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use super::utils::move_tape_and_add_backward_op;
use crate::devices::{Device, DeviceReduce, EqAccum, MinAccum, MulAccum};
use crate::gradients::Tape;
use crate::prelude::*;
pub fn min<T: Reduce<Axes>, Axes>(mut t: T) -> T::Reduced {
let mut result = <T::Reduced as Tensor>::NoTape::zeros();
T::DeviceR::reduce_into::<MinAccum>(result.mut_data(), t.data());
T::DeviceR::broadcast_into_no_reset::<EqAccum>(t.mut_data(), result.data());
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::broadcast_into_no_reset::<MulAccum>(t.mut_data(), result_grad);
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> {
pub fn min<T, Axes>(self) -> T where Self: ReduceTo<T, Axes> {
min(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::*;
use crate::tests::assert_close;
use rand::thread_rng;
#[test]
fn test_valids_min_axis() {
let _: Tensor0D = Tensor1D::<5>::zeros().min();
let _: Tensor1D<3> = Tensor2D::<5, 3>::zeros().min();
let _: Tensor1D<5> = Tensor2D::<5, 3>::zeros().min();
let _: Tensor2D<5, 3> = Tensor3D::<7, 5, 3>::zeros().min();
let _: Tensor2D<7, 3> = Tensor3D::<7, 5, 3>::zeros().min();
let _: Tensor2D<7, 5> = Tensor3D::<7, 5, 3>::zeros().min();
let _: Tensor3D<7, 5, 3> = Tensor4D::<9, 7, 5, 3>::zeros().min();
let _: Tensor3D<9, 5, 3> = Tensor4D::<9, 7, 5, 3>::zeros().min();
let _: Tensor3D<9, 7, 3> = Tensor4D::<9, 7, 5, 3>::zeros().min();
let _: Tensor3D<9, 7, 5> = Tensor4D::<9, 7, 5, 3>::zeros().min();
}
#[test]
fn test_min_axis_0_2d() {
let t: Tensor2D<2, 3> = tensor([[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> = tensor([[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]]);
}
#[test]
fn test_min_axes_3d_to_1d() {
let mut rng = thread_rng();
let t: Tensor3D<2, 3, 4> = TensorCreator::randn(&mut rng);
let r: Tensor1D<4, _> = t.trace().min::<_, Axes2<0, 1>>();
let r2: Tensor1D<4, _> = t.trace().min::<_, Axis<0>>().min::<_, Axis<0>>();
assert_close(r.data(), r2.data());
let g = r.mean().backward();
let g2 = r2.mean().backward();
assert_close(g.ref_gradient(&t), g2.ref_gradient(&t));
}
}