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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
79 lines
2.5 KiB
Plaintext
79 lines
2.5 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_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 <unsupported/Eigen/CXX11/Tensor>
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#include <Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
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using Eigen::Tensor;
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typedef Tensor<float, 1>::DimensionPair DimPair;
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template<int DataLayout>
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void test_gpu_cumsum(int m_size, int k_size, int n_size)
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{
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std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
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Tensor<float, 3, DataLayout> t_input(m_size, k_size, n_size);
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Tensor<float, 3, DataLayout> t_result(m_size, k_size, n_size);
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Tensor<float, 3, DataLayout> t_result_gpu(m_size, k_size, n_size);
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t_input.setRandom();
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std::size_t t_input_bytes = t_input.size() * sizeof(float);
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std::size_t t_result_bytes = t_result.size() * sizeof(float);
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float* d_t_input;
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float* d_t_result;
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gpuMalloc((void**)(&d_t_input), t_input_bytes);
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gpuMalloc((void**)(&d_t_result), t_result_bytes);
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gpuMemcpy(d_t_input, t_input.data(), t_input_bytes, gpuMemcpyHostToDevice);
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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, 3, DataLayout> >
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gpu_t_input(d_t_input, Eigen::array<int, 3>(m_size, k_size, n_size));
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Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> >
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gpu_t_result(d_t_result, Eigen::array<int, 3>(m_size, k_size, n_size));
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gpu_t_result.device(gpu_device) = gpu_t_input.cumsum(1);
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t_result = t_input.cumsum(1);
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gpuMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, gpuMemcpyDeviceToHost);
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for (DenseIndex i = 0; i < t_result.size(); i++) {
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if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) {
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continue;
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}
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if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
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continue;
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}
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std::cout << "mismatch detected at index " << i << ": " << t_result(i)
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<< " vs " << t_result_gpu(i) << std::endl;
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assert(false);
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}
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gpuFree((void*)d_t_input);
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gpuFree((void*)d_t_result);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_scan_gpu)
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{
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CALL_SUBTEST_1(test_gpu_cumsum<ColMajor>(128, 128, 128));
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CALL_SUBTEST_2(test_gpu_cumsum<RowMajor>(128, 128, 128));
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}
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