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| import argparse | |
| import logging | |
| import math | |
| from dataclasses import dataclass | |
| from typing import List, Any, Union, Optional | |
| import torch | |
| import ujson | |
| from accelerate import Accelerator | |
| from accelerate.utils import set_seed | |
| from torch import nn, Tensor | |
| from torch.nn import functional as F | |
| from torch.utils.data import Dataset, RandomSampler, DataLoader, SequentialSampler | |
| from tqdm.auto import tqdm | |
| from transformers import get_scheduler, AutoTokenizer, AutoModel, AdamW, SchedulerType, PreTrainedTokenizerBase, AutoModelForSequenceClassification, BatchEncoding | |
| from transformers.file_utils import PaddingStrategy | |
| logger = logging.getLogger(__name__) | |
| def get_parser(): | |
| parser = argparse.ArgumentParser(description="Train LFQA retriever") | |
| parser.add_argument( | |
| "--dpr_input_file", | |
| type=str, | |
| help="DPR formatted input file with question/positive/negative pairs in a JSONL file", | |
| ) | |
| parser.add_argument( | |
| "--per_device_train_batch_size", | |
| type=int, | |
| default=32, | |
| ) | |
| parser.add_argument( | |
| "--per_device_eval_batch_size", | |
| type=int, | |
| default=32, | |
| help="Batch size (per device) for the evaluation dataloader.", | |
| ) | |
| parser.add_argument( | |
| "--max_length", | |
| type=int, | |
| default=128, | |
| ) | |
| parser.add_argument( | |
| "--pretrained_model_name", | |
| type=str, | |
| default="sentence-transformers/all-MiniLM-L6-v2", | |
| ) | |
| parser.add_argument( | |
| "--ce_model_name", | |
| type=str, | |
| default="cross-encoder/ms-marco-MiniLM-L-6-v2", | |
| ) | |
| parser.add_argument( | |
| "--model_save_name", | |
| type=str, | |
| default="eli5_retriever_model_l-12_h-768_b-512-512", | |
| ) | |
| parser.add_argument( | |
| "--learning_rate", | |
| type=float, | |
| default=2e-5, | |
| ) | |
| parser.add_argument( | |
| "--weight_decay", | |
| type=float, | |
| default=0.01, | |
| ) | |
| parser.add_argument( | |
| "--log_freq", | |
| type=int, | |
| default=500, | |
| help="Log train/validation loss every log_freq update steps" | |
| ) | |
| parser.add_argument( | |
| "--num_train_epochs", | |
| type=int, | |
| default=4, | |
| ) | |
| 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", # this is linear with warmup | |
| 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=100, | |
| help="Number of steps for the warmup in the lr scheduler." | |
| ) | |
| parser.add_argument( | |
| "--warmup_percentage", | |
| type=float, | |
| default=0.08, | |
| help="Number of steps for the warmup in the lr scheduler." | |
| ) | |
| return parser | |
| class InputExample: | |
| guid: str = "" | |
| texts: List[str] = None | |
| label: Union[int, float] = 0 | |
| class DPRDataset(Dataset): | |
| """ | |
| Dataset DPR format of question, answers, positive, negative, and hard negative passages | |
| See https://github.com/facebookresearch/DPR#retriever-input-data-format for more details | |
| """ | |
| def __init__(self, file_path: str, include_all_positive: bool = False) -> None: | |
| super().__init__() | |
| with open(file_path, "r") as fp: | |
| self.data = [] | |
| def dpr_example_to_input_example(idx, dpr_item): | |
| examples = [] | |
| for p_idx, p_item in enumerate(dpr_item["positive_ctxs"]): | |
| for n_idx, n_item in enumerate(dpr_item["negative_ctxs"]): | |
| examples.append(InputExample(guid=[idx, p_idx, n_idx], texts=[dpr_item["question"], | |
| p_item["text"], | |
| n_item["text"]])) | |
| if not include_all_positive: | |
| break | |
| return examples | |
| for idx, line in enumerate(fp): | |
| self.data.extend(dpr_example_to_input_example(idx, ujson.loads(line))) | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, index): | |
| return self.data[index] | |
| def dpr_collate_fn(batch): | |
| query_id, pos_id, neg_id = zip(*[example.guid for example in batch]) | |
| query, pos, neg = zip(*[example.texts for example in batch]) | |
| return (query_id, pos_id, neg_id), (query, pos, neg) | |
| # Mean Pooling - Take attention mask into account for correct averaging | |
| def mean_pooling(model_output, attention_mask): | |
| token_embeddings = model_output[0] # First element of model_output contains all token embeddings | |
| input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() | |
| sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) | |
| sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) | |
| return sum_embeddings / sum_mask | |
| class CrossEncoderCollator: | |
| tokenizer: PreTrainedTokenizerBase | |
| model: Any | |
| target_tokenizer: PreTrainedTokenizerBase | |
| padding: Union[bool, str, PaddingStrategy] = True | |
| max_length: Optional[int] = None | |
| pad_to_multiple_of: Optional[int] = None | |
| return_tensors: str = "pt" | |
| def __call__(self, batch): | |
| query_id, pos_id, neg_id = zip(*[example.guid for example in batch]) | |
| query, pos_passage, neg_passage = zip(*[example.texts for example in batch]) | |
| batch_input: List[List[str]] = list(zip(query, pos_passage)) + list(zip(query, neg_passage)) | |
| features = self.tokenizer(batch_input, padding=self.padding, truncation=True, | |
| return_tensors=self.return_tensors) | |
| with torch.no_grad(): | |
| scores = self.model(**features).logits | |
| labels = scores[:len(query)] - scores[len(query):] | |
| batch_input: List[str] = list(query) + list(pos_passage) + list(neg_passage) | |
| #breakpoint() | |
| encoded_input = self.target_tokenizer(batch_input, padding=True, truncation=True, | |
| max_length=256, return_tensors='pt') | |
| encoded_input["labels"] = labels | |
| return encoded_input | |
| class RetrievalQAEmbedder(torch.nn.Module): | |
| def __init__(self, sent_encoder, sent_tokenizer, batch_size:int = 32): | |
| super(RetrievalQAEmbedder, self).__init__() | |
| dim = sent_encoder.config.hidden_size | |
| self.model = sent_encoder | |
| self.tokenizer = sent_tokenizer | |
| self.scale = 1 | |
| self.similarity_fct = 'dot' | |
| self.batch_size = 32 | |
| self.loss_fct = nn.MSELoss() | |
| def forward(self, examples: BatchEncoding): | |
| # Tokenize sentences | |
| labels = examples.pop("labels") | |
| # Compute token embeddings | |
| model_output = self.model(**examples) | |
| examples["labels"] = labels | |
| # Perform pooling. In this case, mean pooling | |
| sentence_embeddings = mean_pooling(model_output, examples['attention_mask']) | |
| target_shape = (3, self.batch_size, sentence_embeddings.shape[-1]) | |
| sentence_embeddings_reshaped = torch.reshape(sentence_embeddings, target_shape) | |
| #breakpoint() | |
| embeddings_query = sentence_embeddings_reshaped[0] | |
| embeddings_pos = sentence_embeddings_reshaped[1] | |
| embeddings_neg = sentence_embeddings_reshaped[2] | |
| if self.similarity_fct == 'cosine': | |
| embeddings_query = F.normalize(embeddings_query, p=2, dim=1) | |
| embeddings_pos = F.normalize(embeddings_pos, p=2, dim=1) | |
| embeddings_neg = F.normalize(embeddings_neg, p=2, dim=1) | |
| scores_pos = (embeddings_query * embeddings_pos).sum(dim=-1) * self.scale | |
| scores_neg = (embeddings_query * embeddings_neg).sum(dim=-1) * self.scale | |
| margin_pred = scores_pos - scores_neg | |
| #breakpoint() | |
| return self.loss_fct(margin_pred, labels.squeeze()) | |
| def evaluate_qa_retriever(model, data_loader): | |
| # make iterator | |
| epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) | |
| tot_loss = 0.0 | |
| with torch.no_grad(): | |
| for step, batch in enumerate(epoch_iterator): | |
| q_ids, q_mask, a_ids, a_mask = batch | |
| loss = model(q_ids, q_mask, a_ids, a_mask) | |
| tot_loss += loss.item() | |
| return tot_loss / (step + 1) | |
| def train(config): | |
| set_seed(42) | |
| args = config["args"] | |
| # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. | |
| accelerator = 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.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR) | |
| logger.info(accelerator.state) | |
| # prepare torch Dataset objects | |
| train_dataset = DPRDataset(file_path=args.dpr_input_file) | |
| valid_dataset = Dataset() | |
| base_tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name) | |
| base_model = AutoModel.from_pretrained(args.pretrained_model_name) | |
| ce_tokenizer = AutoTokenizer.from_pretrained(args.ce_model_name) | |
| ce_model = AutoModelForSequenceClassification.from_pretrained(args.ce_model_name) | |
| _ = ce_model.eval() | |
| model = RetrievalQAEmbedder(base_model, base_tokenizer) | |
| 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 = AdamW(optimizer_grouped_parameters, lr=args.learning_rate) | |
| cec = CrossEncoderCollator(model=ce_model, tokenizer=ce_tokenizer, target_tokenizer=base_tokenizer) | |
| train_dataloader = DataLoader(train_dataset, batch_size=args.per_device_train_batch_size, | |
| sampler=RandomSampler(train_dataset), collate_fn=cec) | |
| eval_dataloader = DataLoader(valid_dataset, batch_size=args.per_device_eval_batch_size, | |
| sampler=SequentialSampler(valid_dataset), collate_fn=cec) | |
| # train the model | |
| model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(model, optimizer, | |
| train_dataloader, eval_dataloader) | |
| # Scheduler and math around the number of training steps. | |
| 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 | |
| else: | |
| args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) | |
| num_warmup_steps = args.num_warmup_steps if args.num_warmup_steps else math.ceil(args.max_train_steps * | |
| args.warmup_percentage) | |
| scheduler = get_scheduler( | |
| name=args.lr_scheduler_type, | |
| optimizer=optimizer, | |
| num_warmup_steps=args.num_warmup_steps, | |
| num_training_steps=args.max_train_steps, | |
| ) | |
| # 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}") | |
| logger.info(f" Warmup steps = {num_warmup_steps}") | |
| logger.info(f" Logging training progress every {args.log_freq} optimization steps") | |
| loc_loss = 0.0 | |
| current_loss = 0.0 | |
| checkpoint_step = 0 | |
| completed_steps = checkpoint_step | |
| progress_bar = tqdm(range(args.max_train_steps), initial=checkpoint_step, | |
| disable=not accelerator.is_local_main_process) | |
| for epoch in range(args.num_train_epochs): | |
| model.train() | |
| for step, batch in enumerate(train_dataloader, start=checkpoint_step): | |
| # model inputs | |
| pre_loss = model(batch) | |
| loss = pre_loss / args.gradient_accumulation_steps | |
| accelerator.backward(loss) | |
| loc_loss += loss.item() | |
| if ((step + 1) % args.gradient_accumulation_steps == 0) or (step + 1 == len(train_dataloader)): | |
| current_loss = loc_loss | |
| optimizer.step() | |
| scheduler.step() | |
| optimizer.zero_grad() | |
| progress_bar.update(1) | |
| progress_bar.set_postfix(loss=loc_loss) | |
| loc_loss = 0 | |
| completed_steps += 1 | |
| if step % (args.log_freq * args.gradient_accumulation_steps) == 0: | |
| # accelerator.wait_for_everyone() | |
| # unwrapped_model = accelerator.unwrap_model(model) | |
| # eval_loss = evaluate_qa_retriever(unwrapped_model, eval_dataloader) | |
| eval_loss = 0 | |
| logger.info(f"Train loss {current_loss} , eval loss {eval_loss}") | |
| if args.wandb and accelerator.is_local_main_process: | |
| import wandb | |
| wandb.log({"loss": current_loss, "eval_loss": eval_loss, "step": completed_steps}) | |
| if completed_steps >= args.max_train_steps: | |
| break | |
| logger.info("Saving model {}".format(args.model_save_name)) | |
| accelerator.wait_for_everyone() | |
| unwrapped_model = accelerator.unwrap_model(model) | |
| accelerator.save(unwrapped_model.state_dict(), "{}_{}.bin".format(args.model_save_name, epoch)) | |
| eval_loss = evaluate_qa_retriever(unwrapped_model, eval_dataloader) | |
| logger.info("Evaluation loss epoch {:4d}: {:.3f}".format(epoch, eval_loss)) | |
| if __name__ == "__main__": | |
| parser = get_parser() | |
| parser.add_argument( | |
| "--wandb", | |
| action="store_true", | |
| help="Whether to use W&B logging", | |
| ) | |
| main_args, _ = parser.parse_known_args() | |
| config = {"args": main_args} | |
| if main_args.wandb: | |
| import wandb | |
| wandb.init(project="Retriever") | |
| train(config=config) | |