[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, 141 insertions, 0 deletions

diff --git a/unsupported/test/cxx11_tensor_scan_sycl.cpp b/unsupported/test/cxx11_tensor_scan_sycl.cpp
new file mode 100644
index 000000000..09c45fce5
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_scan_sycl.cpp
@@ -0,0 +1,141 @@
+// 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_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+typedef Tensor<float, 1>::DimensionPair DimPair;
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_cumsum(const Eigen::SyclDevice& sycl_device, IndexType m_size,
+                      IndexType k_size, IndexType n_size, int consume_dim,
+                      bool exclusive) {
+  static const DataType error_threshold = 1e-4f;
+  std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size
+            << " consume_dim : " << consume_dim << ")" << std::endl;
+  Tensor<DataType, 3, DataLayout, IndexType> t_input(m_size, k_size, n_size);
+  Tensor<DataType, 3, DataLayout, IndexType> t_result(m_size, k_size, n_size);
+  Tensor<DataType, 3, DataLayout, IndexType> t_result_gpu(m_size, k_size,
+                                                          n_size);
+
+  t_input.setRandom();
+  std::size_t t_input_bytes = t_input.size() * sizeof(DataType);
+  std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
+
+  DataType* gpu_data_in =
+      static_cast<DataType*>(sycl_device.allocate(t_input_bytes));
+  DataType* gpu_data_out =
+      static_cast<DataType*>(sycl_device.allocate(t_result_bytes));
+
+  array<IndexType, 3> tensorRange = {{m_size, k_size, n_size}};
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_input(
+      gpu_data_in, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_result(
+      gpu_data_out, tensorRange);
+  sycl_device.memcpyHostToDevice(gpu_data_in, t_input.data(), t_input_bytes);
+  sycl_device.memcpyHostToDevice(gpu_data_out, t_input.data(), t_input_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_input.cumsum(consume_dim, exclusive);
+
+  t_result = t_input.cumsum(consume_dim, exclusive);
+
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), gpu_data_out,
+                                 t_result_bytes);
+  sycl_device.synchronize();
+
+  for (IndexType i = 0; i < t_result.size(); i++) {
+    if (static_cast<DataType>(std::fabs(static_cast<DataType>(
+            t_result(i) - t_result_gpu(i)))) < error_threshold) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i),
+                                  error_threshold)) {
+      continue;
+    }
+    std::cout << "mismatch detected at index " << i << " CPU : " << t_result(i)
+              << " vs SYCL : " << t_result_gpu(i) << std::endl;
+    assert(false);
+  }
+  sycl_device.deallocate(gpu_data_in);
+  sycl_device.deallocate(gpu_data_out);
+}
+
+template <typename DataType, typename Dev>
+void sycl_scan_test_exclusive_dim0_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0,
+                                                true);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0,
+                                                true);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_exclusive_dim1_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1,
+                                                true);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1,
+                                                true);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_exclusive_dim2_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2,
+                                                true);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2,
+                                                true);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_inclusive_dim0_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0,
+                                                false);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0,
+                                                false);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_inclusive_dim1_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1,
+                                                false);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1,
+                                                false);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_inclusive_dim2_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2,
+                                                false);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2,
+                                                false);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_scan_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_scan_test_exclusive_dim0_per_device<float>(sycl_device));
+    CALL_SUBTEST_2(
+        sycl_scan_test_exclusive_dim1_per_device<float>(sycl_device));
+    CALL_SUBTEST_3(
+        sycl_scan_test_exclusive_dim2_per_device<float>(sycl_device));
+    CALL_SUBTEST_4(
+        sycl_scan_test_inclusive_dim0_per_device<float>(sycl_device));
+    CALL_SUBTEST_5(
+        sycl_scan_test_inclusive_dim1_per_device<float>(sycl_device));
+    CALL_SUBTEST_6(
+        sycl_scan_test_inclusive_dim2_per_device<float>(sycl_device));
+  }
+}