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未验证 提交 eef46770 编写于 作者: C Chen Weihang 提交者： GitHub
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Revert "Revert "[Phi] Move elementwise_floordiv and elementwise_pow to phi... 

Revert "Revert "[Phi] Move elementwise_floordiv and elementwise_pow to phi (#40993)" (#41065)" (#41110)

This reverts commit 3a6f1135.
上级 afe02e9d
隐藏空白更改
内联 并排
Showing with 282 addition and 302 deletion
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc
浏览文件 @ eef46770
......@@ -12,8 +12,6 @@ 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/fluid/operators/elementwise/elementwise_floordiv_op.h"
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
......@@ -63,12 +61,6 @@ namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(elementwise_floordiv, ops::ElementwiseOp,
ops::ElementwiseFloorDivOpMaker);
REGISTER_OP_CPU_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext,
int64_t>);
REGISTER_OP_VERSION(elementwise_floordiv)
.AddCheckpoint(
R"ROC(Register elementwise_floordiv for adding the attribute of Scale_y)ROC",
......
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu 已删除 100644 → 0
浏览文件 @ afe02e9d
/* Copyright (c) 2019 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/fluid/operators/elementwise/elementwise_floordiv_op.h"
namespace paddle {
namespace operators {
template <typename T>
class ElementwiseFloorDivKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
std::vector<const framework::Tensor*> ins;
std::vector<framework::Tensor*> outs;
const auto& cuda_ctx =
ctx.template device_context<platform::CUDADeviceContext>();
int axis = PackTensorsIntoVector<T>(ctx, &ins, &outs);
paddle::operators::LaunchElementwiseCudaKernel<ElementwiseType::kBinary, T,
T>(
cuda_ctx, ins, &outs, axis, FloorDivFunctor<T>());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int64_t>);
paddle/fluid/operators/elementwise/elementwise_floordiv_op.h 已删除 100644 → 0
浏览文件 @ afe02e9d
/* Copyright (c) 2019 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. */
#pragma once
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
void elementwise_floor_div(const framework::ExecutionContext &ctx,
const framework::Tensor *x,
const framework::Tensor *y, framework::Tensor *z) {
int axis = ctx.Attr<int>("axis");
auto x_dims = x->dims();
auto y_dims = y->dims();
if (x_dims.size() >= y_dims.size()) {
ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, FloorDivFunctor<T>(), z);
} else {
ElementwiseComputeEx<InverseFloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, InverseFloorDivFunctor<T>(), z);
}
}
template <typename DeviceContext, typename T>
class ElementwiseFloorDivKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<framework::LoDTensor>("X");
auto *y = ctx.Input<framework::LoDTensor>("Y");
auto *z = ctx.Output<framework::LoDTensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
// dtype of x and y is int64 or int32
elementwise_floor_div<DeviceContext, T>(ctx, x, y, z);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/elementwise/elementwise_functor.h
浏览文件 @ eef46770
......@@ -49,23 +49,6 @@ using DivFunctor = phi::funcs::DivideFunctor<T>;
template <typename T>
using InverseDivFunctor = phi::funcs::InverseDivideFunctor<T>;
// Floor Divide
template <typename T>
struct FloorDivFunctor {
inline HOSTDEVICE T operator()(const T a, const T b) const {
PADDLE_ENFORCE(b != 0, DIV_ERROR_INFO);
return static_cast<T>(std::trunc(a / b));
}
};
template <typename T>
struct InverseFloorDivFunctor {
inline HOSTDEVICE T operator()(const T a, const T b) const {
PADDLE_ENFORCE(a != 0, DIV_ERROR_INFO);
return static_cast<T>(std::trunc(b / a));
}
};
#undef DIV_ERROR_INFO
// Maximum
......
paddle/fluid/operators/elementwise/elementwise_pow_op.cc
浏览文件 @ eef46770
......@@ -9,8 +9,6 @@ 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/fluid/operators/elementwise/elementwise_pow_op.h"
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
......@@ -70,19 +68,6 @@ REGISTER_OPERATOR(elementwise_pow, ops::ElementwiseOp,
ops::ElementwisePowOpGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(elementwise_pow_grad, ops::ElementwiseOpGrad);
REGISTER_OP_CPU_KERNEL(
elementwise_pow,
ops::ElementwisePowKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwisePowKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwisePowKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwisePowKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
elementwise_pow_grad,
ops::ElementwisePowGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwisePowGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwisePowGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwisePowGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_VERSION(elementwise_pow)
.AddCheckpoint(
R"ROC(Register elementwise_pow for adding the attribute of Scale_y)ROC",
......
paddle/fluid/operators/elementwise/elementwise_pow_op.cu 已删除 100644 → 0
浏览文件 @ afe02e9d
/* Copyright (c) 2018 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/fluid/operators/elementwise/elementwise_pow_op.h"
namespace ops = paddle::operators;
namespace paddle {
namespace operators {
template <typename T>
class ElementwisePowKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
std::vector<const framework::Tensor*> ins;
std::vector<framework::Tensor*> outs;
const auto& cuda_ctx =
ctx.template device_context<platform::CUDADeviceContext>();
int axis = PackTensorsIntoVector<T>(ctx, &ins, &outs);
paddle::operators::LaunchElementwiseCudaKernel<ElementwiseType::kBinary, T,
T>(cuda_ctx, ins, &outs,
axis, PowFunctor<T>());
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(
elementwise_pow,
ops::ElementwisePowKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwisePowKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwisePowKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwisePowKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_pow_grad,
ops::ElementwisePowGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwisePowGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwisePowGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwisePowGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/fluid/operators/elementwise/elementwise_pow_op.h 已删除 100644 → 0
浏览文件 @ afe02e9d
/* Copyright (c) 2018 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. */
#pragma once
#include <cmath>
#include <type_traits>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
namespace paddle {
namespace operators {
template <typename T>
struct PowFunctor {
inline HOSTDEVICE T operator()(const T a, const T b) const {
// TODO(wujionghao): A potential speed improvement is supporting different
// types in C++.
#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
// On CUDAPlace, std::pow(3, 1) calls pow(float, float), and
// it will return a float number like 2.99... , which floor to 2
// when cast to int by default and it is wrong.
// Use llrint to cast it to the nearest integer, which is 3.
if (std::is_integral<T>::value) {
return std::llrint(
std::pow(static_cast<double>(a), static_cast<double>(b)));
}
#endif
return std::pow(a, b);
}
};
template <typename DeviceContext, typename T>
class ElementwisePowKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
using Tensor = framework::LoDTensor;
auto* x = ctx.Input<Tensor>("X");
PADDLE_ENFORCE_EQ(x != nullptr, true,
platform::errors::NotFound(
"Cannot get input Variable X, Variable name = %s",
ctx.InputName("X")));
auto* y = ctx.Input<Tensor>("Y");
auto* z = ctx.Output<Tensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
int axis = ctx.Attr<int>("axis");
ElementwiseComputeEx<PowFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
PowFunctor<T>(), z);
}
};
template <typename T>
struct PowGradDX {
HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
if (std::is_integral<T>::value) {
return dout * y *
std::pow(static_cast<double>(x), static_cast<double>(y - 1));
}
#endif
return dout * y * std::pow(x, y - 1);
}
};
template <typename T, typename Enable = void>
struct PowGradDY {
HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
if (std::is_integral<T>::value) {
return dout * std::log(static_cast<double>(x)) *
std::pow(static_cast<double>(x), static_cast<double>(y));
}
#endif
return dout * std::log(x) * std::pow(x, y);
}
};
template <typename DeviceContext, typename T>
class ElementwisePowGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* out = dout;
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
int axis = ctx.Attr<int>("axis");
ElemwiseGradCompute<DeviceContext, T, PowGradDX<T>, PowGradDY<T>>(
ctx, *x, *y, *out, *dout, axis, dx, dy, PowGradDX<T>(), PowGradDY<T>());
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/elementwise/elementwise_pow_op_npu.cc
浏览文件 @ eef46770
......@@ -16,7 +16,6 @@ limitations under the License. */
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_npu.h"
#include "paddle/fluid/operators/elementwise/elementwise_pow_op.h"
#include "paddle/fluid/platform/device/npu/npu_op_runner.h"
namespace paddle {
......
paddle/phi/kernels/cpu/elementwise_grad_kernel.cc
浏览文件 @ eef46770
......@@ -323,3 +323,11 @@ PD_REGISTER_KERNEL(minimum_grad,
int,
int64_t,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(elementwise_pow_grad,
CPU,
ALL_LAYOUT,
phi::ElementwisePowGradKernel,
float,
double,
int,
int64_t) {}
paddle/phi/kernels/cpu/elementwise_kernel.cc
浏览文件 @ eef46770
......@@ -113,6 +113,36 @@ void ModuloRawKernel(const Context& dev_ctx,
}
}
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);
}
// Create the definition of Add
DEFINE_CPU_ELEMENTWISE_OP(Add)
......@@ -207,3 +237,17 @@ PD_REGISTER_KERNEL(modulo_raw,
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) {}
paddle/phi/kernels/elementwise_grad_kernel.h
浏览文件 @ eef46770
......@@ -159,4 +159,13 @@ void MinimumGradKernel(const Context& dev_ctx,
int axis,
DenseTensor* dx,
DenseTensor* dy);
template <typename T, typename Context>
void ElementwisePowGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy);
} // namespace phi
paddle/phi/kernels/elementwise_kernel.cc
浏览文件 @ eef46770
......@@ -81,6 +81,25 @@ void ModuloKernel(const Context& dev_ctx,
int axis = -1;
ModuloRawKernel<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);
}
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);
}
} // namespace phi
using complex64 = ::phi::dtype::complex<float>;
......@@ -151,6 +170,16 @@ PD_REGISTER_KERNEL(minimum,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(
modulo, CPU, ALL_LAYOUT, phi::ModuloKernel, float, double, int, int64_t) {}
PD_REGISTER_KERNEL(
floor_divide, CPU, ALL_LAYOUT, phi::FloorDivideKernel, int, int64_t) {}
PD_REGISTER_KERNEL(elementwise_pow,
CPU,
ALL_LAYOUT,
phi::ElementwisePowKernel,
float,
double,
int,
int64_t) {}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
......@@ -227,4 +256,14 @@ PD_REGISTER_KERNEL(minimum,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(
modulo, GPU, ALL_LAYOUT, phi::ModuloKernel, float, double, int, int64_t) {}
PD_REGISTER_KERNEL(
floor_divide, GPU, ALL_LAYOUT, phi::FloorDivideKernel, int, int64_t) {}
PD_REGISTER_KERNEL(elementwise_pow,
GPU,
ALL_LAYOUT,
phi::ElementwisePowKernel,
float,
double,
int,
int64_t) {}
#endif
paddle/phi/kernels/elementwise_kernel.h
浏览文件 @ eef46770
......@@ -124,6 +124,32 @@ void ModuloKernel(const Context& dev_ctx,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
void FloorDivideRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void FloorDivideKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
void ElementwisePowRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void ElementwisePowKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
DenseTensor Add(const Context& dev_ctx,
const DenseTensor& x,
......@@ -200,4 +226,27 @@ DenseTensor Modulo(const Context& dev_ctx,
ModuloKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
template <typename T, typename Context>
DenseTensor FloorDivide(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
FloorDivideKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
template <typename T, typename Context>
DenseTensor ElementwisePow(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
ElementwisePowKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
} // namespace phi
paddle/phi/kernels/funcs/elementwise_functor.h
浏览文件 @ eef46770
......@@ -538,5 +538,40 @@ struct InverseModuloFunctor<
return res;
}
};
template <typename T>
struct FloorDivideFunctor {
inline HOSTDEVICE T operator()(const T a, const T b) const {
PADDLE_ENFORCE(b != 0, DIV_ERROR_INFO);
return static_cast<T>(std::trunc(a / b));
}
};
template <typename T>
struct InverseFloorDivideFunctor {
inline HOSTDEVICE T operator()(const T a, const T b) const {
PADDLE_ENFORCE(a != 0, DIV_ERROR_INFO);
return static_cast<T>(std::trunc(b / a));
}
};
template <typename T>
struct ElementwisePowFunctor {
inline HOSTDEVICE T operator()(const T a, const T b) const {
// TODO(wujionghao): A potential speed improvement is supporting different
// types in C++.
#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
// On CUDAPlace, std::pow(3, 1) calls pow(float, float), and
// it will return a float number like 2.99... , which floor to 2
// when cast to int by default and it is wrong.
// Use llrint to cast it to the nearest integer, which is 3.
if (std::is_integral<T>::value) {
return std::llrint(
std::pow(static_cast<double>(a), static_cast<double>(b)));
}
#endif
return std::pow(a, b);
}
};
} // namespace funcs
} // namespace phi
paddle/phi/kernels/gpu/elementwise_grad_kernel.cu
浏览文件 @ eef46770
......@@ -382,3 +382,11 @@ PD_REGISTER_KERNEL(minimum_grad,
int64_t,
phi::dtype::float16,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(elementwise_pow_grad,
GPU,
ALL_LAYOUT,
phi::ElementwisePowGradKernel,
float,
double,
int,
int64_t) {}
paddle/phi/kernels/gpu/elementwise_kernel.cu
浏览文件 @ eef46770
......@@ -55,6 +55,10 @@ DEFINE_CUDA_ELEMENTWISE_OP(Maximum)
DEFINE_CUDA_ELEMENTWISE_OP(Minimum)
// Create the definition of Modulo
DEFINE_CUDA_ELEMENTWISE_OP(Modulo)
// Create the definition of FloorDivide
DEFINE_CUDA_ELEMENTWISE_OP(FloorDivide)
// Create the definition of Pow
DEFINE_CUDA_ELEMENTWISE_OP(ElementwisePow)
} // namespace phi
......@@ -148,3 +152,17 @@ PD_REGISTER_KERNEL(modulo_raw,
double,
int,
int64_t) {}
PD_REGISTER_KERNEL(floor_divide_raw,
GPU,
ALL_LAYOUT,
phi::FloorDivideRawKernel,
int,
int64_t) {}
PD_REGISTER_KERNEL(elementwise_pow_raw,
GPU,
ALL_LAYOUT,
phi::ElementwisePowRawKernel,
float,
double,
int,
int64_t) {}
paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h
浏览文件 @ eef46770
......@@ -666,4 +666,44 @@ struct MinGradDy {
return dout * static_cast<T>(x >= y);
}
};
template <typename T>
struct PowGradDX {
HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
if (std::is_integral<T>::value) {
return dout * y *
std::pow(static_cast<double>(x), static_cast<double>(y - 1));
}
#endif
return dout * y * std::pow(x, y - 1);
}
};
template <typename T, typename Enable = void>
struct PowGradDY {
HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
if (std::is_integral<T>::value) {
return dout * std::log(static_cast<double>(x)) *
std::pow(static_cast<double>(x), static_cast<double>(y));
}
#endif
return dout * std::log(x) * std::pow(x, y);
}
};
template <typename T, typename Context>
void ElementwisePowGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& dout,
int axis,
DenseTensor* dx,
DenseTensor* dy) {
funcs::ElementwiseGradPreProcess(dout, dx);
phi::funcs::ElemwiseGradCompute<Context, T, PowGradDX<T>, PowGradDY<T>>(
dev_ctx, x, y, dout, dout, axis, dx, dy, PowGradDX<T>(), PowGradDY<T>());
}
} // namespace phi
paddle/phi/ops/compat/elementwise_sig.cc
浏览文件 @ eef46770
......@@ -86,6 +86,24 @@ KernelSignature ElementwiseModOpArgumentMapping(
return KernelSignature("modulo_raw", {"X", "Y"}, {"axis"}, {"Out"});
}
KernelSignature ElementwiseFloorDivOpArgumentMapping(
const ArgumentMappingContext& ctx) {
int axis = paddle::any_cast<int>(ctx.Attr("axis"));
if (axis == -1) {
return KernelSignature("floor_divide", {"X", "Y"}, {}, {"Out"});
}
return KernelSignature("floor_divide_raw", {"X", "Y"}, {"axis"}, {"Out"});
}
KernelSignature ElementwisePowOpArgumentMapping(
const ArgumentMappingContext& ctx) {
int axis = paddle::any_cast<int>(ctx.Attr("axis"));
if (axis == -1) {
return KernelSignature("elementwise_pow", {"X", "Y"}, {}, {"Out"});
}
return KernelSignature("elementwise_pow_raw", {"X", "Y"}, {"axis"}, {"Out"});
}
KernelSignature ElementwiseAddGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("add_grad",
......@@ -204,6 +222,13 @@ KernelSignature ElementwiseMinGradOpArgumentMapping(
{"axis"},
{GradVarName("X"), GradVarName("Y")});
}
KernelSignature ElementwisePowGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("elementwise_pow_grad",
{"X", "Y", GradVarName("Out")},
{"axis"},
{GradVarName("X"), GradVarName("Y")});
}
} // namespace phi
PD_REGISTER_BASE_KERNEL_NAME(elementwise_add, add);
......@@ -213,6 +238,7 @@ PD_REGISTER_BASE_KERNEL_NAME(elementwise_div, divide);
PD_REGISTER_BASE_KERNEL_NAME(elementwise_max, maximum);
PD_REGISTER_BASE_KERNEL_NAME(elementwise_min, minimum);
PD_REGISTER_BASE_KERNEL_NAME(elementwise_mod, modulo);
PD_REGISTER_BASE_KERNEL_NAME(elementwise_floordiv, floor_divide);
PD_REGISTER_BASE_KERNEL_NAME(elementwise_add_grad, add_grad);
PD_REGISTER_BASE_KERNEL_NAME(elementwise_add_grad_grad, add_double_grad);
PD_REGISTER_BASE_KERNEL_NAME(elementwise_add_triple_grad, add_triple_grad);
......@@ -244,6 +270,10 @@ PD_REGISTER_ARG_MAPPING_FN(elementwise_min,
phi::ElementwiseMinOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(elementwise_mod,
phi::ElementwiseModOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(elementwise_floordiv,
phi::ElementwiseFloorDivOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(elementwise_pow,
phi::ElementwisePowOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(elementwise_add_grad,
phi::ElementwiseAddGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(elementwise_add_grad_grad,
......@@ -276,3 +306,5 @@ PD_REGISTER_ARG_MAPPING_FN(elementwise_max_grad,
phi::ElementwiseMaxGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(elementwise_min_grad,
phi::ElementwiseMinGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(elementwise_pow_grad,
phi::ElementwisePowGradOpArgumentMapping);
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