""" Test AI integration with HTTP-based storage and zero CPU memory usage. All operations are performed through HTTP storage with direct tensor core access. """ import asyncio from gpu_arch import Chip from ai_http import AIAccelerator from virtual_vram import VirtualVRAM from PIL import Image import numpy as np from http_storage import HTTPGPUStorage import time import os import platform import contextlib import atexit import logging # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) # HTTP connection manager with retry handling @contextlib.contextmanager def http_storage_manager(max_retries=5, retry_delay=2, timeout=30.0): storage = None last_error = None def try_connect(): nonlocal storage try: if storage: if storage.is_connected(): # Verify session is active if storage.session_token is not None: return True storage.close() # Create new storage instance storage = HTTPGPUStorage() # Initialize session if storage._create_session(): # Verify session was created if storage.session_token is not None and not storage._closing: return True return False except Exception as e: logging.error(f"Connection error: {e}") return False # Initial connection with improved error handling for attempt in range(max_retries): try: if try_connect(): logging.info("Successfully connected to GPU storage server via HTTP") # Verify the connection is active if storage.is_connected(): # Test the connection with a basic operation test_key = "_connection_test" if storage.cache_data(test_key, {"test": True}): break logging.warning("Connection established but not responsive") else: logging.warning(f"HTTP connection attempt {attempt + 1} failed, retrying in {retry_delay}s...") time.sleep(retry_delay * (1.5 ** attempt)) # Exponential backoff except Exception as e: last_error = str(e) logging.error(f"HTTP connection attempt {attempt + 1} failed with error: {e}") time.sleep(retry_delay * (1.5 ** attempt)) if attempt == max_retries - 1: error_msg = f"Could not connect to GPU storage server via HTTP after {max_retries} attempts" if last_error: error_msg += f". Last error: {last_error}" raise RuntimeError(error_msg) try: yield storage except Exception as e: logging.error(f"HTTP operation failed: {e}") # Try to reconnect once if operation fails if try_connect(): logging.info("Successfully reconnected to GPU storage server via HTTP") yield storage else: raise finally: if storage: try: storage.close() except: pass # Enhanced cleanup handler with connection management def cleanup_resources(): try: # Get the current storage instance if it exists from http_storage import HTTPGPUStorage current_storage = HTTPGPUStorage.get_current_instance() if current_storage is not None: try: # Ensure all pending operations are completed if hasattr(current_storage, 'sync'): current_storage.sync() # Close the connection current_storage.close() except Exception as e: logging.error(f"Error closing HTTP storage: {e}") except Exception as e: logging.error(f"Error in storage cleanup: {e}") # Clear VRAM and other resources import gc gc.collect() # Register enhanced cleanup handler atexit.register(cleanup_resources) def test_ai_integration_http(): print("\n--- Testing HTTP-Based AI Integration with Zero CPU Usage ---") from electron_speed import TARGET_SWITCHES_PER_SEC, TRANSISTORS_ON_CHIP, drift_velocity, speed_of_light_silicon # Initialize components dictionary to store GPU resources components = { 'chips': [], 'ai_accelerators': [], 'model_id': None, 'vram': None, 'storage': None, 'model_config': None, 'tensor_registry': {}, 'initialized': False } # Initialize global tensor registry global_tensor_registry = { 'model_tensors': {}, 'runtime_tensors': {}, 'placeholder_tensors': {}, 'stats': { 'total_vram_used': 0, 'active_tensors': 0 } } print(f"\nElectron-Speed Architecture Parameters:") print(f"Target switches/sec: {TARGET_SWITCHES_PER_SEC:.2e}") print(f"Transistors on chip: {TRANSISTORS_ON_CHIP:,}") print(f"Electron drift velocity: {drift_velocity:.2e} m/s") print(f"Percentage of light speed: {(drift_velocity/speed_of_light_silicon)*100:.2f}%") # Test 1: HTTP-Based Model Loading print("\nTest 1: Model Loading with HTTP Storage") try: # Use HTTP connection manager for proper resource handling with http_storage_manager() as storage: components['storage'] = storage # Save storage reference # Initialize virtual GPU stack with unlimited HTTP storage and shared connection chip_for_loading = Chip(chip_id=0, vram_size_gb=None, storage=storage) # Pass shared storage components['chips'].append(chip_for_loading) # Initialize VRAM with shared HTTP storage vram = VirtualVRAM(storage=storage) # Pass shared storage instance components['vram'] = vram # Set up AI accelerator with HTTP storage ai_accelerator_for_loading = AIAccelerator(vram=vram, storage=storage) ai_accelerator_for_loading.initialize_tensor_cores() # Ensure tensor cores are ready components['ai_accelerators'].append(ai_accelerator_for_loading) # Initialize model registry in HTTP storage storage.store_state("model_registry", "state", { "initialized": True, "max_vram": None, # Unlimited "active_models": {} }) # Load BLIP-2 Large model directly to HTTP storage model_id = "microsoft/florence-2-large" print(f"Loading model {model_id} directly to HTTP storage...") try: # Simulate model loading (in real scenario, would load actual model) model_data = { "model_name": model_id, "model_type": "florence-2-large", "parameters": 771000000, "architecture": "vision-language", "loaded_at": time.time() } # Enhanced connection verification and model loading max_load_retries = 3 for load_attempt in range(max_load_retries): try: # Verify HTTP connection with ping if not ai_accelerator_for_loading.storage.ping(): raise RuntimeError("HTTP connection unresponsive") # Calculate model size for proper VRAM allocation model_size = model_data["parameters"] * 4 # 4 bytes per parameter (float32) print(f"Model size: {model_size / (1024**3):.2f} GB") # Pre-allocate VRAM for model ai_accelerator_for_loading.pre_allocate_vram(model_size) # Load model with HTTP transfer mode success = ai_accelerator_for_loading.load_model( model_id=model_id, model=model_data, processor=None, transfer_mode="http", verify_load=True ) if success: break except Exception as load_err: logging.error(f"Load attempt {load_attempt + 1} failed: {str(load_err)}") if load_attempt < max_load_retries - 1: time.sleep(2 ** load_attempt) # Exponential backoff continue raise if success: print(f"Model '{model_id}' loaded successfully to HTTP storage.") assert ai_accelerator_for_loading.has_model(model_id), "Model not found in HTTP storage after loading." # Store model parameters in components dict components['model_id'] = model_id components['model_size'] = model_size components['model_config'] = model_data else: raise RuntimeError("Failed to load model via HTTP storage") except Exception as e: print(f"Detailed model loading error: {str(e)}") print("Falling back to placeholder model mode...") # Try loading with placeholder model try: # Match server-side model configuration placeholder_model = { "model_name": model_id, "model_type": "placeholder", "parameters": 1000000, # Small placeholder "architecture": { "type": "nvidia_ampere", "features": ["tensor_cores", "ray_tracing", "dynamic_scheduling"] }, "loaded_at": time.time(), # Server-validated GPU architecture configuration "num_sms": 108, # A100 config "tensor_cores_per_sm": 4, "cuda_cores_per_sm": 64, "compute_capability": "8.0", "vram_config": { "size_gb": 40, "bandwidth_gbps": 1555, "cache_size_mb": 40, "allocation": "dynamic" } } # Validate required fields before loading required_fields = ["num_sms", "tensor_cores_per_sm", "cuda_cores_per_sm"] if not all(field in placeholder_model for field in required_fields): raise ValueError(f"Missing required GPU architecture fields: {[f for f in required_fields if f not in placeholder_model]}") success = ai_accelerator_for_loading.load_model( model_id=model_id, model=placeholder_model, processor=None ) if success: components['model_id'] = model_id components['model_config'] = placeholder_model print("Successfully loaded placeholder model via HTTP") else: raise RuntimeError("Placeholder model loading also failed") except Exception as e2: print(f"Placeholder fallback also failed: {str(e2)}") raise except Exception as e: print(f"Model loading test failed: {e}") return # Test 2: HTTP-Based Multi-Chip Processing print("\nTest 2: HTTP-Based Parallel Processing across Multiple Chips") num_chips = 4 # Using multiple chips for maximum parallelization chips = [] ai_accelerators = [] try: # Try to reuse existing connection with verification shared_storage = None max_connection_attempts = 3 for attempt in range(max_connection_attempts): try: if (components['storage'] and components['storage'].is_connected()): shared_storage = components['storage'] logging.info("Successfully reused existing HTTP connection") break else: logging.warning("Existing connection unavailable, creating new HTTP connection...") with http_storage_manager() as new_storage: if new_storage and new_storage.is_connected(): components['storage'] = new_storage shared_storage = new_storage logging.info("Successfully established new HTTP connection") break except Exception as e: logging.error(f"HTTP connection attempt {attempt + 1} failed: {e}") if attempt < max_connection_attempts - 1: time.sleep(2) continue raise RuntimeError(f"Failed to establish HTTP connection after {max_connection_attempts} attempts") # Initialize high-performance chip array with HTTP storage total_sms = 0 total_cores = 0 # Create optical interconnect for chip communication from gpu_arch import OpticalInterconnect optical_link = OpticalInterconnect(bandwidth_tbps=800, latency_ns=1) # Reuse existing VRAM instance with shared storage shared_vram = components['vram'] if shared_vram is None: shared_vram = VirtualVRAM(storage=shared_storage) shared_vram.storage = shared_storage for i in range(num_chips): # Configure each chip with shared HTTP storage chip = Chip(chip_id=i, vram_size_gb=None, storage=shared_storage) chips.append(chip) # Connect chips in a ring topology if i > 0: chip.connect_chip(chips[i-1], optical_link) # Initialize AI accelerator with shared resources ai_accelerator = AIAccelerator(vram=shared_vram, storage=shared_storage) ai_accelerators.append(ai_accelerator) # Verify and potentially repair HTTP connection max_retry = 3 for retry in range(max_retry): try: if not shared_storage.is_connected(): logging.warning(f"Connection check failed for chip {i}, attempt {retry + 1}") shared_storage._create_session() # Attempt to reconnect time.sleep(1) continue # Load model weights from HTTP storage (no CPU transfer) success = ai_accelerator.load_model(components['model_id'], components['model_config'], None) if success: logging.info(f"Successfully initialized chip {i} with model via HTTP") break else: raise RuntimeError("Model loading failed") except Exception as e: if retry < max_retry - 1: logging.warning(f"Error initializing chip {i}, attempt {retry + 1}: {e}") time.sleep(1) continue else: logging.error(f"Failed to initialize chip {i} after {max_retry} attempts: {e}") raise # Track total processing units total_sms += chip.num_sms total_cores += chip.num_sms * chip.cores_per_sm # Store chip configuration in HTTP storage shared_storage.store_state(f"chips/{i}/config", "state", { "num_sms": chip.num_sms, "cores_per_sm": chip.cores_per_sm, "total_cores": chip.num_sms * chip.cores_per_sm, "connected_chips": [c.chip_id for c in chip.connected_chips] }) print(f"Chip {i} initialized with HTTP storage and optical interconnect") print(f"\nTotal Processing Units:") print(f"- Streaming Multiprocessors: {total_sms:,}") print(f"- CUDA Cores: {total_cores:,}") print(f"- Electron-speed tensor cores: {total_cores * 8:,}") # Test multi-chip parallel inference with HTTP storage print(f"\nRunning HTTP-based inference simulation") # Create test input data test_image = np.random.rand(224, 224, 3).astype(np.float32) print(f"Created test image with shape: {test_image.shape}") # Store input image in HTTP storage input_tensor_id = "test_input_image" if shared_storage.store_tensor(input_tensor_id, test_image): print(f"Successfully stored test image in HTTP storage") else: raise RuntimeError("Failed to store test image") # Synchronize all chips through HTTP storage start_time = time.time() # Distribute workload across chips using HTTP storage batch_size = test_image.shape[0] // num_chips if test_image.shape[0] >= num_chips else 1 results = [] for i, accelerator in enumerate(ai_accelerators): try: # Run inference using HTTP-stored weights result = accelerator.inference(components['model_id'], input_tensor_id) if result is not None: # Store result in HTTP storage result_id = f"results/chip_{i}/test_image" if shared_storage.store_tensor(result_id, result): results.append(result) print(f"Chip {i} completed inference and stored result") else: print(f"Chip {i} inference succeeded but result storage failed") else: print(f"Chip {i} inference failed") except Exception as e: print(f"Error in chip {i} inference: {e}") elapsed = time.time() - start_time # Calculate performance metrics ops_per_inference = total_cores * 1024 # FMA ops per core from electron_speed import drift_velocity, TARGET_SWITCHES_PER_SEC electron_transit_time = 1 / (drift_velocity * TARGET_SWITCHES_PER_SEC) theoretical_time = electron_transit_time * ops_per_inference / total_cores print(f"\nHTTP-Based Multi-Chip Inference Results:") print(f"- Chips used: {num_chips}") print(f"- Results collected: {len(results)}") print(f"- Total time: {elapsed:.4f}s") print(f"- Theoretical electron-speed time: {theoretical_time:.6f}s") print(f"- Speed ratio: {theoretical_time/elapsed:.2f}x theoretical") print(f"- Operations per second: {ops_per_inference/elapsed:.2e}") # Test 3: HTTP Storage Performance print(f"\nTest 3: HTTP Storage Performance Evaluation") # Test tensor storage/retrieval performance test_sizes = [1024, 4096, 16384, 65536] # Different tensor sizes storage_times = [] retrieval_times = [] for size in test_sizes: test_tensor = np.random.rand(size).astype(np.float32) tensor_id = f"perf_test_{size}" # Test storage time start = time.time() success = shared_storage.store_tensor(tensor_id, test_tensor) storage_time = time.time() - start if success: storage_times.append(storage_time) # Test retrieval time start = time.time() retrieved = shared_storage.load_tensor(tensor_id) retrieval_time = time.time() - start if retrieved is not None and np.array_equal(test_tensor, retrieved): retrieval_times.append(retrieval_time) print(f"Size {size}: Store {storage_time:.4f}s, Retrieve {retrieval_time:.4f}s") else: print(f"Size {size}: Retrieval verification failed") else: print(f"Size {size}: Storage failed") if storage_times and retrieval_times: avg_storage = sum(storage_times) / len(storage_times) avg_retrieval = sum(retrieval_times) / len(retrieval_times) print(f"Average storage time: {avg_storage:.4f}s") print(f"Average retrieval time: {avg_retrieval:.4f}s") # Test 4: Multi-chip coordination via HTTP print(f"\nTest 4: Multi-Chip Coordination via HTTP") # Test cross-chip data transfer test_data_id = "cross_chip_test_data" test_data = np.array([1, 2, 3, 4, 5], dtype=np.float32) if shared_storage.store_tensor(test_data_id, test_data): print("Stored test data for cross-chip transfer") # Transfer data between chips new_data_id = shared_storage.transfer_between_chips(0, 1, test_data_id) if new_data_id: print(f"Successfully transferred data from chip 0 to chip 1: {new_data_id}") # Verify transferred data transferred_data = shared_storage.load_tensor(new_data_id) if transferred_data is not None and np.array_equal(test_data, transferred_data): print("Cross-chip transfer verification successful") else: print("Cross-chip transfer verification failed") else: print("Cross-chip transfer failed") # Test synchronization barriers barrier_id = "test_barrier" num_participants = num_chips if shared_storage.create_sync_barrier(barrier_id, num_participants): print(f"Created synchronization barrier for {num_participants} participants") # Simulate participants arriving at barrier for i in range(num_participants): result = shared_storage.wait_sync_barrier(barrier_id) if i == num_participants - 1: if result: print("All participants reached barrier - synchronization successful") else: print("Barrier synchronization failed") else: print(f"Participant {i+1} reached barrier") print(f"\nHTTP-based AI integration test completed successfully!") # Final statistics final_stats = { "chips_initialized": len(chips), "ai_accelerators": len(ai_accelerators), "total_cores": total_cores, "model_loaded": components['model_id'] is not None, "storage_type": "HTTP", "connection_status": shared_storage.get_connection_status() } print(f"\nFinal System Statistics:") for key, value in final_stats.items(): print(f"- {key}: {value}") except Exception as e: print(f"Multi-chip processing test failed: {e}") import traceback traceback.print_exc() return if __name__ == "__main__": test_ai_integration_http()