Update evo_model.py

evo_model.py

@@ -1,10 +1,18 @@
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
 class EvoEncoder(nn.Module):
-    def __init__(self, d_model=384, num_heads=6, ffn_dim=1024, num_layers=6, memory_enabled=True):
+    def __init__(self, d_model=512, num_heads=8, ffn_dim=1024, num_layers=6, memory_enabled=True):
         super().__init__()
         self.embedding = nn.Embedding(30522, d_model)
+        self.memory_enabled = memory_enabled
+        if memory_enabled:
+            self.memory_proj = nn.Linear(d_model, d_model)
+            self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model))
+        else:
+            self.memory_token = None
+
         encoder_layer = nn.TransformerEncoderLayer(
             d_model=d_model,
             nhead=num_heads,
@@ -12,34 +20,25 @@ class EvoEncoder(nn.Module):
             batch_first=True
         )
         self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
-        self.memory_enabled = memory_enabled
-        if memory_enabled:
-            self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model))
-            self.memory_proj = nn.Linear(d_model, d_model)
 
     def forward(self, input_ids):
         x = self.embedding(input_ids)
-        if self.memory_enabled:
-            mem = self.memory_token.expand(x.size(0), -1, -1)
+
+        if self.memory_enabled and self.memory_token is not None:
+            mem = self.memory_token.expand(x.size(0), 1, x.size(2))
             x = torch.cat([mem, x], dim=1)
+
         x = self.transformer(x)
         return x
 
 class EvoTransformerV22(nn.Module):
     def __init__(self):
         super().__init__()
-        self.encoder = EvoEncoder(
-            d_model=384,
-            num_heads=6,
-            ffn_dim=1024,
-            num_layers=6,
-            memory_enabled=True
-        )
-        self.pooling = nn.AdaptiveAvgPool1d(1)
-        self.classifier = nn.Linear(384, 2)
+        self.encoder = EvoEncoder(d_model=512, num_heads=8, ffn_dim=1024, num_layers=6, memory_enabled=True)
+        self.pool = nn.AdaptiveAvgPool1d(1)
+        self.classifier = nn.Linear(512, 1)  # ✅ Matches checkpoint
 
     def forward(self, input_ids):
         x = self.encoder(input_ids)
-        x = x.permute(0, 2, 1)  # [B, D, T]
-        x = self.pooling(x).squeeze(-1)
-        return self.classifier(x)
+        x = self.pool(x.transpose(1, 2)).squeeze(-1)
+        return self.classifier(x)  # Output: [batch_size, 1]