fix realtime training

NN/models/advanced_transformer_trading.py

@@ -219,8 +219,8 @@ class MarketRegimeDetector(nn.Module):
         regime_weights = regime_probs.unsqueeze(0).unsqueeze(2).unsqueeze(3)  # (1, batch, 1, 1, n_regimes)
         regime_weights = regime_weights.permute(4, 1, 2, 3, 0).squeeze(-1)  # (n_regimes, batch, 1, 1)
         
-        # Weighted sum across regimes
-        adapted_output = torch.sum(regime_stack * regime_weights, dim=0)
+        # Weighted sum across regimes - clone to avoid inplace errors
+        adapted_output = torch.sum(regime_stack * regime_weights, dim=0).clone()
         
         return adapted_output, regime_probs
 
@@ -634,8 +634,8 @@ class AdvancedTradingTransformer(nn.Module):
         else:
             market_emb = torch.zeros(batch_size, seq_len, self.config.d_model, device=device)
         
-        # Combine all embeddings
-        x = price_emb + cob_emb + tech_emb + market_emb
+        # Combine all embeddings - use clone() to avoid inplace operation errors
+        x = price_emb.clone() + cob_emb + tech_emb + market_emb
         
         # Add position state if provided - critical for loss minimization and profit taking
         if position_state is not None:
@@ -647,8 +647,7 @@ class AdvancedTradingTransformer(nn.Module):
             # This conditions the entire sequence on current position state
             position_emb = position_emb.unsqueeze(1).expand(batch_size, seq_len, -1)  # [batch, seq_len, d_model]
             
-            # Add position embedding to the combined embeddings
-            # This allows the model to modulate its predictions based on position state
+            # Add position embedding to the combined embeddings - create new tensor to avoid inplace
             x = x + position_emb
         
         # Add positional encoding
@@ -670,7 +669,8 @@ class AdvancedTradingTransformer(nn.Module):
             else:
                 layer_output = layer(x, mask)
             
-            x = layer_output['output']
+            # Clone to avoid inplace operation errors during backward pass
+            x = layer_output['output'].clone()
             if layer_output['regime_probs'] is not None:
                 regime_probs_history.append(layer_output['regime_probs'])