xnn 0.2.0

A lightweight ML framework with GPU-first architecture
Documentation 
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//! Sum reduction tests.

use approx::assert_relative_eq;
use xnn::{Context, Tensor};

fn assert_approx(actual: &[f32], expected: &[f32], epsilon: f32) {
    assert_eq!(actual.len(), expected.len(), "length mismatch");
    for (a, e) in actual.iter().zip(expected.iter()) {
        assert_relative_eq!(a, e, epsilon = epsilon);
    }
}

#[test]
fn test_sum_reduce_2d_axis0() {
    let ctx = Context::try_default().unwrap();

    let a =
        Tensor::<f32>::from_shape_slice(&ctx, &[2, 3], &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
    let result = a.sum_reduce(&[0], false).unwrap();

    assert_eq!(result.dimensions(), &[1, 3]);
    assert_approx(&result.to_vec().unwrap(), &[5.0, 7.0, 9.0], 1e-4);
}

#[test]
fn test_sum_reduce_2d_axis1() {
    let ctx = Context::try_default().unwrap();

    let a =
        Tensor::<f32>::from_shape_slice(&ctx, &[2, 3], &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
    let result = a.sum_reduce(&[1], false).unwrap();

    assert_eq!(result.dimensions(), &[2, 1]);
    assert_approx(&result.to_vec().unwrap(), &[6.0, 15.0], 1e-4);
}

#[test]
fn test_sum_reduce_2d_all_axes() {
    let ctx = Context::try_default().unwrap();

    let a =
        Tensor::<f32>::from_shape_slice(&ctx, &[2, 3], &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
    let result = a.sum_reduce(&[0, 1], false).unwrap();

    assert_eq!(result.dimensions(), &[1, 1]);
    assert_approx(&result.to_vec().unwrap(), &[21.0], 1e-4);
}

#[test]
fn test_sum_reduce_3d_axis0() {
    let ctx = Context::try_default().unwrap();

    let data = [
        1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
    ];
    let a = Tensor::<f32>::from_shape_slice(&ctx, &[2, 2, 3], &data).unwrap();
    let result = a.sum_reduce(&[0], false).unwrap();

    assert_eq!(result.dimensions(), &[1, 2, 3]);
    assert_approx(
        &result.to_vec().unwrap(),
        &[8.0, 10.0, 12.0, 14.0, 16.0, 18.0],
        1e-4,
    );
}

#[test]
fn test_sum_reduce_3d_axis1() {
    let ctx = Context::try_default().unwrap();

    let data = [
        1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
    ];
    let a = Tensor::<f32>::from_shape_slice(&ctx, &[2, 2, 3], &data).unwrap();
    let result = a.sum_reduce(&[1], false).unwrap();

    assert_eq!(result.dimensions(), &[2, 1, 3]);
    assert_approx(
        &result.to_vec().unwrap(),
        &[5.0, 7.0, 9.0, 17.0, 19.0, 21.0],
        1e-4,
    );
}

#[test]
fn test_sum_reduce_3d_axis2() {
    let ctx = Context::try_default().unwrap();

    let data = [
        1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
    ];
    let a = Tensor::<f32>::from_shape_slice(&ctx, &[2, 2, 3], &data).unwrap();
    let result = a.sum_reduce(&[2], false).unwrap();

    assert_eq!(result.dimensions(), &[2, 2, 1]);
    assert_approx(&result.to_vec().unwrap(), &[6.0, 15.0, 24.0, 33.0], 1e-4);
}

#[test]
fn test_sum_reduce_3d_multiple_axes() {
    let ctx = Context::try_default().unwrap();

    let data = [
        1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
    ];
    let a = Tensor::<f32>::from_shape_slice(&ctx, &[2, 2, 3], &data).unwrap();
    let result = a.sum_reduce(&[1, 2], false).unwrap();

    assert_eq!(result.dimensions(), &[2, 1, 1]);
    assert_approx(&result.to_vec().unwrap(), &[21.0, 57.0], 1e-4);
}

#[test]
fn test_sum_reduce_normalize_2d() {
    let ctx = Context::try_default().unwrap();

    let a =
        Tensor::<f32>::from_shape_slice(&ctx, &[2, 3], &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
    let result = a.sum_reduce(&[1], true).unwrap();

    assert_eq!(result.dimensions(), &[2, 1]);
    assert_approx(&result.to_vec().unwrap(), &[2.0, 5.0], 1e-4);
}

#[test]
fn test_sum_reduce_normalize_all() {
    let ctx = Context::try_default().unwrap();

    let a =
        Tensor::<f32>::from_shape_slice(&ctx, &[2, 3], &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
    let result = a.sum_reduce(&[0, 1], true).unwrap();

    assert_eq!(result.dimensions(), &[1, 1]);
    assert_approx(&result.to_vec().unwrap(), &[3.5], 1e-4);
}

#[test]
fn test_sum_reduce_non_aligned() {
    let ctx = Context::try_default().unwrap();

    let a = Tensor::<f32>::from_shape_slice(
        &ctx,
        &[2, 5],
        &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
    )
    .unwrap();
    let result = a.sum_reduce(&[1], false).unwrap();

    assert_eq!(result.dimensions(), &[2, 1]);
    assert_approx(&result.to_vec().unwrap(), &[15.0, 40.0], 1e-4);
}

#[test]
fn test_mean_non_aligned() {
    let ctx = Context::try_default().unwrap();

    let a = Tensor::<f32>::from_shape_slice(
        &ctx,
        &[2, 5],
        &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
    )
    .unwrap();
    let result = a.sum_reduce(&[1], true).unwrap();

    assert_eq!(result.dimensions(), &[2, 1]);
    assert_approx(&result.to_vec().unwrap(), &[3.0, 8.0], 1e-4);
}

#[test]
fn test_sum_reduce_i32() {
    let ctx = Context::try_default().unwrap();

    let a = Tensor::<i32>::from_shape_slice(&ctx, &[2, 3], &[1, 2, 3, 4, 5, 6]).unwrap();
    let result = a.sum_reduce(&[1], false).unwrap();

    assert_eq!(result.dimensions(), &[2, 1]);
    assert_eq!(result.to_vec().unwrap(), vec![6, 15]);
}

#[test]
fn test_sum_reduce_u32() {
    let ctx = Context::try_default().unwrap();

    let a = Tensor::<u32>::from_shape_slice(&ctx, &[2, 3], &[1, 2, 3, 4, 5, 6]).unwrap();
    let result = a.sum_reduce(&[1], false).unwrap();

    assert_eq!(result.dimensions(), &[2, 1]);
    assert_eq!(result.to_vec().unwrap(), vec![6, 15]);
}

#[test]
#[allow(clippy::cast_precision_loss)]
fn test_sum_reduce_large() {
    let ctx = Context::try_default().unwrap();

    let size = 1024;
    let data: Vec<f32> = (0..size * size).map(|i| i as f32).collect();

    let a = Tensor::<f32>::from_shape_slice(&ctx, &[size, size], &data).unwrap();
    let result = a.sum_reduce(&[1], false).unwrap();

    assert_eq!(result.dimensions(), &[size, 1]);

    let out = result.to_vec().unwrap();
    for (row, &val) in out.iter().enumerate().take(size) {
        let expected: f32 = (0..size).map(|col| (row * size + col) as f32).sum();
        assert_relative_eq!(val, expected, epsilon = expected * 1e-4);
    }
}

#[test]
fn test_sum_reduce_invalid_axis() {
    let ctx = Context::try_default().unwrap();

    let a =
        Tensor::<f32>::from_shape_slice(&ctx, &[2, 3], &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
    let result = a.sum_reduce(&[5], false);

    assert!(result.is_err());
}

#[test]
fn test_sum_reduce_duplicate_axis() {
    let ctx = Context::try_default().unwrap();

    let a =
        Tensor::<f32>::from_shape_slice(&ctx, &[2, 3], &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
    let result = a.sum_reduce(&[1, 1], false);

    assert!(result.is_err());
}