深度强化学习代码gpt

以下是一个基于PyTorch实现的GPT-2模型的代码示例，包括Transformer Encoder和Transformer Decoder部分。

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import torch.nn.functional as F

然后，定义一个函数用于初始化权重：

def init_weights(module):
    if isinstance(module, nn.Linear):
        module.weight.data.normal_(mean=0.0, std=0.02)
        if module.bias is not None:
            module.bias.data.zero_()
    elif isinstance(module, nn.LayerNorm):
        module.bias.data.zero_()
        module.weight.data.fill_(1.0)

接下来，定义Transformer Encoder部分：

class TransformerEncoder(nn.Module):
    def __init__(self, num_layers, hidden_size, num_heads, dropout_rate):
        super(TransformerEncoder, self).__init__()
        self.layers = nn.ModuleList()
        self.num_layers = num_layers
        self.norm = nn.LayerNorm(hidden_size)

        for _ in range(num_layers):
            self.layers.append(TransformerEncoderLayer(hidden_size, num_heads, dropout_rate))

    def forward(self, x, mask=None):
        for layer in self.layers:
            x = layer(x, mask)
        x = self.norm(x)
        return x

Transformer Encoder由多个Transformer Encoder Layer组成，每个Encoder Layer包含self-attention、feedforward和残差连接等操作。下面是Encoder Layer的代码实现：

class TransformerEncoderLayer(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout_rate):
        super(TransformerEncoderLayer, self).__init__()
        self.self_attention = MultiHeadAttention(hidden_size, num_heads, dropout_rate)
        self.feed_forward = FeedForward(hidden_size, dropout_rate)
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(p=dropout_rate)

    def forward(self, x, mask=None):
        residual = x
        x = self.norm1(x)
        x = self.self_attention(x, x, x, mask)
        x = residual + self.dropout(x)
        residual = x
        x = self.norm2(x)
        x = self.feed_forward(x)
        x = residual + self.dropout(x)
        return x

接下来，定义Transformer Decoder部分：

class TransformerDecoder(nn.Module):
    def __init__(self, num_layers, hidden_size, num_heads, dropout_rate):
        super(TransformerDecoder, self).__init__()
        self.layers = nn.ModuleList()
        self.num_layers = num_layers
        self.norm = nn.LayerNorm(hidden_size)

        for _ in range(num_layers):
            self.layers.append(TransformerDecoderLayer(hidden_size, num_heads, dropout_rate))

    def forward(self, x, memory, src_mask=None, tgt_mask=None):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        x = self.norm(x)
        return x

与Transformer Encoder类似，Transformer Decoder也由多个Transformer Decoder Layer组成。下面是Decoder Layer的代码实现：

class TransformerDecoderLayer(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout_rate):
        super(TransformerDecoderLayer, self).__init__()
        self.self_attention = MultiHeadAttention(hidden_size, num_heads, dropout_rate)
        self.src_attention = MultiHeadAttention(hidden_size, num_heads, dropout_rate)
        self.feed_forward = FeedForward(hidden_size, dropout_rate)
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
        self.norm3 = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(p=dropout_rate)

    def forward(self, x, memory, src_mask=None, tgt_mask=None):
        residual = x
        x = self.norm1(x)
        x = self.self_attention(x, x, x, tgt_mask)
        x = residual + self.dropout(x)
        residual = x
        x = self.norm2(x)
        x = self.src_attention(x, memory, memory, src_mask)
        x = residual + self.dropout(x)
        residual = x
        x = self.norm3(x)
        x = self.feed_forward(x)
        x = residual + self.dropout(x)
        return x

最后，我们需要定义Multi-Head Attention和FeedForward层：

class MultiHeadAttention(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout_rate):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.head_size = hidden_size // num_heads
        self.query = nn.Linear(hidden_size, hidden_size)
        self.key = nn.Linear(hidden_size, hidden_size)
        self.value = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(p=dropout_rate)
        self.output = nn.Linear(hidden_size, hidden_size)

    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)

        query = self.query(query).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
        key = self.key(key).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
        value = self.value(value).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)

        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = query.matmul(key.transpose(-2, -1)) / self.head_size**0.5
            scores.masked_fill_(mask == 0, -1e9)
            attn_weights = F.softmax(scores, dim=-1)
            attn_weights = self.dropout(attn_weights)
            attn_output = attn_weights.matmul(value)
        else:
            scores = query.matmul(key.transpose(-2, -1)) / self.head_size**0.5
            attn_weights = F.softmax(scores, dim=-1)
            attn_weights = self.dropout(attn_weights)
            attn_output = attn_weights.matmul(value)

        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.head_size)
        output = self.output(attn_output)
        return output


class FeedForward(nn.Module):
    def __init__(self, hidden_size, dropout_rate):
        super(FeedForward, self).__init__()
        self.fc1 = nn.Linear(hidden_size, hidden_size * 4)
        self.fc2 = nn.Linear(hidden_size * 4, hidden_size)
        self.dropout = nn.Dropout(p=dropout_rate)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

以上就是一个简单的GPT-2模型的代码实现，可以根据需要进行修改和优化。