KernelGenerator.cc

Go to the documentation of this file.

/*
 * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
 *
 * 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 "KernelGenerator.h"
 
#include "kernel/IfLayer.h"
#include "kernel/PermuteLayer.h"
#include "kernel/WhileLayer.h"
 
#include "exec/FunctionSequence.h"
 
namespace onert
{
namespace backend
{
namespace builtin
{
 
KernelGenerator::KernelGenerator(const ir::Graph &graph, DynamicTensorManager *dyn_tensor_manager,
                                 const std::shared_ptr<TensorRegistry> &tensor_reg,
                                 const std::shared_ptr<ExternalContext> &external_context)
  : basic::KernelGeneratorBase{graph}, _dyn_tensor_manager{dyn_tensor_manager},
    _tensor_reg{tensor_reg}, _tensor_registries{}, _executors{nullptr}, _model_index{},
    _external_context{external_context}
{
  // DO NOTHING
}
 
std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationIndex ind)
{
  assert(_dyn_tensor_manager);
  assert(_tensor_reg);
 
  auto ret = std::make_unique<exec::FunctionSequence>();
 
  // Prepare to handle dynamic tensors later
  auto dyn_ctx = std::make_shared<exec::FunctionSequence::DynamicTensorCtx>();
  {
    dyn_ctx->op = &_graph.operations().at(ind);
    dyn_ctx->dynamic_shape_inferer = std::make_unique<exec::DynamicShapeInferer>(_tensor_reg);
  }
  ret->dynamic_tensor_ctx(dyn_ctx);
 
  auto &op = _graph.operations().at(ind);
  op.accept(*this);
  assert(_return_fn); // _return_fn must have been generated
  ret->append(std::move(_return_fn));
 
  return ret;
}
 
void KernelGenerator::visit(const ir::operation::If &node)
{
  const auto then_subg_index = node.param().then_subg_index;
  const auto else_subg_index = node.param().else_subg_index;
 
  std::vector<backend::IPortableTensor *> input_tensors;
  for (const auto &input_index : node.getInputs())
  {
    auto input_tensor = getPortableTensor(input_index);
    input_tensors.emplace_back(input_tensor);
  }
 
  std::vector<backend::IPortableTensor *> output_tensors;
  for (const auto &output_index : node.getOutputs())
  {
    auto output_tensor = getPortableTensor(output_index);
    output_tensors.emplace_back(output_tensor);
  }
 
  // IfLayer just set Executors instead of then and else executor to avoid complexity of
  // creating executor recusively
  const auto cond_tensor = input_tensors.front();
  input_tensors.erase(input_tensors.begin());
  auto fn = std::make_unique<::onert::backend::builtin::kernel::IfLayer>(
    cond_tensor, input_tensors, output_tensors, then_subg_index, else_subg_index, _executors,
    _model_index, _external_context);
 
  _return_fn = std::move(fn);
}
 
void KernelGenerator::visit(const ir::operation::Permute &node)
{
  const auto output_index{node.getOutputs().at(0)};
  const auto input_index{node.getInputs().at(0)};
 
  // Add PermuteLayer
  std::vector<ITensor *> output_tensors{getTensor(output_index)};
  std::vector<ITensor *> input_tensors{getTensor(input_index)};
  std::vector<ir::PermuteType> permute_types;
 
  // Layout in graph is always NHWC, so layout is not changed
  for (uint32_t i = 0; i < input_tensors.size(); i++)
    permute_types.emplace_back(ir::PermuteType::COPY);
 
  auto fn = std::make_unique<kernel::PermuteLayer>(input_tensors, output_tensors, permute_types,
                                                   _external_context);
  _return_fn = std::move(fn);
}
 
void KernelGenerator::visit(const ir::operation::While &node)
{
  const auto cond_subg_index = node.param().cond_subg_index;
  const auto body_subg_index = node.param().body_subg_index;
 
  // This op does not support input as a constant, because builtin backend does not have
  // TensorBuilder
  std::vector<backend::IPortableTensor *> input_tensors;
  for (const auto &input_index : node.getInputs())
  {
    auto input_tensor = getPortableTensor(input_index);
    input_tensors.emplace_back(input_tensor);
  }
 
  std::vector<backend::IPortableTensor *> output_tensors;
  for (const auto &output_index : node.getOutputs())
  {
    auto output_tensor = getPortableTensor(output_index);
    output_tensors.emplace_back(output_tensor);
  }
 
  // WhileLayer just set Executors instead of cond and body executor to avoid complexity of
  // creating executor recusively
  auto fn = std::make_unique<::onert::backend::builtin::kernel::WhileLayer>(
    input_tensors, output_tensors, cond_subg_index, body_subg_index, _executors, _model_index,
    _dyn_tensor_manager->dynamic_mem_mgr().get(), _external_context);
 
  _return_fn = std::move(fn);
}
 
backend::ITensor *KernelGenerator::getTensor(const ir::OperandIndex &index)
{
  // get Tensor from all tensor registries (for Permute op)
  auto ret = _tensor_registries.getITensor(index);
  assert(ret != nullptr);
  return ret;
}
 
backend::IPortableTensor *KernelGenerator::getPortableTensor(const ir::OperandIndex &index)
{
  auto ret = _tensor_reg->getPortableTensor(index);
  assert(ret != nullptr);
  return ret;
}
 
} // namespace builtin
} // namespace backend
} // namespace onert