aitbc/gpu_acceleration/apple_silicon_provider.py

"""
Apple Silicon GPU Compute Provider Implementation

This module implements the ComputeProvider interface for Apple Silicon GPUs,
providing Metal-based acceleration for ZK operations.
"""

import numpy as np
from typing import Dict, List, Optional, Any, Tuple
import time
import logging
import subprocess
import json

from .compute_provider import (
    ComputeProvider, ComputeDevice, ComputeBackend, 
    ComputeTask, ComputeResult
)

# Configure logging
logger = logging.getLogger(__name__)

# Try to import Metal Python bindings
try:
    import Metal
    METAL_AVAILABLE = True
except ImportError:
    METAL_AVAILABLE = False
    Metal = None


class AppleSiliconDevice(ComputeDevice):
    """Apple Silicon GPU device information."""
    
    def __init__(self, device_id: int, metal_device=None):
        """Initialize Apple Silicon device info."""
        if metal_device:
            name = metal_device.name()
        else:
            name = f"Apple Silicon GPU {device_id}"
        
        super().__init__(
            device_id=device_id,
            name=name,
            backend=ComputeBackend.APPLE_SILICON,
            memory_total=self._get_total_memory(),
            memory_available=self._get_available_memory(),
            is_available=True
        )
        self.metal_device = metal_device
        self._update_utilization()
    
    def _get_total_memory(self) -> int:
        """Get total GPU memory in bytes."""
        try:
            # Try to get memory from system_profiler
            result = subprocess.run(
                ["system_profiler", "SPDisplaysDataType", "-json"],
                capture_output=True, text=True, timeout=10
            )
            if result.returncode == 0:
                data = json.loads(result.stdout)
                # Parse memory from system profiler output
                # This is a simplified approach
                return 8 * 1024 * 1024 * 1024  # 8GB default
        except Exception:
            pass
        
        # Fallback estimate
        return 8 * 1024 * 1024 * 1024  # 8GB
    
    def _get_available_memory(self) -> int:
        """Get available GPU memory in bytes."""
        # For Apple Silicon, this is shared with system memory
        # We'll estimate 70% availability
        return int(self._get_total_memory() * 0.7)
    
    def _update_utilization(self):
        """Update GPU utilization."""
        try:
            # Apple Silicon doesn't expose GPU utilization easily
            # We'll estimate based on system load
            import psutil
            self.utilization = psutil.cpu_percent(interval=1) * 0.5  # Rough estimate
        except Exception:
            self.utilization = 0.0
    
    def update_temperature(self):
        """Update GPU temperature."""
        try:
            # Try to get temperature from powermetrics
            result = subprocess.run(
                ["powermetrics", "--samplers", "gpu_power", "-i", "1", "-n", "1"],
                capture_output=True, text=True, timeout=10
            )
            if result.returncode == 0:
                # Parse temperature from powermetrics output
                # This is a simplified approach
                self.temperature = 65.0  # Typical GPU temperature
            else:
                self.temperature = None
        except Exception:
            self.temperature = None


class AppleSiliconComputeProvider(ComputeProvider):
    """Apple Silicon GPU implementation of ComputeProvider."""
    
    def __init__(self):
        """Initialize Apple Silicon compute provider."""
        self.devices = []
        self.current_device_id = 0
        self.metal_device = None
        self.command_queue = None
        self.initialized = False
        
        if not METAL_AVAILABLE:
            logger.warning("Metal Python bindings not available")
            return
        
        try:
            self._discover_devices()
            logger.info(f"Apple Silicon Compute Provider initialized with {len(self.devices)} devices")
        except Exception as e:
            logger.error(f"Failed to initialize Apple Silicon provider: {e}")
    
    def _discover_devices(self):
        """Discover available Apple Silicon GPU devices."""
        try:
            # Apple Silicon typically has one unified GPU
            device = AppleSiliconDevice(0)
            self.devices = [device]
            
            # Initialize Metal device if available
            if Metal:
                self.metal_device = Metal.MTLCreateSystemDefaultDevice()
                if self.metal_device:
                    self.command_queue = self.metal_device.newCommandQueue()
            
        except Exception as e:
            logger.warning(f"Failed to discover Apple Silicon devices: {e}")
    
    def initialize(self) -> bool:
        """Initialize the Apple Silicon provider."""
        if not METAL_AVAILABLE:
            logger.error("Metal not available")
            return False
        
        try:
            if self.devices and self.metal_device:
                self.initialized = True
                return True
            else:
                logger.error("No Apple Silicon GPU devices available")
                return False
                
        except Exception as e:
            logger.error(f"Apple Silicon initialization failed: {e}")
            return False
    
    def shutdown(self) -> None:
        """Shutdown the Apple Silicon provider."""
        try:
            # Clean up Metal resources
            self.command_queue = None
            self.metal_device = None
            self.initialized = False
            logger.info("Apple Silicon provider shutdown complete")
            
        except Exception as e:
            logger.error(f"Apple Silicon shutdown failed: {e}")
    
    def get_available_devices(self) -> List[ComputeDevice]:
        """Get list of available Apple Silicon devices."""
        return self.devices
    
    def get_device_count(self) -> int:
        """Get number of available Apple Silicon devices."""
        return len(self.devices)
    
    def set_device(self, device_id: int) -> bool:
        """Set the active Apple Silicon device."""
        if device_id >= len(self.devices):
            return False
        
        try:
            self.current_device_id = device_id
            return True
        except Exception as e:
            logger.error(f"Failed to set Apple Silicon device {device_id}: {e}")
            return False
    
    def get_device_info(self, device_id: int) -> Optional[ComputeDevice]:
        """Get information about a specific Apple Silicon device."""
        if device_id < len(self.devices):
            device = self.devices[device_id]
            device._update_utilization()
            device.update_temperature()
            return device
        return None
    
    def allocate_memory(self, size: int, device_id: Optional[int] = None) -> Any:
        """Allocate memory on Apple Silicon GPU."""
        if not self.initialized or not self.metal_device:
            raise RuntimeError("Apple Silicon provider not initialized")
        
        try:
            # Create Metal buffer
            buffer = self.metal_device.newBufferWithLength_options_(size, Metal.MTLResourceStorageModeShared)
            return buffer
        except Exception as e:
            raise RuntimeError(f"Failed to allocate Apple Silicon memory: {e}")
    
    def free_memory(self, memory_handle: Any) -> None:
        """Free allocated Apple Silicon memory."""
        # Metal uses automatic memory management
        # Just set reference to None
        try:
            memory_handle = None
        except Exception as e:
            logger.warning(f"Failed to free Apple Silicon memory: {e}")
    
    def copy_to_device(self, host_data: Any, device_data: Any) -> None:
        """Copy data from host to Apple Silicon GPU."""
        if not self.initialized:
            raise RuntimeError("Apple Silicon provider not initialized")
        
        try:
            if isinstance(host_data, np.ndarray) and hasattr(device_data, 'contents'):
                # Copy numpy array to Metal buffer
                device_data.contents().copy_bytes_from_length_(host_data.tobytes(), host_data.nbytes)
        except Exception as e:
            logger.error(f"Failed to copy to Apple Silicon device: {e}")
    
    def copy_to_host(self, device_data: Any, host_data: Any) -> None:
        """Copy data from Apple Silicon GPU to host."""
        if not self.initialized:
            raise RuntimeError("Apple Silicon provider not initialized")
        
        try:
            if hasattr(device_data, 'contents') and isinstance(host_data, np.ndarray):
                # Copy from Metal buffer to numpy array
                bytes_data = device_data.contents().bytes()
                host_data.flat[:] = np.frombuffer(bytes_data[:host_data.nbytes], dtype=host_data.dtype)
        except Exception as e:
            logger.error(f"Failed to copy from Apple Silicon device: {e}")
    
    def execute_kernel(
        self,
        kernel_name: str,
        grid_size: Tuple[int, int, int],
        block_size: Tuple[int, int, int],
        args: List[Any],
        shared_memory: int = 0
    ) -> bool:
        """Execute a Metal compute kernel."""
        if not self.initialized or not self.metal_device:
            return False
        
        try:
            # This would require Metal shader compilation
            # For now, we'll simulate with CPU operations
            if kernel_name in ["field_add", "field_mul", "field_inverse"]:
                return self._simulate_kernel(kernel_name, args)
            else:
                logger.warning(f"Unknown Apple Silicon kernel: {kernel_name}")
                return False
            
        except Exception as e:
            logger.error(f"Apple Silicon kernel execution failed: {e}")
            return False
    
    def _simulate_kernel(self, kernel_name: str, args: List[Any]) -> bool:
        """Simulate kernel execution with CPU operations."""
        # This is a placeholder for actual Metal kernel execution
        # In practice, this would compile and execute Metal shaders
        try:
            if kernel_name == "field_add" and len(args) >= 3:
                # Simulate field addition
                return True
            elif kernel_name == "field_mul" and len(args) >= 3:
                # Simulate field multiplication
                return True
            elif kernel_name == "field_inverse" and len(args) >= 2:
                # Simulate field inversion
                return True
            return False
        except Exception:
            return False
    
    def synchronize(self) -> None:
        """Synchronize Apple Silicon GPU operations."""
        if self.initialized and self.command_queue:
            try:
                # Wait for command buffer to complete
                # This is a simplified synchronization
                pass
            except Exception as e:
                logger.error(f"Apple Silicon synchronization failed: {e}")
    
    def get_memory_info(self, device_id: Optional[int] = None) -> Tuple[int, int]:
        """Get Apple Silicon memory information."""
        device = self.get_device_info(device_id or self.current_device_id)
        if device:
            return (device.memory_available, device.memory_total)
        return (0, 0)
    
    def get_utilization(self, device_id: Optional[int] = None) -> float:
        """Get Apple Silicon GPU utilization."""
        device = self.get_device_info(device_id or self.current_device_id)
        return device.utilization if device else 0.0
    
    def get_temperature(self, device_id: Optional[int] = None) -> Optional[float]:
        """Get Apple Silicon GPU temperature."""
        device = self.get_device_info(device_id or self.current_device_id)
        return device.temperature if device else None
    
    # ZK-specific operations (Apple Silicon implementations)
    
    def zk_field_add(self, a: np.ndarray, b: np.ndarray, result: np.ndarray) -> bool:
        """Perform field addition using Apple Silicon GPU."""
        try:
            # For now, fall back to CPU operations
            # In practice, this would use Metal compute shaders
            np.add(a, b, out=result, dtype=result.dtype)
            return True
        except Exception as e:
            logger.error(f"Apple Silicon field add failed: {e}")
            return False
    
    def zk_field_mul(self, a: np.ndarray, b: np.ndarray, result: np.ndarray) -> bool:
        """Perform field multiplication using Apple Silicon GPU."""
        try:
            # For now, fall back to CPU operations
            # In practice, this would use Metal compute shaders
            np.multiply(a, b, out=result, dtype=result.dtype)
            return True
        except Exception as e:
            logger.error(f"Apple Silicon field mul failed: {e}")
            return False
    
    def zk_field_inverse(self, a: np.ndarray, result: np.ndarray) -> bool:
        """Perform field inversion using Apple Silicon GPU."""
        try:
            # For now, fall back to CPU operations
            # In practice, this would use Metal compute shaders
            for i in range(len(a)):
                if a[i] != 0:
                    result[i] = 1  # Simplified
                else:
                    result[i] = 0
            return True
        except Exception as e:
            logger.error(f"Apple Silicon field inverse failed: {e}")
            return False
    
    def zk_multi_scalar_mul(
        self,
        scalars: List[np.ndarray],
        points: List[np.ndarray],
        result: np.ndarray
    ) -> bool:
        """Perform multi-scalar multiplication using Apple Silicon GPU."""
        try:
            # For now, fall back to CPU operations
            # In practice, this would use Metal compute shaders
            if len(scalars) != len(points):
                return False
            
            result.fill(0)
            for scalar, point in zip(scalars, points):
                temp = np.multiply(scalar, point, dtype=result.dtype)
                np.add(result, temp, out=result, dtype=result.dtype)
            
            return True
        except Exception as e:
            logger.error(f"Apple Silicon multi-scalar mul failed: {e}")
            return False
    
    def zk_pairing(self, p1: np.ndarray, p2: np.ndarray, result: np.ndarray) -> bool:
        """Perform pairing operation using Apple Silicon GPU."""
        try:
            # For now, fall back to CPU operations
            # In practice, this would use Metal compute shaders
            np.multiply(p1, p2, out=result, dtype=result.dtype)
            return True
        except Exception as e:
            logger.error(f"Apple Silicon pairing failed: {e}")
            return False
    
    # Performance and monitoring
    
    def benchmark_operation(self, operation: str, iterations: int = 100) -> Dict[str, float]:
        """Benchmark an Apple Silicon operation."""
        if not self.initialized:
            return {"error": "Apple Silicon provider not initialized"}
        
        try:
            # Create test data
            test_size = 1024
            a = np.random.randint(0, 2**32, size=test_size, dtype=np.uint64)
            b = np.random.randint(0, 2**32, size=test_size, dtype=np.uint64)
            result = np.zeros_like(a)
            
            # Warm up
            if operation == "add":
                self.zk_field_add(a, b, result)
            elif operation == "mul":
                self.zk_field_mul(a, b, result)
            
            # Benchmark
            start_time = time.time()
            for _ in range(iterations):
                if operation == "add":
                    self.zk_field_add(a, b, result)
                elif operation == "mul":
                    self.zk_field_mul(a, b, result)
            end_time = time.time()
            
            total_time = end_time - start_time
            avg_time = total_time / iterations
            ops_per_second = iterations / total_time
            
            return {
                "total_time": total_time,
                "average_time": avg_time,
                "operations_per_second": ops_per_second,
                "iterations": iterations
            }
            
        except Exception as e:
            return {"error": str(e)}
    
    def get_performance_metrics(self) -> Dict[str, Any]:
        """Get Apple Silicon performance metrics."""
        if not self.initialized:
            return {"error": "Apple Silicon provider not initialized"}
        
        try:
            free_mem, total_mem = self.get_memory_info()
            utilization = self.get_utilization()
            temperature = self.get_temperature()
            
            return {
                "backend": "apple_silicon",
                "device_count": len(self.devices),
                "current_device": self.current_device_id,
                "memory": {
                    "free": free_mem,
                    "total": total_mem,
                    "used": total_mem - free_mem,
                    "utilization": ((total_mem - free_mem) / total_mem) * 100
                },
                "utilization": utilization,
                "temperature": temperature,
                "devices": [
                    {
                        "id": device.device_id,
                        "name": device.name,
                        "memory_total": device.memory_total,
                        "compute_capability": None,
                        "utilization": device.utilization,
                        "temperature": device.temperature
                    }
                    for device in self.devices
                ]
            }
            
        except Exception as e:
            return {"error": str(e)}


# Register the Apple Silicon provider
from .compute_provider import ComputeProviderFactory
ComputeProviderFactory.register_provider(ComputeBackend.APPLE_SILICON, AppleSiliconComputeProvider)