mirror of
https://gitlab.com/libeigen/eigen.git
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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
1015 lines
41 KiB
C++
1015 lines
41 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) 2015
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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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#define EIGEN_HAS_CONSTEXPR 1
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_sum_sycl(
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const Eigen::SyclDevice& sycl_device) {
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const IndexType num_rows = 753;
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const IndexType num_cols = 537;
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array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
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array<IndexType, 2> outRange = {{1, 1}};
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Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 2, DataLayout, IndexType> full_redux(outRange);
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Tensor<DataType, 2, DataLayout, IndexType> full_redux_gpu(outRange);
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in.setRandom();
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auto dim = DSizes<IndexType, 2>(1, 1);
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full_redux = in.sum().reshape(dim);
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DataType* gpu_in_data = static_cast<DataType*>(
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sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_out_data = (DataType*)sycl_device.allocate(
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sizeof(DataType) * (full_redux_gpu.dimensions().TotalSize()));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data,
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tensorRange);
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(gpu_out_data,
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outRange);
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sycl_device.memcpyHostToDevice(
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gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.sum().reshape(dim);
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sycl_device.memcpyDeviceToHost(
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full_redux_gpu.data(), gpu_out_data,
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(full_redux_gpu.dimensions().TotalSize()) * sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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std::cout << "SYCL FULL :" << full_redux_gpu(0, 0)
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<< ", CPU FULL: " << full_redux(0, 0) << "\n";
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VERIFY_IS_APPROX(full_redux_gpu(0, 0), full_redux(0, 0));
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_sum_with_offset_sycl(
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const Eigen::SyclDevice& sycl_device) {
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using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
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using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
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const IndexType num_rows = 64;
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const IndexType num_cols = 64;
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array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
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const IndexType n_elems = internal::array_prod(tensor_range);
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data_tensor in(tensor_range);
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scalar_tensor full_redux;
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scalar_tensor full_redux_gpu;
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in.setRandom();
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array<IndexType, 2> tensor_offset_range(tensor_range);
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tensor_offset_range[0] -= 1;
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const IndexType offset = 64;
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TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
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full_redux = in_offset.sum();
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DataType* gpu_in_data =
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static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
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DataType* gpu_out_data =
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static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
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TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
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TensorMap<scalar_tensor> out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),
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n_elems * sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.sum();
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
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sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_max_sycl(
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const Eigen::SyclDevice& sycl_device) {
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const IndexType num_rows = 4096;
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const IndexType num_cols = 4096;
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array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
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Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 0, DataLayout, IndexType> full_redux;
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Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
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in.setRandom();
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full_redux = in.maximum();
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DataType* gpu_in_data = static_cast<DataType*>(
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sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_out_data = (DataType*)sycl_device.allocate(sizeof(DataType));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data,
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tensorRange);
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TensorMap<Tensor<DataType, 0, DataLayout, IndexType>> out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(
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gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.maximum();
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
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sizeof(DataType));
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VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_max_with_offset_sycl(
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const Eigen::SyclDevice& sycl_device) {
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using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
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using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
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const IndexType num_rows = 64;
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const IndexType num_cols = 64;
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array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
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const IndexType n_elems = internal::array_prod(tensor_range);
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data_tensor in(tensor_range);
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scalar_tensor full_redux;
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scalar_tensor full_redux_gpu;
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in.setRandom();
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array<IndexType, 2> tensor_offset_range(tensor_range);
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tensor_offset_range[0] -= 1;
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// Set the initial value to be the max.
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// As we don't include this in the reduction the result should not be 2.
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in(0) = static_cast<DataType>(2);
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const IndexType offset = 64;
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TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
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full_redux = in_offset.maximum();
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VERIFY_IS_NOT_EQUAL(full_redux(), in(0));
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DataType* gpu_in_data =
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static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
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DataType* gpu_out_data =
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static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
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TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
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TensorMap<scalar_tensor> out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),
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n_elems * sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.maximum();
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
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sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_mean_sycl(
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const Eigen::SyclDevice& sycl_device) {
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const IndexType num_rows = 4096;
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const IndexType num_cols = 4096;
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array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
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array<IndexType, 1> argRange = {{num_cols}};
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Eigen::array<IndexType, 1> red_axis;
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red_axis[0] = 0;
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// red_axis[1]=1;
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Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 2, DataLayout, IndexType> in_arg1(tensorRange);
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Tensor<DataType, 2, DataLayout, IndexType> in_arg2(tensorRange);
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Tensor<bool, 1, DataLayout, IndexType> out_arg_cpu(argRange);
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Tensor<bool, 1, DataLayout, IndexType> out_arg_gpu(argRange);
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Tensor<bool, 1, DataLayout, IndexType> out_arg_gpu_helper(argRange);
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Tensor<DataType, 0, DataLayout, IndexType> full_redux;
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Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
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in.setRandom();
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in_arg1.setRandom();
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in_arg2.setRandom();
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DataType* gpu_in_data = static_cast<DataType*>(
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sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_in_arg1_data = static_cast<DataType*>(sycl_device.allocate(
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in_arg1.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_in_arg2_data = static_cast<DataType*>(sycl_device.allocate(
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in_arg2.dimensions().TotalSize() * sizeof(DataType)));
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bool* gpu_out_arg__gpu_helper_data = static_cast<bool*>(sycl_device.allocate(
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out_arg_gpu.dimensions().TotalSize() * sizeof(DataType)));
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bool* gpu_out_arg_data = static_cast<bool*>(sycl_device.allocate(
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out_arg_gpu.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_out_data = (DataType*)sycl_device.allocate(sizeof(DataType));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data,
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tensorRange);
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_Arg1_gpu(
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gpu_in_arg1_data, tensorRange);
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_Arg2_gpu(
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gpu_in_arg2_data, tensorRange);
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TensorMap<Tensor<bool, 1, DataLayout, IndexType>> out_Argout_gpu(
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gpu_out_arg_data, argRange);
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TensorMap<Tensor<bool, 1, DataLayout, IndexType>> out_Argout_gpu_helper(
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gpu_out_arg__gpu_helper_data, argRange);
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TensorMap<Tensor<DataType, 0, DataLayout, IndexType>> out_gpu(gpu_out_data);
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// CPU VERSION
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out_arg_cpu =
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(in_arg1.argmax(1) == in_arg2.argmax(1))
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.select(out_arg_cpu.constant(true), out_arg_cpu.constant(false));
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full_redux = (out_arg_cpu.template cast<float>())
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.reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
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// GPU VERSION
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sycl_device.memcpyHostToDevice(
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gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
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sycl_device.memcpyHostToDevice(
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gpu_in_arg1_data, in_arg1.data(),
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(in_arg1.dimensions().TotalSize()) * sizeof(DataType));
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sycl_device.memcpyHostToDevice(
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gpu_in_arg2_data, in_arg2.data(),
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(in_arg2.dimensions().TotalSize()) * sizeof(DataType));
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out_Argout_gpu_helper.device(sycl_device) =
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(in_Arg1_gpu.argmax(1) == in_Arg2_gpu.argmax(1));
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out_Argout_gpu.device(sycl_device) =
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(out_Argout_gpu_helper)
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.select(out_Argout_gpu.constant(true),
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out_Argout_gpu.constant(false));
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out_gpu.device(sycl_device) =
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(out_Argout_gpu.template cast<float>())
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.reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
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sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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std::cout << "SYCL : " << full_redux_gpu() << " , CPU : " << full_redux()
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<< '\n';
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VERIFY_IS_EQUAL(full_redux_gpu(), full_redux());
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_in_arg1_data);
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sycl_device.deallocate(gpu_in_arg2_data);
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sycl_device.deallocate(gpu_out_arg__gpu_helper_data);
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sycl_device.deallocate(gpu_out_arg_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_mean_with_offset_sycl(
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const Eigen::SyclDevice& sycl_device) {
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using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
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using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
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const IndexType num_rows = 64;
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const IndexType num_cols = 64;
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array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
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const IndexType n_elems = internal::array_prod(tensor_range);
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data_tensor in(tensor_range);
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scalar_tensor full_redux;
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scalar_tensor full_redux_gpu;
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in.setRandom();
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array<IndexType, 2> tensor_offset_range(tensor_range);
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tensor_offset_range[0] -= 1;
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const IndexType offset = 64;
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TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
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full_redux = in_offset.mean();
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VERIFY_IS_NOT_EQUAL(full_redux(), in(0));
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DataType* gpu_in_data =
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static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
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DataType* gpu_out_data =
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static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
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TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
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TensorMap<scalar_tensor> out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),
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n_elems * sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.mean();
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
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sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_mean_with_odd_offset_sycl(
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const Eigen::SyclDevice& sycl_device) {
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// This is a particular case which illustrates a possible problem when the
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// number of local threads in a workgroup is even, but is not a power of two.
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using data_tensor = Tensor<DataType, 1, DataLayout, IndexType>;
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using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
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// 2177 = (17 * 128) + 1 gives rise to 18 local threads.
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// 8708 = 4 * 2177 = 4 * (17 * 128) + 4 uses 18 vectorised local threads.
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const IndexType n_elems = 8707;
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array<IndexType, 1> tensor_range = {{n_elems}};
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data_tensor in(tensor_range);
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DataType full_redux;
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DataType full_redux_gpu;
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TensorMap<scalar_tensor> red_cpu(&full_redux);
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TensorMap<scalar_tensor> red_gpu(&full_redux_gpu);
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const DataType const_val = static_cast<DataType>(0.6391);
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in = in.constant(const_val);
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Eigen::IndexList<Eigen::type2index<0>> red_axis;
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red_cpu = in.reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
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VERIFY_IS_APPROX(const_val, red_cpu());
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DataType* gpu_in_data =
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static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
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DataType* gpu_out_data =
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static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
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TensorMap<data_tensor> in_gpu(gpu_in_data, tensor_range);
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TensorMap<scalar_tensor> out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),
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n_elems * sizeof(DataType));
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out_gpu.device(sycl_device) =
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in_gpu.reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
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sycl_device.memcpyDeviceToHost(red_gpu.data(), gpu_out_data,
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sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux_gpu, full_redux);
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_full_reductions_min_sycl(
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const Eigen::SyclDevice& sycl_device) {
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const IndexType num_rows = 876;
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const IndexType num_cols = 953;
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array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
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Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 0, DataLayout, IndexType> full_redux;
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Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
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in.setRandom();
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full_redux = in.minimum();
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DataType* gpu_in_data = static_cast<DataType*>(
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sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_out_data = (DataType*)sycl_device.allocate(sizeof(DataType));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data,
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tensorRange);
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TensorMap<Tensor<DataType, 0, DataLayout, IndexType>> out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(
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gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.minimum();
|
|
sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
|
|
sizeof(DataType));
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_full_reductions_min_with_offset_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
|
|
using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
|
|
const IndexType num_rows = 64;
|
|
const IndexType num_cols = 64;
|
|
array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
|
|
const IndexType n_elems = internal::array_prod(tensor_range);
|
|
|
|
data_tensor in(tensor_range);
|
|
scalar_tensor full_redux;
|
|
scalar_tensor full_redux_gpu;
|
|
|
|
in.setRandom();
|
|
array<IndexType, 2> tensor_offset_range(tensor_range);
|
|
tensor_offset_range[0] -= 1;
|
|
// Set the initial value to be the min.
|
|
// As we don't include this in the reduction the result should not be -2.
|
|
in(0) = static_cast<DataType>(-2);
|
|
|
|
const IndexType offset = 64;
|
|
TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
|
|
full_redux = in_offset.minimum();
|
|
VERIFY_IS_NOT_EQUAL(full_redux(), in(0));
|
|
|
|
DataType* gpu_in_data =
|
|
static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
|
|
DataType* gpu_out_data =
|
|
static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
|
|
|
|
TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
|
|
TensorMap<scalar_tensor> out_gpu(gpu_out_data);
|
|
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),
|
|
n_elems * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.minimum();
|
|
sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
|
|
sizeof(DataType));
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_first_dim_reductions_max_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
IndexType dim_x = 145;
|
|
IndexType dim_y = 1;
|
|
IndexType dim_z = 67;
|
|
|
|
array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
|
|
Eigen::array<IndexType, 1> red_axis;
|
|
red_axis[0] = 0;
|
|
array<IndexType, 2> reduced_tensorRange = {{dim_y, dim_z}};
|
|
|
|
Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
|
|
|
|
in.setRandom();
|
|
|
|
redux = in.maximum(red_axis);
|
|
|
|
DataType* gpu_in_data = static_cast<DataType*>(
|
|
sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data,
|
|
tensorRange);
|
|
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(
|
|
gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
|
|
sycl_device.memcpyDeviceToHost(
|
|
redux_gpu.data(), gpu_out_data,
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType));
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
|
|
for (IndexType k = 0; k < reduced_tensorRange[1]; k++)
|
|
VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_first_dim_reductions_max_with_offset_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
|
|
using reduced_tensor = Tensor<DataType, 1, DataLayout, IndexType>;
|
|
|
|
const IndexType num_rows = 64;
|
|
const IndexType num_cols = 64;
|
|
array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
|
|
array<IndexType, 1> reduced_range = {{num_cols}};
|
|
const IndexType n_elems = internal::array_prod(tensor_range);
|
|
const IndexType n_reduced = num_cols;
|
|
|
|
data_tensor in(tensor_range);
|
|
reduced_tensor redux;
|
|
reduced_tensor redux_gpu(reduced_range);
|
|
|
|
in.setRandom();
|
|
array<IndexType, 2> tensor_offset_range(tensor_range);
|
|
tensor_offset_range[0] -= 1;
|
|
// Set maximum value outside of the considered range.
|
|
for (IndexType i = 0; i < n_reduced; i++) {
|
|
in(i) = static_cast<DataType>(2);
|
|
}
|
|
|
|
Eigen::array<IndexType, 1> red_axis;
|
|
red_axis[0] = 0;
|
|
|
|
const IndexType offset = 64;
|
|
TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
|
|
redux = in_offset.maximum(red_axis);
|
|
for (IndexType i = 0; i < n_reduced; i++) {
|
|
VERIFY_IS_NOT_EQUAL(redux(i), in(i));
|
|
}
|
|
|
|
DataType* gpu_in_data =
|
|
static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(
|
|
sycl_device.allocate(n_reduced * sizeof(DataType)));
|
|
|
|
TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
|
|
TensorMap<reduced_tensor> out_gpu(gpu_out_data, reduced_range);
|
|
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),
|
|
n_elems * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
|
|
sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data,
|
|
n_reduced * sizeof(DataType));
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType i = 0; i < n_reduced; i++) {
|
|
VERIFY_IS_APPROX(redux_gpu(i), redux(i));
|
|
}
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_last_dim_reductions_max_with_offset_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
|
|
using reduced_tensor = Tensor<DataType, 1, DataLayout, IndexType>;
|
|
|
|
const IndexType num_rows = 64;
|
|
const IndexType num_cols = 64;
|
|
array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
|
|
array<IndexType, 1> full_reduced_range = {{num_rows}};
|
|
array<IndexType, 1> reduced_range = {{num_rows - 1}};
|
|
const IndexType n_elems = internal::array_prod(tensor_range);
|
|
const IndexType n_reduced = reduced_range[0];
|
|
|
|
data_tensor in(tensor_range);
|
|
reduced_tensor redux(full_reduced_range);
|
|
reduced_tensor redux_gpu(reduced_range);
|
|
|
|
in.setRandom();
|
|
redux.setZero();
|
|
array<IndexType, 2> tensor_offset_range(tensor_range);
|
|
tensor_offset_range[0] -= 1;
|
|
// Set maximum value outside of the considered range.
|
|
for (IndexType i = 0; i < n_reduced; i++) {
|
|
in(i) = static_cast<DataType>(2);
|
|
}
|
|
|
|
Eigen::array<IndexType, 1> red_axis;
|
|
red_axis[0] = 1;
|
|
|
|
const IndexType offset = 64;
|
|
// Introduce an offset in both the input and the output.
|
|
TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
|
|
TensorMap<reduced_tensor> red_offset(redux.data() + 1, reduced_range);
|
|
red_offset = in_offset.maximum(red_axis);
|
|
|
|
// Check that the first value hasn't been changed and that the reduced values
|
|
// are not equal to the previously set maximum in the input outside the range.
|
|
VERIFY_IS_EQUAL(redux(0), static_cast<DataType>(0));
|
|
for (IndexType i = 0; i < n_reduced; i++) {
|
|
VERIFY_IS_NOT_EQUAL(red_offset(i), in(i));
|
|
}
|
|
|
|
DataType* gpu_in_data =
|
|
static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(
|
|
sycl_device.allocate((n_reduced + 1) * sizeof(DataType)));
|
|
|
|
TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
|
|
TensorMap<reduced_tensor> out_gpu(gpu_out_data + 1, reduced_range);
|
|
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),
|
|
n_elems * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
|
|
sycl_device.memcpyDeviceToHost(redux_gpu.data(), out_gpu.data(),
|
|
n_reduced * sizeof(DataType));
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType i = 0; i < n_reduced; i++) {
|
|
VERIFY_IS_APPROX(redux_gpu(i), red_offset(i));
|
|
}
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_first_dim_reductions_sum_sycl(
|
|
const Eigen::SyclDevice& sycl_device, IndexType dim_x, IndexType dim_y) {
|
|
array<IndexType, 2> tensorRange = {{dim_x, dim_y}};
|
|
Eigen::array<IndexType, 1> red_axis;
|
|
red_axis[0] = 0;
|
|
array<IndexType, 1> reduced_tensorRange = {{dim_y}};
|
|
|
|
Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
|
|
Tensor<DataType, 1, DataLayout, IndexType> redux(reduced_tensorRange);
|
|
Tensor<DataType, 1, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
|
|
|
|
in.setRandom();
|
|
redux = in.sum(red_axis);
|
|
|
|
DataType* gpu_in_data = static_cast<DataType*>(
|
|
sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data,
|
|
tensorRange);
|
|
TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> out_gpu(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(
|
|
gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
|
|
sycl_device.memcpyDeviceToHost(
|
|
redux_gpu.data(), gpu_out_data,
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType));
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType i = 0; i < redux.size(); i++) {
|
|
VERIFY_IS_APPROX(redux_gpu.data()[i], redux.data()[i]);
|
|
}
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_first_dim_reductions_mean_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
IndexType dim_x = 145;
|
|
IndexType dim_y = 1;
|
|
IndexType dim_z = 67;
|
|
|
|
array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
|
|
Eigen::array<IndexType, 1> red_axis;
|
|
red_axis[0] = 0;
|
|
array<IndexType, 2> reduced_tensorRange = {{dim_y, dim_z}};
|
|
|
|
Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
|
|
|
|
in.setRandom();
|
|
|
|
redux = in.mean(red_axis);
|
|
|
|
DataType* gpu_in_data = static_cast<DataType*>(
|
|
sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data,
|
|
tensorRange);
|
|
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(
|
|
gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.mean(red_axis);
|
|
sycl_device.memcpyDeviceToHost(
|
|
redux_gpu.data(), gpu_out_data,
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType));
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
|
|
for (IndexType k = 0; k < reduced_tensorRange[1]; k++)
|
|
VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_last_dim_reductions_mean_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
IndexType dim_x = 64;
|
|
IndexType dim_y = 1;
|
|
IndexType dim_z = 32;
|
|
|
|
array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
|
|
Eigen::array<IndexType, 1> red_axis;
|
|
red_axis[0] = 2;
|
|
array<IndexType, 2> reduced_tensorRange = {{dim_x, dim_y}};
|
|
|
|
Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
|
|
|
|
in.setRandom();
|
|
|
|
redux = in.mean(red_axis);
|
|
|
|
DataType* gpu_in_data = static_cast<DataType*>(
|
|
sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data,
|
|
tensorRange);
|
|
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(
|
|
gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.mean(red_axis);
|
|
sycl_device.memcpyDeviceToHost(
|
|
redux_gpu.data(), gpu_out_data,
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType));
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
|
|
for (IndexType k = 0; k < reduced_tensorRange[1]; k++)
|
|
VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_last_dim_reductions_sum_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
IndexType dim_x = 64;
|
|
IndexType dim_y = 1;
|
|
IndexType dim_z = 32;
|
|
|
|
array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
|
|
Eigen::array<IndexType, 1> red_axis;
|
|
red_axis[0] = 2;
|
|
array<IndexType, 2> reduced_tensorRange = {{dim_x, dim_y}};
|
|
|
|
Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
|
|
Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
|
|
|
|
in.setRandom();
|
|
|
|
redux = in.sum(red_axis);
|
|
|
|
DataType* gpu_in_data = static_cast<DataType*>(
|
|
sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data,
|
|
tensorRange);
|
|
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(
|
|
gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
|
|
out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
|
|
sycl_device.memcpyDeviceToHost(
|
|
redux_gpu.data(), gpu_out_data,
|
|
redux_gpu.dimensions().TotalSize() * sizeof(DataType));
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
|
|
for (IndexType k = 0; k < reduced_tensorRange[1]; k++)
|
|
VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_last_reductions_sum_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
auto tensorRange = Sizes<64, 32>(64, 32);
|
|
// auto red_axis = Sizes<0,1>(0,1);
|
|
Eigen::IndexList<Eigen::type2index<1>> red_axis;
|
|
auto reduced_tensorRange = Sizes<64>(64);
|
|
TensorFixedSize<DataType, Sizes<64, 32>, DataLayout> in_fix;
|
|
TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_fix;
|
|
TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_gpu_fix;
|
|
|
|
in_fix.setRandom();
|
|
|
|
redux_fix = in_fix.sum(red_axis);
|
|
|
|
DataType* gpu_in_data = static_cast<DataType*>(
|
|
sycl_device.allocate(in_fix.dimensions().TotalSize() * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(
|
|
redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType)));
|
|
|
|
TensorMap<TensorFixedSize<DataType, Sizes<64, 32>, DataLayout>> in_gpu_fix(
|
|
gpu_in_data, tensorRange);
|
|
TensorMap<TensorFixedSize<DataType, Sizes<64>, DataLayout>> out_gpu_fix(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(
|
|
gpu_in_data, in_fix.data(),
|
|
(in_fix.dimensions().TotalSize()) * sizeof(DataType));
|
|
out_gpu_fix.device(sycl_device) = in_gpu_fix.sum(red_axis);
|
|
sycl_device.memcpyDeviceToHost(
|
|
redux_gpu_fix.data(), gpu_out_data,
|
|
redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType));
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType j = 0; j < reduced_tensorRange[0]; j++) {
|
|
VERIFY_IS_APPROX(redux_gpu_fix(j), redux_fix(j));
|
|
}
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
static void test_last_reductions_mean_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
auto tensorRange = Sizes<64, 32>(64, 32);
|
|
Eigen::IndexList<Eigen::type2index<1>> red_axis;
|
|
auto reduced_tensorRange = Sizes<64>(64);
|
|
TensorFixedSize<DataType, Sizes<64, 32>, DataLayout> in_fix;
|
|
TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_fix;
|
|
TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_gpu_fix;
|
|
|
|
in_fix.setRandom();
|
|
redux_fix = in_fix.mean(red_axis);
|
|
|
|
DataType* gpu_in_data = static_cast<DataType*>(
|
|
sycl_device.allocate(in_fix.dimensions().TotalSize() * sizeof(DataType)));
|
|
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(
|
|
redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType)));
|
|
|
|
TensorMap<TensorFixedSize<DataType, Sizes<64, 32>, DataLayout>> in_gpu_fix(
|
|
gpu_in_data, tensorRange);
|
|
TensorMap<TensorFixedSize<DataType, Sizes<64>, DataLayout>> out_gpu_fix(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(
|
|
gpu_in_data, in_fix.data(),
|
|
(in_fix.dimensions().TotalSize()) * sizeof(DataType));
|
|
out_gpu_fix.device(sycl_device) = in_gpu_fix.mean(red_axis);
|
|
sycl_device.memcpyDeviceToHost(
|
|
redux_gpu_fix.data(), gpu_out_data,
|
|
redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
for (IndexType j = 0; j < reduced_tensorRange[0]; j++) {
|
|
VERIFY_IS_APPROX(redux_gpu_fix(j), redux_fix(j));
|
|
}
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
// SYCL supports a generic case of reduction where the accumulator is a
|
|
// different type than the input data This is an example on how to get if a
|
|
// Tensor contains nan and/or inf in one reduction
|
|
template <typename InT, typename OutT>
|
|
struct CustomReducer {
|
|
static const bool PacketAccess = false;
|
|
static const bool IsStateful = false;
|
|
|
|
static constexpr OutT InfBit = 1;
|
|
static constexpr OutT NanBit = 2;
|
|
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const InT x,
|
|
OutT* accum) const {
|
|
if (Eigen::numext::isinf(x))
|
|
*accum |= InfBit;
|
|
else if (Eigen::numext::isnan(x))
|
|
*accum |= NanBit;
|
|
}
|
|
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const OutT x,
|
|
OutT* accum) const {
|
|
*accum |= x;
|
|
}
|
|
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE OutT initialize() const {
|
|
return OutT(0);
|
|
}
|
|
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE OutT finalize(const OutT accum) const {
|
|
return accum;
|
|
}
|
|
};
|
|
|
|
template <typename DataType, typename AccumType, int DataLayout,
|
|
typename IndexType>
|
|
static void test_full_reductions_custom_sycl(
|
|
const Eigen::SyclDevice& sycl_device) {
|
|
constexpr IndexType InSize = 64;
|
|
auto tensorRange = Sizes<InSize>(InSize);
|
|
Eigen::IndexList<Eigen::type2index<0>> dims;
|
|
auto reduced_tensorRange = Sizes<>();
|
|
TensorFixedSize<DataType, Sizes<InSize>, DataLayout> in_fix;
|
|
TensorFixedSize<AccumType, Sizes<>, DataLayout> redux_gpu_fix;
|
|
|
|
CustomReducer<DataType, AccumType> reducer;
|
|
|
|
in_fix.setRandom();
|
|
|
|
size_t in_size_bytes = in_fix.dimensions().TotalSize() * sizeof(DataType);
|
|
DataType* gpu_in_data =
|
|
static_cast<DataType*>(sycl_device.allocate(in_size_bytes));
|
|
AccumType* gpu_out_data =
|
|
static_cast<AccumType*>(sycl_device.allocate(sizeof(AccumType)));
|
|
|
|
TensorMap<TensorFixedSize<DataType, Sizes<InSize>, DataLayout>> in_gpu_fix(
|
|
gpu_in_data, tensorRange);
|
|
TensorMap<TensorFixedSize<AccumType, Sizes<>, DataLayout>> out_gpu_fix(
|
|
gpu_out_data, reduced_tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(gpu_in_data, in_fix.data(), in_size_bytes);
|
|
out_gpu_fix.device(sycl_device) = in_gpu_fix.reduce(dims, reducer);
|
|
sycl_device.memcpyDeviceToHost(redux_gpu_fix.data(), gpu_out_data,
|
|
sizeof(AccumType));
|
|
VERIFY_IS_EQUAL(redux_gpu_fix(0), AccumType(0));
|
|
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
|
|
template <typename DataType, typename Dev>
|
|
void sycl_reduction_test_full_per_device(const Dev& sycl_device) {
|
|
test_full_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_full_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_full_reductions_min_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_full_reductions_min_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_full_reductions_max_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_full_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
|
|
test_full_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_full_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_full_reductions_custom_sycl<DataType, int, RowMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_custom_sycl<DataType, int, ColMajor, int64_t>(
|
|
sycl_device);
|
|
sycl_device.synchronize();
|
|
}
|
|
|
|
template <typename DataType, typename Dev>
|
|
void sycl_reduction_full_offset_per_device(const Dev& sycl_device) {
|
|
test_full_reductions_sum_with_offset_sycl<DataType, RowMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_sum_with_offset_sycl<DataType, ColMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_min_with_offset_sycl<DataType, RowMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_min_with_offset_sycl<DataType, ColMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_max_with_offset_sycl<DataType, ColMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_max_with_offset_sycl<DataType, RowMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_mean_with_offset_sycl<DataType, RowMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_mean_with_offset_sycl<DataType, ColMajor, int64_t>(
|
|
sycl_device);
|
|
test_full_reductions_mean_with_odd_offset_sycl<DataType, RowMajor, int64_t>(
|
|
sycl_device);
|
|
sycl_device.synchronize();
|
|
}
|
|
|
|
template <typename DataType, typename Dev>
|
|
void sycl_reduction_test_first_dim_per_device(const Dev& sycl_device) {
|
|
test_first_dim_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device,
|
|
4197, 4097);
|
|
test_first_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device,
|
|
4197, 4097);
|
|
test_first_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device,
|
|
129, 8);
|
|
test_first_dim_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_first_dim_reductions_max_with_offset_sycl<DataType, RowMajor, int64_t>(
|
|
sycl_device);
|
|
sycl_device.synchronize();
|
|
}
|
|
|
|
template <typename DataType, typename Dev>
|
|
void sycl_reduction_test_last_dim_per_device(const Dev& sycl_device) {
|
|
test_last_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_last_dim_reductions_max_with_offset_sycl<DataType, RowMajor, int64_t>(
|
|
sycl_device);
|
|
test_last_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_last_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_last_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_last_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
sycl_device.synchronize();
|
|
}
|
|
|
|
EIGEN_DECLARE_TEST(cxx11_tensor_reduction_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(sycl_reduction_test_full_per_device<float>(sycl_device));
|
|
CALL_SUBTEST_2(sycl_reduction_full_offset_per_device<float>(sycl_device));
|
|
CALL_SUBTEST_3(
|
|
sycl_reduction_test_first_dim_per_device<float>(sycl_device));
|
|
CALL_SUBTEST_4(sycl_reduction_test_last_dim_per_device<float>(sycl_device));
|
|
}
|
|
}
|