From 577ce78085d2e09675abb5976ab3026235de8eec Mon Sep 17 00:00:00 2001
From: Mehdi Goli <mehdi.goli@codeplay.com>
Date: Tue, 29 Nov 2016 15:30:42 +0000
Subject: Adding TensorShuffling backend for sycl; adding TensorReshaping
 backend for sycl; cleaning up the sycl backend.

---
 unsupported/test/cxx11_tensor_shuffling_sycl.cpp | 120 +++++++++++++++++++++++
 1 file changed, 120 insertions(+)
 create mode 100644 unsupported/test/cxx11_tensor_shuffling_sycl.cpp

(limited to 'unsupported/test/cxx11_tensor_shuffling_sycl.cpp')

diff --git a/unsupported/test/cxx11_tensor_shuffling_sycl.cpp b/unsupported/test/cxx11_tensor_shuffling_sycl.cpp
new file mode 100644
index 000000000..b2b75cbde
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_shuffling_sycl.cpp
@@ -0,0 +1,120 @@
+// 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>
+// Benoit Steiner <benoit.steiner.goog@gmail.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_shuffling_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#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 IndexTypes>
+static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device)
+{
+  IndexTypes sizeDim1 = 2;
+  IndexTypes sizeDim2 = 3;
+  IndexTypes sizeDim3 = 5;
+  IndexTypes sizeDim4 = 7;
+  array<IndexTypes, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  Tensor<DataType, 4, DataLayout,IndexTypes> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout,IndexTypes> no_shuffle(tensorRange);
+  tensor.setRandom();
+
+  const size_t buffSize =tensor.size()*sizeof(DataType);
+  array<IndexTypes, 4> shuffles;
+  shuffles[0] = 0;
+  shuffles[1] = 1;
+  shuffles[2] = 2;
+  shuffles[3] = 3;
+  DataType* gpu_data1  = static_cast<DataType*>(sycl_device.allocate(buffSize));
+  DataType* gpu_data2  = static_cast<DataType*>(sycl_device.allocate(buffSize));
+
+
+  TensorMap<Tensor<DataType, 4, DataLayout,IndexTypes>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout,IndexTypes>> gpu2(gpu_data2, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);
+
+  gpu2.device(sycl_device)=gpu1.shuffle(shuffles);
+  sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
+
+  VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          VERIFY_IS_EQUAL(tensor(i,j,k,l), no_shuffle(i,j,k,l));
+        }
+      }
+    }
+  }
+
+  shuffles[0] = 2;
+  shuffles[1] = 3;
+  shuffles[2] = 1;
+  shuffles[3] = 0;
+  array<IndexTypes, 4> tensorrangeShuffle = {{sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
+  Tensor<DataType, 4, DataLayout,IndexTypes> shuffle(tensorrangeShuffle);
+  DataType* gpu_data3  = static_cast<DataType*>(sycl_device.allocate(buffSize));
+  TensorMap<Tensor<DataType, 4,DataLayout,IndexTypes>> gpu3(gpu_data3, tensorrangeShuffle);
+
+    gpu3.device(sycl_device)=gpu1.shuffle(shuffles);
+    sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
+
+  VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3);
+  VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4);
+  VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2);
+  VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i));
+        }
+      }
+    }
+  }
+}
+
+
+template<typename DataType, typename dev_Selector> void sycl_shuffling_test_per_device(dev_Selector s){
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_shuffling_sycl<DataType, RowMajor, int>(sycl_device);
+  test_simple_shuffling_sycl<DataType, ColMajor, int>(sycl_device);
+
+  test_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device);
+
+}
+void test_cxx11_tensor_shuffling_sycl()
+{
+  for (const auto& device :Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device));
+  }
+}
-- 
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