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pretrain_Bert_distributed.py
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pretrain_Bert_distributed.py
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# coding=utf-8
'''
@Software:PyCharm
@Time:2024/04/07 2:58 下午
@Author: fffan
'''
from __future__ import absolute_import, division, print_function
import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "4"
import re
import argparse
import logging
import random
import pickle
import numpy as np
import torch
import torch.distributed as dist
import torch.utils.data.distributed
import torch.backends.cudnn as cudnn
from collections import namedtuple
from torch.utils.data import (DataLoader, RandomSampler, Dataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
from torch.nn import CrossEntropyLoss
import collections
from model_info.file_utils import WEIGHTS_NAME, CONFIG_NAME
from model_info.modeling import BertForPreTraining
from model_info.tokenization import BertTokenizer
from model_info.optimization import BertAdam
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
InputFeatures = namedtuple("InputFeatures", "input_ids input_masks segment_ids masked_lm_positions masked_lm_ids masked_lm_weights")
MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
["index", "label"])
class PreTrainingDataset(Dataset):
""" 实际的供给 Dataloader 的数据类 """
def __init__(self):
self.data = []
def add_instance(self, features: collections.OrderedDict):
self.data.append((
features["input_ids"],
features["segment_ids"],
features["input_mask"],
features["masked_lm_ids"],
features["next_sentence_labels"]
))
def __len__(self):
return len(self.data)
def __getitem__(self, index):
input_ids, token_type_ids, attention_mask, masked_lm_labels, next_sentence_label = self.data[index]
return input_ids, token_type_ids, attention_mask, masked_lm_labels, next_sentence_label
class PregeneratedDataset(object):
def __init__(self, training_path, tokenizer, max_seq_len):
self.vocab = tokenizer.vocab
self.tokenizer = tokenizer
logger.info('training_path: {}'.format(training_path))
self.input_ids = []
self.segment_ids = []
self.input_masks = []
self.masked_lm_ids = []
self.next_sentence_labels = []
print("##### 开始读取数据:",training_path)
with open(training_path, 'rb') as f:
one_pkl_dict = pickle.load(f)
for i, feature in enumerate(tqdm(one_pkl_dict.data)):
if not feature:
continue
"""
self.input_ids.append(feature.input_ids)
self.segment_ids.append(feature.segment_ids)
self.input_masks.append(feature.input_masks)
self.masked_lm_ids.append(feature.masked_lm_ids)
self.next_sentence_labels.append(feature.next_sentence_labels)
"""
self.input_ids.append(feature[0])
self.segment_ids.append(feature[1])
self.input_masks.append(feature[2])
self.masked_lm_ids.append(feature[3])
self.next_sentence_labels.append(feature[4])
self.data_size = len(self.input_ids)
def __len__(self):
return self.data_size
def __getitem__(self, item):
return (
torch.tensor(self.input_ids[item]),
torch.tensor(self.input_masks[item]),
torch.tensor(self.segment_ids[item]),
torch.tensor(self.masked_lm_ids[item]),
torch.tensor(self.next_sentence_labels[item]),
)
def save_model(prefix, model, path):
logging.info("** ** * Saving model ** ** * ")
model_name = "{}_{}".format(prefix, WEIGHTS_NAME)
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(path, model_name)
output_config_file = os.path.join(path, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--train_file_path", default="./data/bert_chinese_fffan_data.eval", type=str)
# Required parameters
parser.add_argument("--model_path", default="./pretrain_models/bert-base-chinese", type=str)
parser.add_argument("--output_dir", default="./output_dir/pretrain_fffan", type=str)
# Other parameters
parser.add_argument("--save_model_number",
default=5,
type=int, help="The maximum total input sequence length ")
parser.add_argument("--max_seq_len",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece \n"
" tokenization. Sequences longer than this will be truncated, \n"
"and sequences shorter than this will be padded.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
action='store_true',
# default=True,
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=8,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size", default=1, type=int, help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument('--weight_decay',
'--wd',
default=1e-1,
type=float,
metavar='W',
help='weight decay')
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local-rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed', type=int, default=42, help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing \n"
"a backward/update pass.")
parser.add_argument('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--continue_train',
action='store_true',
help='Whether to train from checkpoints')
# Additional arguments
parser.add_argument('--eval_step', type=int, default=5)
# This is used for running on Huawei Cloud.
parser.add_argument('--data_url', type=str, default="")
args = parser.parse_args()
return args
def main_woker(local_rank, nprocs, args):
dist.init_process_group(backend='nccl')
model = BertForPreTraining.from_scratch(args.model_path)
print("####### local_rank: ",local_rank)
torch.cuda.set_device(local_rank)
model.cuda(local_rank)
args.train_batch_size = int(args.train_batch_size / nprocs)
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[local_rank])
#####
tokenizer = BertTokenizer.from_pretrained(args.model_path, do_lower_case=args.do_lower_case)
if os.path.exists(os.path.join(args.model_path, "vocab.txt")):
os.system("cp " + os.path.join(args.model_path, "vocab.txt") + " " + args.output_dir)
dataset = PregeneratedDataset(args.train_file_path, tokenizer, max_seq_len=args.max_seq_len)
total_train_examples = len(dataset)
print("##### 训练数据量:", total_train_examples)
train_sampler = torch.utils.data.distributed.DistributedSampler(
dataset)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=args.train_batch_size,
num_workers=2,
pin_memory=True,
sampler=train_sampler)
########################
size = 0
for n, p in model.named_parameters():
logger.info('n: {}'.format(n))
logger.info('p: {}'.format(p.nelement()))
size += p.nelement()
logger.info('Total parameters: {}'.format(size))
num_train_optimization_steps = int(total_train_examples / args.train_batch_size /args.num_train_epochs)
print("##### 训练数据总步数:", num_train_optimization_steps)
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
) * args.num_train_epochs
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': 0.01
}, {
'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay': 0.0
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
cudnn.benchmark = True
global_step = 0
for one_epoch in range(int(args.num_train_epochs)):
train_sampler.set_epoch(one_epoch)
# train for one epoch
train(train_loader, model, optimizer, local_rank,
args, global_step)
def reduce_mean(tensor, nprocs):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.ReduceOp.SUM)
rt /= nprocs
return rt
def reduce_value(value, average=True):
world_size = dist.get_world_size()
if world_size < 2: # 单GPU的情况
return value
with torch.no_grad():
dist.all_reduce(value)
if average:
value /= world_size
return value
def train(train_loader, model, optimizer, local_rank, args, global_step):
# switch to train mode
model.train()
all_save_model_list = []
for step, batch in enumerate(tqdm(train_loader, desc="# Iteration", ascii=True)):
#####
#images = images.cuda(local_rank, non_blocking=True)
batch = tuple(t.cuda(local_rank, non_blocking=True) for t in batch)
input_ids, input_mask, segment_ids, masked_lm_ids, next_sentence_labels = batch
if input_ids.size()[0] != args.train_batch_size:
continue
loss = model(input_ids, segment_ids, input_mask, masked_lm_ids, next_sentence_labels)
#print("##### global_step:",global_step," Loss ", loss)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
if step % 100 == 0:
logger.info(f'loss = {loss}')
#loss = reduce_value(loss, average=True)
loss = reduce_mean(loss, args.nprocs)
print("##### global_step:",global_step," Loss ", loss)
optimizer.step()
optimizer.zero_grad()
global_step += 1
if (global_step + 1) % args.eval_step == 0 and local_rank == 0:
result = {}
result['global_step'] = global_step
result['loss'] = loss
output_eval_file = os.path.join(args.output_dir, "log.txt")
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
# Save a trained model
########################################################################
prefix = f"step_{global_step}"
logging.info("** ** * Saving model ** ** * ")
model_name = "{}_{}".format(prefix, WEIGHTS_NAME)
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(args.output_dir, model_name)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
##### 保存的模型数量超过指定数量,删除模型 #############
if len(all_save_model_list) == args.save_model_number:
os.system("rm -rf " + all_save_model_list[0])
print("#### 删除模型:", all_save_model_list[0])
del all_save_model_list[0]
######################################################
all_save_model_list.append(output_model_file)
def main():
args = get_parser()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
args.nprocs = torch.cuda.device_count()
print("########### ",args.nprocs)
main_woker(args.local_rank, args.nprocs, args)
if __name__ == "__main__":
main()