| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2021 Rohit Santhanam <rohit.santhanam@amd.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_GPU |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| |
| using Eigen::Tensor; |
| |
| template<typename> |
| void test_gpu_numext() { |
| Eigen::GpuStreamDevice stream; |
| Eigen::GpuDevice gpu_device(&stream); |
| int num_elem = 101; |
| |
| float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); |
| bool* d_res_bfloat16 = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); |
| bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( |
| d_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_bfloat16( |
| d_res_bfloat16, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<bool, 1>, 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<float>()); |
| // Test bfloat16 specific isnan op. |
| gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::bfloat16>()); |
| |
| Tensor<bool, 1> bfloat16_prec(num_elem); |
| Tensor<bool, 1> full_prec(num_elem); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, 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) { |
| VERIFY_IS_EQUAL(full_prec(i), bfloat16_prec(i)); |
| } |
| |
| gpu_device.deallocate(d_float); |
| gpu_device.deallocate(d_res_bfloat16); |
| gpu_device.deallocate(d_res_float); |
| } |
| |
| |
| #ifdef EIGEN_HAS_GPU_BF16 |
| |
| template<typename> |
| void test_gpu_conversion() { |
| Eigen::GpuStreamDevice stream; |
| Eigen::GpuDevice gpu_device(&stream); |
| int num_elem = 101; |
| |
| float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); |
| Eigen::bfloat16* d_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| 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<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_bfloat16( |
| d_bfloat16, 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_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>(); |
| gpu_conv.device(gpu_device) = gpu_bfloat16.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(static_cast<Eigen::bfloat16>(initial(i)), static_cast<Eigen::bfloat16>(final(i))); |
| } |
| |
| gpu_device.deallocate(d_float); |
| gpu_device.deallocate(d_bfloat16); |
| gpu_device.deallocate(d_conv); |
| } |
| |
| template<typename> |
| void test_gpu_unary() { |
| 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_bfloat16 = (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_bfloat16( |
| d_res_bfloat16, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( |
| d_res_float, num_elem); |
| |
| gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); |
| gpu_float.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().cast<float>(); |
| gpu_res_float.device(gpu_device) = gpu_float.abs(); |
| gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().abs().cast<float>(); |
| |
| Tensor<float, 1> bfloat16_prec(num_elem); |
| Tensor<float, 1> full_prec(num_elem); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, 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) { |
| VERIFY_IS_APPROX(full_prec(i), bfloat16_prec(i)); |
| } |
| |
| gpu_device.deallocate(d_float); |
| gpu_device.deallocate(d_res_bfloat16); |
| gpu_device.deallocate(d_res_float); |
| } |
| |
| template<typename> |
| void test_gpu_elementwise() { |
| Eigen::GpuStreamDevice 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_bfloat16 = (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_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_res_bfloat16( |
| d_res_bfloat16, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( |
| d_res_float, num_elem); |
| |
| gpu_float1.device(gpu_device) = gpu_float1.random(); |
| gpu_float1.device(gpu_device) = gpu_float1.cast<Eigen::bfloat16>().cast<float>(); |
| gpu_float2.device(gpu_device) = gpu_float2.random(); |
| gpu_float2.device(gpu_device) = gpu_float2.cast<Eigen::bfloat16>().cast<float>(); |
| gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1; |
| gpu_res_bfloat16.device(gpu_device) = ((gpu_float1.cast<Eigen::bfloat16>() + gpu_float2.cast<Eigen::bfloat16>()) * gpu_float1.cast<Eigen::bfloat16>()).cast<float>(); |
| |
| Tensor<float, 1> bfloat16_prec(num_elem); |
| Tensor<float, 1> full_prec(num_elem); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, 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) { |
| VERIFY_IS_APPROX(static_cast<Eigen::bfloat16>(full_prec(i)), static_cast<Eigen::bfloat16>(bfloat16_prec(i))); |
| } |
| |
| gpu_device.deallocate(d_float1); |
| gpu_device.deallocate(d_float2); |
| gpu_device.deallocate(d_res_bfloat16); |
| gpu_device.deallocate(d_res_float); |
| } |
| |
| template<typename> |
| void test_gpu_trancendental() { |
| Eigen::GpuStreamDevice 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::bfloat16* d_res1_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res1_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res2_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res2_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res3_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res3_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| |
| 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::bfloat16, 1>, Eigen::Aligned> gpu_res1_bfloat16(d_res1_bfloat16, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res2_bfloat16(d_res2_bfloat16, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res3_bfloat16(d_res3_bfloat16, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res4_bfloat16(d_res3_bfloat16, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem); |
| |
| gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f); |
| gpu_float1.device(gpu_device) = gpu_float1.cast<Eigen::bfloat16>().cast<float>(); |
| gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f); |
| gpu_float2.device(gpu_device) = gpu_float2.cast<Eigen::bfloat16>().cast<float>(); |
| gpu_float3.device(gpu_device) = gpu_float3.random(); |
| gpu_float3.device(gpu_device) = gpu_float3.cast<Eigen::bfloat16>().cast<float>(); |
| gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::bfloat16>(); |
| gpu_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::bfloat16>(); |
| gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast<Eigen::bfloat16>(); |
| gpu_res4_float.device(gpu_device) = gpu_float3.expm1().cast<Eigen::bfloat16>(); |
| |
| gpu_res1_bfloat16.device(gpu_device) = gpu_float1.cast<Eigen::bfloat16>(); |
| gpu_res1_bfloat16.device(gpu_device) = gpu_res1_bfloat16.exp(); |
| |
| gpu_res2_bfloat16.device(gpu_device) = gpu_float2.cast<Eigen::bfloat16>(); |
| gpu_res2_bfloat16.device(gpu_device) = gpu_res2_bfloat16.log(); |
| |
| gpu_res3_bfloat16.device(gpu_device) = gpu_float3.cast<Eigen::bfloat16>(); |
| gpu_res3_bfloat16.device(gpu_device) = gpu_res3_bfloat16.log1p(); |
| |
| gpu_res3_bfloat16.device(gpu_device) = gpu_float3.cast<Eigen::bfloat16>(); |
| gpu_res3_bfloat16.device(gpu_device) = gpu_res3_bfloat16.expm1(); |
| |
| Tensor<float, 1> input1(num_elem); |
| Tensor<Eigen::bfloat16, 1> bfloat16_prec1(num_elem); |
| Tensor<Eigen::bfloat16, 1> full_prec1(num_elem); |
| Tensor<float, 1> input2(num_elem); |
| Tensor<Eigen::bfloat16, 1> bfloat16_prec2(num_elem); |
| Tensor<Eigen::bfloat16, 1> full_prec2(num_elem); |
| Tensor<float, 1> input3(num_elem); |
| Tensor<Eigen::bfloat16, 1> bfloat16_prec3(num_elem); |
| Tensor<Eigen::bfloat16, 1> 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(bfloat16_prec1.data(), d_res1_bfloat16, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec2.data(), d_res2_bfloat16, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec3.data(), d_res3_bfloat16, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.synchronize(); |
| |
| for (int i = 0; i < num_elem; ++i) { |
| VERIFY_IS_APPROX(full_prec1(i), bfloat16_prec1(i)); |
| } |
| for (int i = 0; i < num_elem; ++i) { |
| if(std::abs(input2(i)-1.f)<0.05f) // log lacks accuracy nearby 1 |
| VERIFY_IS_APPROX(full_prec2(i)+Eigen::bfloat16(0.1f), bfloat16_prec2(i)+Eigen::bfloat16(0.1f)); |
| else |
| VERIFY_IS_APPROX(full_prec2(i), bfloat16_prec2(i)); |
| } |
| for (int i = 0; i < num_elem; ++i) { |
| VERIFY_IS_APPROX(full_prec3(i), bfloat16_prec3(i)); |
| } |
| gpu_device.deallocate(d_float1); |
| gpu_device.deallocate(d_float2); |
| gpu_device.deallocate(d_float3); |
| gpu_device.deallocate(d_res1_bfloat16); |
| gpu_device.deallocate(d_res1_float); |
| gpu_device.deallocate(d_res2_bfloat16); |
| gpu_device.deallocate(d_res2_float); |
| gpu_device.deallocate(d_res3_float); |
| gpu_device.deallocate(d_res3_bfloat16); |
| } |
| |
| template<typename> |
| void test_gpu_contractions() { |
| Eigen::GpuStreamDevice 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::bfloat16* d_res_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16)); |
| |
| 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::bfloat16, 2>, Eigen::Aligned> gpu_res_bfloat16( |
| d_res_bfloat16, rows, cols); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 2>, 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<float, 2>::DimensionPair DimPair; |
| Eigen::array<DimPair, 1> dims(DimPair(1, 0)); |
| gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::bfloat16>(); |
| gpu_res_bfloat16.device(gpu_device) = gpu_float1.cast<Eigen::bfloat16>().contract(gpu_float2.cast<Eigen::bfloat16>(), dims); |
| |
| Tensor<Eigen::bfloat16, 2> bfloat16_prec(rows, cols); |
| Tensor<Eigen::bfloat16, 2> full_prec(rows, cols); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(Eigen::bfloat16)); |
| gpu_device.synchronize(); |
| |
| for (int i = 0; i < rows; ++i) { |
| for (int j = 0; j < cols; ++j) { |
| if (numext::abs(full_prec(i, j) - bfloat16_prec(i, j)) > Eigen::bfloat16(1e-2f)) { |
| VERIFY_IS_APPROX(full_prec(i, j), bfloat16_prec(i, j)); |
| } |
| } |
| } |
| |
| gpu_device.deallocate(d_float1); |
| gpu_device.deallocate(d_float2); |
| gpu_device.deallocate(d_res_bfloat16); |
| gpu_device.deallocate(d_res_float); |
| } |
| |
| template<typename> |
| void test_gpu_reductions(int size1, int size2, int redux) { |
| Eigen::GpuStreamDevice stream; |
| Eigen::GpuDevice gpu_device(&stream); |
| int num_elem = size1*size2; |
| int result_size = (redux == 1 ? size1 : size2); |
| |
| float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); |
| Eigen::bfloat16* d_res_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(result_size * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res_float = (Eigen::bfloat16*)gpu_device.allocate(result_size * sizeof(Eigen::bfloat16)); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float( |
| d_float, size1, size2); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res_bfloat16( |
| d_res_bfloat16, result_size); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res_float( |
| d_res_float, result_size); |
| |
| gpu_float.device(gpu_device) = gpu_float.random() * 2.0f; |
| |
| Eigen::array<int, 1> redux_dim = {redux}; |
| gpu_res_float.device(gpu_device) = gpu_float.sum(redux_dim).cast<Eigen::bfloat16>(); |
| gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().sum(redux_dim); |
| |
| Tensor<Eigen::bfloat16, 1> bfloat16_prec(result_size); |
| Tensor<Eigen::bfloat16, 1> full_prec(result_size); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, result_size*sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size*sizeof(Eigen::bfloat16)); |
| gpu_device.synchronize(); |
| |
| for (int i = 0; i < result_size; ++i) { |
| VERIFY_IS_APPROX(full_prec(i), bfloat16_prec(i)); |
| } |
| |
| gpu_device.deallocate(d_float); |
| gpu_device.deallocate(d_res_bfloat16); |
| 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_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); |
| Eigen::bfloat16* d_res_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(1 * sizeof(Eigen::bfloat16)); |
| Eigen::bfloat16* d_res_float = (Eigen::bfloat16*)gpu_device.allocate(1 * sizeof(Eigen::bfloat16)); |
| |
| Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float( |
| d_float, size, size); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 0>, Eigen::Aligned> gpu_res_bfloat16( |
| d_res_bfloat16); |
| Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 0>, Eigen::Aligned> gpu_res_float( |
| d_res_float); |
| |
| gpu_float.device(gpu_device) = gpu_float.random(); |
| |
| gpu_res_float.device(gpu_device) = gpu_float.sum().cast<Eigen::bfloat16>(); |
| gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().sum(); |
| |
| Tensor<Eigen::bfloat16, 0> bfloat16_prec; |
| Tensor<Eigen::bfloat16, 0> full_prec; |
| gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::bfloat16)); |
| gpu_device.synchronize(); |
| |
| VERIFY_IS_APPROX(full_prec(), bfloat16_prec()); |
| |
| gpu_res_float.device(gpu_device) = gpu_float.maximum().cast<Eigen::bfloat16>(); |
| gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().maximum(); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, sizeof(Eigen::bfloat16)); |
| gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::bfloat16)); |
| gpu_device.synchronize(); |
| |
| VERIFY_IS_APPROX(full_prec(), bfloat16_prec()); |
| |
| gpu_device.deallocate(d_float); |
| gpu_device.deallocate(d_res_bfloat16); |
| 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_bfloat16_1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); |
| float* d_res_bfloat16_2 = (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_bfloat16_1( |
| d_res_bfloat16_1, num_elem); |
| Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_bfloat16_2( |
| d_res_bfloat16_2, 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_float.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().cast<float>(); |
| gpu_res_float.device(gpu_device) = gpu_float.abs(); |
| gpu_res_bfloat16_1.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().abs().eval().cast<float>(); |
| gpu_res_bfloat16_2.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().abs().broadcast(no_bcast).eval().cast<float>(); |
| |
| Tensor<float, 1> bfloat16_prec1(num_elem); |
| Tensor<float, 1> bfloat16_prec2(num_elem); |
| Tensor<float, 1> full_prec(num_elem); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec1.data(), d_res_bfloat16_1, num_elem*sizeof(float)); |
| gpu_device.memcpyDeviceToHost(bfloat16_prec2.data(), d_res_bfloat16_2, 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) { |
| VERIFY_IS_APPROX(full_prec(i), bfloat16_prec1(i)); |
| VERIFY_IS_APPROX(full_prec(i), bfloat16_prec2(i)); |
| } |
| |
| gpu_device.deallocate(d_float); |
| gpu_device.deallocate(d_res_bfloat16_1); |
| gpu_device.deallocate(d_res_bfloat16_2); |
| gpu_device.deallocate(d_res_float); |
| } |
| |
| #endif |
| |
| EIGEN_DECLARE_TEST(cxx11_tensor_of_bfloat16_gpu) |
| { |
| CALL_SUBTEST_1(test_gpu_numext<void>()); |
| |
| // The reduction unit tests have been excluded until a working |
| // implementation to expand the accumulator data type to float32 |
| // is available. |
| // TODO: add reduction unit tests |
| #ifdef EIGEN_HAS_GPU_BF16 |
| CALL_SUBTEST_2(test_gpu_conversion<void>()); |
| CALL_SUBTEST_3(test_gpu_unary<void>()); |
| CALL_SUBTEST_4(test_gpu_elementwise<void>()); |
| CALL_SUBTEST_5(test_gpu_trancendental<void>()); |
| CALL_SUBTEST_6(test_gpu_contractions<void>()); |
| CALL_SUBTEST_7(test_gpu_reductions<void>()); |
| CALL_SUBTEST_8(test_gpu_full_reductions<void>()); |
| CALL_SUBTEST_9(test_gpu_forced_evals<void>()); |
| #else |
| std::cout << "bfloat16 floats are not supported by this version of gpu: skipping the test" << std::endl; |
| #endif |
| } |