Update evo_model.py

evo_model.py

@@ -1,42 +1,36 @@
-# evo_model.py — EvoDecoder model with extended positional encoding
+# evo_model.py
 import torch
 import torch.nn as nn
 import math
 
 class PositionalEncoding(nn.Module):
-    def __init__(self, d_model, max_len=512):  # Increased from 128 to 512
+    def __init__(self, d_model, max_len=128):  # ✅ match the saved model's pe shape
         super().__init__()
         pe = torch.zeros(max_len, d_model)
-        position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
-        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        position = torch.arange(0, max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
         pe[:, 0::2] = torch.sin(position * div_term)
         pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0)  # shape: [1, max_len, d_model]
+        pe = pe.unsqueeze(0)  # (1, max_len, d_model)
         self.register_buffer('pe', pe)
 
     def forward(self, x):
         return x + self.pe[:, :x.size(1)]
 
+
 class EvoDecoderModel(nn.Module):
     def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048, dropout=0.1):
         super().__init__()
-        self.embedding = nn.Embedding(vocab_size, d_model)
+        self.token_embed = nn.Embedding(vocab_size, d_model)  # ✅ match checkpoint name
         self.pos_encoder = PositionalEncoding(d_model)
-        decoder_layer = nn.TransformerDecoderLayer(
-            d_model=d_model,
-            nhead=nhead,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            batch_first=True
-        )
+        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, batch_first=True)
         self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
-        self.linear = nn.Linear(d_model, vocab_size)
+        self.lm_head = nn.Linear(d_model, vocab_size)  # ✅ match checkpoint name
 
     def forward(self, input_ids):
-        embedded = self.embedding(input_ids)
-        embedded = self.pos_encoder(embedded)
-        seq_len = embedded.size(1)
-        mask = torch.triu(torch.ones(seq_len, seq_len, device=embedded.device), diagonal=1).bool()
-        output = self.decoder(embedded, embedded, tgt_mask=mask)
-        logits = self.linear(output)
-        return logits
+        x = self.token_embed(input_ids)
+        x = self.pos_encoder(x)
+        seq_len = x.size(1)
+        mask = torch.triu(torch.ones(seq_len, seq_len, device=x.device), diagonal=1).bool()
+        x = self.decoder(x, x, tgt_mask=mask)
+        return self.lm_head(x)