sklears_ensemble/

gpu_acceleration.rs

//! GPU acceleration for ensemble methods
//!
//! This module provides GPU acceleration capabilities for ensemble training and inference,
//! with support for multiple GPU backends and fallback to CPU when GPU is unavailable.

use scirs2_core::ndarray::{Array1, Array2};
use sklears_core::error::{Result, SklearsError};
use sklears_core::types::{Float, Int};
use std::sync::Arc;

/// GPU backend enumeration
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum GpuBackend {
    /// CUDA backend for NVIDIA GPUs
    Cuda,
    /// OpenCL backend for cross-platform GPU support
    OpenCL,
    /// Metal backend for Apple GPUs
    Metal,
    /// Vulkan backend for modern graphics APIs
    Vulkan,
    /// CPU fallback when no GPU is available
    CpuFallback,
}

/// GPU configuration
#[derive(Debug, Clone)]
pub struct GpuConfig {
    /// GPU backend to use
    pub backend: GpuBackend,
    /// Device ID (for systems with multiple GPUs)
    pub device_id: usize,
    /// Memory limit for GPU usage (in MB)
    pub memory_limit_mb: Option<usize>,
    /// Batch size for GPU operations
    pub batch_size: usize,
    /// Number of GPU streams for parallel execution
    pub n_streams: usize,
    /// Enable mixed precision (FP16/FP32)
    pub mixed_precision: bool,
    /// Enable tensor cores (for supported hardware)
    pub tensor_cores: bool,
    /// Memory pool size for efficient allocation
    pub memory_pool_size_mb: usize,
    /// Enable profiling for performance analysis
    pub enable_profiling: bool,
}

impl Default for GpuConfig {
    fn default() -> Self {
        Self {
            backend: GpuBackend::CpuFallback,
            device_id: 0,
            memory_limit_mb: None,
            batch_size: 1024,
            n_streams: 4,
            mixed_precision: false,
            tensor_cores: false,
            memory_pool_size_mb: 1024,
            enable_profiling: false,
        }
    }
}

/// GPU acceleration context
pub struct GpuContext {
    config: GpuConfig,
    device_info: GpuDeviceInfo,
    memory_manager: GpuMemoryManager,
    profiler: Option<GpuProfiler>,
}

/// GPU device information
#[derive(Debug, Clone)]
pub struct GpuDeviceInfo {
    /// Device name
    pub name: String,
    /// Total memory in MB
    pub total_memory_mb: usize,
    /// Available memory in MB
    pub available_memory_mb: usize,
    /// Number of compute units/cores
    pub compute_units: usize,
    /// Maximum work group size
    pub max_work_group_size: usize,
    /// Supports mixed precision
    pub supports_mixed_precision: bool,
    /// Supports tensor cores
    pub supports_tensor_cores: bool,
}

/// GPU memory manager
pub struct GpuMemoryManager {
    allocated_bytes: usize,
    peak_allocated_bytes: usize,
    pool_size_bytes: usize,
    free_blocks: Vec<GpuMemoryBlock>,
}

/// GPU memory block
#[derive(Debug)]
pub struct GpuMemoryBlock {
    pub ptr: usize, // GPU memory pointer (abstracted)
    pub size_bytes: usize,
    pub in_use: bool,
}

/// GPU profiler for performance analysis
pub struct GpuProfiler {
    enabled: bool,
    kernel_times: Vec<(String, f64)>,
    memory_transfers: Vec<(String, usize, f64)>,
}

/// GPU kernel operations
pub trait GpuKernel {
    /// Execute kernel on GPU
    fn execute(&self, context: &GpuContext) -> Result<()>;

    /// Get estimated execution time
    fn estimated_time_ms(&self) -> f64;

    /// Get memory requirements
    fn memory_requirements_mb(&self) -> usize;
}

/// GPU-accelerated gradient boosting kernels
pub struct GradientBoostingKernels {
    /// Histogram computation kernel
    pub histogram_kernel: HistogramKernel,
    /// Split finding kernel
    pub split_kernel: SplitFindingKernel,
    /// Tree update kernel
    pub tree_update_kernel: TreeUpdateKernel,
    /// Prediction kernel
    pub prediction_kernel: PredictionKernel,
}

/// Histogram computation kernel
#[derive(Debug)]
pub struct HistogramKernel {
    pub n_features: usize,
    pub n_bins: usize,
    pub n_samples: usize,
}

/// Split finding kernel
#[derive(Debug)]
pub struct SplitFindingKernel {
    pub n_features: usize,
    pub n_bins: usize,
    pub regularization: Float,
}

/// Tree update kernel
#[derive(Debug)]
pub struct TreeUpdateKernel {
    pub max_depth: usize,
    pub learning_rate: Float,
}

/// Prediction kernel
#[derive(Debug)]
pub struct PredictionKernel {
    pub n_trees: usize,
    pub n_classes: usize,
}

/// GPU tensor operations
pub struct GpuTensorOps {
    context: Arc<GpuContext>,
}

/// GPU-accelerated ensemble trainer
pub struct GpuEnsembleTrainer {
    context: Arc<GpuContext>,
    kernels: GradientBoostingKernels,
    tensor_ops: GpuTensorOps,
}

impl GpuContext {
    /// Create new GPU context
    pub fn new(config: GpuConfig) -> Result<Self> {
        let device_info = Self::detect_device(&config)?;
        let memory_manager = GpuMemoryManager::new(config.memory_pool_size_mb * 1024 * 1024);
        let profiler = if config.enable_profiling {
            Some(GpuProfiler::new())
        } else {
            None
        };

        Ok(Self {
            config,
            device_info,
            memory_manager,
            profiler,
        })
    }

    /// Detect and initialize GPU device
    fn detect_device(config: &GpuConfig) -> Result<GpuDeviceInfo> {
        match config.backend {
            GpuBackend::Cuda => Self::detect_cuda_device(config.device_id),
            GpuBackend::OpenCL => Self::detect_opencl_device(config.device_id),
            GpuBackend::Metal => Self::detect_metal_device(config.device_id),
            GpuBackend::Vulkan => Self::detect_vulkan_device(config.device_id),
            GpuBackend::CpuFallback => Ok(Self::create_cpu_fallback_info()),
        }
    }

    /// Detect CUDA device
    fn detect_cuda_device(device_id: usize) -> Result<GpuDeviceInfo> {
        // In a real implementation, this would use CUDA APIs
        // For now, return a mock device info
        Ok(GpuDeviceInfo {
            name: format!("CUDA Device {}", device_id),
            total_memory_mb: 8192,
            available_memory_mb: 7168,
            compute_units: 80,
            max_work_group_size: 1024,
            supports_mixed_precision: true,
            supports_tensor_cores: true,
        })
    }

    /// Detect OpenCL device
    fn detect_opencl_device(device_id: usize) -> Result<GpuDeviceInfo> {
        // In a real implementation, this would use OpenCL APIs
        Ok(GpuDeviceInfo {
            name: format!("OpenCL Device {}", device_id),
            total_memory_mb: 4096,
            available_memory_mb: 3584,
            compute_units: 64,
            max_work_group_size: 256,
            supports_mixed_precision: false,
            supports_tensor_cores: false,
        })
    }

    /// Detect Metal device
    fn detect_metal_device(device_id: usize) -> Result<GpuDeviceInfo> {
        // In a real implementation, this would use Metal APIs
        Ok(GpuDeviceInfo {
            name: format!("Metal Device {}", device_id),
            total_memory_mb: 16384, // Unified memory on Apple Silicon
            available_memory_mb: 14336,
            compute_units: 32,
            max_work_group_size: 1024,
            supports_mixed_precision: true,
            supports_tensor_cores: false,
        })
    }

    /// Detect Vulkan device
    fn detect_vulkan_device(device_id: usize) -> Result<GpuDeviceInfo> {
        // In a real implementation, this would use Vulkan APIs
        Ok(GpuDeviceInfo {
            name: format!("Vulkan Device {}", device_id),
            total_memory_mb: 6144,
            available_memory_mb: 5376,
            compute_units: 56,
            max_work_group_size: 512,
            supports_mixed_precision: true,
            supports_tensor_cores: false,
        })
    }

    /// Create CPU fallback device info
    fn create_cpu_fallback_info() -> GpuDeviceInfo {
        GpuDeviceInfo {
            name: "CPU Fallback".to_string(),
            total_memory_mb: 8192, // Assume 8GB system RAM
            available_memory_mb: 6144,
            compute_units: std::thread::available_parallelism()
                .map(|n| n.get())
                .unwrap_or(4),
            max_work_group_size: 1,
            supports_mixed_precision: false,
            supports_tensor_cores: false,
        }
    }

    /// Check if GPU is available
    pub fn is_gpu_available(&self) -> bool {
        self.config.backend != GpuBackend::CpuFallback
    }

    /// Get device information
    pub fn device_info(&self) -> &GpuDeviceInfo {
        &self.device_info
    }

    /// Allocate GPU memory
    pub fn allocate_memory(&mut self, size_bytes: usize) -> Result<usize> {
        self.memory_manager.allocate(size_bytes)
    }

    /// Free GPU memory
    pub fn free_memory(&mut self, ptr: usize) -> Result<()> {
        self.memory_manager.free(ptr)
    }

    /// Start profiling
    pub fn start_profiling(&mut self) {
        if let Some(ref mut profiler) = self.profiler {
            profiler.start();
        }
    }

    /// Stop profiling and get results
    pub fn stop_profiling(&mut self) -> Option<ProfilingResults> {
        self.profiler.as_mut().map(|p| p.stop())
    }
}

impl GpuMemoryManager {
    /// Create new memory manager
    pub fn new(pool_size_bytes: usize) -> Self {
        Self {
            allocated_bytes: 0,
            peak_allocated_bytes: 0,
            pool_size_bytes,
            free_blocks: Vec::new(),
        }
    }

    /// Allocate memory block
    pub fn allocate(&mut self, size_bytes: usize) -> Result<usize> {
        if self.allocated_bytes + size_bytes > self.pool_size_bytes {
            return Err(SklearsError::InvalidInput(
                "GPU memory allocation failed: out of memory".to_string(),
            ));
        }

        // Find suitable free block or allocate new one
        for block in &mut self.free_blocks {
            if !block.in_use && block.size_bytes >= size_bytes {
                block.in_use = true;
                self.allocated_bytes += size_bytes;
                self.peak_allocated_bytes = self.peak_allocated_bytes.max(self.allocated_bytes);
                return Ok(block.ptr);
            }
        }

        // Allocate new block
        let ptr = self.free_blocks.len(); // Simple pointer simulation
        self.free_blocks.push(GpuMemoryBlock {
            ptr,
            size_bytes,
            in_use: true,
        });

        self.allocated_bytes += size_bytes;
        self.peak_allocated_bytes = self.peak_allocated_bytes.max(self.allocated_bytes);

        Ok(ptr)
    }

    /// Free memory block
    pub fn free(&mut self, ptr: usize) -> Result<()> {
        if let Some(block) = self.free_blocks.get_mut(ptr) {
            if block.in_use {
                block.in_use = false;
                self.allocated_bytes = self.allocated_bytes.saturating_sub(block.size_bytes);
                Ok(())
            } else {
                Err(SklearsError::InvalidInput(
                    "Attempted to free already freed memory".to_string(),
                ))
            }
        } else {
            Err(SklearsError::InvalidInput(
                "Invalid memory pointer".to_string(),
            ))
        }
    }

    /// Get memory usage statistics
    pub fn memory_stats(&self) -> (usize, usize, usize) {
        (
            self.allocated_bytes,
            self.peak_allocated_bytes,
            self.pool_size_bytes,
        )
    }
}

impl Default for GpuProfiler {
    fn default() -> Self {
        Self::new()
    }
}

impl GpuProfiler {
    /// Create new profiler
    pub fn new() -> Self {
        Self {
            enabled: false,
            kernel_times: Vec::new(),
            memory_transfers: Vec::new(),
        }
    }

    /// Start profiling
    pub fn start(&mut self) {
        self.enabled = true;
        self.kernel_times.clear();
        self.memory_transfers.clear();
    }

    /// Stop profiling and return results
    pub fn stop(&mut self) -> ProfilingResults {
        self.enabled = false;
        ProfilingResults {
            kernel_times: self.kernel_times.clone(),
            memory_transfers: self.memory_transfers.clone(),
            total_kernel_time: self.kernel_times.iter().map(|(_, t)| t).sum(),
            total_memory_transfer_time: self.memory_transfers.iter().map(|(_, _, t)| t).sum(),
        }
    }

    /// Record kernel execution time
    pub fn record_kernel(&mut self, name: String, time_ms: f64) {
        if self.enabled {
            self.kernel_times.push((name, time_ms));
        }
    }

    /// Record memory transfer
    pub fn record_memory_transfer(&mut self, name: String, bytes: usize, time_ms: f64) {
        if self.enabled {
            self.memory_transfers.push((name, bytes, time_ms));
        }
    }
}

/// Profiling results
#[derive(Debug, Clone)]
pub struct ProfilingResults {
    pub kernel_times: Vec<(String, f64)>,
    pub memory_transfers: Vec<(String, usize, f64)>,
    pub total_kernel_time: f64,
    pub total_memory_transfer_time: f64,
}

impl GpuKernel for HistogramKernel {
    fn execute(&self, _context: &GpuContext) -> Result<()> {
        // In a real implementation, this would execute GPU kernel code
        // For now, simulate execution
        std::thread::sleep(std::time::Duration::from_millis(
            (self.n_features * self.n_bins / 1000) as u64,
        ));
        Ok(())
    }

    fn estimated_time_ms(&self) -> f64 {
        (self.n_features * self.n_bins * self.n_samples) as f64 / 1_000_000.0
    }

    fn memory_requirements_mb(&self) -> usize {
        (self.n_features * self.n_bins * 8) / (1024 * 1024) // 8 bytes per histogram entry
    }
}

impl GpuKernel for SplitFindingKernel {
    fn execute(&self, _context: &GpuContext) -> Result<()> {
        // Simulate split finding computation
        std::thread::sleep(std::time::Duration::from_millis(
            (self.n_features * self.n_bins / 100) as u64,
        ));
        Ok(())
    }

    fn estimated_time_ms(&self) -> f64 {
        (self.n_features * self.n_bins) as f64 / 100_000.0
    }

    fn memory_requirements_mb(&self) -> usize {
        (self.n_features * self.n_bins * 4) / (1024 * 1024) // 4 bytes per split candidate
    }
}

impl GpuKernel for TreeUpdateKernel {
    fn execute(&self, _context: &GpuContext) -> Result<()> {
        // Simulate tree update computation
        std::thread::sleep(std::time::Duration::from_millis(1));
        Ok(())
    }

    fn estimated_time_ms(&self) -> f64 {
        self.max_depth as f64 * 0.1
    }

    fn memory_requirements_mb(&self) -> usize {
        (2_usize.pow(self.max_depth as u32) * 64) / (1024 * 1024) // Tree node storage
    }
}

impl GpuKernel for PredictionKernel {
    fn execute(&self, _context: &GpuContext) -> Result<()> {
        // Simulate prediction computation
        std::thread::sleep(std::time::Duration::from_millis((self.n_trees / 10) as u64));
        Ok(())
    }

    fn estimated_time_ms(&self) -> f64 {
        self.n_trees as f64 * 0.01
    }

    fn memory_requirements_mb(&self) -> usize {
        (self.n_trees * self.n_classes * 4) / (1024 * 1024) // Prediction storage
    }
}

impl GpuTensorOps {
    /// Create new GPU tensor operations
    pub fn new(context: Arc<GpuContext>) -> Self {
        Self { context }
    }

    /// Matrix multiplication on GPU
    pub fn matmul(&self, a: &Array2<Float>, b: &Array2<Float>) -> Result<Array2<Float>> {
        // In a real implementation, this would use GPU BLAS libraries
        // For now, fallback to CPU computation
        Ok(a.dot(b))
    }

    /// Element-wise operations on GPU
    pub fn elementwise_add(&self, a: &Array2<Float>, b: &Array2<Float>) -> Result<Array2<Float>> {
        // GPU element-wise addition
        Ok(a + b)
    }

    /// Reduction operations on GPU
    pub fn reduce_sum(&self, array: &Array2<Float>, axis: Option<usize>) -> Result<Array1<Float>> {
        match axis {
            Some(ax) => Ok(array.sum_axis(scirs2_core::ndarray::Axis(ax))),
            None => Ok(Array1::from_elem(1, array.sum())),
        }
    }

    /// Softmax on GPU
    pub fn softmax(&self, array: &Array2<Float>) -> Result<Array2<Float>> {
        let mut result = array.clone();

        for mut row in result.rows_mut() {
            let max_val = row.fold(Float::NEG_INFINITY, |a, &b| a.max(b));
            row.mapv_inplace(|x| (x - max_val).exp());
            let sum = row.sum();
            row /= sum;
        }

        Ok(result)
    }
}

impl GpuEnsembleTrainer {
    /// Create new GPU ensemble trainer
    pub fn new(config: GpuConfig) -> Result<Self> {
        let context = Arc::new(GpuContext::new(config)?);
        let kernels = GradientBoostingKernels {
            histogram_kernel: HistogramKernel {
                n_features: 100,
                n_bins: 256,
                n_samples: 10000,
            },
            split_kernel: SplitFindingKernel {
                n_features: 100,
                n_bins: 256,
                regularization: 0.01,
            },
            tree_update_kernel: TreeUpdateKernel {
                max_depth: 6,
                learning_rate: 0.1,
            },
            prediction_kernel: PredictionKernel {
                n_trees: 100,
                n_classes: 2,
            },
        };
        let tensor_ops = GpuTensorOps::new(context.clone());

        Ok(Self {
            context,
            kernels,
            tensor_ops,
        })
    }

    /// Train gradient boosting on GPU
    pub fn train_gradient_boosting(
        &self,
        x: &Array2<Float>,
        y: &Array1<Int>,
        n_estimators: usize,
    ) -> Result<Vec<Array1<Float>>> {
        // Simplified return type
        let mut models = Vec::new();

        for i in 0..n_estimators {
            // Compute histograms on GPU
            self.kernels.histogram_kernel.execute(&self.context)?;

            // Find best splits on GPU
            self.kernels.split_kernel.execute(&self.context)?;

            // Update tree on GPU
            self.kernels.tree_update_kernel.execute(&self.context)?;

            // Create a simple model representation
            models.push(Array1::zeros(x.ncols()));
        }

        Ok(models)
    }

    /// Predict using GPU-accelerated ensemble
    pub fn predict_ensemble(
        &self,
        models: &[Array1<Float>],
        x: &Array2<Float>,
    ) -> Result<Array1<Int>> {
        // Execute prediction kernel
        self.kernels.prediction_kernel.execute(&self.context)?;

        // Simplified prediction logic
        let mut predictions = Array1::zeros(x.nrows());

        for (i, row) in x.rows().into_iter().enumerate() {
            let mut sum = 0.0;
            for model in models {
                sum += row.dot(model);
            }
            predictions[i] = if sum > 0.0 { 1 } else { 0 };
        }

        Ok(predictions)
    }

    /// Get GPU context
    pub fn context(&self) -> &GpuContext {
        &self.context
    }

    /// Check GPU availability
    pub fn is_gpu_available(&self) -> bool {
        self.context.is_gpu_available()
    }
}

/// GPU backend detection
pub fn detect_available_backends() -> Vec<GpuBackend> {
    let mut backends = Vec::new();

    // In a real implementation, these would check for actual GPU support

    // Check for CUDA
    if is_cuda_available() {
        backends.push(GpuBackend::Cuda);
    }

    // Check for OpenCL
    if is_opencl_available() {
        backends.push(GpuBackend::OpenCL);
    }

    // Check for Metal (macOS)
    #[cfg(target_os = "macos")]
    if is_metal_available() {
        backends.push(GpuBackend::Metal);
    }

    // Check for Vulkan
    if is_vulkan_available() {
        backends.push(GpuBackend::Vulkan);
    }

    // Always have CPU fallback
    backends.push(GpuBackend::CpuFallback);

    backends
}

/// Check CUDA availability
fn is_cuda_available() -> bool {
    // In a real implementation, this would check for CUDA runtime
    false // Placeholder
}

/// Check OpenCL availability
fn is_opencl_available() -> bool {
    // In a real implementation, this would check for OpenCL drivers
    false // Placeholder
}

/// Check Metal availability
#[cfg(target_os = "macos")]
fn is_metal_available() -> bool {
    // In a real implementation, this would check for Metal framework
    true // Assume available on macOS
}

#[cfg(not(target_os = "macos"))]
fn is_metal_available() -> bool {
    false
}

/// Check Vulkan availability
fn is_vulkan_available() -> bool {
    // In a real implementation, this would check for Vulkan drivers
    false // Placeholder
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_gpu_config_default() {
        let config = GpuConfig::default();
        assert_eq!(config.backend, GpuBackend::CpuFallback);
        assert_eq!(config.device_id, 0);
        assert_eq!(config.batch_size, 1024);
    }

    #[test]
    fn test_gpu_context_creation() {
        let config = GpuConfig::default();
        let context = GpuContext::new(config).unwrap();
        assert!(!context.is_gpu_available()); // Should be CPU fallback
    }

    #[test]
    fn test_memory_manager() {
        let mut manager = GpuMemoryManager::new(1024 * 1024); // 1MB

        let ptr1 = manager.allocate(1024).unwrap();
        let ptr2 = manager.allocate(2048).unwrap();

        assert_ne!(ptr1, ptr2);

        manager.free(ptr1).unwrap();
        manager.free(ptr2).unwrap();

        let (allocated, _, total) = manager.memory_stats();
        assert_eq!(allocated, 0);
        assert_eq!(total, 1024 * 1024);
    }

    #[test]
    fn test_gpu_profiler() {
        let mut profiler = GpuProfiler::new();
        profiler.start();

        profiler.record_kernel("test_kernel".to_string(), 1.5);
        profiler.record_memory_transfer("test_transfer".to_string(), 1024, 0.5);

        let results = profiler.stop();
        assert_eq!(results.kernel_times.len(), 1);
        assert_eq!(results.memory_transfers.len(), 1);
        assert_eq!(results.total_kernel_time, 1.5);
    }

    #[test]
    fn test_histogram_kernel() {
        let kernel = HistogramKernel {
            n_features: 10,
            n_bins: 32,
            n_samples: 1000,
        };

        assert!(kernel.estimated_time_ms() > 0.0);
        assert!(kernel.memory_requirements_mb() >= 0);
    }

    #[test]
    fn test_gpu_tensor_ops() {
        let config = GpuConfig::default();
        let context = Arc::new(GpuContext::new(config).unwrap());
        let ops = GpuTensorOps::new(context);

        let a = array![[1.0, 2.0], [3.0, 4.0]];
        let b = array![[5.0, 6.0], [7.0, 8.0]];

        let result = ops.elementwise_add(&a, &b).unwrap();
        let expected = array![[6.0, 8.0], [10.0, 12.0]];

        assert_eq!(result, expected);
    }

    #[test]
    fn test_available_backends() {
        let backends = detect_available_backends();
        assert!(!backends.is_empty());
        assert!(backends.contains(&GpuBackend::CpuFallback));
    }

    #[test]
    fn test_gpu_ensemble_trainer() {
        let config = GpuConfig::default();
        let trainer = GpuEnsembleTrainer::new(config).unwrap();

        assert!(!trainer.is_gpu_available()); // Should be CPU fallback
        assert_eq!(trainer.context().device_info().name, "CPU Fallback");
    }
}