Converting all parallel for lambda to functor in order to prevent kernel duplication name error; adding tensorConcatinationOp backend for sycl.

1 files changed, 180 insertions, 0 deletions

diff --git a/unsupported/test/cxx11_tensor_concatenation_sycl.cpp b/unsupported/test/cxx11_tensor_concatenation_sycl.cpp
new file mode 100644
index 000000000..5a324b44c
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_concatenation_sycl.cpp
@@ -0,0 +1,180 @@
+// 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_concatenation_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template<typename DataType, int DataLayout, typename Index>
+static void test_simple_concatenation(const Eigen::SyclDevice& sycl_device)
+{
+  Index leftDim1 = 2;
+  Index leftDim2 = 3;
+  Index leftDim3 = 1;
+  Eigen::array<Index, 3> leftRange = {{leftDim1, leftDim2, leftDim3}};
+  Index rightDim1 = 2;
+  Index rightDim2 = 3;
+  Index rightDim3 = 1;
+  Eigen::array<Index, 3> rightRange = {{rightDim1, rightDim2, rightDim3}};
+
+  //Index concatDim1 = 3;
+//	Index concatDim2 = 3;
+//	Index concatDim3 = 1;
+  //Eigen::array<Index, 3> concatRange = {{concatDim1, concatDim2, concatDim3}};
+
+  Tensor<DataType, 3, DataLayout, Index> left(leftRange);
+  Tensor<DataType, 3, DataLayout, Index> right(rightRange);
+  left.setRandom();
+  right.setRandom();
+
+  DataType * gpu_in1_data  = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType)));
+  DataType * gpu_in2_data  = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType)));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, Index>> gpu_in1(gpu_in1_data, leftRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, Index>> gpu_in2(gpu_in2_data, rightRange);
+  sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType));
+  ///
+  Tensor<DataType, 3, DataLayout, Index> concatenation1(leftDim1+rightDim1, leftDim2, leftDim3);
+  DataType * gpu_out_data1 =  static_cast<DataType*>(sycl_device.allocate(concatenation1.dimensions().TotalSize()*sizeof(DataType)));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, Index>> gpu_out1(gpu_out_data1, concatenation1.dimensions());
+
+  //concatenation = left.concatenate(right, 0);
+  gpu_out1.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 0);
+  sycl_device.memcpyDeviceToHost(concatenation1.data(), gpu_out_data1,(concatenation1.dimensions().TotalSize())*sizeof(DataType));
+
+  VERIFY_IS_EQUAL(concatenation1.dimension(0), 4);
+  VERIFY_IS_EQUAL(concatenation1.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation1.dimension(2), 1);
+  for (int j = 0; j < 3; ++j) {
+    for (int i = 0; i < 2; ++i) {
+      VERIFY_IS_EQUAL(concatenation1(i, j, 0), left(i, j, 0));
+    }
+    for (int i = 2; i < 4; ++i) {
+      VERIFY_IS_EQUAL(concatenation1(i, j, 0), right(i - 2, j, 0));
+    }
+  }
+
+  sycl_device.deallocate(gpu_out_data1);
+  Tensor<DataType, 3, DataLayout, Index> concatenation2(leftDim1, leftDim2 +rightDim2, leftDim3);
+  DataType * gpu_out_data2 =  static_cast<DataType*>(sycl_device.allocate(concatenation2.dimensions().TotalSize()*sizeof(DataType)));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, Index>> gpu_out2(gpu_out_data2, concatenation2.dimensions());
+  gpu_out2.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 1);
+  sycl_device.memcpyDeviceToHost(concatenation2.data(), gpu_out_data2,(concatenation2.dimensions().TotalSize())*sizeof(DataType));
+
+  //concatenation = left.concatenate(right, 1);
+  VERIFY_IS_EQUAL(concatenation2.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation2.dimension(1), 6);
+  VERIFY_IS_EQUAL(concatenation2.dimension(2), 1);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation2(i, j, 0), left(i, j, 0));
+    }
+    for (int j = 3; j < 6; ++j) {
+      VERIFY_IS_EQUAL(concatenation2(i, j, 0), right(i, j - 3, 0));
+    }
+  }
+  sycl_device.deallocate(gpu_out_data2);
+  Tensor<DataType, 3, DataLayout, Index> concatenation3(leftDim1, leftDim2, leftDim3+rightDim3);
+  DataType * gpu_out_data3 =  static_cast<DataType*>(sycl_device.allocate(concatenation3.dimensions().TotalSize()*sizeof(DataType)));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, Index>> gpu_out3(gpu_out_data3, concatenation3.dimensions());
+  gpu_out3.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 2);
+  sycl_device.memcpyDeviceToHost(concatenation3.data(), gpu_out_data3,(concatenation3.dimensions().TotalSize())*sizeof(DataType));
+
+  //concatenation = left.concatenate(right, 2);
+  VERIFY_IS_EQUAL(concatenation3.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation3.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation3.dimension(2), 2);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation3(i, j, 0), left(i, j, 0));
+      VERIFY_IS_EQUAL(concatenation3(i, j, 1), right(i, j, 0));
+    }
+  }
+  sycl_device.deallocate(gpu_out_data3);
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+}
+template<typename DataType, int DataLayout, typename Index>
+static void test_concatenation_as_lvalue(const Eigen::SyclDevice& sycl_device)
+{
+
+  Index leftDim1 = 2;
+  Index leftDim2 = 3;
+  Eigen::array<Index, 2> leftRange = {{leftDim1, leftDim2}};
+
+  Index rightDim1 = 2;
+  Index rightDim2 = 3;
+  Eigen::array<Index, 2> rightRange = {{rightDim1, rightDim2}};
+
+  Index concatDim1 = 4;
+  Index concatDim2 = 3;
+  Eigen::array<Index, 2> resRange = {{concatDim1, concatDim2}};
+
+  Tensor<DataType, 2, DataLayout, Index> left(leftRange);
+  Tensor<DataType, 2, DataLayout, Index> right(rightRange);
+  Tensor<DataType, 2, DataLayout, Index> result(resRange);
+
+  left.setRandom();
+  right.setRandom();
+  result.setRandom();
+
+  DataType * gpu_in1_data  = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType)));
+  DataType * gpu_in2_data  = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType)));
+  DataType * gpu_out_data =  static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType)));
+
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, Index>> gpu_in1(gpu_in1_data, leftRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, Index>> gpu_in2(gpu_in2_data, rightRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, Index>> gpu_out(gpu_out_data, resRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_out_data, result.data(),(result.dimensions().TotalSize())*sizeof(DataType));
+
+//  t1.concatenate(t2, 0) = result;
+ gpu_in1.concatenate(gpu_in2, 0).device(sycl_device) =gpu_out;
+ sycl_device.memcpyDeviceToHost(left.data(), gpu_in1_data,(left.dimensions().TotalSize())*sizeof(DataType));
+ sycl_device.memcpyDeviceToHost(right.data(), gpu_in2_data,(right.dimensions().TotalSize())*sizeof(DataType));
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(left(i, j), result(i, j));
+      VERIFY_IS_EQUAL(right(i, j), result(i+2, j));
+    }
+  }
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+
+template <typename DataType, typename Dev_selector> void tensorConcat_perDevice(Dev_selector s){
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_concatenation<DataType, RowMajor, int>(sycl_device);
+  test_simple_concatenation<DataType, ColMajor, int>(sycl_device);
+  test_concatenation_as_lvalue<DataType, ColMajor, int>(sycl_device);
+}
+void test_cxx11_tensor_concatenation_sycl() {
+  for (const auto& device :Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(tensorConcat_perDevice<float>(device));
+  }
+}