Adding Sycl Backend for TensorGenerator.h.

1 files changed, 147 insertions, 0 deletions

diff --git a/unsupported/test/cxx11_tensor_generator_sycl.cpp b/unsupported/test/cxx11_tensor_generator_sycl.cpp
new file mode 100644
index 000000000..f551c8d0c
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_generator_sycl.cpp
@@ -0,0 +1,147 @@
+// 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_generator_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+static const float error_threshold =1e-8f;
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+struct Generator1D {
+  Generator1D() { }
+
+  float operator()(const array<Eigen::DenseIndex, 1>& coordinates) const {
+    return coordinates[0];
+  }
+};
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_1D_sycl(const Eigen::SyclDevice& sycl_device)
+{
+
+  IndexType sizeDim1 = 6;
+  array<IndexType, 1> tensorRange = {{sizeDim1}};
+  Tensor<DataType, 1, DataLayout,IndexType> vec(tensorRange);
+  Tensor<DataType, 1, DataLayout,IndexType> result(tensorRange);
+
+  const size_t tensorBuffSize =vec.size()*sizeof(DataType);
+  DataType* gpu_data_vec  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_result  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+
+  TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> gpu_vec(gpu_data_vec, tensorRange);
+  TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_vec, vec.data(), tensorBuffSize);
+  gpu_result.device(sycl_device)=gpu_vec.generate(Generator1D());
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
+
+  for (IndexType i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(result(i), i);
+  }
+}
+
+
+struct Generator2D {
+  Generator2D() { }
+
+  float operator()(const array<Eigen::DenseIndex, 2>& coordinates) const {
+    return 3 * coordinates[0] + 11 * coordinates[1];
+  }
+};
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_2D_sycl(const Eigen::SyclDevice& sycl_device)
+{
+  IndexType sizeDim1 = 5;
+  IndexType sizeDim2 = 7;
+  array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
+  Tensor<DataType, 2, DataLayout,IndexType> matrix(tensorRange);
+  Tensor<DataType, 2, DataLayout,IndexType> result(tensorRange);
+
+  const size_t tensorBuffSize =matrix.size()*sizeof(DataType);
+  DataType* gpu_data_matrix  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_result  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+
+  TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
+  gpu_result.device(sycl_device)=gpu_matrix.generate(Generator2D());
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
+
+  for (IndexType i = 0; i < 5; ++i) {
+    for (IndexType j = 0; j < 5; ++j) {
+      VERIFY_IS_EQUAL(result(i, j), 3*i + 11*j);
+    }
+  }
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_gaussian_sycl(const Eigen::SyclDevice& sycl_device)
+{
+  IndexType rows = 32;
+  IndexType cols = 48;
+  array<DataType, 2> means;
+  means[0] = rows / 2.0f;
+  means[1] = cols / 2.0f;
+  array<DataType, 2> std_devs;
+  std_devs[0] = 3.14f;
+  std_devs[1] = 2.7f;
+  internal::GaussianGenerator<DataType, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs);
+
+  array<IndexType, 2> tensorRange = {{rows, cols}};
+  Tensor<DataType, 2, DataLayout,IndexType> matrix(tensorRange);
+  Tensor<DataType, 2, DataLayout,IndexType> result(tensorRange);
+
+  const size_t tensorBuffSize =matrix.size()*sizeof(DataType);
+  DataType* gpu_data_matrix  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_result  = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+
+  TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
+  gpu_result.device(sycl_device)=gpu_matrix.generate(gaussian_gen);
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
+
+  for (IndexType i = 0; i < rows; ++i) {
+    for (IndexType j = 0; j < cols; ++j) {
+      DataType g_rows = powf(rows/2.0f - i, 2) / (3.14f * 3.14f) * 0.5f;
+      DataType g_cols = powf(cols/2.0f - j, 2) / (2.7f * 2.7f) * 0.5f;
+      DataType gaussian = expf(-g_rows - g_cols);
+      Eigen::internal::isApprox(result(i, j), gaussian, error_threshold);
+    }
+  }
+}
+
+template<typename DataType, typename dev_Selector> void sycl_generator_test_per_device(dev_Selector s){
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_1D_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_1D_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_2D_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_2D_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_gaussian_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_gaussian_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+void test_cxx11_tensor_generator_sycl()
+{
+  for (const auto& device :Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_generator_test_per_device<float>(device));
+  }
+}