Adding Tensor ReverseOp; TensorStriding; TensorConversionOp; Modifying Tensor Contractsycl to be located in any place in the expression tree.

1 files changed, 221 insertions, 0 deletions

diff --git a/unsupported/test/cxx11_tensor_reverse_sycl.cpp b/unsupported/test/cxx11_tensor_reverse_sycl.cpp
new file mode 100644
index 000000000..73b394c18
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_reverse_sycl.cpp
@@ -0,0 +1,221 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015
+// 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_reverse_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+
+template <typename DataType, int DataLayout>
+static void test_simple_reverse(const Eigen::SyclDevice&  sycl_device) {
+
+  int dim1 = 2;
+  int dim2 = 3;
+  int dim3 = 5;
+  int dim4 = 7;
+
+  array<int, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
+  Tensor<DataType, 4, DataLayout> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout> reversed_tensor(tensorRange);
+  tensor.setRandom();
+
+  array<bool, 4> dim_rev;
+  dim_rev[0] = false;
+  dim_rev[1] = true;
+  dim_rev[2] = true;
+  dim_rev[3] = false;
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
+  DataType* gpu_out_data =static_cast<DataType*>(sycl_device.allocate(reversed_tensor.dimensions().TotalSize()*sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 4, DataLayout> >  in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout> >  out_gpu(gpu_out_data, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
+  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(i,2-j,4-k,l));
+        }
+      }
+    }
+  }
+  dim_rev[0] = true;
+  dim_rev[1] = false;
+  dim_rev[2] = false;
+  dim_rev[3] = false;
+
+  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
+  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,l));
+        }
+      }
+    }
+  }
+
+  dim_rev[0] = true;
+  dim_rev[1] = false;
+  dim_rev[2] = false;
+  dim_rev[3] = true;
+  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
+  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,6-l));
+        }
+      }
+    }
+  }
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+
+
+template <typename DataType, int DataLayout>
+static void test_expr_reverse(const Eigen::SyclDevice&  sycl_device, bool LValue)
+{
+  int dim1 = 2;
+  int dim2 = 3;
+  int dim3 = 5;
+  int dim4 = 7;
+
+  array<int, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
+  Tensor<DataType, 4, DataLayout> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout> expected(tensorRange);
+  Tensor<DataType, 4, DataLayout> result(tensorRange);
+  tensor.setRandom();
+
+  array<bool, 4> dim_rev;
+  dim_rev[0] = false;
+  dim_rev[1] = true;
+  dim_rev[2] = false;
+  dim_rev[3] = true;
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
+  DataType* gpu_out_data_expected =static_cast<DataType*>(sycl_device.allocate(expected.dimensions().TotalSize()*sizeof(DataType)));
+  DataType* gpu_out_data_result =static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 4, DataLayout> >  in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout> >  out_gpu_expected(gpu_out_data_expected, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout> >  out_gpu_result(gpu_out_data_result, tensorRange);
+
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));
+
+  if (LValue) {
+    out_gpu_expected.reverse(dim_rev).device(sycl_device) = in_gpu;
+  } else {
+    out_gpu_expected.device(sycl_device) = in_gpu.reverse(dim_rev);
+  }
+  sycl_device.memcpyDeviceToHost(expected.data(), gpu_out_data_expected, expected.dimensions().TotalSize()*sizeof(DataType));
+
+
+  array<int, 4> src_slice_dim;
+  src_slice_dim[0] = 2;
+  src_slice_dim[1] = 3;
+  src_slice_dim[2] = 1;
+  src_slice_dim[3] = 7;
+  array<int, 4> src_slice_start;
+  src_slice_start[0] = 0;
+  src_slice_start[1] = 0;
+  src_slice_start[2] = 0;
+  src_slice_start[3] = 0;
+  array<int, 4> dst_slice_dim = src_slice_dim;
+  array<int, 4> dst_slice_start = src_slice_start;
+
+  for (int i = 0; i < 5; ++i) {
+    if (LValue) {
+        out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
+          in_gpu.slice(src_slice_start, src_slice_dim);
+    } else {
+      out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
+          in_gpu.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
+    }
+    src_slice_start[2] += 1;
+    dst_slice_start[2] += 1;
+  }
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
+        }
+      }
+    }
+  }
+
+  dst_slice_start[2] = 0;
+  result.setRandom();
+  sycl_device.memcpyHostToDevice(gpu_out_data_result, result.data(),(result.dimensions().TotalSize())*sizeof(DataType));
+  for (int i = 0; i < 5; ++i) {
+     if (LValue) {
+       out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
+           in_gpu.slice(dst_slice_start, dst_slice_dim);
+     } else {
+       out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
+           in_gpu.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
+     }
+    dst_slice_start[2] += 1;
+  }
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
+        }
+      }
+    }
+  }
+}
+
+
+
+template<typename DataType> void sycl_reverse_test_per_device(const cl::sycl::device& d){
+  std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
+  QueueInterface queueInterface(d);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_reverse<DataType, RowMajor>(sycl_device);
+  test_simple_reverse<DataType, ColMajor>(sycl_device);
+  test_expr_reverse<DataType, RowMajor>(sycl_device, false);
+  test_expr_reverse<DataType, ColMajor>(sycl_device, false);
+  test_expr_reverse<DataType, RowMajor>(sycl_device, true);
+  test_expr_reverse<DataType, ColMajor>(sycl_device, true);
+}
+void test_cxx11_tensor_reverse_sycl() {
+  for (const auto& device :Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_reverse_test_per_device<float>(device));
+  }
+}