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author | Benoit Steiner <benoit.steiner.goog@gmail.com> | 2016-02-19 06:23:28 +0000 |
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committer | Benoit Steiner <benoit.steiner.goog@gmail.com> | 2016-02-19 06:23:28 +0000 |
commit | f36c0c2c65a78959f6ccbbc29c6e80f86b062bc8 (patch) | |
tree | b17478350333d3257bc6c93ad7d38136061c3a3f /unsupported/test | |
parent | 7151bd876845c15cb6b8abc0886d7917ece635ed (diff) |
Added regression test for float16
Diffstat (limited to 'unsupported/test')
-rw-r--r-- | unsupported/test/cxx11_tensor_of_float16_cuda.cu | 60 |
1 files changed, 60 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_of_float16_cuda.cu b/unsupported/test/cxx11_tensor_of_float16_cuda.cu new file mode 100644 index 000000000..e9f5dd968 --- /dev/null +++ b/unsupported/test/cxx11_tensor_of_float16_cuda.cu @@ -0,0 +1,60 @@ +// 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_of_float16_cuda +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int +#define EIGEN_USE_GPU + + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + +using Eigen::Tensor; + +void test_cuda_conversion() { + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int num_elem = 101; + + float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); + half* d_half = (half*)gpu_device.allocate(num_elem * sizeof(half)); + float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float)); + + Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( + d_float, num_elem); + Eigen::TensorMap<Eigen::Tensor<half, 1>, Eigen::Aligned> gpu_half( + d_half, num_elem); + Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv( + d_conv, num_elem); + + gpu_float.device(gpu_device) = gpu_float.random(); + gpu_half.device(gpu_device) = gpu_float.cast<half>(); + gpu_conv.device(gpu_device) = gpu_half.cast<float>(); + + Tensor<float, 1> initial(num_elem); + Tensor<float, 1> final(num_elem); + gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem*sizeof(float)); + + for (int i = 0; i < num_elem; ++i) { + VERIFY_IS_APPROX(initial(i), final(i)); + } + + gpu_device.deallocate(d_float); + gpu_device.deallocate(d_half); + gpu_device.deallocate(d_conv); +} + + +void test_cxx11_tensor_of_float16_cuda() +{ + CALL_SUBTEST_1(test_cuda_conversion()); +} |