scirs2_core/gpu/kernels/transform/

fft.rs

//! Fast Fourier Transform (FFT) GPU kernels
//!
//! Implements GPU-accelerated FFT for various sizes and dimensions.

use std::collections::HashMap;

use crate::gpu::kernels::{
    BaseKernel, DataType, GpuKernel, KernelMetadata, KernelParams, OperationType,
};
use crate::gpu::{GpuBackend, GpuError};

/// FFT direction
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FftDirection {
    /// Forward FFT (time to frequency)
    Forward,
    /// Inverse FFT (frequency to time)
    Inverse,
}

/// FFT dimension
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FftDimension {
    /// 1D FFT
    One,
    /// 2D FFT
    Two,
    /// 3D FFT
    Three,
}

/// Fast Fourier Transform kernel
pub struct FftKernel {
    base: BaseKernel,
    direction: FftDirection,
    dimension: FftDimension,
}

impl FftKernel {
    /// Create a new FFT kernel with default settings (1D forward FFT)
    pub fn new() -> Self {
        Self::with_params(FftDirection::Forward, FftDimension::One)
    }

    /// Create a new FFT kernel with specified parameters
    pub fn with_params(direction: FftDirection, dimension: FftDimension) -> Self {
        let metadata = KernelMetadata {
            workgroup_size: [256, 1, 1],
            local_memory_usage: 8192, // Varies based on implementation
            supports_tensor_cores: false,
            operationtype: OperationType::ComputeIntensive,
            backend_metadata: HashMap::new(),
        };

        let name = match (direction, dimension) {
            (FftDirection::Forward, FftDimension::One) => "fft_1d_forward",
            (FftDirection::Inverse, FftDimension::One) => "fft_1d_inverse",
            (FftDirection::Forward, FftDimension::Two) => "fft_2d_forward",
            (FftDirection::Inverse, FftDimension::Two) => "fft_2d_inverse",
            (FftDirection::Forward, FftDimension::Three) => "fft_3d_forward",
            (FftDirection::Inverse, FftDimension::Three) => "fft_3d_inverse",
        };

        // For a real implementation, we would have different optimized kernels
        // for each combination of direction and dimension.
        // Here we'll just provide a placeholder for the 1D forward FFT.

        let cuda_source = r#"
// CUDA implementation of FFT
// In a real implementation, we would likely use cuFFT library calls
// or implement the Cooley-Tukey algorithm for powers of 2
extern "C" __global__ void fft_1d_forward(
    const float2* __restrict__ input,
    float2* __restrict__ output,
    int n
) {
    // Implementation would go here
    // This is just a placeholder
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        output[i] = input[i];  // Placeholder, not a real FFT
    }
}
"#
        .to_string();

        let wgpu_source = r#"
// WebGPU implementation of FFT
// Placeholder for actual implementation
struct Complex {
    real: f32,
    imag: f32,
};

struct Uniforms {
    n: u32,
};

@group(0) @binding(0) var<uniform> uniforms: Uniforms;
@group(0) @binding(1) var<storage, read> input: array<Complex>;
@group(0) @binding(2) var<storage, write> output: array<Complex>;

@compute @workgroup_size(256)
#[allow(dead_code)]
fn fft_1d_forward(@builtin(global_invocation_id) global_id: vec3<u32>) {
    let i = global_id.x;

    if (i < uniforms.n) {
        // This is just a placeholder, not a real FFT
        output[i] = input[i];
    }
}
"#
        .to_string();

        let metal_source = r#"
// Metal implementation of FFT
// Placeholder for actual implementation
#include <metal_stdlib>
using namespace metal;

struct Complex {
    float real;
    float imag;
};

kernel void fft_1d_forward(
    const device Complex* input [[buffer(0)]],
    device Complex* output [[buffer(1)]],
    constant uint& n [[buffer(2)]],
    uint global_id [[thread_position_in_grid]])
{
    if (global_id < n) {
        // This is just a placeholder, not a real FFT
        output[global_id] = input[global_id];
    }
}
"#
        .to_string();

        let opencl_source = r#"
// OpenCL implementation of FFT
// Placeholder for actual implementation
typedef struct {
    float real;
    float imag;
} Complex;

__kernel void fft_1d_forward(
    __global const Complex* input,
    __global Complex* output,
    const int n)
{
    int i = get_global_id(0);

    if (i < n) {
        // This is just a placeholder, not a real FFT
        output[i] = input[i];
    }
}
"#
        .to_string();

        // ROCm (HIP) kernel - similar to CUDA
        let rocm_source = cuda_source.clone();

        Self {
            base: BaseKernel::new(
                name,
                &cuda_source,
                &rocm_source,
                &wgpu_source,
                &metal_source,
                &opencl_source,
                metadata,
            ),
            direction,
            dimension,
        }
    }

    /// Generate a kernel specialized for a specific size
    #[allow(dead_code)]
    fn specialized_for_size(&self, size: usize) -> Result<FftKernel, GpuError> {
        // In a real implementation, we would generate different kernels
        // optimized for different sizes (especially powers of 2)

        // For now, just return a new instance with the same parameters
        Ok(FftKernel::with_params(self.direction, self.dimension))
    }
}

impl GpuKernel for FftKernel {
    fn name(&self) -> &str {
        self.base.name()
    }

    fn source_for_backend(&self, backend: GpuBackend) -> Result<String, GpuError> {
        self.base.source_for_backend(backend)
    }

    fn metadata(&self) -> KernelMetadata {
        self.base.metadata()
    }

    fn can_specialize(&self, params: &KernelParams) -> bool {
        // We can specialize for complex types
        match params.datatype {
            DataType::Float32 | DataType::Float64 => {
                // We need input dimensions to specialize
                !params.input_dims.is_empty()
            }
            _ => false,
        }
    }

    fn specialize(&self, params: &KernelParams) -> Result<Box<dyn GpuKernel>, GpuError> {
        if !self.can_specialize(params) {
            return Err(GpuError::SpecializationNotSupported);
        }

        // Extract FFT size from input dimensions
        let _size = *params
            .input_dims
            .first()
            .ok_or_else(|| GpuError::InvalidParameter("input_dims cannot be empty".to_string()))?;

        // Check for direction in parameters
        let direction = if let Some(dir) = params.string_params.get("direction") {
            match dir.as_str() {
                "forward" => FftDirection::Forward,
                "inverse" => FftDirection::Inverse,
                _ => return Err(GpuError::InvalidParameter("direction".to_string())),
            }
        } else {
            self.direction
        };

        // Check for dimension in parameters
        let dimension = if let Some(dim) = params.string_params.get("dimension") {
            match dim.as_str() {
                "1d" => FftDimension::One,
                "2d" => FftDimension::Two,
                "3d" => FftDimension::Three,
                _ => return Err(GpuError::InvalidParameter("dimension".to_string())),
            }
        } else {
            self.dimension
        };

        // Create specialized kernel
        let specialized = FftKernel::with_params(direction, dimension);

        Ok(Box::new(specialized))
    }
}

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