# 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 json from random import random from tqdm import tqdm from functools import reduce, partial import numpy as np import logging import argparse 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().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) #from model.bert import BertConfig, BertModelLayer from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer from ernie.optimization import AdamW, LinearDecay if __name__ == '__main__': parser = argparse.ArgumentParser('classify model with ERNIE') parser.add_argument('--from_pretrained', type=str, required=True, help='pretrained model directory or tag') parser.add_argument('--max_seqlen', type=int, default=128, help='max sentence length, should not greater than 512') parser.add_argument('--bsz', type=int, default=32, help='batchsize') parser.add_argument('--epoch', type=int, default=3, help='epoch') parser.add_argument('--data_dir', type=str, required=True, help='data directory includes train / develop data') parser.add_argument('--max_steps', type=int, required=True, help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE') parser.add_argument('--warmup_proportion', type=float, default=0.1) parser.add_argument('--lr', type=float, default=5e-5, help='learning rate') parser.add_argument('--inference_model_dir', type=str, default=None, help='inference model output directory') 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) #tokenizer = ErnieTinyTokenizer.from_pretrained(args.from_pretrained) feature_column = propeller.data.FeatureColumns([ propeller.data.TextColumn('seg_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize), propeller.data.TextColumn('seg_b', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize), propeller.data.LabelColumn('label', vocab_dict={ b"contradictory": 0, b"contradiction": 0, b"entailment": 1, b"neutral": 2, }), ]) def map_fn(seg_a, seg_b, label): seg_a, seg_b = tokenizer.truncate(seg_a, seg_b, seqlen=args.max_seqlen) sentence, segments = tokenizer.build_for_ernie(seg_a, seg_b) return sentence, segments, label train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=False, use_gz=False) \ .map(map_fn) \ .padded_batch(args.bsz, (0, 0, 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(map_fn) \ .padded_batch(args.bsz, (0, 0, 0)) shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen], [-1]) types = ('int64', 'int64', 'int64') train_ds.data_shapes = shapes train_ds.data_types = types dev_ds.data_shapes = shapes dev_ds.data_types = types place = F.CUDAPlace(0) with FD.guard(place): model = ErnieModelForSequenceClassification.from_pretrained(args.from_pretrained, num_labels=3, name='') 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) g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental for epoch in range(args.epoch): for step, d in enumerate(tqdm(train_ds.start(place), desc='training')): ids, sids, label = d loss, _ = model(ids, sids, labels=label) loss.backward() if step % 10 == 0: log.debug('train loss %.5f lr %.3e' % (loss.numpy(), opt.current_step_lr())) opt.minimize(loss, grad_clip=g_clip) model.clear_gradients() if step % 100 == 0: acc = [] with FD.base._switch_tracer_mode_guard_(is_train=False): model.eval() for step, d in enumerate(tqdm(dev_ds.start(), desc='evaluating %d' % epoch)): ids, sids, label = d loss, logits = model(ids, sids, labels=label) #print('\n'.join(map(str, logits.numpy().tolist()))) a = L.argmax(logits, -1) == label acc.append(a.numpy()) model.train() log.debug('acc %.5f' % np.concatenate(acc).mean()) if args.save_dir is not None: F.save_dygraph(model.state_dict(), args.save_dir) if args.inference_model_dir is not None: log.debug('saving inference model') class InferemceModel(ErnieModelForSequenceClassification): def forward(self, *args, **kwargs): _, logits = super(InferemceModel, self).forward(*args, **kwargs) return logits model.__class__ = InferemceModel #dynamic change model type, to make sure forward output doesn't contain `None` src_placeholder = FD.to_variable(np.ones([1, 1], dtype=np.int64)) sent_placehodler = FD.to_variable(np.zeros([1, 1], dtype=np.int64)) model(src_placeholder, sent_placehodler) _, static_model = FD.TracedLayer.trace(model, inputs=[src_placeholder, sent_placehodler]) static_model.save_inference_model(args.inference_model_dir) log.debug('done')