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82f0ce2726
This provide several advantages: - more flexibility in designing unit tests - unit tests can be glued to speed up compilation - unit tests are compiled with same predefined macros, which is a requirement for zapcc
470 lines
20 KiB
C++
470 lines
20 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 <iostream>
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#include <chrono>
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#include <ctime>
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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#include <iomanip>
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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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static const float error_threshold =1e-4f;
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_larg_expr1D(const Eigen::SyclDevice& sycl_device)
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{
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IndexType indim0 =53;
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IndexType indim1= 55;
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IndexType indim2= 51;
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IndexType outdim0=50;
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IndexType outdim1=55;
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IndexType outdim2=51;
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Eigen::array<IndexType, 3> input_dims = {{indim0, indim1, indim2}};
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Eigen::array<IndexType, 1> kernel_dims = {{4}};
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Eigen::array<IndexType, 3> result_dims = {{outdim0, outdim1, outdim2}};
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Tensor<DataType, 3, DataLayout, IndexType> input(input_dims);
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Tensor<DataType, 1, DataLayout,IndexType> kernel(kernel_dims);
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Tensor<DataType, 3, DataLayout,IndexType> result(result_dims);
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Tensor<DataType, 3, DataLayout,IndexType> result_host(result_dims);
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Eigen::array<IndexType, 1> dims3{{0}};
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input.setRandom();
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kernel.setRandom();
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result.setZero();
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result_host.setZero();
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std::size_t input_bytes = input.size() * sizeof(DataType);
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std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
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std::size_t result_bytes = result.size() * sizeof(DataType);
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DataType * d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
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DataType * d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
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DataType * d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_input(d_input, input_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_result(d_result, result_dims);
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sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
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sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
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gpu_result.device(sycl_device)=gpu_input.convolve(gpu_kernel, dims3);
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sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
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result_host=input.convolve(kernel, dims3);
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for(IndexType i=0; i< outdim0; i++ ){
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for(IndexType j=0; j< outdim1; j++ ){
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for(IndexType k=0; k< outdim2; k++ ){
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if (!(Eigen::internal::isApprox(result(i,j,k), result_host(i,j,k), error_threshold))) {
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std::cout <<std::setprecision(16)<< "mismatch detected at index ( "<< i << " , " << j << ", " << k << " ) " << " \t " << result(i,j,k) << " vs "<< result_host(i,j,k) << std::endl;
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assert(false);
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}
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}
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}
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}
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sycl_device.deallocate(d_input);
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sycl_device.deallocate(d_kernel);
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sycl_device.deallocate(d_result);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_larg_expr2D(const Eigen::SyclDevice& sycl_device)
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{
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IndexType indim0 =53;
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IndexType indim1= 55;
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IndexType indim2= 51;
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IndexType outdim0=50;
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IndexType outdim1=51;
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IndexType outdim2=51;
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Eigen::array<IndexType, 3> input_dims = {{indim0, indim1, indim2}};
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Eigen::array<IndexType, 2> kernel_dims = {{4,5}};
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Eigen::array<IndexType, 3> result_dims = {{outdim0, outdim1, outdim2}};
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Tensor<DataType, 3, DataLayout, IndexType> input(input_dims);
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Tensor<DataType, 2, DataLayout,IndexType> kernel(kernel_dims);
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Tensor<DataType, 3, DataLayout,IndexType> result(result_dims);
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Tensor<DataType, 3, DataLayout,IndexType> result_host(result_dims);
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Eigen::array<IndexType, 2> dims3{{0,1}};
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input.setRandom();
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kernel.setRandom();
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result.setZero();
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result_host.setZero();
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std::size_t input_bytes = input.size() * sizeof(DataType);
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std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
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std::size_t result_bytes = result.size() * sizeof(DataType);
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DataType * d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
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DataType * d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
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DataType * d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_input(d_input, input_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_result(d_result, result_dims);
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sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
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sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
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gpu_result.device(sycl_device)=gpu_input.convolve(gpu_kernel, dims3);
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sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
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result_host=input.convolve(kernel, dims3);
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for(IndexType i=0; i< outdim0; i++ ){
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for(IndexType j=0; j< outdim1; j++ ){
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for(IndexType k=0; k< outdim2; k++ ){
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if (!(Eigen::internal::isApprox(result(i,j,k), result_host(i,j,k), error_threshold))) {
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std::cout <<std::setprecision(16)<< "mismatch detected at index ( "<< i << " , " << j << ", " << k << " ) " << " \t " << result(i,j,k) << " vs "<< result_host(i,j,k) << std::endl;
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assert(false);
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}
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}
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}
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}
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sycl_device.deallocate(d_input);
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sycl_device.deallocate(d_kernel);
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sycl_device.deallocate(d_result);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_larg_expr3D(const Eigen::SyclDevice& sycl_device)
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{
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IndexType indim0 =53;
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IndexType indim1= 55;
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IndexType indim2= 51;
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IndexType outdim0=50;
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IndexType outdim1=51;
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IndexType outdim2=49;
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Eigen::array<IndexType, 3> input_dims = {{indim0, indim1, indim2}};
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Eigen::array<IndexType, 3> kernel_dims = {{4,5,3}};
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Eigen::array<IndexType, 3> result_dims = {{outdim0, outdim1, outdim2}};
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Tensor<DataType, 3, DataLayout, IndexType> input(input_dims);
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Tensor<DataType, 3, DataLayout,IndexType> kernel(kernel_dims);
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Tensor<DataType, 3, DataLayout,IndexType> result(result_dims);
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Tensor<DataType, 3, DataLayout,IndexType> result_host(result_dims);
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Eigen::array<IndexType, 3> dims3{{0,1,2}};
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input.setRandom();
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kernel.setRandom();
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result.setZero();
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result_host.setZero();
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std::size_t input_bytes = input.size() * sizeof(DataType);
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std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
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std::size_t result_bytes = result.size() * sizeof(DataType);
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DataType * d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
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DataType * d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
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DataType * d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_input(d_input, input_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_result(d_result, result_dims);
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sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
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sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
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gpu_result.device(sycl_device)=gpu_input.convolve(gpu_kernel, dims3);
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sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
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result_host=input.convolve(kernel, dims3);
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for(IndexType i=0; i< outdim0; i++ ){
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for(IndexType j=0; j< outdim1; j++ ){
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for(IndexType k=0; k< outdim2; k++ ){
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if (!(Eigen::internal::isApprox(result(i,j,k), result_host(i,j,k), error_threshold))) {
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std::cout <<std::setprecision(16)<< "mismatch detected at index ( "<< i << " , " << j << ", " << k << " ) " << " \t " << result(i,j,k) << " vs "<< result_host(i,j,k) << std::endl;
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assert(false);
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}
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}
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}
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}
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sycl_device.deallocate(d_input);
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sycl_device.deallocate(d_kernel);
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sycl_device.deallocate(d_result);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_evals(const Eigen::SyclDevice& sycl_device)
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{
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Eigen::array<IndexType, 2> input_dims = {{3, 3}};
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Eigen::array<IndexType, 1> kernel_dims = {{2}};
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Eigen::array<IndexType, 2> result_dims = {{2, 3}};
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Tensor<DataType, 2, DataLayout, IndexType> input(input_dims);
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Tensor<DataType, 1, DataLayout,IndexType> kernel(kernel_dims);
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Tensor<DataType, 2, DataLayout,IndexType> result(result_dims);
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Eigen::array<IndexType, 1> dims3{{0}};
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input.setRandom();
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kernel.setRandom();
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result.setZero();
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std::size_t input_bytes = input.size() * sizeof(DataType);
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std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
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std::size_t result_bytes = result.size() * sizeof(DataType);
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DataType * d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
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DataType * d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
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DataType * d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_input(d_input, input_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_result(d_result, result_dims);
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sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
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sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
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gpu_result.device(sycl_device)=gpu_input.convolve(gpu_kernel, dims3);
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sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
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VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0) + input(1,0)*kernel(1)); // index 0
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VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0) + input(1,1)*kernel(1)); // index 2
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VERIFY_IS_APPROX(result(0,2), input(0,2)*kernel(0) + input(1,2)*kernel(1)); // index 4
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VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0) + input(2,0)*kernel(1)); // index 1
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VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0) + input(2,1)*kernel(1)); // index 3
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VERIFY_IS_APPROX(result(1,2), input(1,2)*kernel(0) + input(2,2)*kernel(1)); // index 5
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sycl_device.deallocate(d_input);
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sycl_device.deallocate(d_kernel);
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sycl_device.deallocate(d_result);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_expr(const Eigen::SyclDevice& sycl_device)
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{
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Eigen::array<IndexType, 2> input_dims = {{3, 3}};
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Eigen::array<IndexType, 2> kernel_dims = {{2, 2}};
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Eigen::array<IndexType, 2> result_dims = {{2, 2}};
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Tensor<DataType, 2, DataLayout, IndexType> input(input_dims);
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Tensor<DataType, 2, DataLayout, IndexType> kernel(kernel_dims);
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Tensor<DataType, 2, DataLayout, IndexType> result(result_dims);
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input.setRandom();
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kernel.setRandom();
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Eigen::array<IndexType, 2> dims;
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dims[0] = 0;
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dims[1] = 1;
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std::size_t input_bytes = input.size() * sizeof(DataType);
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std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
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std::size_t result_bytes = result.size() * sizeof(DataType);
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DataType * d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
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DataType * d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
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DataType * d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout,IndexType> > gpu_input(d_input, input_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout,IndexType> > gpu_kernel(d_kernel, kernel_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout,IndexType> > gpu_result(d_result, result_dims);
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sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
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sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
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gpu_result.device(sycl_device)=gpu_input.convolve(gpu_kernel, dims);
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sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
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VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0,0) + input(0,1)*kernel(0,1) +
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input(1,0)*kernel(1,0) + input(1,1)*kernel(1,1));
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VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0,0) + input(0,2)*kernel(0,1) +
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input(1,1)*kernel(1,0) + input(1,2)*kernel(1,1));
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VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0,0) + input(1,1)*kernel(0,1) +
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input(2,0)*kernel(1,0) + input(2,1)*kernel(1,1));
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VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0,0) + input(1,2)*kernel(0,1) +
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input(2,1)*kernel(1,0) + input(2,2)*kernel(1,1));
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sycl_device.deallocate(d_input);
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sycl_device.deallocate(d_kernel);
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sycl_device.deallocate(d_result);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_modes(const Eigen::SyclDevice& sycl_device){
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Eigen::array<IndexType, 1> input_dims = {{3}};
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Eigen::array<IndexType, 1> kernel_dims = {{3}};
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Tensor<DataType, 1, DataLayout, IndexType> input(input_dims);
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Tensor<DataType, 1, DataLayout, IndexType> kernel(kernel_dims);
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input.setRandom();
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kernel.setRandom();
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Eigen::array<IndexType, 1> dims;
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dims[0] = 0;
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input(0) = 1.0f;
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input(1) = 2.0f;
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input(2) = 3.0f;
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kernel(0) = 0.5f;
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kernel(1) = 1.0f;
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kernel(2) = 0.0f;
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Eigen::array<std::pair<IndexType, IndexType>, 1> padding;
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// Emulate VALID mode (as defined in
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// http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
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padding[0] = std::make_pair(0, 0);
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Tensor<DataType, 1, DataLayout, IndexType> valid(1);
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std::size_t input_bytes = input.size() * sizeof(DataType);
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std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
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std::size_t valid_bytes = valid.size() * sizeof(DataType);
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DataType * d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
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DataType * d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
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DataType * d_valid = static_cast<DataType*>(sycl_device.allocate(valid_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_input(d_input, input_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_kernel(d_kernel, kernel_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_valid(d_valid, valid.dimensions());
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sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
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sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
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gpu_valid.device(sycl_device)=gpu_input.pad(padding).convolve(gpu_kernel, dims);
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sycl_device.memcpyDeviceToHost(valid.data(), d_valid, valid_bytes);
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VERIFY_IS_EQUAL(valid.dimension(0), 1);
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VERIFY_IS_APPROX(valid(0), 2.5f);
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// Emulate SAME mode (as defined in
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// http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
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padding[0] = std::make_pair(1, 1);
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Tensor<DataType, 1, DataLayout, IndexType> same(3);
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std::size_t same_bytes = same.size() * sizeof(DataType);
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DataType * d_same = static_cast<DataType*>(sycl_device.allocate(same_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_same(d_same, same.dimensions());
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gpu_same.device(sycl_device)=gpu_input.pad(padding).convolve(gpu_kernel, dims);
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sycl_device.memcpyDeviceToHost(same.data(), d_same, same_bytes);
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VERIFY_IS_EQUAL(same.dimension(0), 3);
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VERIFY_IS_APPROX(same(0), 1.0f);
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VERIFY_IS_APPROX(same(1), 2.5f);
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VERIFY_IS_APPROX(same(2), 4.0f);
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// Emulate FULL mode (as defined in
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// http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
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padding[0] = std::make_pair(2, 2);
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|
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Tensor<DataType, 1, DataLayout, IndexType> full(5);
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std::size_t full_bytes = full.size() * sizeof(DataType);
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DataType * d_full = static_cast<DataType*>(sycl_device.allocate(full_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_full(d_full, full.dimensions());
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gpu_full.device(sycl_device)=gpu_input.pad(padding).convolve(gpu_kernel, dims);
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sycl_device.memcpyDeviceToHost(full.data(), d_full, full_bytes);
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|
|
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VERIFY_IS_EQUAL(full.dimension(0), 5);
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VERIFY_IS_APPROX(full(0), 0.0f);
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VERIFY_IS_APPROX(full(1), 1.0f);
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VERIFY_IS_APPROX(full(2), 2.5f);
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VERIFY_IS_APPROX(full(3), 4.0f);
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VERIFY_IS_APPROX(full(4), 1.5f);
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|
|
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sycl_device.deallocate(d_input);
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|
sycl_device.deallocate(d_kernel);
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|
sycl_device.deallocate(d_valid);
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|
sycl_device.deallocate(d_same);
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|
sycl_device.deallocate(d_full);
|
|
|
|
}
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|
|
|
template <typename DataType, int DataLayout, typename IndexType>
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|
static void test_strides(const Eigen::SyclDevice& sycl_device){
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|
|
|
Eigen::array<IndexType, 1> input_dims = {{13}};
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|
Eigen::array<IndexType, 1> kernel_dims = {{3}};
|
|
|
|
Tensor<DataType, 1, DataLayout, IndexType> input(input_dims);
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|
Tensor<DataType, 1, DataLayout, IndexType> kernel(kernel_dims);
|
|
Tensor<DataType, 1, DataLayout, IndexType> result(2);
|
|
|
|
input.setRandom();
|
|
kernel.setRandom();
|
|
Eigen::array<IndexType, 1> dims;
|
|
dims[0] = 0;
|
|
|
|
Eigen::array<IndexType, 1> stride_of_3;
|
|
stride_of_3[0] = 3;
|
|
Eigen::array<IndexType, 1> stride_of_2;
|
|
stride_of_2[0] = 2;
|
|
|
|
std::size_t input_bytes = input.size() * sizeof(DataType);
|
|
std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
|
|
std::size_t result_bytes = result.size() * sizeof(DataType);
|
|
|
|
DataType * d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
|
|
DataType * d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
|
|
DataType * d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_input(d_input, input_dims);
|
|
Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_kernel(d_kernel, kernel_dims);
|
|
Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout,IndexType> > gpu_result(d_result, result.dimensions());
|
|
sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
|
|
sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
|
|
|
|
gpu_result.device(sycl_device)=gpu_input.stride(stride_of_3).convolve(gpu_kernel, dims).stride(stride_of_2);
|
|
sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
|
|
|
|
VERIFY_IS_EQUAL(result.dimension(0), 2);
|
|
VERIFY_IS_APPROX(result(0), (input(0)*kernel(0) + input(3)*kernel(1) +
|
|
input(6)*kernel(2)));
|
|
VERIFY_IS_APPROX(result(1), (input(6)*kernel(0) + input(9)*kernel(1) +
|
|
input(12)*kernel(2)));
|
|
}
|
|
|
|
template <typename Dev_selector> void tensorConvolutionPerDevice(Dev_selector& s){
|
|
QueueInterface queueInterface(s);
|
|
auto sycl_device=Eigen::SyclDevice(&queueInterface);
|
|
test_larg_expr1D<float, RowMajor, int64_t>(sycl_device);
|
|
test_larg_expr1D<float, ColMajor, int64_t>(sycl_device);
|
|
test_larg_expr2D<float, RowMajor, int64_t>(sycl_device);
|
|
test_larg_expr2D<float, ColMajor, int64_t>(sycl_device);
|
|
test_larg_expr3D<float, RowMajor, int64_t>(sycl_device);
|
|
test_larg_expr3D<float, ColMajor, int64_t>(sycl_device);
|
|
test_evals<float, ColMajor, int64_t>(sycl_device);
|
|
test_evals<float, RowMajor, int64_t>(sycl_device);
|
|
test_expr<float, ColMajor, int64_t>(sycl_device);
|
|
test_expr<float, RowMajor, int64_t>(sycl_device);
|
|
test_modes<float, ColMajor, int64_t>(sycl_device);
|
|
test_modes<float, RowMajor, int64_t>(sycl_device);
|
|
test_strides<float, ColMajor, int64_t>(sycl_device);
|
|
test_strides<float, RowMajor, int64_t>(sycl_device);
|
|
}
|
|
|
|
EIGEN_DECLARE_TEST(cxx11_tensor_convolution_sycl) {
|
|
for (const auto& device :Eigen::get_sycl_supported_devices()) {
|
|
CALL_SUBTEST(tensorConvolutionPerDevice(device));
|
|
}
|
|
}
|