Move gpu_acceleration to dev directory
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- Move GPU acceleration code from root to dev/gpu_acceleration/ - No active imports found in production apps, CLI, or scripts - Contains GPU provider implementations, CUDA kernels, and research code - Belongs in dev/ as development/research code, not production
This commit is contained in:
aitbc
475
dev/gpu_acceleration/apple_silicon_provider.py
Executable file
475
dev/gpu_acceleration/apple_silicon_provider.py
Executable file
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"""
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Apple Silicon GPU Compute Provider Implementation
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This module implements the ComputeProvider interface for Apple Silicon GPUs,
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providing Metal-based acceleration for ZK operations.
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"""
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import numpy as np
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from typing import Dict, List, Optional, Any, Tuple
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import time
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import logging
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import subprocess
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import json
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from .compute_provider import (
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ComputeProvider, ComputeDevice, ComputeBackend,
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ComputeTask, ComputeResult
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)
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# Configure logging
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logger = logging.getLogger(__name__)
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# Try to import Metal Python bindings
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try:
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import Metal
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METAL_AVAILABLE = True
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except ImportError:
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METAL_AVAILABLE = False
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Metal = None
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class AppleSiliconDevice(ComputeDevice):
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"""Apple Silicon GPU device information."""
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def __init__(self, device_id: int, metal_device=None):
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"""Initialize Apple Silicon device info."""
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if metal_device:
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name = metal_device.name()
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else:
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name = f"Apple Silicon GPU {device_id}"
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super().__init__(
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device_id=device_id,
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name=name,
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backend=ComputeBackend.APPLE_SILICON,
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memory_total=self._get_total_memory(),
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memory_available=self._get_available_memory(),
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is_available=True
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)
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self.metal_device = metal_device
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self._update_utilization()
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def _get_total_memory(self) -> int:
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"""Get total GPU memory in bytes."""
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try:
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# Try to get memory from system_profiler
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result = subprocess.run(
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["system_profiler", "SPDisplaysDataType", "-json"],
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capture_output=True, text=True, timeout=10
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)
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if result.returncode == 0:
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data = json.loads(result.stdout)
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# Parse memory from system profiler output
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# This is a simplified approach
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return 8 * 1024 * 1024 * 1024 # 8GB default
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except Exception:
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pass
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# Fallback estimate
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return 8 * 1024 * 1024 * 1024 # 8GB
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def _get_available_memory(self) -> int:
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"""Get available GPU memory in bytes."""
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# For Apple Silicon, this is shared with system memory
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# We'll estimate 70% availability
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return int(self._get_total_memory() * 0.7)
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def _update_utilization(self):
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"""Update GPU utilization."""
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try:
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# Apple Silicon doesn't expose GPU utilization easily
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# We'll estimate based on system load
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import psutil
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self.utilization = psutil.cpu_percent(interval=1) * 0.5 # Rough estimate
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except Exception:
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self.utilization = 0.0
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def update_temperature(self):
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"""Update GPU temperature."""
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try:
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# Try to get temperature from powermetrics
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result = subprocess.run(
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["powermetrics", "--samplers", "gpu_power", "-i", "1", "-n", "1"],
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capture_output=True, text=True, timeout=10
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)
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if result.returncode == 0:
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# Parse temperature from powermetrics output
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# This is a simplified approach
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self.temperature = 65.0 # Typical GPU temperature
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else:
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self.temperature = None
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except Exception:
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self.temperature = None
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class AppleSiliconComputeProvider(ComputeProvider):
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"""Apple Silicon GPU implementation of ComputeProvider."""
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def __init__(self):
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"""Initialize Apple Silicon compute provider."""
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self.devices = []
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self.current_device_id = 0
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self.metal_device = None
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self.command_queue = None
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self.initialized = False
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if not METAL_AVAILABLE:
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logger.warning("Metal Python bindings not available")
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return
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try:
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self._discover_devices()
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logger.info(f"Apple Silicon Compute Provider initialized with {len(self.devices)} devices")
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except Exception as e:
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logger.error(f"Failed to initialize Apple Silicon provider: {e}")
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def _discover_devices(self):
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"""Discover available Apple Silicon GPU devices."""
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try:
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# Apple Silicon typically has one unified GPU
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device = AppleSiliconDevice(0)
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self.devices = [device]
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# Initialize Metal device if available
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if Metal:
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self.metal_device = Metal.MTLCreateSystemDefaultDevice()
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if self.metal_device:
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self.command_queue = self.metal_device.newCommandQueue()
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except Exception as e:
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logger.warning(f"Failed to discover Apple Silicon devices: {e}")
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def initialize(self) -> bool:
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"""Initialize the Apple Silicon provider."""
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if not METAL_AVAILABLE:
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logger.error("Metal not available")
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return False
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try:
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if self.devices and self.metal_device:
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self.initialized = True
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return True
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else:
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logger.error("No Apple Silicon GPU devices available")
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return False
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except Exception as e:
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logger.error(f"Apple Silicon initialization failed: {e}")
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return False
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def shutdown(self) -> None:
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"""Shutdown the Apple Silicon provider."""
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try:
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# Clean up Metal resources
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self.command_queue = None
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self.metal_device = None
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self.initialized = False
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logger.info("Apple Silicon provider shutdown complete")
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except Exception as e:
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logger.error(f"Apple Silicon shutdown failed: {e}")
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def get_available_devices(self) -> List[ComputeDevice]:
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"""Get list of available Apple Silicon devices."""
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return self.devices
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def get_device_count(self) -> int:
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"""Get number of available Apple Silicon devices."""
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return len(self.devices)
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def set_device(self, device_id: int) -> bool:
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"""Set the active Apple Silicon device."""
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if device_id >= len(self.devices):
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return False
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try:
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self.current_device_id = device_id
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return True
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except Exception as e:
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logger.error(f"Failed to set Apple Silicon device {device_id}: {e}")
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return False
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def get_device_info(self, device_id: int) -> Optional[ComputeDevice]:
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"""Get information about a specific Apple Silicon device."""
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if device_id < len(self.devices):
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device = self.devices[device_id]
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device._update_utilization()
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device.update_temperature()
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return device
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return None
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def allocate_memory(self, size: int, device_id: Optional[int] = None) -> Any:
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"""Allocate memory on Apple Silicon GPU."""
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if not self.initialized or not self.metal_device:
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raise RuntimeError("Apple Silicon provider not initialized")
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try:
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# Create Metal buffer
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buffer = self.metal_device.newBufferWithLength_options_(size, Metal.MTLResourceStorageModeShared)
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return buffer
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except Exception as e:
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raise RuntimeError(f"Failed to allocate Apple Silicon memory: {e}")
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def free_memory(self, memory_handle: Any) -> None:
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"""Free allocated Apple Silicon memory."""
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# Metal uses automatic memory management
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# Just set reference to None
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try:
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memory_handle = None
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except Exception as e:
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logger.warning(f"Failed to free Apple Silicon memory: {e}")
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def copy_to_device(self, host_data: Any, device_data: Any) -> None:
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"""Copy data from host to Apple Silicon GPU."""
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if not self.initialized:
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raise RuntimeError("Apple Silicon provider not initialized")
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try:
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if isinstance(host_data, np.ndarray) and hasattr(device_data, 'contents'):
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# Copy numpy array to Metal buffer
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device_data.contents().copy_bytes_from_length_(host_data.tobytes(), host_data.nbytes)
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except Exception as e:
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logger.error(f"Failed to copy to Apple Silicon device: {e}")
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def copy_to_host(self, device_data: Any, host_data: Any) -> None:
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"""Copy data from Apple Silicon GPU to host."""
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if not self.initialized:
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raise RuntimeError("Apple Silicon provider not initialized")
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try:
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if hasattr(device_data, 'contents') and isinstance(host_data, np.ndarray):
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# Copy from Metal buffer to numpy array
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bytes_data = device_data.contents().bytes()
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host_data.flat[:] = np.frombuffer(bytes_data[:host_data.nbytes], dtype=host_data.dtype)
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except Exception as e:
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logger.error(f"Failed to copy from Apple Silicon device: {e}")
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def execute_kernel(
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self,
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kernel_name: str,
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grid_size: Tuple[int, int, int],
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block_size: Tuple[int, int, int],
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args: List[Any],
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shared_memory: int = 0
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) -> bool:
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"""Execute a Metal compute kernel."""
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if not self.initialized or not self.metal_device:
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return False
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try:
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# This would require Metal shader compilation
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# For now, we'll simulate with CPU operations
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if kernel_name in ["field_add", "field_mul", "field_inverse"]:
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return self._simulate_kernel(kernel_name, args)
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else:
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logger.warning(f"Unknown Apple Silicon kernel: {kernel_name}")
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return False
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except Exception as e:
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logger.error(f"Apple Silicon kernel execution failed: {e}")
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return False
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def _simulate_kernel(self, kernel_name: str, args: List[Any]) -> bool:
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"""Simulate kernel execution with CPU operations."""
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# This is a placeholder for actual Metal kernel execution
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# In practice, this would compile and execute Metal shaders
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try:
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if kernel_name == "field_add" and len(args) >= 3:
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# Simulate field addition
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return True
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elif kernel_name == "field_mul" and len(args) >= 3:
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# Simulate field multiplication
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return True
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elif kernel_name == "field_inverse" and len(args) >= 2:
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# Simulate field inversion
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return True
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return False
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except Exception:
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return False
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def synchronize(self) -> None:
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"""Synchronize Apple Silicon GPU operations."""
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if self.initialized and self.command_queue:
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try:
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# Wait for command buffer to complete
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# This is a simplified synchronization
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pass
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except Exception as e:
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logger.error(f"Apple Silicon synchronization failed: {e}")
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def get_memory_info(self, device_id: Optional[int] = None) -> Tuple[int, int]:
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"""Get Apple Silicon memory information."""
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device = self.get_device_info(device_id or self.current_device_id)
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if device:
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return (device.memory_available, device.memory_total)
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return (0, 0)
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def get_utilization(self, device_id: Optional[int] = None) -> float:
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"""Get Apple Silicon GPU utilization."""
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device = self.get_device_info(device_id or self.current_device_id)
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return device.utilization if device else 0.0
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def get_temperature(self, device_id: Optional[int] = None) -> Optional[float]:
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"""Get Apple Silicon GPU temperature."""
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device = self.get_device_info(device_id or self.current_device_id)
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return device.temperature if device else None
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# ZK-specific operations (Apple Silicon implementations)
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def zk_field_add(self, a: np.ndarray, b: np.ndarray, result: np.ndarray) -> bool:
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"""Perform field addition using Apple Silicon GPU."""
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try:
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# For now, fall back to CPU operations
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# In practice, this would use Metal compute shaders
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np.add(a, b, out=result, dtype=result.dtype)
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return True
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except Exception as e:
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logger.error(f"Apple Silicon field add failed: {e}")
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return False
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def zk_field_mul(self, a: np.ndarray, b: np.ndarray, result: np.ndarray) -> bool:
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"""Perform field multiplication using Apple Silicon GPU."""
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try:
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# For now, fall back to CPU operations
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# In practice, this would use Metal compute shaders
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np.multiply(a, b, out=result, dtype=result.dtype)
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return True
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except Exception as e:
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logger.error(f"Apple Silicon field mul failed: {e}")
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return False
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def zk_field_inverse(self, a: np.ndarray, result: np.ndarray) -> bool:
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"""Perform field inversion using Apple Silicon GPU."""
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try:
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# For now, fall back to CPU operations
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# In practice, this would use Metal compute shaders
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for i in range(len(a)):
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if a[i] != 0:
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result[i] = 1 # Simplified
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else:
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result[i] = 0
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return True
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except Exception as e:
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logger.error(f"Apple Silicon field inverse failed: {e}")
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return False
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def zk_multi_scalar_mul(
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self,
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scalars: List[np.ndarray],
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points: List[np.ndarray],
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result: np.ndarray
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) -> bool:
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"""Perform multi-scalar multiplication using Apple Silicon GPU."""
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try:
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# For now, fall back to CPU operations
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# In practice, this would use Metal compute shaders
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if len(scalars) != len(points):
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return False
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result.fill(0)
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for scalar, point in zip(scalars, points):
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temp = np.multiply(scalar, point, dtype=result.dtype)
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np.add(result, temp, out=result, dtype=result.dtype)
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return True
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except Exception as e:
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logger.error(f"Apple Silicon multi-scalar mul failed: {e}")
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return False
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def zk_pairing(self, p1: np.ndarray, p2: np.ndarray, result: np.ndarray) -> bool:
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"""Perform pairing operation using Apple Silicon GPU."""
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try:
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# For now, fall back to CPU operations
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# In practice, this would use Metal compute shaders
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np.multiply(p1, p2, out=result, dtype=result.dtype)
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return True
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except Exception as e:
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logger.error(f"Apple Silicon pairing failed: {e}")
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return False
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# Performance and monitoring
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def benchmark_operation(self, operation: str, iterations: int = 100) -> Dict[str, float]:
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"""Benchmark an Apple Silicon operation."""
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if not self.initialized:
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return {"error": "Apple Silicon provider not initialized"}
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try:
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# Create test data
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test_size = 1024
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a = np.random.randint(0, 2**32, size=test_size, dtype=np.uint64)
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b = np.random.randint(0, 2**32, size=test_size, dtype=np.uint64)
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result = np.zeros_like(a)
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# Warm up
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if operation == "add":
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self.zk_field_add(a, b, result)
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elif operation == "mul":
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self.zk_field_mul(a, b, result)
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# Benchmark
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start_time = time.time()
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for _ in range(iterations):
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if operation == "add":
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self.zk_field_add(a, b, result)
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elif operation == "mul":
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self.zk_field_mul(a, b, result)
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end_time = time.time()
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total_time = end_time - start_time
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avg_time = total_time / iterations
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ops_per_second = iterations / total_time
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return {
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"total_time": total_time,
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"average_time": avg_time,
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"operations_per_second": ops_per_second,
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"iterations": iterations
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}
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except Exception as e:
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return {"error": str(e)}
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def get_performance_metrics(self) -> Dict[str, Any]:
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"""Get Apple Silicon performance metrics."""
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if not self.initialized:
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return {"error": "Apple Silicon provider not initialized"}
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try:
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free_mem, total_mem = self.get_memory_info()
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utilization = self.get_utilization()
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temperature = self.get_temperature()
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return {
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"backend": "apple_silicon",
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"device_count": len(self.devices),
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"current_device": self.current_device_id,
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"memory": {
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"free": free_mem,
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"total": total_mem,
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"used": total_mem - free_mem,
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"utilization": ((total_mem - free_mem) / total_mem) * 100
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},
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"utilization": utilization,
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"temperature": temperature,
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"devices": [
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{
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"id": device.device_id,
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"name": device.name,
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"memory_total": device.memory_total,
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"compute_capability": None,
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"utilization": device.utilization,
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"temperature": device.temperature
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}
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for device in self.devices
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]
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}
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except Exception as e:
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return {"error": str(e)}
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# Register the Apple Silicon provider
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from .compute_provider import ComputeProviderFactory
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ComputeProviderFactory.register_provider(ComputeBackend.APPLE_SILICON, AppleSiliconComputeProvider)
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