dfdx 0.9.0

Ergonomic auto differentiation in Rust, with pytorch like apis.
Documentation 
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use super::utils::move_tape_and_add_backward_op;
use crate::prelude::*;

/// Reduces dimension `I` of the tensor by gathering the minimum value from that dimension.
///
/// **Pytorch equivalent**: `t.amin(I)`
///
/// **NOTE** This evenly distributes gradients between all equal minimum values, instead
/// of only exactly 1 value.
///
/// Example:
/// ```rust
/// # use dfdx::prelude::*;
/// let t = Tensor2D::new([[1.0, 2.0, 3.0], [-1.0, -2.0, -3.0]]);
/// let r: Tensor1D<2> = t.min_axis::<-1>();
/// assert_eq!(r.data(), &[1.0, -3.0]);
/// ```
pub fn min_axis<T: Reduce1<I>, const I: isize>(mut t: T) -> T::Reduced {
    let mut result = <T::Reduced as Tensor>::NoTape::zeros();
    T::DeviceR::reduce_into(t.data(), result.mut_data(), f32::min);

    // store derivative in t
    T::DeviceR::foreach_br(t.mut_data(), result.data(), &mut |l, r| {
        *l = if l == r { 1.0 } else { 0.0 }
    });

    move_tape_and_add_backward_op(t, result, move |mut t, result, grads| {
        let (t_grad, result_grad) = grads.mut_and_ref(&t, &result);

        T::DeviceR::foreach_br(t.mut_data(), result_grad, &mut |d, r| {
            *d *= r;
        });
        T::Device::add(t_grad, t.data());
    })
}

macro_rules! min_axis_impl {
    ($typename:ident, [$($Vs:tt),*]) => {
impl<$(const $Vs: usize, )* H: Tape> $typename<$($Vs, )* H> {
    /// Calls [min_axis()] on `self`.
    pub fn min_axis<const I: isize>(self) -> <Self as Reduce1<I>>::Reduced
    where
        Self: Reduce1<I>,
    {
        min_axis::<Self, I>(self)
    }
}
    };
}

min_axis_impl!(Tensor0D, []);
min_axis_impl!(Tensor1D, [M]);
min_axis_impl!(Tensor2D, [M, N]);
min_axis_impl!(Tensor3D, [M, N, O]);
min_axis_impl!(Tensor4D, [M, N, O, P]);

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

    #[test]
    fn test_valids_min_axis() {
        let _: Tensor0D = Tensor1D::<5>::zeros().min_axis::<-1>();

        let _: Tensor1D<3> = Tensor2D::<5, 3>::zeros().min_axis::<0>();
        let _: Tensor1D<5> = Tensor2D::<5, 3>::zeros().min_axis::<-1>();

        let _: Tensor2D<5, 3> = Tensor3D::<7, 5, 3>::zeros().min_axis::<0>();
        let _: Tensor2D<7, 3> = Tensor3D::<7, 5, 3>::zeros().min_axis::<1>();
        let _: Tensor2D<7, 5> = Tensor3D::<7, 5, 3>::zeros().min_axis::<-1>();

        let _: Tensor3D<7, 5, 3> = Tensor4D::<9, 7, 5, 3>::zeros().min_axis::<0>();
        let _: Tensor3D<9, 5, 3> = Tensor4D::<9, 7, 5, 3>::zeros().min_axis::<1>();
        let _: Tensor3D<9, 7, 3> = Tensor4D::<9, 7, 5, 3>::zeros().min_axis::<2>();
        let _: Tensor3D<9, 7, 5> = Tensor4D::<9, 7, 5, 3>::zeros().min_axis::<-1>();
    }

    #[test]
    fn test_min_axis_0_2d() {
        let t: Tensor2D<2, 3> = Tensor2D::new([[1.0, 1.0, 2.0], [3.0, -2.0, 2.0]]);
        let r = t.trace().min_axis::<0>();
        assert_eq!(r.data(), &[1.0, -2.0, 2.0]);
        let g = r.exp().mean().backward();
        assert_eq!(
            g.ref_gradient(&t),
            &[[0.90609396, 0.0, 2.463019], [0.0, 0.04511176, 2.463019]]
        );
    }

    #[test]
    fn test_min_axis_1_2d() {
        let t: Tensor2D<2, 3> = Tensor2D::new([[1.0, 1.0, 2.0], [3.0, -2.0, 2.0]]);
        let r = t.trace().min_axis::<-1>();
        assert_eq!(r.data(), &[1.0, -2.0]);
        let g = r.sum().backward();
        assert_eq!(g.ref_gradient(&t), &[[1.0, 1.0, 0.0], [0.0, 1.0, 0.0]]);
    }
}