From 7e41c8f1a98c2a3beed667dca416ea8d20ad373e Mon Sep 17 00:00:00 2001 From: Deven Desai Date: Wed, 20 Jun 2018 12:52:30 -0400 Subject: renaming *Cuda files to *Gpu in the unsupported/Eigen/CXX11/src/Tensor and unsupported/test directories --- unsupported/test/cxx11_tensor_of_float16_gpu.cu | 498 ++++++++++++++++++++++++ 1 file changed, 498 insertions(+) create mode 100644 unsupported/test/cxx11_tensor_of_float16_gpu.cu (limited to 'unsupported/test/cxx11_tensor_of_float16_gpu.cu') diff --git a/unsupported/test/cxx11_tensor_of_float16_gpu.cu b/unsupported/test/cxx11_tensor_of_float16_gpu.cu new file mode 100644 index 000000000..7a751ff02 --- /dev/null +++ b/unsupported/test/cxx11_tensor_of_float16_gpu.cu @@ -0,0 +1,498 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Benoit Steiner +// +// 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 + + +using Eigen::Tensor; + +template +void test_cuda_numext() { + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int num_elem = 101; + + float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); + bool* d_res_half = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); + bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float( + d_float, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_half( + d_res_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_float( + d_res_float, num_elem); + + gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); + gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op()); + gpu_res_half.device(gpu_device) = gpu_float.cast().unaryExpr(Eigen::internal::scalar_isnan_op()); + + Tensor half_prec(num_elem); + Tensor full_prec(num_elem); + gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(bool)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(bool)); + gpu_device.synchronize(); + + for (int i = 0; i < num_elem; ++i) { + std::cout << "Checking numext " << i << std::endl; + VERIFY_IS_EQUAL(full_prec(i), half_prec(i)); + } + + gpu_device.deallocate(d_float); + gpu_device.deallocate(d_res_half); + gpu_device.deallocate(d_res_float); +} + + +#ifdef EIGEN_HAS_CUDA_FP16 + +template +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)); + Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float( + d_float, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_half( + d_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_conv( + d_conv, num_elem); + + gpu_float.device(gpu_device) = gpu_float.random(); + gpu_half.device(gpu_device) = gpu_float.cast(); + gpu_conv.device(gpu_device) = gpu_half.cast(); + + Tensor initial(num_elem); + Tensor 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); +} + +template +void test_cuda_unary() { + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int num_elem = 101; + + float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float( + d_float, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_half( + d_res_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_float( + d_res_float, num_elem); + + gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); + gpu_res_float.device(gpu_device) = gpu_float.abs(); + gpu_res_half.device(gpu_device) = gpu_float.cast().abs().cast(); + + Tensor half_prec(num_elem); + Tensor full_prec(num_elem); + gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); + gpu_device.synchronize(); + + for (int i = 0; i < num_elem; ++i) { + std::cout << "Checking unary " << i << std::endl; + VERIFY_IS_APPROX(full_prec(i), half_prec(i)); + } + + gpu_device.deallocate(d_float); + gpu_device.deallocate(d_res_half); + gpu_device.deallocate(d_res_float); +} + +template +void test_cuda_elementwise() { + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int num_elem = 101; + + float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float1( + d_float1, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_float2( + d_float2, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_half( + d_res_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_float( + d_res_float, num_elem); + + gpu_float1.device(gpu_device) = gpu_float1.random(); + gpu_float2.device(gpu_device) = gpu_float2.random(); + gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1; + gpu_res_half.device(gpu_device) = ((gpu_float1.cast() + gpu_float2.cast()) * gpu_float1.cast()).cast(); + + Tensor half_prec(num_elem); + Tensor full_prec(num_elem); + gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); + gpu_device.synchronize(); + + for (int i = 0; i < num_elem; ++i) { + std::cout << "Checking elemwise " << i << ": full prec = " << full_prec(i) << " vs half prec = " << half_prec(i) << std::endl; + VERIFY_IS_APPROX(static_cast(full_prec(i)), static_cast(half_prec(i))); + } + + gpu_device.deallocate(d_float1); + gpu_device.deallocate(d_float2); + gpu_device.deallocate(d_res_half); + gpu_device.deallocate(d_res_float); +} + +template +void test_cuda_trancendental() { + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int num_elem = 101; + + float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_float3 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + Eigen::half* d_res1_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + Eigen::half* d_res1_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + Eigen::half* d_res2_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + Eigen::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + Eigen::half* d_res3_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + Eigen::half* d_res3_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float1(d_float1, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_float2(d_float2, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_float3(d_float3, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res4_half(d_res3_half, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem); + + gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f); + gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f); + gpu_float3.device(gpu_device) = gpu_float3.random(); + gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast(); + gpu_res2_float.device(gpu_device) = gpu_float2.log().cast(); + gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast(); + gpu_res4_float.device(gpu_device) = gpu_float3.expm1().cast(); + + gpu_res1_half.device(gpu_device) = gpu_float1.cast(); + gpu_res1_half.device(gpu_device) = gpu_res1_half.exp(); + + gpu_res2_half.device(gpu_device) = gpu_float2.cast(); + gpu_res2_half.device(gpu_device) = gpu_res2_half.log(); + + gpu_res3_half.device(gpu_device) = gpu_float3.cast(); + gpu_res3_half.device(gpu_device) = gpu_res3_half.log1p(); + + gpu_res3_half.device(gpu_device) = gpu_float3.cast(); + gpu_res3_half.device(gpu_device) = gpu_res3_half.expm1(); + + Tensor input1(num_elem); + Tensor half_prec1(num_elem); + Tensor full_prec1(num_elem); + Tensor input2(num_elem); + Tensor half_prec2(num_elem); + Tensor full_prec2(num_elem); + Tensor input3(num_elem); + Tensor half_prec3(num_elem); + Tensor full_prec3(num_elem); + gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem*sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem*sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem*sizeof(Eigen::half)); + gpu_device.synchronize(); + + for (int i = 0; i < num_elem; ++i) { + std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i) << " half = " << half_prec1(i) << std::endl; + VERIFY_IS_APPROX(full_prec1(i), half_prec1(i)); + } + for (int i = 0; i < num_elem; ++i) { + std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i) << " half = " << half_prec2(i) << std::endl; + if(std::abs(input2(i)-1.f)<0.05f) // log lacks accuracy nearby 1 + VERIFY_IS_APPROX(full_prec2(i)+Eigen::half(0.1f), half_prec2(i)+Eigen::half(0.1f)); + else + VERIFY_IS_APPROX(full_prec2(i), half_prec2(i)); + } + for (int i = 0; i < num_elem; ++i) { + std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i) << " half = " << half_prec3(i) << std::endl; + VERIFY_IS_APPROX(full_prec3(i), half_prec3(i)); + } + gpu_device.deallocate(d_float1); + gpu_device.deallocate(d_float2); + gpu_device.deallocate(d_float3); + gpu_device.deallocate(d_res1_half); + gpu_device.deallocate(d_res1_float); + gpu_device.deallocate(d_res2_half); + gpu_device.deallocate(d_res2_float); + gpu_device.deallocate(d_res3_float); + gpu_device.deallocate(d_res3_half); +} + +template +void test_cuda_contractions() { + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int rows = 23; + int cols = 23; + int num_elem = rows*cols; + + float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float1( + d_float1, rows, cols); + Eigen::TensorMap, Eigen::Aligned> gpu_float2( + d_float2, rows, cols); + Eigen::TensorMap, Eigen::Aligned> gpu_res_half( + d_res_half, rows, cols); + Eigen::TensorMap, Eigen::Aligned> gpu_res_float( + d_res_float, rows, cols); + + gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f); + gpu_float2.device(gpu_device) = gpu_float2.random() - gpu_float2.constant(0.5f); + + typedef Tensor::DimensionPair DimPair; + Eigen::array dims(DimPair(1, 0)); + gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast(); + gpu_res_half.device(gpu_device) = gpu_float1.cast().contract(gpu_float2.cast(), dims); + + Tensor half_prec(rows, cols); + Tensor full_prec(rows, cols); + gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(Eigen::half)); + gpu_device.synchronize(); + + for (int i = 0; i < rows; ++i) { + for (int j = 0; j < cols; ++j) { + std::cout << "Checking contract " << i << " " << j << full_prec(i, j) << " " << half_prec(i, j) << std::endl; + if (numext::abs(full_prec(i, j) - half_prec(i, j)) > Eigen::half(1e-2f)) { + VERIFY_IS_APPROX(full_prec(i, j), half_prec(i, j)); + } + } + } + + gpu_device.deallocate(d_float1); + gpu_device.deallocate(d_float2); + gpu_device.deallocate(d_res_half); + gpu_device.deallocate(d_res_float); +} + +template +void test_cuda_reductions(int size1, int size2, int redux) { + + std::cout << "Reducing " << size1 << " by " << size2 + << " tensor along dim " << redux << std::endl; + + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int num_elem = size1*size2; + int result_size = (redux == 1 ? size1 : size2); + + float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half)); + Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float1( + d_float1, size1, size2); + Eigen::TensorMap, Eigen::Aligned> gpu_float2( + d_float2, size1, size2); + Eigen::TensorMap, Eigen::Aligned> gpu_res_half( + d_res_half, result_size); + Eigen::TensorMap, Eigen::Aligned> gpu_res_float( + d_res_float, result_size); + + gpu_float1.device(gpu_device) = gpu_float1.random() * 2.0f; + gpu_float2.device(gpu_device) = gpu_float2.random() * 2.0f; + + Eigen::array redux_dim = {{redux}}; + gpu_res_float.device(gpu_device) = gpu_float1.sum(redux_dim).cast(); + gpu_res_half.device(gpu_device) = gpu_float1.cast().sum(redux_dim); + + Tensor half_prec(result_size); + Tensor full_prec(result_size); + gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, result_size*sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size*sizeof(Eigen::half)); + gpu_device.synchronize(); + + for (int i = 0; i < result_size; ++i) { + std::cout << "EXPECTED " << full_prec(i) << " GOT " << half_prec(i) << std::endl; + VERIFY_IS_APPROX(full_prec(i), half_prec(i)); + } + + gpu_device.deallocate(d_float1); + gpu_device.deallocate(d_float2); + gpu_device.deallocate(d_res_half); + gpu_device.deallocate(d_res_float); +} + +template +void test_cuda_reductions() { + test_cuda_reductions(13, 13, 0); + test_cuda_reductions(13, 13, 1); + + test_cuda_reductions(35, 36, 0); + test_cuda_reductions(35, 36, 1); + + test_cuda_reductions(36, 35, 0); + test_cuda_reductions(36, 35, 1); +} + +template +void test_cuda_full_reductions() { + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int size = 13; + int num_elem = size*size; + + float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half)); + Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float1( + d_float1, size, size); + Eigen::TensorMap, Eigen::Aligned> gpu_float2( + d_float2, size, size); + Eigen::TensorMap, Eigen::Aligned> gpu_res_half( + d_res_half); + Eigen::TensorMap, Eigen::Aligned> gpu_res_float( + d_res_float); + + gpu_float1.device(gpu_device) = gpu_float1.random(); + gpu_float2.device(gpu_device) = gpu_float2.random(); + + gpu_res_float.device(gpu_device) = gpu_float1.sum().cast(); + gpu_res_half.device(gpu_device) = gpu_float1.cast().sum(); + + Tensor half_prec; + Tensor full_prec; + gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); + gpu_device.synchronize(); + + VERIFY_IS_APPROX(full_prec(), half_prec()); + + gpu_res_float.device(gpu_device) = gpu_float1.maximum().cast(); + gpu_res_half.device(gpu_device) = gpu_float1.cast().maximum(); + gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); + gpu_device.synchronize(); + + VERIFY_IS_APPROX(full_prec(), half_prec()); + + gpu_device.deallocate(d_float1); + gpu_device.deallocate(d_float2); + gpu_device.deallocate(d_res_half); + gpu_device.deallocate(d_res_float); +} + +template +void test_cuda_forced_evals() { + + Eigen::CudaStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + int num_elem = 101; + + float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_res_half1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_res_half2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); + float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); + + Eigen::TensorMap, Eigen::Aligned> gpu_float( + d_float, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_half1( + d_res_half1, num_elem); + Eigen::TensorMap, Eigen::Unaligned> gpu_res_half2( + d_res_half2, num_elem); + Eigen::TensorMap, Eigen::Aligned> gpu_res_float( + d_res_float, num_elem); + + Eigen::array no_bcast; + no_bcast[0] = 1; + + gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); + gpu_res_float.device(gpu_device) = gpu_float.abs(); + gpu_res_half1.device(gpu_device) = gpu_float.cast().abs().eval().cast(); + gpu_res_half2.device(gpu_device) = gpu_float.cast().abs().broadcast(no_bcast).eval().cast(); + + Tensor half_prec1(num_elem); + Tensor half_prec2(num_elem); + Tensor full_prec(num_elem); + gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res_half1, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res_half1, num_elem*sizeof(float)); + gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); + gpu_device.synchronize(); + + for (int i = 0; i < num_elem; ++i) { + std::cout << "Checking forced eval " << i << full_prec(i) << " vs " << half_prec1(i) << " vs " << half_prec2(i) << std::endl; + VERIFY_IS_APPROX(full_prec(i), half_prec1(i)); + VERIFY_IS_APPROX(full_prec(i), half_prec2(i)); + } + + gpu_device.deallocate(d_float); + gpu_device.deallocate(d_res_half1); + gpu_device.deallocate(d_res_half2); + gpu_device.deallocate(d_res_float); +} +#endif + + +void test_cxx11_tensor_of_float16_cuda() +{ + CALL_SUBTEST_1(test_cuda_numext()); + +#ifdef EIGEN_HAS_CUDA_FP16 + CALL_SUBTEST_1(test_cuda_conversion()); + CALL_SUBTEST_1(test_cuda_unary()); + CALL_SUBTEST_1(test_cuda_elementwise()); + CALL_SUBTEST_1(test_cuda_trancendental()); + CALL_SUBTEST_2(test_cuda_contractions()); + CALL_SUBTEST_3(test_cuda_reductions()); + CALL_SUBTEST_4(test_cuda_full_reductions()); + CALL_SUBTEST_5(test_cuda_forced_evals()); +#else + std::cout << "Half floats are not supported by this version of cuda: skipping the test" << std::endl; +#endif +} -- cgit v1.2.3