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use super::utils::move_tape_and_add_backward_op;
use crate::prelude::*;
pub fn sum<T: Tensor<Dtype = f32>>(t: T) -> Tensor0D<T::Tape> {
let result = Tensor0D::<NoneTape>::new(T::Device::reduce_all(t.data(), &mut |a, b| a + b));
move_tape_and_add_backward_op(t, result, move |t, result, grads| {
let (t_grad, result_grad) = grads.mut_and_ref(&t, &result);
T::Device::foreach_m(t_grad, &mut |v| *v += result_grad);
})
}
macro_rules! tensor_impl {
($typename:ident, [$($Vs:tt),*]) => {
impl<$(const $Vs: usize, )* H: Tape> $typename<$($Vs, )* H> {
pub fn sum(self) -> Tensor0D<<Self as Tensor>::Tape> {
sum(self)
}
}
};
}
tensor_impl!(Tensor0D, []);
tensor_impl!(Tensor1D, [M]);
tensor_impl!(Tensor2D, [M, N]);
tensor_impl!(Tensor3D, [M, N, O]);
tensor_impl!(Tensor4D, [M, N, O, P]);
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_sum_0d() {
let t: Tensor0D = Tensor0D::new(3.0);
let r = t.trace().sum();
assert_eq!(r.data(), &3.0);
let gradients = r.backward();
assert_eq!(gradients.ref_gradient(&t), &1.0);
}
#[test]
fn test_sum_1d() {
let t: Tensor1D<3> = Tensor1D::new([1.0, 2.0, 3.0]);
let r: Tensor0D<OwnedTape> = t.trace().sum();
assert_eq!(r.data(), &6.0);
let gradients = r.exp().backward();
assert_eq!(gradients.ref_gradient(&t), &[403.4288; 3]);
}
#[test]
fn test_sum_2d() {
let t: Tensor2D<2, 3> = Tensor2D::new([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]);
let r: Tensor0D<OwnedTape> = t.trace().sum();
assert_eq!(r.data(), &21.0);
let gradients = r.backward();
assert_eq!(gradients.ref_gradient(&t), &[[1.0; 3]; 2]);
}
#[test]
fn test_sum_3d() {
let t: Tensor3D<4, 2, 3> = Tensor3D::ones();
let r: Tensor0D<OwnedTape> = t.trace().sum();
assert_eq!(r.data(), &(4.0 * 2.0 * 3.0));
let gradients = r.backward();
assert_eq!(gradients.ref_gradient(&t), &[[[1.0; 3]; 2]; 4]);
}
}