ronn-providers 0.1.0

//! Custom CUDA kernels for high-performance GPU operations.
//!
//! This module provides custom CUDA kernel implementations for operations that
//! benefit from specialized GPU code beyond what Candle provides by default.

use std::collections::HashMap;
use std::sync::{Arc, Mutex};

use anyhow::{Result, anyhow};
use candle_core::{Device, Tensor as CandleTensor};

/// CUDA kernel compiler and cache manager.
#[derive(Debug)]
pub struct CudaKernelManager {
    /// Device for kernel compilation and execution.
    device: Device,
    /// Compiled kernel cache.
    kernel_cache: Arc<Mutex<HashMap<String, CompiledCudaKernel>>>,
    /// Kernel compilation options.
    compile_options: CudaCompileOptions,
}

/// Options for CUDA kernel compilation.
#[derive(Debug, Clone)]
pub struct CudaCompileOptions {
    /// Optimization level (0-3).
    pub optimization_level: u8,
    /// Enable fast math optimizations.
    pub fast_math: bool,
    /// Target compute capability (e.g., "7.5", "8.0").
    pub compute_capability: Option<String>,
    /// Additional compiler flags.
    pub extra_flags: Vec<String>,
}

impl Default for CudaCompileOptions {
    fn default() -> Self {
        Self {
            optimization_level: 3,
            fast_math: true,
            compute_capability: None,
            extra_flags: vec![],
        }
    }
}

/// A compiled CUDA kernel ready for execution.
#[derive(Debug, Clone)]
pub struct CompiledCudaKernel {
    /// Kernel name for identification.
    name: String,
    /// Compiled kernel binary (placeholder - would be actual CUDA module).
    binary: Vec<u8>,
    /// Kernel grid and block dimensions.
    launch_config: KernelLaunchConfig,
    /// Performance statistics.
    stats: KernelStats,
}

/// Configuration for CUDA kernel launch parameters.
#[derive(Debug, Clone)]
pub struct KernelLaunchConfig {
    /// Grid dimensions (blocks per grid).
    pub grid_dim: (u32, u32, u32),
    /// Block dimensions (threads per block).
    pub block_dim: (u32, u32, u32),
    /// Shared memory size in bytes.
    pub shared_mem_size: u32,
}

impl Default for KernelLaunchConfig {
    fn default() -> Self {
        Self {
            grid_dim: (1, 1, 1),
            block_dim: (256, 1, 1), // Common block size
            shared_mem_size: 0,
        }
    }
}

/// Performance statistics for a CUDA kernel.
#[derive(Debug, Clone, Default)]
pub struct KernelStats {
    /// Number of times the kernel has been executed.
    pub execution_count: u64,
    /// Total execution time in microseconds.
    pub total_time_us: u64,
    /// Average execution time in microseconds.
    pub avg_time_us: f64,
    /// Minimum execution time in microseconds.
    pub min_time_us: u64,
    /// Maximum execution time in microseconds.
    pub max_time_us: u64,
}

/// Predefined CUDA kernel templates for common operations.
pub struct CudaKernelTemplates;

impl CudaKernelManager {
    /// Create a new CUDA kernel manager.
    pub fn new(device: Device) -> Result<Self> {
        if !matches!(device, Device::Cuda(_)) {
            return Err(anyhow!("CUDA kernel manager requires CUDA device"));
        }

        Ok(Self {
            device,
            kernel_cache: Arc::new(Mutex::new(HashMap::new())),
            compile_options: CudaCompileOptions::default(),
        })
    }

    /// Create a kernel manager with custom compilation options.
    pub fn with_options(device: Device, options: CudaCompileOptions) -> Result<Self> {
        if !matches!(device, Device::Cuda(_)) {
            return Err(anyhow!("CUDA kernel manager requires CUDA device"));
        }

        Ok(Self {
            device,
            kernel_cache: Arc::new(Mutex::new(HashMap::new())),
            compile_options: options,
        })
    }

    /// Compile a CUDA kernel from source code.
    pub fn compile_kernel(&self, name: &str, source: &str) -> Result<CompiledCudaKernel> {
        // Check cache first
        {
            let cache = self.kernel_cache.lock().unwrap();
            if let Some(cached_kernel) = cache.get(name) {
                return Ok(cached_kernel.clone());
            }
        }

        // In a real implementation, this would:
        // 1. Compile CUDA source using NVRTC (NVIDIA Runtime Compilation)
        // 2. Optimize the kernel based on compile options
        // 3. Create a CUDA module from the compiled PTX
        // 4. Extract kernel function and metadata

        // For now, simulate kernel compilation
        let binary = self.simulate_kernel_compilation(source)?;
        let launch_config = self.determine_optimal_launch_config(name, source)?;

        let kernel = CompiledCudaKernel {
            name: name.to_string(),
            binary,
            launch_config,
            stats: KernelStats::default(),
        };

        // Cache the compiled kernel
        {
            let mut cache = self.kernel_cache.lock().unwrap();
            cache.insert(name.to_string(), kernel.clone());
        }

        Ok(kernel)
    }

    /// Execute a compiled CUDA kernel.
    pub fn execute_kernel(
        &self,
        kernel: &mut CompiledCudaKernel,
        inputs: &[CandleTensor],
        outputs: &mut [CandleTensor],
    ) -> Result<()> {
        let start = std::time::Instant::now();

        // In a real implementation, this would:
        // 1. Prepare kernel arguments (device pointers)
        // 2. Launch the CUDA kernel with specified grid/block dimensions
        // 3. Synchronize and handle any errors
        // 4. Update performance statistics

        // Simulate kernel execution
        self.simulate_kernel_execution(kernel, inputs, outputs)?;

        let execution_time = start.elapsed().as_micros() as u64;
        self.update_kernel_stats(&mut kernel.stats, execution_time);

        Ok(())
    }

    /// Get or compile a kernel for a specific operation type.
    pub fn get_optimized_kernel(
        &self,
        op_type: &str,
        tensor_shape: &[usize],
    ) -> Result<CompiledCudaKernel> {
        let kernel_source = match op_type {
            "FusedMatMulBias" => CudaKernelTemplates::fused_matmul_bias_kernel(tensor_shape),
            "OptimizedSoftmax" => CudaKernelTemplates::optimized_softmax_kernel(tensor_shape),
            "FusedConvBnRelu" => CudaKernelTemplates::fused_conv_bn_relu_kernel(tensor_shape),
            "WarpReduceSum" => CudaKernelTemplates::warp_reduce_sum_kernel(tensor_shape),
            "TensorCoreGemm" => CudaKernelTemplates::tensor_core_gemm_kernel(tensor_shape),
            "FastGelu" => CudaKernelTemplates::fast_gelu_kernel(tensor_shape),
            _ => {
                return Err(anyhow!(
                    "No optimized CUDA kernel available for operation: {}",
                    op_type
                ));
            }
        };

        let kernel_name = format!("{}_{}", op_type, self.shape_hash(tensor_shape));
        self.compile_kernel(&kernel_name, &kernel_source)
    }

    /// Clear the kernel cache to free memory.
    pub fn clear_cache(&self) {
        let mut cache = self.kernel_cache.lock().unwrap();
        cache.clear();
    }

    /// Get cache statistics.
    pub fn get_cache_stats(&self) -> CacheStats {
        let cache = self.kernel_cache.lock().unwrap();
        CacheStats {
            cached_kernels: cache.len(),
            total_executions: cache.values().map(|k| k.stats.execution_count).sum(),
            average_compile_time: 0.0, // Would track in real implementation
        }
    }

    // Private helper methods

    fn simulate_kernel_compilation(&self, source: &str) -> Result<Vec<u8>> {
        // Simulate compilation time based on source complexity
        let complexity = source.len();
        std::thread::sleep(std::time::Duration::from_millis(complexity as u64 / 100));

        // Return simulated compiled binary
        Ok(source.as_bytes().to_vec())
    }

    fn determine_optimal_launch_config(
        &self,
        name: &str,
        source: &str,
    ) -> Result<KernelLaunchConfig> {
        // Analyze kernel requirements and determine optimal configuration
        let mut config = KernelLaunchConfig::default();

        // Heuristics based on kernel type
        if name.contains("MatMul") || name.contains("Gemm") {
            config.block_dim = (16, 16, 1); // 2D block for matrix operations
            config.shared_mem_size = 16 * 16 * 4; // Shared memory for tiling
        } else if name.contains("Reduce") {
            config.block_dim = (256, 1, 1); // Large 1D block for reductions
            config.shared_mem_size = 256 * 4; // Shared memory for partial sums
        } else if name.contains("Conv") {
            config.block_dim = (16, 8, 1); // Optimized for convolution
            config.shared_mem_size = 8 * 1024; // Larger shared memory for feature maps
        }

        // Adjust based on source code analysis
        if source.contains("__shared__") {
            config.shared_mem_size = config.shared_mem_size.max(4096);
        }

        Ok(config)
    }

    fn simulate_kernel_execution(
        &self,
        kernel: &CompiledCudaKernel,
        _inputs: &[CandleTensor],
        _outputs: &mut [CandleTensor],
    ) -> Result<()> {
        // Simulate execution time based on kernel complexity
        let base_time = 10; // Base 10 microseconds
        let complexity_factor = kernel.binary.len() / 1000;
        let execution_time = base_time + complexity_factor;

        std::thread::sleep(std::time::Duration::from_micros(execution_time as u64));

        Ok(())
    }

    fn update_kernel_stats(&self, stats: &mut KernelStats, execution_time_us: u64) {
        stats.execution_count += 1;
        stats.total_time_us += execution_time_us;
        stats.avg_time_us = stats.total_time_us as f64 / stats.execution_count as f64;

        if stats.execution_count == 1 {
            stats.min_time_us = execution_time_us;
            stats.max_time_us = execution_time_us;
        } else {
            stats.min_time_us = stats.min_time_us.min(execution_time_us);
            stats.max_time_us = stats.max_time_us.max(execution_time_us);
        }
    }

    fn shape_hash(&self, shape: &[usize]) -> String {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        shape.hash(&mut hasher);
        format!("{:x}", hasher.finish())
    }
}

/// Statistics about the kernel cache.
#[derive(Debug, Clone)]
pub struct CacheStats {
    /// Number of kernels currently cached.
    pub cached_kernels: usize,
    /// Total number of kernel executions across all cached kernels.
    pub total_executions: u64,
    /// Average kernel compilation time in milliseconds.
    pub average_compile_time: f64,
}

impl CudaKernelTemplates {
    /// CUDA kernel for fused matrix multiplication with bias addition.
    pub fn fused_matmul_bias_kernel(shape: &[usize]) -> String {
        let m = shape.get(0).unwrap_or(&1);
        let n = shape.get(1).unwrap_or(&1);
        let k = shape.get(2).unwrap_or(&1);

        format!(
            r#"
extern "C" __global__ void fused_matmul_bias_kernel_{}_{}_{} (
    const float* __restrict__ A,
    const float* __restrict__ B,
    const float* __restrict__ bias,
    float* __restrict__ C,
    int M, int N, int K
) {{
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;

    if (row < M && col < N) {{
        float sum = 0.0f;
        for (int i = 0; i < K; ++i) {{
            sum += A[row * K + i] * B[i * N + col];
        }}
        C[row * N + col] = sum + bias[col];
    }}
}}
"#,
            m, n, k
        )
    }

    /// CUDA kernel for optimized softmax with numerical stability.
    pub fn optimized_softmax_kernel(shape: &[usize]) -> String {
        let size = shape.get(0).unwrap_or(&1);

        format!(
            r#"
extern "C" __global__ void optimized_softmax_kernel_{} (
    const float* __restrict__ input,
    float* __restrict__ output,
    int size
) {{
    int tid = blockIdx.x * blockDim.x + threadIdx.x;

    if (tid < size) {{
        // Find max value for numerical stability
        float max_val = input[0];
        for (int i = 1; i < size; ++i) {{
            max_val = fmaxf(max_val, input[i]);
        }}

        // Compute softmax
        float sum = 0.0f;
        float exp_val = expf(input[tid] - max_val);

        // Reduction to compute sum of all exp values
        __shared__ float shared_sum[256];
        shared_sum[threadIdx.x] = exp_val;
        __syncthreads();

        // Parallel reduction
        for (int s = blockDim.x / 2; s > 0; s >>= 1) {{
            if (threadIdx.x < s) {{
                shared_sum[threadIdx.x] += shared_sum[threadIdx.x + s];
            }}
            __syncthreads();
        }}

        if (threadIdx.x == 0) {{
            sum = shared_sum[0];
        }}
        __syncthreads();

        output[tid] = exp_val / sum;
    }}
}}
"#,
            size
        )
    }

    /// CUDA kernel for fused convolution + batch normalization + ReLU.
    pub fn fused_conv_bn_relu_kernel(shape: &[usize]) -> String {
        let channels = shape.get(0).unwrap_or(&1);
        let height = shape.get(1).unwrap_or(&1);
        let width = shape.get(2).unwrap_or(&1);

        format!(
            r#"
extern "C" __global__ void fused_conv_bn_relu_kernel_{}_{}_{}(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bn_weight,
    const float* __restrict__ bn_bias,
    const float* __restrict__ bn_mean,
    const float* __restrict__ bn_var,
    float* __restrict__ output,
    int channels, int height, int width, int kernel_size
) {{
    int c = blockIdx.x * blockDim.x + threadIdx.x;
    int h = blockIdx.y * blockDim.y + threadIdx.y;
    int w = blockIdx.z * blockDim.z + threadIdx.z;

    if (c < channels && h < height && w < width) {{
        float conv_result = 0.0f;

        // Convolution
        for (int kh = 0; kh < kernel_size; ++kh) {{
            for (int kw = 0; kw < kernel_size; ++kw) {{
                int ih = h + kh - kernel_size / 2;
                int iw = w + kw - kernel_size / 2;
                if (ih >= 0 && ih < height && iw >= 0 && iw < width) {{
                    conv_result += input[c * height * width + ih * width + iw] *
                                   weight[c * kernel_size * kernel_size + kh * kernel_size + kw];
                }}
            }}
        }}

        // Batch normalization
        float bn_result = (conv_result - bn_mean[c]) / sqrtf(bn_var[c] + 1e-5f);
        bn_result = bn_result * bn_weight[c] + bn_bias[c];

        // ReLU activation
        float final_result = fmaxf(0.0f, bn_result);

        output[c * height * width + h * width + w] = final_result;
    }}
}}
"#,
            channels, height, width
        )
    }

    /// CUDA kernel for warp-level reduction sum.
    pub fn warp_reduce_sum_kernel(_shape: &[usize]) -> String {
        r#"
extern "C" __global__ void warp_reduce_sum_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int size
) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    int lane = threadIdx.x % 32; // Warp lane ID

    float sum = (tid < size) ? input[tid] : 0.0f;

    // Warp-level reduction using shuffle instructions
    for (int offset = 16; offset > 0; offset /= 2) {
        sum += __shfl_down_sync(0xFFFFFFFF, sum, offset);
    }

    // First thread in each warp writes result
    if (lane == 0) {
        atomicAdd(output, sum);
    }
}
"#
        .to_string()
    }

    /// CUDA kernel optimized for Tensor Core operations (for supported GPUs).
    pub fn tensor_core_gemm_kernel(shape: &[usize]) -> String {
        let m = shape.get(0).unwrap_or(&1);
        let n = shape.get(1).unwrap_or(&1);
        let k = shape.get(2).unwrap_or(&1);

        format!(
            r#"
#include <mma.h>
using namespace nvcuda;

extern "C" __global__ void tensor_core_gemm_kernel_{}_{}_{} (
    const half* __restrict__ A,
    const half* __restrict__ B,
    float* __restrict__ C,
    int M, int N, int K
) {{
    // Tensor Core matrix multiply for 16x16x16 tiles
    const int WMMA_M = 16;
    const int WMMA_N = 16;
    const int WMMA_K = 16;

    int warpM = (blockIdx.x * blockDim.x + threadIdx.x) / 32;
    int warpN = (blockIdx.y * blockDim.y + threadIdx.y) / 32;

    // Declare the fragments
    wmma::fragment<wmma::matrix_a, WMMA_M, WMMA_N, WMMA_K, half, wmma::row_major> a_frag;
    wmma::fragment<wmma::matrix_b, WMMA_M, WMMA_N, WMMA_K, half, wmma::col_major> b_frag;
    wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, float> c_frag;

    // Initialize accumulator to zero
    wmma::fill_fragment(c_frag, 0.0f);

    // Loop over k
    for (int i = 0; i < K; i += WMMA_K) {{
        int aRow = warpM * WMMA_M;
        int aCol = i;
        int bRow = i;
        int bCol = warpN * WMMA_N;

        // Load the inputs
        wmma::load_matrix_sync(a_frag, A + aRow * K + aCol, K);
        wmma::load_matrix_sync(b_frag, B + bRow * N + bCol, N);

        // Perform the matrix multiplication
        wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
    }}

    // Store the output
    int cRow = warpM * WMMA_M;
    int cCol = warpN * WMMA_N;
    wmma::store_matrix_sync(C + cRow * N + cCol, c_frag, N, wmma::mem_row_major);
}}
"#,
            m, n, k
        )
    }

    /// CUDA kernel for fast GELU approximation.
    pub fn fast_gelu_kernel(shape: &[usize]) -> String {
        let size = shape.get(0).unwrap_or(&1);

        format!(
            r#"
extern "C" __global__ void fast_gelu_kernel_{} (
    const float* __restrict__ input,
    float* __restrict__ output,
    int size
) {{
    int tid = blockIdx.x * blockDim.x + threadIdx.x;

    if (tid < size) {{
        float x = input[tid];

        // Fast GELU approximation: x * sigmoid(1.702 * x)
        float sigmoid_arg = 1.702f * x;
        float sigmoid_val = 1.0f / (1.0f + expf(-sigmoid_arg));

        output[tid] = x * sigmoid_val;
    }}
}}
"#,
            size
        )
    }
}