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unary_kernel.cu
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/
unary_kernel.cu
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// 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/sparse/unary_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h"
namespace phi {
namespace sparse {
template <typename T>
struct DivScalarFunctor {
T value_;
explicit DivScalarFunctor(T value) : value_(value) {}
__device__ __forceinline__ T operator()(const T x) const {
return x / value_;
}
};
template <typename T, typename Context>
void DivCooScalarKernel(const Context& dev_ctx,
const SparseCooTensor& x,
float scalar,
SparseCooTensor* out) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out);
std::vector<const DenseTensor*> ins = {&(x.non_zero_elements())};
std::vector<DenseTensor*> outs = {out->mutable_non_zero_elements()};
DivScalarFunctor<T> func(static_cast<T>(scalar));
funcs::ElementwiseKernel<T, DivScalarFunctor<T>>(dev_ctx, ins, &outs, func);
}
template <typename T, typename Context>
void DivCsrScalarKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
float scalar,
SparseCsrTensor* out) {
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out);
std::vector<const DenseTensor*> ins = {&(x.non_zero_elements())};
std::vector<DenseTensor*> outs = {out->mutable_non_zero_elements()};
DivScalarFunctor<T> func(static_cast<T>(scalar));
funcs::ElementwiseKernel<T, DivScalarFunctor<T>>(dev_ctx, ins, &outs, func);
}
} // namespace sparse
} // namespace phi
#define PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(name, prefix) \
PD_REGISTER_KERNEL(name##_coo, \
GPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CooKernel, \
phi::dtype::float16, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(name##_csr, \
GPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CsrKernel, \
phi::dtype::float16, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(sin, Sin)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(tan, Tan)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(asin, Asin)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(atan, Atan)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(sinh, Sinh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(tanh, Tanh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(asinh, Asinh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(atanh, Atanh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(sqrt, Sqrt)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(square, Square)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(log1p, Log1p)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(relu, Relu)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(abs, Abs)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(pow, Pow)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(scale, Scale)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(expm1, Expm1)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(relu6, Relu6)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(leaky_relu, LeakyRelu)
PD_REGISTER_KERNEL(divide_coo_scalar,
GPU,
ALL_LAYOUT,
phi::sparse::DivCooScalarKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(divide_csr_scalar,
GPU,
ALL_LAYOUT,
phi::sparse::DivCsrScalarKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
PD_REGISTER_KERNEL(cast_coo,
GPU,
ALL_LAYOUT,
phi::sparse::CastCooKernel,
phi::dtype::float16,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
PD_REGISTER_KERNEL(cast_csr,
GPU,
ALL_LAYOUT,
phi::sparse::CastCsrKernel,
phi::dtype::float16,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}