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| 1 | +# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li |
| 2 | +# |
| 3 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +# you may not use this file except in compliance with the License. |
| 5 | +# You may obtain a copy of the License at |
| 6 | +# |
| 7 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +# See the License for the specific language governing permissions and |
| 13 | +# limitations under the License. |
| 14 | +from typing import Optional, Tuple, Union |
| 15 | +from transformers.modeling_outputs import ( |
| 16 | + BaseModelOutputWithPastAndCrossAttentions, |
| 17 | + CausalLMOutputWithCrossAttentions, |
| 18 | + QuestionAnsweringModelOutput, |
| 19 | + SequenceClassifierOutputWithPast, |
| 20 | + TokenClassifierOutput, |
| 21 | +) |
| 22 | +import copy |
| 23 | +import logging |
| 24 | +from dataclasses import dataclass, field |
| 25 | +import json |
| 26 | +from typing import Any, Callable, Dict, List, Optional, Tuple, Union,Sequence |
| 27 | + |
| 28 | +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss |
| 29 | +import datasets |
| 30 | +import numpy as np |
| 31 | +import torch |
| 32 | +import transformers |
| 33 | +from torch.utils.data import Dataset |
| 34 | +from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, BloomForCausalLM, LlamaTokenizer |
| 35 | +import random |
| 36 | +# import evaluate |
| 37 | +import utils |
| 38 | +import os |
| 39 | +# metric = evaluate.load("rouge") |
| 40 | + |
| 41 | +IGNORE_INDEX = -100 |
| 42 | +DEFAULT_PAD_TOKEN = "[PAD]" |
| 43 | +DEFAULT_EOS_TOKEN = "</s>" |
| 44 | +DEFAULT_BOS_TOKEN = "</s>" |
| 45 | +DEFAULT_UNK_TOKEN = "</s>" |
| 46 | + |
| 47 | + |
| 48 | +@dataclass |
| 49 | +class ModelArguments: |
| 50 | + model_name_or_path: Optional[str] = field(default="facebook/opt-125m") |
| 51 | + ref_path: Optional[str] = field(default="facebook/opt-125m") |
| 52 | + tokenizer_name_or_path: Optional[str] = field(default="facebook/opt-125m") |
| 53 | + temperature: Optional[float] = field(default=1.0) |
| 54 | + |
| 55 | + top: int = field(default=24) |
| 56 | + |
| 57 | + w_frozen: Optional[bool] = field(default=True) |
| 58 | + |
| 59 | +@dataclass |
| 60 | +class DataArguments: |
| 61 | + train_data_path: str = field(default=None, metadata={"help": "Path to the training data."}) |
| 62 | + train_group_size: int = field(default=-1) |
| 63 | + len_query: int = field(default=64) |
| 64 | + len_doc: int = field(default=438) |
| 65 | + |
| 66 | +@dataclass |
| 67 | +class TrainingArguments(transformers.Seq2SeqTrainingArguments): |
| 68 | + cache_dir: Optional[str] = field(default=None) |
| 69 | + optim: str = field(default="adamw_torch") |
| 70 | + model_max_length: int = field( |
| 71 | + default=2048, |
| 72 | + metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, |
| 73 | + ) |
| 74 | + |
| 75 | + |
| 76 | +def forward( |
| 77 | + self, |
| 78 | + input_ids: Optional[torch.LongTensor] = None, |
| 79 | + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, |
| 80 | + attention_mask: Optional[torch.Tensor] = None, |
| 81 | + head_mask: Optional[torch.Tensor] = None, |
| 82 | + inputs_embeds: Optional[torch.Tensor] = None, |
| 83 | + labels: Optional[torch.Tensor] = None, |
| 84 | + labels_gen: Optional[torch.Tensor] = None, |
| 85 | + use_cache: Optional[bool] = None, |
| 86 | + output_attentions: Optional[bool] = None, |
| 87 | + output_hidden_states: Optional[bool] = None, |
| 88 | + return_dict: Optional[bool] = None, |
| 89 | + **deprecated_arguments, |
| 90 | + ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: |
| 91 | + r""" |
| 92 | + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
| 93 | + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set |
| 94 | + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` |
| 95 | + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` |
| 96 | + """ |
| 97 | + if deprecated_arguments.pop("position_ids", False) is not False: |
| 98 | + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` |
| 99 | + warnings.warn( |
| 100 | + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" |
| 101 | + " passing `position_ids`.", |
| 102 | + FutureWarning, |
| 103 | + ) |
| 104 | + if len(deprecated_arguments) > 0: |
| 105 | + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") |
| 106 | + |
| 107 | + return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
| 108 | + |
| 109 | + transformer_outputs = self.transformer( |
| 110 | + input_ids, |
| 111 | + past_key_values=past_key_values, |
| 112 | + attention_mask=attention_mask, |
| 113 | + head_mask=head_mask, |
| 114 | + inputs_embeds=inputs_embeds, |
| 115 | + use_cache=use_cache, |
| 116 | + output_attentions=output_attentions, |
| 117 | + output_hidden_states=output_hidden_states, |
| 118 | + return_dict=return_dict, |
| 119 | + ) |
| 120 | + hidden_states = transformer_outputs[0] |
| 121 | + |
| 122 | + lm_logits = self.lm_head(hidden_states) |
| 123 | + |
| 124 | + with torch.no_grad(): |
| 125 | + init_lm_logits = self.init_model(input_ids=input_ids,attention_mask=attention_mask)[0] |
| 126 | + |
| 127 | + loss = None |
| 128 | + if labels is not None: |
| 129 | + # move labels to correct device to enable model parallelism |
| 130 | + device = lm_logits.device |
| 131 | + labels = labels.to(device) |
| 132 | + labels_gen = labels_gen.to(device) |
| 133 | + indexs=(labels!=-100).long() |
| 134 | + label_no_ingore = torch.where(labels==-100,torch.ones(labels.shape).long().to(device),labels) |
| 135 | + |
| 136 | + preds = torch.nn.functional.log_softmax(lm_logits,dim=-1) #BLV |
| 137 | + logprobs = torch.gather(preds, -1, label_no_ingore.unsqueeze(dim=-1)).squeeze(dim=-1) # B L |
| 138 | + scores = (logprobs*indexs).sum(dim=-1)/indexs.sum(dim=-1) #B -> bsz*group |
| 139 | + |
| 140 | + |
| 141 | + scores = torch.exp(scores).view(-1,self.train_group_size)/self.temperature # bsz,group |
| 142 | + |
| 143 | + |
| 144 | + target_label=torch.zeros(scores.shape[0], dtype=torch.long).to(device) |
| 145 | + loss1 = self.cross_entropy(scores, target_label) |
| 146 | + |
| 147 | + # generation loss |
| 148 | + _,seq_length,vocab_size = lm_logits.shape |
| 149 | + pos_labels = labels_gen.view(-1,self.train_group_size,seq_length)[:,0] #BL |
| 150 | + pos_lm_logits = lm_logits.view(-1,self.train_group_size, seq_length, vocab_size)[:,0] |
| 151 | + |
| 152 | + loss2 = self.cross_entropy( |
| 153 | + pos_lm_logits.reshape(-1, vocab_size), pos_labels.reshape(-1) |
| 154 | + ) |
| 155 | + |
| 156 | + # kl |
| 157 | + loss3 = self.kl_loss(input=preds.reshape([-1,vocab_size]), target=init_lm_logits.softmax(dim=-1).reshape([-1,vocab_size])) |
| 158 | + |
| 159 | + loss = loss1 + loss2 + loss3 |
| 160 | + |
| 161 | + if not return_dict: |
| 162 | + output = (lm_logits,) + transformer_outputs[1:] |
| 163 | + return ((loss,) + output) if loss is not None else output |
| 164 | + |
| 165 | + return CausalLMOutputWithCrossAttentions( |
| 166 | + loss=loss, |
| 167 | + logits=lm_logits, |
| 168 | + past_key_values=transformer_outputs.past_key_values, |
| 169 | + hidden_states=transformer_outputs.hidden_states, |
| 170 | + attentions=transformer_outputs.attentions, |
| 171 | + ) |
| 172 | + |
| 173 | +def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str): |
| 174 | + """Collects the state dict and dump to disk.""" |
| 175 | + state_dict = trainer.model.state_dict() |
| 176 | + if trainer.args.should_save: |
| 177 | + cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()} |
| 178 | + del state_dict |
| 179 | + trainer._save(output_dir, state_dict=cpu_state_dict) # noqa |
| 180 | + |
| 181 | +def smart_tokenizer_and_embedding_resize( |
| 182 | + special_tokens_dict: Dict, |
| 183 | + tokenizer: transformers.PreTrainedTokenizer, |
| 184 | + model: transformers.PreTrainedModel, |
| 185 | +): |
| 186 | + """Resize tokenizer and embedding. |
| 187 | +
|
| 188 | + Note: This is the unoptimized version that may make your embedding size not be divisible by 64. |
| 189 | + """ |
| 190 | + num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) |
| 191 | + model.resize_token_embeddings(len(tokenizer)) |
| 192 | + |
| 193 | + if num_new_tokens > 0: |
| 194 | + input_embeddings = model.get_input_embeddings().weight.data |
| 195 | + output_embeddings = model.get_output_embeddings().weight.data |
| 196 | + |
| 197 | + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) |
| 198 | + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) |
| 199 | + |
| 200 | + input_embeddings[-num_new_tokens:] = input_embeddings_avg |
| 201 | + output_embeddings[-num_new_tokens:] = output_embeddings_avg |
| 202 | + |
| 203 | +class SupervisedDataset(Dataset): |
| 204 | + def __init__(self, data, train_group_size, tokenizer, len_query, len_doc): |
| 205 | + self.data = data |
| 206 | + self.train_group_size=train_group_size |
| 207 | + self.tokenizer = tokenizer |
| 208 | + self.len_query=len_query |
| 209 | + self.len_doc=len_doc |
| 210 | + |
| 211 | + def __len__(self): |
| 212 | + return len(self.data) |
| 213 | + |
| 214 | + def __getitem__(self, idx): |
| 215 | + ex = self.data[idx] |
| 216 | + all_qd = [] |
| 217 | + |
| 218 | + if len(ex['negative_passages'])<self.train_group_size-1: |
| 219 | + all_qd = random.choices(ex['negative_passages'], k=self.train_group_size-1) |
| 220 | + else: |
| 221 | + all_qd = random.sample(ex['negative_passages'], self.train_group_size-1) |
| 222 | + |
| 223 | + all_qd = [random.choice(ex['positive_passages'])] + all_qd |
| 224 | + |
| 225 | + def truncation(text,length): |
| 226 | + text=self.tokenizer.decode(self.tokenizer.encode(text,max_length=length, add_special_tokens=False)) |
| 227 | + return text |
| 228 | + |
| 229 | + |
| 230 | + query = truncation(ex['query'], self.len_query).replace(self.tokenizer.pad_token,'PAD') |
| 231 | + all_doc = [truncation(qd['text'], self.len_doc).replace(self.tokenizer.pad_token,'PAD') for qd in all_qd] |
| 232 | + |
| 233 | + input_prompt = 'Document: {passage} Query:' |
| 234 | + |
| 235 | + sources = [input_prompt.format(passage = doc) for doc in all_doc] |
| 236 | + targets=[query for _ in sources] |
| 237 | + |
| 238 | + """Preprocess the data by tokenizing.""" |
| 239 | + examples = [s + t for s, t in zip(sources, targets)] |
| 240 | + examples_tokenized, sources_tokenized = [self._tokenize_fn(strings) for strings in (examples, sources)] |
| 241 | + input_ids = examples_tokenized["input_ids"] |
| 242 | + labels = copy.deepcopy(input_ids) |
| 243 | + labels_gen = copy.deepcopy(input_ids) |
| 244 | + for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): |
| 245 | + label[:source_len] = IGNORE_INDEX |
| 246 | + assert len(input_ids)==len(labels) |
| 247 | + |
| 248 | + return dict(input_ids=input_ids, labels=labels, labels_gen=labels_gen) |
| 249 | + |
| 250 | + def _tokenize_fn(self, strings: Sequence[str]) -> Dict: |
| 251 | + """Tokenize a list of strings.""" |
| 252 | + tokenized_list = [ |
| 253 | + self.tokenizer( |
| 254 | + text, |
| 255 | + return_tensors="pt", |
| 256 | + padding="longest", |
| 257 | + max_length=self.tokenizer.model_max_length, |
| 258 | + truncation=True, |
| 259 | + ) |
| 260 | + for text in strings |
| 261 | + ] |
| 262 | + input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] |
| 263 | + input_ids_lens = labels_lens = [ |
| 264 | + tokenized.input_ids.ne(self.tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list |
| 265 | + ] |
| 266 | + return dict( |
| 267 | + input_ids=input_ids, |
| 268 | + labels=labels, |
| 269 | + input_ids_lens=input_ids_lens, |
| 270 | + labels_lens=labels_lens, |
| 271 | + ) |
| 272 | + |
| 273 | +@dataclass |
| 274 | +class DataCollatorForSupervisedDataset(object): |
| 275 | + """Collate examples for supervised fine-tuning.""" |
| 276 | + tokenizer: transformers.PreTrainedTokenizer |
| 277 | + def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: |
| 278 | + input_ids, labels, labels_gen = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels", "labels_gen")) |
| 279 | + input_ids=[item for sublist in input_ids for item in sublist] |
| 280 | + labels=[item for sublist in labels for item in sublist] |
| 281 | + labels_gen=[item for sublist in labels_gen for item in sublist] |
| 282 | + |
| 283 | + for index in range(len(input_ids)): |
| 284 | + input_ids[index]=input_ids[index][:-1] |
| 285 | + |
| 286 | + input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id) |
| 287 | + labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) |
| 288 | + labels_gen = torch.nn.utils.rnn.pad_sequence(labels_gen, batch_first=True, padding_value=IGNORE_INDEX) |
| 289 | + |
| 290 | + labels = labels[..., 1:].contiguous() #BL |
| 291 | + labels_gen = labels_gen[..., 1:].contiguous() #BL |
| 292 | + return dict( |
| 293 | + input_ids=input_ids, |
| 294 | + labels=labels, |
| 295 | + labels_gen=labels_gen, |
| 296 | + attention_mask=input_ids.ne(self.tokenizer.pad_token_id), |
| 297 | + ) |
| 298 | + |
| 299 | + |
| 300 | +def train(): |
| 301 | + parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) |
| 302 | + model_args, data_args, training_args = parser.parse_args_into_dataclasses() |
| 303 | + training_args.predict_with_generate=True |
| 304 | + |
| 305 | + model = transformers.AutoModelForCausalLM.from_pretrained( |
| 306 | + model_args.model_name_or_path, |
| 307 | + cache_dir=training_args.cache_dir, |
| 308 | + ) |
| 309 | + model.bsz = training_args.per_device_train_batch_size |
| 310 | + model.train_group_size = data_args.train_group_size |
| 311 | + model.cross_entropy = torch.nn.CrossEntropyLoss(reduction='mean') |
| 312 | + model.kl_loss = torch.nn.KLDivLoss(reduction="batchmean") |
| 313 | + |
| 314 | + model.temperature = model_args.temperature |
| 315 | + |
| 316 | + model.init_model = transformers.AutoModelForCausalLM.from_pretrained( |
| 317 | + model_args.ref_path, |
| 318 | + cache_dir=training_args.cache_dir |
| 319 | + ).eval() |
| 320 | + |
| 321 | + if model_args.w_frozen: |
| 322 | + # peft |
| 323 | + for name, param in model.named_parameters(): |
| 324 | + param.requires_grad = False |
| 325 | + |
| 326 | + for name, param in model.transformer.h[-1*model_args.top:].named_parameters(): |
| 327 | + param.requires_grad = True |
| 328 | + |
| 329 | + from functools import partial |
| 330 | + model.forward = partial(forward, model) |
| 331 | + |
| 332 | + if 'llama' in model_args.tokenizer_name_or_path.lower(): |
| 333 | + tokenizer = LlamaTokenizer.from_pretrained( |
| 334 | + model_args.tokenizer_name_or_path, |
| 335 | + cache_dir=training_args.cache_dir, |
| 336 | + model_max_length=training_args.model_max_length, |
| 337 | + padding_side="right", |
| 338 | + use_fast=False, |
| 339 | + ) |
| 340 | + else: |
| 341 | + tokenizer = transformers.AutoTokenizer.from_pretrained( |
| 342 | + model_args.tokenizer_name_or_path, |
| 343 | + cache_dir=training_args.cache_dir, |
| 344 | + model_max_length=training_args.model_max_length, |
| 345 | + padding_side="right", |
| 346 | + use_fast=False, |
| 347 | + ) |
| 348 | + |
| 349 | + if tokenizer.pad_token is None: |
| 350 | + smart_tokenizer_and_embedding_resize( |
| 351 | + special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN), |
| 352 | + tokenizer=tokenizer, |
| 353 | + model=model, |
| 354 | + ) |
| 355 | + if "llama" in model_args.model_name_or_path: |
| 356 | + tokenizer.add_special_tokens( |
| 357 | + { |
| 358 | + "eos_token": DEFAULT_EOS_TOKEN, |
| 359 | + "bos_token": DEFAULT_BOS_TOKEN, |
| 360 | + "unk_token": DEFAULT_UNK_TOKEN, |
| 361 | + } |
| 362 | + ) |
| 363 | + |
| 364 | + data = datasets.load_dataset('json',data_files=data_args.train_data_path)['train'] |
| 365 | + |
| 366 | + |
| 367 | + train_dataset = SupervisedDataset(data=data, train_group_size=data_args.train_group_size,tokenizer=tokenizer,len_query=data_args.len_query,len_doc=data_args.len_doc) |
| 368 | + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) |
| 369 | + |
| 370 | + trainer = Seq2SeqTrainer(model=model, tokenizer=tokenizer, args=training_args, train_dataset=train_dataset, data_collator=data_collator) |
| 371 | + trainer.train() |
| 372 | + trainer.save_state() |
| 373 | + safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir) |
| 374 | + |
| 375 | + |
| 376 | +if __name__ == "__main__": |
| 377 | + train() |
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