forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
ActivationSiluKernel.cu
60 lines (51 loc) · 1.75 KB
/
ActivationSiluKernel.cu
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
#define TORCH_ASSERT_NO_OPERATORS
#define _USE_MATH_DEFINES
#include <ATen/native/Activation.h>
#include <cmath>
#include <thrust/tuple.h>
#include <ATen/AccumulateType.h>
#include <ATen/Dispatch.h>
#include <ATen/core/TensorBase.h>
#include <c10/core/Scalar.h>
#include <c10/cuda/CUDAMathCompat.h>
#include <ATen/cuda/ApplyGridUtils.cuh>
#include <ATen/cuda/detail/OffsetCalculator.cuh>
#include <ATen/native/cuda/Loops.cuh>
#include <c10/util/complex.h>
namespace at::native {
namespace {
void silu_kernel(TensorIteratorBase& iter) {
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(
at::ScalarType::Half,
at::ScalarType::BFloat16,
iter.dtype(),
"silu_cuda",
[&]() {
gpu_kernel(iter, [] GPU_LAMBDA(scalar_t x) -> scalar_t {
using opmath_t = at::opmath_type<scalar_t>;
const opmath_t x_acc = static_cast<opmath_t>(x);
return x_acc / (opmath_t(1) + ::exp(-x_acc));
});
});
}
void silu_backward_kernel(TensorIteratorBase& iter) {
AT_DISPATCH_FLOATING_TYPES_AND2(
at::ScalarType::Half,
at::ScalarType::BFloat16,
iter.dtype(),
"silu_backward_cuda",
[&]() {
gpu_kernel(iter, [] GPU_LAMBDA(scalar_t dy, scalar_t x) -> scalar_t {
using opmath_t = at::opmath_type<scalar_t>;
const opmath_t dy_acc = static_cast<opmath_t>(dy);
const opmath_t x_acc = static_cast<opmath_t>(x);
const opmath_t s_acc =
opmath_t(1) / (opmath_t(1) + c10::cuda::compat::exp(-x_acc));
return dy_acc * s_acc * (opmath_t(1) + x_acc * (opmath_t(1) - s_acc));
});
});
}
} // namespace
REGISTER_DISPATCH(silu_stub, &silu_kernel);
REGISTER_DISPATCH(silu_backward_stub, &silu_backward_kernel);
} // namespace at::native