有参考代码吗？

是的，以下是一个使用PyTorch搭建的GPT-2模型的参考代码，供您参考：

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

class GPT2(nn.Module):
    def __init__(self, n_vocab, n_ctx, n_embd, n_head, n_layer):
        super(GPT2, self).__init__()

        self.n_vocab = n_vocab
        self.n_ctx = n_ctx
        self.n_embd = n_embd
        self.n_head = n_head
        self.n_layer = n_layer

        self.wte = nn.Embedding(n_vocab, n_embd)
        self.wpe = nn.Embedding(n_ctx, n_embd)
        self.drop = nn.Dropout(0.1)
        self.h = nn.ModuleList([Block(n_embd, n_head, n_ctx) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd, eps=1e-5)

        self.init_weights()

    def init_weights(self):
        nn.init.normal_(self.wte.weight, std=0.02)
        nn.init.normal_(self.wpe.weight, std=0.01)
        nn.init.normal_(self.ln_f.weight, std=0.02)
        nn.init.zeros_(self.ln_f.bias)

    def forward(self, input_ids, position_ids=None, token_type_ids=None):
        if position_ids is None:
            position_ids = torch.arange(input_ids.shape[1], dtype=torch.long, device=input_ids.device)
            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)

        input_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)
        token_type_embeds = self.wte(token_type_ids)
        hidden_states = input_embeds + position_embeds + token_type_embeds
        hidden_states = self.drop(hidden_states)

        for i in range(self.n_layer):
            block = self.h[i]
            hidden_states = block(hidden_states)

        hidden_states = self.ln_f(hidden_states)

        return hidden_states

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

        self.n_embd = n_embd
        self.n_head = n_head

        self.ln_1 = nn.LayerNorm(n_embd, eps=1e-5)
        self.attn = Attention(n_embd, n_head, n_ctx)
        self.ln_2 = nn.LayerNorm(n_embd, eps=1e-5)
        self.mlp = MLP(n_embd*4, n_embd)

    def forward(self, x):
        h = x

        x = self.ln_1(x)
        x = self.attn(x)
        x = h + x

        h = x

        x = self.ln_2(x)
        x = self.mlp(x)
        x = h + x

        return x

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

        self.n_embd = n_embd
        self.n_head = n_head
        self.split_size = n_embd // n_head
        self.scale = self.split_size ** -0.5

        self.c_attn = nn.Linear(n_embd, n_embd*3)
        self.c_proj = nn.Linear(n_embd, n_embd)

    def split_heads(self, x):
        x = x.view(x.shape[0], x.shape[1], self.n_head, self.split_size)
        return x.permute(0, 2, 1, 3)

    def forward(self, x):
        qkv = self.c_attn(x)
        q, k, v = torch.split(qkv, qkv.shape[-1] // 3, dim=-1)

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

        scores = torch.matmul(q, k.transpose(-1, -2)) * self.scale
        attn_weights = F.softmax(scores, dim=-1)
        attn_output = torch.matmul(attn_weights, v)
        attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
        attn_output = attn_output.view(attn_output.shape[0], attn_output.shape[1], -1)
        attn_output = self.c_proj(attn_output)

        return attn_output

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

        self.n_embd = n_embd
        self.n_hidden = n_hidden

        self.c_fc = nn.Linear(n_embd, n_hidden)
        self.c_proj = nn.Linear(n_hidden, n_embd)

    def forward(self, x):
        x = F.gelu(self.c_fc(x))
        x = self.c_proj(x)
        return x

此代码实现了一个基于GPT-2的语言模型，包括基本的Attention机制、LayerNorm、MLP等模块。您可以根据需要进行修改和扩展。