# 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. import os import re import time import logging import six import json from random import random from tqdm import tqdm from collections import OrderedDict from functools import reduce, partial import numpy as np import multiprocessing import pickle import jieba import logging from sklearn.metrics import f1_score import paddle import paddle.fluid as F import paddle.fluid.dygraph as FD import paddle.fluid.layers as L from propeller import log import propeller.paddle as propeller log.setLevel(logging.DEBUG) logging.getLogger().setLevel(logging.DEBUG) from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification, ErnieModelForTokenClassification from ernie.tokenizing_ernie import ErnieTokenizer from ernie.optimization import AdamW, LinearDecay if __name__ == '__main__': parser = propeller.ArgumentParser('NER model with ERNIE') parser.add_argument('--max_seqlen', type=int, default=256) parser.add_argument('--bsz', type=int, default=32) parser.add_argument('--data_dir', type=str, required=True) parser.add_argument('--epoch', type=int, default=3) parser.add_argument('--warmup_steps', type=int, default=1000) parser.add_argument('--max_steps', type=int, default=30000) parser.add_argument('--from_pretrained', type=str, required=True) parser.add_argument('--lr', type=float, default=5e-5) args = parser.parse_args() tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained) def tokenizer_func(inputs): ret = inputs.split(b'\2') tokens, orig_pos = [], [] for i, r in enumerate(ret): t = tokenizer.tokenize(r) for tt in t: tokens.append(tt) orig_pos.append(i) assert len(tokens) == len(orig_pos) return tokens + orig_pos def tokenizer_func_for_label(inputs): return inputs.split(b'\2') feature_column = propeller.data.FeatureColumns([ propeller.data.TextColumn('text_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer_func), propeller.data.TextColumn('label', unk_id=6, vocab_dict={ b"B-PER": 0, b"I-PER": 1, b"B-ORG": 2, b"I-ORG": 3, b"B-LOC": 4, b"I-LOC": 5, }, tokenizer=tokenizer_func_for_label,) ]) def before(seg, label): seg, orig_pos = np.split(seg, 2) aligned_label = label[orig_pos] seg, _ = tokenizer.truncate(seg, [], args.max_seqlen) aligned_label, _ = tokenizer.truncate(aligned_label, [], args.max_seqlen) orig_pos, _ = tokenizer.truncate(orig_pos, [], args.max_seqlen) sentence, segments = tokenizer.build_for_ernie(seg) #utils.data.build_1_pair(seg, max_seqlen=args.max_seqlen, cls_id=cls_id, sep_id=sep_id) aligned_label = np.concatenate([[-100], aligned_label, [-100]], 0) orig_pos = np.concatenate([[-100], orig_pos, [-100]]) assert len(aligned_label) == len(sentence) == len(orig_pos), (len(aligned_label), len(sentence), len(orig_pos)) # alinged return sentence, segments, aligned_label, label, orig_pos train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=False, use_gz=False) \ .map(before) \ .padded_batch(args.bsz, (0, 0, -100, -100, -100)) \ dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \ .map(before) \ .padded_batch(args.bsz, (0, 0, -100, -100, -100)) \ test_ds = feature_column.build_dataset('test', data_dir=os.path.join(args.data_dir, 'test'), shuffle=False, repeat=False, use_gz=False) \ .map(before) \ .padded_batch(args.bsz, (0, 0, -100, -100, -100)) \ shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen], [-1, args.max_seqlen]) types = ('int64', 'int64', 'int64') train_ds.data_shapes = shapes train_ds.data_types = types dev_ds.data_shapes = shapes dev_ds.data_types = types test_ds.data_shapes = shapes test_ds.data_types = types place = F.CUDAPlace(0) with FD.guard(place): model = ErnieModelForTokenClassification.from_pretrained(args.from_pretrained, num_labels=7, name='') opt = AdamW(learning_rate=LinearDecay(args.lr, args.warmup_steps, args.max_steps), parameter_list=model.parameters(), weight_decay=0.01) #opt = F.optimizer.AdamOptimizer(learning_rate=LinearDecay(args.lr, args.warmup_steps, args.max_steps), parameter_list=model.parameters()) for epoch in range(args.epoch): for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(train_ds.start(place))): loss, _ = model(ids, sids, labels=aligned_label) loss.backward() if step % 10 == 0 : log.debug('train loss %.5f' % loss.numpy()) opt.minimize(loss) model.clear_gradients() if step % 100 == 0 : all_pred, all_label = [], [] with FD.base._switch_tracer_mode_guard_(is_train=False): model.eval() for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(dev_ds.start(place))): loss, logits = model(ids, sids, labels=aligned_label) #print('\n'.join(map(str, logits.numpy().tolist()))) for pos, lo, la in zip(orig_pos.numpy(), logits.numpy(), label.numpy()): _dic = OrderedDict() for p, l in zip(pos, lo): _dic.setdefault(p, []).append(l) del _dic[-100] # delete cls/sep/pad position merged_lo = np.array([np.array(l).mean(0) for _, l in six.iteritems(_dic)]) merged_preds = np.argmax(merged_lo, -1) la = la[np.where(la!=-100)] #remove pad if len(la) > len(merged_preds): log.warn('accuracy loss due to truncation: label len:%d, truncate to %d' % (len(la), len(merged_preds))) merged_preds = np.pad(merged_preds, [0, len(la) - len(merged_preds)], mode='constant', constant_values=-100) all_label.append(la) all_pred.append(merged_preds) model.train() f1 = f1_score(np.concatenate(all_label), np.concatenate(all_pred), average='macro') log.debug('eval f1: %.5f' % f1) F.save_dygraph(model.state_dict(), './saved')