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authorStanislaw Halik <sthalik@misaki.pl>2017-03-25 14:17:07 +0100
committerStanislaw Halik <sthalik@misaki.pl>2017-03-25 14:17:07 +0100
commit35f7829af10c61e33dd2e2a7a015058e11a11ea0 (patch)
tree7135010dcf8fd0a49f3020d52112709bcb883bd6 /eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
parent6e8724193e40a932faf9064b664b529e7301c578 (diff)
update
Diffstat (limited to 'eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp')
-rw-r--r--eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp144
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diff --git a/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
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+++ b/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
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+// 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_broadcast_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_broadcast_sycl_fixed(const Eigen::SyclDevice &sycl_device){
+
+ // BROADCAST test:
+ IndexType inDim1=2;
+ IndexType inDim2=3;
+ IndexType inDim3=5;
+ IndexType inDim4=7;
+ IndexType bDim1=2;
+ IndexType bDim2=3;
+ IndexType bDim3=1;
+ IndexType bDim4=4;
+ array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
+ array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
+ array<IndexType, 4> out_range; // = in_range * broadcasts
+ for (size_t i = 0; i < out_range.size(); ++i)
+ out_range[i] = in_range[i] * broadcasts[i];
+
+ Tensor<DataType, 4, DataLayout, IndexType> input(in_range);
+ Tensor<DataType, 4, DataLayout, IndexType> out(out_range);
+
+ for (size_t i = 0; i < in_range.size(); ++i)
+ VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
+
+
+ for (IndexType i = 0; i < input.size(); ++i)
+ input(i) = static_cast<DataType>(i);
+
+ DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType)));
+ DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
+
+ TensorMap<TensorFixedSize<DataType, Sizes<2, 3, 5, 7>, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range);
+ TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range);
+ sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType));
+ gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
+ sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
+
+ for (IndexType i = 0; i < inDim1*bDim1; ++i) {
+ for (IndexType j = 0; j < inDim2*bDim2; ++j) {
+ for (IndexType k = 0; k < inDim3*bDim3; ++k) {
+ for (IndexType l = 0; l < inDim4*bDim4; ++l) {
+ VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l));
+ }
+ }
+ }
+ }
+ printf("Broadcast Test with fixed size Passed\n");
+ sycl_device.deallocate(gpu_in_data);
+ sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){
+
+ // BROADCAST test:
+ IndexType inDim1=2;
+ IndexType inDim2=3;
+ IndexType inDim3=5;
+ IndexType inDim4=7;
+ IndexType bDim1=2;
+ IndexType bDim2=3;
+ IndexType bDim3=1;
+ IndexType bDim4=4;
+ array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
+ array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
+ array<IndexType, 4> out_range; // = in_range * broadcasts
+ for (size_t i = 0; i < out_range.size(); ++i)
+ out_range[i] = in_range[i] * broadcasts[i];
+
+ Tensor<DataType, 4, DataLayout, IndexType> input(in_range);
+ Tensor<DataType, 4, DataLayout, IndexType> out(out_range);
+
+ for (size_t i = 0; i < in_range.size(); ++i)
+ VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
+
+
+ for (IndexType i = 0; i < input.size(); ++i)
+ input(i) = static_cast<DataType>(i);
+
+ DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType)));
+ DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
+
+ TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range);
+ TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range);
+ sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType));
+ gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
+ sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
+
+ for (IndexType i = 0; i < inDim1*bDim1; ++i) {
+ for (IndexType j = 0; j < inDim2*bDim2; ++j) {
+ for (IndexType k = 0; k < inDim3*bDim3; ++k) {
+ for (IndexType l = 0; l < inDim4*bDim4; ++l) {
+ VERIFY_IS_APPROX(input(i%inDim1,j%inDim2,k%inDim3,l%inDim4), out(i,j,k,l));
+ }
+ }
+ }
+ }
+ printf("Broadcast Test Passed\n");
+ sycl_device.deallocate(gpu_in_data);
+ sycl_device.deallocate(gpu_out_data);
+}
+
+template<typename DataType> void sycl_broadcast_test_per_device(const cl::sycl::device& d){
+ std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
+ QueueInterface queueInterface(d);
+ auto sycl_device = Eigen::SyclDevice(&queueInterface);
+ test_broadcast_sycl<DataType, RowMajor, int64_t>(sycl_device);
+ test_broadcast_sycl<DataType, ColMajor, int64_t>(sycl_device);
+ test_broadcast_sycl_fixed<DataType, RowMajor, int64_t>(sycl_device);
+ test_broadcast_sycl_fixed<DataType, ColMajor, int64_t>(sycl_device);
+}
+
+void test_cxx11_tensor_broadcast_sycl() {
+ for (const auto& device :Eigen::get_sycl_supported_devices()) {
+ CALL_SUBTEST(sycl_broadcast_test_per_device<float>(device));
+ }
+}