Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 846ce406 编写于 作者: Q Qi Li 提交者： GitHub
浏览文件
操作

[ROCM] update eigen cmake and patch, test=develop (#30602)

上级 173660be
展开全部 显示空白变更内容
内联 并排
Showing with 1258 addition and 39 deletion
cmake/external/eigen.cmake
浏览文件 @ 846ce406
......@@ -26,13 +26,6 @@ if(WIN32)
set(EIGEN_TAG 917060c364181f33a735dc023818d5a54f60e54c)
endif()
# eigen on cuda9.1 missing header of math_funtions.hpp
# https://stackoverflow.com/questions/43113508/math-functions-hpp-not-found-when-using-cuda-with-eigen
if(WITH_ROCM_PLATFORM)
set(EIGEN_REPOSITORY ${GIT_URL}/sabreshao/hipeigen.git)
set(EIGEN_TAG 7cb2b6e5a4b4a1efe658abb215cd866c6fb2275e)
endif()
cache_third_party(extern_eigen3
REPOSITORY ${EIGEN_REPOSITORY}
TAG ${EIGEN_TAG}
......@@ -56,14 +49,25 @@ elseif(LINUX)
# add patch to avoid compilation error in c++11
file(TO_NATIVE_PATH ${PADDLE_SOURCE_DIR}/patches/eigen/MathFunctions.h native_src2)
file(TO_NATIVE_PATH ${EIGEN_SOURCE_DIR}/Eigen/src/Core/MathFunctions.h native_dst2)
if(WITH_ROCM)
# For HIPCC Eigen::internal::device::numeric_limits is not EIGEN_DEVICE_FUNC
# which will cause compiler error of using __host__ funciont in __host__ __device__
file(TO_NATIVE_PATH ${PADDLE_SOURCE_DIR}/patches/eigen/Meta.h native_src3)
file(TO_NATIVE_PATH ${EIGEN_SOURCE_DIR}/Eigen/src/Core/util/Meta.h native_dst3)
# For HIPCC Eigen::internal::scalar_sum_op<bool,bool> is not EIGEN_DEVICE_FUNC
# which will cause compiler error of using __host__ funciont in __host__ __device__
file(TO_NATIVE_PATH ${PADDLE_SOURCE_DIR}/patches/eigen/BinaryFunctors.h native_src4)
file(TO_NATIVE_PATH ${EIGEN_SOURCE_DIR}/Eigen/src/Core/functors/BinaryFunctors.h native_dst4)
set(EIGEN_PATCH_COMMAND cp ${native_src1} ${native_dst1} && cp ${native_src2} ${native_dst2} && cp ${native_src3} ${native_dst3} && cp ${native_src4} ${native_dst4})
else()
set(EIGEN_PATCH_COMMAND cp ${native_src1} ${native_dst1} && cp ${native_src2} ${native_dst2})
endif()
endif()
set(EIGEN_INCLUDE_DIR ${EIGEN_SOURCE_DIR})
INCLUDE_DIRECTORIES(${EIGEN_INCLUDE_DIR})
if(WITH_AMD_GPU)
ExternalProject_Add(
ExternalProject_Add(
extern_eigen3
${EXTERNAL_PROJECT_LOG_ARGS}
${SHALLOW_CLONE}
......@@ -76,23 +80,7 @@ if(WITH_AMD_GPU)
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
else()
ExternalProject_Add(
extern_eigen3
${EXTERNAL_PROJECT_LOG_ARGS}
${SHALLOW_CLONE}
"${EIGEN_DOWNLOAD_CMD}"
PREFIX ${EIGEN_PREFIX_DIR}
SOURCE_DIR ${EIGEN_SOURCE_DIR}
UPDATE_COMMAND ""
PATCH_COMMAND ${EIGEN_PATCH_COMMAND}
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
endif()
)
add_library(eigen3 INTERFACE)
......
patches/eigen/BinaryFunctors.h 0 → 100644
浏览文件 @ 846ce406
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// clang-format off
#ifndef EIGEN_BINARY_FUNCTORS_H
#define EIGEN_BINARY_FUNCTORS_H
namespace Eigen {
namespace internal {
//---------- associative binary functors ----------
template<typename Arg1, typename Arg2>
struct binary_op_base
{
typedef Arg1 first_argument_type;
typedef Arg2 second_argument_type;
};
/** \internal
* \brief Template functor to compute the sum of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum()
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_sum_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_sum_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
#else
scalar_sum_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a + b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::padd(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux(a); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_sum_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2, // rough estimate!
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd
// TODO vectorize mixed sum
};
};
/** \internal
* \brief Template specialization to deprecate the summation of boolean expressions.
* This is required to solve Bug 426.
* \sa DenseBase::count(), DenseBase::any(), ArrayBase::cast(), MatrixBase::cast()
*/
template<> struct scalar_sum_op<bool,bool> : scalar_sum_op<int,int> {
EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
scalar_sum_op() {}
};
/** \internal
* \brief Template functor to compute the product of two scalars
*
* \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_product_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_product_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
#else
scalar_product_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmul(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_mul(a); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost)/2, // rough estimate!
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
// TODO vectorize mixed product
};
};
/** \internal
* \brief Template functor to compute the conjugate product of two scalars
*
* This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_conj_product_op : binary_op_base<LhsScalar,RhsScalar>
{
enum {
Conj = NumTraits<LhsScalar>::IsComplex
};
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_conj_product_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
{ return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<LhsScalar>::MulCost,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul
};
};
/** \internal
* \brief Template functor to compute the min of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_min_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_min_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return numext::mini(a, b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmin(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_min(a); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_min_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin
};
};
/** \internal
* \brief Template functor to compute the max of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_max_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_max_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return numext::maxi(a, b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmax(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_max(a); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_max_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax
};
};
/** \internal
* \brief Template functors for comparison of two scalars
* \todo Implement packet-comparisons
*/
template<typename LhsScalar, typename RhsScalar, ComparisonName cmp> struct scalar_cmp_op;
template<typename LhsScalar, typename RhsScalar, ComparisonName cmp>
struct functor_traits<scalar_cmp_op<LhsScalar,RhsScalar, cmp> > {
enum {
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = false
};
};
template<ComparisonName Cmp, typename LhsScalar, typename RhsScalar>
struct result_of<scalar_cmp_op<LhsScalar, RhsScalar, Cmp>(LhsScalar,RhsScalar)> {
typedef bool type;
};
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_EQ> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a==b;}
};
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LT> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<b;}
};
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LE> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<=b;}
};
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GT> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>b;}
};
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GE> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>=b;}
};
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_UNORD> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return !(a<=b || b<=a);}
};
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_NEQ> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a!=b;}
};
/** \internal
* \brief Template functor to compute the hypot of two \b positive \b and \b real scalars
*
* \sa MatrixBase::stableNorm(), class Redux
*/
template<typename Scalar>
struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar>
{
EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar &x, const Scalar &y) const
{
// This functor is used by hypotNorm only for which it is faster to first apply abs
// on all coefficients prior to reduction through hypot.
// This way we avoid calling abs on positive and real entries, and this also permits
// to seamlessly handle complexes. Otherwise we would have to handle both real and complexes
// through the same functor...
return internal::positive_real_hypot(x,y);
}
};
template<typename Scalar>
struct functor_traits<scalar_hypot_op<Scalar,Scalar> > {
enum
{
Cost = 3 * NumTraits<Scalar>::AddCost +
2 * NumTraits<Scalar>::MulCost +
2 * scalar_div_cost<Scalar,false>::value,
PacketAccess = false
};
};
/** \internal
* \brief Template functor to compute the pow of two scalars
*/
template<typename Scalar, typename Exponent>
struct scalar_pow_op : binary_op_base<Scalar,Exponent>
{
typedef typename ScalarBinaryOpTraits<Scalar,Exponent,scalar_pow_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_pow_op)
#else
scalar_pow_op() {
typedef Scalar LhsScalar;
typedef Exponent RhsScalar;
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC
inline result_type operator() (const Scalar& a, const Exponent& b) const { return numext::pow(a, b); }
};
template<typename Scalar, typename Exponent>
struct functor_traits<scalar_pow_op<Scalar,Exponent> > {
enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
};
//---------- non associative binary functors ----------
/** \internal
* \brief Template functor to compute the difference of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::operator-
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_difference_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_difference_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
#else
scalar_difference_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a - b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::psub(a,b); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_difference_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub
};
};
/** \internal
* \brief Template functor to compute the quotient of two scalars
*
* \sa class CwiseBinaryOp, Cwise::operator/()
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_quotient_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_quotient_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
#else
scalar_quotient_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pdiv(a,b); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > {
typedef typename scalar_quotient_op<LhsScalar,RhsScalar>::result_type result_type;
enum {
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv,
Cost = scalar_div_cost<result_type,PacketAccess>::value
};
};
/** \internal
* \brief Template functor to compute the and of two booleans
*
* \sa class CwiseBinaryOp, ArrayBase::operator&&
*/
struct scalar_boolean_and_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a && b; }
};
template<> struct functor_traits<scalar_boolean_and_op> {
enum {
Cost = NumTraits<bool>::AddCost,
PacketAccess = false
};
};
/** \internal
* \brief Template functor to compute the or of two booleans
*
* \sa class CwiseBinaryOp, ArrayBase::operator||
*/
struct scalar_boolean_or_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a || b; }
};
template<> struct functor_traits<scalar_boolean_or_op> {
enum {
Cost = NumTraits<bool>::AddCost,
PacketAccess = false
};
};
/** \internal
* \brief Template functor to compute the xor of two booleans
*
* \sa class CwiseBinaryOp, ArrayBase::operator^
*/
struct scalar_boolean_xor_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_xor_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a ^ b; }
};
template<> struct functor_traits<scalar_boolean_xor_op> {
enum {
Cost = NumTraits<bool>::AddCost,
PacketAccess = false
};
};
/** \internal
* \brief Template functor to compute the absolute difference of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::absolute_difference
*/
template<typename LhsScalar,typename RhsScalar>
struct scalar_absolute_difference_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_absolute_difference_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_absolute_difference_op)
#else
scalar_absolute_difference_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
{ return numext::absdiff(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pabsdiff(a,b); }
};
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_absolute_difference_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAbsDiff
};
};
//---------- binary functors bound to a constant, thus appearing as a unary functor ----------
// The following two classes permits to turn any binary functor into a unary one with one argument bound to a constant value.
// They are analogues to std::binder1st/binder2nd but with the following differences:
// - they are compatible with packetOp
// - they are portable across C++ versions (the std::binder* are deprecated in C++11)
template<typename BinaryOp> struct bind1st_op : BinaryOp {
typedef typename BinaryOp::first_argument_type first_argument_type;
typedef typename BinaryOp::second_argument_type second_argument_type;
typedef typename BinaryOp::result_type result_type;
EIGEN_DEVICE_FUNC explicit bind1st_op(const first_argument_type &val) : m_value(val) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const second_argument_type& b) const { return BinaryOp::operator()(m_value,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& b) const
{ return BinaryOp::packetOp(internal::pset1<Packet>(m_value), b); }
first_argument_type m_value;
};
template<typename BinaryOp> struct functor_traits<bind1st_op<BinaryOp> > : functor_traits<BinaryOp> {};
template<typename BinaryOp> struct bind2nd_op : BinaryOp {
typedef typename BinaryOp::first_argument_type first_argument_type;
typedef typename BinaryOp::second_argument_type second_argument_type;
typedef typename BinaryOp::result_type result_type;
EIGEN_DEVICE_FUNC explicit bind2nd_op(const second_argument_type &val) : m_value(val) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const first_argument_type& a) const { return BinaryOp::operator()(a,m_value); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return BinaryOp::packetOp(a,internal::pset1<Packet>(m_value)); }
second_argument_type m_value;
};
template<typename BinaryOp> struct functor_traits<bind2nd_op<BinaryOp> > : functor_traits<BinaryOp> {};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_BINARY_FUNCTORS_H
// clang-format on
patches/eigen/Meta.h 0 → 100755
浏览文件 @ 846ce406
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或