| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2023 |
| // Alejandro Acosta Codeplay Software Ltd. |
| // Contact: <eigen@codeplay.com> |
| // 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_DEFAULT_DENSE_INDEX_TYPE int |
| #define EIGEN_USE_SYCL |
| #define EIGEN_SYCL_HALF_SUPPORT |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::Tensor; |
| using Eigen::SyclDevice; |
| |
| void test_gpu_numext(const Eigen::SyclDevice &sycl_device) { |
| int num_elem = 101; |
| |
| float* d_float = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float))); |
| bool* d_res_half = static_cast<bool*>(sycl_device.allocate(num_elem * sizeof(bool))); |
| bool* d_res_float = static_cast<bool*>(sycl_device.allocate(num_elem * sizeof(bool))); |
| |
| Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem); |
| Eigen::TensorMap<Tensor<bool, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem); |
| Eigen::TensorMap<Tensor<bool, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem); |
| |
| gpu_float.device(sycl_device) = gpu_float.random() - gpu_float.constant(0.5f); |
| gpu_res_float.device(sycl_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>()); |
| gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>()); |
| |
| Tensor<bool, 1> half_prec(num_elem); |
| Tensor<bool, 1> full_prec(num_elem); |
| |
| sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half,num_elem * sizeof(bool)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float,num_elem * sizeof(bool)); |
| |
| for (int i = 0; i < num_elem; ++i) { |
| std::cout << "Checking numext " << i << std::endl; |
| VERIFY_IS_EQUAL(full_prec(i), half_prec(i)); |
| } |
| } |
| |
| void test_gpu_conversion(const Eigen::SyclDevice &sycl_device) { |
| int num_elem = 101; |
| |
| float* d_float = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float))); |
| Eigen::half* d_half = static_cast<Eigen::half*>(sycl_device.allocate(num_elem * sizeof(Eigen::half))); |
| float* d_conv = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float))); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( |
| d_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::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(sycl_device) = gpu_float.random(); |
| gpu_half.device(sycl_device) = gpu_float.cast<Eigen::half>(); |
| gpu_conv.device(sycl_device) = gpu_half.cast<float>(); |
| |
| Tensor<float, 1> initial(num_elem); |
| Tensor<float, 1> final(num_elem); |
| sycl_device.memcpyDeviceToHost(initial.data(), d_float, num_elem*sizeof(float)); |
| sycl_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)); |
| } |
| } |
| |
| void test_gpu_unary(const Eigen::SyclDevice &sycl_device) { |
| int num_elem = 101; |
| |
| float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_res_half = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_res_float = (float*)sycl_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_half( |
| d_res_half, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( |
| d_res_float, num_elem); |
| |
| gpu_float.device(sycl_device) = gpu_float.random() - gpu_float.constant(0.5f); |
| gpu_res_float.device(sycl_device) = gpu_float.abs(); |
| gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().abs().cast<float>(); |
| |
| Tensor<float, 1> half_prec(num_elem); |
| Tensor<float, 1> full_prec(num_elem); |
| sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); |
| sycl_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)); |
| } |
| } |
| |
| void test_gpu_elementwise(const Eigen::SyclDevice &sycl_device) { |
| int num_elem = 101; |
| |
| float* d_float1 = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float))); |
| float* d_float2 = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float))); |
| float* d_res_half = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float))); |
| float* d_res_float = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float))); |
| |
| Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem); |
| Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem); |
| Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem); |
| Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem); |
| |
| gpu_float1.device(sycl_device) = gpu_float1.random(); |
| gpu_float2.device(sycl_device) = gpu_float2.random(); |
| gpu_res_float.device(sycl_device) = (gpu_float1 + gpu_float2) * gpu_float1; |
| gpu_res_half.device(sycl_device) = ((gpu_float1.cast<Eigen::half>() + gpu_float2.cast<Eigen::half>()) * gpu_float1.cast<Eigen::half>()).cast<float>(); |
| |
| Tensor<float, 1> half_prec(num_elem); |
| Tensor<float, 1> full_prec(num_elem); |
| |
| sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half,num_elem * sizeof(float)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float,num_elem * sizeof(float)); |
| |
| 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<Eigen::half>(full_prec(i)), static_cast<Eigen::half>(half_prec(i))); |
| } |
| } |
| |
| void test_gpu_trancendental(const Eigen::SyclDevice &sycl_device) { |
| int num_elem = 101; |
| |
| float* d_float1 = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_float2 = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_float3 = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| Eigen::half* d_res1_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| Eigen::half* d_res1_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| Eigen::half* d_res2_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| Eigen::half* d_res2_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| Eigen::half* d_res3_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| Eigen::half* d_res3_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_half(d_res3_half, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem); |
| |
| gpu_float1.device(sycl_device) = gpu_float1.random() - gpu_float1.constant(0.5f); |
| gpu_float2.device(sycl_device) = gpu_float2.random() + gpu_float1.constant(0.5f); |
| gpu_float3.device(sycl_device) = gpu_float3.random(); |
| gpu_res1_float.device(sycl_device) = gpu_float1.exp().cast<Eigen::half>(); |
| gpu_res2_float.device(sycl_device) = gpu_float2.log().cast<Eigen::half>(); |
| gpu_res3_float.device(sycl_device) = gpu_float3.log1p().cast<Eigen::half>(); |
| gpu_res4_float.device(sycl_device) = gpu_float3.expm1().cast<Eigen::half>(); |
| |
| gpu_res1_half.device(sycl_device) = gpu_float1.cast<Eigen::half>(); |
| gpu_res1_half.device(sycl_device) = gpu_res1_half.exp(); |
| |
| gpu_res2_half.device(sycl_device) = gpu_float2.cast<Eigen::half>(); |
| gpu_res2_half.device(sycl_device) = gpu_res2_half.log(); |
| |
| gpu_res3_half.device(sycl_device) = gpu_float3.cast<Eigen::half>(); |
| gpu_res3_half.device(sycl_device) = gpu_res3_half.log1p(); |
| |
| gpu_res3_half.device(sycl_device) = gpu_float3.cast<Eigen::half>(); |
| gpu_res3_half.device(sycl_device) = gpu_res3_half.expm1(); |
| |
| Tensor<float, 1> input1(num_elem); |
| Tensor<Eigen::half, 1> half_prec1(num_elem); |
| Tensor<Eigen::half, 1> full_prec1(num_elem); |
| Tensor<float, 1> input2(num_elem); |
| Tensor<Eigen::half, 1> half_prec2(num_elem); |
| Tensor<Eigen::half, 1> full_prec2(num_elem); |
| Tensor<float, 1> input3(num_elem); |
| Tensor<Eigen::half, 1> half_prec3(num_elem); |
| Tensor<Eigen::half, 1> full_prec3(num_elem); |
| sycl_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem*sizeof(float)); |
| sycl_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem*sizeof(float)); |
| sycl_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem*sizeof(float)); |
| sycl_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem*sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem*sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem*sizeof(Eigen::half)); |
| sycl_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)); |
| } |
| } |
| |
| void test_gpu_contractions(const Eigen::SyclDevice &sycl_device) { |
| int rows = 23; |
| int cols = 23; |
| int num_elem = rows*cols; |
| |
| float* d_float1 = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_float2 = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| Eigen::half* d_res_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| Eigen::half* d_res_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half)); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1( |
| d_float1, rows, cols); |
| Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2( |
| d_float2, rows, cols); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half( |
| d_res_half, rows, cols); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float( |
| d_res_float, rows, cols); |
| |
| gpu_float1.device(sycl_device) = gpu_float1.random() - gpu_float1.constant(0.5f); |
| gpu_float2.device(sycl_device) = gpu_float2.random() - gpu_float2.constant(0.5f); |
| |
| typedef typename Tensor<float, 2>::DimensionPair DimPair; |
| Eigen::array<DimPair, 1> dims; |
| gpu_res_float.device(sycl_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::half>(); |
| gpu_res_half.device(sycl_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims); |
| |
| Tensor<Eigen::half, 2> half_prec(rows, cols); |
| Tensor<Eigen::half, 2> full_prec(rows, cols); |
| sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(Eigen::half)); |
| sycl_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)); |
| } |
| } |
| } |
| } |
| |
| void test_gpu_reductions(const Eigen::SyclDevice &sycl_device, int size1, int size2, int redux) { |
| std::cout << "Reducing " << size1 << " by " << size2 |
| << " tensor along dim " << redux << std::endl; |
| |
| int num_elem = size1*size2; |
| int result_size = (redux == 1 ? size1 : size2); |
| |
| float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| Eigen::half* d_res_half = (Eigen::half*)sycl_device.allocate(result_size * sizeof(Eigen::half)); |
| Eigen::half* d_res_float = (Eigen::half*)sycl_device.allocate(result_size * sizeof(Eigen::half)); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float( |
| d_float, size1, size2); |
| 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_float.device(sycl_device) = gpu_float.random() * 2.0f; |
| |
| Eigen::array<int, 1> redux_dim = {redux}; |
| gpu_res_float.device(sycl_device) = gpu_float.sum(redux_dim).cast<Eigen::half>(); |
| gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().sum(redux_dim); |
| |
| Tensor<Eigen::half, 1> half_prec(result_size); |
| Tensor<Eigen::half, 1> full_prec(result_size); |
| sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, result_size*sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size*sizeof(Eigen::half)); |
| sycl_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)); |
| } |
| } |
| |
| void test_gpu_reductions(const Eigen::SyclDevice &sycl_device) { |
| test_gpu_reductions(sycl_device, 13, 13, 0); |
| test_gpu_reductions(sycl_device, 13, 13, 1); |
| |
| test_gpu_reductions(sycl_device, 35, 36, 0); |
| test_gpu_reductions(sycl_device, 35, 36, 1); |
| |
| test_gpu_reductions(sycl_device, 36, 35, 0); |
| test_gpu_reductions(sycl_device, 36, 35, 1); |
| } |
| |
| void test_gpu_full_reductions(const Eigen::SyclDevice &sycl_device) { |
| int size = 13; |
| int num_elem = size*size; |
| |
| float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| Eigen::half* d_res_half = (Eigen::half*)sycl_device.allocate(1 * sizeof(Eigen::half)); |
| Eigen::half* d_res_float = (Eigen::half*)sycl_device.allocate(1 * sizeof(Eigen::half)); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float( |
| d_float, 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_float.device(sycl_device) = gpu_float.random(); |
| |
| gpu_res_float.device(sycl_device) = gpu_float.sum().cast<Eigen::half>(); |
| gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().sum(); |
| |
| Tensor<Eigen::half, 0> half_prec; |
| Tensor<Eigen::half, 0> full_prec; |
| sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); |
| sycl_device.synchronize(); |
| |
| VERIFY_IS_APPROX(full_prec(), half_prec()); |
| |
| gpu_res_float.device(sycl_device) = gpu_float.maximum().cast<Eigen::half>(); |
| gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().maximum(); |
| sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); |
| sycl_device.synchronize(); |
| |
| VERIFY_IS_APPROX(full_prec(), half_prec()); |
| } |
| |
| void test_gpu_forced_evals(const Eigen::SyclDevice &sycl_device) { |
| int num_elem = 101; |
| |
| float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_res_half1 = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_res_half2 = (float*)sycl_device.allocate(num_elem * sizeof(float)); |
| float* d_res_float = (float*)sycl_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(sycl_device) = gpu_float.random() - gpu_float.constant(0.5f); |
| gpu_res_float.device(sycl_device) = gpu_float.abs(); |
| gpu_res_half1.device(sycl_device) = gpu_float.cast<Eigen::half>().abs().eval().cast<float>(); |
| gpu_res_half2.device(sycl_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); |
| sycl_device.memcpyDeviceToHost(half_prec1.data(), d_res_half1, num_elem*sizeof(float)); |
| sycl_device.memcpyDeviceToHost(half_prec2.data(), d_res_half2, num_elem*sizeof(float)); |
| sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); |
| sycl_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)); |
| } |
| } |
| |
| EIGEN_DECLARE_TEST(cxx11_tensor_of_float16_sycl) |
| { |
| for (const auto& s : Eigen::get_sycl_supported_devices()) { |
| QueueInterface queueInterface(s); |
| auto sycl_device = Eigen::SyclDevice(&queueInterface); |
| |
| CALL_SUBTEST_1(test_gpu_numext(sycl_device)); |
| CALL_SUBTEST_1(test_gpu_conversion(sycl_device)); |
| CALL_SUBTEST_1(test_gpu_unary(sycl_device)); |
| CALL_SUBTEST_1(test_gpu_elementwise(sycl_device)); |
| CALL_SUBTEST_1(test_gpu_trancendental(sycl_device)); |
| CALL_SUBTEST_2(test_gpu_contractions(sycl_device)); |
| CALL_SUBTEST_3(test_gpu_reductions(sycl_device)); |
| CALL_SUBTEST_4(test_gpu_full_reductions(sycl_device)); |
| CALL_SUBTEST_5(test_gpu_forced_evals(sycl_device)); |
| } |
| } |