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.

from functools import partial
import argparse
import os
import random
import time

import paddle
import paddlenlp as ppnlp
from paddlenlp.data import JiebaTokenizer, Pad, Stack, Tuple, Vocab
from paddlenlp.datasets import load_dataset

from model import SimNet
from utils import convert_example

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--epochs", type=int, default=10, help="Number of epoches for training.")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument("--lr", type=float, default=5e-4, help="Learning rate used to train.")
parser.add_argument("--save_dir", type=str, default='checkpoints/', help="Directory to save model checkpoint")
parser.add_argument("--batch_size", type=int, default=64, help="Total examples' number of a batch for training.")
parser.add_argument("--vocab_path", type=str, default="./simnet_vocab.txt", help="The directory to dataset.")
parser.add_argument('--network', type=str, default="lstm", help="Which network you would like to choose bow, cnn, lstm or gru ?")
parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
args = parser.parse_args()
# yapf: enable


def create_dataloader(dataset,
                      trans_fn=None,
                      mode='train',
                      batch_size=1,
                      batchify_fn=None):
    """
    Creats dataloader.

    Args:
        dataset(obj:`paddle.io.Dataset`): Dataset instance.
        trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
        mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
        batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
        batchify_fn(obj:`callable`, optional, defaults to `None`): function to generate mini-batch data by merging
            the sample list, None for only stack each fields of sample in axis
            0(same as :attr::`np.stack(..., axis=0)`).

    Returns:
        dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
    """
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == 'train' else False
    if mode == "train":
        sampler = paddle.io.DistributedBatchSampler(
            dataset=dataset, batch_size=batch_size, shuffle=True)
    else:
        sampler = paddle.io.BatchSampler(
            dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    dataloader = paddle.io.DataLoader(
        dataset,
        batch_sampler=sampler,
        return_list=True,
        collate_fn=batchify_fn)
    return dataloader


if __name__ == "__main__":
    paddle.set_device(args.device)

    # Loads vocab.
    if not os.path.exists(args.vocab_path):
        raise RuntimeError('The vocab_path  can not be found in the path %s' %
                           args.vocab_path)
    vocab = Vocab.load_vocabulary(
        args.vocab_path, unk_token='[UNK]', pad_token='[PAD]')

    # Loads dataset.
    train_ds, dev_ds, test_ds = load_dataset(
        "lcqmc", splits=["train", "dev", "test"])

    # Constructs the newtork.
    model = SimNet(
        network=args.network,
        vocab_size=len(vocab),
        num_classes=len(train_ds.label_list))
    model = paddle.Model(model)

    # Reads data and generates mini-batches.
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=vocab.token_to_idx.get('[PAD]', 0)),  # query_ids
        Pad(axis=0, pad_val=vocab.token_to_idx.get('[PAD]', 0)),  # title_ids
        Stack(dtype="int64"),  # query_seq_lens
        Stack(dtype="int64"),  # title_seq_lens
        Stack(dtype="int64")  # label
    ): [data for data in fn(samples)]
    tokenizer = ppnlp.data.JiebaTokenizer(vocab)
    trans_fn = partial(convert_example, tokenizer=tokenizer, is_test=False)
    train_loader = create_dataloader(
        train_ds,
        trans_fn=trans_fn,
        batch_size=args.batch_size,
        mode='train',
        batchify_fn=batchify_fn)
    dev_loader = create_dataloader(
        dev_ds,
        trans_fn=trans_fn,
        batch_size=args.batch_size,
        mode='validation',
        batchify_fn=batchify_fn)
    test_loader = create_dataloader(
        test_ds,
        trans_fn=trans_fn,
        batch_size=args.batch_size,
        mode='test',
        batchify_fn=batchify_fn)

    optimizer = paddle.optimizer.Adam(
        parameters=model.parameters(), learning_rate=args.lr)

    # Defines loss and metric.
    criterion = paddle.nn.CrossEntropyLoss()
    metric = paddle.metric.Accuracy()

    model.prepare(optimizer, criterion, metric)

    # Loads pre-trained parameters.
    if args.init_from_ckpt:
        model.load(args.init_from_ckpt)
        print("Loaded checkpoint from %s" % args.init_from_ckpt)

    # Starts training and evaluating.
    model.fit(
        train_loader,
        dev_loader,
        epochs=args.epochs,
        save_dir=args.save_dir, )