torsh-data 0.1.2

//! GPU-accelerated preprocessing exploration
//!
//! This module explores various approaches to accelerate data preprocessing
//! operations using GPU computing, including CUDA, OpenCL, and compute shaders.

use crate::error::{DataError, Result};
use std::collections::HashMap;
use torsh_tensor::Tensor;

/// GPU acceleration backend types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GpuBackend {
    /// NVIDIA CUDA backend
    Cuda,
    /// OpenCL backend for cross-platform support
    OpenCL,
    /// Vulkan compute shaders
    Vulkan,
    /// Metal Performance Shaders (macOS/iOS)
    Metal,
    /// WebGPU for browser environments
    WebGpu,
}

/// GPU memory management strategy
#[derive(Debug, Clone)]
pub enum MemoryStrategy {
    /// Allocate GPU memory per batch
    PerBatch,
    /// Pre-allocate memory pool
    MemoryPool { pool_size_mb: usize },
    /// Unified memory (CUDA)
    Unified,
    /// Zero-copy memory mapping
    ZeroCopy,
}

/// GPU acceleration configuration
#[derive(Debug, Clone)]
pub struct GpuAccelerationConfig {
    /// Backend to use
    pub backend: GpuBackend,
    /// Memory management strategy
    pub memory_strategy: MemoryStrategy,
    /// Maximum batch size for GPU processing
    pub max_batch_size: usize,
    /// Enable asynchronous processing
    pub async_processing: bool,
    /// Number of concurrent streams
    pub num_streams: usize,
    /// Fallback to CPU if GPU fails
    pub fallback_to_cpu: bool,
}

impl Default for GpuAccelerationConfig {
    fn default() -> Self {
        Self {
            backend: GpuBackend::Cuda,
            memory_strategy: MemoryStrategy::MemoryPool { pool_size_mb: 1024 },
            max_batch_size: 1024,
            async_processing: true,
            num_streams: 4,
            fallback_to_cpu: true,
        }
    }
}

/// GPU-accelerated preprocessing operations
pub trait GpuPreprocessing {
    /// Normalize tensor values on GPU
    fn gpu_normalize(&self, tensor: &Tensor<f32>, mean: f32, std: f32) -> Result<Tensor<f32>>;

    /// Apply data augmentation on GPU
    fn gpu_augment(
        &self,
        tensor: &Tensor<f32>,
        augmentation: &GpuAugmentation,
    ) -> Result<Tensor<f32>>;

    /// Resize images on GPU
    fn gpu_resize(&self, tensor: &Tensor<f32>, new_size: (usize, usize)) -> Result<Tensor<f32>>;

    /// Apply color space transformations
    fn gpu_color_transform(
        &self,
        tensor: &Tensor<f32>,
        transform: ColorTransform,
    ) -> Result<Tensor<f32>>;

    /// Batch multiple operations
    fn gpu_batch_process(
        &self,
        tensors: Vec<Tensor<f32>>,
        operations: &[GpuOperation],
    ) -> Result<Vec<Tensor<f32>>>;
}

/// GPU augmentation operations
#[derive(Debug, Clone)]
pub enum GpuAugmentation {
    /// Random rotation
    Rotation { max_angle: f32 },
    /// Random scaling
    Scale { min_scale: f32, max_scale: f32 },
    /// Random translation
    Translation { max_shift: f32 },
    /// Random brightness adjustment
    Brightness { delta: f32 },
    /// Random contrast adjustment
    Contrast { min_factor: f32, max_factor: f32 },
    /// Gaussian noise injection
    Noise { sigma: f32 },
    /// Random horizontal flip
    HorizontalFlip,
    /// Random vertical flip
    VerticalFlip,
}

/// Color space transformation types
#[derive(Debug, Clone, Copy)]
pub enum ColorTransform {
    /// RGB to grayscale
    RgbToGray,
    /// RGB to HSV
    RgbToHsv,
    /// HSV to RGB
    HsvToRgb,
    /// RGB to LAB
    RgbToLab,
    /// Gamma correction
    GammaCorrection { gamma: f32 },
}

/// GPU operation types for batching
#[derive(Debug, Clone)]
pub enum GpuOperation {
    Normalize { mean: f32, std: f32 },
    Augment(GpuAugmentation),
    Resize { width: usize, height: usize },
    ColorTransform(ColorTransform),
}

/// GPU acceleration manager
pub struct GpuAccelerationManager {
    config: GpuAccelerationConfig,
    // Placeholder fields for future GPU implementation
    #[allow(dead_code)]
    device: Box<dyn std::fmt::Debug>, // Simplified for now since Device trait usage is complex
    #[allow(dead_code)]
    memory_pool: Option<GpuMemoryPool>,
    #[allow(dead_code)]
    backend_handle: Option<BackendHandle>,
}

/// GPU memory pool for efficient memory management
struct GpuMemoryPool {
    // Placeholder fields for future implementation
    #[allow(dead_code)]
    allocated_blocks: HashMap<usize, Vec<*mut u8>>,
    #[allow(dead_code)]
    total_allocated: usize,
    #[allow(dead_code)]
    max_size: usize,
}

/// Backend-specific handle for GPU operations
#[allow(dead_code)]
enum BackendHandle {
    #[cfg(feature = "cuda")]
    Cuda(CudaHandle),
    #[cfg(feature = "opencl")]
    OpenCL(OpenCLHandle),
    #[cfg(feature = "vulkan")]
    Vulkan(VulkanHandle),
    #[cfg(feature = "metal")]
    Metal(MetalHandle),
    #[cfg(feature = "webgpu")]
    WebGpu(WebGpuHandle),
    Mock(MockHandle),
}

// Mock implementations for compilation without GPU features
#[derive(Debug)]
struct MockHandle;

#[cfg(feature = "cuda")]
#[derive(Debug)]
struct CudaHandle {
    // Placeholder fields for future CUDA implementation
    #[allow(dead_code)]
    context: *mut std::ffi::c_void,
    #[allow(dead_code)]
    streams: Vec<*mut std::ffi::c_void>,
}

#[cfg(feature = "opencl")]
#[derive(Debug)]
#[allow(dead_code)] // Placeholder for future OpenCL backend implementation
struct OpenCLHandle {
    context: *mut std::ffi::c_void,
    queue: *mut std::ffi::c_void,
}

#[cfg(feature = "vulkan")]
#[derive(Debug)]
#[allow(dead_code)] // Placeholder for future Vulkan backend implementation
struct VulkanHandle {
    device: *mut std::ffi::c_void,
    queue: *mut std::ffi::c_void,
}

#[cfg(feature = "metal")]
#[derive(Debug)]
#[allow(dead_code)] // Placeholder for future Metal backend implementation
struct MetalHandle {
    device: *mut std::ffi::c_void,
    command_queue: *mut std::ffi::c_void,
}

#[cfg(feature = "webgpu")]
#[derive(Debug)]
#[allow(dead_code)] // Placeholder for future WebGPU backend implementation
struct WebGpuHandle {
    device: *mut std::ffi::c_void,
    queue: *mut std::ffi::c_void,
}

impl GpuAccelerationManager {
    /// Create a new GPU acceleration manager
    pub fn new(config: GpuAccelerationConfig) -> Result<Self> {
        let device = match config.backend {
            GpuBackend::Cuda => {
                // Simplified: in a real implementation, we'd check for CUDA availability
                if cfg!(feature = "cuda") {
                    Box::new("CUDA Device") as Box<dyn std::fmt::Debug>
                } else {
                    return Err(DataError::GpuError("CUDA not available".to_string()));
                }
            }
            _ => Box::new("CPU Device") as Box<dyn std::fmt::Debug>, // Fallback for other backends
        };

        let memory_pool = match config.memory_strategy {
            MemoryStrategy::MemoryPool { pool_size_mb } => {
                Some(GpuMemoryPool::new(pool_size_mb * 1024 * 1024)?)
            }
            _ => None,
        };

        let backend_handle = Self::initialize_backend(&config)?;

        Ok(Self {
            config,
            device,
            memory_pool,
            backend_handle: Some(backend_handle),
        })
    }

    /// Initialize the GPU backend
    fn initialize_backend(config: &GpuAccelerationConfig) -> Result<BackendHandle> {
        match config.backend {
            #[cfg(feature = "cuda")]
            GpuBackend::Cuda => {
                // Initialize CUDA context and streams
                let handle = CudaHandle {
                    context: std::ptr::null_mut(),
                    streams: vec![std::ptr::null_mut(); config.num_streams],
                };
                Ok(BackendHandle::Cuda(handle))
            }
            #[cfg(feature = "opencl")]
            GpuBackend::OpenCL => {
                let handle = OpenCLHandle {
                    context: std::ptr::null_mut(),
                    queue: std::ptr::null_mut(),
                };
                Ok(BackendHandle::OpenCL(handle))
            }
            _ => {
                // Mock implementation for unsupported backends
                Ok(BackendHandle::Mock(MockHandle))
            }
        }
    }

    /// Check if GPU acceleration is available
    pub fn is_available(&self) -> bool {
        match self.config.backend {
            GpuBackend::Cuda => cfg!(feature = "cuda"),
            _ => false, // Other backends would need specific checks
        }
    }

    /// Get performance characteristics
    pub fn get_performance_info(&self) -> GpuPerformanceInfo {
        GpuPerformanceInfo {
            backend: self.config.backend,
            memory_bandwidth_gbps: self.estimate_memory_bandwidth(),
            compute_units: self.get_compute_units(),
            max_threads_per_block: self.get_max_threads_per_block(),
            shared_memory_kb: self.get_shared_memory_size(),
        }
    }

    fn estimate_memory_bandwidth(&self) -> f32 {
        match self.config.backend {
            GpuBackend::Cuda => 500.0, // Typical high-end GPU
            GpuBackend::OpenCL => 200.0,
            _ => 100.0,
        }
    }

    fn get_compute_units(&self) -> u32 {
        match self.config.backend {
            GpuBackend::Cuda => 80, // Typical high-end GPU
            _ => 32,
        }
    }

    fn get_max_threads_per_block(&self) -> u32 {
        match self.config.backend {
            GpuBackend::Cuda => 1024,
            _ => 256,
        }
    }

    fn get_shared_memory_size(&self) -> u32 {
        match self.config.backend {
            GpuBackend::Cuda => 48, // KB
            _ => 16,
        }
    }
}

impl GpuPreprocessing for GpuAccelerationManager {
    fn gpu_normalize(&self, tensor: &Tensor<f32>, mean: f32, std: f32) -> Result<Tensor<f32>> {
        if !self.is_available() && self.config.fallback_to_cpu {
            return self.cpu_normalize(tensor, mean, std);
        }

        // GPU implementation would go here
        // For now, return CPU implementation
        self.cpu_normalize(tensor, mean, std)
    }

    fn gpu_augment(
        &self,
        tensor: &Tensor<f32>,
        augmentation: &GpuAugmentation,
    ) -> Result<Tensor<f32>> {
        if !self.is_available() && self.config.fallback_to_cpu {
            return self.cpu_augment(tensor, augmentation);
        }

        // GPU implementation would go here
        self.cpu_augment(tensor, augmentation)
    }

    fn gpu_resize(&self, tensor: &Tensor<f32>, new_size: (usize, usize)) -> Result<Tensor<f32>> {
        if !self.is_available() && self.config.fallback_to_cpu {
            return self.cpu_resize(tensor, new_size);
        }

        // GPU implementation would go here
        self.cpu_resize(tensor, new_size)
    }

    fn gpu_color_transform(
        &self,
        tensor: &Tensor<f32>,
        transform: ColorTransform,
    ) -> Result<Tensor<f32>> {
        if !self.is_available() && self.config.fallback_to_cpu {
            return self.cpu_color_transform(tensor, transform);
        }

        // GPU implementation would go here
        self.cpu_color_transform(tensor, transform)
    }

    fn gpu_batch_process(
        &self,
        tensors: Vec<Tensor<f32>>,
        operations: &[GpuOperation],
    ) -> Result<Vec<Tensor<f32>>> {
        if !self.is_available() && self.config.fallback_to_cpu {
            return self.cpu_batch_process(tensors, operations);
        }

        // GPU batch implementation would go here
        self.cpu_batch_process(tensors, operations)
    }
}

impl GpuAccelerationManager {
    /// CPU fallback for normalization
    fn cpu_normalize(&self, tensor: &Tensor<f32>, _mean: f32, _std: f32) -> Result<Tensor<f32>> {
        // Simple CPU implementation
        let result = tensor.clone();
        // Normalization logic would go here
        Ok(result)
    }

    /// CPU fallback for augmentation
    fn cpu_augment(
        &self,
        tensor: &Tensor<f32>,
        _augmentation: &GpuAugmentation,
    ) -> Result<Tensor<f32>> {
        // Simple CPU implementation
        Ok(tensor.clone())
    }

    /// CPU fallback for resize
    fn cpu_resize(&self, tensor: &Tensor<f32>, _new_size: (usize, usize)) -> Result<Tensor<f32>> {
        // Simple CPU implementation
        Ok(tensor.clone())
    }

    /// CPU fallback for color transform
    fn cpu_color_transform(
        &self,
        tensor: &Tensor<f32>,
        _transform: ColorTransform,
    ) -> Result<Tensor<f32>> {
        // Simple CPU implementation
        Ok(tensor.clone())
    }

    /// CPU fallback for batch processing
    fn cpu_batch_process(
        &self,
        tensors: Vec<Tensor<f32>>,
        operations: &[GpuOperation],
    ) -> Result<Vec<Tensor<f32>>> {
        let mut results = Vec::new();
        for tensor in tensors {
            let mut result = tensor;
            for operation in operations {
                result = match operation {
                    GpuOperation::Normalize { mean, std } => {
                        self.cpu_normalize(&result, *mean, *std)?
                    }
                    GpuOperation::Augment(aug) => self.cpu_augment(&result, aug)?,
                    GpuOperation::Resize { width, height } => {
                        self.cpu_resize(&result, (*width, *height))?
                    }
                    GpuOperation::ColorTransform(transform) => {
                        self.cpu_color_transform(&result, *transform)?
                    }
                };
            }
            results.push(result);
        }
        Ok(results)
    }
}

impl GpuMemoryPool {
    fn new(max_size: usize) -> Result<Self> {
        Ok(Self {
            allocated_blocks: HashMap::new(),
            total_allocated: 0,
            max_size,
        })
    }
}

/// GPU performance information
#[derive(Debug, Clone)]
pub struct GpuPerformanceInfo {
    pub backend: GpuBackend,
    pub memory_bandwidth_gbps: f32,
    pub compute_units: u32,
    pub max_threads_per_block: u32,
    pub shared_memory_kb: u32,
}

/// GPU preprocessing pipeline for efficient batch processing
pub struct GpuPreprocessingPipeline {
    manager: GpuAccelerationManager,
    operations: Vec<GpuOperation>,
}

impl GpuPreprocessingPipeline {
    /// Create a new GPU preprocessing pipeline
    pub fn new(config: GpuAccelerationConfig) -> Result<Self> {
        let manager = GpuAccelerationManager::new(config)?;
        Ok(Self {
            manager,
            operations: Vec::new(),
        })
    }

    /// Add an operation to the pipeline
    pub fn add_operation(mut self, operation: GpuOperation) -> Self {
        self.operations.push(operation);
        self
    }

    /// Process a batch of tensors through the pipeline
    pub fn process_batch(&self, tensors: Vec<Tensor<f32>>) -> Result<Vec<Tensor<f32>>> {
        self.manager.gpu_batch_process(tensors, &self.operations)
    }

    /// Get performance information
    pub fn get_performance_info(&self) -> GpuPerformanceInfo {
        self.manager.get_performance_info()
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use torsh_tensor::creation::ones;

    #[test]
    fn test_gpu_acceleration_config() {
        let config = GpuAccelerationConfig::default();
        assert_eq!(config.backend, GpuBackend::Cuda);
        assert_eq!(config.max_batch_size, 1024);
        assert!(config.async_processing);
        assert!(config.fallback_to_cpu);
    }

    #[test]
    fn test_gpu_acceleration_manager_creation() {
        let config = GpuAccelerationConfig::default();

        // Should not fail even without GPU (fallback to CPU)
        let result = GpuAccelerationManager::new(config);

        // Since we might not have GPU available, we expect either success or specific error
        match result {
            Ok(manager) => {
                let perf_info = manager.get_performance_info();
                assert_eq!(perf_info.backend, GpuBackend::Cuda);
            }
            Err(DataError::GpuError(_)) => {
                // Expected when CUDA is not available
            }
            Err(e) => panic!("Unexpected error: {:?}", e),
        }
    }

    #[test]
    fn test_gpu_preprocessing_pipeline() -> Result<()> {
        let config = GpuAccelerationConfig::default();
        let pipeline_result = GpuPreprocessingPipeline::new(config);

        match pipeline_result {
            Ok(pipeline) => {
                let pipeline = pipeline
                    .add_operation(GpuOperation::Normalize {
                        mean: 0.5,
                        std: 0.5,
                    })
                    .add_operation(GpuOperation::Resize {
                        width: 224,
                        height: 224,
                    });

                // Test with mock tensor
                let tensor = ones::<f32>(&[1, 3, 128, 128])?;
                let result = pipeline.process_batch(vec![tensor]);

                // Should either succeed or fall back to CPU
                assert!(result.is_ok());
            }
            Err(DataError::GpuError(_)) => {
                // Expected when GPU is not available
            }
            Err(e) => return Err(e),
        }

        Ok(())
    }

    #[test]
    fn test_gpu_augmentation_types() {
        let rotation = GpuAugmentation::Rotation { max_angle: 30.0 };
        let scale = GpuAugmentation::Scale {
            min_scale: 0.8,
            max_scale: 1.2,
        };
        let noise = GpuAugmentation::Noise { sigma: 0.1 };

        // Test that augmentation types are created correctly
        match rotation {
            GpuAugmentation::Rotation { max_angle } => assert_eq!(max_angle, 30.0),
            _ => panic!("Wrong augmentation type"),
        }

        match scale {
            GpuAugmentation::Scale {
                min_scale,
                max_scale,
            } => {
                assert_eq!(min_scale, 0.8);
                assert_eq!(max_scale, 1.2);
            }
            _ => panic!("Wrong augmentation type"),
        }

        match noise {
            GpuAugmentation::Noise { sigma } => assert_eq!(sigma, 0.1),
            _ => panic!("Wrong augmentation type"),
        }
    }

    #[test]
    fn test_color_transforms() {
        let transforms = [
            ColorTransform::RgbToGray,
            ColorTransform::RgbToHsv,
            ColorTransform::HsvToRgb,
            ColorTransform::RgbToLab,
            ColorTransform::GammaCorrection { gamma: 2.2 },
        ];

        // Verify all transform types exist
        assert_eq!(transforms.len(), 5);

        match transforms[4] {
            ColorTransform::GammaCorrection { gamma } => assert_eq!(gamma, 2.2),
            _ => panic!("Wrong transform type"),
        }
    }

    #[test]
    fn test_memory_strategies() {
        let strategies = [
            MemoryStrategy::PerBatch,
            MemoryStrategy::MemoryPool { pool_size_mb: 512 },
            MemoryStrategy::Unified,
            MemoryStrategy::ZeroCopy,
        ];

        assert_eq!(strategies.len(), 4);

        match &strategies[1] {
            MemoryStrategy::MemoryPool { pool_size_mb } => assert_eq!(*pool_size_mb, 512),
            _ => panic!("Wrong memory strategy"),
        }
    }
}