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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) |