text: //*****************************************************************************
  // Copyright 2017-2018 Intel Corporation
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //     http://www.apache.org/licenses/LICENSE-2.0
  //
  // Unless required by applicable law or agreed to in writing, software
  // distributed under the License is distributed on an "AS IS" BASIS,
  // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  // See the License for the specific language governing permissions and
  // limitations under the License.
  //*****************************************************************************
  
  #pragma once
  
  #include <mkldnn.hpp>
  
  #include "ngraph/axis_vector.hpp"
  #include "ngraph/node.hpp"
  #include "ngraph/op/batch_norm.hpp"
  #include "ngraph/runtime/cpu/cpu_layout_descriptor.hpp"
  #include "ngraph/runtime/cpu/op/batch_norm_relu.hpp"
  #include "ngraph/type/element_type.hpp"
  
  namespace ngraph
  {
      namespace runtime
      {
          namespace cpu
          {
              namespace mkldnn_utils
              {
                  extern mkldnn::engine global_cpu_engine;
  
                  mkldnn::memory::format
                      CreateNativeDataFormat(const ngraph::runtime::cpu::LayoutDescriptor& layout);
                  mkldnn::memory::format CreateNativeDataFormat(const Shape& shape);
                  const std::string& get_mkldnn_data_type_string(const ngraph::element::Type& type);
                  mkldnn::memory::data_type get_mkldnn_data_type(const ngraph::element::Type& type);
                  const std::string& get_mkldnn_format_string(mkldnn::memory::format fmt);
  
                  const mkldnn::memory::desc& get_input_mkldnn_md(const Node* node, size_t index);
                  const mkldnn::memory::desc& get_output_mkldnn_md(const Node* node, size_t index);
  
                  mkldnn::memory::desc create_default_mkldnn_md(const Node* node,
                                                                size_t index,
                                                                bool is_output,
                                                                mkldnn::memory::format format);
                  bool is_perm_sorted(const Strides& a, const AxisVector& perm);
                  bool can_create_mkldnn_md(const Shape& dims,
                                            const Strides& strides,
                                            const ngraph::element::Type type);
                  mkldnn::memory::desc create_blocked_mkldnn_md(const Shape& dims,
                                                                const Strides& strides,
                                                                const ngraph::element::Type type);
                  mkldnn::memory::desc try_get_named_md(const mkldnn_memory_desc_t& md);
                  mkldnn::memory::desc rotate_blocked_md(const mkldnn::memory::desc& in,
                                                         const AxisVector& axis_order);
                  mkldnn::memory::desc squeeze_blocked_md(const mkldnn::memory::desc& in,
                                                          AxisVector& axis_list);
                  mkldnn::memory::desc expand_blocked_md(const mkldnn::memory::desc& in,
                                                         AxisVector& axis_list);
  
                  bool compare_mkldnn_formats(mkldnn::memory::format lhs, mkldnn::memory::format rhs);
                  bool compare_mkldnn_mds(const mkldnn::memory::desc& lhs,
                                          const mkldnn::memory::desc& rhs);
                  bool is_mkldnn_padded_layout(const mkldnn::memory::desc& in,
                                               const AxisVector& axis_list);
                  bool is_mkldnn_filter_format(mkldnn::memory::format fmt);
                  bool is_mkldnn_blocked_data_format(mkldnn::memory::format fmt);
                  bool can_use_mkldnn_batchnorm_fprop(const ngraph::Node* node);
                  bool can_use_mkldnn_batchnorm_bprop(const ngraph::Node* node);
  
                  bool use_mkldnn_kernel(const ngraph::Node* node);
                  void assign_mkldnn_kernel(Node* node);
  
                  std::map<element::Type, const mkldnn::memory::data_type>&
                      get_mkldnn_data_type_map();
                  std::map<element::Type, const std::string>& get_mkldnn_data_type_string_map();
                  std::map<mkldnn::memory::format, const std::string>& get_mkldnn_format_string_map();
                  std::set<mkldnn::memory::format>& get_filter_formats();
  
                  template <typename T>
                  bool can_use_mkldnn_conv(ngraph::Node* node)
                  {
                      auto convolution = static_cast<const T*>(node);
                      auto arg0_rank = node->get_input_shape(0).size();
  
                      for (size_t s : convolution->get_data_dilation_strides())
                      {
                          if (s != 1)
                              return false;
                      }
                      if (arg0_rank != 4 && arg0_rank != 5)
                      {
                          return false;
                      }
                      if (node->get_input_element_type(0) != element::f32)
                      {
                          return false;
                      }
  
                      return true;
                  }
              }
          }
      }
  }
  