基于TensorFlow编写transformer代码示例

好的，以下是基于TensorFlow编写transformer的代码示例：

import tensorflow as tf

class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model

        assert d_model % self.num_heads == 0

        self.depth = d_model // self.num_heads

        self.wq = tf.keras.layers.Dense(d_model)
        self.wk = tf.keras.layers.Dense(d_model)
        self.wv = tf.keras.layers.Dense(d_model)

        self.dense = tf.keras.layers.Dense(d_model)

    def split_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, v, k, q, mask):
        batch_size = tf.shape(q)[0]

        q = self.wq(q)
        k = self.wk(k)
        v = self.wv(v)

        q = self.split_heads(q, batch_size)
        k = self.split_heads(k, batch_size)
        v = self.split_heads(v, batch_size)

        scaled_attention, attention_weights = self.scaled_dot_product_attention(q, k, v, mask)

        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))

        output = self.dense(concat_attention)

        return output, attention_weights

    def scaled_dot_product_attention(self, q, k, v, mask):
        matmul_qk = tf.matmul(q, k, transpose_b=True)

        dk = tf.cast(tf.shape(k)[-1], tf.float32)
        scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)

        if mask is not None:
            scaled_attention_logits += (mask * -1e9)

        attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)

        output = tf.matmul(attention_weights, v)

        return output, attention_weights

class LRFutureMask(tf.keras.layers.Layer):
    def __init__(self):
        super(LRFutureMask, self).__init__()

    def call(self, inputs):
        seq_len = tf.shape(inputs)[1]
        mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
        return mask

class PositionalEncoding(tf.keras.layers.Layer):
    def __init__(self, position, d_model):
        super(PositionalEncoding, self).__init__()
        self.pos_encoding = self.positional_encoding(position, d_model)

    def get_angles(self, position, i, d_model):
        angles = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))
        return position * angles

    def positional_encoding(self, position, d_model):
        angle_rads = self.get_angles(tf.range(position)[:, tf.newaxis],
                                tf.range(d_model)[tf.newaxis, :],
                                d_model)

        sines = tf.math.sin(angle_rads[:, 0::2])
        cosines = tf.math.cos(angle_rads[:, 1::2])

        pos_encoding = tf.concat([sines, cosines], axis=-1)
        pos_encoding = pos_encoding[tf.newaxis, ...]
        return tf.cast(pos_encoding, tf.float32)

    def call(self, x):
        return x + self.pos_encoding[:, :tf.shape(x)[1], :]

class EncoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(EncoderLayer, self).__init__()

        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = tf.keras.Sequential([
                                        tf.keras.layers.Dense(dff, activation='relu'),
                                        tf.keras.layers.Dense(d_model)
                                        ])
        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)

        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask):

        attn_output, _ = self.mha(x, x, x, mask)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(x + attn_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layernorm2(out1 + ffn_output)

        return out2

class Encoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
               maximum_position_encoding, rate=0.1):
        super(Encoder, self).__init__()

        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = PositionalEncoding(maximum_position_encoding, self.d_model)

        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) 
                           for _ in range(num_layers)]

        self.dropout = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask):
        seq_len = tf.shape(x)[1]

        x = self.embedding(x) 
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x = self.pos_encoding(x) 
        x = self.dropout(x, training=training)

        for i in range(self.num_layers):
            x = self.enc_layers[i](x, training, mask)

        return x

class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
               maximum_position_encoding, rate=0.1):
        super(Transformer, self).__init__()

        self.encoder = Encoder(num_layers, d_model, num_heads, dff, 
                               input_vocab_size, maximum_position_encoding, rate)

        self.final_layer = tf.keras.layers.Dense(input_vocab_size)

    def call(self, inp, training, enc_padding_mask):

        enc_output = self.encoder(inp, training, enc_padding_mask) 

        final_output = self.final_layer(enc_output) 

        return final_output