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|
use candle_core::backend::BackendStorage;
use candle_core::cpu_backend;
use candle_core::test_utils::to_vec1_round;
use candle_core::{CpuStorage, CustomOp1, DType, Device, Error, Layout, Result, Shape, Tensor};
fn fwd<T: num_traits::Float>(v: T, alpha: f64) -> T {
if v.is_sign_positive() {
v
} else {
let alpha = T::from(alpha).unwrap_or(T::nan());
(v.exp() - T::one()) * alpha
}
}
struct Elu {
alpha: f64,
}
impl CustomOp1 for Elu {
fn name(&self) -> &'static str {
"elu"
}
fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)> {
let storage = candle_core::map_dtype!(
"elu",
s,
|s| cpu_backend::unary_map(s, l, |v| fwd(v, self.alpha)),
(BF16, F16, F32, F64)
);
Ok((storage, l.shape().clone()))
}
}
#[test]
fn custom_op1_no_backward() -> Result<()> {
let cpu = &Device::Cpu;
let t = Tensor::arange(0u32, 12u32, cpu)?.to_dtype(DType::F32)?;
let t = (t - 5.)?;
let elu_t = t.apply_op1_no_bwd(&Elu { alpha: 1. })?;
assert_eq!(
to_vec1_round(&elu_t, 4)?,
&[-0.9933, -0.9817, -0.9502, -0.8647, -0.6321, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
);
Ok(())
}
// Define a similar struct as Elu but with backward support.
fn bwd<T: num_traits::Float>(v: T, alpha: f64) -> T {
if v.is_sign_positive() {
T::one()
} else {
let alpha = T::from(alpha).unwrap_or(T::nan());
v.exp() * alpha
}
}
struct EluBackward {
alpha: f64,
}
impl CustomOp1 for EluBackward {
fn name(&self) -> &'static str {
"elu-bwd"
}
fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)> {
let storage = candle_core::map_dtype!(
"elu-bwd",
s,
|s| cpu_backend::unary_map(s, l, |v| bwd(v, self.alpha)),
(BF16, F16, F32, F64)
);
Ok((storage, l.shape().clone()))
}
}
struct EluWithBackward(Elu);
impl EluWithBackward {
fn new(alpha: f64) -> Self {
Self(Elu { alpha })
}
}
impl CustomOp1 for EluWithBackward {
fn name(&self) -> &'static str {
"elu"
}
fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)> {
self.0.cpu_fwd(s, l)
}
fn bwd(&self, arg: &Tensor, _res: &Tensor, grad_res: &Tensor) -> Result<Option<Tensor>> {
let alpha = self.0.alpha;
let bwd = arg.apply_op1(EluBackward { alpha })?;
Ok(Some(grad_res.mul(&bwd)?))
}
}
#[test]
fn custom_op1_with_backward() -> Result<()> {
let cpu = &Device::Cpu;
let t = candle_core::Var::new(&[-2f32, 0f32, 2f32], cpu)?;
let elu_t = t.apply_op1(EluWithBackward::new(2.))?;
assert_eq!(to_vec1_round(&elu_t, 4)?, &[-1.7293, 0.0, 2.0]);
let grads = elu_t.backward()?;
let grad_x = grads.get(&t).unwrap();
assert_eq!(to_vec1_round(grad_x, 4)?, [0.2707, 1.0, 1.0]);
Ok(())
}
impl candle_core::InplaceOp1 for Elu {
fn name(&self) -> &'static str {
"elu"
}
fn cpu_fwd(&self, s: &mut CpuStorage, _l: &Layout) -> Result<()> {
let alpha = self.alpha;
match s {
CpuStorage::BF16(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
CpuStorage::F16(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
CpuStorage::F32(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
CpuStorage::F64(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
_ => candle_core::bail!("unsupported dtype for inplace elu"),
}
Ok(())
}
}
#[test]
fn inplace_op1() -> Result<()> {
let cpu = &Device::Cpu;
let t = Tensor::arange(0u32, 12u32, cpu)?.to_dtype(DType::F32)?;
let t = (t - 5.)?;
t.inplace_op1(&Elu { alpha: 1. })?;
assert_eq!(
to_vec1_round(&t, 4)?,
&[-0.9933, -0.9817, -0.9502, -0.8647, -0.6321, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
);
Ok(())
}
|
