我的NLP实践之旅06

我的NLP实践之旅06

经历了这些日子的组队学习,这次的NLP实践之路也逐渐接近尾声了,在以后的日子里,我也要努力学习,多多加油啊,扯远了,下面进入正题

Bert

通过这次的组队学习,我终于接触到了目前最热门的自然语言处理模型之一的Bert,起初我听到Bert的概念时是完全懵逼的,不过后来在看了李宏毅在YouTube上的视频后自己逐渐理解了其中的具体含义,下面对Bert进行介绍。

BERT的全称是Bidirectional Encoder Representation from Transformers,即双向Transformer的Encoder,其实它是TransFormer模型的一部分,TransFormer模型的示意图如下:
我的NLP实践之旅06_第1张图片
简单来说,Bert模型为TransFormer模型的左半部分,TransFromer模型训练时需要提供输入和输出数据,其中要输入的输出数据在预测下一段文本的情况下可以理解为下一个词,而Transformer的参数十分的庞大,故大多数情况只运用模型的左边来进行数据的预处理。

从上图中大家应该注意到了模型有一个结构叫Attention,这个结构可以理解为一个特殊的神经网络,在文本预测的场景中,我们通常需要基于上一段文本预测下一段话,因此需要模型有一定的记忆能力;已知的模型如LSTM等可以一定程度上解决这个问题,然而由于在神经网络中会对之前的信息进行削减,而且,关键的信息也可能隔字符出现,以此Attention机制应运而生。

Attention主要通过在神经网络隐层中将k、v、q矩阵进行相乘转换后输出的结果,其中k为键矩阵,v为值矩阵,q为问题矩阵。

bert代码如下:

# build module
import torch.nn as nn
import torch.nn.functional as F


class Attention(nn.Module):
    def __init__(self, hidden_size):
        super(Attention, self).__init__()
        self.weight = nn.Parameter(torch.Tensor(hidden_size, hidden_size))
        self.weight.data.normal_(mean=0.0, std=0.05)

        self.bias = nn.Parameter(torch.Tensor(hidden_size))
        b = np.zeros(hidden_size, dtype=np.float32)
        self.bias.data.copy_(torch.from_numpy(b))

        self.query = nn.Parameter(torch.Tensor(hidden_size))
        self.query.data.normal_(mean=0.0, std=0.05)

    def forward(self, batch_hidden, batch_masks):
        # batch_hidden: b x len x hidden_size (2 * hidden_size of lstm)
        # batch_masks:  b x len

        # linear
        key = torch.matmul(batch_hidden, self.weight) + self.bias  # b x len x hidden

        # compute attention
        outputs = torch.matmul(key, self.query)  # b x len

        masked_outputs = outputs.masked_fill((1 - batch_masks).bool(), float(-1e32))

        attn_scores = F.softmax(masked_outputs, dim=1)  # b x len

        # 对于全零向量,-1e32的结果为 1/len, -inf为nan, 额外补0
        masked_attn_scores = attn_scores.masked_fill((1 - batch_masks).bool(), 0.0)

        # sum weighted sources
        batch_outputs = torch.bmm(masked_attn_scores.unsqueeze(1), key).squeeze(1)  # b x hidden

        return batch_outputs, attn_scores


# build word encoder
bert_path = '../bert-mini/'
dropout = 0.15

from transformers import BertModel


class WordBertEncoder(nn.Module):
    def __init__(self):
        super(WordBertEncoder, self).__init__()
        self.dropout = nn.Dropout(dropout)

        self.tokenizer = WhitespaceTokenizer()
        self.bert = BertModel.from_pretrained(bert_path)

        self.pooled = False
        logging.info('Build Bert encoder with pooled {}.'.format(self.pooled))

    def encode(self, tokens):
        tokens = self.tokenizer.tokenize(tokens)
        return tokens

    def get_bert_parameters(self):
        no_decay = ['bias', 'LayerNorm.weight']
        optimizer_parameters = [
            {'params': [p for n, p in self.bert.named_parameters() if not any(nd in n for nd in no_decay)],
             'weight_decay': 0.01},
            {'params': [p for n, p in self.bert.named_parameters() if any(nd in n for nd in no_decay)],
             'weight_decay': 0.0}
        ]
        return optimizer_parameters

    def forward(self, input_ids, token_type_ids):
        # input_ids: sen_num x bert_len
        # token_type_ids: sen_num  x bert_len

        # sen_num x bert_len x 256, sen_num x 256
        sequence_output, pooled_output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids)

        if self.pooled:
            reps = pooled_output
        else:
            reps = sequence_output[:, 0, :]  # sen_num x 256

        if self.training:
            reps = self.dropout(reps)

        return reps


class WhitespaceTokenizer():
    """WhitespaceTokenizer with vocab."""

    def __init__(self):
        vocab_file = bert_path + 'vocab.txt'
        self._token2id = self.load_vocab(vocab_file)
        self._id2token = {v: k for k, v in self._token2id.items()}
        self.max_len = 256
        self.unk = 1

        logging.info("Build Bert vocab with size %d." % (self.vocab_size))

    def load_vocab(self, vocab_file):
        f = open(vocab_file, 'r')
        lines = f.readlines()
        lines = list(map(lambda x: x.strip(), lines))
        vocab = dict(zip(lines, range(len(lines))))
        return vocab

    def tokenize(self, tokens):
        assert len(tokens) <= self.max_len - 2
        tokens = ["[CLS]"] + tokens + ["[SEP]"]
        output_tokens = self.token2id(tokens)
        return output_tokens

    def token2id(self, xs):
        if isinstance(xs, list):
            return [self._token2id.get(x, self.unk) for x in xs]
        return self._token2id.get(xs, self.unk)

    @property
    def vocab_size(self):
        return len(self._id2token)


# build sent encoder
sent_hidden_size = 256
sent_num_layers = 2


class SentEncoder(nn.Module):
    def __init__(self, sent_rep_size):
        super(SentEncoder, self).__init__()
        self.dropout = nn.Dropout(dropout)

        self.sent_lstm = nn.LSTM(
            input_size=sent_rep_size,
            hidden_size=sent_hidden_size,
            num_layers=sent_num_layers,
            batch_first=True,
            bidirectional=True
        )

    def forward(self, sent_reps, sent_masks):
        # sent_reps:  b x doc_len x sent_rep_size
        # sent_masks: b x doc_len

        sent_hiddens, _ = self.sent_lstm(sent_reps)  # b x doc_len x hidden*2
        sent_hiddens = sent_hiddens * sent_masks.unsqueeze(2)

        if self.training:
            sent_hiddens = self.dropout(sent_hiddens)

        return sent_hiddens
# build model
class Model(nn.Module):
    def __init__(self, vocab):
        super(Model, self).__init__()
        self.sent_rep_size = 256
        self.doc_rep_size = sent_hidden_size * 2
        self.all_parameters = {}
        parameters = []
        self.word_encoder = WordBertEncoder()
        bert_parameters = self.word_encoder.get_bert_parameters()

        self.sent_encoder = SentEncoder(self.sent_rep_size)
        self.sent_attention = Attention(self.doc_rep_size)
        parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_encoder.parameters())))
        parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_attention.parameters())))

        self.out = nn.Linear(self.doc_rep_size, vocab.label_size, bias=True)
        parameters.extend(list(filter(lambda p: p.requires_grad, self.out.parameters())))

        if use_cuda:
            self.to(device)

        if len(parameters) > 0:
            self.all_parameters["basic_parameters"] = parameters
        self.all_parameters["bert_parameters"] = bert_parameters

        logging.info('Build model with bert word encoder, lstm sent encoder.')

        para_num = sum([np.prod(list(p.size())) for p in self.parameters()])
        logging.info('Model param num: %.2f M.' % (para_num / 1e6))

    def forward(self, batch_inputs):
        # batch_inputs(batch_inputs1, batch_inputs2): b x doc_len x sent_len
        # batch_masks : b x doc_len x sent_len
        batch_inputs1, batch_inputs2, batch_masks = batch_inputs
        batch_size, max_doc_len, max_sent_len = batch_inputs1.shape[0], batch_inputs1.shape[1], batch_inputs1.shape[2]
        batch_inputs1 = batch_inputs1.view(batch_size * max_doc_len, max_sent_len)  # sen_num x sent_len
        batch_inputs2 = batch_inputs2.view(batch_size * max_doc_len, max_sent_len)  # sen_num x sent_len
        batch_masks = batch_masks.view(batch_size * max_doc_len, max_sent_len)  # sen_num x sent_len

        sent_reps = self.word_encoder(batch_inputs1, batch_inputs2)  # sen_num x sent_rep_size

        sent_reps = sent_reps.view(batch_size, max_doc_len, self.sent_rep_size)  # b x doc_len x sent_rep_size
        batch_masks = batch_masks.view(batch_size, max_doc_len, max_sent_len)  # b x doc_len x max_sent_len
        sent_masks = batch_masks.bool().any(2).float()  # b x doc_len

        sent_hiddens = self.sent_encoder(sent_reps, sent_masks)  # b x doc_len x doc_rep_size
        doc_reps, atten_scores = self.sent_attention(sent_hiddens, sent_masks)  # b x doc_rep_size

        batch_outputs = self.out(doc_reps)  # b x num_labels

        return batch_outputs
    
model = Model(vocab)
# build optimizer
learning_rate = 2e-4
bert_lr = 5e-5
decay = .75
decay_step = 1000
from transformers import AdamW, get_linear_schedule_with_warmup


class Optimizer:
    def __init__(self, model_parameters, steps):
        self.all_params = []
        self.optims = []
        self.schedulers = []

        for name, parameters in model_parameters.items():
            if name.startswith("basic"):
                optim = torch.optim.Adam(parameters, lr=learning_rate)
                self.optims.append(optim)

                l = lambda step: decay ** (step // decay_step)
                scheduler = torch.optim.lr_scheduler.LambdaLR(optim, lr_lambda=l)
                self.schedulers.append(scheduler)
                self.all_params.extend(parameters)
            elif name.startswith("bert"):
                optim_bert = AdamW(parameters, bert_lr, eps=1e-8)
                self.optims.append(optim_bert)

                scheduler_bert = get_linear_schedule_with_warmup(optim_bert, 0, steps)
                self.schedulers.append(scheduler_bert)

                for group in parameters:
                    for p in group['params']:
                        self.all_params.append(p)
            else:
                Exception("no nameed parameters.")

        self.num = len(self.optims)

    def step(self):
        for optim, scheduler in zip(self.optims, self.schedulers):
            optim.step()
            scheduler.step()
            optim.zero_grad()

    def zero_grad(self):
        for optim in self.optims:
            optim.zero_grad()

    def get_lr(self):
        lrs = tuple(map(lambda x: x.get_lr()[-1], self.schedulers))
        lr = ' %.5f' * self.num
        res = lr % lrs
        return res
# build trainer

import time
from sklearn.metrics import classification_report

clip = 5.0
epochs = 1
early_stops = 3
log_interval = 50

test_batch_size = 16
train_batch_size = 16

save_model = './bert.bin'
save_test = './bert.csv'

class Trainer():
    def __init__(self, model, vocab):
        self.model = model
        self.report = True
        
        self.train_data = get_examples(train_data, model.word_encoder, vocab)
        self.batch_num = int(np.ceil(len(self.train_data) / float(train_batch_size)))
        self.dev_data = get_examples(dev_data, model.word_encoder, vocab)
        self.test_data = get_examples(test_data, model.word_encoder, vocab)

        # criterion
        self.criterion = nn.CrossEntropyLoss()

        # label name
        self.target_names = vocab.target_names

        # optimizer
        self.optimizer = Optimizer(model.all_parameters, steps=self.batch_num * epochs)

        # count
        self.step = 0
        self.early_stop = -1
        self.best_train_f1, self.best_dev_f1 = 0, 0
        self.last_epoch = epochs

    def train(self):
        logging.info('Start training...')
        for epoch in range(1, epochs + 1):
            train_f1 = self._train(epoch)

            dev_f1 = self._eval(epoch)

            if self.best_dev_f1 <= dev_f1:
                logging.info(
                    "Exceed history dev = %.2f, current dev = %.2f" % (self.best_dev_f1, dev_f1))
                torch.save(self.model.state_dict(), save_model)

                self.best_train_f1 = train_f1
                self.best_dev_f1 = dev_f1
                self.early_stop = 0
            else:
                self.early_stop += 1
                if self.early_stop == early_stops:
                    logging.info(
                        "Eearly stop in epoch %d, best train: %.2f, dev: %.2f" % (
                            epoch - early_stops, self.best_train_f1, self.best_dev_f1))
                    self.last_epoch = epoch
                    break
    def test(self):
        self.model.load_state_dict(torch.load(save_model))
        self._eval(self.last_epoch + 1, test=True)

    def _train(self, epoch):
        self.optimizer.zero_grad()
        self.model.train()

        start_time = time.time()
        epoch_start_time = time.time()
        overall_losses = 0
        losses = 0
        batch_idx = 1
        y_pred = []
        y_true = []
        for batch_data in data_iter(self.train_data, train_batch_size, shuffle=True):
            torch.cuda.empty_cache()
            batch_inputs, batch_labels = self.batch2tensor(batch_data)
            batch_outputs = self.model(batch_inputs)
            loss = self.criterion(batch_outputs, batch_labels)
            loss.backward()

            loss_value = loss.detach().cpu().item()
            losses += loss_value
            overall_losses += loss_value

            y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist())
            y_true.extend(batch_labels.cpu().numpy().tolist())

            nn.utils.clip_grad_norm_(self.optimizer.all_params, max_norm=clip)
            for optimizer, scheduler in zip(self.optimizer.optims, self.optimizer.schedulers):
                optimizer.step()
                scheduler.step()
            self.optimizer.zero_grad()

            self.step += 1

            if batch_idx % log_interval == 0:
                elapsed = time.time() - start_time

                lrs = self.optimizer.get_lr()
                logging.info(
                    '| epoch {:3d} | step {:3d} | batch {:3d}/{:3d} | lr{} | loss {:.4f} | s/batch {:.2f}'.format(
                        epoch, self.step, batch_idx, self.batch_num, lrs,
                        losses / log_interval,
                        elapsed / log_interval))

                losses = 0
                start_time = time.time()

            batch_idx += 1

        overall_losses /= self.batch_num
        during_time = time.time() - epoch_start_time

        # reformat
        overall_losses = reformat(overall_losses, 4)
        score, f1 = get_score(y_true, y_pred)

        logging.info(
            '| epoch {:3d} | score {} | f1 {} | loss {:.4f} | time {:.2f}'.format(epoch, score, f1,
                                                                                  overall_losses,
                                                                                  during_time))
        if set(y_true) == set(y_pred) and self.report:
            report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names)
            logging.info('\n' + report)

        return f1

    def _eval(self, epoch, test=False):
        self.model.eval()
        start_time = time.time()
        data = self.test_data if test else self.dev_data
        y_pred = []
        y_true = []
        with torch.no_grad():
            for batch_data in data_iter(data, test_batch_size, shuffle=False):
                torch.cuda.empty_cache()
                batch_inputs, batch_labels = self.batch2tensor(batch_data)
                batch_outputs = self.model(batch_inputs)
                y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist())
                y_true.extend(batch_labels.cpu().numpy().tolist())

            score, f1 = get_score(y_true, y_pred)

            during_time = time.time() - start_time
            
            if test:
                df = pd.DataFrame({'label': y_pred})
                df.to_csv(save_test, index=False, sep=',')
            else:
                logging.info(
                    '| epoch {:3d} | dev | score {} | f1 {} | time {:.2f}'.format(epoch, score, f1,
                                                                              during_time))
                if set(y_true) == set(y_pred) and self.report:
                    report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names)
                    logging.info('\n' + report)

        return f1

    def batch2tensor(self, batch_data):
        '''
            [[label, doc_len, [[sent_len, [sent_id0, ...], [sent_id1, ...]], ...]]
        '''
        batch_size = len(batch_data)
        doc_labels = []
        doc_lens = []
        doc_max_sent_len = []
        for doc_data in batch_data:
            doc_labels.append(doc_data[0])
            doc_lens.append(doc_data[1])
            sent_lens = [sent_data[0] for sent_data in doc_data[2]]
            max_sent_len = max(sent_lens)
            doc_max_sent_len.append(max_sent_len)

        max_doc_len = max(doc_lens)
        max_sent_len = max(doc_max_sent_len)

        batch_inputs1 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64)
        batch_inputs2 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64)
        batch_masks = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.float32)
        batch_labels = torch.LongTensor(doc_labels)

        for b in range(batch_size):
            for sent_idx in range(doc_lens[b]):
                sent_data = batch_data[b][2][sent_idx]
                for word_idx in range(sent_data[0]):
                    batch_inputs1[b, sent_idx, word_idx] = sent_data[1][word_idx]
                    batch_inputs2[b, sent_idx, word_idx] = sent_data[2][word_idx]
                    batch_masks[b, sent_idx, word_idx] = 1

        if use_cuda:
            batch_inputs1 = batch_inputs1.to(device)
            batch_inputs2 = batch_inputs2.to(device)
            batch_masks = batch_masks.to(device)
            batch_labels = batch_labels.to(device)

        return (batch_inputs1, batch_inputs2, batch_masks), batch_labels
# train
trainer = Trainer(model, vocab)
trainer.train()

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