[SYCL] Rebasing the SYCL support branch on top of the Einge upstream master branch.

* 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

1 files changed, 105 insertions, 0 deletions

diff --git a/unsupported/test/cxx11_tensor_math_sycl.cpp b/unsupported/test/cxx11_tensor_math_sycl.cpp
new file mode 100644
index 000000000..029653e27
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_math_sycl.cpp
@@ -0,0 +1,105 @@
+// 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_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;
+
+using Eigen::Tensor;
+using Eigen::RowMajor;
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_tanh_sycl(const Eigen::SyclDevice &sycl_device)
+{
+
+  IndexType sizeDim1 = 4;
+  IndexType sizeDim2 = 4;
+  IndexType sizeDim3 = 1;
+  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out_cpu(tensorRange);
+
+  in = in.random();
+
+  DataType* gpu_data1  = static_cast<DataType*>(sycl_device.allocate(in.size()*sizeof(DataType)));
+  DataType* gpu_data2  = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, in.data(),(in.size())*sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.tanh();
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data2,(out.size())*sizeof(DataType));
+
+  out_cpu=in.tanh();
+
+  for (int i = 0; i < in.size(); ++i) {
+    VERIFY_IS_APPROX(out(i), out_cpu(i));
+  }
+}
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_sigmoid_sycl(const Eigen::SyclDevice &sycl_device)
+{
+
+  IndexType sizeDim1 = 4;
+  IndexType sizeDim2 = 4;
+  IndexType sizeDim3 = 1;
+  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out_cpu(tensorRange);
+
+  in = in.random();
+
+  DataType* gpu_data1  = static_cast<DataType*>(sycl_device.allocate(in.size()*sizeof(DataType)));
+  DataType* gpu_data2  = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, in.data(),(in.size())*sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.sigmoid();
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data2,(out.size())*sizeof(DataType));
+
+  out_cpu=in.sigmoid();
+
+  for (int i = 0; i < in.size(); ++i) {
+    VERIFY_IS_APPROX(out(i), out_cpu(i));
+  }
+}
+
+
+template<typename DataType, typename dev_Selector> void sycl_computing_test_per_device(dev_Selector s){
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_tanh_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_tanh_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_sigmoid_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_sigmoid_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_math_sycl) {
+  for (const auto& device :Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
+  }
+}