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import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
from model.attention import MultiHeadAttention
class FeedForward(nn.Module):
"""Position-wise feed-forward network"""
def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1):
super().__init__()
self.linear1 = nn.Linear(d_model, d_ff)
self.linear2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(dropout)
self.activation = nn.ReLU()
# Layer normalization
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: Input tensor [batch_size, seq_len, d_model]
Returns:
Output tensor [batch_size, seq_len, d_model]
"""
# Store residual
residual = x
# Feed-forward network
x = self.linear1(x)
x = self.activation(x)
x = self.dropout(x)
x = self.linear2(x)
x = self.dropout(x)
# Add and normalize
x = self.layer_norm(x + residual)
return x
class EncoderLayer(nn.Module):
"""Single encoder layer"""
def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float = 0.1):
super().__init__()
# Multi-head attention
self.self_attention = MultiHeadAttention(d_model, n_heads, dropout)
# Feed-forward network
self.feed_forward = FeedForward(d_model, d_ff, dropout)
def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Args:
x: Input tensor [batch_size, seq_len, d_model]
mask: Attention mask
Returns:
Output tensor [batch_size, seq_len, d_model]
"""
# Self-attention
x, _ = self.self_attention(x, x, x, mask)
# Feed-forward
x = self.feed_forward(x)
return x
class DecoderLayer(nn.Module):
"""Single decoder layer"""
def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float = 0.1):
super().__init__()
# Masked self-attention
self.self_attention = MultiHeadAttention(d_model, n_heads, dropout)
# Cross-attention
self.cross_attention = MultiHeadAttention(d_model, n_heads, dropout)
# Feed-forward network
self.feed_forward = FeedForward(d_model, d_ff, dropout)
def forward(self, x: torch.Tensor, memory: torch.Tensor,
tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Args:
x: Input tensor [batch_size, tgt_len, d_model]
memory: Encoder output [batch_size, src_len, d_model]
tgt_mask: Target attention mask
memory_mask: Memory attention mask
Returns:
output: Output tensor [batch_size, tgt_len, d_model]
self_attn: Self-attention weights
cross_attn: Cross-attention weights
"""
# Masked self-attention
x, self_attn = self.self_attention(x, x, x, tgt_mask)
# Cross-attention
x, cross_attn = self.cross_attention(x, memory, memory, memory_mask)
# Feed-forward
x = self.feed_forward(x)
return x, self_attn, cross_attn
class Encoder(nn.Module):
"""Transformer encoder"""
def __init__(self, n_layers: int, d_model: int, n_heads: int,
d_ff: int, dropout: float = 0.1):
super().__init__()
# Stack of encoder layers
self.layers = nn.ModuleList([
EncoderLayer(d_model, n_heads, d_ff, dropout)
for _ in range(n_layers)
])
# Final layer normalization
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Args:
x: Input tensor [batch_size, seq_len, d_model]
mask: Attention mask
Returns:
Output tensor [batch_size, seq_len, d_model]
"""
# Pass through encoder layers
for layer in self.layers:
x = layer(x, mask)
# Final layer normalization
x = self.layer_norm(x)
return x
class Decoder(nn.Module):
"""Transformer decoder"""
def __init__(self, n_layers: int, d_model: int, n_heads: int,
d_ff: int, dropout: float = 0.1):
super().__init__()
# Stack of decoder layers
self.layers = nn.ModuleList([
DecoderLayer(d_model, n_heads, d_ff, dropout)
for _ in range(n_layers)
])
# Final layer normalization
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, x: torch.Tensor, memory: torch.Tensor,
tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Args:
x: Input tensor [batch_size, tgt_len, d_model]
memory: Encoder output [batch_size, src_len, d_model]
tgt_mask: Target attention mask
memory_mask: Memory attention mask
Returns:
Output tensor [batch_size, tgt_len, d_model]
"""
# Pass through decoder layers
for layer in self.layers:
x, _, _ = layer(x, memory, tgt_mask, memory_mask)
# Final layer normalization
x = self.layer_norm(x)
return x
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