# 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 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=6) parser.add_argument('--warmup_proportion', type=float, default=0.1, help='if use_lr_decay is set, ' 'learning rate will raise to `lr` at `warmup_proportion` * `max_steps` and decay to 0. at `max_steps`') parser.add_argument('--max_steps', type=int, required=True, help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE, used in learning rate scheduler') parser.add_argument('--from_pretrained', type=str, required=True) parser.add_argument('--lr', type=float, default=5e-5, help='learning rate') parser.add_argument('--save_dir', type=str, default=None, help='model output directory') parser.add_argument('--wd', type=float, default=0.01, help='weight decay, aka L2 regularizer') 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_map = { b"B-PER": 0, b"I-PER": 1, b"B-ORG": 2, b"I-ORG": 3, b"B-LOC": 4, b"I-LOC": 5, b"O": 6, } other_tag_id = feature_map[b'O'] 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=other_tag_id, vocab_dict=feature_map, 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([[0], aligned_label, [0]], 0) orig_pos = np.concatenate([[0], orig_pos, [0]]) 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, (0,0,0, other_tag_id + 1, 0)) \ 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, (0,0,0, other_tag_id + 1,0)) \ 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, (0,0,0, other_tag_id + 1,0)) \ 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) @FD.no_grad def evaluate(model, dataset): model.eval() chunkf1 = propeller.metrics.ChunkF1(None, None, None, len(feature_map)) for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(dataset.start(place))): loss, logits = model(ids, sids) #print('\n'.join(map(str, logits.numpy().tolist()))) assert orig_pos.shape[0] == logits.shape[0] == ids.shape[0] == label.shape[0] for pos, lo, la, id in zip(orig_pos.numpy(), logits.numpy(), label.numpy(), ids.numpy()): _dic = OrderedDict() assert len(pos) ==len(lo) == len(id) for _pos, _lo, _id in zip(pos, lo, id): if _id > tokenizer.mask_id: # [MASK] is the largest special token _dic.setdefault(_pos, []).append(_lo) 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 != (other_tag_id + 1))] #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=7) else: assert len(la) == len(merged_preds), 'expect label == prediction, got %d vs %d' % (la.shape, merged_preds.shape) chunkf1.update((merged_preds, la, np.array(len(la)))) #f1 = f1_score(np.concatenate(all_label), np.concatenate(all_pred), average='macro') f1 = chunkf1.eval() model.train() return f1 with FD.guard(place): model = ErnieModelForTokenClassification.from_pretrained(args.from_pretrained, num_labels=len(feature_map), name='', has_pooler=False) g_clip = F.clip.GradientClipByGlobalNorm(1.0) #experimental opt = AdamW( learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd, grad_clip=g_clip) #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, logits = model(ids, sids, labels=aligned_label, loss_weights=L.cast(ids > tokenizer.mask_id, 'float32')) # [MASK] is the largest special token loss.backward() if step % 10 == 0 : log.debug('train loss %.5f, lr %.3e' % (loss.numpy(), opt.current_step_lr())) opt.minimize(loss) model.clear_gradients() if step % 100 == 0 : f1 = evaluate(model, dev_ds) log.debug('eval f1: %.5f' % f1) f1 = evaluate(model, dev_ds) log.debug('final eval f1: %.5f' % f1) if args.save_dir is not None: F.save_dygraph(model.state_dict(), args.save_dir)