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[CORE] Change get_input_const_data_as return type from std::unique_ptr to ov::optional #24443

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merged 5 commits into from
May 17, 2024
Merged

[CORE] Change get_input_const_data_as return type from std::unique_ptr to ov::optional #24443

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e0b1225
Change get_input_const_data_as return type from std::unique_ptr to ov…
barnasm1 May 9, 2024
b155cd6
Apply suggestions from code review
barnasm1 May 9, 2024
fe69deb
pass axes by refference to optional
barnasm1 May 10, 2024
b85318f
unify optional return
barnasm1 May 16, 2024
06976f1
Merge branch 'master' into replace_return_type_with_ov_optional
barnasm1 May 17, 2024
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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/fft_base_shape_inference.hpp
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Expand Up @@ -76,7 +76,7 @@ std::vector<TRShape> shape_infer(const util::FFTBase* op,

util::fft_common_validation::shape_validation(op,
input_shapes,
axes.get(),
axes,
util::fft_common_validation::FFTKind::ComplexInput);

output_shape = input_shape;
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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/fft_common_validation.hpp
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Expand Up @@ -101,7 +101,7 @@ void validate_signal_size(const ov::op::util::FFTBase* op,
template <class T>
void shape_validation(const ov::op::util::FFTBase* op,
const std::vector<T>& input_shapes,
std::vector<int64_t>* axes,
ov::optional<std::vector<int64_t>>& axes,
FFTKind fft_kind) {
const auto& input_shape = input_shapes[0];
const auto& axes_shape = input_shapes[1];
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10 changes: 3 additions & 7 deletions src/core/shape_inference/include/interpolate_shape_inference.hpp
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Expand Up @@ -153,20 +153,16 @@ TRShape make_padded_shape(const TShape& input, TInputIter pads_begin, TInputIter
* @return Not null pointer with axes values or null pointer if can't get axes from input.
*/
template <class TShape, class TRes = std::vector<int64_t>>
std::unique_ptr<TRes> get_axes(const Node* const op,
size_t port,
bool has_axes,
size_t rank,
const ITensorAccessor& ta) {
std::unique_ptr<TRes> axes;
ov::optional<TRes> get_axes(const Node* const op, size_t port, bool has_axes, size_t rank, const ITensorAccessor& ta) {
ov::optional<TRes> axes;
if (has_axes) {
using TAxis = typename TRes::value_type;
axes = std::move(get_input_const_data_as<TShape, TAxis, TRes>(op, port, ta));
if (axes) {
validate::axes_values(op, *axes, rank);
}
} else {
axes.reset(new TRes(rank));
axes.emplace(rank);
std::iota(axes->begin(), axes->end(), 0);
}
return axes;
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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/irdft_shape_inference.hpp
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Expand Up @@ -28,7 +28,7 @@ std::vector<TRShape> shape_infer(const IRDFT* op,

util::fft_common_validation::shape_validation(op,
input_shapes,
axes.get(),
axes,
util::fft_common_validation::FFTKind::ComplexInput);

if (input_shape.rank().is_dynamic()) {
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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/rdft_shape_inference.hpp
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Expand Up @@ -39,7 +39,7 @@ std::vector<TRShape> shape_infer(const RDFT* op,

util::fft_common_validation::shape_validation(op,
input_shapes,
axes.get(),
axes,
util::fft_common_validation::FFTKind::RealInput);

if (input_shape.rank().is_dynamic()) {
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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/reduce_shape_inference.hpp
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Expand Up @@ -56,7 +56,7 @@ std::vector<TRShape> reduce_shape_infer(const util::ReductionBase* op,
"Axes input must be a scalar or 1D input. Got: ",
axes_shape);

const auto axes_val = ov::op::get_input_const_data_as<TRShape, int64_t>(op, 1, tensor_accessor);
auto axes_val = ov::op::get_input_const_data_as<TRShape, int64_t>(op, 1, tensor_accessor);

if (data_rank.is_static() && axes_val) {
ov::util::normalize_axes(op, data_rank.get_length(), *axes_val);
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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/rms_norm_shape_inference.hpp
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Expand Up @@ -54,7 +54,7 @@ std::vector<TRShape> shape_infer(const RMSNorm* op,
}

// Axes values validation
if (const auto axes_val = ov::op::get_input_const_data_as<TRShape, int64_t>(op, 1, tensor_accessor)) {
if (auto axes_val = ov::op::get_input_const_data_as<TRShape, int64_t>(op, 1, tensor_accessor)) {
ov::util::normalize_axes(op, data_rank.get_length(), *axes_val);
}

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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/roll_shape_inference.hpp
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Expand Up @@ -39,7 +39,7 @@ std::vector<TRShape> shape_infer(const Roll* op,
"Axes must be a scalar or 1D tensor.");

if (data_pshape.rank().is_static()) {
if (const auto axes = get_input_const_data_as<TRShape, int64_t>(op, 2, ta)) {
if (auto axes = get_input_const_data_as<TRShape, int64_t>(op, 2, ta)) {
ov::util::normalize_axes(op, data_pshape.size(), *axes);
}
}
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4 changes: 2 additions & 2 deletions src/core/shape_inference/include/strided_slice_shape_inference.hpp
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Expand Up @@ -61,12 +61,12 @@ std::vector<TRShape> shape_infer(const StridedSlice* op,
const auto begin = get_input_bounds<TRShape, int64_t>(op, 1, ta);
const auto end = get_input_bounds<TRShape, int64_t>(op, 2, ta);

std::unique_ptr<std::vector<int64_t>> strides;
ov::optional<std::vector<int64_t>> strides;
if (input_shapes.size() > 3) {
strides = get_input_const_data_as<TRShape, int64_t>(op, 3, ta);
} else if (begin) {
// generate default strides
strides.reset(new std::vector<int64_t>(begin->size(), 1));
strides.emplace(begin->size(), 1);
}

// compute and check a number of axes for which begin, end, and strides are defined
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2 changes: 1 addition & 1 deletion src/core/shape_inference/include/transpose_shape_inference.hpp
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Expand Up @@ -74,7 +74,7 @@ std::vector<TRShape> shape_infer(const Transpose* op,
"Input order must have shape [n], where n is the rank of arg.");
}

const auto axes = get_input_const_data_as<TShape, int64_t>(op, Transpose::ORDER, tensor_accessor);
auto axes = get_input_const_data_as<TShape, int64_t>(op, Transpose::ORDER, tensor_accessor);

auto output_shapes = std::vector<TRShape>();
if (axes && input_rank.is_static()) {
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36 changes: 18 additions & 18 deletions src/core/shape_inference/include/utils.hpp
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Expand Up @@ -204,21 +204,21 @@ template <class TShape,
class TRes = std::vector<TData>,
class UnaryOperation = ov::util::Cast<TData>,
typename std::enable_if<!std::is_same<TShape, ov::PartialShape>::value>::type* = nullptr>
std::unique_ptr<TRes> get_input_const_data_as(const ov::Node* op,
size_t idx,
const ITensorAccessor& tensor_accessor,
UnaryOperation&& func = ov::util::Cast<TData>()) {
ov::optional<TRes> get_input_const_data_as(const ov::Node* op,
size_t idx,
const ITensorAccessor& tensor_accessor,
UnaryOperation&& func = ov::util::Cast<TData>()) {
if (auto t = tensor_accessor(idx)) {
return std::unique_ptr<TRes>(new TRes(get_tensor_data_as<TData, TRes>(t, std::forward<UnaryOperation>(func))));
return {get_tensor_data_as<TData, TRes>(t, std::forward<UnaryOperation>(func))};
} else {
const auto& constant = ov::as_type_ptr<ov::opset1::Constant>(op->get_input_node_shared_ptr(idx));
NODE_VALIDATION_CHECK(op, constant != nullptr, "Static shape inference lacks constant data on port ", idx);
const auto& et = constant->get_element_type();
const auto& shape = constant->get_shape();
return std::unique_ptr<TRes>(new TRes(get_raw_data_as<TData, TRes>(et,
constant->get_data_ptr(),
shape_size(shape),
std::forward<UnaryOperation>(func))));
return {get_raw_data_as<TData, TRes>(et,
constant->get_data_ptr(),
shape_size(shape),
std::forward<UnaryOperation>(func))};
}
}

Expand All @@ -245,20 +245,20 @@ template <class TShape,
class TRes = std::vector<TData>,
class UnaryOperation = ov::util::Cast<TData>,
typename std::enable_if<std::is_same<TShape, ov::PartialShape>::value>::type* = nullptr>
std::unique_ptr<TRes> get_input_const_data_as(const ov::Node* op,
size_t idx,
const ITensorAccessor& tensor_accessor,
UnaryOperation&& func = ov::util::Cast<TData>()) {
ov::optional<TRes> get_input_const_data_as(const ov::Node* op,
size_t idx,
const ITensorAccessor& tensor_accessor,
UnaryOperation&& func = ov::util::Cast<TData>()) {
if (auto t = tensor_accessor(idx)) {
return std::unique_ptr<TRes>(new TRes(get_tensor_data_as<TData, TRes>(t, std::forward<UnaryOperation>(func))));
return {get_tensor_data_as<TData, TRes>(t, std::forward<UnaryOperation>(func))};
} else if (const auto& constant =
(idx < op->get_input_size()) ? ov::util::get_constant_from_source(op->input_value(idx)) : nullptr) {
const auto& et = constant->get_element_type();
const auto& shape = constant->get_shape();
return std::unique_ptr<TRes>(new TRes(get_raw_data_as<TData, TRes>(et,
constant->get_data_ptr(),
shape_size(shape),
std::forward<UnaryOperation>(func))));
return {get_raw_data_as<TData, TRes>(et,
constant->get_data_ptr(),
shape_size(shape),
std::forward<UnaryOperation>(func))};
} else {
return {};
}
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