# 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 from functools import partial import numpy as np import paddle from paddle.io import DataLoader from args import parse_args import json import paddlenlp as ppnlp from paddlenlp.datasets import SQuAD, DuReaderRobust, CMRC, DRCD from paddlenlp.data import Pad, Stack, Tuple from paddlenlp.transformers import BertForQuestionAnswering, BertTokenizer, ErnieForQuestionAnswering, ErnieTokenizer from paddlenlp.metrics.squad import squad_evaluate, compute_predictions TASK_CLASSES = {"dureader-robust": DuReaderRobust, "cmrc": CMRC, "drcd": DRCD} 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.cross_entropy( input=start_logits, label=start_position) end_loss = paddle.nn.functional.cross_entropy( input=end_logits, label=end_position) loss = (start_loss + end_loss) / 2 return loss def evaluate(model, data_loader, args, tokenizer, do_pred=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, _, _ = compute_predictions( data_loader.dataset.examples, data_loader.dataset.features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, False, 0.0, args.verbose, tokenizer, False) if do_pred: with open('prediction.json', "w", encoding='utf-8') as writer: writer.write( json.dumps( all_predictions, ensure_ascii=False, indent=4) + "\n") else: squad_evaluate( examples=data_loader.dataset.examples, preds=all_predictions, is_whitespace_splited=False) model.train() def do_train(args): paddle.set_device("gpu" if args.n_gpu else "cpu") if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() task_name = args.task_name.lower() dataset_class = TASK_CLASSES[task_name] 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_ds = dataset_class( tokenizer=tokenizer, root=root, doc_stride=args.doc_stride, max_query_length=args.max_query_length, max_seq_length=args.max_seq_length, mode='train') train_batch_sampler = paddle.io.DistributedBatchSampler( train_ds, 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_ds, batch_sampler=train_batch_sampler, collate_fn=train_batchify_fn, return_list=True) dev_ds = dataset_class( tokenizer=tokenizer, root=root, doc_stride=args.doc_stride, max_query_length=args.max_query_length, max_seq_length=args.max_seq_length, mode='dev') dev_batch_sampler = paddle.io.BatchSampler( dev_ds, 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_ds, 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) lr_scheduler = paddle.optimizer.lr.LambdaDecay( args.learning_rate, lambda current_step, warmup_proportion=args.warmup_proportion, num_training_steps=args.max_steps if args.max_steps > 0 else (len(train_ds.examples)//args.batch_size*args.num_train_epochs): float( current_step) / float(max(1, warmup_proportion*num_training_steps)) if current_step < warmup_proportion*num_training_steps else max( 0.0, float(num_training_steps - current_step) / float( max(1, num_training_steps - warmup_proportion*num_training_steps)))) 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 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: 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: if (not args.n_gpu > 1) or 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 (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0: evaluate(model, dev_data_loader, args, tokenizer) 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)