# 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.modeling_ernie import ErnieModel, ErnieModelForQuestionAnswering from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer #from ernie.optimization import AdamW, LinearDecay from demo.mrc import mrc_reader from demo.mrc import mrc_metrics from demo.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 = len(train_features) * args.epoch // args.bsz 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 = P.optimizer.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: _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: 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=8, help='batchsize') parser.add_argument('--epoch', type=int, default=2, help='epoch') 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=0.1) 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')