xserv/crates/xserv-kernels/tests/gemm_test.rs

use half::bf16;
use xserv_kernels::{matmul, GemmBackend};
use xserv_tensor::{Device, Tensor};

fn cpu_matmul_f32(a: &[f32], b: &[f32], m: usize, n: usize, k: usize) -> Vec<f32> {
    let mut c = vec![0.0f32; m * n];
    for i in 0..m {
        for j in 0..n {
            let mut sum = 0.0f32;
            for kk in 0..k {
                sum += a[i * k + kk] * b[kk * n + j];
            }
            c[i * n + j] = sum;
        }
    }
    c
}

fn check_close_f32(result: &[f32], expected: &[f32], atol: f32) {
    assert_eq!(result.len(), expected.len());
    for (i, (r, e)) in result.iter().zip(expected).enumerate() {
        assert!(
            (r - e).abs() <= atol,
            "mismatch at index {i}: got {r}, expected {e}, diff {}",
            (r - e).abs()
        );
    }
}

fn check_close_bf16(result: &[bf16], expected: &[f32], atol: f32) {
    assert_eq!(result.len(), expected.len());
    for (i, (r, e)) in result.iter().zip(expected).enumerate() {
        let rv = r.to_f32();
        assert!(
            (rv - e).abs() <= atol,
            "mismatch at index {i}: got {rv}, expected {e}, diff {}",
            (rv - e).abs()
        );
    }
}

fn run_gemm_test_f32(backend: GemmBackend, m: usize, n: usize, k: usize) {
    xserv_cuda::device::set_device(0).unwrap();

    let a_data: Vec<f32> = (0..m * k).map(|i| ((i % 7) as f32 - 3.0) * 0.1).collect();
    let b_data: Vec<f32> = (0..k * n).map(|i| ((i % 11) as f32 - 5.0) * 0.1).collect();
    let expected = cpu_matmul_f32(&a_data, &b_data, m, n, k);

    let a = Tensor::from_slice(&a_data, &[m, k]).to_device(Device::Cuda(0));
    let b = Tensor::from_slice(&b_data, &[k, n]).to_device(Device::Cuda(0));
    let c = matmul(&a, &b, backend);

    let c_cpu = c.to_device(Device::Cpu);
    check_close_f32(c_cpu.as_slice::<f32>(), &expected, 1e-4);
}

fn run_gemm_test_bf16(backend: GemmBackend, m: usize, n: usize, k: usize) {
    xserv_cuda::device::set_device(0).unwrap();

    let a_f32: Vec<f32> = (0..m * k).map(|i| ((i % 7) as f32 - 3.0) * 0.1).collect();
    let b_f32: Vec<f32> = (0..k * n).map(|i| ((i % 11) as f32 - 5.0) * 0.1).collect();
    let expected = cpu_matmul_f32(&a_f32, &b_f32, m, n, k);

    let a_data: Vec<bf16> = a_f32.iter().map(|&v| bf16::from_f32(v)).collect();
    let b_data: Vec<bf16> = b_f32.iter().map(|&v| bf16::from_f32(v)).collect();

    let a = Tensor::from_slice(&a_data, &[m, k]).to_device(Device::Cuda(0));
    let b = Tensor::from_slice(&b_data, &[k, n]).to_device(Device::Cuda(0));
    let c = matmul(&a, &b, backend);

    let c_cpu = c.to_device(Device::Cpu);
    check_close_bf16(c_cpu.as_slice::<bf16>(), &expected, 0.1);
}

// --- F32 tests ---

#[test]
fn test_gemm_naive_f32_small() { run_gemm_test_f32(GemmBackend::Naive, 4, 4, 4); }

#[test]
fn test_gemm_naive_f32_medium() { run_gemm_test_f32(GemmBackend::Naive, 64, 64, 64); }

#[test]
fn test_gemm_naive_f32_rect() { run_gemm_test_f32(GemmBackend::Naive, 32, 64, 48); }

#[test]
fn test_gemm_tiled_f32_small() { run_gemm_test_f32(GemmBackend::Tiled, 4, 4, 4); }

#[test]
fn test_gemm_tiled_f32_medium() { run_gemm_test_f32(GemmBackend::Tiled, 128, 128, 128); }

#[test]
fn test_gemm_tiled_f32_rect() { run_gemm_test_f32(GemmBackend::Tiled, 65, 33, 97); }

#[test]
fn test_gemm_cublas_f32_small() { run_gemm_test_f32(GemmBackend::CuBlas, 4, 4, 4); }

#[test]
fn test_gemm_cublas_f32_medium() { run_gemm_test_f32(GemmBackend::CuBlas, 256, 256, 256); }

#[test]
fn test_gemm_cublas_f32_rect() { run_gemm_test_f32(GemmBackend::CuBlas, 65, 33, 97); }

// --- BF16 tests ---

#[test]
fn test_gemm_naive_bf16_small() { run_gemm_test_bf16(GemmBackend::Naive, 4, 4, 4); }

#[test]
fn test_gemm_naive_bf16_medium() { run_gemm_test_bf16(GemmBackend::Naive, 64, 64, 64); }

#[test]
fn test_gemm_tiled_bf16_small() { run_gemm_test_bf16(GemmBackend::Tiled, 4, 4, 4); }

#[test]
fn test_gemm_tiled_bf16_medium() { run_gemm_test_bf16(GemmBackend::Tiled, 128, 128, 128); }

#[test]
fn test_gemm_cublas_bf16_small() { run_gemm_test_bf16(GemmBackend::CuBlas, 4, 4, 4); }

#[test]
fn test_gemm_cublas_bf16_medium() { run_gemm_test_bf16(GemmBackend::CuBlas, 256, 256, 256); }

// --- Larger benchmark-style tests ---

#[test]
fn test_gemm_cublas_f32_1024() { run_gemm_test_f32(GemmBackend::CuBlas, 1024, 1024, 1024); }

#[test]
fn test_gemm_consistency_all_backends() {
    xserv_cuda::device::set_device(0).unwrap();

    let m = 64;
    let n = 64;
    let k = 64;
    let a_data: Vec<f32> = (0..m * k).map(|i| ((i % 7) as f32 - 3.0) * 0.1).collect();
    let b_data: Vec<f32> = (0..k * n).map(|i| ((i % 11) as f32 - 5.0) * 0.1).collect();

    let a = Tensor::from_slice(&a_data, &[m, k]).to_device(Device::Cuda(0));
    let b = Tensor::from_slice(&b_data, &[k, n]).to_device(Device::Cuda(0));

    let c_naive = matmul(&a, &b, GemmBackend::Naive).to_device(Device::Cpu);
    let c_tiled = matmul(&a, &b, GemmBackend::Tiled).to_device(Device::Cpu);
    let c_cublas = matmul(&a, &b, GemmBackend::CuBlas).to_device(Device::Cpu);

    let naive = c_naive.as_slice::<f32>();
    let tiled = c_tiled.as_slice::<f32>();
    let cublas = c_cublas.as_slice::<f32>();

    check_close_f32(naive, cublas, 1e-4);
    check_close_f32(tiled, cublas, 1e-4);
}