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import torch
import torch.nn as nn
from src.constants.tokens import PAD_ID
class TinyTransformer(nn.Module):
def __init__(self, vocab_size, d_model=256, nhead=4, num_layers=2, dim_feedforward=512, dropout=0.1):
super().__init__()
self.embedding = nn.Embedding(vocab_size, d_model, padding_idx=PAD_ID)
self.pos_encoder = PositionalEncoding(d_model, dropout)
encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, batch_first=True)
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, batch_first=True)
self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
self.out = nn.Linear(d_model, vocab_size)
# Keep tensors in batch-first format
def forward(self, src, tgt):
tgt_mask = nn.Transformer.generate_square_subsequent_mask(tgt.size(1)).to(src.device).bool()
src_emb = self.pos_encoder(self.embedding(src))
tgt_emb = self.pos_encoder(self.embedding(tgt))
# Create padding masks
src_padding_mask = (src == PAD_ID).bool()
tgt_padding_mask = (tgt == PAD_ID).bool()
memory = self.encoder(src_emb, src_key_padding_mask=src_padding_mask)
output = self.decoder(tgt_emb, memory, tgt_mask=tgt_mask, tgt_key_padding_mask=tgt_padding_mask)
return self.out(output) # (batch, seq_len, vocab)
def generate_src_mask(self, size):
return torch.zeros((size, size), device='cpu').type(torch.bool)
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=512):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(10000.0)) / d_model)
)
pe = torch.zeros(max_len, d_model)
pe[:, 0::2] = torch.sin(position * div_term) # even indices
pe[:, 1::2] = torch.cos(position * div_term) # odd indices
self.register_buffer('pe', pe.unsqueeze(0))
def forward(self, x):
x = x + self.pe[:, :x.size(1), :].to(x.device)
return self.dropout(x)
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