mod cpu_kernel;
#[cfg(feature = "cuda")]
mod cuda_kernel;
use super::ops::{try_unary_op, UnaryKernel};
use crate::{shapes::*, tensor::*};
#[repr(C)]
#[derive(Debug, Default, Copy, Clone)]
pub struct LnKernelOp;
pub fn ln<S: Shape, E: Dtype, D: UnaryKernel<LnKernelOp, E>, T: Tape<E, D>>(
t: Tensor<S, E, D, T>,
) -> Tensor<S, E, D, T> {
t.ln()
}
impl<S: Shape, E: Dtype, D: UnaryKernel<LnKernelOp, E>, T: Tape<E, D>> Tensor<S, E, D, T> {
pub fn ln(self) -> Self {
self.try_ln().unwrap()
}
pub fn try_ln(self) -> Result<Self, D::Err> {
try_unary_op(LnKernelOp, self)
}
}
#[cfg(test)]
mod tests {
use crate::{tensor::*, tensor_ops::*, tests::*};
#[test]
fn test_ln() {
let dev: TestDevice = Default::default();
let x = dev
.tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
.to_dtype::<TestDtype>();
let r = x.leaky_trace().ln();
let r_array = r.array();
assert!(r_array[0].is_nan());
assert!(r_array[1].is_nan());
assert!(r_array[2].is_infinite() && r_array[2].is_sign_negative());
assert_eq!(r_array[3], TestDtype::default());
let t: TestDtype = NumCast::from(2.0f64.ln()).unwrap();
assert_eq!(r_array[4], t);
let g = r.mean().backward();
assert_close_to_literal!(g.get(&x), [-0.1, -0.2, f64::INFINITY, 0.2, 0.1]);
}
}