train.py

# 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 argparse
import os
import time
from functools import partial

import paddle
from data import convert_example, load_dataset, load_vocab
from model import BiGruCrf

from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.metrics import ChunkEvaluator
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.utils.log import logger

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.")
parser.add_argument("--init_checkpoint", type=str, default=None, help="Path to init model.")
parser.add_argument("--model_save_dir", type=str, default=None, help="The model will be saved in this path.")
parser.add_argument("--epochs", type=int, default=10, help="Corpus iteration num.")
parser.add_argument("--batch_size", type=int, default=300, help="The number of sequences contained in a mini-batch.")
parser.add_argument("--max_seq_len", type=int, default=64, help="Number of words of the longest seqence.")
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.")
parser.add_argument("--base_lr", type=float, default=0.001, help="The basic learning rate that affects the entire network.")
parser.add_argument("--crf_lr", type=float, default=0.2, help="The learning rate ratio that affects CRF layers.")
parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.")
parser.add_argument("--logging_steps", type=int, default=10, help="Log every X updates steps.")
parser.add_argument("--save_steps", type=int, default=100, help="Save checkpoint every X updates steps.")
parser.add_argument("--do_eval", type=strtobool, default=True, help="To evaluate the model if True.")
# yapf: enable


@paddle.no_grad()
def evaluate(model, metric, data_loader):
 model.eval()
 metric.reset()
 for batch in data_loader:
 token_ids, length, labels = batch
 preds = model(token_ids, length)
 num_infer_chunks, num_label_chunks, num_correct_chunks = metric.compute(length, preds, labels)
 metric.update(num_infer_chunks.numpy(), num_label_chunks.numpy(), num_correct_chunks.numpy())
 precision, recall, f1_score = metric.accumulate()
 logger.info("eval precision: %f, recall: %f, f1: %f" % (precision, recall, f1_score))
 model.train()
 return precision, recall, f1_score


def train(args):
 paddle.set_device(args.device)

 trainer_num = paddle.distributed.get_world_size()
 if trainer_num > 1:
 paddle.distributed.init_parallel_env()
 rank = paddle.distributed.get_rank()
 # Create dataset.
 train_ds, test_ds = load_dataset(
 datafiles=(os.path.join(args.data_dir, "train.tsv"), os.path.join(args.data_dir, "test.tsv"))
 )

 word_vocab = load_vocab(os.path.join(args.data_dir, "word.dic"))
 label_vocab = load_vocab(os.path.join(args.data_dir, "tag.dic"))
 # q2b.dic is used to replace DBC case to SBC case
 normlize_vocab = load_vocab(os.path.join(args.data_dir, "q2b.dic"))

 trans_func = partial(
 convert_example,
 max_seq_len=args.max_seq_len,
 word_vocab=word_vocab,
 label_vocab=label_vocab,
 normlize_vocab=normlize_vocab,
 )
 train_ds.map(trans_func)
 test_ds.map(trans_func)

 batchify_fn = lambda samples, fn=Tuple(
 Pad(axis=0, pad_val=word_vocab.get("[PAD]", 0), dtype="int64"), # word_ids
 Stack(dtype="int64"), # length
 Pad(axis=0, pad_val=label_vocab.get("O", 0), dtype="int64"), # label_ids
 ): fn(samples)

 # Create sampler for dataloader
 train_sampler = paddle.io.DistributedBatchSampler(
 dataset=train_ds, batch_size=args.batch_size, shuffle=True, drop_last=True
 )
 train_loader = paddle.io.DataLoader(
 dataset=train_ds, batch_sampler=train_sampler, return_list=True, collate_fn=batchify_fn
 )

 test_sampler = paddle.io.BatchSampler(dataset=test_ds, batch_size=args.batch_size, shuffle=False, drop_last=False)
 test_loader = paddle.io.DataLoader(
 dataset=test_ds, batch_sampler=test_sampler, return_list=True, collate_fn=batchify_fn
 )

 # Define the model netword and its loss
 model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab), crf_lr=args.crf_lr)
 # Prepare optimizer, loss and metric evaluator
 optimizer = paddle.optimizer.Adam(learning_rate=args.base_lr, parameters=model.parameters())
 chunk_evaluator = ChunkEvaluator(label_list=label_vocab.keys(), suffix=True)

 if args.init_checkpoint:
 if os.path.exists(args.init_checkpoint):
 logger.info("Init checkpoint from %s" % args.init_checkpoint)
 model_dict = paddle.load(args.init_checkpoint)
 model.load_dict(model_dict)
 else:
 logger.info("Cannot init checkpoint from %s which doesn't exist" % args.init_checkpoint)
 logger.info("Start training")
 # Start training
 global_step = 0
 last_step = args.epochs * len(train_loader)
 train_reader_cost = 0.0
 train_run_cost = 0.0
 total_samples = 0
 reader_start = time.time()
 max_f1_score = -1
 for epoch in range(args.epochs):
 for step, batch in enumerate(train_loader):
 train_reader_cost += time.time() - reader_start
 global_step += 1
 token_ids, length, label_ids = batch
 train_start = time.time()
 loss = model(token_ids, length, label_ids)
 avg_loss = paddle.mean(loss)
 train_run_cost += time.time() - train_start
 total_samples += args.batch_size
 if global_step % args.logging_steps == 0:
 logger.info(
 "global step %d / %d, loss: %f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, avg_samples: %.5f, ips: %.5f sequences/sec"
 % (
 global_step,
 last_step,
 avg_loss,
 train_reader_cost / args.logging_steps,
 (train_reader_cost + train_run_cost) / args.logging_steps,
 total_samples / args.logging_steps,
 total_samples / (train_reader_cost + train_run_cost),
 )
 )
 train_reader_cost = 0.0
 train_run_cost = 0.0
 total_samples = 0
 avg_loss.backward()
 optimizer.step()
 optimizer.clear_grad()
 if global_step % args.save_steps == 0 or global_step == last_step:
 if rank == 0:
 paddle.save(
 model.state_dict(), os.path.join(args.model_save_dir, "model_%d.pdparams" % global_step)
 )
 logger.info("Save %d steps model." % (global_step))
 if args.do_eval:
 precision, recall, f1_score = evaluate(model, chunk_evaluator, test_loader)
 if f1_score > max_f1_score:
 max_f1_score = f1_score
 paddle.save(model.state_dict(), os.path.join(args.model_save_dir, "best_model.pdparams"))
 logger.info("Save best model.")

 reader_start = time.time()


if __name__ == "__main__":
 args = parser.parse_args()
 train(args)