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Move unsqueeze evaluate to new API (#19948)

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* Move unsqueeze evaluate to new API

* Add new header

* Fixed comments

* Fixed build

* Revert redundant changes

* Fixed comments
This commit is contained in:
Ilya Churaev 2023-09-21 16:07:10 +04:00 committed by GitHub
parent 2489356121
commit e277d48535
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 32 additions and 99 deletions
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4
src/core/include/openvino/op/unsqueeze.hpp
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@ -23,9 +23,7 @@ public:
void validate_and_infer_types() override;
bool visit_attributes(AttributeVisitor& visitor) override;
OPENVINO_SUPPRESS_DEPRECATED_START
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
OPENVINO_SUPPRESS_DEPRECATED_END
bool evaluate(ov::TensorVector& outputs, const ov::TensorVector& inputs) const override;
bool has_evaluate() const override;
bool evaluate_lower(TensorVector& output_values) const override;
bool evaluate_upper(TensorVector& output_values) const override;

127
src/core/src/op/unsqueeze.cpp
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@ -2,27 +2,23 @@
// SPDX-License-Identifier: Apache-2.0
//
#include "ngraph/op/unsqueeze.hpp"
#include "openvino/op/unsqueeze.hpp"
#include <cstddef>
#include <functional>
#include <set>
#include "bound_evaluate.hpp"
#include "element_visitor.hpp"
#include "itt.hpp"
#include "ngraph/validation_util.hpp"
#include "openvino/reference/copy.hpp"
#include "openvino/core/validation_util.hpp"
#include "unsqueeze_shape_inference.hpp"
using namespace std;
using namespace ngraph;
op::v0::Unsqueeze::Unsqueeze(const Output<Node>& data, const Output<Node>& axes) : Op({data, axes}) {
ov::op::v0::Unsqueeze::Unsqueeze(const ov::Output<ov::Node>& data, const ov::Output<ov::Node>& axes)
: Op({data, axes}) {
constructor_validate_and_infer_types();
}
void op::v0::Unsqueeze::validate_and_infer_types() {
void ov::op::v0::Unsqueeze::validate_and_infer_types() {
OV_OP_SCOPE(v0_Unsqueeze_validate_and_infer_types);
OPENVINO_SUPPRESS_DEPRECATED_START
@ -33,112 +29,51 @@ void op::v0::Unsqueeze::validate_and_infer_types() {
set_output_type(0, get_input_element_type(0), output_shapes[0]);
}
bool op::v0::Unsqueeze::visit_attributes(AttributeVisitor& visitor) {
bool ov::op::v0::Unsqueeze::visit_attributes(AttributeVisitor& visitor) {
OV_OP_SCOPE(v0_Unsqueeze_visit_attributes);
return true;
}
shared_ptr<Node> op::v0::Unsqueeze::clone_with_new_inputs(const OutputVector& new_args) const {
std::shared_ptr<ov::Node> ov::op::v0::Unsqueeze::clone_with_new_inputs(const OutputVector& new_args) const {
OV_OP_SCOPE(v0_Unsqueeze_clone_with_new_inputs);
if (new_args.size() != 2) {
OPENVINO_THROW("Incorrect number of new arguments");
}
return make_shared<Unsqueeze>(new_args.at(0), new_args.at(1));
return std::make_shared<Unsqueeze>(new_args.at(0), new_args.at(1));
}
OPENVINO_SUPPRESS_DEPRECATED_START
namespace ov {
namespace op {
namespace unsqueeze {
struct Evaluate : element::NoAction<bool> {
using element::NoAction<bool>::visit;
template <element::Type_t ET>
static result_type visit(const HostTensorPtr& arg0, const HostTensorPtr& out, const size_t count) {
ov::reference::copy(arg0->get_data_ptr<ET>(), out->get_data_ptr<ET>(), count);
return true;
}
};
// The evaluate cannot use shape_infer for output shape calculation as shape inference accepts
// repeated axis and evaluate not. When shape inference will changed to be compatible with `numpy` then
// evaluate and inference can use same function to calculate output shape. TODO for next version for this operator.
namespace {
bool evaluate_unsqueeze(const Node* node,
const HostTensorPtr& arg0,
const HostTensorPtr& arg1,
const HostTensorPtr& out) {
auto element_type = arg0->get_element_type();
out->set_element_type(element_type);
const auto& axes_shape = arg1->get_shape();
ov::op::v0::check_unsqueeze_axes_rank(node, Rank(axes_shape.size()));
const auto& data_shape = arg0->get_shape();
const auto out_rank = static_cast<int64_t>(data_shape.size() + shape_size(axes_shape));
// Get axes and normalize
OPENVINO_SUPPRESS_DEPRECATED_START
auto axes = read_index_vector(arg1);
normalize_axes(node, out_rank, axes);
OPENVINO_SUPPRESS_DEPRECATED_END
// Sort in increasing order
std::set<int64_t> axes_set(axes.begin(), axes.end());
NGRAPH_CHECK(axes.size() == axes_set.size(), "Axes has duplicate axis.");
auto out_shape = data_shape;
for (int64_t axis : axes_set) {
out_shape.insert(out_shape.begin() + axis, 1);
}
out->set_shape(out_shape);
using namespace ov::element;
return IfTypeOf<i32, i64, u32, u64, f16, f32, f64, bf16>::apply<Evaluate>(element_type,
arg0,
out,
shape_size(out_shape));
}
} // namespace
} // namespace unsqueeze
} // namespace op
} // namespace ov
bool op::v0::Unsqueeze::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
bool ov::op::v0::Unsqueeze::evaluate(ov::TensorVector& outputs, const ov::TensorVector& inputs) const {
OV_OP_SCOPE(v0_Unsqueeze_evaluate);
OPENVINO_SUPPRESS_DEPRECATED_START
NGRAPH_CHECK(validate_host_tensor_vector(inputs, 2));
NGRAPH_CHECK(validate_host_tensor_vector(outputs, 1));
OPENVINO_SUPPRESS_DEPRECATED_END
return unsqueeze::evaluate_unsqueeze(this, inputs[0], inputs[1], outputs[0]);
}
bool op::v0::Unsqueeze::has_evaluate() const {
OV_OP_SCOPE(v0_Unsqueeze_has_evaluate);
switch (get_input_element_type(0)) {
case ngraph::element::i32:
case ngraph::element::i64:
case ngraph::element::u32:
case ngraph::element::u64:
case ngraph::element::f16:
case ngraph::element::f32:
case ngraph::element::f64:
case ngraph::element::bf16:
return true;
default:
return false;
OPENVINO_ASSERT(inputs.size() == 2);
if (outputs.empty()) {
outputs.emplace_back(ov::Tensor(inputs[0].get_element_type(), {0}));
} else {
OPENVINO_ASSERT(outputs.size() == 1);
}
const auto& output_shape = shape_infer(this,
std::vector<ov::PartialShape>{inputs[0].get_shape(), inputs[1].get_shape()},
make_tensor_accessor(inputs))
.front()
.to_shape();
outputs[0].set_shape(output_shape);
std::memcpy(outputs[0].data(), inputs[0].data(), outputs[0].get_byte_size());
return true;
}
bool op::v0::Unsqueeze::evaluate_lower(ov::TensorVector& output_values) const {
bool ov::op::v0::Unsqueeze::has_evaluate() const {
OV_OP_SCOPE(v0_Unsqueeze_has_evaluate);
return true;
}
bool ov::op::v0::Unsqueeze::evaluate_lower(ov::TensorVector& output_values) const {
return get_input_tensor(1).has_and_set_bound() && default_lower_bound_evaluator(this, output_values);
}
bool op::v0::Unsqueeze::evaluate_upper(ov::TensorVector& output_values) const {
bool ov::op::v0::Unsqueeze::evaluate_upper(ov::TensorVector& output_values) const {
return get_input_tensor(1).has_and_set_bound() && default_upper_bound_evaluator(this, output_values);
}
bool op::v0::Unsqueeze::evaluate_label(TensorLabelVector& output_labels) const {
bool ov::op::v0::Unsqueeze::evaluate_label(TensorLabelVector& output_labels) const {
if (!get_input_tensor(1).has_and_set_bound())
return false;
OPENVINO_SUPPRESS_DEPRECATED_START
@ -146,7 +81,7 @@ bool op::v0::Unsqueeze::evaluate_label(TensorLabelVector& output_labels) const {
OPENVINO_SUPPRESS_DEPRECATED_END
}
bool op::v0::Unsqueeze::constant_fold(OutputVector& output_values, const OutputVector& inputs_values) {
bool ov::op::v0::Unsqueeze::constant_fold(OutputVector& output_values, const OutputVector& inputs_values) {
if (get_output_partial_shape(0).is_dynamic() || is_const_fold_disabled()) {
return false;
}