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187 lines
6.5 KiB
Plaintext
187 lines
6.5 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
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#define EIGEN_USE_GPU
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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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void test_cuda_nullary() {
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Tensor<std::complex<float>, 1, 0, int> in1(2);
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Tensor<std::complex<float>, 1, 0, int> in2(2);
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in1.setRandom();
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in2.setRandom();
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std::size_t float_bytes = in1.size() * sizeof(float);
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std::size_t complex_bytes = in1.size() * sizeof(std::complex<float>);
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std::complex<float>* d_in1;
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std::complex<float>* d_in2;
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float* d_out2;
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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_out2), float_bytes);
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cudaMemcpy(d_in1, in1.data(), complex_bytes, cudaMemcpyHostToDevice);
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cudaMemcpy(d_in2, in2.data(), complex_bytes, cudaMemcpyHostToDevice);
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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<float>, 1, 0, int>, Eigen::Aligned> gpu_in1(
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d_in1, 2);
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Eigen::TensorMap<Eigen::Tensor<std::complex<float>, 1, 0, int>, Eigen::Aligned> gpu_in2(
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d_in2, 2);
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Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_out2(
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d_out2, 2);
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gpu_in1.device(gpu_device) = gpu_in1.constant(std::complex<float>(3.14f, 2.7f));
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gpu_out2.device(gpu_device) = gpu_in2.abs();
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Tensor<std::complex<float>, 1, 0, int> new1(2);
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Tensor<float, 1, 0, int> new2(2);
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assert(cudaMemcpyAsync(new1.data(), d_in1, complex_bytes, cudaMemcpyDeviceToHost,
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gpu_device.stream()) == cudaSuccess);
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assert(cudaMemcpyAsync(new2.data(), d_out2, float_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 < 2; ++i) {
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VERIFY_IS_APPROX(new1(i), std::complex<float>(3.14f, 2.7f));
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VERIFY_IS_APPROX(new2(i), std::abs(in2(i)));
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}
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cudaFree(d_in1);
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cudaFree(d_in2);
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cudaFree(d_out2);
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}
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static void test_cuda_sum_reductions() {
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Eigen::CudaStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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const int num_rows = internal::random<int>(1024, 5*1024);
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const int num_cols = internal::random<int>(1024, 5*1024);
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Tensor<std::complex<float>, 2> in(num_rows, num_cols);
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in.setRandom();
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Tensor<std::complex<float>, 0> full_redux;
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full_redux = in.sum();
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std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
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std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
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std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
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std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
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gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
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TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
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TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
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out_gpu.device(gpu_device) = in_gpu.sum();
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Tensor<std::complex<float>, 0> full_redux_gpu;
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gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
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gpu_device.synchronize();
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
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gpu_device.deallocate(gpu_in_ptr);
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gpu_device.deallocate(gpu_out_ptr);
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}
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static void test_cuda_mean_reductions() {
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Eigen::CudaStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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const int num_rows = internal::random<int>(1024, 5*1024);
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const int num_cols = internal::random<int>(1024, 5*1024);
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Tensor<std::complex<float>, 2> in(num_rows, num_cols);
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in.setRandom();
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Tensor<std::complex<float>, 0> full_redux;
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full_redux = in.mean();
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std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
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std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
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std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
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std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
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gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
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TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
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TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
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out_gpu.device(gpu_device) = in_gpu.mean();
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Tensor<std::complex<float>, 0> full_redux_gpu;
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gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
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gpu_device.synchronize();
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
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gpu_device.deallocate(gpu_in_ptr);
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gpu_device.deallocate(gpu_out_ptr);
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}
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static void test_cuda_product_reductions() {
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Eigen::CudaStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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const int num_rows = internal::random<int>(1024, 5*1024);
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const int num_cols = internal::random<int>(1024, 5*1024);
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Tensor<std::complex<float>, 2> in(num_rows, num_cols);
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in.setRandom();
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Tensor<std::complex<float>, 0> full_redux;
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full_redux = in.prod();
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std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
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std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
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std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
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std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
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gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
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TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
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TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
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out_gpu.device(gpu_device) = in_gpu.prod();
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Tensor<std::complex<float>, 0> full_redux_gpu;
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gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
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gpu_device.synchronize();
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
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gpu_device.deallocate(gpu_in_ptr);
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gpu_device.deallocate(gpu_out_ptr);
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}
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void test_cxx11_tensor_complex()
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{
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CALL_SUBTEST(test_cuda_nullary());
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CALL_SUBTEST(test_cuda_sum_reductions());
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CALL_SUBTEST(test_cuda_mean_reductions());
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CALL_SUBTEST(test_cuda_product_reductions());
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}
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