稀疏transformer完整代码

稀疏Transformer是一种优化版的Transformer模型，可以在处理大规模数据时提高计算效率。以下是一个基于PyTorch实现的稀疏Transformer的完整代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class SparseSelfAttention(nn.Module):
    def __init__(self, d_model, num_heads, dropout=0.1):
        super(SparseSelfAttention, self).__init__()

        self.d_model = d_model
        self.num_heads = num_heads
        self.dropout = nn.Dropout(dropout)

        assert d_model % num_heads == 0
        self.depth = d_model // num_heads

        self.qkv = nn.Linear(d_model, 3 * d_model)
        self.proj = nn.Linear(d_model, d_model)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        batch_size, seq_len, d_model = x.size()

        qkv = self.qkv(x).view(batch_size, seq_len, 3, self.num_heads, self.depth)
        qkv = qkv.permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        scores = torch.matmul(q, k.transpose(-2, -1))
        scores = scores / (self.depth ** 0.5)

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

        attn = self.softmax(scores)
        attn = self.dropout(attn)

        context = torch.matmul(attn, v)
        context = context.permute(1, 2, 0, 3).contiguous().view(batch_size, seq_len, -1)

        output = self.proj(context)

        return output

class SparseTransformer(nn.Module):
    def __init__(self, d_model, num_heads, num_layers, dropout=0.1):
        super(SparseTransformer, self).__init__()

        self.d_model = d_model
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.dropout = nn.Dropout(dropout)

        self.layers = nn.ModuleList([SparseSelfAttention(d_model, num_heads, dropout) for _ in range(num_layers)])

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

        return x

上述代码中，SparseSelfAttention类实现了稀疏自注意力机制，SparseTransformer类则实现了整个稀疏Transformer模型。在训练和测试时，可以通过调用SparseTransformer的forward方法来获取模型的输出。