Delete multi_head_Attention.py

multi_head_Attention.py

@@ -1,44 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Optional, Tuple
-
-# Multi-Head Attention Mechanism
-class MultiHeadAttention(nn.Module):
-    def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1):
-        super().__init__()
-        assert d_model % num_heads == 0
-
-        self.d_model = d_model
-        self.num_heads = num_heads
-        self.head_dim = d_model // num_heads
-
-        self.q_proj = nn.Linear(d_model, d_model)
-        self.k_proj = nn.Linear(d_model, d_model)
-        self.v_proj = nn.Linear(d_model, d_model)
-        self.o_proj = nn.Linear(d_model, d_model)
-
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
-        batch_size, seq_len, d_model = x.shape
-        
-        # Project and reshape for multi-head attention
-        q = self.q_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
-        k = self.k_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
-        v = self.v_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
-
-        # Transpose for attention computation
-        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
-
-        # Compute attention scores
-        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, float('-inf'))
-        
-        attn_weights = F.softmax(scores, dim=-1)
-        attn_weights = self.dropout(attn_weights)
-        
-        # Apply attention to values
-        out = torch.matmul(attn_weights, v).transpose(1, 2).reshape(batch_size, seq_len, d_model)
-        return self.o_proj(out)