burn-jit 0.16.1

Generic backend that can be compiled just-in-time to any shader language target
Documentation 
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#[burn_tensor_testgen::testgen(select_assign)]
mod tests {
    use super::*;
    use burn_tensor::{backend::Backend, Distribution, Int, Tensor};

    #[test]
    fn select_assign_should_work_with_multiple_workgroups_2d_dim0() {
        select_assign_same_as_ref(0, [256, 6]);
    }

    #[test]
    fn select_assign_should_work_with_multiple_workgroups_2d_dim1() {
        select_assign_same_as_ref(1, [6, 256]);
    }

    #[test]
    fn select_assign_should_work_with_multiple_workgroups_3d_dim0() {
        select_assign_same_as_ref(0, [256, 6, 6]);
    }

    #[test]
    fn select_assign_should_work_with_multiple_workgroups_3d_dim1() {
        select_assign_same_as_ref(1, [6, 256, 6]);
    }

    #[test]
    fn select_assign_should_work_with_multiple_workgroups_3d_dim2() {
        select_assign_same_as_ref(2, [6, 6, 256]);
    }

    fn select_assign_same_as_ref<const D: usize>(dim: usize, shape: [usize; D]) {
        TestBackend::seed(0);
        let tensor =
            Tensor::<TestBackend, D>::random(shape, Distribution::Default, &Default::default());
        let value =
            Tensor::<TestBackend, D>::random(shape, Distribution::Default, &Default::default());
        let indices = Tensor::<TestBackend, 1, Int>::from_data(
            Tensor::<TestBackend, 1>::random(
                [shape[dim]],
                Distribution::Uniform(0., shape[dim] as f64),
                &Default::default(),
            )
            .into_data(),
            &Default::default(),
        );
        let tensor_ref =
            Tensor::<ReferenceBackend, D>::from_data(tensor.to_data(), &Default::default());
        let value_ref =
            Tensor::<ReferenceBackend, D>::from_data(value.to_data(), &Default::default());
        let indices_ref =
            Tensor::<ReferenceBackend, 1, Int>::from_data(indices.to_data(), &Default::default());

        let actual = tensor.select_assign(dim, indices, value);
        let expected = tensor_ref.select_assign(dim, indices_ref, value_ref);

        expected
            .into_data()
            .assert_approx_eq(&actual.into_data(), 3);
    }
}