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未验证 提交 92afe146 编写于 作者: Z zhiboniu 提交者： GitHub
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p_norm transfer to phi kernels (#40819)

上级 22a5035e
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Showing with 610 addition and 426 deletion
paddle/fluid/operators/p_norm_op.cc
浏览文件 @ 92afe146
......@@ -11,12 +11,15 @@ 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/p_norm_op.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/unary.h"
namespace paddle {
namespace operators {
......@@ -81,68 +84,11 @@ where, $\sum_i $ is calculated along the `axis` dimension.
class PnormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "p_norm");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "p_norm");
auto x_dim = ctx->GetInputDim("X");
auto x_rank = x_dim.size();
int axis = ctx->Attrs().Get<int>("axis");
bool keepdim = ctx->Attrs().Get<bool>("keepdim");
PADDLE_ENFORCE_GE(axis, -x_rank,
platform::errors::InvalidArgument(
"Attr(axis) value should be in range [-R, R-1], R is "
"the rank of Input(X). But received axis: %d, R: %d. "
"Current Input(X)'s shape is=[%s].",
axis, x_rank, x_dim));
PADDLE_ENFORCE_LT(axis, x_rank,
platform::errors::InvalidArgument(
"Attr(axis) value should be in range [-R, R-1], R is "
"the rank of Input(X). But received axis: %d, R: %d. "
"Current Input(X)'s shape is=[%s].",
axis, x_rank, x_dim));
std::vector<int> reduce_dims;
bool asvector = ctx->Attrs().Get<bool>("asvector");
if (asvector) {
reduce_dims.emplace_back(1);
if (keepdim) {
for (int i = 1; i < x_dim.size(); ++i) {
reduce_dims.emplace_back(1);
}
x_dim = phi::make_ddim(reduce_dims);
}
} else {
if (axis < 0) axis = x_dim.size() + axis;
for (int i = 0; i < x_dim.size(); ++i) {
if (i != axis) reduce_dims.emplace_back(x_dim[i]);
}
if (reduce_dims.size() == 0) {
reduce_dims.emplace_back(1);
}
}
x_dim[axis] = 1;
if (keepdim) {
ctx->SetOutputDim("Out", x_dim);
} else {
ctx->SetOutputDim("Out", phi::make_ddim(reduce_dims));
}
}
};
class PnormOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "p_norm");
OP_INOUT_CHECK(ctx->HasInput("Out"), "Input", "Out", "p_norm");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
"Out@GRAD", "p_norm");
OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
"X@GRAD", "p_norm");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
template <typename T>
......@@ -167,14 +113,17 @@ class PnormOpGradOpMaker : public framework::SingleGradOpMaker<T> {
namespace ops = paddle::operators;
using CPU = paddle::platform::CPUDeviceContext;
DECLARE_INFER_SHAPE_FUNCTOR(p_norm, PNormInferShapeFunctor,
PD_INFER_META(phi::PNormInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(p_norm_grad, PNormGradInferShapeFunctor,
PD_INFER_META(phi::GeneralUnaryGradInferMeta));
REGISTER_OPERATOR(p_norm, ops::PnormOp, ops::PnormOpMaker,
ops::PnormOpGradOpMaker<paddle::framework::OpDesc>,
ops::PnormOpGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(p_norm_grad, ops::PnormOpGrad);
REGISTER_OP_CPU_KERNEL(p_norm, ops::PnormKernel<CPU, float>,
ops::PnormKernel<CPU, double>);
REGISTER_OP_CPU_KERNEL(p_norm_grad, ops::PnormGradKernel<CPU, float>,
ops::PnormGradKernel<CPU, double>);
ops::PnormOpGradOpMaker<paddle::imperative::OpBase>,
PNormInferShapeFunctor);
REGISTER_OPERATOR(p_norm_grad, ops::PnormOpGrad, PNormGradInferShapeFunctor);
REGISTER_OP_VERSION(p_norm)
.AddCheckpoint(
R"ROC(
......
paddle/fluid/operators/p_norm_op.cu 已删除 100644 → 0
浏览文件 @ 22a5035e
/* Copyright (c) 2020 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.
Indicesou 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 <algorithm>
#ifdef __NVCC__
#include "cub/cub.cuh"
#endif
#ifdef __HIPCC__
#include <hipcub/hipcub.hpp>
namespace cub = hipcub;
#endif
#include "paddle/fluid/operators/amp/fp16_type_traits.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_impl.cu.h"
#include "paddle/fluid/operators/fc_op.h"
#include "paddle/fluid/operators/p_norm_op.h"
#include "paddle/fluid/operators/reduce_ops/reduce_op.cu.h"
#include "paddle/fluid/operators/reduce_ops/reduce_op.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
template <typename T>
__device__ __forceinline__ int sgn(T val) {
return (T(0) < val) - (val < T(0));
}
__device__ __forceinline__ platform::float16 inline_abs(platform::float16 x) {
return static_cast<platform::float16>(abs(static_cast<float>(x)));
}
__device__ __forceinline__ platform::bfloat16 inline_abs(platform::bfloat16 x) {
return static_cast<platform::bfloat16>(abs(static_cast<float>(x)));
}
__device__ __forceinline__ float inline_abs(float x) { return abs(x); }
__device__ __forceinline__ double inline_abs(double x) { return abs(x); }
__device__ __forceinline__ int inline_sign(platform::float16 x) {
return sgn<platform::float16>(x);
}
__device__ __forceinline__ int inline_sign(float x) { return sgn<float>(x); }
__device__ __forceinline__ int inline_sign(double x) { return sgn<double>(x); }
__device__ __forceinline__ platform::float16 inline_pow(
platform::float16 base, platform::float16 exponent) {
return static_cast<platform::float16>(
pow(static_cast<float>(base), static_cast<float>(exponent)));
}
__device__ __forceinline__ platform::bfloat16 inline_pow(
platform::bfloat16 base, platform::bfloat16 exponent) {
return static_cast<platform::bfloat16>(
pow(static_cast<float>(base), static_cast<float>(exponent)));
}
__device__ __forceinline__ float inline_pow(float base, float exponent) {
return pow(base, exponent);
}
__device__ __forceinline__ double inline_pow(double base, double exponent) {
return pow(base, exponent);
}
template <typename T>
struct NonzeroFunctor {
HOSTDEVICE explicit inline NonzeroFunctor() {}
HOSTDEVICE inline T operator()(const T x) const {
return static_cast<T>(static_cast<double>(x) != 0);
}
};
template <typename T>
struct AbsFunctor {
HOSTDEVICE explicit inline AbsFunctor() {}
HOSTDEVICE inline T operator()(const T x) const {
return static_cast<T>(inline_abs(x));
}
};
template <typename T>
struct UnsignedPowFunctor {
HOSTDEVICE explicit inline UnsignedPowFunctor(float porder) {
this->porder = porder;
}
HOSTDEVICE inline T operator()(const T x) const {
return static_cast<T>(inline_pow(inline_abs(x), static_cast<T>(porder)));
}
float porder;
};
template <typename DeviceContext, typename T>
class PnormCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_x = ctx.Input<framework::Tensor>("X");
auto* out_norm = ctx.Output<framework::Tensor>("Out");
const T* x = in_x->data<T>();
T* norm = out_norm->mutable_data<T>(ctx.GetPlace());
auto xdim = in_x->dims();
float porder = ctx.Attr<float>("porder");
bool asvector = ctx.Attr<bool>("asvector");
int axis = ctx.Attr<int>("axis");
std::vector<int> reduce_axis = {axis};
reduce_axis = GetReduceDim(reduce_axis, xdim.size(), asvector);
auto stream = ctx.cuda_device_context().stream();
using MT = typename details::MPTypeTrait<T>::Type;
if (porder == 0) {
TensorReduceImpl<T, T, kps::AddFunctor, NonzeroFunctor<T>>(
ctx.cuda_device_context(), *in_x, out_norm, NonzeroFunctor<T>(),
reduce_axis, stream);
} else if (porder == INFINITY) {
TensorReduceImpl<T, T, kps::MaxFunctor, AbsFunctor<T>>(
ctx.cuda_device_context(), *in_x, out_norm, AbsFunctor<T>(),
reduce_axis, stream);
} else if (porder == -INFINITY) {
TensorReduceImpl<T, T, kps::MinFunctor, AbsFunctor<T>>(
ctx.cuda_device_context(), *in_x, out_norm, AbsFunctor<T>(),
reduce_axis, stream);
} else {
TensorReduceImpl<T, T, kps::AddFunctor, UnsignedPowFunctor<T>>(
ctx.cuda_device_context(), *in_x, out_norm,
UnsignedPowFunctor<T>(porder), reduce_axis, stream);
const framework::Tensor* tmp_norm = out_norm;
std::vector<const framework::Tensor*> ins = {tmp_norm};
std::vector<framework::Tensor*> outs = {out_norm};
const auto& cuda_ctx =
ctx.template device_context<platform::CUDADeviceContext>();
paddle::operators::LaunchSameDimsElementwiseCudaKernel<T>(
cuda_ctx, ins, &outs, UnsignedPowFunctor<T>(1. / porder));
}
}
};
template <typename T>
struct AbsMaxAndMinGradFunctor {
template <typename DeviceContext, typename X, typename Y, typename DX,
typename DY, typename Dim>
void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
const Dim& dim, int size) {
dx->device(place) = dy->broadcast(dim) * (*x).sign() *
((*x).abs() == y->broadcast(dim)).template cast<T>();
}
};
template <typename T>
struct PNormGradFunctor {
HOSTDEVICE explicit inline PNormGradFunctor(float porder) {
this->porder = static_cast<T>(porder - 1.);
}
template <typename DeviceContext, typename X, typename Y, typename DX,
typename DY, typename Dim>
void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
const Dim& dim, int size) {
dx->device(place) = (*x).abs().pow(this->porder) * (*x).sign() *
dy->broadcast(dim) *
(*y).pow(-this->porder).broadcast(dim);
}
T porder;
};
template <typename DeviceContext, typename T, typename AttrType = T>
class PnormGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_x = ctx.Input<framework::Tensor>("X");
auto* in_norm = ctx.Input<framework::Tensor>("Out");
auto* in_norm_dy =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* out_dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
T* dx = out_dx->mutable_data<T>(ctx.GetPlace());
auto xdim = in_x->dims();
float porder = ctx.Attr<float>("porder");
int axis = ctx.Attr<int>("axis");
bool reduce_all = (in_norm->numel() == 1);
if (axis < 0) axis = xdim.size() + axis;
const std::vector<int> dims = {axis};
auto& cuda_ctx = ctx.template device_context<DeviceContext>();
if (porder == 0) {
phi::funcs::SetConstant<DeviceContext, T> set_zero;
set_zero(cuda_ctx, out_dx, static_cast<T>(0));
} else if (porder == INFINITY || porder == -INFINITY) {
AbsMaxAndMinGradFunctor<T> functor;
LaunchReduceGradKernel<DeviceContext, T, AbsMaxAndMinGradFunctor<T>>(
ctx, in_x, in_norm, in_norm_dy, out_dx, functor, dims, reduce_all);
} else {
auto functor = PNormGradFunctor<T>(porder);
LaunchReduceGradKernel<DeviceContext, T, PNormGradFunctor<T>>(
ctx, in_x, in_norm, in_norm_dy, out_dx, functor, dims, reduce_all);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(p_norm,
ops::PnormCUDAKernel<CUDA, paddle::platform::float16>,
ops::PnormCUDAKernel<CUDA, paddle::platform::bfloat16>,
ops::PnormCUDAKernel<CUDA, float>,
ops::PnormCUDAKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(
p_norm_grad, ops::PnormGradCUDAKernel<CUDA, paddle::platform::float16>,
ops::PnormGradCUDAKernel<CUDA, paddle::platform::bfloat16>,
ops::PnormGradCUDAKernel<CUDA, float>,
ops::PnormGradCUDAKernel<CUDA, double>);
paddle/fluid/operators/p_norm_op.h 已删除 100644 → 0
浏览文件 @ 22a5035e
/* Copyright (c) 2020 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.
Indicesou 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/framework/op_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace paddle {
namespace operators {
inline void GetDims(const framework::DDim& dim, int axis, int* pre, int* n,
int* post, bool asvector) {
*pre = 1;
*post = 1;
*n = dim[axis];
if (asvector) {
*n = product(dim);
} else {
for (int i = 0; i < axis; ++i) {
(*pre) *= dim[i];
}
for (int i = axis + 1; i < dim.size(); ++i) {
(*post) *= dim[i];
}
}
}
template <typename DeviceContext, typename T>
class PnormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_x = ctx.Input<framework::Tensor>("X");
auto* out_norm = ctx.Output<framework::Tensor>("Out");
out_norm->mutable_data<T>(ctx.GetPlace());
auto xdim = in_x->dims();
float porder = ctx.Attr<float>("porder");
int axis = ctx.Attr<int>("axis");
bool asvector = ctx.Attr<bool>("asvector");
if (axis < 0) axis = xdim.size() + axis;
int pre, n, post;
GetDims(xdim, axis, &pre, &n, &post, asvector);
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
Eigen::DSizes<int, 3> shape(pre, n, post);
Eigen::DSizes<int, 2> norm_shape(pre, post);
auto x_e = framework::EigenVector<T>::Flatten(*in_x);
auto norm_e = framework::EigenVector<T>::Flatten(*out_norm);
auto x = x_e.reshape(shape);
auto norm = norm_e.reshape(norm_shape);
// p=0 means number of non-zero elements of (x)
// p=inf means the maximum of |x|
// p=-inf means the minimum of |x|
// otherwise, Lp-norm = pow(sum(pow(|x|, p)), 1/p)
Eigen::DSizes<int, 1> rdim(1);
if (porder == 0) {
norm.device(*place) = (x != x.constant(0)).template cast<T>().sum(rdim);
} else if (porder == INFINITY) {
norm.device(*place) = x.abs().maximum(rdim);
} else if (porder == -INFINITY) {
norm.device(*place) = x.abs().minimum(rdim);
} else {
norm.device(*place) = x.abs().pow(porder).sum(rdim).pow(1.0f / porder);
}
}
};
template <typename DeviceContext, typename T, typename AttrType = T>
class PnormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_x = ctx.Input<framework::Tensor>("X");
auto* in_norm = ctx.Input<framework::Tensor>("Out");
auto* in_norm_dy =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* out_dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
out_dx->mutable_data<T>(ctx.GetPlace());
T eps = static_cast<T>(ctx.Attr<float>("epsilon"));
auto xdim = in_x->dims();
float porder = ctx.Attr<float>("porder");
int axis = ctx.Attr<int>("axis");
bool asvector = ctx.Attr<bool>("asvector");
if (axis < 0) axis = xdim.size() + axis;
int pre, n, post;
GetDims(xdim, axis, &pre, &n, &post, asvector);
Eigen::DSizes<int, 3> shape(pre, n, post);
Eigen::DSizes<int, 3> rshape(pre, 1, post);
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
auto x_e = framework::EigenVector<T>::Flatten(*in_x);
auto dx_e = framework::EigenVector<T>::Flatten(*out_dx);
auto norm_e = framework::EigenVector<T>::Flatten(*in_norm);
auto norm_dy_e = framework::EigenVector<T>::Flatten(*in_norm_dy);
auto x = x_e.reshape(shape);
auto dx = dx_e.reshape(shape);
auto norm = norm_e.reshape(rshape);
auto norm_dy = norm_dy_e.reshape(rshape);
Eigen::DSizes<int, 1> rdim(1);
Eigen::DSizes<int, 3> bcast(1, n, 1);
if (porder == 0) {
phi::funcs::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
set_zero(dev_ctx, out_dx, static_cast<T>(0));
} else if (porder == INFINITY || porder == -INFINITY) {
dx.device(*place) =
(x.abs() == norm.broadcast(bcast)).template cast<T>() * x.sign() *
norm_dy.broadcast(bcast);
} else {
dx.device(*place) =
(x.abs()).pow(porder - 1.0f) /
((norm.broadcast(bcast)).pow(porder - 1.0f) + x.constant(eps));
dx.device(*place) = dx * norm_dy.broadcast(bcast) * x.sign();
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/p_norm_op_npu.cc
浏览文件 @ 92afe146
......@@ -12,7 +12,7 @@ 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/p_norm_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/npu/npu_op_runner.h"
namespace paddle {
......
paddle/phi/infermeta/unary.cc
浏览文件 @ 92afe146
......@@ -1012,6 +1012,63 @@ void PixelShuffleInferMeta(const MetaTensor& x,
out->set_dims(output_dims);
}
void PNormInferMeta(const MetaTensor& x,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
MetaTensor* out) {
auto x_dim = x.dims();
auto x_rank = x_dim.size();
PADDLE_ENFORCE_GE(axis,
-x_rank,
errors::InvalidArgument(
"Attr(axis) value should be in range [-R, R-1], R is "
"the rank of Input(X). But received axis: %d, R: %d. "
"Current Input(X)'s shape is=[%s].",
axis,
x_rank,
x_dim));
PADDLE_ENFORCE_LT(axis,
x_rank,
errors::InvalidArgument(
"Attr(axis) value should be in range [-R, R-1], R is "
"the rank of Input(X). But received axis: %d, R: %d. "
"Current Input(X)'s shape is=[%s].",
axis,
x_rank,
x_dim));
std::vector<int> reduce_dims;
if (asvector) {
reduce_dims.emplace_back(1);
if (keepdim) {
for (int i = 1; i < x_dim.size(); ++i) {
reduce_dims.emplace_back(1);
}
x_dim = phi::make_ddim(reduce_dims);
}
} else {
if (axis < 0) axis = x_dim.size() + axis;
for (int i = 0; i < x_dim.size(); ++i) {
if (i != axis) reduce_dims.emplace_back(x_dim[i]);
}
if (reduce_dims.size() == 0) {
reduce_dims.emplace_back(1);
}
}
x_dim[axis] = 1;
if (keepdim) {
out->set_dims(x_dim);
} else {
out->set_dims(phi::make_ddim(reduce_dims));
}
out->set_dtype(x.dtype());
}
void PoolInferMeta(const MetaTensor& x,
const std::vector<int>& kernel_size,
const std::vector<int>& strides,
......
paddle/phi/infermeta/unary.h
浏览文件 @ 92afe146
......@@ -166,6 +166,14 @@ void PixelShuffleInferMeta(const MetaTensor& x,
const std::string& data_format,
MetaTensor* out);
void PNormInferMeta(const MetaTensor& x,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
MetaTensor* out);
void PoolInferMeta(const MetaTensor& x,
const std::vector<int>& kernel_size,
const std::vector<int>& strides,
......
paddle/phi/kernels/cpu/p_norm_grad_kernel.cc 0 → 100644
浏览文件 @ 92afe146
// 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/p_norm_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
inline void GetDims(const phi::DDim& dim,
int axis,
int* pre,
int* n,
int* post,
bool asvector) {
*pre = 1;
*post = 1;
*n = dim[axis];
if (asvector) {
*n = product(dim);
} else {
for (int i = 0; i < axis; ++i) {
(*pre) *= dim[i];
}
for (int i = axis + 1; i < dim.size(); ++i) {
(*post) *= dim[i];
}
}
}
template <typename T, typename Context>
void PNormGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* x_grad) {
auto* in_x = &x;
auto* in_norm = &out;
auto* in_norm_dy = &out_grad;
auto* out_dx = x_grad;
dev_ctx.template Alloc<T>(out_dx);
T eps = static_cast<T>(epsilon);
auto xdim = in_x->dims();
if (axis < 0) axis = xdim.size() + axis;
int pre, n, post;
GetDims(xdim, axis, &pre, &n, &post, asvector);
Eigen::DSizes<int, 3> shape(pre, n, post);
Eigen::DSizes<int, 3> rshape(pre, 1, post);
auto* place = dev_ctx.eigen_device();
auto x_e = phi::EigenVector<T>::Flatten(*in_x);
auto dx_e = phi::EigenVector<T>::Flatten(*out_dx);
auto norm_e = phi::EigenVector<T>::Flatten(*in_norm);
auto norm_dy_e = phi::EigenVector<T>::Flatten(*in_norm_dy);
auto xr = x_e.reshape(shape);
auto dx = dx_e.reshape(shape);
auto norm = norm_e.reshape(rshape);
auto norm_dy = norm_dy_e.reshape(rshape);
Eigen::DSizes<int, 1> rdim(1);
Eigen::DSizes<int, 3> bcast(1, n, 1);
if (porder == 0) {
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, out_dx, static_cast<T>(0));
} else if (porder == INFINITY || porder == -INFINITY) {
dx.device(*place) = (xr.abs() == norm.broadcast(bcast)).template cast<T>() *
xr.sign() * norm_dy.broadcast(bcast);
} else {
dx.device(*place) =
(xr.abs()).pow(porder - 1.0f) /
((norm.broadcast(bcast)).pow(porder - 1.0f) + xr.constant(eps));
dx.device(*place) = dx * norm_dy.broadcast(bcast) * xr.sign();
}
}
} // namespace phi
PD_REGISTER_KERNEL(
p_norm_grad, CPU, ALL_LAYOUT, phi::PNormGradKernel, float, double) {}
paddle/phi/kernels/cpu/p_norm_kernel.cc 0 → 100644
浏览文件 @ 92afe146
// 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/p_norm_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/common_shape.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
inline void GetDims(const phi::DDim& dim,
int axis,
int* pre,
int* n,
int* post,
bool asvector) {
*pre = 1;
*post = 1;
*n = dim[axis];
if (asvector) {
*n = product(dim);
} else {
for (int i = 0; i < axis; ++i) {
(*pre) *= dim[i];
}
for (int i = axis + 1; i < dim.size(); ++i) {
(*post) *= dim[i];
}
}
}
template <typename T, typename Context>
void PNormKernel(const Context& dev_ctx,
const DenseTensor& x,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* out) {
auto* in_x = &x;
dev_ctx.template Alloc<T>(out);
auto xdim = in_x->dims();
if (axis < 0) axis = xdim.size() + axis;
int pre, n, post;
GetDims(xdim, axis, &pre, &n, &post, asvector);
auto* place = dev_ctx.eigen_device();
Eigen::DSizes<int, 3> shape(pre, n, post);
Eigen::DSizes<int, 2> norm_shape(pre, post);
auto x_e = phi::EigenVector<T>::Flatten(*in_x);
auto norm_e = phi::EigenVector<T>::Flatten(*out);
auto xr = x_e.reshape(shape);
auto norm = norm_e.reshape(norm_shape);
// p=0 means number of non-zero elements of (xr)
// p=inf means the maximum of |xr|
// p=-inf means the minimum of |xr|
// otherwise, Lp-norm = pow(sum(pow(|xr|, p)), 1/p)
Eigen::DSizes<int, 1> rdim(1);
if (porder == 0) {
norm.device(*place) = (xr != xr.constant(0)).template cast<T>().sum(rdim);
} else if (porder == INFINITY) {
norm.device(*place) = xr.abs().maximum(rdim);
} else if (porder == -INFINITY) {
norm.device(*place) = xr.abs().minimum(rdim);
} else {
norm.device(*place) = xr.abs().pow(porder).sum(rdim).pow(1.0f / porder);
}
}
} // namespace phi
PD_REGISTER_KERNEL(p_norm, CPU, ALL_LAYOUT, phi::PNormKernel, float, double) {}
paddle/phi/kernels/gpu/p_norm_grad_kernel.cu 0 → 100644
浏览文件 @ 92afe146
// 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/p_norm_grad_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/reduce_grad_functions.h"
namespace phi {
template <typename T>
struct AbsMaxAndMinGradFunctor {
template <typename Context,
typename X,
typename Y,
typename DX,
typename DY,
typename Dim>
void operator()(const Context& place,
X* x,
Y* y,
DX* dx,
DY* dy,
const Dim& dim,
int size) {
dx->device(place) = dy->broadcast(dim) * (*x).sign() *
((*x).abs() == y->broadcast(dim)).template cast<T>();
}
};
template <typename T>
struct PNormGradFunctor {
HOSTDEVICE explicit inline PNormGradFunctor(float porder) {
this->porder = static_cast<T>(porder - 1.);
}
template <typename Context,
typename X,
typename Y,
typename DX,
typename DY,
typename Dim>
void operator()(const Context& place,
X* x,
Y* y,
DX* dx,
DY* dy,
const Dim& dim,
int size) {
dx->device(place) = (*x).abs().pow(this->porder) * (*x).sign() *
dy->broadcast(dim) *
(*y).pow(-this->porder).broadcast(dim);
}
T porder;
};
template <typename T, typename Context>
void PNormGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* x_grad) {
auto* in_x = &x;
auto* in_norm = &out;
auto* in_norm_dy = &out_grad;
auto* out_dx = x_grad;
dev_ctx.template Alloc<T>(out_dx);
auto xdim = in_x->dims();
bool reduce_all = (in_norm->numel() == 1);
if (axis < 0) axis = xdim.size() + axis;
const std::vector<int> dims = {axis};
if (porder == 0) {
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, out_dx, static_cast<T>(0));
} else if (porder == INFINITY || porder == -INFINITY) {
AbsMaxAndMinGradFunctor<T> functor;
funcs::LaunchReduceGradKernel<Context, T, AbsMaxAndMinGradFunctor<T>>(
dev_ctx, in_x, in_norm, in_norm_dy, out_dx, functor, dims, reduce_all);
} else {
auto functor = PNormGradFunctor<T>(porder);
funcs::LaunchReduceGradKernel<Context, T, PNormGradFunctor<T>>(
dev_ctx, in_x, in_norm, in_norm_dy, out_dx, functor, dims, reduce_all);
}
}
} // namespace phi
PD_REGISTER_KERNEL(p_norm_grad,
GPU,
ALL_LAYOUT,
phi::PNormGradKernel,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16) {}
paddle/phi/kernels/gpu/p_norm_kernel.cu 0 → 100644
浏览文件 @ 92afe146
// 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/p_norm_kernel.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_impl.cu.h"
#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/reduce_function.h"
#include "paddle/phi/kernels/gpu/reduce.h"
namespace phi {
template <typename T>
__device__ __forceinline__ int sgn(T val) {
return (T(0) < val) - (val < T(0));
}
__device__ __forceinline__ dtype::float16 inline_abs(dtype::float16 x) {
return static_cast<dtype::float16>(abs(static_cast<float>(x)));
}
__device__ __forceinline__ dtype::bfloat16 inline_abs(dtype::bfloat16 x) {
return static_cast<dtype::bfloat16>(abs(static_cast<float>(x)));
}
__device__ __forceinline__ float inline_abs(float x) { return abs(x); }
__device__ __forceinline__ double inline_abs(double x) { return abs(x); }
__device__ __forceinline__ int inline_sign(dtype::float16 x) {
return sgn<dtype::float16>(x);
}
__device__ __forceinline__ int inline_sign(float x) { return sgn<float>(x); }
__device__ __forceinline__ int inline_sign(double x) { return sgn<double>(x); }
__device__ __forceinline__ dtype::float16 inline_pow(dtype::float16 base,
dtype::float16 exponent) {
return static_cast<dtype::float16>(
pow(static_cast<float>(base), static_cast<float>(exponent)));
}
__device__ __forceinline__ dtype::bfloat16 inline_pow(
dtype::bfloat16 base, dtype::bfloat16 exponent) {
return static_cast<dtype::bfloat16>(
pow(static_cast<float>(base), static_cast<float>(exponent)));
}
__device__ __forceinline__ float inline_pow(float base, float exponent) {
return pow(base, exponent);
}
__device__ __forceinline__ double inline_pow(double base, double exponent) {
return pow(base, exponent);
}
template <typename T>
struct NonzeroFunctor {
HOSTDEVICE explicit inline NonzeroFunctor() {}
HOSTDEVICE inline T operator()(const T x) const {
return static_cast<T>(static_cast<double>(x) != 0);
}
};
template <typename T>
struct AbsFunctor {
HOSTDEVICE explicit inline AbsFunctor() {}
HOSTDEVICE inline T operator()(const T x) const {
return static_cast<T>(inline_abs(x));
}
};
template <typename T>
struct UnsignedPowFunctor {
HOSTDEVICE explicit inline UnsignedPowFunctor(float porder) {
this->porder = porder;
}
HOSTDEVICE inline T operator()(const T x) const {
return static_cast<T>(inline_pow(inline_abs(x), static_cast<T>(porder)));
}
float porder;
};
template <typename T, typename Context>
void PNormKernel(const Context& dev_ctx,
const DenseTensor& x,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* out) {
auto* in_x = &x;
auto* out_norm = out;
T* norm = dev_ctx.template Alloc<T>(out);
auto xdim = in_x->dims();
std::vector<int64_t> axis_dims = {static_cast<int64_t>(axis)};
std::vector<int> reduce_axis =
funcs::details::GetReduceDim(axis_dims, xdim.size(), asvector);
using MT = typename dtype::MPTypeTrait<T>::Type;
if (porder == 0) {
phi::funcs::ReduceKernel<T, T, kps::AddFunctor, NonzeroFunctor<T>>(
dev_ctx, *in_x, out_norm, NonzeroFunctor<T>(), reduce_axis);
} else if (porder == INFINITY) {
phi::funcs::ReduceKernel<T, T, kps::MaxFunctor, AbsFunctor<T>>(
dev_ctx, *in_x, out_norm, AbsFunctor<T>(), reduce_axis);
} else if (porder == -INFINITY) {
phi::funcs::ReduceKernel<T, T, kps::MinFunctor, AbsFunctor<T>>(
dev_ctx, *in_x, out_norm, AbsFunctor<T>(), reduce_axis);
} else {
phi::funcs::ReduceKernel<T, T, kps::AddFunctor, UnsignedPowFunctor<T>>(
dev_ctx, *in_x, out_norm, UnsignedPowFunctor<T>(porder), reduce_axis);
const DenseTensor* tmp_norm = out_norm;
std::vector<const DenseTensor*> ins = {tmp_norm};
std::vector<DenseTensor*> outs = {out_norm};
phi::funcs::ElementwiseKernel<T>(
dev_ctx, ins, &outs, UnsignedPowFunctor<T>(1. / porder));
}
}
} // namespace phi
PD_REGISTER_KERNEL(p_norm,
GPU,
ALL_LAYOUT,
phi::PNormKernel,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16) {}
paddle/phi/kernels/p_norm_grad_kernel.h 0 → 100644
浏览文件 @ 92afe146
// 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.
#pragma once
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void PNormGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* x_grad);
} // namespace phi
paddle/phi/kernels/p_norm_kernel.h 0 → 100644
浏览文件 @ 92afe146
// 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.
#pragma once
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void PNormKernel(const Context& dev_ctx,
const DenseTensor& x,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* out);
} // namespace phi
paddle/phi/ops/compat/p_norm_sig.cc 0 → 100644
浏览文件 @ 92afe146
// 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/core/compat/op_utils.h"
namespace phi {
KernelSignature PNormGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature("p_norm_grad",
{"X", "Out", GradVarName("Out")},
{"porder", "axis", "epsilon", "keepdim", "asvector"},
{GradVarName("X")});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(p_norm_grad, phi::PNormGradOpArgumentMapping);
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