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Added a test to validate the conversion of half floats into floats on Kepler GPUs.

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Restricted the testing of the random number generation code to GPU architecture greater than or equal to 3.5.
This commit is contained in:
Benoit Steiner 2016-03-03 10:37:25 -08:00
parent 1032441c6f
commit dac58d7c35
2 changed files with 80 additions and 2 deletions
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9
unsupported/test/CMakeLists.txt
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@ -170,10 +170,15 @@ if(CUDA_FOUND)
ei_add_test(cxx11_tensor_cuda)
ei_add_test(cxx11_tensor_contract_cuda)
ei_add_test(cxx11_tensor_reduction_cuda)
ei_add_test(cxx11_tensor_random_cuda)
ei_add_test(cxx11_tensor_argmax_cuda)
ei_add_test(cxx11_tensor_cast_float16_cuda)
# Half floats are only supported starting with arch 5.3
# The random number generation code requires arch 3.5 or greater.
if (${EIGEN_CUDA_COMPUTE_ARCH} GREATER 34)
ei_add_test(cxx11_tensor_random_cuda)
endif()
# Operations other that casting of half floats are only supported starting with arch 5.3
if (${EIGEN_CUDA_COMPUTE_ARCH} GREATER 52)
ei_add_test(cxx11_tensor_of_float16_cuda)
endif()

73
unsupported/test/cxx11_tensor_cast_float16_cuda.cu Normal file
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@ -0,0 +1,73 @@
// 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_cast_float16_cuda
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_GPU
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
#ifdef EIGEN_HAS_CUDA_FP16
void test_cuda_conversion() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
int num_elem = 101;
Tensor<float, 1> floats(num_elem);
floats.setRandom();
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_device.memcpyHostToDevice(d_float, floats.data(), num_elem*sizeof(float));
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));
gpu_device.synchronize();
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);
}
#endif
void test_cxx11_tensor_cast_float16_cuda()
{
#ifdef EIGEN_HAS_CUDA_FP16
CALL_SUBTEST(test_cuda_conversion());
#else
std::cout << "Half floats are not supported by this version of cuda: skipping the test" << std::endl;
#endif
}