onert::backend::basic Namespace Reference

Namespaces
namespace	train

Data Structures
class	Allocator
	Class to allocate memory. More...
struct	Block
	Structure to have memory offset and size. More...
class	BumpPlanner
	Class to plan memory by bump way. More...
class	DynamicMemoryManager
class	DynamicTensorManager
	Class to manage dynamic tensor and its memory. More...
class	ExternalTensor
	Class that uses data from external memory that is not managed by a backend instead of allocating and copying the data. (ex. constant data from model file) ExternalTensor's data pointer points to an address of memory such as where memory is already allocated, or mmapped area. This is meaning that ExternalTensor can take all of types' ir::Data. To support this, assume below things no padding, always NHWC layout, constant tensor and not dynamic. More...
class	FirstFitPlanner
	Class to plan memory by firstfit way. More...
struct	IMemoryPlanner
	Interface to plan memory. More...
class	KernelGeneratorBase
class	MemoryManager
class	MemoryPlannerFactory
class	StaticTensorManager
class	Tensor
class	TensorBuilder
class	WICPlanner
	Class to plan memory by Weighted Interval Color algorithm. More...

Typedefs
using	TensorRegistry = PortableTensorRegistryTemplate< basic::Tensor >

Functions
template<typename T_TensorBuilder >
void	planTensors (const std::shared_ptr< T_TensorBuilder > &tensor_builder, const ir::Graph &graph, const util::Set< ir::OperandIndex > &external_operands, const std::vector< onert::ir::OperationIndex > &op_order)
template<typename T_TensorBuilder >
util::Set< ir::OperandIndex >	register_source_memory_tensors (const std::shared_ptr< T_TensorBuilder > &tensor_builder, const ir::Graph &graph, const util::Set< ir::OperandIndex > &external_operands, const ir::OperandIndexMap< ir::OperandIndex > &shared_memory_operand_idx)
template<typename T_TensorBuilder >
ITensorRegistry *	genTensors (const std::shared_ptr< T_TensorBuilder > &tensor_builder, const ir::Graph &graph, const util::Set< ir::OperandIndex > &external_operands, const std::shared_ptr< ITensorRegistry > &tensor_registry, const std::vector< onert::ir::OperationIndex > &op_order, const ir::OperandIndexMap< ir::OperandIndex > &shared_memory_operand_idx)
template<typename T_BackendContext >
ITensorRegistry *	genTensors (T_BackendContext &ctx)
void	initSharedMemoryConsts (const ir::Operands &operands, const util::Set< ir::OperandIndex > &external_operands, TensorRegistry *tensor_registry, const ir::OperandIndexMap< ir::OperandIndex > &shared_memory_operands_map)
void	initConsts (const ir::Operands &operands, const util::Set< ir::OperandIndex > &external_operands, ITensorRegistry *tensor_registry, const ir::OperandIndexMap< ir::OperandIndex > &shared_memory_operands_map)
void	initConsts (BackendContext &ctx)

Typedef Documentation

TensorRegistry

using onert::backend::basic::TensorRegistry = typedef PortableTensorRegistryTemplate<basic::Tensor>

Definition at line 26 of file TensorRegistry.h.

Function Documentation

genTensors() [1/2]

template<typename T_TensorBuilder >

ITensorRegistry * onert::backend::basic::genTensors	(	const std::shared_ptr< T_TensorBuilder > &	tensor_builder,
		const ir::Graph &	graph,
		const util::Set< ir::OperandIndex > &	external_operands,
		const std::shared_ptr< ITensorRegistry > &	tensor_registry,
		const std::vector< onert::ir::OperationIndex > &	op_order,
		const ir::OperandIndexMap< ir::OperandIndex > &	shared_memory_operand_idx
	)

Definition at line 197 of file BackendContextHelpers.h.

{
  const auto registered_source_ind = register_source_memory_tensors(
    tensor_builder, graph, external_operands, shared_memory_operand_idx);
  graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
    if (external_operands.contains(ind))
      return;
    if (registered_source_ind.contains(ind)) // skip tensors already registered
      return;
    tensor_builder->registerTensorInfo(ind, obj.info());
  });
 
  // TODO Get compiler options from compiler, and use it rather than getting it from Env
  if (util::getConfigString(util::config::EXECUTOR) == "Linear")
  {
    basic::planTensors(tensor_builder, graph, external_operands, op_order);
  }
  else
  {
    // For the executors that does not have fixed linear execution order:
    // To make tensors never be deallocated, this is a workaround to use static memory planner
    graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &) {
      if (tensor_builder->isRegistered(ind))
        tensor_builder->notifyFirstUse(ind);
    });
  }
 
  tensor_builder->allocate();
 
  return tensor_registry.get();
}

References onert::util::Set< Element >::contains(), onert::util::getConfigString(), planTensors(), and register_source_memory_tensors().

Referenced by onert::backend::cpu::BackendContext::genTensors(), onert::backend::ruy::BackendContext::genTensors(), onert::backend::trix::BackendContext::genTensors(), onert::backend::builtin::BackendContext::genTensors(), and genTensors().

genTensors() [2/2]

template<typename T_BackendContext >

ITensorRegistry * onert::backend::basic::genTensors

(

T_BackendContext &

ctx

)

Definition at line 234 of file BackendContextHelpers.h.

{
  return genTensors(ctx.tensor_builder, *ctx.graph(), ctx.external_operands(), ctx.tensor_registry,
                    ctx.data().op_order, {});
}

References genTensors().

initConsts() [1/2]

void onert::backend::basic::initConsts ( BackendContext &  ctx )

inline

Definition at line 296 of file BackendContextHelpers.h.

{
  initConsts(ctx.graph()->operands(), ctx.external_operands(), ctx.tensor_registry.get(), {});
}

References onert::backend::BackendContext::external_operands(), onert::backend::BackendContext::graph(), initConsts(), onert::ir::Graph::operands(), and onert::backend::BackendContext::tensor_registry.

initConsts() [2/2]

void onert::backend::basic::initConsts ( const ir::Operands &  operands, const util::Set< ir::OperandIndex > &  external_operands, ITensorRegistry *  tensor_registry, const ir::OperandIndexMap< ir::OperandIndex > &  shared_memory_operands_map  )

inline

Definition at line 266 of file BackendContextHelpers.h.

{
  operands.iterate([&](const ir::OperandIndex &ind, const ir::Operand &operand) {
    if (external_operands.contains(ind) || !operand.isConstant())
      return;
    const bool has_const_shared_source =
      shared_memory_operands_map.find(ind) != std::end(shared_memory_operands_map) &&
      operands.at(shared_memory_operands_map.at(ind)).isConstant();
    if (has_const_shared_source)
      return; // tensors with shared memory are processed in initSharedMemoryConsts
 
    auto tensor = tensor_registry->getNativeITensor(ind);
    assert(tensor != nullptr);
 
    VERBOSE(FillOperandData) << "Fill data for " << ind << std::endl;
 
    auto data = operand.shareData();
    assert(data && data->base());
    ExternalTensor *ext_tensor = dynamic_cast<ExternalTensor *>(tensor);
 
    if (ext_tensor == nullptr)
      throw std::runtime_error{"This tensor is not external tensor"};
 
    ext_tensor->setData(data);
  });
}

References onert::util::ObjectManager< Index, Object >::at(), onert::util::Set< Element >::contains(), onert::backend::ITensorRegistry::getNativeITensor(), onert::ir::Operand::isConstant(), onert::util::ObjectManager< Index, Object >::iterate(), onert::backend::basic::ExternalTensor::setData(), onert::ir::Operand::shareData(), and VERBOSE.

Referenced by onert::backend::cpu::BackendContext::genKernels(), onert::backend::ruy::BackendContext::genKernels(), onert::backend::trix::BackendContext::genKernels(), onert::backend::builtin::BackendContext::genKernels(), and initConsts().

initSharedMemoryConsts()

void onert::backend::basic::initSharedMemoryConsts ( const ir::Operands &  operands, const util::Set< ir::OperandIndex > &  external_operands, TensorRegistry *  tensor_registry, const ir::OperandIndexMap< ir::OperandIndex > &  shared_memory_operands_map  )

inline

Definition at line 241 of file BackendContextHelpers.h.

{
  operands.iterate([&](const ir::OperandIndex &ind, const ir::Operand &) {
    if (external_operands.contains(ind))
      return;
    const auto shared_mem_it = shared_memory_operands_map.find(ind);
    if (shared_mem_it == std::end(shared_memory_operands_map))
      return; // no shared memory source
    if (!operands.at(shared_mem_it->second).isConstant())
      return; // source operand not a constant
 
    VERBOSE(FillOperandData) << "Fill shared data for " << ind << std::endl;
 
    const auto &source_operand_ind = operands.at(shared_mem_it->second);
    auto memory_source_data = source_operand_ind.shareData();
    assert(memory_source_data && memory_source_data->base());
    auto tensor = tensor_registry->getNativeTensor(ind);
    assert(tensor != nullptr);
    tensor->setBuffer(const_cast<uint8_t *>(memory_source_data->base()));
  });
}

References onert::util::ObjectManager< Index, Object >::at(), onert::util::Set< Element >::contains(), onert::backend::PortableTensorRegistryTemplate< T_Tensor >::getNativeTensor(), onert::util::ObjectManager< Index, Object >::iterate(), and VERBOSE.

Referenced by onert::backend::cpu::BackendContext::genKernels().

planTensors()

template<typename T_TensorBuilder >

void onert::backend::basic::planTensors	(	const std::shared_ptr< T_TensorBuilder > &	tensor_builder,
		const ir::Graph &	graph,
		const util::Set< ir::OperandIndex > &	external_operands,
		const std::vector< onert::ir::OperationIndex > &	op_order
	)

Definition at line 35 of file BackendContextHelpers.h.

{
  ir::OperandIndexMap<uint32_t> uses_map;
  ir::OperandIndexMap<uint32_t> def_map;
  ir::OperandIndexSequence constants;
 
  // Prepare scanning
  graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
    if (external_operands.contains(ind))
      return;
 
    // TODO Check if we need to handle unused tensors
 
    uses_map[ind] = obj.getUses().size();
    def_map[ind] = obj.getDef().valid() ? 1 : 0;
 
    if (obj.isConstant())
      constants.append(ind);
 
    if (!tensor_builder->isRegistered(ind))
    {
      // These tensors do not exist in any  (No use and def)
      const auto &info = obj.info();
      tensor_builder->registerTensorInfo(ind, info);
    }
  });
 
  // Start scanning to do notify{First|Last}Use for each tensor
 
  // If a tensor is a constant, increase the use of the tensor and allocate it first.
  // Increasing use count here makes the tensor never be deallocated, i.e it they will be
  // deallocated last.
  for (const auto &ind : constants)
  {
    uses_map[ind]++;
    tensor_builder->notifyFirstUse(ind);
  }
 
  for (const auto &[ind, def_count] : def_map)
  {
    if (def_count == 0)
      tensor_builder->notifyFirstUse(ind);
  }
 
  // This is a workaround to keep the operands over the execution
  // (the operands look like they are unused)
  std::vector<ir::OperandIndex> operands_last_until_end;
  for (const auto &[ind, use_count] : uses_map)
  {
    if (use_count == 0)
      operands_last_until_end.push_back(ind);
  }
 
  // At each operation,
  // 1. Scan DEF of outputs. If the DEF, allocate it
  // 2. Scan DEF of inputs. If variable tensor, allocate it
  // 3. Scan USE of inputs. Decrease the USE and deallocate if the USE is 0
  for (const auto &op_ind : op_order)
  {
    const auto &op = graph.operations().at(op_ind);
    auto op_inputs = op.getInputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED;
    auto op_outputs = op.getOutputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED;
 
    // Define outputs
    for (const auto &ind : op_outputs)
    {
      if (external_operands.contains(ind))
        continue;
      if (!tensor_builder->isRegistered(ind))
        continue;
      assert(def_map.find(ind) != def_map.end());
      if (def_map[ind])
      {
        def_map[ind] = 0;
        tensor_builder->notifyFirstUse(ind);
      }
    }
 
    // Scan variable tensors
    // This tensor has features like constant. But OperandInfo and LowerInfo treat them as
    // non-constant because of less memory usage by memory planning in here
    for (const auto &ind : op_inputs)
    {
      if (external_operands.contains(ind))
        continue;
      if (!tensor_builder->isRegistered(ind))
        continue;
      const auto &operand = graph.operands().at(ind);
      if (operand.info().isVariable())
      {
        // The variable tensor with buffer is not supported yet
        assert(operand.data() == nullptr);
        assert(operand.getUses().size() == 1 && !operand.getDef().valid());
        assert(uses_map[ind] == 1 && def_map[ind] == 0);
        tensor_builder->notifyFirstUse(ind);
      }
    }
 
    for (const auto &ind : op_inputs)
    {
      if (external_operands.contains(ind))
        continue;
      if (!tensor_builder->isRegistered(ind))
        continue;
      assert(uses_map.find(ind) != uses_map.end());
      assert(uses_map[ind] > 0);
      uses_map[ind]--;
      if (uses_map[ind] == 0)
      {
        // plan for deallocation of static tensornode
        tensor_builder->notifyLastUse(ind);
      }
    }
  }
 
  for (const auto &ind : operands_last_until_end)
  {
    tensor_builder->notifyLastUse(ind);
  }
 
  // Dispose and validate
  for (const auto &ind : constants)
  {
    --uses_map[ind];
    if (uses_map[ind] == 0) // To prevent notifyLastUse from being called twice
    {
      tensor_builder->notifyLastUse(ind);
    }
  }
 
  assert(
    std::all_of(uses_map.begin(), uses_map.end(),
                [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; }));
 
  assert(
    std::all_of(def_map.begin(), def_map.end(),
                [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; }));
}

References onert::ir::OperandIndexSequence::append(), onert::util::Set< Element >::contains(), onert::ir::DUPLICATED, info, and onert::ir::UNDEFINED.

Referenced by genTensors().

register_source_memory_tensors()

template<typename T_TensorBuilder >

util::Set< ir::OperandIndex > onert::backend::basic::register_source_memory_tensors	(	const std::shared_ptr< T_TensorBuilder > &	tensor_builder,
		const ir::Graph &	graph,
		const util::Set< ir::OperandIndex > &	external_operands,
		const ir::OperandIndexMap< ir::OperandIndex > &	shared_memory_operand_idx
	)

Definition at line 177 of file BackendContextHelpers.h.

{
  // process source tensors that share memory at first
  util::Set<ir::OperandIndex> registered_source_ind;
  for (const auto &[_, source_ind] : shared_memory_operand_idx)
  {
    if (external_operands.contains(source_ind))
      continue;
    if (tensor_builder->isRegistered(source_ind)) // some tensors can have the same source
      continue;
    tensor_builder->registerTensorInfo(source_ind, graph.operands().at(source_ind).info());
    registered_source_ind.add(source_ind);
  }
  return registered_source_ind;
}

References onert::util::Set< Element >::add(), and onert::util::Set< Element >::contains().

Referenced by genTensors().