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Diffstat (limited to 'unsupported/test/cxx11_tensor_contract_sycl.cpp')
| -rw-r--r-- | unsupported/test/cxx11_tensor_contract_sycl.cpp | 1026 |
1 files changed, 1026 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..fbcc29358 --- /dev/null +++ b/unsupported/test/cxx11_tensor_contract_sycl.cpp @@ -0,0 +1,1026 @@ +// 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_DEFAULT_DENSE_INDEX_TYPE int64_t +#define EIGEN_USE_SYCL + +#include <algorithm> +#include <chrono> +#include <ctime> +#include <iostream> + +#include "main.h" + +#include <unsupported/Eigen/CXX11/Tensor> + +using Eigen::array; +using Eigen::SyclDevice; +using Eigen::Tensor; +using Eigen::TensorMap; + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void static test_sycl_contraction(const Device &sycl_device, IndexType m_size, + IndexType k_size, IndexType n_size) { + typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair + DimPair; + static const DataType error_threshold = DataType(1e-4); + // 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<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size); + Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size); + Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size); + Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(m_size, n_size); + Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; + Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}}; + Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}}; + Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}}; + + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(DataType); + std::size_t t_right_bytes = t_right.size() * sizeof(DataType); + std::size_t t_result_bytes = t_result.size() * sizeof(DataType); + + DataType *d_t_left = + static_cast<DataType *>(sycl_device.allocate(t_left_bytes)); + DataType *d_t_right = + static_cast<DataType *>(sycl_device.allocate(t_right_bytes)); + DataType *d_t_result = + static_cast<DataType *>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + 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); + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, + t_result_bytes); + + t_result = t_left.contract(t_right, dims); + + for (IndexType i = 0; i < t_result.size(); i++) { + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result(i) - t_result_gpu(i)))) < error_threshold) { + continue; + } + if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), + error_threshold)) { + continue; + } + + std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size + << ", mismatch detected at IndexType " << i << ": " << t_result(i) + << " vs " << t_result_gpu(i) << std::endl; + VERIFY_IS_APPROX(t_result_gpu(i), t_result(i)); + } + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void test_sycl_contraction_m(const Device &sycl_device) { + for (IndexType k = 32; k < 256; k++) { + test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, k, 128, + 128); + } +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void test_sycl_contraction_k(const Device &sycl_device) { + for (IndexType k = 32; k < 256; k++) { + test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128, k, + 128); + } +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void test_sycl_contraction_n(const Device &sycl_device) { + for (IndexType k = 32; k < 256; k++) { + test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128, + 128, k); + } +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void test_sycl_contraction_sizes(const Device &sycl_device) { + IndexType m_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255, + 257, 511, 512, 513, 1023, 1024, 1025}; + + IndexType n_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255, + 257, 511, 512, 513, 1023, 1024, 1025}; + + IndexType k_sizes[] = {31, 39, 63, 64, 65, 95, 96, 127, 129, + 255, 257, 511, 512, 513, 1023, 1024, 1025}; + + for (IndexType i = 0; i < 15; i++) { + for (IndexType j = 0; j < 15; j++) { + for (IndexType k = 0; k < 17; k++) { + test_sycl_contraction<DataLayout, DataType, IndexType>( + sycl_device, m_sizes[i], n_sizes[j], k_sizes[k]); + } + } + } +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void static test_no_out_of_bounds(const Device &sycl_device, IndexType m_size, + IndexType k_size, IndexType n_size) { + typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair + DimPair; + static const DataType error_threshold = DataType(1e-4); + Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size); + Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size); + Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size); + + Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; + Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}}; + Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}}; + Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}}; + + t_left.setRandom(); + t_right.setRandom(); + + // Allocate buffers twice as big to check for invalid read and write + auto padded_left_size = 2 * t_left.size(); + auto padded_right_size = 2 * t_right.size(); + auto padded_result_size = 2 * t_result.size(); + + std::size_t t_left_bytes = padded_left_size * sizeof(DataType); + std::size_t t_right_bytes = padded_right_size * sizeof(DataType); + std::size_t t_result_bytes = padded_result_size * sizeof(DataType); + + DataType *d_t_left = + static_cast<DataType *>(sycl_device.allocate(t_left_bytes)); + DataType *d_t_right = + static_cast<DataType *>(sycl_device.allocate(t_right_bytes)); + DataType *d_t_result = + static_cast<DataType *>(sycl_device.allocate(t_result_bytes)); + + // TensorMaps are still of the same size than the Tensors + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_result(d_t_result, result_dims); + + // Write nan after the actual buffer to propagate nans everywhere in case of + // invalid reads + DataType nan = std::numeric_limits<DataType>::quiet_NaN(); + auto host_left_data = new DataType[padded_left_size]; + std::copy_n(t_left.data(), t_left.size(), host_left_data); + std::fill_n(host_left_data + t_left.size(), t_left.size(), nan); + auto host_right_data = new DataType[padded_right_size]; + std::copy_n(t_right.data(), t_right.size(), host_right_data); + std::fill_n(host_right_data + t_right.size(), t_right.size(), nan); + auto host_result_data = new DataType[padded_result_size]; + std::fill_n(host_result_data, padded_result_size, nan); + + sycl_device.memcpyHostToDevice(d_t_left, host_left_data, t_left_bytes); + sycl_device.memcpyHostToDevice(d_t_right, host_right_data, t_right_bytes); + sycl_device.memcpyHostToDevice(d_t_result, host_result_data, t_result_bytes); + + gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims); + sycl_device.memcpyDeviceToHost(host_result_data, d_t_result, t_result_bytes); + + t_result = t_left.contract(t_right, dims); + + for (IndexType i = 0; i < t_result.size(); i++) { + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result(i) - host_result_data[i]))) < error_threshold) { + continue; + } + if (Eigen::internal::isApprox(t_result(i), host_result_data[i], + error_threshold)) { + continue; + } + if (std::isnan(host_result_data[i])) { + std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size + << ", invalid read detected at IndexType " << i << ": " + << t_result(i) << " vs " << host_result_data[i] << std::endl; + } else { + std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size + << ", mismatch detected at IndexType " << i << ": " + << t_result(i) << " vs " << host_result_data[i] << std::endl; + } + VERIFY_IS_APPROX(host_result_data[i], t_result(i)); + } + // Make sure that the rest of the result is still nans + for (IndexType i = t_result.size(); i < padded_result_size; i++) { + if (std::isnan(host_result_data[i])) { + continue; + } + std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size + << ", invalid write detected at IndexType " << i << ": " + << host_result_data[i] << std::endl; + VERIFY_IS_APPROX(host_result_data[i], t_result(i)); + } + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); + + delete[] host_left_data; + delete[] host_right_data; + delete[] host_result_data; +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void test_scalar(const Device &sycl_device, IndexType m_size, IndexType k_size, + IndexType 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 + typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair + DimPair; + static const DataType error_threshold = DataType(1e-4); + Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size); + Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size); + Tensor<DataType, 0, DataLayout, IndexType> t_result; + Tensor<DataType, 0, DataLayout, IndexType> t_result_gpu; + Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}}; + Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}}; + Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}}; + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(DataType); + std::size_t t_right_bytes = t_right.size() * sizeof(DataType); + std::size_t t_result_bytes = sizeof(DataType); + + DataType *d_t_left = + static_cast<DataType *>(sycl_device.allocate(t_left_bytes)); + DataType *d_t_right = + static_cast<DataType *>(sycl_device.allocate(t_right_bytes)); + DataType *d_t_result = + static_cast<DataType *>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 0, DataLayout, IndexType>> + 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); + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, + t_result_bytes); + + t_result = t_left.contract(t_right, dims); + + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result() - t_result_gpu()))) > error_threshold && + !Eigen::internal::isApprox(t_result(), t_result_gpu(), error_threshold)) { + std::cout << "K: " << k_size << ", N: " << n_size << ", M: " << m_size + << " : mismatch detected: " << t_result() << " vs " + << t_result_gpu() << std::endl; + VERIFY_IS_APPROX(t_result_gpu(), t_result()); + } + + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void contraction_batch(const Device &sycl_device, IndexType m_size, + IndexType k_size, IndexType n_size, IndexType m_batch, + IndexType start, IndexType limit) { + typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair + DimPair; + static const DataType error_threshold = DataType(1e-4); + typedef Eigen::array<IndexType, 3> TensorDim; + typedef Eigen::Tensor<DataType, 3, DataLayout, IndexType> TensorType; + TensorDim left_dims = {{m_batch, k_size, m_size}}; + TensorDim right_dims = {{m_batch, n_size, k_size}}; + TensorDim res_dims = {{m_batch, m_size, n_size}}; + Eigen::array<DimPair, 1> contract_pairs = {{DimPair(0, 1)}}; + + TensorType t_left(left_dims); + TensorType t_right(right_dims); + TensorType t_result_gpu(res_dims); + TensorType t_result(res_dims); + + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(DataType); + std::size_t t_right_bytes = t_right.size() * sizeof(DataType); + std::size_t t_result_bytes = t_result.size() * sizeof(DataType); + + DataType *d_t_left = + static_cast<DataType *>(sycl_device.allocate(t_left_bytes)); + DataType *d_t_right = + static_cast<DataType *>(sycl_device.allocate(t_right_bytes)); + DataType *d_t_result = + static_cast<DataType *>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<TensorType> gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<TensorType> gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<TensorType> gpu_t_result(d_t_result, res_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); + for (int i = start; i < limit; ++i) { + auto x = gpu_t_left.template chip<0>(i); + auto y = gpu_t_right.template chip<0>(i); + auto z = gpu_t_result.template chip<0>(i); + z.device(sycl_device) = x.contract(y, contract_pairs); + } + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, + t_result_bytes); + + for (int i = start; i < limit; ++i) { + auto x = t_left.template chip<0>(i); + auto y = t_right.template chip<0>(i); + auto z = t_result.template chip<0>(i); + z = x.contract(y, contract_pairs); + } + + for (IndexType i = 0; i < t_result.size(); i++) { + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result(i) - t_result_gpu(i)))) < error_threshold) { + continue; + } + if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), + error_threshold)) { + continue; + } + std::cout << "mismatch detected at IndexType " << i << ": " << t_result(i) + << " vs " << t_result_gpu(i) << std::endl; + VERIFY_IS_APPROX(t_result_gpu(i), t_result(i)); + } + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void contraction_rhs_transposed(const Device &sycl_device, IndexType m_size, + IndexType k_size, IndexType n_size) { + typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair + DimPair; + static const DataType error_threshold = DataType(1e-4); + Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}}; + Eigen::array<IndexType, 2> right_dims = {{n_size, k_size}}; + Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}}; + Eigen::array<DimPair, 1> dims = {{DimPair(1, 1)}}; + + Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims); + + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(DataType); + std::size_t t_right_bytes = t_right.size() * sizeof(DataType); + std::size_t t_result_bytes = t_result.size() * sizeof(DataType); + + DataType *d_t_left = + static_cast<DataType *>(sycl_device.allocate(t_left_bytes)); + DataType *d_t_right = + static_cast<DataType *>(sycl_device.allocate(t_right_bytes)); + DataType *d_t_result = + static_cast<DataType *>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_result(d_t_result, res_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); + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, + t_result_bytes); + + t_result = t_left.contract(t_right, dims); + + for (IndexType j = 0; j < m_size; j++) { + for (IndexType i = 0; i < n_size; i++) { + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result(j, i) - t_result_gpu(j, i)))) < error_threshold) { + continue; + } + if (Eigen::internal::isApprox(t_result(j, i), t_result_gpu(j, i), + error_threshold)) { + continue; + } + std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size + << ", mismatch detected at IndexType m: " << j << " n: " << i + << " CPU : " << t_result(j, i) + << " vs SYCL:" << t_result_gpu(j, i) << std::endl; + VERIFY_IS_APPROX(t_result_gpu(j, i), t_result(j, i)); + } + } + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void contraction_lhs_transposed(const Device &sycl_device, IndexType m_size, + IndexType k_size, IndexType n_size) { + typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair + DimPair; + static const DataType error_threshold = DataType(1e-4); + Eigen::array<IndexType, 2> left_dims = {{k_size, m_size}}; + Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}}; + Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}}; + Eigen::array<DimPair, 1> dims = {{DimPair(0, 0)}}; + + Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims); + + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(DataType); + std::size_t t_right_bytes = t_right.size() * sizeof(DataType); + std::size_t t_result_bytes = t_result.size() * sizeof(DataType); + + DataType *d_t_left = + static_cast<DataType *>(sycl_device.allocate(t_left_bytes)); + DataType *d_t_right = + static_cast<DataType *>(sycl_device.allocate(t_right_bytes)); + DataType *d_t_result = + static_cast<DataType *>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_result(d_t_result, res_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); + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, + t_result_bytes); + + t_result = t_left.contract(t_right, dims); + + for (IndexType i = 0; i < t_result.size(); i++) { + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result(i) - t_result_gpu(i)))) < error_threshold) { + continue; + } + if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), + error_threshold)) { + continue; + } + std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size + << ", mismatch detected at IndexType " << i << ": " << t_result(i) + << " vs " << t_result_gpu(i) << std::endl; + VERIFY_IS_APPROX(t_result_gpu(i), t_result(i)); + } + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + +template <int DataLayout, typename DataType, typename IndexType, + typename Device> +void contraction_both_transposed(const Device &sycl_device, IndexType m_size, + IndexType k_size, IndexType n_size) { + typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair + DimPair; + static const DataType error_threshold = DataType(1e-4); + Eigen::array<IndexType, 2> left_dims = {{k_size, m_size}}; + Eigen::array<IndexType, 2> right_dims = {{n_size, k_size}}; + Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}}; + Eigen::array<DimPair, 1> dims = {{DimPair(0, 1)}}; + + Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims); + Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims); + + t_left.setRandom(); + t_right.setRandom(); + + std::size_t t_left_bytes = t_left.size() * sizeof(DataType); + std::size_t t_right_bytes = t_right.size() * sizeof(DataType); + std::size_t t_result_bytes = t_result.size() * sizeof(DataType); + + DataType *d_t_left = + static_cast<DataType *>(sycl_device.allocate(t_left_bytes)); + DataType *d_t_right = + static_cast<DataType *>(sycl_device.allocate(t_right_bytes)); + DataType *d_t_result = + static_cast<DataType *>(sycl_device.allocate(t_result_bytes)); + + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_left(d_t_left, left_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_right(d_t_right, right_dims); + Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> + gpu_t_result(d_t_result, res_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); + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, + t_result_bytes); + + t_result = t_left.contract(t_right, dims); + + for (IndexType i = 0; i < t_result.size(); i++) { + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result(i) - t_result_gpu(i)))) < error_threshold) { + continue; + } + if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), + error_threshold)) { + continue; + } + std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size + << ", mismatch detected at IndexType " << i << ": " << t_result(i) + << " vs " << t_result_gpu(i) << std::endl; + + VERIFY_IS_APPROX(t_result_gpu(i), t_result(i)); + } + sycl_device.deallocate(d_t_left); + sycl_device.deallocate(d_t_right); + sycl_device.deallocate(d_t_result); +} + +template <typename Dev> +void inline tensorOutofBound(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Test out of bound for Tensor-Tensor + test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, + 1024); + test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, + 4096); + test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 4096, 1024, + 2048); + test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 784, 2048, + 1024); + test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 2048, 1024, + 784); + test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, + 10); + test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 513, 4096, + 513); + test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 783, 1024, + 783); + test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 784, 2048, + 784); + test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 11, 1024, + 11); + 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 << "tensor out of bound tests finished computation at " + << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; +} + +template <typename Dev> +void inline tensorTensor(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Tensor Tensor Contraction + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 128, 128, + 128); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 128, 128, + 128); + 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 << "tensor tensor tests finished computation at " + << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; +} + +template <typename Dev> +void inline tensorTensor_m(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Tensor Tensor Contraction + test_sycl_contraction_m<ColMajor, DataType, IndexType>(sycl_device); + test_sycl_contraction_m<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " + << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; +} + +template <typename Dev> +void inline tensorTensor_n(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Tensor Tensor Contraction + test_sycl_contraction_n<ColMajor, DataType, IndexType>(sycl_device); + test_sycl_contraction_n<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " + << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; +} + +template <typename Dev> +void inline tensorTensor_k(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + test_sycl_contraction_k<ColMajor, DataType, IndexType>(sycl_device); + test_sycl_contraction_k<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " + << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; +} + +template <typename Dev> +void inline tensorTensor_sizes(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Tensor Tensor Contraction + test_sycl_contraction_sizes<ColMajor, DataType, IndexType>(sycl_device); + test_sycl_contraction_sizes<RowMajor, DataType, IndexType>(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 << "tensor tensor tests finished computation at " + << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; +} +template <typename Dev> +void inline vectorVector(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // VECTOR-VECTOR + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1025, 1, + 1025); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1025, 1, + 1025); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1024, 1, + 1024); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1024, 1, + 1024); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1023, 1, + 1023); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1023, 1, + 1023); + + 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 << "contracted tensor tests finished computation at " + << std::ctime(&end_time) + << "elapsed time: " << elapsed_seconds.count() << "s\n"; +} + +template <typename Dev> +void inline vectorTensor(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Vector-Tensor + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1025, + 1025); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1025, + 1025); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1024, + 1024); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1024, + 1024); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1023, + 1023); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1023, + 1023); + + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4097, + 4097); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4097, + 4097); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4096, + 4096); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4096, + 4096); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4095, + 4095); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4095, + 4095); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 802816, + 32); + + 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"; +} + +template <typename Dev> +void inline tensorVector(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Matrix-Vector + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1025, 1025, + 1); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1125, 1025, + 1); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1224, 1024, + 1); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, + 1); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1023, 1023, + 1); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1023, 1023, + 1); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4097, 4197, + 1); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4097, 4097, + 1); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4096, 4096, + 1); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4096, 8196, + 1); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4095, 4095, + 1); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4095, 4095, + 1); +// If the GEMV disabled it will creates one kernel to calculate the contraction. +// Therefore the acumuation of float number will overflow the precision +// threshold for float and cause the test to fail. While it the GMV multiple +// kernel will be created and each one run the overflow of accumutation breaks +// among the kernels. +#ifndef EIGEN_SYCL_DISABLE_GEMV + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 32, 802032, + 1); +#endif + + 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"; +} + +template <typename Dev> +void inline tensorScalar(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // SCALAR Contraction + test_scalar<ColMajor, DataType, IndexType>(sycl_device, 127, 127, 127); + test_scalar<RowMajor, DataType, IndexType>(sycl_device, 127, 127, 127); + test_scalar<ColMajor, DataType, IndexType>(sycl_device, 128, 128, 128); + test_scalar<RowMajor, DataType, IndexType>(sycl_device, 128, 128, 128); + test_scalar<ColMajor, DataType, IndexType>(sycl_device, 129, 129, 129); + test_scalar<RowMajor, DataType, IndexType>(sycl_device, 129, 129, 129); + + 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"; +} + +template <typename Dev> +void inline skinnyTensor_row(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Tensor Tensor Contraction + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 16, 4, 16); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 257, 131073, + 257); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 256, 131072, + 256); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 16, 131073, + 16); + test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 17, 131072, + 17); + 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"; +} + +template <typename Dev> +void inline skinnyTensor_col(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + // Tensor Tensor Contraction + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 16, 4, 16); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 257, 131073, + 257); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 256, 131072, + 256); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 16, 131073, + 16); + test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 17, 131072, + 17); + 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"; +} + +template <typename Dev> +void inline tensor_contraction_batch_per_device(const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + + contraction_batch<RowMajor, DataType, IndexType>(sycl_device, 64, 75, 30, 4, + 0, 4); + contraction_batch<ColMajor, DataType, IndexType>(sycl_device, 64, 75, 30, 4, + 0, 4); + 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"; +} + +template <typename Dev> +void inline tensor_contraction_lhs_transposed_per_device( + const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + + contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 8, 4, + 8); + contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, + 32); + contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, + 64); + contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 784, + 2048, 1024); + contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 1024, + 10, 1024); + contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 4096, + 1024, 1024); + contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 2048, + 4096, 1024); + 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"; +} + +template <typename Dev> +void inline tensor_contraction_rhs_transposed_per_device( + const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 16, 4, + 16); + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 17, 5, + 17); + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, + 32); + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, + 64); + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 10, + 1024, 1024); + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 1024, + 1024, 4096); + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 4096, + 1024, 2048); + contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 2048, + 1024, 784); + 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"; +} + +template <typename Dev> +void inline tensor_contraction_both_transposed_per_device( + const Dev &sycl_device) { + typedef float DataType; + typedef int64_t IndexType; + std::chrono::time_point<std::chrono::system_clock> start, end; + start = std::chrono::system_clock::now(); + + contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 17, 5, + 17); + contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, + 32); + contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, + 16, 64); + 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"; +} + +EIGEN_DECLARE_TEST(cxx11_tensor_contract_sycl) { + for (const auto &device : Eigen::get_sycl_supported_devices()) { + std::cout << "Running on " + << device.template get_info<cl::sycl::info::device::name>() + << std::endl; + QueueInterface queueInterface(device); + auto sycl_device = Eigen::SyclDevice(&queueInterface); + CALL_SUBTEST_1(tensorOutofBound(sycl_device)); + CALL_SUBTEST_2(tensorTensor(sycl_device)); + CALL_SUBTEST_2(tensorTensor_m(sycl_device)); + CALL_SUBTEST_2(tensorTensor_n(sycl_device)); + CALL_SUBTEST_2(tensorTensor_k(sycl_device)); + CALL_SUBTEST_2(tensorTensor_sizes(sycl_device)); + CALL_SUBTEST_3(vectorVector(sycl_device)); + CALL_SUBTEST_4(vectorTensor(sycl_device)); + CALL_SUBTEST_5(tensorVector(sycl_device)); + CALL_SUBTEST_6(tensorScalar(sycl_device)); + CALL_SUBTEST_7(skinnyTensor_row(sycl_device)); + CALL_SUBTEST_7(skinnyTensor_col(sycl_device)); + CALL_SUBTEST_8(tensor_contraction_batch_per_device(sycl_device)); + CALL_SUBTEST_9(tensor_contraction_lhs_transposed_per_device(sycl_device)); + CALL_SUBTEST_10(tensor_contraction_rhs_transposed_per_device(sycl_device)); + CALL_SUBTEST_11(tensor_contraction_both_transposed_per_device(sycl_device)); + } +} |