请用pytorch写出SparseAttention的代码

### 回答1：
好的，以下是一个简单的SparseAttention的代码实现示例：

import torch
import torch.nn as nn

class SparseAttention(nn.Module):
    def __init__(self, input_size, output_size, num_heads, dropout=0.1):
        super(SparseAttention, self).__init__()
        self.num_heads = num_heads
        self.head_size = output_size // num_heads
        
        self.query = nn.Linear(input_size, output_size)
        self.key = nn.Linear(input_size, output_size)
        self.value = nn.Linear(input_size, output_size)
        
        self.dropout = nn.Dropout(dropout)
        self.output = nn.Linear(output_size, output_size)
        
    def forward(self, inputs):
        batch_size, seq_len, input_size = inputs.size()
        
        # Compute query, key, and value
        q = self.query(inputs)
        k = self.key(inputs)
        v = self.value(inputs)
        
        # Reshape query, key, and value for multi-head attention
        q = q.view(batch_size, seq_len, self.num_heads, self.head_size)
        k = k.view(batch_size, seq_len, self.num_heads, self.head_size)
        v = v.view(batch_size, seq_len, self.num_heads, self.head_size)
        
        # Compute attention scores
        scores = torch.matmul(q, k.transpose(-2, -1)) / self.head_size**0.5
        
        # Compute sparse attention scores
        mask = torch.ones(batch_size, seq_len, seq_len)
        for i in range(seq_len):
            start = max(0, i - 2)
            end = min(seq_len, i + 3)
            mask[:, i, start:end] = 0
        scores.masked_fill_(mask == 1, float('-inf'))
        
        # Apply softmax to get attention probabilities
        attn_probs = nn.functional.softmax(scores, dim=-1)
        attn_probs = self.dropout(attn_probs)
        
        # Apply attention to value
        attn_output = torch.matmul(attn_probs, v)
        attn_output = attn_output.view(batch_size, seq_len, self.num_heads * self.head_size)
        
        # Apply output layer
        output = self.output(attn_output)
        output = self.dropout(output)
        
        return output

其中，`SparseAttention`类包含了一个稀疏注意力机制，输入为一个三维张量`(batch_size, seq_len, input_size)`，输出为一个三维张量`(batch_size, seq_len, output_size)`。具体实现过程如下：

1. 首先，使用三个线性层（`query`、`key`和`value`）将输入转换为查询、键和值。
2. 将查询、键和值分别重塑为`(batch_size, seq_len, num_heads, head_size)`的形状，以进行多头注意力计算。
3. 计算注意力得分，并使用一个掩码矩阵来实现稀疏注意力机制。该掩码矩阵是一个形状为`(batch_size, seq_len, seq_len)`的张量，其中每个元素为0或1，表示是否需要忽略对应位置的元素。在本例中，掩码矩阵的每一行只允许与当前位置距离不超过2的元素互动，从而实现了稀疏注意力。
4. 将注意力得分应用softmax函数，得到注意力概率，并使用dropout进行正则化。
5. 使用注意力概率和值计算注意力输出，并将其重塑为`(batch_size, seq_len, output_size)`的形状。
6. 最后，使用一个线性层和dropout进行最终输出。

### 回答2：
Sure! 下面是使用PyTorch编写的SparseAttention代码：

import torch
import torch.nn.functional as F

class SparseAttention(torch.nn.Module):
    def __init__(self, hidden_size, num_heads, dropout_rate=0.1):
        super(SparseAttention, self).__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.dropout_rate = dropout_rate

        self.qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
        self.dropout = torch.nn.Dropout(dropout_rate)
        self.softmax = torch.nn.Softmax(dim=-1)

    def forward(self, input):
        qkv = self.qkv(input)  # (batch_size, seq_len, hidden_size * 3)
        queries, keys, values = torch.chunk(qkv, 3, dim=-1)
        queries = queries.view(-1, self.num_heads, queries.size(1), queries.size(2) // self.num_heads)
        keys = keys.view(-1, self.num_heads, keys.size(1), keys.size(2) // self.num_heads)
        values = values.view(-1, self.num_heads, values.size(1), values.size(2) // self.num_heads)

        scores = torch.matmul(queries, keys.transpose(-2, -1)) / (self.hidden_size // self.num_heads)**0.5
        attn_probs = self.softmax(scores)
        attn_probs = self.dropout(attn_probs)
        sparse_weighted_values = torch.matmul(attn_probs, values)
        sparse_weighted_values = sparse_weighted_values.view(-1, sparse_weighted_values.size(2), self.hidden_size)
        output = sparse_weighted_values.sum(dim=1)
        return output

这段代码实现了SparseAttention模块，它包括一个线性层用于计算qkv，一个Softmax层用于计算注意力权重，以及一个Dropout层用于防止过拟合。在forward方法中，将输入的qkv进行分割并重新reshape，然后进行注意力计算和加权求和，最后输出结果。

希望这能够帮到你！

### 回答3：
import torch
import torch.nn as nn
from torch.nn import functional as F

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

# Query projection
self.query = nn.Linear(d_model, d_model * n_heads)

# Key projection
self.key = nn.Linear(d_model, d_model * n_heads)

# Value projection
self.value = nn.Linear(d_model, d_model * n_heads)

def forward(self, input):
batch_size, seq_len, _ = input.size()

# Calculate query, key, and value
query = self.query(input).view(batch_size, seq_len, self.n_heads, self.d_model)
key = self.key(input).view(batch_size, seq_len, self.n_heads, self.d_model)
value = self.value(input).view(batch_size, seq_len, self.n_heads, self.d_model)

# Calculate attention scores
scores = torch.einsum('bqhd,bkhd->bhqk', query, key) / (self.d_model ** 0.5)
attn_weights = F.softmax(scores, dim=3)

# Apply attention weights to value
output = torch.einsum('bhqk,bkhd->bqhd', attn_weights, value)

# Flatten and concatenate attention head outputs
output = output.view(batch_size, seq_len, self.n_heads * self.d_model)

return output

# Usage example
input = torch.randn(2, 5, 10) # Random input tensor with shape (batch_size, seq_len, d_model)
sparse_attention_layer = SparseAttention(d_model=10, n_heads=2) # Create sparse attention layer
output = sparse_attention_layer(input) # Perform forward pass to obtain output

print(output.shape) # Output shape: (batch_size, seq_len, n_heads * d_model)