多头注意力机制代码语音

抱歉，语音回答需要语音合成技术，目前我还没有这个能力。但我可以为你提供多头注意力机制的代码示例：

import torch
import torch.nn as nn

class MultiHeadAttention(nn.Module):
    def __init__(self, n_heads, d_model, dropout=0.1):
        super().__init__()
        assert d_model % n_heads == 0
        self.n_heads = n_heads
        self.d_model = d_model
        self.head_dim = d_model // n_heads
        self.q_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.out_linear = nn.Linear(d_model, d_model)

    def forward(self, query, key, value, mask=None):
        batch_size = query.shape[0]

        # Project inputs to multi-head attention space
        query = self.q_linear(query)
        key = self.k_linear(key)
        value = self.v_linear(value)

        # Reshape inputs to [batch_size, n_heads, seq_len, head_dim]
        query = query.view(batch_size, -1, self.n_heads, self.head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, self.n_heads, self.head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, self.n_heads, self.head_dim).transpose(1, 2)

        # Compute attention scores and apply mask if given
        scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32))
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)

        # Apply softmax to get attention probabilities
        attn_probs = torch.softmax(scores, dim=-1)
        attn_probs = self.dropout(attn_probs)

        # Compute weighted sum of values
        weighted_sum = torch.matmul(attn_probs, value)

        # Reshape and project back to original space
        weighted_sum = weighted_sum.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
        output = self.out_linear(weighted_sum)

        return output, attn_probs

这是一个用 PyTorch 实现的多头注意力机制模块，可以用于自然语言处理等任务中。需要注意的是，在使用时需要传入 query、key 和 value 三个输入，并且可以选择传入掩码 mask，以便在计算注意力分数时过滤掉无效的信息。