PaddleDetection代码解析之Transformer encoder源码实现分析（下）

2021SC@SDUSC

接我的上一篇博客：PaddleDetection代码解析之Transformer encoder源码实现分析（上）。

依旧是对代码的解释在下面代码的注释中：

MultiHeadAttentionLayer

几个维度：

self._emb_size = config['hidden_size']   # 768

d_key=self._emb_size // self._n_head,

d_value=self._emb_size // self._n_head,

d_model=self._emb_size,

d_inner_hid=self._emb_size * 4

得到q、k、v的矩阵：

  
        q = self._q_fc(queries)
        k = self._k_fc(keys)
        v = self._v_fc(values)

class MultiHeadAttentionLayer(Layer):
    """
    MultiHeadAttentionLayer
    """

    def __init__(self,
                 d_key,
                 d_value,
                 d_model,
                 n_head=1,
                 dropout_rate=0.,
                 cache=None,
                 gather_idx=None,
                 static_kv=False,
                 param_initializer=None,
                 name=""):
        super(MultiHeadAttentionLayer, self).__init__()
        self._n_head = n_head
        self._d_key = d_key
        self._d_value = d_value
        self._d_model = d_model
        self._dropout_rate = dropout_rate

        self._q_fc = Linear(
            input_dim=d_model,
            output_dim=d_key * n_head,
            param_attr=fluid.ParamAttr(
                name=name + '_query_fc.w_0', initializer=param_initializer),
            bias_attr=name + '_query_fc.b_0')

        self._k_fc = Linear(
            input_dim=d_model,
            output_dim=d_key * n_head,
            param_attr=fluid.ParamAttr(
                name=name + '_key_fc.w_0', initializer=param_initializer),
            bias_attr=name + '_key_fc.b_0')

        self._v_fc = Linear(
            input_dim=d_model,
            output_dim=d_value * n_head,
            param_attr=fluid.ParamAttr(
                name=name + '_value_fc.w_0', initializer=param_initializer),
            bias_attr=name + '_value_fc.b_0')

        self._proj_fc = Linear(
            input_dim=d_value * n_head,
            output_dim=d_model,
            param_attr=fluid.ParamAttr(
                name=name + '_output_fc.w_0', initializer=param_initializer),
            bias_attr=name + '_output_fc.b_0')

    def forward(self, queries, keys, values, attn_bias):
        """
        forward
        :param queries:
        :param keys:
        :param values:
        :param attn_bias:
        :return:
        """
        # compute q ,k ,v
        keys = queries if keys is None else keys
        values = keys if values is None else values
        # 得到q k v 矩阵
        q = self._q_fc(queries)
        k = self._k_fc(keys)
        v = self._v_fc(values)

        # split head

        q_hidden_size = q.shape[-1]     
        eshaped_q = fluid.layers.reshape(
            x=q,
            shape=[0, 0, self._n_head, q_hidden_size // self._n_head],
            inplace=False)
        transpose_q = fluid.layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])

        k_hidden_size = k.shape[-1]
        reshaped_k = fluid.layers.reshape(
            x=k,
            shape=[0, 0, self._n_head, k_hidden_size // self._n_head],
            inplace=False)
        transpose_k = fluid.layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3])

        v_hidden_size = v.shape[-1]
        reshaped_v = fluid.layers.reshape(
            x=v,
            shape=[0, 0, self._n_head, v_hidden_size // self._n_head],
            inplace=False)
        transpose_v = fluid.layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3])

        scaled_q = fluid.layers.scale(x=transpose_q, scale=self._d_key**-0.5)
        # scale dot product attention
        product = fluid.layers.matmul(
            #x=transpose_q,
            x=scaled_q,
            y=transpose_k,
            transpose_y=True)
        #alpha=self._d_model**-0.5)
        if attn_bias:
            product += attn_bias
        weights = fluid.layers.softmax(product)
        if self._dropout_rate:
            weights_droped = fluid.layers.dropout(
                weights,
                dropout_prob=self._dropout_rate,
                dropout_implementation="upscale_in_train",
                is_test=False)
            out = fluid.layers.matmul(weights_droped, transpose_v)
        else:       
                out = fluid.layers.matmul(weights, transpose_v)

        # combine heads
        if len(out.shape) != 4:
            raise ValueError("Input(x) should be a 4-D Tensor.")
        trans_x = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
        final_out = fluid.layers.reshape(
            x=trans_x,
            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
            inplace=False)

        # fc to output
        proj_out = self._proj_fc(final_out)
        return proj_out

EncoderSubLayer

预处理：

 
        self._preprocess_layer = PrePostProcessLayer(
            self._preprocess_cmd,
            d_model,
            prepostprocess_dropout,
            name=name + "_pre_att")

多头注意力：

        
        self._multihead_attention_layer = MultiHeadAttentionLayer(
            d_key,
            d_value,
            d_model,
            n_head,
            attention_dropout,
            None,
            None,
            False,
            param_initializer,
            name=name + "_multi_head_att")

class EncoderSubLayer(Layer):
    """
    EncoderSubLayer
    """

    def __init__(self,
                 hidden_act,
                 n_head,
                 d_key,
                 d_value,
                 d_model,
                 d_inner_hid,
                 prepostprocess_dropout,
                 attention_dropout,
                 relu_dropout,
                 preprocess_cmd="n",
                 postprocess_cmd="da",
                 param_initializer=None,
                 name=""):

        super(EncoderSubLayer, self).__init__()
        self.name = name
        self._preprocess_cmd = preprocess_cmd
        self._postprocess_cmd = postprocess_cmd
        self._prepostprocess_dropout = prepostprocess_dropout
        # 预处理
        self._preprocess_layer = PrePostProcessLayer(
            self._preprocess_cmd,
            d_model,
            prepostprocess_dropout,
            name=name + "_pre_att")
        # 多头注意力
        self._multihead_attention_layer = MultiHeadAttentionLayer(
            d_key,
            d_value,
            d_model,
            n_head,
            attention_dropout,
            None,
            None,
            False,
            param_initializer,
            name=name + "_multi_head_att")

        self._postprocess_layer = PrePostProcessLayer(
            self._postprocess_cmd,
            d_model,
            self._prepostprocess_dropout,
            name=name + "_post_att")
        self._preprocess_layer2 = PrePostProcessLayer(
            self._preprocess_cmd,
            d_model,
            self._prepostprocess_dropout,
            name=name + "_pre_ffn")

        self._positionwise_feed_forward = PositionwiseFeedForwardLayer(
            hidden_act,
            d_inner_hid,
            d_model,
            relu_dropout,
            param_initializer,
            name=name + "_ffn")

        self._postprocess_layer2 = PrePostProcessLayer(
            self._postprocess_cmd,
            d_model,
            self._prepostprocess_dropout,
            name=name + "_post_ffn")

    def forward(self, enc_input, attn_bias):
        """
        forward
        :param enc_input: encoder 输入
        :param attn_bias: attention 偏置
        :return: 一层encoder encode输入之后的结果
        """
        # 在进行多头attention前，先进行预处理
        pre_process_multihead = self._preprocess_layer(enc_input)
        # 预处理之后的结果给到多头attention层
        attn_output = self._multihead_attention_layer(pre_process_multihead,
                                                      None, None, attn_bias)
        # 经过attention之后进行后处理
        attn_output = self._postprocess_layer(attn_output, enc_input)
        # 在给到FFN层前进行预处理
        pre_process2_output = self._preprocess_layer2(attn_output)
        # 得到FFN层的结果
        ffd_output = self._positionwise_feed_forward(pre_process2_output)
        # 返回后处理后的结果
        return self._postprocess_layer2(ffd_output, attn_output)

EncoderLayer

# 使用add_sublayer方法添加子层：

                self.add_sublayer(
                    'esl_%d' % i,
                    EncoderSubLayer(
                        hidden_act,
                        n_head,
                        d_key,
                        d_value,
                        d_model,
                        d_inner_hid,
                        prepostprocess_dropout,
                        attention_dropout,
                        relu_dropout,
                        preprocess_cmd,
                        postprocess_cmd,
                        param_initializer,
                        name=name + '_layer_' + str(i))))

class EncoderLayer(Layer):
    """
    encoder
    """

    def __init__(self,
                 hidden_act,
                 n_layer, # encoder子层数量 / encoder深度
                 n_head, # 注意力机制中head数量
                 d_key,
                 d_value,
                 d_model,
                 d_inner_hid,
                 prepostprocess_dropout, # 处理层的dropout概率
                 attention_dropout, # attention层的dropout概率
                 relu_dropout, # 激活函数层的dropout概率
                 preprocess_cmd="n", # 前处理，正则化
                 postprocess_cmd="da", # 后处理，dropout + 残差连接
                 param_initializer=None,
                 name=""):

        super(EncoderLayer, self).__init__()
        self._preprocess_cmd = preprocess_cmd
        self._encoder_sublayers = list()
        self._prepostprocess_dropout = prepostprocess_dropout
        self._n_layer = n_layer
        self._hidden_act = hidden_act
        # 后处理层，这里是层正则化
        self._preprocess_layer = PrePostProcessLayer(
            self._preprocess_cmd, 3, self._prepostprocess_dropout,
            "post_encoder")
        # 根据n_layer的设置（bert_base中是12）迭代定义几个encoder子层
        for i in range(n_layer):
            self._encoder_sublayers.append(
                # 使用add_sublayer方法添加子层
                self.add_sublayer(
                    'esl_%d' % i,
                    EncoderSubLayer(
                        hidden_act,
                        n_head,
                        d_key,
                        d_value,
                        d_model,
                        d_inner_hid,
                        prepostprocess_dropout,
                        attention_dropout,
                        relu_dropout,
                        preprocess_cmd,
                        postprocess_cmd,
                        param_initializer,
                        name=name + '_layer_' + str(i))))

    def forward(self, enc_input, attn_bias):
        """
        forward
        :param enc_input: 模型输入
        :param attn_bias: bias项可根据具体情况选择是否保留
        :return: encode之后的结果
        """
        # 迭代多个encoder子层，例如 bert base 的encoder子层数为12(self._n_layer)
        for i in range(self._n_layer):
            # 得到子层的输出，参数为 enc_input, attn_bias
            enc_output = self._encoder_sublayers[i](enc_input, attn_bias)
            # 该子层的输出作为下一子层的输入
            enc_input = enc_output
        # 返回处理过的层
        return self._preprocess_layer(enc_output)