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 random
import time
from functools import partial

import numpy as np
import paddle
from model import SentenceTransformer

from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup

# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--save_dir", default='./checkpoint', type=str, help="The output directory where the model checkpoints will be written.")
parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--epochs", default=3, type=int, help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proportion over the training process.")
parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
parser.add_argument("--seed", type=int, default=1000, help="random seed for initialization")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
args = parser.parse_args()
# fmt: on


def set_seed(seed):
    """sets random seed"""
    random.seed(seed)
    np.random.seed(seed)
    paddle.seed(seed)


@paddle.no_grad()
def evaluate(model, criterion, metric, data_loader):
    """
    Given a dataset, it evals model and computes the metric.

    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
        criterion(obj:`paddle.nn.Layer`): It can compute the loss.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
    """
    model.eval()
    metric.reset()
    losses = []
    for batch in data_loader:
        query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids, labels = batch
        logits = model(
            query_input_ids=query_input_ids,
            title_input_ids=title_input_ids,
            query_token_type_ids=query_token_type_ids,
            title_token_type_ids=title_token_type_ids,
        )
        loss = criterion(logits, labels)
        losses.append(loss.numpy())
        correct = metric.compute(logits, labels)
        metric.update(correct)
        accu = metric.accumulate()
    print("eval loss: %.5f, accu: %.5f" % (np.mean(losses), accu))
    model.train()
    metric.reset()


def convert_example(example, tokenizer, max_seq_length=512, is_test=False):
    """
    Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
    by concatenating and adding special tokens. And creates a mask from the two sequences passed
    to be used in a sequence-pair classification task.

    A BERT sequence has the following format:

    - single sequence: ``[CLS] X [SEP]``
    - pair of sequences: ``[CLS] A [SEP] B [SEP]``

    A BERT sequence pair mask has the following format:
    ::
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |

    If only one sequence, only returns the first portion of the mask (0's).


    Args:
        example(obj:`list[str]`): List of input data, containing query, title and label if it have label.
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
            Sequences longer than this will be truncated, sequences shorter will be padded.
        is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.

    Returns:
        query_input_ids(obj:`list[int]`): The list of query token ids.
        query_token_type_ids(obj: `list[int]`): List of query sequence pair mask.
        title_input_ids(obj:`list[int]`): The list of title token ids.
        title_token_type_ids(obj: `list[int]`): List of title sequence pair mask.
        label(obj:`numpy.array`, data type of int64, optional): The input label if not is_test.
    """
    query, title = example["query"], example["title"]

    query_encoded_inputs = tokenizer(text=query, max_seq_len=max_seq_length)
    query_input_ids = query_encoded_inputs["input_ids"]
    query_token_type_ids = query_encoded_inputs["token_type_ids"]

    title_encoded_inputs = tokenizer(text=title, max_seq_len=max_seq_length)
    title_input_ids = title_encoded_inputs["input_ids"]
    title_token_type_ids = title_encoded_inputs["token_type_ids"]

    if not is_test:
        label = np.array([example["label"]], dtype="int64")
        return query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids, label
    else:
        return query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids


def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
    if trans_fn:
        dataset = dataset.map(trans_fn)

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

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


def do_train():
    paddle.set_device(args.device)
    rank = paddle.distributed.get_rank()
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()

    set_seed(args.seed)

    train_ds, dev_ds = load_dataset("lcqmc", splits=["train", "dev"])

    pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")

    tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")

    trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # query_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # query_segment
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # title_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # title_segment
        Stack(dtype="int64"),  # label
    ): [data for data in fn(samples)]
    train_data_loader = create_dataloader(
        train_ds, mode="train", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
    )
    dev_data_loader = create_dataloader(
        dev_ds, mode="dev", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
    )

    model = SentenceTransformer(pretrained_model)

    if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
        state_dict = paddle.load(args.init_from_ckpt)
        model.set_dict(state_dict)
    model = paddle.DataParallel(model)

    num_training_steps = len(train_data_loader) * args.epochs

    lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, args.warmup_proportion)

    # Generate parameter names needed to perform weight decay.
    # All bias and LayerNorm parameters are excluded.
    decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])]
    optimizer = paddle.optimizer.AdamW(
        learning_rate=lr_scheduler,
        parameters=model.parameters(),
        weight_decay=args.weight_decay,
        apply_decay_param_fun=lambda x: x in decay_params,
    )

    criterion = paddle.nn.loss.CrossEntropyLoss()
    metric = paddle.metric.Accuracy()

    global_step = 0
    tic_train = time.time()
    for epoch in range(1, args.epochs + 1):
        for step, batch in enumerate(train_data_loader, start=1):
            query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids, labels = batch
            logits = model(
                query_input_ids=query_input_ids,
                title_input_ids=title_input_ids,
                query_token_type_ids=query_token_type_ids,
                title_token_type_ids=title_token_type_ids,
            )
            loss = criterion(logits, labels)
            correct = metric.compute(logits, labels)
            metric.update(correct)
            acc = metric.accumulate()

            global_step += 1
            if global_step % 10 == 0 and rank == 0:
                print(
                    "global step %d, epoch: %d, batch: %d, loss: %.5f, accu: %.5f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss, acc, 10 / (time.time() - tic_train))
                )
                tic_train = time.time()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_grad()
            if global_step % 100 == 0 and rank == 0:
                save_dir = os.path.join(args.save_dir, "model_%d" % global_step)
                if not os.path.exists(save_dir):
                    os.makedirs(save_dir)
                evaluate(model, criterion, metric, dev_data_loader)
                save_param_path = os.path.join(save_dir, "model_state.pdparams")
                paddle.save(model.state_dict(), save_param_path)
                tokenizer.save_pretrained(save_dir)


if __name__ == "__main__":
    do_train()