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train.py
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train.py
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# -*- coding:utf-8 -*-
# @project: BlockShuffleTest
# @filename: train
# @author: swift
# @source: https://github.com/liucongg/BlockShuffleTest
"""
文件说明:
"""
import torch
import os
import random
import numpy as np
import argparse
import logging
from transformers import BertTokenizer
from data_set import SentimentAnalysisDataSet, collate_func_sentiment_analysis
from mysampler import MySampler
from model import SentimentAnalysisModel
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch import nn
from transformers import AdamW, get_linear_schedule_with_warmup
from tqdm import tqdm, trange
from sklearn.metrics import f1_score, accuracy_score
import json
import time
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__)
def evaluate(model, device, dev_data, args):
test_sampler = SequentialSampler(dev_data)
test_data_loader = DataLoader(dev_data, sampler=test_sampler,
batch_size=args.test_batch_size, collate_fn=collate_func_sentiment_analysis)
iter_bar = tqdm(test_data_loader, desc="iter", disable=False)
true_label = []
pre_label = []
model.eval()
for step, batch in enumerate(iter_bar):
with torch.no_grad():
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
label = batch["label"].to(device)
predict_label, _ = model.forward(input_ids, attention_mask)
true_label.extend(label.cpu().numpy())
pre_label.extend(predict_label.cpu().numpy())
true_label = np.array(true_label)
pre_label = np.array(pre_label)
f1 = f1_score(true_label, pre_label, average='macro')
acc = accuracy_score(true_label, pre_label)
return acc, f1
def train_ori_time(model, device, tokenizer, args):
train_batch_size = int(args.train_batch_size)
train_data = SentimentAnalysisDataSet(tokenizer, args.max_len, args.data_dir, "train", args.train_file_path)
train_sampler = RandomSampler(train_data)
train_data_loader = DataLoader(train_data, sampler=train_sampler,
batch_size=train_batch_size, collate_fn=collate_func_sentiment_analysis)
total_steps = int(len(train_data_loader) * args.num_train_epochs)
logger.info("总训练步数为:{}".format(total_steps))
model.to(device)
no_decay = ["bias", "LayerNorm.weight"]
param_optimizer = list(model.named_parameters())
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay,
'lr': args.learning_rate},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay': 0.0,
'lr': args.learning_rate}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=int(args.warmup_proportion * total_steps),
num_training_steps=total_steps)
criterion = nn.CrossEntropyLoss()
torch.cuda.empty_cache()
model.train()
T1 = time.time()
for iepoch in trange(0, args.num_train_epochs, desc="Epoch", disable=False):
iter_bar = tqdm(train_data_loader, desc="Iter", disable=False)
for step, batch in enumerate(iter_bar):
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
label = batch["label"].to(device)
_, logits = model.forward(input_ids, attention_mask)
loss = criterion(logits, label)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
T2 = time.time()
print("原始DataLoader，运行2个epoch时间为{}秒".format(T2 - T1))
dev_data = SentimentAnalysisDataSet(tokenizer, args.max_len, args.data_dir, "dev", args.dev_file_path)
acc, f1 = evaluate(model, device, dev_data, args)
print("origin train acc: {} f1: {}".format(acc, f1))
def train_block_shuffle_time(model, device, tokenizer, args):
train_batch_size = int(args.train_batch_size)
train_data = SentimentAnalysisDataSet(tokenizer, args.max_len, args.data_dir, "train", args.train_file_path)
train_data_sampler = MySampler(train_data, train_batch_size)
train_data_loader = DataLoader(train_data, batch_sampler=train_data_sampler, collate_fn=collate_func_sentiment_analysis)
total_steps = int(len(train_data_loader) * args.num_train_epochs)
logger.info("总训练步数为:{}".format(total_steps))
model.to(device)
no_decay = ["bias", "LayerNorm.weight"]
param_optimizer = list(model.named_parameters())
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay, 'lr': args.learning_rate},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0,
'lr': args.learning_rate}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=int(args.warmup_proportion * total_steps),
num_training_steps=total_steps)
torch.cuda.empty_cache()
model.train()
T1 = time.time()
for iepoch in trange(0, args.num_train_epochs, desc="Epoch", disable=False):
iter_bar = tqdm(train_data_loader, desc="Iter", disable=False)
for step, batch in enumerate(iter_bar):
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
label = batch["label"].to(device)
outputs = model.forward(input_ids, attention_mask, label)
loss = outputs[0]
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
T2 = time.time()
print("BlockShuffleDataLoader，运行2个epoch时间为{}秒".format(T2 - T1))
dev_data = SentimentAnalysisDataSet(tokenizer, args.max_len, args.data_dir, "dev", args.dev_file_path)
acc, f1 = evaluate(model, device, dev_data, args)
print("shuffle train acc: {} f1: {}".format(acc, f1))
def set_args():
parser = argparse.ArgumentParser()
parser.add_argument('--device', default=0, type=int, help='gpu device id')
parser.add_argument('--train_file_path', default='data/train.json', type=str, help='')
parser.add_argument('--dev_file_path', default='data/test.json', type=str, help='')
parser.add_argument('--data_dir', default='data/', type=str, help='')
parser.add_argument('--num_train_epochs', default=2, type=int, help='')
parser.add_argument('--train_batch_size', default=4, type=int, help='')
parser.add_argument('--test_batch_size', default=4, type=int, help='')
parser.add_argument('--learning_rate', default=5e-5, type=float, help='')
parser.add_argument('--warmup_proportion', default=0.1, type=float, help='')
parser.add_argument("--weight_decay", default=0.01, type=float, help="")
parser.add_argument('--adam_epsilon', default=1e-8, type=float, help='')
parser.add_argument('--save_model_steps', default=12, type=int, help='')
parser.add_argument('--logging_steps', default=5, type=int, help='')
parser.add_argument('--gradient_accumulation_steps', default=1, type=int, help='')
parser.add_argument('--max_grad_norm', default=1.0, type=float, help='')
parser.add_argument('--output_dir', default='output_dir', type=str,
help='')
parser.add_argument('--is_block_shuffle', type=bool, default=True, help='')
parser.add_argument('--seed', type=int, default=2020, help='')
parser.add_argument('--max_len', type=int, default=256, help='')
parser.add_argument('--num_labels', type=int, default=6, help='')
return parser.parse_args()
def main():
args = set_args()
device = torch.device("cuda:{}".format(args.device) if torch.cuda.is_available() and int(args.device) >= 0 else "cpu")
if args.seed:
torch.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
model = SentimentAnalysisModel(num_labels=args.num_labels)
tokenizer = BertTokenizer.from_pretrained("bert-base-chinese")
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
train_ori_time(model, device, tokenizer, args)
train_block_shuffle_time(model, device, tokenizer, args)
if __name__ == '__main__':
main()