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elementwise_kernel.cc
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elementwise_kernel.cc
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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/cpu/elementwise.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/bfloat16.h"
#include "paddle/phi/common/complex.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/impl/elementwise_kernel_impl.h"
namespace phi {
#define DEFINE_CPU_ELEMENTWISE_OP(name) \
template <typename T, typename Context> \
void name##RawKernel(const Context& dev_ctx, \
const DenseTensor& x, \
const DenseTensor& y, \
int axis, \
DenseTensor* out) { \
dev_ctx.template Alloc<T>(out); \
if (x.dims() == y.dims()) { \
SameDimsElementwiseCompute<SameDims##name##Functor<CPUContext, T>>()( \
dev_ctx, x, y, out); \
} else { \
auto x_dims = x.dims(); \
auto y_dims = y.dims(); \
if (x_dims.size() >= y_dims.size()) { \
funcs::ElementwiseCompute<funcs::name##Functor<T>, T>( \
dev_ctx, x, y, axis, funcs::name##Functor<T>(), out); \
} else { \
funcs::ElementwiseCompute<funcs::Inverse##name##Functor<T>, T>( \
dev_ctx, x, y, axis, funcs::Inverse##name##Functor<T>(), out); \
} \
} \
}
template <typename T, typename Context>
void DivideRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out) {
// allocate memory for out
dev_ctx.template Alloc<T>(out);
if (x.dims() == y.dims() && std::is_floating_point<T>::value) {
SameDimsElementwiseCompute<SameDimsDivideFunctor<CPUContext, T>>()(
dev_ctx, x, y, out);
} else {
auto x_dims = x.dims();
auto y_dims = y.dims();
if (x_dims.size() >= y_dims.size()) {
funcs::ElementwiseCompute<funcs::DivideFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::DivideFunctor<T>(), out);
} else {
funcs::ElementwiseCompute<funcs::InverseDivideFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::InverseDivideFunctor<T>(), out);
}
}
}
template <typename T, typename Context>
void MaximumRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out) {
// allocate memory for out
dev_ctx.template Alloc<T>(out);
funcs::ElementwiseCompute<funcs::MaximumFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::MaximumFunctor<T>(), out);
}
template <typename T, typename Context>
void MinimumRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out) {
// allocate memory for out
dev_ctx.template Alloc<T>(out);
funcs::ElementwiseCompute<funcs::MinimumFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::MinimumFunctor<T>(), out);
}
template <typename T, typename Context>
void ModuloRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out) {
// allocate memory for out
dev_ctx.template Alloc<T>(out);
auto x_dims = x.dims();
auto y_dims = y.dims();
if (x_dims.size() >= y_dims.size()) {
funcs::ElementwiseCompute<funcs::ModuloFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::ModuloFunctor<T>(), out);
} else {
funcs::ElementwiseCompute<funcs::InverseModuloFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::InverseModuloFunctor<T>(), out);
}
}
template <typename T, typename Context>
void FloorDivideRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out) {
// allocate memory for out
dev_ctx.template Alloc<T>(out);
auto x_dims = x.dims();
auto y_dims = y.dims();
if (x_dims.size() >= y_dims.size()) {
funcs::ElementwiseCompute<funcs::FloorDivideFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::FloorDivideFunctor<T>(), out);
} else {
funcs::ElementwiseCompute<funcs::InverseFloorDivideFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::InverseFloorDivideFunctor<T>(), out);
}
}
template <typename T, typename Context>
void ElementwisePowRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out) {
// allocate memory for out
dev_ctx.template Alloc<T>(out);
funcs::ElementwiseCompute<funcs::ElementwisePowFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::ElementwisePowFunctor<T>(), out);
}
template <typename T, typename Context>
void ElementwiseHeavisideRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out) {
// allocate memory for out
dev_ctx.template Alloc<T>(out);
funcs::ElementwiseCompute<funcs::ElementwiseHeavisideFunctor<T>, T>(
dev_ctx, x, y, axis, funcs::ElementwiseHeavisideFunctor<T>(), out);
}
// Create the definition of Add
DEFINE_CPU_ELEMENTWISE_OP(Add)
// Create the definition of Subtract
DEFINE_CPU_ELEMENTWISE_OP(Subtract)
// Create the definition of Multiply
DEFINE_CPU_ELEMENTWISE_OP(Multiply)
} // namespace phi
using complex64 = ::phi::dtype::complex<float>;
using complex128 = ::phi::dtype::complex<double>;
// NOTE(chenweihang): using bfloat16 will cause redefine with xpu bfloat16
// using bfloat16 = ::phi::dtype::bfloat16;
PD_REGISTER_KERNEL(
fmax, CPU, ALL_LAYOUT, phi::FMaxKernel, float, double, int, int64_t) {}
PD_REGISTER_KERNEL(
fmin, CPU, ALL_LAYOUT, phi::FMinKernel, float, double, int, int64_t) {}
PD_REGISTER_KERNEL(add_raw,
CPU,
ALL_LAYOUT,
phi::AddRawKernel,
float,
double,
int16_t,
int,
int64_t,
complex64,
complex128) {}
PD_REGISTER_KERNEL(subtract_raw,
CPU,
ALL_LAYOUT,
phi::SubtractRawKernel,
float,
double,
int16_t,
int,
int64_t,
complex64,
complex128,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(divide_raw,
CPU,
ALL_LAYOUT,
phi::DivideRawKernel,
float,
double,
int,
int64_t,
complex64,
complex128) {}
PD_REGISTER_KERNEL(multiply_raw,
CPU,
ALL_LAYOUT,
phi::MultiplyRawKernel,
float,
double,
int,
int64_t,
bool,
complex64,
complex128,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(maximum_raw,
CPU,
ALL_LAYOUT,
phi::MaximumRawKernel,
float,
double,
int,
int64_t,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(minimum_raw,
CPU,
ALL_LAYOUT,
phi::MinimumRawKernel,
float,
double,
int,
int64_t,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(modulo_raw,
CPU,
ALL_LAYOUT,
phi::ModuloRawKernel,
float,
double,
int,
int64_t) {}
PD_REGISTER_KERNEL(floor_divide_raw,
CPU,
ALL_LAYOUT,
phi::FloorDivideRawKernel,
int,
int64_t) {}
PD_REGISTER_KERNEL(elementwise_pow_raw,
CPU,
ALL_LAYOUT,
phi::ElementwisePowRawKernel,
float,
double,
int,
int64_t) {}
PD_REGISTER_KERNEL(elementwise_heaviside_raw,
CPU,
ALL_LAYOUT,
phi::ElementwiseHeavisideRawKernel,
float,
double,
int,
int64_t) {}