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f124f07965
Also, a few minor fixes for GPU tests running in HIP mode. 1. Adding an include for hip/hip_runtime.h in the Macros.h file For HIP __host__ and __device__ are macros which are defined in hip headers. Their definitions need to be included before their use in the file. 2. Fixing the compile failure in TensorContractionGpu introduced by the commit to "Fuse computations into the Tensor contractions using output kernel" 3. Fixing a HIP/clang specific compile error by making the struct-member assignment explicit
87 lines
2.3 KiB
Plaintext
87 lines
2.3 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) 2014 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_NO_COMPLEX
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
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#define EIGEN_USE_GPU
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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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#include <Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
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void test_gpu_random_uniform()
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{
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Tensor<float, 2> out(72,97);
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out.setZero();
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std::size_t out_bytes = out.size() * sizeof(float);
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float* d_out;
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gpuMalloc((void**)(&d_out), out_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<float, 2> > gpu_out(d_out, 72,97);
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gpu_out.device(gpu_device) = gpu_out.random();
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assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
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assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
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// For now we just check this code doesn't crash.
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// TODO: come up with a valid test of randomness
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}
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void test_gpu_random_normal()
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{
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Tensor<float, 2> out(72,97);
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out.setZero();
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std::size_t out_bytes = out.size() * sizeof(float);
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float* d_out;
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gpuMalloc((void**)(&d_out), out_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<float, 2> > gpu_out(d_out, 72,97);
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Eigen::internal::NormalRandomGenerator<float> gen(true);
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gpu_out.device(gpu_device) = gpu_out.random(gen);
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assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
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assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
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}
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static void test_complex()
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{
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Tensor<std::complex<float>, 1> vec(6);
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vec.setRandom();
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// Fixme: we should check that the generated numbers follow a uniform
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// distribution instead.
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for (int i = 1; i < 6; ++i) {
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VERIFY_IS_NOT_EQUAL(vec(i), vec(i-1));
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}
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_random_gpu)
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{
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CALL_SUBTEST(test_gpu_random_uniform());
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CALL_SUBTEST(test_gpu_random_normal());
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CALL_SUBTEST(test_complex());
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}
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