train_pairwise.py

# Copyright (c) 2021 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 numpy as np
import paddle
import paddle.nn.functional as F

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

from data import create_dataloader, gen_pair
from data import convert_pairwise_example as convert_example
from model import PairwiseMatching

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--margin", default=0.2, type=float, help="Margin for pos_score and neg_score.")
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("--eval_step", default=100, type=int, help="Step interval for evaluation.")
parser.add_argument('--save_step', default=10000, type=int, help="Step interval for saving checkpoint.")
parser.add_argument('--max_step', default=10000, type=int, help="Max steps for training.")
parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proption 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()
# yapf: enable


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


@paddle.no_grad()
def evaluate(model, metric, data_loader, phase="dev"):
    """
    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.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
    """
    model.eval()
    metric.reset()

    for idx, batch in enumerate(data_loader):
        input_ids, token_type_ids, labels = batch

        pos_probs = model.predict(input_ids=input_ids,
                                  token_type_ids=token_type_ids)

        neg_probs = 1.0 - pos_probs

        preds = np.concatenate((neg_probs, pos_probs), axis=1)
        metric.update(preds=preds, labels=labels)

    print("eval_{} auc:{:.2}".format(phase, metric.accumulate()))
    metric.reset()
    model.train()


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"])

    train_ds = gen_pair(train_ds)

    pretrained_model = AutoModel.from_pretrained('ernie-3.0-medium-zh')
    tokenizer = AutoTokenizer.from_pretrained('ernie-3.0-medium-zh')

    trans_func_train = partial(convert_example,
                               tokenizer=tokenizer,
                               max_seq_length=args.max_seq_length)

    trans_func_eval = partial(convert_example,
                              tokenizer=tokenizer,
                              max_seq_length=args.max_seq_length,
                              phase="eval")

    batchify_fn_train = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # pos_pair_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # pos_pair_segment
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # neg_pair_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id)  # neg_pair_segment
    ): [data for data in fn(samples)]

    batchify_fn_eval = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # pair_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # pair_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_train,
                                          trans_fn=trans_func_train)

    dev_data_loader = create_dataloader(dev_ds,
                                        mode='dev',
                                        batch_size=args.batch_size,
                                        batchify_fn=batchify_fn_eval,
                                        trans_fn=trans_func_eval)

    model = PairwiseMatching(pretrained_model, margin=args.margin)

    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)

    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)

    metric = paddle.metric.Auc()

    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):
            pos_input_ids, pos_token_type_ids, neg_input_ids, neg_token_type_ids = batch

            loss = model(pos_input_ids=pos_input_ids,
                         neg_input_ids=neg_input_ids,
                         pos_token_type_ids=pos_token_type_ids,
                         neg_token_type_ids=neg_token_type_ids)

            global_step += 1

            if global_step > args.max_step:
                print(
                    "Training steps have achieved max_step, training is stopped."
                )
                return

            if global_step % 10 == 0 and rank == 0:
                print(
                    "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()

            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_grad()

            if global_step % args.eval_step == 0 and rank == 0:
                evaluate(model, metric, dev_data_loader, "dev")

            if global_step % args.save_step == 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)
                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()