35.微调BERT

import json
import multiprocessing
import os
import torch
from torch import nn
from d2l import torch as d2l
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#下载预训练的bert模型:base and small
d2l.DATA_HUB['bert.base'] = (d2l.DATA_URL + 'bert.base.torch.zip','225d66f04cae318b841a13d32af3acc165f253ac')
d2l.DATA_HUB['bert.small'] = (d2l.DATA_URL + 'bert.small.torch.zip','c72329e68a732bef0452e4b96a1c341c8910f81f')
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#加载预训练模型:
def load_pretrained_model(pretrained_model,num_hiddens,ffn_num_hiddens,num_heads,num_layers,dropout,max_len,devices):data_dir=d2l.download_extract(pretrained_model)vocab=d2l.Vocab()vocab.idx_to_token=json.load(open(os.path.join(data_dir,'vocab.json')))vocab.token_to_idx = {token: idx for idx, token in enumerate(vocab.idx_to_token)}bert = d2l.BERTModel(len(vocab), num_hiddens, norm_shape=[256],ffn_num_input=256, ffn_num_hiddens=ffn_num_hiddens,num_heads=4, num_layers=2, dropout=0.2,max_len=max_len, key_size=256, query_size=256,value_size=256, hid_in_features=256,mlm_in_features=256, nsp_in_features=256)bert.load_state_dict(torch.load(os.path.join(data_dir,'pretrained.params')))return bert,vocab
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class SNLIBERTDataset(torch.utils.data.Dataset):def __init__(self, dataset, max_len, vocab=None):all_premise_hypothesis_tokens = [[p_tokens, h_tokens] for p_tokens, h_tokens in zip(*[d2l.tokenize([s.lower() for s in sentences])for sentences in dataset[:2]])]self.labels = torch.tensor(dataset[2])self.vocab = vocabself.max_len = max_len(self.all_token_ids, self.all_segments,self.valid_lens) = self._preprocess(all_premise_hypothesis_tokens)print('read ' + str(len(self.all_token_ids)) + ' examples')def _preprocess(self, all_premise_hypothesis_tokens):pool = multiprocessing.Pool(4)  # 使用4个进程out = pool.map(self._mp_worker, all_premise_hypothesis_tokens)all_token_ids = [token_ids for token_ids, segments, valid_len in out]all_segments = [segments for token_ids, segments, valid_len in out]valid_lens = [valid_len for token_ids, segments, valid_len in out]return (torch.tensor(all_token_ids, dtype=torch.long),torch.tensor(all_segments, dtype=torch.long),torch.tensor(valid_lens))def _mp_worker(self, premise_hypothesis_tokens):p_tokens, h_tokens = premise_hypothesis_tokensself._truncate_pair_of_tokens(p_tokens, h_tokens)tokens, segments = d2l.get_tokens_and_segments(p_tokens, h_tokens)token_ids = self.vocab[tokens] + [self.vocab['<pad>']] \* (self.max_len - len(tokens))segments = segments + [0] * (self.max_len - len(segments))valid_len = len(tokens)return token_ids, segments, valid_lendef _truncate_pair_of_tokens(self, p_tokens, h_tokens):# 为BERT输入中的'<CLS>'、'<SEP>'和'<SEP>'词元保留位置while len(p_tokens) + len(h_tokens) > self.max_len - 3:if len(p_tokens) > len(h_tokens):p_tokens.pop()else:h_tokens.pop()def __getitem__(self, idx):return (self.all_token_ids[idx], self.all_segments[idx],self.valid_lens[idx]), self.labels[idx]def __len__(self):return len(self.all_token_ids)
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class BERTClassifier(nn.Module):def __init__(self, bert):super(BERTClassifier,self).__init__()self.encoder=bert.encoderself.hidden=bert.hiddenself.output=nn.Linear(256,3)def forward(self,inputs):tokens_X,segments_X,valid_len_x=inputsencoded_X=self.encoder(tokens_X,segments_X,valid_len_x)#只取<CLS> token进行分类return self.output(self.hidden(encoded_X[:,0,:]))
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#预训练模型加载
devices = d2l.try_all_gpus()
bert,vocab=load_pretrained_model('bert.small',num_hiddens=256,ffn_num_hiddens=512,num_heads=4,num_layers=2,dropout=0.1,max_len=512,devices=devices)
#数据集加载
batch_size, max_len, num_workers = 512, 128,d2l.get_dataloader_workers()
data_dir=r"/data1/zhongyan/deepl/pytorch/13_应用自然语言模型/snli_1.0"
train_set = SNLIBERTDataset(d2l.read_snli(data_dir, True), max_len, vocab)
test_set = SNLIBERTDataset(d2l.read_snli(data_dir, False), max_len, vocab)
train_iter = torch.utils.data.DataLoader(train_set, batch_size, shuffle=True,num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(test_set, batch_size,num_workers=num_workers)
#模型训练与测试:
print("1")
net = BERTClassifier(bert)
lr,num_epochs=1e-4,5
trainer=torch.optim.Adam(net.parameters(),lr=lr)
loss=nn.CrossEntropyLoss(reduction='none')
d2l.train_ch13(net,train_iter,test_iter,loss,trainer,num_epochs,devices)
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