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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super().__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:x.size(0), :]
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff):
super().__init__()
self.linear1 = nn.Linear(d_model, d_ff)
self.linear2 = nn.Linear(d_ff, d_model)
def forward(self, x):
return self.linear2(F.relu(self.linear1(x)))
class EncoderLayer(nn.Module):
def __init__(self, d_model, n_heads, d_ff, dropout=0.1):
super().__init__()
# Using PyTorch's built-in MultiheadAttention
self.self_attention = nn.MultiheadAttention(
embed_dim=d_model,
num_heads=n_heads,
dropout=dropout,
batch_first=False # PyTorch default: (seq_len, batch, embed_dim)
)
self.feed_forward = FeedForward(d_model, d_ff)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, key_padding_mask=None):
# x shape: (seq_len, batch_size, d_model)
# Multi-head self-attention with residual connection and layer norm
attn_output, _ = self.self_attention(
query=x,
key=x,
value=x,
key_padding_mask=key_padding_mask,
need_weights=False
)
x = self.norm1(x + self.dropout(attn_output))
# Feed forward with residual connection and layer norm
ff_output = self.feed_forward(x)
x = self.norm2(x + self.dropout(ff_output))
return x
class DecoderLayer(nn.Module):
def __init__(self, d_model, n_heads, d_ff, dropout=0.1):
super().__init__()
# Masked self-attention
self.masked_self_attention = nn.MultiheadAttention(
embed_dim=d_model,
num_heads=n_heads,
dropout=dropout,
batch_first=False
)
# Cross-attention (decoder attends to encoder)
self.cross_attention = nn.MultiheadAttention(
embed_dim=d_model,
num_heads=n_heads,
dropout=dropout,
batch_first=False
)
self.feed_forward = FeedForward(d_model, d_ff)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, enc_output, tgt_mask=None, memory_key_padding_mask=None, tgt_key_padding_mask=None):
# x shape: (tgt_seq_len, batch_size, d_model)
# enc_output shape: (src_seq_len, batch_size, d_model)
# Masked multi-head self-attention
attn_output, _ = self.masked_self_attention(
query=x,
key=x,
value=x,
attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask,
need_weights=False
)
x = self.norm1(x + self.dropout(attn_output))
# Multi-head cross-attention (decoder attends to encoder)
attn_output, _ = self.cross_attention(
query=x,
key=enc_output,
value=enc_output,
key_padding_mask=memory_key_padding_mask,
need_weights=False
)
x = self.norm2(x + self.dropout(attn_output))
# Feed forward
ff_output = self.feed_forward(x)
x = self.norm3(x + self.dropout(ff_output))
return x
class Transformer(nn.Module):
def __init__(self, src_vocab_size, tgt_vocab_size, d_model=512, n_heads=8,
n_encoder_layers=6, n_decoder_layers=6, d_ff=2048, dropout=0.1, pad_idx=0):
super().__init__()
self.d_model = d_model
self.pad_idx = pad_idx
# Embeddings
self.src_embedding = nn.Embedding(src_vocab_size, d_model, padding_idx=pad_idx)
self.tgt_embedding = nn.Embedding(tgt_vocab_size, d_model, padding_idx=pad_idx)
# Positional encodings
self.pos_encoding = PositionalEncoding(d_model)
# Encoder layers
self.encoder_layers = nn.ModuleList([
EncoderLayer(d_model, n_heads, d_ff, dropout)
for _ in range(n_encoder_layers)
])
# Decoder layers
self.decoder_layers = nn.ModuleList([
DecoderLayer(d_model, n_heads, d_ff, dropout)
for _ in range(n_decoder_layers)
])
# Output projection
self.linear = nn.Linear(d_model, tgt_vocab_size)
self.dropout = nn.Dropout(dropout)
# Initialize weights
self._init_weights()
def _init_weights(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def create_padding_mask(self, seq):
"""Create padding mask for sequences (True for padding tokens)"""
return seq == self.pad_idx
def create_look_ahead_mask(self, size):
"""Create look-ahead mask for decoder (upper triangular matrix)"""
mask = torch.triu(torch.ones(size, size), diagonal=1)
return mask.bool()
def encode(self, src, src_key_padding_mask=None):
"""Encode source sequence"""
# src shape: (batch_size, src_seq_len)
# Convert to (src_seq_len, batch_size, d_model)
# Source embedding + positional encoding
src_emb = self.src_embedding(src) * math.sqrt(self.d_model) # (batch, seq, d_model)
src_emb = src_emb.transpose(0, 1) # (seq, batch, d_model)
src_emb = self.pos_encoding(src_emb)
src_emb = self.dropout(src_emb)
# Pass through encoder layers
enc_output = src_emb
for layer in self.encoder_layers:
enc_output = layer(enc_output, key_padding_mask=src_key_padding_mask)
return enc_output
def decode(self, tgt, enc_output, tgt_mask=None, memory_key_padding_mask=None, tgt_key_padding_mask=None):
"""Decode target sequence"""
# tgt shape: (batch_size, tgt_seq_len)
# Convert to (tgt_seq_len, batch_size, d_model)
# Target embedding + positional encoding
tgt_emb = self.tgt_embedding(tgt) * math.sqrt(self.d_model) # (batch, seq, d_model)
tgt_emb = tgt_emb.transpose(0, 1) # (seq, batch, d_model)
tgt_emb = self.pos_encoding(tgt_emb)
tgt_emb = self.dropout(tgt_emb)
# Pass through decoder layers
dec_output = tgt_emb
for layer in self.decoder_layers:
dec_output = layer(
dec_output,
enc_output,
tgt_mask=tgt_mask,
memory_key_padding_mask=memory_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask
)
return dec_output
def forward(self, src, tgt):
"""Forward pass"""
# src shape: (batch_size, src_seq_len)
# tgt shape: (batch_size, tgt_seq_len)
batch_size, src_seq_len = src.shape
batch_size, tgt_seq_len = tgt.shape
# Create masks
src_key_padding_mask = self.create_padding_mask(src) # (batch, src_seq)
tgt_key_padding_mask = self.create_padding_mask(tgt) # (batch, tgt_seq)
tgt_mask = self.create_look_ahead_mask(tgt_seq_len).to(tgt.device) # (tgt_seq, tgt_seq)
# Encode
enc_output = self.encode(src, src_key_padding_mask)
# Decode
dec_output = self.decode(
tgt,
enc_output,
tgt_mask=tgt_mask,
memory_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask
)
# Final linear transformation
# Convert back to (batch, seq, d_model)
dec_output = dec_output.transpose(0, 1)
output = self.linear(dec_output)
# Apply softmax to get probabilities
output_probs = F.softmax(output, dim=-1)
return output_probs
def generate(self, src, max_len=50, start_token=1, end_token=2):
"""Generate sequence using greedy decoding"""
self.eval()
device = src.device
batch_size = src.size(0)
# Encode source
src_key_padding_mask = self.create_padding_mask(src)
enc_output = self.encode(src, src_key_padding_mask)
# Initialize target with start token
tgt = torch.full((batch_size, 1), start_token, device=device, dtype=torch.long)
for i in range(max_len - 1):
# Create masks for current target
tgt_key_padding_mask = self.create_padding_mask(tgt)
tgt_mask = self.create_look_ahead_mask(tgt.size(1)).to(device)
# Decode
dec_output = self.decode(
tgt,
enc_output,
tgt_mask=tgt_mask,
memory_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask
)
# Get next token probabilities
dec_output = dec_output.transpose(0, 1) # (batch, seq, d_model)
next_token_logits = self.linear(dec_output[:, -1, :]) # (batch, vocab_size)
next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True) # (batch, 1)
# Append to target sequence
tgt = torch.cat([tgt, next_token], dim=1)
# Check if all sequences have generated end token
if (next_token == end_token).all():
break
return tgt |