""" Enhanced Hardware Abstraction Layer (HAL) for Virtual GPU Integrates with DuckDB for state management and multi-GPU support """ import duckdb import numpy as np import json from typing import Dict, List, Optional, Union, Tuple, Any from enum import Enum import logging from config import get_db_url, get_hf_token_cached class HardwareType(Enum): COMPUTE_UNIT = "compute_unit" TENSOR_CORE = "tensor_core" SHADER_UNIT = "shader_unit" MEMORY_CONTROLLER = "memory_controller" DMA_ENGINE = "dma_engine" OPTICAL_INTERCONNECT = "optical_interconnect" class HardwareAbstractionLayer: DB_URL = "hf://datasets/Fred808/helium/storage.json" def __init__(self, db_url: Optional[str] = None): """Initialize HAL with remote database connection""" self.db_url = db_url or self.DB_URL self.max_retries = 3 self._connect_with_retries() def _connect_with_retries(self): """Establish database connection with retry logic""" for attempt in range(self.max_retries): try: self._init_db_connection() self._setup_database() return except Exception as e: if attempt == self.max_retries - 1: raise RuntimeError(f"Failed to initialize database after {self.max_retries} attempts: {str(e)}") logging.warning(f"Database connection attempt {attempt + 1} failed, retrying...") def _init_db_connection(self) -> duckdb.DuckDBPyConnection: """Initialize database connection with HuggingFace configuration""" # Convert HF URL to S3 path and connect directly _, _, owner, dataset, db_file = self.db_url.split('/', 4) db_path = f"s3://datasets-cached/{owner}/{dataset}/{db_file}" # Connect directly to remote database self.conn = duckdb.connect(db_path) self.conn.execute(""" INSTALL httpfs; LOAD httpfs; SET s3_region='us-east-1'; SET s3_endpoint='s3.us-east-1.amazonaws.com'; SET s3_url_style='path'; SET s3_access_key_id='none'; SET s3_secret_access_key=?; """, [self.HF_TOKEN]) def ensure_connection(self): """Ensure database connection is active and valid""" try: self.conn.execute("SELECT 1") except: logging.warning("Database connection lost, attempting to reconnect...") self._connect_with_retries() def _setup_database(self): """Initialize database tables for hardware state tracking""" self.ensure_connection() # GPU Chips table self.conn.execute(""" CREATE TABLE IF NOT EXISTS gpu_chips ( chip_id INTEGER PRIMARY KEY, sm_count INTEGER, clock_speed_mhz INTEGER, memory_size_gb FLOAT, state_json JSON ) """) # Streaming Multiprocessors self.conn.execute(""" CREATE TABLE IF NOT EXISTS streaming_multiprocessors ( sm_id INTEGER, chip_id INTEGER, core_count INTEGER, tensor_core_count INTEGER, state_json JSON, PRIMARY KEY (sm_id, chip_id) ) """) # Optical Interconnects self.conn.execute(""" CREATE TABLE IF NOT EXISTS optical_interconnects ( link_id VARCHAR PRIMARY KEY, chip_a_id INTEGER, chip_b_id INTEGER, bandwidth_tbps FLOAT, latency_ns FLOAT, state_json JSON ) """) # Hardware Queues self.conn.execute(""" CREATE TABLE IF NOT EXISTS hardware_queues ( queue_id INTEGER PRIMARY KEY, hardware_type VARCHAR, chip_id INTEGER, sm_id INTEGER, instructions JSON ) """) # Memory Map self.conn.execute(""" CREATE TABLE IF NOT EXISTS memory_map ( address INTEGER PRIMARY KEY, chip_id INTEGER, size INTEGER, allocation_type VARCHAR, metadata JSON ) """) # Shader Units self.conn.execute(""" CREATE TABLE IF NOT EXISTS shader_units ( unit_id INTEGER, chip_id INTEGER, sm_id INTEGER, current_program_id VARCHAR, state_json JSON, PRIMARY KEY (unit_id, chip_id, sm_id) ) """) self.conn.commit() def get_chip(self, chip_id: int) -> Dict: """Get or create a GPU chip""" self.ensure_connection() result = self.conn.execute(""" SELECT * FROM gpu_chips WHERE chip_id = ? """, [chip_id]).fetchone() if not result: # Initialize new chip self.ensure_connection() self.conn.execute(""" INSERT INTO gpu_chips ( chip_id, sm_count, clock_speed_mhz, memory_size_gb, state_json ) VALUES (?, 64, 1500, 24.0, ?) """, [chip_id, json.dumps({"power_state": "idle", "temperature": 30})]) # Initialize SMs for the chip sm_values = [(i, chip_id, 128, 4, json.dumps({"active": False})) for i in range(64)] self.ensure_connection() self.conn.executemany(""" INSERT INTO streaming_multiprocessors ( sm_id, chip_id, core_count, tensor_core_count, state_json ) VALUES (?, ?, ?, ?, ?) """, sm_values) # Initialize shader units shader_values = [(i, chip_id, j, None, json.dumps({"active": False})) for j in range(64) for i in range(16)] self.ensure_connection() self.conn.executemany(""" INSERT INTO shader_units ( unit_id, chip_id, sm_id, current_program_id, state_json ) VALUES (?, ?, ?, ?, ?) """, shader_values) self.conn.commit() self.ensure_connection() result = self.conn.execute(""" SELECT * FROM gpu_chips WHERE chip_id = ? """, [chip_id]).fetchone() return { "chip_id": result[0], "sm_count": result[1], "clock_speed_mhz": result[2], "memory_size_gb": result[3], "state": json.loads(result[4]) } def connect_chips(self, chip_id_a: int, chip_id_b: int, bandwidth_tbps: float = 800, latency_ns: float = 1): """Connect two chips with an optical interconnect""" link_id = f"link_{chip_id_a}_{chip_id_b}" # Ensure both chips exist self.get_chip(chip_id_a) self.get_chip(chip_id_b) # Create interconnect self.ensure_connection() self.conn.execute(""" INSERT INTO optical_interconnects ( link_id, chip_a_id, chip_b_id, bandwidth_tbps, latency_ns, state_json ) VALUES (?, ?, ?, ?, ?, ?) ON CONFLICT (link_id) DO UPDATE SET bandwidth_tbps = excluded.bandwidth_tbps, latency_ns = excluded.latency_ns """, [link_id, chip_id_a, chip_id_b, bandwidth_tbps, latency_ns, json.dumps({"active": True, "errors": 0})]) self.conn.commit() def execute_tensor_core_matmul(self, chip_id: int, sm_id: int, A: np.ndarray, B: np.ndarray) -> Optional[np.ndarray]: """Execute matrix multiplication on tensor core""" # Verify SM exists and has tensor cores self.ensure_connection() result = self.conn.execute(""" SELECT tensor_core_count, state_json FROM streaming_multiprocessors WHERE chip_id = ? AND sm_id = ? """, [chip_id, sm_id]).fetchone() if not result or result[0] == 0: return None # Update SM state sm_state = json.loads(result[1]) sm_state["active"] = True sm_state["operation"] = "matmul" self.ensure_connection() self.conn.execute(""" UPDATE streaming_multiprocessors SET state_json = ? WHERE chip_id = ? AND sm_id = ? """, [json.dumps(sm_state), chip_id, sm_id]) # Execute matmul try: result = np.matmul(A, B) # Update state on completion sm_state["active"] = False sm_state["last_operation"] = "matmul" sm_state["last_operation_status"] = "success" self.conn.execute(""" UPDATE streaming_multiprocessors SET state_json = ? WHERE chip_id = ? AND sm_id = ? """, [json.dumps(sm_state), chip_id, sm_id]) return result except Exception as e: sm_state["active"] = False sm_state["last_operation"] = "matmul" sm_state["last_operation_status"] = "error" sm_state["last_error"] = str(e) self.conn.execute(""" UPDATE streaming_multiprocessors SET state_json = ? WHERE chip_id = ? AND sm_id = ? """, [json.dumps(sm_state), chip_id, sm_id]) return None """ Run vertex shader using provided instructions. Supports AI/ML ops: matmul, activation, softmax, etc. """ chip = self.get_chip(chip_id) if not chip.sms: return vertex_data sm = chip.sms[0] # Use first SM for shader execution registers = list(vertex_data) for instr in shader_program.get('instructions', []): op = instr.get('opcode') args = instr.get('args', []) if op == 'load_vertex_data': continue elif op == 'transform_vertex': registers = [v * 2 for v in registers] elif op == 'matmul': A = args[0] if args else [[v] for v in registers] B = args[1] if len(args) > 1 else [[1.0] * len(registers)] result = sm.tensor_core_matmul(np.array(A), np.array(B)) if result is not None: registers = result.flatten().tolist() elif op == 'activation': registers = [max(0, v) for v in registers] # ReLU elif op == 'softmax': import math exp_vals = [math.exp(v) for v in registers] s = sum(exp_vals) registers = [v / s for v in exp_vals] return registers def v2_fragment_shader(self, chip_id: int, fragment_data: Dict[str, Any], shader_program: Dict[str, Any]) -> Tuple[float, float, float, float]: """ Run fragment shader using provided instructions. Supports AI/ML ops: matmul, activation, softmax, etc. """ chip = self.get_chip(chip_id) if not chip.sms: return (1.0, 1.0, 1.0, 1.0) sm = chip.sms[0] # Use first SM for shader execution color = [1.0, 1.0, 1.0, 1.0] # Default white for instr in shader_program.get('instructions', []): op = instr.get('opcode') args = instr.get('args', []) if op == 'load_fragment_data': continue elif op == 'compute_color': x = fragment_data.get('x', 0) y = fragment_data.get('y', 0) color = [x % 256 / 255.0, y % 256 / 255.0, 0.5, 1.0] elif op == 'matmul': A = args[0] if args else [[c] for c in color] B = args[1] if len(args) > 1 else [[1.0] * len(color)] result = sm.tensor_core_matmul(np.array(A), np.array(B)) if result is not None: color = result.flatten().tolist() elif op == 'activation': color = [max(0, v) for v in color] # ReLU elif op == 'softmax': import math exp_vals = [math.exp(v) for v in color] s = sum(exp_vals) color = [v / s for v in exp_vals] return tuple(color[:4]) # Ensure RGBA output def allocate_vram(self, chip_id: int, size_bytes: int) -> Optional[str]: """Allocate VRAM on specified chip""" chip = self.get_chip(chip_id) return chip.allocate_memory(size_bytes) def transfer_data(self, src_chip_id: int, dst_chip_id: int, size_bytes: int) -> float: """Transfer data between chips, returns transfer time""" src_chip = self.get_chip(src_chip_id) dst_chip = self.get_chip(dst_chip_id) return src_chip.transfer_data(dst_chip, size_bytes)