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"""
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)
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