20_BERT微调训练
1.导包
import json #通过路径加载预训练模型
import os
import torch
from torch import nn
import dltools
2.加载预训练模型BERT函数¶
def load_pretrained_model(pretrained_model, num_hiddens, ffn_num_hiddens,num_heads, num_layers, dropout, max_len, devices):data_dir = "./bert.small.torch/"# 定义空词表以加载预定义词表vocab = dltools.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 = dltools.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)# 加载预训练BERT参数bert.load_state_dict(torch.load(os.path.join(data_dir,'pretrained.params')))return bert, vocabdevices = dltools.try_all_gpus()
#调用加载预训练模型BERT的封装函数
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)# standford natural language inference
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(*[dltools.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):out = [self._mp_worker(x) for x in 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 = dltools.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)#若出现显存不足错误，请减少‘batch——size’。在原始的BERT模型中，max_len=512
batch_size, max_len, num_workers = 128, 128, dltools.get_dataloader_workers()
data_dir = './snli_1.0/'
train_set = SNLIBERTDataset(dltools.read_snli(data_dir, True), max_len, vocab)
test_set = SNLIBERTDataset(dltools.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)read 549367 examples read 9824 examples
train_data = dltools.read_snli(data_dir, is_train=True)
for x0, x1, y in zip(train_data[0][:3], train_data[1][:3], train_data[2][:3]):print('premise', x0)print('hypothesis:', x1)print('label', y)
premise A person on a horse jumps over a broken down airplane . hypothesis: A person is training his horse for a competition . label 2 premise A person on a horse jumps over a broken down airplane . hypothesis: A person is at a diner , ordering an omelette . label 1 premise A person on a horse jumps over a broken down airplane . hypothesis: A person is outdoors , on a horse . label 0
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_lens_x = inputsencoded_X = self.encoder(tokens_X, segments_X, valid_lens_x)return self.output(self.hidden(encoded_X[:, 0, :])) #去除中间的维度net = BERTClassifier(bert)lr, num_epochs = 1e-4, 2
trainer = torch.optim.Adam(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss(reduction='none')
dltools.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)loss 0.640, train acc 0.733, test acc 0.762 2658.2 examples/sec on [device(type='cuda', index=0)]
