finetune_mrc.py

#   Copyright (c) 2018 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 __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals

import os
import re
import time
import logging
import json
from pathlib import Path
from random import random
from tqdm import tqdm
from functools import reduce, partial
import pickle
import argparse
from functools import partial
from io import open

import numpy as np
import logging

import paddle as P

from propeller import log
import propeller.paddle as propeller
from ernie_gram.optimization import AdamW

from ernie.modeling_ernie import ErnieModel, ErnieModelForQuestionAnswering
from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
#from ernie.optimization import AdamW, LinearDecay

from ernie_gram.mrc import mrc_reader
from ernie_gram.mrc import mrc_metrics
from ernie_gram.utils import create_if_not_exists, get_warmup_and_linear_decay

log.setLevel(logging.DEBUG)
logging.getLogger().setLevel(logging.DEBUG)


def evaluate(model, ds, all_examples, all_features, tokenizer, args):
    dev_file = json.loads(open(args.dev_file, encoding='utf8').read())
    with P.no_grad():
        log.debug('start eval')
        model.eval()
        all_res = []
        for step, (uids, token_ids, token_type_ids, _, __) in enumerate(
                P.io.DataLoader(
                    ds, places=P.CUDAPlace(env.dev_id), batch_size=None)):
            _, start_logits, end_logits = model(token_ids, token_type_ids)
            res = [
                mrc_metrics.RawResult(
                    unique_id=u, start_logits=s, end_logits=e)
                for u, s, e in zip(uids.numpy(),
                                   start_logits.numpy(), end_logits.numpy())
            ]
            all_res += res
        open('all_res', 'wb').write(pickle.dumps(all_res))
        all_pred, all_nbests = mrc_metrics.make_results(
            tokenizer,
            all_examples,
            all_features,
            all_res,
            n_best_size=args.n_best_size,
            max_answer_length=args.max_answer_length,
            do_lower_case=tokenizer.lower)
        f1, em, _, __ = mrc_metrics.evaluate(dev_file, all_pred)
        model.train()
        log.debug('done eval')
        return f1, em


def train(model, train_dataset, dev_dataset, dev_examples, dev_features,
          tokenizer, args):
    model = P.DataParallel(model)

    max_steps = args.max_steps


    g_clip = P.nn.ClipGradByGlobalNorm(1.0)  #experimental
    lr_scheduler = P.optimizer.lr.LambdaDecay(
        args.lr,
        get_warmup_and_linear_decay(max_steps,
                                    int(args.warmup_proportion * max_steps)))

    opt = AdamW(
        lr_scheduler,
        parameters=model.parameters(),
        weight_decay=args.wd,
        grad_clip=g_clip)

    train_dataset = train_dataset \
            .cache_shuffle_shard(env.nranks, env.dev_id, drop_last=True) \
            .padded_batch(args.bsz)

    log.debug('init training with args: %s' % repr(args))
    scaler = P.amp.GradScaler(enable=args.use_amp)
    create_if_not_exists(args.save_dir)

    with P.amp.auto_cast(enable=args.use_amp):
        for step, (_, token_ids, token_type_ids, start_pos,
                   end_pos) in enumerate(
                       P.io.DataLoader(
                           train_dataset,
                           places=P.CUDAPlace(env.dev_id),
                           batch_size=None)):
            loss, _, __ = model(
                token_ids,
                token_type_ids,
                start_pos=start_pos,
                end_pos=end_pos)
            loss = scaler.scale(loss)
            loss.backward()
            scaler.minimize(opt, loss)
            model.clear_gradients()
            lr_scheduler.step()

            if env.dev_id == 0 and step % 10==0 and step:
                _lr = lr_scheduler.get_lr()
                if args.use_amp:
                    _l = (loss / scaler._scale).numpy()
                    msg = '[rank-%d][step-%d] train loss %.5f lr %.3e scaling %.3e' % (
                        env.dev_id, step, _l, _lr, scaler._scale.numpy())
                else:
                    _l = loss.numpy()
                    msg = '[rank-%d][step-%d] train loss %.5f lr %.3e' % (
                        env.dev_id, step, _l, _lr)
                log.debug(msg)

            if env.dev_id == 0 and step % 100==0 and step:
                print(step)
                f1, em = evaluate(model, dev_dataset, dev_examples,
                                  dev_features, tokenizer, args)
                log.debug('[step %d] eval result: f1 %.5f em %.5f' %
                          (step, f1, em))
                if env.dev_id == 0 and args.save_dir is not None:
                    P.save(model.state_dict(), args.save_dir / 'ckpt.bin')
            if step > max_steps:
                break


if __name__ == "__main__":
    parser = argparse.ArgumentParser('MRC model with ERNIE')
    parser.add_argument(
        '--from_pretrained',
        type=Path,
        required=True,
        help='pretrained model directory or tag')
    parser.add_argument(
        '--max_seqlen',
        type=int,
        default=512,
        help='max sentence length, should not greater than 512')
    parser.add_argument('--bsz', type=int, default=16, help='batchsize')
    parser.add_argument('--max_steps', type=int, required=True, help='max steps')
    parser.add_argument(
        '--train_file',
        type=str,
        required=True,
        help='data directory includes train / develop data')
    parser.add_argument(
        '--dev_file',
        type=str,
        required=True,
        help='data directory includes train / develop data')
    parser.add_argument('--warmup_proportion', type=float, default=0.0)
    parser.add_argument('--lr', type=float, default=3e-5, help='learning rate')
    parser.add_argument(
        '--save_dir', type=Path, required=True, help='model output directory')
    parser.add_argument(
        '--n_best_size', type=int, default=20, help='nbest prediction to keep')
    parser.add_argument(
        '--max_answer_length', type=int, default=100, help='max answer span')
    parser.add_argument(
        '--wd',
        type=float,
        default=0.01,
        help='weight decay, aka L2 regularizer')
    parser.add_argument(
        '--use_amp',
        action='store_true',
        help='only activate AMP(auto mixed precision accelatoin) on TensorCore compatible devices'
    )

    args = parser.parse_args()

    env = P.distributed.ParallelEnv()
    P.distributed.init_parallel_env()

    tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)

    if not os.path.exists(args.train_file):
        raise RuntimeError('input data not found at %s' % args.train_file)
    if not os.path.exists(args.dev_file):
        raise RuntimeError('input data not found at %s' % args.dev_file)

    log.info('making train/dev data...')
    train_examples = mrc_reader.read_files(args.train_file, is_training=True)
    train_features = mrc_reader.convert_example_to_features(
        train_examples, args.max_seqlen, tokenizer, is_training=True)

    dev_examples = mrc_reader.read_files(args.dev_file, is_training=False)
    dev_features = mrc_reader.convert_example_to_features(
        dev_examples, args.max_seqlen, tokenizer, is_training=False)

    log.info('train examples: %d, features: %d' %
             (len(train_examples), len(train_features)))

    def map_fn(unique_id, example_index, doc_span_index, tokens,
               token_to_orig_map, token_is_max_context, token_ids,
               position_ids, text_type_ids, start_position, end_position):
        if start_position is None:
            start_position = 0
        if end_position is None:
            end_position = 0
        return np.array(unique_id), np.array(token_ids), np.array(
            text_type_ids), np.array(start_position), np.array(end_position)

    train_dataset = propeller.data.Dataset.from_list(train_features).map(
        map_fn)

    dev_dataset = propeller.data.Dataset.from_list(dev_features).map(
        map_fn).padded_batch(args.bsz)

    model = ErnieModelForQuestionAnswering.from_pretrained(
        args.from_pretrained, name='')

    train(model, train_dataset, dev_dataset, dev_examples, dev_features,
          tokenizer, args)

    if env.dev_id == 0:
        f1, em = evaluate(model, dev_dataset, dev_examples, dev_features,
                          tokenizer, args)
        log.debug('final eval result: f1 %.5f em %.5f' % (f1, em))
    if env.dev_id == 0 and args.save_dir is not None:
        P.save(model.state_dict(), args.save_dir / 'ckpt.bin')