Update evo_model.py

evo_model.py

@@ -1,39 +1,32 @@
 import torch
 import torch.nn as nn
-from torch.nn import TransformerEncoder, TransformerEncoderLayer
+import torch.nn.functional as F
 
 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 __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)
-        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
+        x = x.mean(dim=1)
+        return self.memory_proj(x)
 
 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 __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  # shape: [batch, d_model]
+        return x