train.py

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

import megengine as mge
import megengine.functional as F
import megengine.optimizer as optim
from megengine.jit import trace
from tqdm import tqdm

from config import get_args
from model import BertForSequenceClassification, create_hub_bert
from mrpc_dataset import MRPCDataset

args = get_args()
logger = mge.get_logger(__name__)


@trace(symbolic=True)
def net_eval(input_ids, segment_ids, input_mask, label_ids, opt=None, net=None):
    net.eval()
    results = net(input_ids, segment_ids, input_mask, label_ids)
    logits, loss = results
    return loss, logits, label_ids


@trace(symbolic=True)
def net_train(input_ids, segment_ids, input_mask, label_ids, opt=None, net=None):
    net.train()
    results = net(input_ids, segment_ids, input_mask, label_ids)
    logits, loss = results
    opt.backward(loss)
    return loss, logits, label_ids


def accuracy(out, labels):
    outputs = F.argmax(out, axis=1)
    return F.sum(outputs == labels)


def eval(dataloader, net):
    logger.info("***** Running evaluation *****")
    logger.info("batch size = %d", args.eval_batch_size)

    sum_loss, sum_accuracy, total_steps, total_examples = 0, 0, 0, 0

    for step, batch in enumerate(tqdm(dataloader, desc="Iteration")):
        input_ids, input_mask, segment_ids, label_ids = tuple(
            mge.tensor(t) for t in batch
        )
        batch_size = input_ids.shape[0]
        if batch_size != args.eval_batch_size:
            break
        loss, logits, label_ids = net_eval(
            input_ids, segment_ids, input_mask, label_ids, net=net
        )
        sum_loss += loss.mean().item()
        sum_accuracy += accuracy(logits, label_ids)
        total_examples += batch_size
        total_steps += 1

    result = {
        "eval_loss": sum_loss / total_steps,
        "eval_accuracy": sum_accuracy / total_examples,
    }

    logger.info("***** Eval results *****")
    for key in sorted(result.keys()):
        logger.info("%s = %s", key, str(result[key]))


def train(dataloader, net, opt):
    logger.info("***** Running training *****")
    logger.info("batch size = %d", args.train_batch_size)
    sum_loss, sum_accuracy, total_steps, total_examples = 0, 0, 0, 0

    for step, batch in enumerate(tqdm(dataloader, desc="Iteration")):
        input_ids, input_mask, segment_ids, label_ids = tuple(
            mge.tensor(t) for t in batch
        )
        batch_size = input_ids.shape[0]
        opt.zero_grad()
        loss, logits, label_ids = net_train(
            input_ids, segment_ids, input_mask, label_ids, opt=opt, net=net
        )
        optimizer.step()
        sum_loss += loss.mean().item()
        sum_accuracy += accuracy(logits, label_ids)
        total_examples += batch_size
        total_steps += 1

    result = {
        "train_loss": sum_loss / total_steps,
        "train_accuracy": sum_accuracy / total_examples,
    }

    logger.info("***** Train results *****")
    for key in sorted(result.keys()):
        logger.info("%s = %s", key, str(result[key]))


if __name__ == "__main__":
    bert, config, vocab_file = create_hub_bert(args.pretrained_bert, pretrained=True)
    args.vocab_file = vocab_file
    model = BertForSequenceClassification(config, num_labels=2, bert=bert)
    mrpc_dataset = MRPCDataset(args)
    optimizer = optim.Adam(model.parameters(requires_grad=True), lr=args.learning_rate,)
    mrpc_dataset = MRPCDataset(args)
    train_dataloader, train_size = mrpc_dataset.get_train_dataloader()
    eval_dataloader, eval_size = mrpc_dataset.get_eval_dataloader()
    for epoch in range(args.num_train_epochs):
        logger.info("***** Epoch {} *****".format(epoch + 1))
        train(train_dataloader, model, optimizer)
        mge.save(model.state_dict(), args.save_model_path)
        eval(eval_dataloader, model)