# 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 functools import reduce, partial from visualdl import LogWriter import numpy as np import logging import argparse from pathlib import Path import paddle as P from propeller import log import propeller.paddle as propeller log.setLevel(logging.DEBUG) 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 from demo.utils import create_if_not_exists, get_warmup_and_linear_decay parser = argparse.ArgumentParser('classify 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=128, help='max sentence length, should not greater than 512') parser.add_argument( '--bsz', type=int, default=128, help='global batch size for each optimizer step') parser.add_argument( '--micro_bsz', type=int, default=32, help='batch size for each device. if `--bsz` > `--micro_bsz` * num_device, will do grad accumulate' ) 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( '--use_lr_decay', action='store_true', help='if set, learning rate will decay to zero at `max_steps`') 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('--lr', type=float, default=5e-5, help='learning rate') parser.add_argument( '--inference_model_dir', type=Path, default=None, help='inference model output directory') parser.add_argument( '--save_dir', type=Path, required=True, help='model output directory') parser.add_argument( '--max_steps', type=int, default=None, help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE') parser.add_argument( '--wd', type=float, default=0.01, help='weight decay, aka L2 regularizer') parser.add_argument( '--init_checkpoint', type=str, default=None, help='checkpoint to warm start from') parser.add_argument( '--use_amp', action='store_true', help='only activate AMP(auto mixed precision accelatoin) on TensorCore compatible devices' ) args = parser.parse_args() if args.bsz > args.micro_bsz: assert args.bsz % args.micro_bsz == 0, 'cannot perform gradient accumulate with bsz:%d micro_bsz:%d' % ( args.bsz, args.micro_bsz) acc_step = args.bsz // args.micro_bsz log.info( 'performing gradient accumulate: global_bsz:%d, micro_bsz:%d, accumulate_steps:%d' % (args.bsz, args.micro_bsz, acc_step)) args.bsz = args.micro_bsz else: acc_step = 1 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)) place = P.CUDAPlace(0) model = ErnieModelForSequenceClassification.from_pretrained( args.from_pretrained, num_labels=3, name='') if args.init_checkpoint is not None: log.info('loading checkpoint from %s' % args.init_checkpoint) sd = P.load(str(args.init_checkpoint)) model.set_state_dict(sd) g_clip = P.nn.ClipGradByGlobalNorm(1.0) #experimental param_name_to_exclue_from_weight_decay = re.compile( r'.*layer_norm_scale|.*layer_norm_bias|.*b_0') if args.use_lr_decay: lr_scheduler = P.optimizer.lr.LambdaDecay( args.lr, get_warmup_and_linear_decay( args.max_steps, int(args.warmup_proportion * args.max_steps))) opt = P.optimizer.AdamW( lr_scheduler, parameters=model.parameters(), weight_decay=args.wd, apply_decay_param_fun=lambda n: not param_name_to_exclue_from_weight_decay.match(n), grad_clip=g_clip) else: lr_scheduler = None opt = P.optimizer.AdamW( args.lr, parameters=model.parameters(), weight_decay=args.wd, apply_decay_param_fun=lambda n: not param_name_to_exclue_from_weight_decay.match(n), grad_clip=g_clip) scaler = P.amp.GradScaler(enable=args.use_amp) step, inter_step = 0, 0 with LogWriter( logdir=str(create_if_not_exists(args.save_dir / 'vdl'))) as log_writer: with P.amp.auto_cast(enable=args.use_amp): for epoch in range(args.epoch): for ids, sids, label in P.io.DataLoader( train_ds, places=P.CUDAPlace(0), batch_size=None): inter_step += 1 loss, _ = model(ids, sids, labels=label) loss /= acc_step loss = scaler.scale(loss) loss.backward() if inter_step % acc_step != 0: continue step += 1 scaler.minimize(opt, loss) model.clear_gradients() lr_scheduler and lr_scheduler.step() if step % 10 == 0: _lr = lr_scheduler.get_lr( ) if args.use_lr_decay else args.lr if args.use_amp: _l = (loss / scaler._scale).numpy() msg = '[step-%d] train loss %.5f lr %.3e scaling %.3e' % ( step, _l, _lr, scaler._scale.numpy()) else: _l = loss.numpy() msg = '[step-%d] train loss %.5f lr %.3e' % (step, _l, _lr) log.debug(msg) log_writer.add_scalar('loss', _l, step=step) log_writer.add_scalar('lr', _lr, step=step) if step % 100 == 0: acc = [] with P.no_grad(): model.eval() for ids, sids, label in P.io.DataLoader( dev_ds, places=P.CUDAPlace(0), batch_size=None): loss, logits = model(ids, sids, labels=label) #print('\n'.join(map(str, logits.numpy().tolist()))) a = (logits.argmax(-1) == label) acc.append(a.numpy()) model.train() acc = np.concatenate(acc).mean() log_writer.add_scalar('eval/acc', acc, step=step) log.debug('acc %.5f' % acc) if args.save_dir is not None: P.save(model.state_dict(), str(args.save_dir / 'ckpt.bin')) if args.save_dir is not None: P.save(model.state_dict(),str( args.save_dir / 'ckpt.bin')) if args.inference_model_dir is not None: class InferenceModel(ErnieModelForSequenceClassification): def forward(self, ids, sids): _, logits = super(InferenceModel, self).forward(ids, sids) return logits model.__class__ = InferenceModel log.debug('saving inference model') src_placeholder = P.zeros([2, 2], dtype='int64') sent_placehodler = P.zeros([2, 2], dtype='int64') _, static = P.jit.TracedLayer.trace( model, inputs=[src_placeholder, sent_placehodler]) static.save_inference_model(str(args.inference_model_dir)) #class InferenceModel(ErnieModelForSequenceClassification): # @P.jit.to_static # def forward(self, ids, sids): # _, logits = super(InferenceModel, self).forward(ids, sids, labels=None) # return logits #model.__class__ = InferenceModel #src_placeholder = P.zeros([2, 2], dtype='int64') #sent_placehodler = P.zeros([2, 2], dtype='int64') #P.jit.save(model, args.inference_model_dir, input_var=[src_placeholder, sent_placehodler]) log.debug('done')