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elementwise_kernel.cu
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elementwise_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/backends/gpu/gpu_context.h"
#ifndef PADDLE_WITH_XPU_KP
#include "paddle/phi/common/complex.h"
#include "paddle/phi/common/float16.h"
#endif
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/impl/elementwise_kernel_impl.h"
#include "paddle/phi/kernels/legacy/elementwise_add_kernel.h"
#include "paddle/phi/kernels/legacy/elementwise_divide_kernel.h"
#include "paddle/phi/kernels/legacy/elementwise_kernel.h"
#include "paddle/phi/kernels/legacy/elementwise_multipy_kernel.h"
#include "paddle/phi/kernels/legacy/elementwise_subtract_kernel.h"
namespace phi {
template <typename T, typename Context>
void SubtractKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
phi::SubtractRawKernel<T, Context>(dev_ctx, x, y, -1, out);
}
template <typename T, typename Context>
void MultiplyKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
phi::MultiplyRawKernel<T, Context>(dev_ctx, x, y, -1, out);
}
template <typename T, typename Context>
void DivideKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
phi::DivideRawKernel<T, Context>(dev_ctx, x, y, -1, out);
}
template <typename T, typename Context>
void MultiPrecisionAddKernelImpl(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
std::vector<const DenseTensor*> inputs = {&x, &y};
std::vector<DenseTensor*> outputs = {out};
if (y.dtype() == phi::DataType::BFLOAT16) {
funcs::BroadcastKernel<T>(
dev_ctx,
inputs,
&outputs,
funcs::MultiPrecisionAddFunctor<T, phi::bfloat16>(),
-1);
} else if (y.dtype() == phi::DataType::FLOAT16) {
funcs::BroadcastKernel<T>(
dev_ctx,
inputs,
&outputs,
funcs::MultiPrecisionAddFunctor<T, phi::float16>(),
-1);
} else {
PADDLE_THROW(common::errors::InvalidArgument(
"Unsupport x dtype:%s, y dtype:%s for add(x, y) operation",
phi::DataTypeToString(x.type()),
phi::DataTypeToString(y.type())));
}
}
template <typename T, typename Context>
void AddKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
#ifdef PADDLE_WITH_CUDA
if (x.dtype() == phi::DataType::FLOAT32 &&
(y.dtype() == phi::DataType::BFLOAT16 ||
y.dtype() == phi::DataType::FLOAT16)) {
MultiPrecisionAddKernelImpl<float, Context>(dev_ctx, x, y, out);
} else {
#endif
phi::AddRawKernel<T, Context>(dev_ctx, x, y, -1, out);
#ifdef PADDLE_WITH_CUDA
}
#endif
}
template <typename T, typename Context>
void GradAddKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
phi::AddRawKernel<T>(dev_ctx, x, y, -1, out);
}
template <typename T, typename Context>
void MaximumKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
int axis = -1;
MaximumRawKernel<T>(dev_ctx, x, y, axis, out);
}
template <typename T, typename Context>
void MinimumKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
int axis = -1;
MinimumRawKernel<T>(dev_ctx, x, y, axis, out);
}
template <typename T, typename Context>
void RemainderKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
int axis = -1;
RemainderRawKernel<T>(dev_ctx, x, y, axis, out);
}
template <typename T, typename Context>
void FloorDivideKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
int axis = -1;
FloorDivideRawKernel<T>(dev_ctx, x, y, axis, out);
}
// Create the definition of Heaviside
template <typename T, typename Context>
void HeavisideKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
std::vector<const DenseTensor*> inputs = {&x, &y};
std::vector<DenseTensor*> outputs = {out};
dev_ctx.template Alloc<T>(out);
funcs::BroadcastKernel<T>(
dev_ctx, inputs, &outputs, funcs::ElementwiseHeavisideFunctor<T>());
}
template <typename T, typename Context>
void ElementwisePowKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
int axis = -1;
ElementwisePowRawKernel<T>(dev_ctx, x, y, axis, out);
}
template <typename T, typename Context>
void CopySignKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
std::vector<const DenseTensor*> inputs = {&x, &y};
std::vector<DenseTensor*> outputs = {out};
dev_ctx.template Alloc<T>(out);
funcs::BroadcastKernel<T>(
dev_ctx, inputs, &outputs, funcs::CopySignFunctor<T>());
}
} // namespace phi
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL(maximum,
KPS,
ALL_LAYOUT,
phi::MaximumKernel,
float,
double,
int,
int64_t,
phi::dtype::float16,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(minimum,
KPS,
ALL_LAYOUT,
phi::MinimumKernel,
float,
double,
int,
int64_t,
phi::dtype::float16,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(remainder,
GPU,
ALL_LAYOUT,
phi::RemainderKernel,
float,
double,
int,
int64_t,
phi::dtype::float16,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(floor_divide,
KPS,
ALL_LAYOUT,
phi::FloorDivideKernel,
uint8_t,
int8_t,
int16_t,
int,
int64_t,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(elementwise_pow,
KPS,
ALL_LAYOUT,
phi::ElementwisePowKernel,
float,
double,
int,
int64_t,
phi::dtype::float16,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(copysign,
GPU,
ALL_LAYOUT,
phi::CopySignKernel,
bool,
uint8_t,
int8_t,
int16_t,
int,
int64_t,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16) {}
#endif
#ifdef PADDLE_WITH_XPU_KP
PD_REGISTER_KERNEL(maximum, KPS, ALL_LAYOUT, phi::MaximumKernel, float) {}
PD_REGISTER_KERNEL(minimum, KPS, ALL_LAYOUT, phi::MinimumKernel, float) {}
PD_REGISTER_KERNEL(divide, KPS, ALL_LAYOUT, phi::DivideKernel, float) {}
PD_REGISTER_KERNEL(multiply, KPS, ALL_LAYOUT, phi::MultiplyKernel, float) {}
PD_REGISTER_KERNEL(add, KPS, ALL_LAYOUT, phi::AddKernel, float) {}
PD_REGISTER_KERNEL(subtract, KPS, ALL_LAYOUT, phi::SubtractKernel, float) {}
PD_REGISTER_KERNEL(floor_divide, KPS, ALL_LAYOUT, phi::FloorDivideKernel, int) {
}
PD_REGISTER_KERNEL(
elementwise_pow, KPS, ALL_LAYOUT, phi::ElementwisePowKernel, float) {}
#else
using float16 = phi::dtype::float16;
using bfloat16 = phi::dtype::bfloat16;
using complex64 = ::phi::dtype::complex<float>;
using complex128 = ::phi::dtype::complex<double>;
PD_REGISTER_KERNEL(fmax,
KPS,
ALL_LAYOUT,
phi::FMaxKernel,
float,
double,
int,
float16,
bfloat16,
int64_t) {}
PD_REGISTER_KERNEL(fmin,
KPS,
ALL_LAYOUT,
phi::FMinKernel,
float,
double,
int,
float16,
bfloat16,
int64_t) {}
PD_REGISTER_KERNEL(heaviside,
KPS,
ALL_LAYOUT,
phi::HeavisideKernel,
float,
double,
int,
float16,
bfloat16,
int64_t) {}
PD_REGISTER_KERNEL(add,
KPS,
ALL_LAYOUT,
phi::AddKernel,
float,
double,
int16_t,
int,
bool,
uint8_t,
int8_t,
int64_t,
phi::dtype::float16,
phi::dtype::bfloat16,
complex64,
complex128) {}
PD_REGISTER_KERNEL(grad_add,
KPS,
ALL_LAYOUT,
phi::GradAddKernel,
float,
double,
int16_t,
int,
bool,
uint8_t,
int8_t,
int64_t,
phi::dtype::float16,
phi::dtype::bfloat16,
complex64,
complex128) {}
PD_REGISTER_KERNEL(divide,
KPS,
ALL_LAYOUT,
phi::DivideKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool,
float16,
bfloat16,
complex64,
complex128) {}
PD_REGISTER_KERNEL(multiply,
KPS,
ALL_LAYOUT,
phi::MultiplyKernel,
float,
double,
int,
int64_t,
bool,
float16,
complex64,
complex128,
bfloat16) {}
PD_REGISTER_KERNEL(subtract,
KPS,
ALL_LAYOUT,
phi::SubtractKernel,
float,
double,
int16_t,
int,
int64_t,
float16,
bfloat16,
complex64,
complex128) {}
#endif