462 lines
16 KiB
Plaintext
462 lines
16 KiB
Plaintext
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// 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_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 complex_sqrt {
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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 typename T::Scalar ComplexType;
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typedef typename T::Scalar::value_type ValueType;
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const int num_special_inputs = 18;
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if (i == 0) {
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const ValueType nan = std::numeric_limits<ValueType>::quiet_NaN();
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typedef Eigen::Vector<ComplexType, num_special_inputs> SpecialInputs;
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SpecialInputs special_in;
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special_in.setZero();
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int idx = 0;
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special_in[idx++] = ComplexType(0, 0);
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special_in[idx++] = ComplexType(-0, 0);
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special_in[idx++] = ComplexType(0, -0);
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special_in[idx++] = ComplexType(-0, -0);
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// GCC's fallback sqrt implementation fails for inf inputs.
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// It is called when _GLIBCXX_USE_C99_COMPLEX is false or if
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// clang includes the GCC header (which temporarily disables
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// _GLIBCXX_USE_C99_COMPLEX)
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#if !defined(_GLIBCXX_COMPLEX) || \
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(_GLIBCXX_USE_C99_COMPLEX && !defined(__CLANG_CUDA_WRAPPERS_COMPLEX))
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const ValueType inf = std::numeric_limits<ValueType>::infinity();
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special_in[idx++] = ComplexType(1.0, inf);
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special_in[idx++] = ComplexType(nan, inf);
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special_in[idx++] = ComplexType(1.0, -inf);
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special_in[idx++] = ComplexType(nan, -inf);
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special_in[idx++] = ComplexType(-inf, 1.0);
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special_in[idx++] = ComplexType(inf, 1.0);
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special_in[idx++] = ComplexType(-inf, -1.0);
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special_in[idx++] = ComplexType(inf, -1.0);
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special_in[idx++] = ComplexType(-inf, nan);
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special_in[idx++] = ComplexType(inf, nan);
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#endif
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special_in[idx++] = ComplexType(1.0, nan);
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special_in[idx++] = ComplexType(nan, 1.0);
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special_in[idx++] = ComplexType(nan, -1.0);
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special_in[idx++] = ComplexType(nan, nan);
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Map<SpecialInputs> special_out(out);
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special_out = special_in.cwiseSqrt();
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}
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T x1(in + i);
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Map<T> res(out + num_special_inputs + i*T::MaxSizeAtCompileTime);
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res = x1.cwiseSqrt();
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}
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};
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template<typename T>
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struct complex_operators {
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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 typename T::Scalar ComplexType;
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typedef typename T::Scalar::value_type ValueType;
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const int num_scalar_operators = 24;
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const int num_vector_operators = 23; // no unary + operator.
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int out_idx = i * (num_scalar_operators + num_vector_operators * T::MaxSizeAtCompileTime);
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// Scalar operators.
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const ComplexType a = in[i];
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const ComplexType b = in[i + 1];
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out[out_idx++] = +a;
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out[out_idx++] = -a;
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out[out_idx++] = a + b;
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out[out_idx++] = a + numext::real(b);
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out[out_idx++] = numext::real(a) + b;
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out[out_idx++] = a - b;
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out[out_idx++] = a - numext::real(b);
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out[out_idx++] = numext::real(a) - b;
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out[out_idx++] = a * b;
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out[out_idx++] = a * numext::real(b);
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out[out_idx++] = numext::real(a) * b;
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out[out_idx++] = a / b;
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out[out_idx++] = a / numext::real(b);
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out[out_idx++] = numext::real(a) / b;
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out[out_idx] = a; out[out_idx++] += b;
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out[out_idx] = a; out[out_idx++] -= b;
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out[out_idx] = a; out[out_idx++] *= b;
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out[out_idx] = a; out[out_idx++] /= b;
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const ComplexType true_value = ComplexType(ValueType(1), ValueType(0));
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const ComplexType false_value = ComplexType(ValueType(0), ValueType(0));
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out[out_idx++] = (a == b ? true_value : false_value);
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out[out_idx++] = (a == numext::real(b) ? true_value : false_value);
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out[out_idx++] = (numext::real(a) == b ? true_value : false_value);
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out[out_idx++] = (a != b ? true_value : false_value);
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out[out_idx++] = (a != numext::real(b) ? true_value : false_value);
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out[out_idx++] = (numext::real(a) != b ? true_value : false_value);
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// Vector versions.
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T x1(in + i);
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T x2(in + i + 1);
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const int res_size = T::MaxSizeAtCompileTime * num_scalar_operators;
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const int size = T::MaxSizeAtCompileTime;
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int block_idx = 0;
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Map<VectorX<ComplexType>> res(out + out_idx, res_size);
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res.segment(block_idx, size) = -x1;
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block_idx += size;
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res.segment(block_idx, size) = x1 + x2;
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block_idx += size;
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res.segment(block_idx, size) = x1 + x2.real();
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block_idx += size;
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res.segment(block_idx, size) = x1.real() + x2;
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block_idx += size;
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res.segment(block_idx, size) = x1 - x2;
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block_idx += size;
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res.segment(block_idx, size) = x1 - x2.real();
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block_idx += size;
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res.segment(block_idx, size) = x1.real() - x2;
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block_idx += size;
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res.segment(block_idx, size) = x1.array() * x2.array();
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block_idx += size;
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res.segment(block_idx, size) = x1.array() * x2.real().array();
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block_idx += size;
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res.segment(block_idx, size) = x1.real().array() * x2.array();
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block_idx += size;
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res.segment(block_idx, size) = x1.array() / x2.array();
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block_idx += size;
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res.segment(block_idx, size) = x1.array() / x2.real().array();
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block_idx += size;
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res.segment(block_idx, size) = x1.real().array() / x2.array();
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block_idx += size;
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res.segment(block_idx, size) = x1; res.segment(block_idx, size) += x2;
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block_idx += size;
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res.segment(block_idx, size) = x1; res.segment(block_idx, size) -= x2;
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block_idx += size;
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res.segment(block_idx, size) = x1; res.segment(block_idx, size).array() *= x2.array();
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block_idx += size;
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res.segment(block_idx, size) = x1; res.segment(block_idx, size).array() /= x2.array();
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block_idx += size;
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const T true_vector = T::Constant(true_value);
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const T false_vector = T::Constant(false_value);
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res.segment(block_idx, size) = (x1 == x2 ? true_vector : false_vector);
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block_idx += size;
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// Mixing types in equality comparison does not work.
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// res.segment(block_idx, size) = (x1 == x2.real() ? true_vector : false_vector);
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// block_idx += size;
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// res.segment(block_idx, size) = (x1.real() == x2 ? true_vector : false_vector);
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// block_idx += size;
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res.segment(block_idx, size) = (x1 != x2 ? true_vector : false_vector);
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block_idx += size;
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// res.segment(block_idx, size) = (x1 != x2.real() ? true_vector : false_vector);
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// block_idx += size;
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// res.segment(block_idx, size) = (x1.real() != x2 ? true_vector : false_vector);
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// block_idx += size;
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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 alloc_new_delete {
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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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int offset = 2*i*T::MaxSizeAtCompileTime;
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T* x = new T(in + offset);
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Eigen::Map<T> u(out + offset);
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u = *x;
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delete x;
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offset += T::MaxSizeAtCompileTime;
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T* y = new T[1];
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y[0] = T(in + offset);
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Eigen::Map<T> v(out + offset);
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v = y[0];
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delete[] y;
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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(A);
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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(A);
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|
|
res = eig.eigenvalues();
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
template<typename T>
|
||
|
|
struct matrix_inverse {
|
||
|
|
EIGEN_DEVICE_FUNC
|
||
|
|
void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
|
||
|
|
{
|
||
|
|
using namespace Eigen;
|
||
|
|
T M(in+i);
|
||
|
|
Map<T> res(out+i*T::MaxSizeAtCompileTime);
|
||
|
|
res = M.inverse();
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
template<typename T>
|
||
|
|
struct numeric_limits_test {
|
||
|
|
EIGEN_DEVICE_FUNC
|
||
|
|
void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
|
||
|
|
{
|
||
|
|
EIGEN_UNUSED_VARIABLE(in)
|
||
|
|
int out_idx = i * 5;
|
||
|
|
out[out_idx++] = numext::numeric_limits<float>::epsilon();
|
||
|
|
out[out_idx++] = (numext::numeric_limits<float>::max)();
|
||
|
|
out[out_idx++] = (numext::numeric_limits<float>::min)();
|
||
|
|
out[out_idx++] = numext::numeric_limits<float>::infinity();
|
||
|
|
out[out_idx++] = numext::numeric_limits<float>::quiet_NaN();
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
template<typename Type1, typename Type2>
|
||
|
|
bool verifyIsApproxWithInfsNans(const Type1& a, const Type2& b, typename Type1::Scalar* = 0) // Enabled for Eigen's type only
|
||
|
|
{
|
||
|
|
if (a.rows() != b.rows()) {
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
if (a.cols() != b.cols()) {
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
for (Index r = 0; r < a.rows(); ++r) {
|
||
|
|
for (Index c = 0; c < a.cols(); ++c) {
|
||
|
|
if (a(r, c) != b(r, c)
|
||
|
|
&& !((numext::isnan)(a(r, c)) && (numext::isnan)(b(r, c)))
|
||
|
|
&& !test_isApprox(a(r, c), b(r, c))) {
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
|
||
|
|
template<typename Kernel, typename Input, typename Output>
|
||
|
|
void test_with_infs_nans(const Kernel& ker, int n, const Input& in, Output& out)
|
||
|
|
{
|
||
|
|
Output out_ref, out_gpu;
|
||
|
|
#if !defined(EIGEN_GPU_COMPILE_PHASE)
|
||
|
|
out_ref = out_gpu = out;
|
||
|
|
#else
|
||
|
|
EIGEN_UNUSED_VARIABLE(in);
|
||
|
|
EIGEN_UNUSED_VARIABLE(out);
|
||
|
|
#endif
|
||
|
|
run_on_cpu (ker, n, in, out_ref);
|
||
|
|
run_on_gpu(ker, n, in, out_gpu);
|
||
|
|
#if !defined(EIGEN_GPU_COMPILE_PHASE)
|
||
|
|
verifyIsApproxWithInfsNans(out_ref, out_gpu);
|
||
|
|
#endif
|
||
|
|
}
|
||
|
|
|
||
|
|
EIGEN_DECLARE_TEST(gpu_basic)
|
||
|
|
{
|
||
|
|
ei_test_init_gpu();
|
||
|
|
|
||
|
|
int nthreads = 100;
|
||
|
|
Eigen::VectorXf in, out;
|
||
|
|
Eigen::VectorXcf cfin, cfout;
|
||
|
|
|
||
|
|
#if !defined(EIGEN_GPU_COMPILE_PHASE)
|
||
|
|
int data_size = nthreads * 512;
|
||
|
|
in.setRandom(data_size);
|
||
|
|
out.setConstant(data_size, -1);
|
||
|
|
cfin.setRandom(data_size);
|
||
|
|
cfout.setConstant(data_size, -1);
|
||
|
|
#endif
|
||
|
|
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(coeff_wise<Vector3f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(coeff_wise<Array44f>(), nthreads, in, out) );
|
||
|
|
|
||
|
|
#if !defined(EIGEN_USE_HIP)
|
||
|
|
// FIXME
|
||
|
|
// These subtests result in a compile failure on the HIP platform
|
||
|
|
//
|
||
|
|
// eigen-upstream/Eigen/src/Core/Replicate.h:61:65: error:
|
||
|
|
// base class 'internal::dense_xpr_base<Replicate<Array<float, 4, 1, 0, 4, 1>, -1, -1> >::type'
|
||
|
|
// (aka 'ArrayBase<Eigen::Replicate<Eigen::Array<float, 4, 1, 0, 4, 1>, -1, -1> >') has protected default constructor
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(replicate<Array4f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(replicate<Array33f>(), nthreads, in, out) );
|
||
|
|
|
||
|
|
// HIP does not support new/delete on device.
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(alloc_new_delete<Vector3f>(), nthreads, in, out) );
|
||
|
|
#endif
|
||
|
|
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(redux<Array4f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(redux<Matrix3f>(), nthreads, in, out) );
|
||
|
|
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(prod_test<Matrix3f,Matrix3f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(prod_test<Matrix4f,Vector4f>(), nthreads, in, out) );
|
||
|
|
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(diagonal<Matrix3f,Vector3f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(diagonal<Matrix4f,Vector4f>(), nthreads, in, out) );
|
||
|
|
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(matrix_inverse<Matrix2f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(matrix_inverse<Matrix3f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(matrix_inverse<Matrix4f>(), nthreads, in, out) );
|
||
|
|
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(eigenvalues_direct<Matrix3f>(), nthreads, in, out) );
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(eigenvalues_direct<Matrix2f>(), nthreads, in, out) );
|
||
|
|
|
||
|
|
// Test std::complex.
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(complex_operators<Vector3cf>(), nthreads, cfin, cfout) );
|
||
|
|
CALL_SUBTEST( test_with_infs_nans(complex_sqrt<Vector3cf>(), nthreads, cfin, cfout) );
|
||
|
|
|
||
|
|
// numeric_limits
|
||
|
|
CALL_SUBTEST( test_with_infs_nans(numeric_limits_test<Vector3f>(), 1, in, out) );
|
||
|
|
|
||
|
|
#if defined(__NVCC__)
|
||
|
|
// FIXME
|
||
|
|
// These subtests compiles only with nvcc and fail with HIPCC and clang-cuda
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(eigenvalues<Matrix4f>(), nthreads, in, out) );
|
||
|
|
typedef Matrix<float,6,6> Matrix6f;
|
||
|
|
CALL_SUBTEST( run_and_compare_to_gpu(eigenvalues<Matrix6f>(), nthreads, in, out) );
|
||
|
|
#endif
|
||
|
|
}
|