Rasmus Munk LarsenandRasmus Munk Larsen
8a3d61c790
libeigen/eigen!2509 Closes #2868 Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
92 lines
2.8 KiB
Plaintext
92 lines
2.8 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) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
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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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// SPDX-License-Identifier: MPL-2.0
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_USE_GPU
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#include "main.h"
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#include <unsupported/Eigen/Tensor>
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using Eigen::Tensor;
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template <typename T>
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void test_cuda_complex_cwise_ops() {
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const int kNumItems = 2;
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std::size_t complex_bytes = kNumItems * sizeof(std::complex<T>);
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std::complex<T>* d_in1;
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std::complex<T>* d_in2;
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std::complex<T>* d_out;
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cudaMalloc((void**)(&d_in1), complex_bytes);
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cudaMalloc((void**)(&d_in2), complex_bytes);
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cudaMalloc((void**)(&d_out), complex_bytes);
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_in1(d_in1, kNumItems);
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Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_in2(d_in2, kNumItems);
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Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_out(d_out, kNumItems);
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const std::complex<T> a(static_cast<T>(3.14), static_cast<T>(2.7));
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const std::complex<T> b(static_cast<T>(-10.6), static_cast<T>(1.4));
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gpu_in1.device(gpu_device) = gpu_in1.constant(a);
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gpu_in2.device(gpu_device) = gpu_in2.constant(b);
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enum CwiseOp { Add = 0, Sub, Mul, Div, Neg, NbOps };
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Tensor<std::complex<T>, 1, 0, int> actual(kNumItems);
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for (int op = Add; op < NbOps; op++) {
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std::complex<T> expected;
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switch (static_cast<CwiseOp>(op)) {
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case Add:
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gpu_out.device(gpu_device) = gpu_in1 + gpu_in2;
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expected = a + b;
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break;
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case Sub:
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gpu_out.device(gpu_device) = gpu_in1 - gpu_in2;
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expected = a - b;
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break;
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case Mul:
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gpu_out.device(gpu_device) = gpu_in1 * gpu_in2;
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expected = a * b;
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break;
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case Div:
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gpu_out.device(gpu_device) = gpu_in1 / gpu_in2;
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expected = a / b;
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break;
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case Neg:
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gpu_out.device(gpu_device) = -gpu_in1;
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expected = -a;
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break;
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case NbOps:
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break;
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}
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assert(cudaMemcpyAsync(actual.data(), d_out, complex_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) ==
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cudaSuccess);
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assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
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for (int i = 0; i < kNumItems; ++i) {
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VERIFY_IS_APPROX(actual(i), expected);
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}
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}
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cudaFree(d_in1);
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cudaFree(d_in2);
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cudaFree(d_out);
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}
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EIGEN_DECLARE_TEST(test_tensor_complex_cwise_ops) {
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CALL_SUBTEST(test_cuda_complex_cwise_ops<float>());
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CALL_SUBTEST(test_cuda_complex_cwise_ops<double>());
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}
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