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author | Mehdi Goli <mehdi.goli@codeplay.com> | 2016-12-14 15:30:37 +0000 |
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committer | Mehdi Goli <mehdi.goli@codeplay.com> | 2016-12-14 15:30:37 +0000 |
commit | 2d4a091beb9e55664c1475137af7166d524cbc1d (patch) | |
tree | d9e4baec0be3eb3c8a4bb2451701f7e49730daa1 /unsupported/test/cxx11_tensor_contract_sycl.cpp | |
parent | 3d59a477201d4d4f34b4332fda699c21387cf726 (diff) |
Adding tensor contraction operation backend for Sycl; adding test for contractionOp sycl backend; adding temporary solution to prevent memory leak in buffer; cleaning up cxx11_tensor_buildins_sycl.h
Diffstat (limited to 'unsupported/test/cxx11_tensor_contract_sycl.cpp')
-rw-r--r-- | unsupported/test/cxx11_tensor_contract_sycl.cpp | 218 |
1 files changed, 218 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_contract_sycl.cpp b/unsupported/test/cxx11_tensor_contract_sycl.cpp new file mode 100644 index 000000000..0221da110 --- /dev/null +++ b/unsupported/test/cxx11_tensor_contract_sycl.cpp @@ -0,0 +1,218 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#define EIGEN_TEST_NO_LONGDOUBLE +#define EIGEN_TEST_NO_COMPLEX +#define EIGEN_TEST_FUNC cxx11_tensor_contract_sycl +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int +#define EIGEN_USE_SYCL + +#include <iostream> +#include <chrono> +#include <ctime> + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + +using Eigen::array; +using Eigen::SyclDevice; +using Eigen::Tensor; +using Eigen::TensorMap; +typedef Tensor<float, 1>::DimensionPair DimPair; +template<int DataLayout, typename Device> +void test_sycl_contraction(const Device& sycl_device, int m_size, int k_size, int n_size) +{ +// std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl; + // with these dimensions, the output has 300 * 140 elements, which is + // more than 30 * 1024, which is the number of threads in blocks on + // a 15 SM GK110 GPU + Tensor<float, 2, DataLayout> t_left(m_size, k_size); + Tensor<float, 2, DataLayout> t_right(k_size, n_size); + Tensor<float, 2, DataLayout> t_result(m_size, n_size); + Tensor<float, 2, DataLayout> t_result_gpu(m_size, n_size); +// Eigen::array<DimPair, 1> dims(DimPair(1, 0)); + Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; + Eigen::array<int, 2> left_dims = {{m_size, k_size}}; + Eigen::array<int, 2> right_dims = {{k_size, n_size}}; + Eigen::array<int, 2> result_dims = {{m_size, n_size}}; + + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(float); + std::size_t t_right_bytes = t_right.size() * sizeof(float); + std::size_t t_result_bytes = t_result.size() * sizeof(float); + + float * d_t_left = static_cast<float*>(sycl_device.allocate(t_left_bytes)); + float * d_t_right = static_cast<float*>(sycl_device.allocate(t_right_bytes)); + float * d_t_result = static_cast<float*>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_result(d_t_result, result_dims); + + sycl_device.memcpyHostToDevice(d_t_left, t_left.data(),t_left_bytes); + sycl_device.memcpyHostToDevice(d_t_right, t_right.data(),t_right_bytes); + + gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims); + t_result = t_left.contract(t_right, dims); + + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes); + + + for (DenseIndex i = 0; i < t_result.size(); i++) { + if (static_cast<float>(fabs(t_result(i) - t_result_gpu(i))) < 1e-4f) { + continue; + } + if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) { + continue; + } + std::cout << "mismatch detected at index " << i << ": " << t_result(i) + << " vs " << t_result_gpu(i) << std::endl; + assert(false); + } + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + + +template<int DataLayout, typename Device> +void test_scalar(const Device& sycl_device, int m_size, int k_size, int n_size) +{ + //std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl; + // with these dimensions, the output has 300 * 140 elements, which is + // more than 30 * 1024, which is the number of threads in blocks on + // a 15 SM GK110 GPU + Tensor<float, 2, DataLayout> t_left(m_size, k_size); + Tensor<float, 2, DataLayout> t_right(k_size, n_size); + Tensor<float, 0, DataLayout> t_result; + Tensor<float, 0, DataLayout> t_result_gpu; + Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}}; + Eigen::array<int, 2> left_dims = {{m_size, k_size}}; + Eigen::array<int, 2> right_dims = {{k_size, n_size}}; + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(float); + std::size_t t_right_bytes = t_right.size() * sizeof(float); + std::size_t t_result_bytes = sizeof(float); + + + float * d_t_left = static_cast<float*>(sycl_device.allocate(t_left_bytes)); + float * d_t_right = static_cast<float*>(sycl_device.allocate(t_right_bytes)); + float * d_t_result = static_cast<float*>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<float, 0, DataLayout> > gpu_t_result(d_t_result); + + sycl_device.memcpyHostToDevice(d_t_left, t_left.data(),t_left_bytes); + sycl_device.memcpyHostToDevice(d_t_right, t_right.data(),t_right_bytes); + + gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims); + t_result = t_left.contract(t_right, dims); + + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes); + if (static_cast<float>(fabs(t_result() - t_result_gpu())) > 1e-4f && + !Eigen::internal::isApprox(t_result(), t_result_gpu(), 1e-4f)) { + std::cout << "mismatch detected: " << t_result() + << " vs " << t_result_gpu() << std::endl; + assert(false); + } + + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + + +template<int DataLayout, typename Device> +void test_sycl_contraction_m(const Device& sycl_device) { + for (int k = 32; k < 256; k++) { + test_sycl_contraction<DataLayout>(sycl_device, k, 128, 128); + } +} + +template<int DataLayout, typename Device> +void test_sycl_contraction_k(const Device& sycl_device) { + for (int k = 32; k < 256; k++) { + test_sycl_contraction<DataLayout>(sycl_device, 128, k, 128); + } +} + +template<int DataLayout, typename Device> +void test_sycl_contraction_n(const Device& sycl_device) { + for (int k = 32; k < 256; k++) { + test_sycl_contraction<DataLayout>(sycl_device, 128, 128, k); + } +} + + +template<int DataLayout, typename Device> +void test_sycl_contraction_sizes(const Device& sycl_device) { + int m_sizes[] = { 31, 39, 63, 64, 65, + 127, 129, 255, 257 , 511, + 512, 513, 1023, 1024, 1025}; + + int n_sizes[] = { 31, 39, 63, 64, 65, + 127, 129, 255, 257, 511, + 512, 513, 1023, 1024, 1025}; + + int k_sizes[] = { 31, 39, 63, 64, 65, + 95, 96, 127, 129, 255, + 257, 511, 512, 513, 1023, + 1024, 1025}; + + for (int i = 0; i < 15; i++) { + for (int j = 0; j < 15; j++) { + for (int k = 0; k < 17; k++) { + test_sycl_contraction<DataLayout>(sycl_device, m_sizes[i], n_sizes[j], k_sizes[k]); + } + } + } +} + +template <typename Dev_selector> void tensorContractionPerDevice(Dev_selector& s){ + QueueInterface queueInterface(s); + auto sycl_device=Eigen::SyclDevice(&queueInterface); + test_sycl_contraction<ColMajor>(sycl_device, 32, 32, 32); + test_sycl_contraction<RowMajor>(sycl_device, 32, 32, 32); + test_scalar<ColMajor>(sycl_device, 32, 32, 32); + test_scalar<RowMajor>(sycl_device, 32, 32, 32); + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + test_sycl_contraction<ColMajor>(sycl_device, 128, 128, 128); + test_sycl_contraction<RowMajor>(sycl_device, 128, 128, 128); + test_scalar<ColMajor>(sycl_device, 128, 128, 128); + test_scalar<RowMajor>(sycl_device, 128, 128, 128); + test_sycl_contraction_m<ColMajor>(sycl_device); + test_sycl_contraction_m<RowMajor>(sycl_device); + test_sycl_contraction_n<ColMajor>(sycl_device); + test_sycl_contraction_n<RowMajor>(sycl_device); + test_sycl_contraction_k<ColMajor>(sycl_device); + test_sycl_contraction_k<RowMajor>(sycl_device); + test_sycl_contraction_sizes<ColMajor>(sycl_device); + test_sycl_contraction_sizes<RowMajor>(sycl_device); + end = std::chrono::system_clock::now(); + std::chrono::duration<double> elapsed_seconds = end-start; + std::time_t end_time = std::chrono::system_clock::to_time_t(end); + std::cout << "finished computation at " << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; + +} +void test_cxx11_tensor_contract_sycl() { + for (const auto& device :Eigen::get_sycl_supported_devices()) { + CALL_SUBTEST(tensorContractionPerDevice(device)); + } +} |