【ChatGPT】基于tensorflow2实现transformer（GPT-4）

请记住，您是一位NLP领域的专家和优秀的算法工程师。使用带有 tensorflow2.0 subclass api 的 python 从头开始实现 transformer 模型。 全部内容如下：

1. 构建transformer模型架构和依赖层；
2. 生成并预处理一些假样本数据，用于训练上面构建的模型；
3. 上面生成的样本数据的训练模型示例教程；
4. 上面生成的样本数据的预测模型示例教程；
5. 上面生成的示例数据的部署模型示例教程；

最后，您所有的答案都以markdown格式呈现。

You:
Remember you’re an expert and excellent algorithm engineer in NLP area. Implement transformer model from scratch using python with tensorflow2.0 subclass api step by step. All content is as follows:

1. Build the transformer model architecture and dependency layers;
2. Generate and preprocess some fake sample data for training the model built above;
3. A training model example tutorial with sample data generated above;
4. A predicting model example tutorial with sample data generated above;
5. A deploying model example tutorial with sample data generated above;

Finally, All of your answers are presented in markdown format.

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ChatGPT:

I would be happy to help you build a Transformer model from scratch using Python with TensorFlow 2.0’s Subclass API. Here’s a detailed, step by step guide:

Step 1: Build the Transformer model architecture and dependency layers

First, we need to implement the dependency layers such as Multi-Head Attention, Position-wise Feed-Forward, and Positional Encoding. Let’s start by importing the necessary libraries:

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Layer
from tensorflow.keras.models import Model

Now, define the Multi-Head Attention layer:

class MultiHeadAttention(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, q, k, v, 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)
        scaledattention,  = 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

    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

Next, define the Position-wise Feed-Forward layer:

class PositionWiseFeedForward(Layer):
    def init(self, d_model, dff):
        super(PositionWiseFeedForward, self).init()
        self.fc1 = tf.keras.layers.Dense(dff, activation='relu')
        self.fc2 = tf.keras.layers.Dense(d_model)

    def call(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        return x

Then, implement the Positional Encoding:

def positional_encoding(position, d_model):
    pos = np.arange(position)[:, np.newaxis]
    div_term = np.exp(
        np.arange(0, d_model, 2) * -(np.log(10000.0) / d_model))
    pos_enc = np.zeros((1, position, d_model))
    pos_enc[0, :, 0::2] = np.sin(pos * div_term)
    pos_enc[0, :, 1::2] = np.cos(pos * div_term)
    return tf.cast(pos_enc, dtype=tf.float32)

Finally, build the Transformer model architecture by connecting these layers:

class Transformer(Model):
    def init(self, num_layers, d_model, num_heads, dff, input_vocab_size, pe_input, rate=0.1):
        super(Transformer, self).init()
        self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(pe_input, d_model)
        self.dropout = tf.keras.layers.Dropout(rate)

        self.encoder_layers = [
            self.create_encoder_layer(d_model, numheads, dff, rate) for  in range(num_layers)
        ]
        self.fc = tf.keras.layers.Dense(input_vocab_size)

    def create_encoder_layer(self, d_model, num_heads, dff, rate):
        inputs = tf.keras.Input(shape=(None, d_model))
        attnoutput,  = MultiHeadAttention(d_model, num_heads)(inputs, inputs, inputs, None)
        attn_output = tf.keras.layers.Dropout(rate)(attn_output)
        out1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)(inputs + attn_output)
        ffn_output = PositionWiseFeedForward(d_model, dff)(out1)
        ffn_output = tf.keras.layers.Dropout(rate)(ffn_output)
        out2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)(out1 + ffn_output)
        return Model(inputs, out2)

    def call(self, x, training):
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :tf.shape(x)[1], :]
        x = self.dropout(x, training=training)

        for layer in self.encoder_layers:
            x = layer(x)

        x = self.fc(x)
        return x

Step 2: Generate and preprocess some fake sample data for training the model built above

To generate some fake data for training, let’s assume we have sequences of integers as input and output data. We will use the following function to create fake data:

def generate_fake_data(num_samples, sequence_length, vocab_size):
    inputs = np.random.randint(1, vocab_size, size=(num_samples, sequence_length))
    outputs = np.random.randint(1, vocab_size, size=(num_samples, sequence_length))
    return inputs, outputs

Preprocess the data by applying some padding to both input and target sequences to have the same length:

def preprocess_data(inputs, outputs, maxlen):
    inputs = tf.keras.preprocessing.sequence.pad_sequences(inputs, maxlen=maxlen, padding='post')
    outputs = tf.keras.preprocessing.sequence.pad_sequences(outputs, maxlen=maxlen, padding='post')
    return inputs, outputs

Step 3: A training model example tutorial with sample data generated above

We first generate and preprocess the fake data:

# Parameters for the data
num_samples = 10000
sequence_length = 20
vocab_size = 100
maxlen = 25

inputs, outputs = generate_fake_data(num_samples, sequence_length, vocab_size)
inputs, outputs = preprocess_data(inputs, outputs, maxlen)

# Split the data into training and validation
train_frac = 0.8
train_size = int(num_samples * train_frac)
train_inputs, val_inputs = inputs[:train_size], inputs[train_size:]
train_outputs, val_outputs = outputs[:train_size], outputs[train_size:]

Then, build the Transformer model and compile it:

# Parameters for the Transformer model
num_layers = 2
d_model = 128
num_heads = 8
dff = 512
pe_input = maxlen

model = Transformer(num_layers, d_model, num_heads, dff, vocab_size, pe_input)

# Set learning rate, loss function, and metric for the model
learning_rate = 0.001
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
optimizer = tf.keras.optimizers.Adam(learning_rate)

def loss_function(y_true, y_pred):
    mask = tf.math.logical_not(tf.math.equal(y_true, 0))
    loss = loss_object(y_true, y_pred)
    mask = tf.cast(mask, dtype=loss.dtype)
    loss *= mask
    return tf.reduce_sum(loss) / tf.reduce_sum(mask)

model.compile(optimizer=optimizer, loss=loss_function, metrics=['accuracy'])

Finally, train the model:

# Parameters for training
epochs = 10
batch_size = 32

history = model.fit(train_inputs, train_outputs, epochs=epochs, batch_size=batch_size,
                    validation_data=(val_inputs, val_outputs))

Step 4: A predicting model example tutorial with sample data generated above

To use the trained model for prediction:

input_sequence = np.random.randint(1, vocab_size, size=(1, sequence_length))
input_sequence = tf.keras.preprocessing.sequence.pad_sequences(input_sequence, maxlen=maxlen, padding='post')

predictions = model.predict(input_sequence)
output_sequence = np.argmax(predictions, axis=-1)

Step 5: A deploying model example tutorial with sample data generated above

To deploy the trained model, save it and load it for prediction:

model.save('transformer_model.h5')
loaded_model = tf.keras.models.load_model('transformer_model.h5', custom_objects={'loss_function': loss_function})

input_sequence = np.random.randint(1, vocab_size, size=(1, sequence_length))
input_sequence = tf.keras.preprocessing.sequence.pad_sequences(input_sequence, maxlen=maxlen, padding='post')

predictions = loaded_model.predict(input_sequence)
output_sequence = np.argmax(predictions, axis=-1)

That’s it! You’ve got a fully functional Transformer model built from scratch using Python with TensorFlow 2.0’s Subclass API.