383 lines
15 KiB
C++
383 lines
15 KiB
C++
// 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) 2023
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// Alejandro Acosta Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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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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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
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#define EIGEN_USE_SYCL
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#include "main.h"
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#include <Eigen/Dense>
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template <bool verifyNan = false, bool singleTask = false, typename Operation, typename Input, typename Output>
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void run_and_verify(Operation& ope, size_t num_elements, const Input& in, Output& out) {
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Output out_gpu, out_cpu;
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out_gpu = out_cpu = out;
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auto queue = sycl::queue{sycl::default_selector_v};
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auto in_size_bytes = sizeof(typename Input::Scalar) * in.size();
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auto out_size_bytes = sizeof(typename Output::Scalar) * out.size();
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auto in_d = sycl::malloc_device<typename Input::Scalar>(in.size(), queue);
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auto out_d = sycl::malloc_device<typename Output::Scalar>(out.size(), queue);
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queue.memcpy(in_d, in.data(), in_size_bytes).wait();
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queue.memcpy(out_d, out.data(), out_size_bytes).wait();
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if constexpr (singleTask) {
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queue.single_task([=]() { ope(in_d, out_d); }).wait();
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} else {
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queue
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.parallel_for(sycl::range{num_elements},
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[=](sycl::id<1> idx) {
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auto id = idx[0];
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ope(id, in_d, out_d);
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})
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.wait();
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}
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queue.memcpy(out_gpu.data(), out_d, out_size_bytes).wait();
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sycl::free(in_d, queue);
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sycl::free(out_d, queue);
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queue.throw_asynchronous();
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// Run on CPU and compare the output
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if constexpr (singleTask == 1) {
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ope(in.data(), out_cpu.data());
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} else {
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for (size_t i = 0; i < num_elements; ++i) {
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ope(i, in.data(), out_cpu.data());
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}
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}
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if constexpr (verifyNan) {
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VERIFY_IS_CWISE_APPROX(out_gpu, out_cpu);
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} else {
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VERIFY_IS_APPROX(out_gpu, out_cpu);
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}
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}
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template <typename DataType, typename Input, typename Output>
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void test_coeff_wise(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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DataType x1(in + i);
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DataType x2(in + i + 1);
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DataType x3(in + i + 2);
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Map<DataType> res(out + i * DataType::MaxSizeAtCompileTime);
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res.array() += (in[0] * x1 + x2).array() * x3.array();
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};
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run_and_verify(operation, num_elements, in, out);
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}
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template <typename DataType, typename Input, typename Output>
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void test_complex_sqrt(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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using namespace Eigen;
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typedef typename DataType::Scalar ComplexType;
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typedef typename DataType::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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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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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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DataType x1(in + i);
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Map<DataType> res(out + num_special_inputs + i * DataType::MaxSizeAtCompileTime);
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res = x1.cwiseSqrt();
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};
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run_and_verify<true>(operation, num_elements, in, out);
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}
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template <typename DataType, typename Input, typename Output>
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void test_complex_operators(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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using namespace Eigen;
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typedef typename DataType::Scalar ComplexType;
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typedef typename DataType::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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size_t out_idx = i * (num_scalar_operators + num_vector_operators * DataType::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;
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out[out_idx++] += b;
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out[out_idx] = a;
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out[out_idx++] -= b;
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out[out_idx] = a;
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out[out_idx++] *= b;
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out[out_idx] = a;
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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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DataType x1(in + i);
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DataType x2(in + i + 1);
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const int res_size = DataType::MaxSizeAtCompileTime * num_scalar_operators;
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const int size = DataType::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;
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res.segment(block_idx, size) += x2;
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block_idx += size;
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res.segment(block_idx, size) = x1;
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res.segment(block_idx, size) -= x2;
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block_idx += size;
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res.segment(block_idx, size) = x1;
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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;
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res.segment(block_idx, size).array() /= x2.array();
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block_idx += size;
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const DataType true_vector = DataType::Constant(true_value);
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const DataType false_vector = DataType::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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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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run_and_verify<true>(operation, num_elements, in, out);
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}
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template <typename DataType, typename Input, typename Output>
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void test_redux(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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using namespace Eigen;
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int N = 10;
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DataType 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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run_and_verify(operation, num_elements, in, out);
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}
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template <typename DataType, typename Input, typename Output>
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void test_replicate(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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using namespace Eigen;
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DataType 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 DataType::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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run_and_verify(operation, num_elements, in, out);
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}
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template <typename DataType1, typename DataType2, typename Input, typename Output>
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void test_product(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType1::Scalar* in, typename DataType1::Scalar* out) {
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using namespace Eigen;
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typedef Matrix<typename DataType1::Scalar, DataType1::RowsAtCompileTime, DataType2::ColsAtCompileTime> DataType3;
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DataType1 x1(in + i);
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DataType2 x2(in + i + 1);
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Map<DataType3> res(out + i * DataType3::MaxSizeAtCompileTime);
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res += in[i] * x1 * x2;
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};
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run_and_verify(operation, num_elements, in, out);
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}
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template <typename DataType1, typename DataType2, typename Input, typename Output>
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void test_diagonal(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType1::Scalar* in, typename DataType1::Scalar* out) {
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using namespace Eigen;
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DataType1 x1(in + i);
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Map<DataType2> res(out + i * DataType2::MaxSizeAtCompileTime);
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res += x1.diagonal();
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};
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run_and_verify(operation, num_elements, in, out);
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}
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template <typename DataType, typename Input, typename Output>
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void test_eigenvalues_direct(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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using namespace Eigen;
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typedef Matrix<typename DataType::Scalar, DataType::RowsAtCompileTime, 1> Vec;
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DataType M(in + i);
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Map<Vec> res(out + i * Vec::MaxSizeAtCompileTime);
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DataType A = M * M.adjoint();
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SelfAdjointEigenSolver<DataType> eig;
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eig.computeDirect(A);
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res = eig.eigenvalues();
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};
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run_and_verify(operation, num_elements, in, out);
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}
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template <typename DataType, typename Input, typename Output>
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void test_matrix_inverse(size_t num_elements, const Input& in, Output& out) {
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auto operation = [](size_t i, const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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using namespace Eigen;
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DataType M(in + i);
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Map<DataType> res(out + i * DataType::MaxSizeAtCompileTime);
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res = M.inverse();
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};
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run_and_verify(operation, num_elements, in, out);
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}
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template <typename DataType, typename Input, typename Output>
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void test_numeric_limits(const Input& in, Output& out) {
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auto operation = [](const typename DataType::Scalar* in, typename DataType::Scalar* out) {
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EIGEN_UNUSED_VARIABLE(in)
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out[0] = numext::numeric_limits<float>::epsilon();
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out[1] = (numext::numeric_limits<float>::max)();
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out[2] = (numext::numeric_limits<float>::min)();
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out[3] = numext::numeric_limits<float>::infinity();
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out[4] = numext::numeric_limits<float>::quiet_NaN();
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};
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run_and_verify<true, true>(operation, 1, in, out);
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}
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EIGEN_DECLARE_TEST(sycl_basic) {
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Eigen::VectorXf in, out;
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Eigen::VectorXcf cfin, cfout;
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constexpr size_t num_elements = 100;
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constexpr size_t data_size = num_elements * 512;
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in.setRandom(data_size);
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out.setConstant(data_size, -1);
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cfin.setRandom(data_size);
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cfout.setConstant(data_size, -1);
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CALL_SUBTEST(test_coeff_wise<Vector3f>(num_elements, in, out));
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CALL_SUBTEST(test_coeff_wise<Array44f>(num_elements, in, out));
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CALL_SUBTEST(test_complex_operators<Vector3cf>(num_elements, cfin, cfout));
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CALL_SUBTEST(test_complex_sqrt<Vector3cf>(num_elements, cfin, cfout));
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CALL_SUBTEST(test_redux<Array4f>(num_elements, in, out));
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CALL_SUBTEST(test_redux<Matrix3f>(num_elements, in, out));
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CALL_SUBTEST(test_replicate<Array4f>(num_elements, in, out));
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CALL_SUBTEST(test_replicate<Array33f>(num_elements, in, out));
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auto test_prod_mm = [&]() { test_product<Matrix3f, Matrix3f>(num_elements, in, out); };
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auto test_prod_mv = [&]() { test_product<Matrix4f, Vector4f>(num_elements, in, out); };
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CALL_SUBTEST(test_prod_mm());
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CALL_SUBTEST(test_prod_mv());
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auto test_diagonal_mv3f = [&]() { test_diagonal<Matrix3f, Vector3f>(num_elements, in, out); };
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auto test_diagonal_mv4f = [&]() { test_diagonal<Matrix4f, Vector4f>(num_elements, in, out); };
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CALL_SUBTEST(test_diagonal_mv3f());
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CALL_SUBTEST(test_diagonal_mv4f());
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CALL_SUBTEST(test_eigenvalues_direct<Matrix3f>(num_elements, in, out));
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CALL_SUBTEST(test_eigenvalues_direct<Matrix2f>(num_elements, in, out));
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CALL_SUBTEST(test_matrix_inverse<Matrix2f>(num_elements, in, out));
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CALL_SUBTEST(test_matrix_inverse<Matrix3f>(num_elements, in, out));
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CALL_SUBTEST(test_matrix_inverse<Matrix4f>(num_elements, in, out));
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CALL_SUBTEST(test_numeric_limits<Vector3f>(in, out));
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}
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