# 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 on sequence classification on GLUE.""" import argparse import glob import json import logging import os import random import numpy as np import torch from emmental import MaskedBertConfig, MaskedBertForSequenceClassification from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange from transformers import ( WEIGHTS_NAME, AdamW, BertConfig, BertForSequenceClassification, BertTokenizer, get_linear_schedule_with_warmup, ) from transformers import glue_compute_metrics as compute_metrics from transformers import glue_convert_examples_to_features as convert_examples_to_features from transformers import glue_output_modes as output_modes from transformers import glue_processors as processors try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForSequenceClassification, BertTokenizer), "masked_bert": (MaskedBertConfig, MaskedBertForSequenceClassification, 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 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 = 0 # 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: global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) except ValueError: global_step = 0 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) 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], ) set_seed(args) # Added here for reproducibility 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], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids if "masked" in args.model_type: inputs["threshold"] = threshold outputs = model(**inputs) loss, logits_stu = outputs # model outputs are always tuple in transformers (see doc) # Distillation loss if teacher is not None: if "token_type_ids" not in inputs: inputs["token_type_ids"] = None if args.teacher_type == "xlm" else batch[2] with torch.no_grad(): (logits_tea,) = teacher( input_ids=inputs["input_ids"], token_type_ids=inputs["token_type_ids"], attention_mask=inputs["attention_mask"], ) loss_logits = nn.functional.kl_div( input=nn.functional.log_softmax(logits_stu / args.temperature, dim=-1), target=nn.functional.softmax(logits_tea / args.temperature, dim=-1), reduction="batchmean", ) * (args.temperature**2) 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 or ( # last step in epoch but step is always smaller than gradient_accumulation_steps len(epoch_iterator) <= args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator) ): 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) 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 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: logs = {} if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): eval_key = "eval_{}".format(key) logs[eval_key] = value loss_scalar = (tr_loss - logging_loss) / args.logging_steps learning_rate_scalar = scheduler.get_lr() logs["learning_rate"] = learning_rate_scalar[0] if len(learning_rate_scalar) > 1: for idx, lr in enumerate(learning_rate_scalar[1:]): logs[f"learning_rate/{idx+1}"] = lr logs["loss"] = loss_scalar if teacher is not None: logs["loss/distil"] = loss_logits.item() if args.regularization is not None: logs["loss/regularization"] = regu_.item() if (teacher is not None) or (args.regularization is not None): if (teacher is not None) and (args.regularization is not None): logs["loss/instant_ce"] = ( loss.item() - regu_lambda * logs["loss/regularization"] - args.alpha_distil * logs["loss/distil"] ) / args.alpha_ce elif teacher is not None: logs["loss/instant_ce"] = ( loss.item() - args.alpha_distil * logs["loss/distil"] ) / args.alpha_ce else: logs["loss/instant_ce"] = loss.item() - regu_lambda * logs["loss/regularization"] logging_loss = tr_loss for key, value in logs.items(): tb_writer.add_scalar(key, value, global_step) print(json.dumps({**logs, **{"step": global_step}})) if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training 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=""): # Loop to handle MNLI double evaluation (matched, mis-matched) eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) eval_outputs_dirs = (args.output_dir, args.output_dir + "/MM") if args.task_name == "mnli" else (args.output_dir,) results = {} for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: os.makedirs(eval_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(eval_dataset) eval_dataloader = DataLoader(eval_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(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None # 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], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids 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) tmp_eval_loss, logits = outputs[:2] eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps if args.output_mode == "classification": from scipy.special import softmax probs = softmax(preds, axis=-1) entropy = np.exp((-probs * np.log(probs)).sum(axis=-1).mean()) preds = np.argmax(preds, axis=1) elif args.output_mode == "regression": preds = np.squeeze(preds) result = compute_metrics(eval_task, preds, out_label_ids) results.update(result) if entropy is not None: result["eval_avg_entropy"] = entropy output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results {} *****".format(prefix)) for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) return results def load_and_cache_examples(args, task, tokenizer, evaluate=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache processor = processors[task]() output_mode = output_modes[task] # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), str(task), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) label_list = processor.get_labels() if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]: # HACK(label indices are swapped in RoBERTa pretrained model) label_list[1], label_list[2] = label_list[2], label_list[1] examples = ( processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) ) features = convert_examples_to_features( examples, tokenizer, max_length=args.max_seq_length, label_list=label_list, output_mode=output_mode, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) if output_mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif output_mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.", ) 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( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(processors.keys()), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) # Other parameters 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( "--max_seq_length", default=128, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) 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 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("--logging_steps", type=int, default=50, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=50, 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="Avoid using 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( "--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("--local_rank", type=int, default=-1, help="For distributed training: local_rank") args = parser.parse_args() # Regularization if args.regularization == "null": args.regularization = None 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( f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to" " overcome." ) # 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) # Prepare GLUE task args.task_name = args.task_name.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] label_list = processor.get_labels() num_labels = len(label_list) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab 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, num_labels=num_labels, finetuning_task=args.task_name, 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, cache_dir=args.cache_dir if args.cache_dir else None, do_lower_case=args.do_lower_case, ) 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: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=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) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() 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()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training 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) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) 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)) ] logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) result = evaluate(args, model, tokenizer, prefix=prefix) result = {k + "_{}".format(global_step): v for k, v in result.items()} results.update(result) return results if __name__ == "__main__": main()