from typing import List, Union, Dict, Mapping, Optional, Tuple, TypedDict import torch import os import json import numpy as np from functools import partial from contextlib import nullcontext from transformers import AutoModel, PreTrainedTokenizerFast, BatchEncoding, DataCollatorWithPadding from transformers.modeling_utils import PreTrainedModel from transformers.models.auto import AutoTokenizer from transformers.models.llama.modeling_llama import LLAMA_INPUTS_DOCSTRING from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa from transformers import LlamaModel, LlamaConfig from transformers.cache_utils import Cache, DynamicCache from transformers.utils import ( add_start_docstrings_to_model_forward, logging, ) from einops import rearrange, repeat from tqdm.auto import tqdm from datasets import Dataset from torch.utils.data import DataLoader from .configuration_gigarembed import GigarEmbedConfig, LatentAttentionConfig, BidirectionalLlamaConfig logger = logging.get_logger(__name__) class GigarEmbedFeatures(TypedDict): input_dict: torch.Tensor attention_mask: torch.Tensor pool_mask: torch.Tensor class BidirectionalLlamaModel(LlamaModel): config_class = BidirectionalLlamaConfig def __init__(self, config: LlamaConfig): super().__init__(config) for layer in self.layers: layer.self_attn.is_causal = False self._attn_implementation = "eager" @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = 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 # 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: batch_size, seq_length = input_ids.shape elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False past_key_values_length = 0 if use_cache: use_legacy_cache = not isinstance(past_key_values, Cache) if use_legacy_cache: past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values_length = past_key_values.get_usable_length(seq_length) if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0).view(-1, seq_length) else: position_ids = position_ids.view(-1, seq_length).long() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: is_padding_right = attention_mask[:, -1].sum().item() != batch_size if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Llama. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if self._attn_implementation == "flash_attention_2": # 2d mask is passed through the layers attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None elif self._attn_implementation == "sdpa" and not output_attentions: # output_attentions=True can not be supported when using SDPA, and we fall back on # the manual implementation that requires a 4D causal mask in all cases. attention_mask = _prepare_4d_attention_mask_for_sdpa( attention_mask, inputs_embeds.dtype ) else: # 4d mask is passed through the layers attention_mask = _prepare_4d_attention_mask( attention_mask, inputs_embeds.dtype, ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_values, output_attentions, use_cache, position_embeddings=position_embeddings ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, position_embeddings=position_embeddings ) 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],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = None if use_cache: next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache 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, ) def _move_to_device(maybe_tensor, device: torch.device): if torch.is_tensor(maybe_tensor): return maybe_tensor.to(device, non_blocking=device.type == "cuda") elif isinstance(maybe_tensor, dict): return {key: _move_to_device(value, device) for key, value in maybe_tensor.items()} elif isinstance(maybe_tensor, list): return [_move_to_device(x, device) for x in maybe_tensor] elif isinstance(maybe_tensor, tuple): return tuple([_move_to_device(x, device) for x in maybe_tensor]) elif isinstance(maybe_tensor, Mapping): return type(maybe_tensor)({k: _move_to_device(v, device) for k, v in maybe_tensor.items()}) else: return maybe_tensor def move_to_device(sample, device: torch.device): if device.type == "cpu": return sample if len(sample) == 0: return {} return _move_to_device(sample, device) def input_transform_func( tokenizer: PreTrainedTokenizerFast, examples: Dict[str, List], max_length: int, instruction: str, ) -> BatchEncoding: examples['input_texts'] = [instruction + input_example for input_example in examples['input_texts']] batch_dict = tokenizer( examples['input_texts'], max_length=max_length, padding=True, return_token_type_ids=False, return_tensors="pt", truncation=True) return batch_dict class PreNorm(torch.nn.Module): def __init__(self, dim, fn, context_dim = None): super().__init__() # TODO remove this layer, we don't use it def forward(self, x, **kwargs): return x class GEGLU(torch.nn.Module): def forward(self, x): x, gates = x.chunk(2, dim = -1) return x * torch.nn.functional.gelu(gates) class FeedForward(torch.nn.Module): def __init__(self, dim, mult = 4): super().__init__() self.net = torch.nn.Sequential( torch.nn.Linear(dim, 2 * dim * mult), GEGLU(), torch.nn.Linear(dim * mult, dim) ) def forward(self, x): return self.net(x) def exists(val): return val is not None def default(val, d): return val if exists(val) else d class Attention(torch.nn.Module): def __init__(self, query_dim, context_dim = None, heads = 8, dim_head = 64): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.scale = dim_head ** -0.5 self.heads = heads self.to_q = torch.nn.Linear(query_dim, inner_dim, bias = False) self.to_kv = torch.nn.Linear(context_dim, inner_dim * 2, bias = False) self.to_out = torch.nn.Linear(inner_dim, query_dim, bias = False) def forward(self, x, context = None, mask = None): h = self.heads q = self.to_q(x) context = default(context, x) k, v = self.to_kv(context).chunk(2, dim = -1) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h = h), (q, k, v)) attn_weights = torch.matmul(q, k.transpose(-1, -2)) / self.scale mask_value = torch.finfo(attn_weights.dtype).min mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device) padding_mask = mask[:, :, None].repeat(self.heads, 1, 1).bool() attn_weights = torch.where(padding_mask, attn_weights, mask_value) attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1) out = torch.matmul(attn_weights, v) out = rearrange(out, '(b h) n d -> b n (h d)', h = h) return self.to_out(out) class LatentAttentionModel(PreTrainedModel): config_class = LatentAttentionConfig def __init__(self, config: LatentAttentionConfig): super().__init__(config) ## cross-attention block num_latents, latent_dim, cross_heads, cross_dim_head = config.num_latents_value, config.latent_dim, config.num_cross_heads, config.cross_dim_head dim = config.hidden_dim # init latent_attention and latents self.cross_attend_blocks = torch.nn.ModuleList([ PreNorm(latent_dim, Attention(latent_dim, dim, heads = cross_heads, dim_head = cross_dim_head), context_dim = dim), PreNorm(latent_dim, FeedForward(latent_dim)), ]) self.output_normalize = config.output_normalize self.register_parameter("latents", torch.nn.Parameter(torch.randn(num_latents, latent_dim))) def forward(self, hiddens, attention_mask: torch.Tensor=None): # cross-attention block cross_attn, cross_ff = self.cross_attend_blocks b, *_, device = *hiddens.shape, hiddens.device x = repeat(self.latents, 'n d -> b n d', b = b) hiddens = cross_attn(hiddens, context=x, mask=attention_mask) + hiddens hiddens = cross_ff(hiddens) + hiddens if attention_mask != None: s = torch.sum(hiddens * attention_mask.unsqueeze(-1).float(), dim=1) d = attention_mask.sum(dim=1, keepdim=True).float() hiddens = s / d if self.output_normalize: hiddens = torch.nn.functional.normalize(hiddens, p=2, dim=-1) return hiddens class GigarEmbedModel(PreTrainedModel): config_class = GigarEmbedConfig _no_split_modules = ["LlamaDecoderLayer", "LatentAttentionModel"] def __init__(self, config: GigarEmbedConfig): super().__init__(config) self.latent_attention_model = AutoModel.from_config(config.latent_attention_config).float() self.model = AutoModel.from_config( config.text_config, ) if config.text_config is not None else None self.tokenizer = AutoTokenizer.from_pretrained(config.text_config._name_or_path) if config.text_config is not None else None self.padding_side = config.padding_side self.is_mask_instruction = config.is_mask_instruction self.add_eos = config.add_eos self.mask_type = config.mask_type if config.add_pad_token and self.tokenizer is not None: self.add_pad_token() self.latent_attention_model.apply(self._init_weights) def _init_weights(self, module): if isinstance(module, torch.nn.Linear): torch.nn.init.xavier_normal_(module.weight) def add_pad_token(self): self.tokenizer.pad_token_id = 0 self.tokenizer.padding_side = self.padding_side def prepare_kwargs_from_batch(self, batch_dict: dict, instruction_lens: int, device: torch.device): batch_dict = move_to_device(batch_dict, device) attention_mask = batch_dict['attention_mask'].clone() if 'attention_mask' in batch_dict else None if (attention_mask is not None and self.padding_side == "right" and self.is_mask_instruction == True and instruction_lens > 0): # Mask out the instruction tokens for mean-pooling attention_mask[:, :instruction_lens] = 0 features: GigarEmbedFeatures = { 'input_ids': batch_dict['input_ids'], 'attention_mask': batch_dict['attention_mask'], 'pool_mask': attention_mask, } return features @torch.no_grad() def _do_encode(self, prompts: List[str], batch_size: int=1, instruction: str="", max_length: int=4096, num_workers: int=32, **kwargs ) -> Union[np.ndarray, torch.FloatTensor]: dataset: Dataset = Dataset.from_dict({'input_texts': prompts}) dataset.set_transform(partial(input_transform_func, self.tokenizer, max_length=max_length, instruction=instruction)) data_collator = DataCollatorWithPadding(self.tokenizer) data_loader = DataLoader( dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers, collate_fn=data_collator, pin_memory=True) if self.padding_side == "right" and self.is_mask_instruction == True and len(instruction) > 0: instruction_lens = len(self.tokenizer.tokenize(instruction)) else: instruction_lens = 0 encoded_embeds = [] device = next(self.model.parameters()).device for batch_dict in tqdm(data_loader, desc='encoding', mininterval=10): features = self.prepare_kwargs_from_batch(batch_dict, instruction_lens, device=device) embeds=self(**features)["sentence_embeddings"].squeeze(1) encoded_embeds.append(embeds) encoded_embeds = torch.cat(encoded_embeds, axis=0) if "return_numpy" in kwargs and kwargs.get("return_numpy"): encoded_embeds = encoded_embeds.cpu().detach().numpy() return encoded_embeds def forward(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, pool_mask: Optional[torch.Tensor]=None, return_dict: bool=True, **kwargs): kwargs.pop('token_type_ids', None) outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) embeds = self.latent_attention_model( outputs.last_hidden_state, attention_mask, ) if not return_dict: return (embeds,) return {"sentence_embeddings": embeds} @torch.no_grad() def encode(self, prompts: List[str], instruction: str="", max_length: int=4096, **kwargs): if self.padding_side == "right" and self.is_mask_instruction == True and len(instruction) > 0: instruction_lens = len(self.tokenizer.tokenize(instruction)) else: instruction_lens = 0 device = next(self.model.parameters()).device batch_dict = input_transform_func(self.tokenizer, {"input_texts": [prompt for prompt in prompts]}, max_length=max_length, instruction=instruction) features: GigarEmbedFeatures = self.prepare_kwargs_from_batch(batch_dict, instruction_lens, device=device) return self(**features)["sentence_embeddings"].squeeze(1) ## AutoModel Register AutoModel.register(GigarEmbedConfig, GigarEmbedModel) AutoModel.register(LatentAttentionConfig, LatentAttentionModel) AutoModel.register(BidirectionalLlamaConfig, BidirectionalLlamaModel) ## Register for auto class GigarEmbedModel.register_for_auto_class("AutoModel") LatentAttentionModel.register_for_auto_class("AutoModel") BidirectionalLlamaModel.register_for_auto_class("AutoModel")