深度强化学习代码gpt

以下是使用PyTorch实现GPT-2模型的示例代码：

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

class GPT2(nn.Module):
    def __init__(self, vocab_size, n_embd, n_layer, n_head, n_positions):
        super(GPT2, self).__init__()
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_positions = n_positions
        self.vocab_size = vocab_size

        self.wte = nn.Embedding(vocab_size, n_embd)
        self.wpe = nn.Embedding(n_positions, n_embd)
        self.drop = nn.Dropout(0.1)
        self.h = nn.ModuleList([Block(n_embd, n_head, n_positions) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)

    def forward(self, x, positions):
        h = self.wte(x)
        position_embed = self.wpe(positions)
        h = h + position_embed
        h = self.drop(h)

        for block in self.h:
            h = block(h)

        h = self.ln_f(h)
        logits = torch.matmul(h, self.wte.weight.t())

        return logits

class Block(nn.Module):
    def __init__(self, n_embd, n_head, n_positions):
        super(Block, self).__init__()

        self.ln1 = nn.LayerNorm(n_embd)
        self.attn = Attention(n_embd, n_head, n_positions)
        self.ln2 = nn.LayerNorm(n_embd)
        self.mlp = MLP(n_embd)

    def forward(self, x):
        h = self.ln1(x)
        h = self.attn(h)
        x = x + h
        h = self.ln2(x)
        h = self.mlp(h)
        x = x + h

        return x

class Attention(nn.Module):
    def __init__(self, n_embd, n_head, n_positions):
        super(Attention, self).__init__()

        self.c_attn = nn.Conv1d(n_embd, n_embd * 3, 1, bias=False)
        self.c_proj = nn.Conv1d(n_embd, n_embd, 1, bias=False)
        self.n_head = n_head
        self.split_size = n_embd
        self.scale = 1 / (n_embd // n_head) ** 0.5
        self.n_positions = n_positions

    def forward(self, x):
        query, key, value = self.c_attn(x).chunk(3, dim=1)
        query = self.split_heads(query)
        key = self.split_heads(key)
        value = self.split_heads(value)
        a = torch.matmul(query, key.transpose(-1, -2)) * self.scale
        a = F.softmax(a, dim=-1)
        a = self.dropout(a)
        o = torch.matmul(a, value)
        o = self.merge_heads(o)
        o = self.c_proj(o)
        o = self.dropout(o)
        x = x + o
        return x

    def split_heads(self, x):
        batch_size, length, hidden_size = x.size()
        x = x.view(batch_size, length, self.n_head, hidden_size // self.n_head)
        x = x.transpose(1, 2)
        return x

    def merge_heads(self, x):
        batch_size, _, length, hidden_size = x.size()
        x = x.transpose(1, 2).contiguous()
        x = x.view(batch_size, length, hidden_size * self.n_head)
        return x

    def dropout(self, x):
        return F.dropout(x, p=0.1, training=self.training)

class MLP(nn.Module):
    def __init__(self, n_embd):
        super(MLP, self).__init__()

        self.c_fc = nn.Conv1d(n_embd, n_embd * 4, 1)
        self.c_proj = nn.Conv1d(n_embd * 4, n_embd, 1)
        self.act = F.gelu

    def forward(self, x):
        h = self.act(self.c_fc(x))
        h = self.c_proj(h)
        return h

需要注意的是，以上代码是GPT-2模型的简化版本，实际应用中可能需要进行一些修改和优化。此外，使用深度强化学习训练GPT-2模型需要大量数据和计算资源，需要有充足的准备。