forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
DistributionGeometricKernel.cu
60 lines (52 loc) · 2.09 KB
/
DistributionGeometricKernel.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
#include <ATen/Dispatch.h>
#include <ATen/ExpandUtils.h>
#include <ATen/NativeFunctions.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>
#include <ATen/AccumulateType.h>
#include <ATen/CUDAGenerator.h>
#include <ATen/native/UnaryOps.h>
#include <ATen/native/cuda/DistributionTemplates.h>
#include <curand.h>
#include <curand_kernel.h>
#include <curand_philox4x32_x.h>
#include <utility>
#include <functional>
#include <ATen/native/Distributions.h>
#include <ATen/native/cuda/Loops.cuh>
#include <ATen/native/TensorIterator.h>
#include <ATen/LegacyTHFunctionsCUDA.h>
#include <THC/THCGeneral.h>
#include <THC/THCApply.cuh>
#include <THC/THCDeviceUtils.cuh>
#include <cstdint>
#include <limits>
#include <utility>
#include <type_traits>
namespace at { namespace native {
void geometric_kernel_cuda(TensorIterator& iter, double p_, Generator gen_) {
auto gen = get_generator_or_default<CUDAGenerator>(gen_, cuda::detail::getDefaultCUDAGenerator());
AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, iter.dtype(), "geometric_cuda", [&] {
if (std::is_same<scalar_t, double>::value) {
// define lambda for geometric transformation
auto geometric_func = [p_] __device__ (double rand) {
return static_cast<scalar_t>(::ceil(::log(rand) / ::log(static_cast<double>(1.0)-p_)));
};
distribution_nullary_kernel<scalar_t, double, curand4_engine_calls/2>(iter,
gen,
[] __device__ (curandStatePhilox4_32_10_t* state) { return curand_uniform2_double(state); },
geometric_func);
} else {
auto p = static_cast<float>(p_);
auto geometric_func = [p] __device__ (float rand) {
// use __logf fast approximation for peak bandwidth
return static_cast<scalar_t>(::ceil(__logf(rand) / __logf(static_cast<float>(1.0)-p)));
};
distribution_nullary_kernel<scalar_t, float, curand4_engine_calls>(iter,
gen,
[] __device__ (curandStatePhilox4_32_10_t* state) { return curand_uniform4(state); },
geometric_func);
}
});
}
REGISTER_DISPATCH(geometric_stub, &geometric_kernel_cuda);
}} // namespace at::native