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Added a test to validate the tensor scan evaluation on GPU. The test is currently disabled since the code segfaults.

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Benoit Steiner 2016-06-27 16:02:52 -07:00
parent 75c333f94c
commit 1a9f92e781
2 changed files with 78 additions and 0 deletions
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1
unsupported/test/CMakeLists.txt
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@ -220,6 +220,7 @@ if(CUDA_FOUND AND EIGEN_TEST_CUDA)
ei_add_test(cxx11_tensor_reduction_cuda)
ei_add_test(cxx11_tensor_argmax_cuda)
ei_add_test(cxx11_tensor_cast_float16_cuda)
# ei_add_test(cxx11_tensor_scan_cuda)
# The random number generation code requires arch 3.5 or greater.
if (${EIGEN_CUDA_COMPUTE_ARCH} GREATER 34)

77
unsupported/test/cxx11_tensor_scan_cuda.cu Normal file
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@ -0,0 +1,77 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_tensor_scan_cuda
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_GPU
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
typedef Tensor<float, 1>::DimensionPair DimPair;
template<int DataLayout>
void test_cuda_cumsum(int m_size, int k_size, int n_size)
{
std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
Tensor<float, 3, DataLayout> t_input(m_size, k_size, n_size);
Tensor<float, 3, DataLayout> t_result(m_size, k_size, n_size);
Tensor<float, 3, DataLayout> t_result_gpu(m_size, k_size, n_size);
t_input.setRandom();
std::size_t t_input_bytes = t_input.size() * sizeof(float);
std::size_t t_result_bytes = t_result.size() * sizeof(float);
float* d_t_input;
float* d_t_result;
cudaMalloc((void**)(&d_t_input), t_input_bytes);
cudaMalloc((void**)(&d_t_result), t_result_bytes);
cudaMemcpy(d_t_input, t_input.data(), t_input_bytes, cudaMemcpyHostToDevice);
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> >
gpu_t_input(d_t_input, Eigen::array<int, 3>(m_size, k_size, n_size));
Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> >
gpu_t_result(d_t_result, Eigen::array<int, 3>(m_size, k_size, n_size));
gpu_t_result.device(gpu_device) = gpu_t_input.cumsum(1);
t_result = t_input.cumsum(1);
cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
for (size_t i = 0; i < t_result.size(); i++) {
if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
continue;
}
std::cout << "mismatch detected at index " << i << ": " << t_result(i)
<< " vs " << t_result_gpu(i) << std::endl;
assert(false);
}
cudaFree((void*)d_t_input);
cudaFree((void*)d_t_result);
}
void test_cxx11_tensor_scan_cuda()
{
CALL_SUBTEST_1(test_cuda_cumsum<ColMajor>(128, 128, 128));
CALL_SUBTEST_2(test_cuda_cumsum<RowMajor>(128, 128, 128));
}