evo_model.py

import torch
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer

class EvoEncoder(nn.Module):
    def __init__(self, vocab_size=30522, d_model=512, nhead=8, num_layers=6, dim_feedforward=1024, dropout=0.1):
        super(EvoEncoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model))
        self.positional_encoding = nn.Parameter(torch.zeros(1, 512, 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.norm = nn.LayerNorm(d_model)

    def forward(self, input_ids):
        x = self.embedding(input_ids)
        bsz = x.size(0)

        # Add memory token
        mem_token = self.memory_token.expand(bsz, -1, -1)  # [B, 1, D]
        x = torch.cat([mem_token, x], dim=1)

        x = x + self.positional_encoding[:, :x.size(1), :]
        x = self.transformer(x)
        x = self.norm(x)
        return x[:, 0]  # return memory token output

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.encoder = EvoEncoder(vocab_size, d_model, nhead, num_layers, dim_feedforward, dropout)
        self.classifier = nn.Linear(d_model, 1)

    def forward(self, input_ids):
        x = self.encoder(input_ids)
        return x  # shape: [batch, d_model]