B_, N, C = x.shape qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) q = q * self.scale attn = (q @ k.transpose(-2, -1))

这是一个 Transformer 模型中的 self-attention 的实现代码。其中 x 是输入的数据，qkv 是对输入数据进行线性变换后得到的三个向量 q、k、v，表示查询、键、值。num_heads 表示头的数目，C 表示每个头的维度。在这段代码中，将 qkv reshape 后，将 q、k 进行转置，然后计算注意力得分，得到注意力矩阵 attn。注意力矩阵可以用来加权求和值向量，即 v，得到最终的 self-attention 输出结果。这个过程可以并行计算，因为注意力矩阵的每一行都只与其他行独立计算。

相关问题

用tensorflow的layers.Layer模块改写class SelfAttention(nn.Module): def init(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).init() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias def __getPosCode(self,fm_sz,out_c): x = [] for i in range(fm_sz): x.append([np.sin,np.cos][i % 2](1 / (10000 ** (i // 2 / fm_sz)))) x = torch.from_numpy(np.array([x])).float() return torch.cat([(x + x.t()).unsqueeze(0) for i in range(out_c)]) def forward(self,x): q,k,v = self.w_q(x),self.w_k(x),self.w_v(x) pos_code = torch.cat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])]).to(x.device) if self.pos_bias: att_map = torch.matmul(q,k.permute(0,1,3,2)) + pos_code else: att_map = torch.matmul(q,k.permute(0,1,3,2)) + torch.matmul(q,pos_code.permute(0,1,3,2)) am_shape = att_map.shape att_map = self.softmax(att_map.view(am_shape[0],am_shape[1],am_shape[2] * am_shape[3])).view(am_shape) return att_map * v

以下是使用tensorflow的layers.Layer模块改写的代码：

import tensorflow as tf

class SelfAttention(tf.keras.layers.Layer):
    def __init__(self, in_c, out_c, fm_sz, pos_bias=False):
        super(SelfAttention, self).__init__()
        self.w_q = tf.keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=[None, None, in_c])
        self.w_k = tf.keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=[None, None, in_c])
        self.w_v = tf.keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=[None, None, in_c])
        self.pos_code = self.__getPosCode(fm_sz, out_c)
        self.softmax = tf.keras.layers.Softmax(axis=2)
        self.pos_bias = pos_bias
        
    def __getPosCode(self, fm_sz, out_c):
        x = []
        for i in range(fm_sz):
            x.append([np.sin, np.cos][i % 2](1 / (10000 ** (i // 2 / fm_sz))))
        x = np.array([x], dtype=np.float32)
        return tf.concat([(x + tf.transpose(x)).unsqueeze(0) for i in range(out_c)], axis=0)
    
    def call(self, x):
        q, k, v = self.w_q(x), self.w_k(x), self.w_v(x)
        pos_code = tf.concat([self.pos_code[None, ...] for i in range(tf.shape(x)[0])], axis=0)
        if self.pos_bias:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + pos_code
        else:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + tf.matmul(q, tf.transpose(pos_code, perm=[0, 1, 3, 2]))
        am_shape = tf.shape(att_map)
        att_map = self.softmax(tf.reshape(att_map, [am_shape[0], am_shape[1], am_shape[2] * am_shape[3]]))
        att_map = tf.reshape(att_map, am_shape)
        return att_map * v

需要注意的是，tensorflow中没有nn.Module这个概念，而是使用tf.keras.layers.Layer作为基类。因此，我们需要在类定义中加入`tf.keras.layers.Layer`，并对一些函数名称进行修改。此外，tensorflow默认使用NHWC格式的数据，因此在调用transpose函数时需要加上perm参数进行转置，以保证维度的正确性。

class SelfAttention(nn.Module): def __init__(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).__init__() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias 改写为twensorflow形式

可以将这段PyTorch代码改写为如下的TensorFlow代码：

import tensorflow as tf
from tensorflow import keras

class SelfAttention(keras.layers.Layer):
    def __init__(self, in_c, out_c, fm_sz, pos_bias=False):
        super(SelfAttention, self).__init__()
        self.w_q = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.w_k = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.w_v = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.pos_code = self.__getPosCode(fm_sz, out_c)
        self.softmax = keras.layers.Softmax(axis=2)
        self.pos_bias = pos_bias
        
    def __getPosCode(self, fm_sz, out_c):
        x = []
        for i in range(fm_sz):
            x.append([np.sin, np.cos][i % 2](1 / (10000 ** (i // 2 / fm_sz))))
        x = tf.convert_to_tensor([x], dtype=tf.float32)
        return tf.concat([(x + tf.transpose(x)).unsqueeze(0) for i in range(out_c)], axis=0)
    
    def call(self, x):
        q, k, v = self.w_q(x), self.w_k(x), self.w_v(x)
        pos_code = tf.concat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])], axis=0)
        if self.pos_bias:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + pos_code
        else:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + tf.matmul(q, tf.transpose(pos_code, perm=[0, 1, 3, 2]))
        am_shape = att_map.shape
        att_map = self.softmax(tf.reshape(att_map, [am_shape[0], am_shape[1], am_shape[2] * am_shape[3]]))
        att_map = tf.reshape(att_map, am_shape)
        return att_map * v

需要注意的是，这里的代码只是一种可能的TensorFlow实现方式，具体还需要根据实际情况进行微调。