wehub-resource-sync
123 lines
5.2 KiB
Python
123 lines
5.2 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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from functools import partial
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import paddle
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from model import SimNet
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from utils import convert_example
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from paddlenlp.data import JiebaTokenizer, Pad, Stack, Tuple, Vocab
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from paddlenlp.datasets import load_dataset
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--epochs", type=int, default=10, help="Number of epoches for training.")
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parser.add_argument('--device', choices=['cpu', 'gpu', 'npu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--lr", type=float, default=5e-4, help="Learning rate used to train.")
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parser.add_argument("--save_dir", type=str, default='checkpoints/', help="Directory to save model checkpoint")
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parser.add_argument("--batch_size", type=int, default=64, help="Total examples' number of a batch for training.")
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parser.add_argument("--vocab_path", type=str, default="./simnet_vocab.txt", help="The directory to dataset.")
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parser.add_argument('--network', type=str, default="lstm", help="Which network you would like to choose bow, cnn, lstm or gru ?")
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parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
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args = parser.parse_args()
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# yapf: enable
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def create_dataloader(dataset, trans_fn=None, mode="train", batch_size=1, batchify_fn=None):
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"""
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Creates dataloader.
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Args:
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dataset(obj:`paddle.io.Dataset`): Dataset instance.
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trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
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mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
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batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
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batchify_fn(obj:`callable`, optional, defaults to `None`): function to generate mini-batch data by merging
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the sample list, None for only stack each fields of sample in axis
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0(same as :attr::`np.stack(..., axis=0)`).
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Returns:
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dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
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"""
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if trans_fn:
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dataset = dataset.map(trans_fn)
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shuffle = True if mode == "train" else False
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if mode == "train":
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sampler = paddle.io.DistributedBatchSampler(dataset=dataset, batch_size=batch_size, shuffle=True)
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else:
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sampler = paddle.io.BatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
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dataloader = paddle.io.DataLoader(dataset, batch_sampler=sampler, return_list=True, collate_fn=batchify_fn)
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return dataloader
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if __name__ == "__main__":
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paddle.set_device(args.device)
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# Loads vocab.
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if not os.path.exists(args.vocab_path):
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raise RuntimeError("The vocab_path can not be found in the path %s" % args.vocab_path)
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vocab = Vocab.load_vocabulary(args.vocab_path, unk_token="[UNK]", pad_token="[PAD]")
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# Loads dataset.
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train_ds, dev_ds, test_ds = load_dataset("lcqmc", splits=["train", "dev", "test"])
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# Constructs the network.
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model = SimNet(network=args.network, vocab_size=len(vocab), num_classes=len(train_ds.label_list))
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model = paddle.Model(model)
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# Reads data and generates mini-batches.
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=vocab.token_to_idx.get("[PAD]", 0)), # query_ids
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Pad(axis=0, pad_val=vocab.token_to_idx.get("[PAD]", 0)), # title_ids
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Stack(dtype="int64"), # query_seq_lens
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Stack(dtype="int64"), # title_seq_lens
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Stack(dtype="int64"), # label
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): [data for data in fn(samples)]
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tokenizer = JiebaTokenizer(vocab)
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trans_fn = partial(convert_example, tokenizer=tokenizer, is_test=False)
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train_loader = create_dataloader(
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train_ds, trans_fn=trans_fn, batch_size=args.batch_size, mode="train", batchify_fn=batchify_fn
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)
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dev_loader = create_dataloader(
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dev_ds, trans_fn=trans_fn, batch_size=args.batch_size, mode="validation", batchify_fn=batchify_fn
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)
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test_loader = create_dataloader(
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test_ds, trans_fn=trans_fn, batch_size=args.batch_size, mode="test", batchify_fn=batchify_fn
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)
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optimizer = paddle.optimizer.Adam(parameters=model.parameters(), learning_rate=args.lr)
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# Defines loss and metric.
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criterion = paddle.nn.CrossEntropyLoss()
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metric = paddle.metric.Accuracy()
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model.prepare(optimizer, criterion, metric)
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# Loads pre-trained parameters.
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if args.init_from_ckpt:
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model.load(args.init_from_ckpt)
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print("Loaded checkpoint from %s" % args.init_from_ckpt)
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# Starts training and evaluating.
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model.fit(
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train_loader,
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dev_loader,
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epochs=args.epochs,
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save_dir=args.save_dir,
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)
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