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323 lines
14 KiB
Python
323 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
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GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
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using a masked language modeling (MLM) loss.
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"""
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from __future__ import absolute_import
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import os
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from models import CloneModel
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import logging
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import argparse
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import math
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import numpy as np
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from io import open
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from tqdm import tqdm
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
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from torch.utils.data.distributed import DistributedSampler
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from transformers import (AdamW, get_linear_schedule_with_warmup,
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RobertaConfig, RobertaModel, RobertaTokenizer,
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BartConfig, BartForConditionalGeneration, BartTokenizer,
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T5Config, T5ForConditionalGeneration, T5Tokenizer)
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import multiprocessing
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from sklearn.metrics import recall_score, precision_score, f1_score
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import time
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from configs import add_args, set_seed
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from utils import get_filenames, get_elapse_time, load_and_cache_clone_data
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from models import get_model_size
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MODEL_CLASSES = {'roberta': (RobertaConfig, RobertaModel, RobertaTokenizer),
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't5': (T5Config, T5ForConditionalGeneration, T5Tokenizer),
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'codet5': (T5Config, T5ForConditionalGeneration, RobertaTokenizer),
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'bart': (BartConfig, BartForConditionalGeneration, BartTokenizer)}
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cpu_cont = multiprocessing.cpu_count()
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logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
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datefmt='%m/%d/%Y %H:%M:%S',
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level=logging.INFO)
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logger = logging.getLogger(__name__)
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def evaluate(args, model, eval_examples, eval_data, write_to_pred=False):
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eval_sampler = SequentialSampler(eval_data)
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eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
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# Eval!
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logger.info("***** Running evaluation *****")
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logger.info(" Num examples = %d", len(eval_examples))
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logger.info(" Batch size = %d", args.eval_batch_size)
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eval_loss = 0.0
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nb_eval_steps = 0
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model.eval()
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logits = []
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y_trues = []
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for batch in tqdm(eval_dataloader, total=len(eval_dataloader), desc="Evaluating"):
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inputs = batch[0].to(args.device)
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labels = batch[1].to(args.device)
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with torch.no_grad():
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lm_loss, logit = model(inputs, labels)
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eval_loss += lm_loss.mean().item()
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logits.append(logit.cpu().numpy())
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y_trues.append(labels.cpu().numpy())
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nb_eval_steps += 1
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logits = np.concatenate(logits, 0)
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y_trues = np.concatenate(y_trues, 0)
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best_threshold = 0.5
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y_preds = logits[:, 1] > best_threshold
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recall = recall_score(y_trues, y_preds)
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precision = precision_score(y_trues, y_preds)
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f1 = f1_score(y_trues, y_preds)
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result = {
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"eval_recall": float(recall),
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"eval_precision": float(precision),
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"eval_f1": float(f1),
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"eval_threshold": best_threshold,
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}
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logger.info("***** Eval results *****")
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for key in sorted(result.keys()):
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logger.info(" %s = %s", key, str(round(result[key], 4)))
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logger.info(" " + "*" * 20)
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if write_to_pred:
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with open(os.path.join(args.output_dir, "predictions.txt"), 'w') as f:
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for example, pred in zip(eval_examples, y_preds):
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if pred:
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f.write(example.url1 + '\t' + example.url2 + '\t' + '1' + '\n')
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else:
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f.write(example.url1 + '\t' + example.url2 + '\t' + '0' + '\n')
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return result
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def main():
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parser = argparse.ArgumentParser()
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t0 = time.time()
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args = add_args(parser)
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logger.info(args)
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# Setup CUDA, GPU & distributed training
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if args.local_rank == -1 or args.no_cuda:
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device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
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args.n_gpu = torch.cuda.device_count()
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else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
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torch.cuda.set_device(args.local_rank)
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device = torch.device("cuda", args.local_rank)
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torch.distributed.init_process_group(backend='nccl')
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args.n_gpu = 1
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logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, cpu count: %d",
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args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), cpu_cont)
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args.device = device
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set_seed(args)
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# Build model
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config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
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config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
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model = model_class.from_pretrained(args.model_name_or_path)
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tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name)
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model = CloneModel(model, config, tokenizer, args)
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logger.info("Finish loading model [%s] from %s", get_model_size(model), args.model_name_or_path)
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if args.load_model_path is not None:
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logger.info("Reload model from {}".format(args.load_model_path))
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model.load_state_dict(torch.load(args.load_model_path))
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model.to(device)
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pool = multiprocessing.Pool(cpu_cont)
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args.train_filename, args.dev_filename, args.test_filename = get_filenames(args.data_dir, args.task, args.sub_task)
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fa = open(os.path.join(args.output_dir, 'summary.log'), 'a+')
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if args.do_train:
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if args.n_gpu > 1:
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# multi-gpu training
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model = torch.nn.DataParallel(model)
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if args.local_rank in [-1, 0] and args.data_num == -1:
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summary_fn = '{}/{}'.format(args.summary_dir, '/'.join(args.output_dir.split('/')[1:]))
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tb_writer = SummaryWriter(summary_fn)
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# Prepare training data loader
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train_examples, train_data = load_and_cache_clone_data(args, args.train_filename, pool, tokenizer, 'train',
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is_sample=False)
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if args.local_rank == -1:
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train_sampler = RandomSampler(train_data)
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else:
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train_sampler = DistributedSampler(train_data)
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train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
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num_train_optimization_steps = args.num_train_epochs * len(train_dataloader)
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save_steps = max(len(train_dataloader) // 5, 1)
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# Prepare optimizer and schedule (linear warmup and decay)
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no_decay = ['bias', 'LayerNorm.weight']
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optimizer_grouped_parameters = [
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{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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'weight_decay': args.weight_decay},
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{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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if args.warmup_steps < 1:
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warmup_steps = num_train_optimization_steps * args.warmup_steps
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else:
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warmup_steps = int(args.warmup_steps)
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps,
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num_training_steps=num_train_optimization_steps)
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# Start training
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train_example_num = len(train_data)
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", train_example_num)
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logger.info(" Batch size = %d", args.train_batch_size)
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logger.info(" Batch num = %d", math.ceil(train_example_num / args.train_batch_size))
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logger.info(" Num epoch = %d", args.num_train_epochs)
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global_step, best_f1 = 0, 0
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not_f1_inc_cnt = 0
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is_early_stop = False
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for cur_epoch in range(args.start_epoch, int(args.num_train_epochs)):
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bar = tqdm(train_dataloader, total=len(train_dataloader), desc="Training")
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nb_tr_examples, nb_tr_steps, tr_loss = 0, 0, 0
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model.train()
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for step, batch in enumerate(bar):
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batch = tuple(t.to(device) for t in batch)
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source_ids, labels = batch
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# pdb.set_trace()
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loss, logits = model(source_ids, labels)
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if args.n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu.
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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tr_loss += loss.item()
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nb_tr_examples += source_ids.size(0)
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nb_tr_steps += 1
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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if nb_tr_steps % args.gradient_accumulation_steps == 0:
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# Update parameters
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optimizer.step()
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optimizer.zero_grad()
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scheduler.step()
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global_step += 1
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train_loss = round(tr_loss * args.gradient_accumulation_steps / nb_tr_steps, 4)
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bar.set_description("[{}] Train loss {}".format(cur_epoch, round(train_loss, 3)))
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if (step + 1) % save_steps == 0 and args.do_eval:
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logger.info("***** CUDA.empty_cache() *****")
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torch.cuda.empty_cache()
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eval_examples, eval_data = load_and_cache_clone_data(args, args.dev_filename, pool, tokenizer,
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'valid', is_sample=True)
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result = evaluate(args, model, eval_examples, eval_data)
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eval_f1 = result['eval_f1']
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if args.data_num == -1:
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tb_writer.add_scalar('dev_f1', round(eval_f1, 4), cur_epoch)
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# save last checkpoint
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last_output_dir = os.path.join(args.output_dir, 'checkpoint-last')
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if not os.path.exists(last_output_dir):
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os.makedirs(last_output_dir)
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if True or args.data_num == -1 and args.save_last_checkpoints:
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model_to_save = model.module if hasattr(model, 'module') else model
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output_model_file = os.path.join(last_output_dir, "pytorch_model.bin")
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torch.save(model_to_save.state_dict(), output_model_file)
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logger.info("Save the last model into %s", output_model_file)
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if eval_f1 > best_f1:
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not_f1_inc_cnt = 0
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logger.info(" Best f1: %s", round(eval_f1, 4))
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logger.info(" " + "*" * 20)
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fa.write("[%d] Best f1 changed into %.4f\n" % (cur_epoch, round(eval_f1, 4)))
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best_f1 = eval_f1
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# Save best checkpoint for best ppl
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output_dir = os.path.join(args.output_dir, 'checkpoint-best-f1')
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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if args.data_num == -1 or True:
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model_to_save = model.module if hasattr(model, 'module') else model
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output_model_file = os.path.join(output_dir, "pytorch_model.bin")
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torch.save(model_to_save.state_dict(), output_model_file)
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logger.info("Save the best ppl model into %s", output_model_file)
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else:
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not_f1_inc_cnt += 1
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logger.info("F1 does not increase for %d epochs", not_f1_inc_cnt)
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if not_f1_inc_cnt > args.patience:
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logger.info("Early stop as f1 do not increase for %d times", not_f1_inc_cnt)
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fa.write("[%d] Early stop as not_f1_inc_cnt=%d\n" % (cur_epoch, not_f1_inc_cnt))
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is_early_stop = True
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break
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model.train()
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if is_early_stop:
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break
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logger.info("***** CUDA.empty_cache() *****")
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torch.cuda.empty_cache()
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if args.local_rank in [-1, 0] and args.data_num == -1:
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tb_writer.close()
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if args.do_test:
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logger.info(" " + "***** Testing *****")
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logger.info(" Batch size = %d", args.eval_batch_size)
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for criteria in ['best-f1']:
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file = os.path.join(args.output_dir, 'checkpoint-{}/pytorch_model.bin'.format(criteria))
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logger.info("Reload model from {}".format(file))
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model.load_state_dict(torch.load(file))
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if args.n_gpu > 1:
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# multi-gpu training
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model = torch.nn.DataParallel(model)
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eval_examples, eval_data = load_and_cache_clone_data(args, args.test_filename, pool, tokenizer, 'test',
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False)
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result = evaluate(args, model, eval_examples, eval_data, write_to_pred=True)
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logger.info(" test_f1=%.4f", result['eval_f1'])
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logger.info(" test_prec=%.4f", result['eval_precision'])
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logger.info(" test_rec=%.4f", result['eval_recall'])
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logger.info(" " + "*" * 20)
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fa.write("[%s] test-f1: %.4f, precision: %.4f, recall: %.4f\n" % (
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criteria, result['eval_f1'], result['eval_precision'], result['eval_recall']))
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if args.res_fn:
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with open(args.res_fn, 'a+') as f:
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f.write('[Time: {}] {}\n'.format(get_elapse_time(t0), file))
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f.write("[%s] f1: %.4f, precision: %.4f, recall: %.4f\n\n" % (
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criteria, result['eval_f1'], result['eval_precision'], result['eval_recall']))
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fa.close()
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if __name__ == "__main__":
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main()
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