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multi-gpu-dataparallel-cls.py
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multi-gpu-dataparallel-cls.py
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import json
import time
import random
import torch
import torch.nn as nn
import numpy as np
from sklearn.metrics import classification_report
from torch.utils.data import DataLoader
from collections import Counter
from transformers import BertForMaskedLM, BertTokenizer, BertForSequenceClassification, BertConfig, AdamW
def set_seed(seed=123):
"""
设置随机数种子,保证实验可重现
:param seed:
:return:
"""
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed_all(seed)
def get_data():
with open("data/train.json", "r", encoding="utf-8") as fp:
data = fp.read()
data = json.loads(data)
return data
def load_data():
data = get_data()
return_data = []
# [(文本, 标签id)]
for d in data:
text = d[0]
label = d[1]
return_data.append(("".join(text.split(" ")).strip(), label))
return return_data
class Collate:
def __init__(self,
tokenizer,
max_seq_len,
):
self.tokenizer = tokenizer
self.max_seq_len = max_seq_len
def collate_fn(self, batch):
input_ids_all = []
token_type_ids_all = []
attention_mask_all = []
label_all = []
for data in batch:
text = data[0]
label = data[1]
inputs = self.tokenizer.encode_plus(text=text,
max_length=self.max_seq_len,
padding="max_length",
truncation="longest_first",
return_attention_mask=True,
return_token_type_ids=True)
input_ids = inputs["input_ids"]
token_type_ids = inputs["token_type_ids"]
attention_mask = inputs["attention_mask"]
input_ids_all.append(input_ids)
token_type_ids_all.append(token_type_ids)
attention_mask_all.append(attention_mask)
label_all.append(label)
input_ids_all = torch.tensor(input_ids_all, dtype=torch.long)
token_type_ids_all = torch.tensor(token_type_ids_all, dtype=torch.long)
attention_mask_all = torch.tensor(attention_mask_all, dtype=torch.long)
label_all = torch.tensor(label_all, dtype=torch.long)
return_data = {
"input_ids": input_ids_all,
"attention_mask": attention_mask_all,
"token_type_ids": token_type_ids_all,
"label": label_all
}
return return_data
def build_optimizer(model, args):
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
'weight_decay': 0.0}
]
# optimizer = AdamW(model.parameters(), lr=learning_rate)
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
return optimizer
class Trainer:
def __init__(self,
args,
config,
model,
criterion,
optimizer):
self.args = args
self.config = config
self.model = model
self.criterion =criterion
self.optimizer = optimizer
def on_step(self, batch_data):
label = batch_data["label"].cuda()
input_ids = batch_data["input_ids"].cuda()
token_type_ids = batch_data["token_type_ids"].cuda()
attention_mask = batch_data["attention_mask"].cuda()
output = self.model(input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
labels=label)
logits = output[1]
return logits, label
def train(self, train_loader, dev_loader=None):
gloabl_step = 1
best_acc = 0.
start = time.time()
for epoch in range(1, self.args.epochs + 1):
for step, batch_data in enumerate(train_loader):
self.model.train()
logits, label = self.on_step(batch_data)
loss = self.criterion(logits, label)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
print("【train】 epoch:{}/{} step:{}/{} loss:{:.6f}".format(
epoch, self.args.epochs, gloabl_step, self.args.total_step, loss.item()
))
gloabl_step += 1
if self.args.dev:
if gloabl_step % self.args.eval_step == 0:
loss, accuracy = self.dev(dev_loader)
print("【dev】 loss:{:.6f} accuracy:{:.4f}".format(loss, accuracy))
if accuracy > best_acc:
best_acc = accuracy
print("【best accuracy】 {:.4f}".format(best_acc))
torch.save(self.model.state_dict(), self.args.ckpt_path)
end = time.time()
print("耗时:{}分钟".format((end - start) / 60))
if not self.args.dev:
torch.save(self.model.state_dict(), self.args.ckpt_path)
def dev(self, dev_loader):
self.model.eval()
correct_total = 0
num_total = 0
loss_total = 0.
with torch.no_grad():
for step, batch_data in enumerate(dev_loader):
logits, label = self.on_step(batch_data)
loss = self.criterion(logits, label)
loss_total += loss.item()
logits = logits.detach().cpu().numpy()
label = label.view(-1).detach().cpu().numpy()
num_total += len(label)
preds = np.argmax(logits, axis=1).flatten()
correct_num = (preds == label).sum()
correct_total += correct_num
return loss_total, correct_total / num_total
def test(self, model, test_loader, labels):
self.model = model
self.model.eval()
preds = []
trues = []
with torch.no_grad():
for step, batch_data in enumerate(test_loader):
logits, label = self.on_step(batch_data)
label = label.view(-1).detach().cpu().numpy().tolist()
logits = logits.detach().cpu().numpy()
pred = np.argmax(logits, axis=1).flatten().tolist()
trues.extend(label)
preds.extend(pred)
print(trues, preds, labels)
report = classification_report(trues, preds, target_names=labels)
return report
class Args:
model_path = "model_hub/chinese-bert-wwm-ext"
ckpt_path = "output/multi-gpu-dataparallel-cls.pt"
max_seq_len = 128
ratio = 0.92
train_batch_size = 32
dev_batch_size = 32
weight_decay = 0.01
epochs = 1
learning_rate = 3e-5
eval_step = 100
dev = False
gpu_ids = [0, 1]
def main():
# =======================================
# 定义相关参数
set_seed()
label2id = {
"其他": 0,
"喜好": 1,
"悲伤": 2,
"厌恶": 3,
"愤怒": 4,
"高兴": 5,
}
args = Args()
tokenizer = BertTokenizer.from_pretrained(args.model_path)
# =======================================
# =======================================
# 加载数据集
data = load_data()
# 取1万条数据出来
data = data[:10000]
random.shuffle(data)
train_num = int(len(data) * args.ratio)
train_data = data[:train_num]
dev_data = data[train_num:]
collate = Collate(tokenizer, args.max_seq_len)
train_loader = DataLoader(train_data,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=2,
collate_fn=collate.collate_fn)
total_step = len(train_loader) * args.epochs
args.total_step = total_step
dev_loader = DataLoader(dev_data,
batch_size=args.dev_batch_size,
shuffle=False,
num_workers=2,
collate_fn=collate.collate_fn)
test_loader = dev_loader
# =======================================
# =======================================
# 定义模型、优化器、损失函数
config = BertConfig.from_pretrained(args.model_path, num_labels=6)
model = BertForSequenceClassification.from_pretrained(args.model_path,
config=config)
model.cuda()
model = nn.DataParallel(model, device_ids=args.gpu_ids, output_device=args.gpu_ids[0])
criterion = torch.nn.CrossEntropyLoss()
optimizer = build_optimizer(model, args)
# =======================================
# 定义训练器
trainer = Trainer(args,
config,
model,
criterion,
optimizer)
# 训练和验证
trainer.train(train_loader, dev_loader)
# 测试
labels = list(label2id.keys())
config = BertConfig.from_pretrained(args.model_path, num_labels=6)
model = BertForSequenceClassification.from_pretrained(args.model_path, config=config)
model.cuda()
model = nn.DataParallel(model, device_ids=args.gpu_ids, output_device=args.gpu_ids[0])
model.load_state_dict(torch.load(args.ckpt_path))
report = trainer.test(model, test_loader, labels)
print(report)
# =======================================
if __name__ == '__main__':
main()