基于pytorch的transformer架构代码

以下是一个基于PyTorch实现的Transformer架构代码示例：

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


class MultiHeadAttention(nn.Module):
    def __init__(self, n_heads, d_model):
        super().__init__()

        self.n_heads = n_heads
        self.d_k = d_model // n_heads

        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.out_linear = nn.Linear(d_model, d_model)

    def forward(self, q, k, v, mask=None):
        bs = q.size(0)

        # perform linear operation and split into N heads
        k = self.k_linear(k).view(bs, -1, self.n_heads, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.n_heads, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.n_heads, self.d_k)

        # transpose to get dimensions bs * N * sl * d_model
        k = k.transpose(1, 2)
        q = q.transpose(1, 2)
        v = v.transpose(1, 2)

        # calculate attention using function we will define next
        scores = self.attention(q, k, v, self.d_k, mask, self.n_heads)

        # concatenate heads and put through final linear layer
        concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_k)

        output = self.out_linear(concat)

        return output

    def attention(self, q, k, v, d_k, mask=None, n_heads=None):
        scores = torch.matmul(q, k.transpose(-2, -1)) / torch.sqrt(torch.tensor(d_k).float())

        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = scores.masked_fill(mask == 0, -1e9)

        scores = F.softmax(scores, dim=-1)

        if n_heads is not None:
            scores = scores[:, n_heads, :, :]

        output = torch.matmul(scores, v)

        return output


class PositionwiseFeedforward(nn.Module):
    def __init__(self, d_model, d_ff):
        super().__init__()

        self.linear1 = nn.Linear(d_model, d_ff)
        self.linear2 = nn.Linear(d_ff, d_model)

    def forward(self, x):
        x = self.linear1(x)
        x = F.relu(x)
        x = self.linear2(x)

        return x


class EncoderBlock(nn.Module):
    def __init__(self, d_model, n_heads, d_ff):
        super().__init__()

        self.multihead_attention = MultiHeadAttention(n_heads, d_model)
        self.layer_norm1 = nn.LayerNorm(d_model)

        self.positionwise_feedforward = PositionwiseFeedforward(d_model, d_ff)
        self.layer_norm2 = nn.LayerNorm(d_model)

    def forward(self, x, mask):
        attn_output = self.multihead_attention(x, x, x, mask)
        x = self.layer_norm1(x + attn_output)

        ff_output = self.positionwise_feedforward(x)
        x = self.layer_norm2(x + ff_output)

        return x


class Transformer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, n_layers, n_classes):
        super().__init__()

        self.d_model = d_model
        self.n_heads = n_heads
        self.d_ff = d_ff
        self.n_layers = n_layers

        self.embedding = nn.Embedding(n_classes, d_model)
        self.pos_embedding = nn.Embedding(1000, d_model)  # positional embedding

        self.encoder_blocks = nn.ModuleList([EncoderBlock(d_model, n_heads, d_ff) for _ in range(n_layers)])

        self.out_linear = nn.Linear(d_model, n_classes)

    def forward(self, x, mask=None):
        x = self.embedding(x)
        pos = torch.arange(0, x.size(1)).unsqueeze(0).repeat(x.size(0), 1).to(x.device)
        pos = self.pos_embedding(pos)
        x = x + pos

        if mask is not None:
            mask = mask.unsqueeze(1).unsqueeze(2)

        for encoder in self.encoder_blocks:
            x = encoder(x, mask)

        x = self.out_linear(x[:, 0, :])

        return x

这个代码实现了Transformer的核心部分，包括多头注意力机制、前馈神经网络和编码器块。你可以使用这个代码来实现自己的Transformer模型。