mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-27 07:29:52 +08:00
00f32752f7
* Unifying all loadLocalTile from lhs and rhs to an extract_block function. * Adding get_tensor operation which was missing in TensorContractionMapper. * Adding the -D method missing from cmake for Disable_Skinny Contraction operation. * Wrapping all the indices in TensorScanSycl into Scan parameter struct. * Fixing typo in Device SYCL * Unifying load to private register for tall/skinny no shared * Unifying load to vector tile for tensor-vector/vector-tensor operation * Removing all the LHS/RHS class for extracting data from global * Removing Outputfunction from TensorContractionSkinnyNoshared. * Combining the local memory version of tall/skinny and normal tensor contraction into one kernel. * Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel. * Combining General Tensor-Vector and VectorTensor contraction into one kernel. * Making double buffering optional for Tensor contraction when local memory is version is used. * Modifying benchmark to accept custom Reduction Sizes * Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host * Adding Test for SYCL * Modifying SYCL CMake
1027 lines
46 KiB
C++
1027 lines
46 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2016
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// Mehdi Goli Codeplay Software Ltd.
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// Ralph Potter Codeplay Software Ltd.
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// Luke Iwanski Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
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#define EIGEN_USE_SYCL
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#include <algorithm>
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#include <chrono>
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#include <ctime>
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#include <iostream>
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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using Eigen::array;
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using Eigen::SyclDevice;
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using Eigen::Tensor;
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using Eigen::TensorMap;
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void static test_sycl_contraction(const Device &sycl_device, IndexType m_size,
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IndexType k_size, IndexType n_size) {
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typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair
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DimPair;
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static const DataType error_threshold = DataType(1e-4);
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// with these dimensions, the output has 300 * 140 elements, which is
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// more than 30 * 1024, which is the number of threads in blocks on
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// a 15 SM GK110 GPU
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Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
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Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
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Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size);
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Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(m_size, n_size);
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Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
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Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
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Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
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Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}};
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t_left.setRandom();
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t_right.setRandom();
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std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
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std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
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std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
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DataType *d_t_left =
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static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
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DataType *d_t_right =
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static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
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DataType *d_t_result =
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static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_left(d_t_left, left_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_right(d_t_right, right_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_result(d_t_result, result_dims);
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sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
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sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
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gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
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sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result,
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t_result_bytes);
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t_result = t_left.contract(t_right, dims);
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for (IndexType i = 0; i < t_result.size(); i++) {
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if (static_cast<DataType>(std::fabs(static_cast<DataType>(
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t_result(i) - t_result_gpu(i)))) < error_threshold) {
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continue;
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}
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if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i),
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error_threshold)) {
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continue;
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}
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std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
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<< ", mismatch detected at IndexType " << i << ": " << t_result(i)
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<< " vs " << t_result_gpu(i) << std::endl;
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VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
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}
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sycl_device.deallocate(d_t_left);
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sycl_device.deallocate(d_t_right);
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sycl_device.deallocate(d_t_result);
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}
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void test_sycl_contraction_m(const Device &sycl_device) {
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for (IndexType k = 32; k < 256; k++) {
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test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, k, 128,
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128);
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}
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}
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void test_sycl_contraction_k(const Device &sycl_device) {
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for (IndexType k = 32; k < 256; k++) {
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test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128, k,
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128);
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}
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}
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void test_sycl_contraction_n(const Device &sycl_device) {
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for (IndexType k = 32; k < 256; k++) {
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test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128,
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128, k);
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}
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}
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void test_sycl_contraction_sizes(const Device &sycl_device) {
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IndexType m_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255,
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257, 511, 512, 513, 1023, 1024, 1025};
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IndexType n_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255,
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257, 511, 512, 513, 1023, 1024, 1025};
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IndexType k_sizes[] = {31, 39, 63, 64, 65, 95, 96, 127, 129,
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255, 257, 511, 512, 513, 1023, 1024, 1025};
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for (IndexType i = 0; i < 15; i++) {
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for (IndexType j = 0; j < 15; j++) {
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for (IndexType k = 0; k < 17; k++) {
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test_sycl_contraction<DataLayout, DataType, IndexType>(
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sycl_device, m_sizes[i], n_sizes[j], k_sizes[k]);
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}
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}
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}
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}
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void static test_no_out_of_bounds(const Device &sycl_device, IndexType m_size,
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IndexType k_size, IndexType n_size) {
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typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair
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DimPair;
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static const DataType error_threshold = DataType(1e-4);
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Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
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Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
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Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size);
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Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
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Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
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Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
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Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}};
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t_left.setRandom();
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t_right.setRandom();
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// Allocate buffers twice as big to check for invalid read and write
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auto padded_left_size = 2 * t_left.size();
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auto padded_right_size = 2 * t_right.size();
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auto padded_result_size = 2 * t_result.size();
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std::size_t t_left_bytes = padded_left_size * sizeof(DataType);
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std::size_t t_right_bytes = padded_right_size * sizeof(DataType);
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std::size_t t_result_bytes = padded_result_size * sizeof(DataType);
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DataType *d_t_left =
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static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
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DataType *d_t_right =
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static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
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DataType *d_t_result =
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static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
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// TensorMaps are still of the same size than the Tensors
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_left(d_t_left, left_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_right(d_t_right, right_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_result(d_t_result, result_dims);
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// Write nan after the actual buffer to propagate nans everywhere in case of
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// invalid reads
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DataType nan = std::numeric_limits<DataType>::quiet_NaN();
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auto host_left_data = new DataType[padded_left_size];
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std::copy_n(t_left.data(), t_left.size(), host_left_data);
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std::fill_n(host_left_data + t_left.size(), t_left.size(), nan);
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auto host_right_data = new DataType[padded_right_size];
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std::copy_n(t_right.data(), t_right.size(), host_right_data);
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std::fill_n(host_right_data + t_right.size(), t_right.size(), nan);
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auto host_result_data = new DataType[padded_result_size];
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std::fill_n(host_result_data, padded_result_size, nan);
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sycl_device.memcpyHostToDevice(d_t_left, host_left_data, t_left_bytes);
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sycl_device.memcpyHostToDevice(d_t_right, host_right_data, t_right_bytes);
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sycl_device.memcpyHostToDevice(d_t_result, host_result_data, t_result_bytes);
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gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
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sycl_device.memcpyDeviceToHost(host_result_data, d_t_result, t_result_bytes);
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t_result = t_left.contract(t_right, dims);
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for (IndexType i = 0; i < t_result.size(); i++) {
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if (static_cast<DataType>(std::fabs(static_cast<DataType>(
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t_result(i) - host_result_data[i]))) < error_threshold) {
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continue;
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}
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if (Eigen::internal::isApprox(t_result(i), host_result_data[i],
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error_threshold)) {
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continue;
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}
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if (std::isnan(host_result_data[i])) {
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std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
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<< ", invalid read detected at IndexType " << i << ": "
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<< t_result(i) << " vs " << host_result_data[i] << std::endl;
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} else {
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std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
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<< ", mismatch detected at IndexType " << i << ": "
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<< t_result(i) << " vs " << host_result_data[i] << std::endl;
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}
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VERIFY_IS_APPROX(host_result_data[i], t_result(i));
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}
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// Make sure that the rest of the result is still nans
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for (IndexType i = t_result.size(); i < padded_result_size; i++) {
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if (std::isnan(host_result_data[i])) {
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continue;
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}
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std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
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<< ", invalid write detected at IndexType " << i << ": "
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<< host_result_data[i] << std::endl;
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VERIFY_IS_APPROX(host_result_data[i], t_result(i));
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}
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sycl_device.deallocate(d_t_left);
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sycl_device.deallocate(d_t_right);
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sycl_device.deallocate(d_t_result);
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delete[] host_left_data;
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delete[] host_right_data;
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delete[] host_result_data;
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}
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void test_scalar(const Device &sycl_device, IndexType m_size, IndexType k_size,
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IndexType n_size) {
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// std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size <<
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// ")" << std::endl;
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// with these dimensions, the output has 300 * 140 elements, which is
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// more than 30 * 1024, which is the number of threads in blocks on
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// a 15 SM GK110 GPU
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typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair
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DimPair;
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static const DataType error_threshold = DataType(1e-4);
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Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
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Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
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Tensor<DataType, 0, DataLayout, IndexType> t_result;
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Tensor<DataType, 0, DataLayout, IndexType> t_result_gpu;
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Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}};
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Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
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Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
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t_left.setRandom();
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t_right.setRandom();
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std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
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std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
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std::size_t t_result_bytes = sizeof(DataType);
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DataType *d_t_left =
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static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
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DataType *d_t_right =
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static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
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DataType *d_t_result =
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static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_left(d_t_left, left_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>>
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gpu_t_right(d_t_right, right_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 0, DataLayout, IndexType>>
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gpu_t_result(d_t_result);
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sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
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sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
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gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
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sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result,
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t_result_bytes);
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t_result = t_left.contract(t_right, dims);
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if (static_cast<DataType>(std::fabs(static_cast<DataType>(
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t_result() - t_result_gpu()))) > error_threshold &&
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!Eigen::internal::isApprox(t_result(), t_result_gpu(), error_threshold)) {
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std::cout << "K: " << k_size << ", N: " << n_size << ", M: " << m_size
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<< " : mismatch detected: " << t_result() << " vs "
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<< t_result_gpu() << std::endl;
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VERIFY_IS_APPROX(t_result_gpu(), t_result());
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}
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sycl_device.deallocate(d_t_left);
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sycl_device.deallocate(d_t_right);
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sycl_device.deallocate(d_t_result);
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}
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template <int DataLayout, typename DataType, typename IndexType,
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typename Device>
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void contraction_batch(const Device &sycl_device, IndexType m_size,
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IndexType k_size, IndexType n_size, IndexType m_batch,
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IndexType start, IndexType limit) {
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typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair
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DimPair;
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static const DataType error_threshold = DataType(1e-4);
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typedef Eigen::array<IndexType, 3> TensorDim;
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typedef Eigen::Tensor<DataType, 3, DataLayout, IndexType> TensorType;
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TensorDim left_dims = {{m_batch, k_size, m_size}};
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TensorDim right_dims = {{m_batch, n_size, k_size}};
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TensorDim res_dims = {{m_batch, m_size, n_size}};
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Eigen::array<DimPair, 1> contract_pairs = {{DimPair(0, 1)}};
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TensorType t_left(left_dims);
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TensorType t_right(right_dims);
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TensorType t_result_gpu(res_dims);
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TensorType t_result(res_dims);
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t_left.setRandom();
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t_right.setRandom();
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std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
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std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
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std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
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DataType *d_t_left =
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static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
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DataType *d_t_right =
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static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
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DataType *d_t_result =
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static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
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Eigen::TensorMap<TensorType> gpu_t_left(d_t_left, left_dims);
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Eigen::TensorMap<TensorType> gpu_t_right(d_t_right, right_dims);
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Eigen::TensorMap<TensorType> gpu_t_result(d_t_result, res_dims);
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sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
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sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
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for (int i = start; i < limit; ++i) {
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auto x = gpu_t_left.template chip<0>(i);
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auto y = gpu_t_right.template chip<0>(i);
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auto z = gpu_t_result.template chip<0>(i);
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z.device(sycl_device) = x.contract(y, contract_pairs);
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}
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sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result,
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t_result_bytes);
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for (int i = start; i < limit; ++i) {
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auto x = t_left.template chip<0>(i);
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auto y = t_right.template chip<0>(i);
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auto z = t_result.template chip<0>(i);
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z = x.contract(y, contract_pairs);
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}
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for (IndexType i = 0; i < t_result.size(); i++) {
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if (static_cast<DataType>(std::fabs(static_cast<DataType>(
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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));
|
|
}
|
|
}
|