mirror of
https://gitlab.com/libeigen/eigen.git
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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
499 lines
21 KiB
Plaintext
499 lines
21 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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using Eigen::Tensor;
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template<typename>
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void test_gpu_numext() {
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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int num_elem = 101;
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float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
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bool* d_res_half = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
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bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(
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d_float, num_elem);
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Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_half(
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d_res_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_float(
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d_res_float, num_elem);
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gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
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gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>());
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gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>());
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Tensor<bool, 1> half_prec(num_elem);
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Tensor<bool, 1> full_prec(num_elem);
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gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(bool));
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gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(bool));
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gpu_device.synchronize();
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for (int i = 0; i < num_elem; ++i) {
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std::cout << "Checking numext " << i << std::endl;
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VERIFY_IS_EQUAL(full_prec(i), half_prec(i));
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}
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gpu_device.deallocate(d_float);
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gpu_device.deallocate(d_res_half);
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gpu_device.deallocate(d_res_float);
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}
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#ifdef EIGEN_HAS_GPU_FP16
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template<typename>
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void test_gpu_conversion() {
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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int num_elem = 101;
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float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
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Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float));
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(
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d_float, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half(
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d_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(
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d_conv, num_elem);
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gpu_float.device(gpu_device) = gpu_float.random();
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gpu_half.device(gpu_device) = gpu_float.cast<Eigen::half>();
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gpu_conv.device(gpu_device) = gpu_half.cast<float>();
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Tensor<float, 1> initial(num_elem);
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Tensor<float, 1> final(num_elem);
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gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem*sizeof(float));
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gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem*sizeof(float));
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for (int i = 0; i < num_elem; ++i) {
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VERIFY_IS_APPROX(initial(i), final(i));
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}
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gpu_device.deallocate(d_float);
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gpu_device.deallocate(d_half);
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gpu_device.deallocate(d_conv);
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}
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template<typename>
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void test_gpu_unary() {
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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int num_elem = 101;
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float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(
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d_float, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(
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d_res_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(
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d_res_float, num_elem);
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gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
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gpu_res_float.device(gpu_device) = gpu_float.abs();
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gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().cast<float>();
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Tensor<float, 1> half_prec(num_elem);
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Tensor<float, 1> full_prec(num_elem);
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gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float));
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gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float));
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gpu_device.synchronize();
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for (int i = 0; i < num_elem; ++i) {
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std::cout << "Checking unary " << i << std::endl;
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VERIFY_IS_APPROX(full_prec(i), half_prec(i));
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}
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gpu_device.deallocate(d_float);
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gpu_device.deallocate(d_res_half);
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gpu_device.deallocate(d_res_float);
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}
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template<typename>
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void test_gpu_elementwise() {
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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int num_elem = 101;
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float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(
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d_float1, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(
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d_float2, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(
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d_res_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(
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d_res_float, num_elem);
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gpu_float1.device(gpu_device) = gpu_float1.random();
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gpu_float2.device(gpu_device) = gpu_float2.random();
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gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1;
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gpu_res_half.device(gpu_device) = ((gpu_float1.cast<Eigen::half>() + gpu_float2.cast<Eigen::half>()) * gpu_float1.cast<Eigen::half>()).cast<float>();
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Tensor<float, 1> half_prec(num_elem);
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Tensor<float, 1> full_prec(num_elem);
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gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float));
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gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float));
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gpu_device.synchronize();
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for (int i = 0; i < num_elem; ++i) {
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std::cout << "Checking elemwise " << i << ": full prec = " << full_prec(i) << " vs half prec = " << half_prec(i) << std::endl;
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VERIFY_IS_APPROX(static_cast<Eigen::half>(full_prec(i)), static_cast<Eigen::half>(half_prec(i)));
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}
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gpu_device.deallocate(d_float1);
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gpu_device.deallocate(d_float2);
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gpu_device.deallocate(d_res_half);
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gpu_device.deallocate(d_res_float);
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}
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template<typename>
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void test_gpu_trancendental() {
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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int num_elem = 101;
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float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_float3 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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Eigen::half* d_res1_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::half* d_res1_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::half* d_res2_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::half* d_res3_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::half* d_res3_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
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Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_half(d_res3_half, num_elem);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem);
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gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
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gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f);
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gpu_float3.device(gpu_device) = gpu_float3.random();
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gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::half>();
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gpu_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::half>();
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gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast<Eigen::half>();
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gpu_res4_float.device(gpu_device) = gpu_float3.expm1().cast<Eigen::half>();
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gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>();
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gpu_res1_half.device(gpu_device) = gpu_res1_half.exp();
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gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>();
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gpu_res2_half.device(gpu_device) = gpu_res2_half.log();
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gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>();
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gpu_res3_half.device(gpu_device) = gpu_res3_half.log1p();
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gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>();
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gpu_res3_half.device(gpu_device) = gpu_res3_half.expm1();
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Tensor<float, 1> input1(num_elem);
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Tensor<Eigen::half, 1> half_prec1(num_elem);
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Tensor<Eigen::half, 1> full_prec1(num_elem);
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Tensor<float, 1> input2(num_elem);
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Tensor<Eigen::half, 1> half_prec2(num_elem);
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Tensor<Eigen::half, 1> full_prec2(num_elem);
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Tensor<float, 1> input3(num_elem);
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Tensor<Eigen::half, 1> half_prec3(num_elem);
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Tensor<Eigen::half, 1> full_prec3(num_elem);
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gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem*sizeof(float));
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gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem*sizeof(float));
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gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem*sizeof(float));
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gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(Eigen::half));
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gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(Eigen::half));
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gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem*sizeof(Eigen::half));
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gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::half));
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gpu_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem*sizeof(Eigen::half));
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gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem*sizeof(Eigen::half));
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gpu_device.synchronize();
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for (int i = 0; i < num_elem; ++i) {
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std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i) << " half = " << half_prec1(i) << std::endl;
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VERIFY_IS_APPROX(full_prec1(i), half_prec1(i));
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}
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for (int i = 0; i < num_elem; ++i) {
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std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i) << " half = " << half_prec2(i) << std::endl;
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if(std::abs(input2(i)-1.f)<0.05f) // log lacks accuracy nearby 1
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VERIFY_IS_APPROX(full_prec2(i)+Eigen::half(0.1f), half_prec2(i)+Eigen::half(0.1f));
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else
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VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
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}
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for (int i = 0; i < num_elem; ++i) {
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std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i) << " half = " << half_prec3(i) << std::endl;
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VERIFY_IS_APPROX(full_prec3(i), half_prec3(i));
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}
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gpu_device.deallocate(d_float1);
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gpu_device.deallocate(d_float2);
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gpu_device.deallocate(d_float3);
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gpu_device.deallocate(d_res1_half);
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gpu_device.deallocate(d_res1_float);
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gpu_device.deallocate(d_res2_half);
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gpu_device.deallocate(d_res2_float);
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gpu_device.deallocate(d_res3_float);
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gpu_device.deallocate(d_res3_half);
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}
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template<typename>
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void test_gpu_contractions() {
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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int rows = 23;
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int cols = 23;
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int num_elem = rows*cols;
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float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
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Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(
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d_float1, rows, cols);
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Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(
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d_float2, rows, cols);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half(
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d_res_half, rows, cols);
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Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float(
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d_res_float, rows, cols);
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gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
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gpu_float2.device(gpu_device) = gpu_float2.random() - gpu_float2.constant(0.5f);
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typedef Tensor<float, 2>::DimensionPair DimPair;
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Eigen::array<DimPair, 1> dims(DimPair(1, 0));
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gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::half>();
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gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims);
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Tensor<Eigen::half, 2> half_prec(rows, cols);
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Tensor<Eigen::half, 2> full_prec(rows, cols);
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gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(Eigen::half));
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gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(Eigen::half));
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gpu_device.synchronize();
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for (int i = 0; i < rows; ++i) {
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for (int j = 0; j < cols; ++j) {
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std::cout << "Checking contract " << i << " " << j << full_prec(i, j) << " " << half_prec(i, j) << std::endl;
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if (numext::abs(full_prec(i, j) - half_prec(i, j)) > Eigen::half(1e-2f)) {
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VERIFY_IS_APPROX(full_prec(i, j), half_prec(i, j));
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}
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}
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}
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gpu_device.deallocate(d_float1);
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gpu_device.deallocate(d_float2);
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gpu_device.deallocate(d_res_half);
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gpu_device.deallocate(d_res_float);
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}
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template<typename>
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void test_gpu_reductions(int size1, int size2, int redux) {
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std::cout << "Reducing " << size1 << " by " << size2
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<< " tensor along dim " << redux << std::endl;
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Eigen::GpuStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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int num_elem = size1*size2;
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int result_size = (redux == 1 ? size1 : size2);
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float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
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Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));
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Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));
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Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(
|
|
d_float1, size1, size2);
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|
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(
|
|
d_float2, size1, size2);
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|
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half(
|
|
d_res_half, result_size);
|
|
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, 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<int, 1> redux_dim = {{redux}};
|
|
gpu_res_float.device(gpu_device) = gpu_float1.sum(redux_dim).cast<Eigen::half>();
|
|
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum(redux_dim);
|
|
|
|
Tensor<Eigen::half, 1> half_prec(result_size);
|
|
Tensor<Eigen::half, 1> full_prec(result_size);
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|
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<typename>
|
|
void test_gpu_reductions() {
|
|
test_gpu_reductions<void>(13, 13, 0);
|
|
test_gpu_reductions<void>(13, 13, 1);
|
|
|
|
test_gpu_reductions<void>(35, 36, 0);
|
|
test_gpu_reductions<void>(35, 36, 1);
|
|
|
|
test_gpu_reductions<void>(36, 35, 0);
|
|
test_gpu_reductions<void>(36, 35, 1);
|
|
}
|
|
|
|
template<typename>
|
|
void test_gpu_full_reductions() {
|
|
Eigen::GpuStreamDevice 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::Tensor<float, 2>, Eigen::Aligned> gpu_float1(
|
|
d_float1, size, size);
|
|
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(
|
|
d_float2, size, size);
|
|
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_half(
|
|
d_res_half);
|
|
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, 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<Eigen::half>();
|
|
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum();
|
|
|
|
Tensor<Eigen::half, 0> half_prec;
|
|
Tensor<Eigen::half, 0> 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<Eigen::half>();
|
|
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().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<typename>
|
|
void test_gpu_forced_evals() {
|
|
|
|
Eigen::GpuStreamDevice 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::Tensor<float, 1>, Eigen::Aligned> gpu_float(
|
|
d_float, num_elem);
|
|
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half1(
|
|
d_res_half1, num_elem);
|
|
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_half2(
|
|
d_res_half2, num_elem);
|
|
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(
|
|
d_res_float, num_elem);
|
|
|
|
Eigen::array<int, 1> 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<Eigen::half>().abs().eval().cast<float>();
|
|
gpu_res_half2.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().broadcast(no_bcast).eval().cast<float>();
|
|
|
|
Tensor<float, 1> half_prec1(num_elem);
|
|
Tensor<float, 1> half_prec2(num_elem);
|
|
Tensor<float, 1> 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
|
|
|
|
|
|
EIGEN_DECLARE_TEST(cxx11_tensor_of_float16_gpu)
|
|
{
|
|
CALL_SUBTEST_1(test_gpu_numext<void>());
|
|
|
|
#ifdef EIGEN_HAS_GPU_FP16
|
|
CALL_SUBTEST_1(test_gpu_conversion<void>());
|
|
CALL_SUBTEST_1(test_gpu_unary<void>());
|
|
CALL_SUBTEST_1(test_gpu_elementwise<void>());
|
|
CALL_SUBTEST_1(test_gpu_trancendental<void>());
|
|
CALL_SUBTEST_2(test_gpu_contractions<void>());
|
|
CALL_SUBTEST_3(test_gpu_reductions<void>());
|
|
CALL_SUBTEST_4(test_gpu_full_reductions<void>());
|
|
CALL_SUBTEST_5(test_gpu_forced_evals<void>());
|
|
#else
|
|
std::cout << "Half floats are not supported by this version of gpu: skipping the test" << std::endl;
|
|
#endif
|
|
}
|