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import torch
import torch.nn as nn
import torch.nn.functional as F
class EvoEncoder(nn.Module):
def __init__(self, d_model=384, nhead=6, dim_feedforward=1024, num_layers=6):
super().__init__()
self.embedding = nn.Embedding(30522, d_model) # BERT-base vocab size
encoder_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
batch_first=True,
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
self.memory_proj = nn.Linear(d_model, d_model)
def forward(self, input_ids):
x = self.embedding(input_ids)
x = self.transformer(x)
x = x.mean(dim=1)
return self.memory_proj(x)
class EvoTransformer(nn.Module):
def __init__(self, d_model=384):
super().__init__()
self.encoder = EvoEncoder(d_model=d_model)
self.classifier = nn.Linear(d_model, 2)
def forward(self, input_ids):
x = self.encoder(input_ids)
return x
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