" + key]
for key in kwargs_decoder.keys():
del kwargs["decoder_" + key]
# Load and initialize the encoder and decoder
# The distinction between encoder and decoder at the model level is made
# by the value of the flag `is_decoder` that we need to set correctly.
encoder = kwargs_encoder.pop("model", None)
if encoder is None:
if encoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_encoder:
encoder_config, kwargs_encoder = AutoConfig.from_pretrained(
encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
)
if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
logger.info(
f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
"from a decoder model. Cross-attention and casual mask are disabled."
)
encoder_config.is_decoder = False
encoder_config.add_cross_attention = False
kwargs_encoder["config"] = encoder_config
encoder = FlaxAutoModel.from_pretrained(
encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder
)
decoder = kwargs_decoder.pop("model", None)
if decoder is None:
if decoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_decoder:
decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
)
if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
logger.info(
f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
)
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
kwargs_decoder["config"] = decoder_config
if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
logger.warning(
f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
"make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
"passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
"`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
)
decoder = FlaxAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
# instantiate config with corresponding kwargs
dtype = kwargs.pop("dtype", jnp.float32)
config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
# make sure input & output word embeddings are not tied
config.tie_word_embeddings = False
# init model
model = cls(config, dtype=dtype)
model.params["encoder"] = encoder.params
model.params["decoder"] = decoder.params
return model
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Classes to support Flax Speech-Encoder-Decoder architectures"""
import os
from typing import Optional, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput
from ...modeling_flax_utils import FlaxPreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_flax_auto import FlaxAutoModel, FlaxAutoModelForCausalLM
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "SpeechEncoderDecoderConfig"
SPEECH_ENCODER_DECODER_START_DOCSTRING = r"""
This class can be used to initialize a speech-sequence-to-text-sequence model with any pretrained speech
autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is
loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via
[`~AutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder
and should be fine-tuned on a downstream generative task, like summarization.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
Additionally, in [Large-Scale Self- and Semi-Supervised Learning for Speech
Translation](https://arxiv.org/abs/2104.06678) it is shown how leveraging large pretrained speech models for speech
translation yields a significant performance improvement.
After such an Speech-Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other
models (see the examples for more information).
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Parameters:
config ([`SpeechEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
Args:
inputs (`jnp.ndarray` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*):
Float values of input raw speech waveform or speech features. Values can be obtained by loading a *.flac*
or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile
library (*pip install soundfile*). To prepare the array into *inputs*, either the [`Wav2Vec2Processor`] or
[`Speech2TextProcessor`] should be used for padding and conversion into a tensor of type
*torch.FloatTensor*.
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
For sequence to sequence training, `decoder_input_ids` should be provided. `decoder_input_ids` should be
created outside of the model by shifting the `labels` to the right, replacing -100 by the `pad_token_id`
and prepending them with the `decoder_start_token_id`.
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.decoder.max_position_embeddings - 1]`.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple.
"""
SPEECH_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r"""
Args:
inputs (`jnp.ndarray` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*):
Float values of input raw speech waveform or speech features. Values can be obtained by loading a *.flac*
or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile
library (*pip install soundfile*). To prepare the array into *inputs*, either the [`Wav2Vec2Processor`] or
[`Speech2TextProcessor`] should be used for padding and conversion into a tensor of type
*torch.FloatTensor*.
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple.
"""
SPEECH_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
For sequence to sequence training, `decoder_input_ids` should be provided. `decoder_input_ids` should be
created outside of the model by shifting the `labels` to the right, replacing -100 by the `pad_token_id`
and prepending them with the `decoder_start_token_id`.
encoder_outputs (`tuple(tuple(jnp.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.decoder.max_position_embeddings - 1]`.
past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a
plain tuple.
"""
class FlaxSpeechEncoderDecoderModule(nn.Module):
config: SpeechEncoderDecoderConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
encoder_config = self.config.encoder
decoder_config = self.config.decoder
# Copied from `modeling_hybrid_clip.py` with modifications.
from ...models.auto.modeling_flax_auto import FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_MAPPING
encoder_module = FLAX_MODEL_MAPPING[encoder_config.__class__].module_class
decoder_module = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING[decoder_config.__class__].module_class
self.encoder = encoder_module(encoder_config, dtype=self.dtype)
self.decoder = decoder_module(decoder_config, dtype=self.dtype)
# encoder outputs might need to be projected to different dimension for decoder
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
self.enc_to_dec_proj = nn.Dense(
self.decoder.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.decoder.config.initializer_range),
dtype=self.dtype,
)
else:
self.enc_to_dec_proj = None
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
"""
Computes the output length of the convolutional layers
"""
add_adapter = self.config.encoder.add_adapter if add_adapter is None else add_adapter
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return (input_length - kernel_size) // stride + 1
for kernel_size, stride in zip(self.config.encoder.conv_kernel, self.config.encoder.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
if add_adapter:
for _ in range(self.config.encoder.num_adapter_layers):
input_lengths = _conv_out_length(input_lengths, 1, self.config.encoder.adapter_stride)
return input_lengths
def _get_encoder_module(self):
return self.encoder
def _get_projection_module(self):
return self.enc_to_dec_proj
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
inputs,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
encoder_outputs=None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
freeze_feature_encoder: bool = False,
):
if encoder_outputs is None:
encoder_outputs = self.encoder(
inputs,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
freeze_feature_encoder=freeze_feature_encoder,
)
encoder_hidden_states = encoder_outputs[0]
# optionally project encoder_hidden_states
if self.enc_to_dec_proj is not None:
encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
# compute correct encoder attention mask
if attention_mask is not None:
encoder_attention_mask = self.encoder._get_feature_vector_attention_mask(
encoder_hidden_states.shape[1], attention_mask
)
else:
encoder_attention_mask = None
# flax script modeling_flax_wav2vec2.py
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqLMOutput(
logits=decoder_outputs.logits,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_hidden_states,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(SPEECH_ENCODER_DECODER_START_DOCSTRING)
class FlaxSpeechEncoderDecoderModel(FlaxPreTrainedModel):
r"""
[`FlaxSpeechEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture
with the module (flax.nn.Module) of one of the base model classes of the library as encoder module and another one
as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method for the
encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder.
"""
config_class = SpeechEncoderDecoderConfig
base_model_prefix: str = "speech_encoder_decoder"
module_class = FlaxSpeechEncoderDecoderModule
def __init__(
self,
config: SpeechEncoderDecoderConfig,
input_shape: Optional[Tuple] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs
):
if not _do_init:
raise ValueError(
"`FlaxSpeechEncoderDecoderModel` cannot be created without initializing, `_do_init` must be `True`."
)
if config.decoder.cross_attention_hidden_size is not None:
# Raise ValueError or option to project enc to dec hidden_size (eg EncAdapterLayer)
if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
raise ValueError(
"If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
" `config.encoder.hidden_size`."
)
# make sure input & output embeddings are not tied
config.tie_word_embeddings = False
module = self.module_class(config=config, dtype=dtype, **kwargs)
if input_shape is None:
# speech encoders almost always downsample the sequence length dimension
encoder_input_length = 1024
decoder_input_length = module._get_feat_extract_output_lengths(encoder_input_length)
input_shape = ((1, encoder_input_length), (1, decoder_input_length))
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
encoder_input_shape, decoder_input_shape = input_shape
# init input DeviceArrays
inputs = jnp.zeros(encoder_input_shape, dtype="f4")
attention_mask = jnp.ones_like(inputs, dtype="i4")
decoder_input_ids = jnp.zeros(decoder_input_shape, dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
batch_size, sequence_length = inputs.shape
decoder_batch_size, decoder_sequence_length = decoder_input_ids.shape
if not decoder_batch_size == batch_size:
raise ValueError(
f"The inputs of encoder and decoder should have the same batch size, but got {batch_size} for encoder"
f" and {decoder_batch_size} for decoder."
)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_sequence_length)[None, :], (decoder_batch_size, decoder_sequence_length)
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
inputs,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter)
@add_start_docstrings(SPEECH_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=_CONFIG_FOR_DOC)
def encode(
self,
inputs: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
freeze_feature_encoder: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel
>>> # initialize a wav2vec2-2-bart from pretrained wav2vec2 and bart models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
... "facebook/wav2vec2-large-lv60", "facebook/bart-large"
... )
>>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)
>>> encoder_outputs = model.encode(inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
if attention_mask is None:
attention_mask = jnp.ones_like(inputs, dtype="i4")
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, inputs, attention_mask, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(inputs, attention_mask, **kwargs)
outputs = self.module.apply(
{"params": params or self.params},
inputs=jnp.array(inputs, dtype="f4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
freeze_feature_encoder=freeze_feature_encoder,
rngs=rngs,
method=_encoder_forward,
)
if return_dict:
outputs = FlaxBaseModelOutput(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
return outputs
@add_start_docstrings(SPEECH_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel
>>> import jax.numpy as jnp
>>> # initialize a wav2vec2-2-bart from pretrained wav2vec2 and bart models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
... "facebook/wav2vec2-large-lv60", "facebook/bart-large"
... )
>>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)
>>> encoder_outputs = model.encode(inputs)
>>> decoder_start_token_id = model.config.decoder.bos_token_id
>>> decoder_input_ids = jnp.ones((inputs.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
params = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxBartAttention module
if past_key_values:
params["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(
module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs
):
projection_module = module._get_projection_module()
decoder_module = module._get_decoder_module()
# optionally project encoder_hidden_states
if projection_module is not None:
encoder_hidden_states = projection_module(encoder_hidden_states)
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
encoder_hidden_states=encoder_hidden_states,
**kwargs,
)
outputs = self.module.apply(
params,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
@add_start_docstrings_to_model_forward(SPEECH_ENCODER_DECODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def __call__(
self,
inputs: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
freeze_feature_encoder: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Examples:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel, BartTokenizer
>>> # load a fine-tuned wav2vec2-2-bart model
>>> model = FlaxSpeechEncoderDecoderModel.from_pretrained("patrickvonplaten/wav2vec2-2-bart-large")
>>> # load output tokenizer
>>> tokenizer_output = BartTokenizer.from_pretrained("facebook/bart-large")
>>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)
>>> # use bart's special bos, pad and eos tokens
>>> model.config.decoder_start_token_id = model.decoder.config.bos_token_id
>>> model.config.pad_token_id = model.decoder.config.pad_token_id
>>> model.config.eos_token_id = model.decoder.config.eos_token_id
>>> outputs = model.generate(inputs)
# Assert something? More interesting input? dtype correct?
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(inputs, dtype="i4")
# prepare decoder inputs
if decoder_input_ids is None:
raise ValueError(
"`decoder_input_ids` cannot be `None`. For sequence to sequence training, `decoder_position_ids` must"
" be specified as an input argument."
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
if decoder_position_ids is None:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
inputs=jnp.array(inputs, dtype="f4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
freeze_feature_encoder=freeze_feature_encoder,
rngs=rngs,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jnp.DeviceArray] = None,
decoder_attention_mask: Optional[jnp.DeviceArray] = None,
encoder_outputs=None,
**kwargs
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if decoder_attention_mask is not None:
decoder_position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
)
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": decoder_position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
@classmethod
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
*model_args,
**kwargs
) -> FlaxPreTrainedModel:
r"""
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Params:
encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*):
Information necessary to initiate the encoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
user or organization name, like `dbmdz/bert-base-german-cased`.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
decoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*, defaults to `None`):
Information necessary to initiate the decoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
user or organization name, like `dbmdz/bert-base-german-cased`.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
model_args (remaining positional arguments, *optional*):
All remaning positional arguments will be passed to the underlying model's `__init__` method.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
- To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
- To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a `config` is provided or automatically loaded.
Example:
```python
>>> from transformers import FlaxSpeechEncoderDecoderModel
>>> # initialize a wav2vec2-2-bart from pretrained wav2vec2 and bart models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
... "facebook/wav2vec2-large-lv60", "facebook/bart-large"
... )
>>> # saving model after fine-tuning
>>> model.save_pretrained("./wav2vec2-2-bart-large")
>>> # load fine-tuned model
>>> model = FlaxSpeechEncoderDecoderModel.from_pretrained("./wav2vec2-2-bart-large")
```"""
kwargs_encoder = {
argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
}
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
# remove encoder, decoder kwargs from kwargs
for key in kwargs_encoder.keys():
del kwargs["encoder_" + key]
for key in kwargs_decoder.keys():
del kwargs["decoder_" + key]
# Load and initialize the encoder and decoder
# The distinction between encoder and decoder at the model level is made
# by the value of the flag `is_decoder` that we need to set correctly.
encoder = kwargs_encoder.pop("model", None)
if encoder is None:
if encoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_encoder:
encoder_config, kwargs_encoder = AutoConfig.from_pretrained(
encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
)
if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
logger.info(
f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
"from a decoder model. Cross-attention and casual mask are disabled."
)
encoder_config.is_decoder = False
encoder_config.add_cross_attention = False
kwargs_encoder["config"] = encoder_config
encoder = FlaxAutoModel.from_pretrained(
encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder
)
decoder = kwargs_decoder.pop("model", None)
if decoder is None:
if decoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_decoder:
decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
)
if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
logger.info(
f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
)
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
kwargs_decoder["config"] = decoder_config
if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
logger.warning(
f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
"make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
"passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
"`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
)
decoder = FlaxAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
# instantiate config with corresponding kwargs
dtype = kwargs.pop("dtype", jnp.float32)
config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
# make sure input & output word embeddings are not tied
config.tie_word_embeddings = False
# init model
model = cls(config, dtype=dtype)
model.params["encoder"] = encoder.params
model.params["decoder"] = decoder.params
return model
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./docs/source/it/pipeline_tutorial.mdx | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Pipeline per l'inferenza
La [`pipeline`] rende semplice usare qualsiasi modello dal [Model Hub](https://huggingface.co/models) per fare inferenza su diversi compiti come generazione del testo, segmentazione di immagini e classificazione di audio. Anche se non hai esperienza con una modalità specifica o non comprendi bene il codice che alimenta i modelli, è comunque possibile utilizzarli con l'opzione [`pipeline`]! Questa esercitazione ti insegnerà a:
* Usare una [`pipeline`] per fare inferenza.
* Usare uno specifico tokenizer o modello.
* Usare una [`pipeline`] per compiti che riguardano audio e video.
<Tip>
Dai un'occhiata alla documentazione di [`pipeline`] per una lista completa dei compiti supportati.
</Tip>
## Utilizzo della Pipeline
Nonostante ogni compito abbia una [`pipeline`] associata, è più semplice utilizzare l'astrazione generica della [`pipeline`] che contiene tutte quelle specifiche per ogni mansione. La [`pipeline`] carica automaticamente un modello predefinito e un tokenizer in grado di fare inferenza per il tuo compito.
1. Inizia creando una [`pipeline`] e specificando il compito su cui fare inferenza:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation")
```
2. Inserisci il testo in input nella [`pipeline`]:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
```
Se hai più di un input, inseriscilo in una lista:
```py
>>> generator(
... [
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
... ]
... ) # doctest: +SKIP
```
Qualsiasi parametro addizionale per il tuo compito può essere incluso nella [`pipeline`]. La mansione `text-generation` ha un metodo [`~generation.GenerationMixin.generate`] con diversi parametri per controllare l'output. Ad esempio, se desideri generare più di un output, utilizza il parametro `num_return_sequences`:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... num_return_sequences=2,
... ) # doctest: +SKIP
```
### Scegliere modello e tokenizer
La [`pipeline`] accetta qualsiasi modello dal [Model Hub](https://huggingface.co/models). Ci sono tag nel Model Hub che consentono di filtrare i modelli per attività. Una volta che avrai scelto il modello appropriato, caricalo usando la corrispondente classe `AutoModelFor` e [`AutoTokenizer`]. Ad esempio, carica la classe [`AutoModelForCausalLM`] per un compito di causal language modeling:
```py
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
```
Crea una [`pipeline`] per il tuo compito, specificando il modello e il tokenizer che hai caricato:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
```
Inserisci il testo di input nella [`pipeline`] per generare del testo:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
```
## Audio pipeline
La flessibilità della [`pipeline`] fa si che possa essere estesa ad attività sugli audio.
Per esempio, classifichiamo le emozioni in questo clip audio:
```py
>>> from datasets import load_dataset
>>> import torch
>>> torch.manual_seed(42) # doctest: +IGNORE_RESULT
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> audio_file = ds[0]["audio"]["path"]
```
Trova un modello per la [classificazione audio](https://huggingface.co/models?pipeline_tag=audio-classification) sul Model Hub per eseguire un compito di riconoscimento automatico delle emozioni e caricalo nella [`pipeline`]:
```py
>>> from transformers import pipeline
>>> audio_classifier = pipeline(
... task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
... )
```
Inserisci il file audio nella [`pipeline`]:
```py
>>> preds = audio_classifier(audio_file)
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]
```
## Vision pipeline
Infine, usare la [`pipeline`] per le attività sulle immagini è praticamente la stessa cosa.
Specifica la tua attività e inserisci l'immagine nel classificatore. L'immagine può essere sia un link che un percorso sul tuo pc in locale. Per esempio, quale specie di gatto è raffigurata qui sotto?
```py
>>> from transformers import pipeline
>>> vision_classifier = pipeline(task="image-classification")
>>> preds = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
```
| <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Pipeline per l'inferenza
La [`pipeline`] rende semplice usare qualsiasi modello dal [Model Hub](https://huggingface.co/models) per fare inferenza su diversi compiti come generazione del testo, segmentazione di immagini e classificazione di audio. Anche se non hai esperienza con una modalità specifica o non comprendi bene il codice che alimenta i modelli, è comunque possibile utilizzarli con l'opzione [`pipeline`]! Questa esercitazione ti insegnerà a:
* Usare una [`pipeline`] per fare inferenza.
* Usare uno specifico tokenizer o modello.
* Usare una [`pipeline`] per compiti che riguardano audio e video.
<Tip>
Dai un'occhiata alla documentazione di [`pipeline`] per una lista completa dei compiti supportati.
</Tip>
## Utilizzo della Pipeline
Nonostante ogni compito abbia una [`pipeline`] associata, è più semplice utilizzare l'astrazione generica della [`pipeline`] che contiene tutte quelle specifiche per ogni mansione. La [`pipeline`] carica automaticamente un modello predefinito e un tokenizer in grado di fare inferenza per il tuo compito.
1. Inizia creando una [`pipeline`] e specificando il compito su cui fare inferenza:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation")
```
2. Inserisci il testo in input nella [`pipeline`]:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
```
Se hai più di un input, inseriscilo in una lista:
```py
>>> generator(
... [
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
... ]
... ) # doctest: +SKIP
```
Qualsiasi parametro addizionale per il tuo compito può essere incluso nella [`pipeline`]. La mansione `text-generation` ha un metodo [`~generation.GenerationMixin.generate`] con diversi parametri per controllare l'output. Ad esempio, se desideri generare più di un output, utilizza il parametro `num_return_sequences`:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... num_return_sequences=2,
... ) # doctest: +SKIP
```
### Scegliere modello e tokenizer
La [`pipeline`] accetta qualsiasi modello dal [Model Hub](https://huggingface.co/models). Ci sono tag nel Model Hub che consentono di filtrare i modelli per attività. Una volta che avrai scelto il modello appropriato, caricalo usando la corrispondente classe `AutoModelFor` e [`AutoTokenizer`]. Ad esempio, carica la classe [`AutoModelForCausalLM`] per un compito di causal language modeling:
```py
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
```
Crea una [`pipeline`] per il tuo compito, specificando il modello e il tokenizer che hai caricato:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
```
Inserisci il testo di input nella [`pipeline`] per generare del testo:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
```
## Audio pipeline
La flessibilità della [`pipeline`] fa si che possa essere estesa ad attività sugli audio.
Per esempio, classifichiamo le emozioni in questo clip audio:
```py
>>> from datasets import load_dataset
>>> import torch
>>> torch.manual_seed(42) # doctest: +IGNORE_RESULT
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> audio_file = ds[0]["audio"]["path"]
```
Trova un modello per la [classificazione audio](https://huggingface.co/models?pipeline_tag=audio-classification) sul Model Hub per eseguire un compito di riconoscimento automatico delle emozioni e caricalo nella [`pipeline`]:
```py
>>> from transformers import pipeline
>>> audio_classifier = pipeline(
... task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
... )
```
Inserisci il file audio nella [`pipeline`]:
```py
>>> preds = audio_classifier(audio_file)
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]
```
## Vision pipeline
Infine, usare la [`pipeline`] per le attività sulle immagini è praticamente la stessa cosa.
Specifica la tua attività e inserisci l'immagine nel classificatore. L'immagine può essere sia un link che un percorso sul tuo pc in locale. Per esempio, quale specie di gatto è raffigurata qui sotto?
```py
>>> from transformers import pipeline
>>> vision_classifier = pipeline(task="image-classification")
>>> preds = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
```
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/gpt_neox_japanese/__init__.py | -1 |
||
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./docs/source/en/model_doc/layoutxlm.mdx | <!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# LayoutXLM
## Overview
LayoutXLM was proposed in [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
Zhang, Furu Wei. It's a multilingual extension of the [LayoutLMv2 model](https://arxiv.org/abs/2012.14740) trained
on 53 languages.
The abstract from the paper is the following:
*Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document
understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In
this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to
bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also
introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in
7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled
for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA
cross-lingual pre-trained models on the XFUN dataset.*
One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so:
```python
from transformers import LayoutLMv2Model
model = LayoutLMv2Model.from_pretrained("microsoft/layoutxlm-base")
```
Note that LayoutXLM has its own tokenizer, based on
[`LayoutXLMTokenizer`]/[`LayoutXLMTokenizerFast`]. You can initialize it as
follows:
```python
from transformers import LayoutXLMTokenizer
tokenizer = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
```
Similar to LayoutLMv2, you can use [`LayoutXLMProcessor`] (which internally applies
[`LayoutLMv2FeatureExtractor`] and
[`LayoutXLMTokenizer`]/[`LayoutXLMTokenizerFast`] in sequence) to prepare all
data for the model.
As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to [LayoutLMv2's documentation page](layoutlmv2) for all tips, code examples and notebooks.
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm).
## LayoutXLMTokenizer
[[autodoc]] LayoutXLMTokenizer
- __call__
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## LayoutXLMTokenizerFast
[[autodoc]] LayoutXLMTokenizerFast
- __call__
## LayoutXLMProcessor
[[autodoc]] LayoutXLMProcessor
- __call__
| <!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# LayoutXLM
## Overview
LayoutXLM was proposed in [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
Zhang, Furu Wei. It's a multilingual extension of the [LayoutLMv2 model](https://arxiv.org/abs/2012.14740) trained
on 53 languages.
The abstract from the paper is the following:
*Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document
understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In
this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to
bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also
introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in
7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled
for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA
cross-lingual pre-trained models on the XFUN dataset.*
One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so:
```python
from transformers import LayoutLMv2Model
model = LayoutLMv2Model.from_pretrained("microsoft/layoutxlm-base")
```
Note that LayoutXLM has its own tokenizer, based on
[`LayoutXLMTokenizer`]/[`LayoutXLMTokenizerFast`]. You can initialize it as
follows:
```python
from transformers import LayoutXLMTokenizer
tokenizer = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
```
Similar to LayoutLMv2, you can use [`LayoutXLMProcessor`] (which internally applies
[`LayoutLMv2FeatureExtractor`] and
[`LayoutXLMTokenizer`]/[`LayoutXLMTokenizerFast`] in sequence) to prepare all
data for the model.
As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to [LayoutLMv2's documentation page](layoutlmv2) for all tips, code examples and notebooks.
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm).
## LayoutXLMTokenizer
[[autodoc]] LayoutXLMTokenizer
- __call__
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## LayoutXLMTokenizerFast
[[autodoc]] LayoutXLMTokenizerFast
- __call__
## LayoutXLMProcessor
[[autodoc]] LayoutXLMProcessor
- __call__
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/roformer/test_modeling_tf_roformer.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class TFRoFormerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = True
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RoFormerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_lm_head(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TFRoFormerForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
prediction_scores = model(inputs)["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerForMaskedLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRoFormerForSequenceClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = TFRoFormerForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRoFormerForTokenClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerForQuestionAnswering(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFRoFormerModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFRoFormerModelTester(self)
self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base")
self.assertIsNotNone(model)
@require_tf
class TFRoFormerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 50000
expected_shape = [1, 6, vocab_size]
self.assertEqual(output.shape, expected_shape)
print(output[:, :3, :3])
# TODO Replace values below with what was printed above.
expected_slice = tf.constant(
[
[
[-0.12053341, -1.0264901, 0.29221946],
[-1.5133783, 0.197433, 0.15190607],
[-5.0135403, -3.900256, -0.84038764],
]
]
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
@require_tf
class TFRoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_basic(self):
input_ids = tf.constant([[4, 10]])
emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6)
emb = emb1(input_ids.shape)
desired_weights = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]
)
tf.debugging.assert_near(emb, desired_weights, atol=self.tolerance)
def test_positional_emb_weights_against_roformer(self):
desired_weights = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
]
)
emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512)
emb1([2, 16, 512])
weights = emb1.weight[:3, :5]
tf.debugging.assert_near(weights, desired_weights, atol=self.tolerance)
@require_tf
class TFRoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_apply_rotary_position_embeddings(self):
# 2,12,16,64
query_layer = tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
key_layer = -tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
embed_positions = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64)
sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :]
query_layer, key_layer = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
sinusoidal_pos, query_layer, key_layer
)
desired_query_layer = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
]
)
desired_key_layer = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
]
)
tf.debugging.assert_near(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance)
tf.debugging.assert_near(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance)
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class TFRoFormerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = True
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RoFormerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_lm_head(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TFRoFormerForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
prediction_scores = model(inputs)["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerForMaskedLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRoFormerForSequenceClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = TFRoFormerForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRoFormerForTokenClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRoFormerForQuestionAnswering(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFRoFormerModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFRoFormerModelTester(self)
self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base")
self.assertIsNotNone(model)
@require_tf
class TFRoFormerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 50000
expected_shape = [1, 6, vocab_size]
self.assertEqual(output.shape, expected_shape)
print(output[:, :3, :3])
# TODO Replace values below with what was printed above.
expected_slice = tf.constant(
[
[
[-0.12053341, -1.0264901, 0.29221946],
[-1.5133783, 0.197433, 0.15190607],
[-5.0135403, -3.900256, -0.84038764],
]
]
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
@require_tf
class TFRoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_basic(self):
input_ids = tf.constant([[4, 10]])
emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6)
emb = emb1(input_ids.shape)
desired_weights = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]
)
tf.debugging.assert_near(emb, desired_weights, atol=self.tolerance)
def test_positional_emb_weights_against_roformer(self):
desired_weights = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
]
)
emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512)
emb1([2, 16, 512])
weights = emb1.weight[:3, :5]
tf.debugging.assert_near(weights, desired_weights, atol=self.tolerance)
@require_tf
class TFRoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_apply_rotary_position_embeddings(self):
# 2,12,16,64
query_layer = tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
key_layer = -tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
embed_positions = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64)
sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :]
query_layer, key_layer = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
sinusoidal_pos, query_layer, key_layer
)
desired_query_layer = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
]
)
desired_key_layer = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
]
)
tf.debugging.assert_near(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance)
tf.debugging.assert_near(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/utils/test_add_new_model_like.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers.commands.add_new_model_like import (
ModelPatterns,
_re_class_func,
add_content_to_file,
add_content_to_text,
clean_frameworks_in_init,
duplicate_doc_file,
duplicate_module,
filter_framework_files,
find_base_model_checkpoint,
get_model_files,
get_module_from_file,
parse_module_content,
replace_model_patterns,
retrieve_info_for_model,
retrieve_model_classes,
simplify_replacements,
)
from transformers.testing_utils import require_flax, require_tf, require_torch
BERT_MODEL_FILES = {
"src/transformers/models/bert/__init__.py",
"src/transformers/models/bert/configuration_bert.py",
"src/transformers/models/bert/tokenization_bert.py",
"src/transformers/models/bert/tokenization_bert_fast.py",
"src/transformers/models/bert/modeling_bert.py",
"src/transformers/models/bert/modeling_flax_bert.py",
"src/transformers/models/bert/modeling_tf_bert.py",
"src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py",
"src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py",
"src/transformers/models/bert/convert_bert_pytorch_checkpoint_to_original_tf.py",
}
VIT_MODEL_FILES = {
"src/transformers/models/vit/__init__.py",
"src/transformers/models/vit/configuration_vit.py",
"src/transformers/models/vit/convert_dino_to_pytorch.py",
"src/transformers/models/vit/convert_vit_timm_to_pytorch.py",
"src/transformers/models/vit/feature_extraction_vit.py",
"src/transformers/models/vit/modeling_vit.py",
"src/transformers/models/vit/modeling_tf_vit.py",
"src/transformers/models/vit/modeling_flax_vit.py",
}
WAV2VEC2_MODEL_FILES = {
"src/transformers/models/wav2vec2/__init__.py",
"src/transformers/models/wav2vec2/configuration_wav2vec2.py",
"src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py",
"src/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py",
"src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py",
"src/transformers/models/wav2vec2/processing_wav2vec2.py",
"src/transformers/models/wav2vec2/tokenization_wav2vec2.py",
}
REPO_PATH = Path(transformers.__path__[0]).parent.parent
@require_torch
@require_tf
@require_flax
class TestAddNewModelLike(unittest.TestCase):
def init_file(self, file_name, content):
with open(file_name, "w", encoding="utf-8") as f:
f.write(content)
def check_result(self, file_name, expected_result):
with open(file_name, "r", encoding="utf-8") as f:
self.assertEqual(f.read(), expected_result)
def test_re_class_func(self):
self.assertEqual(_re_class_func.search("def my_function(x, y):").groups()[0], "my_function")
self.assertEqual(_re_class_func.search("class MyClass:").groups()[0], "MyClass")
self.assertEqual(_re_class_func.search("class MyClass(SuperClass):").groups()[0], "MyClass")
def test_model_patterns_defaults(self):
model_patterns = ModelPatterns("GPT-New new", "huggingface/gpt-new-base")
self.assertEqual(model_patterns.model_type, "gpt-new-new")
self.assertEqual(model_patterns.model_lower_cased, "gpt_new_new")
self.assertEqual(model_patterns.model_camel_cased, "GPTNewNew")
self.assertEqual(model_patterns.model_upper_cased, "GPT_NEW_NEW")
self.assertEqual(model_patterns.config_class, "GPTNewNewConfig")
self.assertIsNone(model_patterns.tokenizer_class)
self.assertIsNone(model_patterns.feature_extractor_class)
self.assertIsNone(model_patterns.processor_class)
def test_parse_module_content(self):
test_code = """SOME_CONSTANT = a constant
CONSTANT_DEFINED_ON_SEVERAL_LINES = [
first_item,
second_item
]
def function(args):
some code
# Copied from transformers.some_module
class SomeClass:
some code
"""
expected_parts = [
"SOME_CONSTANT = a constant\n",
"CONSTANT_DEFINED_ON_SEVERAL_LINES = [\n first_item,\n second_item\n]",
"",
"def function(args):\n some code\n",
"# Copied from transformers.some_module\nclass SomeClass:\n some code\n",
]
self.assertEqual(parse_module_content(test_code), expected_parts)
def test_add_content_to_text(self):
test_text = """all_configs = {
"gpt": "GPTConfig",
"bert": "BertConfig",
"t5": "T5Config",
}"""
expected = """all_configs = {
"gpt": "GPTConfig",
"gpt2": "GPT2Config",
"bert": "BertConfig",
"t5": "T5Config",
}"""
line = ' "gpt2": "GPT2Config",'
self.assertEqual(add_content_to_text(test_text, line, add_before="bert"), expected)
self.assertEqual(add_content_to_text(test_text, line, add_before="bert", exact_match=True), test_text)
self.assertEqual(
add_content_to_text(test_text, line, add_before=' "bert": "BertConfig",', exact_match=True), expected
)
self.assertEqual(add_content_to_text(test_text, line, add_before=re.compile('^\s*"bert":')), expected)
self.assertEqual(add_content_to_text(test_text, line, add_after="gpt"), expected)
self.assertEqual(add_content_to_text(test_text, line, add_after="gpt", exact_match=True), test_text)
self.assertEqual(
add_content_to_text(test_text, line, add_after=' "gpt": "GPTConfig",', exact_match=True), expected
)
self.assertEqual(add_content_to_text(test_text, line, add_after=re.compile('^\s*"gpt":')), expected)
def test_add_content_to_file(self):
test_text = """all_configs = {
"gpt": "GPTConfig",
"bert": "BertConfig",
"t5": "T5Config",
}"""
expected = """all_configs = {
"gpt": "GPTConfig",
"gpt2": "GPT2Config",
"bert": "BertConfig",
"t5": "T5Config",
}"""
line = ' "gpt2": "GPT2Config",'
with tempfile.TemporaryDirectory() as tmp_dir:
file_name = os.path.join(tmp_dir, "code.py")
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before="bert")
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before="bert", exact_match=True)
self.check_result(file_name, test_text)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before=' "bert": "BertConfig",', exact_match=True)
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before=re.compile('^\s*"bert":'))
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after="gpt")
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after="gpt", exact_match=True)
self.check_result(file_name, test_text)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after=' "gpt": "GPTConfig",', exact_match=True)
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after=re.compile('^\s*"gpt":'))
self.check_result(file_name, expected)
def test_simplify_replacements(self):
self.assertEqual(simplify_replacements([("Bert", "NewBert")]), [("Bert", "NewBert")])
self.assertEqual(
simplify_replacements([("Bert", "NewBert"), ("bert", "new-bert")]),
[("Bert", "NewBert"), ("bert", "new-bert")],
)
self.assertEqual(
simplify_replacements([("BertConfig", "NewBertConfig"), ("Bert", "NewBert"), ("bert", "new-bert")]),
[("Bert", "NewBert"), ("bert", "new-bert")],
)
def test_replace_model_patterns(self):
bert_model_patterns = ModelPatterns("Bert", "bert-base-cased")
new_bert_model_patterns = ModelPatterns("New Bert", "huggingface/bert-new-base")
bert_test = '''class TFBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
is_parallelizable = True
supports_gradient_checkpointing = True
model_type = "bert"
BERT_CONSTANT = "value"
'''
bert_expected = '''class TFNewBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NewBertConfig
load_tf_weights = load_tf_weights_in_new_bert
base_model_prefix = "new_bert"
is_parallelizable = True
supports_gradient_checkpointing = True
model_type = "new-bert"
NEW_BERT_CONSTANT = "value"
'''
bert_converted, replacements = replace_model_patterns(bert_test, bert_model_patterns, new_bert_model_patterns)
self.assertEqual(bert_converted, bert_expected)
# Replacements are empty here since bert as been replaced by bert_new in some instances and bert-new
# in others.
self.assertEqual(replacements, "")
# If we remove the model type, we will get replacements
bert_test = bert_test.replace(' model_type = "bert"\n', "")
bert_expected = bert_expected.replace(' model_type = "new-bert"\n', "")
bert_converted, replacements = replace_model_patterns(bert_test, bert_model_patterns, new_bert_model_patterns)
self.assertEqual(bert_converted, bert_expected)
self.assertEqual(replacements, "BERT->NEW_BERT,Bert->NewBert,bert->new_bert")
gpt_model_patterns = ModelPatterns("GPT2", "gpt2")
new_gpt_model_patterns = ModelPatterns("GPT-New new", "huggingface/gpt-new-base")
gpt_test = '''class GPT2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
load_tf_weights = load_tf_weights_in_gpt2
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointing = True
GPT2_CONSTANT = "value"
'''
gpt_expected = '''class GPTNewNewPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTNewNewConfig
load_tf_weights = load_tf_weights_in_gpt_new_new
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointing = True
GPT_NEW_NEW_CONSTANT = "value"
'''
gpt_converted, replacements = replace_model_patterns(gpt_test, gpt_model_patterns, new_gpt_model_patterns)
self.assertEqual(gpt_converted, gpt_expected)
# Replacements are empty here since GPT2 as been replaced by GPTNewNew in some instances and GPT_NEW_NEW
# in others.
self.assertEqual(replacements, "")
roberta_model_patterns = ModelPatterns("RoBERTa", "roberta-base", model_camel_cased="Roberta")
new_roberta_model_patterns = ModelPatterns(
"RoBERTa-New", "huggingface/roberta-new-base", model_camel_cased="RobertaNew"
)
roberta_test = '''# Copied from transformers.models.bert.BertModel with Bert->Roberta
class RobertaModel(RobertaPreTrainedModel):
""" The base RoBERTa model. """
checkpoint = roberta-base
base_model_prefix = "roberta"
'''
roberta_expected = '''# Copied from transformers.models.bert.BertModel with Bert->RobertaNew
class RobertaNewModel(RobertaNewPreTrainedModel):
""" The base RoBERTa-New model. """
checkpoint = huggingface/roberta-new-base
base_model_prefix = "roberta_new"
'''
roberta_converted, replacements = replace_model_patterns(
roberta_test, roberta_model_patterns, new_roberta_model_patterns
)
self.assertEqual(roberta_converted, roberta_expected)
def test_get_module_from_file(self):
self.assertEqual(
get_module_from_file("/git/transformers/src/transformers/models/bert/modeling_tf_bert.py"),
"transformers.models.bert.modeling_tf_bert",
)
self.assertEqual(
get_module_from_file("/transformers/models/gpt2/modeling_gpt2.py"),
"transformers.models.gpt2.modeling_gpt2",
)
with self.assertRaises(ValueError):
get_module_from_file("/models/gpt2/modeling_gpt2.py")
def test_duplicate_module(self):
bert_model_patterns = ModelPatterns("Bert", "bert-base-cased")
new_bert_model_patterns = ModelPatterns("New Bert", "huggingface/bert-new-base")
bert_test = '''class TFBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
is_parallelizable = True
supports_gradient_checkpointing = True
BERT_CONSTANT = "value"
'''
bert_expected = '''class TFNewBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NewBertConfig
load_tf_weights = load_tf_weights_in_new_bert
base_model_prefix = "new_bert"
is_parallelizable = True
supports_gradient_checkpointing = True
NEW_BERT_CONSTANT = "value"
'''
bert_expected_with_copied_from = (
"# Copied from transformers.bert_module.TFBertPreTrainedModel with Bert->NewBert,bert->new_bert\n"
+ bert_expected
)
with tempfile.TemporaryDirectory() as tmp_dir:
work_dir = os.path.join(tmp_dir, "transformers")
os.makedirs(work_dir)
file_name = os.path.join(work_dir, "bert_module.py")
dest_file_name = os.path.join(work_dir, "new_bert_module.py")
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns)
self.check_result(dest_file_name, bert_expected_with_copied_from)
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns, add_copied_from=False)
self.check_result(dest_file_name, bert_expected)
def test_duplicate_module_with_copied_from(self):
bert_model_patterns = ModelPatterns("Bert", "bert-base-cased")
new_bert_model_patterns = ModelPatterns("New Bert", "huggingface/bert-new-base")
bert_test = '''# Copied from transformers.models.xxx.XxxModel with Xxx->Bert
class TFBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
is_parallelizable = True
supports_gradient_checkpointing = True
BERT_CONSTANT = "value"
'''
bert_expected = '''# Copied from transformers.models.xxx.XxxModel with Xxx->NewBert
class TFNewBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NewBertConfig
load_tf_weights = load_tf_weights_in_new_bert
base_model_prefix = "new_bert"
is_parallelizable = True
supports_gradient_checkpointing = True
NEW_BERT_CONSTANT = "value"
'''
with tempfile.TemporaryDirectory() as tmp_dir:
work_dir = os.path.join(tmp_dir, "transformers")
os.makedirs(work_dir)
file_name = os.path.join(work_dir, "bert_module.py")
dest_file_name = os.path.join(work_dir, "new_bert_module.py")
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns)
# There should not be a new Copied from statement, the old one should be adapated.
self.check_result(dest_file_name, bert_expected)
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns, add_copied_from=False)
self.check_result(dest_file_name, bert_expected)
def test_filter_framework_files(self):
files = ["modeling_tf_bert.py", "modeling_bert.py", "modeling_flax_bert.py", "configuration_bert.py"]
self.assertEqual(filter_framework_files(files), files)
self.assertEqual(set(filter_framework_files(files, ["pt", "tf", "flax"])), set(files))
self.assertEqual(set(filter_framework_files(files, ["pt"])), {"modeling_bert.py", "configuration_bert.py"})
self.assertEqual(set(filter_framework_files(files, ["tf"])), {"modeling_tf_bert.py", "configuration_bert.py"})
self.assertEqual(
set(filter_framework_files(files, ["flax"])), {"modeling_flax_bert.py", "configuration_bert.py"}
)
self.assertEqual(
set(filter_framework_files(files, ["pt", "tf"])),
{"modeling_tf_bert.py", "modeling_bert.py", "configuration_bert.py"},
)
self.assertEqual(
set(filter_framework_files(files, ["tf", "flax"])),
{"modeling_tf_bert.py", "modeling_flax_bert.py", "configuration_bert.py"},
)
self.assertEqual(
set(filter_framework_files(files, ["pt", "flax"])),
{"modeling_bert.py", "modeling_flax_bert.py", "configuration_bert.py"},
)
def test_get_model_files(self):
# BERT
bert_files = get_model_files("bert")
doc_file = str(Path(bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["model_files"]}
self.assertEqual(model_files, BERT_MODEL_FILES)
self.assertEqual(bert_files["module_name"], "bert")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
"tests/test_modeling_tf_bert.py",
"tests/test_modeling_flax_bert.py",
}
self.assertEqual(test_files, bert_test_files)
# VIT
vit_files = get_model_files("vit")
doc_file = str(Path(vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["model_files"]}
self.assertEqual(model_files, VIT_MODEL_FILES)
self.assertEqual(vit_files["module_name"], "vit")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_vit.py",
"tests/test_modeling_tf_vit.py",
"tests/test_modeling_flax_vit.py",
}
self.assertEqual(test_files, vit_test_files)
# Wav2Vec2
wav2vec2_files = get_model_files("wav2vec2")
doc_file = str(Path(wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["model_files"]}
self.assertEqual(model_files, WAV2VEC2_MODEL_FILES)
self.assertEqual(wav2vec2_files["module_name"], "wav2vec2")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_wav2vec2.py",
"tests/test_modeling_tf_wav2vec2.py",
"tests/test_modeling_flax_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
def test_get_model_files_only_pt(self):
# BERT
bert_files = get_model_files("bert", frameworks=["pt"])
doc_file = str(Path(bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["model_files"]}
bert_model_files = BERT_MODEL_FILES - {
"src/transformers/models/bert/modeling_tf_bert.py",
"src/transformers/models/bert/modeling_flax_bert.py",
}
self.assertEqual(model_files, bert_model_files)
self.assertEqual(bert_files["module_name"], "bert")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
}
self.assertEqual(test_files, bert_test_files)
# VIT
vit_files = get_model_files("vit", frameworks=["pt"])
doc_file = str(Path(vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["model_files"]}
vit_model_files = VIT_MODEL_FILES - {
"src/transformers/models/vit/modeling_tf_vit.py",
"src/transformers/models/vit/modeling_flax_vit.py",
}
self.assertEqual(model_files, vit_model_files)
self.assertEqual(vit_files["module_name"], "vit")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_vit.py",
}
self.assertEqual(test_files, vit_test_files)
# Wav2Vec2
wav2vec2_files = get_model_files("wav2vec2", frameworks=["pt"])
doc_file = str(Path(wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["model_files"]}
wav2vec2_model_files = WAV2VEC2_MODEL_FILES - {
"src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py",
}
self.assertEqual(model_files, wav2vec2_model_files)
self.assertEqual(wav2vec2_files["module_name"], "wav2vec2")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
def test_get_model_files_tf_and_flax(self):
# BERT
bert_files = get_model_files("bert", frameworks=["tf", "flax"])
doc_file = str(Path(bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["model_files"]}
bert_model_files = BERT_MODEL_FILES - {"src/transformers/models/bert/modeling_bert.py"}
self.assertEqual(model_files, bert_model_files)
self.assertEqual(bert_files["module_name"], "bert")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_tf_bert.py",
"tests/test_modeling_flax_bert.py",
}
self.assertEqual(test_files, bert_test_files)
# VIT
vit_files = get_model_files("vit", frameworks=["tf", "flax"])
doc_file = str(Path(vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["model_files"]}
vit_model_files = VIT_MODEL_FILES - {"src/transformers/models/vit/modeling_vit.py"}
self.assertEqual(model_files, vit_model_files)
self.assertEqual(vit_files["module_name"], "vit")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_tf_vit.py",
"tests/test_modeling_flax_vit.py",
}
self.assertEqual(test_files, vit_test_files)
# Wav2Vec2
wav2vec2_files = get_model_files("wav2vec2", frameworks=["tf", "flax"])
doc_file = str(Path(wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["model_files"]}
wav2vec2_model_files = WAV2VEC2_MODEL_FILES - {"src/transformers/models/wav2vec2/modeling_wav2vec2.py"}
self.assertEqual(model_files, wav2vec2_model_files)
self.assertEqual(wav2vec2_files["module_name"], "wav2vec2")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_tf_wav2vec2.py",
"tests/test_modeling_flax_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
def test_find_base_model_checkpoint(self):
self.assertEqual(find_base_model_checkpoint("bert"), "bert-base-uncased")
self.assertEqual(find_base_model_checkpoint("gpt2"), "gpt2")
def test_retrieve_model_classes(self):
gpt_classes = {k: set(v) for k, v in retrieve_model_classes("gpt2").items()}
expected_gpt_classes = {
"pt": {"GPT2ForTokenClassification", "GPT2Model", "GPT2LMHeadModel", "GPT2ForSequenceClassification"},
"tf": {"TFGPT2Model", "TFGPT2ForSequenceClassification", "TFGPT2LMHeadModel"},
"flax": {"FlaxGPT2Model", "FlaxGPT2LMHeadModel"},
}
self.assertEqual(gpt_classes, expected_gpt_classes)
del expected_gpt_classes["flax"]
gpt_classes = {k: set(v) for k, v in retrieve_model_classes("gpt2", frameworks=["pt", "tf"]).items()}
self.assertEqual(gpt_classes, expected_gpt_classes)
del expected_gpt_classes["pt"]
gpt_classes = {k: set(v) for k, v in retrieve_model_classes("gpt2", frameworks=["tf"]).items()}
self.assertEqual(gpt_classes, expected_gpt_classes)
def test_retrieve_info_for_model_with_bert(self):
bert_info = retrieve_info_for_model("bert")
bert_classes = [
"BertForTokenClassification",
"BertForQuestionAnswering",
"BertForNextSentencePrediction",
"BertForSequenceClassification",
"BertForMaskedLM",
"BertForMultipleChoice",
"BertModel",
"BertForPreTraining",
"BertLMHeadModel",
]
expected_model_classes = {
"pt": set(bert_classes),
"tf": {f"TF{m}" for m in bert_classes},
"flax": {f"Flax{m}" for m in bert_classes[:-1]},
}
self.assertEqual(set(bert_info["frameworks"]), {"pt", "tf", "flax"})
model_classes = {k: set(v) for k, v in bert_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_bert_files = bert_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["model_files"]}
self.assertEqual(model_files, BERT_MODEL_FILES)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
"tests/test_modeling_tf_bert.py",
"tests/test_modeling_flax_bert.py",
}
self.assertEqual(test_files, bert_test_files)
doc_file = str(Path(all_bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
self.assertEqual(all_bert_files["module_name"], "bert")
bert_model_patterns = bert_info["model_patterns"]
self.assertEqual(bert_model_patterns.model_name, "BERT")
self.assertEqual(bert_model_patterns.checkpoint, "bert-base-uncased")
self.assertEqual(bert_model_patterns.model_type, "bert")
self.assertEqual(bert_model_patterns.model_lower_cased, "bert")
self.assertEqual(bert_model_patterns.model_camel_cased, "Bert")
self.assertEqual(bert_model_patterns.model_upper_cased, "BERT")
self.assertEqual(bert_model_patterns.config_class, "BertConfig")
self.assertEqual(bert_model_patterns.tokenizer_class, "BertTokenizer")
self.assertIsNone(bert_model_patterns.feature_extractor_class)
self.assertIsNone(bert_model_patterns.processor_class)
def test_retrieve_info_for_model_pt_tf_with_bert(self):
bert_info = retrieve_info_for_model("bert", frameworks=["pt", "tf"])
bert_classes = [
"BertForTokenClassification",
"BertForQuestionAnswering",
"BertForNextSentencePrediction",
"BertForSequenceClassification",
"BertForMaskedLM",
"BertForMultipleChoice",
"BertModel",
"BertForPreTraining",
"BertLMHeadModel",
]
expected_model_classes = {"pt": set(bert_classes), "tf": {f"TF{m}" for m in bert_classes}}
self.assertEqual(set(bert_info["frameworks"]), {"pt", "tf"})
model_classes = {k: set(v) for k, v in bert_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_bert_files = bert_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["model_files"]}
bert_model_files = BERT_MODEL_FILES - {"src/transformers/models/bert/modeling_flax_bert.py"}
self.assertEqual(model_files, bert_model_files)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
"tests/test_modeling_tf_bert.py",
}
self.assertEqual(test_files, bert_test_files)
doc_file = str(Path(all_bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
self.assertEqual(all_bert_files["module_name"], "bert")
bert_model_patterns = bert_info["model_patterns"]
self.assertEqual(bert_model_patterns.model_name, "BERT")
self.assertEqual(bert_model_patterns.checkpoint, "bert-base-uncased")
self.assertEqual(bert_model_patterns.model_type, "bert")
self.assertEqual(bert_model_patterns.model_lower_cased, "bert")
self.assertEqual(bert_model_patterns.model_camel_cased, "Bert")
self.assertEqual(bert_model_patterns.model_upper_cased, "BERT")
self.assertEqual(bert_model_patterns.config_class, "BertConfig")
self.assertEqual(bert_model_patterns.tokenizer_class, "BertTokenizer")
self.assertIsNone(bert_model_patterns.feature_extractor_class)
self.assertIsNone(bert_model_patterns.processor_class)
def test_retrieve_info_for_model_with_vit(self):
vit_info = retrieve_info_for_model("vit")
vit_classes = ["ViTForImageClassification", "ViTModel"]
expected_model_classes = {
"pt": set(vit_classes),
"tf": {f"TF{m}" for m in vit_classes},
"flax": {f"Flax{m}" for m in vit_classes},
}
self.assertEqual(set(vit_info["frameworks"]), {"pt", "tf", "flax"})
model_classes = {k: set(v) for k, v in vit_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_vit_files = vit_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_vit_files["model_files"]}
self.assertEqual(model_files, VIT_MODEL_FILES)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_vit.py",
"tests/test_modeling_tf_vit.py",
"tests/test_modeling_flax_vit.py",
}
self.assertEqual(test_files, vit_test_files)
doc_file = str(Path(all_vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
self.assertEqual(all_vit_files["module_name"], "vit")
vit_model_patterns = vit_info["model_patterns"]
self.assertEqual(vit_model_patterns.model_name, "ViT")
self.assertEqual(vit_model_patterns.checkpoint, "google/vit-base-patch16-224")
self.assertEqual(vit_model_patterns.model_type, "vit")
self.assertEqual(vit_model_patterns.model_lower_cased, "vit")
self.assertEqual(vit_model_patterns.model_camel_cased, "ViT")
self.assertEqual(vit_model_patterns.model_upper_cased, "VIT")
self.assertEqual(vit_model_patterns.config_class, "ViTConfig")
self.assertEqual(vit_model_patterns.feature_extractor_class, "ViTFeatureExtractor")
self.assertIsNone(vit_model_patterns.tokenizer_class)
self.assertIsNone(vit_model_patterns.processor_class)
def test_retrieve_info_for_model_with_wav2vec2(self):
wav2vec2_info = retrieve_info_for_model("wav2vec2")
wav2vec2_classes = [
"Wav2Vec2Model",
"Wav2Vec2ForPreTraining",
"Wav2Vec2ForAudioFrameClassification",
"Wav2Vec2ForCTC",
"Wav2Vec2ForMaskedLM",
"Wav2Vec2ForSequenceClassification",
"Wav2Vec2ForXVector",
]
expected_model_classes = {
"pt": set(wav2vec2_classes),
"tf": {f"TF{m}" for m in wav2vec2_classes[:1]},
"flax": {f"Flax{m}" for m in wav2vec2_classes[:2]},
}
self.assertEqual(set(wav2vec2_info["frameworks"]), {"pt", "tf", "flax"})
model_classes = {k: set(v) for k, v in wav2vec2_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_wav2vec2_files = wav2vec2_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_wav2vec2_files["model_files"]}
self.assertEqual(model_files, WAV2VEC2_MODEL_FILES)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_wav2vec2.py",
"tests/test_modeling_tf_wav2vec2.py",
"tests/test_modeling_flax_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
doc_file = str(Path(all_wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
self.assertEqual(all_wav2vec2_files["module_name"], "wav2vec2")
wav2vec2_model_patterns = wav2vec2_info["model_patterns"]
self.assertEqual(wav2vec2_model_patterns.model_name, "Wav2Vec2")
self.assertEqual(wav2vec2_model_patterns.checkpoint, "facebook/wav2vec2-base-960h")
self.assertEqual(wav2vec2_model_patterns.model_type, "wav2vec2")
self.assertEqual(wav2vec2_model_patterns.model_lower_cased, "wav2vec2")
self.assertEqual(wav2vec2_model_patterns.model_camel_cased, "Wav2Vec2")
self.assertEqual(wav2vec2_model_patterns.model_upper_cased, "WAV_2_VEC_2")
self.assertEqual(wav2vec2_model_patterns.config_class, "Wav2Vec2Config")
self.assertEqual(wav2vec2_model_patterns.feature_extractor_class, "Wav2Vec2FeatureExtractor")
self.assertEqual(wav2vec2_model_patterns.processor_class, "Wav2Vec2Processor")
self.assertEqual(wav2vec2_model_patterns.tokenizer_class, "Wav2Vec2CTCTokenizer")
def test_clean_frameworks_in_init_with_gpt(self):
test_init = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
"tokenization_gpt2": ["GPT2Tokenizer"],
}
if is_tokenizers_available():
_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if is_tf_available():
_import_structure["modeling_tf_gpt2"] = ["TFGPT2Model"]
if is_flax_available():
_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
from .tokenization_gpt2 import GPT2Tokenizer
if is_tokenizers_available():
from .tokenization_gpt2_fast import GPT2TokenizerFast
if is_torch_available():
from .modeling_gpt2 import GPT2Model
if is_tf_available():
from .modeling_tf_gpt2 import TFGPT2Model
if is_flax_available():
from .modeling_flax_gpt2 import FlaxGPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_no_tokenizer = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
}
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if is_tf_available():
_import_structure["modeling_tf_gpt2"] = ["TFGPT2Model"]
if is_flax_available():
_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
if is_torch_available():
from .modeling_gpt2 import GPT2Model
if is_tf_available():
from .modeling_tf_gpt2 import TFGPT2Model
if is_flax_available():
from .modeling_flax_gpt2 import FlaxGPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
"tokenization_gpt2": ["GPT2Tokenizer"],
}
if is_tokenizers_available():
_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
from .tokenization_gpt2 import GPT2Tokenizer
if is_tokenizers_available():
from .tokenization_gpt2_fast import GPT2TokenizerFast
if is_torch_available():
from .modeling_gpt2 import GPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only_no_tokenizer = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
}
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
if is_torch_available():
from .modeling_gpt2 import GPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
with tempfile.TemporaryDirectory() as tmp_dir:
file_name = os.path.join(tmp_dir, "../__init__.py")
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, keep_processing=False)
self.check_result(file_name, init_no_tokenizer)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"])
self.check_result(file_name, init_pt_only)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"], keep_processing=False)
self.check_result(file_name, init_pt_only_no_tokenizer)
def test_clean_frameworks_in_init_with_vit(self):
test_init = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available, is_vision_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_vision_available():
_import_structure["feature_extraction_vit"] = ["ViTFeatureExtractor"]
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if is_tf_available():
_import_structure["modeling_tf_vit"] = ["TFViTModel"]
if is_flax_available():
_import_structure["modeling_flax_vit"] = ["FlaxViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_vision_available():
from .feature_extraction_vit import ViTFeatureExtractor
if is_torch_available():
from .modeling_vit import ViTModel
if is_tf_available():
from .modeling_tf_vit import ViTModel
if is_flax_available():
from .modeling_flax_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_no_feature_extractor = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if is_tf_available():
_import_structure["modeling_tf_vit"] = ["TFViTModel"]
if is_flax_available():
_import_structure["modeling_flax_vit"] = ["FlaxViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_torch_available():
from .modeling_vit import ViTModel
if is_tf_available():
from .modeling_tf_vit import ViTModel
if is_flax_available():
from .modeling_flax_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_vision_available():
_import_structure["feature_extraction_vit"] = ["ViTFeatureExtractor"]
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_vision_available():
from .feature_extraction_vit import ViTFeatureExtractor
if is_torch_available():
from .modeling_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only_no_feature_extractor = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_torch_available():
from .modeling_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
with tempfile.TemporaryDirectory() as tmp_dir:
file_name = os.path.join(tmp_dir, "../__init__.py")
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, keep_processing=False)
self.check_result(file_name, init_no_feature_extractor)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"])
self.check_result(file_name, init_pt_only)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"], keep_processing=False)
self.check_result(file_name, init_pt_only_no_feature_extractor)
def test_duplicate_doc_file(self):
test_doc = """
# GPT2
## Overview
Overview of the model.
## GPT2Config
[[autodoc]] GPT2Config
## GPT2Tokenizer
[[autodoc]] GPT2Tokenizer
- save_vocabulary
## GPT2TokenizerFast
[[autodoc]] GPT2TokenizerFast
## GPT2 specific outputs
[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
## GPT2Model
[[autodoc]] GPT2Model
- forward
## TFGPT2Model
[[autodoc]] TFGPT2Model
- call
## FlaxGPT2Model
[[autodoc]] FlaxGPT2Model
- __call__
"""
test_new_doc = """
# GPT-New New
## Overview
The GPT-New New model was proposed in [<INSERT PAPER NAME HERE>(<INSERT PAPER LINK HERE>) by <INSERT AUTHORS HERE>.
<INSERT SHORT SUMMARY HERE>
The abstract from the paper is the following:
*<INSERT PAPER ABSTRACT HERE>*
Tips:
<INSERT TIPS ABOUT MODEL HERE>
This model was contributed by [INSERT YOUR HF USERNAME HERE](<https://huggingface.co/<INSERT YOUR HF USERNAME HERE>).
The original code can be found [here](<INSERT LINK TO GITHUB REPO HERE>).
## GPTNewNewConfig
[[autodoc]] GPTNewNewConfig
## GPTNewNewTokenizer
[[autodoc]] GPTNewNewTokenizer
- save_vocabulary
## GPTNewNewTokenizerFast
[[autodoc]] GPTNewNewTokenizerFast
## GPTNewNew specific outputs
[[autodoc]] models.gpt_new_new.modeling_gpt_new_new.GPTNewNewDoubleHeadsModelOutput
[[autodoc]] models.gpt_new_new.modeling_tf_gpt_new_new.TFGPTNewNewDoubleHeadsModelOutput
## GPTNewNewModel
[[autodoc]] GPTNewNewModel
- forward
## TFGPTNewNewModel
[[autodoc]] TFGPTNewNewModel
- call
## FlaxGPTNewNewModel
[[autodoc]] FlaxGPTNewNewModel
- __call__
"""
with tempfile.TemporaryDirectory() as tmp_dir:
doc_file = os.path.join(tmp_dir, "gpt2.mdx")
new_doc_file = os.path.join(tmp_dir, "gpt-new-new.mdx")
gpt2_model_patterns = ModelPatterns("GPT2", "gpt2", tokenizer_class="GPT2Tokenizer")
new_model_patterns = ModelPatterns(
"GPT-New New", "huggingface/gpt-new-new", tokenizer_class="GPTNewNewTokenizer"
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns)
self.check_result(new_doc_file, test_new_doc)
test_new_doc_pt_only = test_new_doc.replace(
"""
## TFGPTNewNewModel
[[autodoc]] TFGPTNewNewModel
- call
## FlaxGPTNewNewModel
[[autodoc]] FlaxGPTNewNewModel
- __call__
""",
"",
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns, frameworks=["pt"])
self.check_result(new_doc_file, test_new_doc_pt_only)
test_new_doc_no_tok = test_new_doc.replace(
"""
## GPTNewNewTokenizer
[[autodoc]] GPTNewNewTokenizer
- save_vocabulary
## GPTNewNewTokenizerFast
[[autodoc]] GPTNewNewTokenizerFast
""",
"",
)
new_model_patterns = ModelPatterns(
"GPT-New New", "huggingface/gpt-new-new", tokenizer_class="GPT2Tokenizer"
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns)
print(test_new_doc_no_tok)
self.check_result(new_doc_file, test_new_doc_no_tok)
test_new_doc_pt_only_no_tok = test_new_doc_no_tok.replace(
"""
## TFGPTNewNewModel
[[autodoc]] TFGPTNewNewModel
- call
## FlaxGPTNewNewModel
[[autodoc]] FlaxGPTNewNewModel
- __call__
""",
"",
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns, frameworks=["pt"])
self.check_result(new_doc_file, test_new_doc_pt_only_no_tok)
| # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers.commands.add_new_model_like import (
ModelPatterns,
_re_class_func,
add_content_to_file,
add_content_to_text,
clean_frameworks_in_init,
duplicate_doc_file,
duplicate_module,
filter_framework_files,
find_base_model_checkpoint,
get_model_files,
get_module_from_file,
parse_module_content,
replace_model_patterns,
retrieve_info_for_model,
retrieve_model_classes,
simplify_replacements,
)
from transformers.testing_utils import require_flax, require_tf, require_torch
BERT_MODEL_FILES = {
"src/transformers/models/bert/__init__.py",
"src/transformers/models/bert/configuration_bert.py",
"src/transformers/models/bert/tokenization_bert.py",
"src/transformers/models/bert/tokenization_bert_fast.py",
"src/transformers/models/bert/modeling_bert.py",
"src/transformers/models/bert/modeling_flax_bert.py",
"src/transformers/models/bert/modeling_tf_bert.py",
"src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py",
"src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py",
"src/transformers/models/bert/convert_bert_pytorch_checkpoint_to_original_tf.py",
}
VIT_MODEL_FILES = {
"src/transformers/models/vit/__init__.py",
"src/transformers/models/vit/configuration_vit.py",
"src/transformers/models/vit/convert_dino_to_pytorch.py",
"src/transformers/models/vit/convert_vit_timm_to_pytorch.py",
"src/transformers/models/vit/feature_extraction_vit.py",
"src/transformers/models/vit/modeling_vit.py",
"src/transformers/models/vit/modeling_tf_vit.py",
"src/transformers/models/vit/modeling_flax_vit.py",
}
WAV2VEC2_MODEL_FILES = {
"src/transformers/models/wav2vec2/__init__.py",
"src/transformers/models/wav2vec2/configuration_wav2vec2.py",
"src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py",
"src/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py",
"src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py",
"src/transformers/models/wav2vec2/processing_wav2vec2.py",
"src/transformers/models/wav2vec2/tokenization_wav2vec2.py",
}
REPO_PATH = Path(transformers.__path__[0]).parent.parent
@require_torch
@require_tf
@require_flax
class TestAddNewModelLike(unittest.TestCase):
def init_file(self, file_name, content):
with open(file_name, "w", encoding="utf-8") as f:
f.write(content)
def check_result(self, file_name, expected_result):
with open(file_name, "r", encoding="utf-8") as f:
self.assertEqual(f.read(), expected_result)
def test_re_class_func(self):
self.assertEqual(_re_class_func.search("def my_function(x, y):").groups()[0], "my_function")
self.assertEqual(_re_class_func.search("class MyClass:").groups()[0], "MyClass")
self.assertEqual(_re_class_func.search("class MyClass(SuperClass):").groups()[0], "MyClass")
def test_model_patterns_defaults(self):
model_patterns = ModelPatterns("GPT-New new", "huggingface/gpt-new-base")
self.assertEqual(model_patterns.model_type, "gpt-new-new")
self.assertEqual(model_patterns.model_lower_cased, "gpt_new_new")
self.assertEqual(model_patterns.model_camel_cased, "GPTNewNew")
self.assertEqual(model_patterns.model_upper_cased, "GPT_NEW_NEW")
self.assertEqual(model_patterns.config_class, "GPTNewNewConfig")
self.assertIsNone(model_patterns.tokenizer_class)
self.assertIsNone(model_patterns.feature_extractor_class)
self.assertIsNone(model_patterns.processor_class)
def test_parse_module_content(self):
test_code = """SOME_CONSTANT = a constant
CONSTANT_DEFINED_ON_SEVERAL_LINES = [
first_item,
second_item
]
def function(args):
some code
# Copied from transformers.some_module
class SomeClass:
some code
"""
expected_parts = [
"SOME_CONSTANT = a constant\n",
"CONSTANT_DEFINED_ON_SEVERAL_LINES = [\n first_item,\n second_item\n]",
"",
"def function(args):\n some code\n",
"# Copied from transformers.some_module\nclass SomeClass:\n some code\n",
]
self.assertEqual(parse_module_content(test_code), expected_parts)
def test_add_content_to_text(self):
test_text = """all_configs = {
"gpt": "GPTConfig",
"bert": "BertConfig",
"t5": "T5Config",
}"""
expected = """all_configs = {
"gpt": "GPTConfig",
"gpt2": "GPT2Config",
"bert": "BertConfig",
"t5": "T5Config",
}"""
line = ' "gpt2": "GPT2Config",'
self.assertEqual(add_content_to_text(test_text, line, add_before="bert"), expected)
self.assertEqual(add_content_to_text(test_text, line, add_before="bert", exact_match=True), test_text)
self.assertEqual(
add_content_to_text(test_text, line, add_before=' "bert": "BertConfig",', exact_match=True), expected
)
self.assertEqual(add_content_to_text(test_text, line, add_before=re.compile('^\s*"bert":')), expected)
self.assertEqual(add_content_to_text(test_text, line, add_after="gpt"), expected)
self.assertEqual(add_content_to_text(test_text, line, add_after="gpt", exact_match=True), test_text)
self.assertEqual(
add_content_to_text(test_text, line, add_after=' "gpt": "GPTConfig",', exact_match=True), expected
)
self.assertEqual(add_content_to_text(test_text, line, add_after=re.compile('^\s*"gpt":')), expected)
def test_add_content_to_file(self):
test_text = """all_configs = {
"gpt": "GPTConfig",
"bert": "BertConfig",
"t5": "T5Config",
}"""
expected = """all_configs = {
"gpt": "GPTConfig",
"gpt2": "GPT2Config",
"bert": "BertConfig",
"t5": "T5Config",
}"""
line = ' "gpt2": "GPT2Config",'
with tempfile.TemporaryDirectory() as tmp_dir:
file_name = os.path.join(tmp_dir, "code.py")
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before="bert")
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before="bert", exact_match=True)
self.check_result(file_name, test_text)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before=' "bert": "BertConfig",', exact_match=True)
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_before=re.compile('^\s*"bert":'))
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after="gpt")
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after="gpt", exact_match=True)
self.check_result(file_name, test_text)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after=' "gpt": "GPTConfig",', exact_match=True)
self.check_result(file_name, expected)
self.init_file(file_name, test_text)
add_content_to_file(file_name, line, add_after=re.compile('^\s*"gpt":'))
self.check_result(file_name, expected)
def test_simplify_replacements(self):
self.assertEqual(simplify_replacements([("Bert", "NewBert")]), [("Bert", "NewBert")])
self.assertEqual(
simplify_replacements([("Bert", "NewBert"), ("bert", "new-bert")]),
[("Bert", "NewBert"), ("bert", "new-bert")],
)
self.assertEqual(
simplify_replacements([("BertConfig", "NewBertConfig"), ("Bert", "NewBert"), ("bert", "new-bert")]),
[("Bert", "NewBert"), ("bert", "new-bert")],
)
def test_replace_model_patterns(self):
bert_model_patterns = ModelPatterns("Bert", "bert-base-cased")
new_bert_model_patterns = ModelPatterns("New Bert", "huggingface/bert-new-base")
bert_test = '''class TFBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
is_parallelizable = True
supports_gradient_checkpointing = True
model_type = "bert"
BERT_CONSTANT = "value"
'''
bert_expected = '''class TFNewBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NewBertConfig
load_tf_weights = load_tf_weights_in_new_bert
base_model_prefix = "new_bert"
is_parallelizable = True
supports_gradient_checkpointing = True
model_type = "new-bert"
NEW_BERT_CONSTANT = "value"
'''
bert_converted, replacements = replace_model_patterns(bert_test, bert_model_patterns, new_bert_model_patterns)
self.assertEqual(bert_converted, bert_expected)
# Replacements are empty here since bert as been replaced by bert_new in some instances and bert-new
# in others.
self.assertEqual(replacements, "")
# If we remove the model type, we will get replacements
bert_test = bert_test.replace(' model_type = "bert"\n', "")
bert_expected = bert_expected.replace(' model_type = "new-bert"\n', "")
bert_converted, replacements = replace_model_patterns(bert_test, bert_model_patterns, new_bert_model_patterns)
self.assertEqual(bert_converted, bert_expected)
self.assertEqual(replacements, "BERT->NEW_BERT,Bert->NewBert,bert->new_bert")
gpt_model_patterns = ModelPatterns("GPT2", "gpt2")
new_gpt_model_patterns = ModelPatterns("GPT-New new", "huggingface/gpt-new-base")
gpt_test = '''class GPT2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
load_tf_weights = load_tf_weights_in_gpt2
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointing = True
GPT2_CONSTANT = "value"
'''
gpt_expected = '''class GPTNewNewPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTNewNewConfig
load_tf_weights = load_tf_weights_in_gpt_new_new
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointing = True
GPT_NEW_NEW_CONSTANT = "value"
'''
gpt_converted, replacements = replace_model_patterns(gpt_test, gpt_model_patterns, new_gpt_model_patterns)
self.assertEqual(gpt_converted, gpt_expected)
# Replacements are empty here since GPT2 as been replaced by GPTNewNew in some instances and GPT_NEW_NEW
# in others.
self.assertEqual(replacements, "")
roberta_model_patterns = ModelPatterns("RoBERTa", "roberta-base", model_camel_cased="Roberta")
new_roberta_model_patterns = ModelPatterns(
"RoBERTa-New", "huggingface/roberta-new-base", model_camel_cased="RobertaNew"
)
roberta_test = '''# Copied from transformers.models.bert.BertModel with Bert->Roberta
class RobertaModel(RobertaPreTrainedModel):
""" The base RoBERTa model. """
checkpoint = roberta-base
base_model_prefix = "roberta"
'''
roberta_expected = '''# Copied from transformers.models.bert.BertModel with Bert->RobertaNew
class RobertaNewModel(RobertaNewPreTrainedModel):
""" The base RoBERTa-New model. """
checkpoint = huggingface/roberta-new-base
base_model_prefix = "roberta_new"
'''
roberta_converted, replacements = replace_model_patterns(
roberta_test, roberta_model_patterns, new_roberta_model_patterns
)
self.assertEqual(roberta_converted, roberta_expected)
def test_get_module_from_file(self):
self.assertEqual(
get_module_from_file("/git/transformers/src/transformers/models/bert/modeling_tf_bert.py"),
"transformers.models.bert.modeling_tf_bert",
)
self.assertEqual(
get_module_from_file("/transformers/models/gpt2/modeling_gpt2.py"),
"transformers.models.gpt2.modeling_gpt2",
)
with self.assertRaises(ValueError):
get_module_from_file("/models/gpt2/modeling_gpt2.py")
def test_duplicate_module(self):
bert_model_patterns = ModelPatterns("Bert", "bert-base-cased")
new_bert_model_patterns = ModelPatterns("New Bert", "huggingface/bert-new-base")
bert_test = '''class TFBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
is_parallelizable = True
supports_gradient_checkpointing = True
BERT_CONSTANT = "value"
'''
bert_expected = '''class TFNewBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NewBertConfig
load_tf_weights = load_tf_weights_in_new_bert
base_model_prefix = "new_bert"
is_parallelizable = True
supports_gradient_checkpointing = True
NEW_BERT_CONSTANT = "value"
'''
bert_expected_with_copied_from = (
"# Copied from transformers.bert_module.TFBertPreTrainedModel with Bert->NewBert,bert->new_bert\n"
+ bert_expected
)
with tempfile.TemporaryDirectory() as tmp_dir:
work_dir = os.path.join(tmp_dir, "transformers")
os.makedirs(work_dir)
file_name = os.path.join(work_dir, "bert_module.py")
dest_file_name = os.path.join(work_dir, "new_bert_module.py")
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns)
self.check_result(dest_file_name, bert_expected_with_copied_from)
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns, add_copied_from=False)
self.check_result(dest_file_name, bert_expected)
def test_duplicate_module_with_copied_from(self):
bert_model_patterns = ModelPatterns("Bert", "bert-base-cased")
new_bert_model_patterns = ModelPatterns("New Bert", "huggingface/bert-new-base")
bert_test = '''# Copied from transformers.models.xxx.XxxModel with Xxx->Bert
class TFBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
is_parallelizable = True
supports_gradient_checkpointing = True
BERT_CONSTANT = "value"
'''
bert_expected = '''# Copied from transformers.models.xxx.XxxModel with Xxx->NewBert
class TFNewBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NewBertConfig
load_tf_weights = load_tf_weights_in_new_bert
base_model_prefix = "new_bert"
is_parallelizable = True
supports_gradient_checkpointing = True
NEW_BERT_CONSTANT = "value"
'''
with tempfile.TemporaryDirectory() as tmp_dir:
work_dir = os.path.join(tmp_dir, "transformers")
os.makedirs(work_dir)
file_name = os.path.join(work_dir, "bert_module.py")
dest_file_name = os.path.join(work_dir, "new_bert_module.py")
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns)
# There should not be a new Copied from statement, the old one should be adapated.
self.check_result(dest_file_name, bert_expected)
self.init_file(file_name, bert_test)
duplicate_module(file_name, bert_model_patterns, new_bert_model_patterns, add_copied_from=False)
self.check_result(dest_file_name, bert_expected)
def test_filter_framework_files(self):
files = ["modeling_tf_bert.py", "modeling_bert.py", "modeling_flax_bert.py", "configuration_bert.py"]
self.assertEqual(filter_framework_files(files), files)
self.assertEqual(set(filter_framework_files(files, ["pt", "tf", "flax"])), set(files))
self.assertEqual(set(filter_framework_files(files, ["pt"])), {"modeling_bert.py", "configuration_bert.py"})
self.assertEqual(set(filter_framework_files(files, ["tf"])), {"modeling_tf_bert.py", "configuration_bert.py"})
self.assertEqual(
set(filter_framework_files(files, ["flax"])), {"modeling_flax_bert.py", "configuration_bert.py"}
)
self.assertEqual(
set(filter_framework_files(files, ["pt", "tf"])),
{"modeling_tf_bert.py", "modeling_bert.py", "configuration_bert.py"},
)
self.assertEqual(
set(filter_framework_files(files, ["tf", "flax"])),
{"modeling_tf_bert.py", "modeling_flax_bert.py", "configuration_bert.py"},
)
self.assertEqual(
set(filter_framework_files(files, ["pt", "flax"])),
{"modeling_bert.py", "modeling_flax_bert.py", "configuration_bert.py"},
)
def test_get_model_files(self):
# BERT
bert_files = get_model_files("bert")
doc_file = str(Path(bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["model_files"]}
self.assertEqual(model_files, BERT_MODEL_FILES)
self.assertEqual(bert_files["module_name"], "bert")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
"tests/test_modeling_tf_bert.py",
"tests/test_modeling_flax_bert.py",
}
self.assertEqual(test_files, bert_test_files)
# VIT
vit_files = get_model_files("vit")
doc_file = str(Path(vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["model_files"]}
self.assertEqual(model_files, VIT_MODEL_FILES)
self.assertEqual(vit_files["module_name"], "vit")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_vit.py",
"tests/test_modeling_tf_vit.py",
"tests/test_modeling_flax_vit.py",
}
self.assertEqual(test_files, vit_test_files)
# Wav2Vec2
wav2vec2_files = get_model_files("wav2vec2")
doc_file = str(Path(wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["model_files"]}
self.assertEqual(model_files, WAV2VEC2_MODEL_FILES)
self.assertEqual(wav2vec2_files["module_name"], "wav2vec2")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_wav2vec2.py",
"tests/test_modeling_tf_wav2vec2.py",
"tests/test_modeling_flax_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
def test_get_model_files_only_pt(self):
# BERT
bert_files = get_model_files("bert", frameworks=["pt"])
doc_file = str(Path(bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["model_files"]}
bert_model_files = BERT_MODEL_FILES - {
"src/transformers/models/bert/modeling_tf_bert.py",
"src/transformers/models/bert/modeling_flax_bert.py",
}
self.assertEqual(model_files, bert_model_files)
self.assertEqual(bert_files["module_name"], "bert")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
}
self.assertEqual(test_files, bert_test_files)
# VIT
vit_files = get_model_files("vit", frameworks=["pt"])
doc_file = str(Path(vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["model_files"]}
vit_model_files = VIT_MODEL_FILES - {
"src/transformers/models/vit/modeling_tf_vit.py",
"src/transformers/models/vit/modeling_flax_vit.py",
}
self.assertEqual(model_files, vit_model_files)
self.assertEqual(vit_files["module_name"], "vit")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_vit.py",
}
self.assertEqual(test_files, vit_test_files)
# Wav2Vec2
wav2vec2_files = get_model_files("wav2vec2", frameworks=["pt"])
doc_file = str(Path(wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["model_files"]}
wav2vec2_model_files = WAV2VEC2_MODEL_FILES - {
"src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py",
"src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py",
}
self.assertEqual(model_files, wav2vec2_model_files)
self.assertEqual(wav2vec2_files["module_name"], "wav2vec2")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
def test_get_model_files_tf_and_flax(self):
# BERT
bert_files = get_model_files("bert", frameworks=["tf", "flax"])
doc_file = str(Path(bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["model_files"]}
bert_model_files = BERT_MODEL_FILES - {"src/transformers/models/bert/modeling_bert.py"}
self.assertEqual(model_files, bert_model_files)
self.assertEqual(bert_files["module_name"], "bert")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_tf_bert.py",
"tests/test_modeling_flax_bert.py",
}
self.assertEqual(test_files, bert_test_files)
# VIT
vit_files = get_model_files("vit", frameworks=["tf", "flax"])
doc_file = str(Path(vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["model_files"]}
vit_model_files = VIT_MODEL_FILES - {"src/transformers/models/vit/modeling_vit.py"}
self.assertEqual(model_files, vit_model_files)
self.assertEqual(vit_files["module_name"], "vit")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_tf_vit.py",
"tests/test_modeling_flax_vit.py",
}
self.assertEqual(test_files, vit_test_files)
# Wav2Vec2
wav2vec2_files = get_model_files("wav2vec2", frameworks=["tf", "flax"])
doc_file = str(Path(wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["model_files"]}
wav2vec2_model_files = WAV2VEC2_MODEL_FILES - {"src/transformers/models/wav2vec2/modeling_wav2vec2.py"}
self.assertEqual(model_files, wav2vec2_model_files)
self.assertEqual(wav2vec2_files["module_name"], "wav2vec2")
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_tf_wav2vec2.py",
"tests/test_modeling_flax_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
def test_find_base_model_checkpoint(self):
self.assertEqual(find_base_model_checkpoint("bert"), "bert-base-uncased")
self.assertEqual(find_base_model_checkpoint("gpt2"), "gpt2")
def test_retrieve_model_classes(self):
gpt_classes = {k: set(v) for k, v in retrieve_model_classes("gpt2").items()}
expected_gpt_classes = {
"pt": {"GPT2ForTokenClassification", "GPT2Model", "GPT2LMHeadModel", "GPT2ForSequenceClassification"},
"tf": {"TFGPT2Model", "TFGPT2ForSequenceClassification", "TFGPT2LMHeadModel"},
"flax": {"FlaxGPT2Model", "FlaxGPT2LMHeadModel"},
}
self.assertEqual(gpt_classes, expected_gpt_classes)
del expected_gpt_classes["flax"]
gpt_classes = {k: set(v) for k, v in retrieve_model_classes("gpt2", frameworks=["pt", "tf"]).items()}
self.assertEqual(gpt_classes, expected_gpt_classes)
del expected_gpt_classes["pt"]
gpt_classes = {k: set(v) for k, v in retrieve_model_classes("gpt2", frameworks=["tf"]).items()}
self.assertEqual(gpt_classes, expected_gpt_classes)
def test_retrieve_info_for_model_with_bert(self):
bert_info = retrieve_info_for_model("bert")
bert_classes = [
"BertForTokenClassification",
"BertForQuestionAnswering",
"BertForNextSentencePrediction",
"BertForSequenceClassification",
"BertForMaskedLM",
"BertForMultipleChoice",
"BertModel",
"BertForPreTraining",
"BertLMHeadModel",
]
expected_model_classes = {
"pt": set(bert_classes),
"tf": {f"TF{m}" for m in bert_classes},
"flax": {f"Flax{m}" for m in bert_classes[:-1]},
}
self.assertEqual(set(bert_info["frameworks"]), {"pt", "tf", "flax"})
model_classes = {k: set(v) for k, v in bert_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_bert_files = bert_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["model_files"]}
self.assertEqual(model_files, BERT_MODEL_FILES)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
"tests/test_modeling_tf_bert.py",
"tests/test_modeling_flax_bert.py",
}
self.assertEqual(test_files, bert_test_files)
doc_file = str(Path(all_bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
self.assertEqual(all_bert_files["module_name"], "bert")
bert_model_patterns = bert_info["model_patterns"]
self.assertEqual(bert_model_patterns.model_name, "BERT")
self.assertEqual(bert_model_patterns.checkpoint, "bert-base-uncased")
self.assertEqual(bert_model_patterns.model_type, "bert")
self.assertEqual(bert_model_patterns.model_lower_cased, "bert")
self.assertEqual(bert_model_patterns.model_camel_cased, "Bert")
self.assertEqual(bert_model_patterns.model_upper_cased, "BERT")
self.assertEqual(bert_model_patterns.config_class, "BertConfig")
self.assertEqual(bert_model_patterns.tokenizer_class, "BertTokenizer")
self.assertIsNone(bert_model_patterns.feature_extractor_class)
self.assertIsNone(bert_model_patterns.processor_class)
def test_retrieve_info_for_model_pt_tf_with_bert(self):
bert_info = retrieve_info_for_model("bert", frameworks=["pt", "tf"])
bert_classes = [
"BertForTokenClassification",
"BertForQuestionAnswering",
"BertForNextSentencePrediction",
"BertForSequenceClassification",
"BertForMaskedLM",
"BertForMultipleChoice",
"BertModel",
"BertForPreTraining",
"BertLMHeadModel",
]
expected_model_classes = {"pt": set(bert_classes), "tf": {f"TF{m}" for m in bert_classes}}
self.assertEqual(set(bert_info["frameworks"]), {"pt", "tf"})
model_classes = {k: set(v) for k, v in bert_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_bert_files = bert_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["model_files"]}
bert_model_files = BERT_MODEL_FILES - {"src/transformers/models/bert/modeling_flax_bert.py"}
self.assertEqual(model_files, bert_model_files)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_bert_files["test_files"]}
bert_test_files = {
"tests/test_tokenization_bert.py",
"tests/test_modeling_bert.py",
"tests/test_modeling_tf_bert.py",
}
self.assertEqual(test_files, bert_test_files)
doc_file = str(Path(all_bert_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/bert.mdx")
self.assertEqual(all_bert_files["module_name"], "bert")
bert_model_patterns = bert_info["model_patterns"]
self.assertEqual(bert_model_patterns.model_name, "BERT")
self.assertEqual(bert_model_patterns.checkpoint, "bert-base-uncased")
self.assertEqual(bert_model_patterns.model_type, "bert")
self.assertEqual(bert_model_patterns.model_lower_cased, "bert")
self.assertEqual(bert_model_patterns.model_camel_cased, "Bert")
self.assertEqual(bert_model_patterns.model_upper_cased, "BERT")
self.assertEqual(bert_model_patterns.config_class, "BertConfig")
self.assertEqual(bert_model_patterns.tokenizer_class, "BertTokenizer")
self.assertIsNone(bert_model_patterns.feature_extractor_class)
self.assertIsNone(bert_model_patterns.processor_class)
def test_retrieve_info_for_model_with_vit(self):
vit_info = retrieve_info_for_model("vit")
vit_classes = ["ViTForImageClassification", "ViTModel"]
expected_model_classes = {
"pt": set(vit_classes),
"tf": {f"TF{m}" for m in vit_classes},
"flax": {f"Flax{m}" for m in vit_classes},
}
self.assertEqual(set(vit_info["frameworks"]), {"pt", "tf", "flax"})
model_classes = {k: set(v) for k, v in vit_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_vit_files = vit_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_vit_files["model_files"]}
self.assertEqual(model_files, VIT_MODEL_FILES)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_vit_files["test_files"]}
vit_test_files = {
"tests/test_feature_extraction_vit.py",
"tests/test_modeling_vit.py",
"tests/test_modeling_tf_vit.py",
"tests/test_modeling_flax_vit.py",
}
self.assertEqual(test_files, vit_test_files)
doc_file = str(Path(all_vit_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/vit.mdx")
self.assertEqual(all_vit_files["module_name"], "vit")
vit_model_patterns = vit_info["model_patterns"]
self.assertEqual(vit_model_patterns.model_name, "ViT")
self.assertEqual(vit_model_patterns.checkpoint, "google/vit-base-patch16-224")
self.assertEqual(vit_model_patterns.model_type, "vit")
self.assertEqual(vit_model_patterns.model_lower_cased, "vit")
self.assertEqual(vit_model_patterns.model_camel_cased, "ViT")
self.assertEqual(vit_model_patterns.model_upper_cased, "VIT")
self.assertEqual(vit_model_patterns.config_class, "ViTConfig")
self.assertEqual(vit_model_patterns.feature_extractor_class, "ViTFeatureExtractor")
self.assertIsNone(vit_model_patterns.tokenizer_class)
self.assertIsNone(vit_model_patterns.processor_class)
def test_retrieve_info_for_model_with_wav2vec2(self):
wav2vec2_info = retrieve_info_for_model("wav2vec2")
wav2vec2_classes = [
"Wav2Vec2Model",
"Wav2Vec2ForPreTraining",
"Wav2Vec2ForAudioFrameClassification",
"Wav2Vec2ForCTC",
"Wav2Vec2ForMaskedLM",
"Wav2Vec2ForSequenceClassification",
"Wav2Vec2ForXVector",
]
expected_model_classes = {
"pt": set(wav2vec2_classes),
"tf": {f"TF{m}" for m in wav2vec2_classes[:1]},
"flax": {f"Flax{m}" for m in wav2vec2_classes[:2]},
}
self.assertEqual(set(wav2vec2_info["frameworks"]), {"pt", "tf", "flax"})
model_classes = {k: set(v) for k, v in wav2vec2_info["model_classes"].items()}
self.assertEqual(model_classes, expected_model_classes)
all_wav2vec2_files = wav2vec2_info["model_files"]
model_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_wav2vec2_files["model_files"]}
self.assertEqual(model_files, WAV2VEC2_MODEL_FILES)
test_files = {str(Path(f).relative_to(REPO_PATH)) for f in all_wav2vec2_files["test_files"]}
wav2vec2_test_files = {
"tests/test_feature_extraction_wav2vec2.py",
"tests/test_modeling_wav2vec2.py",
"tests/test_modeling_tf_wav2vec2.py",
"tests/test_modeling_flax_wav2vec2.py",
"tests/test_processor_wav2vec2.py",
"tests/test_tokenization_wav2vec2.py",
}
self.assertEqual(test_files, wav2vec2_test_files)
doc_file = str(Path(all_wav2vec2_files["doc_file"]).relative_to(REPO_PATH))
self.assertEqual(doc_file, "docs/source/model_doc/wav2vec2.mdx")
self.assertEqual(all_wav2vec2_files["module_name"], "wav2vec2")
wav2vec2_model_patterns = wav2vec2_info["model_patterns"]
self.assertEqual(wav2vec2_model_patterns.model_name, "Wav2Vec2")
self.assertEqual(wav2vec2_model_patterns.checkpoint, "facebook/wav2vec2-base-960h")
self.assertEqual(wav2vec2_model_patterns.model_type, "wav2vec2")
self.assertEqual(wav2vec2_model_patterns.model_lower_cased, "wav2vec2")
self.assertEqual(wav2vec2_model_patterns.model_camel_cased, "Wav2Vec2")
self.assertEqual(wav2vec2_model_patterns.model_upper_cased, "WAV_2_VEC_2")
self.assertEqual(wav2vec2_model_patterns.config_class, "Wav2Vec2Config")
self.assertEqual(wav2vec2_model_patterns.feature_extractor_class, "Wav2Vec2FeatureExtractor")
self.assertEqual(wav2vec2_model_patterns.processor_class, "Wav2Vec2Processor")
self.assertEqual(wav2vec2_model_patterns.tokenizer_class, "Wav2Vec2CTCTokenizer")
def test_clean_frameworks_in_init_with_gpt(self):
test_init = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
"tokenization_gpt2": ["GPT2Tokenizer"],
}
if is_tokenizers_available():
_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if is_tf_available():
_import_structure["modeling_tf_gpt2"] = ["TFGPT2Model"]
if is_flax_available():
_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
from .tokenization_gpt2 import GPT2Tokenizer
if is_tokenizers_available():
from .tokenization_gpt2_fast import GPT2TokenizerFast
if is_torch_available():
from .modeling_gpt2 import GPT2Model
if is_tf_available():
from .modeling_tf_gpt2 import TFGPT2Model
if is_flax_available():
from .modeling_flax_gpt2 import FlaxGPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_no_tokenizer = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
}
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if is_tf_available():
_import_structure["modeling_tf_gpt2"] = ["TFGPT2Model"]
if is_flax_available():
_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
if is_torch_available():
from .modeling_gpt2 import GPT2Model
if is_tf_available():
from .modeling_tf_gpt2 import TFGPT2Model
if is_flax_available():
from .modeling_flax_gpt2 import FlaxGPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
"tokenization_gpt2": ["GPT2Tokenizer"],
}
if is_tokenizers_available():
_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
from .tokenization_gpt2 import GPT2Tokenizer
if is_tokenizers_available():
from .tokenization_gpt2_fast import GPT2TokenizerFast
if is_torch_available():
from .modeling_gpt2 import GPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only_no_tokenizer = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
_import_structure = {
"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
}
if is_torch_available():
_import_structure["modeling_gpt2"] = ["GPT2Model"]
if TYPE_CHECKING:
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
if is_torch_available():
from .modeling_gpt2 import GPT2Model
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
with tempfile.TemporaryDirectory() as tmp_dir:
file_name = os.path.join(tmp_dir, "../__init__.py")
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, keep_processing=False)
self.check_result(file_name, init_no_tokenizer)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"])
self.check_result(file_name, init_pt_only)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"], keep_processing=False)
self.check_result(file_name, init_pt_only_no_tokenizer)
def test_clean_frameworks_in_init_with_vit(self):
test_init = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available, is_vision_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_vision_available():
_import_structure["feature_extraction_vit"] = ["ViTFeatureExtractor"]
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if is_tf_available():
_import_structure["modeling_tf_vit"] = ["TFViTModel"]
if is_flax_available():
_import_structure["modeling_flax_vit"] = ["FlaxViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_vision_available():
from .feature_extraction_vit import ViTFeatureExtractor
if is_torch_available():
from .modeling_vit import ViTModel
if is_tf_available():
from .modeling_tf_vit import ViTModel
if is_flax_available():
from .modeling_flax_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_no_feature_extractor = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if is_tf_available():
_import_structure["modeling_tf_vit"] = ["TFViTModel"]
if is_flax_available():
_import_structure["modeling_flax_vit"] = ["FlaxViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_torch_available():
from .modeling_vit import ViTModel
if is_tf_available():
from .modeling_tf_vit import ViTModel
if is_flax_available():
from .modeling_flax_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_vision_available():
_import_structure["feature_extraction_vit"] = ["ViTFeatureExtractor"]
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_vision_available():
from .feature_extraction_vit import ViTFeatureExtractor
if is_torch_available():
from .modeling_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
init_pt_only_no_feature_extractor = """
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_torch_available
_import_structure = {
"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
}
if is_torch_available():
_import_structure["modeling_vit"] = ["ViTModel"]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
if is_torch_available():
from .modeling_vit import ViTModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
with tempfile.TemporaryDirectory() as tmp_dir:
file_name = os.path.join(tmp_dir, "../__init__.py")
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, keep_processing=False)
self.check_result(file_name, init_no_feature_extractor)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"])
self.check_result(file_name, init_pt_only)
self.init_file(file_name, test_init)
clean_frameworks_in_init(file_name, frameworks=["pt"], keep_processing=False)
self.check_result(file_name, init_pt_only_no_feature_extractor)
def test_duplicate_doc_file(self):
test_doc = """
# GPT2
## Overview
Overview of the model.
## GPT2Config
[[autodoc]] GPT2Config
## GPT2Tokenizer
[[autodoc]] GPT2Tokenizer
- save_vocabulary
## GPT2TokenizerFast
[[autodoc]] GPT2TokenizerFast
## GPT2 specific outputs
[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
## GPT2Model
[[autodoc]] GPT2Model
- forward
## TFGPT2Model
[[autodoc]] TFGPT2Model
- call
## FlaxGPT2Model
[[autodoc]] FlaxGPT2Model
- __call__
"""
test_new_doc = """
# GPT-New New
## Overview
The GPT-New New model was proposed in [<INSERT PAPER NAME HERE>(<INSERT PAPER LINK HERE>) by <INSERT AUTHORS HERE>.
<INSERT SHORT SUMMARY HERE>
The abstract from the paper is the following:
*<INSERT PAPER ABSTRACT HERE>*
Tips:
<INSERT TIPS ABOUT MODEL HERE>
This model was contributed by [INSERT YOUR HF USERNAME HERE](<https://huggingface.co/<INSERT YOUR HF USERNAME HERE>).
The original code can be found [here](<INSERT LINK TO GITHUB REPO HERE>).
## GPTNewNewConfig
[[autodoc]] GPTNewNewConfig
## GPTNewNewTokenizer
[[autodoc]] GPTNewNewTokenizer
- save_vocabulary
## GPTNewNewTokenizerFast
[[autodoc]] GPTNewNewTokenizerFast
## GPTNewNew specific outputs
[[autodoc]] models.gpt_new_new.modeling_gpt_new_new.GPTNewNewDoubleHeadsModelOutput
[[autodoc]] models.gpt_new_new.modeling_tf_gpt_new_new.TFGPTNewNewDoubleHeadsModelOutput
## GPTNewNewModel
[[autodoc]] GPTNewNewModel
- forward
## TFGPTNewNewModel
[[autodoc]] TFGPTNewNewModel
- call
## FlaxGPTNewNewModel
[[autodoc]] FlaxGPTNewNewModel
- __call__
"""
with tempfile.TemporaryDirectory() as tmp_dir:
doc_file = os.path.join(tmp_dir, "gpt2.mdx")
new_doc_file = os.path.join(tmp_dir, "gpt-new-new.mdx")
gpt2_model_patterns = ModelPatterns("GPT2", "gpt2", tokenizer_class="GPT2Tokenizer")
new_model_patterns = ModelPatterns(
"GPT-New New", "huggingface/gpt-new-new", tokenizer_class="GPTNewNewTokenizer"
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns)
self.check_result(new_doc_file, test_new_doc)
test_new_doc_pt_only = test_new_doc.replace(
"""
## TFGPTNewNewModel
[[autodoc]] TFGPTNewNewModel
- call
## FlaxGPTNewNewModel
[[autodoc]] FlaxGPTNewNewModel
- __call__
""",
"",
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns, frameworks=["pt"])
self.check_result(new_doc_file, test_new_doc_pt_only)
test_new_doc_no_tok = test_new_doc.replace(
"""
## GPTNewNewTokenizer
[[autodoc]] GPTNewNewTokenizer
- save_vocabulary
## GPTNewNewTokenizerFast
[[autodoc]] GPTNewNewTokenizerFast
""",
"",
)
new_model_patterns = ModelPatterns(
"GPT-New New", "huggingface/gpt-new-new", tokenizer_class="GPT2Tokenizer"
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns)
print(test_new_doc_no_tok)
self.check_result(new_doc_file, test_new_doc_no_tok)
test_new_doc_pt_only_no_tok = test_new_doc_no_tok.replace(
"""
## TFGPTNewNewModel
[[autodoc]] TFGPTNewNewModel
- call
## FlaxGPTNewNewModel
[[autodoc]] FlaxGPTNewNewModel
- __call__
""",
"",
)
self.init_file(doc_file, test_doc)
duplicate_doc_file(doc_file, gpt2_model_patterns, new_model_patterns, frameworks=["pt"])
self.check_result(new_doc_file, test_new_doc_pt_only_no_tok)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/utils/dummy_sentencepiece_and_speech_objects.py | # This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class Speech2TextProcessor(metaclass=DummyObject):
_backends = ["sentencepiece", "speech"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece", "speech"])
| # This file is autogenerated by the command `make fix-copies`, do not edit.
# flake8: noqa
from ..utils import DummyObject, requires_backends
class Speech2TextProcessor(metaclass=DummyObject):
_backends = ["sentencepiece", "speech"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece", "speech"])
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py | # coding=utf-8
# Copyright The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" VisionTextDualEncoder model configuration"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig
logger = logging.get_logger(__name__)
class VisionTextDualEncoderConfig(PretrainedConfig):
r"""
[`VisionTextDualEncoderConfig`] is the configuration class to store the configuration of a
[`VisionTextDualEncoderModel`]. It is used to instantiate [`VisionTextDualEncoderModel`] model according to the
specified arguments, defining the text model and vision model configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`):
Dictionary of configuration options that defines text model config.
vision_config (`dict`):
Dictionary of configuration options that defines vison model config.
projection_dim (`int`, *optional*, defaults to 512):
Dimentionality of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation.
kwargs (*optional*):
Dictionary of keyword arguments.
Examples:
```python
>>> from transformers import ViTConfig, BertConfig, VisionTextDualEncoderConfig, VisionTextDualEncoderModel
>>> # Initializing a BERT and ViT configuration
>>> config_vision = ViTConfig()
>>> config_text = BertConfig()
>>> config = VisionTextDualEncoderConfig.from_vision_text_configs(config_vision, config_text, projection_dim=512)
>>> # Initializing a BERT and ViT model (with random weights)
>>> model = VisionTextDualEncoderModel(config=config)
>>> # Accessing the model configuration
>>> config_vision = model.config.vision_config
>>> config_text = model.config.text_config
>>> # Saving the model, including its configuration
>>> model.save_pretrained("vit-bert")
>>> # loading model and config from pretrained folder
>>> vision_text_config = VisionTextDualEncoderConfig.from_pretrained("vit-bert")
>>> model = VisionTextDualEncoderModel.from_pretrained("vit-bert", config=vision_text_config)
```"""
model_type = "vision-text-dual-encoder"
is_composition = True
def __init__(self, projection_dim=512, logit_scale_init_value=2.6592, **kwargs):
super().__init__(**kwargs)
if "vision_config" not in kwargs:
raise ValueError("`vision_config` can not be `None`.")
if "text_config" not in kwargs:
raise ValueError("`text_config` can not be `None`.")
vision_config = kwargs.pop("vision_config")
text_config = kwargs.pop("text_config")
vision_model_type = vision_config.pop("model_type")
text_model_type = text_config.pop("model_type")
if vision_model_type == "clip":
self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config
elif vision_model_type == "clip_vision_model":
self.vision_config = CLIPVisionConfig(**vision_config)
else:
self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
self.text_config = AutoConfig.for_model(text_model_type, **text_config)
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
@classmethod
def from_vision_text_configs(cls, vision_config: PretrainedConfig, text_config: PretrainedConfig, **kwargs):
r"""
Instantiate a [`VisionTextDualEncoderConfig`] (or a derived class) from text model configuration and vision
model configuration.
Returns:
[`VisionTextDualEncoderConfig`]: An instance of a configuration object
"""
return cls(vision_config=vision_config.to_dict(), text_config=text_config.to_dict(), **kwargs)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["vision_config"] = self.vision_config.to_dict()
output["text_config"] = self.text_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
| # coding=utf-8
# Copyright The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" VisionTextDualEncoder model configuration"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig
logger = logging.get_logger(__name__)
class VisionTextDualEncoderConfig(PretrainedConfig):
r"""
[`VisionTextDualEncoderConfig`] is the configuration class to store the configuration of a
[`VisionTextDualEncoderModel`]. It is used to instantiate [`VisionTextDualEncoderModel`] model according to the
specified arguments, defining the text model and vision model configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`):
Dictionary of configuration options that defines text model config.
vision_config (`dict`):
Dictionary of configuration options that defines vison model config.
projection_dim (`int`, *optional*, defaults to 512):
Dimentionality of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation.
kwargs (*optional*):
Dictionary of keyword arguments.
Examples:
```python
>>> from transformers import ViTConfig, BertConfig, VisionTextDualEncoderConfig, VisionTextDualEncoderModel
>>> # Initializing a BERT and ViT configuration
>>> config_vision = ViTConfig()
>>> config_text = BertConfig()
>>> config = VisionTextDualEncoderConfig.from_vision_text_configs(config_vision, config_text, projection_dim=512)
>>> # Initializing a BERT and ViT model (with random weights)
>>> model = VisionTextDualEncoderModel(config=config)
>>> # Accessing the model configuration
>>> config_vision = model.config.vision_config
>>> config_text = model.config.text_config
>>> # Saving the model, including its configuration
>>> model.save_pretrained("vit-bert")
>>> # loading model and config from pretrained folder
>>> vision_text_config = VisionTextDualEncoderConfig.from_pretrained("vit-bert")
>>> model = VisionTextDualEncoderModel.from_pretrained("vit-bert", config=vision_text_config)
```"""
model_type = "vision-text-dual-encoder"
is_composition = True
def __init__(self, projection_dim=512, logit_scale_init_value=2.6592, **kwargs):
super().__init__(**kwargs)
if "vision_config" not in kwargs:
raise ValueError("`vision_config` can not be `None`.")
if "text_config" not in kwargs:
raise ValueError("`text_config` can not be `None`.")
vision_config = kwargs.pop("vision_config")
text_config = kwargs.pop("text_config")
vision_model_type = vision_config.pop("model_type")
text_model_type = text_config.pop("model_type")
if vision_model_type == "clip":
self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config
elif vision_model_type == "clip_vision_model":
self.vision_config = CLIPVisionConfig(**vision_config)
else:
self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
self.text_config = AutoConfig.for_model(text_model_type, **text_config)
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
@classmethod
def from_vision_text_configs(cls, vision_config: PretrainedConfig, text_config: PretrainedConfig, **kwargs):
r"""
Instantiate a [`VisionTextDualEncoderConfig`] (or a derived class) from text model configuration and vision
model configuration.
Returns:
[`VisionTextDualEncoderConfig`]: An instance of a configuration object
"""
return cls(vision_config=vision_config.to_dict(), text_config=text_config.to_dict(), **kwargs)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["vision_config"] = self.vision_config.to_dict()
output["text_config"] = self.text_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/commands/transformers_cli.py | #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def main():
parser = ArgumentParser("Transformers CLI tool", usage="transformers-cli <command> [<args>]")
commands_parser = parser.add_subparsers(help="transformers-cli command helpers")
# Register commands
ConvertCommand.register_subcommand(commands_parser)
DownloadCommand.register_subcommand(commands_parser)
EnvironmentCommand.register_subcommand(commands_parser)
RunCommand.register_subcommand(commands_parser)
ServeCommand.register_subcommand(commands_parser)
UserCommands.register_subcommand(commands_parser)
AddNewModelCommand.register_subcommand(commands_parser)
AddNewModelLikeCommand.register_subcommand(commands_parser)
LfsCommands.register_subcommand(commands_parser)
PTtoTFCommand.register_subcommand(commands_parser)
# Let's go
args = parser.parse_args()
if not hasattr(args, "func"):
parser.print_help()
exit(1)
# Run
service = args.func(args)
service.run()
if __name__ == "__main__":
main()
| #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def main():
parser = ArgumentParser("Transformers CLI tool", usage="transformers-cli <command> [<args>]")
commands_parser = parser.add_subparsers(help="transformers-cli command helpers")
# Register commands
ConvertCommand.register_subcommand(commands_parser)
DownloadCommand.register_subcommand(commands_parser)
EnvironmentCommand.register_subcommand(commands_parser)
RunCommand.register_subcommand(commands_parser)
ServeCommand.register_subcommand(commands_parser)
UserCommands.register_subcommand(commands_parser)
AddNewModelCommand.register_subcommand(commands_parser)
AddNewModelLikeCommand.register_subcommand(commands_parser)
LfsCommands.register_subcommand(commands_parser)
PTtoTFCommand.register_subcommand(commands_parser)
# Let's go
args = parser.parse_args()
if not hasattr(args, "func"):
parser.print_help()
exit(1)
# Run
service = args.func(args)
service.run()
if __name__ == "__main__":
main()
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/legacy/seq2seq/minify_dataset.py | #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import fire
def minify(src_dir: str, dest_dir: str, n: int):
"""Write first n lines of each file f in src_dir to dest_dir/f"""
src_dir = Path(src_dir)
dest_dir = Path(dest_dir)
dest_dir.mkdir(exist_ok=True)
for path in src_dir.iterdir():
new = [x.rstrip() for x in list(path.open().readlines())][:n]
dest_path = dest_dir.joinpath(path.name)
print(dest_path)
dest_path.open("w").write("\n".join(new))
if __name__ == "__main__":
fire.Fire(minify)
| #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import fire
def minify(src_dir: str, dest_dir: str, n: int):
"""Write first n lines of each file f in src_dir to dest_dir/f"""
src_dir = Path(src_dir)
dest_dir = Path(dest_dir)
dest_dir.mkdir(exist_ok=True)
for path in src_dir.iterdir():
new = [x.rstrip() for x in list(path.open().readlines())][:n]
dest_path = dest_dir.joinpath(path.name)
print(dest_path)
dest_path.open("w").write("\n".join(new))
if __name__ == "__main__":
fire.Fire(minify)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/opt/modeling_opt.py | # coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch OPT model."""
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_opt import OPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
_CONFIG_FOR_DOC = "OPTConfig"
_TOKENIZER_FOR_DOC = "GPT2Tokenizer"
# Base model docstring
_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
# SequenceClassification docstring
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
_SEQ_CLASS_EXPECTED_LOSS = 1.71
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/opt-125m",
"facebook/opt-350m",
"facebook/opt-1.3b",
"facebook/opt-2.7b",
"facebook/opt-6.7b",
"facebook/opt-13b",
"facebook/opt-30b",
# See all OPT models at https://huggingface.co/models?filter=opt
]
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class OPTLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
super().__init__(num_embeddings + self.offset, embedding_dim)
def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
attention_mask = attention_mask.long()
# create positions depending on attention_mask
positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1
# cut positions if `past_key_values_length` is > 0
positions = positions[:, past_key_values_length:]
return super().forward(positions + self.offset)
class OPTAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
if attn_weights.dtype == torch.float16:
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
else:
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class OPTDecoderLayer(nn.Module):
def __init__(self, config: OPTConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = OPTAttention(
embed_dim=self.embed_dim,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.do_layer_norm_before = config.do_layer_norm_before
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Fully Connected
hidden_states_shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = (residual + hidden_states).view(hidden_states_shape)
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
OPT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`OPTConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare OPT Model outputting raw hidden-states without any specific head on top.",
OPT_START_DOCSTRING,
)
class OPTPreTrainedModel(PreTrainedModel):
config_class = OPTConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["OPTDecoderLayer"]
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (OPTDecoder)):
module.gradient_checkpointing = value
OPT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class OPTDecoder(OPTPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
Args:
config: OPTConfig
"""
def __init__(self, config: OPTConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
else:
self.project_in = None
# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
# with checkpoints that have been fine-tuned before transformers v4.20.1
# see https://github.com/facebookresearch/metaseq/pull/164
if config.do_layer_norm_before and not config._remove_final_layer_norm:
self.final_layer_norm = nn.LayerNorm(config.hidden_size)
else:
self.final_layer_norm = None
self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(inputs_embeds.device)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device)
pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
if self.project_in is not None:
inputs_embeds = self.project_in(inputs_embeds)
hidden_states = inputs_embeds + pos_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
# check if head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if self.final_layer_norm is not None:
hidden_states = self.final_layer_norm(hidden_states)
if self.project_out is not None:
hidden_states = self.project_out(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
@add_start_docstrings(
"The bare OPT Model outputting raw hidden-states without any specific head on top.",
OPT_START_DOCSTRING,
)
class OPTModel(OPTPreTrainedModel):
def __init__(self, config: OPTConfig):
super().__init__(config)
self.decoder = OPTDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.decoder.embed_tokens
def set_input_embeddings(self, value):
self.decoder.embed_tokens = value
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs
return BaseModelOutputWithPast(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
)
class OPTForCausalLM(OPTPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = OPTModel(config)
# the lm_head weight is automatically tied to the embed tokens weight
self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import GPT2Tokenizer, OPTForCausalLM
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
>>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0]).contiguous()
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past:
input_ids = input_ids[:, -1:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
@add_start_docstrings(
"""
The OPT Model transformer with a sequence classification head on top (linear layer).
[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
OPT_START_DOCSTRING,
)
class OPTForSequenceClassification(OPTPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config: OPTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = OPTModel(config)
self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size, sequence_length = input_ids.shape[:2]
else:
batch_size, sequence_length = inputs_embeds.shape[:2]
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
@add_start_docstrings(
"""
The OPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD
(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
OPT_START_DOCSTRING,
)
class OPTForQuestionAnswering(OPTPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config: OPTConfig):
super().__init__(config)
self.model = OPTModel(config)
self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
Returns:
Example:
```python
>>> from transformers import GPT2Tokenizer, OPTForQuestionAnswering
>>> import torch
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
>>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
>>> # so the head will be randomly initialized, hence the predictions will be random
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> predicted = tokenizer.decode(predict_answer_tokens)
>>> predicted
' Henson?'
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.qa_outputs(hidden_states)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + transformer_outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
| # coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch OPT model."""
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_opt import OPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
_CONFIG_FOR_DOC = "OPTConfig"
_TOKENIZER_FOR_DOC = "GPT2Tokenizer"
# Base model docstring
_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
# SequenceClassification docstring
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
_SEQ_CLASS_EXPECTED_LOSS = 1.71
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/opt-125m",
"facebook/opt-350m",
"facebook/opt-1.3b",
"facebook/opt-2.7b",
"facebook/opt-6.7b",
"facebook/opt-13b",
"facebook/opt-30b",
# See all OPT models at https://huggingface.co/models?filter=opt
]
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class OPTLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
super().__init__(num_embeddings + self.offset, embedding_dim)
def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
attention_mask = attention_mask.long()
# create positions depending on attention_mask
positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1
# cut positions if `past_key_values_length` is > 0
positions = positions[:, past_key_values_length:]
return super().forward(positions + self.offset)
class OPTAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
if attn_weights.dtype == torch.float16:
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
else:
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class OPTDecoderLayer(nn.Module):
def __init__(self, config: OPTConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = OPTAttention(
embed_dim=self.embed_dim,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.do_layer_norm_before = config.do_layer_norm_before
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Fully Connected
hidden_states_shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = (residual + hidden_states).view(hidden_states_shape)
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
OPT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`OPTConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare OPT Model outputting raw hidden-states without any specific head on top.",
OPT_START_DOCSTRING,
)
class OPTPreTrainedModel(PreTrainedModel):
config_class = OPTConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["OPTDecoderLayer"]
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (OPTDecoder)):
module.gradient_checkpointing = value
OPT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class OPTDecoder(OPTPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
Args:
config: OPTConfig
"""
def __init__(self, config: OPTConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
else:
self.project_in = None
# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
# with checkpoints that have been fine-tuned before transformers v4.20.1
# see https://github.com/facebookresearch/metaseq/pull/164
if config.do_layer_norm_before and not config._remove_final_layer_norm:
self.final_layer_norm = nn.LayerNorm(config.hidden_size)
else:
self.final_layer_norm = None
self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(inputs_embeds.device)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device)
pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
if self.project_in is not None:
inputs_embeds = self.project_in(inputs_embeds)
hidden_states = inputs_embeds + pos_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
# check if head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if self.final_layer_norm is not None:
hidden_states = self.final_layer_norm(hidden_states)
if self.project_out is not None:
hidden_states = self.project_out(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
@add_start_docstrings(
"The bare OPT Model outputting raw hidden-states without any specific head on top.",
OPT_START_DOCSTRING,
)
class OPTModel(OPTPreTrainedModel):
def __init__(self, config: OPTConfig):
super().__init__(config)
self.decoder = OPTDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.decoder.embed_tokens
def set_input_embeddings(self, value):
self.decoder.embed_tokens = value
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs
return BaseModelOutputWithPast(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
)
class OPTForCausalLM(OPTPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = OPTModel(config)
# the lm_head weight is automatically tied to the embed tokens weight
self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import GPT2Tokenizer, OPTForCausalLM
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
>>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0]).contiguous()
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past:
input_ids = input_ids[:, -1:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
@add_start_docstrings(
"""
The OPT Model transformer with a sequence classification head on top (linear layer).
[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
OPT_START_DOCSTRING,
)
class OPTForSequenceClassification(OPTPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config: OPTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = OPTModel(config)
self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size, sequence_length = input_ids.shape[:2]
else:
batch_size, sequence_length = inputs_embeds.shape[:2]
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
@add_start_docstrings(
"""
The OPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD
(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
OPT_START_DOCSTRING,
)
class OPTForQuestionAnswering(OPTPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config: OPTConfig):
super().__init__(config)
self.model = OPTModel(config)
self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
Returns:
Example:
```python
>>> from transformers import GPT2Tokenizer, OPTForQuestionAnswering
>>> import torch
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
>>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
>>> # so the head will be randomly initialized, hence the predictions will be random
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> predicted = tokenizer.decode(predict_answer_tokens)
>>> predicted
' Henson?'
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.qa_outputs(hidden_states)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + transformer_outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./templates/adding_a_missing_tokenization_test/cookiecutter-template-{{cookiecutter.modelname}}/test_tokenization_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}}. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the {{cookiecutter.modelname}} tokenizer. """
import unittest
{% if cookiecutter.has_slow_class == "True" and cookiecutter.has_fast_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}TokenizerFast
{% elif cookiecutter.has_slow_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer
{% elif cookiecutter.has_fast_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}TokenizerFast
{% endif -%}
{% if cookiecutter.has_fast_class == "True" and cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
from transformers.testing_utils import require_sentencepiece, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_sentencepiece
@require_tokenizers
{% elif cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
from transformers.testing_utils import require_sentencepiece
from ...test_tokenization_common import TokenizerTesterMixin
@require_sentencepiece
{% elif cookiecutter.has_fast_class == "True" -%}
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
{% else -%}
from ...test_tokenization_common import TokenizerTesterMixin
{% endif -%}
class {{cookiecutter.camelcase_modelname}}TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
{% if cookiecutter.has_slow_class == "True" -%}
tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
test_slow_tokenizer = True
{% else -%}
tokenizer_class = None
test_slow_tokenizer = False
{% endif -%}
{% if cookiecutter.has_fast_class == "True" -%}
rust_tokenizer_class = {{cookiecutter.camelcase_modelname}}TokenizerFast
test_rust_tokenizer = True
{% else -%}
rust_tokenizer_class = None
test_rust_tokenizer = False
{% endif -%}
{% if cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
test_sentencepiece = True
{% endif -%}
# TODO: Check in `TokenizerTesterMixin` if other attributes need to be changed
def setUp(self):
super().setUp()
raise NotImplementedError(
"Here you have to implement the saving of a toy tokenizer in "
"`self.tmpdirname`."
)
# TODO: add tests with hard-coded target values | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}}. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the {{cookiecutter.modelname}} tokenizer. """
import unittest
{% if cookiecutter.has_slow_class == "True" and cookiecutter.has_fast_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}TokenizerFast
{% elif cookiecutter.has_slow_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer
{% elif cookiecutter.has_fast_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}TokenizerFast
{% endif -%}
{% if cookiecutter.has_fast_class == "True" and cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
from transformers.testing_utils import require_sentencepiece, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_sentencepiece
@require_tokenizers
{% elif cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
from transformers.testing_utils import require_sentencepiece
from ...test_tokenization_common import TokenizerTesterMixin
@require_sentencepiece
{% elif cookiecutter.has_fast_class == "True" -%}
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
{% else -%}
from ...test_tokenization_common import TokenizerTesterMixin
{% endif -%}
class {{cookiecutter.camelcase_modelname}}TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
{% if cookiecutter.has_slow_class == "True" -%}
tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
test_slow_tokenizer = True
{% else -%}
tokenizer_class = None
test_slow_tokenizer = False
{% endif -%}
{% if cookiecutter.has_fast_class == "True" -%}
rust_tokenizer_class = {{cookiecutter.camelcase_modelname}}TokenizerFast
test_rust_tokenizer = True
{% else -%}
rust_tokenizer_class = None
test_rust_tokenizer = False
{% endif -%}
{% if cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
test_sentencepiece = True
{% endif -%}
# TODO: Check in `TokenizerTesterMixin` if other attributes need to be changed
def setUp(self):
super().setUp()
raise NotImplementedError(
"Here you have to implement the saving of a toy tokenizer in "
"`self.tmpdirname`."
)
# TODO: add tests with hard-coded target values | -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/bert_japanese/__init__.py | -1 |
||
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/layoutlmv2/processing_layoutlmv2.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for LayoutLMv2.
"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class LayoutLMv2Processor(ProcessorMixin):
r"""
Constructs a LayoutLMv2 processor which combines a LayoutLMv2 feature extractor and a LayoutLMv2 tokenizer into a
single processor.
[`LayoutLMv2Processor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv2FeatureExtractor`] to resize document images to a fixed size, and optionally applies OCR
to get words and normalized bounding boxes. These are then provided to [`LayoutLMv2Tokenizer`] or
[`LayoutLMv2TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
`attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
into token-level `labels` for token classification tasks (such as FUNSD, CORD).
Args:
feature_extractor (`LayoutLMv2FeatureExtractor`):
An instance of [`LayoutLMv2FeatureExtractor`]. The feature extractor is a required input.
tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`):
An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input.
"""
feature_extractor_class = "LayoutLMv2FeatureExtractor"
tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
def __call__(
self,
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
) -> BatchEncoding:
"""
This method first forwards the `images` argument to [`~LayoutLMv2FeatureExtractor.__call__`]. In case
[`LayoutLMv2FeatureExtractor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output,
together with resized `images`. In case [`LayoutLMv2FeatureExtractor`] was initialized with `apply_ocr` set to
`False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images``.
Please refer to the docstring of the above two methods for more information.
"""
# verify input
if self.feature_extractor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes "
"if you initialized the feature extractor with apply_ocr set to True."
)
if self.feature_extractor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the feature extractor with apply_ocr set to True."
)
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")
# first, apply the feature extractor
features = self.feature_extractor(images=images, return_tensors=return_tensors)
# second, apply the tokenizer
if text is not None and self.feature_extractor.apply_ocr and text_pair is None:
if isinstance(text, str):
text = [text] # add batch dimension (as the feature extractor always adds a batch dimension)
text_pair = features["words"]
encoded_inputs = self.tokenizer(
text=text if text is not None else features["words"],
text_pair=text_pair if text_pair is not None else None,
boxes=boxes if boxes is not None else features["boxes"],
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
# add pixel values
images = features.pop("pixel_values")
if return_overflowing_tokens is True:
images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
encoded_inputs["image"] = images
return encoded_inputs
def get_overflowing_images(self, images, overflow_to_sample_mapping):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
images_with_overflow = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx])
if len(images_with_overflow) != len(overflow_to_sample_mapping):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
)
return images_with_overflow
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for LayoutLMv2.
"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class LayoutLMv2Processor(ProcessorMixin):
r"""
Constructs a LayoutLMv2 processor which combines a LayoutLMv2 feature extractor and a LayoutLMv2 tokenizer into a
single processor.
[`LayoutLMv2Processor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv2FeatureExtractor`] to resize document images to a fixed size, and optionally applies OCR
to get words and normalized bounding boxes. These are then provided to [`LayoutLMv2Tokenizer`] or
[`LayoutLMv2TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
`attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
into token-level `labels` for token classification tasks (such as FUNSD, CORD).
Args:
feature_extractor (`LayoutLMv2FeatureExtractor`):
An instance of [`LayoutLMv2FeatureExtractor`]. The feature extractor is a required input.
tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`):
An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input.
"""
feature_extractor_class = "LayoutLMv2FeatureExtractor"
tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
def __call__(
self,
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
) -> BatchEncoding:
"""
This method first forwards the `images` argument to [`~LayoutLMv2FeatureExtractor.__call__`]. In case
[`LayoutLMv2FeatureExtractor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output,
together with resized `images`. In case [`LayoutLMv2FeatureExtractor`] was initialized with `apply_ocr` set to
`False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images``.
Please refer to the docstring of the above two methods for more information.
"""
# verify input
if self.feature_extractor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes "
"if you initialized the feature extractor with apply_ocr set to True."
)
if self.feature_extractor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the feature extractor with apply_ocr set to True."
)
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")
# first, apply the feature extractor
features = self.feature_extractor(images=images, return_tensors=return_tensors)
# second, apply the tokenizer
if text is not None and self.feature_extractor.apply_ocr and text_pair is None:
if isinstance(text, str):
text = [text] # add batch dimension (as the feature extractor always adds a batch dimension)
text_pair = features["words"]
encoded_inputs = self.tokenizer(
text=text if text is not None else features["words"],
text_pair=text_pair if text_pair is not None else None,
boxes=boxes if boxes is not None else features["boxes"],
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
# add pixel values
images = features.pop("pixel_values")
if return_overflowing_tokens is True:
images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
encoded_inputs["image"] = images
return encoded_inputs
def get_overflowing_images(self, images, overflow_to_sample_mapping):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
images_with_overflow = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx])
if len(images_with_overflow) != len(overflow_to_sample_mapping):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
)
return images_with_overflow
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./docs/source/it/preprocessing.mdx | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Preprocess
[[open-in-colab]]
Prima di poter usare i dati in un modello, bisogna processarli in un formato accettabile per quest'ultimo. Un modello non comprende il testo grezzo, le immagini o l'audio. Bisogna convertire questi input in numeri e assemblarli all'interno di tensori. In questa esercitazione, tu potrai:
* Preprocessare dati testuali con un tokenizer.
* Preprocessare immagini o dati audio con un estrattore di caratteristiche.
* Preprocessare dati per attività multimodali mediante un processore.
## NLP
<Youtube id="Yffk5aydLzg"/>
Lo strumento principale per processare dati testuali è un [tokenizer](main_classes/tokenizer). Un tokenizer inizia separando il testo in *tokens* secondo una serie di regole. I tokens sono convertiti in numeri, questi vengono utilizzati per costruire i tensori di input del modello. Anche altri input addizionali se richiesti dal modello vengono aggiunti dal tokenizer.
<Tip>
Se stai pensando si utilizzare un modello preaddestrato, è importante utilizzare il tokenizer preaddestrato associato. Questo assicura che il testo sia separato allo stesso modo che nel corpus usato per l'addestramento, e venga usata la stessa mappatura tokens-to-index (solitamente indicato come il *vocabolario*) come nel preaddestramento.
</Tip>
Iniziamo subito caricando un tokenizer preaddestrato con la classe [`AutoTokenizer`]. Questo scarica il *vocabolario* usato quando il modello è stato preaddestrato.
### Tokenize
Carica un tokenizer preaddestrato con [`AutoTokenizer.from_pretrained`]:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
```
Poi inserisci le tue frasi nel tokenizer:
```py
>>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
>>> print(encoded_input)
{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
Il tokenizer restituisce un dizionario contenente tre oggetti importanti:
* [input_ids](glossary#input-ids) sono gli indici che corrispondono ad ogni token nella frase.
* [attention_mask](glossary#attention-mask) indicata se un token deve essere elaborato o no.
* [token_type_ids](glossary#token-type-ids) identifica a quale sequenza appartiene un token se è presente più di una sequenza.
Si possono decodificare gli `input_ids` per farsi restituire l'input originale:
```py
>>> tokenizer.decode(encoded_input["input_ids"])
'[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'
```
Come si può vedere, il tokenizer aggiunge due token speciali - `CLS` e `SEP` (classificatore e separatore) - alla frase. Non tutti i modelli hanno bisogno dei token speciali, ma se servono, il tokenizer li aggiungerà automaticamente.
Se ci sono più frasi che vuoi processare, passale come una lista al tokenizer:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_inputs = tokenizer(batch_sentences)
>>> print(encoded_inputs)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1]]}
```
### Pad
Questo è un argomento importante. Quando processi un insieme di frasi potrebbero non avere tutte la stessa lunghezza. Questo è un problema perchè i tensori, in input del modello, devono avere dimensioni uniformi. Il padding è una strategia per assicurarsi che i tensori siano rettangolari aggiungendo uno speciale *padding token* alle frasi più corte.
Imposta il parametro `padding` a `True` per imbottire le frasi più corte nel gruppo in modo che combacino con la massima lunghezza presente:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
Nota che il tokenizer aggiunge alle sequenze degli `0` perchè sono troppo corte!
### Truncation
L'altra faccia della medaglia è che avolte le sequenze possono essere troppo lunghe per essere gestite dal modello. In questo caso, avrai bisogno di troncare la sequenza per avere una lunghezza minore.
Imposta il parametro `truncation` a `True` per troncare una sequenza alla massima lunghezza accettata dal modello:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
### Costruire i tensori
Infine, vuoi che il tokenizer restituisca i tensori prodotti dal modello.
Imposta il parametro `return_tensors` su `pt` per PyTorch, o `tf` per TensorFlow:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch, padding=True, truncation=True, return_tensors="pt")
>>> print(encoded_input)
{'input_ids': tensor([[ 101, 153, 7719, 21490, 1122, 1114, 9582, 1623, 102],
[ 101, 5226, 1122, 9649, 1199, 2610, 1236, 102, 0]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]]),
'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 0]])}
===PT-TF-SPLIT===
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch, padding=True, truncation=True, return_tensors="tf")
>>> print(encoded_input)
{'input_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[ 101, 153, 7719, 21490, 1122, 1114, 9582, 1623, 102],
[ 101, 5226, 1122, 9649, 1199, 2610, 1236, 102, 0]],
dtype=int32)>,
'token_type_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>,
'attention_mask': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 0]], dtype=int32)>}
```
## Audio
Gli input audio sono processati in modo differente rispetto al testo, ma l'obiettivo rimane lo stesso: creare sequenze numeriche che il modello può capire. Un [estrattore di caratteristiche](main_classes/feature_extractor) è progettato con lo scopo preciso di estrarre caratteristiche da immagini o dati audio grezzi e convertirli in tensori. Prima di iniziare, installa 🤗 Datasets per caricare un dataset audio e sperimentare:
```bash
pip install datasets
```
Carica il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) (vedi il 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) per avere maggiori dettagli su come caricare un dataset):
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
```
Accedi al primo elemento della colonna `audio` per dare uno sguardo all'input. Richiamando la colonna `audio` sarà caricato automaticamente e ricampionato il file audio:
```py
>>> dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
```
Questo restituisce tre oggetti:
* `array` è il segnale vocale caricato - e potenzialmente ricampionato - come vettore 1D.
* `path` il percorso del file audio.
* `sampling_rate` si riferisce al numero di campioni del segnale vocale misurati al secondo.
### Ricampionamento
Per questo tutorial, puoi usare il modello [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base). Come puoi vedere dalla model card, il modello Wav2Vec2 è preaddestrato su un campionamento vocale a 16kHz.È importante che la frequenza di campionamento dei tuoi dati audio combaci con la frequenza di campionamento del dataset usato per preaddestrare il modello. Se la frequenza di campionamento dei tuoi dati non è uguale dovrai ricampionare i tuoi dati audio.
Per esempio, il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) ha una frequenza di campionamento di 8000kHz. Utilizzando il modello Wav2Vec2 su questo dataset, alzala a 16kHz:
```py
>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
>>> dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
```
1. Usa il metodo di 🤗 Datasets' [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.cast_column) per alzare la frequenza di campionamento a 16kHz:
```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
```
2. Carica il file audio:
```py
>>> dataset[0]["audio"]
{'array': array([ 2.3443763e-05, 2.1729663e-04, 2.2145823e-04, ...,
3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 16000}
```
Come puoi notare, la `sampling_rate` adesso è 16kHz!
### Feature extractor
Il prossimo passo è caricare un estrattore di caratteristiche per normalizzare e fare padding sull'input. Quando applichiamo il padding sui dati testuali, uno `0` è aggiunto alle sequenze più brevi. La stessa idea si applica ai dati audio, l'estrattore di caratteristiche per gli audio aggiungerà uno `0` - interpretato come silenzio - agli `array`.
Carica l'estrattore delle caratteristiche con [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
```
Inserisci l' `array` audio nell'estrattore delle caratteristiche. Noi raccomandiamo sempre di aggiungere il parametro `sampling_rate` nell'estrattore delle caratteristiche per correggere meglio qualche errore, dovuto ai silenzi, che potrebbe verificarsi.
```py
>>> audio_input = [dataset[0]["audio"]["array"]]
>>> feature_extractor(audio_input, sampling_rate=16000)
{'input_values': [array([ 3.8106556e-04, 2.7506407e-03, 2.8015103e-03, ...,
5.6335266e-04, 4.6588284e-06, -1.7142107e-04], dtype=float32)]}
```
### Pad e truncate
Come per il tokenizer, puoi applicare le operazioni padding o truncation per manipolare sequenze di variabili a lotti. Dai uno sguaro alla lunghezza delle sequenze di questi due campioni audio:
```py
>>> dataset[0]["audio"]["array"].shape
(173398,)
>>> dataset[1]["audio"]["array"].shape
(106496,)
```
Come puoi vedere, il primo campione ha una sequenza più lunga del secondo. Crea una funzione che preprocesserà il dataset. Specifica una lunghezza massima del campione, e l'estrattore di features si occuperà di riempire o troncare la sequenza per coincidervi:
```py
>>> def preprocess_function(examples):
... audio_arrays = [x["array"] for x in examples["audio"]]
... inputs = feature_extractor(
... audio_arrays,
... sampling_rate=16000,
... padding=True,
... max_length=100000,
... truncation=True,
... )
... return inputs
```
Applica la funzione ai primi esempi nel dataset:
```py
>>> processed_dataset = preprocess_function(dataset[:5])
```
Adesso guarda la lunghezza dei campioni elaborati:
```py
>>> processed_dataset["input_values"][0].shape
(100000,)
>>> processed_dataset["input_values"][1].shape
(100000,)
```
La lunghezza dei campioni adesso coincide con la massima lunghezza impostata nelle funzione.
## Vision
Un estrattore di caratteristiche si può usare anche per processare immagini e per compiti di visione. Ancora una volta, l'obiettivo è convertire l'immagine grezza in un lotto di tensori come input.
Carica il dataset [food101](https://huggingface.co/datasets/food101) per questa esercitazione. Usa il parametro `split` di 🤗 Datasets per caricare solo un piccolo campione dal dataset di addestramento poichè il set di dati è molto grande:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("food101", split="train[:100]")
```
Secondo passo, dai uno sguardo alle immagini usando la caratteristica [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image) di 🤗 Datasets:
```py
>>> dataset[0]["image"]
```
### Feature extractor
Carica l'estrattore di caratteristiche [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
```
### Data augmentation
Per le attività di visione, è usuale aggiungere alcuni tipi di data augmentation alle immagini come parte del preprocessing. Puoi aggiungere augmentations con qualsiasi libreria che preferisci, ma in questa esercitazione, userai il modulo [`transforms`](https://pytorch.org/vision/stable/transforms.html) di torchvision.
1. Normalizza l'immagine e usa [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) per concatenare alcune trasformazioni - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) e [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) - insieme:
```py
>>> from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ColorJitter, ToTensor
>>> normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
>>> _transforms = Compose(
... [RandomResizedCrop(feature_extractor.size), ColorJitter(brightness=0.5, hue=0.5), ToTensor(), normalize]
... )
```
2. Il modello accetta [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) come input. Questo valore è generato dall'estrattore di caratteristiche. Crea una funzione che genera `pixel_values` dai transforms:
```py
>>> def transforms(examples):
... examples["pixel_values"] = [_transforms(image.convert("RGB")) for image in examples["image"]]
... return examples
```
3. Poi utilizza 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform)per applicare al volo la trasformazione:
```py
>>> dataset.set_transform(transforms)
```
4. Adesso quando accedi all'immagine, puoi notare che l'estrattore di caratteristiche ha aggiunto `pixel_values` allo schema di input:
```py
>>> dataset[0]["image"]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F1A7B0630D0>,
'label': 6,
'pixel_values': tensor([[[ 0.0353, 0.0745, 0.1216, ..., -0.9922, -0.9922, -0.9922],
[-0.0196, 0.0667, 0.1294, ..., -0.9765, -0.9843, -0.9922],
[ 0.0196, 0.0824, 0.1137, ..., -0.9765, -0.9686, -0.8667],
...,
[ 0.0275, 0.0745, 0.0510, ..., -0.1137, -0.1216, -0.0824],
[ 0.0667, 0.0824, 0.0667, ..., -0.0588, -0.0745, -0.0980],
[ 0.0353, 0.0353, 0.0431, ..., -0.0039, -0.0039, -0.0588]],
[[ 0.2078, 0.2471, 0.2863, ..., -0.9451, -0.9373, -0.9451],
[ 0.1608, 0.2471, 0.3098, ..., -0.9373, -0.9451, -0.9373],
[ 0.2078, 0.2706, 0.3020, ..., -0.9608, -0.9373, -0.8275],
...,
[-0.0353, 0.0118, -0.0039, ..., -0.2392, -0.2471, -0.2078],
[ 0.0196, 0.0353, 0.0196, ..., -0.1843, -0.2000, -0.2235],
[-0.0118, -0.0039, -0.0039, ..., -0.0980, -0.0980, -0.1529]],
[[ 0.3961, 0.4431, 0.4980, ..., -0.9216, -0.9137, -0.9216],
[ 0.3569, 0.4510, 0.5216, ..., -0.9059, -0.9137, -0.9137],
[ 0.4118, 0.4745, 0.5216, ..., -0.9137, -0.8902, -0.7804],
...,
[-0.2314, -0.1922, -0.2078, ..., -0.4196, -0.4275, -0.3882],
[-0.1843, -0.1686, -0.2000, ..., -0.3647, -0.3804, -0.4039],
[-0.1922, -0.1922, -0.1922, ..., -0.2941, -0.2863, -0.3412]]])}
```
Di seguito come si vede l'immagine dopo la fase di preprocessing. Come ci si aspetterebbe dalle trasformazioni applicate, l'immagine è stata ritagliata in modo casuale e le proprietà del colore sono diverse.
```py
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> img = dataset[0]["pixel_values"]
>>> plt.imshow(img.permute(1, 2, 0))
```
## Multimodal
Per attività multimodali userai una combinazione di tutto quello che hai imparato poco fa e applicherai le tue competenze alla comprensione automatica del parlato (Automatic Speech Recognition - ASR). Questo significa che avrai bisogno di:
* Un estrattore delle caratteristiche per processare i dati audio.
* Il Tokenizer per processare i testi.
Ritorna sul datasere [LJ Speech](https://huggingface.co/datasets/lj_speech):
```py
>>> from datasets import load_dataset
>>> lj_speech = load_dataset("lj_speech", split="train")
```
Visto che sei interessato solo alle colonne `audio` e `text`, elimina tutte le altre:
```py
>>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
```
Adesso guarda le colonne `audio` e `text`:
```py
>>> lj_speech[0]["audio"]
{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ...,
7.3242188e-04, 2.1362305e-04, 6.1035156e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
'sampling_rate': 22050}
>>> lj_speech[0]["text"]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
```
Ricorda dalla sezione precedente sull'elaborazione dei dati audio, tu dovresti sempre [ricampionare](preprocessing#audio) la frequenza di campionamento dei tuoi dati audio per farla coincidere con quella del dataset usato dal modello preaddestrato:
```py
>>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
```
### Processor
Un processor combina un estrattore di caratteristiche e un tokenizer. Carica un processor con [`AutoProcessor.from_pretrained]:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
```
1. Crea una funzione che processi i dati audio in `input_values`, e tokenizza il testo in `labels`. Questi sono i tuoi input per il modello:
```py
>>> def prepare_dataset(example):
... audio = example["audio"]
... example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
... return example
```
2. Applica la funzione `prepare_dataset` ad un campione:
```py
>>> prepare_dataset(lj_speech[0])
```
Nota che il processor ha aggiunto `input_values` e `labels`. La frequenza di campionamento è stata corretta riducendola a 16kHz.
Fantastico, ora dovresti essere in grado di preelaborare i dati per qualsiasi modalità e persino di combinare modalità diverse! Nella prossima esercitazione, impareremo a mettere a punto un modello sui dati appena pre-elaborati. | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Preprocess
[[open-in-colab]]
Prima di poter usare i dati in un modello, bisogna processarli in un formato accettabile per quest'ultimo. Un modello non comprende il testo grezzo, le immagini o l'audio. Bisogna convertire questi input in numeri e assemblarli all'interno di tensori. In questa esercitazione, tu potrai:
* Preprocessare dati testuali con un tokenizer.
* Preprocessare immagini o dati audio con un estrattore di caratteristiche.
* Preprocessare dati per attività multimodali mediante un processore.
## NLP
<Youtube id="Yffk5aydLzg"/>
Lo strumento principale per processare dati testuali è un [tokenizer](main_classes/tokenizer). Un tokenizer inizia separando il testo in *tokens* secondo una serie di regole. I tokens sono convertiti in numeri, questi vengono utilizzati per costruire i tensori di input del modello. Anche altri input addizionali se richiesti dal modello vengono aggiunti dal tokenizer.
<Tip>
Se stai pensando si utilizzare un modello preaddestrato, è importante utilizzare il tokenizer preaddestrato associato. Questo assicura che il testo sia separato allo stesso modo che nel corpus usato per l'addestramento, e venga usata la stessa mappatura tokens-to-index (solitamente indicato come il *vocabolario*) come nel preaddestramento.
</Tip>
Iniziamo subito caricando un tokenizer preaddestrato con la classe [`AutoTokenizer`]. Questo scarica il *vocabolario* usato quando il modello è stato preaddestrato.
### Tokenize
Carica un tokenizer preaddestrato con [`AutoTokenizer.from_pretrained`]:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
```
Poi inserisci le tue frasi nel tokenizer:
```py
>>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
>>> print(encoded_input)
{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
Il tokenizer restituisce un dizionario contenente tre oggetti importanti:
* [input_ids](glossary#input-ids) sono gli indici che corrispondono ad ogni token nella frase.
* [attention_mask](glossary#attention-mask) indicata se un token deve essere elaborato o no.
* [token_type_ids](glossary#token-type-ids) identifica a quale sequenza appartiene un token se è presente più di una sequenza.
Si possono decodificare gli `input_ids` per farsi restituire l'input originale:
```py
>>> tokenizer.decode(encoded_input["input_ids"])
'[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'
```
Come si può vedere, il tokenizer aggiunge due token speciali - `CLS` e `SEP` (classificatore e separatore) - alla frase. Non tutti i modelli hanno bisogno dei token speciali, ma se servono, il tokenizer li aggiungerà automaticamente.
Se ci sono più frasi che vuoi processare, passale come una lista al tokenizer:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_inputs = tokenizer(batch_sentences)
>>> print(encoded_inputs)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1]]}
```
### Pad
Questo è un argomento importante. Quando processi un insieme di frasi potrebbero non avere tutte la stessa lunghezza. Questo è un problema perchè i tensori, in input del modello, devono avere dimensioni uniformi. Il padding è una strategia per assicurarsi che i tensori siano rettangolari aggiungendo uno speciale *padding token* alle frasi più corte.
Imposta il parametro `padding` a `True` per imbottire le frasi più corte nel gruppo in modo che combacino con la massima lunghezza presente:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
Nota che il tokenizer aggiunge alle sequenze degli `0` perchè sono troppo corte!
### Truncation
L'altra faccia della medaglia è che avolte le sequenze possono essere troppo lunghe per essere gestite dal modello. In questo caso, avrai bisogno di troncare la sequenza per avere una lunghezza minore.
Imposta il parametro `truncation` a `True` per troncare una sequenza alla massima lunghezza accettata dal modello:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
### Costruire i tensori
Infine, vuoi che il tokenizer restituisca i tensori prodotti dal modello.
Imposta il parametro `return_tensors` su `pt` per PyTorch, o `tf` per TensorFlow:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch, padding=True, truncation=True, return_tensors="pt")
>>> print(encoded_input)
{'input_ids': tensor([[ 101, 153, 7719, 21490, 1122, 1114, 9582, 1623, 102],
[ 101, 5226, 1122, 9649, 1199, 2610, 1236, 102, 0]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]]),
'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 0]])}
===PT-TF-SPLIT===
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch, padding=True, truncation=True, return_tensors="tf")
>>> print(encoded_input)
{'input_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[ 101, 153, 7719, 21490, 1122, 1114, 9582, 1623, 102],
[ 101, 5226, 1122, 9649, 1199, 2610, 1236, 102, 0]],
dtype=int32)>,
'token_type_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>,
'attention_mask': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 0]], dtype=int32)>}
```
## Audio
Gli input audio sono processati in modo differente rispetto al testo, ma l'obiettivo rimane lo stesso: creare sequenze numeriche che il modello può capire. Un [estrattore di caratteristiche](main_classes/feature_extractor) è progettato con lo scopo preciso di estrarre caratteristiche da immagini o dati audio grezzi e convertirli in tensori. Prima di iniziare, installa 🤗 Datasets per caricare un dataset audio e sperimentare:
```bash
pip install datasets
```
Carica il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) (vedi il 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) per avere maggiori dettagli su come caricare un dataset):
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
```
Accedi al primo elemento della colonna `audio` per dare uno sguardo all'input. Richiamando la colonna `audio` sarà caricato automaticamente e ricampionato il file audio:
```py
>>> dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
```
Questo restituisce tre oggetti:
* `array` è il segnale vocale caricato - e potenzialmente ricampionato - come vettore 1D.
* `path` il percorso del file audio.
* `sampling_rate` si riferisce al numero di campioni del segnale vocale misurati al secondo.
### Ricampionamento
Per questo tutorial, puoi usare il modello [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base). Come puoi vedere dalla model card, il modello Wav2Vec2 è preaddestrato su un campionamento vocale a 16kHz.È importante che la frequenza di campionamento dei tuoi dati audio combaci con la frequenza di campionamento del dataset usato per preaddestrare il modello. Se la frequenza di campionamento dei tuoi dati non è uguale dovrai ricampionare i tuoi dati audio.
Per esempio, il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) ha una frequenza di campionamento di 8000kHz. Utilizzando il modello Wav2Vec2 su questo dataset, alzala a 16kHz:
```py
>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
>>> dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
```
1. Usa il metodo di 🤗 Datasets' [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.cast_column) per alzare la frequenza di campionamento a 16kHz:
```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
```
2. Carica il file audio:
```py
>>> dataset[0]["audio"]
{'array': array([ 2.3443763e-05, 2.1729663e-04, 2.2145823e-04, ...,
3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 16000}
```
Come puoi notare, la `sampling_rate` adesso è 16kHz!
### Feature extractor
Il prossimo passo è caricare un estrattore di caratteristiche per normalizzare e fare padding sull'input. Quando applichiamo il padding sui dati testuali, uno `0` è aggiunto alle sequenze più brevi. La stessa idea si applica ai dati audio, l'estrattore di caratteristiche per gli audio aggiungerà uno `0` - interpretato come silenzio - agli `array`.
Carica l'estrattore delle caratteristiche con [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
```
Inserisci l' `array` audio nell'estrattore delle caratteristiche. Noi raccomandiamo sempre di aggiungere il parametro `sampling_rate` nell'estrattore delle caratteristiche per correggere meglio qualche errore, dovuto ai silenzi, che potrebbe verificarsi.
```py
>>> audio_input = [dataset[0]["audio"]["array"]]
>>> feature_extractor(audio_input, sampling_rate=16000)
{'input_values': [array([ 3.8106556e-04, 2.7506407e-03, 2.8015103e-03, ...,
5.6335266e-04, 4.6588284e-06, -1.7142107e-04], dtype=float32)]}
```
### Pad e truncate
Come per il tokenizer, puoi applicare le operazioni padding o truncation per manipolare sequenze di variabili a lotti. Dai uno sguaro alla lunghezza delle sequenze di questi due campioni audio:
```py
>>> dataset[0]["audio"]["array"].shape
(173398,)
>>> dataset[1]["audio"]["array"].shape
(106496,)
```
Come puoi vedere, il primo campione ha una sequenza più lunga del secondo. Crea una funzione che preprocesserà il dataset. Specifica una lunghezza massima del campione, e l'estrattore di features si occuperà di riempire o troncare la sequenza per coincidervi:
```py
>>> def preprocess_function(examples):
... audio_arrays = [x["array"] for x in examples["audio"]]
... inputs = feature_extractor(
... audio_arrays,
... sampling_rate=16000,
... padding=True,
... max_length=100000,
... truncation=True,
... )
... return inputs
```
Applica la funzione ai primi esempi nel dataset:
```py
>>> processed_dataset = preprocess_function(dataset[:5])
```
Adesso guarda la lunghezza dei campioni elaborati:
```py
>>> processed_dataset["input_values"][0].shape
(100000,)
>>> processed_dataset["input_values"][1].shape
(100000,)
```
La lunghezza dei campioni adesso coincide con la massima lunghezza impostata nelle funzione.
## Vision
Un estrattore di caratteristiche si può usare anche per processare immagini e per compiti di visione. Ancora una volta, l'obiettivo è convertire l'immagine grezza in un lotto di tensori come input.
Carica il dataset [food101](https://huggingface.co/datasets/food101) per questa esercitazione. Usa il parametro `split` di 🤗 Datasets per caricare solo un piccolo campione dal dataset di addestramento poichè il set di dati è molto grande:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("food101", split="train[:100]")
```
Secondo passo, dai uno sguardo alle immagini usando la caratteristica [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image) di 🤗 Datasets:
```py
>>> dataset[0]["image"]
```
### Feature extractor
Carica l'estrattore di caratteristiche [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
```
### Data augmentation
Per le attività di visione, è usuale aggiungere alcuni tipi di data augmentation alle immagini come parte del preprocessing. Puoi aggiungere augmentations con qualsiasi libreria che preferisci, ma in questa esercitazione, userai il modulo [`transforms`](https://pytorch.org/vision/stable/transforms.html) di torchvision.
1. Normalizza l'immagine e usa [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) per concatenare alcune trasformazioni - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) e [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) - insieme:
```py
>>> from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ColorJitter, ToTensor
>>> normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
>>> _transforms = Compose(
... [RandomResizedCrop(feature_extractor.size), ColorJitter(brightness=0.5, hue=0.5), ToTensor(), normalize]
... )
```
2. Il modello accetta [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) come input. Questo valore è generato dall'estrattore di caratteristiche. Crea una funzione che genera `pixel_values` dai transforms:
```py
>>> def transforms(examples):
... examples["pixel_values"] = [_transforms(image.convert("RGB")) for image in examples["image"]]
... return examples
```
3. Poi utilizza 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform)per applicare al volo la trasformazione:
```py
>>> dataset.set_transform(transforms)
```
4. Adesso quando accedi all'immagine, puoi notare che l'estrattore di caratteristiche ha aggiunto `pixel_values` allo schema di input:
```py
>>> dataset[0]["image"]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F1A7B0630D0>,
'label': 6,
'pixel_values': tensor([[[ 0.0353, 0.0745, 0.1216, ..., -0.9922, -0.9922, -0.9922],
[-0.0196, 0.0667, 0.1294, ..., -0.9765, -0.9843, -0.9922],
[ 0.0196, 0.0824, 0.1137, ..., -0.9765, -0.9686, -0.8667],
...,
[ 0.0275, 0.0745, 0.0510, ..., -0.1137, -0.1216, -0.0824],
[ 0.0667, 0.0824, 0.0667, ..., -0.0588, -0.0745, -0.0980],
[ 0.0353, 0.0353, 0.0431, ..., -0.0039, -0.0039, -0.0588]],
[[ 0.2078, 0.2471, 0.2863, ..., -0.9451, -0.9373, -0.9451],
[ 0.1608, 0.2471, 0.3098, ..., -0.9373, -0.9451, -0.9373],
[ 0.2078, 0.2706, 0.3020, ..., -0.9608, -0.9373, -0.8275],
...,
[-0.0353, 0.0118, -0.0039, ..., -0.2392, -0.2471, -0.2078],
[ 0.0196, 0.0353, 0.0196, ..., -0.1843, -0.2000, -0.2235],
[-0.0118, -0.0039, -0.0039, ..., -0.0980, -0.0980, -0.1529]],
[[ 0.3961, 0.4431, 0.4980, ..., -0.9216, -0.9137, -0.9216],
[ 0.3569, 0.4510, 0.5216, ..., -0.9059, -0.9137, -0.9137],
[ 0.4118, 0.4745, 0.5216, ..., -0.9137, -0.8902, -0.7804],
...,
[-0.2314, -0.1922, -0.2078, ..., -0.4196, -0.4275, -0.3882],
[-0.1843, -0.1686, -0.2000, ..., -0.3647, -0.3804, -0.4039],
[-0.1922, -0.1922, -0.1922, ..., -0.2941, -0.2863, -0.3412]]])}
```
Di seguito come si vede l'immagine dopo la fase di preprocessing. Come ci si aspetterebbe dalle trasformazioni applicate, l'immagine è stata ritagliata in modo casuale e le proprietà del colore sono diverse.
```py
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> img = dataset[0]["pixel_values"]
>>> plt.imshow(img.permute(1, 2, 0))
```
## Multimodal
Per attività multimodali userai una combinazione di tutto quello che hai imparato poco fa e applicherai le tue competenze alla comprensione automatica del parlato (Automatic Speech Recognition - ASR). Questo significa che avrai bisogno di:
* Un estrattore delle caratteristiche per processare i dati audio.
* Il Tokenizer per processare i testi.
Ritorna sul datasere [LJ Speech](https://huggingface.co/datasets/lj_speech):
```py
>>> from datasets import load_dataset
>>> lj_speech = load_dataset("lj_speech", split="train")
```
Visto che sei interessato solo alle colonne `audio` e `text`, elimina tutte le altre:
```py
>>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
```
Adesso guarda le colonne `audio` e `text`:
```py
>>> lj_speech[0]["audio"]
{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ...,
7.3242188e-04, 2.1362305e-04, 6.1035156e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
'sampling_rate': 22050}
>>> lj_speech[0]["text"]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
```
Ricorda dalla sezione precedente sull'elaborazione dei dati audio, tu dovresti sempre [ricampionare](preprocessing#audio) la frequenza di campionamento dei tuoi dati audio per farla coincidere con quella del dataset usato dal modello preaddestrato:
```py
>>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
```
### Processor
Un processor combina un estrattore di caratteristiche e un tokenizer. Carica un processor con [`AutoProcessor.from_pretrained]:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
```
1. Crea una funzione che processi i dati audio in `input_values`, e tokenizza il testo in `labels`. Questi sono i tuoi input per il modello:
```py
>>> def prepare_dataset(example):
... audio = example["audio"]
... example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
... return example
```
2. Applica la funzione `prepare_dataset` ad un campione:
```py
>>> prepare_dataset(lj_speech[0])
```
Nota che il processor ha aggiunto `input_values` e `labels`. La frequenza di campionamento è stata corretta riducendola a 16kHz.
Fantastico, ora dovresti essere in grado di preelaborare i dati per qualsiasi modalità e persino di combinare modalità diverse! Nella prossima esercitazione, impareremo a mettere a punto un modello sui dati appena pre-elaborati. | -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/unispeech/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_unispeech"] = [
"UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechForCTC",
"UniSpeechForPreTraining",
"UniSpeechForSequenceClassification",
"UniSpeechModel",
"UniSpeechPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_unispeech"] = [
"UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechForCTC",
"UniSpeechForPreTraining",
"UniSpeechForSequenceClassification",
"UniSpeechModel",
"UniSpeechPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./.github/workflows/self-nightly-scheduled.yml | name: Self-hosted runner (nightly)
# Note that each job's dependencies go into a corresponding docker file.
#
# For example for `run_all_tests_torch_cuda_extensions_gpu` the docker image is
# `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at
# `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile`
on:
repository_dispatch:
# Disable temporarily until the test suite can be run under 12 hours.
# schedule:
# - cron: "0 16 * * *"
env:
HF_HOME: /mnt/cache
TRANSFORMERS_IS_CI: yes
OMP_NUM_THREADS: 8
MKL_NUM_THREADS: 8
RUN_SLOW: yes
SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
TF_FORCE_GPU_ALLOW_GROWTH: true
RUN_PT_TF_CROSS_TESTS: 1
jobs:
check_runner_status:
name: Check Runner Status
runs-on: ubuntu-latest
steps:
- name: Checkout transformers
uses: actions/checkout@v2
with:
fetch-depth: 2
- name: Check Runner Status
run: python utils/check_self_hosted_runner.py --target_runners single-gpu-scheduled-ci-runner-docker,multi-gpu-scheduled-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
check_runners:
name: Check Runners
needs: check_runner_status
strategy:
matrix:
machine_type: [single-gpu, multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: NVIDIA-SMI
run: |
nvidia-smi
setup:
name: Setup
needs: check_runners
strategy:
matrix:
machine_type: [single-gpu, multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
outputs:
matrix: ${{ steps.set-matrix.outputs.matrix }}
steps:
- name: Update clone
working-directory: /transformers
run: |
git fetch && git checkout ${{ github.sha }}
- name: Cleanup
working-directory: /transformers
run: |
rm -rf tests/__pycache__
rm -rf tests/models/__pycache__
rm -rf reports
- name: Show installed libraries and their versions
working-directory: /transformers
run: pip freeze
- id: set-matrix
name: Identify models to test
working-directory: /transformers/tests
run: |
echo "::set-output name=matrix::$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')"
- name: NVIDIA-SMI
run: |
nvidia-smi
run_tests_single_gpu:
name: Model tests
strategy:
fail-fast: false
matrix:
folders: ${{ fromJson(needs.setup.outputs.matrix) }}
machine_type: [single-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
needs: setup
steps:
- name: Echo folder ${{ matrix.folders }}
shell: bash
# For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
# set the artifact folder names (because the character `/` is not allowed).
run: |
echo "${{ matrix.folders }}"
matrix_folders=${{ matrix.folders }}
matrix_folders=${matrix_folders/'models/'/'models_'}
echo "$matrix_folders"
echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
- name: Update clone
working-directory: /transformers
run: git fetch && git checkout ${{ github.sha }}
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Environment
working-directory: /transformers
run: |
python3 utils/print_env.py
- name: Show installed libraries and their versions
working-directory: /transformers
run: pip freeze
- name: Run all tests on GPU
working-directory: /transformers
run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }}
- name: Failure short reports
if: ${{ failure() }}
continue-on-error: true
run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}
run_tests_multi_gpu:
name: Model tests
strategy:
fail-fast: false
matrix:
folders: ${{ fromJson(needs.setup.outputs.matrix) }}
machine_type: [multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
needs: setup
steps:
- name: Echo folder ${{ matrix.folders }}
shell: bash
# For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
# set the artifact folder names (because the character `/` is not allowed).
run: |
echo "${{ matrix.folders }}"
matrix_folders=${{ matrix.folders }}
matrix_folders=${matrix_folders/'models/'/'models_'}
echo "$matrix_folders"
echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
- name: Update clone
working-directory: /transformers
run: git fetch && git checkout ${{ github.sha }}
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Environment
working-directory: /transformers
run: |
python3 utils/print_env.py
- name: Show installed libraries and their versions
working-directory: /transformers
run: pip freeze
- name: Run all tests on GPU
working-directory: /transformers
run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }}
- name: Failure short reports
if: ${{ failure() }}
continue-on-error: true
run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}
run_all_tests_torch_cuda_extensions_gpu:
name: Torch CUDA extension tests
strategy:
fail-fast: false
matrix:
machine_type: [single-gpu, multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
needs: setup
container:
image: huggingface/transformers-pytorch-deepspeed-nightly-gpu
options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: Update clone
working-directory: /workspace/transformers
run: git fetch && git checkout ${{ github.sha }}
- name: Remove cached torch extensions
run: rm -rf /github/home/.cache/torch_extensions/
# To avoid unknown test failures
- name: Pre build DeepSpeed *again*
working-directory: /workspace
run: |
python3 -m pip uninstall -y deepspeed
rm -rf DeepSpeed
git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build
DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Environment
working-directory: /workspace/transformers
run: |
python utils/print_env.py
- name: Show installed libraries and their versions
working-directory: /workspace/transformers
run: pip freeze
- name: Run all tests on GPU
working-directory: /workspace/transformers
run: |
python -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu tests/deepspeed tests/extended
- name: Failure short reports
if: ${{ failure() }}
continue-on-error: true
run: cat /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu/failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: ${{ matrix.machine_type }}_run_tests_torch_cuda_extensions_gpu_test_reports
path: /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu
send_results:
name: Send results to webhook
runs-on: ubuntu-latest
if: always()
needs: [
check_runner_status,
check_runners,
setup,
run_tests_single_gpu,
run_tests_multi_gpu,
run_all_tests_torch_cuda_extensions_gpu
]
steps:
- name: Preliminary job status
shell: bash
# For the meaning of these environment variables, see the job `Setup`
run: |
echo "Runner availability: ${{ needs.check_runner_status.result }}"
echo "Runner status: ${{ needs.check_runners.result }}"
echo "Setup status: ${{ needs.setup.result }}"
- uses: actions/checkout@v2
- uses: actions/download-artifact@v2
- name: Send message to Slack
env:
CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_PAST_FUTURE }}
CI_EVENT: nightly-build
RUNNER_STATUS: ${{ needs.check_runner_status.result }}
RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
SETUP_STATUS: ${{ needs.setup.result }}
# We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
# `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
run: |
pip install slack_sdk
pip show slack_sdk
python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"
| name: Self-hosted runner (nightly)
# Note that each job's dependencies go into a corresponding docker file.
#
# For example for `run_all_tests_torch_cuda_extensions_gpu` the docker image is
# `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at
# `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile`
on:
repository_dispatch:
# Disable temporarily until the test suite can be run under 12 hours.
# schedule:
# - cron: "0 16 * * *"
env:
HF_HOME: /mnt/cache
TRANSFORMERS_IS_CI: yes
OMP_NUM_THREADS: 8
MKL_NUM_THREADS: 8
RUN_SLOW: yes
SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
TF_FORCE_GPU_ALLOW_GROWTH: true
RUN_PT_TF_CROSS_TESTS: 1
jobs:
check_runner_status:
name: Check Runner Status
runs-on: ubuntu-latest
steps:
- name: Checkout transformers
uses: actions/checkout@v2
with:
fetch-depth: 2
- name: Check Runner Status
run: python utils/check_self_hosted_runner.py --target_runners single-gpu-scheduled-ci-runner-docker,multi-gpu-scheduled-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
check_runners:
name: Check Runners
needs: check_runner_status
strategy:
matrix:
machine_type: [single-gpu, multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: NVIDIA-SMI
run: |
nvidia-smi
setup:
name: Setup
needs: check_runners
strategy:
matrix:
machine_type: [single-gpu, multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
outputs:
matrix: ${{ steps.set-matrix.outputs.matrix }}
steps:
- name: Update clone
working-directory: /transformers
run: |
git fetch && git checkout ${{ github.sha }}
- name: Cleanup
working-directory: /transformers
run: |
rm -rf tests/__pycache__
rm -rf tests/models/__pycache__
rm -rf reports
- name: Show installed libraries and their versions
working-directory: /transformers
run: pip freeze
- id: set-matrix
name: Identify models to test
working-directory: /transformers/tests
run: |
echo "::set-output name=matrix::$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')"
- name: NVIDIA-SMI
run: |
nvidia-smi
run_tests_single_gpu:
name: Model tests
strategy:
fail-fast: false
matrix:
folders: ${{ fromJson(needs.setup.outputs.matrix) }}
machine_type: [single-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
needs: setup
steps:
- name: Echo folder ${{ matrix.folders }}
shell: bash
# For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
# set the artifact folder names (because the character `/` is not allowed).
run: |
echo "${{ matrix.folders }}"
matrix_folders=${{ matrix.folders }}
matrix_folders=${matrix_folders/'models/'/'models_'}
echo "$matrix_folders"
echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
- name: Update clone
working-directory: /transformers
run: git fetch && git checkout ${{ github.sha }}
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Environment
working-directory: /transformers
run: |
python3 utils/print_env.py
- name: Show installed libraries and their versions
working-directory: /transformers
run: pip freeze
- name: Run all tests on GPU
working-directory: /transformers
run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }}
- name: Failure short reports
if: ${{ failure() }}
continue-on-error: true
run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}
run_tests_multi_gpu:
name: Model tests
strategy:
fail-fast: false
matrix:
folders: ${{ fromJson(needs.setup.outputs.matrix) }}
machine_type: [multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
container:
image: huggingface/transformers-all-latest-torch-nightly-gpu
options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
needs: setup
steps:
- name: Echo folder ${{ matrix.folders }}
shell: bash
# For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
# set the artifact folder names (because the character `/` is not allowed).
run: |
echo "${{ matrix.folders }}"
matrix_folders=${{ matrix.folders }}
matrix_folders=${matrix_folders/'models/'/'models_'}
echo "$matrix_folders"
echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
- name: Update clone
working-directory: /transformers
run: git fetch && git checkout ${{ github.sha }}
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Environment
working-directory: /transformers
run: |
python3 utils/print_env.py
- name: Show installed libraries and their versions
working-directory: /transformers
run: pip freeze
- name: Run all tests on GPU
working-directory: /transformers
run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }}
- name: Failure short reports
if: ${{ failure() }}
continue-on-error: true
run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}
run_all_tests_torch_cuda_extensions_gpu:
name: Torch CUDA extension tests
strategy:
fail-fast: false
matrix:
machine_type: [single-gpu, multi-gpu]
runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
needs: setup
container:
image: huggingface/transformers-pytorch-deepspeed-nightly-gpu
options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: Update clone
working-directory: /workspace/transformers
run: git fetch && git checkout ${{ github.sha }}
- name: Remove cached torch extensions
run: rm -rf /github/home/.cache/torch_extensions/
# To avoid unknown test failures
- name: Pre build DeepSpeed *again*
working-directory: /workspace
run: |
python3 -m pip uninstall -y deepspeed
rm -rf DeepSpeed
git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build
DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Environment
working-directory: /workspace/transformers
run: |
python utils/print_env.py
- name: Show installed libraries and their versions
working-directory: /workspace/transformers
run: pip freeze
- name: Run all tests on GPU
working-directory: /workspace/transformers
run: |
python -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu tests/deepspeed tests/extended
- name: Failure short reports
if: ${{ failure() }}
continue-on-error: true
run: cat /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu/failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: ${{ matrix.machine_type }}_run_tests_torch_cuda_extensions_gpu_test_reports
path: /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu
send_results:
name: Send results to webhook
runs-on: ubuntu-latest
if: always()
needs: [
check_runner_status,
check_runners,
setup,
run_tests_single_gpu,
run_tests_multi_gpu,
run_all_tests_torch_cuda_extensions_gpu
]
steps:
- name: Preliminary job status
shell: bash
# For the meaning of these environment variables, see the job `Setup`
run: |
echo "Runner availability: ${{ needs.check_runner_status.result }}"
echo "Runner status: ${{ needs.check_runners.result }}"
echo "Setup status: ${{ needs.setup.result }}"
- uses: actions/checkout@v2
- uses: actions/download-artifact@v2
- name: Send message to Slack
env:
CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_PAST_FUTURE }}
CI_EVENT: nightly-build
RUNNER_STATUS: ${{ needs.check_runner_status.result }}
RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
SETUP_STATUS: ${{ needs.setup.result }}
# We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
# `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
run: |
pip install slack_sdk
pip show slack_sdk
python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./.github/ISSUE_TEMPLATE/config.yml | blank_issues_enabled: true
version: 2.1
contact_links:
- name: Model checkpoints on the Hugging Face Hub
url: https://huggingface.co/models
about: Open a Pull request / Discussion related to a specific model checkpoint directly on the Hugging Face Hub
- name: Website Related
url: https://github.com/huggingface/hub-docs/issues
about: Feature requests and bug reports related to the website
- name: Forum
url: https://discuss.huggingface.co/
about: General usage questions and community discussions
| blank_issues_enabled: true
version: 2.1
contact_links:
- name: Model checkpoints on the Hugging Face Hub
url: https://huggingface.co/models
about: Open a Pull request / Discussion related to a specific model checkpoint directly on the Hugging Face Hub
- name: Website Related
url: https://github.com/huggingface/hub-docs/issues
about: Feature requests and bug reports related to the website
- name: Forum
url: https://discuss.huggingface.co/
about: General usage questions and community discussions
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/poolformer/feature_extraction_poolformer.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for PoolFormer."""
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
logger = logging.get_logger(__name__)
PoolFormerFeatureExtractor = PoolFormerImageProcessor
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for PoolFormer."""
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
logger = logging.get_logger(__name__)
PoolFormerFeatureExtractor = PoolFormerImageProcessor
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/convert_slow_tokenizers_checkpoints_to_fast.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Convert slow tokenizers checkpoints in fast (serialization format of the `tokenizers` library)"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}
def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.")
if tokenizer_name is None:
tokenizer_names = TOKENIZER_CLASSES
else:
tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")}
logger.info(f"Loading tokenizer classes: {tokenizer_names}")
for tokenizer_name in tokenizer_names:
tokenizer_class = TOKENIZER_CLASSES[tokenizer_name]
add_prefix = True
if checkpoint_name is None:
checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys())
else:
checkpoint_names = [checkpoint_name]
logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}")
for checkpoint in checkpoint_names:
logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}")
# Load tokenizer
tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download)
# Save fast tokenizer
logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}")
# For organization names we create sub-directories
if "/" in checkpoint:
checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/")
dump_path_full = os.path.join(dump_path, checkpoint_directory)
elif add_prefix:
checkpoint_prefix_name = checkpoint
dump_path_full = dump_path
else:
checkpoint_prefix_name = None
dump_path_full = dump_path
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
next_char = file_path.split(checkpoint)[-1][0]
if next_char == "/":
dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
checkpoint_prefix_name = None
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
file_names = tokenizer.save_pretrained(
dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name
)
logger.info(f"=> File names {file_names}")
for file_name in file_names:
if not file_name.endswith("tokenizer.json"):
os.remove(file_name)
logger.info(f"=> removing {file_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help=(
f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will "
"download and convert all the checkpoints from AWS."
),
)
parser.add_argument(
"--checkpoint_name",
default=None,
type=str,
help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
)
parser.add_argument(
"--force_download",
action="store_true",
help="Re-download checkpoints.",
)
args = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Convert slow tokenizers checkpoints in fast (serialization format of the `tokenizers` library)"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}
def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.")
if tokenizer_name is None:
tokenizer_names = TOKENIZER_CLASSES
else:
tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")}
logger.info(f"Loading tokenizer classes: {tokenizer_names}")
for tokenizer_name in tokenizer_names:
tokenizer_class = TOKENIZER_CLASSES[tokenizer_name]
add_prefix = True
if checkpoint_name is None:
checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys())
else:
checkpoint_names = [checkpoint_name]
logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}")
for checkpoint in checkpoint_names:
logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}")
# Load tokenizer
tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download)
# Save fast tokenizer
logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}")
# For organization names we create sub-directories
if "/" in checkpoint:
checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/")
dump_path_full = os.path.join(dump_path, checkpoint_directory)
elif add_prefix:
checkpoint_prefix_name = checkpoint
dump_path_full = dump_path
else:
checkpoint_prefix_name = None
dump_path_full = dump_path
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
next_char = file_path.split(checkpoint)[-1][0]
if next_char == "/":
dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
checkpoint_prefix_name = None
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
file_names = tokenizer.save_pretrained(
dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name
)
logger.info(f"=> File names {file_names}")
for file_name in file_names:
if not file_name.endswith("tokenizer.json"):
os.remove(file_name)
logger.info(f"=> removing {file_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help=(
f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will "
"download and convert all the checkpoints from AWS."
),
)
parser.add_argument(
"--checkpoint_name",
default=None,
type=str,
help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
)
parser.add_argument(
"--force_download",
action="store_true",
help="Re-download checkpoints.",
)
args = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./examples/research_projects/codeparrot/scripts/arguments.py | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class TrainingArguments:
"""
Configuration for training model.
"""
model_ckpt: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be trained."}
)
save_dir: Optional[str] = field(
default="./", metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}
)
dataset_name_train: Optional[str] = field(
default="codeparrot/codeparrot-clean-train", metadata={"help": "Name or path of training dataset."}
)
dataset_name_valid: Optional[str] = field(
default="codeparrot/codeparrot-clean-valid", metadata={"help": "Name or path of validation dataset."}
)
train_batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size for training."})
valid_batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size for evaluation."})
weight_decay: Optional[float] = field(default=0.1, metadata={"help": "Value of weight decay."})
shuffle_buffer: Optional[int] = field(
default=10000, metadata={"help": "Size of buffer used to shuffle streaming dataset."}
)
learning_rate: Optional[float] = field(default=2e-4, metadata={"help": "Learning rate fo training."})
lr_scheduler_type: Optional[str] = field(default="cosine", metadata={"help": "Learning rate."})
num_warmup_steps: Optional[int] = field(
default=750, metadata={"help": "Number of warmup steps in the learning rate schedule."}
)
gradient_accumulation_steps: Optional[int] = field(
default=16, metadata={"help": "Number of gradient accumulation steps."}
)
gradient_checkpointing: Optional[bool] = field(
default=True, metadata={"help": "Use gradient checkpointing to reduce memory footprint."}
)
max_train_steps: Optional[int] = field(default=50000, metadata={"help": "Maximum number of training steps."})
max_eval_steps: Optional[int] = field(
default=-1, metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}
)
seq_length: Optional[int] = field(default=1024, metadata={"help": "Sequence lengths used for training."})
seed: Optional[int] = field(default=1, metadata={"help": "Training seed."})
save_checkpoint_steps: Optional[int] = field(
default=1024,
metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."},
)
resume_from_checkpoint: Optional[str] = field(
default=None, metadata={"help": "States path if the training should continue from a checkpoint folder."}
)
tokenized: Optional[bool] = field(default=False, metadata={"help": "If True the data is pretokenized."})
@dataclass
class EvaluationArguments:
"""
Configuration for evaluating model.
"""
model_ckpt: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be evaluated."}
)
dataset_name: Optional[str] = field(
default="codeparrot/codeparrot-clean-valid", metadata={"help": "Name or path of validation dataset."}
)
batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size used for evaluation."})
max_eval_steps: Optional[int] = field(
default=-1, metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}
)
seq_length: Optional[int] = field(default=1024, metadata={"help": "Length of sequences to be evaluated."})
seed: Optional[int] = field(default=1, metadata={"help": "Random seed used for evaluation."})
@dataclass
class HumanEvalArguments:
"""
Configuration for running evaluation on HumanEval dataset.
"""
model_ckpt: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be evaluated."}
)
num_workers: Optional[int] = field(default=None, metadata={"help": "Number of workers used for code evaluation."})
num_tasks: Optional[int] = field(
default=None,
metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."},
)
do_sample: Optional[bool] = field(
default=True, metadata={"help": "Sample from the language model's output distribution."}
)
temperature: Optional[float] = field(default=0.2, metadata={"help": "Sampling temperature used for generation."})
max_new_tokens: Optional[int] = field(default=256, metadata={"help": "Maximum number of newly generated tokens."})
top_k: Optional[int] = field(default=0, metadata={"help": "Top-k parameter used for generation."})
top_p: Optional[float] = field(default=0.95, metadata={"help": "Top-p parameter used for nucleus sampling."})
batch_size: Optional[int] = field(default=10, metadata={"help": "Number of generations to run in parallel."})
n_samples: Optional[int] = field(
default=200, metadata={"help": "Number of completions to generate for each sample."}
)
seed: Optional[int] = field(default=1, metadata={"help": "Random seed used for evaluation."})
output_file: Optional[str] = field(
default="eval_results.json", metadata={"help": "Random seed used for evaluation."}
)
HF_ALLOW_CODE_EVAL: Optional[str] = field(
default="0", metadata={"help": "Allow `code_eval` to execute Python code on machine"}
)
device_int: Optional[int] = field(
default=-1,
metadata={
"help": (
"Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
" number corresponds to which GPU device id to run on."
)
},
)
@dataclass
class PreprocessingArguments:
"""
Configuration for preprocessing data.
"""
num_workers: Optional[int] = field(
default=None,
metadata={
"help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
},
)
dataset_name: Optional[str] = field(
default="transformersbook/codeparrot", metadata={"help": "Folder or name of dataset to process."}
)
output_dir: Optional[str] = field(
default="codeparrot-clean", metadata={"help": "Folder to save processed processed dataset."}
)
samples_per_file: Optional[int] = field(
default=100_000, metadata={"help": "Number of files to save per JSON output file."}
)
text_column: Optional[str] = field(default="content", metadata={"help": "Column containing text data to process."})
line_max: Optional[float] = field(
default=1000, metadata={"help": "Maximum line length in file, otherwise file is filtered."}
)
line_mean: Optional[float] = field(
default=100, metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}
)
alpha_frac: Optional[float] = field(
default=0.25, metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}
)
min_token_ratio: Optional[float] = field(
default=1.5, metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}
)
filter_proba: Optional[float] = field(
default=0.7, metadata={"help": "Probability for filtering config, test and uncommon files."}
)
tokenizer: Optional[str] = field(
default="codeparrot/codeparrot",
metadata={"help": "Name or path to the tokenizer."},
)
near_deduplication: Optional[bool] = field(
default=False, metadata={"help": "If True, near-duplicate samples are removed."}
)
jaccard_threshold: Optional[float] = field(
default=0.85, metadata={"help": "Jaccard threshold for near-duplicate samples."}
)
@dataclass
class TokenizerTrainingArguments:
"""
Configuration for tokenizer training.
"""
base_tokenizer: Optional[str] = field(
default="gpt2", metadata={"help": "Base tokenizer to build new tokenizer from."}
)
dataset_name: Optional[str] = field(
default="transformersbook/codeparrot-train", metadata={"help": "Dataset to train tokenizer on."}
)
text_column: Optional[str] = field(default="content", metadata={"help": "Column containing text data to process."})
vocab_size: Optional[int] = field(default=200_000, metadata={"help": "Number of examples to train tokenizer on."})
n_examples: Optional[int] = field(
default=32768, metadata={"help": "Number of examples to train the tokenizer on."}
)
tokenizer_name: Optional[str] = field(default="codeparrot", metadata={"help": "Name of new tokenizer."})
push_to_hub: Optional[bool] = field(default=True, metadata={"help": "Push saved tokenizer to the hub."})
@dataclass
class PretokenizationArguments:
"""
Configuration for data pretokenization.
"""
tokenizer_dir: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Name or path to the tokenizer."}
)
dataset_name: Optional[str] = field(
default="codeparrot/codeparrot-clean-train", metadata={"help": "Name or path to the dataset to pretokenize."}
)
tokenized_data_repo: Optional[str] = field(
default="tokenized-codeparrot-train", metadata={"help": "Repo name of the pretokenized data."}
)
num_workers: Optional[int] = field(default=None, metadata={"help": "Number of workers used for code evaluation."})
@dataclass
class InitializationArguments:
"""
Configuration for initializing new model.
"""
config_name: Optional[str] = field(
default="gpt2-large", metadata={"help": "Configuration to use for model initialization."}
)
tokenizer_name: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Tokenizer attached to model."}
)
model_name: Optional[str] = field(default="codeparrot", metadata={"help": "Name of the created model."})
push_to_hub: Optional[bool] = field(default=True, metadata={"help": "Push saved tokenizer to the hub."})
| from dataclasses import dataclass, field
from typing import Optional
@dataclass
class TrainingArguments:
"""
Configuration for training model.
"""
model_ckpt: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be trained."}
)
save_dir: Optional[str] = field(
default="./", metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}
)
dataset_name_train: Optional[str] = field(
default="codeparrot/codeparrot-clean-train", metadata={"help": "Name or path of training dataset."}
)
dataset_name_valid: Optional[str] = field(
default="codeparrot/codeparrot-clean-valid", metadata={"help": "Name or path of validation dataset."}
)
train_batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size for training."})
valid_batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size for evaluation."})
weight_decay: Optional[float] = field(default=0.1, metadata={"help": "Value of weight decay."})
shuffle_buffer: Optional[int] = field(
default=10000, metadata={"help": "Size of buffer used to shuffle streaming dataset."}
)
learning_rate: Optional[float] = field(default=2e-4, metadata={"help": "Learning rate fo training."})
lr_scheduler_type: Optional[str] = field(default="cosine", metadata={"help": "Learning rate."})
num_warmup_steps: Optional[int] = field(
default=750, metadata={"help": "Number of warmup steps in the learning rate schedule."}
)
gradient_accumulation_steps: Optional[int] = field(
default=16, metadata={"help": "Number of gradient accumulation steps."}
)
gradient_checkpointing: Optional[bool] = field(
default=True, metadata={"help": "Use gradient checkpointing to reduce memory footprint."}
)
max_train_steps: Optional[int] = field(default=50000, metadata={"help": "Maximum number of training steps."})
max_eval_steps: Optional[int] = field(
default=-1, metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}
)
seq_length: Optional[int] = field(default=1024, metadata={"help": "Sequence lengths used for training."})
seed: Optional[int] = field(default=1, metadata={"help": "Training seed."})
save_checkpoint_steps: Optional[int] = field(
default=1024,
metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."},
)
resume_from_checkpoint: Optional[str] = field(
default=None, metadata={"help": "States path if the training should continue from a checkpoint folder."}
)
tokenized: Optional[bool] = field(default=False, metadata={"help": "If True the data is pretokenized."})
@dataclass
class EvaluationArguments:
"""
Configuration for evaluating model.
"""
model_ckpt: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be evaluated."}
)
dataset_name: Optional[str] = field(
default="codeparrot/codeparrot-clean-valid", metadata={"help": "Name or path of validation dataset."}
)
batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size used for evaluation."})
max_eval_steps: Optional[int] = field(
default=-1, metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}
)
seq_length: Optional[int] = field(default=1024, metadata={"help": "Length of sequences to be evaluated."})
seed: Optional[int] = field(default=1, metadata={"help": "Random seed used for evaluation."})
@dataclass
class HumanEvalArguments:
"""
Configuration for running evaluation on HumanEval dataset.
"""
model_ckpt: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be evaluated."}
)
num_workers: Optional[int] = field(default=None, metadata={"help": "Number of workers used for code evaluation."})
num_tasks: Optional[int] = field(
default=None,
metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."},
)
do_sample: Optional[bool] = field(
default=True, metadata={"help": "Sample from the language model's output distribution."}
)
temperature: Optional[float] = field(default=0.2, metadata={"help": "Sampling temperature used for generation."})
max_new_tokens: Optional[int] = field(default=256, metadata={"help": "Maximum number of newly generated tokens."})
top_k: Optional[int] = field(default=0, metadata={"help": "Top-k parameter used for generation."})
top_p: Optional[float] = field(default=0.95, metadata={"help": "Top-p parameter used for nucleus sampling."})
batch_size: Optional[int] = field(default=10, metadata={"help": "Number of generations to run in parallel."})
n_samples: Optional[int] = field(
default=200, metadata={"help": "Number of completions to generate for each sample."}
)
seed: Optional[int] = field(default=1, metadata={"help": "Random seed used for evaluation."})
output_file: Optional[str] = field(
default="eval_results.json", metadata={"help": "Random seed used for evaluation."}
)
HF_ALLOW_CODE_EVAL: Optional[str] = field(
default="0", metadata={"help": "Allow `code_eval` to execute Python code on machine"}
)
device_int: Optional[int] = field(
default=-1,
metadata={
"help": (
"Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
" number corresponds to which GPU device id to run on."
)
},
)
@dataclass
class PreprocessingArguments:
"""
Configuration for preprocessing data.
"""
num_workers: Optional[int] = field(
default=None,
metadata={
"help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
},
)
dataset_name: Optional[str] = field(
default="transformersbook/codeparrot", metadata={"help": "Folder or name of dataset to process."}
)
output_dir: Optional[str] = field(
default="codeparrot-clean", metadata={"help": "Folder to save processed processed dataset."}
)
samples_per_file: Optional[int] = field(
default=100_000, metadata={"help": "Number of files to save per JSON output file."}
)
text_column: Optional[str] = field(default="content", metadata={"help": "Column containing text data to process."})
line_max: Optional[float] = field(
default=1000, metadata={"help": "Maximum line length in file, otherwise file is filtered."}
)
line_mean: Optional[float] = field(
default=100, metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}
)
alpha_frac: Optional[float] = field(
default=0.25, metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}
)
min_token_ratio: Optional[float] = field(
default=1.5, metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}
)
filter_proba: Optional[float] = field(
default=0.7, metadata={"help": "Probability for filtering config, test and uncommon files."}
)
tokenizer: Optional[str] = field(
default="codeparrot/codeparrot",
metadata={"help": "Name or path to the tokenizer."},
)
near_deduplication: Optional[bool] = field(
default=False, metadata={"help": "If True, near-duplicate samples are removed."}
)
jaccard_threshold: Optional[float] = field(
default=0.85, metadata={"help": "Jaccard threshold for near-duplicate samples."}
)
@dataclass
class TokenizerTrainingArguments:
"""
Configuration for tokenizer training.
"""
base_tokenizer: Optional[str] = field(
default="gpt2", metadata={"help": "Base tokenizer to build new tokenizer from."}
)
dataset_name: Optional[str] = field(
default="transformersbook/codeparrot-train", metadata={"help": "Dataset to train tokenizer on."}
)
text_column: Optional[str] = field(default="content", metadata={"help": "Column containing text data to process."})
vocab_size: Optional[int] = field(default=200_000, metadata={"help": "Number of examples to train tokenizer on."})
n_examples: Optional[int] = field(
default=32768, metadata={"help": "Number of examples to train the tokenizer on."}
)
tokenizer_name: Optional[str] = field(default="codeparrot", metadata={"help": "Name of new tokenizer."})
push_to_hub: Optional[bool] = field(default=True, metadata={"help": "Push saved tokenizer to the hub."})
@dataclass
class PretokenizationArguments:
"""
Configuration for data pretokenization.
"""
tokenizer_dir: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Name or path to the tokenizer."}
)
dataset_name: Optional[str] = field(
default="codeparrot/codeparrot-clean-train", metadata={"help": "Name or path to the dataset to pretokenize."}
)
tokenized_data_repo: Optional[str] = field(
default="tokenized-codeparrot-train", metadata={"help": "Repo name of the pretokenized data."}
)
num_workers: Optional[int] = field(default=None, metadata={"help": "Number of workers used for code evaluation."})
@dataclass
class InitializationArguments:
"""
Configuration for initializing new model.
"""
config_name: Optional[str] = field(
default="gpt2-large", metadata={"help": "Configuration to use for model initialization."}
)
tokenizer_name: Optional[str] = field(
default="codeparrot/codeparrot", metadata={"help": "Tokenizer attached to model."}
)
model_name: Optional[str] = field(default="codeparrot", metadata={"help": "Name of the created model."})
push_to_hub: Optional[bool] = field(default=True, metadata={"help": "Push saved tokenizer to the hub."})
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./docs/source/de/pipeline_tutorial.mdx | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Pipelines für Inferenzen
Die [`pipeline`] macht es einfach, jedes beliebige Modell aus dem [Hub](https://huggingface.co/models) für die Inferenz auf jede Sprache, Computer Vision, Sprache und multimodale Aufgaben zu verwenden. Selbst wenn Sie keine Erfahrung mit einer bestimmten Modalität haben oder nicht mit dem zugrundeliegenden Code hinter den Modellen vertraut sind, können Sie sie mit der [`pipeline`] für Inferenzen verwenden! In diesem Beispiel lernen Sie, wie:
* Eine [`pipeline`] für Inferenz zu verwenden.
* Einen bestimmten Tokenizer oder ein bestimmtes Modell zu verwenden.
* Eine [`pipeline`] für Audio-, Vision- und multimodale Aufgaben zu verwenden.
<Tip>
Eine vollständige Liste der unterstützten Aufgaben und verfügbaren Parameter finden Sie in der [`pipeline`]-Dokumentation.
</Tip>
## Verwendung von Pipelines
Obwohl jede Aufgabe eine zugehörige [`pipeline`] hat, ist es einfacher, die allgemeine [`pipeline`]-Abstraktion zu verwenden, die alle aufgabenspezifischen Pipelines enthält. Die [`pipeline`] lädt automatisch ein Standardmodell und eine Vorverarbeitungsklasse, die für Ihre Aufgabe inferenzfähig ist.
1. Beginnen Sie mit der Erstellung einer [`pipeline`] und geben Sie eine Inferenzaufgabe an:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation")
```
2. Übergeben Sie Ihren Eingabetext an die [`pipeline`]:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
```
Wenn Sie mehr als eine Eingabe haben, übergeben Sie die Eingabe als Liste:
```py
>>> generator(
... [
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
... ]
... ) # doctest: +SKIP
```
Alle zusätzlichen Parameter für Ihre Aufgabe können auch in die [`pipeline`] aufgenommen werden. Die Aufgabe `Text-Generierung` hat eine [`~generation.GenerationMixin.generate`]-Methode mit mehreren Parametern zur Steuerung der Ausgabe. Wenn Sie zum Beispiel mehr als eine Ausgabe erzeugen wollen, setzen Sie den Parameter `num_return_sequences`:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... num_return_sequences=2,
... ) # doctest: +SKIP
```
### Wählen Sie ein Modell und einen Tokenizer
Die [`pipeline`] akzeptiert jedes Modell aus dem [Hub] (https://huggingface.co/models). Auf dem Hub gibt es Tags, mit denen Sie nach einem Modell filtern können, das Sie für Ihre Aufgabe verwenden möchten. Sobald Sie ein passendes Modell ausgewählt haben, laden Sie es mit der entsprechenden `AutoModelFor` und [`AutoTokenizer`] Klasse. Laden Sie zum Beispiel die Klasse [`AutoModelForCausalLM`] für eine kausale Sprachmodellierungsaufgabe:
```py
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
```
Erstellen Sie eine [`pipeline`] für Ihre Aufgabe, und geben Sie das Modell und den Tokenizer an, die Sie geladen haben:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
```
Übergeben Sie Ihren Eingabetext an die [`pipeline`] , um einen Text zu erzeugen:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
```
## Audio-Pipeline
Die [`pipeline`] unterstützt auch Audioaufgaben wie Audioklassifizierung und automatische Spracherkennung.
Lassen Sie uns zum Beispiel die Emotion in diesem Audioclip klassifizieren:
```py
>>> from datasets import load_dataset
>>> import torch
>>> torch.manual_seed(42) # doctest: +IGNORE_RESULT
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> audio_file = ds[0]["audio"]["path"]
```
Finden Sie ein [Audioklassifikation](https://huggingface.co/models?pipeline_tag=audio-classification) Modell auf dem Model Hub für Emotionserkennung und laden Sie es in die [`pipeline`]:
```py
>>> from transformers import pipeline
>>> audio_classifier = pipeline(
... task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
... )
```
Übergeben Sie die Audiodatei an die [`pipeline`]:
```py
>>> preds = audio_classifier(audio_file)
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]
```
## Bildverarbeitungs-Pipeline
Die Verwendung einer [`pipeline`] für Bildverarbeitungsaufgaben ist praktisch identisch.
Geben Sie Ihre Aufgabe an und übergeben Sie Ihr Bild an den Klassifikator. Das Bild kann ein Link oder ein lokaler Pfad zu dem Bild sein. Zum Beispiel: Welche Katzenart ist unten abgebildet?
```py
>>> from transformers import pipeline
>>> vision_classifier = pipeline(task="image-classification")
>>> preds = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
```
## Multimodale Pipeline
Die [`pipeline`] unterstützt mehr als eine Modalität. Eine Aufgabe zur Beantwortung visueller Fragen (VQA) kombiniert zum Beispiel Text und Bild. Verwenden Sie einen beliebigen Bildlink und eine Frage, die Sie zu dem Bild stellen möchten. Das Bild kann eine URL oder ein lokaler Pfad zu dem Bild sein.
Wenn Sie zum Beispiel das gleiche Bild wie in der obigen Vision-Pipeline verwenden:
```py
>>> image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
>>> question = "Where is the cat?"
```
Erstellen Sie eine Pipeline für "vqa" und übergeben Sie ihr das Bild und die Frage:
```py
>>> from transformers import pipeline
>>> vqa = pipeline(task="vqa")
>>> preds = vqa(image=image, question=question)
>>> preds = [{"score": round(pred["score"], 4), "answer": pred["answer"]} for pred in preds]
>>> preds
[{'score': 0.9112, 'answer': 'snow'}, {'score': 0.8796, 'answer': 'in snow'}, {'score': 0.6717, 'answer': 'outside'}, {'score': 0.0291, 'answer': 'on ground'}, {'score': 0.027, 'answer': 'ground'}]
```
| <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Pipelines für Inferenzen
Die [`pipeline`] macht es einfach, jedes beliebige Modell aus dem [Hub](https://huggingface.co/models) für die Inferenz auf jede Sprache, Computer Vision, Sprache und multimodale Aufgaben zu verwenden. Selbst wenn Sie keine Erfahrung mit einer bestimmten Modalität haben oder nicht mit dem zugrundeliegenden Code hinter den Modellen vertraut sind, können Sie sie mit der [`pipeline`] für Inferenzen verwenden! In diesem Beispiel lernen Sie, wie:
* Eine [`pipeline`] für Inferenz zu verwenden.
* Einen bestimmten Tokenizer oder ein bestimmtes Modell zu verwenden.
* Eine [`pipeline`] für Audio-, Vision- und multimodale Aufgaben zu verwenden.
<Tip>
Eine vollständige Liste der unterstützten Aufgaben und verfügbaren Parameter finden Sie in der [`pipeline`]-Dokumentation.
</Tip>
## Verwendung von Pipelines
Obwohl jede Aufgabe eine zugehörige [`pipeline`] hat, ist es einfacher, die allgemeine [`pipeline`]-Abstraktion zu verwenden, die alle aufgabenspezifischen Pipelines enthält. Die [`pipeline`] lädt automatisch ein Standardmodell und eine Vorverarbeitungsklasse, die für Ihre Aufgabe inferenzfähig ist.
1. Beginnen Sie mit der Erstellung einer [`pipeline`] und geben Sie eine Inferenzaufgabe an:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation")
```
2. Übergeben Sie Ihren Eingabetext an die [`pipeline`]:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
```
Wenn Sie mehr als eine Eingabe haben, übergeben Sie die Eingabe als Liste:
```py
>>> generator(
... [
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
... ]
... ) # doctest: +SKIP
```
Alle zusätzlichen Parameter für Ihre Aufgabe können auch in die [`pipeline`] aufgenommen werden. Die Aufgabe `Text-Generierung` hat eine [`~generation.GenerationMixin.generate`]-Methode mit mehreren Parametern zur Steuerung der Ausgabe. Wenn Sie zum Beispiel mehr als eine Ausgabe erzeugen wollen, setzen Sie den Parameter `num_return_sequences`:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
... num_return_sequences=2,
... ) # doctest: +SKIP
```
### Wählen Sie ein Modell und einen Tokenizer
Die [`pipeline`] akzeptiert jedes Modell aus dem [Hub] (https://huggingface.co/models). Auf dem Hub gibt es Tags, mit denen Sie nach einem Modell filtern können, das Sie für Ihre Aufgabe verwenden möchten. Sobald Sie ein passendes Modell ausgewählt haben, laden Sie es mit der entsprechenden `AutoModelFor` und [`AutoTokenizer`] Klasse. Laden Sie zum Beispiel die Klasse [`AutoModelForCausalLM`] für eine kausale Sprachmodellierungsaufgabe:
```py
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
```
Erstellen Sie eine [`pipeline`] für Ihre Aufgabe, und geben Sie das Modell und den Tokenizer an, die Sie geladen haben:
```py
>>> from transformers import pipeline
>>> generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
```
Übergeben Sie Ihren Eingabetext an die [`pipeline`] , um einen Text zu erzeugen:
```py
>>> generator(
... "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
... ) # doctest: +SKIP
[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
```
## Audio-Pipeline
Die [`pipeline`] unterstützt auch Audioaufgaben wie Audioklassifizierung und automatische Spracherkennung.
Lassen Sie uns zum Beispiel die Emotion in diesem Audioclip klassifizieren:
```py
>>> from datasets import load_dataset
>>> import torch
>>> torch.manual_seed(42) # doctest: +IGNORE_RESULT
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> audio_file = ds[0]["audio"]["path"]
```
Finden Sie ein [Audioklassifikation](https://huggingface.co/models?pipeline_tag=audio-classification) Modell auf dem Model Hub für Emotionserkennung und laden Sie es in die [`pipeline`]:
```py
>>> from transformers import pipeline
>>> audio_classifier = pipeline(
... task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
... )
```
Übergeben Sie die Audiodatei an die [`pipeline`]:
```py
>>> preds = audio_classifier(audio_file)
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]
```
## Bildverarbeitungs-Pipeline
Die Verwendung einer [`pipeline`] für Bildverarbeitungsaufgaben ist praktisch identisch.
Geben Sie Ihre Aufgabe an und übergeben Sie Ihr Bild an den Klassifikator. Das Bild kann ein Link oder ein lokaler Pfad zu dem Bild sein. Zum Beispiel: Welche Katzenart ist unten abgebildet?
```py
>>> from transformers import pipeline
>>> vision_classifier = pipeline(task="image-classification")
>>> preds = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
```
## Multimodale Pipeline
Die [`pipeline`] unterstützt mehr als eine Modalität. Eine Aufgabe zur Beantwortung visueller Fragen (VQA) kombiniert zum Beispiel Text und Bild. Verwenden Sie einen beliebigen Bildlink und eine Frage, die Sie zu dem Bild stellen möchten. Das Bild kann eine URL oder ein lokaler Pfad zu dem Bild sein.
Wenn Sie zum Beispiel das gleiche Bild wie in der obigen Vision-Pipeline verwenden:
```py
>>> image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
>>> question = "Where is the cat?"
```
Erstellen Sie eine Pipeline für "vqa" und übergeben Sie ihr das Bild und die Frage:
```py
>>> from transformers import pipeline
>>> vqa = pipeline(task="vqa")
>>> preds = vqa(image=image, question=question)
>>> preds = [{"score": round(pred["score"], 4), "answer": pred["answer"]} for pred in preds]
>>> preds
[{'score': 0.9112, 'answer': 'snow'}, {'score': 0.8796, 'answer': 'in snow'}, {'score': 0.6717, 'answer': 'outside'}, {'score': 0.0291, 'answer': 'on ground'}, {'score': 0.027, 'answer': 'ground'}]
```
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./templates/adding_a_missing_tokenization_test/cookiecutter.json | {
"modelname": "BrandNewBERT",
"uppercase_modelname": "BRAND_NEW_BERT",
"lowercase_modelname": "brand_new_bert",
"camelcase_modelname": "BrandNewBert",
"has_slow_class": ["True", "False"],
"has_fast_class": ["True", "False"],
"slow_tokenizer_use_sentencepiece": ["True", "False"],
"authors": "The HuggingFace Team"
}
| {
"modelname": "BrandNewBERT",
"uppercase_modelname": "BRAND_NEW_BERT",
"lowercase_modelname": "brand_new_bert",
"camelcase_modelname": "BrandNewBert",
"has_slow_class": ["True", "False"],
"has_fast_class": ["True", "False"],
"slow_tokenizer_use_sentencepiece": ["True", "False"],
"authors": "The HuggingFace Team"
}
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/bert_japanese/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_import_structure = {"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_import_structure = {"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/big_bird/tokenization_big_bird_fast.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization classes for Big Bird model."""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
BigBirdTokenizer = None
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
},
"tokenizer_file": {
"google/bigbird-roberta-base": (
"https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
),
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
),
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/bigbird-roberta-base": 4096,
"google/bigbird-roberta-large": 4096,
"google/bigbird-base-trivia-itc": 4096,
}
SPIECE_UNDERLINE = "▁"
class BigBirdTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" BigBird tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the `sep_token`.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = BigBirdTokenizer
model_input_names = ["input_ids", "attention_mask"]
prefix_tokens: List[int] = []
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="<pad>",
sep_token="[SEP]",
mask_token="[MASK]",
cls_token="[CLS]",
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs,
)
self.vocab_file = vocab_file
self.can_save_slow_tokenizer = False if not self.vocab_file else True
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An BigBird sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
| # coding=utf-8
# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization classes for Big Bird model."""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
BigBirdTokenizer = None
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
},
"tokenizer_file": {
"google/bigbird-roberta-base": (
"https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
),
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
),
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/bigbird-roberta-base": 4096,
"google/bigbird-roberta-large": 4096,
"google/bigbird-base-trivia-itc": 4096,
}
SPIECE_UNDERLINE = "▁"
class BigBirdTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" BigBird tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the `sep_token`.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = BigBirdTokenizer
model_input_names = ["input_ids", "attention_mask"]
prefix_tokens: List[int] = []
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="<pad>",
sep_token="[SEP]",
mask_token="[MASK]",
cls_token="[CLS]",
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs,
)
self.vocab_file = vocab_file
self.can_save_slow_tokenizer = False if not self.vocab_file else True
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An BigBird sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/generation/tf_utils.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice
from ..modeling_tf_outputs import TFCausalLMOutputWithPast, TFSeq2SeqLMOutput
from ..models.auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
)
from ..tf_utils import shape_list, stable_softmax
from ..utils import ModelOutput, logging
from .tf_logits_process import (
TFForcedBOSTokenLogitsProcessor,
TFForcedEOSTokenLogitsProcessor,
TFForceTokensLogitsProcessor,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
TFNoBadWordsLogitsProcessor,
TFNoRepeatNGramLogitsProcessor,
TFRepetitionPenaltyLogitsProcessor,
TFSuppressTokensAtBeginLogitsProcessor,
TFSuppressTokensLogitsProcessor,
TFTemperatureLogitsWarper,
TFTopKLogitsWarper,
TFTopPLogitsWarper,
)
logger = logging.get_logger(__name__)
@dataclass
class TFGreedySearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFGreedySearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size*num_return_sequences,
num_heads, sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_return_sequences, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam search.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. `Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length,
sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam sample.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_beams, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_beams, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFContrastiveSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using contrastive search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFContrastiveSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using contrastive search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
TFGreedySearchOutput = Union[TFGreedySearchEncoderDecoderOutput, TFGreedySearchDecoderOnlyOutput]
TFSampleOutput = Union[TFSampleEncoderDecoderOutput, TFSampleDecoderOnlyOutput]
TFBeamSearchOutput = Union[TFBeamSearchEncoderDecoderOutput, TFBeamSearchDecoderOnlyOutput]
TFBeamSampleOutput = Union[TFBeamSampleEncoderDecoderOutput, TFBeamSampleDecoderOnlyOutput]
TFContrastiveSearchOutput = Union[TFContrastiveSearchEncoderDecoderOutput, TFContrastiveSearchDecoderOnlyOutput]
TFGenerateOutput = Union[
TFGreedySearchOutput, TFSampleOutput, TFBeamSearchOutput, TFBeamSampleOutput, TFContrastiveSearchOutput
]
class TFGenerationMixin:
"""
A class containing all of the functions supporting generation, to be used as a mixin in [`TFPreTrainedModel`].
The class exposes [`~generation.TFGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`.
- *contrastive search* by calling [`~generation.TFGenerationMixin.contrastive_search`] if `penalty_alpha>0` and
`top_k>1`
- *multinomial sampling* by calling [`~generation.TFGenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`.
- *beam-search decoding* by calling [`~generation.TFGenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`.
"""
_seed_generator = None
@property
def seed_generator(self):
warnings.warn("`seed_generator` is deprecated and will be removed in a future version.", UserWarning)
if self._seed_generator is None:
self._seed_generator = tf.random.Generator.from_non_deterministic_state()
return self._seed_generator
supports_xla_generation = True
def _use_cache(self, outputs, use_cache):
"""During generation, decide whether to pass the `past` variable to the next forward pass."""
use_cache = getattr(self.config, "use_cache", False)
if len(outputs) <= 1 or use_cache is False:
return False
if hasattr(self.config, "mem_len") and self.config.mem_len == 0:
return False
return True
def generate(
self,
input_ids=None,
max_length=None,
max_new_tokens=None,
min_length=None,
do_sample=None,
early_stopping=None,
num_beams=None,
temperature=None,
penalty_alpha=None,
top_k=None,
top_p=None,
repetition_penalty=None,
bad_words_ids=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
attention_mask=None,
decoder_start_token_id=None,
use_cache=None,
output_scores=None,
output_attentions=None,
output_hidden_states=None,
return_dict_in_generate=None,
forced_bos_token_id=None,
forced_eos_token_id=None,
suppress_tokens: Optional[List[int]] = None,
begin_suppress_tokens: Optional[List[int]] = None,
forced_decoder_ids: Optional[List[List[int]]] = None,
**model_kwargs,
) -> Union[TFGenerateOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head. The method supports the following
generation methods for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`.
- *contrastive search* by calling [`~generation.TFGenerationMixin.contrastive_search`] if `penalty_alpha>0`
and `top_k>1`
- *multinomial sampling* by calling [`~generation.TFGenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`.
- *beam-search decoding* by calling [`~generation.TFGenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`.
Adapted in part from [Facebook's XLM beam search
code](https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529).
Apart from `input_ids` and `attention_mask`, all the arguments below will default to the value of the attribute
of the same name inside the [`PretrainedConfig`] of the model. The default values indicated are the default
values of those config.
Most of these parameters are explained in more detail in [this blog
post](https://huggingface.co/blog/how-to-generate).
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, `(batch_size, sequence_length,
feature_dim)` or `(batch_size, num_channels, height, width)`, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
`input_ids`, `input_values`, `input_features`, or `pixel_values`.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 10):
The minimum length of the sequence to be generated.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
early_stopping (`bool`, *optional*, defaults to `False`):
Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
num_beams (`int`, *optional*, defaults to 1):
Number of beams for beam search. 1 means no beam search.
temperature (`float`, *optional*, defaults to 1.0):
The value used to module the next token probabilities.
penalty_alpha (`float`, *optional*):
The values balance the model confidence and the degeneration penalty in contrastive search decoding.
top_k (`int`, *optional*, defaults to 50):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*, defaults to 1.0):
If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher
are kept for generation.
repetition_penalty (`float`, *optional*, defaults to 1.0):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
bos_token_id (`int`, *optional*):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent
to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size can only occur once.
bad_words_ids(`List[int]`, *optional*):
List of token ids that are not allowed to be generated. In order to get the tokens of the words that
should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
attention_mask (`tf.Tensor` of `dtype=tf.int32` and shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values are in `[0, 1]`, 1 for tokens
that are not masked, and 0 for masked tokens.
If not provided, will default to a tensor the same shape as `input_ids` that masks the pad token.
[What are attention masks?](../glossary#attention-mask)
decoder_start_token_id (`int`, *optional*):
If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
forced_bos_token_id (`int`, *optional*):
The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
the target language token.
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached.
suppress_tokens (`List[int]`, *optional*, defaults to `model.config.suppress_tokens`):
A list of tokens that will be supressed at generation. The `SupressTokens` logit processor will set
their log probs to `-inf` so that they are not sampled.
begin_suppress_tokens (`List[int]`, *optional*, defaults to `model.config.begin_suppress_tokens`):
A list of tokens that will be supressed at the begining of the generation. The `SupressBeginTokens`
logit processor will set their log probs to `-inf` so that they are not sampled.
forced_decoder_ids (`List[List[int]]`, *optional*, defaults to `model.config.forced_decoder_ids`):
A list of pairs of integers which indicates a mapping from generation indices to token indices that
will be forced before sampling. For example, `[[1, 123]]` means the second generated token will always
be a token of index 123.
model_specific_kwargs:
Additional model specific kwargs will be forwarded to the `forward` function of the model.
Return:
[`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a `tf.Tensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchDecoderOnlyOutput`],
- [`~generation.TFSampleDecoderOnlyOutput`],
- [`~generation.TFBeamSearchDecoderOnlyOutput`],
- [`~generation.TFBeamSampleDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchEncoderDecoderOutput`],
- [`~generation.TFSampleEncoderDecoderOutput`],
- [`~generation.TFBeamSearchEncoderDecoderOutput`],
- [`~generation.TFBeamSampleEncoderDecoderOutput`]
Examples:
```python
tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"distilgpt2"
) # Download model and configuration from huggingface.co and cache.
outputs = model.generate(max_length=40) # do greedy decoding
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("openai-gpt") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"openai-gpt"
) # Download model and configuration from huggingface.co and cache.
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5
) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
for i in range(3): # 3 output sequences were generated
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"distilgpt2"
) # Download model and configuration from huggingface.co and cache.
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True
) # generate 3 candidates using sampling
for i in range(3): # 3 output sequences were generated
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("ctrl") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"ctrl"
) # Download model and configuration from huggingface.co and cache.
input_context = "Legal My neighbor is" # "Legal" is one of the control codes for ctrl
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2
) # generate sequences
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("gpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"gpt2"
) # Download model and configuration from huggingface.co and cache.
input_context = "My cute dog"
bad_words_ids = [
tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ["idiot", "stupid", "shut up"]
]
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids
) # generate sequences without allowing bad_words to be generated
```"""
num_beams = num_beams if num_beams is not None else self.config.num_beams
do_sample = do_sample if do_sample is not None else self.config.do_sample
if do_sample is False or num_beams == 1:
seed = model_kwargs.pop("seed", None)
return self._generate(
input_ids=input_ids,
max_length=max_length,
max_new_tokens=max_new_tokens,
min_length=min_length,
do_sample=do_sample,
early_stopping=early_stopping,
num_beams=num_beams,
temperature=temperature,
penalty_alpha=penalty_alpha,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
bad_words_ids=bad_words_ids,
bos_token_id=bos_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
length_penalty=length_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
num_return_sequences=num_return_sequences,
attention_mask=attention_mask,
decoder_start_token_id=decoder_start_token_id,
use_cache=use_cache,
seed=seed,
output_scores=output_scores,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
suppress_tokens=suppress_tokens,
begin_suppress_tokens=begin_suppress_tokens,
forced_decoder_ids=forced_decoder_ids,
**model_kwargs,
)
# We cannot generate if the model does not have a LM head
if self.get_output_embeddings() is None:
raise AttributeError(
"You tried to generate sequences with a model that does not have a LM Head. Please use another model"
" class (e.g. `TFOpenAIGPTLMHeadModel`, `TFXLNetLMHeadModel`, `TFGPT2LMHeadModel`,"
" `TFCTRLLMHeadModel`, `TFT5ForConditionalGeneration`, `TFTransfoXLLMHeadModel`)"
)
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
temperature = temperature if temperature is not None else self.config.temperature
top_k = top_k if top_k is not None else self.config.top_k
top_p = top_p if top_p is not None else self.config.top_p
repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
no_repeat_ngram_size = (
no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
)
bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
forced_bos_token_id = (
forced_bos_token_id if forced_bos_token_id is not None else self.config.forced_bos_token_id
)
forced_eos_token_id = (
forced_eos_token_id if forced_eos_token_id is not None else self.config.forced_eos_token_id
)
suppress_tokens = suppress_tokens if suppress_tokens is not None else self.config.suppress_tokens
begin_suppress_tokens = (
begin_suppress_tokens if begin_suppress_tokens is not None else self.config.begin_suppress_tokens
)
if forced_decoder_ids is None and hasattr(self.config, "forced_decoder_ids"):
forced_decoder_ids = self.config.forced_decoder_ids
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
model_kwargs["output_scores"] = output_scores
model_kwargs["output_attentions"] = output_attentions
model_kwargs["output_hidden_states"] = output_hidden_states
if self.config.is_encoder_decoder:
model_kwargs["encoder_attentions"] = None
model_kwargs["encoder_hidden_states"] = None
if input_ids is not None:
batch_size = shape_list(input_ids)[0] # overridden by the input batch_size
else:
batch_size = 1
assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
assert temperature > 0, "`temperature` should be strictly positive."
assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
assert input_ids is not None or (
isinstance(bos_token_id, int) and bos_token_id >= 0
), "If input_ids is not defined, `bos_token_id` should be a positive integer."
assert pad_token_id is None or (
isinstance(pad_token_id, int) and (pad_token_id >= 0)
), "`pad_token_id` should be a positive integer."
assert (eos_token_id is None) or (
isinstance(eos_token_id, int) and (eos_token_id >= 0)
), "`eos_token_id` should be a positive integer."
assert length_penalty > 0, "`length_penalty` should be strictly positive."
assert (
isinstance(num_return_sequences, int) and num_return_sequences > 0
), "`num_return_sequences` should be a strictly positive integer."
assert (
bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"
# This block corresponds to the following line in `generation`:
# "input_ids = self._prepare_input_ids_for_generation(bos_token_id, model_kwargs.get("encoder_outputs"))"
# with the following differences:
# 1. In PT, `generate()`'s `model_kwargs` can accept `encoder_outputs`, but not the case in TF.
# 2. There is no shape checking in PT.
# In both PT/TF, if `input_ids` is `None`, we try to create it as it is for a text model.
if input_ids is None:
assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
"you should either supply a context to complete as `input_ids` input "
"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
)
input_ids = tf.fill((batch_size, 1), bos_token_id)
# not allow to duplicate outputs when greedy decoding
if do_sample is False:
if num_beams == 1:
# no_beam_search greedy generation conditions
assert num_return_sequences == 1, (
"Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences >"
" 1. Please set num_return_sequences = 1"
)
else:
# beam_search greedy generation conditions
assert num_beams >= num_return_sequences, (
"Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams"
" >= num_return_sequences"
)
# create attention mask if necessary
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.call).parameters.keys())
if accepts_attention_mask:
if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids.numpy()):
attention_mask = tf.cast(tf.math.not_equal(input_ids, pad_token_id), dtype=tf.int32)
elif attention_mask is None:
attention_mask = tf.ones(shape_list(input_ids)[:2], dtype=tf.int32)
if pad_token_id is None and eos_token_id is not None:
logger.warning(f"Setting `pad_token_id` to {eos_token_id} (first `eos_token_id`) to generate sequence")
pad_token_id = eos_token_id
# current position and vocab size
cur_len = shape_list(input_ids)[1] # unused
vocab_size = getattr(self.config, "vocab_size", None)
if vocab_size is None and self.config.is_encoder_decoder:
decoder_config = getattr(self.config, "decoder", None)
if decoder_config is not None:
vocab_size = getattr(self.config.decoder, "vocab_size", None)
# set effective batch size and effective batch multiplier according to do_sample
if do_sample:
effective_batch_size = batch_size * num_return_sequences
effective_batch_mult = num_return_sequences
else:
effective_batch_size = batch_size
effective_batch_mult = 1
if self.config.is_encoder_decoder:
if decoder_start_token_id is None:
decoder_start_token_id = bos_token_id
assert (
decoder_start_token_id is not None
), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
assert hasattr(self, "get_encoder"), f"{self} should have a 'get_encoder' function defined"
assert callable(self.get_encoder), f"{self.get_encoder} should be a method"
# get encoder and store encoder outputs
encoder = self.get_encoder()
encoder_kwargs = {
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict_in_generate,
}
if accepts_attention_mask:
encoder_kwargs["attention_mask"] = attention_mask
encoder_outputs = encoder(input_ids, **encoder_kwargs)
if return_dict_in_generate:
if output_attentions:
model_kwargs["encoder_attentions"] = encoder_outputs.attentions
if output_hidden_states:
model_kwargs["encoder_hidden_states"] = encoder_outputs.hidden_states
expanded_batch_idxs = tf.reshape(
tf.repeat(tf.expand_dims(tf.range(batch_size), -1), repeats=num_beams * effective_batch_mult, axis=1),
shape=(-1,),
)
# prepares text-based inputs
if len(shape_list(input_ids)) == 2:
input_ids = tf.gather(input_ids, expanded_batch_idxs, axis=0)
if accepts_attention_mask:
attention_mask = tf.gather(attention_mask, expanded_batch_idxs, axis=0)
if self.config.is_encoder_decoder:
# create empty decoder_input_ids
input_ids = (
tf.ones(
(effective_batch_size * num_beams, 1),
dtype=tf.int32,
)
* decoder_start_token_id
)
cur_len = 1
assert (
batch_size == encoder_outputs[0].shape[0]
), f"expected encoder_outputs[0] to have 1st dimension bs={batch_size}, got {encoder_outputs[0].shape[0]} "
# expand encoder_outputs
encoder_outputs = (tf.gather(encoder_outputs[0], expanded_batch_idxs, axis=0),)
else:
encoder_outputs = None
cur_len = shape_list(input_ids)[-1]
assert cur_len < max_length, (
f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that"
" `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or"
" `config.max_length = ...`"
)
return self._generate_beam_search(
input_ids,
cur_len=cur_len,
max_length=max_length,
min_length=min_length,
do_sample=do_sample,
early_stopping=early_stopping,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
bad_words_ids=bad_words_ids,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
batch_size=effective_batch_size,
num_return_sequences=num_return_sequences,
length_penalty=length_penalty,
num_beams=num_beams,
vocab_size=vocab_size,
encoder_outputs=encoder_outputs,
attention_mask=attention_mask,
use_cache=use_cache,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
def _generate_beam_search(
self,
input_ids,
cur_len,
max_length,
min_length,
do_sample,
early_stopping,
temperature,
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
bad_words_ids,
pad_token_id,
eos_token_id,
batch_size,
num_return_sequences,
length_penalty,
num_beams,
vocab_size,
encoder_outputs,
attention_mask,
use_cache,
forced_bos_token_id,
forced_eos_token_id,
return_dict_in_generate,
**kwargs,
) -> Union[TFBeamSearchOutput, TFBeamSampleOutput, tf.Tensor]:
"""Generate sequences for each example with beam search."""
# generated hypotheses
generated_hyps = [
BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
for _ in range(batch_size)
]
# for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
if do_sample is False:
beam_scores_begin = tf.zeros((batch_size, 1), dtype=tf.float32)
beam_scores_end = tf.ones((batch_size, num_beams - 1), dtype=tf.float32) * (-1e9)
beam_scores = tf.concat([beam_scores_begin, beam_scores_end], -1)
else:
beam_scores = tf.zeros((batch_size, num_beams), dtype=tf.float32)
beam_scores = tf.reshape(beam_scores, (batch_size * num_beams,))
# variable to cache compute states
past = None
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and kwargs["output_scores"]) else None
decoder_attentions = () if (return_dict_in_generate and kwargs["output_attentions"]) else None
cross_attentions = () if (return_dict_in_generate and kwargs["output_attentions"]) else None
decoder_hidden_states = () if (return_dict_in_generate and kwargs["output_hidden_states"]) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if self.config.is_encoder_decoder:
encoder_attentions = (
kwargs["encoder_attentions"] if (return_dict_in_generate and kwargs["encoder_attentions"]) else None
)
encoder_hidden_states = (
kwargs["encoder_hidden_states"]
if (return_dict_in_generate and kwargs["encoder_hidden_states"])
else None
)
# the refactored generate, without the encoder outputs in `past`, expects the `encoder_outputs`
# variable to contain all (encoder_outputs, encoder_hidden_states, encoder_attentions) in
# `prepare_inputs_for_generation`
if encoder_hidden_states is not None:
encoder_outputs = (*encoder_outputs, encoder_hidden_states)
if encoder_attentions is not None:
encoder_outputs = (*encoder_outputs, encoder_attentions)
# done sentences
done = [False for _ in range(batch_size)]
while cur_len < max_length:
model_inputs = self.prepare_inputs_for_generation(
input_ids,
past=past,
attention_mask=attention_mask,
use_cache=use_cache,
encoder_outputs=encoder_outputs,
**kwargs,
)
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=kwargs["output_attentions"],
output_hidden_states=kwargs["output_hidden_states"],
)
next_token_logits = outputs.logits[:, -1, :] # (batch_size * num_beams, vocab_size)
# if model has past, then set the past variable to speed up decoding
if self._use_cache(outputs, use_cache):
past = outputs[1]
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0:
next_token_logits_penalties = _create_next_token_logits_penalties(
input_ids, next_token_logits, repetition_penalty
)
next_token_logits = tf.math.multiply(next_token_logits, next_token_logits_penalties)
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
next_token_logits = next_token_logits / temperature
if self.config.is_encoder_decoder and do_sample is False:
next_token_logits = self.adjust_logits_during_generation(
next_token_logits,
cur_len=cur_len,
max_length=max_length,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
)
# calculate log softmax score
scores = tf.nn.log_softmax(next_token_logits, axis=-1) # (batch_size * num_beams, vocab_size)
# set eos token prob to zero if min_length is not reached
if eos_token_id is not None and cur_len < min_length:
# create eos_token_id boolean mask
num_batch_hypotheses = batch_size * num_beams
is_token_logit_eos_token = tf.convert_to_tensor(
[True if token == eos_token_id else False for token in range(vocab_size)], dtype=tf.bool
)
eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [num_batch_hypotheses, vocab_size])
scores = tf.where(eos_token_indices_mask, -float("inf"), scores)
if no_repeat_ngram_size > 0:
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
num_batch_hypotheses = batch_size * num_beams
banned_tokens = calc_banned_ngram_tokens(
input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len
)
# create banned_tokens boolean mask
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = tf.where(
tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores
)
if bad_words_ids is not None:
# calculate a list of banned tokens according to bad words
banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = tf.where(
tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores
)
assert shape_list(scores) == [batch_size * num_beams, vocab_size]
if do_sample:
_scores = scores + tf.broadcast_to(
beam_scores[:, None], (batch_size * num_beams, vocab_size)
) # (batch_size * num_beams, vocab_size)
# Top-p/top-k filtering
_scores = tf_top_k_top_p_filtering(
_scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
) # (batch_size * num_beams, vocab_size)
# Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
_scores = tf.reshape(_scores, (batch_size, num_beams * vocab_size))
next_tokens = sample_without_replacement(
_scores, num_samples=2 * num_beams
) # (batch_size, 2 * num_beams)
# Compute next scores
next_scores = tf.gather(_scores, next_tokens, batch_dims=1) # (batch_size, 2 * num_beams)
# sort the sampled vector to make sure that the first num_beams samples are the best
next_scores_indices = tf.argsort(next_scores, direction="DESCENDING", axis=1)
next_scores = tf.gather(next_scores, next_scores_indices, batch_dims=1) # (batch_size, num_beams * 2)
next_tokens = tf.gather(next_tokens, next_scores_indices, batch_dims=1) # (batch_size, num_beams * 2)
else:
# Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product)
next_scores = scores + tf.broadcast_to(
beam_scores[:, None], (batch_size * num_beams, vocab_size)
) # (batch_size * num_beams, vocab_size)
# re-organize to group the beam together (we are keeping top hypothesis across beams)
next_scores = tf.reshape(
next_scores, (batch_size, num_beams * vocab_size)
) # (batch_size, num_beams * vocab_size)
next_scores, next_tokens = tf.math.top_k(next_scores, k=2 * num_beams, sorted=True)
assert shape_list(next_scores) == shape_list(next_tokens) == [batch_size, 2 * num_beams]
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if kwargs["output_scores"]:
scores += (next_token_logits,)
if kwargs["output_attentions"]:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if kwargs["output_hidden_states"]:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# next batch beam content
next_batch_beam = []
# for each sentence
for batch_idx in range(batch_size):
# if we are done with this sentence
if done[batch_idx]:
assert (
len(generated_hyps[batch_idx]) >= num_beams
), f"Batch can only be done if at least {num_beams} beams have been generated."
assert (
eos_token_id is not None and pad_token_id is not None
), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch
continue
# next sentence beam content
next_sent_beam = []
# next tokens for this sentence
for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx])
):
# get beam and token IDs
beam_id = beam_token_id // vocab_size
token_id = beam_token_id % vocab_size
effective_beam_id = batch_idx * num_beams + beam_id
# add to generated hypotheses if end of sentence or last iteration
if (eos_token_id is not None) and (token_id.numpy() == eos_token_id):
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
if is_beam_token_worse_than_top_num_beams:
continue
generated_hyps[batch_idx].add(
tf.identity(input_ids[effective_beam_id]), beam_token_score.numpy()
)
else:
# add next predicted token if it is not eos_token
next_sent_beam.append((beam_token_score, token_id, effective_beam_id))
# the beam for next step is full
if len(next_sent_beam) == num_beams:
break
# Check if we are done so that we can save a pad step if all(done)
done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
tf.reduce_max(next_scores[batch_idx]).numpy(), cur_len
)
# update next beam content
assert len(next_sent_beam) == num_beams, "Beam should always be full"
next_batch_beam.extend(next_sent_beam)
assert len(next_batch_beam) == num_beams * (batch_idx + 1)
# stop when we are done with each sentence
if all(done):
break
# sanity check / prepare next batch
assert len(next_batch_beam) == batch_size * num_beams
beam_scores = tf.convert_to_tensor([x[0] for x in next_batch_beam], dtype=tf.float32)
beam_tokens = tf.convert_to_tensor([x[1] for x in next_batch_beam], dtype=tf.int32)
beam_idx = tf.convert_to_tensor([x[2] for x in next_batch_beam], dtype=tf.int32)
# re-order batch and update current length
input_ids = tf.stack([tf.identity(input_ids[x, :]) for x in beam_idx])
input_ids = tf.concat([input_ids, tf.expand_dims(beam_tokens, 1)], axis=-1)
cur_len = cur_len + 1
# re-order internal states
if past is not None:
past = self._reorder_cache(past, beam_idx)
# extend attention_mask for new generated input if only decoder
if self.config.is_encoder_decoder is False:
attention_mask = tf.concat(
[attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
)
# finalize all open beam hypotheses and end to generated hypotheses
for batch_idx in range(batch_size):
# Add all open beam hypothesis to generated_hyps
if done[batch_idx]:
continue
# test that beam scores match previously calculated scores if not eos and batch_idx not done
if eos_token_id is not None and all(
(token_id % vocab_size).numpy().item() != eos_token_id for token_id in next_tokens[batch_idx]
):
if not tf.reduce_all(
next_scores[batch_idx, :num_beams] == tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]
):
raise ValueError(
f"If batch_idx is not done, final next scores: {next_scores[:, :num_beams][batch_idx]} have "
"to equal to accumulated beam_scores: "
f"{tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]}"
)
# need to add best num_beams hypotheses to generated hyps
for beam_id in range(num_beams):
effective_beam_id = batch_idx * num_beams + beam_id
final_score = beam_scores[effective_beam_id].numpy().item()
final_tokens = input_ids[effective_beam_id]
generated_hyps[batch_idx].add(final_tokens, final_score)
# depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences
# select the best hypotheses
sent_lengths_list = []
best = []
# retrieve best hypotheses
for i, hypotheses in enumerate(generated_hyps):
sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
for j in range(output_num_return_sequences_per_batch):
best_hyp = sorted_hyps.pop()[1]
sent_lengths_list.append(len(best_hyp))
best.append(best_hyp)
assert output_batch_size == len(
best
), f"Output batch size {output_batch_size} must match output beam hypotheses {len(best)}"
sent_lengths = tf.convert_to_tensor(sent_lengths_list, dtype=tf.int32)
# shorter batches are filled with pad_token
if tf.reduce_min(sent_lengths).numpy() != tf.reduce_max(sent_lengths).numpy():
assert pad_token_id is not None, "`Pad_token_id` has to be defined"
sent_max_len = min(tf.reduce_max(sent_lengths).numpy() + 1, max_length)
decoded_list = []
# fill with hypothesis and eos_token_id if necessary
for i, hypo in enumerate(best):
assert sent_lengths[i] == shape_list(hypo)[0]
# if sent_length is max_len do not pad
if sent_lengths[i] == sent_max_len:
decoded_slice = hypo
else:
# else pad to sent_max_len
num_pad_tokens = sent_max_len - sent_lengths[i]
padding = pad_token_id * tf.ones((num_pad_tokens,), dtype=tf.int32)
decoded_slice = tf.concat([hypo, padding], axis=-1)
# finish sentence with EOS token
if sent_lengths[i] < max_length:
decoded_slice = tf.where(
tf.range(sent_max_len, dtype=tf.int32) == sent_lengths[i],
eos_token_id * tf.ones((sent_max_len,), dtype=tf.int32),
decoded_slice,
)
# add to list
decoded_list.append(decoded_slice)
decoded = tf.stack(decoded_list)
else:
# none of the hypotheses have an eos_token
assert (len(hypo) == max_length for hypo in best)
decoded = tf.stack(best)
if return_dict_in_generate:
if do_sample and self.config.is_encoder_decoder:
return TFBeamSampleEncoderDecoderOutput(
sequences=decoded,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
elif do_sample and not self.config.is_encoder_decoder:
return TFBeamSampleDecoderOnlyOutput(
sequences=decoded,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
elif self.config.is_encoder_decoder:
return TFBeamSearchEncoderDecoderOutput(
sequences=decoded,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFBeamSearchDecoderOnlyOutput(
sequences=decoded,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return decoded
@staticmethod
def _reorder_cache(past, beam_idx):
return tuple(tf.gather(layer_past, beam_idx, axis=1) for layer_past in past)
def adjust_logits_during_generation(
self, logits, cur_len, max_length, forced_bos_token_id, forced_eos_token_id, **kwargs
):
"""
Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
"""
vocab_size = getattr(self.config, "vocab_size", None)
if vocab_size is None and self.config.is_encoder_decoder:
decoder_config = getattr(self.config, "decoder", None)
if decoder_config is not None:
vocab_size = getattr(self.config.decoder, "vocab_size", None)
if cur_len == 1 and forced_bos_token_id is not None:
vocab_range = tf.constant(range(vocab_size))
return tf.where(vocab_range != forced_bos_token_id, -1e8, logits)
elif cur_len == max_length - 1 and forced_eos_token_id is not None:
vocab_range = tf.constant(range(vocab_size))
return tf.where(vocab_range != forced_eos_token_id, -1e8, logits)
else:
return logits
def _validate_model_class(self):
"""
Confirms that the model class is compatible with generation. If not, raises an exception that points to the
right class to use.
"""
if not hasattr(self, "prepare_inputs_for_generation"):
generate_compatible_mappings = [
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
]
generate_compatible_classes = set()
for model_mapping in generate_compatible_mappings:
supported_models = model_mapping.get(type(self.config), default=None)
if supported_models is not None:
generate_compatible_classes.add(supported_models.__name__)
exception_message = (
f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
"it doesn't have a language model head."
)
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message)
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
# Excludes arguments that are handled before calling any model function
if self.config.is_encoder_decoder:
for key in ["decoder_input_ids"]:
model_kwargs.pop(key, None)
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
if "kwargs" in model_args or "model_kwargs" in model_args:
model_args |= set(inspect.signature(self.call).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
def _generate(
self,
input_ids=None,
max_length=None,
max_new_tokens=None,
min_length=None,
do_sample=None,
early_stopping=None,
num_beams=None,
temperature=None,
penalty_alpha=None,
top_k=None,
top_p=None,
repetition_penalty=None,
bad_words_ids=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
attention_mask=None,
decoder_start_token_id=None,
use_cache=None,
seed=None,
output_scores=None,
output_attentions=None,
output_hidden_states=None,
return_dict_in_generate=None,
forced_bos_token_id=None,
forced_eos_token_id=None,
suppress_tokens=None,
begin_suppress_tokens=None,
forced_decoder_ids=None,
**model_kwargs,
) -> Union[TFGenerateOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head. The method supports the following
generation methods for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`.
- *contrastive search* by calling [`~generation.TFGenerationMixin.contrastive_search`] if `penalty_alpha>0`
and `top_k>1`
- *multinomial sampling* by calling [`~generation.TFGenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`.
- *beam-search decoding* by calling [`~generation.TFGenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`.
Adapted in part from [Facebook's XLM beam search
code](https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529).
Apart from `input_ids` and `attention_mask`, all the arguments below will default to the value of the attribute
of the same name inside the [`PretrainedConfig`] of the model. The default values indicated are the default
values of those config.
Most of these parameters are explained in more detail in [this blog
post](https://huggingface.co/blog/how-to-generate).
Parameters:
input_ids (`tf.Tensor` of `dtype=tf.int32` and shape `(batch_size, sequence_length)`, *optional*):
The sequence used as a prompt for the generation. If `None` the method initializes it with
`bos_token_id` and a batch size of 1.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 10):
The minimum length of the sequence to be generated.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
early_stopping (`bool`, *optional*, defaults to `False`):
Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
num_beams (`int`, *optional*, defaults to 1):
Number of beams for beam search. 1 means no beam search.
temperature (`float`, *optional*, defaults to 1.0):
The value used to module the next token probabilities.
penalty_alpha (`float`, *optional*):
The values balance the model confidence and the degeneration penalty in contrastive search decoding.
top_k (`int`, *optional*, defaults to 50):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*, defaults to 1.0):
If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher
are kept for generation.
repetition_penalty (`float`, *optional*, defaults to 1.0):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
bos_token_id (`int`, *optional*):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent
to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size can only occur once.
bad_words_ids(`List[int]`, *optional*):
List of token ids that are not allowed to be generated. In order to get the tokens of the words that
should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
attention_mask (`tf.Tensor` of `dtype=tf.int32` and shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values are in `[0, 1]`, 1 for tokens
that are not masked, and 0 for masked tokens.
If not provided, will default to a tensor the same shape as `input_ids` that masks the pad token.
[What are attention masks?](../glossary#attention-mask)
decoder_start_token_id (`int`, *optional*):
If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.
seed (`List[int]`, *optional*):
Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
`seed` argument from stateless functions in `tf.random`.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
forced_bos_token_id (`int`, *optional*):
The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
the target language token.
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached.
suppress_tokens (`List[int]`, *optional*, defaults to `model.config.suppress_tokens`):
A list of tokens that will be supressed at generation. The `SupressTokens` logit processor will set
their log probs to `-inf` so that they are not sampled.
begin_suppress_tokens (`List[int]`, *optional*, defaults to `model.config.begin_suppress_tokens`):
A list of tokens that will be supressed at the begining of the generation. The `SupressBeginTokens`
logit processor will set their log probs to `-inf` so that they are not sampled.
forced_decoder_ids (`List[List[int]]`, *optional*, defaults to `model.config.forced_decoder_ids`):
A list of pairs of integers which indicates a mapping from generation indices to token indices that
will be forced before sampling. For example, `[[1, 123]]` means the second generated token will always
be a token of index 123.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model.
Return:
[`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a `tf.Tensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchDecoderOnlyOutput`],
- [`~generation.TFSampleDecoderOnlyOutput`],
- [`~generation.TFBeamSearchDecoderOnlyOutput`],
- [`~generation.TFBeamSampleDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchEncoderDecoderOutput`],
- [`~generation.TFSampleEncoderDecoderOutput`],
- [`~generation.TFBeamSearchEncoderDecoderOutput`],
- [`~generation.TFBeamSampleEncoderDecoderOutput`]
Examples:
```python
tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained("distilgpt2")
# Greedy decoding
outputs = model.generate(max_length=40)
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("openai-gpt")
model = TFAutoModelWithLMHead.from_pretrained("openai-gpt")
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
# Generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)
# 3 output sequences were generated
for i in range(3):
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = TFAutoModelWithLMHead.from_pretrained("distilgpt2")
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf")
# Generate 3 candidates using sampling
outputs = model.generate(
input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True
)
# 3 output sequences were generated
for i in range(3):
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("ctrl")
model = TFAutoModelWithLMHead.from_pretrained("ctrl")
# "Legal" is one of the control codes for ctrl
input_context = "Legal My neighbor is"
input_ids = tokenizer.encode(input_context, return_tensors="tf")
outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = TFAutoModelWithLMHead.from_pretrained("gpt2")
input_context = "My cute dog"
bad_words_ids = [
tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ["idiot", "stupid", "shut up"]
]
input_ids = tokenizer.encode(input_context, return_tensors="tf")
# generate sequences without allowing bad_words to be generated
outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)
```"""
# 0. Validate the `.generate()` call
self._validate_model_class()
self._validate_model_kwargs(model_kwargs.copy())
# 1. Cast input dtypes to tf.int32 unless they're floats (which happens for some image models)
if input_ids is not None:
if isinstance(input_ids, tf.Tensor) and input_ids.dtype.is_floating:
pass
elif isinstance(input_ids, np.ndarray) and np.issubdtype(input_ids.dtype, np.floating):
pass
else:
input_ids = tf.cast(input_ids, tf.int32)
if attention_mask is not None:
attention_mask = tf.cast(attention_mask, tf.int32)
if "decoder_input_ids" in model_kwargs:
if (
isinstance(model_kwargs["decoder_input_ids"], tf.Tensor)
and model_kwargs["decoder_input_ids"].dtype.is_floating
):
pass
elif isinstance(model_kwargs["decoder_input_ids"], np.ndarray) and np.issubdtype(
model_kwargs["decoder_input_ids"].dtype, np.floating
):
pass
else:
model_kwargs["decoder_input_ids"] = tf.cast(model_kwargs["decoder_input_ids"], tf.int32)
# 2. Set generation parameters if not already defined
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
forced_bos_token_id = (
forced_bos_token_id if forced_bos_token_id is not None else self.config.forced_bos_token_id
)
forced_eos_token_id = (
forced_eos_token_id if forced_eos_token_id is not None else self.config.forced_eos_token_id
)
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
num_beams = num_beams if num_beams is not None else self.config.num_beams
do_sample = do_sample if do_sample is not None else self.config.do_sample
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
if pad_token_id is None and eos_token_id is not None:
if attention_mask is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
logger.warning(f"Setting `pad_token_id` to {eos_token_id} (first `eos_token_id`) to generate sequence")
pad_token_id = eos_token_id
use_xla = not tf.executing_eagerly()
if use_xla and not self.supports_xla_generation:
raise ValueError(
"The selected model does not support Graph mode nor XLA generation (e.g. from tf.function())"
)
# 3. Define model inputs
input_ids = self._prepare_model_inputs(input_ids, bos_token_id)
# inputs_ids now has to be defined and cannot be None anymore
batch_size = shape_list(input_ids)[0]
# 4. Prepare other model kwargs
if output_attentions is not None:
model_kwargs["output_attentions"] = output_attentions
if output_hidden_states is not None:
model_kwargs["output_hidden_states"] = output_hidden_states
if use_cache is not None:
model_kwargs["use_cache"] = use_cache
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.call).parameters.keys())
requires_attention_mask = "encoder_outputs" not in model_kwargs
if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
input_ids, pad_token_id, eos_token_id
)
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder:
if pad_token_id is not None and tf.math.reduce_any(input_ids[:, -1] == pad_token_id):
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
# 5. Prepare model inputs which will be used for auto-regressive generation
if self.config.is_encoder_decoder:
# if encoder-decoder, we create encoder_outputs and add to `model_kwargs`
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, model_kwargs)
# if encoder-decoder then `input_ids` come from `decoder_start_token_id`
input_ids = self._prepare_decoder_input_ids_for_generation(
batch_size,
decoder_start_token_id=decoder_start_token_id,
bos_token_id=bos_token_id,
model_kwargs=model_kwargs,
)
# 6. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
if max_length is None and max_new_tokens is None:
warnings.warn(
"Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to "
f"{self.config.max_length} (`self.config.max_length`). Controlling `max_length` via the config is "
"deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend "
"using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif max_length is None and max_new_tokens is not None:
max_length = max_new_tokens + input_ids_seq_length
elif max_length is not None and max_new_tokens is not None:
raise ValueError(
"Both `max_new_tokens` and `max_length` have been set but they serve the same purpose -- setting a"
" limit to the generated output length. Remove one of those arguments. Please refer to the"
" documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
# default to config if still None
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
if min_length is not None and min_length > max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
f"length ({max_length})"
)
if input_ids_seq_length >= max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {max_length}. This can lead to unexpected behavior. You should consider increasing"
"`max_new_tokens`."
)
# 7. determine generation mode
# TODO(Matt, Joao, Patrick) - add more use cases here
is_contrastive_search_gen_mode = (
top_k is not None and top_k > 1 and do_sample is False and penalty_alpha is not None and penalty_alpha > 0
)
is_greedy_gen_mode = not is_contrastive_search_gen_mode and (num_beams == 1) and do_sample is False
is_beam_gen_mode = not is_contrastive_search_gen_mode and (num_beams > 1) and do_sample is False
is_sample_gen_mode = (num_beams == 1) and do_sample is True
# 8. prepare distribution pre_processing samplers
logits_processor = self._get_logits_processor(
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
input_ids_seq_length=input_ids_seq_length,
bad_words_ids=bad_words_ids,
min_length=min_length,
max_length=max_length,
eos_token_id=eos_token_id,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
suppress_tokens=suppress_tokens,
begin_suppress_tokens=begin_suppress_tokens,
forced_decoder_ids=forced_decoder_ids,
)
# 9. go into different generation modes
if is_greedy_gen_mode:
if num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {num_return_sequences} when doing greedy search."
)
# 10. run greedy search
return self.greedy_search(
input_ids,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
logits_processor=logits_processor,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
elif is_contrastive_search_gen_mode:
if num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {num_return_sequences} when doing contrastive search."
)
# 10. run contrastive search
return self.contrastive_search(
input_ids,
top_k=top_k,
penalty_alpha=penalty_alpha,
logits_processor=logits_processor,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
elif is_sample_gen_mode:
# 10. prepare logits warper
logits_warper = self._get_logits_warper(top_k=top_k, top_p=top_p, temperature=temperature)
# 11. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 12. run sample
return self.sample(
input_ids,
logits_processor=logits_processor,
logits_warper=logits_warper,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
seed=seed,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
elif is_beam_gen_mode:
if num_beams < num_return_sequences:
raise ValueError(
"Greedy beam search decoding cannot return more sequences than it has beams. Please set "
f"num_beams >= num_return_sequences, got {num_beams} and {num_return_sequences} (respectivelly)"
)
# 10. broadcast inputs to the desired number of beams
input_ids = self._expand_to_num_beams(input_ids, num_beams=num_beams)
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = self._expand_to_num_beams(
model_kwargs["encoder_outputs"]["last_hidden_state"], num_beams=num_beams
)
if "attention_mask" in model_kwargs:
model_kwargs["attention_mask"] = self._expand_to_num_beams(
model_kwargs["attention_mask"], num_beams=num_beams
)
# 11. run beam search
return self.beam_search(
input_ids,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
length_penalty=length_penalty,
early_stopping=early_stopping,
logits_processor=logits_processor,
return_dict_in_generate=return_dict_in_generate,
num_return_sequences=num_return_sequences,
**model_kwargs,
)
else:
# TODO(Matt, Joao, Patrick) - add more sub-generation methods here
raise NotImplementedError("Beam sampling is currently not implemented.")
@staticmethod
def _expand_to_num_beams(tensor: tf.Tensor, num_beams: int) -> tf.Tensor:
shape = shape_list(tensor)
return tf.broadcast_to(tensor[:, None], (shape[0], num_beams) + tuple(shape[1:]))
def _prepare_attention_mask_for_generation(
self,
inputs: tf.Tensor,
pad_token_id: Optional[int],
eos_token_id: Optional[int],
) -> tf.Tensor:
is_input_ids = len(inputs.shape) == 2 and inputs.dtype in (tf.int32, tf.int64)
is_pad_token_in_inputs = (pad_token_id is not None) and tf.math.reduce_any(inputs == pad_token_id)
is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (pad_token_id != eos_token_id)
# Check if input is input_ids and padded -> only then is attention_mask defined
if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
return tf.cast(tf.math.not_equal(inputs, pad_token_id), dtype=tf.int32)
else:
return tf.ones(inputs.shape[:2], dtype=tf.int32)
def _prepare_encoder_decoder_kwargs_for_generation(self, inputs_tensor: tf.Tensor, model_kwargs) -> Dict[str, Any]:
# get encoder and store encoder outputs
encoder = self.get_encoder()
# prepare encoder args and encoder kwargs from model kwargs
irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
encoder_kwargs = {
argument: value
for argument, value in model_kwargs.items()
if not any(argument.startswith(p) for p in irrelevant_prefix)
}
# vision models don't use `attention_mask`.
encoder_kwargs["return_dict"] = True
encoder_kwargs[self.main_input_name] = inputs_tensor
encoder_outputs = encoder(**encoder_kwargs)
model_kwargs["encoder_outputs"] = encoder_outputs
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
decoder_start_token_id: int = None,
bos_token_id: int = None,
model_kwargs: Optional[Dict[str, tf.Tensor]] = None,
) -> tf.Tensor:
# prepare `input_ids` for decoder if model is encoder-decoder
if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
return model_kwargs.pop("decoder_input_ids")
else:
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
return tf.ones((batch_size, 1), dtype=tf.int32) * decoder_start_token_id
def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
# retrieve decoder_start_token_id for encoder-decoder models
# fall back to bos_token_id if necessary
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
if decoder_start_token_id is not None:
return decoder_start_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "decoder_start_token_id")
and self.config.decoder.decoder_start_token_id is not None
):
return self.config.decoder.decoder_start_token_id
elif bos_token_id is not None:
return bos_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "bos_token_id")
and self.config.decoder.bos_token_id is not None
):
return self.config.decoder.bos_token_id
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
)
@staticmethod
def _expand_inputs_for_generation(
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[tf.Tensor] = None,
**model_kwargs,
) -> Tuple[tf.Tensor, Dict[str, Any]]:
"""Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""
if input_ids is not None:
input_ids = tf.repeat(input_ids, expand_size, axis=0)
if model_kwargs.get("token_type_ids") is not None:
model_kwargs["token_type_ids"] = tf.repeat(model_kwargs["token_type_ids"], expand_size, axis=0)
if model_kwargs.get("attention_mask") is not None:
model_kwargs["attention_mask"] = tf.repeat(model_kwargs["attention_mask"], expand_size, axis=0)
if model_kwargs.get("decoder_attention_mask") is not None:
model_kwargs["decoder_attention_mask"] = tf.repeat(
model_kwargs["decoder_attention_mask"], expand_size, axis=0
)
if is_encoder_decoder:
encoder_outputs = model_kwargs.get("encoder_outputs")
if encoder_outputs is None:
raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, expand_size, axis=0)
model_kwargs["encoder_outputs"] = encoder_outputs
return input_ids, model_kwargs
def _prepare_model_inputs(self, inputs: Optional[tf.Tensor] = None, bos_token_id: Optional[int] = None):
# TODO(Patrick) - adapt this function when making `generate` more flexible
# for all kinds of input types
if inputs is None:
# if no `inputs` are passed create prompt of size (1,1) filled with BOS token
if not isinstance(bos_token_id, int) or bos_token_id < 0:
raise ValueError(
"you should either supply a context to complete as `input_ids` input "
"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
)
return tf.cast(tf.fill((1, 1), bos_token_id), dtype=tf.int32)
return inputs
@staticmethod
def _extract_past_from_model_output(outputs: ModelOutput):
past = None
if "past_key_values" in outputs:
past = outputs.past_key_values
elif "mems" in outputs:
past = outputs.mems
elif "past_buckets_states" in outputs:
past = outputs.past_buckets_states
return past
def _update_model_kwargs_for_generation(
self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
) -> Dict[str, Any]:
# update past
model_kwargs["past"] = self._extract_past_from_model_output(outputs)
# update attention mask
if not is_encoder_decoder:
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = tf.concat(
[attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
)
return model_kwargs
def _update_model_kwargs_for_xla_generation(
self,
model_outputs: ModelOutput,
model_kwargs: Dict[str, Any],
cur_len: int,
max_length: int,
batch_size: int,
is_encoder_decoder: bool = False,
batch_axis: int = 0,
):
def _initialize_attention(model_kwargs, num_padding_values, is_encoder_decoder):
"""initializes the appropriate attention mask -- encoder-decoder models use `decoder_attention_mask`"""
if is_encoder_decoder:
# One 1 for decoder_start_token_id, 0s for the currently-unfilled locations in the past tensor,
# 1s for the actual input_ids
decoder_attention_mask = tf.concat(
[
tf.ones((batch_size, 1), dtype=tf.int32),
tf.zeros((batch_size, num_padding_values), dtype=tf.int32),
tf.ones((batch_size, 1), dtype=tf.int32),
],
axis=1,
)
mask = {"decoder_attention_mask": decoder_attention_mask}
else:
attention_mask = model_kwargs.pop("attention_mask")
# 0s for the currently-unfilled locations in the past tensor, 1s for the actual input_ids
attention_mask = tf.concat(
[
attention_mask,
tf.zeros((batch_size, num_padding_values), dtype=attention_mask.dtype),
tf.ones((batch_size, 1), dtype=attention_mask.dtype),
],
axis=1,
)
mask = {"attention_mask": attention_mask}
return mask
def _update_attention(model_kwargs, new_past_index, is_encoder_decoder):
"""updates the appropriate attention mask -- encoder-decoder models use `decoder_attention_mask`"""
update_start = tf.constant([0, 1], dtype=tf.int32) * new_past_index
if is_encoder_decoder:
decoder_attention_mask = model_kwargs.pop("decoder_attention_mask")
decoder_attention_mask_update_slice = tf.ones((batch_size, 1), dtype=decoder_attention_mask.dtype)
decoder_attention_mask = dynamic_update_slice(
decoder_attention_mask, decoder_attention_mask_update_slice, update_start
)
mask = {"decoder_attention_mask": decoder_attention_mask}
else:
attention_mask = model_kwargs.pop("attention_mask")
attention_mask_update_slice = tf.ones((batch_size, 1), dtype=attention_mask.dtype)
attention_mask = dynamic_update_slice(attention_mask, attention_mask_update_slice, update_start)
mask = {"attention_mask": attention_mask}
return mask
def _initialize_past(past, num_padding_values, batch_axis):
"""initialize past with zeros -- the structure depends on `batch_axis`"""
if batch_axis == 0:
padding_values = tf.constant([[0, 0], [0, 0], [0, num_padding_values], [0, 0]], dtype=tf.int32)
new_past = ()
for past_layer in past:
new_past_layer = list(past_layer)
for i in range(len(new_past_layer[:2])):
new_past_layer[i] = tf.pad(past_layer[i], padding_values)
new_past += (tuple(new_past_layer),)
else:
padding_values = tf.scatter_nd(indices=[[3, 1]], updates=[num_padding_values], shape=(5, 2))
new_past = list(past)
for i in range(len(past)):
new_past[i] = tf.pad(past[i], padding_values)
return new_past
def _update_past(past, new_past_index, batch_axis):
if batch_axis == 0:
slice_start_base = tf.constant([0, 0, 1, 0])
new_past = ()
for past_layer in past:
new_past_layer = list(past_layer)
for i in range(len(new_past_layer[:2])):
update_slice = past_layer[i][:, :, -1:]
# Write the last slice to the first open location in the padded past array
# and then truncate the last slice off the array
new_past_layer[i] = dynamic_update_slice(
past_layer[i][:, :, :-1], update_slice, slice_start_base * new_past_index
)
new_past += (tuple(new_past_layer),)
else:
slice_start_base = tf.constant([0, 0, 0, 1, 0])
new_past = [None for _ in range(len(past))]
for i in range(len(past)):
update_slice = past[i][:, :, :, -1:]
# Write the last slice to the first open location in the padded past array
# and then truncate the last slice off the array
new_past[i] = dynamic_update_slice(
past[i][:, :, :, :-1], update_slice, slice_start_base * new_past_index
)
return new_past
past = self._extract_past_from_model_output(model_outputs)
if past is None:
raise ValueError(
f"No known past variable found in model outputs (model outputs keys: {list(model_outputs.keys())})"
)
is_past_initialized = model_kwargs.pop("past", None) is not None
if not is_past_initialized:
# The padded version of `past` has a length of `max_length - 1`, as `past` holds information relative to
# previous autoregressive generation steps (step 0 has no past, step 1 has 1 past value, ..., the last step
# has `max_length - 1` past values).
num_padding_values = max_length - cur_len - 1
mask = _initialize_attention(model_kwargs, num_padding_values, is_encoder_decoder)
new_past = _initialize_past(past, num_padding_values, batch_axis)
else:
# The new index of past to be filled corresponds to the current length of the sequence, with two
# subtractions: -1 because past holds information regarding previous generation steps (read comment above)
# and -1 again because in an array the index is the length of the array minus 1.
new_past_index = cur_len - 2
mask = _update_attention(model_kwargs, new_past_index, is_encoder_decoder)
new_past = _update_past(past, new_past_index, batch_axis)
# sets the updated variables (mask and past)
model_kwargs.update(mask)
model_kwargs["past"] = tuple(new_past)
return model_kwargs
def _get_logits_warper(
self,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None,
) -> TFLogitsProcessorList:
"""
This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`TFLogitsWarper`]
instances used for multinomial sampling.
"""
# init warp parameters
top_k = top_k if top_k is not None else self.config.top_k
top_p = top_p if top_p is not None else self.config.top_p
temperature = temperature if temperature is not None else self.config.temperature
# instantiate warpers list
warpers = TFLogitsProcessorList()
# the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
# all samplers can be found in `generation_utils_samplers.py`
if temperature is not None and temperature != 1.0:
warpers.append(TFTemperatureLogitsWarper(temperature))
if top_k is not None and top_k != 0:
warpers.append(TFTopKLogitsWarper(top_k=top_k, min_tokens_to_keep=1))
if top_p is not None and top_p < 1.0:
warpers.append(TFTopPLogitsWarper(top_p=top_p, min_tokens_to_keep=1))
return warpers
def _get_logits_processor(
self,
repetition_penalty: float,
no_repeat_ngram_size: int,
input_ids_seq_length: int,
bad_words_ids: List[List[int]],
min_length: int,
max_length: int,
eos_token_id: int,
forced_bos_token_id: int,
forced_eos_token_id: int,
suppress_tokens: Optional[List[int]] = None,
begin_suppress_tokens: Optional[List[int]] = None,
forced_decoder_ids: Optional[List[List[int]]] = None,
) -> TFLogitsProcessorList:
"""
This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`TFLogitsProcessor`]
instances used to modify the scores of the language model head.
"""
processors = TFLogitsProcessorList()
repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
no_repeat_ngram_size = (
no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
)
bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
suppress_tokens = suppress_tokens if suppress_tokens is not None else self.config.suppress_tokens
begin_suppress_tokens = (
begin_suppress_tokens if begin_suppress_tokens is not None else self.config.begin_suppress_tokens
)
if forced_decoder_ids is None and hasattr(self.config, "forced_decoder_ids"):
forced_decoder_ids = self.config.forced_decoder_ids
# instantiate processors list
if repetition_penalty is not None and repetition_penalty != 1.0:
processors.append(TFRepetitionPenaltyLogitsProcessor(penalty=repetition_penalty))
if no_repeat_ngram_size is not None and no_repeat_ngram_size > 0:
processors.append(TFNoRepeatNGramLogitsProcessor(no_repeat_ngram_size))
if bad_words_ids is not None:
processors.append(TFNoBadWordsLogitsProcessor(bad_words_ids, eos_token_id))
if min_length is not None and eos_token_id is not None and min_length > 0:
processors.append(TFMinLengthLogitsProcessor(min_length, eos_token_id))
if forced_bos_token_id is not None:
processors.append(TFForcedBOSTokenLogitsProcessor(forced_bos_token_id))
if forced_eos_token_id is not None:
processors.append(TFForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id))
if suppress_tokens is not None:
processors.append(TFSuppressTokensLogitsProcessor(suppress_tokens))
if begin_suppress_tokens is not None:
begin_index = input_ids_seq_length
begin_index = begin_index if (input_ids_seq_length > 1 or forced_bos_token_id is None) else begin_index + 1
if forced_decoder_ids is not None:
begin_index += forced_decoder_ids[-1][0] # generation starts after the last token that is forced
processors.append(TFSuppressTokensAtBeginLogitsProcessor(begin_suppress_tokens, begin_index))
if forced_decoder_ids is not None:
processors.append(TFForceTokensLogitsProcessor(forced_decoder_ids))
return processors
def greedy_search(
self,
input_ids: tf.Tensor,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFGreedySearchOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using greedy decoding.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `call` function of the model. If
model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFGreedySearchDecoderOnlyOutput`], [`~generation.TFGreedySearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFGreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFGreedySearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForCausalLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [
... TFMinLengthLogitsProcessor(15, eos_token_id=model.config.eos_token_id),
... ]
... )
>>> outputs = model.greedy_search(input_ids, logits_processor=logits_processor)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
["Today is a beautiful day, and I'm so happy to be here. I'm so happy to"]
```"""
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# some models, like XLNet, need more than the last token in the presence of past
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (`input_ids` padded with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define condition fn
def greedy_search_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state termination condition fn."""
return ~tf.reduce_all(finished_sequences)
# define condition fn
def greedy_search_body_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state update fn."""
if model_kwargs.get("past") is None or needs_full_input:
input_ids = generated[:, :cur_len]
else:
input_ids = tf.expand_dims(generated[:, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token logits
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
next_token_logits = model_outputs.logits[:, -1]
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(next_token_logits)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# pre-process distribution
next_tokens_scores = logits_processor(generated, next_token_logits, cur_len)
# argmax
next_tokens = tf.argmax(next_tokens_scores, axis=-1, output_type=tf.int32)
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
finished_sequences = finished_sequences | (next_tokens == eos_token_id)
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past key values we need the whole input
if model_kwargs.get("past", None) is None:
# let's throw out `past` since we don't want `None` tensors
model_kwargs.pop("past", None)
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past`
generated, finished_sequences, cur_len, model_kwargs = greedy_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
if greedy_search_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
maximum_iterations = max_length - cur_len
generated, _, cur_len, _ = tf.while_loop(
greedy_search_cond_fn,
greedy_search_body_fn,
(generated, finished_sequences, cur_len, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFGreedySearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFGreedySearchDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def sample(
self,
input_ids: tf.Tensor,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
seed: Optional[Tuple[int, int]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFSampleOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using multinomial sampling.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
seed (`List[int]`, *optional*):
Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
`seed` argument from stateless functions in `tf.random`.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFSampleDecoderOnlyOutput`], [`~generation.TFSampleEncoderDecoderOutput`] or `tf.Tensor`: A
`tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFSampleEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForCausalLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... TFTopKLogitsWarper,
... TFTemperatureLogitsWarper,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [
... TFMinLengthLogitsProcessor(15, eos_token_id=model.config.eos_token_id),
... ]
... )
>>> # instantiate logits processors
>>> logits_warper = TFLogitsProcessorList(
... [
... TFTopKLogitsWarper(50),
... TFTemperatureLogitsWarper(0.7),
... ]
... )
>>> tf.random.set_seed(0)
>>> outputs = model.sample(input_ids, logits_processor=logits_processor, logits_warper=logits_warper)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Today is a beautiful day, and I love my country. But when I look at Donald Trump,']
```"""
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# some models, like XLNet, need more than the last token in the presence of past
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (pre-populated with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
def sample_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
return ~tf.reduce_all(finished_sequences)
def sample_body_fn(generated, finished_sequences, cur_len, model_kwargs):
if model_kwargs.get("past") is None or needs_full_input:
input_ids = generated[:, :cur_len]
else:
input_ids = tf.expand_dims(generated[:, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token logits
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
next_token_logits = model_outputs.logits[:, -1]
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(next_token_logits)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# pre-process distribution
next_tokens_scores = logits_processor(generated, next_token_logits, cur_len)
next_tokens_scores = logits_warper(generated, next_tokens_scores, cur_len)
# sample
if seed is not None:
sample_seed = seed
else:
sample_seed = tf.experimental.numpy.random.randint(tf.int32.min, tf.int32.max, (2,), dtype=tf.int32)
next_tokens = tf.squeeze(
tf.random.stateless_categorical(
logits=next_tokens_scores, num_samples=1, seed=sample_seed, dtype=tf.int32
),
axis=1,
)
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
finished_sequences = finished_sequences | (next_tokens == eos_token_id)
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past key values we need the whole input
if model_kwargs.get("past", None) is None:
# let's throw out `past` since we don't want `None` tensors
model_kwargs.pop("past", None)
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past`
generated, finished_sequences, cur_len, model_kwargs = sample_body_fn(
generated, finished_sequences, cur_len, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
if sample_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
maximum_iterations = max_length - cur_len
generated, _, cur_len, _ = tf.while_loop(
sample_cond_fn,
sample_body_fn,
(generated, finished_sequences, cur_len, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFSampleEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFSampleDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def beam_search(
self,
input_ids: tf.Tensor,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
length_penalty: Optional[float] = None,
early_stopping: Optional[bool] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
num_return_sequences: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFBeamSearchOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using beam search with multinomial sampling.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent
to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
early_stopping (`bool`, *optional*, defaults to `False`):
Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
logits_processor (`[TFLogitsProcessorList]`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFBeamSearchDecoderOnlyOutput`], [`~generation.TFBeamSearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFBeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFBeamSearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForSeq2SeqLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... )
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="tf").input_ids
>>> # lets run beam search using 3 beams
>>> num_beams = 3
>>> # define decoder start token ids
>>> input_ids = tf.ones((1, num_beams, 1), dtype=tf.int32)
>>> input_ids = input_ids * model.config.decoder_start_token_id
>>> # add encoder_outputs to model keyword arguments
>>> encoder_outputs = model.get_encoder()(encoder_input_ids, return_dict=True)
>>> encoder_outputs.last_hidden_state = tf.repeat(
... tf.expand_dims(encoder_outputs.last_hidden_state, axis=0), num_beams, axis=1
... )
>>> model_kwargs = {"encoder_outputs": encoder_outputs}
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [TFMinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id)]
... )
>>> outputs = model.beam_search(input_ids, logits_processor=logits_processor, **model_kwargs)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Wie alt bist du?']
```"""
def flatten_beam_dim(tensor, batch_axis=0):
"""Flattens the first two dimensions of a non-scalar array."""
shape = shape_list(tensor)
return tf.reshape(
tensor,
shape[:batch_axis] + [shape[batch_axis] * shape[batch_axis + 1]] + shape[batch_axis + 2 :],
)
def unflatten_beam_dim(tensor, batch_size, num_beams, batch_axis=0):
"""Unflattens the first, flat batch*beam dimension of a non-scalar array."""
shape = shape_list(tensor)
return tf.reshape(tensor, shape[:batch_axis] + [batch_size, num_beams] + shape[batch_axis + 1 :])
def gather_beams(nested, beam_indices, batch_axis=0):
"""Gathers the beam slices indexed by beam_indices into new beam array."""
def gather_fn(tensor):
if batch_axis > 0:
# pushes all dimentions before the batch to the end, so we get (batch, beam_id, ...)
perm = tf.concat((tf.range(tf.rank(tensor))[batch_axis:], tf.range(batch_axis)), axis=0)
tensor = tf.transpose(tensor, perm=perm)
gathered_tensor = tf.gather(params=tensor, indices=beam_indices, axis=1, batch_dims=1)
if batch_axis > 0:
# transposes back to the original dimensions
perm = tf.concat((tf.range(tf.rank(tensor))[batch_axis:], tf.range(batch_axis)), axis=0)
perm = tf.math.invert_permutation(perm)
gathered_tensor = tf.transpose(gathered_tensor, perm=perm)
return gathered_tensor
return tf.nest.map_structure(gather_fn, nested)
# 1. init beam_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_scores = output_scores if output_scores is not None else self.config.output_scores
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# some models, like XLNet, need more than the last token in the presence of past
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, num_beams, cur_len = shape_list(input_ids)
# per batch, beam-item holding current token in loop, pre-populated with `pad_token_id`
input_ids_padding = tf.ones((batch_size, num_beams, max_length - cur_len), dtype=tf.int32) * (
pad_token_id or 0
)
running_sequences = tf.concat([input_ids, input_ids_padding], axis=-1)
sequences = tf.ones((batch_size, num_beams, max_length), dtype=tf.int32) * (pad_token_id or 0)
# per batch,beam-item state bit indicating if sentence has finished.
is_sent_finished = tf.zeros((batch_size, num_beams), dtype=tf.bool)
# per batch, beam-item score, logprobs
running_scores = tf.tile(
tf.expand_dims(tf.convert_to_tensor([0.0] + [-1.0e9] * (num_beams - 1)), axis=0), [batch_size, 1]
)
scores = tf.ones((batch_size, num_beams)) * -1.0e9
# flatten beam dim
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = flatten_beam_dim(
model_kwargs["encoder_outputs"]["last_hidden_state"]
)
if "attention_mask" in model_kwargs:
model_kwargs["attention_mask"] = flatten_beam_dim(model_kwargs["attention_mask"])
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define stop-condition and auto-regressive function
def beam_search_cond_fn(
cur_len,
running_sequences,
running_scores,
sequences,
scores,
is_sent_finished,
model_kwargs,
):
"""
Beam Search termination condition function -- halts the generation loop if any of these conditions becomes
False
"""
# 1. is less than max length?
not_max_length_yet = cur_len < max_length
# 2. can the new beams still improve?
best_running_score = running_scores[:, :1] / (max_length**length_penalty)
worst_finished_score = tf.where(
is_sent_finished, tf.math.reduce_min(scores, axis=1, keepdims=True), -1.0e9
)
improvement_still_possible = tf.math.reduce_all(worst_finished_score < best_running_score)
# 3. is there still a beam that has not finished?
still_open_beam = ~(tf.math.reduce_all(is_sent_finished) & early_stopping)
return not_max_length_yet & (still_open_beam | improvement_still_possible)
def beam_search_body_fn(
cur_len,
running_sequences,
running_scores,
sequences,
scores,
is_sent_finished,
model_kwargs,
):
"""
Beam Search iterative update function -- each iteration adds a new token and updates the best sequences
seen so far
"""
# 1. Forward current tokens
if model_kwargs.get("past") is None or needs_full_input:
input_ids = running_sequences[:, :, :cur_len]
else:
input_ids = tf.expand_dims(running_sequences[:, :, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(flatten_beam_dim(input_ids), **model_kwargs)
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
logits = unflatten_beam_dim(model_outputs.logits[:, -1], batch_size, num_beams)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(model_outputs.logits[:, -1])
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# 2. Compute log probs
# get log probabilities from logits, process logits with processors (*e.g.* min_length, ...), and
# add new logprobs to existing running logprobs scores.
log_probs = tf.nn.log_softmax(logits)
log_probs = logits_processor(flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), cur_len)
log_probs = unflatten_beam_dim(log_probs, batch_size, num_beams)
log_probs = log_probs + tf.expand_dims(running_scores, axis=2)
vocab_size = log_probs.shape[2]
log_probs = tf.reshape(log_probs, (batch_size, num_beams * vocab_size))
# 3. Retrieve top-K
# Each item in batch has num_beams * vocab_size candidate sequences. For each item, get the top 2*k
# candidates with the highest log-probabilities. We gather the top 2*K beams here so that even if the
# best K sequences reach EOS simultaneously, we have another K sequences remaining to continue the live
# beam search.
# Gather the top 2*K scores from _all_ beams.
# Gather 2*k top beams.
# Recover the beam index by floor division.
# Recover token id by modulo division and expand Id array for broadcasting.
# Update sequences for the 2*K top-k new sequences.
beams_to_keep = 2 * num_beams
topk_log_probs, topk_indices = tf.math.top_k(log_probs, k=beams_to_keep)
topk_beam_indices = topk_indices // vocab_size
topk_running_sequences = gather_beams(running_sequences, topk_beam_indices)
topk_ids = topk_indices % vocab_size
# writes the new token
indices_batch = tf.repeat(tf.range(batch_size), [beams_to_keep])
indices_beam = tf.tile(tf.range(beams_to_keep), [batch_size])
update_indices = tf.stack(
[indices_batch, indices_beam, tf.broadcast_to(cur_len, [batch_size * beams_to_keep])], axis=-1
)
topk_sequences = tf.tensor_scatter_nd_update(
tensor=topk_running_sequences,
indices=update_indices,
updates=tf.reshape(topk_ids, [batch_size * beams_to_keep]),
)
# 4. Check which sequences have ended
# Update current sequences: Did the top `num_beams` sequences reach an end marker?
# To prevent these just finished sequences from being added to the current sequences
# set of active beam search sequences, set their log probs to a very large negative value.
eos_in_next_token = topk_sequences[:, :, cur_len] == eos_token_id
if eos_token_id is None:
eos_in_next_token = tf.broadcast_to(eos_in_next_token, topk_sequences[:, :, cur_len].shape)
did_topk_just_finished = eos_in_next_token & tf.broadcast_to(
tf.concat((tf.ones((num_beams), dtype=tf.bool), tf.zeros((num_beams), dtype=tf.bool)), axis=0),
shape_list(eos_in_next_token),
)
# non-top `num_beams` eos tokens can't be used to finish a beam, but the others can't be used in the next
# running sentences either
running_topk_log_probs = topk_log_probs + tf.cast(eos_in_next_token, tf.float32) * -1.0e9
# 5. Get running sequences scores for next
# Determine the top k beam indices (from top 2*k beams) from log probs and gather top k beams
# (from top 2*k beams).
next_topk_indices = tf.math.top_k(running_topk_log_probs, k=num_beams)[1]
next_running_sequences, next_running_scores = gather_beams(
[topk_sequences, running_topk_log_probs], next_topk_indices
)
# 6. Process topk logits
# Further process log probs:
# - add length penalty
# - make sure no scores can be added anymore if beam is full
# - make sure still running sequences cannot be chosen as finalized beam
topk_log_probs = topk_log_probs / (tf.cast(cur_len, dtype=tf.float32) ** length_penalty)
beams_in_batch_are_full = (
tf.broadcast_to(
tf.math.reduce_all(is_sent_finished, axis=-1, keepdims=True), shape_list(did_topk_just_finished)
)
& early_stopping
)
add_penalty = ~did_topk_just_finished | beams_in_batch_are_full
topk_log_probs += tf.cast(add_penalty, tf.float32) * -1.0e9
# 7. Get scores, sequences, is sentence finished for next.
# Combine sequences, scores, and flags along the beam dimension and compare new finished sequence scores
# to existing finished scores and select the best from the new set of beams
merged_sequences = tf.concat([sequences, topk_sequences], axis=1)
merged_scores = tf.concat([scores, topk_log_probs], axis=1)
merged_is_sent_finished = tf.concat([is_sent_finished, did_topk_just_finished], axis=1)
topk_merged_indices = tf.math.top_k(merged_scores, k=num_beams)[1]
next_sequences, next_scores, next_is_sent_finished = gather_beams(
[merged_sequences, merged_scores, merged_is_sent_finished], topk_merged_indices
)
# 8. Prepare data for the next iteration
# Determine the top k beam indices from the original set of all beams. With these, gather the top k
# beam-associated caches.
cur_len = cur_len + 1
if "past_key_values" in model_outputs:
cache = tf.nest.map_structure(
lambda tensor: unflatten_beam_dim(tensor, batch_size, num_beams, batch_axis=cache_batch_axis),
model_outputs.past_key_values,
)
next_running_indices = gather_beams(topk_beam_indices, next_topk_indices)
next_cache = gather_beams(cache, next_running_indices, batch_axis=cache_batch_axis)
model_outputs["past_key_values"] = tf.nest.map_structure(
lambda tensor: flatten_beam_dim(tensor, batch_axis=cache_batch_axis), next_cache
)
if use_xla:
next_model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=(batch_size * num_beams),
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
next_model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past key values we need the whole input
if model_kwargs.get("past", None) is None:
# let's throw out `past` since we don't want `None` tensors
model_kwargs.pop("past", None)
return (
cur_len,
next_running_sequences,
next_running_scores,
next_sequences,
next_scores,
next_is_sent_finished,
next_model_kwargs,
)
# 5. run generation
# 1st generation step has to be run before to initialize `past` (if active)
(
cur_len,
running_sequences,
running_scores,
sequences,
scores,
is_sent_finished,
model_kwargs,
) = beam_search_body_fn(
cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion (only in case 1st generation step does
# NOT yield EOS token though)
if beam_search_cond_fn(
cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, model_kwargs
):
maximum_iterations = max_length - cur_len
cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, _ = tf.while_loop(
beam_search_cond_fn,
beam_search_body_fn,
(cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
# Account for the edge-case where there are no finished sequences for a particular batch item. If so, return
# running sequences for that batch item.
none_finished = tf.math.reduce_any(is_sent_finished, axis=1)
sequences = tf.where(none_finished[:, None, None], sequences, running_sequences)
scores = tf.where(none_finished[:, None], scores, running_scores)
# Take best beams for each batch (the score is sorted in ascending order)
sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
scores = flatten_beam_dim(scores[:, :num_return_sequences])
if not use_xla:
# Cut for backward compatibility
sequences = sequences[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
return TFBeamSearchEncoderDecoderOutput(
sequences=sequences,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFBeamSearchDecoderOnlyOutput(
sequences=sequences,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequences
def contrastive_search(
self,
input_ids: tf.Tensor,
top_k: Optional[int] = 1,
penalty_alpha: Optional[float] = 0,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFContrastiveSearchOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **contrastive search** and can
be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
top_k (`int`, *optional*, defaults to 1):
The size of the candidate set that is used to re-rank for contrastive search
penalty_alpha (`float`, *optional*, defaults to 0):
The degeneration penalty for contrastive search; activate when it is larger than 0
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `call` function of the model. If
model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFContrastiveSearchDecoderOnlyOutput`],
[`~generation.TFContrastiveSearchEncoderDecoderOutput`] or `tf.Tensor`: A `tf.Tensor` containing the
generated tokens (default behaviour) or a [`~generation.TFContrastiveySearchDecoderOnlyOutput`] if
`model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a
[`~generation.TFContrastiveSearchEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import AutoTokenizer, TFAutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
>>> model = TFAutoModelForCausalLM.from_pretrained("facebook/opt-125m")
>>> # set pad_token_id to eos_token_id because OPT does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "DeepMind Company is"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf")
>>> outputs = model.contrastive_search(**input_ids, penalty_alpha=0.6, top_k=4, max_length=64)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['DeepMind Company is a company that focuses on the development and commercialization of artificial intelligence (AI). DeepMind’s mission is to help people understand and solve problems that are difficult to solve in the world today.\n\nIn this post, we talk about the benefits of deep learning in business and how it']
```"""
def gather_best_candidate(nested, selected_idx_stacked, batch_axis=0):
"""Gathers the slices indexed by selected_idx_stacked from a potentially nested structure of tensors."""
def gather_fn(tensor):
gathered_tensor = tf.gather(params=tensor, indices=selected_idx_stacked, axis=batch_axis)
return gathered_tensor
return tf.nest.map_structure(gather_fn, nested)
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (`input_ids` padded with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define condition fn
def contrastive_search_cond_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
"""state termination condition fn."""
return ~tf.reduce_all(finished_sequences)
# define condition fn
def contrastive_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
"""state update fn."""
# if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
# (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
if model_kwargs.get("past") is None:
# prepare inputs
model_inputs = self.prepare_inputs_for_generation(generated[:, :cur_len], **model_kwargs)
model_inputs["use_cache"] = True
# encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
# the `encoder_outputs`
outputs = self(
**model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
# last decoder hidden states will be used to compute the degeneration penalty (cosine similarity with
# previous tokens)
if self.config.is_encoder_decoder:
last_hidden_states = outputs.decoder_hidden_states[-1]
else:
last_hidden_states = outputs.hidden_states[-1]
# XLA: last_hidden_states normally grows at each step, but in XLA it is padded so as to be used across
# iterations (with fixed shapes)
if use_xla:
last_hidden_states = tf.pad(last_hidden_states, [[0, 0], [0, max_length - cur_len], [0, 0]])
# next logit for contrastive search to select top-k candidate tokens
logit_for_next_step = outputs.logits[:, -1, :]
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# Expands model inputs top_k times, for batched forward passes (akin to beam search).
_, model_kwargs = self._expand_inputs_for_generation(
expand_size=top_k, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs
)
past = model_kwargs.get("past")
if past is None:
raise ValueError(
f"{self.__class__.__name__} does not support caching and therefore **can't** be used "
"for contrastive search."
)
elif not isinstance(past[0], (tuple, tf.Tensor)) or past[0][0].shape[0] != batch_size:
raise ValueError(
f"{self.__class__.__name__} does not have a standard cache format and therefore **can't** be "
"used for contrastive search without further modifications."
)
else:
logit_for_next_step = next_step_cached_variables["logit_for_next_step"]
last_hidden_states = next_step_cached_variables["last_hidden_states"]
outputs = next_step_cached_variables["outputs"]
# contrastive_search main logic start:
# contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
# degeneration penalty
logit_for_next_step = logits_processor(generated, logit_for_next_step, cur_len)
logit_for_next_step = logits_warper(generated, logit_for_next_step, cur_len)
next_probs = stable_softmax(logit_for_next_step, axis=-1)
top_k_probs, top_k_ids = tf.math.top_k(next_probs, k=top_k)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(outputs.logits[:, -1])
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(outputs.hidden_states)
# Replicates the new past_key_values to match the `top_k` candidates
model_kwargs["past"] = tf.nest.map_structure(
lambda tensor: tf.repeat(tensor, top_k, axis=cache_batch_axis), model_kwargs["past"]
)
# compute the candidate tokens by the language model and collects their hidden_states
next_model_inputs = self.prepare_inputs_for_generation(tf.reshape(top_k_ids, [-1, 1]), **model_kwargs)
next_model_inputs["use_cache"] = True
outputs = self(
**next_model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
next_past_key_values = self._extract_past_from_model_output(outputs)
logits = outputs.logits[:, -1, :]
# name is different for encoder-decoder and decoder-only models
if self.config.is_encoder_decoder:
next_hidden = outputs.decoder_hidden_states[-1]
full_hidden_states = outputs.decoder_hidden_states
else:
next_hidden = outputs.hidden_states[-1]
full_hidden_states = outputs.hidden_states
context_hidden = tf.repeat(last_hidden_states[:, :cur_len, :], top_k, axis=0)
# compute the degeneration penalty and re-rank the candidates based on the degeneration penalty and the
# model confidence
selected_idx = _ranking_fast(context_hidden, next_hidden, top_k_probs, penalty_alpha, top_k)
# converts indices to a dimension of top_k to the stacked top_k * batch_size dimension, for indexing
# without a need to reshape on tensors that have these two dimensions stacked
selected_idx_stacked = selected_idx + tf.range(selected_idx.shape[0], dtype=tf.int64) * top_k
# prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
# the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
# (model confidence minus degeneration penalty); (6) decoder hidden_states
next_tokens = tf.gather(top_k_ids, selected_idx, axis=1, batch_dims=1)
next_hidden = gather_best_candidate(next_hidden, selected_idx_stacked)
# XLA: last_hidden_states normally grows at each step, but in XLA it is padded so as to be used across
# iterations (with fixed shapes)
if use_xla:
last_hidden_states = dynamic_update_slice(last_hidden_states, next_hidden, [0, cur_len, 0])
else:
last_hidden_states = tf.concat([last_hidden_states, next_hidden], axis=1)
next_decoder_hidden_states = gather_best_candidate(full_hidden_states, selected_idx_stacked)
next_past_key_values = gather_best_candidate(
next_past_key_values, selected_idx_stacked, batch_axis=cache_batch_axis
)
logit_for_next_step = gather_best_candidate(logits, selected_idx_stacked)
# Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
if self.config.is_encoder_decoder:
next_step_cross_attentions = ()
next_step_decoder_attentions = ()
if output_attentions:
next_step_cross_attentions = gather_best_candidate(outputs.cross_attentions, selected_idx_stacked)
next_step_decoder_attentions = gather_best_candidate(
outputs.decoder_attentions, selected_idx_stacked
)
outputs = TFSeq2SeqLMOutput(
past_key_values=next_past_key_values,
decoder_hidden_states=next_decoder_hidden_states,
decoder_attentions=next_step_decoder_attentions or None,
cross_attentions=next_step_cross_attentions or None,
)
else:
next_step_attentions = ()
if output_attentions:
next_step_attentions = gather_best_candidate(outputs.attentions, selected_idx_stacked)
outputs = TFCausalLMOutputWithPast(
past_key_values=next_past_key_values,
hidden_states=next_decoder_hidden_states,
attentions=next_step_attentions or None,
)
# contrastive_search main logic end
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
finished_sequences = finished_sequences | (next_tokens == eos_token_id)
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
if use_xla:
# NOTE: 1) relative to other generation strategies, contrastive search is always running forward
# passes one step ahead -- hence the `cur_len=cur_len + 1`; 2) the attention mask here is expanded from
# [batch_size, ...] to [batch_size*top_k, ...] -- hence the `batch_size=batch_size * top_k`
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=outputs,
model_kwargs=model_kwargs,
cur_len=cur_len + 1,
max_length=max_length,
batch_size=batch_size * top_k,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
next_step_cached_variables = {
"logit_for_next_step": logit_for_next_step,
"last_hidden_states": last_hidden_states,
"outputs": outputs,
}
return generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
# 5. run generation
# 1st generation step has to be run before to initialize `past`
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables = contrastive_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs, None
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
if contrastive_search_cond_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
maximum_iterations = max_length - cur_len
generated, _, cur_len, _, _, = tf.while_loop(
contrastive_search_cond_fn,
contrastive_search_body_fn,
(generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFContrastiveSearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFContrastiveSearchDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def _create_next_token_logits_penalties(input_ids, logits, repetition_penalty):
# create logit penalties for already seen input_ids
token_penalties = np.ones(shape_list(logits))
prev_input_ids = [np.unique(input_id) for input_id in input_ids.numpy()]
for i, prev_input_id in enumerate(prev_input_ids):
logit_penalized = logits[i].numpy()[prev_input_id]
logit_penalties = np.zeros(logit_penalized.shape)
# if previous logit score is < 0 then multiply repetition penalty else divide
logit_penalties[logit_penalized < 0] = repetition_penalty
logit_penalties[logit_penalized > 0] = 1 / repetition_penalty
np.put(token_penalties[i], prev_input_id, logit_penalties)
return tf.convert_to_tensor(token_penalties, dtype=tf.float32)
def calc_banned_ngram_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, cur_len):
# Copied from fairseq for no_repeat_ngram in beam_search
if cur_len + 1 < no_repeat_ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].numpy().tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - no_repeat_ngram_size
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].numpy().tolist())
return generated_ngrams[hypo_idx].get(ngram_idx, [])
banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
return banned_tokens
def calc_banned_bad_words_ids(prev_input_ids, bad_words_ids):
banned_tokens = []
def _tokens_match(prev_tokens, tokens):
if len(tokens) == 0:
# if bad word tokens is just one token always ban it
return True
if len(tokens) > len(prev_tokens):
# if bad word tokens are longer than prev tokens they can't be equal
return False
if prev_tokens[-len(tokens) :] == tokens:
# if tokens match
return True
else:
return False
for prev_input_ids_slice in prev_input_ids:
banned_tokens_slice = []
for banned_token_seq in bad_words_ids:
assert (
len(banned_token_seq) > 0
), f"Banned words token sequences { bad_words_ids} cannot have an empty list"
if _tokens_match(prev_input_ids_slice.numpy().tolist(), banned_token_seq[:-1]) is False:
# if tokens do not match continue
continue
banned_tokens_slice.append(banned_token_seq[-1])
banned_tokens.append(banned_tokens_slice)
return banned_tokens
def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
"""
Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
top_k (`int`, *optional*, defaults to 0):
If > 0, only keep the top k tokens with highest probability (top-k filtering)
top_p (`float`, *optional*, defaults to 1.0):
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimumber of tokens we keep per batch example in the output.
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
logits_shape = shape_list(logits)
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep), logits_shape[-1]) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < tf.math.top_k(logits, k=top_k)[0][..., -1, None]
logits = tf.where(indices_to_remove, filter_value, logits)
if top_p < 1.0:
sorted_indices = tf.argsort(logits, direction="DESCENDING")
sorted_logits = tf.gather(
logits, sorted_indices, axis=-1, batch_dims=1
) # expects logits to be of dim (batch_size, vocab_size)
cumulative_probs = tf.math.cumsum(stable_softmax(sorted_logits, axis=-1), axis=-1)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove = tf.concat(
[
tf.zeros_like(sorted_indices_to_remove[:, :min_tokens_to_keep]),
sorted_indices_to_remove[:, min_tokens_to_keep:],
],
-1,
)
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove = tf.concat(
[tf.zeros_like(sorted_indices_to_remove[:, :1]), sorted_indices_to_remove[:, :-1]],
-1,
)
# scatter sorted tensors to original indexing
indices_to_remove = scatter_values_on_batch_indices(sorted_indices_to_remove, sorted_indices)
logits = tf.where(indices_to_remove, filter_value, logits)
return logits
def scatter_values_on_batch_indices(values, batch_indices):
shape = shape_list(batch_indices)
# broadcast batch dim to shape
broad_casted_batch_dims = tf.reshape(tf.broadcast_to(tf.expand_dims(tf.range(shape[0]), axis=-1), shape), [1, -1])
# transform batch_indices to pair_indices
pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
# scatter values to pair indices
return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), shape)
def sample_without_replacement(logits, num_samples):
"""
categorical sampling without replacement is currently not implemented the gumbel-max trick will do for now see
https://github.com/tensorflow/tensorflow/issues/9260 for more info
"""
z = -tf.math.log(tf.random.uniform(shape_list(logits), 0, 1))
_, indices = tf.nn.top_k(logits + z, num_samples)
return indices
class BeamHypotheses(object):
def __init__(self, num_beams, max_length, length_penalty, early_stopping):
"""
Initialize n-best list of hypotheses.
"""
self.max_length = max_length - 1 # ignoring bos_token
self.length_penalty = length_penalty
self.early_stopping = early_stopping
self.num_beams = num_beams
self.beams = []
self.worst_score = 1e9
def __len__(self):
"""
Number of hypotheses in the list.
"""
return len(self.beams)
def add(self, hyp, sum_logprobs):
"""
Add a new hypothesis to the list.
"""
score = sum_logprobs / len(hyp) ** self.length_penalty
if len(self) < self.num_beams or score > self.worst_score:
self.beams.append((score, hyp))
if len(self) > self.num_beams:
sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
del self.beams[sorted_scores[0][1]]
self.worst_score = sorted_scores[1][0]
else:
self.worst_score = min(score, self.worst_score)
def is_done(self, best_sum_logprobs, cur_len):
"""
If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst
one in the heap, then we are done with this sentence.
"""
if len(self) < self.num_beams:
return False
elif self.early_stopping:
return True
else:
cur_score = best_sum_logprobs / cur_len**self.length_penalty
ret = self.worst_score >= cur_score
return ret
def _ranking_fast(
context_hidden: tf.Tensor,
next_hidden: tf.Tensor,
next_top_k_probs: tf.Tensor,
alpha: float,
beam_width: int,
) -> tf.Tensor:
"""
Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described
in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each
row in the batch.
"""
norm_context_hidden = context_hidden / tf.norm(context_hidden, axis=2, keepdims=True)
norm_next_hidden = next_hidden / tf.norm(next_hidden, axis=2, keepdims=True)
cosine_matrix = tf.squeeze(tf.linalg.matmul(norm_context_hidden, norm_next_hidden, transpose_b=True), axis=-1)
degeneration_penalty = tf.reduce_max(cosine_matrix, axis=-1)
next_top_k_probs = tf.reshape(next_top_k_probs, shape=[-1])
contrastive_score = (1.0 - alpha) * next_top_k_probs - alpha * degeneration_penalty
contrastive_score = tf.reshape(contrastive_score, shape=[-1, beam_width])
selected_idx = tf.argmax(contrastive_score, axis=1)
return selected_idx
| # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice
from ..modeling_tf_outputs import TFCausalLMOutputWithPast, TFSeq2SeqLMOutput
from ..models.auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
)
from ..tf_utils import shape_list, stable_softmax
from ..utils import ModelOutput, logging
from .tf_logits_process import (
TFForcedBOSTokenLogitsProcessor,
TFForcedEOSTokenLogitsProcessor,
TFForceTokensLogitsProcessor,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
TFNoBadWordsLogitsProcessor,
TFNoRepeatNGramLogitsProcessor,
TFRepetitionPenaltyLogitsProcessor,
TFSuppressTokensAtBeginLogitsProcessor,
TFSuppressTokensLogitsProcessor,
TFTemperatureLogitsWarper,
TFTopKLogitsWarper,
TFTopPLogitsWarper,
)
logger = logging.get_logger(__name__)
@dataclass
class TFGreedySearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFGreedySearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size*num_return_sequences,
num_heads, sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_return_sequences, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam search.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. `Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length,
sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam sample.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_beams, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_beams, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFContrastiveSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using contrastive search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFContrastiveSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using contrastive search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
TFGreedySearchOutput = Union[TFGreedySearchEncoderDecoderOutput, TFGreedySearchDecoderOnlyOutput]
TFSampleOutput = Union[TFSampleEncoderDecoderOutput, TFSampleDecoderOnlyOutput]
TFBeamSearchOutput = Union[TFBeamSearchEncoderDecoderOutput, TFBeamSearchDecoderOnlyOutput]
TFBeamSampleOutput = Union[TFBeamSampleEncoderDecoderOutput, TFBeamSampleDecoderOnlyOutput]
TFContrastiveSearchOutput = Union[TFContrastiveSearchEncoderDecoderOutput, TFContrastiveSearchDecoderOnlyOutput]
TFGenerateOutput = Union[
TFGreedySearchOutput, TFSampleOutput, TFBeamSearchOutput, TFBeamSampleOutput, TFContrastiveSearchOutput
]
class TFGenerationMixin:
"""
A class containing all of the functions supporting generation, to be used as a mixin in [`TFPreTrainedModel`].
The class exposes [`~generation.TFGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`.
- *contrastive search* by calling [`~generation.TFGenerationMixin.contrastive_search`] if `penalty_alpha>0` and
`top_k>1`
- *multinomial sampling* by calling [`~generation.TFGenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`.
- *beam-search decoding* by calling [`~generation.TFGenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`.
"""
_seed_generator = None
@property
def seed_generator(self):
warnings.warn("`seed_generator` is deprecated and will be removed in a future version.", UserWarning)
if self._seed_generator is None:
self._seed_generator = tf.random.Generator.from_non_deterministic_state()
return self._seed_generator
supports_xla_generation = True
def _use_cache(self, outputs, use_cache):
"""During generation, decide whether to pass the `past` variable to the next forward pass."""
use_cache = getattr(self.config, "use_cache", False)
if len(outputs) <= 1 or use_cache is False:
return False
if hasattr(self.config, "mem_len") and self.config.mem_len == 0:
return False
return True
def generate(
self,
input_ids=None,
max_length=None,
max_new_tokens=None,
min_length=None,
do_sample=None,
early_stopping=None,
num_beams=None,
temperature=None,
penalty_alpha=None,
top_k=None,
top_p=None,
repetition_penalty=None,
bad_words_ids=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
attention_mask=None,
decoder_start_token_id=None,
use_cache=None,
output_scores=None,
output_attentions=None,
output_hidden_states=None,
return_dict_in_generate=None,
forced_bos_token_id=None,
forced_eos_token_id=None,
suppress_tokens: Optional[List[int]] = None,
begin_suppress_tokens: Optional[List[int]] = None,
forced_decoder_ids: Optional[List[List[int]]] = None,
**model_kwargs,
) -> Union[TFGenerateOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head. The method supports the following
generation methods for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`.
- *contrastive search* by calling [`~generation.TFGenerationMixin.contrastive_search`] if `penalty_alpha>0`
and `top_k>1`
- *multinomial sampling* by calling [`~generation.TFGenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`.
- *beam-search decoding* by calling [`~generation.TFGenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`.
Adapted in part from [Facebook's XLM beam search
code](https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529).
Apart from `input_ids` and `attention_mask`, all the arguments below will default to the value of the attribute
of the same name inside the [`PretrainedConfig`] of the model. The default values indicated are the default
values of those config.
Most of these parameters are explained in more detail in [this blog
post](https://huggingface.co/blog/how-to-generate).
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, `(batch_size, sequence_length,
feature_dim)` or `(batch_size, num_channels, height, width)`, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
`input_ids`, `input_values`, `input_features`, or `pixel_values`.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 10):
The minimum length of the sequence to be generated.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
early_stopping (`bool`, *optional*, defaults to `False`):
Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
num_beams (`int`, *optional*, defaults to 1):
Number of beams for beam search. 1 means no beam search.
temperature (`float`, *optional*, defaults to 1.0):
The value used to module the next token probabilities.
penalty_alpha (`float`, *optional*):
The values balance the model confidence and the degeneration penalty in contrastive search decoding.
top_k (`int`, *optional*, defaults to 50):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*, defaults to 1.0):
If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher
are kept for generation.
repetition_penalty (`float`, *optional*, defaults to 1.0):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
bos_token_id (`int`, *optional*):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent
to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size can only occur once.
bad_words_ids(`List[int]`, *optional*):
List of token ids that are not allowed to be generated. In order to get the tokens of the words that
should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
attention_mask (`tf.Tensor` of `dtype=tf.int32` and shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values are in `[0, 1]`, 1 for tokens
that are not masked, and 0 for masked tokens.
If not provided, will default to a tensor the same shape as `input_ids` that masks the pad token.
[What are attention masks?](../glossary#attention-mask)
decoder_start_token_id (`int`, *optional*):
If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
forced_bos_token_id (`int`, *optional*):
The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
the target language token.
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached.
suppress_tokens (`List[int]`, *optional*, defaults to `model.config.suppress_tokens`):
A list of tokens that will be supressed at generation. The `SupressTokens` logit processor will set
their log probs to `-inf` so that they are not sampled.
begin_suppress_tokens (`List[int]`, *optional*, defaults to `model.config.begin_suppress_tokens`):
A list of tokens that will be supressed at the begining of the generation. The `SupressBeginTokens`
logit processor will set their log probs to `-inf` so that they are not sampled.
forced_decoder_ids (`List[List[int]]`, *optional*, defaults to `model.config.forced_decoder_ids`):
A list of pairs of integers which indicates a mapping from generation indices to token indices that
will be forced before sampling. For example, `[[1, 123]]` means the second generated token will always
be a token of index 123.
model_specific_kwargs:
Additional model specific kwargs will be forwarded to the `forward` function of the model.
Return:
[`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a `tf.Tensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchDecoderOnlyOutput`],
- [`~generation.TFSampleDecoderOnlyOutput`],
- [`~generation.TFBeamSearchDecoderOnlyOutput`],
- [`~generation.TFBeamSampleDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchEncoderDecoderOutput`],
- [`~generation.TFSampleEncoderDecoderOutput`],
- [`~generation.TFBeamSearchEncoderDecoderOutput`],
- [`~generation.TFBeamSampleEncoderDecoderOutput`]
Examples:
```python
tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"distilgpt2"
) # Download model and configuration from huggingface.co and cache.
outputs = model.generate(max_length=40) # do greedy decoding
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("openai-gpt") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"openai-gpt"
) # Download model and configuration from huggingface.co and cache.
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5
) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
for i in range(3): # 3 output sequences were generated
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"distilgpt2"
) # Download model and configuration from huggingface.co and cache.
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True
) # generate 3 candidates using sampling
for i in range(3): # 3 output sequences were generated
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("ctrl") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"ctrl"
) # Download model and configuration from huggingface.co and cache.
input_context = "Legal My neighbor is" # "Legal" is one of the control codes for ctrl
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2
) # generate sequences
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("gpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"gpt2"
) # Download model and configuration from huggingface.co and cache.
input_context = "My cute dog"
bad_words_ids = [
tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ["idiot", "stupid", "shut up"]
]
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids
) # generate sequences without allowing bad_words to be generated
```"""
num_beams = num_beams if num_beams is not None else self.config.num_beams
do_sample = do_sample if do_sample is not None else self.config.do_sample
if do_sample is False or num_beams == 1:
seed = model_kwargs.pop("seed", None)
return self._generate(
input_ids=input_ids,
max_length=max_length,
max_new_tokens=max_new_tokens,
min_length=min_length,
do_sample=do_sample,
early_stopping=early_stopping,
num_beams=num_beams,
temperature=temperature,
penalty_alpha=penalty_alpha,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
bad_words_ids=bad_words_ids,
bos_token_id=bos_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
length_penalty=length_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
num_return_sequences=num_return_sequences,
attention_mask=attention_mask,
decoder_start_token_id=decoder_start_token_id,
use_cache=use_cache,
seed=seed,
output_scores=output_scores,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
suppress_tokens=suppress_tokens,
begin_suppress_tokens=begin_suppress_tokens,
forced_decoder_ids=forced_decoder_ids,
**model_kwargs,
)
# We cannot generate if the model does not have a LM head
if self.get_output_embeddings() is None:
raise AttributeError(
"You tried to generate sequences with a model that does not have a LM Head. Please use another model"
" class (e.g. `TFOpenAIGPTLMHeadModel`, `TFXLNetLMHeadModel`, `TFGPT2LMHeadModel`,"
" `TFCTRLLMHeadModel`, `TFT5ForConditionalGeneration`, `TFTransfoXLLMHeadModel`)"
)
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
temperature = temperature if temperature is not None else self.config.temperature
top_k = top_k if top_k is not None else self.config.top_k
top_p = top_p if top_p is not None else self.config.top_p
repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
no_repeat_ngram_size = (
no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
)
bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
forced_bos_token_id = (
forced_bos_token_id if forced_bos_token_id is not None else self.config.forced_bos_token_id
)
forced_eos_token_id = (
forced_eos_token_id if forced_eos_token_id is not None else self.config.forced_eos_token_id
)
suppress_tokens = suppress_tokens if suppress_tokens is not None else self.config.suppress_tokens
begin_suppress_tokens = (
begin_suppress_tokens if begin_suppress_tokens is not None else self.config.begin_suppress_tokens
)
if forced_decoder_ids is None and hasattr(self.config, "forced_decoder_ids"):
forced_decoder_ids = self.config.forced_decoder_ids
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
model_kwargs["output_scores"] = output_scores
model_kwargs["output_attentions"] = output_attentions
model_kwargs["output_hidden_states"] = output_hidden_states
if self.config.is_encoder_decoder:
model_kwargs["encoder_attentions"] = None
model_kwargs["encoder_hidden_states"] = None
if input_ids is not None:
batch_size = shape_list(input_ids)[0] # overridden by the input batch_size
else:
batch_size = 1
assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
assert temperature > 0, "`temperature` should be strictly positive."
assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
assert input_ids is not None or (
isinstance(bos_token_id, int) and bos_token_id >= 0
), "If input_ids is not defined, `bos_token_id` should be a positive integer."
assert pad_token_id is None or (
isinstance(pad_token_id, int) and (pad_token_id >= 0)
), "`pad_token_id` should be a positive integer."
assert (eos_token_id is None) or (
isinstance(eos_token_id, int) and (eos_token_id >= 0)
), "`eos_token_id` should be a positive integer."
assert length_penalty > 0, "`length_penalty` should be strictly positive."
assert (
isinstance(num_return_sequences, int) and num_return_sequences > 0
), "`num_return_sequences` should be a strictly positive integer."
assert (
bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"
# This block corresponds to the following line in `generation`:
# "input_ids = self._prepare_input_ids_for_generation(bos_token_id, model_kwargs.get("encoder_outputs"))"
# with the following differences:
# 1. In PT, `generate()`'s `model_kwargs` can accept `encoder_outputs`, but not the case in TF.
# 2. There is no shape checking in PT.
# In both PT/TF, if `input_ids` is `None`, we try to create it as it is for a text model.
if input_ids is None:
assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
"you should either supply a context to complete as `input_ids` input "
"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
)
input_ids = tf.fill((batch_size, 1), bos_token_id)
# not allow to duplicate outputs when greedy decoding
if do_sample is False:
if num_beams == 1:
# no_beam_search greedy generation conditions
assert num_return_sequences == 1, (
"Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences >"
" 1. Please set num_return_sequences = 1"
)
else:
# beam_search greedy generation conditions
assert num_beams >= num_return_sequences, (
"Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams"
" >= num_return_sequences"
)
# create attention mask if necessary
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.call).parameters.keys())
if accepts_attention_mask:
if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids.numpy()):
attention_mask = tf.cast(tf.math.not_equal(input_ids, pad_token_id), dtype=tf.int32)
elif attention_mask is None:
attention_mask = tf.ones(shape_list(input_ids)[:2], dtype=tf.int32)
if pad_token_id is None and eos_token_id is not None:
logger.warning(f"Setting `pad_token_id` to {eos_token_id} (first `eos_token_id`) to generate sequence")
pad_token_id = eos_token_id
# current position and vocab size
cur_len = shape_list(input_ids)[1] # unused
vocab_size = getattr(self.config, "vocab_size", None)
if vocab_size is None and self.config.is_encoder_decoder:
decoder_config = getattr(self.config, "decoder", None)
if decoder_config is not None:
vocab_size = getattr(self.config.decoder, "vocab_size", None)
# set effective batch size and effective batch multiplier according to do_sample
if do_sample:
effective_batch_size = batch_size * num_return_sequences
effective_batch_mult = num_return_sequences
else:
effective_batch_size = batch_size
effective_batch_mult = 1
if self.config.is_encoder_decoder:
if decoder_start_token_id is None:
decoder_start_token_id = bos_token_id
assert (
decoder_start_token_id is not None
), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
assert hasattr(self, "get_encoder"), f"{self} should have a 'get_encoder' function defined"
assert callable(self.get_encoder), f"{self.get_encoder} should be a method"
# get encoder and store encoder outputs
encoder = self.get_encoder()
encoder_kwargs = {
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict_in_generate,
}
if accepts_attention_mask:
encoder_kwargs["attention_mask"] = attention_mask
encoder_outputs = encoder(input_ids, **encoder_kwargs)
if return_dict_in_generate:
if output_attentions:
model_kwargs["encoder_attentions"] = encoder_outputs.attentions
if output_hidden_states:
model_kwargs["encoder_hidden_states"] = encoder_outputs.hidden_states
expanded_batch_idxs = tf.reshape(
tf.repeat(tf.expand_dims(tf.range(batch_size), -1), repeats=num_beams * effective_batch_mult, axis=1),
shape=(-1,),
)
# prepares text-based inputs
if len(shape_list(input_ids)) == 2:
input_ids = tf.gather(input_ids, expanded_batch_idxs, axis=0)
if accepts_attention_mask:
attention_mask = tf.gather(attention_mask, expanded_batch_idxs, axis=0)
if self.config.is_encoder_decoder:
# create empty decoder_input_ids
input_ids = (
tf.ones(
(effective_batch_size * num_beams, 1),
dtype=tf.int32,
)
* decoder_start_token_id
)
cur_len = 1
assert (
batch_size == encoder_outputs[0].shape[0]
), f"expected encoder_outputs[0] to have 1st dimension bs={batch_size}, got {encoder_outputs[0].shape[0]} "
# expand encoder_outputs
encoder_outputs = (tf.gather(encoder_outputs[0], expanded_batch_idxs, axis=0),)
else:
encoder_outputs = None
cur_len = shape_list(input_ids)[-1]
assert cur_len < max_length, (
f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that"
" `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or"
" `config.max_length = ...`"
)
return self._generate_beam_search(
input_ids,
cur_len=cur_len,
max_length=max_length,
min_length=min_length,
do_sample=do_sample,
early_stopping=early_stopping,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
bad_words_ids=bad_words_ids,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
batch_size=effective_batch_size,
num_return_sequences=num_return_sequences,
length_penalty=length_penalty,
num_beams=num_beams,
vocab_size=vocab_size,
encoder_outputs=encoder_outputs,
attention_mask=attention_mask,
use_cache=use_cache,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
def _generate_beam_search(
self,
input_ids,
cur_len,
max_length,
min_length,
do_sample,
early_stopping,
temperature,
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
bad_words_ids,
pad_token_id,
eos_token_id,
batch_size,
num_return_sequences,
length_penalty,
num_beams,
vocab_size,
encoder_outputs,
attention_mask,
use_cache,
forced_bos_token_id,
forced_eos_token_id,
return_dict_in_generate,
**kwargs,
) -> Union[TFBeamSearchOutput, TFBeamSampleOutput, tf.Tensor]:
"""Generate sequences for each example with beam search."""
# generated hypotheses
generated_hyps = [
BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
for _ in range(batch_size)
]
# for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
if do_sample is False:
beam_scores_begin = tf.zeros((batch_size, 1), dtype=tf.float32)
beam_scores_end = tf.ones((batch_size, num_beams - 1), dtype=tf.float32) * (-1e9)
beam_scores = tf.concat([beam_scores_begin, beam_scores_end], -1)
else:
beam_scores = tf.zeros((batch_size, num_beams), dtype=tf.float32)
beam_scores = tf.reshape(beam_scores, (batch_size * num_beams,))
# variable to cache compute states
past = None
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and kwargs["output_scores"]) else None
decoder_attentions = () if (return_dict_in_generate and kwargs["output_attentions"]) else None
cross_attentions = () if (return_dict_in_generate and kwargs["output_attentions"]) else None
decoder_hidden_states = () if (return_dict_in_generate and kwargs["output_hidden_states"]) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if self.config.is_encoder_decoder:
encoder_attentions = (
kwargs["encoder_attentions"] if (return_dict_in_generate and kwargs["encoder_attentions"]) else None
)
encoder_hidden_states = (
kwargs["encoder_hidden_states"]
if (return_dict_in_generate and kwargs["encoder_hidden_states"])
else None
)
# the refactored generate, without the encoder outputs in `past`, expects the `encoder_outputs`
# variable to contain all (encoder_outputs, encoder_hidden_states, encoder_attentions) in
# `prepare_inputs_for_generation`
if encoder_hidden_states is not None:
encoder_outputs = (*encoder_outputs, encoder_hidden_states)
if encoder_attentions is not None:
encoder_outputs = (*encoder_outputs, encoder_attentions)
# done sentences
done = [False for _ in range(batch_size)]
while cur_len < max_length:
model_inputs = self.prepare_inputs_for_generation(
input_ids,
past=past,
attention_mask=attention_mask,
use_cache=use_cache,
encoder_outputs=encoder_outputs,
**kwargs,
)
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=kwargs["output_attentions"],
output_hidden_states=kwargs["output_hidden_states"],
)
next_token_logits = outputs.logits[:, -1, :] # (batch_size * num_beams, vocab_size)
# if model has past, then set the past variable to speed up decoding
if self._use_cache(outputs, use_cache):
past = outputs[1]
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0:
next_token_logits_penalties = _create_next_token_logits_penalties(
input_ids, next_token_logits, repetition_penalty
)
next_token_logits = tf.math.multiply(next_token_logits, next_token_logits_penalties)
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
next_token_logits = next_token_logits / temperature
if self.config.is_encoder_decoder and do_sample is False:
next_token_logits = self.adjust_logits_during_generation(
next_token_logits,
cur_len=cur_len,
max_length=max_length,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
)
# calculate log softmax score
scores = tf.nn.log_softmax(next_token_logits, axis=-1) # (batch_size * num_beams, vocab_size)
# set eos token prob to zero if min_length is not reached
if eos_token_id is not None and cur_len < min_length:
# create eos_token_id boolean mask
num_batch_hypotheses = batch_size * num_beams
is_token_logit_eos_token = tf.convert_to_tensor(
[True if token == eos_token_id else False for token in range(vocab_size)], dtype=tf.bool
)
eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [num_batch_hypotheses, vocab_size])
scores = tf.where(eos_token_indices_mask, -float("inf"), scores)
if no_repeat_ngram_size > 0:
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
num_batch_hypotheses = batch_size * num_beams
banned_tokens = calc_banned_ngram_tokens(
input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len
)
# create banned_tokens boolean mask
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = tf.where(
tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores
)
if bad_words_ids is not None:
# calculate a list of banned tokens according to bad words
banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = tf.where(
tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores
)
assert shape_list(scores) == [batch_size * num_beams, vocab_size]
if do_sample:
_scores = scores + tf.broadcast_to(
beam_scores[:, None], (batch_size * num_beams, vocab_size)
) # (batch_size * num_beams, vocab_size)
# Top-p/top-k filtering
_scores = tf_top_k_top_p_filtering(
_scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
) # (batch_size * num_beams, vocab_size)
# Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
_scores = tf.reshape(_scores, (batch_size, num_beams * vocab_size))
next_tokens = sample_without_replacement(
_scores, num_samples=2 * num_beams
) # (batch_size, 2 * num_beams)
# Compute next scores
next_scores = tf.gather(_scores, next_tokens, batch_dims=1) # (batch_size, 2 * num_beams)
# sort the sampled vector to make sure that the first num_beams samples are the best
next_scores_indices = tf.argsort(next_scores, direction="DESCENDING", axis=1)
next_scores = tf.gather(next_scores, next_scores_indices, batch_dims=1) # (batch_size, num_beams * 2)
next_tokens = tf.gather(next_tokens, next_scores_indices, batch_dims=1) # (batch_size, num_beams * 2)
else:
# Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product)
next_scores = scores + tf.broadcast_to(
beam_scores[:, None], (batch_size * num_beams, vocab_size)
) # (batch_size * num_beams, vocab_size)
# re-organize to group the beam together (we are keeping top hypothesis across beams)
next_scores = tf.reshape(
next_scores, (batch_size, num_beams * vocab_size)
) # (batch_size, num_beams * vocab_size)
next_scores, next_tokens = tf.math.top_k(next_scores, k=2 * num_beams, sorted=True)
assert shape_list(next_scores) == shape_list(next_tokens) == [batch_size, 2 * num_beams]
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if kwargs["output_scores"]:
scores += (next_token_logits,)
if kwargs["output_attentions"]:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if kwargs["output_hidden_states"]:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# next batch beam content
next_batch_beam = []
# for each sentence
for batch_idx in range(batch_size):
# if we are done with this sentence
if done[batch_idx]:
assert (
len(generated_hyps[batch_idx]) >= num_beams
), f"Batch can only be done if at least {num_beams} beams have been generated."
assert (
eos_token_id is not None and pad_token_id is not None
), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch
continue
# next sentence beam content
next_sent_beam = []
# next tokens for this sentence
for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx])
):
# get beam and token IDs
beam_id = beam_token_id // vocab_size
token_id = beam_token_id % vocab_size
effective_beam_id = batch_idx * num_beams + beam_id
# add to generated hypotheses if end of sentence or last iteration
if (eos_token_id is not None) and (token_id.numpy() == eos_token_id):
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
if is_beam_token_worse_than_top_num_beams:
continue
generated_hyps[batch_idx].add(
tf.identity(input_ids[effective_beam_id]), beam_token_score.numpy()
)
else:
# add next predicted token if it is not eos_token
next_sent_beam.append((beam_token_score, token_id, effective_beam_id))
# the beam for next step is full
if len(next_sent_beam) == num_beams:
break
# Check if we are done so that we can save a pad step if all(done)
done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
tf.reduce_max(next_scores[batch_idx]).numpy(), cur_len
)
# update next beam content
assert len(next_sent_beam) == num_beams, "Beam should always be full"
next_batch_beam.extend(next_sent_beam)
assert len(next_batch_beam) == num_beams * (batch_idx + 1)
# stop when we are done with each sentence
if all(done):
break
# sanity check / prepare next batch
assert len(next_batch_beam) == batch_size * num_beams
beam_scores = tf.convert_to_tensor([x[0] for x in next_batch_beam], dtype=tf.float32)
beam_tokens = tf.convert_to_tensor([x[1] for x in next_batch_beam], dtype=tf.int32)
beam_idx = tf.convert_to_tensor([x[2] for x in next_batch_beam], dtype=tf.int32)
# re-order batch and update current length
input_ids = tf.stack([tf.identity(input_ids[x, :]) for x in beam_idx])
input_ids = tf.concat([input_ids, tf.expand_dims(beam_tokens, 1)], axis=-1)
cur_len = cur_len + 1
# re-order internal states
if past is not None:
past = self._reorder_cache(past, beam_idx)
# extend attention_mask for new generated input if only decoder
if self.config.is_encoder_decoder is False:
attention_mask = tf.concat(
[attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
)
# finalize all open beam hypotheses and end to generated hypotheses
for batch_idx in range(batch_size):
# Add all open beam hypothesis to generated_hyps
if done[batch_idx]:
continue
# test that beam scores match previously calculated scores if not eos and batch_idx not done
if eos_token_id is not None and all(
(token_id % vocab_size).numpy().item() != eos_token_id for token_id in next_tokens[batch_idx]
):
if not tf.reduce_all(
next_scores[batch_idx, :num_beams] == tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]
):
raise ValueError(
f"If batch_idx is not done, final next scores: {next_scores[:, :num_beams][batch_idx]} have "
"to equal to accumulated beam_scores: "
f"{tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]}"
)
# need to add best num_beams hypotheses to generated hyps
for beam_id in range(num_beams):
effective_beam_id = batch_idx * num_beams + beam_id
final_score = beam_scores[effective_beam_id].numpy().item()
final_tokens = input_ids[effective_beam_id]
generated_hyps[batch_idx].add(final_tokens, final_score)
# depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences
# select the best hypotheses
sent_lengths_list = []
best = []
# retrieve best hypotheses
for i, hypotheses in enumerate(generated_hyps):
sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
for j in range(output_num_return_sequences_per_batch):
best_hyp = sorted_hyps.pop()[1]
sent_lengths_list.append(len(best_hyp))
best.append(best_hyp)
assert output_batch_size == len(
best
), f"Output batch size {output_batch_size} must match output beam hypotheses {len(best)}"
sent_lengths = tf.convert_to_tensor(sent_lengths_list, dtype=tf.int32)
# shorter batches are filled with pad_token
if tf.reduce_min(sent_lengths).numpy() != tf.reduce_max(sent_lengths).numpy():
assert pad_token_id is not None, "`Pad_token_id` has to be defined"
sent_max_len = min(tf.reduce_max(sent_lengths).numpy() + 1, max_length)
decoded_list = []
# fill with hypothesis and eos_token_id if necessary
for i, hypo in enumerate(best):
assert sent_lengths[i] == shape_list(hypo)[0]
# if sent_length is max_len do not pad
if sent_lengths[i] == sent_max_len:
decoded_slice = hypo
else:
# else pad to sent_max_len
num_pad_tokens = sent_max_len - sent_lengths[i]
padding = pad_token_id * tf.ones((num_pad_tokens,), dtype=tf.int32)
decoded_slice = tf.concat([hypo, padding], axis=-1)
# finish sentence with EOS token
if sent_lengths[i] < max_length:
decoded_slice = tf.where(
tf.range(sent_max_len, dtype=tf.int32) == sent_lengths[i],
eos_token_id * tf.ones((sent_max_len,), dtype=tf.int32),
decoded_slice,
)
# add to list
decoded_list.append(decoded_slice)
decoded = tf.stack(decoded_list)
else:
# none of the hypotheses have an eos_token
assert (len(hypo) == max_length for hypo in best)
decoded = tf.stack(best)
if return_dict_in_generate:
if do_sample and self.config.is_encoder_decoder:
return TFBeamSampleEncoderDecoderOutput(
sequences=decoded,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
elif do_sample and not self.config.is_encoder_decoder:
return TFBeamSampleDecoderOnlyOutput(
sequences=decoded,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
elif self.config.is_encoder_decoder:
return TFBeamSearchEncoderDecoderOutput(
sequences=decoded,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFBeamSearchDecoderOnlyOutput(
sequences=decoded,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return decoded
@staticmethod
def _reorder_cache(past, beam_idx):
return tuple(tf.gather(layer_past, beam_idx, axis=1) for layer_past in past)
def adjust_logits_during_generation(
self, logits, cur_len, max_length, forced_bos_token_id, forced_eos_token_id, **kwargs
):
"""
Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
"""
vocab_size = getattr(self.config, "vocab_size", None)
if vocab_size is None and self.config.is_encoder_decoder:
decoder_config = getattr(self.config, "decoder", None)
if decoder_config is not None:
vocab_size = getattr(self.config.decoder, "vocab_size", None)
if cur_len == 1 and forced_bos_token_id is not None:
vocab_range = tf.constant(range(vocab_size))
return tf.where(vocab_range != forced_bos_token_id, -1e8, logits)
elif cur_len == max_length - 1 and forced_eos_token_id is not None:
vocab_range = tf.constant(range(vocab_size))
return tf.where(vocab_range != forced_eos_token_id, -1e8, logits)
else:
return logits
def _validate_model_class(self):
"""
Confirms that the model class is compatible with generation. If not, raises an exception that points to the
right class to use.
"""
if not hasattr(self, "prepare_inputs_for_generation"):
generate_compatible_mappings = [
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
]
generate_compatible_classes = set()
for model_mapping in generate_compatible_mappings:
supported_models = model_mapping.get(type(self.config), default=None)
if supported_models is not None:
generate_compatible_classes.add(supported_models.__name__)
exception_message = (
f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
"it doesn't have a language model head."
)
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message)
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
# Excludes arguments that are handled before calling any model function
if self.config.is_encoder_decoder:
for key in ["decoder_input_ids"]:
model_kwargs.pop(key, None)
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
if "kwargs" in model_args or "model_kwargs" in model_args:
model_args |= set(inspect.signature(self.call).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
def _generate(
self,
input_ids=None,
max_length=None,
max_new_tokens=None,
min_length=None,
do_sample=None,
early_stopping=None,
num_beams=None,
temperature=None,
penalty_alpha=None,
top_k=None,
top_p=None,
repetition_penalty=None,
bad_words_ids=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
attention_mask=None,
decoder_start_token_id=None,
use_cache=None,
seed=None,
output_scores=None,
output_attentions=None,
output_hidden_states=None,
return_dict_in_generate=None,
forced_bos_token_id=None,
forced_eos_token_id=None,
suppress_tokens=None,
begin_suppress_tokens=None,
forced_decoder_ids=None,
**model_kwargs,
) -> Union[TFGenerateOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head. The method supports the following
generation methods for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`.
- *contrastive search* by calling [`~generation.TFGenerationMixin.contrastive_search`] if `penalty_alpha>0`
and `top_k>1`
- *multinomial sampling* by calling [`~generation.TFGenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`.
- *beam-search decoding* by calling [`~generation.TFGenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`.
Adapted in part from [Facebook's XLM beam search
code](https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529).
Apart from `input_ids` and `attention_mask`, all the arguments below will default to the value of the attribute
of the same name inside the [`PretrainedConfig`] of the model. The default values indicated are the default
values of those config.
Most of these parameters are explained in more detail in [this blog
post](https://huggingface.co/blog/how-to-generate).
Parameters:
input_ids (`tf.Tensor` of `dtype=tf.int32` and shape `(batch_size, sequence_length)`, *optional*):
The sequence used as a prompt for the generation. If `None` the method initializes it with
`bos_token_id` and a batch size of 1.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 10):
The minimum length of the sequence to be generated.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
early_stopping (`bool`, *optional*, defaults to `False`):
Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
num_beams (`int`, *optional*, defaults to 1):
Number of beams for beam search. 1 means no beam search.
temperature (`float`, *optional*, defaults to 1.0):
The value used to module the next token probabilities.
penalty_alpha (`float`, *optional*):
The values balance the model confidence and the degeneration penalty in contrastive search decoding.
top_k (`int`, *optional*, defaults to 50):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*, defaults to 1.0):
If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher
are kept for generation.
repetition_penalty (`float`, *optional*, defaults to 1.0):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
bos_token_id (`int`, *optional*):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent
to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size can only occur once.
bad_words_ids(`List[int]`, *optional*):
List of token ids that are not allowed to be generated. In order to get the tokens of the words that
should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
attention_mask (`tf.Tensor` of `dtype=tf.int32` and shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values are in `[0, 1]`, 1 for tokens
that are not masked, and 0 for masked tokens.
If not provided, will default to a tensor the same shape as `input_ids` that masks the pad token.
[What are attention masks?](../glossary#attention-mask)
decoder_start_token_id (`int`, *optional*):
If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.
seed (`List[int]`, *optional*):
Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
`seed` argument from stateless functions in `tf.random`.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
forced_bos_token_id (`int`, *optional*):
The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
the target language token.
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached.
suppress_tokens (`List[int]`, *optional*, defaults to `model.config.suppress_tokens`):
A list of tokens that will be supressed at generation. The `SupressTokens` logit processor will set
their log probs to `-inf` so that they are not sampled.
begin_suppress_tokens (`List[int]`, *optional*, defaults to `model.config.begin_suppress_tokens`):
A list of tokens that will be supressed at the begining of the generation. The `SupressBeginTokens`
logit processor will set their log probs to `-inf` so that they are not sampled.
forced_decoder_ids (`List[List[int]]`, *optional*, defaults to `model.config.forced_decoder_ids`):
A list of pairs of integers which indicates a mapping from generation indices to token indices that
will be forced before sampling. For example, `[[1, 123]]` means the second generated token will always
be a token of index 123.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model.
Return:
[`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a `tf.Tensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchDecoderOnlyOutput`],
- [`~generation.TFSampleDecoderOnlyOutput`],
- [`~generation.TFBeamSearchDecoderOnlyOutput`],
- [`~generation.TFBeamSampleDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchEncoderDecoderOutput`],
- [`~generation.TFSampleEncoderDecoderOutput`],
- [`~generation.TFBeamSearchEncoderDecoderOutput`],
- [`~generation.TFBeamSampleEncoderDecoderOutput`]
Examples:
```python
tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained("distilgpt2")
# Greedy decoding
outputs = model.generate(max_length=40)
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("openai-gpt")
model = TFAutoModelWithLMHead.from_pretrained("openai-gpt")
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
# Generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)
# 3 output sequences were generated
for i in range(3):
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = TFAutoModelWithLMHead.from_pretrained("distilgpt2")
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf")
# Generate 3 candidates using sampling
outputs = model.generate(
input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True
)
# 3 output sequences were generated
for i in range(3):
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("ctrl")
model = TFAutoModelWithLMHead.from_pretrained("ctrl")
# "Legal" is one of the control codes for ctrl
input_context = "Legal My neighbor is"
input_ids = tokenizer.encode(input_context, return_tensors="tf")
outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = TFAutoModelWithLMHead.from_pretrained("gpt2")
input_context = "My cute dog"
bad_words_ids = [
tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ["idiot", "stupid", "shut up"]
]
input_ids = tokenizer.encode(input_context, return_tensors="tf")
# generate sequences without allowing bad_words to be generated
outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)
```"""
# 0. Validate the `.generate()` call
self._validate_model_class()
self._validate_model_kwargs(model_kwargs.copy())
# 1. Cast input dtypes to tf.int32 unless they're floats (which happens for some image models)
if input_ids is not None:
if isinstance(input_ids, tf.Tensor) and input_ids.dtype.is_floating:
pass
elif isinstance(input_ids, np.ndarray) and np.issubdtype(input_ids.dtype, np.floating):
pass
else:
input_ids = tf.cast(input_ids, tf.int32)
if attention_mask is not None:
attention_mask = tf.cast(attention_mask, tf.int32)
if "decoder_input_ids" in model_kwargs:
if (
isinstance(model_kwargs["decoder_input_ids"], tf.Tensor)
and model_kwargs["decoder_input_ids"].dtype.is_floating
):
pass
elif isinstance(model_kwargs["decoder_input_ids"], np.ndarray) and np.issubdtype(
model_kwargs["decoder_input_ids"].dtype, np.floating
):
pass
else:
model_kwargs["decoder_input_ids"] = tf.cast(model_kwargs["decoder_input_ids"], tf.int32)
# 2. Set generation parameters if not already defined
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
forced_bos_token_id = (
forced_bos_token_id if forced_bos_token_id is not None else self.config.forced_bos_token_id
)
forced_eos_token_id = (
forced_eos_token_id if forced_eos_token_id is not None else self.config.forced_eos_token_id
)
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
num_beams = num_beams if num_beams is not None else self.config.num_beams
do_sample = do_sample if do_sample is not None else self.config.do_sample
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
if pad_token_id is None and eos_token_id is not None:
if attention_mask is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
logger.warning(f"Setting `pad_token_id` to {eos_token_id} (first `eos_token_id`) to generate sequence")
pad_token_id = eos_token_id
use_xla = not tf.executing_eagerly()
if use_xla and not self.supports_xla_generation:
raise ValueError(
"The selected model does not support Graph mode nor XLA generation (e.g. from tf.function())"
)
# 3. Define model inputs
input_ids = self._prepare_model_inputs(input_ids, bos_token_id)
# inputs_ids now has to be defined and cannot be None anymore
batch_size = shape_list(input_ids)[0]
# 4. Prepare other model kwargs
if output_attentions is not None:
model_kwargs["output_attentions"] = output_attentions
if output_hidden_states is not None:
model_kwargs["output_hidden_states"] = output_hidden_states
if use_cache is not None:
model_kwargs["use_cache"] = use_cache
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.call).parameters.keys())
requires_attention_mask = "encoder_outputs" not in model_kwargs
if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
input_ids, pad_token_id, eos_token_id
)
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder:
if pad_token_id is not None and tf.math.reduce_any(input_ids[:, -1] == pad_token_id):
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
# 5. Prepare model inputs which will be used for auto-regressive generation
if self.config.is_encoder_decoder:
# if encoder-decoder, we create encoder_outputs and add to `model_kwargs`
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, model_kwargs)
# if encoder-decoder then `input_ids` come from `decoder_start_token_id`
input_ids = self._prepare_decoder_input_ids_for_generation(
batch_size,
decoder_start_token_id=decoder_start_token_id,
bos_token_id=bos_token_id,
model_kwargs=model_kwargs,
)
# 6. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
if max_length is None and max_new_tokens is None:
warnings.warn(
"Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to "
f"{self.config.max_length} (`self.config.max_length`). Controlling `max_length` via the config is "
"deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend "
"using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif max_length is None and max_new_tokens is not None:
max_length = max_new_tokens + input_ids_seq_length
elif max_length is not None and max_new_tokens is not None:
raise ValueError(
"Both `max_new_tokens` and `max_length` have been set but they serve the same purpose -- setting a"
" limit to the generated output length. Remove one of those arguments. Please refer to the"
" documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
# default to config if still None
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
if min_length is not None and min_length > max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
f"length ({max_length})"
)
if input_ids_seq_length >= max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {max_length}. This can lead to unexpected behavior. You should consider increasing"
"`max_new_tokens`."
)
# 7. determine generation mode
# TODO(Matt, Joao, Patrick) - add more use cases here
is_contrastive_search_gen_mode = (
top_k is not None and top_k > 1 and do_sample is False and penalty_alpha is not None and penalty_alpha > 0
)
is_greedy_gen_mode = not is_contrastive_search_gen_mode and (num_beams == 1) and do_sample is False
is_beam_gen_mode = not is_contrastive_search_gen_mode and (num_beams > 1) and do_sample is False
is_sample_gen_mode = (num_beams == 1) and do_sample is True
# 8. prepare distribution pre_processing samplers
logits_processor = self._get_logits_processor(
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
input_ids_seq_length=input_ids_seq_length,
bad_words_ids=bad_words_ids,
min_length=min_length,
max_length=max_length,
eos_token_id=eos_token_id,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
suppress_tokens=suppress_tokens,
begin_suppress_tokens=begin_suppress_tokens,
forced_decoder_ids=forced_decoder_ids,
)
# 9. go into different generation modes
if is_greedy_gen_mode:
if num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {num_return_sequences} when doing greedy search."
)
# 10. run greedy search
return self.greedy_search(
input_ids,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
logits_processor=logits_processor,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
elif is_contrastive_search_gen_mode:
if num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {num_return_sequences} when doing contrastive search."
)
# 10. run contrastive search
return self.contrastive_search(
input_ids,
top_k=top_k,
penalty_alpha=penalty_alpha,
logits_processor=logits_processor,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
elif is_sample_gen_mode:
# 10. prepare logits warper
logits_warper = self._get_logits_warper(top_k=top_k, top_p=top_p, temperature=temperature)
# 11. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 12. run sample
return self.sample(
input_ids,
logits_processor=logits_processor,
logits_warper=logits_warper,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
seed=seed,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
**model_kwargs,
)
elif is_beam_gen_mode:
if num_beams < num_return_sequences:
raise ValueError(
"Greedy beam search decoding cannot return more sequences than it has beams. Please set "
f"num_beams >= num_return_sequences, got {num_beams} and {num_return_sequences} (respectivelly)"
)
# 10. broadcast inputs to the desired number of beams
input_ids = self._expand_to_num_beams(input_ids, num_beams=num_beams)
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = self._expand_to_num_beams(
model_kwargs["encoder_outputs"]["last_hidden_state"], num_beams=num_beams
)
if "attention_mask" in model_kwargs:
model_kwargs["attention_mask"] = self._expand_to_num_beams(
model_kwargs["attention_mask"], num_beams=num_beams
)
# 11. run beam search
return self.beam_search(
input_ids,
max_length=max_length,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
length_penalty=length_penalty,
early_stopping=early_stopping,
logits_processor=logits_processor,
return_dict_in_generate=return_dict_in_generate,
num_return_sequences=num_return_sequences,
**model_kwargs,
)
else:
# TODO(Matt, Joao, Patrick) - add more sub-generation methods here
raise NotImplementedError("Beam sampling is currently not implemented.")
@staticmethod
def _expand_to_num_beams(tensor: tf.Tensor, num_beams: int) -> tf.Tensor:
shape = shape_list(tensor)
return tf.broadcast_to(tensor[:, None], (shape[0], num_beams) + tuple(shape[1:]))
def _prepare_attention_mask_for_generation(
self,
inputs: tf.Tensor,
pad_token_id: Optional[int],
eos_token_id: Optional[int],
) -> tf.Tensor:
is_input_ids = len(inputs.shape) == 2 and inputs.dtype in (tf.int32, tf.int64)
is_pad_token_in_inputs = (pad_token_id is not None) and tf.math.reduce_any(inputs == pad_token_id)
is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (pad_token_id != eos_token_id)
# Check if input is input_ids and padded -> only then is attention_mask defined
if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
return tf.cast(tf.math.not_equal(inputs, pad_token_id), dtype=tf.int32)
else:
return tf.ones(inputs.shape[:2], dtype=tf.int32)
def _prepare_encoder_decoder_kwargs_for_generation(self, inputs_tensor: tf.Tensor, model_kwargs) -> Dict[str, Any]:
# get encoder and store encoder outputs
encoder = self.get_encoder()
# prepare encoder args and encoder kwargs from model kwargs
irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
encoder_kwargs = {
argument: value
for argument, value in model_kwargs.items()
if not any(argument.startswith(p) for p in irrelevant_prefix)
}
# vision models don't use `attention_mask`.
encoder_kwargs["return_dict"] = True
encoder_kwargs[self.main_input_name] = inputs_tensor
encoder_outputs = encoder(**encoder_kwargs)
model_kwargs["encoder_outputs"] = encoder_outputs
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
decoder_start_token_id: int = None,
bos_token_id: int = None,
model_kwargs: Optional[Dict[str, tf.Tensor]] = None,
) -> tf.Tensor:
# prepare `input_ids` for decoder if model is encoder-decoder
if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
return model_kwargs.pop("decoder_input_ids")
else:
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
return tf.ones((batch_size, 1), dtype=tf.int32) * decoder_start_token_id
def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
# retrieve decoder_start_token_id for encoder-decoder models
# fall back to bos_token_id if necessary
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
if decoder_start_token_id is not None:
return decoder_start_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "decoder_start_token_id")
and self.config.decoder.decoder_start_token_id is not None
):
return self.config.decoder.decoder_start_token_id
elif bos_token_id is not None:
return bos_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "bos_token_id")
and self.config.decoder.bos_token_id is not None
):
return self.config.decoder.bos_token_id
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
)
@staticmethod
def _expand_inputs_for_generation(
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[tf.Tensor] = None,
**model_kwargs,
) -> Tuple[tf.Tensor, Dict[str, Any]]:
"""Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""
if input_ids is not None:
input_ids = tf.repeat(input_ids, expand_size, axis=0)
if model_kwargs.get("token_type_ids") is not None:
model_kwargs["token_type_ids"] = tf.repeat(model_kwargs["token_type_ids"], expand_size, axis=0)
if model_kwargs.get("attention_mask") is not None:
model_kwargs["attention_mask"] = tf.repeat(model_kwargs["attention_mask"], expand_size, axis=0)
if model_kwargs.get("decoder_attention_mask") is not None:
model_kwargs["decoder_attention_mask"] = tf.repeat(
model_kwargs["decoder_attention_mask"], expand_size, axis=0
)
if is_encoder_decoder:
encoder_outputs = model_kwargs.get("encoder_outputs")
if encoder_outputs is None:
raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, expand_size, axis=0)
model_kwargs["encoder_outputs"] = encoder_outputs
return input_ids, model_kwargs
def _prepare_model_inputs(self, inputs: Optional[tf.Tensor] = None, bos_token_id: Optional[int] = None):
# TODO(Patrick) - adapt this function when making `generate` more flexible
# for all kinds of input types
if inputs is None:
# if no `inputs` are passed create prompt of size (1,1) filled with BOS token
if not isinstance(bos_token_id, int) or bos_token_id < 0:
raise ValueError(
"you should either supply a context to complete as `input_ids` input "
"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
)
return tf.cast(tf.fill((1, 1), bos_token_id), dtype=tf.int32)
return inputs
@staticmethod
def _extract_past_from_model_output(outputs: ModelOutput):
past = None
if "past_key_values" in outputs:
past = outputs.past_key_values
elif "mems" in outputs:
past = outputs.mems
elif "past_buckets_states" in outputs:
past = outputs.past_buckets_states
return past
def _update_model_kwargs_for_generation(
self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
) -> Dict[str, Any]:
# update past
model_kwargs["past"] = self._extract_past_from_model_output(outputs)
# update attention mask
if not is_encoder_decoder:
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = tf.concat(
[attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
)
return model_kwargs
def _update_model_kwargs_for_xla_generation(
self,
model_outputs: ModelOutput,
model_kwargs: Dict[str, Any],
cur_len: int,
max_length: int,
batch_size: int,
is_encoder_decoder: bool = False,
batch_axis: int = 0,
):
def _initialize_attention(model_kwargs, num_padding_values, is_encoder_decoder):
"""initializes the appropriate attention mask -- encoder-decoder models use `decoder_attention_mask`"""
if is_encoder_decoder:
# One 1 for decoder_start_token_id, 0s for the currently-unfilled locations in the past tensor,
# 1s for the actual input_ids
decoder_attention_mask = tf.concat(
[
tf.ones((batch_size, 1), dtype=tf.int32),
tf.zeros((batch_size, num_padding_values), dtype=tf.int32),
tf.ones((batch_size, 1), dtype=tf.int32),
],
axis=1,
)
mask = {"decoder_attention_mask": decoder_attention_mask}
else:
attention_mask = model_kwargs.pop("attention_mask")
# 0s for the currently-unfilled locations in the past tensor, 1s for the actual input_ids
attention_mask = tf.concat(
[
attention_mask,
tf.zeros((batch_size, num_padding_values), dtype=attention_mask.dtype),
tf.ones((batch_size, 1), dtype=attention_mask.dtype),
],
axis=1,
)
mask = {"attention_mask": attention_mask}
return mask
def _update_attention(model_kwargs, new_past_index, is_encoder_decoder):
"""updates the appropriate attention mask -- encoder-decoder models use `decoder_attention_mask`"""
update_start = tf.constant([0, 1], dtype=tf.int32) * new_past_index
if is_encoder_decoder:
decoder_attention_mask = model_kwargs.pop("decoder_attention_mask")
decoder_attention_mask_update_slice = tf.ones((batch_size, 1), dtype=decoder_attention_mask.dtype)
decoder_attention_mask = dynamic_update_slice(
decoder_attention_mask, decoder_attention_mask_update_slice, update_start
)
mask = {"decoder_attention_mask": decoder_attention_mask}
else:
attention_mask = model_kwargs.pop("attention_mask")
attention_mask_update_slice = tf.ones((batch_size, 1), dtype=attention_mask.dtype)
attention_mask = dynamic_update_slice(attention_mask, attention_mask_update_slice, update_start)
mask = {"attention_mask": attention_mask}
return mask
def _initialize_past(past, num_padding_values, batch_axis):
"""initialize past with zeros -- the structure depends on `batch_axis`"""
if batch_axis == 0:
padding_values = tf.constant([[0, 0], [0, 0], [0, num_padding_values], [0, 0]], dtype=tf.int32)
new_past = ()
for past_layer in past:
new_past_layer = list(past_layer)
for i in range(len(new_past_layer[:2])):
new_past_layer[i] = tf.pad(past_layer[i], padding_values)
new_past += (tuple(new_past_layer),)
else:
padding_values = tf.scatter_nd(indices=[[3, 1]], updates=[num_padding_values], shape=(5, 2))
new_past = list(past)
for i in range(len(past)):
new_past[i] = tf.pad(past[i], padding_values)
return new_past
def _update_past(past, new_past_index, batch_axis):
if batch_axis == 0:
slice_start_base = tf.constant([0, 0, 1, 0])
new_past = ()
for past_layer in past:
new_past_layer = list(past_layer)
for i in range(len(new_past_layer[:2])):
update_slice = past_layer[i][:, :, -1:]
# Write the last slice to the first open location in the padded past array
# and then truncate the last slice off the array
new_past_layer[i] = dynamic_update_slice(
past_layer[i][:, :, :-1], update_slice, slice_start_base * new_past_index
)
new_past += (tuple(new_past_layer),)
else:
slice_start_base = tf.constant([0, 0, 0, 1, 0])
new_past = [None for _ in range(len(past))]
for i in range(len(past)):
update_slice = past[i][:, :, :, -1:]
# Write the last slice to the first open location in the padded past array
# and then truncate the last slice off the array
new_past[i] = dynamic_update_slice(
past[i][:, :, :, :-1], update_slice, slice_start_base * new_past_index
)
return new_past
past = self._extract_past_from_model_output(model_outputs)
if past is None:
raise ValueError(
f"No known past variable found in model outputs (model outputs keys: {list(model_outputs.keys())})"
)
is_past_initialized = model_kwargs.pop("past", None) is not None
if not is_past_initialized:
# The padded version of `past` has a length of `max_length - 1`, as `past` holds information relative to
# previous autoregressive generation steps (step 0 has no past, step 1 has 1 past value, ..., the last step
# has `max_length - 1` past values).
num_padding_values = max_length - cur_len - 1
mask = _initialize_attention(model_kwargs, num_padding_values, is_encoder_decoder)
new_past = _initialize_past(past, num_padding_values, batch_axis)
else:
# The new index of past to be filled corresponds to the current length of the sequence, with two
# subtractions: -1 because past holds information regarding previous generation steps (read comment above)
# and -1 again because in an array the index is the length of the array minus 1.
new_past_index = cur_len - 2
mask = _update_attention(model_kwargs, new_past_index, is_encoder_decoder)
new_past = _update_past(past, new_past_index, batch_axis)
# sets the updated variables (mask and past)
model_kwargs.update(mask)
model_kwargs["past"] = tuple(new_past)
return model_kwargs
def _get_logits_warper(
self,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None,
) -> TFLogitsProcessorList:
"""
This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`TFLogitsWarper`]
instances used for multinomial sampling.
"""
# init warp parameters
top_k = top_k if top_k is not None else self.config.top_k
top_p = top_p if top_p is not None else self.config.top_p
temperature = temperature if temperature is not None else self.config.temperature
# instantiate warpers list
warpers = TFLogitsProcessorList()
# the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
# all samplers can be found in `generation_utils_samplers.py`
if temperature is not None and temperature != 1.0:
warpers.append(TFTemperatureLogitsWarper(temperature))
if top_k is not None and top_k != 0:
warpers.append(TFTopKLogitsWarper(top_k=top_k, min_tokens_to_keep=1))
if top_p is not None and top_p < 1.0:
warpers.append(TFTopPLogitsWarper(top_p=top_p, min_tokens_to_keep=1))
return warpers
def _get_logits_processor(
self,
repetition_penalty: float,
no_repeat_ngram_size: int,
input_ids_seq_length: int,
bad_words_ids: List[List[int]],
min_length: int,
max_length: int,
eos_token_id: int,
forced_bos_token_id: int,
forced_eos_token_id: int,
suppress_tokens: Optional[List[int]] = None,
begin_suppress_tokens: Optional[List[int]] = None,
forced_decoder_ids: Optional[List[List[int]]] = None,
) -> TFLogitsProcessorList:
"""
This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`TFLogitsProcessor`]
instances used to modify the scores of the language model head.
"""
processors = TFLogitsProcessorList()
repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
no_repeat_ngram_size = (
no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
)
bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
suppress_tokens = suppress_tokens if suppress_tokens is not None else self.config.suppress_tokens
begin_suppress_tokens = (
begin_suppress_tokens if begin_suppress_tokens is not None else self.config.begin_suppress_tokens
)
if forced_decoder_ids is None and hasattr(self.config, "forced_decoder_ids"):
forced_decoder_ids = self.config.forced_decoder_ids
# instantiate processors list
if repetition_penalty is not None and repetition_penalty != 1.0:
processors.append(TFRepetitionPenaltyLogitsProcessor(penalty=repetition_penalty))
if no_repeat_ngram_size is not None and no_repeat_ngram_size > 0:
processors.append(TFNoRepeatNGramLogitsProcessor(no_repeat_ngram_size))
if bad_words_ids is not None:
processors.append(TFNoBadWordsLogitsProcessor(bad_words_ids, eos_token_id))
if min_length is not None and eos_token_id is not None and min_length > 0:
processors.append(TFMinLengthLogitsProcessor(min_length, eos_token_id))
if forced_bos_token_id is not None:
processors.append(TFForcedBOSTokenLogitsProcessor(forced_bos_token_id))
if forced_eos_token_id is not None:
processors.append(TFForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id))
if suppress_tokens is not None:
processors.append(TFSuppressTokensLogitsProcessor(suppress_tokens))
if begin_suppress_tokens is not None:
begin_index = input_ids_seq_length
begin_index = begin_index if (input_ids_seq_length > 1 or forced_bos_token_id is None) else begin_index + 1
if forced_decoder_ids is not None:
begin_index += forced_decoder_ids[-1][0] # generation starts after the last token that is forced
processors.append(TFSuppressTokensAtBeginLogitsProcessor(begin_suppress_tokens, begin_index))
if forced_decoder_ids is not None:
processors.append(TFForceTokensLogitsProcessor(forced_decoder_ids))
return processors
def greedy_search(
self,
input_ids: tf.Tensor,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFGreedySearchOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using greedy decoding.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `call` function of the model. If
model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFGreedySearchDecoderOnlyOutput`], [`~generation.TFGreedySearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFGreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFGreedySearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForCausalLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [
... TFMinLengthLogitsProcessor(15, eos_token_id=model.config.eos_token_id),
... ]
... )
>>> outputs = model.greedy_search(input_ids, logits_processor=logits_processor)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
["Today is a beautiful day, and I'm so happy to be here. I'm so happy to"]
```"""
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# some models, like XLNet, need more than the last token in the presence of past
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (`input_ids` padded with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define condition fn
def greedy_search_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state termination condition fn."""
return ~tf.reduce_all(finished_sequences)
# define condition fn
def greedy_search_body_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state update fn."""
if model_kwargs.get("past") is None or needs_full_input:
input_ids = generated[:, :cur_len]
else:
input_ids = tf.expand_dims(generated[:, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token logits
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
next_token_logits = model_outputs.logits[:, -1]
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(next_token_logits)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# pre-process distribution
next_tokens_scores = logits_processor(generated, next_token_logits, cur_len)
# argmax
next_tokens = tf.argmax(next_tokens_scores, axis=-1, output_type=tf.int32)
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
finished_sequences = finished_sequences | (next_tokens == eos_token_id)
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past key values we need the whole input
if model_kwargs.get("past", None) is None:
# let's throw out `past` since we don't want `None` tensors
model_kwargs.pop("past", None)
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past`
generated, finished_sequences, cur_len, model_kwargs = greedy_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
if greedy_search_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
maximum_iterations = max_length - cur_len
generated, _, cur_len, _ = tf.while_loop(
greedy_search_cond_fn,
greedy_search_body_fn,
(generated, finished_sequences, cur_len, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFGreedySearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFGreedySearchDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def sample(
self,
input_ids: tf.Tensor,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
seed: Optional[Tuple[int, int]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFSampleOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using multinomial sampling.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
seed (`List[int]`, *optional*):
Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
`seed` argument from stateless functions in `tf.random`.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFSampleDecoderOnlyOutput`], [`~generation.TFSampleEncoderDecoderOutput`] or `tf.Tensor`: A
`tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFSampleEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForCausalLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... TFTopKLogitsWarper,
... TFTemperatureLogitsWarper,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [
... TFMinLengthLogitsProcessor(15, eos_token_id=model.config.eos_token_id),
... ]
... )
>>> # instantiate logits processors
>>> logits_warper = TFLogitsProcessorList(
... [
... TFTopKLogitsWarper(50),
... TFTemperatureLogitsWarper(0.7),
... ]
... )
>>> tf.random.set_seed(0)
>>> outputs = model.sample(input_ids, logits_processor=logits_processor, logits_warper=logits_warper)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Today is a beautiful day, and I love my country. But when I look at Donald Trump,']
```"""
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# some models, like XLNet, need more than the last token in the presence of past
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (pre-populated with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
def sample_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
return ~tf.reduce_all(finished_sequences)
def sample_body_fn(generated, finished_sequences, cur_len, model_kwargs):
if model_kwargs.get("past") is None or needs_full_input:
input_ids = generated[:, :cur_len]
else:
input_ids = tf.expand_dims(generated[:, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token logits
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
next_token_logits = model_outputs.logits[:, -1]
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(next_token_logits)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# pre-process distribution
next_tokens_scores = logits_processor(generated, next_token_logits, cur_len)
next_tokens_scores = logits_warper(generated, next_tokens_scores, cur_len)
# sample
if seed is not None:
sample_seed = seed
else:
sample_seed = tf.experimental.numpy.random.randint(tf.int32.min, tf.int32.max, (2,), dtype=tf.int32)
next_tokens = tf.squeeze(
tf.random.stateless_categorical(
logits=next_tokens_scores, num_samples=1, seed=sample_seed, dtype=tf.int32
),
axis=1,
)
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
finished_sequences = finished_sequences | (next_tokens == eos_token_id)
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past key values we need the whole input
if model_kwargs.get("past", None) is None:
# let's throw out `past` since we don't want `None` tensors
model_kwargs.pop("past", None)
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past`
generated, finished_sequences, cur_len, model_kwargs = sample_body_fn(
generated, finished_sequences, cur_len, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
if sample_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
maximum_iterations = max_length - cur_len
generated, _, cur_len, _ = tf.while_loop(
sample_cond_fn,
sample_body_fn,
(generated, finished_sequences, cur_len, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFSampleEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFSampleDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def beam_search(
self,
input_ids: tf.Tensor,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
length_penalty: Optional[float] = None,
early_stopping: Optional[bool] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
num_return_sequences: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFBeamSearchOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using beam search with multinomial sampling.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent
to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
early_stopping (`bool`, *optional*, defaults to `False`):
Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
logits_processor (`[TFLogitsProcessorList]`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFBeamSearchDecoderOnlyOutput`], [`~generation.TFBeamSearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFBeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFBeamSearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForSeq2SeqLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... )
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="tf").input_ids
>>> # lets run beam search using 3 beams
>>> num_beams = 3
>>> # define decoder start token ids
>>> input_ids = tf.ones((1, num_beams, 1), dtype=tf.int32)
>>> input_ids = input_ids * model.config.decoder_start_token_id
>>> # add encoder_outputs to model keyword arguments
>>> encoder_outputs = model.get_encoder()(encoder_input_ids, return_dict=True)
>>> encoder_outputs.last_hidden_state = tf.repeat(
... tf.expand_dims(encoder_outputs.last_hidden_state, axis=0), num_beams, axis=1
... )
>>> model_kwargs = {"encoder_outputs": encoder_outputs}
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [TFMinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id)]
... )
>>> outputs = model.beam_search(input_ids, logits_processor=logits_processor, **model_kwargs)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Wie alt bist du?']
```"""
def flatten_beam_dim(tensor, batch_axis=0):
"""Flattens the first two dimensions of a non-scalar array."""
shape = shape_list(tensor)
return tf.reshape(
tensor,
shape[:batch_axis] + [shape[batch_axis] * shape[batch_axis + 1]] + shape[batch_axis + 2 :],
)
def unflatten_beam_dim(tensor, batch_size, num_beams, batch_axis=0):
"""Unflattens the first, flat batch*beam dimension of a non-scalar array."""
shape = shape_list(tensor)
return tf.reshape(tensor, shape[:batch_axis] + [batch_size, num_beams] + shape[batch_axis + 1 :])
def gather_beams(nested, beam_indices, batch_axis=0):
"""Gathers the beam slices indexed by beam_indices into new beam array."""
def gather_fn(tensor):
if batch_axis > 0:
# pushes all dimentions before the batch to the end, so we get (batch, beam_id, ...)
perm = tf.concat((tf.range(tf.rank(tensor))[batch_axis:], tf.range(batch_axis)), axis=0)
tensor = tf.transpose(tensor, perm=perm)
gathered_tensor = tf.gather(params=tensor, indices=beam_indices, axis=1, batch_dims=1)
if batch_axis > 0:
# transposes back to the original dimensions
perm = tf.concat((tf.range(tf.rank(tensor))[batch_axis:], tf.range(batch_axis)), axis=0)
perm = tf.math.invert_permutation(perm)
gathered_tensor = tf.transpose(gathered_tensor, perm=perm)
return gathered_tensor
return tf.nest.map_structure(gather_fn, nested)
# 1. init beam_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_scores = output_scores if output_scores is not None else self.config.output_scores
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# some models, like XLNet, need more than the last token in the presence of past
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, num_beams, cur_len = shape_list(input_ids)
# per batch, beam-item holding current token in loop, pre-populated with `pad_token_id`
input_ids_padding = tf.ones((batch_size, num_beams, max_length - cur_len), dtype=tf.int32) * (
pad_token_id or 0
)
running_sequences = tf.concat([input_ids, input_ids_padding], axis=-1)
sequences = tf.ones((batch_size, num_beams, max_length), dtype=tf.int32) * (pad_token_id or 0)
# per batch,beam-item state bit indicating if sentence has finished.
is_sent_finished = tf.zeros((batch_size, num_beams), dtype=tf.bool)
# per batch, beam-item score, logprobs
running_scores = tf.tile(
tf.expand_dims(tf.convert_to_tensor([0.0] + [-1.0e9] * (num_beams - 1)), axis=0), [batch_size, 1]
)
scores = tf.ones((batch_size, num_beams)) * -1.0e9
# flatten beam dim
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = flatten_beam_dim(
model_kwargs["encoder_outputs"]["last_hidden_state"]
)
if "attention_mask" in model_kwargs:
model_kwargs["attention_mask"] = flatten_beam_dim(model_kwargs["attention_mask"])
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define stop-condition and auto-regressive function
def beam_search_cond_fn(
cur_len,
running_sequences,
running_scores,
sequences,
scores,
is_sent_finished,
model_kwargs,
):
"""
Beam Search termination condition function -- halts the generation loop if any of these conditions becomes
False
"""
# 1. is less than max length?
not_max_length_yet = cur_len < max_length
# 2. can the new beams still improve?
best_running_score = running_scores[:, :1] / (max_length**length_penalty)
worst_finished_score = tf.where(
is_sent_finished, tf.math.reduce_min(scores, axis=1, keepdims=True), -1.0e9
)
improvement_still_possible = tf.math.reduce_all(worst_finished_score < best_running_score)
# 3. is there still a beam that has not finished?
still_open_beam = ~(tf.math.reduce_all(is_sent_finished) & early_stopping)
return not_max_length_yet & (still_open_beam | improvement_still_possible)
def beam_search_body_fn(
cur_len,
running_sequences,
running_scores,
sequences,
scores,
is_sent_finished,
model_kwargs,
):
"""
Beam Search iterative update function -- each iteration adds a new token and updates the best sequences
seen so far
"""
# 1. Forward current tokens
if model_kwargs.get("past") is None or needs_full_input:
input_ids = running_sequences[:, :, :cur_len]
else:
input_ids = tf.expand_dims(running_sequences[:, :, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(flatten_beam_dim(input_ids), **model_kwargs)
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
logits = unflatten_beam_dim(model_outputs.logits[:, -1], batch_size, num_beams)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(model_outputs.logits[:, -1])
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# 2. Compute log probs
# get log probabilities from logits, process logits with processors (*e.g.* min_length, ...), and
# add new logprobs to existing running logprobs scores.
log_probs = tf.nn.log_softmax(logits)
log_probs = logits_processor(flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), cur_len)
log_probs = unflatten_beam_dim(log_probs, batch_size, num_beams)
log_probs = log_probs + tf.expand_dims(running_scores, axis=2)
vocab_size = log_probs.shape[2]
log_probs = tf.reshape(log_probs, (batch_size, num_beams * vocab_size))
# 3. Retrieve top-K
# Each item in batch has num_beams * vocab_size candidate sequences. For each item, get the top 2*k
# candidates with the highest log-probabilities. We gather the top 2*K beams here so that even if the
# best K sequences reach EOS simultaneously, we have another K sequences remaining to continue the live
# beam search.
# Gather the top 2*K scores from _all_ beams.
# Gather 2*k top beams.
# Recover the beam index by floor division.
# Recover token id by modulo division and expand Id array for broadcasting.
# Update sequences for the 2*K top-k new sequences.
beams_to_keep = 2 * num_beams
topk_log_probs, topk_indices = tf.math.top_k(log_probs, k=beams_to_keep)
topk_beam_indices = topk_indices // vocab_size
topk_running_sequences = gather_beams(running_sequences, topk_beam_indices)
topk_ids = topk_indices % vocab_size
# writes the new token
indices_batch = tf.repeat(tf.range(batch_size), [beams_to_keep])
indices_beam = tf.tile(tf.range(beams_to_keep), [batch_size])
update_indices = tf.stack(
[indices_batch, indices_beam, tf.broadcast_to(cur_len, [batch_size * beams_to_keep])], axis=-1
)
topk_sequences = tf.tensor_scatter_nd_update(
tensor=topk_running_sequences,
indices=update_indices,
updates=tf.reshape(topk_ids, [batch_size * beams_to_keep]),
)
# 4. Check which sequences have ended
# Update current sequences: Did the top `num_beams` sequences reach an end marker?
# To prevent these just finished sequences from being added to the current sequences
# set of active beam search sequences, set their log probs to a very large negative value.
eos_in_next_token = topk_sequences[:, :, cur_len] == eos_token_id
if eos_token_id is None:
eos_in_next_token = tf.broadcast_to(eos_in_next_token, topk_sequences[:, :, cur_len].shape)
did_topk_just_finished = eos_in_next_token & tf.broadcast_to(
tf.concat((tf.ones((num_beams), dtype=tf.bool), tf.zeros((num_beams), dtype=tf.bool)), axis=0),
shape_list(eos_in_next_token),
)
# non-top `num_beams` eos tokens can't be used to finish a beam, but the others can't be used in the next
# running sentences either
running_topk_log_probs = topk_log_probs + tf.cast(eos_in_next_token, tf.float32) * -1.0e9
# 5. Get running sequences scores for next
# Determine the top k beam indices (from top 2*k beams) from log probs and gather top k beams
# (from top 2*k beams).
next_topk_indices = tf.math.top_k(running_topk_log_probs, k=num_beams)[1]
next_running_sequences, next_running_scores = gather_beams(
[topk_sequences, running_topk_log_probs], next_topk_indices
)
# 6. Process topk logits
# Further process log probs:
# - add length penalty
# - make sure no scores can be added anymore if beam is full
# - make sure still running sequences cannot be chosen as finalized beam
topk_log_probs = topk_log_probs / (tf.cast(cur_len, dtype=tf.float32) ** length_penalty)
beams_in_batch_are_full = (
tf.broadcast_to(
tf.math.reduce_all(is_sent_finished, axis=-1, keepdims=True), shape_list(did_topk_just_finished)
)
& early_stopping
)
add_penalty = ~did_topk_just_finished | beams_in_batch_are_full
topk_log_probs += tf.cast(add_penalty, tf.float32) * -1.0e9
# 7. Get scores, sequences, is sentence finished for next.
# Combine sequences, scores, and flags along the beam dimension and compare new finished sequence scores
# to existing finished scores and select the best from the new set of beams
merged_sequences = tf.concat([sequences, topk_sequences], axis=1)
merged_scores = tf.concat([scores, topk_log_probs], axis=1)
merged_is_sent_finished = tf.concat([is_sent_finished, did_topk_just_finished], axis=1)
topk_merged_indices = tf.math.top_k(merged_scores, k=num_beams)[1]
next_sequences, next_scores, next_is_sent_finished = gather_beams(
[merged_sequences, merged_scores, merged_is_sent_finished], topk_merged_indices
)
# 8. Prepare data for the next iteration
# Determine the top k beam indices from the original set of all beams. With these, gather the top k
# beam-associated caches.
cur_len = cur_len + 1
if "past_key_values" in model_outputs:
cache = tf.nest.map_structure(
lambda tensor: unflatten_beam_dim(tensor, batch_size, num_beams, batch_axis=cache_batch_axis),
model_outputs.past_key_values,
)
next_running_indices = gather_beams(topk_beam_indices, next_topk_indices)
next_cache = gather_beams(cache, next_running_indices, batch_axis=cache_batch_axis)
model_outputs["past_key_values"] = tf.nest.map_structure(
lambda tensor: flatten_beam_dim(tensor, batch_axis=cache_batch_axis), next_cache
)
if use_xla:
next_model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=(batch_size * num_beams),
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
next_model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past key values we need the whole input
if model_kwargs.get("past", None) is None:
# let's throw out `past` since we don't want `None` tensors
model_kwargs.pop("past", None)
return (
cur_len,
next_running_sequences,
next_running_scores,
next_sequences,
next_scores,
next_is_sent_finished,
next_model_kwargs,
)
# 5. run generation
# 1st generation step has to be run before to initialize `past` (if active)
(
cur_len,
running_sequences,
running_scores,
sequences,
scores,
is_sent_finished,
model_kwargs,
) = beam_search_body_fn(
cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion (only in case 1st generation step does
# NOT yield EOS token though)
if beam_search_cond_fn(
cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, model_kwargs
):
maximum_iterations = max_length - cur_len
cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, _ = tf.while_loop(
beam_search_cond_fn,
beam_search_body_fn,
(cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
# Account for the edge-case where there are no finished sequences for a particular batch item. If so, return
# running sequences for that batch item.
none_finished = tf.math.reduce_any(is_sent_finished, axis=1)
sequences = tf.where(none_finished[:, None, None], sequences, running_sequences)
scores = tf.where(none_finished[:, None], scores, running_scores)
# Take best beams for each batch (the score is sorted in ascending order)
sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
scores = flatten_beam_dim(scores[:, :num_return_sequences])
if not use_xla:
# Cut for backward compatibility
sequences = sequences[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
return TFBeamSearchEncoderDecoderOutput(
sequences=sequences,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFBeamSearchDecoderOnlyOutput(
sequences=sequences,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequences
def contrastive_search(
self,
input_ids: tf.Tensor,
top_k: Optional[int] = 1,
penalty_alpha: Optional[float] = 0,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFContrastiveSearchOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **contrastive search** and can
be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
top_k (`int`, *optional*, defaults to 1):
The size of the candidate set that is used to re-rank for contrastive search
penalty_alpha (`float`, *optional*, defaults to 0):
The degeneration penalty for contrastive search; activate when it is larger than 0
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`int`, *optional*):
The id of the *end-of-sequence* token.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `call` function of the model. If
model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFContrastiveSearchDecoderOnlyOutput`],
[`~generation.TFContrastiveSearchEncoderDecoderOutput`] or `tf.Tensor`: A `tf.Tensor` containing the
generated tokens (default behaviour) or a [`~generation.TFContrastiveySearchDecoderOnlyOutput`] if
`model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a
[`~generation.TFContrastiveSearchEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import AutoTokenizer, TFAutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
>>> model = TFAutoModelForCausalLM.from_pretrained("facebook/opt-125m")
>>> # set pad_token_id to eos_token_id because OPT does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "DeepMind Company is"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf")
>>> outputs = model.contrastive_search(**input_ids, penalty_alpha=0.6, top_k=4, max_length=64)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['DeepMind Company is a company that focuses on the development and commercialization of artificial intelligence (AI). DeepMind’s mission is to help people understand and solve problems that are difficult to solve in the world today.\n\nIn this post, we talk about the benefits of deep learning in business and how it']
```"""
def gather_best_candidate(nested, selected_idx_stacked, batch_axis=0):
"""Gathers the slices indexed by selected_idx_stacked from a potentially nested structure of tensors."""
def gather_fn(tensor):
gathered_tensor = tf.gather(params=tensor, indices=selected_idx_stacked, axis=batch_axis)
return gathered_tensor
return tf.nest.map_structure(gather_fn, nested)
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
output_scores = output_scores if output_scores is not None else self.config.output_scores
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any([model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")]) else 0
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (`input_ids` padded with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define condition fn
def contrastive_search_cond_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
"""state termination condition fn."""
return ~tf.reduce_all(finished_sequences)
# define condition fn
def contrastive_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
"""state update fn."""
# if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
# (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
if model_kwargs.get("past") is None:
# prepare inputs
model_inputs = self.prepare_inputs_for_generation(generated[:, :cur_len], **model_kwargs)
model_inputs["use_cache"] = True
# encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
# the `encoder_outputs`
outputs = self(
**model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
# last decoder hidden states will be used to compute the degeneration penalty (cosine similarity with
# previous tokens)
if self.config.is_encoder_decoder:
last_hidden_states = outputs.decoder_hidden_states[-1]
else:
last_hidden_states = outputs.hidden_states[-1]
# XLA: last_hidden_states normally grows at each step, but in XLA it is padded so as to be used across
# iterations (with fixed shapes)
if use_xla:
last_hidden_states = tf.pad(last_hidden_states, [[0, 0], [0, max_length - cur_len], [0, 0]])
# next logit for contrastive search to select top-k candidate tokens
logit_for_next_step = outputs.logits[:, -1, :]
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# Expands model inputs top_k times, for batched forward passes (akin to beam search).
_, model_kwargs = self._expand_inputs_for_generation(
expand_size=top_k, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs
)
past = model_kwargs.get("past")
if past is None:
raise ValueError(
f"{self.__class__.__name__} does not support caching and therefore **can't** be used "
"for contrastive search."
)
elif not isinstance(past[0], (tuple, tf.Tensor)) or past[0][0].shape[0] != batch_size:
raise ValueError(
f"{self.__class__.__name__} does not have a standard cache format and therefore **can't** be "
"used for contrastive search without further modifications."
)
else:
logit_for_next_step = next_step_cached_variables["logit_for_next_step"]
last_hidden_states = next_step_cached_variables["last_hidden_states"]
outputs = next_step_cached_variables["outputs"]
# contrastive_search main logic start:
# contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
# degeneration penalty
logit_for_next_step = logits_processor(generated, logit_for_next_step, cur_len)
logit_for_next_step = logits_warper(generated, logit_for_next_step, cur_len)
next_probs = stable_softmax(logit_for_next_step, axis=-1)
top_k_probs, top_k_ids = tf.math.top_k(next_probs, k=top_k)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(outputs.logits[:, -1])
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(outputs.hidden_states)
# Replicates the new past_key_values to match the `top_k` candidates
model_kwargs["past"] = tf.nest.map_structure(
lambda tensor: tf.repeat(tensor, top_k, axis=cache_batch_axis), model_kwargs["past"]
)
# compute the candidate tokens by the language model and collects their hidden_states
next_model_inputs = self.prepare_inputs_for_generation(tf.reshape(top_k_ids, [-1, 1]), **model_kwargs)
next_model_inputs["use_cache"] = True
outputs = self(
**next_model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
next_past_key_values = self._extract_past_from_model_output(outputs)
logits = outputs.logits[:, -1, :]
# name is different for encoder-decoder and decoder-only models
if self.config.is_encoder_decoder:
next_hidden = outputs.decoder_hidden_states[-1]
full_hidden_states = outputs.decoder_hidden_states
else:
next_hidden = outputs.hidden_states[-1]
full_hidden_states = outputs.hidden_states
context_hidden = tf.repeat(last_hidden_states[:, :cur_len, :], top_k, axis=0)
# compute the degeneration penalty and re-rank the candidates based on the degeneration penalty and the
# model confidence
selected_idx = _ranking_fast(context_hidden, next_hidden, top_k_probs, penalty_alpha, top_k)
# converts indices to a dimension of top_k to the stacked top_k * batch_size dimension, for indexing
# without a need to reshape on tensors that have these two dimensions stacked
selected_idx_stacked = selected_idx + tf.range(selected_idx.shape[0], dtype=tf.int64) * top_k
# prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
# the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
# (model confidence minus degeneration penalty); (6) decoder hidden_states
next_tokens = tf.gather(top_k_ids, selected_idx, axis=1, batch_dims=1)
next_hidden = gather_best_candidate(next_hidden, selected_idx_stacked)
# XLA: last_hidden_states normally grows at each step, but in XLA it is padded so as to be used across
# iterations (with fixed shapes)
if use_xla:
last_hidden_states = dynamic_update_slice(last_hidden_states, next_hidden, [0, cur_len, 0])
else:
last_hidden_states = tf.concat([last_hidden_states, next_hidden], axis=1)
next_decoder_hidden_states = gather_best_candidate(full_hidden_states, selected_idx_stacked)
next_past_key_values = gather_best_candidate(
next_past_key_values, selected_idx_stacked, batch_axis=cache_batch_axis
)
logit_for_next_step = gather_best_candidate(logits, selected_idx_stacked)
# Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
if self.config.is_encoder_decoder:
next_step_cross_attentions = ()
next_step_decoder_attentions = ()
if output_attentions:
next_step_cross_attentions = gather_best_candidate(outputs.cross_attentions, selected_idx_stacked)
next_step_decoder_attentions = gather_best_candidate(
outputs.decoder_attentions, selected_idx_stacked
)
outputs = TFSeq2SeqLMOutput(
past_key_values=next_past_key_values,
decoder_hidden_states=next_decoder_hidden_states,
decoder_attentions=next_step_decoder_attentions or None,
cross_attentions=next_step_cross_attentions or None,
)
else:
next_step_attentions = ()
if output_attentions:
next_step_attentions = gather_best_candidate(outputs.attentions, selected_idx_stacked)
outputs = TFCausalLMOutputWithPast(
past_key_values=next_past_key_values,
hidden_states=next_decoder_hidden_states,
attentions=next_step_attentions or None,
)
# contrastive_search main logic end
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
finished_sequences = finished_sequences | (next_tokens == eos_token_id)
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
if use_xla:
# NOTE: 1) relative to other generation strategies, contrastive search is always running forward
# passes one step ahead -- hence the `cur_len=cur_len + 1`; 2) the attention mask here is expanded from
# [batch_size, ...] to [batch_size*top_k, ...] -- hence the `batch_size=batch_size * top_k`
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=outputs,
model_kwargs=model_kwargs,
cur_len=cur_len + 1,
max_length=max_length,
batch_size=batch_size * top_k,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
next_step_cached_variables = {
"logit_for_next_step": logit_for_next_step,
"last_hidden_states": last_hidden_states,
"outputs": outputs,
}
return generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
# 5. run generation
# 1st generation step has to be run before to initialize `past`
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables = contrastive_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs, None
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
if contrastive_search_cond_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
maximum_iterations = max_length - cur_len
generated, _, cur_len, _, _, = tf.while_loop(
contrastive_search_cond_fn,
contrastive_search_body_fn,
(generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFContrastiveSearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFContrastiveSearchDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def _create_next_token_logits_penalties(input_ids, logits, repetition_penalty):
# create logit penalties for already seen input_ids
token_penalties = np.ones(shape_list(logits))
prev_input_ids = [np.unique(input_id) for input_id in input_ids.numpy()]
for i, prev_input_id in enumerate(prev_input_ids):
logit_penalized = logits[i].numpy()[prev_input_id]
logit_penalties = np.zeros(logit_penalized.shape)
# if previous logit score is < 0 then multiply repetition penalty else divide
logit_penalties[logit_penalized < 0] = repetition_penalty
logit_penalties[logit_penalized > 0] = 1 / repetition_penalty
np.put(token_penalties[i], prev_input_id, logit_penalties)
return tf.convert_to_tensor(token_penalties, dtype=tf.float32)
def calc_banned_ngram_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, cur_len):
# Copied from fairseq for no_repeat_ngram in beam_search
if cur_len + 1 < no_repeat_ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].numpy().tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - no_repeat_ngram_size
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].numpy().tolist())
return generated_ngrams[hypo_idx].get(ngram_idx, [])
banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
return banned_tokens
def calc_banned_bad_words_ids(prev_input_ids, bad_words_ids):
banned_tokens = []
def _tokens_match(prev_tokens, tokens):
if len(tokens) == 0:
# if bad word tokens is just one token always ban it
return True
if len(tokens) > len(prev_tokens):
# if bad word tokens are longer than prev tokens they can't be equal
return False
if prev_tokens[-len(tokens) :] == tokens:
# if tokens match
return True
else:
return False
for prev_input_ids_slice in prev_input_ids:
banned_tokens_slice = []
for banned_token_seq in bad_words_ids:
assert (
len(banned_token_seq) > 0
), f"Banned words token sequences { bad_words_ids} cannot have an empty list"
if _tokens_match(prev_input_ids_slice.numpy().tolist(), banned_token_seq[:-1]) is False:
# if tokens do not match continue
continue
banned_tokens_slice.append(banned_token_seq[-1])
banned_tokens.append(banned_tokens_slice)
return banned_tokens
def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
"""
Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
top_k (`int`, *optional*, defaults to 0):
If > 0, only keep the top k tokens with highest probability (top-k filtering)
top_p (`float`, *optional*, defaults to 1.0):
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimumber of tokens we keep per batch example in the output.
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
logits_shape = shape_list(logits)
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep), logits_shape[-1]) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < tf.math.top_k(logits, k=top_k)[0][..., -1, None]
logits = tf.where(indices_to_remove, filter_value, logits)
if top_p < 1.0:
sorted_indices = tf.argsort(logits, direction="DESCENDING")
sorted_logits = tf.gather(
logits, sorted_indices, axis=-1, batch_dims=1
) # expects logits to be of dim (batch_size, vocab_size)
cumulative_probs = tf.math.cumsum(stable_softmax(sorted_logits, axis=-1), axis=-1)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove = tf.concat(
[
tf.zeros_like(sorted_indices_to_remove[:, :min_tokens_to_keep]),
sorted_indices_to_remove[:, min_tokens_to_keep:],
],
-1,
)
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove = tf.concat(
[tf.zeros_like(sorted_indices_to_remove[:, :1]), sorted_indices_to_remove[:, :-1]],
-1,
)
# scatter sorted tensors to original indexing
indices_to_remove = scatter_values_on_batch_indices(sorted_indices_to_remove, sorted_indices)
logits = tf.where(indices_to_remove, filter_value, logits)
return logits
def scatter_values_on_batch_indices(values, batch_indices):
shape = shape_list(batch_indices)
# broadcast batch dim to shape
broad_casted_batch_dims = tf.reshape(tf.broadcast_to(tf.expand_dims(tf.range(shape[0]), axis=-1), shape), [1, -1])
# transform batch_indices to pair_indices
pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
# scatter values to pair indices
return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), shape)
def sample_without_replacement(logits, num_samples):
"""
categorical sampling without replacement is currently not implemented the gumbel-max trick will do for now see
https://github.com/tensorflow/tensorflow/issues/9260 for more info
"""
z = -tf.math.log(tf.random.uniform(shape_list(logits), 0, 1))
_, indices = tf.nn.top_k(logits + z, num_samples)
return indices
class BeamHypotheses(object):
def __init__(self, num_beams, max_length, length_penalty, early_stopping):
"""
Initialize n-best list of hypotheses.
"""
self.max_length = max_length - 1 # ignoring bos_token
self.length_penalty = length_penalty
self.early_stopping = early_stopping
self.num_beams = num_beams
self.beams = []
self.worst_score = 1e9
def __len__(self):
"""
Number of hypotheses in the list.
"""
return len(self.beams)
def add(self, hyp, sum_logprobs):
"""
Add a new hypothesis to the list.
"""
score = sum_logprobs / len(hyp) ** self.length_penalty
if len(self) < self.num_beams or score > self.worst_score:
self.beams.append((score, hyp))
if len(self) > self.num_beams:
sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
del self.beams[sorted_scores[0][1]]
self.worst_score = sorted_scores[1][0]
else:
self.worst_score = min(score, self.worst_score)
def is_done(self, best_sum_logprobs, cur_len):
"""
If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst
one in the heap, then we are done with this sentence.
"""
if len(self) < self.num_beams:
return False
elif self.early_stopping:
return True
else:
cur_score = best_sum_logprobs / cur_len**self.length_penalty
ret = self.worst_score >= cur_score
return ret
def _ranking_fast(
context_hidden: tf.Tensor,
next_hidden: tf.Tensor,
next_top_k_probs: tf.Tensor,
alpha: float,
beam_width: int,
) -> tf.Tensor:
"""
Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described
in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each
row in the batch.
"""
norm_context_hidden = context_hidden / tf.norm(context_hidden, axis=2, keepdims=True)
norm_next_hidden = next_hidden / tf.norm(next_hidden, axis=2, keepdims=True)
cosine_matrix = tf.squeeze(tf.linalg.matmul(norm_context_hidden, norm_next_hidden, transpose_b=True), axis=-1)
degeneration_penalty = tf.reduce_max(cosine_matrix, axis=-1)
next_top_k_probs = tf.reshape(next_top_k_probs, shape=[-1])
contrastive_score = (1.0 - alpha) * next_top_k_probs - alpha * degeneration_penalty
contrastive_score = tf.reshape(contrastive_score, shape=[-1, beam_width])
selected_idx = tf.argmax(contrastive_score, axis=1)
return selected_idx
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/big_bird/tokenization_big_bird.py | # coding=utf-8
# Copyright 2021 Google Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for BigBird."""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/bigbird-roberta-base": 4096,
"google/bigbird-roberta-large": 4096,
"google/bigbird-base-trivia-itc": 4096,
}
class BigBirdTokenizer(PreTrainedTokenizer):
"""
Construct a BigBird tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The begin of sequence token.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
prefix_tokens: List[int] = []
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="<pad>",
sep_token="[SEP]",
mask_token="[MASK]",
cls_token="[CLS]",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs
) -> None:
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
sep_token=sep_token,
mask_token=mask_token,
cls_token=cls_token,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
@property
def vocab_size(self):
return self.sp_model.get_piece_size()
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.sp_model.IdToPiece(index)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
spaces_between_special_tokens: bool = True,
**kwargs
) -> str:
self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
text = re.sub(r" (\[(MASK|SEP)\])", r"\1", " ".join(sub_texts))
else:
text = "".join(sub_texts)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Big Bird sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second
sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
| # coding=utf-8
# Copyright 2021 Google Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for BigBird."""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/bigbird-roberta-base": 4096,
"google/bigbird-roberta-large": 4096,
"google/bigbird-base-trivia-itc": 4096,
}
class BigBirdTokenizer(PreTrainedTokenizer):
"""
Construct a BigBird tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The begin of sequence token.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
prefix_tokens: List[int] = []
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="<pad>",
sep_token="[SEP]",
mask_token="[MASK]",
cls_token="[CLS]",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs
) -> None:
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
sep_token=sep_token,
mask_token=mask_token,
cls_token=cls_token,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
@property
def vocab_size(self):
return self.sp_model.get_piece_size()
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.sp_model.IdToPiece(index)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
spaces_between_special_tokens: bool = True,
**kwargs
) -> str:
self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
text = re.sub(r" (\[(MASK|SEP)\])", r"\1", " ".join(sub_texts))
else:
text = "".join(sub_texts)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Big Bird sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second
sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/mpnet/configuration_mpnet.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MPNet model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}
class MPNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MPNetModel`] or a [`TFMPNetModel`]. It is used to
instantiate a MPNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the MPNet
[microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30527):
Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MPNetModel`] or [`TFMPNetModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer.
Examples:
```python
>>> from transformers import MPNetModel, MPNetConfig
>>> # Initializing a MPNet mpnet-base style configuration
>>> configuration = MPNetConfig()
>>> # Initializing a model from the mpnet-base style configuration
>>> model = MPNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mpnet"
def __init__(
self,
vocab_size=30527,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
layer_norm_eps=1e-12,
relative_attention_num_buckets=32,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.relative_attention_num_buckets = relative_attention_num_buckets
| # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MPNet model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}
class MPNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MPNetModel`] or a [`TFMPNetModel`]. It is used to
instantiate a MPNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the MPNet
[microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30527):
Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MPNetModel`] or [`TFMPNetModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer.
Examples:
```python
>>> from transformers import MPNetModel, MPNetConfig
>>> # Initializing a MPNet mpnet-base style configuration
>>> configuration = MPNetConfig()
>>> # Initializing a model from the mpnet-base style configuration
>>> model = MPNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mpnet"
def __init__(
self,
vocab_size=30527,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
layer_norm_eps=1e-12,
relative_attention_num_buckets=32,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.relative_attention_num_buckets = relative_attention_num_buckets
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./src/transformers/models/wavlm/configuration_wavlm.py | # coding=utf-8
# Copyright 2021 The Fairseq Authors, Microsoft Research, and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" WavLM model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class WavLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`WavLMModel`]. It is used to instantiate an WavLM
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the WavLM
[microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the WavLM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`WavLMModel`]. Vocabulary size of the model. Defines the different tokens
that can be represented by the *inputs_ids* passed to the forward method of [`WavLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`WavLMForCTC`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for quantized feature encoder states.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
False` corresponds to applying layer norm after the attention layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Propability of each feature vector along the time axis to be chosen as the start of the vector span to be
masked. Approximately `mask_time_prob * sequence_length // mask_time_length` feature vectors will be masked
along the time axis. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Propability of each feature vector along the feature axis to be chosen as the start of the vector span to
be masked. Approximately `mask_time_prob * hidden_size // mask_time_length` feature vectors will be masked
along the time axis. This is only relevant if `apply_spec_augment is True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
num_codevectors_per_group (`int`, *optional*, defaults to 320):
Number of entries in each quantization codebook (group).
num_codevector_groups (`int`, *optional*, defaults to 2):
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for the output of the feature encoder that's used by the quantizer.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the quantized feature vectors.
proj_codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`WavLMForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`WavLMForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`WavLMForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Wav2Vec2 Encoder. Can be very useful for
warm-starting Wav2Vec2 for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
Example:
```python
```
Example:
```python
>>> from transformers import WavLMConfig, WavLMModel
>>> # Initializing a WavLM facebook/wavlm-base-960h style configuration
>>> configuration = WavLMConfig()
>>> # Initializing a model (with random weights) from the facebook/wavlm-base-960h style configuration
>>> model = WavLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "wavlm"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
feat_quantizer_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
num_buckets=320,
max_bucket_distance=800,
do_stable_layer_norm=False,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
num_codevectors_per_group=320,
num_codevector_groups=2,
contrastive_logits_temperature=0.1,
num_negatives=100,
codevector_dim=256,
proj_codevector_dim=256,
diversity_loss_weight=0.1,
ctc_loss_reduction="mean",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
tdnn_dim=(512, 512, 512, 512, 1500),
tdnn_kernel=(5, 3, 3, 1, 1),
tdnn_dilation=(1, 2, 3, 1, 1),
xvector_output_dim=512,
num_ctc_classes=80,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
add_adapter=False,
adapter_kernel_size=3,
adapter_stride=2,
num_adapter_layers=3,
output_hidden_size=None,
**kwargs
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_buckets = num_buckets
self.max_bucket_distance = max_bucket_distance
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.num_ctc_classes = num_ctc_classes
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.use_weighted_layer_sum = use_weighted_layer_sum
self.classifier_proj_size = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
# parameters for pretraining with codevector quantized representations
self.num_codevectors_per_group = num_codevectors_per_group
self.num_codevector_groups = num_codevector_groups
self.contrastive_logits_temperature = contrastive_logits_temperature
self.feat_quantizer_dropout = feat_quantizer_dropout
self.num_negatives = num_negatives
self.codevector_dim = codevector_dim
self.proj_codevector_dim = proj_codevector_dim
self.diversity_loss_weight = diversity_loss_weight
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
self.tdnn_dim = list(tdnn_dim)
self.tdnn_kernel = list(tdnn_kernel)
self.tdnn_dilation = list(tdnn_dilation)
self.xvector_output_dim = xvector_output_dim
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
| # coding=utf-8
# Copyright 2021 The Fairseq Authors, Microsoft Research, and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" WavLM model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class WavLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`WavLMModel`]. It is used to instantiate an WavLM
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the WavLM
[microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the WavLM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`WavLMModel`]. Vocabulary size of the model. Defines the different tokens
that can be represented by the *inputs_ids* passed to the forward method of [`WavLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`WavLMForCTC`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for quantized feature encoder states.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
False` corresponds to applying layer norm after the attention layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Propability of each feature vector along the time axis to be chosen as the start of the vector span to be
masked. Approximately `mask_time_prob * sequence_length // mask_time_length` feature vectors will be masked
along the time axis. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Propability of each feature vector along the feature axis to be chosen as the start of the vector span to
be masked. Approximately `mask_time_prob * hidden_size // mask_time_length` feature vectors will be masked
along the time axis. This is only relevant if `apply_spec_augment is True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
num_codevectors_per_group (`int`, *optional*, defaults to 320):
Number of entries in each quantization codebook (group).
num_codevector_groups (`int`, *optional*, defaults to 2):
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for the output of the feature encoder that's used by the quantizer.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the quantized feature vectors.
proj_codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`WavLMForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`WavLMForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`WavLMForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Wav2Vec2 Encoder. Can be very useful for
warm-starting Wav2Vec2 for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
Example:
```python
```
Example:
```python
>>> from transformers import WavLMConfig, WavLMModel
>>> # Initializing a WavLM facebook/wavlm-base-960h style configuration
>>> configuration = WavLMConfig()
>>> # Initializing a model (with random weights) from the facebook/wavlm-base-960h style configuration
>>> model = WavLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "wavlm"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
feat_quantizer_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
num_buckets=320,
max_bucket_distance=800,
do_stable_layer_norm=False,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
num_codevectors_per_group=320,
num_codevector_groups=2,
contrastive_logits_temperature=0.1,
num_negatives=100,
codevector_dim=256,
proj_codevector_dim=256,
diversity_loss_weight=0.1,
ctc_loss_reduction="mean",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
tdnn_dim=(512, 512, 512, 512, 1500),
tdnn_kernel=(5, 3, 3, 1, 1),
tdnn_dilation=(1, 2, 3, 1, 1),
xvector_output_dim=512,
num_ctc_classes=80,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
add_adapter=False,
adapter_kernel_size=3,
adapter_stride=2,
num_adapter_layers=3,
output_hidden_size=None,
**kwargs
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_buckets = num_buckets
self.max_bucket_distance = max_bucket_distance
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.num_ctc_classes = num_ctc_classes
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.use_weighted_layer_sum = use_weighted_layer_sum
self.classifier_proj_size = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
# parameters for pretraining with codevector quantized representations
self.num_codevectors_per_group = num_codevectors_per_group
self.num_codevector_groups = num_codevector_groups
self.contrastive_logits_temperature = contrastive_logits_temperature
self.feat_quantizer_dropout = feat_quantizer_dropout
self.num_negatives = num_negatives
self.codevector_dim = codevector_dim
self.proj_codevector_dim = proj_codevector_dim
self.diversity_loss_weight = diversity_loss_weight
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
self.tdnn_dim = list(tdnn_dim)
self.tdnn_kernel = list(tdnn_kernel)
self.tdnn_dilation = list(tdnn_dilation)
self.xvector_output_dim = xvector_output_dim
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
| -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./docs/source/en/perf_train_tpu.mdx | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-->
# Training on TPUs
<Tip>
Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [mutli-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section.
</Tip>
This document will be completed soon with information on how to train on TPUs. | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-->
# Training on TPUs
<Tip>
Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [mutli-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section.
</Tip>
This document will be completed soon with information on how to train on TPUs. | -1 |
huggingface/transformers | 20,340 | [FLAX] Add dtype to embedding for bert/bart/opt/t5 | ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | merrymercy | "2022-11-21T00:53:45Z" | "2022-11-28T15:21:43Z" | 667ccea72235504ab7876024e4f8c113ca62190f | ac2f6674a33e8eaffdf868e1fa6cbc8e722f469e | [FLAX] Add dtype to embedding for bert/bart/opt/t5. ## What does this PR do?
This PR is the follow-up of #18462. It adds dtype to `nn.Embed` for more common Flax models, including bert, bart, opt, t5, and their copies.
This dtype is necessary for mixed precision training.
## Who can review?
@patrickvonplaten, @LysandreJik, @sanchit-gandhi | ./tests/models/layoutlmv2/test_modeling_layoutlmv2.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch LayoutLMv2 model. """
import os
import random
import tempfile
import unittest
from transformers.testing_utils import require_detectron2, require_torch, require_torch_multi_gpu, slow, torch_device
from transformers.utils import is_detectron2_available, is_torch_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
LayoutLMv2Config,
LayoutLMv2ForQuestionAnswering,
LayoutLMv2ForSequenceClassification,
LayoutLMv2ForTokenClassification,
LayoutLMv2Model,
)
from transformers.models.layoutlmv2.modeling_layoutlmv2 import LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_detectron2_available():
from detectron2.structures.image_list import ImageList
class LayoutLMv2ModelTester:
def __init__(
self,
parent,
batch_size=2,
num_channels=3,
image_size=4,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=36,
num_hidden_layers=3,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
image_feature_pool_shape=[7, 7, 256],
coordinate_size=6,
shape_size=6,
num_labels=3,
num_choices=4,
scope=None,
range_bbox=1000,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.image_feature_pool_shape = image_feature_pool_shape
self.coordinate_size = coordinate_size
self.shape_size = shape_size
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.range_bbox = range_bbox
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
t = bbox[i, j, 3]
bbox[i, j, 3] = bbox[i, j, 1]
bbox[i, j, 1] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
t = bbox[i, j, 2]
bbox[i, j, 2] = bbox[i, j, 0]
bbox[i, j, 0] = t
image = ImageList(
torch.zeros(self.batch_size, self.num_channels, self.image_size, self.image_size, device=torch_device),
self.image_size,
)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = LayoutLMv2Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
image_feature_pool_shape=self.image_feature_pool_shape,
coordinate_size=self.coordinate_size,
shape_size=self.shape_size,
)
# use smaller resnet backbone to make tests faster
config.detectron2_config_args["MODEL.RESNETS.DEPTH"] = 18
config.detectron2_config_args["MODEL.RESNETS.RES2_OUT_CHANNELS"] = 64
config.detectron2_config_args["MODEL.RESNETS.NUM_GROUPS"] = 1
return config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
def create_and_check_model(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
model = LayoutLMv2Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, bbox=bbox, image=image, token_type_ids=token_type_ids)
result = model(input_ids, bbox=bbox, image=image)
# LayoutLMv2 has a different expected sequence length, namely also visual tokens are added
expected_seq_len = self.seq_length + self.image_feature_pool_shape[0] * self.image_feature_pool_shape[1]
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
config.num_labels = self.num_labels
model = LayoutLMv2ForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
image=image,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
config.num_labels = self.num_labels
model = LayoutLMv2ForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
image=image,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
model = LayoutLMv2ForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
image=image,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
bbox,
image,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"bbox": bbox,
"image": image,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
@require_detectron2
class LayoutLMv2ModelTest(ModelTesterMixin, unittest.TestCase):
test_pruning = False
test_torchscript = True
test_mismatched_shapes = False
all_model_classes = (
(
LayoutLMv2Model,
LayoutLMv2ForSequenceClassification,
LayoutLMv2ForTokenClassification,
LayoutLMv2ForQuestionAnswering,
)
if is_torch_available()
else ()
)
def setUp(self):
self.model_tester = LayoutLMv2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMv2Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@require_torch_multi_gpu
@unittest.skip(
reason=(
"LayoutLMV2 and its dependency `detectron2` have some layers using `add_module` which doesn't work well"
" with `nn.DataParallel`"
)
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_save_load_fast_init_from_base(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
base_class = MODEL_MAPPING[config.__class__]
if isinstance(base_class, tuple):
base_class = base_class[0]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
# make a copy of model class to not break future tests
# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
class CopyClass(model_class):
pass
model_class_copy = CopyClass
# make sure that all keys are expected for test
model_class_copy._keys_to_ignore_on_load_missing = []
# make init deterministic, but make sure that
# non-initialized weights throw errors nevertheless
model_class_copy._init_weights = self._mock_init_weights
model = base_class(config)
state_dict = model.state_dict()
# this will often delete a single weight of a multi-weight module
# to test an edge case
random_key_to_del = random.choice(list(state_dict.keys()))
del state_dict[random_key_to_del]
# check that certain keys didn't get saved with the model
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))
model_fast_init = model_class_copy.from_pretrained(tmpdirname)
model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False)
for key in model_fast_init.state_dict().keys():
if key == "layoutlmv2.visual_segment_embedding":
# we skip the visual segment embedding as it has a custom initialization scheme
continue
max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
# LayoutLMv2 has a different expected sequence length
expected_seq_len = (
self.model_tester.seq_length
+ self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1]
)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# LayoutLMv2 has a different expected sequence length
expected_seq_len = (
self.model_tester.seq_length
+ self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1]
)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[expected_seq_len, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
@slow
def test_model_from_pretrained(self):
for model_name in LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = LayoutLMv2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "backbone" in name or "visual_segment_embedding" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def prepare_layoutlmv2_batch_inputs():
# Here we prepare a batch of 2 sequences to test a LayoutLMv2 forward pass on:
# fmt: off
input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231
bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231
image = ImageList(torch.randn((2,3,224,224)), image_sizes=[(224,224), (224,224)]) # noqa: E231
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231
token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231
# fmt: on
return input_ids, bbox, image, attention_mask, token_type_ids
@require_torch
@require_detectron2
class LayoutLMv2ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased").to(torch_device)
(
input_ids,
bbox,
image,
attention_mask,
token_type_ids,
) = prepare_layoutlmv2_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids.to(torch_device),
bbox=bbox.to(torch_device),
image=image.to(torch_device),
attention_mask=attention_mask.to(torch_device),
token_type_ids=token_type_ids.to(torch_device),
)
# verify the sequence output
expected_shape = torch.Size(
(
2,
input_ids.shape[1]
+ model.config.image_feature_pool_shape[0] * model.config.image_feature_pool_shape[1],
model.config.hidden_size,
)
)
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[-0.1087, 0.0727, -0.3075], [0.0799, -0.0427, -0.0751], [-0.0367, 0.0480, -0.1358]], device=torch_device
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
# verify the pooled output
expected_shape = torch.Size((2, model.config.hidden_size))
self.assertEqual(outputs.pooler_output.shape, expected_shape)
| # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch LayoutLMv2 model. """
import os
import random
import tempfile
import unittest
from transformers.testing_utils import require_detectron2, require_torch, require_torch_multi_gpu, slow, torch_device
from transformers.utils import is_detectron2_available, is_torch_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
LayoutLMv2Config,
LayoutLMv2ForQuestionAnswering,
LayoutLMv2ForSequenceClassification,
LayoutLMv2ForTokenClassification,
LayoutLMv2Model,
)
from transformers.models.layoutlmv2.modeling_layoutlmv2 import LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_detectron2_available():
from detectron2.structures.image_list import ImageList
class LayoutLMv2ModelTester:
def __init__(
self,
parent,
batch_size=2,
num_channels=3,
image_size=4,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=36,
num_hidden_layers=3,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
image_feature_pool_shape=[7, 7, 256],
coordinate_size=6,
shape_size=6,
num_labels=3,
num_choices=4,
scope=None,
range_bbox=1000,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.image_feature_pool_shape = image_feature_pool_shape
self.coordinate_size = coordinate_size
self.shape_size = shape_size
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.range_bbox = range_bbox
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
t = bbox[i, j, 3]
bbox[i, j, 3] = bbox[i, j, 1]
bbox[i, j, 1] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
t = bbox[i, j, 2]
bbox[i, j, 2] = bbox[i, j, 0]
bbox[i, j, 0] = t
image = ImageList(
torch.zeros(self.batch_size, self.num_channels, self.image_size, self.image_size, device=torch_device),
self.image_size,
)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = LayoutLMv2Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
image_feature_pool_shape=self.image_feature_pool_shape,
coordinate_size=self.coordinate_size,
shape_size=self.shape_size,
)
# use smaller resnet backbone to make tests faster
config.detectron2_config_args["MODEL.RESNETS.DEPTH"] = 18
config.detectron2_config_args["MODEL.RESNETS.RES2_OUT_CHANNELS"] = 64
config.detectron2_config_args["MODEL.RESNETS.NUM_GROUPS"] = 1
return config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
def create_and_check_model(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
model = LayoutLMv2Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, bbox=bbox, image=image, token_type_ids=token_type_ids)
result = model(input_ids, bbox=bbox, image=image)
# LayoutLMv2 has a different expected sequence length, namely also visual tokens are added
expected_seq_len = self.seq_length + self.image_feature_pool_shape[0] * self.image_feature_pool_shape[1]
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
config.num_labels = self.num_labels
model = LayoutLMv2ForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
image=image,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
config.num_labels = self.num_labels
model = LayoutLMv2ForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
image=image,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels
):
model = LayoutLMv2ForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
image=image,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
bbox,
image,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"bbox": bbox,
"image": image,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
@require_detectron2
class LayoutLMv2ModelTest(ModelTesterMixin, unittest.TestCase):
test_pruning = False
test_torchscript = True
test_mismatched_shapes = False
all_model_classes = (
(
LayoutLMv2Model,
LayoutLMv2ForSequenceClassification,
LayoutLMv2ForTokenClassification,
LayoutLMv2ForQuestionAnswering,
)
if is_torch_available()
else ()
)
def setUp(self):
self.model_tester = LayoutLMv2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMv2Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@require_torch_multi_gpu
@unittest.skip(
reason=(
"LayoutLMV2 and its dependency `detectron2` have some layers using `add_module` which doesn't work well"
" with `nn.DataParallel`"
)
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_save_load_fast_init_from_base(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
base_class = MODEL_MAPPING[config.__class__]
if isinstance(base_class, tuple):
base_class = base_class[0]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
# make a copy of model class to not break future tests
# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
class CopyClass(model_class):
pass
model_class_copy = CopyClass
# make sure that all keys are expected for test
model_class_copy._keys_to_ignore_on_load_missing = []
# make init deterministic, but make sure that
# non-initialized weights throw errors nevertheless
model_class_copy._init_weights = self._mock_init_weights
model = base_class(config)
state_dict = model.state_dict()
# this will often delete a single weight of a multi-weight module
# to test an edge case
random_key_to_del = random.choice(list(state_dict.keys()))
del state_dict[random_key_to_del]
# check that certain keys didn't get saved with the model
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))
model_fast_init = model_class_copy.from_pretrained(tmpdirname)
model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False)
for key in model_fast_init.state_dict().keys():
if key == "layoutlmv2.visual_segment_embedding":
# we skip the visual segment embedding as it has a custom initialization scheme
continue
max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
# LayoutLMv2 has a different expected sequence length
expected_seq_len = (
self.model_tester.seq_length
+ self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1]
)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# LayoutLMv2 has a different expected sequence length
expected_seq_len = (
self.model_tester.seq_length
+ self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1]
)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[expected_seq_len, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
@slow
def test_model_from_pretrained(self):
for model_name in LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = LayoutLMv2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "backbone" in name or "visual_segment_embedding" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def prepare_layoutlmv2_batch_inputs():
# Here we prepare a batch of 2 sequences to test a LayoutLMv2 forward pass on:
# fmt: off
input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231
bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231
image = ImageList(torch.randn((2,3,224,224)), image_sizes=[(224,224), (224,224)]) # noqa: E231
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231
token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231
# fmt: on
return input_ids, bbox, image, attention_mask, token_type_ids
@require_torch
@require_detectron2
class LayoutLMv2ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased").to(torch_device)
(
input_ids,
bbox,
image,
attention_mask,
token_type_ids,
) = prepare_layoutlmv2_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids.to(torch_device),
bbox=bbox.to(torch_device),
image=image.to(torch_device),
attention_mask=attention_mask.to(torch_device),
token_type_ids=token_type_ids.to(torch_device),
)
# verify the sequence output
expected_shape = torch.Size(
(
2,
input_ids.shape[1]
+ model.config.image_feature_pool_shape[0] * model.config.image_feature_pool_shape[1],
model.config.hidden_size,
)
)
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[-0.1087, 0.0727, -0.3075], [0.0799, -0.0427, -0.0751], [-0.0367, 0.0480, -0.1358]], device=torch_device
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
# verify the pooled output
expected_shape = torch.Size((2, model.config.hidden_size))
self.assertEqual(outputs.pooler_output.shape, expected_shape)
| -1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./src/transformers/models/dinat/configuration_dinat.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Dilated Neighborhood Attention Transformer model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class DinatConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Dinat
[shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 64):
Dimensionality of patch embedding.
depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
Number of layers in each level of the encoder.
num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
Number of attention heads in each layer of the Transformer encoder.
kernel_size (`int`, *optional*, defaults to 7):
Neighborhood Attention kernel size.
dilations (`List[List[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`):
Dilation value of each NA layer in the Transformer encoder.
mlp_ratio (`float`, *optional*, defaults to 3.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
Example:
```python
>>> from transformers import DinatConfig, DinatModel
>>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration
>>> configuration = DinatConfig()
>>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration
>>> model = DinatModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "dinat"
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
patch_size=4,
num_channels=3,
embed_dim=64,
depths=[3, 4, 6, 5],
num_heads=[2, 4, 8, 16],
kernel_size=7,
dilations=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]],
mlp_ratio=3.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
**kwargs
):
super().__init__(**kwargs)
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.kernel_size = kernel_size
self.dilations = dilations
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Dilated Neighborhood Attention Transformer model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class DinatConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Dinat
[shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 64):
Dimensionality of patch embedding.
depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
Number of layers in each level of the encoder.
num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
Number of attention heads in each layer of the Transformer encoder.
kernel_size (`int`, *optional*, defaults to 7):
Neighborhood Attention kernel size.
dilations (`List[List[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`):
Dilation value of each NA layer in the Transformer encoder.
mlp_ratio (`float`, *optional*, defaults to 3.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 0.0):
The initial value for the layer scale. Disabled if <=0.
Example:
```python
>>> from transformers import DinatConfig, DinatModel
>>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration
>>> configuration = DinatConfig()
>>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration
>>> model = DinatModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "dinat"
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
patch_size=4,
num_channels=3,
embed_dim=64,
depths=[3, 4, 6, 5],
num_heads=[2, 4, 8, 16],
kernel_size=7,
dilations=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]],
mlp_ratio=3.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
layer_scale_init_value=0.0,
**kwargs
):
super().__init__(**kwargs)
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.kernel_size = kernel_size
self.dilations = dilations
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
self.layer_scale_init_value = layer_scale_init_value
| 1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./src/transformers/models/dinat/modeling_dinat.py | # coding=utf-8
# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Dilated Neighborhood Attention Transformer model."""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
OptionalDependencyNotAvailable,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_natten_available,
logging,
requires_backends,
)
from .configuration_dinat import DinatConfig
if is_natten_available():
from natten.functional import natten2dav, natten2dqkrpb
else:
def natten2dqkrpb(*args, **kwargs):
raise OptionalDependencyNotAvailable()
def natten2dav(*args, **kwargs):
raise OptionalDependencyNotAvailable()
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "DinatConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoImageProcessor"
# Base docstring
_CHECKPOINT_FOR_DOC = "shi-labs/dinat-mini-in1k-224"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 7, 512]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "shi-labs/dinat-mini-in1k-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
DINAT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"shi-labs/dinat-mini-in1k-224",
# See all Dinat models at https://huggingface.co/models?filter=dinat
]
# drop_path and DinatDropPath are from the timm library.
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatEncoderOutput with Nat->Dinat
class DinatEncoderOutput(ModelOutput):
"""
Dinat encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatModelOutput with Nat->Dinat
class DinatModelOutput(ModelOutput):
"""
Dinat model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
Average pooling of the last layer hidden-state.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatImageClassifierOutput with Nat->Dinat
class DinatImageClassifierOutput(ModelOutput):
"""
Dinat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.nat.modeling_nat.NatEmbeddings with Nat->Dinat
class DinatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = DinatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]:
embeddings = self.patch_embeddings(pixel_values)
embeddings = self.norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.nat.modeling_nat.NatPatchEmbeddings with Nat->Dinat
class DinatPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
patch_size = config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
self.num_channels = num_channels
if patch_size == 4:
pass
else:
# TODO: Support arbitrary patch sizes.
raise ValueError("Dinat only supports patch size of 4 at the moment.")
self.projection = nn.Sequential(
nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.projection(pixel_values)
embeddings = embeddings.permute(0, 2, 3, 1)
return embeddings
# Copied from transformers.models.nat.modeling_nat.NatDownsampler with Nat->Dinat
class DinatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
super().__init__()
self.dim = dim
self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
self.norm = norm_layer(2 * dim)
def forward(self, input_feature: torch.Tensor) -> torch.Tensor:
input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
input_feature = self.norm(input_feature)
return input_feature
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input, drop_prob=0.0, training=False, scale_by_keep=True):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Dinat
class DinatDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, x: torch.Tensor) -> torch.Tensor:
return drop_path(x, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
self.num_attention_heads = num_heads
self.attention_head_size = int(dim / num_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.kernel_size = kernel_size
self.dilation = dilation
# rpb is learnable relative positional biases; same concept is used Swin.
self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1)))
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttention.transpose_for_scores with Nat->Dinat
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 3, 1, 2, 4)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# Apply the scale factor before computing attention weights. It's usually more efficient because
# attention weights are typically a bigger tensor compared to query.
# It gives identical results because scalars are commutable in matrix multiplication.
query_layer = query_layer / math.sqrt(self.attention_head_size)
# Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.dilation)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = natten2dav(attention_probs, value_layer, self.dilation)
context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionOutput
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size, dilation)
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_heads = set()
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionModule.prune_heads
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionModule.forward
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.nat.modeling_nat.NatIntermediate with Nat->Dinat
class DinatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.nat.modeling_nat.NatOutput with Nat->Dinat
class DinatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class DinatLayer(nn.Module):
def __init__(self, config, dim, num_heads, dilation, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.dilation = dilation
self.window_size = self.kernel_size * self.dilation
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.attention = NeighborhoodAttentionModule(
config, dim, num_heads, kernel_size=self.kernel_size, dilation=self.dilation
)
self.drop_path = DinatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.intermediate = DinatIntermediate(config, dim)
self.output = DinatOutput(config, dim)
def maybe_pad(self, hidden_states, height, width):
window_size = self.window_size
pad_values = (0, 0, 0, 0, 0, 0)
if height < window_size or width < window_size:
pad_l = pad_t = 0
pad_r = max(0, window_size - width)
pad_b = max(0, window_size - height)
pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b)
hidden_states = nn.functional.pad(hidden_states, pad_values)
return hidden_states, pad_values
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size, height, width, channels = hidden_states.size()
shortcut = hidden_states
hidden_states = self.layernorm_before(hidden_states)
# pad hidden_states if they are smaller than kernel size x dilation
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
_, height_pad, width_pad, _ = hidden_states.shape
attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)
attention_output = attention_outputs[0]
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
attention_output = attention_output[:, :height, :width, :].contiguous()
hidden_states = shortcut + self.drop_path(attention_output)
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
layer_output = hidden_states + self.output(layer_output)
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs
class DinatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, dilations, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
DinatLayer(
config=config,
dim=dim,
num_heads=num_heads,
dilation=dilations[i],
drop_path_rate=drop_path_rate[i],
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm)
else:
self.downsample = None
self.pointing = False
# Copied from transformers.models.nat.modeling_nat.NatStage.forward
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
_, height, width, _ = hidden_states.size()
for i, layer_module in enumerate(self.layers):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if self.downsample is not None:
height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
output_dimensions = (height, width, height_downsampled, width_downsampled)
hidden_states = self.downsample(layer_outputs[0])
else:
output_dimensions = (height, width, height, width)
stage_outputs = (hidden_states, output_dimensions)
if output_attentions:
stage_outputs += layer_outputs[1:]
return stage_outputs
class DinatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
DinatStage(
config=config,
dim=int(config.embed_dim * 2**i_layer),
depth=config.depths[i_layer],
num_heads=config.num_heads[i_layer],
dilations=config.dilations[i_layer],
drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
downsample=DinatDownsampler if (i_layer < self.num_levels - 1) else None,
)
for i_layer in range(self.num_levels)
]
)
# Copied from transformers.models.nat.modeling_nat.NatEncoder.forward with Nat->Dinat
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, DinatEncoderOutput]:
all_hidden_states = () if output_hidden_states else None
all_reshaped_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
for i, layer_module in enumerate(self.levels):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
if output_attentions:
all_self_attentions += layer_outputs[2:]
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return DinatEncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
reshaped_hidden_states=all_reshaped_hidden_states,
)
class DinatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DinatConfig
base_model_prefix = "dinat"
main_input_name = "pixel_values"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module: DinatEncoder, value: bool = False) -> None:
pass
DINAT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`DinatConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
DINAT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Dinat Model transformer outputting raw hidden-states without any specific head on top.",
DINAT_START_DOCSTRING,
)
# Copied from transformers.models.nat.modeling_nat.NatModel with Nat->Dinat, NAT->DINAT
class DinatModel(DinatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1))
self.embeddings = DinatEmbeddings(config)
self.encoder = DinatEncoder(config)
self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=DinatModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, DinatModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = None
if self.pooler is not None:
pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2))
pooled_output = torch.flatten(pooled_output, 1)
if not return_dict:
output = (sequence_output, pooled_output) + encoder_outputs[1:]
return output
return DinatModelOutput(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
)
@add_start_docstrings(
"""
Dinat Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.
""",
DINAT_START_DOCSTRING,
)
class DinatForImageClassification(DinatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.dinat = DinatModel(config)
# Classifier head
self.classifier = (
nn.Linear(self.dinat.num_features, config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=DinatImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, DinatImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.dinat(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return DinatImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
| # coding=utf-8
# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Dilated Neighborhood Attention Transformer model."""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
OptionalDependencyNotAvailable,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_natten_available,
logging,
requires_backends,
)
from .configuration_dinat import DinatConfig
if is_natten_available():
from natten.functional import natten2dav, natten2dqkrpb
else:
def natten2dqkrpb(*args, **kwargs):
raise OptionalDependencyNotAvailable()
def natten2dav(*args, **kwargs):
raise OptionalDependencyNotAvailable()
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "DinatConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoImageProcessor"
# Base docstring
_CHECKPOINT_FOR_DOC = "shi-labs/dinat-mini-in1k-224"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 7, 512]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "shi-labs/dinat-mini-in1k-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
DINAT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"shi-labs/dinat-mini-in1k-224",
# See all Dinat models at https://huggingface.co/models?filter=dinat
]
# drop_path and DinatDropPath are from the timm library.
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatEncoderOutput with Nat->Dinat
class DinatEncoderOutput(ModelOutput):
"""
Dinat encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatModelOutput with Nat->Dinat
class DinatModelOutput(ModelOutput):
"""
Dinat model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
Average pooling of the last layer hidden-state.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatImageClassifierOutput with Nat->Dinat
class DinatImageClassifierOutput(ModelOutput):
"""
Dinat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.nat.modeling_nat.NatEmbeddings with Nat->Dinat
class DinatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = DinatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]:
embeddings = self.patch_embeddings(pixel_values)
embeddings = self.norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.nat.modeling_nat.NatPatchEmbeddings with Nat->Dinat
class DinatPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
patch_size = config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
self.num_channels = num_channels
if patch_size == 4:
pass
else:
# TODO: Support arbitrary patch sizes.
raise ValueError("Dinat only supports patch size of 4 at the moment.")
self.projection = nn.Sequential(
nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.projection(pixel_values)
embeddings = embeddings.permute(0, 2, 3, 1)
return embeddings
# Copied from transformers.models.nat.modeling_nat.NatDownsampler with Nat->Dinat
class DinatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
super().__init__()
self.dim = dim
self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
self.norm = norm_layer(2 * dim)
def forward(self, input_feature: torch.Tensor) -> torch.Tensor:
input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
input_feature = self.norm(input_feature)
return input_feature
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input, drop_prob=0.0, training=False, scale_by_keep=True):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Dinat
class DinatDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, x: torch.Tensor) -> torch.Tensor:
return drop_path(x, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
self.num_attention_heads = num_heads
self.attention_head_size = int(dim / num_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.kernel_size = kernel_size
self.dilation = dilation
# rpb is learnable relative positional biases; same concept is used Swin.
self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1)))
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttention.transpose_for_scores with Nat->Dinat
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 3, 1, 2, 4)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# Apply the scale factor before computing attention weights. It's usually more efficient because
# attention weights are typically a bigger tensor compared to query.
# It gives identical results because scalars are commutable in matrix multiplication.
query_layer = query_layer / math.sqrt(self.attention_head_size)
# Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.dilation)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = natten2dav(attention_probs, value_layer, self.dilation)
context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionOutput
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size, dilation)
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_heads = set()
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionModule.prune_heads
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionModule.forward
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.nat.modeling_nat.NatIntermediate with Nat->Dinat
class DinatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.nat.modeling_nat.NatOutput with Nat->Dinat
class DinatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class DinatLayer(nn.Module):
def __init__(self, config, dim, num_heads, dilation, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.dilation = dilation
self.window_size = self.kernel_size * self.dilation
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.attention = NeighborhoodAttentionModule(
config, dim, num_heads, kernel_size=self.kernel_size, dilation=self.dilation
)
self.drop_path = DinatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.intermediate = DinatIntermediate(config, dim)
self.output = DinatOutput(config, dim)
self.layer_scale_parameters = (
nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True)
if config.layer_scale_init_value > 0
else None
)
def maybe_pad(self, hidden_states, height, width):
window_size = self.window_size
pad_values = (0, 0, 0, 0, 0, 0)
if height < window_size or width < window_size:
pad_l = pad_t = 0
pad_r = max(0, window_size - width)
pad_b = max(0, window_size - height)
pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b)
hidden_states = nn.functional.pad(hidden_states, pad_values)
return hidden_states, pad_values
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size, height, width, channels = hidden_states.size()
shortcut = hidden_states
hidden_states = self.layernorm_before(hidden_states)
# pad hidden_states if they are smaller than kernel size x dilation
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
_, height_pad, width_pad, _ = hidden_states.shape
attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)
attention_output = attention_outputs[0]
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
attention_output = attention_output[:, :height, :width, :].contiguous()
if self.layer_scale_parameters is not None:
attention_output = self.layer_scale_parameters[0] * attention_output
hidden_states = shortcut + self.drop_path(attention_output)
layer_output = self.layernorm_after(hidden_states)
layer_output = self.output(self.intermediate(layer_output))
if self.layer_scale_parameters is not None:
layer_output = self.layer_scale_parameters[1] * layer_output
layer_output = hidden_states + self.drop_path(layer_output)
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs
class DinatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, dilations, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
DinatLayer(
config=config,
dim=dim,
num_heads=num_heads,
dilation=dilations[i],
drop_path_rate=drop_path_rate[i],
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm)
else:
self.downsample = None
self.pointing = False
# Copied from transformers.models.nat.modeling_nat.NatStage.forward
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
_, height, width, _ = hidden_states.size()
for i, layer_module in enumerate(self.layers):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if self.downsample is not None:
height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
output_dimensions = (height, width, height_downsampled, width_downsampled)
hidden_states = self.downsample(layer_outputs[0])
else:
output_dimensions = (height, width, height, width)
stage_outputs = (hidden_states, output_dimensions)
if output_attentions:
stage_outputs += layer_outputs[1:]
return stage_outputs
class DinatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
DinatStage(
config=config,
dim=int(config.embed_dim * 2**i_layer),
depth=config.depths[i_layer],
num_heads=config.num_heads[i_layer],
dilations=config.dilations[i_layer],
drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
downsample=DinatDownsampler if (i_layer < self.num_levels - 1) else None,
)
for i_layer in range(self.num_levels)
]
)
# Copied from transformers.models.nat.modeling_nat.NatEncoder.forward with Nat->Dinat
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, DinatEncoderOutput]:
all_hidden_states = () if output_hidden_states else None
all_reshaped_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
for i, layer_module in enumerate(self.levels):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
if output_attentions:
all_self_attentions += layer_outputs[2:]
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return DinatEncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
reshaped_hidden_states=all_reshaped_hidden_states,
)
class DinatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DinatConfig
base_model_prefix = "dinat"
main_input_name = "pixel_values"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module: DinatEncoder, value: bool = False) -> None:
pass
DINAT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`DinatConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
DINAT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Dinat Model transformer outputting raw hidden-states without any specific head on top.",
DINAT_START_DOCSTRING,
)
# Copied from transformers.models.nat.modeling_nat.NatModel with Nat->Dinat, NAT->DINAT
class DinatModel(DinatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1))
self.embeddings = DinatEmbeddings(config)
self.encoder = DinatEncoder(config)
self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=DinatModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, DinatModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = None
if self.pooler is not None:
pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2))
pooled_output = torch.flatten(pooled_output, 1)
if not return_dict:
output = (sequence_output, pooled_output) + encoder_outputs[1:]
return output
return DinatModelOutput(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
)
@add_start_docstrings(
"""
Dinat Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.
""",
DINAT_START_DOCSTRING,
)
class DinatForImageClassification(DinatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.dinat = DinatModel(config)
# Classifier head
self.classifier = (
nn.Linear(self.dinat.num_features, config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=DinatImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, DinatImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.dinat(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return DinatImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
| 1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./src/transformers/models/nat/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_nat"] = [
"NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
"NatForImageClassification",
"NatModel",
"NatPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nat import (
NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
NatForImageClassification,
NatModel,
NatPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_nat"] = [
"NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
"NatForImageClassification",
"NatModel",
"NatPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nat import (
NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
NatForImageClassification,
NatModel,
NatPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./src/transformers/models/nat/configuration_nat.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Neighborhood Attention Transformer model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
NAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
# See all Nat models at https://huggingface.co/models?filter=nat
}
class NatConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Nat
[shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 64):
Dimensionality of patch embedding.
depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
Number of layers in each level of the encoder.
num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
Number of attention heads in each layer of the Transformer encoder.
kernel_size (`int`, *optional*, defaults to 7):
Neighborhood Attention kernel size.
mlp_ratio (`float`, *optional*, defaults to 3.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
Example:
```python
>>> from transformers import NatConfig, NatModel
>>> # Initializing a Nat shi-labs/nat-mini-in1k-224 style configuration
>>> configuration = NatConfig()
>>> # Initializing a model (with random weights) from the shi-labs/nat-mini-in1k-224 style configuration
>>> model = NatModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "nat"
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
patch_size=4,
num_channels=3,
embed_dim=64,
depths=[3, 4, 6, 5],
num_heads=[2, 4, 8, 16],
kernel_size=7,
mlp_ratio=3.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
**kwargs
):
super().__init__(**kwargs)
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.kernel_size = kernel_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
| # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Neighborhood Attention Transformer model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
NAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
# See all Nat models at https://huggingface.co/models?filter=nat
}
class NatConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Nat
[shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 64):
Dimensionality of patch embedding.
depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
Number of layers in each level of the encoder.
num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
Number of attention heads in each layer of the Transformer encoder.
kernel_size (`int`, *optional*, defaults to 7):
Neighborhood Attention kernel size.
mlp_ratio (`float`, *optional*, defaults to 3.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
patch_norm (`bool`, *optional*, defaults to `True`):
Whether or not to add layer normalization after patch embedding.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 0.0):
The initial value for the layer scale. Disabled if <=0.
Example:
```python
>>> from transformers import NatConfig, NatModel
>>> # Initializing a Nat shi-labs/nat-mini-in1k-224 style configuration
>>> configuration = NatConfig()
>>> # Initializing a model (with random weights) from the shi-labs/nat-mini-in1k-224 style configuration
>>> model = NatModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "nat"
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
patch_size=4,
num_channels=3,
embed_dim=64,
depths=[3, 4, 6, 5],
num_heads=[2, 4, 8, 16],
kernel_size=7,
mlp_ratio=3.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
layer_scale_init_value=0.0,
**kwargs
):
super().__init__(**kwargs)
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.kernel_size = kernel_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.path_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
self.layer_scale_init_value = layer_scale_init_value
| 1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./src/transformers/models/nat/modeling_nat.py | # coding=utf-8
# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Neighborhood Attention Transformer model."""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
OptionalDependencyNotAvailable,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_natten_available,
logging,
requires_backends,
)
from .configuration_nat import NatConfig
if is_natten_available():
from natten.functional import natten2dav, natten2dqkrpb
else:
def natten2dqkrpb(*args, **kwargs):
raise OptionalDependencyNotAvailable()
def natten2dav(*args, **kwargs):
raise OptionalDependencyNotAvailable()
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "NatConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoImageProcessor"
# Base docstring
_CHECKPOINT_FOR_DOC = "shi-labs/nat-mini-in1k-224"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 7, 512]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "shi-labs/nat-mini-in1k-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
NAT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"shi-labs/nat-mini-in1k-224",
# See all Nat models at https://huggingface.co/models?filter=nat
]
# drop_path and NatDropPath are from the timm library.
@dataclass
class NatEncoderOutput(ModelOutput):
"""
Nat encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class NatModelOutput(ModelOutput):
"""
Nat model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
Average pooling of the last layer hidden-state.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class NatImageClassifierOutput(ModelOutput):
"""
Nat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class NatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = NatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]:
embeddings = self.patch_embeddings(pixel_values)
embeddings = self.norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class NatPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
patch_size = config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
self.num_channels = num_channels
if patch_size == 4:
pass
else:
# TODO: Support arbitrary patch sizes.
raise ValueError("Dinat only supports patch size of 4 at the moment.")
self.projection = nn.Sequential(
nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.projection(pixel_values)
embeddings = embeddings.permute(0, 2, 3, 1)
return embeddings
class NatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
super().__init__()
self.dim = dim
self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
self.norm = norm_layer(2 * dim)
def forward(self, input_feature: torch.Tensor) -> torch.Tensor:
input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
input_feature = self.norm(input_feature)
return input_feature
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input, drop_prob=0.0, training=False, scale_by_keep=True):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Nat
class NatDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, x: torch.Tensor) -> torch.Tensor:
return drop_path(x, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
self.num_attention_heads = num_heads
self.attention_head_size = int(dim / num_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.kernel_size = kernel_size
# rpb is learnable relative positional biases; same concept is used Swin.
self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1)))
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 3, 1, 2, 4)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# Apply the scale factor before computing attention weights. It's usually more efficient because
# attention weights are typically a bigger tensor compared to query.
# It gives identical results because scalars are commutable in matrix multiplication.
query_layer = query_layer / math.sqrt(self.attention_head_size)
# Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, 1)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = natten2dav(attention_probs, value_layer, 1)
context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size)
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class NatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class NatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class NatLayer(nn.Module):
def __init__(self, config, dim, num_heads, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.attention = NeighborhoodAttentionModule(config, dim, num_heads, kernel_size=self.kernel_size)
self.drop_path = NatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.intermediate = NatIntermediate(config, dim)
self.output = NatOutput(config, dim)
def maybe_pad(self, hidden_states, height, width):
window_size = self.kernel_size
pad_values = (0, 0, 0, 0, 0, 0)
if height < window_size or width < window_size:
pad_l = pad_t = 0
pad_r = max(0, window_size - width)
pad_b = max(0, window_size - height)
pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b)
hidden_states = nn.functional.pad(hidden_states, pad_values)
return hidden_states, pad_values
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size, height, width, channels = hidden_states.size()
shortcut = hidden_states
hidden_states = self.layernorm_before(hidden_states)
# pad hidden_states if they are smaller than kernel size
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
_, height_pad, width_pad, _ = hidden_states.shape
attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)
attention_output = attention_outputs[0]
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
attention_output = attention_output[:, :height, :width, :].contiguous()
hidden_states = shortcut + self.drop_path(attention_output)
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
layer_output = hidden_states + self.output(layer_output)
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs
class NatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
NatLayer(
config=config,
dim=dim,
num_heads=num_heads,
drop_path_rate=drop_path_rate[i],
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm)
else:
self.downsample = None
self.pointing = False
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
_, height, width, _ = hidden_states.size()
for i, layer_module in enumerate(self.layers):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if self.downsample is not None:
height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
output_dimensions = (height, width, height_downsampled, width_downsampled)
hidden_states = self.downsample(layer_outputs[0])
else:
output_dimensions = (height, width, height, width)
stage_outputs = (hidden_states, output_dimensions)
if output_attentions:
stage_outputs += layer_outputs[1:]
return stage_outputs
class NatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
NatStage(
config=config,
dim=int(config.embed_dim * 2**i_layer),
depth=config.depths[i_layer],
num_heads=config.num_heads[i_layer],
drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
downsample=NatDownsampler if (i_layer < self.num_levels - 1) else None,
)
for i_layer in range(self.num_levels)
]
)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, NatEncoderOutput]:
all_hidden_states = () if output_hidden_states else None
all_reshaped_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
for i, layer_module in enumerate(self.levels):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
if output_attentions:
all_self_attentions += layer_outputs[2:]
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return NatEncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
reshaped_hidden_states=all_reshaped_hidden_states,
)
class NatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NatConfig
base_model_prefix = "nat"
main_input_name = "pixel_values"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module: NatEncoder, value: bool = False) -> None:
pass
NAT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`NatConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
NAT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Nat Model transformer outputting raw hidden-states without any specific head on top.",
NAT_START_DOCSTRING,
)
class NatModel(NatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1))
self.embeddings = NatEmbeddings(config)
self.encoder = NatEncoder(config)
self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=NatModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, NatModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = None
if self.pooler is not None:
pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2))
pooled_output = torch.flatten(pooled_output, 1)
if not return_dict:
output = (sequence_output, pooled_output) + encoder_outputs[1:]
return output
return NatModelOutput(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
)
@add_start_docstrings(
"""
Nat Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.
""",
NAT_START_DOCSTRING,
)
class NatForImageClassification(NatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.nat = NatModel(config)
# Classifier head
self.classifier = (
nn.Linear(self.nat.num_features, config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=NatImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, NatImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nat(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return NatImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
| # coding=utf-8
# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Neighborhood Attention Transformer model."""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
OptionalDependencyNotAvailable,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_natten_available,
logging,
requires_backends,
)
from .configuration_nat import NatConfig
if is_natten_available():
from natten.functional import natten2dav, natten2dqkrpb
else:
def natten2dqkrpb(*args, **kwargs):
raise OptionalDependencyNotAvailable()
def natten2dav(*args, **kwargs):
raise OptionalDependencyNotAvailable()
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "NatConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoImageProcessor"
# Base docstring
_CHECKPOINT_FOR_DOC = "shi-labs/nat-mini-in1k-224"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 7, 512]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "shi-labs/nat-mini-in1k-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
NAT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"shi-labs/nat-mini-in1k-224",
# See all Nat models at https://huggingface.co/models?filter=nat
]
# drop_path and NatDropPath are from the timm library.
@dataclass
class NatEncoderOutput(ModelOutput):
"""
Nat encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class NatModelOutput(ModelOutput):
"""
Nat model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
Average pooling of the last layer hidden-state.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class NatImageClassifierOutput(ModelOutput):
"""
Nat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, hidden_size, height, width)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class NatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = NatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]:
embeddings = self.patch_embeddings(pixel_values)
embeddings = self.norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class NatPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
patch_size = config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
self.num_channels = num_channels
if patch_size == 4:
pass
else:
# TODO: Support arbitrary patch sizes.
raise ValueError("Dinat only supports patch size of 4 at the moment.")
self.projection = nn.Sequential(
nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.projection(pixel_values)
embeddings = embeddings.permute(0, 2, 3, 1)
return embeddings
class NatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
super().__init__()
self.dim = dim
self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
self.norm = norm_layer(2 * dim)
def forward(self, input_feature: torch.Tensor) -> torch.Tensor:
input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
input_feature = self.norm(input_feature)
return input_feature
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input, drop_prob=0.0, training=False, scale_by_keep=True):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Nat
class NatDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, x: torch.Tensor) -> torch.Tensor:
return drop_path(x, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
self.num_attention_heads = num_heads
self.attention_head_size = int(dim / num_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.kernel_size = kernel_size
# rpb is learnable relative positional biases; same concept is used Swin.
self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1)))
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 3, 1, 2, 4)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# Apply the scale factor before computing attention weights. It's usually more efficient because
# attention weights are typically a bigger tensor compared to query.
# It gives identical results because scalars are commutable in matrix multiplication.
query_layer = query_layer / math.sqrt(self.attention_head_size)
# Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, 1)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = natten2dav(attention_probs, value_layer, 1)
context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size)
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class NatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class NatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class NatLayer(nn.Module):
def __init__(self, config, dim, num_heads, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.attention = NeighborhoodAttentionModule(config, dim, num_heads, kernel_size=self.kernel_size)
self.drop_path = NatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.intermediate = NatIntermediate(config, dim)
self.output = NatOutput(config, dim)
self.layer_scale_parameters = (
nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True)
if config.layer_scale_init_value > 0
else None
)
def maybe_pad(self, hidden_states, height, width):
window_size = self.kernel_size
pad_values = (0, 0, 0, 0, 0, 0)
if height < window_size or width < window_size:
pad_l = pad_t = 0
pad_r = max(0, window_size - width)
pad_b = max(0, window_size - height)
pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b)
hidden_states = nn.functional.pad(hidden_states, pad_values)
return hidden_states, pad_values
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size, height, width, channels = hidden_states.size()
shortcut = hidden_states
hidden_states = self.layernorm_before(hidden_states)
# pad hidden_states if they are smaller than kernel size
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
_, height_pad, width_pad, _ = hidden_states.shape
attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)
attention_output = attention_outputs[0]
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
attention_output = attention_output[:, :height, :width, :].contiguous()
if self.layer_scale_parameters is not None:
attention_output = self.layer_scale_parameters[0] * attention_output
hidden_states = shortcut + self.drop_path(attention_output)
layer_output = self.layernorm_after(hidden_states)
layer_output = self.output(self.intermediate(layer_output))
if self.layer_scale_parameters is not None:
layer_output = self.layer_scale_parameters[1] * layer_output
layer_output = hidden_states + self.drop_path(layer_output)
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs
class NatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
NatLayer(
config=config,
dim=dim,
num_heads=num_heads,
drop_path_rate=drop_path_rate[i],
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm)
else:
self.downsample = None
self.pointing = False
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
_, height, width, _ = hidden_states.size()
for i, layer_module in enumerate(self.layers):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if self.downsample is not None:
height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
output_dimensions = (height, width, height_downsampled, width_downsampled)
hidden_states = self.downsample(layer_outputs[0])
else:
output_dimensions = (height, width, height, width)
stage_outputs = (hidden_states, output_dimensions)
if output_attentions:
stage_outputs += layer_outputs[1:]
return stage_outputs
class NatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
NatStage(
config=config,
dim=int(config.embed_dim * 2**i_layer),
depth=config.depths[i_layer],
num_heads=config.num_heads[i_layer],
drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
downsample=NatDownsampler if (i_layer < self.num_levels - 1) else None,
)
for i_layer in range(self.num_levels)
]
)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, NatEncoderOutput]:
all_hidden_states = () if output_hidden_states else None
all_reshaped_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
for i, layer_module in enumerate(self.levels):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if output_hidden_states:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
if output_attentions:
all_self_attentions += layer_outputs[2:]
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return NatEncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
reshaped_hidden_states=all_reshaped_hidden_states,
)
class NatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NatConfig
base_model_prefix = "nat"
main_input_name = "pixel_values"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module: NatEncoder, value: bool = False) -> None:
pass
NAT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`NatConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
NAT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Nat Model transformer outputting raw hidden-states without any specific head on top.",
NAT_START_DOCSTRING,
)
class NatModel(NatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1))
self.embeddings = NatEmbeddings(config)
self.encoder = NatEncoder(config)
self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=NatModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, NatModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = None
if self.pooler is not None:
pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2))
pooled_output = torch.flatten(pooled_output, 1)
if not return_dict:
output = (sequence_output, pooled_output) + encoder_outputs[1:]
return output
return NatModelOutput(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
)
@add_start_docstrings(
"""
Nat Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.
""",
NAT_START_DOCSTRING,
)
class NatForImageClassification(NatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.nat = NatModel(config)
# Classifier head
self.classifier = (
nn.Linear(self.nat.num_features, config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=NatImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, NatImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nat(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return NatImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
| 1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./examples/legacy/pytorch-lightning/lightning_base.py | import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
logger = logging.getLogger(__name__)
require_version("pytorch_lightning>=1.0.4")
MODEL_MODES = {
"base": AutoModel,
"sequence-classification": AutoModelForSequenceClassification,
"question-answering": AutoModelForQuestionAnswering,
"pretraining": AutoModelForPreTraining,
"token-classification": AutoModelForTokenClassification,
"language-modeling": AutoModelWithLMHead,
"summarization": AutoModelForSeq2SeqLM,
"translation": AutoModelForSeq2SeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
arg_to_scheduler = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
arg_to_scheduler_choices = sorted(arg_to_scheduler.keys())
arg_to_scheduler_metavar = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class BaseTransformer(pl.LightningModule):
def __init__(
self,
hparams: argparse.Namespace,
num_labels=None,
mode="base",
config=None,
tokenizer=None,
model=None,
**config_kwargs
):
"""Initialize a model, tokenizer and config."""
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(hparams)
self.step_count = 0
self.output_dir = Path(self.hparams.output_dir)
cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
self.config = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
**({"num_labels": num_labels} if num_labels is not None else {}),
cache_dir=cache_dir,
**config_kwargs,
)
else:
self.config: PretrainedConfig = config
extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(self.hparams, p, None):
assert hasattr(self.config, p), f"model config doesn't have a `{p}` attribute"
setattr(self.config, p, getattr(self.hparams, p))
if tokenizer is None:
self.tokenizer = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
cache_dir=cache_dir,
)
else:
self.tokenizer: PreTrainedTokenizer = tokenizer
self.model_type = MODEL_MODES[mode]
if model is None:
self.model = self.model_type.from_pretrained(
self.hparams.model_name_or_path,
from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
config=self.config,
cache_dir=cache_dir,
)
else:
self.model = model
def load_hf_checkpoint(self, *args, **kwargs):
self.model = self.model_type.from_pretrained(*args, **kwargs)
def get_lr_scheduler(self):
get_schedule_func = arg_to_scheduler[self.hparams.lr_scheduler]
scheduler = get_schedule_func(
self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps()
)
scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return scheduler
def configure_optimizers(self):
"""Prepare optimizer and schedule (linear warmup and decay)"""
model = self.model
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
if self.hparams.adafactor:
optimizer = Adafactor(
optimizer_grouped_parameters, lr=self.hparams.learning_rate, scale_parameter=False, relative_step=False
)
else:
optimizer = AdamW(
optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon
)
self.opt = optimizer
scheduler = self.get_lr_scheduler()
return [optimizer], [scheduler]
def test_step(self, batch, batch_nb):
return self.validation_step(batch, batch_nb)
def test_epoch_end(self, outputs):
return self.validation_end(outputs)
def total_steps(self) -> int:
"""The number of total training steps that will be run. Used for lr scheduler purposes."""
num_devices = max(1, self.hparams.gpus) # TODO: consider num_tpu_cores
effective_batch_size = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def setup(self, mode):
if mode == "test":
self.dataset_size = len(self.test_dataloader().dataset)
else:
self.train_loader = self.get_dataloader("train", self.hparams.train_batch_size, shuffle=True)
self.dataset_size = len(self.train_dataloader().dataset)
def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False):
raise NotImplementedError("You must implement this for your task")
def train_dataloader(self):
return self.train_loader
def val_dataloader(self):
return self.get_dataloader("dev", self.hparams.eval_batch_size, shuffle=False)
def test_dataloader(self):
return self.get_dataloader("test", self.hparams.eval_batch_size, shuffle=False)
def _feature_file(self, mode):
return os.path.join(
self.hparams.data_dir,
"cached_{}_{}_{}".format(
mode,
list(filter(None, self.hparams.model_name_or_path.split("/"))).pop(),
str(self.hparams.max_seq_length),
),
)
@pl.utilities.rank_zero_only
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
save_path = self.output_dir.joinpath("best_tfmr")
self.model.config.save_step = self.step_count
self.model.save_pretrained(save_path)
self.tokenizer.save_pretrained(save_path)
@staticmethod
def add_model_specific_args(parser, root_dir):
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from huggingface.co",
)
parser.add_argument(
"--encoder_layerdrop",
type=float,
help="Encoder layer dropout probability (Optional). Goes into model.config",
)
parser.add_argument(
"--decoder_layerdrop",
type=float,
help="Decoder layer dropout probability (Optional). Goes into model.config",
)
parser.add_argument(
"--dropout",
type=float,
help="Dropout probability (Optional). Goes into model.config",
)
parser.add_argument(
"--attention_dropout",
type=float,
help="Attention dropout probability (Optional). Goes into model.config",
)
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument(
"--lr_scheduler",
default="linear",
choices=arg_to_scheduler_choices,
metavar=arg_to_scheduler_metavar,
type=str,
help="Learning rate scheduler",
)
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--num_workers", default=4, type=int, help="kwarg passed to DataLoader")
parser.add_argument("--num_train_epochs", dest="max_epochs", default=3, type=int)
parser.add_argument("--train_batch_size", default=32, type=int)
parser.add_argument("--eval_batch_size", default=32, type=int)
parser.add_argument("--adafactor", action="store_true")
class LoggingCallback(pl.Callback):
def on_batch_end(self, trainer, pl_module):
lr_scheduler = trainer.lr_schedulers[0]["scheduler"]
lrs = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
pl_module.logger.log_metrics(lrs)
def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
rank_zero_info("***** Validation results *****")
metrics = trainer.callback_metrics
# Log results
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
rank_zero_info("***** Test results *****")
metrics = trainer.callback_metrics
# Log and save results to file
output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
with open(output_test_results_file, "w") as writer:
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
writer.write("{} = {}\n".format(key, str(metrics[key])))
def add_generic_args(parser, root_dir) -> None:
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O2",
help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
),
)
parser.add_argument("--n_tpu_cores", dest="tpu_cores", type=int)
parser.add_argument("--max_grad_norm", dest="gradient_clip_val", default=1.0, type=float, help="Max gradient norm")
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.")
parser.add_argument(
"--gradient_accumulation_steps",
dest="accumulate_grad_batches",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
)
def generic_train(
model: BaseTransformer,
args: argparse.Namespace,
early_stopping_callback=None,
logger=True, # can pass WandbLogger() here
extra_callbacks=[],
checkpoint_callback=None,
logging_callback=None,
**extra_train_kwargs
):
pl.seed_everything(args.seed)
# init model
odir = Path(model.hparams.output_dir)
odir.mkdir(exist_ok=True)
# add custom checkpoints
if checkpoint_callback is None:
checkpoint_callback = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=1
)
if early_stopping_callback:
extra_callbacks.append(early_stopping_callback)
if logging_callback is None:
logging_callback = LoggingCallback()
train_params = {}
# TODO: remove with PyTorch 1.6 since pl uses native amp
if args.fp16:
train_params["precision"] = 16
train_params["amp_level"] = args.fp16_opt_level
if args.gpus > 1:
train_params["distributed_backend"] = "ddp"
train_params["accumulate_grad_batches"] = args.accumulate_grad_batches
train_params["accelerator"] = extra_train_kwargs.get("accelerator", None)
train_params["profiler"] = extra_train_kwargs.get("profiler", None)
trainer = pl.Trainer.from_argparse_args(
args,
weights_summary=None,
callbacks=[logging_callback] + extra_callbacks,
logger=logger,
checkpoint_callback=checkpoint_callback,
**train_params,
)
if args.do_train:
trainer.fit(model)
return trainer
| import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
logger = logging.getLogger(__name__)
require_version("pytorch_lightning>=1.0.4")
MODEL_MODES = {
"base": AutoModel,
"sequence-classification": AutoModelForSequenceClassification,
"question-answering": AutoModelForQuestionAnswering,
"pretraining": AutoModelForPreTraining,
"token-classification": AutoModelForTokenClassification,
"language-modeling": AutoModelWithLMHead,
"summarization": AutoModelForSeq2SeqLM,
"translation": AutoModelForSeq2SeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
arg_to_scheduler = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
arg_to_scheduler_choices = sorted(arg_to_scheduler.keys())
arg_to_scheduler_metavar = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class BaseTransformer(pl.LightningModule):
def __init__(
self,
hparams: argparse.Namespace,
num_labels=None,
mode="base",
config=None,
tokenizer=None,
model=None,
**config_kwargs
):
"""Initialize a model, tokenizer and config."""
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(hparams)
self.step_count = 0
self.output_dir = Path(self.hparams.output_dir)
cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
self.config = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
**({"num_labels": num_labels} if num_labels is not None else {}),
cache_dir=cache_dir,
**config_kwargs,
)
else:
self.config: PretrainedConfig = config
extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(self.hparams, p, None):
assert hasattr(self.config, p), f"model config doesn't have a `{p}` attribute"
setattr(self.config, p, getattr(self.hparams, p))
if tokenizer is None:
self.tokenizer = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
cache_dir=cache_dir,
)
else:
self.tokenizer: PreTrainedTokenizer = tokenizer
self.model_type = MODEL_MODES[mode]
if model is None:
self.model = self.model_type.from_pretrained(
self.hparams.model_name_or_path,
from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
config=self.config,
cache_dir=cache_dir,
)
else:
self.model = model
def load_hf_checkpoint(self, *args, **kwargs):
self.model = self.model_type.from_pretrained(*args, **kwargs)
def get_lr_scheduler(self):
get_schedule_func = arg_to_scheduler[self.hparams.lr_scheduler]
scheduler = get_schedule_func(
self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps()
)
scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return scheduler
def configure_optimizers(self):
"""Prepare optimizer and schedule (linear warmup and decay)"""
model = self.model
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
if self.hparams.adafactor:
optimizer = Adafactor(
optimizer_grouped_parameters, lr=self.hparams.learning_rate, scale_parameter=False, relative_step=False
)
else:
optimizer = AdamW(
optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon
)
self.opt = optimizer
scheduler = self.get_lr_scheduler()
return [optimizer], [scheduler]
def test_step(self, batch, batch_nb):
return self.validation_step(batch, batch_nb)
def test_epoch_end(self, outputs):
return self.validation_end(outputs)
def total_steps(self) -> int:
"""The number of total training steps that will be run. Used for lr scheduler purposes."""
num_devices = max(1, self.hparams.gpus) # TODO: consider num_tpu_cores
effective_batch_size = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def setup(self, mode):
if mode == "test":
self.dataset_size = len(self.test_dataloader().dataset)
else:
self.train_loader = self.get_dataloader("train", self.hparams.train_batch_size, shuffle=True)
self.dataset_size = len(self.train_dataloader().dataset)
def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False):
raise NotImplementedError("You must implement this for your task")
def train_dataloader(self):
return self.train_loader
def val_dataloader(self):
return self.get_dataloader("dev", self.hparams.eval_batch_size, shuffle=False)
def test_dataloader(self):
return self.get_dataloader("test", self.hparams.eval_batch_size, shuffle=False)
def _feature_file(self, mode):
return os.path.join(
self.hparams.data_dir,
"cached_{}_{}_{}".format(
mode,
list(filter(None, self.hparams.model_name_or_path.split("/"))).pop(),
str(self.hparams.max_seq_length),
),
)
@pl.utilities.rank_zero_only
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
save_path = self.output_dir.joinpath("best_tfmr")
self.model.config.save_step = self.step_count
self.model.save_pretrained(save_path)
self.tokenizer.save_pretrained(save_path)
@staticmethod
def add_model_specific_args(parser, root_dir):
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from huggingface.co",
)
parser.add_argument(
"--encoder_layerdrop",
type=float,
help="Encoder layer dropout probability (Optional). Goes into model.config",
)
parser.add_argument(
"--decoder_layerdrop",
type=float,
help="Decoder layer dropout probability (Optional). Goes into model.config",
)
parser.add_argument(
"--dropout",
type=float,
help="Dropout probability (Optional). Goes into model.config",
)
parser.add_argument(
"--attention_dropout",
type=float,
help="Attention dropout probability (Optional). Goes into model.config",
)
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument(
"--lr_scheduler",
default="linear",
choices=arg_to_scheduler_choices,
metavar=arg_to_scheduler_metavar,
type=str,
help="Learning rate scheduler",
)
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--num_workers", default=4, type=int, help="kwarg passed to DataLoader")
parser.add_argument("--num_train_epochs", dest="max_epochs", default=3, type=int)
parser.add_argument("--train_batch_size", default=32, type=int)
parser.add_argument("--eval_batch_size", default=32, type=int)
parser.add_argument("--adafactor", action="store_true")
class LoggingCallback(pl.Callback):
def on_batch_end(self, trainer, pl_module):
lr_scheduler = trainer.lr_schedulers[0]["scheduler"]
lrs = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
pl_module.logger.log_metrics(lrs)
def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
rank_zero_info("***** Validation results *****")
metrics = trainer.callback_metrics
# Log results
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
rank_zero_info("***** Test results *****")
metrics = trainer.callback_metrics
# Log and save results to file
output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
with open(output_test_results_file, "w") as writer:
for key in sorted(metrics):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
writer.write("{} = {}\n".format(key, str(metrics[key])))
def add_generic_args(parser, root_dir) -> None:
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O2",
help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
),
)
parser.add_argument("--n_tpu_cores", dest="tpu_cores", type=int)
parser.add_argument("--max_grad_norm", dest="gradient_clip_val", default=1.0, type=float, help="Max gradient norm")
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.")
parser.add_argument(
"--gradient_accumulation_steps",
dest="accumulate_grad_batches",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
)
def generic_train(
model: BaseTransformer,
args: argparse.Namespace,
early_stopping_callback=None,
logger=True, # can pass WandbLogger() here
extra_callbacks=[],
checkpoint_callback=None,
logging_callback=None,
**extra_train_kwargs
):
pl.seed_everything(args.seed)
# init model
odir = Path(model.hparams.output_dir)
odir.mkdir(exist_ok=True)
# add custom checkpoints
if checkpoint_callback is None:
checkpoint_callback = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=1
)
if early_stopping_callback:
extra_callbacks.append(early_stopping_callback)
if logging_callback is None:
logging_callback = LoggingCallback()
train_params = {}
# TODO: remove with PyTorch 1.6 since pl uses native amp
if args.fp16:
train_params["precision"] = 16
train_params["amp_level"] = args.fp16_opt_level
if args.gpus > 1:
train_params["distributed_backend"] = "ddp"
train_params["accumulate_grad_batches"] = args.accumulate_grad_batches
train_params["accelerator"] = extra_train_kwargs.get("accelerator", None)
train_params["profiler"] = extra_train_kwargs.get("profiler", None)
trainer = pl.Trainer.from_argparse_args(
args,
weights_summary=None,
callbacks=[logging_callback] + extra_callbacks,
logger=logger,
checkpoint_callback=checkpoint_callback,
**train_params,
)
if args.do_train:
trainer.fit(model)
return trainer
| -1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./src/transformers/models/roberta/convert_roberta_original_pytorch_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RoBERTa checkpoint."""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
raise Exception("requires fairseq >= 0.9.0")
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
SAMPLE_TEXT = "Hello world! cécé herlolip"
def convert_roberta_checkpoint_to_pytorch(
roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool
):
"""
Copy/paste/tweak roberta's weights to our BERT structure.
"""
roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
roberta.eval() # disable dropout
roberta_sent_encoder = roberta.model.encoder.sentence_encoder
config = RobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
hidden_size=roberta.args.encoder_embed_dim,
num_hidden_layers=roberta.args.encoder_layers,
num_attention_heads=roberta.args.encoder_attention_heads,
intermediate_size=roberta.args.encoder_ffn_embed_dim,
max_position_embeddings=514,
type_vocab_size=1,
layer_norm_eps=1e-5, # PyTorch default used in fairseq
)
if classification_head:
config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our BERT config:", config)
model = RobertaForSequenceClassification(config) if classification_head else RobertaForMaskedLM(config)
model.eval()
# Now let's copy all the weights.
# Embeddings
model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight
) # just zero them out b/c RoBERTa doesn't use them.
model.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
model.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
layer: BertLayer = model.roberta.encoder.layer[i]
roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
# self attention
self_attn: BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
)
self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight
self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias
self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight
self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias
self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight
self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias
# self-attention output
self_output: BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
# intermediate
intermediate: BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
intermediate.dense.weight = roberta_layer.fc1.weight
intermediate.dense.bias = roberta_layer.fc1.bias
# output
bert_output: BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
bert_output.dense.weight = roberta_layer.fc2.weight
bert_output.dense.bias = roberta_layer.fc2.bias
bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
# end of layer
if classification_head:
model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight
model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias
model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight
model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight
model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias
model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight
model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias
model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight
model.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
our_output = model(input_ids)[0]
if classification_head:
their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids))
else:
their_output = roberta.model(input_ids)[0]
print(our_output.shape, their_output.shape)
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
success = torch.allclose(our_output, their_output, atol=1e-3)
print("Do both models output the same tensors?", "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
args = parser.parse_args()
convert_roberta_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RoBERTa checkpoint."""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
raise Exception("requires fairseq >= 0.9.0")
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
SAMPLE_TEXT = "Hello world! cécé herlolip"
def convert_roberta_checkpoint_to_pytorch(
roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool
):
"""
Copy/paste/tweak roberta's weights to our BERT structure.
"""
roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
roberta.eval() # disable dropout
roberta_sent_encoder = roberta.model.encoder.sentence_encoder
config = RobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
hidden_size=roberta.args.encoder_embed_dim,
num_hidden_layers=roberta.args.encoder_layers,
num_attention_heads=roberta.args.encoder_attention_heads,
intermediate_size=roberta.args.encoder_ffn_embed_dim,
max_position_embeddings=514,
type_vocab_size=1,
layer_norm_eps=1e-5, # PyTorch default used in fairseq
)
if classification_head:
config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our BERT config:", config)
model = RobertaForSequenceClassification(config) if classification_head else RobertaForMaskedLM(config)
model.eval()
# Now let's copy all the weights.
# Embeddings
model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight
) # just zero them out b/c RoBERTa doesn't use them.
model.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
model.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
layer: BertLayer = model.roberta.encoder.layer[i]
roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
# self attention
self_attn: BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
)
self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight
self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias
self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight
self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias
self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight
self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias
# self-attention output
self_output: BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
# intermediate
intermediate: BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
intermediate.dense.weight = roberta_layer.fc1.weight
intermediate.dense.bias = roberta_layer.fc1.bias
# output
bert_output: BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
bert_output.dense.weight = roberta_layer.fc2.weight
bert_output.dense.bias = roberta_layer.fc2.bias
bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
# end of layer
if classification_head:
model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight
model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias
model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight
model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight
model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias
model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight
model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias
model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight
model.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
our_output = model(input_ids)[0]
if classification_head:
their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids))
else:
their_output = roberta.model(input_ids)[0]
print(our_output.shape, their_output.shape)
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
success = torch.allclose(our_output, their_output, atol=1e-3)
print("Do both models output the same tensors?", "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
args = parser.parse_args()
convert_roberta_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| -1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./examples/research_projects/distillation/scripts/extract_distilbert.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocessing script before training DistilBERT.
Specific to BERT -> DistilBERT.
"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="bert", choices=["bert"])
parser.add_argument("--model_name", default="bert-base-uncased", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
args = parser.parse_args()
if args.model_type == "bert":
model = BertForMaskedLM.from_pretrained(args.model_name)
prefix = "bert"
else:
raise ValueError('args.model_type should be "bert".')
state_dict = model.state_dict()
compressed_sd = {}
for w in ["word_embeddings", "position_embeddings"]:
compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"]
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]
std_idx = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
]
std_idx += 1
compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"]
compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"]
compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"]
print(f"N layers selected for distillation: {std_idx}")
print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")
print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
torch.save(compressed_sd, args.dump_checkpoint)
| # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocessing script before training DistilBERT.
Specific to BERT -> DistilBERT.
"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="bert", choices=["bert"])
parser.add_argument("--model_name", default="bert-base-uncased", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
args = parser.parse_args()
if args.model_type == "bert":
model = BertForMaskedLM.from_pretrained(args.model_name)
prefix = "bert"
else:
raise ValueError('args.model_type should be "bert".')
state_dict = model.state_dict()
compressed_sd = {}
for w in ["word_embeddings", "position_embeddings"]:
compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"]
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]
std_idx = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
]
std_idx += 1
compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"]
compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"]
compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"]
print(f"N layers selected for distillation: {std_idx}")
print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")
print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
torch.save(compressed_sd, args.dump_checkpoint)
| -1 |
huggingface/transformers | 20,325 | Add LayerScale to NAT/DiNAT |
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | alihassanijr | "2022-11-18T22:01:53Z" | "2022-11-21T14:08:35Z" | d28448c5cd8fa8dfb64190c7f55275d80e256a9e | 11f3ec7224c83c9e5c379a774b9d3984e68d26fa | Add LayerScale to NAT/DiNAT.
# What does this PR do?
This follows PR #20219 .
I completely dropped the ball on LayerScale in the original PR.
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@sgugger @NielsRogge . | ./tests/models/data2vec/test_modeling_data2vec_audio.py | # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Data2VecAudio model. """
import math
import unittest
import numpy as np
from datasets import load_dataset
from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from transformers import Data2VecAudioConfig, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_soundfile, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init
if is_torch_available():
import torch
from transformers import (
Data2VecAudioForAudioFrameClassification,
Data2VecAudioForCTC,
Data2VecAudioForSequenceClassification,
Data2VecAudioForXVector,
Data2VecAudioModel,
Wav2Vec2Processor,
)
from transformers.models.data2vec.modeling_data2vec_audio import _compute_mask_indices
class Data2VecAudioModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=1024, # speech is longer
is_training=False,
hidden_size=16,
feat_extract_dropout=0.0,
feat_extract_activation="gelu",
conv_dim=(32, 32, 32),
conv_stride=(4, 4, 4),
conv_kernel=(8, 8, 8),
conv_bias=False,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=4,
num_attention_heads=2,
hidden_dropout_prob=0.1,
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
mask_time_prob=0.5,
mask_time_length=2,
vocab_size=32,
num_adapter_layers=1,
adapter_stride=2,
tdnn_dim=(32, 32),
tdnn_kernel=(5, 3),
tdnn_dilation=(1, 2),
xvector_output_dim=32,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feat_extract_dropout = feat_extract_dropout
self.feat_extract_activation = feat_extract_activation
self.conv_dim = conv_dim
self.conv_stride = conv_stride
self.conv_kernel = conv_kernel
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.num_adapter_layers = num_adapter_layers
self.adapter_stride = adapter_stride
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.scope = scope
self.tdnn_dim = tdnn_dim
self.tdnn_kernel = tdnn_kernel
self.tdnn_dilation = tdnn_dilation
self.xvector_output_dim = xvector_output_dim
output_seq_length = self.seq_length
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
output_seq_length = (output_seq_length - (kernel - 1)) / stride
self.output_seq_length = int(math.ceil(output_seq_length))
self.encoder_seq_length = self.output_seq_length
self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
return config, input_values, attention_mask
def get_config(self):
return Data2VecAudioConfig(
hidden_size=self.hidden_size,
feat_extract_dropout=self.feat_extract_dropout,
feat_extract_activation=self.feat_extract_activation,
conv_dim=self.conv_dim,
conv_stride=self.conv_stride,
conv_kernel=self.conv_kernel,
conv_bias=self.conv_bias,
mask_time_prob=self.mask_time_prob,
mask_time_length=self.mask_time_length,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
num_adapter_layers=self.num_adapter_layers,
adapter_stride=self.adapter_stride,
tdnn_dim=self.tdnn_dim,
tdnn_kernel=self.tdnn_kernel,
tdnn_dilation=self.tdnn_dilation,
xvector_output_dim=self.xvector_output_dim,
)
def create_and_check_model(self, config, input_values, attention_mask):
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter(self, config, input_values, attention_mask):
config.add_adapter = True
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask):
config.add_adapter = True
config.output_hidden_size = 8
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
)
def create_and_check_batch_inference(self, config, input_values, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0.0
batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
for i in range(input_values.shape[0]):
input_slice = input_values[i : i + 1, : input_lengths[i]]
output = model(input_slice).last_hidden_state
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
def check_ctc_loss(self, config, input_values, *args):
model = Data2VecAudioForCTC(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
self.parent.assertTrue(isinstance(sum_loss, float))
self.parent.assertTrue(isinstance(mean_loss, float))
def check_seq_classifier_loss(self, config, input_values, *args):
model = Data2VecAudioForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
unmasked_loss = model(input_values, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_ctc_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Data2VecAudioForCTC(config=config)
model.to(torch_device)
model.train()
# freeze feature encoder
model.freeze_feature_encoder()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_seq_classifier_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Data2VecAudioForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_xvector_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Data2VecAudioForXVector(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_labels_out_of_vocab(self, config, input_values, *args):
model = Data2VecAudioForCTC(config)
model.to(torch_device)
model.train()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
with self.parent.assertRaises(ValueError):
model(input_values, labels=labels)
def prepare_config_and_inputs_for_common(self):
config, input_values, attention_mask = self.prepare_config_and_inputs()
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class Data2VecAudioModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
Data2VecAudioForCTC,
Data2VecAudioModel,
Data2VecAudioForSequenceClassification,
Data2VecAudioForAudioFrameClassification,
Data2VecAudioForXVector,
)
if is_torch_available()
else ()
)
test_pruning = False
test_headmasking = False
def setUp(self):
self.model_tester = Data2VecAudioModelTester(self)
self.config_tester = ConfigTester(self, config_class=Data2VecAudioConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
def test_model_with_adapter_proj_dim(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_seq_classifier_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
def test_ctc_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_training(*config_and_inputs)
def test_seq_classifier_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_training(*config_and_inputs)
def test_xvector_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_xvector_training(*config_and_inputs)
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
# Data2VecAudio has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# Data2VecAudio cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# Data2VecAudio has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_flax_to_pt(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_pt_to_flax(self):
pass
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv.weight",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
]
if param.requires_grad:
if any([x in name for x in uniform_init_parms]):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight_g is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "weight_v") and module.weight_v is not None:
module.weight_v.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
if hasattr(module, "codevectors") and module.codevectors is not None:
module.codevectors.data.fill_(3)
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
module.masked_spec_embed.data.fill_(3)
def test_mask_feature_prob_ctc(self):
model = Data2VecAudioForCTC.from_pretrained(
"hf-internal-testing/tiny-random-data2vec-seq-class", mask_feature_prob=0.2, mask_feature_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
def test_mask_time_prob_ctc(self):
model = Data2VecAudioForCTC.from_pretrained(
"facebook/data2vec-audio-base-960h", mask_time_prob=0.2, mask_time_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 299, 32))
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
@slow
def test_model_from_pretrained(self):
model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base")
self.assertIsNotNone(model)
@require_torch
class Data2VecAudioUtilsTest(unittest.TestCase):
def test_compute_mask_indices(self):
batch_size = 4
sequence_length = 60
mask_prob = 0.5
mask_length = 1
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
def test_compute_mask_indices_low_prob(self):
# with these settings num_masked_spans=0.5, which means probabilistic rounding
# ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
# the other 5 out of 10, cases num_masked_spans=1
n_trials = 100
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
count_dimensions_masked = 0
count_dimensions_not_masked = 0
for _ in range(n_trials):
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
num_masks = torch.sum(mask).item()
if num_masks > 0:
count_dimensions_masked += 1
else:
count_dimensions_not_masked += 1
# as we test for at least 10 masked dimension and at least
# 10 non-masked dimension, this test could fail with probability:
# P(100 coin flips, at most 9 heads) = 1.66e-18
self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
def test_compute_mask_indices_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
def test_compute_mask_indices_attn_mask_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
attention_mask[:2, sequence_length // 2 :] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
)
mask = torch.from_numpy(mask).to(torch_device)
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
def test_compute_mask_indices_short_audio(self):
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
# force one example to be heavily padded
attention_mask[0, 5:] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
)
# make sure that non-padded examples cannot be padded
self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
@require_torch
@require_soundfile
@slow
class Data2VecAudioModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _load_superb(self, task, num_samples):
ds = load_dataset("anton-l/superb_dummy", task, split="test")
return ds[:num_samples]
def test_inference_ctc_normal(self):
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
model.to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
input_speech = self._load_datasamples(1)
input_values = processor(input_speech, return_tensors="pt").input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_ctc_batched(self):
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h").to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
" him with thousands of spectators were trivialities not worth thinking about",
"his instant of panic was followed by a small sharp blow high on his chest",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
| # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Data2VecAudio model. """
import math
import unittest
import numpy as np
from datasets import load_dataset
from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from transformers import Data2VecAudioConfig, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_soundfile, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init
if is_torch_available():
import torch
from transformers import (
Data2VecAudioForAudioFrameClassification,
Data2VecAudioForCTC,
Data2VecAudioForSequenceClassification,
Data2VecAudioForXVector,
Data2VecAudioModel,
Wav2Vec2Processor,
)
from transformers.models.data2vec.modeling_data2vec_audio import _compute_mask_indices
class Data2VecAudioModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=1024, # speech is longer
is_training=False,
hidden_size=16,
feat_extract_dropout=0.0,
feat_extract_activation="gelu",
conv_dim=(32, 32, 32),
conv_stride=(4, 4, 4),
conv_kernel=(8, 8, 8),
conv_bias=False,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=4,
num_attention_heads=2,
hidden_dropout_prob=0.1,
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
mask_time_prob=0.5,
mask_time_length=2,
vocab_size=32,
num_adapter_layers=1,
adapter_stride=2,
tdnn_dim=(32, 32),
tdnn_kernel=(5, 3),
tdnn_dilation=(1, 2),
xvector_output_dim=32,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feat_extract_dropout = feat_extract_dropout
self.feat_extract_activation = feat_extract_activation
self.conv_dim = conv_dim
self.conv_stride = conv_stride
self.conv_kernel = conv_kernel
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.num_adapter_layers = num_adapter_layers
self.adapter_stride = adapter_stride
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.scope = scope
self.tdnn_dim = tdnn_dim
self.tdnn_kernel = tdnn_kernel
self.tdnn_dilation = tdnn_dilation
self.xvector_output_dim = xvector_output_dim
output_seq_length = self.seq_length
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
output_seq_length = (output_seq_length - (kernel - 1)) / stride
self.output_seq_length = int(math.ceil(output_seq_length))
self.encoder_seq_length = self.output_seq_length
self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
return config, input_values, attention_mask
def get_config(self):
return Data2VecAudioConfig(
hidden_size=self.hidden_size,
feat_extract_dropout=self.feat_extract_dropout,
feat_extract_activation=self.feat_extract_activation,
conv_dim=self.conv_dim,
conv_stride=self.conv_stride,
conv_kernel=self.conv_kernel,
conv_bias=self.conv_bias,
mask_time_prob=self.mask_time_prob,
mask_time_length=self.mask_time_length,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
num_adapter_layers=self.num_adapter_layers,
adapter_stride=self.adapter_stride,
tdnn_dim=self.tdnn_dim,
tdnn_kernel=self.tdnn_kernel,
tdnn_dilation=self.tdnn_dilation,
xvector_output_dim=self.xvector_output_dim,
)
def create_and_check_model(self, config, input_values, attention_mask):
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter(self, config, input_values, attention_mask):
config.add_adapter = True
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask):
config.add_adapter = True
config.output_hidden_size = 8
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
)
def create_and_check_batch_inference(self, config, input_values, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
model = Data2VecAudioModel(config=config)
model.to(torch_device)
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0.0
batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
for i in range(input_values.shape[0]):
input_slice = input_values[i : i + 1, : input_lengths[i]]
output = model(input_slice).last_hidden_state
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
def check_ctc_loss(self, config, input_values, *args):
model = Data2VecAudioForCTC(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
self.parent.assertTrue(isinstance(sum_loss, float))
self.parent.assertTrue(isinstance(mean_loss, float))
def check_seq_classifier_loss(self, config, input_values, *args):
model = Data2VecAudioForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
unmasked_loss = model(input_values, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_ctc_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Data2VecAudioForCTC(config=config)
model.to(torch_device)
model.train()
# freeze feature encoder
model.freeze_feature_encoder()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_seq_classifier_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Data2VecAudioForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_xvector_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Data2VecAudioForXVector(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_labels_out_of_vocab(self, config, input_values, *args):
model = Data2VecAudioForCTC(config)
model.to(torch_device)
model.train()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
with self.parent.assertRaises(ValueError):
model(input_values, labels=labels)
def prepare_config_and_inputs_for_common(self):
config, input_values, attention_mask = self.prepare_config_and_inputs()
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class Data2VecAudioModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
Data2VecAudioForCTC,
Data2VecAudioModel,
Data2VecAudioForSequenceClassification,
Data2VecAudioForAudioFrameClassification,
Data2VecAudioForXVector,
)
if is_torch_available()
else ()
)
test_pruning = False
test_headmasking = False
def setUp(self):
self.model_tester = Data2VecAudioModelTester(self)
self.config_tester = ConfigTester(self, config_class=Data2VecAudioConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
def test_model_with_adapter_proj_dim(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_seq_classifier_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
def test_ctc_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_training(*config_and_inputs)
def test_seq_classifier_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_training(*config_and_inputs)
def test_xvector_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_xvector_training(*config_and_inputs)
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
# Data2VecAudio has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# Data2VecAudio cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# Data2VecAudio has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_flax_to_pt(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_pt_to_flax(self):
pass
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv.weight",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
]
if param.requires_grad:
if any([x in name for x in uniform_init_parms]):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight_g is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "weight_v") and module.weight_v is not None:
module.weight_v.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
if hasattr(module, "codevectors") and module.codevectors is not None:
module.codevectors.data.fill_(3)
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
module.masked_spec_embed.data.fill_(3)
def test_mask_feature_prob_ctc(self):
model = Data2VecAudioForCTC.from_pretrained(
"hf-internal-testing/tiny-random-data2vec-seq-class", mask_feature_prob=0.2, mask_feature_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
def test_mask_time_prob_ctc(self):
model = Data2VecAudioForCTC.from_pretrained(
"facebook/data2vec-audio-base-960h", mask_time_prob=0.2, mask_time_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 299, 32))
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
@slow
def test_model_from_pretrained(self):
model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base")
self.assertIsNotNone(model)
@require_torch
class Data2VecAudioUtilsTest(unittest.TestCase):
def test_compute_mask_indices(self):
batch_size = 4
sequence_length = 60
mask_prob = 0.5
mask_length = 1
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
def test_compute_mask_indices_low_prob(self):
# with these settings num_masked_spans=0.5, which means probabilistic rounding
# ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
# the other 5 out of 10, cases num_masked_spans=1
n_trials = 100
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
count_dimensions_masked = 0
count_dimensions_not_masked = 0
for _ in range(n_trials):
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
num_masks = torch.sum(mask).item()
if num_masks > 0:
count_dimensions_masked += 1
else:
count_dimensions_not_masked += 1
# as we test for at least 10 masked dimension and at least
# 10 non-masked dimension, this test could fail with probability:
# P(100 coin flips, at most 9 heads) = 1.66e-18
self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
def test_compute_mask_indices_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
def test_compute_mask_indices_attn_mask_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
attention_mask[:2, sequence_length // 2 :] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
)
mask = torch.from_numpy(mask).to(torch_device)
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
def test_compute_mask_indices_short_audio(self):
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
# force one example to be heavily padded
attention_mask[0, 5:] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
)
# make sure that non-padded examples cannot be padded
self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
@require_torch
@require_soundfile
@slow
class Data2VecAudioModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _load_superb(self, task, num_samples):
ds = load_dataset("anton-l/superb_dummy", task, split="test")
return ds[:num_samples]
def test_inference_ctc_normal(self):
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
model.to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
input_speech = self._load_datasamples(1)
input_values = processor(input_speech, return_tensors="pt").input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_ctc_batched(self):
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h").to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
" him with thousands of spectators were trivialities not worth thinking about",
"his instant of panic was followed by a small sharp blow high on his chest",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
| -1 |