# Copyright (c) 2020 PaddlePaddle Authors. 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. import collections import os import random import time import json from functools import partial import numpy as np import paddle from paddle.io import DataLoader from args import parse_args import io import paddlenlp as ppnlp from paddlenlp.data import Pad, Stack, Tuple from paddlenlp.transformers import BertForQuestionAnswering, BertTokenizer, ErnieForQuestionAnswering, ErnieTokenizer from paddlenlp.transformers import LinearDecayWithWarmup from paddlenlp.metrics.dureader import dureader_evaluate, compute_predictions MODEL_CLASSES = { "bert": (BertForQuestionAnswering, BertTokenizer), "ernie": (ErnieForQuestionAnswering, ErnieTokenizer) } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) paddle.seed(args.seed) class CrossEntropyLossForSQuAD(paddle.nn.Layer): def __init__(self): super(CrossEntropyLossForSQuAD, self).__init__() def forward(self, y, label): start_logits, end_logits = y start_position, end_position = label start_position = paddle.unsqueeze(start_position, axis=-1) end_position = paddle.unsqueeze(end_position, axis=-1) start_loss = paddle.nn.functional.softmax_with_cross_entropy( logits=start_logits, label=start_position, soft_label=False) start_loss = paddle.mean(start_loss) end_loss = paddle.nn.functional.softmax_with_cross_entropy( logits=end_logits, label=end_position, soft_label=False) end_loss = paddle.mean(end_loss) loss = (start_loss + end_loss) / 2 return loss def evaluate(model, data_loader, args, predict=False): model.eval() RawResult = collections.namedtuple( "RawResult", ["unique_id", "start_logits", "end_logits"]) all_results = [] tic_eval = time.time() for batch in data_loader: input_ids, segment_ids, unipue_ids = batch start_logits_tensor, end_logits_tensor = model(input_ids, segment_ids) for idx in range(unipue_ids.shape[0]): if len(all_results) % 1000 == 0 and len(all_results): print("Processing example: %d" % len(all_results)) print('time per 1000:', time.time() - tic_eval) tic_eval = time.time() unique_id = int(unipue_ids[idx]) start_logits = [float(x) for x in start_logits_tensor.numpy()[idx]] end_logits = [float(x) for x in end_logits_tensor.numpy()[idx]] all_results.append( RawResult( unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) all_predictions_eval, all_predictions_test = compute_predictions( data_loader.dataset.examples, data_loader.dataset.data, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, args.verbose, data_loader.dataset.tokenizer) if predict: with open('prediction.json', "w") as writer: for pred in all_predictions_test: writer.write(json.dumps(pred, ensure_ascii=False) + "\n") else: dureader_evaluate(data_loader.dataset.examples, all_predictions_eval) model.train() def do_train(args): if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() args.model_type = args.model_type.lower() model_class, tokenizer_class = MODEL_CLASSES[args.model_type] tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) root = args.data_path set_seed(args) train_dataset = ppnlp.datasets.DuReader( tokenizer=tokenizer, doc_stride=args.doc_stride, root=root, max_query_length=args.max_query_length, max_seq_length=args.max_seq_length, mode="train") train_batch_sampler = paddle.io.DistributedBatchSampler( train_dataset, batch_size=args.batch_size, shuffle=True) train_batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # input Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # segment Stack(), # unipue_id Stack(dtype="int64"), # start_pos Stack(dtype="int64") # end_pos ): [data for i, data in enumerate(fn(samples)) if i != 2] train_data_loader = DataLoader( dataset=train_dataset, batch_sampler=train_batch_sampler, collate_fn=train_batchify_fn, return_list=True) dev_dataset = ppnlp.datasets.DuReader( tokenizer=tokenizer, doc_stride=args.doc_stride, root=root, max_query_length=args.max_query_length, max_seq_length=args.max_seq_length, mode="dev") dev_batch_sampler = paddle.io.BatchSampler( dev_dataset, batch_size=args.batch_size, shuffle=False) dev_batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # input Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # segment Stack() # unipue_id ): fn(samples) dev_data_loader = DataLoader( dataset=dev_dataset, batch_sampler=dev_batch_sampler, collate_fn=dev_batchify_fn, return_list=True) model = model_class.from_pretrained(args.model_name_or_path) if paddle.distributed.get_world_size() > 1: model = paddle.DataParallel(model) num_training_steps = args.max_steps if args.max_steps > 0 else len( train_dataset.examples) // args.batch_size * args.num_train_epochs lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, args.warmup_proportion) optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, epsilon=args.adam_epsilon, parameters=model.parameters(), weight_decay=args.weight_decay, apply_decay_param_fun=lambda x: x in [ p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"]) ]) criterion = CrossEntropyLossForSQuAD() global_step = 0 tic_train = time.time() for epoch in range(args.num_train_epochs): for step, batch in enumerate(train_data_loader): global_step += 1 * args.n_gpu input_ids, segment_ids, start_positions, end_positions = batch logits = model(input_ids=input_ids, token_type_ids=segment_ids) loss = criterion(logits, (start_positions, end_positions)) if global_step % args.logging_steps == 0 and paddle.distributed.get_rank( ) == 0: print( "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s" % (global_step, epoch, step, loss, args.logging_steps / (time.time() - tic_train))) tic_train = time.time() loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_gradients() if global_step % args.save_steps == 0 and paddle.distributed.get_rank( ) == 0: output_dir = os.path.join(args.output_dir, "model_%d" % global_step) if not os.path.exists(output_dir): os.makedirs(output_dir) # need better way to get inner model of DataParallel model_to_save = model._layers if isinstance( model, paddle.DataParallel) else model model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) print('Saving checkpoint to:', output_dir) if paddle.distributed.get_rank() == 0: evaluate(model, dev_data_loader, args) if __name__ == "__main__": args = parse_args() if args.n_gpu > 1: paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_gpu) else: do_train(args)