Update evo_model.py

evo_model.py

@@ -2,8 +2,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-class EvoEncoder(nn.Module):
-    def __init__(self, d_model=512, num_heads=8, ffn_dim=1024, num_layers=6, memory_enabled=True):
+class EvoTransformerV22(nn.Module):
+    def __init__(self, d_model=384, num_heads=6, ffn_dim=1024, num_layers=6, memory_enabled=False):
         super().__init__()
         self.embedding = nn.Embedding(30522, d_model)
         encoder_layer = nn.TransformerEncoderLayer(
@@ -15,8 +15,14 @@ class EvoEncoder(nn.Module):
         self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
         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))
+            self.memory_proj = nn.Linear(d_model, d_model)
+        self.pooling = nn.AdaptiveAvgPool1d(1)
+        self.classifier = nn.Sequential(
+            nn.Linear(d_model, 128),
+            nn.ReLU(),
+            nn.Linear(128, 2)
+        )
 
     def forward(self, input_ids):
         x = self.embedding(input_ids)
@@ -24,14 +30,6 @@ class EvoEncoder(nn.Module):
             mem = self.memory_token.expand(x.size(0), -1, -1)
             x = torch.cat([mem, x], dim=1)
         x = self.transformer(x)
-        return x[:, 0]  # Return memory token or first token
-
-class EvoTransformer(nn.Module):
-    def __init__(self, d_model=512, num_heads=8, ffn_dim=1024, num_layers=6, num_classes=1, memory_enabled=True):
-        super().__init__()
-        self.encoder = EvoEncoder(d_model, num_heads, ffn_dim, num_layers, memory_enabled)
-        self.classifier = nn.Linear(d_model, num_classes)
-
-    def forward(self, input_ids):
-        x = self.encoder(input_ids)
+        x = x.permute(0, 2, 1)  # for pooling
+        x = self.pooling(x).squeeze(-1)
         return self.classifier(x)