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| import argparse | |
| import functools | |
| import logging | |
| import math | |
| from random import choice, randint | |
| import torch | |
| from accelerate import Accelerator | |
| from accelerate.utils import set_seed | |
| from datasets import load_dataset | |
| from torch.utils import checkpoint | |
| from torch.utils.data import Dataset, RandomSampler, DataLoader, SequentialSampler | |
| from tqdm.auto import tqdm | |
| from transformers import get_scheduler, AutoTokenizer, AdamW, SchedulerType, AutoModelForSequenceClassification | |
| logger = logging.getLogger(__name__) | |
| def get_parser(): | |
| parser = argparse.ArgumentParser(description="Train ELI5 retriever") | |
| parser.add_argument( | |
| "--dataset_name", | |
| type=str, | |
| default="vblagoje/lfqa", | |
| help="The name of the dataset to use (via the datasets library).", | |
| ) | |
| parser.add_argument( | |
| "--per_device_train_batch_size", | |
| type=int, | |
| default=1024, | |
| ) | |
| parser.add_argument( | |
| "--per_device_eval_batch_size", | |
| type=int, | |
| default=1024, | |
| help="Batch size (per device) for the evaluation dataloader.", | |
| ) | |
| parser.add_argument( | |
| "--max_length", | |
| type=int, | |
| default=128, | |
| ) | |
| parser.add_argument( | |
| "--checkpoint_batch_size", | |
| type=int, | |
| default=32, | |
| ) | |
| parser.add_argument( | |
| "--pretrained_model_name", | |
| type=str, | |
| default="google/bert_uncased_L-8_H-768_A-12", | |
| ) | |
| 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-4, | |
| ) | |
| parser.add_argument( | |
| "--weight_decay", | |
| type=float, | |
| default=0.2, | |
| ) | |
| 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 RetrievalQAEmbedder(torch.nn.Module): | |
| def __init__(self, sent_encoder): | |
| super(RetrievalQAEmbedder, self).__init__() | |
| dim = sent_encoder.config.hidden_size | |
| self.bert_query = sent_encoder | |
| self.output_dim = 128 | |
| self.project_query = torch.nn.Linear(dim, self.output_dim, bias=False) | |
| self.project_doc = torch.nn.Linear(dim, self.output_dim, bias=False) | |
| self.ce_loss = torch.nn.CrossEntropyLoss(reduction="mean") | |
| def embed_sentences_checkpointed(self, input_ids, attention_mask, checkpoint_batch_size=-1): | |
| # reproduces BERT forward pass with checkpointing | |
| if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: | |
| return self.bert_query(input_ids, attention_mask=attention_mask)[1] | |
| else: | |
| # prepare implicit variables | |
| device = input_ids.device | |
| input_shape = input_ids.size() | |
| token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) | |
| head_mask = [None] * self.bert_query.config.num_hidden_layers | |
| extended_attention_mask: torch.Tensor = self.bert_query.get_extended_attention_mask( | |
| attention_mask, input_shape, device | |
| ) | |
| # define function for checkpointing | |
| def partial_encode(*inputs): | |
| encoder_outputs = self.bert_query.encoder(inputs[0], attention_mask=inputs[1], head_mask=head_mask, ) | |
| sequence_output = encoder_outputs[0] | |
| pooled_output = self.bert_query.pooler(sequence_output) | |
| return pooled_output | |
| # run embedding layer on everything at once | |
| embedding_output = self.bert_query.embeddings( | |
| input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None | |
| ) | |
| # run encoding and pooling on one mini-batch at a time | |
| pooled_output_list = [] | |
| for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): | |
| b_embedding_output = embedding_output[b * checkpoint_batch_size: (b + 1) * checkpoint_batch_size] | |
| b_attention_mask = extended_attention_mask[b * checkpoint_batch_size: (b + 1) * checkpoint_batch_size] | |
| pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) | |
| pooled_output_list.append(pooled_output) | |
| return torch.cat(pooled_output_list, dim=0) | |
| def embed_questions(self, q_ids, q_mask, checkpoint_batch_size=-1): | |
| q_reps = self.embed_sentences_checkpointed(q_ids, q_mask, checkpoint_batch_size) | |
| return self.project_query(q_reps) | |
| def embed_answers(self, a_ids, a_mask, checkpoint_batch_size=-1): | |
| a_reps = self.embed_sentences_checkpointed(a_ids, a_mask, checkpoint_batch_size) | |
| return self.project_doc(a_reps) | |
| def forward(self, q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=-1): | |
| device = q_ids.device | |
| q_reps = self.embed_questions(q_ids, q_mask, checkpoint_batch_size) | |
| a_reps = self.embed_answers(a_ids, a_mask, checkpoint_batch_size) | |
| compare_scores = torch.mm(q_reps, a_reps.t()) | |
| loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) | |
| loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) | |
| loss = (loss_qa + loss_aq) / 2 | |
| return loss | |
| class ELI5DatasetQARetriever(Dataset): | |
| def __init__(self, examples_array, extra_answer_threshold=3, min_answer_length=64, training=True, n_samples=None): | |
| self.data = examples_array | |
| self.answer_thres = extra_answer_threshold | |
| self.min_length = min_answer_length | |
| self.training = training | |
| self.n_samples = self.data.num_rows if n_samples is None else n_samples | |
| def __len__(self): | |
| return self.n_samples | |
| def make_example(self, idx): | |
| example = self.data[idx] | |
| question = example["title"] | |
| if self.training: | |
| answers = [a for i, (a, sc) in enumerate(zip(example["answers"]["text"], example["answers"]["score"]))] | |
| answer_tab = choice(answers).split(" ") | |
| start_idx = randint(0, max(0, len(answer_tab) - self.min_length)) | |
| answer_span = " ".join(answer_tab[start_idx:]) | |
| else: | |
| answer_span = example["answers"]["text"][0] | |
| return question, answer_span | |
| def __getitem__(self, idx): | |
| return self.make_example(idx % self.data.num_rows) | |
| def make_qa_retriever_batch(qa_list, tokenizer, max_len=64): | |
| q_ls = [q for q, a in qa_list] | |
| a_ls = [a for q, a in qa_list] | |
| q_toks = tokenizer(q_ls, padding="max_length", max_length=max_len, truncation=True) | |
| q_ids, q_mask = ( | |
| torch.LongTensor(q_toks["input_ids"]), | |
| torch.LongTensor(q_toks["attention_mask"]) | |
| ) | |
| a_toks = tokenizer(a_ls, padding="max_length", max_length=max_len, truncation=True) | |
| a_ids, a_mask = ( | |
| torch.LongTensor(a_toks["input_ids"]), | |
| torch.LongTensor(a_toks["attention_mask"]), | |
| ) | |
| return q_ids, q_mask, a_ids, a_mask | |
| 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"] | |
| data_files = {"train": "train.json", "validation": "validation.json", "test": "test.json"} | |
| eli5 = load_dataset(args.dataset_name, data_files=data_files) | |
| # 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 = ELI5DatasetQARetriever(eli5['train'], training=True) | |
| valid_dataset = ELI5DatasetQARetriever(eli5['validation'], training=False) | |
| tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name) | |
| base_model = AutoModel.from_pretrained(args.pretrained_model_name) | |
| model = RetrievalQAEmbedder(base_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': 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, weight_decay=args.weight_decay) | |
| model_collate_fn = functools.partial(make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length) | |
| train_dataloader = DataLoader(train_dataset, batch_size=args.per_device_train_batch_size, | |
| sampler=RandomSampler(train_dataset), collate_fn=model_collate_fn) | |
| model_collate_fn = functools.partial(make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length) | |
| eval_dataloader = DataLoader(valid_dataset, batch_size=args.per_device_eval_batch_size, | |
| sampler=SequentialSampler(valid_dataset), collate_fn=model_collate_fn) | |
| # 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() | |
| batch = next(iter(train_dataloader)) | |
| for step in range(1000): | |
| #for step, batch in enumerate(train_dataloader, start=checkpoint_step): | |
| # model inputs | |
| q_ids, q_mask, a_ids, a_mask = batch | |
| pre_loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size) | |
| loss = pre_loss.sum() / 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) | |
| 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) | |