Update evo_model.py

evo_model.py

@@ -1,35 +1,34 @@
-
 import torch
-
 import torch.nn as nn
 from torch.nn import TransformerEncoder, TransformerEncoderLayer
 
 class EvoEncoder(nn.Module):
-    def __init__(self, d_model=512, nhead=8, dim_feedforward=1024, num_layers=6, dropout=0.1):
-        super().__init__()
-        self.embedding = nn.Embedding(30522, d_model)
-        self.positional_encoding = nn.Parameter(torch.zeros(1, 512, d_model))
-        encoder_layers = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout)
-        self.transformer = TransformerEncoder(encoder_layers, num_layers)
+    def __init__(self, d_model=512, nhead=8, num_layers=6, dim_feedforward=1024, dropout=0.1):
+        super(EvoEncoder, self).__init__()
+        self.positional_encoding = nn.Parameter(torch.zeros(1, 512, d_model))  # Assuming max seq length = 512
+
+        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)
         self.memory_proj = nn.Linear(d_model, d_model)
-        self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model))
 
-    def forward(self, input_ids):
-        x = self.embedding(input_ids)
-        x += self.positional_encoding[:, :x.size(1)]
-        memory_token = self.memory_token.expand(x.size(0), -1, -1)
-        x = torch.cat([memory_token, x], dim=1)
+    def forward(self, x):
+        x = x + self.positional_encoding[:, :x.size(1), :]
         x = self.transformer(x)
         x = self.norm(x)
-        return self.memory_proj(x[:, 0, :])
+        memory_output = self.memory_proj(x[:, 0])  # Use first token
+        return memory_output
 
 class EvoTransformer(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.encoder = EvoEncoder()
-        self.classifier = nn.Linear(512, 1)
+    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.embedding = nn.Embedding(vocab_size, d_model)
+        self.encoder = EvoEncoder(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
+        x = self.embedding(input_ids)
+        memory_output = self.encoder(x)
+        return memory_output