98 lines
2.8 KiB
Plaintext
98 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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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_FUNC cxx11_tensor_complex_cwise_ops
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#define EIGEN_USE_GPU
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#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500
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#include <cuda_fp16.h>
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#endif
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#include "main.h"
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#include <unsupported/Eigen/CXX11/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::CudaStreamDevice 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(
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d_in1, kNumItems);
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Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_in2(
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d_in2, kNumItems);
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Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_out(
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d_out, kNumItems);
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const std::complex<T> a(3.14f, 2.7f);
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const std::complex<T> b(-10.6f, 1.4f);
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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 {
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Add = 0,
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Sub,
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Mul,
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Div
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};
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Tensor<std::complex<T>, 1, 0, int> actual(kNumItems);
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for (int op = Add; op <= Div; 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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}
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assert(cudaMemcpyAsync(actual.data(), d_out, complex_bytes, cudaMemcpyDeviceToHost,
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gpu_device.stream()) == 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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void test_cxx11_tensor_complex_cwise_ops()
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{
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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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