mirror/eigen
2
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2024-12-21 07:19:46 +08:00
Code Issues Packages Projects Releases Wiki Activity
d1d22ef0f4
eigen/unsupported/test/cxx11_tensor_concatenation_sycl.cpp

181 lines
8.3 KiB
C++
Raw Normal View History

Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli Codeplay Software Ltd.
// Ralph Potter Codeplay Software Ltd.
// Luke Iwanski Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_tensor_concatenation_sycl
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
template<typename DataType, int DataLayout, typename IndexType>
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
static void test_simple_concatenation(const Eigen::SyclDevice& sycl_device)
{
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
IndexType leftDim1 = 2;
IndexType leftDim2 = 3;
IndexType leftDim3 = 1;
Eigen::array<IndexType, 3> leftRange = {{leftDim1, leftDim2, leftDim3}};
IndexType rightDim1 = 2;
IndexType rightDim2 = 3;
IndexType rightDim3 = 1;
Eigen::array<IndexType, 3> rightRange = {{rightDim1, rightDim2, rightDim3}};
//IndexType concatDim1 = 3;
// IndexType concatDim2 = 3;
// IndexType concatDim3 = 1;
//Eigen::array<IndexType, 3> concatRange = {{concatDim1, concatDim2, concatDim3}};
Tensor<DataType, 3, DataLayout, IndexType> left(leftRange);
Tensor<DataType, 3, DataLayout, IndexType> right(rightRange);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
left.setRandom();
right.setRandom();
DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType)));
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType));
///
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Tensor<DataType, 3, DataLayout, IndexType> concatenation1(leftDim1+rightDim1, leftDim2, leftDim3);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
DataType * gpu_out_data1 = static_cast<DataType*>(sycl_device.allocate(concatenation1.dimensions().TotalSize()*sizeof(DataType)));
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out1(gpu_out_data1, concatenation1.dimensions());
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
//concatenation = left.concatenate(right, 0);
gpu_out1.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 0);
sycl_device.memcpyDeviceToHost(concatenation1.data(), gpu_out_data1,(concatenation1.dimensions().TotalSize())*sizeof(DataType));
VERIFY_IS_EQUAL(concatenation1.dimension(0), 4);
VERIFY_IS_EQUAL(concatenation1.dimension(1), 3);
VERIFY_IS_EQUAL(concatenation1.dimension(2), 1);
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
for (IndexType j = 0; j < 3; ++j) {
for (IndexType i = 0; i < 2; ++i) {
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
VERIFY_IS_EQUAL(concatenation1(i, j, 0), left(i, j, 0));
}
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
for (IndexType i = 2; i < 4; ++i) {
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
VERIFY_IS_EQUAL(concatenation1(i, j, 0), right(i - 2, j, 0));
}
}
sycl_device.deallocate(gpu_out_data1);
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Tensor<DataType, 3, DataLayout, IndexType> concatenation2(leftDim1, leftDim2 +rightDim2, leftDim3);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
DataType * gpu_out_data2 = static_cast<DataType*>(sycl_device.allocate(concatenation2.dimensions().TotalSize()*sizeof(DataType)));
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out2(gpu_out_data2, concatenation2.dimensions());
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
gpu_out2.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 1);
sycl_device.memcpyDeviceToHost(concatenation2.data(), gpu_out_data2,(concatenation2.dimensions().TotalSize())*sizeof(DataType));
//concatenation = left.concatenate(right, 1);
VERIFY_IS_EQUAL(concatenation2.dimension(0), 2);
VERIFY_IS_EQUAL(concatenation2.dimension(1), 6);
VERIFY_IS_EQUAL(concatenation2.dimension(2), 1);
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 3; ++j) {
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
VERIFY_IS_EQUAL(concatenation2(i, j, 0), left(i, j, 0));
}
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
for (IndexType j = 3; j < 6; ++j) {
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
VERIFY_IS_EQUAL(concatenation2(i, j, 0), right(i, j - 3, 0));
}
}
sycl_device.deallocate(gpu_out_data2);
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Tensor<DataType, 3, DataLayout, IndexType> concatenation3(leftDim1, leftDim2, leftDim3+rightDim3);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
DataType * gpu_out_data3 = static_cast<DataType*>(sycl_device.allocate(concatenation3.dimensions().TotalSize()*sizeof(DataType)));
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out3(gpu_out_data3, concatenation3.dimensions());
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
gpu_out3.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 2);
sycl_device.memcpyDeviceToHost(concatenation3.data(), gpu_out_data3,(concatenation3.dimensions().TotalSize())*sizeof(DataType));
//concatenation = left.concatenate(right, 2);
VERIFY_IS_EQUAL(concatenation3.dimension(0), 2);
VERIFY_IS_EQUAL(concatenation3.dimension(1), 3);
VERIFY_IS_EQUAL(concatenation3.dimension(2), 2);
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 3; ++j) {
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
VERIFY_IS_EQUAL(concatenation3(i, j, 0), left(i, j, 0));
VERIFY_IS_EQUAL(concatenation3(i, j, 1), right(i, j, 0));
}
}
sycl_device.deallocate(gpu_out_data3);
sycl_device.deallocate(gpu_in1_data);
sycl_device.deallocate(gpu_in2_data);
}
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
template<typename DataType, int DataLayout, typename IndexType>
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
static void test_concatenation_as_lvalue(const Eigen::SyclDevice& sycl_device)
{
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
IndexType leftDim1 = 2;
IndexType leftDim2 = 3;
Eigen::array<IndexType, 2> leftRange = {{leftDim1, leftDim2}};
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
IndexType rightDim1 = 2;
IndexType rightDim2 = 3;
Eigen::array<IndexType, 2> rightRange = {{rightDim1, rightDim2}};
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
IndexType concatDim1 = 4;
IndexType concatDim2 = 3;
Eigen::array<IndexType, 2> resRange = {{concatDim1, concatDim2}};
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Tensor<DataType, 2, DataLayout, IndexType> left(leftRange);
Tensor<DataType, 2, DataLayout, IndexType> right(rightRange);
Tensor<DataType, 2, DataLayout, IndexType> result(resRange);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
left.setRandom();
right.setRandom();
result.setRandom();
DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType)));
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(gpu_out_data, resRange);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_out_data, result.data(),(result.dimensions().TotalSize())*sizeof(DataType));
// t1.concatenate(t2, 0) = result;
gpu_in1.concatenate(gpu_in2, 0).device(sycl_device) =gpu_out;
sycl_device.memcpyDeviceToHost(left.data(), gpu_in1_data,(left.dimensions().TotalSize())*sizeof(DataType));
sycl_device.memcpyDeviceToHost(right.data(), gpu_in2_data,(right.dimensions().TotalSize())*sizeof(DataType));
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 3; ++j) {
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
VERIFY_IS_EQUAL(left(i, j), result(i, j));
VERIFY_IS_EQUAL(right(i, j), result(i+2, j));
}
}
sycl_device.deallocate(gpu_in1_data);
sycl_device.deallocate(gpu_in2_data);
sycl_device.deallocate(gpu_out_data);
}
template <typename DataType, typename Dev_selector> void tensorConcat_perDevice(Dev_selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
Reducing warnings in Sycl backend.
2017-02-01 23:29:53 +08:00
test_simple_concatenation<DataType, RowMajor, int64_t>(sycl_device);
test_simple_concatenation<DataType, ColMajor, int64_t>(sycl_device);
test_concatenation_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.
2016-12-17 03:46:45 +08:00
}
void test_cxx11_tensor_concatenation_sycl() {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(tensorConcat_perDevice<float>(device));
}
}
Reference in New Issue Copy Permalink