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import torch
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer
class EvoTransformer(nn.Module):
def __init__(self, vocab_size=30522, d_model=512, nhead=8, num_layers=6, dim_feedforward=1024, dropout=0.1):
super(EvoTransformer, self).__init__()
self.embedding = nn.Embedding(vocab_size, d_model)
self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model))
encoder_layer = TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout)
self.transformer = TransformerEncoder(encoder_layer, num_layers=num_layers)
self.memory_proj = nn.Linear(d_model, d_model)
self.norm = nn.LayerNorm(d_model)
self.classifier = nn.Linear(d_model, 1) # Matches saved model: output is a single logit
def forward(self, input_ids):
x = self.embedding(input_ids)
memory_token = self.memory_token.expand(x.size(0), -1, -1)
x = torch.cat([memory_token, x], dim=1)
x = self.transformer(x)
x = self.norm(x)
memory_output = self.memory_proj(x[:, 0])
return memory_output
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