# 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. """ Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa).""" import argparse import glob import json import logging import os import re import shutil import random from multiprocessing import Pool from typing import Dict, List, Tuple from copy import deepcopy import numpy as np import torch 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, AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer, BertConfig, BertForSequenceClassification, BertForLongSequenceClassification, BertForLongSequenceClassificationCat, BertTokenizer, DNATokenizer, DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer, FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, XLMConfig, XLMForSequenceClassification, XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer, 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__) ALL_MODELS = sum( ( tuple(conf.pretrained_config_archive_map.keys()) for conf in ( BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig, AlbertConfig, XLMRobertaConfig, FlaubertConfig, ) ), (), ) MODEL_CLASSES = { "dna": (BertConfig, BertForSequenceClassification, DNATokenizer), "dnalong": (BertConfig, BertForLongSequenceClassification, DNATokenizer), "dnalongcat": (BertConfig, BertForLongSequenceClassificationCat, DNATokenizer), "bert": (BertConfig, BertForSequenceClassification, BertTokenizer), "xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer), "xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer), "roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer), "distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer), "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer), "xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer), "flaubert": (FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer), } TOKEN_ID_GROUP = ["bert", "dnalong", "dnalongcat", "xlnet", "albert"] 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 _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]: ordering_and_checkpoint_path = [] glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-*".format(checkpoint_prefix))) for path in glob_checkpoints: if use_mtime: ordering_and_checkpoint_path.append((os.path.getmtime(path), path)) else: regex_match = re.match(".*{}-([0-9]+)".format(checkpoint_prefix), path) if regex_match and regex_match.groups(): ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path)) checkpoints_sorted = sorted(ordering_and_checkpoint_path) checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted] return checkpoints_sorted def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None: if not args.save_total_limit: return if args.save_total_limit <= 0: return # Check if we should delete older checkpoint(s) checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime) if len(checkpoints_sorted) <= args.save_total_limit: return number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit) checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete] for checkpoint in checkpoints_to_be_deleted: logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint)) shutil.rmtree(checkpoint) def train(args, train_dataset, model, tokenizer): """ Train the model """ if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() 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 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}, ] warmup_steps = args.warmup_steps if args.warmup_percent == 0 else int(args.warmup_percent*t_total) optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon, betas=(args.beta1,args.beta2)) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=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 = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = torch.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) global_step = 0 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 gobal_step of last saved checkpoint from model path try: global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) except: 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 reproductibility best_auc = 0 last_auc = 0 stop_count = 0 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) 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 TOKEN_ID_GROUP else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in transformers (see doc) 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: torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) 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) if args.task_name == "dna690": # record the best auc if results["auc"] > best_auc: best_auc = results["auc"] if args.early_stop != 0: # record current auc to perform early stop if results["auc"] < last_auc: stop_count += 1 else: stop_count = 0 last_auc = results["auc"] if stop_count == args.early_stop: logger.info("Early stop") return global_step, tr_loss / global_step 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()[0] logs["learning_rate"] = learning_rate_scalar logs["loss"] = loss_scalar 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: if args.task_name == "dna690" and results["auc"] < best_auc: continue checkpoint_prefix = "checkpoint" # 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) logger.info("Saving model checkpoint to %s", output_dir) _rotate_checkpoints(args, checkpoint_prefix) if args.task_name != "dna690": torch.save(args, os.path.join(output_dir, "training_args.bin")) 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="", evaluate=True): # 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,) if args.task_name[:3] == "dna": softmax = torch.nn.Softmax(dim=1) 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=evaluate) 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, torch.nn.DataParallel): model = torch.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 probs = None out_label_ids = 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 TOKEN_ID_GROUP else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids 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": if args.task_name[:3] == "dna" and args.task_name != "dnasplice": if args.do_ensemble_pred: probs = softmax(torch.tensor(preds, dtype=torch.float32)).numpy() else: probs = softmax(torch.tensor(preds, dtype=torch.float32))[:,1].numpy() elif args.task_name == "dnasplice": probs = softmax(torch.tensor(preds, dtype=torch.float32)).numpy() preds = np.argmax(preds, axis=1) elif args.output_mode == "regression": preds = np.squeeze(preds) if args.do_ensemble_pred: result = compute_metrics(eval_task, preds, out_label_ids, probs[:,1]) else: result = compute_metrics(eval_task, preds, out_label_ids, probs) results.update(result) if args.task_name == "dna690": eval_output_dir = args.result_dir if not os.path.exists(args.result_dir): os.makedirs(args.result_dir) output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") with open(output_eval_file, "a") as writer: if args.task_name[:3] == "dna": eval_result = args.data_dir.split('/')[-1] + " " else: eval_result = "" logger.info("***** Eval results {} *****".format(prefix)) for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) eval_result = eval_result + str(result[key])[:5] + " " writer.write(eval_result + "\n") if args.do_ensemble_pred: return results, eval_task, preds, out_label_ids, probs else: return results def predict(args, model, tokenizer, prefix=""): # Loop to handle MNLI double evaluation (matched, mis-matched) pred_task_names = (args.task_name,) pred_outputs_dirs = (args.predict_dir,) if not os.path.exists(args.predict_dir): os.makedirs(args.predict_dir) softmax = torch.nn.Softmax(dim=1) predictions = {} for pred_task, pred_output_dir in zip(pred_task_names, pred_outputs_dirs): pred_dataset = load_and_cache_examples(args, pred_task, tokenizer, evaluate=True) if not os.path.exists(pred_output_dir) and args.local_rank in [-1, 0]: os.makedirs(pred_output_dir) args.pred_batch_size = args.per_gpu_pred_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly pred_sampler = SequentialSampler(pred_dataset) pred_dataloader = DataLoader(pred_dataset, sampler=pred_sampler, batch_size=args.pred_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel): model = torch.nn.DataParallel(model) # Eval! logger.info("***** Running prediction {} *****".format(prefix)) logger.info(" Num examples = %d", len(pred_dataset)) logger.info(" Batch size = %d", args.pred_batch_size) pred_loss = 0.0 nb_pred_steps = 0 preds = None out_label_ids = None for batch in tqdm(pred_dataloader, desc="Predicting"): 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 TOKEN_ID_GROUP else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids outputs = model(**inputs) _, logits = outputs[:2] 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) if args.output_mode == "classification": if args.task_name[:3] == "dna" and args.task_name != "dnasplice": if args.do_ensemble_pred: probs = softmax(torch.tensor(preds, dtype=torch.float32)).numpy() else: probs = softmax(torch.tensor(preds, dtype=torch.float32))[:,1].numpy() elif args.task_name == "dnasplice": probs = softmax(torch.tensor(preds, dtype=torch.float32)).numpy() preds = np.argmax(preds, axis=1) elif args.output_mode == "regression": preds = np.squeeze(preds) if args.do_ensemble_pred: result = compute_metrics(pred_task, preds, out_label_ids, probs[:,1]) else: result = compute_metrics(pred_task, preds, out_label_ids, probs) pred_output_dir = args.predict_dir if not os.path.exists(pred_output_dir): os.makedir(pred_output_dir) output_pred_file = os.path.join(pred_output_dir, "pred_results.npy") logger.info("***** Pred results {} *****".format(prefix)) for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) np.save(output_pred_file, probs) def format_attention(attention): squeezed = [] for layer_attention in attention: # 1 x num_heads x seq_len x seq_len if len(layer_attention.shape) != 4: raise ValueError("The attention tensor does not have the correct number of dimensions. Make sure you set " "output_attentions=True when initializing your model.") squeezed.append(layer_attention.squeeze(0)) # num_layers x num_heads x seq_len x seq_len return torch.stack(squeezed).unsqueeze(0) def visualize(args, model, tokenizer, kmer, prefix=""): # Loop to handle MNLI double evaluation (matched, mis-matched) pred_task_names = (args.task_name,) pred_outputs_dirs = (args.predict_dir,) if not os.path.exists(args.predict_dir): os.makedirs(args.predict_dir) softmax = torch.nn.Softmax(dim=1) for pred_task, pred_output_dir in zip(pred_task_names, pred_outputs_dirs): ''' if args.task_name != "dna690": args.data_dir = os.path.join(args.visualize_data_dir, str(kmer)) else: args.data_dir = deepcopy(args.visualize_data_dir).replace("/690", "/690/" + str(kmer)) ''' evaluate = False if args.visualize_train else True pred_dataset = load_and_cache_examples(args, pred_task, tokenizer, evaluate=evaluate) if not os.path.exists(pred_output_dir) and args.local_rank in [-1, 0]: os.makedirs(pred_output_dir) args.pred_batch_size = args.per_gpu_pred_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly pred_sampler = SequentialSampler(pred_dataset) pred_dataloader = DataLoader(pred_dataset, sampler=pred_sampler, batch_size=args.pred_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel): model = torch.nn.DataParallel(model) # Eval! logger.info("***** Running prediction {} *****".format(prefix)) logger.info(" Num examples = %d", len(pred_dataset)) logger.info(" Batch size = %d", args.pred_batch_size) pred_loss = 0.0 nb_pred_steps = 0 batch_size = args.pred_batch_size if args.task_name != "dnasplice": preds = np.zeros([len(pred_dataset),2]) else: preds = np.zeros([len(pred_dataset),3]) attention_scores = np.zeros([len(pred_dataset), 12, args.max_seq_length, args.max_seq_length]) for index, batch in enumerate(tqdm(pred_dataloader, desc="Predicting")): 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 TOKEN_ID_GROUP else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids outputs = model(**inputs) attention = outputs[-1][-1] _, logits = outputs[:2] preds[index*batch_size:index*batch_size+len(batch[0]),:] = logits.detach().cpu().numpy() attention_scores[index*batch_size:index*batch_size+len(batch[0]),:,:,:] = attention.cpu().numpy() # if preds is None: # preds = logits.detach().cpu().numpy() # else: # preds = np.concatenate((preds, logits.detach().cpu().numpy()), axis=0) # if attention_scores is not None: # attention_scores = np.concatenate((attention_scores, attention.cpu().numpy()), 0) # else: # attention_scores = attention.cpu().numpy() if args.task_name != "dnasplice": probs = softmax(torch.tensor(preds, dtype=torch.float32))[:,1].numpy() else: probs = softmax(torch.tensor(preds, dtype=torch.float32)).numpy() scores = np.zeros([attention_scores.shape[0], attention_scores.shape[-1]]) for index, attention_score in enumerate(attention_scores): attn_score = [] for i in range(1, attention_score.shape[-1]-kmer+2): attn_score.append(float(attention_score[:,0,i].sum())) for i in range(len(attn_score)-1): if attn_score[i+1] == 0: attn_score[i] = 0 break # attn_score[0] = 0 counts = np.zeros([len(attn_score)+kmer-1]) real_scores = np.zeros([len(attn_score)+kmer-1]) for i, score in enumerate(attn_score): for j in range(kmer): counts[i+j] += 1.0 real_scores[i+j] += score real_scores = real_scores / counts real_scores = real_scores / np.linalg.norm(real_scores) # print(index) # print(real_scores) # print(len(real_scores)) scores[index] = real_scores return scores, probs 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 args.do_predict: cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}".format( "dev" if evaluate else "train", 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) ) print("finish loading examples") # params for convert_examples_to_features max_length = args.max_seq_length pad_on_left = bool(args.model_type in ["xlnet"]) pad_token = tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0] pad_token_segment_id = 4 if args.model_type in ["xlnet"] else 0 if args.n_process == 1: features = convert_examples_to_features( examples, tokenizer, label_list=label_list, max_length=max_length, output_mode=output_mode, pad_on_left=pad_on_left, # pad on the left for xlnet pad_token=pad_token, pad_token_segment_id=pad_token_segment_id,) else: n_proc = int(args.n_process) if evaluate: n_proc = max(int(n_proc/4),1) print("number of processes for converting feature: " + str(n_proc)) p = Pool(n_proc) indexes = [0] len_slice = int(len(examples)/n_proc) for i in range(1, n_proc+1): if i != n_proc: indexes.append(len_slice*(i)) else: indexes.append(len(examples)) results = [] for i in range(n_proc): results.append(p.apply_async(convert_examples_to_features, args=(examples[indexes[i]:indexes[i+1]], tokenizer, max_length, None, label_list, output_mode, pad_on_left, pad_token, pad_token_segment_id, True, ))) print(str(i+1) + ' processor started !') p.close() p.join() features = [] for result in results: features.extend(result.get()) 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( "--n_process", default=2, type=int, help="number of processes used for data process", ) parser.add_argument( "--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir" ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_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( "--visualize_data_dir", default=None, type=str, help="The input data dir. Should contain the .tsv files (or other data files) for the task.", ) parser.add_argument( "--result_dir", default=None, type=str, help="The directory where the dna690 and mouse will save results.", ) 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 s3", ) parser.add_argument( "--predict_dir", default=None, type=str, help="The output directory of predicted result. (when do_predict)", ) 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("--do_predict", action="store_true", help="Whether to do prediction on the given dataset.") parser.add_argument("--do_visualize", action="store_true", help="Whether to calculate attention score.") parser.add_argument("--visualize_train", action="store_true", help="Whether to visualize train.tsv or dev.tsv.") parser.add_argument("--do_ensemble_pred", action="store_true", help="Whether to do ensemble prediction with kmer 3456.") 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( "--per_gpu_pred_batch_size", default=8, type=int, help="Batch size per GPU/CPU for prediction.", ) parser.add_argument( "--early_stop", default=0, type=int, help="set this to a positive integet if you want to perfrom early stop. The model will stop \ if the auc keep decreasing early_stop times", ) parser.add_argument( "--predict_scan_size", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) 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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") 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("--beta1", default=0.9, type=float, help="Beta1 for Adam optimizer.") parser.add_argument("--beta2", default=0.999, type=float, help="Beta2 for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--attention_probs_dropout_prob", default=0.1, type=float, help="Dropout rate of attention.") parser.add_argument("--hidden_dropout_prob", default=0.1, type=float, help="Dropout rate of intermidiete layer.") parser.add_argument("--rnn_dropout", default=0.0, type=float, help="Dropout rate of intermidiete layer.") parser.add_argument("--rnn", default="lstm", type=str, help="What kind of RNN to use") parser.add_argument("--num_rnn_layer", default=2, type=int, help="Number of rnn layers in dnalong model.") parser.add_argument("--rnn_hidden", default=768, type=int, help="Number of hidden unit in a rnn layer.") 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("--warmup_percent", default=0, type=float, help="Linear warmup over warmup_percent*total_steps.") 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( "--save_total_limit", type=int, default=None, help="Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default", ) 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( "--visualize_models", type=int, default=None, help="The model used to do visualization. If None, use 3456.", ) 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") parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() if args.should_continue: sorted_checkpoints = _sorted_checkpoints(args) if len(sorted_checkpoints) == 0: raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.") else: args.model_name_or_path = sorted_checkpoints[-1] 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 = torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing 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] if not args.do_visualize and not args.do_ensemble_pred: 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, ) config.hidden_dropout_prob = args.hidden_dropout_prob config.attention_probs_dropout_prob = args.attention_probs_dropout_prob if args.model_type in ["dnalong", "dnalongcat"]: assert args.max_seq_length % 512 == 0 config.split = int(args.max_seq_length/512) config.rnn = args.rnn config.num_rnn_layer = args.num_rnn_layer config.rnn_dropout = args.rnn_dropout config.rnn_hidden = args.rnn_hidden 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, ) logger.info('finish loading model') 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) 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) and args.task_name != "dna690": # Create output directory if needed if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) 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) 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 = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging 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 = dict((k + "_{}".format(global_step), v) for k, v in result.items()) results.update(result) # Prediction predictions = {} if args.do_predict and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) checkpoint = args.output_dir logger.info("Predict using the following checkpoint: %s", checkpoint) prefix = '' model = model_class.from_pretrained(checkpoint) model.to(args.device) prediction = predict(args, model, tokenizer, prefix=prefix) # Visualize if args.do_visualize and args.local_rank in [-1, 0]: visualization_models = [3,4,5,6] if not args.visualize_models else [args.visualize_models] scores = None all_probs = None for kmer in visualization_models: output_dir = args.output_dir.replace("/690", "/690/" + str(kmer)) #checkpoint_name = os.listdir(output_dir)[0] #output_dir = os.path.join(output_dir, checkpoint_name) tokenizer = tokenizer_class.from_pretrained( "dna"+str(kmer), do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) checkpoint = output_dir logger.info("Calculate attention score using the following checkpoint: %s", checkpoint) prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" config = config_class.from_pretrained( output_dir, num_labels=num_labels, finetuning_task=args.task_name, cache_dir=args.cache_dir if args.cache_dir else None, ) config.output_attentions = True model = model_class.from_pretrained( checkpoint, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) model.to(args.device) attention_scores, probs = visualize(args, model, tokenizer, prefix=prefix, kmer=kmer) if scores is not None: all_probs += probs scores += attention_scores else: all_probs = deepcopy(probs) scores = deepcopy(attention_scores) all_probs = all_probs/float(len(visualization_models)) np.save(os.path.join(args.predict_dir, "atten.npy"), scores) np.save(os.path.join(args.predict_dir, "pred_results.npy"), all_probs) # ensemble prediction if args.do_ensemble_pred and args.local_rank in [-1, 0]: for kmer in range(3,7): output_dir = os.path.join(args.output_dir, str(kmer)) tokenizer = tokenizer_class.from_pretrained( "dna"+str(kmer), do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) checkpoint = output_dir logger.info("Calculate attention score using the following checkpoint: %s", checkpoint) prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" config = config_class.from_pretrained( output_dir, num_labels=num_labels, finetuning_task=args.task_name, cache_dir=args.cache_dir if args.cache_dir else None, ) config.output_attentions = True 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, ) model.to(args.device) if kmer == 3: args.data_dir = os.path.join(args.data_dir, str(kmer)) else: args.data_dir = args.data_dir.replace("/"+str(kmer-1), "/"+str(kmer)) if args.result_dir.split('/')[-1] == "test.npy": results, eval_task, _, out_label_ids, probs = evaluate(args, model, tokenizer, prefix=prefix) elif args.result_dir.split('/')[-1] == "train.npy": results, eval_task, _, out_label_ids, probs = evaluate(args, model, tokenizer, prefix=prefix, evaluate=False) else: raise ValueError("file name in result_dir should be either test.npy or train.npy") if kmer == 3: all_probs = deepcopy(probs) cat_probs = deepcopy(probs) else: all_probs += probs cat_probs = np.concatenate((cat_probs, probs), axis=1) print(cat_probs[0]) all_probs = all_probs / 4.0 all_preds = np.argmax(all_probs, axis=1) # save label and data for stuck ensemble labels = np.array(out_label_ids) labels = labels.reshape(labels.shape[0],1) data = np.concatenate((cat_probs, labels), axis=1) random.shuffle(data) root_path = args.result_dir.replace(args.result_dir.split('/')[-1],'') if not os.path.exists(root_path): os.makedirs(root_path) # data_path = os.path.join(root_path, "data") # pred_path = os.path.join(root_path, "pred") # if not os.path.exists(data_path): # os.makedirs(data_path) # if not os.path.exists(pred_path): # os.makedirs(pred_path) # np.save(os.path.join(data_path, args.result_dir.split('/')[-1]), data) # np.save(os.path.join(pred_path, "pred_results.npy", all_probs[:,1])) np.save(args.result_dir, data) ensemble_results = compute_metrics(eval_task, all_preds, out_label_ids, all_probs[:,1]) logger.info("***** Ensemble results {} *****".format(prefix)) for key in sorted(ensemble_results.keys()): logger.info(" %s = %s", key, str(ensemble_results[key])) return results if __name__ == "__main__": main()