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Transformer Decoder with Pre-Layer Normalization
"""
import torch
import torch.nn as nn
from typing import Optional
from .attention import MultiHeadAttention
from .feed_forward import FeedForward
from .layer_norm import LayerNorm
from .embeddings import ScaledEmbedding
from .positional_encoding import PositionalEncoding
class DecoderLayer(nn.Module):
"""
Transformer Decoder Layer with Pre-Layer Normalization
Structure:
1. LayerNorm -> Masked Self-Attention -> Dropout -> Residual
2. LayerNorm -> Cross-Attention -> Dropout -> Residual
3. LayerNorm -> Feed-Forward -> Dropout -> Residual
"""
def __init__(self, d_model: int, n_heads: int, d_ff: int,
dropout: float = 0.1, attention_dropout: float = 0.1,
activation_dropout: float = 0.0):
"""
Args:
d_model: Model dimension
n_heads: Number of attention heads
d_ff: Feed-forward dimension
dropout: Dropout rate
attention_dropout: Dropout rate for attention
activation_dropout: Dropout rate for FFN activation
"""
super().__init__()
# Pre-layer normalization
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
# Masked self-attention
self.self_attn = MultiHeadAttention(
d_model, n_heads, dropout, attention_dropout
)
# Cross-attention
self.cross_attn = MultiHeadAttention(
d_model, n_heads, dropout, attention_dropout
)
# Feed-forward network
self.ffn = FeedForward(d_model, d_ff, dropout, activation_dropout)
# Dropout for residual connections
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
def forward(self, x, enc_output, src_mask: Optional[torch.Tensor] = None,
tgt_mask: Optional[torch.Tensor] = None,
return_attention: bool = False):
"""
Args:
x: [batch_size, tgt_len, d_model]
enc_output: [batch_size, src_len, d_model]
src_mask: [batch_size, 1, 1, src_len]
tgt_mask: [batch_size, 1, tgt_len, tgt_len]
return_attention: Whether to return attention weights
Returns:
output: [batch_size, tgt_len, d_model]
self_attn: [batch_size, n_heads, tgt_len, tgt_len] (if return_attention)
cross_attn: [batch_size, n_heads, tgt_len, src_len] (if return_attention)
"""
# Masked self-attention block with pre-norm
residual = x
x = self.norm1(x)
if return_attention:
self_attn_out, self_attn = self.self_attn(
x, x, x, tgt_mask, return_attention=True
)
x = residual + self.dropout1(self_attn_out)
else:
x = residual + self.dropout1(self.self_attn(x, x, x, tgt_mask))
self_attn = None
# Cross-attention block with pre-norm
residual = x
x = self.norm2(x)
if return_attention:
cross_attn_out, cross_attn = self.cross_attn(
x, enc_output, enc_output, src_mask, return_attention=True
)
x = residual + self.dropout2(cross_attn_out)
else:
x = residual + self.dropout2(
self.cross_attn(x, enc_output, enc_output, src_mask)
)
cross_attn = None
# Feed-forward block with pre-norm
residual = x
x = self.norm3(x)
x = residual + self.dropout3(self.ffn(x))
if return_attention:
return x, self_attn, cross_attn
return x
class TransformerDecoder(nn.Module):
"""
Complete Transformer Decoder
"""
def __init__(self, vocab_size: int, d_model: int, n_heads: int,
d_ff: int, n_layers: int, max_len: int = 5000,
dropout: float = 0.1, attention_dropout: float = 0.1,
activation_dropout: float = 0.0, pad_idx: int = 0,
scale_embedding: bool = True):
"""
Args:
vocab_size: Target vocabulary size
d_model: Model dimension
n_heads: Number of attention heads
d_ff: Feed-forward dimension
n_layers: Number of decoder layers
max_len: Maximum sequence length
dropout: Dropout rate
attention_dropout: Dropout rate for attention
activation_dropout: Dropout rate for FFN activation
pad_idx: Padding token index
scale_embedding: Whether to scale embeddings
"""
super().__init__()
self.d_model = d_model
self.pad_idx = pad_idx
# Embedding layer
self.embedding = ScaledEmbedding(
vocab_size, d_model, pad_idx, scale=scale_embedding, dropout=0.0
)
# Positional encoding
self.pos_encoding = PositionalEncoding(d_model, max_len, dropout)
# Stack of decoder layers
self.layers = nn.ModuleList([
DecoderLayer(
d_model, n_heads, d_ff, dropout,
attention_dropout, activation_dropout
)
for _ in range(n_layers)
])
# Final layer norm (important for Pre-LN)
self.final_norm = LayerNorm(d_model)
# Output projection
self.fc_out = nn.Linear(d_model, vocab_size)
self.dropout = nn.Dropout(dropout)
def forward(self, tgt, enc_output, src_mask: Optional[torch.Tensor] = None,
tgt_mask: Optional[torch.Tensor] = None,
return_attention: bool = False):
"""
Args:
tgt: [batch_size, tgt_len]
enc_output: [batch_size, src_len, d_model]
src_mask: [batch_size, 1, 1, src_len]
tgt_mask: [batch_size, 1, tgt_len, tgt_len]
return_attention: Whether to return attention weights
Returns:
output: [batch_size, tgt_len, vocab_size]
self_attentions: List of self-attention weights (if return_attention)
cross_attentions: List of cross-attention weights (if return_attention)
"""
# Embedding + positional encoding
x = self.embedding(tgt)
x = self.pos_encoding(x)
# Pass through decoder layers
self_attentions = [] if return_attention else None
cross_attentions = [] if return_attention else None
for layer in self.layers:
if return_attention:
x, self_attn, cross_attn = layer(
x, enc_output, src_mask, tgt_mask, return_attention=True
)
self_attentions.append(self_attn)
cross_attentions.append(cross_attn)
else:
x = layer(x, enc_output, src_mask, tgt_mask, return_attention=False)
# Final layer normalization
x = self.final_norm(x)
# Project to vocabulary
output = self.fc_out(x)
if return_attention:
return output, self_attentions, cross_attentions
return output
def init_weights(self, init_std: float = 0.02):
"""Initialize model weights"""
# Initialize embeddings
self.embedding.init_weights(init_std)
# Initialize linear layers
for module in self.modules():
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=init_std)
if module.bias is not None:
nn.init.zeros_(module.bias)
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