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 os.path as osp
from functools import partial

import data
import paddle
import paddle.nn as nn

import paddlenlp
from paddlenlp.data import Pad, Stack, Tuple, Vocab
from paddlenlp.datasets import load_dataset
from paddlenlp.embeddings import TokenEmbedding
from paddlenlp.utils.downloader import get_path_from_url

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--epochs", type=int, default=5, help="Number of epoches for training.")
parser.add_argument("--device", type=str, default="gpu", help="Select cpu, gpu, xpu devices to train model.")
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("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
parser.add_argument("--use_token_embedding", type=eval, default=True, help="Whether use pretrained embedding")
parser.add_argument("--embedding_name", type=str, default="w2v.baidu_encyclopedia.target.word-word.dim300", help="The name of pretrained embedding")
parser.add_argument("--vdl_dir", type=str, default="vdl_dir/", help="VisualDL log directory")
args = parser.parse_args()
# yapf: enable

WORD_DICT_URL = "https://bj.bcebos.com/paddlenlp/data/dict.txt"


def create_dataloader(dataset, trans_fn=None, mode="train", batch_size=1, pad_token_id=0):
    """
    Creats dataloader.
    Args:
        dataset(obj:`paddle.io.Dataset`): Dataset instance.
        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.
        pad_token_id(obj:`int`, optional, defaults to 0): The pad token index.
    Returns:
        dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
    """
    if trans_fn:
        dataset = dataset.map(trans_fn, lazy=True)

    shuffle = True if mode == "train" else False
    sampler = paddle.io.BatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)

    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=vocab.get("[PAD]", 0)),  # input_ids
        Stack(dtype="int32"),  # seq len
        Stack(dtype="int64"),  # label
    ): [data for data in fn(samples)]

    dataloader = paddle.io.DataLoader(dataset, batch_sampler=sampler, return_list=True, collate_fn=batchify_fn)
    return dataloader


class BoWModel(nn.Layer):
    """
    This class implements the Bag of Words Classification Network model to classify texts.
    At a high level, the model starts by embedding the tokens and running them through
    a word embedding. Then, we encode these representations with a `BoWEncoder`.
    Lastly, we take the output of the encoder to create a final representation,
    which is passed through some feed-forward layers to output a logits (`output_layer`).
    Args:
        vocab_size (obj:`int`): The vocabulary size.
        emb_dim (obj:`int`, optional, defaults to 300):  The embedding dimension.
        hidden_size (obj:`int`, optional, defaults to 128): The first full-connected layer hidden size.
        fc_hidden_size (obj:`int`, optional, defaults to 96): The second full-connected layer hidden size.
        num_classes (obj:`int`): All the labels that the data has.
    """

    def __init__(
        self,
        vocab_size,
        num_classes,
        vocab_path,
        emb_dim=300,
        hidden_size=128,
        fc_hidden_size=96,
        use_token_embedding=True,
    ):
        super().__init__()
        if use_token_embedding:
            self.embedder = TokenEmbedding(args.embedding_name, extended_vocab_path=vocab_path)
            emb_dim = self.embedder.embedding_dim
        else:
            padding_idx = vocab_size - 1
            self.embedder = nn.Embedding(vocab_size, emb_dim, padding_idx=padding_idx)
        self.bow_encoder = paddlenlp.seq2vec.BoWEncoder(emb_dim)
        self.fc1 = nn.Linear(self.bow_encoder.get_output_dim(), hidden_size)
        self.fc2 = nn.Linear(hidden_size, fc_hidden_size)
        self.dropout = nn.Dropout(p=0.3, axis=1)
        self.output_layer = nn.Linear(fc_hidden_size, num_classes)

    def forward(self, text, seq_len=None):
        # Shape: (batch_size, num_tokens, embedding_dim)
        embedded_text = self.embedder(text)

        # Shape: (batch_size, embedding_dim)
        summed = self.bow_encoder(embedded_text)
        summed = self.dropout(summed)
        encoded_text = paddle.tanh(summed)

        # Shape: (batch_size, hidden_size)
        fc1_out = paddle.tanh(self.fc1(encoded_text))
        # Shape: (batch_size, fc_hidden_size)
        fc2_out = paddle.tanh(self.fc2(fc1_out))
        # Shape: (batch_size, num_classes)
        logits = self.output_layer(fc2_out)
        return logits


if __name__ == "__main__":
    assert args.device in ["cpu", "gpu", "xpu"], "Invalid device! Available device should be cpu, gpu, or xpu."
    paddle.set_device(args.device)

    # Loads vocab.
    vocab_path = "./dict.txt"
    if not os.path.exists(vocab_path):
        # download in current directory
        get_path_from_url(WORD_DICT_URL, "./")
    vocab = data.load_vocab(vocab_path)

    if "[PAD]" not in vocab:
        vocab["[PAD]"] = len(vocab)
    # Loads dataset.
    train_ds, dev_ds = load_dataset("chnsenticorp", splits=["train", "dev"])

    # Constructs the network.
    model = BoWModel(
        vocab_size=len(vocab),
        num_classes=len(train_ds.label_list),
        vocab_path=vocab_path,
        use_token_embedding=args.use_token_embedding,
    )
    if args.use_token_embedding:
        vocab = model.embedder.vocab
        data.set_tokenizer(vocab)
        vocab = vocab.token_to_idx
    else:
        v = Vocab.from_dict(vocab, unk_token="[UNK]", pad_token="[PAD]")
        data.set_tokenizer(v)
    model = paddle.Model(model)

    # Reads data and generates mini-batches.
    trans_fn = partial(data.convert_example, vocab=vocab, unk_token_id=vocab["[UNK]"], is_test=False)
    train_loader = create_dataloader(
        train_ds, trans_fn=trans_fn, batch_size=args.batch_size, mode="train", pad_token_id=vocab["[PAD]"]
    )
    dev_loader = create_dataloader(
        dev_ds, trans_fn=trans_fn, batch_size=args.batch_size, mode="validation", pad_token_id=vocab["[PAD]"]
    )

    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.
    log_dir = "use_normal_embedding"
    if args.use_token_embedding:
        log_dir = "use_token_embedding"
    log_dir = osp.join(args.vdl_dir, log_dir)
    callback = paddle.callbacks.VisualDL(log_dir=log_dir)
    model.fit(train_loader, dev_loader, epochs=args.epochs, save_dir=args.save_dir, callbacks=callback)