mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
876f392c39
The major changes are
1. Moving CUDA/PacketMath.h to GPU/PacketMath.h
2. Moving CUDA/MathFunctions.h to GPU/MathFunction.h
3. Moving CUDA/CudaSpecialFunctions.h to GPU/GpuSpecialFunctions.h
The above three changes effectively enable the Eigen "Packet" layer for the HIP platform
4. Merging the "hip_basic" and "cuda_basic" unit tests into one ("gpu_basic")
5. Updating the "EIGEN_DEVICE_FUNC" marking in some places
The change has been tested on the HIP and CUDA platforms.
213 lines
6.8 KiB
Plaintext
213 lines
6.8 KiB
Plaintext
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2015-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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// workaround issue between gcc >= 4.7 and cuda 5.5
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#if (defined __GNUC__) && (__GNUC__>4 || __GNUC_MINOR__>=7)
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#undef _GLIBCXX_ATOMIC_BUILTINS
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#undef _GLIBCXX_USE_INT128
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#endif
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_TEST_FUNC gpu_basic
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
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#include "main.h"
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#include "gpu_common.h"
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// Check that dense modules can be properly parsed by nvcc
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#include <Eigen/Dense>
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// struct Foo{
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// EIGEN_DEVICE_FUNC
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// void operator()(int i, const float* mats, float* vecs) const {
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// using namespace Eigen;
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// // Matrix3f M(data);
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// // Vector3f x(data+9);
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// // Map<Vector3f>(data+9) = M.inverse() * x;
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// Matrix3f M(mats+i/16);
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// Vector3f x(vecs+i*3);
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// // using std::min;
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// // using std::sqrt;
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// Map<Vector3f>(vecs+i*3) << x.minCoeff(), 1, 2;// / x.dot(x);//(M.inverse() * x) / x.x();
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// //x = x*2 + x.y() * x + x * x.maxCoeff() - x / x.sum();
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// }
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// };
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template<typename T>
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struct coeff_wise {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
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{
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using namespace Eigen;
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T x1(in+i);
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T x2(in+i+1);
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T x3(in+i+2);
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Map<T> res(out+i*T::MaxSizeAtCompileTime);
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res.array() += (in[0] * x1 + x2).array() * x3.array();
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}
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};
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template<typename T>
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struct replicate {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
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{
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using namespace Eigen;
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T x1(in+i);
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int step = x1.size() * 4;
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int stride = 3 * step;
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typedef Map<Array<typename T::Scalar,Dynamic,Dynamic> > MapType;
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MapType(out+i*stride+0*step, x1.rows()*2, x1.cols()*2) = x1.replicate(2,2);
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MapType(out+i*stride+1*step, x1.rows()*3, x1.cols()) = in[i] * x1.colwise().replicate(3);
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MapType(out+i*stride+2*step, x1.rows(), x1.cols()*3) = in[i] * x1.rowwise().replicate(3);
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}
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};
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template<typename T>
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struct redux {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
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{
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using namespace Eigen;
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int N = 10;
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T x1(in+i);
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out[i*N+0] = x1.minCoeff();
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out[i*N+1] = x1.maxCoeff();
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out[i*N+2] = x1.sum();
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out[i*N+3] = x1.prod();
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out[i*N+4] = x1.matrix().squaredNorm();
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out[i*N+5] = x1.matrix().norm();
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out[i*N+6] = x1.colwise().sum().maxCoeff();
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out[i*N+7] = x1.rowwise().maxCoeff().sum();
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out[i*N+8] = x1.matrix().colwise().squaredNorm().sum();
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}
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};
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template<typename T1, typename T2>
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struct prod_test {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const
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{
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using namespace Eigen;
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typedef Matrix<typename T1::Scalar, T1::RowsAtCompileTime, T2::ColsAtCompileTime> T3;
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T1 x1(in+i);
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T2 x2(in+i+1);
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Map<T3> res(out+i*T3::MaxSizeAtCompileTime);
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res += in[i] * x1 * x2;
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}
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};
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template<typename T1, typename T2>
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struct diagonal {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const
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{
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using namespace Eigen;
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T1 x1(in+i);
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Map<T2> res(out+i*T2::MaxSizeAtCompileTime);
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res += x1.diagonal();
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}
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};
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template<typename T>
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struct eigenvalues_direct {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
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{
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using namespace Eigen;
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typedef Matrix<typename T::Scalar, T::RowsAtCompileTime, 1> Vec;
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T M(in+i);
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Map<Vec> res(out+i*Vec::MaxSizeAtCompileTime);
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T A = M*M.adjoint();
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SelfAdjointEigenSolver<T> eig;
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eig.computeDirect(M);
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res = eig.eigenvalues();
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}
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};
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template<typename T>
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struct eigenvalues {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
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{
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using namespace Eigen;
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typedef Matrix<typename T::Scalar, T::RowsAtCompileTime, 1> Vec;
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T M(in+i);
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Map<Vec> res(out+i*Vec::MaxSizeAtCompileTime);
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T A = M*M.adjoint();
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SelfAdjointEigenSolver<T> eig;
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eig.compute(M);
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res = eig.eigenvalues();
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}
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};
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template<typename T>
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struct matrix_inverse {
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EIGEN_DEVICE_FUNC
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void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
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{
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using namespace Eigen;
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T M(in+i);
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Map<T> res(out+i*T::MaxSizeAtCompileTime);
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res = M.inverse();
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}
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};
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void test_gpu_basic()
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{
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ei_test_init_gpu();
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int nthreads = 100;
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Eigen::VectorXf in, out;
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#if !defined(__CUDA_ARCH__) && !defined(__HIP_DEVICE_COMPILE__)
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int data_size = nthreads * 512;
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in.setRandom(data_size);
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out.setRandom(data_size);
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#endif
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CALL_SUBTEST( run_and_compare_to_gpu(coeff_wise<Vector3f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(coeff_wise<Array44f>(), nthreads, in, out) );
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#if !defined(EIGEN_USE_HIP)
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// FIXME
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// These subtests result in a compile failure on the HIP platform
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//
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// eigen-upstream/Eigen/src/Core/Replicate.h:61:65: error:
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// base class 'internal::dense_xpr_base<Replicate<Array<float, 4, 1, 0, 4, 1>, -1, -1> >::type'
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// (aka 'ArrayBase<Eigen::Replicate<Eigen::Array<float, 4, 1, 0, 4, 1>, -1, -1> >') has protected default constructor
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CALL_SUBTEST( run_and_compare_to_gpu(replicate<Array4f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(replicate<Array33f>(), nthreads, in, out) );
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#endif
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CALL_SUBTEST( run_and_compare_to_gpu(redux<Array4f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(redux<Matrix3f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(prod_test<Matrix3f,Matrix3f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(prod_test<Matrix4f,Vector4f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(diagonal<Matrix3f,Vector3f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(diagonal<Matrix4f,Vector4f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(matrix_inverse<Matrix2f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(matrix_inverse<Matrix3f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(matrix_inverse<Matrix4f>(), nthreads, in, out) );
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#if !defined(EIGEN_USE_HIP)
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// FIXME
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// These subtests result in a linking error on the HIP platform
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CALL_SUBTEST( run_and_compare_to_gpu(eigenvalues_direct<Matrix3f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(eigenvalues_direct<Matrix2f>(), nthreads, in, out) );
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CALL_SUBTEST( run_and_compare_to_gpu(eigenvalues<Matrix4f>(), nthreads, in, out) );
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#endif
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}
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