# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-pruning Masked BERT for question-answering on SQuAD.""" import argparse import glob import logging import os import random import timeit import numpy as np import torch from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange from transformers import ( WEIGHTS_NAME, AdamW, BertConfig, BertForQuestionAnswering, BertTokenizer, get_linear_schedule_with_warmup, squad_convert_examples_to_features, ) from transformers.data.metrics.squad_metrics import ( compute_predictions_log_probs, compute_predictions_logits, squad_evaluate, ) from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer), "masked_bert": (MaskedBertConfig, MaskedBertForQuestionAnswering, BertTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def schedule_threshold( step: int, total_step: int, warmup_steps: int, initial_threshold: float, final_threshold: float, initial_warmup: int, final_warmup: int, final_lambda: float, ): if step <= initial_warmup * warmup_steps: threshold = initial_threshold elif step > (total_step - final_warmup * warmup_steps): threshold = final_threshold else: spars_warmup_steps = initial_warmup * warmup_steps spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps) threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff**3) regu_lambda = final_lambda * threshold / final_threshold return threshold, regu_lambda def regularization(model: nn.Module, mode: str): regu, counter = 0, 0 for name, param in model.named_parameters(): if "mask_scores" in name: if mode == "l1": regu += torch.norm(torch.sigmoid(param), p=1) / param.numel() elif mode == "l0": regu += torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1)).sum() / param.numel() else: ValueError("Don't know this mode.") counter += 1 return regu / counter def to_list(tensor): return tensor.detach().cpu().tolist() def train(args, train_dataset, model, tokenizer, teacher=None): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter(log_dir=args.output_dir) args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if "mask_score" in n and p.requires_grad], "lr": args.mask_scores_learning_rate, }, { "params": [ p for n, p in model.named_parameters() if "mask_score" not in n and p.requires_grad and not any(nd in n for nd in no_decay) ], "lr": args.learning_rate, "weight_decay": args.weight_decay, }, { "params": [ p for n, p in model.named_parameters() if "mask_score" not in n and p.requires_grad and any(nd in n for nd in no_decay) ], "lr": args.learning_rate, "weight_decay": 0.0, }, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) # Distillation if teacher is not None: logger.info(" Training with distillation") global_step = 1 # Global TopK if args.global_topk: threshold_mem = None epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to global_step of last saved checkpoint from model path try: checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0] global_step = int(checkpoint_suffix) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) except ValueError: logger.info(" Starting fine-tuning.") tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] ) # Added here for reproducibility set_seed(args) for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) threshold, regu_lambda = schedule_threshold( step=global_step, total_step=t_total, warmup_steps=args.warmup_steps, final_threshold=args.final_threshold, initial_threshold=args.initial_threshold, final_warmup=args.final_warmup, initial_warmup=args.initial_warmup, final_lambda=args.final_lambda, ) # Global TopK if args.global_topk: if threshold == 1.0: threshold = -1e2 # Or an indefinitely low quantity else: if (threshold_mem is None) or (global_step % args.global_topk_frequency_compute == 0): # Sort all the values to get the global topK concat = torch.cat( [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name] ) n = concat.numel() kth = max(n - (int(n * threshold) + 1), 1) threshold_mem = concat.kthvalue(kth).values.item() threshold = threshold_mem else: threshold = threshold_mem inputs = { "input_ids": batch[0], "attention_mask": batch[1], "token_type_ids": batch[2], "start_positions": batch[3], "end_positions": batch[4], } if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]: del inputs["token_type_ids"] if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[5], "p_mask": batch[6]}) if args.version_2_with_negative: inputs.update({"is_impossible": batch[7]}) if hasattr(model, "config") and hasattr(model.config, "lang2id"): inputs.update( {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)} ) if "masked" in args.model_type: inputs["threshold"] = threshold outputs = model(**inputs) # model outputs are always tuple in transformers (see doc) loss, start_logits_stu, end_logits_stu = outputs # Distillation loss if teacher is not None: with torch.no_grad(): start_logits_tea, end_logits_tea = teacher( input_ids=inputs["input_ids"], token_type_ids=inputs["token_type_ids"], attention_mask=inputs["attention_mask"], ) loss_start = nn.functional.kl_div( input=nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1), target=nn.functional.softmax(start_logits_tea / args.temperature, dim=-1), reduction="batchmean", ) * (args.temperature**2) loss_end = nn.functional.kl_div( input=nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1), target=nn.functional.softmax(end_logits_tea / args.temperature, dim=-1), reduction="batchmean", ) * (args.temperature**2) loss_logits = (loss_start + loss_end) / 2.0 loss = args.alpha_distil * loss_logits + args.alpha_ce * loss # Regularization if args.regularization is not None: regu_ = regularization(model=model, mode=args.regularization) loss = loss + regu_lambda * regu_ if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: tb_writer.add_scalar("threshold", threshold, global_step) for name, param in model.named_parameters(): if not param.requires_grad: continue tb_writer.add_scalar("parameter_mean/" + name, param.data.mean(), global_step) tb_writer.add_scalar("parameter_std/" + name, param.data.std(), global_step) tb_writer.add_scalar("parameter_min/" + name, param.data.min(), global_step) tb_writer.add_scalar("parameter_max/" + name, param.data.max(), global_step) if "pooler" in name: continue tb_writer.add_scalar("grad_mean/" + name, param.grad.data.mean(), global_step) tb_writer.add_scalar("grad_std/" + name, param.grad.data.std(), global_step) if args.regularization is not None and "mask_scores" in name: if args.regularization == "l1": perc = (torch.sigmoid(param) > threshold).sum().item() / param.numel() elif args.regularization == "l0": perc = (torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1))).sum().item() / param.numel() tb_writer.add_scalar("retained_weights_perc/" + name, perc, global_step) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 # Log metrics if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well if args.local_rank == -1 and args.evaluate_during_training: results = evaluate(args, model, tokenizer) for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) learning_rate_scalar = scheduler.get_lr() tb_writer.add_scalar("lr", learning_rate_scalar[0], global_step) if len(learning_rate_scalar) > 1: for idx, lr in enumerate(learning_rate_scalar[1:]): tb_writer.add_scalar(f"lr/{idx+1}", lr, global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) if teacher is not None: tb_writer.add_scalar("loss/distil", loss_logits.item(), global_step) if args.regularization is not None: tb_writer.add_scalar("loss/regularization", regu_.item(), global_step) if (teacher is not None) or (args.regularization is not None): if (teacher is not None) and (args.regularization is not None): tb_writer.add_scalar( "loss/instant_ce", (loss.item() - regu_lambda * regu_.item() - args.alpha_distil * loss_logits.item()) / args.alpha_ce, global_step, ) elif teacher is not None: tb_writer.add_scalar( "loss/instant_ce", (loss.item() - args.alpha_distil * loss_logits.item()) / args.alpha_ce, global_step, ) else: tb_writer.add_scalar( "loss/instant_ce", loss.item() - regu_lambda * regu_.item(), global_step ) logging_loss = tr_loss # Save model checkpoint if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) # Take care of distributed/parallel training model_to_save = model.module if hasattr(model, "module") else model model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) all_results = [] start_time = timeit.default_timer() # Global TopK if args.global_topk: threshold_mem = None for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "token_type_ids": batch[2], } if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]: del inputs["token_type_ids"] example_indices = batch[3] # XLNet and XLM use more arguments for their predictions if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[4], "p_mask": batch[5]}) # for lang_id-sensitive xlm models if hasattr(model, "config") and hasattr(model.config, "lang2id"): inputs.update( {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)} ) if "masked" in args.model_type: inputs["threshold"] = args.final_threshold if args.global_topk: if threshold_mem is None: concat = torch.cat( [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name] ) n = concat.numel() kth = max(n - (int(n * args.final_threshold) + 1), 1) threshold_mem = concat.kthvalue(kth).values.item() inputs["threshold"] = threshold_mem outputs = model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) output = [to_list(output[i]) for output in outputs] # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler" # models only use two. if len(output) >= 5: start_logits = output[0] start_top_index = output[1] end_logits = output[2] end_top_index = output[3] cls_logits = output[4] result = SquadResult( unique_id, start_logits, end_logits, start_top_index=start_top_index, end_top_index=end_top_index, cls_logits=cls_logits, ) else: start_logits, end_logits = output result = SquadResult(unique_id, start_logits, end_logits) all_results.append(result) evalTime = timeit.default_timer() - start_time logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset)) # Compute predictions output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix)) output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix)) if args.version_2_with_negative: output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix)) else: output_null_log_odds_file = None # XLNet and XLM use a more complex post-processing procedure if args.model_type in ["xlnet", "xlm"]: start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top predictions = compute_predictions_log_probs( examples, features, all_results, args.n_best_size, args.max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, start_n_top, end_n_top, args.version_2_with_negative, tokenizer, args.verbose_logging, ) else: predictions = compute_predictions_logits( examples, features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold, tokenizer, ) # Compute the F1 and exact scores. results = squad_evaluate(examples, predictions) return results def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0] and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() # Load data features from cache or dataset file input_dir = args.data_dir if args.data_dir else "." cached_features_file = os.path.join( input_dir, "cached_{}_{}_{}_{}".format( "dev" if evaluate else "train", args.tokenizer_name if args.tokenizer_name else list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), list(filter(None, args.predict_file.split("/"))).pop() if evaluate else list(filter(None, args.train_file.split("/"))).pop(), ), ) # Init features and dataset from cache if it exists if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features_and_dataset = torch.load(cached_features_file) features, dataset, examples = ( features_and_dataset["features"], features_and_dataset["dataset"], features_and_dataset["examples"], ) else: logger.info("Creating features from dataset file at %s", input_dir) if not args.data_dir and ((evaluate and not args.predict_file) or (not evaluate and not args.train_file)): try: import tensorflow_datasets as tfds except ImportError: raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.") if args.version_2_with_negative: logger.warning("tensorflow_datasets does not handle version 2 of SQuAD.") tfds_examples = tfds.load("squad") examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate) else: processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor() if evaluate: examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file) else: examples = processor.get_train_examples(args.data_dir, filename=args.train_file) features, dataset = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate, return_dataset="pt", threads=args.threads, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file) if args.local_rank == 0 and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() if output_examples: return dataset, examples, features return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained model or model identifier from huggingface.co/models", ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.", ) # Other parameters parser.add_argument( "--data_dir", default=None, type=str, help="The input data dir. Should contain the .json files for the task." + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--train_file", default=None, type=str, help="The input training file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--predict_file", default=None, type=str, help="The input evaluation file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--version_2_with_negative", action="store_true", help="If true, the SQuAD examples contain some that do not have an answer.", ) parser.add_argument( "--null_score_diff_threshold", type=float, default=0.0, help="If null_score - best_non_null is greater than the threshold predict null.", ) parser.add_argument( "--max_seq_length", default=384, type=int, help=( "The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ), ) parser.add_argument( "--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.", ) parser.add_argument( "--max_query_length", default=64, type=int, help=( "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step." ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model." ) parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") # Pruning parameters parser.add_argument( "--mask_scores_learning_rate", default=1e-2, type=float, help="The Adam initial learning rate of the mask scores.", ) parser.add_argument( "--initial_threshold", default=1.0, type=float, help="Initial value of the threshold (for scheduling)." ) parser.add_argument( "--final_threshold", default=0.7, type=float, help="Final value of the threshold (for scheduling)." ) parser.add_argument( "--initial_warmup", default=1, type=int, help=( "Run `initial_warmup` * `warmup_steps` steps of threshold warmup during which threshold stays " "at its `initial_threshold` value (sparsity schedule)." ), ) parser.add_argument( "--final_warmup", default=2, type=int, help=( "Run `final_warmup` * `warmup_steps` steps of threshold cool-down during which threshold stays " "at its final_threshold value (sparsity schedule)." ), ) parser.add_argument( "--pruning_method", default="topK", type=str, help=( "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning," " sigmoied_threshold = Soft movement pruning)." ), ) parser.add_argument( "--mask_init", default="constant", type=str, help="Initialization method for the mask scores. Choices: constant, uniform, kaiming.", ) parser.add_argument( "--mask_scale", default=0.0, type=float, help="Initialization parameter for the chosen initialization method." ) parser.add_argument("--regularization", default=None, help="Add L0 or L1 regularization to the mask scores.") parser.add_argument( "--final_lambda", default=0.0, type=float, help="Regularization intensity (used in conjunction with `regularization`.", ) parser.add_argument("--global_topk", action="store_true", help="Global TopK on the Scores.") parser.add_argument( "--global_topk_frequency_compute", default=25, type=int, help="Frequency at which we compute the TopK global threshold.", ) # Distillation parameters (optional) parser.add_argument( "--teacher_type", default=None, type=str, help=( "Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for" " distillation." ), ) parser.add_argument( "--teacher_name_or_path", default=None, type=str, help="Path to the already SQuAD fine-tuned teacher model. Only for distillation.", ) parser.add_argument( "--alpha_ce", default=0.5, type=float, help="Cross entropy loss linear weight. Only for distillation." ) parser.add_argument( "--alpha_distil", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation." ) parser.add_argument( "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation." ) 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("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument( "--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json output file.", ) parser.add_argument( "--max_answer_length", default=30, type=int, help=( "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another." ), ) parser.add_argument( "--verbose_logging", action="store_true", help=( "If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation." ), ) parser.add_argument( "--lang_id", default=0, type=int, help=( "language id of input for language-specific xlm models (see" " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)" ), ) parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. " "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features") args = parser.parse_args() # Regularization if args.regularization == "null": args.regularization = None if args.doc_stride >= args.max_seq_length - args.max_query_length: logger.warning( "WARNING - You've set a doc stride which may be superior to the document length in some " "examples. This could result in errors when building features from the examples. Please reduce the doc " "stride or increase the maximum length to ensure the features are correctly built." ) if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of synchronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None, pruning_method=args.pruning_method, mask_init=args.mask_init, mask_scale=args.mask_scale, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.teacher_type is not None: assert args.teacher_name_or_path is not None assert args.alpha_distil > 0.0 assert args.alpha_distil + args.alpha_ce > 0.0 teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type] teacher_config = teacher_config_class.from_pretrained(args.teacher_name_or_path) teacher = teacher_model_class.from_pretrained( args.teacher_name_or_path, from_tf=False, config=teacher_config, cache_dir=args.cache_dir if args.cache_dir else None, ) teacher.to(args.device) else: teacher = None if args.local_rank == 0: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set. # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will # remove the need for this code, but it is still valid. if args.fp16: try: import apex apex.amp.register_half_function(torch, "einsum") except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` # Take care of distributed/parallel training model_to_save = model.module if hasattr(model, "module") else model model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) # , force_download=True) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: if args.do_train: logger.info("Loading checkpoints saved during training for evaluation") checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = [ os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ] else: logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path) checkpoints = [args.model_name_or_path] logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) # , force_download=True) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = {k + ("_{}".format(global_step) if global_step else ""): v for k, v in result.items()} results.update(result) logger.info("Results: {}".format(results)) predict_file = list(filter(None, args.predict_file.split("/"))).pop() if not os.path.exists(os.path.join(args.output_dir, predict_file)): os.makedirs(os.path.join(args.output_dir, predict_file)) output_eval_file = os.path.join(args.output_dir, predict_file, "eval_results.txt") with open(output_eval_file, "w") as writer: for key in sorted(results.keys()): writer.write("%s = %s\n" % (key, str(results[key]))) return results if __name__ == "__main__": main()