text: //*****************************************************************************
  // Copyright 2017-2020 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.
  //*****************************************************************************
  
  #include "int_executable.hpp"
  #include "backend_manager.hpp"
  #include "ngraph/chrome_trace.hpp"
  #include "ngraph/except.hpp"
  #include "ngraph/op/util/op_types.hpp"
  #include "ngraph/ops.hpp"
  #include "ngraph/pass/manager.hpp"
  #include "ngraph/util.hpp"
  #include "pass/fused_op_decomposition.hpp"
  #include "pass/like_replacement.hpp"
  #include "pass/liveness.hpp"
  #include "pass/opset0_downgrade.hpp"
  #include "pass/opset1_downgrade.hpp"
  
  using namespace std;
  using namespace ngraph;
  
  NGRAPH_SUPPRESS_DEPRECATED_START
  
  runtime::interpreter::OP_TYPEID runtime::interpreter::INTExecutable::get_typeid(const Node& node)
  {
      const NodeTypeInfo& type_info = node.get_type_info();
      // This expands the op list in op_tbl.hpp into a list of enumerations that look like this:
      // {Abs::type_info, OP_TYPEID::Abs},
      // {Acos::type_info, OP_TYPEID::Acos},
      // ...
      static const map<NodeTypeInfo, OP_TYPEID> type_info_map{
  #define NGRAPH_OP(NAME, NAMESPACE) {NAMESPACE::NAME::type_info, OP_TYPEID::ID_SUFFIX(NAME)},
  #include "opset_int_tbl.hpp"
  #undef NGRAPH_OP
      };
      OP_TYPEID rc = OP_TYPEID::UnknownOp;
  
      auto it = type_info_map.find(type_info);
      if (it != type_info_map.end())
      {
          rc = it->second;
      }
      return rc;
  }
  
  runtime::interpreter::INTExecutable::INTExecutable(const shared_ptr<Function>& function,
                                                     bool enable_performance_collection)
      : m_is_compiled{true}
      , m_performance_counters_enabled{enable_performance_collection}
  {
      m_function = clone_function(*function);
      auto is_supported = [](const Node& node) {
          bool retval = false;
          switch (INTExecutable::get_typeid(node))
          {
          case OP_TYPEID::Clamp:
          case OP_TYPEID::MatMul:
          case OP_TYPEID::Squeeze:
          case OP_TYPEID::PRelu:
          case OP_TYPEID::Unsqueeze: retval = true; break;
          default: break;
          }
          return retval;
      };
      pass::Manager pass_manager;
      pass_manager.register_pass<pass::LikeReplacement>();
      pass_manager.register_pass<pass::FusedOpDecomposition>(is_supported);
      pass_manager.register_pass<pass::Opset1Downgrade>();
      pass_manager.register_pass<pass::Opset0Downgrade>();
      // Need to decompose any v0 fused ops, which were produced by the downgrade pass
      pass_manager.register_pass<pass::FusedOpDecomposition>(is_supported);
      pass_manager.run_passes(m_function);
      for (auto node : m_function->get_ordered_ops())
      {
          m_nodes.push_back(node);
      }
      set_parameters_and_results(*m_function);
  }
  
  bool runtime::interpreter::INTExecutable::call(const vector<shared_ptr<runtime::Tensor>>& outputs,
                                                 const vector<shared_ptr<runtime::Tensor>>& inputs)
  {
      event::Duration d1("call", "Interpreter");
  
      // convert inputs to HostTensor
      vector<shared_ptr<HostTensor>> func_inputs;
      for (auto tensor : inputs)
      {
          auto host_tensor = static_pointer_cast<runtime::HostTensor>(tensor);
          func_inputs.push_back(host_tensor);
      }
      if (m_nan_check_enabled)
      {
          perform_nan_check(func_inputs);
      }
  
      // convert outputs to HostTensor
      vector<shared_ptr<HostTensor>> func_outputs;
      for (auto tensor : outputs)
      {
          auto host_tensor = static_pointer_cast<runtime::HostTensor>(tensor);
          func_outputs.push_back(host_tensor);
      }
  
      // map function params -> HostTensor
      unordered_map<descriptor::Tensor*, shared_ptr<HostTensor>> tensor_map;
      size_t input_count = 0;
      for (auto param : get_parameters())
      {
          for (size_t i = 0; i < param->get_output_size(); ++i)
          {
              descriptor::Tensor* tensor = &param->output(i).get_tensor();
              tensor_map.insert({tensor, func_inputs[input_count++]});
          }
      }
  
      // map function outputs -> HostTensor
      for (size_t output_count = 0; output_count < get_results().size(); ++output_count)
      {
          auto output = get_results()[output_count];
          if (!is_type<op::Result>(output))
          {
              throw ngraph_error("One of function's outputs isn't op::Result");
          }
          descriptor::Tensor* tensor = &output->get_output_tensor(0);
          tensor_map.insert({tensor, func_outputs[output_count]});
      }
  
      // for each ordered op in the graph
      for (auto op : m_nodes)
      {
          event::Duration d2(op->description(), "Interpreter");
          if (op::is_parameter(op))
          {
              continue;
          }
  
          // get op inputs from map
          vector<shared_ptr<HostTensor>> op_inputs;
          for (auto input : op->inputs())
          {
              descriptor::Tensor* tensor = &input.get_tensor();
              op_inputs.push_back(tensor_map.at(tensor));
          }
  
          // get op outputs from map or create
          vector<shared_ptr<HostTensor>> op_outputs;
          for (size_t i = 0; i < op->get_output_size(); ++i)
          {
              descriptor::Tensor* tensor = &op->output(i).get_tensor();
              shared_ptr<HostTensor> host_tensor;
              auto it = tensor_map.find(tensor);
              if (it == tensor_map.end())
              {
                  host_tensor = make_shared<HostTensor>(op->output(i));
                  tensor_map.insert({tensor, host_tensor});
              }
              else
              {
                  host_tensor = it->second;
              }
              op_outputs.push_back(host_tensor);
          }
  
          // get op type
          element::Type type;
          if (is_type<op::Convert>(op) || is_type<op::Quantize>(op) || is_type<op::Dequantize>(op) ||
              is_type<op::PriorBox>(op))
          {
              type = op->get_input_element_type(0);
          }
          else if (is_type<op::Equal>(op) || is_type<op::Greater>(op) || is_type<op::GreaterEq>(op) ||
                   is_type<op::Less>(op) || is_type<op::LessEq>(op) || is_type<op::NotEqual>(op))
          {
              // Get the type of the second input, not the first
              // All BinaryElementwiseComparision ops have the same type for inputs
              // Select has bool for first input and the type we are interested in for the second
              type = op->get_input_element_type(1);
          }
          else if (is_type<op::TopK>(op))
          {
              type = op->get_output_element_type(1);
          }
          else
          {
              type = op->get_output_element_type(0);
          }
  
          if (m_performance_counters_enabled)
          {
              m_timer_map[op].start();
          }
          if (!op->evaluate(op_outputs, op_inputs))
          {
              generate_calls(type, *op.get(), op_outputs, op_inputs);
          }
          if (m_performance_counters_enabled)
          {
              m_timer_map[op].stop();
          }
          if (m_nan_check_enabled)
          {
              perform_nan_check(op_outputs, op.get());
          }
      }
  
      return true;
  }
  
  void runtime::interpreter::INTExecutable::generate_calls(const element::Type& type,
                                                           const Node& op,
                                                           const vector<shared_ptr<HostTensor>>& out,
                                                           const vector<shared_ptr<HostTensor>>& in)
  {
      stringstream ss;
      switch (type)
      {
      case element::Type_t::boolean: op_engine<char>(op, out, in); break;
      case element::Type_t::f32: op_engine<float>(op, out, in); break;
      case element::Type_t::f64: op_engine<double>(op, out, in); break;
      case element::Type_t::i8: op_engine<int8_t>(op, out, in); break;
      case element::Type_t::i16: op_engine<int16_t>(op, out, in); break;
      case element::Type_t::i32: op_engine<int32_t>(op, out, in); break;
      case element::Type_t::i64: op_engine<int64_t>(op, out, in); break;
      case element::Type_t::u8: op_engine<uint8_t>(op, out, in); break;
      case element::Type_t::u16: op_engine<uint16_t>(op, out, in); break;
      case element::Type_t::u32: op_engine<uint32_t>(op, out, in); break;
      case element::Type_t::u64: op_engine<uint64_t>(op, out, in); break;
      case element::Type_t::undefined:
      case element::Type_t::dynamic:
      case element::Type_t::u1:
      case element::Type_t::bf16:
      case element::Type_t::f16:
          ss << "unsupported element type " << type << " op " << op.get_name();
          throw ngraph_error(ss.str());
      }
  }
  
  void runtime::interpreter::INTExecutable::set_nan_check(bool enable)
  {
      m_nan_check_enabled = enable;
  }
  
  vector<runtime::PerformanceCounter>
      runtime::interpreter::INTExecutable::get_performance_data() const
  {
      vector<runtime::PerformanceCounter> rc;
      for (const pair<shared_ptr<const Node>, stopwatch> p : m_timer_map)
      {
          rc.emplace_back(p.first, p.second.get_total_microseconds(), p.second.get_call_count());
      }
      return rc;
  }
  
  void runtime::interpreter::INTExecutable::perform_nan_check(
      const vector<shared_ptr<HostTensor>>& tensors, const Node* op)
  {
      size_t arg_number = 1;
      for (const shared_ptr<HostTensor>& tensor : tensors)
      {
          const element::Type& type = tensor->get_element_type();
          if (type == element::f32)
          {
              const float* data = tensor->get_data_ptr<float>();
              for (size_t i = 0; i < tensor->get_element_count(); i++)
              {
                  if (std::isnan(data[i]))
                  {
                      if (op)
                      {
                          throw runtime_error("nan found in op '" + op->get_name() + "' output");
                      }
                      else
                      {
                          throw runtime_error("nan found in function's input tensor number " +
                                              to_string(arg_number));
                      }
                  }
              }
          }
          else if (type == element::f64)
          {
              const double* data = tensor->get_data_ptr<double>();
              for (size_t i = 0; i < tensor->get_element_count(); i++)
              {
                  if (std::isnan(data[i]))
                  {
                      if (op)
                      {
                          throw runtime_error("nan found in op '" + op->get_name() + "' output");
                      }
                      else
                      {
                          throw runtime_error("nan found in function's input tensor number " +
                                              to_string(arg_number));
                      }
                  }
              }
          }
          arg_number++;
      }
  }
  
  shared_ptr<ngraph::op::Parameter>
      runtime::interpreter::INTExecutable::get_parameter(size_t index) const
  {
      const ParameterVector& parameters = get_parameters();
      NGRAPH_CHECK(index < parameters.size(), "create_tensor for input out of bounds");
      return parameters[index];
  }
  
  shared_ptr<ngraph::op::Result> runtime::interpreter::INTExecutable::get_result(size_t index) const
  {
      const ResultVector& results = get_results();
      NGRAPH_CHECK(index < results.size(), "create_tensor for input out of bounds");
      return results[index];
  }
  shared_ptr<runtime::Tensor>
      runtime::interpreter::INTExecutable::create_input_tensor(size_t input_index)
  {
      shared_ptr<op::Parameter> parameter = get_parameter(input_index);
      return make_shared<runtime::HostTensor>(parameter->get_element_type(), parameter->get_shape());
  }
  
  shared_ptr<runtime::Tensor>
      runtime::interpreter::INTExecutable::create_output_tensor(size_t output_index)
  {
      shared_ptr<op::Result> result = get_result(output_index);
      return make_shared<runtime::HostTensor>(result->get_element_type(), result->get_shape());
  }
  
  vector<shared_ptr<runtime::Tensor>>
      runtime::interpreter::INTExecutable::create_input_tensor(size_t input_index,
                                                               size_t pipeline_depth)
  {
      vector<shared_ptr<runtime::HostTensor>> tensors;
      shared_ptr<op::Parameter> parameter = get_parameter(input_index);
      for (size_t i = 0; i < pipeline_depth; i++)
      {
          shared_ptr<runtime::HostTensor> tensor;
          auto t =
              make_shared<runtime::HostTensor>(parameter->get_element_type(), parameter->get_shape());
          tensor = static_pointer_cast<runtime::HostTensor>(t);
          tensors.push_back(tensor);
      }
      vector<shared_ptr<runtime::Tensor>> result_tensors;
      for (const shared_ptr<runtime::HostTensor>& tensor : tensors)
      {
          result_tensors.push_back(tensor);
      }
      return result_tensors;
  }
  
  vector<shared_ptr<runtime::Tensor>>
      runtime::interpreter::INTExecutable::create_output_tensor(size_t output_index,
                                                                size_t pipeline_depth)
  {
      vector<shared_ptr<runtime::HostTensor>> tensors;
      shared_ptr<op::Result> result = get_result(output_index);
      for (size_t i = 0; i < pipeline_depth; i++)
      {
          shared_ptr<runtime::HostTensor> tensor;
          auto t = make_shared<runtime::HostTensor>(result->get_element_type(), result->get_shape());
          tensor = static_pointer_cast<runtime::HostTensor>(t);
          tensors.push_back(tensor);
      }
      vector<shared_ptr<runtime::Tensor>> result_tensors;
      for (const shared_ptr<runtime::HostTensor>& tensor : tensors)
      {
          result_tensors.push_back(tensor);
      }
      return result_tensors;
  }
  