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[onert] Remove UNUSED_RELEASE macro #14336

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Nov 22, 2024
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[onert] Remove UNUSED_RELEASE macro #14336

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18 changes: 5 additions & 13 deletions runtime/onert/backend/acl_cl/KernelGenerator.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,6 @@
#include "exec/NopFunction.h"
#include "exec/FunctionSequence.h"
#include "util/logging.h"
#include "util/Utils.h"
#include "AclKernelGen.h"

namespace onert
Expand Down Expand Up @@ -419,14 +418,11 @@ void KernelGenerator::visit(const ir::operation::Slice &node)
assert(_ctx.at(sizes_index).data());
auto beginData_base = _ctx.at(begins_index).data()->base();
auto sizeData_base = _ctx.at(sizes_index).data()->base();
const int beginData_size = _ctx.at(begins_index).shape().num_elements();
const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();
[[maybe_unused]] const int beginData_size = _ctx.at(begins_index).shape().num_elements();
[[maybe_unused]] const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();

using ir::DataType;

UNUSED_RELEASE(beginData_size);
UNUSED_RELEASE(sizeData_size);

assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32);
assert(beginData_size == input_rank);
Expand Down Expand Up @@ -486,16 +482,12 @@ void KernelGenerator::visit(const ir::operation::StridedSlice &node)
auto startData_base = _ctx.at(starts_index).data()->base();
auto endData_base = _ctx.at(ends_index).data()->base();
auto stridesData_base = _ctx.at(strides_index).data()->base();
const int startData_size = _ctx.at(starts_index).shape().num_elements();
const int endData_size = _ctx.at(ends_index).shape().num_elements();
const int stridesData_size = _ctx.at(strides_index).shape().num_elements();
[[maybe_unused]] const int startData_size = _ctx.at(starts_index).shape().num_elements();
[[maybe_unused]] const int endData_size = _ctx.at(ends_index).shape().num_elements();
[[maybe_unused]] const int stridesData_size = _ctx.at(strides_index).shape().num_elements();

using ir::DataType;

UNUSED_RELEASE(startData_size);
UNUSED_RELEASE(endData_size);
UNUSED_RELEASE(stridesData_size);

assert(_ctx.at(starts_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(ends_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(strides_index).typeInfo().type() == DataType::INT32);
Expand Down
6 changes: 2 additions & 4 deletions runtime/onert/backend/acl_common/AclKernelGen.h
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Expand Up @@ -238,9 +238,8 @@ kernelGenFullyConnected(const ir::operation::FullyConnected &node, const ir::Ope

const auto input_rank = operands.at(input_index).shape().rank();

const auto output_size =
[[maybe_unused]] const auto output_size =
operands.at(output_index).shape().dim(operands.at(output_index).shape().rank() - 1);
UNUSED_RELEASE(output_size);
assert(bias_index.undefined() || operands.at(bias_index).shape().dim(0) == output_size);
assert(operands.at(weight_index).shape().dim(0) == output_size);
const auto batch_size =
Expand All @@ -254,13 +253,12 @@ kernelGenFullyConnected(const ir::operation::FullyConnected &node, const ir::Ope
if (input_rank == 3 || input_rank == 4)
{
const auto &ifm_shape = operands.at(input_index).shape();
auto feature_size = 1;
[[maybe_unused]] auto feature_size = 1;
for (int i = 0; i < ifm_shape.rank(); ++i)
{
feature_size *= ifm_shape.dim(i);
}

UNUSED_RELEASE(feature_size);
assert(feature_size == batch_size * input_size);

// for reshaping
Expand Down
21 changes: 6 additions & 15 deletions runtime/onert/backend/acl_neon/KernelGenerator.cc
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Expand Up @@ -29,7 +29,6 @@
#include "ir/InternalType.h"
#include "exec/NopFunction.h"
#include "util/logging.h"
#include "util/Utils.h"
#include "AclKernelGen.h"

namespace onert
Expand Down Expand Up @@ -751,8 +750,7 @@ void KernelGenerator::visit(const ir::operation::Pad &node)
padding_list[axis] = ::arm_compute::PaddingInfo{from[0], from[1]};
}

const auto input_type = _ctx.at(input_index).typeInfo();
UNUSED_RELEASE(input_type);
[[maybe_unused]] const auto input_type = _ctx.at(input_index).typeInfo();
assert(input->info()->data_type() == acl_common::asDataType(input_type.type()));
assert(input->info()->quantization_info() ==
::arm_compute::QuantizationInfo(input_type.scale(), input_type.zero_point()));
Expand Down Expand Up @@ -1041,14 +1039,11 @@ void KernelGenerator::visit(const ir::operation::Slice &node)
{
auto beginData_base = _ctx.at(begins_index).data()->base();
auto sizeData_base = _ctx.at(sizes_index).data()->base();
const int beginData_size = _ctx.at(begins_index).shape().num_elements();
const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();
[[maybe_unused]] const int beginData_size = _ctx.at(begins_index).shape().num_elements();
[[maybe_unused]] const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();

using ir::DataType;

UNUSED_RELEASE(beginData_size);
UNUSED_RELEASE(sizeData_size);

assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32);
assert(beginData_size == input_rank);
Expand Down Expand Up @@ -1105,16 +1100,12 @@ void KernelGenerator::visit(const ir::operation::StridedSlice &node)
auto startData_base = _ctx.at(starts_index).data()->base();
auto endData_base = _ctx.at(ends_index).data()->base();
auto stridesData_base = _ctx.at(strides_index).data()->base();
const int startData_size = _ctx.at(starts_index).shape().num_elements();
const int endData_size = _ctx.at(ends_index).shape().num_elements();
const int stridesData_size = _ctx.at(strides_index).shape().num_elements();
[[maybe_unused]] const int startData_size = _ctx.at(starts_index).shape().num_elements();
[[maybe_unused]] const int endData_size = _ctx.at(ends_index).shape().num_elements();
[[maybe_unused]] const int stridesData_size = _ctx.at(strides_index).shape().num_elements();

using ir::DataType;

UNUSED_RELEASE(startData_size);
UNUSED_RELEASE(endData_size);
UNUSED_RELEASE(stridesData_size);

assert(_ctx.at(starts_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(ends_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(strides_index).typeInfo().type() == DataType::INT32);
Expand Down
2 changes: 1 addition & 1 deletion runtime/onert/backend/cpu/KernelGenerator.cc
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Expand Up @@ -251,7 +251,7 @@ std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationI
auto dyn_ctx = std::make_shared<exec::FunctionSequence::DynamicTensorCtx>();
{
dyn_ctx->op = &_operations_ctx.at(ind);
dyn_ctx->dynamic_shape_inferer = std::make_shared<exec::DynamicShapeInferer>(_ctx, _tensor_reg);
dyn_ctx->dynamic_shape_inferer = std::make_shared<exec::DynamicShapeInferer>(_tensor_reg);
}
ret->dynamic_tensor_ctx(dyn_ctx);

Expand Down
4 changes: 2 additions & 2 deletions runtime/onert/backend/cpu/ops/OperationUtils.cc
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Expand Up @@ -85,10 +85,10 @@ void GetQuantizedConvolutionMultiplier(const IPortableTensor *input, const IPort
double *multiplier)
{
const double input_product_scale = input->data_scale() * filter->data_scale();
const double bias_scale = (bias != nullptr) ? bias->data_scale() : input_product_scale;
[[maybe_unused]] const double bias_scale =
(bias != nullptr) ? bias->data_scale() : input_product_scale;
const double output_scale = output->data_scale();
// The following conditions must be guaranteed by the training pipeline.
UNUSED_RELEASE(bias_scale);
assert(std::abs(input_product_scale - bias_scale) <=
1e-6 * std::min(input_product_scale, bias_scale));
assert(input_product_scale >= 0);
Expand Down
6 changes: 4 additions & 2 deletions runtime/onert/backend/ruy/KernelGenerator.cc
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Expand Up @@ -46,7 +46,7 @@ std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationI
auto dyn_ctx = std::make_shared<exec::FunctionSequence::DynamicTensorCtx>();
{
dyn_ctx->op = &_operations_ctx.at(ind);
dyn_ctx->dynamic_shape_inferer = std::make_shared<exec::DynamicShapeInferer>(_ctx, _tensor_reg);
dyn_ctx->dynamic_shape_inferer = std::make_shared<exec::DynamicShapeInferer>(_tensor_reg);
}
ret->dynamic_tensor_ctx(dyn_ctx);

Expand Down Expand Up @@ -137,6 +137,8 @@ void KernelGenerator::visit(const ir::operation::FullyConnected &node)
const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)};
const auto activation = node.param().activation;
const auto weights_format = node.param().weights_format;
if (weights_format != ir::FullyConnectedWeightsFormat::Default)
throw std::runtime_error("Unsupported FullyConnected Weights Format");
Comment on lines +140 to +141
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weights_format param is not used on ruy backend. ruy backend supports default format only.


auto output_tensor = _tensor_reg->getPortableTensor(output_index);
auto input_tensor = _tensor_reg->getPortableTensor(input_index);
Expand All @@ -145,7 +147,7 @@ void KernelGenerator::visit(const ir::operation::FullyConnected &node)

auto fn = std::make_unique<ops::FullyConnectedLayer>();

fn->configure(input_tensor, weight_tensor, bias_tensor, activation, weights_format, output_tensor,
fn->configure(input_tensor, weight_tensor, bias_tensor, activation, output_tensor,
_external_context);

_return_fn = std::move(fn);
Expand Down
2 changes: 0 additions & 2 deletions runtime/onert/backend/ruy/ops/FullyConnectedLayer.cc
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Expand Up @@ -60,11 +60,9 @@ void FullyConnectedLayer::fullyConnectedFloat32()

void FullyConnectedLayer::configure(const IPortableTensor *input, const IPortableTensor *weights,
const IPortableTensor *bias, ir::Activation activation,
ir::FullyConnectedWeightsFormat weights_format,
IPortableTensor *output,
const std::shared_ptr<ExternalContext> &external_context)
{
UNUSED_RELEASE(weights_format);
_input = input;
_weights = weights;
_bias = bias;
Expand Down
3 changes: 1 addition & 2 deletions runtime/onert/backend/ruy/ops/FullyConnectedLayer.h
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Expand Up @@ -42,8 +42,7 @@ class FullyConnectedLayer : public ::onert::exec::IFunction
void fullyConnectedFloat32();

void configure(const IPortableTensor *input, const IPortableTensor *weights,
const IPortableTensor *bias, ir::Activation activation,
ir::FullyConnectedWeightsFormat weights_format, IPortableTensor *output,
const IPortableTensor *bias, ir::Activation activation, IPortableTensor *output,
const std::shared_ptr<ExternalContext> &external_context);

void run() override;
Expand Down
5 changes: 2 additions & 3 deletions runtime/onert/backend/train/KernelGenerator.cc
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Expand Up @@ -34,7 +34,6 @@
#include <backend/Backend.h>
#include <backend/IConfig.h>
#include <memory>
#include <util/Utils.h>
#include <util/logging.h>
#include <exec/DynamicShapeInferer.h>

Expand Down Expand Up @@ -622,8 +621,8 @@ IPortableTensor *KernelGenerator::getBackPropIn(const ir::IOperation &node,
_tensor_reg->getDisposableBackPropTensor(DisposableTensorIndex{op_index, operand_index});
if (disposable_tensor != nullptr)
{
const auto &training_usedefs = _tgraph.trainingUseDefs().at(backwarding_operand_index);
UNUSED_RELEASE(training_usedefs);
[[maybe_unused]] const auto &training_usedefs =
_tgraph.trainingUseDefs().at(backwarding_operand_index);
assert(std::count_if(training_usedefs.getTrainingDefs().begin(),
training_usedefs.getTrainingDefs().end(),
[&](const ir::train::TrainingOperationIndex &op_index) {
Expand Down
3 changes: 0 additions & 3 deletions runtime/onert/backend/train/TensorPlanner.cc
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Expand Up @@ -30,9 +30,6 @@ TensorPlanner::TensorPlanner(const ir::train::TrainableGraph &tgraph,
: _tgraph{tgraph}, _external_operands{external_operands}
{
// DO NOTHING
// TODO Remove the following lines
UNUSED_RELEASE(_tgraph);
UNUSED_RELEASE(_external_operands);
}

void TensorPlanner::planNonConstTensors(TensorBuilder *tensor_builder)
Expand Down
2 changes: 1 addition & 1 deletion runtime/onert/backend/xnnpack/KernelGenerator.cc
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Expand Up @@ -59,7 +59,7 @@ std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationI
auto dyn_ctx = std::make_shared<exec::FunctionSequence::DynamicTensorCtx>();
{
dyn_ctx->op = &_operations_ctx.at(ind);
dyn_ctx->dynamic_shape_inferer = std::make_shared<exec::DynamicShapeInferer>(_ctx, _tensor_reg);
dyn_ctx->dynamic_shape_inferer = std::make_shared<exec::DynamicShapeInferer>(_tensor_reg);
}
ret->dynamic_tensor_ctx(dyn_ctx);

Expand Down
4 changes: 1 addition & 3 deletions runtime/onert/core/include/backend/ITensor.h
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Expand Up @@ -26,7 +26,6 @@
#include "ir/Layout.h"
#include "ir/Shape.h"
#include "ir/Coordinates.h"
#include "util/Utils.h"

namespace onert
{
Expand Down Expand Up @@ -96,9 +95,8 @@ class ITensor
* @brief Set the shape of tenser to new_shape
* @note Higer dimension will be placed on front.
*/
virtual void setShape(const ir::Shape &new_shape)
virtual void setShape(const ir::Shape &)
{
UNUSED_RELEASE(new_shape);
throw std::runtime_error("This backend does not support dynamic setShape");
}

Expand Down
12 changes: 3 additions & 9 deletions runtime/onert/core/include/exec/DynamicShapeInferer.h
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Expand Up @@ -36,12 +36,10 @@ namespace exec
class DynamicShapeInferer : public ir::OperationVisitor
{
public:
DynamicShapeInferer(const ir::Operands &operands,
const std::shared_ptr<backend::ITensorRegistry> &tensor_registry)
: _operands(operands), _tensor_registry(tensor_registry)
DynamicShapeInferer(const std::shared_ptr<backend::ITensorRegistry> &tensor_registry)
: _tensor_registry(tensor_registry)
{
UNUSED_RELEASE(_operands);
UNUSED_RELEASE(_tensor_registry);
// DO NOTHING
}

public:
Expand Down Expand Up @@ -120,10 +118,6 @@ class DynamicShapeInferer : public ir::OperationVisitor
bool currently_static(backend::ITensor *op_input) { return !op_input->is_dynamic(); }

private:
/**
* @brief To get operand-level info, e.g., ir::Operand::isConstant()
*/
const ir::Operands &_operands;
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_operands is not used in DynamicShapeInferer.

/**
* @brief To get tensor object and access tensor-level info, e.g., ITensor::buffer()
*/
Expand Down
6 changes: 1 addition & 5 deletions runtime/onert/core/include/util/Utils.h
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Expand Up @@ -25,8 +25,6 @@
#include "ir/Coordinates.h"
#include "ir/Shape.h"

#define UNUSED_RELEASE(a) (void)(a)

template <size_t rest> struct ForEachDimension
{
template <typename L>
Expand All @@ -53,10 +51,8 @@ template <size_t rest> struct ForEachDimension
template <> struct ForEachDimension<0>
{
template <typename L>
static void unroll(const onert::ir::Shape &shape, onert::ir::Coordinates &coords,
L lambda_function)
static void unroll(const onert::ir::Shape &, onert::ir::Coordinates &coords, L lambda_function)
{
UNUSED_RELEASE(shape);
lambda_function(coords);
}
};
Expand Down
7 changes: 2 additions & 5 deletions runtime/onert/core/src/backend/builtin/KernelGenerator.cc
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Expand Up @@ -36,9 +36,7 @@ KernelGenerator::KernelGenerator(const ir::Graph &graph, DynamicTensorManager *d
_tensor_reg{tensor_reg}, _tensor_registries{}, _executors{nullptr}, _model_index{},
_external_context{external_context}
{
UNUSED_RELEASE(_graph);
UNUSED_RELEASE(_tensor_registries);
UNUSED_RELEASE(_executors);
// DO NOTHING
}

std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationIndex ind)
Expand All @@ -52,8 +50,7 @@ std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationI
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>(_graph.operands(), _tensor_reg);
dyn_ctx->dynamic_shape_inferer = std::make_unique<exec::DynamicShapeInferer>(_tensor_reg);
}
ret->dynamic_tensor_ctx(dyn_ctx);

Expand Down
21 changes: 10 additions & 11 deletions runtime/onert/core/src/compiler/ExecutorFactory.cc
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Expand Up @@ -174,8 +174,8 @@ createBackendContexts(compiler::ILoweredGraph &lgraph, bool linear_executor,
auto new_operand = std::make_unique<ir::Operand>(operand);
new_operand->clearDefUse();
operand.releaseData(); // Deref data of LoweredGraph
auto new_operand_ind = partial_graph.addOperand(operand_ind, std::move(new_operand));
UNUSED_RELEASE(new_operand_ind);
[[maybe_unused]] auto new_operand_ind =
partial_graph.addOperand(operand_ind, std::move(new_operand));
assert(new_operand_ind == operand_ind);
});
// Separate operations into partial graphs
Expand All @@ -202,15 +202,14 @@ createBackendContexts(compiler::ILoweredGraph &lgraph, bool linear_executor,
const auto &operand = whole_graph.operands().at(operand_ind);
auto new_operand = std::make_unique<ir::Operand>(operand);
new_operand->clearDefUse();
auto new_operand_ind = partial_graph.addOperand(operand_ind, std::move(new_operand));
UNUSED_RELEASE(new_operand_ind);
[[maybe_unused]] auto new_operand_ind =
partial_graph.addOperand(operand_ind, std::move(new_operand));
assert(new_operand_ind == operand_ind);

external_operands.add(operand_ind);
}

auto new_op_ind = partial_graph.addOperation(op_ind, clone(operation));
UNUSED_RELEASE(new_op_ind);
[[maybe_unused]] auto new_op_ind = partial_graph.addOperation(op_ind, clone(operation));
assert(new_op_ind == op_ind);
}
});
Expand Down Expand Up @@ -649,7 +648,8 @@ exec::IExecutor *ExecutorFactory::createTrainableExecutor(
const onert::ir::IOperation &op) {
try
{
UNUSED_RELEASE(dynamic_cast<const ir::train::ITrainableOperation &>(op));
[[maybe_unused]] const auto &casted_op =
dynamic_cast<const ir::train::ITrainableOperation &>(op);
}
catch (std::bad_cast &)
{
Expand All @@ -674,8 +674,7 @@ exec::IExecutor *ExecutorFactory::createTrainableExecutor(
[&](const onert::ir::OperationIndex &op_index, const onert::ir::IOperation &) {
const auto &orig_tgraph = lowered_graph->trainable_graph();
const auto &trainable_op = orig_tgraph.operation(op_index);
auto gen_index = tgraph->replaceOperation(op_index, trainable_op.clone());
UNUSED_RELEASE(gen_index);
[[maybe_unused]] auto gen_index = tgraph->replaceOperation(op_index, trainable_op.clone());
assert(gen_index == op_index);
});
data.graph->operands().iterate([&](const ir::OperandIndex &index, const ir::Operand &) {
Expand All @@ -684,8 +683,8 @@ exec::IExecutor *ExecutorFactory::createTrainableExecutor(
{
const auto &bwd_operand = orig_tgraph.backward_operands().at(index);
auto new_bwd_operand = std::make_unique<ir::Operand>(bwd_operand);
auto gen_index = tgraph->addBackwardOperand(index, std::move(new_bwd_operand));
UNUSED_RELEASE(gen_index);
[[maybe_unused]] auto gen_index =
tgraph->addBackwardOperand(index, std::move(new_bwd_operand));
assert(gen_index == index);
}
});
Expand Down
8 changes: 4 additions & 4 deletions runtime/onert/core/src/compiler/HEScheduler.cc
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Expand Up @@ -289,10 +289,10 @@ void HEScheduler::makeRank()
[&](const ir::OperationIndex &index, const ir::IOperation &) { DFSMaxRank(index); });

// Check that ranks are calculated for all operations(nodes)
_graph->operations().iterate([&](const ir::OperationIndex &index, const ir::IOperation &) {
UNUSED_RELEASE(index);
assert(_op_to_rank->find(index) != _op_to_rank->end());
});
_graph->operations().iterate(
[&]([[maybe_unused]] const ir::OperationIndex &index, const ir::IOperation &) {
assert(_op_to_rank->find(index) != _op_to_rank->end());
});
VERBOSE(HEScheduler::makeRank) << "task prioritizing finished" << std::endl;
}

Expand Down
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