//! CUDA kernels for tensor operations

// Allow unused variables for CUDA kernel stubs
#![allow(unused_variables)]

pub mod neural_ops;
pub mod reduction_ops;
pub mod tensor_ops;

/// Stub module for CUDA kernel functions
///
/// These functions are placeholders that will be replaced with actual PTX kernel
/// implementations when CUDA kernel compilation is set up. Currently these are
/// no-ops to allow the code to compile.
#[allow(unused_variables)]
pub mod cuda_kernels {
    use crate::cuda::cudaStream_t as CUstream;

    // Neural network operation stubs
    pub unsafe fn launch_conv2d_f32(
        _input: *mut f32,
        _weight: *mut f32,
        _bias: *mut f32,
        _output: *mut f32,
        _batch_size: i32,
        _in_channels: i32,
        _out_channels: i32,
        _input_height: i32,
        _input_width: i32,
        _kernel_height: i32,
        _kernel_width: i32,
        _pad_h: i32,
        _pad_w: i32,
        _stride_h: i32,
        _stride_w: i32,
        _dilation_h: i32,
        _dilation_w: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_maxpool2d_f32(
        _input: *mut f32,
        _output: *mut f32,
        _batch_size: i32,
        _channels: i32,
        _input_height: i32,
        _input_width: i32,
        _output_height: i32,
        _output_width: i32,
        _kernel_height: i32,
        _kernel_width: i32,
        _pad_h: i32,
        _pad_w: i32,
        _stride_h: i32,
        _stride_w: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_batchnorm2d_f32(
        _input: *mut f32,
        _output: *mut f32,
        _weight: *mut f32,
        _bias: *mut f32,
        _running_mean: *mut f32,
        _running_var: *mut f32,
        _batch_size: i32,
        _channels: i32,
        _height: i32,
        _width: i32,
        _eps: f32,
        _momentum: f32,
        _training: bool,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_softmax_f32(
        _input: *mut f32,
        _output: *mut f32,
        _batch_size: i32,
        _classes: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    // Reduction operation stubs
    pub unsafe fn launch_sum_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _axis: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_mean_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _axis: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_max_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _axis: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_min_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _axis: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    // Tensor operation stubs
    pub unsafe fn launch_elementwise_add_f32(
        a: *mut f32,
        b: *mut f32,
        output: *mut f32,
        size: i32,
        stream: CUstream,
    ) {
        // CPU-based fallback for testing until PTX kernels are implemented
        let size_usize = size as usize;
        let mut host_a = vec![0.0f32; size_usize];
        let mut host_b = vec![0.0f32; size_usize];

        // Copy from device to host
        use crate::cuda::cuda_sys_compat as cuda_sys;
        use std::ffi::c_void;

        let _ = cuda_sys::cudaMemcpyAsync(
            host_a.as_mut_ptr() as *mut c_void,
            a as *const c_void,
            size_usize * std::mem::size_of::<f32>(),
            cuda_sys::cudaMemcpyKind_cudaMemcpyDeviceToHost,
            stream,
        );

        let _ = cuda_sys::cudaMemcpyAsync(
            host_b.as_mut_ptr() as *mut c_void,
            b as *const c_void,
            size_usize * std::mem::size_of::<f32>(),
            cuda_sys::cudaMemcpyKind_cudaMemcpyDeviceToHost,
            stream,
        );

        // Synchronize to ensure copies complete
        let _ = cuda_sys::cudaStreamSynchronize(stream);

        // Perform operation on CPU
        let host_output: Vec<f32> = host_a
            .iter()
            .zip(host_b.iter())
            .map(|(x, y)| x + y)
            .collect();

        // Copy result back to device
        let _ = cuda_sys::cudaMemcpyAsync(
            output as *mut c_void,
            host_output.as_ptr() as *const c_void,
            size_usize * std::mem::size_of::<f32>(),
            cuda_sys::cudaMemcpyKind_cudaMemcpyHostToDevice,
            stream,
        );
    }

    pub unsafe fn launch_elementwise_mul_f32(
        a: *mut f32,
        b: *mut f32,
        output: *mut f32,
        size: i32,
        stream: CUstream,
    ) {
        // CPU-based fallback for testing until PTX kernels are implemented
        let size_usize = size as usize;
        let mut host_a = vec![0.0f32; size_usize];
        let mut host_b = vec![0.0f32; size_usize];

        use crate::cuda::cuda_sys_compat as cuda_sys;
        use std::ffi::c_void;

        let _ = cuda_sys::cudaMemcpyAsync(
            host_a.as_mut_ptr() as *mut c_void,
            a as *const c_void,
            size_usize * std::mem::size_of::<f32>(),
            cuda_sys::cudaMemcpyKind_cudaMemcpyDeviceToHost,
            stream,
        );

        let _ = cuda_sys::cudaMemcpyAsync(
            host_b.as_mut_ptr() as *mut c_void,
            b as *const c_void,
            size_usize * std::mem::size_of::<f32>(),
            cuda_sys::cudaMemcpyKind_cudaMemcpyDeviceToHost,
            stream,
        );

        let _ = cuda_sys::cudaStreamSynchronize(stream);

        let host_output: Vec<f32> = host_a
            .iter()
            .zip(host_b.iter())
            .map(|(x, y)| x * y)
            .collect();

        let _ = cuda_sys::cudaMemcpyAsync(
            output as *mut c_void,
            host_output.as_ptr() as *const c_void,
            size_usize * std::mem::size_of::<f32>(),
            cuda_sys::cudaMemcpyKind_cudaMemcpyHostToDevice,
            stream,
        );
    }

    pub unsafe fn launch_elementwise_sub_f32(
        _a: *mut f32,
        _b: *mut f32,
        _output: *mut f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_elementwise_div_f32(
        _a: *mut f32,
        _b: *mut f32,
        _output: *mut f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_elementwise_relu_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_elementwise_sigmoid_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_elementwise_tanh_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_elementwise_gelu_f32(
        _input: *mut f32,
        _output: *mut f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_transpose_f32(
        _input: *mut f32,
        _output: *mut f32,
        _rows: i32,
        _cols: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_scalar_mul_f32(
        _input: *mut f32,
        _output: *mut f32,
        _scalar: f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    // Mixed precision conversion stubs
    pub unsafe fn launch_f32_to_f16(
        _input: *const f32,
        _output: *mut u16, // f16 stored as u16
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }

    pub unsafe fn launch_f16_to_f32(
        _input: *const u16, // f16 stored as u16
        _output: *mut f32,
        _size: i32,
        _stream: CUstream,
    ) {
        // Stub: No-op until PTX kernels are implemented
    }
}

use crate::cuda::error::CudaResult;
use crate::cuda::stream::CudaStream;
use cust::memory::DeviceCopy;
use cust::prelude::DevicePointer;

/// Launch configuration for CUDA kernels
#[derive(Debug, Clone)]
pub struct LaunchConfig {
    pub grid_size: (u32, u32, u32),
    pub block_size: (u32, u32, u32),
    pub shared_memory: usize,
}

impl LaunchConfig {
    /// Create 1D launch configuration
    pub fn new_1d(total_threads: usize, block_size: u32) -> Self {
        let grid_size = ((total_threads as u32 + block_size - 1) / block_size, 1, 1);
        Self {
            grid_size,
            block_size: (block_size, 1, 1),
            shared_memory: 0,
        }
    }

    /// Create 2D launch configuration
    pub fn new_2d(width: usize, height: usize, block_x: u32, block_y: u32) -> Self {
        let grid_x = (width as u32 + block_x - 1) / block_x;
        let grid_y = (height as u32 + block_y - 1) / block_y;

        Self {
            grid_size: (grid_x, grid_y, 1),
            block_size: (block_x, block_y, 1),
            shared_memory: 0,
        }
    }

    /// Set shared memory size
    pub fn with_shared_memory(mut self, shared_memory: usize) -> Self {
        self.shared_memory = shared_memory;
        self
    }

    /// Get optimal block size for 1D operations
    pub fn optimal_block_size_1d(device_props: &crate::cuda::device::DeviceProperties) -> u32 {
        // Use warp size as base, typically 32 for NVIDIA GPUs
        let warp_size = device_props.warp_size;

        // Choose block size based on compute capability
        if device_props.compute_capability >= 75 {
            512.min(device_props.max_threads_per_block)
        } else if device_props.compute_capability >= 50 {
            256.min(device_props.max_threads_per_block)
        } else {
            128.min(device_props.max_threads_per_block)
        }
    }

    /// Get optimal block size for 2D operations
    pub fn optimal_block_size_2d(
        device_props: &crate::cuda::device::DeviceProperties,
    ) -> (u32, u32) {
        let total_threads = Self::optimal_block_size_1d(device_props);

        // For 2D operations, use square blocks when possible
        let side = (total_threads as f32).sqrt() as u32;
        let side = (side / device_props.warp_size) * device_props.warp_size;

        if side * side <= device_props.max_threads_per_block {
            (side, side)
        } else {
            // Fall back to rectangular blocks
            (
                device_props.warp_size,
                total_threads / device_props.warp_size,
            )
        }
    }
}

/// Kernel function trait
pub trait KernelFunction<Args> {
    fn launch(&self, config: &LaunchConfig, stream: &CudaStream, args: Args) -> CudaResult<()>;
}

/// Generic kernel launcher
pub struct Kernel<F, Args> {
    function: F,
    _phantom: std::marker::PhantomData<Args>,
}

impl<F, Args> Kernel<F, Args> {
    pub fn new(function: F) -> Self {
        Self {
            function,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<F, Args> KernelFunction<Args> for Kernel<F, Args>
where
    F: Fn(&LaunchConfig, &CudaStream, Args) -> CudaResult<()>,
{
    fn launch(&self, config: &LaunchConfig, stream: &CudaStream, args: Args) -> CudaResult<()> {
        (self.function)(config, stream, args)
    }
}

// TODO: Kernel registry implementation requires proper cust version with Module/Function support
// Commented out until PTX compilation infrastructure is set up
/*
/// Kernel registry for compiled kernels
pub struct KernelRegistry {
    module: cust::Module,
}

impl KernelRegistry {
    /// Load kernels from PTX
    pub fn load_from_ptx(ptx: &str) -> CudaResult<Self> {
        let module = cust::Module::load_from_string(ptx)?;
        Ok(Self { module })
    }

    /// Get kernel function by name
    pub fn get_function(&self, name: &str) -> CudaResult<cust::Function> {
        Ok(self.module.get_function(name)?)
    }

    /// Launch kernel by name
    pub fn launch_kernel(
        &self,
        name: &str,
        config: &LaunchConfig,
        stream: &CudaStream,
        args: &[&dyn cust::DeviceCopy],
    ) -> CudaResult<()> {
        let func = self.get_function(name)?;
        let stream_raw = stream.raw();

        unsafe {
            cust::launch!(
                func<<<config.grid_size, config.block_size, config.shared_memory, stream_raw>>>(
                    args.as_ptr()
                )
            )?;
        }

        Ok(())
    }
}
*/

/// Kernel registry stub - placeholder until PTX compilation infrastructure is set up
#[derive(Debug, Clone, Default)]
pub struct KernelRegistry {
    /// Placeholder for registered kernels
    #[allow(dead_code)]
    kernels: std::collections::HashMap<String, ()>,
}

impl KernelRegistry {
    /// Create a new kernel registry
    pub fn new() -> Self {
        Self {
            kernels: std::collections::HashMap::new(),
        }
    }

    /// Load kernels from PTX (stub implementation)
    pub fn load_from_ptx(_ptx: &str) -> CudaResult<Self> {
        // Stub implementation - PTX loading infrastructure not yet set up
        Ok(Self::new())
    }
}

/// Common kernel arguments
#[derive(Debug)]
pub struct ElementwiseArgs<T: DeviceCopy> {
    pub input: DevicePointer<T>,
    pub output: DevicePointer<T>,
    pub size: usize,
}

#[derive(Debug)]
pub struct BinaryArgs<T: DeviceCopy> {
    pub lhs: DevicePointer<T>,
    pub rhs: DevicePointer<T>,
    pub output: DevicePointer<T>,
    pub size: usize,
}

#[derive(Debug)]
pub struct ReductionArgs<T: DeviceCopy> {
    pub input: DevicePointer<T>,
    pub output: DevicePointer<T>,
    pub size: usize,
    pub axis: i32,
}

#[derive(Debug)]
pub struct MatmulArgs<T: DeviceCopy> {
    pub a: DevicePointer<T>,
    pub b: DevicePointer<T>,
    pub c: DevicePointer<T>,
    pub m: usize,
    pub n: usize,
    pub k: usize,
    pub lda: usize,
    pub ldb: usize,
    pub ldc: usize,
}

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

    #[test]
    fn test_launch_config_1d() {
        let config = LaunchConfig::new_1d(1000, 256);
        assert_eq!(config.grid_size, (4, 1, 1)); // (1000 + 256 - 1) / 256 = 4
        assert_eq!(config.block_size, (256, 1, 1));
    }

    #[test]
    fn test_launch_config_2d() {
        let config = LaunchConfig::new_2d(1024, 768, 16, 16);
        assert_eq!(config.grid_size, (64, 48, 1)); // 1024/16 = 64, 768/16 = 48
        assert_eq!(config.block_size, (16, 16, 1));
    }

    #[test]
    fn test_optimal_block_sizes() {
        let props = crate::cuda::device::DeviceProperties {
            name: "Test GPU".to_string(),
            total_memory: 8 * 1024 * 1024 * 1024, // 8GB
            compute_capability: 75,
            multiprocessor_count: 80,
            max_threads_per_block: 1024,
            warp_size: 32,
            max_threads_per_multiprocessor: 1024,
            shared_memory_per_multiprocessor: 65536,
            max_blocks_per_multiprocessor: 16,
            registers_per_multiprocessor: 65536,
        };

        let block_1d = LaunchConfig::optimal_block_size_1d(&props);
        assert_eq!(block_1d, 512);

        let (block_x, block_y) = LaunchConfig::optimal_block_size_2d(&props);
        assert!(block_x * block_y <= props.max_threads_per_block);
    }
}
