# search for JAANDOUI for the parts I have modified, and for JAANDOUI TODO for the parts that might need to be changed. # Copyright 2022 MosaicML Examples authors # SPDX-License-Identifier: Apache-2.0 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2022, Tri Dao. import copy import logging import math import warnings from typing import List, Optional, Tuple, Union import torch import torch.nn as nn from einops import rearrange from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present from transformers.activations import ACT2FN from transformers.modeling_outputs import (MaskedLMOutput, SequenceClassifierOutput) from transformers.models.bert.modeling_bert import BertPreTrainedModel from transformers.modeling_utils import PreTrainedModel from .bert_padding import (index_first_axis, index_put_first_axis, pad_input, unpad_input, unpad_input_only) try: from .flash_attn_triton import flash_attn_qkvpacked_func except ImportError as e: flash_attn_qkvpacked_func = None logger = logging.getLogger(__name__) class BertEmbeddings(nn.Module): def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) # ALiBi doesn't use position embeddings 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.dropout = nn.Dropout(config.hidden_dropout_prob) self.register_buffer('token_type_ids', torch.zeros(config.max_position_embeddings, dtype=torch.long), persistent=False) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values_length: int = 0, ) -> torch.Tensor: if (input_ids is not None) == (inputs_embeds is not None): raise ValueError('Must specify either input_ids or input_embeds!') if input_ids is not None: input_shape = input_ids.size() else: assert inputs_embeds is not None # just for type checking input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: # great! ALiBi pass # Setting the token_type_ids to the registered buffer in constructor # where it is all zeros, which usually occurs when it's 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'): assert isinstance(self.token_type_ids, torch.LongTensor) 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 # type: ignore else: token_type_ids = torch.zeros(input_shape, # type: ignore dtype=torch.long, device=self.word_embeddings.device) # type: ignore # yapf: disable if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings # no position embeddings! ALiBi embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertUnpadSelfAttention(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.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.p_dropout = config.attention_probs_dropout_prob self.Wqkv = nn.Linear(self.all_head_size, 3 * config.hidden_size) # Warn if defaulting to pytorch because of import issues if flash_attn_qkvpacked_func is None: warnings.warn( 'Unable to import Triton; defaulting MosaicBERT attention implementation to pytorch (this will reduce throughput when using this model).' ) def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, max_seqlen_in_batch: int, indices: torch.Tensor, attn_mask: torch.Tensor, bias: torch.Tensor) -> torch.Tensor: """Perform self-attention. If dropout is zero, then we can use the Triton kernel, so we do that. However, if not, we send through a standard PyTorch implementation of self-attention. The arguments are unpadded, and our implementations of attention require padded arguments, so we first call `pad_input`. Once we compute attention, we re-unpad our outputs for the other layers. The pad/unpad operations add overhead, but not sending pad tokens through ffs saves compute. It is possible to write an unpadded implementation of attention (in Triton and PyTorch), which we will eventually do. Args: hidden_states: (total_nnz, dim) cu_seqlens: (batch + 1,) max_seqlen_in_batch: int indices: (total_nnz,) attn_mask: (batch, max_seqlen_in_batch) bias: (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch) Returns: attention: (total_nnz, dim) """ qkv = self.Wqkv(hidden_states) qkv = pad_input(qkv, indices, cu_seqlens.shape[0] - 1, max_seqlen_in_batch) # batch, max_seqlen_in_batch, thd qkv = rearrange(qkv, 'b s (t h d) -> b s t h d', t=3, h=self.num_attention_heads) if self.p_dropout or flash_attn_qkvpacked_func is None: # if we have nonzero attention dropout (e.g. during fine-tuning) or no Triton, compute attention in PyTorch q = qkv[:, :, 0, :, :].permute(0, 2, 1, 3) # b h s d k = qkv[:, :, 1, :, :].permute(0, 2, 3, 1) # b h d s v = qkv[:, :, 2, :, :].permute(0, 2, 1, 3) # b h s d attention_scores = torch.matmul(q, k) / math.sqrt( self.attention_head_size) attention_scores = attention_scores + bias attention_probs = nn.functional.softmax(attention_scores, dim=-1) attention_probs = self.dropout(attention_probs) attention = torch.matmul(attention_probs, v).permute(0, 2, 1, 3) # b s h d else: # Triton implementation only supports 0 attention dropout convert_dtype = qkv.dtype not in [torch.float16, torch.bfloat16] if convert_dtype: # Triton implementation only supports fp16 and bf16 orig_dtype = qkv.dtype qkv = qkv.to(torch.float16) bias_dtype = bias.dtype bias = bias.to(torch.float16) attention = flash_attn_qkvpacked_func(qkv, bias) attention = attention.to(orig_dtype) bias = bias.to(bias_dtype) else: attention = flash_attn_qkvpacked_func(qkv, bias) # attn_mask is 1 for attend and 0 for don't attention = unpad_input_only(attention, torch.squeeze(attn_mask) == 1) return rearrange(attention, 'nnz h d -> nnz (h d)') # Copy of transformer's library BertSelfOutput that will not be caught by surgery methods looking for HF BERT modules. class BertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) 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: torch.Tensor, input_tensor: torch.Tensor) -> torch.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 BertUnpadAttention(nn.Module): """Chains attention, Dropout, and LayerNorm for Mosaic BERT.""" def __init__(self, config): super().__init__() self.self = BertUnpadSelfAttention(config) self.output = BertSelfOutput(config) def forward( self, input_tensor: torch.Tensor, cu_seqlens: torch.Tensor, max_s: int, subset_idx: Optional[torch.Tensor] = None, indices: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Forward pass for scaled self-attention without padding. Arguments: input_tensor: (total_nnz, dim) cu_seqlens: (batch + 1,) max_s: int subset_idx: () set of indices whose values we care about at the end of the layer (e.g., the masked tokens, if this is the final layer). indices: None or (total_nnz,) attn_mask: None or (batch, max_seqlen_in_batch) bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch) """ self_output = self.self(input_tensor, cu_seqlens, max_s, indices, attn_mask, bias) if subset_idx is not None: return self.output(index_first_axis(self_output, subset_idx), index_first_axis(input_tensor, subset_idx)) else: return self.output(self_output, input_tensor) class BertGatedLinearUnitMLP(nn.Module): """Applies the FFN at the end of each Mosaic BERT layer. Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate` and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality, but introduces Gated Linear Units. Note: Mosaic BERT adds parameters in order to implement Gated Linear Units. To keep parameter count consistent with that of a standard Hugging Face BERT, scale down `config.intermediate_size` by 2/3. For example, a Mosaic BERT constructed with `config.intermediate_size=2048` will have the same parameter footprint as its Hugging Face BERT counterpart constructed with the `config.intermediate_size=3072`. However, in most cases it will not be necessary to adjust `config.intermediate_size` since, despite the increased parameter size, Mosaic BERT typically offers a net higher throughput than a Hugging Face BERT built from the same `config`. """ def __init__(self, config): super().__init__() self.config = config self.gated_layers = nn.Linear(config.hidden_size, config.intermediate_size * 2, bias=False) self.act = nn.GELU(approximate='none') self.wo = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: """Compute new hidden states from current hidden states. Args: hidden_states (torch.Tensor): The (unpadded) hidden states from the attention layer [nnz, dim]. """ residual_connection = hidden_states # compute the activation hidden_states = self.gated_layers(hidden_states) gated = hidden_states[:, :self.config.intermediate_size] non_gated = hidden_states[:, self.config.intermediate_size:] hidden_states = self.act(gated) * non_gated hidden_states = self.dropout(hidden_states) # multiply by the second matrix hidden_states = self.wo(hidden_states) # add the residual connection and post-LN hidden_states = self.layernorm(hidden_states + residual_connection) return hidden_states class BertLayer(nn.Module): """Composes the Mosaic BERT attention and FFN blocks into a single layer.""" def __init__(self, config): super(BertLayer, self).__init__() self.attention = BertUnpadAttention(config) self.mlp = BertGatedLinearUnitMLP(config) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, seqlen: int, subset_idx: Optional[torch.Tensor] = None, indices: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Forward pass for a BERT layer, including both attention and MLP. Args: hidden_states: (total_nnz, dim) cu_seqlens: (batch + 1,) seqlen: int subset_idx: () set of indices whose values we care about at the end of the layer (e.g., the masked tokens, if this is the final layer). indices: None or (total_nnz,) attn_mask: None or (batch, max_seqlen_in_batch) bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch) """ attention_output = self.attention(hidden_states, cu_seqlens, seqlen, subset_idx, indices, attn_mask, bias) layer_output = self.mlp(attention_output) return layer_output, attention_output # JAANDOUI: this only returns layer_output in the original work. class BertEncoder(nn.Module): """A stack of BERT layers providing the backbone of Mosaic BERT. This module is modeled after the Hugging Face BERT's :class:`~transformers.model.bert.modeling_bert.BertEncoder`, but with substantial modifications to implement unpadding and ALiBi. Compared to the analogous Hugging Face BERT module, this module handles unpadding to reduce unnecessary computation at padded tokens, and pre-computes attention biases to implement ALiBi. """ def __init__(self, config): super().__init__() layer = BertLayer(config) # JAANDOUI: In this line we define the BertLayer, note that now the forward of this class returns attention too!! 2 values instead of 1 self.layer = nn.ModuleList( [copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) self.num_attention_heads = config.num_attention_heads # The alibi mask will be dynamically expanded if it is too small for # the input the model receives. But it generally helps to initialize it # to a reasonably large size to help pre-allocate CUDA memory. # The default `alibi_starting_size` is 512. self._current_alibi_size = int(config.alibi_starting_size) self.alibi = torch.zeros( (1, self.num_attention_heads, self._current_alibi_size, self._current_alibi_size)) self.rebuild_alibi_tensor(size=config.alibi_starting_size) def rebuild_alibi_tensor(self, size: int, device: Optional[Union[torch.device, str]] = None): # Alibi # Following https://github.com/ofirpress/attention_with_linear_biases/issues/5 (Implementation 1) # In the causal case, you can exploit the fact that softmax is invariant to a uniform translation # of the logits, which makes the math work out *after* applying causal masking. If no causal masking # will be applied, it is necessary to construct the diagonal mask. n_heads = self.num_attention_heads def _get_alibi_head_slopes(n_heads: int) -> List[float]: def get_slopes_power_of_2(n_heads: int) -> List[float]: start = (2**(-2**-(math.log2(n_heads) - 3))) ratio = start return [start * ratio**i for i in range(n_heads)] # In the paper, they only train models that have 2^a heads for some a. This function # has some good properties that only occur when the input is a power of 2. To # maintain that even when the number of heads is not a power of 2, we use a # workaround. if math.log2(n_heads).is_integer(): return get_slopes_power_of_2(n_heads) closest_power_of_2 = 2**math.floor(math.log2(n_heads)) slopes_a = get_slopes_power_of_2(closest_power_of_2) slopes_b = _get_alibi_head_slopes(2 * closest_power_of_2) slopes_b = slopes_b[0::2][:n_heads - closest_power_of_2] return slopes_a + slopes_b context_position = torch.arange(size, device=device)[:, None] memory_position = torch.arange(size, device=device)[None, :] relative_position = torch.abs(memory_position - context_position) # [n_heads, max_token_length, max_token_length] relative_position = relative_position.unsqueeze(0).expand( n_heads, -1, -1) slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device) alibi = slopes.unsqueeze(1).unsqueeze(1) * -relative_position # [1, n_heads, max_token_length, max_token_length] alibi = alibi.unsqueeze(0) assert alibi.shape == torch.Size([1, n_heads, size, size]) self._current_alibi_size = size self.alibi = alibi def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, output_all_encoded_layers: Optional[bool] = True, subset_mask: Optional[torch.Tensor] = None, ) -> Tuple[List[torch.Tensor], torch.Tensor]: # Modify return type to include attention weights extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) extended_attention_mask = extended_attention_mask.to( dtype=torch.float32) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 attention_mask_bool = attention_mask.bool() batch, seqlen = hidden_states.shape[:2] # Unpad inputs and mask. It will remove tokens that are padded. # Assume ntokens is total number of tokens (padded and non-padded) # and ntokens_unpad is total number of non-padded tokens. # Then unpadding performs the following compression of the inputs: # hidden_states[ntokens,hidden] -> hidden_states[ntokens_unpad,hidden] hidden_states, indices, cu_seqlens, _ = unpad_input( hidden_states, attention_mask_bool) # Add alibi matrix to extended_attention_mask if self._current_alibi_size < seqlen: # Rebuild the alibi tensor when needed warnings.warn( f'Increasing alibi size from {self._current_alibi_size} to {seqlen}' ) self.rebuild_alibi_tensor(size=seqlen, device=hidden_states.device) elif self.alibi.device != hidden_states.device: # Device catch-up self.alibi = self.alibi.to(hidden_states.device) alibi_bias = self.alibi[:, :, :seqlen, :seqlen] attn_bias = extended_attention_mask[:, :, :seqlen, :seqlen] alibi_attn_mask = attn_bias + alibi_bias all_encoder_layers = [] all_attention_weights = [] # List to store attention weights if subset_mask is None: for layer_module in self.layer: # JAANDOUI: Since we get now attention too, we need to unpack 2 elements instead of 1. hidden_states, attention_weights = layer_module(hidden_states, cu_seqlens, seqlen, None, indices, attn_mask=attention_mask, bias=alibi_attn_mask) # JAANDOUI # print(f'Inner Attention: {attention_weights}') # print(f'Inner Attention shape: {attention_weights.shape}') all_attention_weights.append(attention_weights) # Store attention weights if output_all_encoded_layers: all_encoder_layers.append(hidden_states) # Pad inputs and mask. It will insert back zero-padded tokens. # Assume ntokens is total number of tokens (padded and non-padded) # and ntokens_unpad is total number of non-padded tokens. # Then padding performs the following de-compression: # hidden_states[ntokens_unpad,hidden] -> hidden_states[ntokens,hidden] hidden_states = pad_input(hidden_states, indices, batch, seqlen) else: for i in range(len(self.layer) - 1): layer_module = self.layer[i] # JAANDOUI: Since we get now attention too, we need to unpack 2 elements instead of 1. hidden_states, attention_weights = layer_module(hidden_states, cu_seqlens, seqlen, None, indices, attn_mask=attention_mask, bias=alibi_attn_mask) all_attention_weights.append(attention_weights) # JAANDOUI: Store attention weights if output_all_encoded_layers: all_encoder_layers.append(hidden_states) subset_idx = torch.nonzero(subset_mask[attention_mask_bool], as_tuple=False).flatten() # JAANDOUI: Since we get now attention too, we need to unpack 2 elements instead of 1. hidden_states, attention_weights = self.layer[-1](hidden_states, cu_seqlens, seqlen, subset_idx=subset_idx, indices=indices, attn_mask=attention_mask, bias=alibi_attn_mask) all_attention_weights.append(attention_weights) # JAANDOUI: appending the attention of different layers together. # print(f'here is the matrix of attentions inside encoder: \n {all_attention_weights}') # print(f'and this is the shape inside encoder: \n {all_attention_weights.shape}') # print(f'NUMBER6: {all_attention_weights}') if not output_all_encoded_layers: all_encoder_layers.append(hidden_states) # JAANDOUI: Since we now return both, we need to handle them wherever BertEncoder forward is called. return all_encoder_layers, all_attention_weights # Return both hidden states and attention weights # return all_encoder_layers # JAANDOUI: original return. class BertPooler(nn.Module): def __init__(self, config): super(BertPooler, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor, pool: Optional[bool] = True) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] if pool else hidden_states pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class BertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class BertModel(BertPreTrainedModel): """Overall BERT model. Args: config: a BertConfig class instance with the configuration to build a new model Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`) `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. Outputs: Tuple of (encoded_layers, pooled_output) `encoded_layers`: controlled by `output_all_encoded_layers` argument: - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size], - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding to the last attention block of shape [batch_size, sequence_length, hidden_size], `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLS`) to train on the Next-Sentence task (see BERT's paper). Example usage: ```python # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertModel(config=config) all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) ``` """ def __init__(self, config, add_pooling_layer=True): super(BertModel, self).__init__(config) self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def forward( self, input_ids: torch.Tensor, token_type_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_all_encoded_layers: Optional[bool] = False, masked_tokens_mask: Optional[torch.Tensor] = None, **kwargs ) -> Tuple[Union[List[torch.Tensor], torch.Tensor], Optional[torch.Tensor]]: if attention_mask is None: attention_mask = torch.ones_like(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) embedding_output = self.embeddings(input_ids, token_type_ids, position_ids) subset_mask = [] first_col_mask = [] if masked_tokens_mask is None: subset_mask = None else: first_col_mask = torch.zeros_like(masked_tokens_mask) first_col_mask[:, 0] = True subset_mask = masked_tokens_mask | first_col_mask # JAANDOUI: first part where we call self.encoder (which is the instance of BertEncoder defined here) # JAANDOUI: need to return the attention weights here too. encoder_outputs, all_attention_weights = self.encoder( embedding_output, attention_mask, output_all_encoded_layers=output_all_encoded_layers, subset_mask=subset_mask) # print(f'NUMBER7: {all_attention_weights}') # print(f'here is the matrix of attentions in BERT: \n {all_attention_weights}') # print(f'and this is the shape in BERT: \n {all_attention_weights.shape}') if masked_tokens_mask is None: sequence_output = encoder_outputs[-1] pooled_output = self.pooler( sequence_output) if self.pooler is not None else None else: # TD [2022-03-01]: the indexing here is very tricky. attention_mask_bool = attention_mask.bool() subset_idx = subset_mask[attention_mask_bool] # type: ignore sequence_output = encoder_outputs[-1][ masked_tokens_mask[attention_mask_bool][subset_idx]] if self.pooler is not None: pool_input = encoder_outputs[-1][ first_col_mask[attention_mask_bool][subset_idx]] pooled_output = self.pooler(pool_input, pool=False) else: pooled_output = None if not output_all_encoded_layers: encoder_outputs = sequence_output # JAANDOUI: returning all_attention_weights too if self.pooler is not None: # print(f'NUMBER8: {all_attention_weights}') return encoder_outputs, pooled_output, all_attention_weights # JAANDOUI: returning all_attention_weights too # print(f'NUMBER9: {all_attention_weights}') return encoder_outputs, None, all_attention_weights # JAANDOUI: need to handle the returned elements wherever BertModel is instantiated. ################### # Bert Heads ################### class BertLMPredictionHead(nn.Module): def __init__(self, config, bert_model_embedding_weights): super().__init__() self.transform = BertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(bert_model_embedding_weights.size(1), bert_model_embedding_weights.size(0)) self.decoder.weight = bert_model_embedding_weights def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class BertOnlyMLMHead(nn.Module): def __init__(self, config, bert_model_embedding_weights): super().__init__() self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores class BertOnlyNSPHead(nn.Module): def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output: torch.Tensor) -> torch.Tensor: seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score class BertForMaskedLM(BertPreTrainedModel): def __init__(self, config): super().__init__(config) if config.is_decoder: warnings.warn( 'If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for ' 'bi-directional self-attention.') # JAANDOUI: this part is only for the pretraining, I don't think it is called if we finetune # there handle the returned elements (we now get 3 elements) of BertModel if pretraining self.bert = BertModel(config, add_pooling_layer=False) self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: # labels should be a `torch.LongTensor` of shape # `(batch_size, sequence_length)`. These are used for computing the # masked language modeling loss. # # Indices should be in `[-100, 0, ..., config.vocab_size]` (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]` # # Prediction scores are only computed for masked tokens and the (bs, # seqlen) dimensions are flattened if (input_ids is not None) == (inputs_embeds is not None): raise ValueError('Must specify either input_ids or input_embeds!') if labels is None: masked_tokens_mask = None else: masked_tokens_mask = labels > 0 return_dict = return_dict if return_dict is not None else self.config.use_return_dict # JAANDOUI: for the pretraining: return handled here. outputs = self.bert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, 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, masked_tokens_mask=masked_tokens_mask, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) loss = None if labels is not None: # Compute loss loss_fct = nn.CrossEntropyLoss() masked_token_idx = torch.nonzero(labels.flatten() > 0, as_tuple=False).flatten() loss = loss_fct(prediction_scores, labels.flatten()[masked_token_idx]) assert input_ids is not None, 'Coding error; please open an issue' batch, seqlen = input_ids.shape[:2] prediction_scores = rearrange(index_put_first_axis( prediction_scores, masked_token_idx, batch * seqlen), '(b s) d -> b s d', b=batch) if not return_dict: output = (prediction_scores,) + outputs[2:] # JAANDOUI TODO: might need to handle this part and everywhere where we get outputs (now outputs has 3 elements not 2) return ((loss,) + output) if loss is not None else output return MaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs[0], attentions=None, ) def prepare_inputs_for_generation(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **model_kwargs): input_shape = input_ids.shape effective_batch_size = input_shape[0] # add a dummy token if self.config.pad_token_id is None: raise ValueError('The PAD token should be defined for generation') attention_mask = torch.cat([ attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1)) ], dim=-1) dummy_token = torch.full((effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device) input_ids = torch.cat([input_ids, dummy_token], dim=1) return {'input_ids': input_ids, 'attention_mask': attention_mask} # JAANDOUI: this model is the one used for finetuning. class BertForSequenceClassification(BertPreTrainedModel): """Bert Model transformer with a sequence classification/regression head. This head is just a linear layer on top of the pooled output. Used for, e.g., GLUE tasks. """ def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.bert = BertModel(config) classifier_dropout = (config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: # 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 outputs = self.bert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] # JAANDOUI: all_attention_weights = outputs[2] # print(f'last: {all_attention_weights}') pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: # Compute loss 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 = nn.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 = nn.CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == 'multi_label_classification': loss_fct = nn.BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: # JAANDOUI TODO maybe. output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output # print(outputs.attentions) return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs[0], #JAANDOUI: returning all_attention_weights here attentions=torch.stack(outputs[2], dim=0), )