tenflowers-core 0.1.1

//! Advanced GPU Kernel Manager for TenfloweRS
//!
//! This module provides high-level integration of cutting-edge GPU optimization
//! techniques including Tensor Cores, SIMD-group operations, and wavefront
//! primitives across CUDA, Metal, and ROCm platforms.
//!
//! The goal is to achieve state-of-the-art performance by leveraging the latest
//! GPU architecture features and vendor-specific optimizations.

use crate::{DType, Device, Result, Tensor, TensorError};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use wgpu::util::DeviceExt;

/// Advanced GPU kernel execution strategies
#[derive(Debug, Clone, PartialEq)]
pub enum KernelStrategy {
    /// Use NVIDIA Tensor Cores for matrix operations (requires compute capability 7.0+)
    TensorCore {
        precision: TensorCorePrecision,
        tile_size: usize,
        /// Enable Hopper/Ada architecture optimizations (H100, RTX 40-series)
        hopper_optimizations: bool,
        /// Use FP8 precision for latest architectures
        fp8_support: bool,
    },
    /// Use Apple SIMD-group operations for Apple Silicon and AMD GPUs on macOS
    SIMDGroup {
        group_size: usize,
        vectorization_width: usize,
        /// Enable M3/M4 architecture-specific optimizations
        apple_neural_engine: bool,
        /// Advanced memory bandwidth optimization
        memory_coalescing: bool,
    },
    /// Use AMD wavefront primitives for optimal RDNA/GCN performance
    Wavefront {
        wavefront_size: usize,
        lds_optimization: bool,
        /// Enable RDNA3/4 architecture optimizations
        rdna3_optimizations: bool,
        /// Use AI accelerator units on RDNA3+
        ai_accelerator: bool,
    },
    /// Intel Arc GPU optimizations with Xe-Core utilization
    IntelXe {
        /// Xe-Core thread group size
        xe_thread_groups: usize,
        /// Enable Intel XMX AI acceleration
        xmx_acceleration: bool,
        /// Optimize for Arc Alchemist/Battlemage
        intel_gpu_gen: IntelGpuGeneration,
    },
    /// Fallback to standard WGPU compute shaders
    StandardCompute,
}

/// Tensor Core precision modes
#[derive(Debug, Clone, PartialEq)]
pub enum TensorCorePrecision {
    /// Half precision (FP16) for maximum throughput
    Float16,
    /// Brain Float 16 for training workloads
    BFloat16,
    /// Int8 for maximum inference performance
    Int8,
    /// FP8 precision for latest Hopper/Ada architectures
    Float8 { e4m3: bool, e5m2: bool },
    /// Int4 for extreme quantization on latest hardware
    Int4,
    /// Mixed precision with FP32 accumulation
    Mixed,
    /// Adaptive precision that switches based on compute requirements
    Adaptive,
}

/// Intel GPU generation for architecture-specific optimizations
#[derive(Debug, Clone, PartialEq)]
pub enum IntelGpuGeneration {
    /// Arc Alchemist (DG2)
    Alchemist,
    /// Arc Battlemage (BMG)
    Battlemage,
    /// Future Celestial architecture
    Celestial,
    /// Integrated Xe graphics
    XeIntegrated,
}

/// Advanced memory optimization strategies
#[derive(Debug, Clone, PartialEq)]
pub enum MemoryStrategy {
    /// Use GPU shared memory for data reuse
    SharedMemoryTiling {
        tile_size: usize,
        bank_conflict_avoidance: bool,
    },
    /// Optimize for memory bandwidth saturation
    BandwidthOptimized {
        prefetch_distance: usize,
        vectorization_factor: usize,
    },
    /// Use texture memory for read-only data
    TextureMemory { cache_optimization: bool },
    /// HBM optimization for high-end GPUs
    HbmOptimized {
        memory_channels: usize,
        interleaving: bool,
    },
}

/// Performance optimization hints for kernel selection
#[derive(Debug, Clone)]
pub struct OptimizationHints {
    /// Operation is compute-bound vs memory-bound
    pub compute_intensity: ComputeIntensity,
    /// Expected tensor shapes for optimization
    pub tensor_shapes: Vec<Vec<usize>>,
    /// Whether this operation will be repeated many times
    pub is_repetitive: bool,
    /// Memory access pattern characteristics
    pub memory_pattern: MemoryPattern,
    /// Target precision requirements
    pub precision_requirements: PrecisionRequirements,
}

#[derive(Debug, Clone, PartialEq)]
pub enum ComputeIntensity {
    /// Memory bandwidth limited (e.g., element-wise operations)
    MemoryBound,
    /// Compute limited (e.g., large matrix multiplications)
    ComputeBound,
    /// Balanced compute and memory requirements
    Balanced,
}

#[derive(Debug, Clone, PartialEq)]
pub enum MemoryPattern {
    /// Sequential access patterns (good for vectorization)
    Sequential,
    /// Strided access patterns (may benefit from prefetching)
    Strided { stride: usize },
    /// Random access patterns (cache-unfriendly)
    Random,
    /// Coalesced access suitable for GPU memory hierarchy
    Coalesced,
}

#[derive(Debug, Clone, PartialEq)]
pub enum PrecisionRequirements {
    /// Maximum precision required (FP64)
    HighPrecision,
    /// Standard precision (FP32)
    StandardPrecision,
    /// Reduced precision acceptable (FP16/BF16)
    ReducedPrecision,
    /// Minimum precision for inference (INT8)
    MinimalPrecision,
}

/// Advanced kernel manager that automatically selects optimal implementations
pub struct AdvancedKernelManager {
    /// Device capabilities and features
    device_info: Arc<RwLock<DeviceCapabilities>>,
    /// Cache of compiled kernels for reuse
    kernel_cache: Arc<RwLock<HashMap<String, CompiledKernel>>>,
    /// Performance profiling data
    performance_data: Arc<RwLock<HashMap<String, KernelPerformanceData>>>,
    /// Current optimization strategy
    strategy: KernelStrategy,
}

/// Device capabilities for kernel selection
#[derive(Debug, Clone)]
pub struct DeviceCapabilities {
    /// GPU vendor (NVIDIA, AMD, Apple, Intel)
    pub vendor: GpuVendor,
    /// Compute capability or architecture version
    pub compute_capability: String,
    /// Available memory bandwidth (GB/s)
    pub memory_bandwidth: f64,
    /// Number of compute units/streaming multiprocessors
    pub compute_units: usize,
    /// Supports Tensor Cores or equivalent
    pub has_tensor_cores: bool,
    /// Maximum threads per block/workgroup
    pub max_threads_per_block: usize,
    /// Shared memory size per block
    pub shared_memory_size: usize,
    /// Supports half precision operations
    pub supports_fp16: bool,
    /// Supports brain float 16
    pub supports_bf16: bool,
    /// Supports cooperative groups/SIMD-groups
    pub supports_coop_groups: bool,
}

#[derive(Debug, Clone, PartialEq)]
pub enum GpuVendor {
    Nvidia,
    AMD,
    Apple,
    Intel,
    Unknown,
}

/// Compiled kernel information
#[derive(Debug, Clone)]
pub struct CompiledKernel {
    /// Unique kernel identifier
    pub id: String,
    /// Kernel strategy used
    pub strategy: KernelStrategy,
    /// Compilation timestamp
    pub compiled_at: std::time::SystemTime,
    /// Kernel-specific parameters
    pub parameters: KernelParameters,
    /// Platform-specific kernel handle
    pub handle: KernelHandle,
}

/// Platform-agnostic kernel handle
#[derive(Debug, Clone)]
pub enum KernelHandle {
    #[cfg(feature = "cuda")]
    Cuda {
        module: String,
        function: String,
    },
    #[cfg(feature = "metal")]
    Metal {
        library: String,
        function: String,
    },
    #[cfg(feature = "rocm")]
    ROCm {
        module: String,
        function: String,
    },
    WGPU {
        shader: String,
        entry_point: String,
    },
}

/// Kernel execution parameters
#[derive(Debug, Clone)]
pub struct KernelParameters {
    /// Grid/threadgroup dimensions
    pub grid_size: (usize, usize, usize),
    /// Block/thread dimensions
    pub block_size: (usize, usize, usize),
    /// Shared memory requirements
    pub shared_memory: usize,
    /// Expected register usage
    pub register_count: usize,
}

/// Performance profiling data for kernel optimization
#[derive(Debug, Clone)]
pub struct KernelPerformanceData {
    /// Average execution time in microseconds
    pub avg_execution_time: f64,
    /// Memory bandwidth utilization percentage
    pub memory_bandwidth_util: f64,
    /// Compute utilization percentage
    pub compute_utilization: f64,
    /// Number of times this kernel has been executed
    pub execution_count: usize,
    /// Performance improvement over baseline
    pub speedup_factor: f64,
}

impl AdvancedKernelManager {
    /// Create a new kernel manager with device introspection
    pub fn new(device: &Device) -> Result<Self> {
        let device_info = Self::detect_device_capabilities(device)?;
        let strategy = Self::select_optimal_strategy(&device_info);

        Ok(AdvancedKernelManager {
            device_info: Arc::new(RwLock::new(device_info)),
            kernel_cache: Arc::new(RwLock::new(HashMap::new())),
            performance_data: Arc::new(RwLock::new(HashMap::new())),
            strategy,
        })
    }

    /// Detect device capabilities through runtime introspection
    fn detect_device_capabilities(device: &Device) -> Result<DeviceCapabilities> {
        match device {
            #[cfg(feature = "gpu")]
            Device::Gpu(gpu_device) => {
                // Query GPU properties through appropriate APIs
                let vendor = Self::detect_gpu_vendor()?;
                let compute_capability = Self::query_compute_capability(&vendor)?;

                Ok(DeviceCapabilities {
                    vendor,
                    compute_capability,
                    memory_bandwidth: Self::measure_memory_bandwidth()?,
                    compute_units: Self::query_compute_units()?,
                    has_tensor_cores: Self::detect_tensor_cores()?,
                    max_threads_per_block: Self::query_max_threads_per_block()?,
                    shared_memory_size: Self::query_shared_memory_size()?,
                    supports_fp16: Self::test_fp16_support()?,
                    supports_bf16: Self::test_bf16_support()?,
                    supports_coop_groups: Self::test_cooperative_groups()?,
                })
            }
            _ => Err(TensorError::InvalidArgument {
                operation: "AdvancedKernelManager::detect_device_capabilities".to_string(),
                reason: "Advanced kernels only supported on GPU devices".to_string(),
                context: None,
            }),
        }
    }

    /// Select optimal kernel strategy based on device capabilities
    fn select_optimal_strategy(capabilities: &DeviceCapabilities) -> KernelStrategy {
        match capabilities.vendor {
            GpuVendor::Nvidia if capabilities.has_tensor_cores => {
                KernelStrategy::TensorCore {
                    precision: if capabilities.supports_bf16 {
                        TensorCorePrecision::BFloat16
                    } else {
                        TensorCorePrecision::Float16
                    },
                    tile_size: 16, // 16x16 Tensor Core tiles
                    hopper_optimizations: capabilities.compute_capability.as_str() >= "9.0",
                    fp8_support: capabilities.compute_capability.as_str() >= "8.9",
                }
            }
            GpuVendor::Apple => {
                KernelStrategy::SIMDGroup {
                    group_size: 32,            // Apple Silicon SIMD-group size
                    vectorization_width: 8,    // Optimal for unified memory
                    apple_neural_engine: true, // Assume M-series chips have Neural Engine
                    memory_coalescing: true,
                }
            }
            GpuVendor::AMD => {
                KernelStrategy::Wavefront {
                    wavefront_size: 64, // AMD wavefront size
                    lds_optimization: true,
                    rdna3_optimizations: true, // Assume modern AMD GPUs
                    ai_accelerator: false,     // Conservative default
                }
            }
            _ => KernelStrategy::StandardCompute,
        }
    }

    /// Execute optimized matrix multiplication using the best available strategy
    pub fn optimized_matmul<T>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        hints: OptimizationHints,
    ) -> Result<Tensor<T>>
    where
        T: bytemuck::Pod + bytemuck::Zeroable + Clone + Default + Send + Sync + 'static,
    {
        let kernel_id = format!("matmul_{}x{}x{}", a.shape()[0], a.shape()[1], b.shape()[1]);

        // Check cache for pre-compiled kernel
        if let Some(kernel) = self.get_cached_kernel(&kernel_id)? {
            return self.execute_cached_matmul(a, b, &kernel);
        }

        // Select and compile optimal kernel based on strategy and hints
        let kernel = self.compile_optimal_matmul_kernel(a, b, &hints)?;
        self.cache_kernel(kernel_id.clone(), kernel.clone())?;

        // Execute the kernel
        let result = self.execute_matmul_kernel(a, b, &kernel)?;

        // Update performance data
        self.update_performance_data(&kernel_id, &kernel)?;

        Ok(result)
    }

    /// Compile optimal matrix multiplication kernel based on current strategy
    fn compile_optimal_matmul_kernel<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        hints: &OptimizationHints,
    ) -> Result<CompiledKernel> {
        match &self.strategy {
            KernelStrategy::TensorCore {
                precision,
                tile_size,
                ..
            } => self.compile_tensor_core_matmul(a, b, precision, *tile_size),
            KernelStrategy::SIMDGroup {
                group_size,
                vectorization_width,
                ..
            } => self.compile_simd_group_matmul(a, b, *group_size, *vectorization_width),
            KernelStrategy::Wavefront {
                wavefront_size,
                lds_optimization,
                ..
            } => self.compile_wavefront_matmul(a, b, *wavefront_size, *lds_optimization),
            KernelStrategy::StandardCompute => self.compile_standard_matmul(a, b),
            KernelStrategy::IntelXe {
                xe_thread_groups,
                xmx_acceleration,
                intel_gpu_gen,
            } => self.compile_intel_xe_matmul(
                a,
                b,
                *xe_thread_groups,
                *xmx_acceleration,
                intel_gpu_gen,
            ),
        }
    }

    /// Compile Tensor Core optimized matrix multiplication
    #[cfg(feature = "cuda")]
    fn compile_tensor_core_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        precision: &TensorCorePrecision,
        tile_size: usize,
    ) -> Result<CompiledKernel> {
        let kernel_source = match precision {
            TensorCorePrecision::Float16 => {
                include_str!("cuda_kernels/tensor_core_matmul.ptx")
            }
            TensorCorePrecision::BFloat16 => {
                include_str!("cuda_kernels/tensor_core_matmul.ptx") // BF16 variant
            }
            TensorCorePrecision::Int8 => {
                include_str!("cuda_kernels/tensor_core_matmul.ptx") // INT8 variant
            }
            TensorCorePrecision::Mixed => {
                include_str!("cuda_kernels/tensor_core_matmul.ptx") // Mixed precision variant
            }
            TensorCorePrecision::Float8 { .. } => {
                include_str!("cuda_kernels/tensor_core_matmul.ptx") // Float8 variant
            }
            TensorCorePrecision::Int4 => {
                include_str!("cuda_kernels/tensor_core_matmul.ptx") // Int4 variant
            }
            TensorCorePrecision::Adaptive => {
                include_str!("cuda_kernels/tensor_core_matmul.ptx") // Adaptive variant
            }
        };

        // Calculate optimal grid and block dimensions for Tensor Cores
        let m = a.shape()[0];
        let n = b.shape()[1];
        let grid_x = (m + tile_size - 1) / tile_size;
        let grid_y = (n + tile_size - 1) / tile_size;
        let block_x = 32; // Warp size
        let block_y = 8; // 4 warps per block

        Ok(CompiledKernel {
            id: format!("tensor_core_{}_{:?}", tile_size, precision),
            strategy: self.strategy.clone(),
            compiled_at: std::time::SystemTime::now(),
            parameters: KernelParameters {
                grid_size: (grid_x, grid_y, 1),
                block_size: (block_x, block_y, 1),
                shared_memory: tile_size * tile_size * 4, // 2 tiles * sizeof(element)
                register_count: 64,                       // Tensor Core register requirements
            },
            handle: KernelHandle::Cuda {
                module: "tensor_core_matmul".to_string(),
                function: match precision {
                    TensorCorePrecision::Float16 => "tensor_core_gemm_f16".to_string(),
                    TensorCorePrecision::BFloat16 => "tensor_core_gemm_bf16".to_string(),
                    TensorCorePrecision::Int8 => "tensor_core_gemm_int8".to_string(),
                    TensorCorePrecision::Mixed => "tensor_core_gemm_mixed".to_string(),
                    TensorCorePrecision::Float8 { .. } => "tensor_core_gemm_f8".to_string(),
                    TensorCorePrecision::Int4 => "tensor_core_gemm_int4".to_string(),
                    TensorCorePrecision::Adaptive => "tensor_core_gemm_adaptive".to_string(),
                },
            },
        })
    }

    /// Compile SIMD-group optimized matrix multiplication for Apple Silicon
    #[cfg(feature = "metal")]
    fn compile_simd_group_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        group_size: usize,
        vectorization_width: usize,
    ) -> Result<CompiledKernel> {
        let kernel_source = include_str!("metal_shaders/simd_group_matmul.metal");

        let m = a.shape()[0];
        let n = b.shape()[1];
        let tile_size = 32; // Optimal for Apple Silicon

        let threadgroup_x = (m + tile_size - 1) / tile_size;
        let threadgroup_y = (n + tile_size - 1) / tile_size;

        Ok(CompiledKernel {
            id: format!("simd_group_{}_{}", group_size, vectorization_width),
            strategy: self.strategy.clone(),
            compiled_at: std::time::SystemTime::now(),
            parameters: KernelParameters {
                grid_size: (threadgroup_x, threadgroup_y, 1),
                block_size: (group_size, 1, 1),
                shared_memory: tile_size * tile_size * 8, // Tile memory for A and B
                register_count: 32,                       // SIMD-group register usage
            },
            handle: KernelHandle::Metal {
                library: "simd_group_matmul".to_string(),
                function: match a.dtype() {
                    DType::Float32 => "simd_group_matmul_f32".to_string(),
                    DType::Float16 => "simd_group_matmul_f16".to_string(),
                    _ => {
                        return Err(TensorError::unsupported_operation_simple(format!(
                            "SIMD-group matmul: Unsupported dtype: {:?}",
                            a.dtype()
                        )))
                    }
                },
            },
        })
    }

    /// Compile wavefront optimized matrix multiplication for AMD GPUs
    #[cfg(feature = "rocm")]
    fn compile_wavefront_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        wavefront_size: usize,
        lds_optimization: bool,
    ) -> Result<CompiledKernel> {
        let kernel_source = include_str!("rocm_kernels/wavefront_optimized_matmul.hip");

        let m = a.shape()[0];
        let n = b.shape()[1];
        let tile_size = 64; // Optimal for GCN/RDNA

        let grid_x = (m + tile_size - 1) / tile_size;
        let grid_y = (n + tile_size - 1) / tile_size;
        let block_size = wavefront_size * 4; // 4 wavefronts per workgroup

        Ok(CompiledKernel {
            id: format!("wavefront_{}_{}", wavefront_size, lds_optimization),
            strategy: self.strategy.clone(),
            compiled_at: std::time::SystemTime::now(),
            parameters: KernelParameters {
                grid_size: (grid_x, grid_y, 1),
                block_size: (block_size, 1, 1),
                shared_memory: if lds_optimization {
                    tile_size * tile_size * 8 // LDS for A and B tiles
                } else {
                    0
                },
                register_count: 48, // Wavefront register requirements
            },
            handle: KernelHandle::ROCm {
                module: "wavefront_matmul".to_string(),
                function: match a.dtype() {
                    DType::Float32 => "wavefront_gemm_f32".to_string(),
                    DType::Float16 => "wavefront_gemm_f16".to_string(),
                    _ => {
                        return Err(TensorError::unsupported_operation_simple(format!(
                            "Wavefront matmul: Unsupported dtype: {:?}",
                            a.dtype()
                        )))
                    }
                },
            },
        })
    }

    /// Fallback to standard compute shader implementation
    fn compile_standard_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
    ) -> Result<CompiledKernel> {
        // Use existing WGPU matmul implementation as fallback
        let m = a.shape()[0];
        let n = b.shape()[1];
        let tile_size = 16; // Conservative tile size for compatibility

        let grid_x = (m + tile_size - 1) / tile_size;
        let grid_y = (n + tile_size - 1) / tile_size;

        Ok(CompiledKernel {
            id: "standard_compute".to_string(),
            strategy: self.strategy.clone(),
            compiled_at: std::time::SystemTime::now(),
            parameters: KernelParameters {
                grid_size: (grid_x, grid_y, 1),
                block_size: (tile_size, tile_size, 1),
                shared_memory: 0,
                register_count: 16,
            },
            handle: KernelHandle::WGPU {
                shader: "matmul_ops.wgsl".to_string(),
                entry_point: "matmul_kernel".to_string(),
            },
        })
    }

    // Helper methods for device detection and kernel management
    fn detect_gpu_vendor() -> Result<GpuVendor> {
        // Implementation would query GPU vendor through appropriate APIs
        // This is a placeholder returning Unknown for now
        Ok(GpuVendor::Unknown)
    }

    fn query_compute_capability(vendor: &GpuVendor) -> Result<String> {
        match vendor {
            GpuVendor::Nvidia => Ok("7.5".to_string()), // Example: RTX 2080
            GpuVendor::AMD => Ok("gfx906".to_string()), // Example: RX 5700 XT
            GpuVendor::Apple => Ok("M1".to_string()),   // Example: M1/M2
            _ => Ok("unknown".to_string()),
        }
    }

    fn measure_memory_bandwidth() -> Result<f64> {
        // Run a simple memory bandwidth test
        // This is a placeholder returning a typical value
        Ok(448.0) // GB/s for RTX 2080
    }

    fn query_compute_units() -> Result<usize> {
        Ok(46) // Example: RTX 2080 has 46 SMs
    }

    fn detect_tensor_cores() -> Result<bool> {
        Ok(true) // Would detect based on compute capability >= 7.0
    }

    fn query_max_threads_per_block() -> Result<usize> {
        Ok(1024) // Standard for modern GPUs
    }

    fn query_shared_memory_size() -> Result<usize> {
        Ok(49152) // 48KB for compute capability 7.0+
    }

    fn test_fp16_support() -> Result<bool> {
        Ok(true) // Most modern GPUs support FP16
    }

    fn test_bf16_support() -> Result<bool> {
        Ok(false) // Only newer GPUs support BF16
    }

    fn test_cooperative_groups() -> Result<bool> {
        Ok(true) // Compute capability 6.0+
    }

    fn get_cached_kernel(&self, kernel_id: &str) -> Result<Option<CompiledKernel>> {
        let cache = self
            .kernel_cache
            .read()
            .map_err(|_| TensorError::InvalidArgument {
                operation: "get_cached_kernel".to_string(),
                reason: "Failed to acquire read lock".to_string(),
                context: None,
            })?;
        Ok(cache.get(kernel_id).cloned())
    }

    fn cache_kernel(&self, kernel_id: String, kernel: CompiledKernel) -> Result<()> {
        let mut cache = self
            .kernel_cache
            .write()
            .map_err(|_| TensorError::InvalidArgument {
                operation: "cache_kernel".to_string(),
                reason: "Failed to acquire write lock".to_string(),
                context: None,
            })?;
        cache.insert(kernel_id, kernel);
        Ok(())
    }

    fn execute_cached_matmul<T>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        kernel: &CompiledKernel,
    ) -> Result<Tensor<T>>
    where
        T: bytemuck::Pod + bytemuck::Zeroable + Clone + Default + Send + Sync + 'static,
    {
        // Execute the cached kernel - implementation depends on kernel type
        self.execute_matmul_kernel(a, b, kernel)
    }

    fn execute_matmul_kernel<T>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        kernel: &CompiledKernel,
    ) -> Result<Tensor<T>>
    where
        T: bytemuck::Pod + bytemuck::Zeroable + Clone + Default + Send + Sync + 'static,
    {
        // Platform-specific kernel execution
        match &kernel.handle {
            #[cfg(feature = "cuda")]
            KernelHandle::Cuda { module, function } => self.execute_cuda_kernel(a, b, kernel),
            #[cfg(feature = "metal")]
            KernelHandle::Metal { library, function } => self.execute_metal_kernel(a, b, kernel),
            #[cfg(feature = "rocm")]
            KernelHandle::ROCm { module, function } => self.execute_rocm_kernel(a, b, kernel),
            KernelHandle::WGPU {
                shader,
                entry_point,
            } => self.execute_wgpu_kernel(a, b, kernel),
        }
    }

    #[cfg(feature = "cuda")]
    fn execute_cuda_kernel<T>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        kernel: &CompiledKernel,
    ) -> Result<Tensor<T>> {
        // CUDA kernel execution implementation
        // This would interface with the CUDA runtime using cuLaunchKernel or similar APIs

        // For now, return an informative error that CUDA execution is not yet implemented
        // Future implementation would:
        // 1. Load CUDA module from kernel.handle.module
        // 2. Get CUDA function from kernel.handle.function
        // 3. Allocate device memory for inputs/outputs
        // 4. Configure kernel launch parameters using kernel.parameters
        // 5. Launch kernel with cuLaunchKernel
        // 6. Synchronize and copy results back
        // 7. Create result tensor with GPU storage

        Err(TensorError::unsupported_operation_simple(
            "CUDA kernel execution: CUDA kernel execution not yet implemented. CUDA feature is enabled but requires cuLaunchKernel integration.".to_string()
        ))
    }

    #[cfg(feature = "metal")]
    fn execute_metal_kernel<T>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        kernel: &CompiledKernel,
    ) -> Result<Tensor<T>> {
        // Metal kernel execution implementation
        // This would interface with Metal Performance Shaders and Metal compute pipelines

        // For now, return an informative error that Metal execution is not yet implemented
        // Future implementation would:
        // 1. Create MTLDevice and MTLCommandQueue
        // 2. Load Metal library from kernel.handle.library
        // 3. Get Metal function from kernel.handle.function
        // 4. Create MTLComputePipelineState from function
        // 5. Allocate MTLBuffer objects for inputs/outputs
        // 6. Create MTLComputeCommandEncoder
        // 7. Set compute pipeline state and buffers
        // 8. Dispatch threadgroups using kernel.parameters dimensions
        // 9. Commit command buffer and wait for completion
        // 10. Create result tensor with Metal buffer storage

        Err(TensorError::unsupported_operation_simple(
            "Metal kernel execution: Metal kernel execution not yet implemented. Metal feature is enabled but requires Metal Performance Shaders integration.".to_string()
        ))
    }

    #[cfg(feature = "rocm")]
    fn execute_rocm_kernel<T>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        kernel: &CompiledKernel,
    ) -> Result<Tensor<T>> {
        // ROCm kernel execution implementation
        // This would interface with HIP runtime for AMD GPU execution

        // For now, return an informative error that ROCm execution is not yet implemented
        // Future implementation would:
        // 1. Initialize HIP runtime with hipInit()
        // 2. Load HIP module from kernel.handle.module using hipModuleLoad()
        // 3. Get HIP function from kernel.handle.function using hipModuleGetFunction()
        // 4. Allocate device memory with hipMalloc()
        // 5. Copy input data to device with hipMemcpy()
        // 6. Configure launch parameters using kernel.parameters
        // 7. Launch kernel with hipModuleLaunchKernel()
        // 8. Synchronize with hipDeviceSynchronize()
        // 9. Copy results back to host and create result tensor
        // 10. Free device memory with hipFree()

        Err(TensorError::unsupported_operation_simple(
            "ROCm kernel execution: ROCm kernel execution not yet implemented. ROCm feature is enabled but requires HIP runtime integration.".to_string()
        ))
    }

    fn execute_wgpu_kernel<T>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        kernel: &CompiledKernel,
    ) -> Result<Tensor<T>>
    where
        T: bytemuck::Pod + bytemuck::Zeroable + Clone + Default + Send + Sync + 'static,
    {
        use crate::gpu::buffer::GpuBuffer;
        use crate::tensor::TensorStorage;

        if let (TensorStorage::Gpu(gpu_a), TensorStorage::Gpu(gpu_b)) = (&a.storage, &b.storage) {
            let device_arc = Arc::clone(&gpu_a.device);
            let queue_arc = Arc::clone(&gpu_a.queue);

            let a_shape = a.shape().dims();
            let b_shape = b.shape().dims();
            let a_ndim = a_shape.len();
            let b_ndim = b_shape.len();

            // Calculate dimensions
            let m = a_shape[a_ndim - 2];
            let k = a_shape[a_ndim - 1];
            let n = b_shape[b_ndim - 1];
            let batch_size = kernel.parameters.grid_size.2.max(1);

            // Create output buffer based on result dimensions
            let result_shape = vec![batch_size, m, n];
            let output_size: usize = result_shape.iter().product();

            let output_buffer = device_arc.create_buffer(&wgpu::BufferDescriptor {
                label: Some("advanced_matmul_output"),
                size: (output_size * 4) as u64, // Assuming f32
                usage: wgpu::BufferUsages::STORAGE
                    | wgpu::BufferUsages::COPY_SRC
                    | wgpu::BufferUsages::COPY_DST,
                mapped_at_creation: false,
            });

            // Create parameters uniform buffer
            #[repr(C)]
            #[derive(Copy, Clone, bytemuck::Pod, bytemuck::Zeroable)]
            struct MatMulParams {
                m: u32,
                k: u32,
                n: u32,
                batch_size: u32,
            }

            let params = MatMulParams {
                m: m as u32,
                k: k as u32,
                n: n as u32,
                batch_size: batch_size as u32,
            };

            let params_buffer = device_arc.create_buffer_init(&wgpu::util::BufferInitDescriptor {
                label: Some("advanced_matmul_params"),
                contents: bytemuck::cast_slice(&[params]),
                usage: wgpu::BufferUsages::UNIFORM,
            });

            // Load compute shader based on kernel handle
            let shader_source = match &kernel.handle {
                KernelHandle::WGPU { shader, .. } => match shader.as_str() {
                    "matmul_ops.wgsl" => include_str!("shaders/matmul_ops.wgsl"),
                    _ => include_str!("shaders/matmul_ops.wgsl"), // Fallback
                },
                #[cfg(any(feature = "cuda", feature = "metal", feature = "rocm"))]
                _ => {
                    return Err(TensorError::InvalidArgument {
                        operation: "execute_wgpu_kernel".to_string(),
                        reason: "Expected WGPU kernel handle".to_string(),
                        context: None,
                    })
                }
            };

            let shader_module = device_arc.create_shader_module(wgpu::ShaderModuleDescriptor {
                label: Some("advanced_matmul_shader"),
                source: wgpu::ShaderSource::Wgsl(shader_source.into()),
            });

            // Create bind group layout
            let bind_group_layout =
                device_arc.create_bind_group_layout(&wgpu::BindGroupLayoutDescriptor {
                    label: Some("advanced_matmul_bind_group_layout"),
                    entries: &[
                        wgpu::BindGroupLayoutEntry {
                            binding: 0,
                            visibility: wgpu::ShaderStages::COMPUTE,
                            ty: wgpu::BindingType::Buffer {
                                ty: wgpu::BufferBindingType::Storage { read_only: true },
                                has_dynamic_offset: false,
                                min_binding_size: None,
                            },
                            count: None,
                        },
                        wgpu::BindGroupLayoutEntry {
                            binding: 1,
                            visibility: wgpu::ShaderStages::COMPUTE,
                            ty: wgpu::BindingType::Buffer {
                                ty: wgpu::BufferBindingType::Storage { read_only: true },
                                has_dynamic_offset: false,
                                min_binding_size: None,
                            },
                            count: None,
                        },
                        wgpu::BindGroupLayoutEntry {
                            binding: 2,
                            visibility: wgpu::ShaderStages::COMPUTE,
                            ty: wgpu::BindingType::Buffer {
                                ty: wgpu::BufferBindingType::Storage { read_only: false },
                                has_dynamic_offset: false,
                                min_binding_size: None,
                            },
                            count: None,
                        },
                        wgpu::BindGroupLayoutEntry {
                            binding: 3,
                            visibility: wgpu::ShaderStages::COMPUTE,
                            ty: wgpu::BindingType::Buffer {
                                ty: wgpu::BufferBindingType::Uniform,
                                has_dynamic_offset: false,
                                min_binding_size: None,
                            },
                            count: None,
                        },
                    ],
                });

            // Create bind group
            let bind_group = device_arc.create_bind_group(&wgpu::BindGroupDescriptor {
                label: Some("advanced_matmul_bind_group"),
                layout: &bind_group_layout,
                entries: &[
                    wgpu::BindGroupEntry {
                        binding: 0,
                        resource: gpu_a.buffer().as_entire_binding(),
                    },
                    wgpu::BindGroupEntry {
                        binding: 1,
                        resource: gpu_b.buffer().as_entire_binding(),
                    },
                    wgpu::BindGroupEntry {
                        binding: 2,
                        resource: output_buffer.as_entire_binding(),
                    },
                    wgpu::BindGroupEntry {
                        binding: 3,
                        resource: params_buffer.as_entire_binding(),
                    },
                ],
            });

            // Create compute pipeline
            let pipeline_layout =
                device_arc.create_pipeline_layout(&wgpu::PipelineLayoutDescriptor {
                    label: Some("advanced_matmul_pipeline_layout"),
                    bind_group_layouts: &[Some(&bind_group_layout)],
                    immediate_size: 0,
                });

            let entry_point = match &kernel.handle {
                KernelHandle::WGPU { entry_point, .. } => entry_point.as_str(),
                #[cfg(any(feature = "cuda", feature = "metal", feature = "rocm"))]
                _ => "matmul_kernel", // Fallback
            };

            let compute_pipeline =
                device_arc.create_compute_pipeline(&wgpu::ComputePipelineDescriptor {
                    label: Some("advanced_matmul_pipeline"),
                    layout: Some(&pipeline_layout),
                    module: &shader_module,
                    entry_point: Some(entry_point),
                    cache: None,
                    compilation_options: Default::default(),
                });

            // Dispatch compute shader using kernel parameters
            let mut encoder = device_arc.create_command_encoder(&wgpu::CommandEncoderDescriptor {
                label: Some("advanced_matmul_encoder"),
            });

            {
                let mut compute_pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
                    label: Some("advanced_matmul_pass"),
                    timestamp_writes: None,
                });

                compute_pass.set_pipeline(&compute_pipeline);
                compute_pass.set_bind_group(0, &bind_group, &[]);

                // Use kernel parameters for dispatch
                let grid_x = kernel.parameters.grid_size.0 as u32;
                let grid_y = kernel.parameters.grid_size.1 as u32;
                let grid_z = kernel.parameters.grid_size.2 as u32;

                compute_pass.dispatch_workgroups(grid_x, grid_y, grid_z);
            }

            queue_arc.submit(std::iter::once(encoder.finish()));
            device_arc.poll(wgpu::PollType::wait_indefinitely()).ok();

            // Create result tensor
            let device_id = match a.device() {
                crate::Device::Gpu(id) => *id,
                _ => {
                    return Err(TensorError::InvalidArgument {
                        operation: "execute_wgpu_kernel".to_string(),
                        reason: "Expected GPU device".to_string(),
                        context: None,
                    })
                }
            };

            let result_gpu = GpuBuffer::from_wgpu_buffer(
                output_buffer,
                device_arc,
                queue_arc,
                crate::Device::Gpu(device_id),
                output_size,
            );

            Ok(Tensor::from_gpu_buffer(
                result_gpu,
                crate::Shape::new(result_shape),
            ))
        } else {
            Err(TensorError::InvalidArgument {
                operation: "execute_wgpu_kernel".to_string(),
                reason: "Expected GPU tensors".to_string(),
                context: None,
            })
        }
    }

    fn update_performance_data(&self, kernel_id: &str, kernel: &CompiledKernel) -> Result<()> {
        // Update performance profiling data for kernel optimization
        let mut perf_data =
            self.performance_data
                .write()
                .map_err(|_| TensorError::InvalidArgument {
                    operation: "update_performance_data".to_string(),
                    reason: "Failed to acquire write lock".to_string(),
                    context: None,
                })?;

        // This would include actual timing and profiling measurements
        perf_data.insert(
            kernel_id.to_string(),
            KernelPerformanceData {
                avg_execution_time: 0.0, // Would be measured
                memory_bandwidth_util: 0.0,
                compute_utilization: 0.0,
                execution_count: 1,
                speedup_factor: 1.0,
            },
        );

        Ok(())
    }
}

/// Public API for advanced GPU optimizations
impl AdvancedKernelManager {
    /// Enable advanced GPU optimizations with automatic kernel selection
    pub fn enable_advanced_optimizations(&mut self) -> Result<()> {
        // Benchmark different strategies and select the best one
        self.benchmark_strategies()?;
        Ok(())
    }

    /// Benchmark different kernel strategies to find optimal performance
    fn benchmark_strategies(&mut self) -> Result<()> {
        // This would run micro-benchmarks on different kernel types
        // and select the fastest one for the current hardware
        Ok(())
    }

    /// Get current performance statistics
    pub fn get_performance_stats(&self) -> Result<HashMap<String, KernelPerformanceData>> {
        let perf_data = self
            .performance_data
            .read()
            .map_err(|_| TensorError::InvalidArgument {
                operation: "get_performance_stats".to_string(),
                reason: "Failed to acquire read lock".to_string(),
                context: None,
            })?;
        Ok(perf_data.clone())
    }

    /// Clear kernel cache and performance data
    pub fn clear_cache(&mut self) -> Result<()> {
        let mut cache = self
            .kernel_cache
            .write()
            .map_err(|_| TensorError::InvalidArgument {
                operation: "clear_cache".to_string(),
                reason: "Failed to acquire write lock".to_string(),
                context: None,
            })?;
        cache.clear();

        let mut perf_data =
            self.performance_data
                .write()
                .map_err(|_| TensorError::InvalidArgument {
                    operation: "clear_cache".to_string(),
                    reason: "Failed to acquire write lock".to_string(),
                    context: None,
                })?;
        perf_data.clear();

        Ok(())
    }

    /// Stub implementation for TensorCore matmul when CUDA feature is not available
    #[cfg(not(feature = "cuda"))]
    fn compile_tensor_core_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        _precision: &TensorCorePrecision,
        _tile_size: usize,
    ) -> Result<CompiledKernel> {
        self.compile_standard_matmul(a, b)
    }

    /// Stub implementation for SIMD group matmul when Metal feature is not available
    #[cfg(not(feature = "metal"))]
    fn compile_simd_group_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        _group_size: usize,
        _vectorization_width: usize,
    ) -> Result<CompiledKernel> {
        self.compile_standard_matmul(a, b)
    }

    /// Stub implementation for wavefront matmul when ROCm feature is not available
    #[cfg(not(feature = "rocm"))]
    fn compile_wavefront_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        _wavefront_size: usize,
        _lds_optimization: bool,
    ) -> Result<CompiledKernel> {
        self.compile_standard_matmul(a, b)
    }

    /// Compile Intel Xe optimized matrix multiplication
    ///
    /// Intel Arc GPUs feature Xe-Cores with XMX (Xe Matrix Extensions) for AI acceleration
    /// Similar to NVIDIA Tensor Cores and AMD Matrix Cores
    ///
    /// Note: Intel Xe GPU optimizations not yet fully implemented. Falls back to standard WGPU.
    /// Future implementation would use Intel oneAPI Level Zero or SYCL for XMX acceleration.
    fn compile_intel_xe_matmul<T: 'static>(
        &self,
        a: &Tensor<T>,
        b: &Tensor<T>,
        _xe_thread_groups: usize,
        _xmx_acceleration: bool,
        _intel_gpu_gen: &IntelGpuGeneration,
    ) -> Result<CompiledKernel> {
        // NOTE(v0.2): Implement Intel Xe GPU optimizations with XMX matrix extensions
        // For now, fallback to standard WGPU compute shader which works across all platforms
        self.compile_standard_matmul(a, b)
    }
}