device tensor fix

test_device_fix.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""
+Test script to verify device mismatch fixes for GPU training
+"""
+
+import torch
+import logging
+import sys
+import os
+
+# Add the project root to the path
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from core.enhanced_cnn_adapter import EnhancedCNNAdapter
+from core.data_models import BaseDataInput, OHLCVBar
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def test_device_consistency():
+    """Test that all tensors are on the same device"""
+    
+    logger.info("Testing device consistency for EnhancedCNN...")
+    
+    # Check if CUDA is available
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    logger.info(f"Using device: {device}")
+    
+    try:
+        # Initialize the adapter
+        adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
+        
+        # Verify adapter device
+        logger.info(f"Adapter device: {adapter.device}")
+        logger.info(f"Model device: {next(adapter.model.parameters()).device}")
+        
+        # Create sample data
+        sample_ohlcv = [
+            OHLCVBar(
+                symbol="ETH/USDT",
+                timeframe="1s",
+                timestamp=1640995200.0,  # 2022-01-01
+                open=50000.0,
+                high=51000.0,
+                low=49000.0,
+                close=50500.0,
+                volume=1000.0
+            )
+        ] * 300  # 300 frames
+        
+        base_data = BaseDataInput(
+            symbol="ETH/USDT",
+            timestamp=1640995200.0,
+            ohlcv_1s=sample_ohlcv,
+            ohlcv_1m=sample_ohlcv,
+            ohlcv_5m=sample_ohlcv,
+            ohlcv_15m=sample_ohlcv,
+            btc_ohlcv=sample_ohlcv,
+            cob_data={},
+            ma_data={},
+            technical_indicators={},
+            last_predictions={}
+        )
+        
+        # Test prediction
+        logger.info("Testing prediction...")
+        prediction = adapter.predict(base_data)
+        logger.info(f"Prediction successful: {prediction.predictions['action']} (confidence: {prediction.confidence:.3f})")
+        
+        # Test training sample addition
+        logger.info("Testing training sample addition...")
+        adapter.add_training_sample(base_data, "BUY", 0.1)
+        adapter.add_training_sample(base_data, "SELL", -0.05)
+        adapter.add_training_sample(base_data, "HOLD", 0.02)
+        
+        # Test training
+        logger.info("Testing training...")
+        training_results = adapter.train(epochs=1)
+        logger.info(f"Training results: {training_results}")
+        
+        logger.info("✅ All device consistency tests passed!")
+        return True
+        
+    except Exception as e:
+        logger.error(f"❌ Device consistency test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_orchestrator_inference_history():
+    """Test that orchestrator properly initializes inference history"""
+    
+    logger.info("Testing orchestrator inference history initialization...")
+    
+    try:
+        from core.orchestrator import TradingOrchestrator
+        from core.data_provider import DataProvider
+        
+        # Initialize orchestrator
+        data_provider = DataProvider()
+        orchestrator = TradingOrchestrator(data_provider=data_provider)
+        
+        # Check if inference history is initialized
+        logger.info(f"Inference history keys: {list(orchestrator.inference_history.keys())}")
+        
+        # Check if models are registered
+        logger.info(f"Registered models: {list(orchestrator.model_registry.models.keys())}")
+        
+        # Verify each registered model has inference history
+        for model_name in orchestrator.model_registry.models.keys():
+            if model_name in orchestrator.inference_history:
+                logger.info(f"✅ {model_name} has inference history initialized")
+            else:
+                logger.warning(f"❌ {model_name} missing inference history")
+        
+        logger.info("✅ Orchestrator inference history test completed!")
+        return True
+        
+    except Exception as e:
+        logger.error(f"❌ Orchestrator test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    logger.info("Starting device fix verification tests...")
+    
+    # Test 1: Device consistency
+    test1_passed = test_device_consistency()
+    
+    # Test 2: Orchestrator inference history
+    test2_passed = test_orchestrator_inference_history()
+    
+    # Summary
+    if test1_passed and test2_passed:
+        logger.info("🎉 All tests passed! Device issues should be fixed.")
+        sys.exit(0)
+    else:
+        logger.error("❌ Some tests failed. Please check the logs above.")
+        sys.exit(1)