text: /*
  // Copyright (c) 2016-2019 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 "primitive_inst.h"
  #include "data_inst.h"
  #include "mutable_data_inst.h"
  #include "generic_layer_inst.h"
  #include "input_layout_inst.h"
  #include "max_unpooling_inst.h"
  #include "arg_max_min_inst.h"
  #include "fused_conv_eltwise_inst.h"
  
  #include "network_impl.h"
  #include "engine_impl.h"
  #include "memory_impl.h"
  
  #include "error_handler.h"
  #include "json_object.h"
  #include <string>
  #include <vector>
  #include <memory>
  #include <algorithm>
  
  namespace cldnn {
  
  uint32_t primitive_inst::get_network_id() const { return _network.get_id(); }
  
  void primitive_inst::check_memory_to_set(const memory_impl& mem, const layout& layout) const {
      CLDNN_ERROR_LAYOUT_MISMATCH("network layout",
          "set memory layout",
          mem.get_layout(),
          "expected layout",
          layout,
          "");
  
      // check shared image/buffer compatibility, if applicable
      auto params = mem.get_internal_params();
      if (params.mem_type != shared_mem_type::shared_mem_empty) {
          if (!mem.is_allocated_by(get_network().get_engine())) {
              CLDNN_ERROR_MESSAGE(_node.id(), "Memory object is not suitable");
          }
  
          switch (params.mem_type) {
          case shared_mem_type::shared_mem_vasurface:
          case shared_mem_type::shared_mem_image:
              if (!layout.format.is_image_2d())
                  CLDNN_ERROR_MESSAGE(_node.id(), "Attempt to set user-supplied input or output image instead of a buffer");
              break;
          case shared_mem_type::shared_mem_buffer:
          case shared_mem_type::shared_mem_dxbuffer:
              if (layout.format.is_image_2d())
                  CLDNN_ERROR_MESSAGE(_node.id(), "Attempt to set user-supplied input or output buffer instead of an image");
              break;
          default:
              CLDNN_ERROR_MESSAGE(_node.id(), "Attempt to set user-supplied input or output memory of unknown/invalid type");
              break;
          }
      }
  }
  
  void primitive_inst::set_output_memory(memory_impl& mem) {
      auto ol = _node.get_output_layout();
  
      check_memory_to_set(mem, ol);
  
      _output = (memory_impl::ptr) &mem;
  }
  
  event_impl::ptr primitive_inst::execute(const std::vector<event_impl::ptr>& events) {
      const auto primitive_id = id();
      CLDNN_ERROR_BOOL(primitive_id,
                       "Invalid/unset input",
                       !_has_valid_input,
                       "Cannot execute primitive " + primitive_id + " with invalid/unset input");
      on_execute();
  
      if (_exec_deps.empty())
          return _impl->execute(events, *this);
  
      std::vector<event_impl::ptr> dependencies;
      dependencies.reserve(_exec_deps.size());
      for (auto& input : _exec_deps) {
          auto id = input->id();
          try {
              // if the requested event deos not exits it means that it has not been executed, so the processing_order is
              // wrong or synchronization failed.
              auto ev = get_network().get_primitive_event(id);
              dependencies.emplace_back(ev);
          } catch (const std::out_of_range& oor) {
              std::string temp = std::string("internal CLDNN error: execution order corrupted.") + std::string("\n") +
                                 std::string(oor.what() + std::string("\n"));
              CLDNN_ERROR_MESSAGE(id, temp);
          }
      }
      return _impl->execute(dependencies, *this);
  }
  
  void primitive_inst::set_arguments() {
      const auto primitive_id = id();
      CLDNN_ERROR_BOOL(primitive_id,
                       "Invalid/unset input",
                       !_has_valid_input,
                       "Cannot set arguments for primitive " + primitive_id + " with invalid/unset input");
  
      _impl->set_arguments(*this);
  }
  
  void primitive_inst::cleanup() {
      _impl->cleanup(*this);
  }
  
  void primitive_inst::build_deps() {
      if (_deps.empty() && !_node.get_dependencies().empty()) {
          _deps = _network.get_primitives(_node.get_dependencies());
          _exec_deps = build_exec_deps(_deps);
      }
  }
  
  primitive_inst::primitive_inst(network_impl& network, program_node const& node, bool allocate_memory)
      : _network(network), _node(node), _impl(node.get_selected_impl()), _output(), _output_changed(false) {
      if (allocate_memory) {
          // In case when output is mutable_data primitive, and other users dependencies are only used for
          // suychronization, The output memory of such primitive will be fused with mutable_data
          auto users = node.get_users();
          auto user_count = users.size();
          uint32_t mutable_data_count = 0;
          for (auto& user : users) {
              // Get mutable_data nodes count from nodes users
              if (user->is_type<mutable_data>()) {
                  mutable_data_count++;
              } else if (user->is_type<fused_conv_eltwise>()) {
                  if (!user->as<fused_conv_eltwise>().get_users().empty() &&
                      (*user->as<fused_conv_eltwise>().get_users().begin())->is_type<mutable_data>()) {
                      if (user->as<fused_conv_eltwise>().get_dependency(1).id() == node.id()) {
                          user_count--;
                      }
                  }
              }
          }
  
          // TODO: Remove WA for arg_max_min node.
          // For now it's required to handle the case when only second output of TopK primitive is used in plugin,
          // but kernels always write both outputs to the same memory object which leads to wrong result.
          if (user_count == 1 && mutable_data_count == 1 && !node.is_type<arg_max_min>()) {
              for (auto& user : node.get_users())
                  if (user->is_type<mutable_data>())
                      _output = user->as<mutable_data>().get_attached_memory_ptr();
          } else {
              _output = allocate_output();
          }
      }
  }
  
  memory_impl::ptr primitive_inst::allocate_output() {
      auto layout = _node.get_output_layout();
      auto net_id = get_network_id();
      auto& engine = get_network().get_engine();
  
      // For outputs, cpu prim we want to have lockable alloc type
      // Also if the successor of a node is an cpu, then memory needs to be lockable.
      auto use_lockable_memory = _node.is_output() || _node.get_selected_impl()->is_cpu()
                                 || std::any_of(_node.get_users().begin(), _node.get_users().end(),
                                                [](const program_node* n) {return n->get_selected_impl()->is_cpu() || n->can_be_optimized(); })
                                 || engine.supports_allocation(allocation_type::usm_device) == false;
      allocation_type alloc_type = use_lockable_memory ?
                                   engine.get_lockable_preffered_memory_allocation_type(layout.format.is_image_2d())
                                                       : allocation_type::usm_device;
      if (!_network.is_internal() && (_node.can_be_optimized() || _node.is_type<generic_layer>())) {
          return engine.allocate_memory(layout,
                                        _node.id(),
                                        net_id,
                                        _node.get_memory_dependencies(),
                                        alloc_type,
                                        false);
      } else if (_network.is_internal() || (!_node.can_share_buffer()) || _node.can_be_optimized() || _node.is_output()) {
          return engine.allocate_memory(layout, alloc_type, net_id);
      }
      return engine.allocate_memory(layout,
                                    _node.id(),
                                    net_id,
                                    _node.get_memory_dependencies(),
                                    alloc_type,
                                    true);
  }
  
  std::vector<std::shared_ptr<primitive_inst>> primitive_inst::build_exec_deps(
      std::vector<std::shared_ptr<primitive_inst>> const& deps) {
      std::vector<std::shared_ptr<primitive_inst>> exec_deps;
      exec_deps.reserve(deps.size());
      for (auto& dep : deps)
          if (dep->get_impl() != nullptr)
              exec_deps.push_back(dep);
  
      return exec_deps;
  }
  
  std::string primitive_inst::generic_to_string(program_node const& node, const char* type_name) {
      auto node_info = node.desc_to_json();
  
      std::stringstream primitive_description;
      std::stringstream ss_inputs;
  
      for (size_t i = 0; i < node.get_dependencies().size(); ++i) {
          auto& in = node.get_dependency(i);
          ss_inputs << in.id();
          ss_inputs << ", count: " << in.get_output_layout().count();
          i != (node.get_dependencies().size() - 1) ? ss_inputs << ", " : ss_inputs << "";
      }
  
      json_composite generic_info;
      generic_info.add("type_name", type_name);
      generic_info.add("deps count", node.get_dependencies().size());
      generic_info.add("deps", ss_inputs.str());
  
      node_info->add("generic info", generic_info);
      node_info->dump(primitive_description);
  
      return primitive_description.str();
  }
  
  }  // namespace cldnn
  