wehub-resource-sync
265 lines
9.3 KiB
Plaintext
265 lines
9.3 KiB
Plaintext
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/phi/kernels/gaussian_kernel.h"
|
|
|
|
#include <thrust/random.h>
|
|
|
|
#include "paddle/phi/backends/gpu/gpu_context.h"
|
|
#include "paddle/phi/common/amp_type_traits.h"
|
|
#include "paddle/phi/common/type_traits.h"
|
|
#include "paddle/phi/core/dense_tensor.h"
|
|
#include "paddle/phi/core/generator.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/complex_kernel.h"
|
|
#include "paddle/phi/kernels/funcs/distribution_helper.h"
|
|
#include "paddle/phi/kernels/funcs/index_impl.cu.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T>
|
|
using ComplexType = dtype::complex<T>;
|
|
|
|
template <typename T>
|
|
struct GaussianGenerator {
|
|
T mean_, std_;
|
|
unsigned int seed_;
|
|
unsigned int offset_ = 0;
|
|
|
|
__host__ __device__ GaussianGenerator(T mean, T std, int seed)
|
|
: mean_(mean), std_(std), seed_(seed) {}
|
|
|
|
__host__ __device__ GaussianGenerator(T mean, T std, int seed, int offset)
|
|
: mean_(mean), std_(std), seed_(seed), offset_(offset) {}
|
|
|
|
__host__ __device__ T operator()(const unsigned int n) const {
|
|
thrust::minstd_rand rng;
|
|
rng.seed(seed_);
|
|
using MT = typename MPTypeTrait<T>::Type;
|
|
thrust::normal_distribution<MT> dist(static_cast<MT>(mean_),
|
|
static_cast<MT>(std_));
|
|
unsigned int new_n = n + offset_;
|
|
rng.discard(new_n);
|
|
MT out = dist(rng);
|
|
return static_cast<T>(out);
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct GaussianGenerator<ComplexType<T>> {
|
|
T mean_, std_;
|
|
unsigned int seed_;
|
|
unsigned int offset_ = 0;
|
|
|
|
__host__ __device__ GaussianGenerator(T mean, T std, int seed)
|
|
: mean_(mean), std_(std), seed_(seed) {}
|
|
|
|
__host__ __device__ GaussianGenerator(T mean, T std, int seed, int offset)
|
|
: mean_(mean), std_(std), seed_(seed), offset_(offset) {}
|
|
|
|
__host__ __device__ ComplexType<T> operator()(const unsigned int n) const {
|
|
thrust::minstd_rand rng_real;
|
|
thrust::minstd_rand rng_img;
|
|
rng_real.seed(seed_);
|
|
rng_img.seed(seed_);
|
|
thrust::normal_distribution<T> dist(mean_, std_);
|
|
unsigned int new_n = n + offset_;
|
|
rng_real.discard(new_n);
|
|
rng_img.discard(new_n);
|
|
T real = dist(rng_real);
|
|
T imag = dist(rng_img);
|
|
return ComplexType<T>(real, imag);
|
|
}
|
|
};
|
|
|
|
// If T is not complex
|
|
template <typename T,
|
|
typename Context,
|
|
std::enable_if_t<!std::is_same<T, complex64>::value &&
|
|
!std::is_same<T, complex128>::value,
|
|
bool> = true>
|
|
void GaussianRandom(const Context& dev_ctx,
|
|
const IntArray& shape,
|
|
double mean,
|
|
double std,
|
|
int seed,
|
|
DataType dtype,
|
|
DenseTensor* out) {
|
|
out->Resize(shape.GetData());
|
|
dev_ctx.template Alloc<T>(out);
|
|
if (seed == 0) {
|
|
// use global Generator seed
|
|
using MT = typename MPTypeTrait<T>::Type;
|
|
funcs::normal_distribution<MT> dist;
|
|
funcs::normal_transform<MT> trans(static_cast<MT>(mean),
|
|
static_cast<MT>(std));
|
|
funcs::distribution_and_transform<T>(dev_ctx, out, dist, trans);
|
|
} else {
|
|
// use OP seed
|
|
auto func =
|
|
GaussianGenerator<T>(static_cast<T>(mean), static_cast<T>(std), seed);
|
|
IndexKernel<T, GaussianGenerator<T>>(dev_ctx, out, func);
|
|
}
|
|
}
|
|
|
|
// If T is complex
|
|
template <typename T,
|
|
typename Context,
|
|
std::enable_if_t<std::is_same<T, complex64>::value ||
|
|
std::is_same<T, complex128>::value,
|
|
bool> = true>
|
|
void GaussianRandom(const Context& dev_ctx,
|
|
const IntArray& shape,
|
|
double mean,
|
|
double std,
|
|
int seed,
|
|
DataType dtype,
|
|
DenseTensor* out) {
|
|
out->Resize(shape.GetData());
|
|
dev_ctx.template Alloc<T>(out);
|
|
using RealT = dtype::Real<T>;
|
|
RealT std_of_real_or_imag = static_cast<RealT>(std::sqrt(std * std / 2.0));
|
|
RealT mean_real = static_cast<RealT>(mean);
|
|
if (seed == 0) {
|
|
// use global Generator seed
|
|
DenseTensor out_real;
|
|
DenseTensor out_imag;
|
|
out_real.Resize(shape.GetData());
|
|
out_imag.Resize(shape.GetData());
|
|
dev_ctx.template Alloc<T>(&out_real);
|
|
dev_ctx.template Alloc<T>(&out_imag);
|
|
funcs::normal_distribution<RealT> dist;
|
|
funcs::normal_distribution<RealT> dist_imag;
|
|
funcs::normal_transform<RealT> trans(mean_real, std_of_real_or_imag);
|
|
funcs::distribution_and_transform<RealT>(dev_ctx, &out_real, dist, trans);
|
|
funcs::distribution_and_transform<RealT>(
|
|
dev_ctx, &out_imag, dist_imag, trans);
|
|
ComplexKernel<RealT>(dev_ctx, out_real, out_imag, out);
|
|
} else {
|
|
// use OP seed
|
|
auto func = GaussianGenerator<T>(mean_real, std_of_real_or_imag, seed);
|
|
IndexKernel<T, GaussianGenerator<T>>(dev_ctx, out, func);
|
|
}
|
|
}
|
|
|
|
// If T is not complex
|
|
template <typename T,
|
|
typename Context,
|
|
std::enable_if_t<!std::is_same<T, complex64>::value &&
|
|
!std::is_same<T, complex128>::value,
|
|
bool> = true>
|
|
void GaussianRandomInplace(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
float mean,
|
|
float std,
|
|
int seed,
|
|
DenseTensor* out) {
|
|
dev_ctx.template Alloc<T>(out);
|
|
if (seed == 0) {
|
|
// use global Generator seed
|
|
using MT = typename MPTypeTrait<T>::Type;
|
|
funcs::normal_distribution<MT> dist;
|
|
funcs::normal_transform<MT> trans(static_cast<MT>(mean),
|
|
static_cast<MT>(std));
|
|
funcs::distribution_and_transform<T>(dev_ctx, out, dist, trans);
|
|
} else {
|
|
// use OP seed
|
|
auto func =
|
|
GaussianGenerator<T>(static_cast<T>(mean), static_cast<T>(std), seed);
|
|
IndexKernel<T, GaussianGenerator<T>>(dev_ctx, out, func);
|
|
}
|
|
}
|
|
|
|
// If T is complex
|
|
template <typename T,
|
|
typename Context,
|
|
std::enable_if_t<std::is_same<T, complex64>::value ||
|
|
std::is_same<T, complex128>::value,
|
|
bool> = true>
|
|
void GaussianRandomInplace(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
float mean,
|
|
float std,
|
|
int seed,
|
|
DenseTensor* out) {
|
|
dev_ctx.template Alloc<T>(out);
|
|
float std_of_real_or_imag = std::sqrt(std::pow(std, 2) / 2);
|
|
if (seed == 0) {
|
|
// use global Generator seed
|
|
DenseTensor out_real;
|
|
DenseTensor out_imag;
|
|
out_real.Resize(x.dims());
|
|
out_imag.Resize(x.dims());
|
|
dev_ctx.template Alloc<T>(&out_real);
|
|
dev_ctx.template Alloc<T>(&out_imag);
|
|
funcs::normal_distribution<dtype::Real<T>> dist;
|
|
funcs::normal_distribution<dtype::Real<T>> dist_imag;
|
|
funcs::normal_transform<dtype::Real<T>> trans(mean, std_of_real_or_imag);
|
|
funcs::distribution_and_transform<dtype::Real<T>>(
|
|
dev_ctx, &out_real, dist, trans);
|
|
funcs::distribution_and_transform<dtype::Real<T>>(
|
|
dev_ctx, &out_imag, dist_imag, trans);
|
|
ComplexKernel<dtype::Real<T>>(dev_ctx, out_real, out_imag, out);
|
|
} else {
|
|
// use OP seed
|
|
auto func = GaussianGenerator<T>(mean, std_of_real_or_imag, seed);
|
|
IndexKernel<T, GaussianGenerator<T>>(dev_ctx, out, func);
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
PADDLE_API void GaussianKernel(const Context& dev_ctx,
|
|
const IntArray& shape,
|
|
double mean,
|
|
double std,
|
|
int seed,
|
|
DataType dtype,
|
|
DenseTensor* out) {
|
|
GaussianRandom<T>(dev_ctx, shape, mean, std, seed, dtype, out);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void GaussianInplaceKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
float mean,
|
|
float std,
|
|
int seed,
|
|
DenseTensor* out) {
|
|
GaussianRandomInplace<T>(dev_ctx, x, mean, std, seed, out);
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(gaussian,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::GaussianKernel,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
float,
|
|
double,
|
|
phi::complex64,
|
|
phi::complex128) {}
|
|
|
|
PD_REGISTER_KERNEL(gaussian_inplace,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::GaussianInplaceKernel,
|
|
phi::float16,
|
|
phi::bfloat16,
|
|
float,
|
|
double,
|
|
phi::complex64,
|
|
phi::complex128) {}
|