listen to all IPs

NN/models/advanced_transformer_trading.py

@@ -599,10 +599,11 @@ class AdvancedTradingTransformer(nn.Module):
         batched_tfs = stacked_tfs.reshape(batch_size * num_tfs, seq_len, self.config.d_model)
         
         # Apply single cross-timeframe attention layer
-        batched_tfs = self.cross_timeframe_layer(batched_tfs)
+        # Use new variable to avoid inplace modification issues
+        cross_tf_encoded = self.cross_timeframe_layer(batched_tfs)
         
         # Reshape back: [batch*num_tfs, seq_len, d_model] -> [batch, num_tfs, seq_len, d_model]
-        cross_tf_output = batched_tfs.reshape(batch_size, num_tfs, seq_len, self.config.d_model)
+        cross_tf_output = cross_tf_encoded.reshape(batch_size, num_tfs, seq_len, self.config.d_model)
         
         # Average across timeframes to get unified representation
         # [batch, num_tfs, seq_len, d_model] -> [batch, seq_len, d_model]
@@ -1346,6 +1347,10 @@ class TradingTransformerTrainer:
                 for param in self.model.parameters():
                     if param.grad is not None:
                         param.grad = None
+                
+                # Clear CUDA cache to prevent tensor version conflicts
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
             
             # OPTIMIZATION: Only move batch to device if not already there
             # Check if first tensor is already on correct device