wehub-resource-sync
179 lines
8.0 KiB
Python
179 lines
8.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. 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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import argparse
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import os
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import time
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from functools import partial
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import paddle
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from data import convert_example, load_dataset, load_vocab
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from model import BiGruCrf
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from paddlenlp.data import Pad, Stack, Tuple
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from paddlenlp.metrics import ChunkEvaluator
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from paddlenlp.trainer.argparser import strtobool
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from paddlenlp.utils.log import logger
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.")
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parser.add_argument("--init_checkpoint", type=str, default=None, help="Path to init model.")
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parser.add_argument("--model_save_dir", type=str, default=None, help="The model will be saved in this path.")
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parser.add_argument("--epochs", type=int, default=10, help="Corpus iteration num.")
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parser.add_argument("--batch_size", type=int, default=300, help="The number of sequences contained in a mini-batch.")
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parser.add_argument("--max_seq_len", type=int, default=64, help="Number of words of the longest sequence.")
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parser.add_argument("--device", default="gpu", type=str, choices=["cpu", "gpu"], help="The device to select to train the model, is must be cpu/gpu.")
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parser.add_argument("--base_lr", type=float, default=0.001, help="The basic learning rate that affects the entire network.")
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parser.add_argument("--crf_lr", type=float, default=0.2, help="The learning rate ratio that affects CRF layers.")
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parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
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parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.")
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parser.add_argument("--logging_steps", type=int, default=10, help="Log every X updates steps.")
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parser.add_argument("--save_steps", type=int, default=100, help="Save checkpoint every X updates steps.")
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parser.add_argument("--do_eval", type=strtobool, default=True, help="To evaluate the model if True.")
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# yapf: enable
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@paddle.no_grad()
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def evaluate(model, metric, data_loader):
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model.eval()
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metric.reset()
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for batch in data_loader:
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token_ids, length, labels = batch
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preds = model(token_ids, length)
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num_infer_chunks, num_label_chunks, num_correct_chunks = metric.compute(length, preds, labels)
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metric.update(num_infer_chunks.numpy(), num_label_chunks.numpy(), num_correct_chunks.numpy())
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precision, recall, f1_score = metric.accumulate()
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logger.info("eval precision: %f, recall: %f, f1: %f" % (precision, recall, f1_score))
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model.train()
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return precision, recall, f1_score
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def train(args):
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paddle.set_device(args.device)
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trainer_num = paddle.distributed.get_world_size()
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if trainer_num > 1:
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paddle.distributed.init_parallel_env()
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rank = paddle.distributed.get_rank()
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# Create dataset.
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train_ds, test_ds = load_dataset(
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datafiles=(os.path.join(args.data_dir, "train.tsv"), os.path.join(args.data_dir, "test.tsv"))
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)
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word_vocab = load_vocab(os.path.join(args.data_dir, "word.dic"))
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label_vocab = load_vocab(os.path.join(args.data_dir, "tag.dic"))
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# q2b.dic is used to replace DBC case to SBC case
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normlize_vocab = load_vocab(os.path.join(args.data_dir, "q2b.dic"))
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trans_func = partial(
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convert_example,
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max_seq_len=args.max_seq_len,
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word_vocab=word_vocab,
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label_vocab=label_vocab,
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normlize_vocab=normlize_vocab,
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)
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train_ds.map(trans_func)
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test_ds.map(trans_func)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=word_vocab.get("[PAD]", 0), dtype="int64"), # word_ids
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Stack(dtype="int64"), # length
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Pad(axis=0, pad_val=label_vocab.get("O", 0), dtype="int64"), # label_ids
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): fn(samples)
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# Create sampler for dataloader
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train_sampler = paddle.io.DistributedBatchSampler(
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dataset=train_ds, batch_size=args.batch_size, shuffle=True, drop_last=True
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)
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train_loader = paddle.io.DataLoader(
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dataset=train_ds, batch_sampler=train_sampler, return_list=True, collate_fn=batchify_fn
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)
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test_sampler = paddle.io.BatchSampler(dataset=test_ds, batch_size=args.batch_size, shuffle=False, drop_last=False)
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test_loader = paddle.io.DataLoader(
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dataset=test_ds, batch_sampler=test_sampler, return_list=True, collate_fn=batchify_fn
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)
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# Define the model netword and its loss
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model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab), crf_lr=args.crf_lr)
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# Prepare optimizer, loss and metric evaluator
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optimizer = paddle.optimizer.Adam(learning_rate=args.base_lr, parameters=model.parameters())
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chunk_evaluator = ChunkEvaluator(label_list=label_vocab.keys(), suffix=True)
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if args.init_checkpoint:
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if os.path.exists(args.init_checkpoint):
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logger.info("Init checkpoint from %s" % args.init_checkpoint)
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model_dict = paddle.load(args.init_checkpoint)
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model.load_dict(model_dict)
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else:
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logger.info("Cannot init checkpoint from %s which doesn't exist" % args.init_checkpoint)
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logger.info("Start training")
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# Start training
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global_step = 0
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last_step = args.epochs * len(train_loader)
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train_reader_cost = 0.0
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train_run_cost = 0.0
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total_samples = 0
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reader_start = time.time()
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max_f1_score = -1
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for epoch in range(args.epochs):
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for step, batch in enumerate(train_loader):
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train_reader_cost += time.time() - reader_start
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global_step += 1
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token_ids, length, label_ids = batch
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train_start = time.time()
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loss = model(token_ids, length, label_ids)
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avg_loss = paddle.mean(loss)
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train_run_cost += time.time() - train_start
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total_samples += args.batch_size
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if global_step % args.logging_steps == 0:
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logger.info(
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"global step %d / %d, loss: %f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, avg_samples: %.5f, ips: %.5f sequences/sec"
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% (
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global_step,
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last_step,
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avg_loss,
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train_reader_cost / args.logging_steps,
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(train_reader_cost + train_run_cost) / args.logging_steps,
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total_samples / args.logging_steps,
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total_samples / (train_reader_cost + train_run_cost),
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)
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)
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train_reader_cost = 0.0
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train_run_cost = 0.0
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total_samples = 0
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avg_loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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if global_step % args.save_steps == 0 or global_step == last_step:
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if rank == 0:
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paddle.save(
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model.state_dict(), os.path.join(args.model_save_dir, "model_%d.pdparams" % global_step)
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)
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logger.info("Save %d steps model." % (global_step))
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if args.do_eval:
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precision, recall, f1_score = evaluate(model, chunk_evaluator, test_loader)
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if f1_score > max_f1_score:
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max_f1_score = f1_score
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paddle.save(model.state_dict(), os.path.join(args.model_save_dir, "best_model.pdparams"))
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logger.info("Save best model.")
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reader_start = time.time()
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if __name__ == "__main__":
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args = parser.parse_args()
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train(args)
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