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from data.bert_dataloader.load import BertDataModule
from transformers import (
BertTokenizer,
BertConfig,
BertForPreTraining,
BertModel,
BertForMaskedLM
)
from pytorch_lightning import (
LightningDataModule,
LightningModule,
loggers,
Trainer,
)
from pytorch_lightning.callbacks import (
ModelCheckpoint,
LearningRateMonitor,
)
from typing import Optional
from torch.utils.data import DataLoader
from transformers.optimization import get_linear_schedule_with_warmup
import argparse
import sys
import torch
import os
import re
import jieba
import numpy as np
# 如果没有安装fengshen模块，请把Fengshenbang-LM/fengshen加入到系统环境变量
sys.path.insert(0, '../../../fengshen')
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
class DataCollate(object):
def __init__(self, tokenizer, max_length, mask_rate=0.15, max_ngram=3, if_padding=True) -> None:
self.tokenizer = tokenizer
self.max_length = max_length
self.word_cuter = jieba.cut
self.vocab_length = len(tokenizer)
self.mask_rate = mask_rate
self.ignore_labels = -100
self.ngrams = np.arange(1, max_ngram + 1, dtype=np.int64)
pvals = 1. / np.arange(1, max_ngram + 1)
pvals /= pvals.sum(keepdims=True) # p(n) = 1/n / sigma(1/k)
self.pvals = pvals
self.padding = if_padding
def token_process(self, token_id):
rand = np.random.random()
if rand <= 0.8:
return self.tokenizer.mask_token_id
elif rand <= 0.9:
return token_id
else:
return np.random.randint(1, self.vocab_length)
def __call__(self, samples):
input_ids = []
attention_mask = []
token_type_ids = []
batch_labels = []
# print('^-^ batch size :',len(samples))
for sample in samples:
word_list = list(self.word_cuter(sample['text']))
mask_ids, labels = [], []
record = []
for i in range(len(word_list)):
rands = np.random.random()
if i in record:
continue
word = word_list[i]
if rands > self.mask_rate and len(word) < 4:
word = word_list[i]
word_encode = tokenizer.encode(word, add_special_tokens=False)
for token in word_encode:
mask_ids.append(token)
labels.append(self.ignore_labels)
record.append(i)
else:
n = np.random.choice(self.ngrams, p=self.pvals)
for index in range(n):
ind = index + i
if ind in record or ind >= len(word_list):
continue
record.append(ind)
word = word_list[ind]
word_encode = tokenizer.encode(word, add_special_tokens=False)
for token in word_encode:
mask_ids.append(self.token_process(token))
labels.append(token)
if self.padding:
if len(mask_ids) > self.max_length:
input_ids.append(mask_ids[:self.max_length])
batch_labels.append(labels[:self.max_length])
else:
lenght = len(mask_ids)
mask_ids.extend([0]*(self.max_length-lenght))
labels.extend([-100]*(self.max_length-lenght))
input_ids.append(mask_ids)
batch_labels.append(labels)
attention_mask.append([1]*self.max_length)
token_type_ids.append([0]*self.max_length)
# print('sentence:',sample['text'])
# print('input_ids:',mask_ids)
# print('decode inputids:',self.tokenizer.decode(mask_ids))
# print('labels',labels)
# print('decode labels:',self.tokenizer.decode(labels))
# print('*'*20)
return {
'input_ids': torch.tensor(input_ids),
'labels': torch.tensor(batch_labels),
'attention_mask': torch.tensor(attention_mask),
'token_type_ids': torch.tensor(token_type_ids)
}
class Bert(LightningModule):
@staticmethod
def add_module_specific_args(args_parser):
parser = args_parser.add_argument_group('Bert')
parser.add_argument('--model_path', type=str, default='')
parser.add_argument('--learning_rate', default=1e-5, type=float)
parser.add_argument('--weight_decay', default=0.1, type=float)
parser.add_argument('--warmup', default=0.01, type=float)
return args_parser
def __init__(self, args):
super().__init__()
self.save_hyperparameters(args)
self.bertconfig = BertConfig.from_pretrained(args.model_path)
# self.model = BertForPreTraining(self.bertconfig)
self.model = BertForMaskedLM(self.bertconfig)
def setup(self, stage) -> None:
if stage == 'fit':
train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
# Calculate total steps
tb_size = self.hparams.train_batchsize * max(1, self.trainer.gpus)
ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs)
self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size
def configure_optimizers(self):
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
paras = list(
filter(lambda p: p[1].requires_grad, self.named_parameters()))
paras = [{
'params':
[p for n, p in paras if not any(nd in n for nd in no_decay)],
'weight_decay': self.hparams.weight_decay
}, {
'params': [p for n, p in paras if any(nd in n for nd in no_decay)],
'weight_decay': 0.0
}]
optimizer = torch.optim.AdamW(paras, lr=self.hparams.learning_rate)
scheduler = get_linear_schedule_with_warmup(
optimizer, int(self.total_steps * self.hparams.warmup),
self.total_steps)
return [{
'optimizer': optimizer,
'lr_scheduler': {
'scheduler': scheduler,
'interval': 'step',
'frequency': 1
}
}]
def training_step(self, batch, batch_idx):
output = self.model(**batch)
# print(output)
self.log('train_loss', output.loss)
return output.loss
def comput_metrix(self, logits, labels):
ones = torch.ones_like(labels)
zero = torch.zeros_like(labels)
mask = torch.where(labels < 0, zero, ones)
mask = mask.view(size=(-1,)).float()
# y_true=labels.view(size=(-1,)).float()
y_pred = torch.argmax(logits, dim=-1)
y_pred = y_pred.view(size=(-1,))
y_true = labels.view(size=(-1,)).float()
corr = torch.eq(y_pred, y_true)
corr = torch.multiply(corr.float(), mask)
acc = torch.sum(corr.float()) / torch.sum(mask)
return acc
def validation_step(self, batch, batch_idx):
output = self.model(**batch)
# print(output)
acc = self.comput_metrix(output.logits, batch['labels'])
print('val_loss ', output.loss)
self.log('val_loss', output.loss)
self.log('val_acc', acc)
# pass
def predict_step(self, batch, batch_idx):
output = self.model(**batch)
return output.prediction_logits
class CustomCKPT:
@staticmethod
def add_argparse_args(parent_args):
parser = parent_args.add_argument_group('ckpt call back')
parser.add_argument('--monitor', default='train_loss', type=str)
parser.add_argument('--mode', default='min', type=str)
parser.add_argument('--dirpath', default='./ckpt/', type=str)
parser.add_argument(
'--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str)
parser.add_argument('--save_last', action='store_true', default=True)
parser.add_argument('--save_top_k', default=3, type=float)
parser.add_argument('--every_n_train_steps', default=100, type=float)
parser.add_argument('--save_weights_only', action='store_true', default=False)
return parent_args
def __init__(self, args):
self.callbacks = ModelCheckpoint(monitor=args.monitor,
save_top_k=args.save_top_k,
mode=args.mode,
every_n_train_steps=args.every_n_train_steps,
save_weights_only=args.save_weights_only,
dirpath=args.dirpath,
filename=args.filename,
save_last=args.save_last)
if __name__ == '__main__':
args_parser = argparse.ArgumentParser()
args_parser = BertDataModule.add_data_specific_args(args_parser)
args_parser = Trainer.add_argparse_args(args_parser)
args_parser = Bert.add_module_specific_args(args_parser)
args_parser = CustomCKPT.add_argparse_args(args_parser)
args_parser.add_argument('--deepspeed')
args_parser.add_argument('--seq_max_length')
args = args_parser.parse_args()
tokenizer = BertTokenizer.from_pretrained(args.model_path)
collate_fn = DataCollate(tokenizer, 512)
data_module = BertDataModule(tokenizer=tokenizer, args=args, collate_fn=collate_fn)
print('data load complete')
model = Bert(args)
print('model load complete')
lr_monitor = LearningRateMonitor(logging_interval='step')
logger = loggers.TensorBoardLogger(save_dir=os.path.join(
args.default_root_dir, 'logs/'),
name=os.path.basename(os.path.dirname(args.model_path)))
checkpoint_callback = CustomCKPT(args).callbacks
if args.resume_from_checkpoint is not None and \
not os.path.exists(args.resume_from_checkpoint):
print('--------warning no checkpoint found--------, remove args')
del args.resume_from_checkpoint
# autotuning
if args.deepspeed is not None:
os.environ['PL_DEEPSPEED_CONFIG_PATH'] = args.deepspeed
trainer = Trainer.from_argparse_args(args, logger=logger,
callbacks=[
lr_monitor,
checkpoint_callback])
trainer.fit(model, data_module)
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