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# evo_model.py — EvoDecoder model with extended positional encoding
import torch
import torch.nn as nn
import math
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=512): # Increased from 128 to 512
super().__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float32).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) # shape: [1, max_len, d_model]
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:, :x.size(1)]
class EvoDecoderModel(nn.Module):
def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048, dropout=0.1):
super().__init__()
self.embedding = nn.Embedding(vocab_size, d_model)
self.pos_encoder = PositionalEncoding(d_model)
decoder_layer = nn.TransformerDecoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
batch_first=True
)
self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
self.linear = nn.Linear(d_model, vocab_size)
def forward(self, input_ids):
embedded = self.embedding(input_ids)
embedded = self.pos_encoder(embedded)
seq_len = embedded.size(1)
mask = torch.triu(torch.ones(seq_len, seq_len, device=embedded.device), diagonal=1).bool()
output = self.decoder(embedded, embedded, tgt_mask=mask)
logits = self.linear(output)
return logits
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