[core] Migrate Log operator to new API (#20917)

* Drop ngraph remains

* Use ov::Tensor

instaed of ngraph::HostTensor

* Remove useless code
This commit is contained in:
Tomasz Jankowski 2023-11-13 12:00:09 +01:00 committed by GitHub
parent 7c595f8773
commit 70a2736695
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 47 additions and 61 deletions

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@ -21,11 +21,8 @@ public:
/// \param arg Node that produces the input tensor.
Log(const Output<Node>& arg);
bool visit_attributes(AttributeVisitor& visitor) override;
std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
OPENVINO_SUPPRESS_DEPRECATED_START
bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
OPENVINO_SUPPRESS_DEPRECATED_END
bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
bool has_evaluate() const override;
};
} // namespace v0

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@ -4,6 +4,7 @@
#pragma once
#include <algorithm>
#include <cmath>
#include <cstddef>
@ -11,9 +12,9 @@ namespace ov {
namespace reference {
template <typename T>
void log(const T* arg, T* out, size_t count) {
for (size_t i = 0; i < count; i++) {
out[i] = static_cast<T>(std::log(arg[i]));
}
std::transform(arg, arg + count, out, [](const T v) {
return static_cast<T>(std::log(v));
});
}
} // namespace reference
} // namespace ov

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@ -2,78 +2,66 @@
// SPDX-License-Identifier: Apache-2.0
//
#include "ngraph/op/log.hpp"
#include "openvino/op/log.hpp"
#include "element_visitor.hpp"
#include "itt.hpp"
#include "ngraph/op/divide.hpp"
#include "ngraph/runtime/host_tensor.hpp"
#include "openvino/reference/log.hpp"
using namespace std;
using namespace ngraph;
namespace ov {
namespace op {
namespace log {
struct Evaluate : element::NoAction<bool> {
using element::NoAction<bool>::visit;
op::Log::Log(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
template <element::Type_t ET, class T = fundamental_type_for<ET>>
static result_type visit(const Tensor& in, Tensor& out, const size_t count) {
reference::log(in.data<const T>(), out.data<T>(), count);
return true;
}
};
} // namespace log
namespace v0 {
Log::Log(const Output<Node>& arg) : UnaryElementwiseArithmetic(arg) {
constructor_validate_and_infer_types();
}
bool ngraph::op::v0::Log::visit_attributes(AttributeVisitor& visitor) {
OV_OP_SCOPE(v0_Log_visit_attributes);
return true;
}
shared_ptr<Node> op::Log::clone_with_new_inputs(const OutputVector& new_args) const {
std::shared_ptr<Node> Log::clone_with_new_inputs(const OutputVector& new_args) const {
OV_OP_SCOPE(v0_Log_clone_with_new_inputs);
check_new_args_count(this, new_args);
return make_shared<Log>(new_args.at(0));
return std::make_shared<Log>(new_args.at(0));
}
OPENVINO_SUPPRESS_DEPRECATED_START
namespace logop {
namespace {
template <element::Type_t ET>
inline bool evaluate(const HostTensorPtr& arg0, const HostTensorPtr& out, const size_t count) {
using T = typename element_type_traits<ET>::value_type;
ov::reference::log<T>(arg0->get_data_ptr<ET>(), out->get_data_ptr<ET>(), count);
return true;
}
bool evaluate_log(const HostTensorPtr& arg0, const HostTensorPtr& out, const size_t count) {
bool rc = true;
out->set_unary(arg0);
switch (arg0->get_element_type()) {
OPENVINO_TYPE_CASE(evaluate_log, i32, arg0, out, count);
OPENVINO_TYPE_CASE(evaluate_log, i64, arg0, out, count);
OPENVINO_TYPE_CASE(evaluate_log, u32, arg0, out, count);
OPENVINO_TYPE_CASE(evaluate_log, u64, arg0, out, count);
OPENVINO_TYPE_CASE(evaluate_log, f16, arg0, out, count);
OPENVINO_TYPE_CASE(evaluate_log, f32, arg0, out, count);
default:
rc = false;
break;
}
return rc;
}
} // namespace
} // namespace logop
bool op::Log::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
bool Log::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
OV_OP_SCOPE(v0_Log_evaluate);
return logop::evaluate_log(inputs[0], outputs[0], shape_size(inputs[0]->get_shape()));
OPENVINO_ASSERT(outputs.size() == 1);
OPENVINO_ASSERT(inputs.size() == 1);
const auto& input_shape = inputs[0].get_shape();
const auto count = shape_size(input_shape);
outputs[0].set_shape(input_shape);
using namespace ov::element;
return IfTypeOf<f16, f32, i32, i64, u32, u64>::apply<log::Evaluate>(inputs[0].get_element_type(),
inputs[0],
outputs[0],
count);
}
bool op::Log::has_evaluate() const {
bool Log::has_evaluate() const {
OV_OP_SCOPE(v0_Log_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 element::f16:
case element::f32:
case element::i32:
case element::i64:
case element::u32:
case element::u64:
return true;
default:
break;
return false;
}
return false;
}
} // namespace v0
} // namespace op
} // namespace ov