【NLP实践】使用Pytorch进行文本分类——Transformer

前言

文本分类不是生成式的任务，因此只使用Transformer的编码部分（Encoder）进行特征提取。如果不熟悉Transformer模型的原理请移步。

网络结构

代码实现

• 自注意力模型：

class TextSlfAttnNet(nn.Module):
    ''' 自注意力模型 '''

    def __init__(self,
                 config: TextSlfAttnConfig,
                 char_size=5000,
                 pinyin_size=5000):
        super(TextSlfAttnNet, self).__init__()
        # 字向量
        self.char_embedding = nn.Embedding(char_size, config.embedding_size)
        # 拼音向量
        self.pinyin_embedding = nn.Embedding(pinyin_size, config.embedding_size)
        # 位置向量
        self.pos_embedding = nn.Embedding.from_pretrained(
            get_sinusoid_encoding_table(config.max_sen_len, config.embedding_size, padding_idx=0),
            freeze=True)

        self.layer_stack = nn.ModuleList([
            EncoderLayer(config.embedding_size, config.hidden_dims, config.n_heads, config.k_dims, config.v_dims, dropout=config.keep_dropout)
            for _ in range(config.hidden_layers)
        ])

        self.fc_out = nn.Sequential(
            nn.Dropout(config.keep_dropout),
            nn.Linear(config.embedding_size, config.hidden_dims),
            nn.ReLU(inplace=True),
            nn.Dropout(config.keep_dropout),
            nn.Linear(config.hidden_dims, config.num_classes),

        )


    def forward(self, char_id, pinyin_id, pos_id):
        char_inputs = self.char_embedding(char_id)
        pinyin_iputs = self.pinyin_embedding(pinyin_id)

        sen_inputs = torch.cat((char_inputs, pinyin_iputs), dim=1)
        # sentence_length = sen_inputs.size()[1]
        # pos_id = torch.LongTensor(np.array([i for i in range(sentence_length)]))
        pos_inputs = self.pos_embedding(pos_id)
        # batch_size * sen_len * embedding_size
        inputs = sen_inputs + pos_inputs

        for layer in self.layer_stack:
            inputs, _ = layer(inputs)

        enc_outs = inputs.permute(0, 2, 1)
        enc_outs = torch.sum(enc_outs, dim=-1)
        return self.fc_out(enc_outs)

• 编码层（EncoderLayer）：

class EncoderLayer(nn.Module):
    '''编码层'''

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        '''

        :param d_model: 模型输入维度
        :param d_inner: 前馈神经网络隐层维度
        :param n_head:  多头注意力
        :param d_k:     键向量
        :param d_v:     值向量
        :param dropout:
        '''
        super(EncoderLayer, self).__init__()
        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)

    def forward(self, enc_input, non_pad_mask=None, slf_attn_mask=None):
        '''

        :param enc_input:
        :param non_pad_mask:
        :param slf_attn_mask:
        :return:
        '''
        enc_output, enc_slf_attn = self.slf_attn(enc_input, enc_input, enc_input, mask=slf_attn_mask)
        if non_pad_mask is not None:
            enc_output *= non_pad_mask

        enc_output = self.pos_ffn(enc_output)
        if non_pad_mask is not None:
            enc_output *= non_pad_mask
        return enc_output, enc_slf_attn

• 多头注意力（MultiHeadAttention）

class MultiHeadAttention(nn.Module):
    '''
        “多头”注意力模型
    '''

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        '''

        :param n_head: “头”数
        :param d_model: 输入维度
        :param d_k: 键向量维度
        :param d_v: 值向量维度
        :param dropout:
        '''
        super(MultiHeadAttention, self).__init__()

        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v
        # 产生 查询向量q，键向量k， 值向量v
        self.w_qs = nn.Linear(d_model, n_head * d_k)
        self.w_ks = nn.Linear(d_model, n_head * d_k)
        self.w_vs = nn.Linear(d_model, n_head * d_v)

        nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))

        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
        self.layer_normal = nn.LayerNorm(d_model)

        self.fc = nn.Linear(n_head * d_v, d_model)
        nn.init.xavier_normal_(self.fc.weight)

        self.dropout = nn.Dropout(dropout)

    def forward(self, q, k, v, mask=None):
        '''
        计算多头注意力
        :param q: 用于产生  查询向量
        :param k: 用于产生  键向量
        :param v:  用于产生 值向量
        :param mask:
        :return:
        '''
        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head

        sz_b, len_q, _ = q.size()
        sz_b, len_k, _ = k.size()
        sz_b, len_v, _ = v.size()

        residual = q

        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)

        # (n*b) x lq x dk
        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k)
        # (n*b) x lk x dk
        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k)
        # (n*b) x lv x dv
        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v)

        # mask = mask.repeat(n_head, 1, 1)  # (n*b) x .. x ..
        #
        output, attn = self.attention(q, k, v, mask=None)
        # (n_heads * batch_size) * lq * dv
        output = output.view(n_head, sz_b, len_q, d_v)
        # batch_size * len_q * (n_heads * dv)
        output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1)
        output = self.dropout(self.fc(output))
        output = self.layer_normal(output + residual)
        return output, attn

• 前馈神经网络（PositionwiseFeedForward）

class PositionwiseFeedForward(nn.Module):
    '''
        前馈神经网络
    '''

    def __init__(self, d_in, d_hid, dropout=0.1):
        '''

        :param d_in:    输入维度
        :param d_hid:   隐藏层维度
        :param dropout:
        '''
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Conv1d(d_in, d_hid, 1)
        self.w_2 = nn.Conv1d(d_hid, d_in, 1)
        self.layer_normal = nn.LayerNorm(d_in)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        residual = x
        output = x.transpose(1, 2)
        output = self.w_2(F.relu(self.w_1(output)))
        output = output.transpose(1, 2)
        output = self.dropout(output)
        output = self.layer_normal(output + residual)
        return output

• 位置函数

def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
    '''
    计算位置向量
    :param n_position:      位置的最大值
    :param d_hid:           位置向量的维度，和字向量维度相同（要相加求和）
    :param padding_idx: 
    :return: 
    '''

    def cal_angle(position, hid_idx):
        return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)

    def get_posi_angle_vec(position):
        return [cal_angle(position, hid_j) for hid_j in range(d_hid)]

    sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])

    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

    if padding_idx is not None:
        # zero vector for padding dimension
        sinusoid_table[padding_idx] = 0.

    return torch.FloatTensor(sinusoid_table)

实践经验

在分类任务中，与BILSTM+ATTENTION（链接）相比：

1. 模型比LSTM大很多，同样的任务LSTM模型6M左右，Transformer模型55M；
2. 收敛速度比较慢；
3. 超参比较多，不易调参，但同时也意味着弹性比较大；
4. 效果和BILSTM模型差不多；

相关文章

• 使用TextCNN模型进行文本分类（链接）；
• 使用BILSTM+ATTENTION模型进行文本分类（链接）