train.py

# Copyright (c) 2022 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 re
import json
import functools
import random
import time
import os
import argparse

import numpy as np

import paddle
import paddle.nn.functional as F
from paddle.io import DataLoader, BatchSampler, DistributedBatchSampler
from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import AutoModelForSequenceClassification, AutoTokenizer, LinearDecayWithWarmup
from paddlenlp.utils.log import logger

from metric import MetricReport
from utils import evaluate, preprocess_function, read_local_dataset

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument('--device', default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument("--dataset_dir", required=True, default=None, type=str, help="Local dataset directory should include train.txt, dev.txt and label.txt")
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('--model_name', default="ernie-3.0-medium-zh", help="Select model to train, defaults to ernie-3.0-medium-zh.",
                    choices=["ernie-1.0-large-zh-cw","ernie-3.0-xbase-zh", "ernie-3.0-base-zh", "ernie-3.0-medium-zh", "ernie-3.0-micro-zh", "ernie-3.0-mini-zh", "ernie-3.0-nano-zh", "ernie-2.0-base-en", "ernie-2.0-large-en","ernie-m-base","ernie-m-large"])
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--learning_rate", default=3e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--epochs", default=10, type=int, help="Total number of training epochs to perform.")
parser.add_argument('--early_stop', action='store_true', help='Epoch before early stop.')
parser.add_argument('--early_stop_nums', type=int, default=3, help='Number of epoch before early stop.')
parser.add_argument("--logging_steps", default=5, type=int, help="The interval steps to logging.")
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument('--warmup', action='store_true', help="whether use warmup strategy")
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup steps 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=3, help="random seed for initialization")
parser.add_argument("--train_file", type=str, default="train.txt", help="Train dataset file name")
parser.add_argument("--dev_file", type=str, default="dev.txt", help="Dev dataset file name")
parser.add_argument("--label_file", type=str, default="label.txt", help="Label file name")
args = parser.parse_args()
# yapf: enable


def set_seed(seed):
    """
    Sets random seed
    """
    random.seed(seed)
    np.random.seed(seed)
    paddle.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)


def args_saving():
    argsDict = args.__dict__
    with open(os.path.join(args.save_dir, "setting.txt"), "w") as f:
        f.writelines("------------------ start ------------------" + "\n")
        for eachArg, value in argsDict.items():
            f.writelines(eachArg + " : " + str(value) + "\n")
        f.writelines("------------------- end -------------------")


def train():
    """
    Training a hierarchical classification model
    """

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)
    args_saving()
    set_seed(args.seed)
    paddle.set_device(args.device)

    rank = paddle.distributed.get_rank()
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()

    # load and preprocess dataset
    label_list = {}
    with open(os.path.join(args.dataset_dir, args.label_file), "r", encoding="utf-8") as f:
        for i, line in enumerate(f):
            l = line.strip()
            label_list[l] = i
    train_ds = load_dataset(
        read_local_dataset, path=os.path.join(args.dataset_dir, args.train_file), label_list=label_list, lazy=False
    )
    dev_ds = load_dataset(
        read_local_dataset, path=os.path.join(args.dataset_dir, args.dev_file), label_list=label_list, lazy=False
    )

    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
    trans_func = functools.partial(
        preprocess_function, tokenizer=tokenizer, max_seq_length=args.max_seq_length, label_nums=len(label_list)
    )
    train_ds = train_ds.map(trans_func)
    dev_ds = dev_ds.map(trans_func)

    # batchify dataset
    collate_fn = DataCollatorWithPadding(tokenizer)
    if paddle.distributed.get_world_size() > 1:
        train_batch_sampler = DistributedBatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)
    else:
        train_batch_sampler = BatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)
    dev_batch_sampler = BatchSampler(dev_ds, batch_size=args.batch_size, shuffle=False)
    train_data_loader = DataLoader(dataset=train_ds, batch_sampler=train_batch_sampler, collate_fn=collate_fn)
    dev_data_loader = DataLoader(dataset=dev_ds, batch_sampler=dev_batch_sampler, collate_fn=collate_fn)

    # define model
    model = AutoModelForSequenceClassification.from_pretrained(args.model_name, num_classes=len(label_list))
    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_steps)

    # 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.BCEWithLogitsLoss()
    metric = MetricReport()

    global_step = 0
    best_f1_score = 0
    early_stop_count = 0
    tic_train = time.time()

    for epoch in range(1, args.epochs + 1):

        if args.early_stop and early_stop_count >= args.early_stop_nums:
            logger.info("Early stop!")
            break

        for step, batch in enumerate(train_data_loader, start=1):

            labels = batch.pop("labels")
            logits = model(**batch)
            loss = criterion(logits, labels)

            loss.backward()
            optimizer.step()
            if args.warmup:
                lr_scheduler.step()
            optimizer.clear_grad()

            global_step += 1
            if global_step % args.logging_steps == 0 and rank == 0:
                logger.info(
                    "global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss, 10 / (time.time() - tic_train))
                )
                tic_train = time.time()

        early_stop_count += 1
        micro_f1_score, macro_f1_score = evaluate(model, criterion, metric, dev_data_loader)

        save_best_path = args.save_dir
        if not os.path.exists(save_best_path):
            os.makedirs(save_best_path)

        # save models
        if macro_f1_score > best_f1_score:
            early_stop_count = 0
            best_f1_score = macro_f1_score
            model._layers.save_pretrained(save_best_path)
            tokenizer.save_pretrained(save_best_path)
        logger.info("Current best macro f1 score: %.5f" % (best_f1_score))
    logger.info("Final best macro f1 score: %.5f" % (best_f1_score))
    logger.info("Save best macro f1 text classification model in %s" % (args.save_dir))


if __name__ == "__main__":
    train()
    print(args.train_file)