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Mark as deprecated nGraph API (#17647)

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* Mark as deprecated nGraph API

* Fixed code style

* Added IN_OV_LIBRARY define

* Suppress warnings for log

* Suppress warning

* Updated nGraph headers

* Fixed build for macOS

* Fixed lpt and snippets

* Fixed build all on macOS

* Suppress some warnings

* Fixed some new warnings

* Fixed new warnings

* Try to fix some warnings

* More warnings

* Soome change

* Suppress more warnings

* Suppress warnings for transformations

* Suppress warnings for LPT

* One more fix

* Suppress more warnings

* Try to fix opset error

* Remove opset constructor

* Cannot fix opset warning

* Suppress warnings for offline transfromations

* Fixed some warnings for Windows

* Fixed code style

* Suppress some warnings for onnx FE

* Revert "Suppress some warnings for onnx FE"

This reverts commit 75d23b64fc.

* Revert "Fixed code style"

This reverts commit c6eba63116.

* Revert "Fixed some warnings for Windows"

This reverts commit 23d7ed88b6.

* Revert "Suppress warnings for offline transfromations"

This reverts commit 0b9f6317bf.

* Revert "Cannot fix opset warning"

This reverts commit 19ea658639.

* Revert "Remove opset constructor"

This reverts commit 06afb1bc20.

* Revert "Suppress warnings for LPT"

This reverts commit 58b1c0f5a0.

* Revert "Suppress warnings for transformations"

This reverts commit f8bb9814a1.

* Revert "Suppress more warnings"

This reverts commit f9f0da9acb.

* Revert "Soome change"

This reverts commit e545d4984e.

* Remove deprecation for ngraph::OpSet and FactoryRegistry
This commit is contained in:
Ilya Churaev 2023-06-01 12:44:28 +04:00 committed by GitHub
parent 6b3a252f92
commit ea04f8217d
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504 changed files with 4355 additions and 1154 deletions
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47
docs/snippets/lpt_intel_cpu_plugin.cpp
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@ -10,6 +10,7 @@
#include <low_precision/network_helper.hpp> #include <low_precision/network_helper.hpp>
#include <transformations/common_optimizations/convert_quantize_dequantize.hpp> #include <transformations/common_optimizations/convert_quantize_dequantize.hpp>
#include <transformations/op_conversions/convert_subtract.hpp> #include <transformations/op_conversions/convert_subtract.hpp>
#include "openvino/pass/manager.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -28,7 +29,7 @@ public:
int main() { int main() {
std::shared_ptr<ov::Model> nGraphFunc; std::shared_ptr<ov::Model> nGraphFunc;
ngraph::pass::Manager manager; ov::pass::Manager manager;
auto pass_config = manager.get_pass_config(); auto pass_config = manager.get_pass_config();
//! [lpt_common] //! [lpt_common]
// check if the function is quantized to ignore LPT transformations for not quantized function to speed up model loading // check if the function is quantized to ignore LPT transformations for not quantized function to speed up model loading
@ -69,19 +70,19 @@ using namespace ngraph::pass::low_precision;
if (useLpt) { if (useLpt) {
// Low precision transformations plugin specific configuration: restrictions definition // Low precision transformations plugin specific configuration: restrictions definition
auto supportedPrecisions = std::vector<PrecisionsRestriction>({ auto supportedPrecisions = std::vector<PrecisionsRestriction>({
PrecisionsRestriction::create<ngraph::opset1::Convolution>({ PrecisionsRestriction::create<ov::opset1::Convolution>({
{{0}, {ngraph::element::u8}}, {{0}, {ngraph::element::u8}},
{{1}, {ngraph::element::i8}}, {{1}, {ngraph::element::i8}},
}), }),
PrecisionsRestriction::create<ngraph::opset1::ConvolutionBackpropData>({ PrecisionsRestriction::create<ov::opset1::ConvolutionBackpropData>({
{{0}, {ngraph::element::u8, ngraph::element::i8}}, {{0}, {ngraph::element::u8, ngraph::element::i8}},
{{1}, {ngraph::element::i8}} {{1}, {ngraph::element::i8}}
}), }),
PrecisionsRestriction::create<ngraph::opset1::GroupConvolution>({ PrecisionsRestriction::create<ov::opset1::GroupConvolution>({
{{0}, {ngraph::element::u8}}, {{0}, {ngraph::element::u8}},
{{1}, {ngraph::element::i8}} {{1}, {ngraph::element::i8}}
}), }),
PrecisionsRestriction::create<ngraph::opset1::Multiply>({ PrecisionsRestriction::create<ov::opset1::Multiply>({
{{0}, {ngraph::element::u8}}, {{0}, {ngraph::element::u8}},
{{1}, {ngraph::element::i8}}, {{1}, {ngraph::element::i8}},
}), }),
@ -89,17 +90,17 @@ if (useLpt) {
// Low precision transformations plugin specific configuration: per-tensor quantization operations definition // Low precision transformations plugin specific configuration: per-tensor quantization operations definition
auto perTensorQuantization = std::vector<QuantizationGranularityRestriction>({ auto perTensorQuantization = std::vector<QuantizationGranularityRestriction>({
QuantizationGranularityRestriction::create<ngraph::opset1::Convolution>({0}), QuantizationGranularityRestriction::create<ov::opset1::Convolution>({0}),
QuantizationGranularityRestriction::create<ngraph::opset1::ConvolutionBackpropData>({0}) QuantizationGranularityRestriction::create<ov::opset1::ConvolutionBackpropData>({0})
}); });
// Low precision transformations instantiation and registration in pass manager // Low precision transformations instantiation and registration in pass manager
ngraph::pass::Manager lptManager; ov::pass::Manager lptManager;
lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(supportedPrecisions, perTensorQuantization); lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(supportedPrecisions, perTensorQuantization);
// Low precision transformations plugin specific configuration: transformation callbacks definition // Low precision transformations plugin specific configuration: transformation callbacks definition
lptManager.get_pass_config()->set_callback<MarkupPrecisions>([](const std::shared_ptr<const ngraph::Node>& node) -> bool { lptManager.get_pass_config()->set_callback<MarkupPrecisions>([](const std::shared_ptr<const ngraph::Node>& node) -> bool {
if (const auto multiply = std::dynamic_pointer_cast<const ngraph::opset1::Multiply>(node)) { if (const auto multiply = std::dynamic_pointer_cast<const ov::opset1::Multiply>(node)) {
return !MultiplyToGroupConvolutionTransformation::canBeTransformedToGroupConvolution(multiply); return !MultiplyToGroupConvolutionTransformation::canBeTransformedToGroupConvolution(multiply);
} }
return false; return false;
@ -117,7 +118,7 @@ if (useLpt) {
//! [lpt_execution] //! [lpt_execution]
//! [lpt_device] //! [lpt_device]
ngraph::pass::Manager deviceSpecificManager; ov::pass::Manager deviceSpecificManager;
deviceSpecificManager.register_pass<ngraph::pass::device::ConvertOpSet1ToDeviceSpecific>(); deviceSpecificManager.register_pass<ngraph::pass::device::ConvertOpSet1ToDeviceSpecific>();
deviceSpecificManager.run_passes(nGraphFunc); deviceSpecificManager.run_passes(nGraphFunc);
//! [lpt_device] //! [lpt_device]
@ -127,23 +128,23 @@ return 0;
int lpt_supported_precisions() { int lpt_supported_precisions() {
std::shared_ptr<ov::Model> nGraphFunc; std::shared_ptr<ov::Model> nGraphFunc;
ngraph::pass::Manager manager; ov::pass::Manager manager;
using namespace ngraph::pass::low_precision; using namespace ngraph::pass::low_precision;
//! [lpt_supported_precisions] //! [lpt_supported_precisions]
auto supportedPrecisions = std::vector<PrecisionsRestriction>({ auto supportedPrecisions = std::vector<PrecisionsRestriction>({
PrecisionsRestriction::create<ngraph::opset1::Convolution>({ PrecisionsRestriction::create<ov::opset1::Convolution>({
{{0}, {ngraph::element::u8}}, {{0}, {ngraph::element::u8}},
{{1}, {ngraph::element::i8}}, {{1}, {ngraph::element::i8}},
}), }),
}); });
ngraph::pass::Manager lptManager; ov::pass::Manager lptManager;
lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(supportedPrecisions); lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(supportedPrecisions);
lptManager.run_passes(nGraphFunc); lptManager.run_passes(nGraphFunc);
//! [lpt_supported_precisions] //! [lpt_supported_precisions]
ngraph::pass::Manager deviceSpecificManager; ov::pass::Manager deviceSpecificManager;
deviceSpecificManager.register_pass<ngraph::pass::device::ConvertOpSet1ToDeviceSpecific>(); deviceSpecificManager.register_pass<ngraph::pass::device::ConvertOpSet1ToDeviceSpecific>();
deviceSpecificManager.run_passes(nGraphFunc); deviceSpecificManager.run_passes(nGraphFunc);
@ -158,10 +159,10 @@ using namespace ngraph::pass::low_precision;
const std::vector<PrecisionsRestriction> emptyRestrictions; const std::vector<PrecisionsRestriction> emptyRestrictions;
auto perTensorQuantization = std::vector<QuantizationGranularityRestriction>({ auto perTensorQuantization = std::vector<QuantizationGranularityRestriction>({
QuantizationGranularityRestriction::create<ngraph::opset1::Convolution>({0}) QuantizationGranularityRestriction::create<ov::opset1::Convolution>({0})
}); });
ngraph::pass::Manager lptManager; ov::pass::Manager lptManager;
lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(emptyRestrictions, perTensorQuantization); lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(emptyRestrictions, perTensorQuantization);
lptManager.run_passes(nGraphFunc); lptManager.run_passes(nGraphFunc);
//! [per_tensor_quantization] //! [per_tensor_quantization]
@ -171,13 +172,13 @@ return 0;
int asymmetric_quantization(const std::vector<ngraph::element::Type>& defaultPrecisions) { int asymmetric_quantization(const std::vector<ngraph::element::Type>& defaultPrecisions) {
std::shared_ptr<ov::Model> nGraphFunc; std::shared_ptr<ov::Model> nGraphFunc;
ngraph::pass::Manager manager; ov::pass::Manager manager;
auto pass_config = manager.get_pass_config(); auto pass_config = manager.get_pass_config();
//! [asymmetric_quantization] //! [asymmetric_quantization]
using namespace ngraph::pass::low_precision; using namespace ngraph::pass::low_precision;
ngraph::pass::Manager lptManager; ov::pass::Manager lptManager;
lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(); lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>();
lptManager.get_pass_config()->set_callback<ConvolutionBackpropDataTransformation>([&defaultPrecisions](const std::shared_ptr<const ngraph::Node>& node) -> bool { lptManager.get_pass_config()->set_callback<ConvolutionBackpropDataTransformation>([&defaultPrecisions](const std::shared_ptr<const ngraph::Node>& node) -> bool {
@ -191,27 +192,27 @@ return 0;
int lpt_markup_pipeline() { int lpt_markup_pipeline() {
std::shared_ptr<ov::Model> nGraphFunc; std::shared_ptr<ov::Model> nGraphFunc;
ngraph::pass::Manager manager; ov::pass::Manager manager;
using namespace ngraph::pass::low_precision; using namespace ngraph::pass::low_precision;
//! [lpt_markup_pipeline] //! [lpt_markup_pipeline]
auto supportedPrecisions = std::vector<PrecisionsRestriction>({ auto supportedPrecisions = std::vector<PrecisionsRestriction>({
PrecisionsRestriction::create<ngraph::opset1::Convolution>({ PrecisionsRestriction::create<ov::opset1::Convolution>({
{{0}, {ngraph::element::u8}}, {{0}, {ngraph::element::u8}},
{{1}, {ngraph::element::i8}}, {{1}, {ngraph::element::i8}},
}), }),
}); });
auto perTensorQuantization = std::vector<QuantizationGranularityRestriction>({ auto perTensorQuantization = std::vector<QuantizationGranularityRestriction>({
QuantizationGranularityRestriction::create<ngraph::opset1::Convolution>({0}) QuantizationGranularityRestriction::create<ov::opset1::Convolution>({0})
}); });
ngraph::pass::Manager lptManager; ov::pass::Manager lptManager;
lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(supportedPrecisions, perTensorQuantization); lptManager.register_pass<ngraph::pass::low_precision::LowPrecision>(supportedPrecisions, perTensorQuantization);
lptManager.run_passes(nGraphFunc); lptManager.run_passes(nGraphFunc);
//! [lpt_markup_pipeline] //! [lpt_markup_pipeline]
ngraph::pass::Manager deviceSpecificManager; ov::pass::Manager deviceSpecificManager;
deviceSpecificManager.register_pass<ngraph::pass::device::ConvertOpSet1ToDeviceSpecific>(); deviceSpecificManager.register_pass<ngraph::pass::device::ConvertOpSet1ToDeviceSpecific>();
deviceSpecificManager.run_passes(nGraphFunc); deviceSpecificManager.run_passes(nGraphFunc);

15
docs/snippets/ngraph.cpp
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@ -1,8 +1,7 @@
// Copyright (C) 2018-2021 Intel Corporation // Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0 // SPDX-License-Identifier: Apache-2.0
// //
#include <ngraph/ngraph.hpp> #include <openvino/opsets/opset8.hpp>
#include <ngraph/opsets/opset8.hpp>
int main() { int main() {
//! [ngraph:graph] //! [ngraph:graph]
@ -26,23 +25,21 @@ int main() {
// | Result | // | Result |
// | result | // | result |
// |_____________| // |_____________|
auto data1 = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1, 3, 2, 2}); auto data1 = std::make_shared<ov::opset8::Parameter>(ov::element::i64, ov::Shape{1, 3, 2, 2});
data1->set_friendly_name("data1"); // operation name data1->set_friendly_name("data1"); // operation name
data1->output(0).set_names({"data1_t"}); // tensor names data1->output(0).set_names({"data1_t"}); // tensor names
auto data2 = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1, 2, 2, 2}); auto data2 = std::make_shared<ov::opset8::Parameter>(ov::element::i64, ov::Shape{1, 2, 2, 2});
data2->set_friendly_name("data2"); // operation name data2->set_friendly_name("data2"); // operation name
data2->output(0).set_names({"data2_t"}); // tensor names data2->output(0).set_names({"data2_t"}); // tensor names
auto concat = std::make_shared<ngraph::opset8::Concat>(ngraph::OutputVector{data1, data2}, 1); auto concat = std::make_shared<ov::opset8::Concat>(ov::OutputVector{data1, data2}, 1);
concat->set_friendly_name("concat"); // operation name concat->set_friendly_name("concat"); // operation name
concat->output(0).set_names({"concat_t"}); // tensor name concat->output(0).set_names({"concat_t"}); // tensor name
auto result = std::make_shared<ngraph::opset8::Result>(concat); auto result = std::make_shared<ov::opset8::Result>(concat);
result->set_friendly_name("result"); // operation name result->set_friendly_name("result"); // operation name
auto f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result}, auto f = std::make_shared<ov::Model>(ov::ResultVector{result}, ov::ParameterVector{data1, data2}, "function_name");
ngraph::ParameterVector{data1, data2},
"function_name");
//! [ngraph:graph] //! [ngraph:graph]
return 0; return 0;
} }

4
src/bindings/python/src/pyopenvino/graph/node_factory.cpp
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@ -18,12 +18,12 @@
#include "dict_attribute_visitor.hpp" #include "dict_attribute_visitor.hpp"
#include "ngraph/check.hpp" #include "ngraph/check.hpp"
#include "ngraph/log.hpp"
#include "openvino/core/except.hpp" #include "openvino/core/except.hpp"
#include "openvino/core/node.hpp" #include "openvino/core/node.hpp"
#include "openvino/op/util/op_types.hpp" #include "openvino/op/util/op_types.hpp"
#include "openvino/op/util/variable.hpp" #include "openvino/op/util/variable.hpp"
#include "openvino/opsets/opset.hpp" #include "openvino/opsets/opset.hpp"
#include "openvino/util/log.hpp"
#include "pyopenvino/core/common.hpp" #include "pyopenvino/core/common.hpp"
namespace py = pybind11; namespace py = pybind11;
@ -61,7 +61,7 @@ public:
"Currently NodeFactory doesn't support Constant node: ", "Currently NodeFactory doesn't support Constant node: ",
op_type_name); op_type_name);
NGRAPH_WARN << "Empty op created! Please assign inputs and attributes and run validate() before op is used."; OPENVINO_WARN << "Empty op created! Please assign inputs and attributes and run validate() before op is used.";
return op_node; return op_node;
} }

6
src/bindings/python/src/pyopenvino/graph/ops/if.cpp
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@ -6,9 +6,9 @@
#include <string> #include <string>
#include "ngraph/log.hpp"
#include "openvino/core/node.hpp" #include "openvino/core/node.hpp"
#include "openvino/op/util/multi_subgraph_base.hpp" #include "openvino/op/util/multi_subgraph_base.hpp"
#include "openvino/util/log.hpp"
#include "pyopenvino/core/common.hpp" #include "pyopenvino/core/common.hpp"
#include "pyopenvino/graph/ops/if.hpp" #include "pyopenvino/graph/ops/if.hpp"
#include "pyopenvino/graph/ops/util/multisubgraph.hpp" #include "pyopenvino/graph/ops/util/multisubgraph.hpp"
@ -24,8 +24,8 @@ void regclass_graph_op_If(py::module m) {
if (MultiSubgraphHelpers::is_constant_or_parameter(execution_condition)) { if (MultiSubgraphHelpers::is_constant_or_parameter(execution_condition)) {
return std::make_shared<ov::op::v8::If>(execution_condition->output(0)); return std::make_shared<ov::op::v8::If>(execution_condition->output(0));
} else { } else {
NGRAPH_WARN << "Please specify execution_condition as Constant or Parameter. Default If() " OPENVINO_WARN << "Please specify execution_condition as Constant or Parameter. Default If() "
"constructor was applied."; "constructor was applied.";
return std::make_shared<ov::op::v8::If>(); return std::make_shared<ov::op::v8::If>();
} }
}), }),

4
src/bindings/python/src/pyopenvino/graph/ops/loop.cpp
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@ -6,9 +6,9 @@
#include <string> #include <string>
#include "ngraph/log.hpp"
#include "openvino/core/node.hpp" #include "openvino/core/node.hpp"
#include "openvino/op/loop.hpp" #include "openvino/op/loop.hpp"
#include "openvino/util/log.hpp"
#include "pyopenvino/core/common.hpp" #include "pyopenvino/core/common.hpp"
#include "pyopenvino/graph/ops/util/multisubgraph.hpp" #include "pyopenvino/graph/ops/util/multisubgraph.hpp"
@ -28,7 +28,7 @@ void regclass_graph_op_Loop(py::module m) {
MultiSubgraphHelpers::is_constant_or_parameter(execution_condition)) { MultiSubgraphHelpers::is_constant_or_parameter(execution_condition)) {
return std::make_shared<ov::op::v5::Loop>(trip_count->output(0), execution_condition->output(0)); return std::make_shared<ov::op::v5::Loop>(trip_count->output(0), execution_condition->output(0));
} else { } else {
NGRAPH_WARN OPENVINO_WARN
<< "Please specify execution_condition and trip_count as Constant or Parameter. Default Loop() " << "Please specify execution_condition and trip_count as Constant or Parameter. Default Loop() "
"constructor was applied."; "constructor was applied.";
return std::make_shared<ov::op::v5::Loop>(); return std::make_shared<ov::op::v5::Loop>();

8
src/common/low_precision_transformations/include/low_precision/align_quantization_intervals.hpp
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@ -5,9 +5,9 @@
#pragma once #pragma once
#include <memory> #include <memory>
#include <ngraph/pass/pass.hpp>
#include "low_precision/lpt_visibility.hpp" #include "low_precision/lpt_visibility.hpp"
#include "low_precision/layer_transformation.hpp" #include "low_precision/layer_transformation.hpp"
#include "openvino/pass/pass.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -28,11 +28,11 @@ class LP_TRANSFORMATIONS_API AlignQuantizationIntervals;
* [AlignQuantizationIntervals](@ref openvino_docs_OV_UG_lpt_AlignQuantizationIntervals) page * [AlignQuantizationIntervals](@ref openvino_docs_OV_UG_lpt_AlignQuantizationIntervals) page
* in the Inference Engine Developer Guide. * in the Inference Engine Developer Guide.
*/ */
class ngraph::pass::low_precision::AlignQuantizationIntervals : public ngraph::pass::FunctionPass { class ngraph::pass::low_precision::AlignQuantizationIntervals : public ov::pass::ModelPass {
public: public:
OPENVINO_RTTI("AlignQuantizationIntervals", "0"); OPENVINO_RTTI("AlignQuantizationIntervals", "0");
AlignQuantizationIntervals(const std::vector<ngraph::element::Type>& defaultPrecisions = ngraph::pass::low_precision::precision_set::int8_support); AlignQuantizationIntervals(const std::vector<ov::element::Type>& defaultPrecisions = ngraph::pass::low_precision::precision_set::int8_support);
bool run_on_model(const std::shared_ptr<ngraph::Function>& m) override; bool run_on_model(const std::shared_ptr<ov::Model>& m) override;
private: private:
const std::vector<ngraph::element::Type> defaultPrecisions; const std::vector<ngraph::element::Type> defaultPrecisions;
}; };

2
src/common/low_precision_transformations/include/low_precision/avg_pool.hpp
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@ -23,7 +23,7 @@ class LP_TRANSFORMATIONS_API AvgPoolTransformation : public LayerTransformation
public: public:
OPENVINO_RTTI("AvgPoolTransformation", "0"); OPENVINO_RTTI("AvgPoolTransformation", "0");
AvgPoolTransformation(const Params& params = Params()); AvgPoolTransformation(const Params& params = Params());
bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override;
}; };

36
src/common/low_precision_transformations/include/low_precision/common/fake_quantize_dequantization.hpp
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@ -6,15 +6,15 @@
#include <memory> #include <memory>
#include <tuple> #include <tuple>
#include <ngraph/ngraph.hpp>
#include <ngraph/opsets/opset1.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include "openvino/core/node.hpp"
#include "openvino/opsets/opset1.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
namespace low_precision { namespace low_precision {
typedef std::tuple<std::shared_ptr<Node>, std::shared_ptr<Node>> FakeQuantizeDequantizationValues; typedef std::tuple<std::shared_ptr<ov::Node>, std::shared_ptr<ov::Node>> FakeQuantizeDequantizationValues;
class LP_TRANSFORMATIONS_API FakeQuantizeDequantization { class LP_TRANSFORMATIONS_API FakeQuantizeDequantization {
public: public:
@ -22,12 +22,12 @@ public:
FakeQuantizeDequantization( FakeQuantizeDequantization(
const Output<Node>& data, const Output<Node>& data,
const std::shared_ptr<ngraph::opset1::Convert>& convert, const std::shared_ptr<ov::opset1::Convert>& convert,
const std::shared_ptr<ngraph::opset1::Subtract>& subtract, const std::shared_ptr<ov::opset1::Subtract>& subtract,
const std::shared_ptr<ngraph::opset1::Convert>& subtractConvert, const std::shared_ptr<ov::opset1::Convert>& subtractConvert,
const std::shared_ptr<ngraph::opset1::Constant>& subtractConstant, const std::shared_ptr<ov::opset1::Constant>& subtractConstant,
const std::shared_ptr<ngraph::opset1::Multiply>& multiply, const std::shared_ptr<ov::opset1::Multiply>& multiply,
const std::shared_ptr<ngraph::opset1::Constant>& multiplyConstant); const std::shared_ptr<ov::opset1::Constant>& multiplyConstant);
bool empty() const noexcept; bool empty() const noexcept;
bool multiplyHasZeroOrDenormal() const; bool multiplyHasZeroOrDenormal() const;
@ -41,21 +41,21 @@ public:
static int fillDequantizationParams( static int fillDequantizationParams(
const std::shared_ptr<ngraph::Node>& elementwise, const std::shared_ptr<ngraph::Node>& elementwise,
std::shared_ptr<ngraph::opset1::Convert>& convert, std::shared_ptr<ov::opset1::Convert>& convert,
std::shared_ptr<ngraph::opset1::Constant>& constant); std::shared_ptr<ov::opset1::Constant>& constant);
static int fillDequantizationParams( static int fillDequantizationParams(
const std::shared_ptr<ngraph::Node>& elementwise, const std::shared_ptr<ngraph::Node>& elementwise,
std::shared_ptr<ngraph::opset1::Constant>& constant); std::shared_ptr<ov::opset1::Constant>& constant);
size_t channelDimIndex; size_t channelDimIndex;
Output<Node> data; Output<Node> data;
std::shared_ptr<opset1::Convert> convert; std::shared_ptr<ov::opset1::Convert> convert;
std::shared_ptr<opset1::Subtract> subtract; std::shared_ptr<ov::opset1::Subtract> subtract;
std::shared_ptr<ngraph::opset1::Convert> subtractConvert; std::shared_ptr<ov::opset1::Convert> subtractConvert;
std::shared_ptr<ngraph::opset1::Constant> subtractConstant; std::shared_ptr<ov::opset1::Constant> subtractConstant;
std::shared_ptr<opset1::Multiply> multiply; std::shared_ptr<ov::opset1::Multiply> multiply;
std::shared_ptr<ngraph::opset1::Constant> multiplyConstant; std::shared_ptr<ov::opset1::Constant> multiplyConstant;
}; };
} // namespace low_precision } // namespace low_precision

1
src/common/low_precision_transformations/include/low_precision/common/port_quantization_granularity_restriction.hpp
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@ -8,7 +8,6 @@
#include <unordered_map> #include <unordered_map>
#include <vector> #include <vector>
#include <ngraph/pass/pass.hpp>
#include "low_precision/lpt_visibility.hpp" #include "low_precision/lpt_visibility.hpp"
#include "low_precision/rt_info/quantization_granularity_attribute.hpp" #include "low_precision/rt_info/quantization_granularity_attribute.hpp"

4
src/common/low_precision_transformations/include/low_precision/common/precisions_restriction.hpp
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@ -9,10 +9,8 @@
#include <unordered_set> #include <unordered_set>
#include <vector> #include <vector>
#include <ngraph/node.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include <ngraph/pass/graph_rewrite.hpp> #include "openvino/core/node.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {

1
src/common/low_precision_transformations/include/low_precision/common/quantization_granularity_restriction.hpp
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@ -9,7 +9,6 @@
#include <ngraph/node.hpp> #include <ngraph/node.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include <ngraph/pass/graph_rewrite.hpp>
#include <low_precision/rt_info/quantization_granularity_attribute.hpp> #include <low_precision/rt_info/quantization_granularity_attribute.hpp>
#include <low_precision/common/port_quantization_granularity_restriction.hpp> #include <low_precision/common/port_quantization_granularity_restriction.hpp>

4
src/common/low_precision_transformations/include/low_precision/convert_subtract_constant.hpp
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@ -8,7 +8,7 @@
#include <utility> #include <utility>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include <ngraph/pass/graph_rewrite.hpp> #include "openvino/pass/graph_rewrite.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -29,7 +29,7 @@ class LP_TRANSFORMATIONS_API ConvertSubtractConstant;
* [ConvertSubtractConstant](@ref openvino_docs_OV_UG_lpt_ConvertSubtractConstant) page * [ConvertSubtractConstant](@ref openvino_docs_OV_UG_lpt_ConvertSubtractConstant) page
* in the Inference Engine Developer Guide. * in the Inference Engine Developer Guide.
*/ */
class ngraph::pass::low_precision::ConvertSubtractConstant : public ngraph::pass::MatcherPass { class ngraph::pass::low_precision::ConvertSubtractConstant : public ov::pass::MatcherPass {
public: public:
OPENVINO_RTTI("ConvertSubtractConstant", "0"); OPENVINO_RTTI("ConvertSubtractConstant", "0");
ConvertSubtractConstant(const std::vector<ngraph::element::Type>& constantPrecisions = {}); ConvertSubtractConstant(const std::vector<ngraph::element::Type>& constantPrecisions = {});

3
src/common/low_precision_transformations/include/low_precision/convolution.hpp
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@ -4,7 +4,6 @@
#pragma once #pragma once
#include <ngraph/ngraph.hpp>
#include "weightable_layer_transformation.hpp" #include "weightable_layer_transformation.hpp"
namespace ngraph { namespace ngraph {
@ -23,7 +22,7 @@ class LP_TRANSFORMATIONS_API ConvolutionTransformation : public WeightableLayerT
public: public:
OPENVINO_RTTI("ConvolutionTransformation", "0"); OPENVINO_RTTI("ConvolutionTransformation", "0");
ConvolutionTransformation(const Params& params = Params()); ConvolutionTransformation(const Params& params = Params());
bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) override;
bool isQuantized(const std::shared_ptr<const Node>& layer, bool isQuantized(const std::shared_ptr<const Node>& layer,
const std::vector<ngraph::element::Type>&defaultPrecisions) const override; const std::vector<ngraph::element::Type>&defaultPrecisions) const override;
static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer, static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer,

3
src/common/low_precision_transformations/include/low_precision/convolution_backprop_data.hpp
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@ -4,7 +4,6 @@
#pragma once #pragma once
#include <ngraph/ngraph.hpp>
#include "weightable_layer_transformation.hpp" #include "weightable_layer_transformation.hpp"
namespace ngraph { namespace ngraph {
@ -22,7 +21,7 @@ namespace low_precision {
class LP_TRANSFORMATIONS_API ConvolutionBackpropDataTransformation : public WeightableLayerTransformation { class LP_TRANSFORMATIONS_API ConvolutionBackpropDataTransformation : public WeightableLayerTransformation {
public: public:
ConvolutionBackpropDataTransformation(const Params& params = Params()); ConvolutionBackpropDataTransformation(const Params& params = Params());
bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> op) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> op) const override;
bool isQuantized(const std::shared_ptr<const Node>& layer, bool isQuantized(const std::shared_ptr<const Node>& layer,
const std::vector<ngraph::element::Type>&defaultPrecisions) const override; const std::vector<ngraph::element::Type>&defaultPrecisions) const override;

2
src/common/low_precision_transformations/include/low_precision/depth_to_space.hpp
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@ -22,7 +22,7 @@ class LP_TRANSFORMATIONS_API DepthToSpaceTransformation : public TransparentBase
public: public:
OPENVINO_RTTI("DepthToSpaceTransformation", "0"); OPENVINO_RTTI("DepthToSpaceTransformation", "0");
DepthToSpaceTransformation(const Params& params = Params()); DepthToSpaceTransformation(const Params& params = Params());
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<ov::Node> layer) const override;
}; };
} // namespace low_precision } // namespace low_precision

7
src/common/low_precision_transformations/include/low_precision/fake_quantize.hpp
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@ -5,7 +5,6 @@
#pragma once #pragma once
#include <memory> #include <memory>
#include <ngraph/ngraph.hpp>
#include "layer_transformation.hpp" #include "layer_transformation.hpp"
namespace ngraph { namespace ngraph {
@ -24,15 +23,15 @@ class LP_TRANSFORMATIONS_API FakeQuantizeTransformation : public LayerTransforma
public: public:
OPENVINO_RTTI("FakeQuantizeTransformation", "0"); OPENVINO_RTTI("FakeQuantizeTransformation", "0");
FakeQuantizeTransformation(const Params& params = Params()); FakeQuantizeTransformation(const Params& params = Params());
bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
static bool checkElementwise(const std::shared_ptr<Node>& eltwise); static bool checkElementwise(const std::shared_ptr<Node>& eltwise);
static std::shared_ptr<opset1::FakeQuantize> fuseElementwise( static std::shared_ptr<ov::opset1::FakeQuantize> fuseElementwise(
TransformationContext& context, TransformationContext& context,
MatcherPass* matcherPass, MatcherPass* matcherPass,
const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize, const std::shared_ptr<ov::opset1::FakeQuantize>& fakeQuantize,
const bool updatePrecisions); const bool updatePrecisions);
}; };

2
src/common/low_precision_transformations/include/low_precision/gather.hpp
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@ -15,7 +15,7 @@ class LP_TRANSFORMATIONS_API GatherTransformation : public LayerTransformation {
public: public:
OPENVINO_RTTI("GatherTransformation", "0"); OPENVINO_RTTI("GatherTransformation", "0");
GatherTransformation(const Params& params = Params()); GatherTransformation(const Params& params = Params());
bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override;
}; };

14
src/common/low_precision_transformations/include/low_precision/layer_transformation.hpp
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@ -11,9 +11,7 @@
#include <memory> #include <memory>
#include <vector> #include <vector>
#include <ngraph/ngraph.hpp> #include "openvino/pass/graph_rewrite.hpp"
#include <ngraph/pass/graph_rewrite.hpp>
#include "transformation_context.hpp" #include "transformation_context.hpp"
#include "quantization_details.hpp" #include "quantization_details.hpp"
#include "low_precision/common/ie_lpt_exception.hpp" #include "low_precision/common/ie_lpt_exception.hpp"
@ -234,7 +232,7 @@ inline std::ostream &operator << (std::ostream &os, const DataPrecision& value)
* @ingroup ie_transformation_common_api * @ingroup ie_transformation_common_api
* @brief Base class for low precision transformation. * @brief Base class for low precision transformation.
*/ */
class LP_TRANSFORMATIONS_API LayerTransformation : public ngraph::pass::MatcherPass { class LP_TRANSFORMATIONS_API LayerTransformation : public ov::pass::MatcherPass {
public: public:
class Params { class Params {
public: public:
@ -285,7 +283,7 @@ public:
LayerTransformation(const Params& params); LayerTransformation(const Params& params);
virtual ~LayerTransformation() = default; virtual ~LayerTransformation() = default;
virtual bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) = 0; virtual bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) = 0;
void setContext(TransformationContext* context) noexcept; void setContext(TransformationContext* context) noexcept;
@ -369,19 +367,19 @@ protected:
std::shared_ptr<ngraph::Node> lastNode, std::shared_ptr<ngraph::Node> lastNode,
std::string originalName) const; std::string originalName) const;
void addPattern(ngraph::pass::GraphRewrite& pass, TransformationContext& context, std::shared_ptr<Node> patternRoot); void addPattern(ov::pass::GraphRewrite& pass, TransformationContext& context, std::shared_ptr<Node> patternRoot);
//TODO: replace with canBeTransformed when quantization by special dimension is supported for all transformations //TODO: replace with canBeTransformed when quantization by special dimension is supported for all transformations
bool canBeTransformedSpatialDimension(const TransformationContext& context, std::shared_ptr<Node> layer) const; bool canBeTransformedSpatialDimension(const TransformationContext& context, std::shared_ptr<Node> layer) const;
template <typename Operation> template <typename Operation>
void addSingleNodePattern(ngraph::pass::GraphRewrite& pass, TransformationContext& context) const { void addSingleNodePattern(ov::pass::GraphRewrite& pass, TransformationContext& context) const {
using namespace ngraph; using namespace ngraph;
auto is_op_type = [](std::shared_ptr<Node> n) { auto is_op_type = [](std::shared_ptr<Node> n) {
return !!as_type_ptr<Operation>(n); return !!as_type_ptr<Operation>(n);
}; };
auto p_node = std::make_shared<pattern::op::Label>(element::f32, Shape{}, is_op_type); auto p_node = std::make_shared<ov::pass::pattern::op::Label>(element::f32, Shape{}, is_op_type);
addPattern(pass, context, p_node); addPattern(pass, context, p_node);
} }

11
src/common/low_precision_transformations/include/low_precision/low_precision.hpp
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@ -20,11 +20,12 @@
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include <ngraph/pass/graph_rewrite.hpp>
#include <low_precision/common/quantization_granularity_restriction.hpp> #include <low_precision/common/quantization_granularity_restriction.hpp>
#include <low_precision/common/precisions_restriction.hpp> #include <low_precision/common/precisions_restriction.hpp>
#include "low_precision/layer_transformation.hpp" #include "low_precision/layer_transformation.hpp"
#include "low_precision/markup_precisions.hpp" #include "low_precision/markup_precisions.hpp"
#include "openvino/pass/graph_rewrite.hpp"
#include "openvino/pass/pass.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -38,27 +39,27 @@ class LP_TRANSFORMATIONS_API LowPrecision;
} // namespace pass } // namespace pass
} // namespace ngraph } // namespace ngraph
class ngraph::pass::low_precision::MarkupOptimizations : public ngraph::pass::FunctionPass { class ngraph::pass::low_precision::MarkupOptimizations : public ov::pass::ModelPass {
public: public:
OPENVINO_RTTI("MarkupOptimizations", "0"); OPENVINO_RTTI("MarkupOptimizations", "0");
MarkupOptimizations( MarkupOptimizations(
const std::vector<PrecisionsRestriction>& precisionRestrictions, const std::vector<PrecisionsRestriction>& precisionRestrictions,
const std::vector<QuantizationGranularityRestriction>& quantizationRestrictions, const std::vector<QuantizationGranularityRestriction>& quantizationRestrictions,
const AttributeParameters& params); const AttributeParameters& params);
bool run_on_model(const std::shared_ptr<ngraph::Function>& m) override; bool run_on_model(const std::shared_ptr<ov::Model>& m) override;
private: private:
const std::vector<PrecisionsRestriction>& precisionRestrictions; const std::vector<PrecisionsRestriction>& precisionRestrictions;
const std::vector<QuantizationGranularityRestriction>& quantizationRestrictions; const std::vector<QuantizationGranularityRestriction>& quantizationRestrictions;
const AttributeParameters& params; const AttributeParameters& params;
}; };
class ngraph::pass::low_precision::TypeRelaxedReplacer : public ngraph::pass::GraphRewrite { class ngraph::pass::low_precision::TypeRelaxedReplacer : public ov::pass::GraphRewrite {
public: public:
OPENVINO_RTTI("TypeRelaxedReplacer", "0"); OPENVINO_RTTI("TypeRelaxedReplacer", "0");
TypeRelaxedReplacer(); TypeRelaxedReplacer();
}; };
class ngraph::pass::low_precision::LowPrecision : public ngraph::pass::FunctionPass { class ngraph::pass::low_precision::LowPrecision : public ov::pass::ModelPass {
public: public:
OPENVINO_RTTI("LowPrecision", "0"); OPENVINO_RTTI("LowPrecision", "0");
LowPrecision( LowPrecision(

8
src/common/low_precision_transformations/include/low_precision/markup_avg_pool_precision_preserved.hpp
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@ -5,9 +5,9 @@
#pragma once #pragma once
#include <memory> #include <memory>
#include <ngraph/pass/pass.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include "low_precision/layer_transformation.hpp" #include "low_precision/layer_transformation.hpp"
#include "openvino/pass/pass.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -27,11 +27,11 @@ class LP_TRANSFORMATIONS_API MarkupAvgPoolPrecisionPreserved;
* [MarkupAvgPoolPrecisionPreserved](@ref openvino_docs_OV_UG_lpt_MarkupAvgPoolPrecisionPreserved) page * [MarkupAvgPoolPrecisionPreserved](@ref openvino_docs_OV_UG_lpt_MarkupAvgPoolPrecisionPreserved) page
* in the Inference Engine Developer Guide. * in the Inference Engine Developer Guide.
*/ */
class ngraph::pass::low_precision::MarkupAvgPoolPrecisionPreserved : public ngraph::pass::FunctionPass { class ngraph::pass::low_precision::MarkupAvgPoolPrecisionPreserved : public ov::pass::ModelPass {
public: public:
OPENVINO_RTTI("MarkupAvgPoolPrecisionPreserved", "0"); OPENVINO_RTTI("MarkupAvgPoolPrecisionPreserved", "0");
MarkupAvgPoolPrecisionPreserved(const std::vector<ngraph::element::Type> defaultPrecisions = ngraph::pass::low_precision::precision_set::int8_support); MarkupAvgPoolPrecisionPreserved(const std::vector<ov::element::Type> defaultPrecisions = ngraph::pass::low_precision::precision_set::int8_support);
bool run_on_model(const std::shared_ptr<ngraph::Function>& m) override; bool run_on_model(const std::shared_ptr<ov::Model>& m) override;
private: private:
const std::vector<ngraph::element::Type> defaultPrecisions; const std::vector<ngraph::element::Type> defaultPrecisions;
}; };

6
src/common/low_precision_transformations/include/low_precision/markup_precisions.hpp
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@ -8,9 +8,9 @@
#include <set> #include <set>
#include <vector> #include <vector>
#include <ngraph/pass/pass.hpp>
#include "low_precision/lpt_visibility.hpp" #include "low_precision/lpt_visibility.hpp"
#include "low_precision/common/precisions_restriction.hpp" #include "low_precision/common/precisions_restriction.hpp"
#include "openvino/pass/pass.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -34,7 +34,7 @@ class LP_TRANSFORMATIONS_API MarkupPrecisions;
* [MarkupPrecisions](@ref openvino_docs_OV_UG_lpt_MarkupPrecisions) page * [MarkupPrecisions](@ref openvino_docs_OV_UG_lpt_MarkupPrecisions) page
* in the Inference Engine Developer Guide. * in the Inference Engine Developer Guide.
*/ */
class ngraph::pass::low_precision::MarkupPrecisions : public ngraph::pass::FunctionPass { class ngraph::pass::low_precision::MarkupPrecisions : public ov::pass::ModelPass {
public: public:
class Restriction { class Restriction {
public: public:
@ -68,7 +68,7 @@ public:
OPENVINO_RTTI("MarkupPrecisions", "0"); OPENVINO_RTTI("MarkupPrecisions", "0");
explicit MarkupPrecisions(const std::vector<PrecisionsRestriction>& restrictions = {}, explicit MarkupPrecisions(const std::vector<PrecisionsRestriction>& restrictions = {},
const std::vector<ngraph::element::Type>& defaultPrecisions = { ngraph::element::u8, ngraph::element::i8 }); const std::vector<ngraph::element::Type>& defaultPrecisions = { ngraph::element::u8, ngraph::element::i8 });
bool run_on_model(const std::shared_ptr<ngraph::Function>& m) override; bool run_on_model(const std::shared_ptr<ov::Model>& m) override;
private: private:
static bool isPrecisionPreserved(const std::shared_ptr<Node>& node); static bool isPrecisionPreserved(const std::shared_ptr<Node>& node);

2
src/common/low_precision_transformations/include/low_precision/mat_mul.hpp
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@ -23,7 +23,7 @@ class LP_TRANSFORMATIONS_API MatMulTransformation : public LayerTransformation {
public: public:
OPENVINO_RTTI("MatMulTransformation", "0"); OPENVINO_RTTI("MatMulTransformation", "0");
MatMulTransformation(const Params& params = Params()); MatMulTransformation(const Params& params = Params());
bool transform(TransformationContext &context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext &context, ov::pass::pattern::Matcher &m) override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override;
}; };

3
src/common/low_precision_transformations/include/low_precision/multiply_to_group_convolution.hpp
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@ -5,7 +5,6 @@
#pragma once #pragma once
#include <memory> #include <memory>
#include <ngraph/ngraph.hpp>
#include "low_precision/layer_transformation.hpp" #include "low_precision/layer_transformation.hpp"
#include "common/precisions_restriction.hpp" #include "common/precisions_restriction.hpp"
@ -28,7 +27,7 @@ public:
const Params& params = Params(), const Params& params = Params(),
const PrecisionsRestriction::PrecisionsByPorts& restrictions = {}); const PrecisionsRestriction::PrecisionsByPorts& restrictions = {});
~MultiplyToGroupConvolutionTransformation() override {} ~MultiplyToGroupConvolutionTransformation() override {}
bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
bool isQuantized(const std::shared_ptr<const Node>& layer, bool isQuantized(const std::shared_ptr<const Node>& layer,

2
src/common/low_precision_transformations/include/low_precision/mvn.hpp
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@ -22,7 +22,7 @@ class LP_TRANSFORMATIONS_API MVNTransformation : public LayerTransformation {
public: public:
OPENVINO_RTTI("MVNTransformation", "0"); OPENVINO_RTTI("MVNTransformation", "0");
MVNTransformation(const Params& params = Params()); MVNTransformation(const Params& params = Params());
bool transform(TransformationContext &context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext &context, ov::pass::pattern::Matcher &m) override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
}; };

58
src/common/low_precision_transformations/include/low_precision/network_helper.hpp
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@ -10,11 +10,7 @@
#include <vector> #include <vector>
#include <unordered_set> #include <unordered_set>
#include <ngraph/ngraph.hpp>
#include <ngraph/pattern/matcher.hpp>
#include <ngraph/opsets/opset1.hpp>
#include "ov_ops/type_relaxed.hpp" #include "ov_ops/type_relaxed.hpp"
#include <ngraph/rt_info.hpp>
#include "rt_info/shared_value_attribute.hpp" #include "rt_info/shared_value_attribute.hpp"
#include "rt_info/precisions_attribute.hpp" #include "rt_info/precisions_attribute.hpp"
@ -26,6 +22,7 @@
#include "common/fake_quantize_dequantization.hpp" #include "common/fake_quantize_dequantization.hpp"
#include "common/ie_lpt_exception.hpp" #include "common/ie_lpt_exception.hpp"
#include "layer_transformation.hpp" #include "layer_transformation.hpp"
#include "openvino/opsets/opset1.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -54,8 +51,8 @@ public:
static std::shared_ptr<Node> setOutDataPrecision(std::shared_ptr<OperationType> operation, const element::Type& precision); static std::shared_ptr<Node> setOutDataPrecision(std::shared_ptr<OperationType> operation, const element::Type& precision);
// applies constant folding of operation to constant and returns the specified output // applies constant folding of operation to constant and returns the specified output
static std::shared_ptr<opset1::Constant> foldDequantizationConstant( static std::shared_ptr<ov::opset1::Constant> foldDequantizationConstant(
const std::shared_ptr<opset1::Constant>& foldingConstant, const std::shared_ptr<ov::opset1::Constant>& foldingConstant,
const std::shared_ptr<Node>& operation, const std::shared_ptr<Node>& operation,
const size_t outIdx = 0); const size_t outIdx = 0);
@ -73,7 +70,7 @@ public:
// Remove node by connecting its 0th input with 0th output // Remove node by connecting its 0th input with 0th output
static void removeLayer(std::shared_ptr<Node> node); static void removeLayer(std::shared_ptr<Node> node);
static std::shared_ptr<Node> swapMultiplyAndAdd(std::shared_ptr<opset1::Add> addAfterMultiply, const int multiplyBranch); static std::shared_ptr<Node> swapMultiplyAndAdd(std::shared_ptr<ov::opset1::Add> addAfterMultiply, const int multiplyBranch);
static void copyInfo(const std::vector<std::shared_ptr<Node>>& sources, const std::vector<std::shared_ptr<Node>>& targets, bool overrideName = true); static void copyInfo(const std::vector<std::shared_ptr<Node>>& sources, const std::vector<std::shared_ptr<Node>>& targets, bool overrideName = true);
@ -81,11 +78,11 @@ public:
static void copyInfo(const std::shared_ptr<Node>& source, const std::shared_ptr<Node>& target, bool overrideName = true); static void copyInfo(const std::shared_ptr<Node>& source, const std::shared_ptr<Node>& target, bool overrideName = true);
static bool isScalarLike(std::shared_ptr<opset1::Constant> constant); static bool isScalarLike(std::shared_ptr<ov::opset1::Constant> constant);
static bool isZero(std::shared_ptr<opset1::Constant> constant); static bool isZero(std::shared_ptr<ov::opset1::Constant> constant);
static std::shared_ptr<opset1::Constant> toScalar(std::shared_ptr<opset1::Constant> constant); static std::shared_ptr<ov::opset1::Constant> toScalar(std::shared_ptr<ov::opset1::Constant> constant);
static std::shared_ptr<Node> getConstantInput(const std::shared_ptr<const Node>& node, const bool convertIsExpected = false); static std::shared_ptr<Node> getConstantInput(const std::shared_ptr<const Node>& node, const bool convertIsExpected = false);
@ -95,15 +92,15 @@ public:
const std::vector<size_t>& reshapeValues); const std::vector<size_t>& reshapeValues);
// Optimizes the series of multiplies after a given output port // Optimizes the series of multiplies after a given output port
static std::shared_ptr<ngraph::opset1::Multiply> optimizeMultipliesAfter(std::shared_ptr<Node> multiply); static std::shared_ptr<ov::opset1::Multiply> optimizeMultipliesAfter(std::shared_ptr<Node> multiply);
static std::shared_ptr<opset1::Constant> round(std::shared_ptr<Node> node, element::Type target_type); static std::shared_ptr<ov::opset1::Constant> round(std::shared_ptr<Node> node, element::Type target_type);
static std::shared_ptr<opset1::FakeQuantize> composeFakeQuantize(const std::shared_ptr<opset1::FakeQuantize>& fq, static std::shared_ptr<ov::opset1::FakeQuantize> composeFakeQuantize(const std::shared_ptr<ov::opset1::FakeQuantize>& fq,
const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support); const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support);
static std::tuple<std::shared_ptr<Node>, std::shared_ptr<Node>> decomposeFakeQuantize( static std::tuple<std::shared_ptr<Node>, std::shared_ptr<Node>> decomposeFakeQuantize(
std::shared_ptr<opset1::FakeQuantize> fq, std::shared_ptr<ov::opset1::FakeQuantize> fq,
const element::Type precision, const element::Type precision,
const float min, const float min,
const float max, const float max,
@ -112,8 +109,8 @@ public:
const element::Type deqPrecision = element::f32, const element::Type deqPrecision = element::f32,
const size_t outChannelsShapeIndex = 0); const size_t outChannelsShapeIndex = 0);
static std::shared_ptr<opset1::FakeQuantize> updateFakeQuantize( static std::shared_ptr<ov::opset1::FakeQuantize> updateFakeQuantize(
std::shared_ptr<opset1::FakeQuantize> fq, std::shared_ptr<ov::opset1::FakeQuantize> fq,
element::Type precision, element::Type precision,
float min, float min,
float max, float max,
@ -138,7 +135,7 @@ public:
static bool areQuantizeAndDequantizeSupportedForMultiply(const std::shared_ptr<const ngraph::Node>& node, static bool areQuantizeAndDequantizeSupportedForMultiply(const std::shared_ptr<const ngraph::Node>& node,
const std::vector<ngraph::element::Type>& _defaultPrecisions = precision_set::int8_support); const std::vector<ngraph::element::Type>& _defaultPrecisions = precision_set::int8_support);
static bool isQuantizeSupported(const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize); static bool isQuantizeSupported(const std::shared_ptr<ov::opset1::FakeQuantize>& fakeQuantize);
static FakeQuantizeDequantization getDequantization(const std::shared_ptr<const Node>& node, static FakeQuantizeDequantization getDequantization(const std::shared_ptr<const Node>& node,
const std::vector<ngraph::element::Type> _defaultPrecisions = precision_set::int8_support, const std::vector<ngraph::element::Type> _defaultPrecisions = precision_set::int8_support,
@ -149,14 +146,14 @@ public:
static FakeQuantizeDequantization normalizeDequantization(FakeQuantizeDequantization dequantization); static FakeQuantizeDequantization normalizeDequantization(FakeQuantizeDequantization dequantization);
static std::shared_ptr<opset1::Constant> normalizeDequantizationShape( static std::shared_ptr<ov::opset1::Constant> normalizeDequantizationShape(
const std::shared_ptr<Node>& eltwise, const std::shared_ptr<Node>& eltwise,
const bool convertIsExpected = true); const bool convertIsExpected = true);
// 1. remove Convert if possible // 1. remove Convert if possible
// 2. optimize Constant if possible // 2. optimize Constant if possible
// 3. remove Subtract if Constant on the second branch is zero // 3. remove Subtract if Constant on the second branch is zero
static std::shared_ptr<Node> optimizeSubtract(std::shared_ptr<opset1::Subtract> add); static std::shared_ptr<Node> optimizeSubtract(std::shared_ptr<ov::opset1::Subtract> add);
class InsertDequantizationResult { class InsertDequantizationResult {
public: public:
@ -181,9 +178,9 @@ public:
const bool updatePrecision, const bool updatePrecision,
const bool moveSubtract); const bool moveSubtract);
static std::vector<std::vector<std::shared_ptr<ngraph::opset1::Constant>>> splitConstantsBeforeConcat( static std::vector<std::vector<std::shared_ptr<ov::opset1::Constant>>> splitConstantsBeforeConcat(
const std::shared_ptr<ov::Node> concat, const std::shared_ptr<ov::Node> concat,
const std::vector<std::shared_ptr<opset1::Constant>> currConstants); const std::vector<std::shared_ptr<ov::opset1::Constant>> currConstants);
static bool checkConstantValuePrecision(const element::Type expectedPrecision, const std::shared_ptr<Node>& constant); static bool checkConstantValuePrecision(const element::Type expectedPrecision, const std::shared_ptr<Node>& constant);
@ -198,8 +195,8 @@ public:
static std::shared_ptr<Node> toScalarIfPossible(std::shared_ptr<Node> node); static std::shared_ptr<Node> toScalarIfPossible(std::shared_ptr<Node> node);
static std::shared_ptr<Node> fold_fake_quantize(const std::shared_ptr<opset1::FakeQuantize>& fq); static std::shared_ptr<Node> fold_fake_quantize(const std::shared_ptr<ov::opset1::FakeQuantize>& fq);
static std::shared_ptr<Node> fold_fake_quantize(const std::shared_ptr<opset1::FakeQuantize>& fq, const bool roundValues); static std::shared_ptr<Node> fold_fake_quantize(const std::shared_ptr<ov::opset1::FakeQuantize>& fq, const bool roundValues);
static FakeQuantizeDequantization foldDequantization(const std::shared_ptr<Node>& node, static FakeQuantizeDequantization foldDequantization(const std::shared_ptr<Node>& node,
const size_t branchIndex, const size_t branchIndex,
@ -209,7 +206,7 @@ public:
static std::shared_ptr<ngraph::Node> separateInStandaloneBranch(std::shared_ptr<ngraph::Node> node, static std::shared_ptr<ngraph::Node> separateInStandaloneBranch(std::shared_ptr<ngraph::Node> node,
const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support); const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support);
static std::shared_ptr<opset1::FakeQuantize> fuseConvert(const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize); static std::shared_ptr<ov::opset1::FakeQuantize> fuseConvert(const std::shared_ptr<ov::opset1::FakeQuantize>& fakeQuantize);
static std::vector<element::Type> precisionIntersection( static std::vector<element::Type> precisionIntersection(
const std::vector<element::Type>& v1, const std::vector<element::Type>& v1,
@ -254,7 +251,7 @@ public:
private: private:
static std::shared_ptr<Node> foldFakeQuantize( static std::shared_ptr<Node> foldFakeQuantize(
const std::shared_ptr<opset1::FakeQuantize>& fq, const std::shared_ptr<ov::opset1::FakeQuantize>& fq,
const bool roundValues, const bool roundValues,
const bool roundValuesWasSet); const bool roundValuesWasSet);
@ -296,7 +293,8 @@ std::shared_ptr<Node> NetworkHelper::setOutDataPrecision(std::shared_ptr<Operati
template <typename T> template <typename T>
std::shared_ptr<Node> make_op_pattern(const ngraph::NodeVector& args) { std::shared_ptr<Node> make_op_pattern(const ngraph::NodeVector& args) {
return std::make_shared<ngraph::pattern::op::Any>(element::undefined, PartialShape{}, [](std::shared_ptr<Node> n) {return !!ov::as_type_ptr<T>(n); }, args); return std::make_shared<ov::pass::pattern::op::Any>(element::undefined, PartialShape{},
[](std::shared_ptr<Node> n) {return !!ov::as_type_ptr<T>(n); }, args);
} }
template <typename T, typename... Args> template <typename T, typename... Args>
@ -317,14 +315,14 @@ template <typename T, typename... Args>
std::shared_ptr<Node> fold_reshape(Args&&... args) { std::shared_ptr<Node> fold_reshape(Args&&... args) {
std::shared_ptr<Node> node = std::make_shared<T>(args...); std::shared_ptr<Node> node = std::make_shared<T>(args...);
if (node->get_output_size() == 1) { if (node->get_output_size() == 1) {
const auto data_const = ov::as_type_ptr<opset1::Constant>(node->get_input_node_shared_ptr(0)); const auto data_const = ov::as_type_ptr<ov::opset1::Constant>(node->get_input_node_shared_ptr(0));
const auto target_shape = ov::as_type_ptr<opset1::Constant>(node->get_input_node_shared_ptr(1)); const auto target_shape = ov::as_type_ptr<ov::opset1::Constant>(node->get_input_node_shared_ptr(1));
if (data_const && target_shape) { if (data_const && target_shape) {
return std::make_shared<opset1::Constant>(node->get_input_element_type(0), return std::make_shared<ov::opset1::Constant>(node->get_input_element_type(0),
node->get_output_shape(0), node->get_output_shape(0),
data_const->get_data_ptr()); data_const->get_data_ptr());
} }
return fold<opset1::Reshape>(std::forward<Args>(args)...); return fold<ov::opset1::Reshape>(std::forward<Args>(args)...);
} }
return node; return node;
} }

2
src/common/low_precision_transformations/include/low_precision/normalize_l2.hpp
View File

@ -22,7 +22,7 @@ class LP_TRANSFORMATIONS_API NormalizeL2Transformation : public LayerTransformat
public: public:
OPENVINO_RTTI("NormalizeL2Transformation", "0"); OPENVINO_RTTI("NormalizeL2Transformation", "0");
NormalizeL2Transformation(const Params& params = Params()); NormalizeL2Transformation(const Params& params = Params());
bool transform(TransformationContext &context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext &context, ov::pass::pattern::Matcher &m) override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
}; };

7
src/common/low_precision_transformations/include/low_precision/propagate_precisions.hpp
View File

@ -7,10 +7,9 @@
#include <memory> #include <memory>
#include <vector> #include <vector>
#include <ngraph/node.hpp>
#include <ngraph/pass/graph_rewrite.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include "low_precision/rt_info/attribute_parameters.hpp" #include "low_precision/rt_info/attribute_parameters.hpp"
#include "openvino/pass/pass.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -30,11 +29,11 @@ class LP_TRANSFORMATIONS_API PropagatePrecisions;
* [PropagatePrecisions](@ref openvino_docs_OV_UG_lpt_PropagatePrecisions) page * [PropagatePrecisions](@ref openvino_docs_OV_UG_lpt_PropagatePrecisions) page
* in the Inference Engine Developer Guide. * in the Inference Engine Developer Guide.
*/ */
class ngraph::pass::low_precision::PropagatePrecisions : public ngraph::pass::FunctionPass { class ngraph::pass::low_precision::PropagatePrecisions : public ov::pass::ModelPass {
public: public:
OPENVINO_RTTI("PropagatePrecisions", "0"); OPENVINO_RTTI("PropagatePrecisions", "0");
PropagatePrecisions(const AttributeParameters& params = AttributeParameters()); PropagatePrecisions(const AttributeParameters& params = AttributeParameters());
bool run_on_model(const std::shared_ptr<ngraph::Function>& m) override; bool run_on_model(const std::shared_ptr<ov::Model>& m) override;
private: private:
const AttributeParameters params; const AttributeParameters params;

4
src/common/low_precision_transformations/include/low_precision/propagate_through_precision_preserved.hpp
View File

@ -104,9 +104,9 @@ private:
auto getInput = [&defaultPrecisions](const std::shared_ptr<ngraph::Node>& node, const size_t index) -> Input<Node> { auto getInput = [&defaultPrecisions](const std::shared_ptr<ngraph::Node>& node, const size_t index) -> Input<Node> {
const auto dequantization = NetworkHelper::getDequantization(node, defaultPrecisions, index); const auto dequantization = NetworkHelper::getDequantization(node, defaultPrecisions, index);
if (!dequantization.empty() && if (!dequantization.empty() &&
ov::is_type<opset1::Convert>(dequantization.data.get_node()) && ov::is_type<ov::opset1::Convert>(dequantization.data.get_node()) &&
(dequantization.data.get_node()->get_input_size() == 1ul) && (dequantization.data.get_node()->get_input_size() == 1ul) &&
ov::is_type<opset1::FakeQuantize>(dequantization.data.get_node()->get_input_node_ptr(0))) { ov::is_type<ov::opset1::FakeQuantize>(dequantization.data.get_node()->get_input_node_ptr(0))) {
return dequantization.data.get_node()->input(0); return dequantization.data.get_node()->input(0);
} }

13
src/common/low_precision_transformations/include/low_precision/quantization_details.hpp
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@ -9,10 +9,7 @@
#include <vector> #include <vector>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include "openvino/opsets/opset1.hpp"
#include <ngraph/node.hpp>
#include <ngraph/opsets/opset1.hpp>
#include <ngraph/type.hpp>
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -29,19 +26,19 @@ public:
const std::vector<float>& outputLowValues, const std::vector<float>& outputLowValues,
const std::vector<float>& outputHighValues); const std::vector<float>& outputHighValues);
static bool outputLayoutIsSupported(std::shared_ptr<opset1::FakeQuantize> quantize, bool isConvertExpected = false); static bool outputLayoutIsSupported(std::shared_ptr<ov::opset1::FakeQuantize> quantize, bool isConvertExpected = false);
static void getInputIntervals( static void getInputIntervals(
std::shared_ptr<opset1::FakeQuantize> quantize, std::shared_ptr<ov::opset1::FakeQuantize> quantize,
std::vector<float>& inputLowValues, std::vector<float>& inputLowValues,
std::vector<float>& inputHighValues); std::vector<float>& inputHighValues);
static void getOutputIntervals( static void getOutputIntervals(
std::shared_ptr<opset1::FakeQuantize> quantize, std::shared_ptr<ov::opset1::FakeQuantize> quantize,
std::vector<float>& outputLowValues, std::vector<float>& outputLowValues,
std::vector<float>& outputHighValues); std::vector<float>& outputHighValues);
static QuantizationDetails getDetails(std::shared_ptr<opset1::FakeQuantize>); static QuantizationDetails getDetails(std::shared_ptr<ov::opset1::FakeQuantize>);
bool hasNegativeOutput() const; bool hasNegativeOutput() const;
float maxOutput(const size_t channel) const; float maxOutput(const size_t channel) const;
float maxInput(const size_t channel) const; float maxInput(const size_t channel) const;

2
src/common/low_precision_transformations/include/low_precision/reshape.hpp
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@ -23,7 +23,7 @@ class LP_TRANSFORMATIONS_API ReshapeTransformation : public LayerTransformation
public: public:
OPENVINO_RTTI("ReshapeTransformation", "0"); OPENVINO_RTTI("ReshapeTransformation", "0");
ReshapeTransformation(const Params& params = Params()); ReshapeTransformation(const Params& params = Params());
bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher &m) override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> op) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> op) const override;

3
src/common/low_precision_transformations/include/low_precision/rt_info/precisions_attribute.hpp
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@ -9,9 +9,6 @@
#include <unordered_set> #include <unordered_set>
#include <vector> #include <vector>
#include <ngraph/node.hpp>
#include <ngraph/pass/graph_rewrite.hpp>
#include "low_precision/lpt_visibility.hpp" #include "low_precision/lpt_visibility.hpp"
#include "low_precision/rt_info/attribute_parameters.hpp" #include "low_precision/rt_info/attribute_parameters.hpp"
#include "low_precision/rt_info/shared_value_attribute.hpp" #include "low_precision/rt_info/shared_value_attribute.hpp"

3
src/common/low_precision_transformations/include/low_precision/rt_info/quantization_alignment_attribute.hpp
View File

@ -9,10 +9,7 @@
#include <unordered_set> #include <unordered_set>
#include <vector> #include <vector>
#include <ngraph/node.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include <ngraph/pass/graph_rewrite.hpp>
#include "shared_value_attribute.hpp" #include "shared_value_attribute.hpp"
#include "attribute_parameters.hpp" #include "attribute_parameters.hpp"

3
src/common/low_precision_transformations/include/low_precision/rt_info/quantization_granularity_attribute.hpp
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@ -4,10 +4,7 @@
#pragma once #pragma once
#include <ngraph/node.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include <ngraph/pass/graph_rewrite.hpp>
#include "low_precision/rt_info/shared_value_attribute.hpp" #include "low_precision/rt_info/shared_value_attribute.hpp"
#include "low_precision/layer_transformation.hpp" #include "low_precision/layer_transformation.hpp"
#include "attribute_parameters.hpp" #include "attribute_parameters.hpp"

1
src/common/low_precision_transformations/include/low_precision/rt_info/shared_value_attribute.hpp
View File

@ -11,7 +11,6 @@
#include <ngraph/node.hpp> #include <ngraph/node.hpp>
#include <low_precision/lpt_visibility.hpp> #include <low_precision/lpt_visibility.hpp>
#include <ngraph/pass/graph_rewrite.hpp>
template <class T> template <class T>
class LP_TRANSFORMATIONS_API SharedAttribute : public ov::RuntimeAttribute { class LP_TRANSFORMATIONS_API SharedAttribute : public ov::RuntimeAttribute {

2
src/common/low_precision_transformations/include/low_precision/split.hpp
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@ -25,7 +25,7 @@ class LP_TRANSFORMATIONS_API SplitTransformation : public LayerTransformation {
public: public:
OPENVINO_RTTI("SplitTransformation", "0"); OPENVINO_RTTI("SplitTransformation", "0");
SplitTransformation(const Params& params = Params()); SplitTransformation(const Params& params = Params());
bool transform(TransformationContext& context, ngraph::pattern::Matcher& m) override; bool transform(TransformationContext& context, ov::pass::pattern::Matcher& m) override;
bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override; bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override; bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const override;
void updateOutputs( void updateOutputs(

1
src/common/low_precision_transformations/include/low_precision/transformation_context.hpp
View File

@ -6,7 +6,6 @@
#include <string> #include <string>
#include <unordered_set> #include <unordered_set>
#include <ngraph/ngraph.hpp>
#include "low_precision/quantization_details.hpp" #include "low_precision/quantization_details.hpp"
namespace ngraph { namespace ngraph {

16
src/common/low_precision_transformations/include/low_precision/update_shared_precision_preserved.hpp
View File

@ -34,23 +34,23 @@ class UpdateSharedPrecisionPreserved;
* in the Inference Engine Developer Guide. * in the Inference Engine Developer Guide.
*/ */
template <typename AttributeType, typename ExpectedAttributeType = AttributeType> template <typename AttributeType, typename ExpectedAttributeType = AttributeType>
class ngraph::pass::low_precision::UpdateSharedPrecisionPreserved : public ngraph::pass::MatcherPass { class ngraph::pass::low_precision::UpdateSharedPrecisionPreserved : public ov::pass::MatcherPass {
public: public:
UpdateSharedPrecisionPreserved(const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support) { UpdateSharedPrecisionPreserved(const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support) {
ngraph::graph_rewrite_callback callback = [&](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [&](ov::pass::pattern::Matcher& m) {
auto node = m.get_match_root(); auto node = m.get_match_root();
const bool needToCheckExpectedAttributeType = !std::is_same<ExpectedAttributeType, AttributeType>::value; const bool needToCheckExpectedAttributeType = !std::is_same<ExpectedAttributeType, AttributeType>::value;
if (!needToCheckExpectedAttributeType) { if (!needToCheckExpectedAttributeType) {
// expected attribute is ignored, set attributes for node inputs except Result & FakeQuantize operations // expected attribute is ignored, set attributes for node inputs except Result & FakeQuantize operations
if (ov::is_type<ngraph::opset1::Result>(node) || if (ov::is_type<ov::opset1::Result>(node) ||
ov::is_type<ngraph::opset1::FakeQuantize>(node) || ov::is_type<ov::opset1::FakeQuantize>(node) ||
transformation_callback(node)) { transformation_callback(node)) {
return false; return false;
} }
} }
if (ngraph::pass::low_precision::NetworkHelper::isPrecisionPreserved(node) || ov::is_type<opset1::FakeQuantize>(node)) { if (ngraph::pass::low_precision::NetworkHelper::isPrecisionPreserved(node) || ov::is_type<ov::opset1::FakeQuantize>(node)) {
return false; return false;
} }
@ -90,7 +90,7 @@ public:
return true; return true;
}; };
auto matcher = std::make_shared<ngraph::pattern::Matcher>(pattern::any_input(), "UpdateSharedPrecisionPreserved"); auto matcher = std::make_shared<ov::pass::pattern::Matcher>(ov::pass::pattern::any_input(), "UpdateSharedPrecisionPreserved");
this->register_matcher(matcher, callback); this->register_matcher(matcher, callback);
} }
@ -98,8 +98,8 @@ private:
Input<Node> getDequantizationInput(const Input<Node>& input, const std::vector<ngraph::element::Type>& defaultPrecisions) { Input<Node> getDequantizationInput(const Input<Node>& input, const std::vector<ngraph::element::Type>& defaultPrecisions) {
const auto dequantization = NetworkHelper::getDequantization(input.get_node()->shared_from_this(), defaultPrecisions, input.get_index()); const auto dequantization = NetworkHelper::getDequantization(input.get_node()->shared_from_this(), defaultPrecisions, input.get_index());
if (!dequantization.empty() && if (!dequantization.empty() &&
(ov::is_type<opset1::Convert>(dequantization.data.get_node())) && (ov::is_type<ov::opset1::Convert>(dequantization.data.get_node())) &&
ov::is_type<opset1::FakeQuantize>(dequantization.data.get_node()->get_input_node_ptr(0))) { ov::is_type<ov::opset1::FakeQuantize>(dequantization.data.get_node()->get_input_node_ptr(0))) {
assert(dequantization.data.get_target_inputs().size() == 1ul); assert(dequantization.data.get_target_inputs().size() == 1ul);
return *dequantization.data.get_target_inputs().begin(); return *dequantization.data.get_target_inputs().begin();
} }

4
src/common/low_precision_transformations/include/low_precision/weightable_layer_transformation.hpp
View File

@ -5,9 +5,9 @@
#pragma once #pragma once
#include <memory> #include <memory>
#include <ngraph/ngraph.hpp>
#include "transformation_context.hpp" #include "transformation_context.hpp"
#include "layer_transformation.hpp" #include "layer_transformation.hpp"
#include "openvino/opsets/opset1.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -42,7 +42,7 @@ protected:
virtual size_t getInputChannels(const std::shared_ptr<ngraph::Node> conv) const = 0; virtual size_t getInputChannels(const std::shared_ptr<ngraph::Node> conv) const = 0;
public: public:
static std::shared_ptr<opset1::FakeQuantize> getFakeQuantizeOnWeights(const std::shared_ptr<Node>& node); static std::shared_ptr<ov::opset1::FakeQuantize> getFakeQuantizeOnWeights(const std::shared_ptr<Node>& node);
static DataPrecision getDataPrecisionOnWeights(const std::shared_ptr<Node>& node, const std::vector<ngraph::element::Type>& defaultPrecisions); static DataPrecision getDataPrecisionOnWeights(const std::shared_ptr<Node>& node, const std::vector<ngraph::element::Type>& defaultPrecisions);
static bool isAsymmetricOnWeights(const std::shared_ptr<const Node>& node, static bool isAsymmetricOnWeights(const std::shared_ptr<const Node>& node,
const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support); const std::vector<ngraph::element::Type>& defaultPrecisions = precision_set::int8_support);

52
src/common/low_precision_transformations/src/add.cpp
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@ -24,29 +24,29 @@ namespace low_precision {
namespace { namespace {
std::shared_ptr<opset1::Subtract> replaceToSubtract(const std::shared_ptr<Node>& op) { std::shared_ptr<ov::opset1::Subtract> replaceToSubtract(const std::shared_ptr<Node>& op) {
// TODO: separate this part to standalone transformation: AddToSubtractTransformation // TODO: separate this part to standalone transformation: AddToSubtractTransformation
// motivation: // motivation:
// - single responsibility // - single responsibility
// - keep AddTransformation and AddToSubtractTransformation transformations independent and optional // - keep AddTransformation and AddToSubtractTransformation transformations independent and optional
const auto add = ov::as_type_ptr<opset1::Add>(op); const auto add = ov::as_type_ptr<ov::opset1::Add>(op);
if (add == nullptr || ov::marked_as_bias(add)) { if (add == nullptr || ov::marked_as_bias(add)) {
return nullptr; return nullptr;
} }
// TODO: use general way from getDequantization: is eltwise with Constant // TODO: use general way from getDequantization: is eltwise with Constant
const int constBranchIndex = ov::is_type<opset1::Constant>(add->get_input_node_ptr(0)) ? const int constBranchIndex = ov::is_type<ov::opset1::Constant>(add->get_input_node_ptr(0)) ?
0 : 0 :
(ov::is_type<opset1::Constant>(add->get_input_node_ptr(1)) ? 1 : -1); (ov::is_type<ov::opset1::Constant>(add->get_input_node_ptr(1)) ? 1 : -1);
if (constBranchIndex == -1) { if (constBranchIndex == -1) {
return nullptr; return nullptr;
} }
const size_t dataBranchIndex = constBranchIndex == 0 ? 1ul : 0; const size_t dataBranchIndex = constBranchIndex == 0 ? 1ul : 0;
auto constant = fold<opset1::Negative>(add->input_value(constBranchIndex)); auto constant = fold<ov::opset1::Negative>(add->input_value(constBranchIndex));
auto constOutput = constant->output(0); auto constOutput = constant->output(0);
const auto subtract = std::make_shared<ov::op::TypeRelaxed<opset1::Subtract>>( const auto subtract = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Subtract>>(
std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{element::f32, element::f32},
std::vector<element::Type>{ op->get_output_element_type(0) }, std::vector<element::Type>{ op->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(add->input_value(dataBranchIndex), element::f32).get(), ov::op::TemporaryReplaceOutputType(add->input_value(dataBranchIndex), element::f32).get(),
@ -59,20 +59,20 @@ std::shared_ptr<opset1::Subtract> replaceToSubtract(const std::shared_ptr<Node>&
return subtract; return subtract;
} }
std::shared_ptr<opset1::Subtract> fuseWithSubtract(const std::shared_ptr<Node>& op) { std::shared_ptr<ov::opset1::Subtract> fuseWithSubtract(const std::shared_ptr<Node>& op) {
const auto add = ov::as_type_ptr<opset1::Add>(op); const auto add = ov::as_type_ptr<ov::opset1::Add>(op);
if ((add == nullptr) || if ((add == nullptr) ||
!ov::is_type<opset1::Subtract>(add->get_input_node_shared_ptr(0)) || !ov::is_type<ov::opset1::Subtract>(add->get_input_node_shared_ptr(0)) ||
// TODO: use general way from getDequantization: is eltwise with Constant // TODO: use general way from getDequantization: is eltwise with Constant
!ov::is_type<opset1::Constant>(add->get_input_node_shared_ptr(0)->get_input_node_shared_ptr(1))) { !ov::is_type<ov::opset1::Constant>(add->get_input_node_shared_ptr(0)->get_input_node_shared_ptr(1))) {
return nullptr; return nullptr;
} }
const auto newSubConst = fold<opset1::Subtract>( const auto newSubConst = fold<ov::opset1::Subtract>(
add->get_input_node_shared_ptr(0)->input_value(1), add->get_input_node_shared_ptr(0)->input_value(1),
add->input_value(1)); add->input_value(1));
const auto newSubtract = std::make_shared<ov::op::TypeRelaxed<opset1::Subtract>>( const auto newSubtract = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Subtract>>(
std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{element::f32, element::f32},
std::vector<element::Type>{ op->get_output_element_type(0) }, std::vector<element::Type>{ op->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(add->get_input_node_shared_ptr(0)->input_value(0), element::f32).get(), ov::op::TemporaryReplaceOutputType(add->get_input_node_shared_ptr(0)->input_value(0), element::f32).get(),
@ -87,7 +87,7 @@ std::shared_ptr<opset1::Subtract> fuseWithSubtract(const std::shared_ptr<Node>&
AddTransformation::AddTransformation(const Params& params) : EltwiseBaseTransformation(params) { AddTransformation::AddTransformation(const Params& params) : EltwiseBaseTransformation(params) {
MATCHER_SCOPE(AddTransformation); MATCHER_SCOPE(AddTransformation);
auto matcher = ngraph::pattern::wrap_type<opset1::Add>(); auto matcher = ngraph::pattern::wrap_type<ov::opset1::Add>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -102,7 +102,7 @@ AddTransformation::AddTransformation(const Params& params) : EltwiseBaseTransfor
} }
bool AddTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) { bool AddTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) {
std::shared_ptr<opset1::Add> op = ov::as_type_ptr<opset1::Add>(m.get_match_root()); std::shared_ptr<ov::opset1::Add> op = ov::as_type_ptr<ov::opset1::Add>(m.get_match_root());
if ((op == nullptr) || (!canBeTransformed(context, op))) { if ((op == nullptr) || (!canBeTransformed(context, op))) {
return false; return false;
} }
@ -111,7 +111,7 @@ bool AddTransformation::transform(TransformationContext& context, ngraph::patter
NetworkHelper::normalizeDequantization(NetworkHelper::getDequantization(op, defaultPrecisions, 1)); NetworkHelper::normalizeDequantization(NetworkHelper::getDequantization(op, defaultPrecisions, 1));
std::shared_ptr<Node> addNode = NetworkHelper::separateInStandaloneBranch(op, defaultPrecisions); std::shared_ptr<Node> addNode = NetworkHelper::separateInStandaloneBranch(op, defaultPrecisions);
std::shared_ptr<opset1::Add> add = ov::as_type_ptr<opset1::Add>(addNode); std::shared_ptr<ov::opset1::Add> add = ov::as_type_ptr<ov::opset1::Add>(addNode);
const int fullPathIndex = getNotEmpty(add); const int fullPathIndex = getNotEmpty(add);
std::shared_ptr<Node> newMultiply; std::shared_ptr<Node> newMultiply;
@ -131,7 +131,7 @@ bool AddTransformation::transform(TransformationContext& context, ngraph::patter
newMultiply = NetworkHelper::swapMultiplyAndAdd(add, multiplyBranch.first); newMultiply = NetworkHelper::swapMultiplyAndAdd(add, multiplyBranch.first);
ngraph::copy_runtime_info({ add, newMultiply }, newMultiply); ngraph::copy_runtime_info({ add, newMultiply }, newMultiply);
if (ov::is_type<opset1::Add>(newMultiply->get_input_node_shared_ptr(0))) { if (ov::is_type<ov::opset1::Add>(newMultiply->get_input_node_shared_ptr(0))) {
newAddOrSubtract = newMultiply->get_input_node_shared_ptr(0); newAddOrSubtract = newMultiply->get_input_node_shared_ptr(0);
auto subtract = fuseWithSubtract(newAddOrSubtract); auto subtract = fuseWithSubtract(newAddOrSubtract);
@ -172,13 +172,13 @@ bool AddTransformation::transform(TransformationContext& context, ngraph::patter
// after : Y = SC2 * ( SC1' * (X1 - SH1') + X2 ) , where : // after : Y = SC2 * ( SC1' * (X1 - SH1') + X2 ) , where :
// SC1' = SC1 / SC2 // SC1' = SC1 / SC2
// SH1' = SH1 + SC2 * SH2 / SC1 // SH1' = SH1 + SC2 * SH2 / SC1
auto newSubtractFullPathValues = fold<opset1::Add>( auto newSubtractFullPathValues = fold<ov::opset1::Add>(
subtractFullPathValues, subtractFullPathValues,
fold<opset1::Divide>( fold<ov::opset1::Divide>(
fold<opset1::Multiply>(subtractEmptyPathValues, multiplyEmptyPathValues), fold<ov::opset1::Multiply>(subtractEmptyPathValues, multiplyEmptyPathValues),
multiplyFullPathValues)); multiplyFullPathValues));
auto newMultiplyFullPathValues = fold<opset1::Divide>(multiplyFullPathValues, multiplyEmptyPathValues); auto newMultiplyFullPathValues = fold<ov::opset1::Divide>(multiplyFullPathValues, multiplyEmptyPathValues);
// Transformation can't be applied if new full path values brake accuracy because of Inf values // Transformation can't be applied if new full path values brake accuracy because of Inf values
if (!NetworkHelper::checkConstantNotInf(newSubtractFullPathValues) || if (!NetworkHelper::checkConstantNotInf(newSubtractFullPathValues) ||
@ -202,25 +202,25 @@ bool AddTransformation::transform(TransformationContext& context, ngraph::patter
// newMultiply // newMultiply
inputs[emptyPathIndex] = dequantizationEmptyPath.data; inputs[emptyPathIndex] = dequantizationEmptyPath.data;
inputs[fullPathIndex] = std::make_shared<opset1::Multiply>( inputs[fullPathIndex] = std::make_shared<ov::opset1::Multiply>(
newSubtractFullPathValues == nullptr ? newSubtractFullPathValues == nullptr ?
(fullPathInput.get_element_type() != newMultiplyFullPathValues->get_element_type() ? (fullPathInput.get_element_type() != newMultiplyFullPathValues->get_element_type() ?
std::make_shared<opset1::Convert>(fullPathInput, newMultiplyFullPathValues->get_element_type()) : std::make_shared<ov::opset1::Convert>(fullPathInput, newMultiplyFullPathValues->get_element_type()) :
fullPathInput) : fullPathInput) :
std::make_shared<opset1::Subtract>( std::make_shared<ov::opset1::Subtract>(
// precision on branch with dequantization operations can be different with dequantization precision, // precision on branch with dequantization operations can be different with dequantization precision,
// for example: FP16 model with FP32 dequantization // for example: FP16 model with FP32 dequantization
fullPathInput.get_element_type() != newSubtractFullPathValues->get_element_type() ? fullPathInput.get_element_type() != newSubtractFullPathValues->get_element_type() ?
std::make_shared<opset1::Convert>(fullPathInput, newSubtractFullPathValues->get_element_type()) : std::make_shared<ov::opset1::Convert>(fullPathInput, newSubtractFullPathValues->get_element_type()) :
fullPathInput, fullPathInput,
newSubtractFullPathValues), newSubtractFullPathValues),
newMultiplyFullPathValues); newMultiplyFullPathValues);
newAddOrSubtract = std::make_shared<ov::op::TypeRelaxed<opset1::Add>>( newAddOrSubtract = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Add>>(
std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{ element::f32 }, std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{ element::f32 },
ov::op::TemporaryReplaceOutputType(inputs[0], element::f32).get(), ov::op::TemporaryReplaceOutputType(inputs[0], element::f32).get(),
ov::op::TemporaryReplaceOutputType(inputs[1], element::f32).get()); ov::op::TemporaryReplaceOutputType(inputs[1], element::f32).get());
newMultiply = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( newMultiply = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{ add->get_output_element_type(0) }, std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{ add->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(newAddOrSubtract, element::f32).get(), ov::op::TemporaryReplaceOutputType(newAddOrSubtract, element::f32).get(),
ov::op::TemporaryReplaceOutputType(multiplyEmptyPathValues, element::f32).get()); ov::op::TemporaryReplaceOutputType(multiplyEmptyPathValues, element::f32).get());

3
src/common/low_precision_transformations/src/align_quantization_intervals.cpp
View File

@ -10,6 +10,7 @@
#include "low_precision/rt_info/intervals_alignment_attribute.hpp" #include "low_precision/rt_info/intervals_alignment_attribute.hpp"
#include "low_precision/rt_info/attribute_parameters.hpp" #include "low_precision/rt_info/attribute_parameters.hpp"
#include "itt.hpp" #include "itt.hpp"
#include "openvino/pass/manager.hpp"
using namespace ngraph; using namespace ngraph;
using namespace ngraph::pass::low_precision; using namespace ngraph::pass::low_precision;
@ -19,7 +20,7 @@ ngraph::pass::low_precision::AlignQuantizationIntervals::AlignQuantizationInterv
bool ngraph::pass::low_precision::AlignQuantizationIntervals::run_on_model(const std::shared_ptr<ngraph::Function>& f) { bool ngraph::pass::low_precision::AlignQuantizationIntervals::run_on_model(const std::shared_ptr<ngraph::Function>& f) {
RUN_ON_FUNCTION_SCOPE(AlignQuantizationIntervals); RUN_ON_FUNCTION_SCOPE(AlignQuantizationIntervals);
ngraph::pass::Manager manager; ov::pass::Manager manager;
manager.set_per_pass_validation(false); manager.set_per_pass_validation(false);
std::shared_ptr<ngraph::pass::GraphRewrite> intervalsAlignment = manager.register_pass<ngraph::pass::GraphRewrite>(); std::shared_ptr<ngraph::pass::GraphRewrite> intervalsAlignment = manager.register_pass<ngraph::pass::GraphRewrite>();
intervalsAlignment->add_matcher<low_precision::CreateAttribute<IntervalsAlignmentAttribute, opset1::FakeQuantize>>( intervalsAlignment->add_matcher<low_precision::CreateAttribute<IntervalsAlignmentAttribute, opset1::FakeQuantize>>(

3
src/common/low_precision_transformations/src/align_quantization_parameters.cpp
View File

@ -11,6 +11,7 @@
#include "low_precision/rt_info/quantization_granularity_attribute.hpp" #include "low_precision/rt_info/quantization_granularity_attribute.hpp"
#include "low_precision/update_shared_precision_preserved.hpp" #include "low_precision/update_shared_precision_preserved.hpp"
#include "itt.hpp" #include "itt.hpp"
#include "openvino/pass/manager.hpp"
using namespace ngraph; using namespace ngraph;
using namespace ngraph::pass::low_precision; using namespace ngraph::pass::low_precision;
@ -20,7 +21,7 @@ ngraph::pass::low_precision::AlignQuantizationParameters::AlignQuantizationParam
bool ngraph::pass::low_precision::AlignQuantizationParameters::run_on_model(const std::shared_ptr<ngraph::Function>& f) { bool ngraph::pass::low_precision::AlignQuantizationParameters::run_on_model(const std::shared_ptr<ngraph::Function>& f) {
RUN_ON_FUNCTION_SCOPE(AlignQuantizationParameters); RUN_ON_FUNCTION_SCOPE(AlignQuantizationParameters);
ngraph::pass::Manager manager; ov::pass::Manager manager;
manager.set_per_pass_validation(false); manager.set_per_pass_validation(false);
std::shared_ptr<ngraph::pass::GraphRewrite> propagation = manager.register_pass<ngraph::pass::GraphRewrite>(); std::shared_ptr<ngraph::pass::GraphRewrite> propagation = manager.register_pass<ngraph::pass::GraphRewrite>();
propagation->add_matcher<low_precision::CreateAttribute<QuantizationAlignmentAttribute>>(); propagation->add_matcher<low_precision::CreateAttribute<QuantizationAlignmentAttribute>>();

10
src/common/low_precision_transformations/src/assign_and_read_value.cpp
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@ -21,8 +21,8 @@ namespace low_precision {
AssignAndReadValueTransformation::AssignAndReadValueTransformation(const std::shared_ptr<ngraph::Function> function, const Params& params) : AssignAndReadValueTransformation::AssignAndReadValueTransformation(const std::shared_ptr<ngraph::Function> function, const Params& params) :
LayerTransformation(params), function(function) { LayerTransformation(params), function(function) {
MATCHER_SCOPE(AssignAndReadValueTransformation); MATCHER_SCOPE(AssignAndReadValueTransformation);
auto assign3 = pattern::wrap_type<opset3::Assign>({ pattern::wrap_type<opset1::Multiply>() }); auto assign3 = pattern::wrap_type<opset3::Assign>({ pattern::wrap_type<ov::opset1::Multiply>() });
auto assign6 = pattern::wrap_type<opset6::Assign>({ pattern::wrap_type<opset1::Multiply>() }); auto assign6 = pattern::wrap_type<opset6::Assign>({ pattern::wrap_type<ov::opset1::Multiply>() });
ngraph::graph_rewrite_callback callback = [=](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [=](pattern::Matcher& m) {
const auto& opsMap = m.get_pattern_value_map(); const auto& opsMap = m.get_pattern_value_map();
@ -86,15 +86,15 @@ bool AssignAndReadValueTransformation::transform(TransformationContext& context,
if (nextLayers.size() > 1) { if (nextLayers.size() > 1) {
return true; return true;
} }
const auto fakeQuantize = as_type_ptr<opset1::FakeQuantize>(nextLayers.begin()->get_node()->shared_from_this()); const auto fakeQuantize = as_type_ptr<ov::opset1::FakeQuantize>(nextLayers.begin()->get_node()->shared_from_this());
if (fakeQuantize == nullptr) { if (fakeQuantize == nullptr) {
return true; return true;
} }
auto fakeQuantizeInputs = fakeQuantize->input_values(); auto fakeQuantizeInputs = fakeQuantize->input_values();
const auto inputLow = as_type_ptr<opset1::Constant>(fakeQuantizeInputs[1].get_node_shared_ptr()); const auto inputLow = as_type_ptr<ov::opset1::Constant>(fakeQuantizeInputs[1].get_node_shared_ptr());
const auto inputHigh = as_type_ptr<opset1::Constant>(fakeQuantizeInputs[2].get_node_shared_ptr()); const auto inputHigh = as_type_ptr<ov::opset1::Constant>(fakeQuantizeInputs[2].get_node_shared_ptr());
if (inputLow == nullptr || inputHigh == nullptr) { if (inputLow == nullptr || inputHigh == nullptr) {
return true; return true;

8
src/common/low_precision_transformations/src/clamp.cpp
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@ -17,7 +17,7 @@ namespace low_precision {
ClampTransformation::ClampTransformation(const Params& params) : LayerTransformation(params) { ClampTransformation::ClampTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(ClampTransformation); MATCHER_SCOPE(ClampTransformation);
auto matcher = pattern::wrap_type<opset1::Clamp>({ pattern::wrap_type<opset1::Multiply>() }); auto matcher = pattern::wrap_type<ov::opset1::Clamp>({ pattern::wrap_type<ov::opset1::Multiply>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -45,9 +45,9 @@ bool ClampTransformation::transform(TransformationContext& context, ngraph::patt
return false; return false;
} }
const auto newClamp = ov::as_type_ptr<opset1::Clamp>(moveDequantizationAfter(context, clamp, dequantization, false, moveSubtract)); const auto newClamp = ov::as_type_ptr<ov::opset1::Clamp>(moveDequantizationAfter(context, clamp, dequantization, false, moveSubtract));
std::shared_ptr<ngraph::opset1::Clamp> replacement; std::shared_ptr<ov::opset1::Clamp> replacement;
{ {
double min = newClamp->get_min(); double min = newClamp->get_min();
double max = newClamp->get_max(); double max = newClamp->get_max();
@ -67,7 +67,7 @@ bool ClampTransformation::transform(TransformationContext& context, ngraph::patt
max += shift; max += shift;
} }
replacement = std::make_shared<ngraph::opset1::Clamp>(newClamp->input_value(0), min, max); replacement = std::make_shared<ov::opset1::Clamp>(newClamp->input_value(0), min, max);
} }
replace_node_update_name(newClamp, replacement); replace_node_update_name(newClamp, replacement);

8
src/common/low_precision_transformations/src/convert.cpp
View File

@ -22,7 +22,7 @@ namespace low_precision {
ConvertTransformation::ConvertTransformation(const Params& params) : LayerTransformation(params) { ConvertTransformation::ConvertTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(ConvertTransformation); MATCHER_SCOPE(ConvertTransformation);
auto matcher = pattern::wrap_type<opset1::Convert>(); auto matcher = pattern::wrap_type<ov::opset1::Convert>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -37,7 +37,7 @@ ConvertTransformation::ConvertTransformation(const Params& params) : LayerTransf
} }
bool ConvertTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) { bool ConvertTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) {
std::shared_ptr<opset1::Convert> convert = ov::as_type_ptr<opset1::Convert>(m.get_match_root()); std::shared_ptr<ov::opset1::Convert> convert = ov::as_type_ptr<ov::opset1::Convert>(m.get_match_root());
if (!convert) { if (!convert) {
return false; return false;
} }
@ -48,9 +48,9 @@ bool ConvertTransformation::transform(TransformationContext& context, ngraph::pa
const ngraph::element::Type precisionBefore = convert->get_input_element_type(0); const ngraph::element::Type precisionBefore = convert->get_input_element_type(0);
std::shared_ptr<opset1::Subtract> subtract = std::make_shared<ov::op::TypeRelaxed<opset1::Subtract>>( std::shared_ptr<ov::opset1::Subtract> subtract = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Subtract>>(
convert->input_value(0), convert->input_value(0),
std::make_shared<opset1::Constant>(precisionBefore, Shape{}, std::vector<size_t>({ 0 }))); std::make_shared<ov::opset1::Constant>(precisionBefore, Shape{}, std::vector<size_t>({ 0 })));
NetworkHelper::setOutDataPrecision(subtract, convert->get_output_element_type(0)); NetworkHelper::setOutDataPrecision(subtract, convert->get_output_element_type(0));
replace_node(convert, subtract); replace_node(convert, subtract);

2
src/common/low_precision_transformations/src/convert_subtract_constant.cpp
View File

@ -44,7 +44,7 @@ ngraph::pass::low_precision::ConvertSubtractConstant::ConvertSubtractConstant(co
auto multiplyConstantWrapper = ngraph::pattern::wrap_type<opset1::Constant>(pattern::consumers_count(1)); auto multiplyConstantWrapper = ngraph::pattern::wrap_type<opset1::Constant>(pattern::consumers_count(1));
auto multiplyWrapper = ngraph::pattern::wrap_type<opset1::Multiply>({ subtractWrapper, multiplyConstantWrapper }, pattern::consumers_count(1)); auto multiplyWrapper = ngraph::pattern::wrap_type<opset1::Multiply>({ subtractWrapper, multiplyConstantWrapper }, pattern::consumers_count(1));
ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher & m) -> bool { ov::matcher_pass_callback callback = [=](ngraph::pattern::Matcher & m) -> bool {
const auto& opsMap = m.get_pattern_value_map(); const auto& opsMap = m.get_pattern_value_map();
const auto weightsConvert = opsMap.at(weightsConvertWrapper).get_node_shared_ptr(); const auto weightsConvert = opsMap.at(weightsConvertWrapper).get_node_shared_ptr();
const auto quantizePrecision = weightsConvert->get_input_element_type(0); const auto quantizePrecision = weightsConvert->get_input_element_type(0);

76
src/common/low_precision_transformations/src/convolution.cpp
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@ -22,16 +22,16 @@ namespace low_precision {
ConvolutionTransformation::ConvolutionTransformation(const Params& params) : WeightableLayerTransformation(params) { ConvolutionTransformation::ConvolutionTransformation(const Params& params) : WeightableLayerTransformation(params) {
MATCHER_SCOPE(ConvolutionTransformation); MATCHER_SCOPE(ConvolutionTransformation);
auto matcher = ngraph::pattern::wrap_type<opset1::Convolution>({ auto matcher = ngraph::pattern::wrap_type<ov::opset1::Convolution>({
ngraph::pattern::wrap_type<opset1::Multiply>(), ngraph::pattern::wrap_type<ov::opset1::Multiply>(),
std::make_shared<pattern::op::Or>(OutputVector { std::make_shared<pattern::op::Or>(OutputVector {
pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<ov::opset1::Multiply>(),
pattern::wrap_type<opset1::FakeQuantize>() pattern::wrap_type<ov::opset1::FakeQuantize>()
}) })
}); });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -64,7 +64,7 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
if (!canConvolutionBeTransformed(context, convolution, defaultPrecisions)) { if (!canConvolutionBeTransformed(context, convolution, defaultPrecisions)) {
const auto weightInput = convolution->get_input_node_shared_ptr(1); const auto weightInput = convolution->get_input_node_shared_ptr(1);
const auto reshapeFromWeights = ov::as_type_ptr<opset1::Reshape>(weightInput); const auto reshapeFromWeights = ov::as_type_ptr<ov::opset1::Reshape>(weightInput);
FakeQuantizeDequantization dequantization = reshapeFromWeights == nullptr ? FakeQuantizeDequantization dequantization = reshapeFromWeights == nullptr ?
NetworkHelper::getDequantization(convolution, defaultPrecisions, 1ul) : NetworkHelper::getDequantization(convolution, defaultPrecisions, 1ul) :
NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions); NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions);
@ -72,12 +72,12 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
const auto fqOnWeights = getFakeQuantizeOnWeights(convolution); const auto fqOnWeights = getFakeQuantizeOnWeights(convolution);
std::shared_ptr<ngraph::Node> resultConstant = NetworkHelper::fold_fake_quantize(fqOnWeights); std::shared_ptr<ngraph::Node> resultConstant = NetworkHelper::fold_fake_quantize(fqOnWeights);
if (reshapeFromWeights != nullptr) { if (reshapeFromWeights != nullptr) {
resultConstant = fold_reshape<opset1::Reshape>( resultConstant = fold_reshape<ov::opset1::Reshape>(
resultConstant, resultConstant,
reshapeFromWeights->input_value(1), reshapeFromWeights->input_value(1),
false); false);
} }
if (ov::is_type<opset1::Constant>(resultConstant)) { if (ov::is_type<ov::opset1::Constant>(resultConstant)) {
replace_node(weightInput, resultConstant); replace_node(weightInput, resultConstant);
} }
} else { } else {
@ -97,13 +97,13 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
std::shared_ptr<Node> newMultiplyAfter; std::shared_ptr<Node> newMultiplyAfter;
{ {
std::shared_ptr<opset1::Subtract> subtract; std::shared_ptr<ov::opset1::Subtract> subtract;
if (dequantization.subtract != nullptr) { if (dequantization.subtract != nullptr) {
auto optimizedSubtract = NetworkHelper::optimizeSubtract(dequantization.subtract); auto optimizedSubtract = NetworkHelper::optimizeSubtract(dequantization.subtract);
if (optimizedSubtract == nullptr) { if (optimizedSubtract == nullptr) {
optimizedSubtract = dequantization.subtract; optimizedSubtract = dequantization.subtract;
} }
subtract = ov::as_type_ptr<opset1::Subtract>(optimizedSubtract); subtract = ov::as_type_ptr<ov::opset1::Subtract>(optimizedSubtract);
} }
// workaround normalizes shape of Subtract to match CPU plugin expectations // workaround normalizes shape of Subtract to match CPU plugin expectations
@ -114,14 +114,14 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
Shape broadcastShape(length, 1); Shape broadcastShape(length, 1);
broadcastShape[1] = getInputChannels(convolution); broadcastShape[1] = getInputChannels(convolution);
std::shared_ptr<Node> newShift = fold<opset1::Broadcast>( std::shared_ptr<Node> newShift = fold<ov::opset1::Broadcast>(
subtract->input_value(1), subtract->input_value(1),
std::make_shared<opset1::Constant>( std::make_shared<ov::opset1::Constant>(
element::i64, element::i64,
Shape{ length }, Shape{ length },
broadcastShape)); broadcastShape));
const auto newSubtract = ov::as_type_ptr<opset1::Subtract>(subtract->clone_with_new_inputs({ const auto newSubtract = ov::as_type_ptr<ov::opset1::Subtract>(subtract->clone_with_new_inputs({
subtract->input_value(0), subtract->input_value(0),
newShift })); newShift }));
NetworkHelper::copyInfo(subtract, newSubtract); NetworkHelper::copyInfo(subtract, newSubtract);
@ -155,35 +155,35 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
Shape newMulShape(outputPShape.size(), 1); Shape newMulShape(outputPShape.size(), 1);
newMulShape[1] = outputScales.size(); newMulShape[1] = outputScales.size();
newMultiplyAfterConst = std::make_shared<opset1::Constant>( newMultiplyAfterConst = std::make_shared<ov::opset1::Constant>(
dequantization.multiplyConstant->get_element_type(), dequantization.multiplyConstant->get_element_type(),
newMulShape, newMulShape,
outputScales); outputScales);
} }
} else { } else {
newMultiplyAfterConst = std::make_shared<opset1::Constant>( newMultiplyAfterConst = std::make_shared<ov::opset1::Constant>(
dequantization.multiplyConstant->get_element_type(), dequantization.multiplyConstant->get_element_type(),
Shape{ 1 }, Shape{ 1 },
dequantization.multiplyConstant->cast_vector<float>()[0]); dequantization.multiplyConstant->cast_vector<float>()[0]);
} }
const auto copyNode = convolution->clone_with_new_inputs({ dequantization.multiply->input_value(0), convolution->input_value(1) }); const auto copyNode = convolution->clone_with_new_inputs({ dequantization.multiply->input_value(0), convolution->input_value(1) });
auto conv = ov::as_type_ptr<opset1::Convolution>(copyNode); auto conv = ov::as_type_ptr<ov::opset1::Convolution>(copyNode);
std::shared_ptr<Node> relaxedNewConvolution; std::shared_ptr<Node> relaxedNewConvolution;
if (conv) { if (conv) {
relaxedNewConvolution = std::make_shared<ov::op::TypeRelaxed<opset1::Convolution>>( relaxedNewConvolution = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Convolution>>(
*conv, *conv,
std::vector<element::Type>{deqPrecision, deqPrecision}, std::vector<element::Type>{deqPrecision, deqPrecision},
std::vector<element::Type>{deqPrecision}); std::vector<element::Type>{deqPrecision});
} else { } else {
relaxedNewConvolution = std::make_shared<ov::op::TypeRelaxed<opset1::GroupConvolution>>( relaxedNewConvolution = std::make_shared<ov::op::TypeRelaxed<ov::opset1::GroupConvolution>>(
*ov::as_type_ptr<opset1::GroupConvolution>(copyNode), *ov::as_type_ptr<ov::opset1::GroupConvolution>(copyNode),
std::vector<element::Type>{deqPrecision, deqPrecision}, std::vector<element::Type>{deqPrecision, deqPrecision},
std::vector<element::Type>{deqPrecision}); std::vector<element::Type>{deqPrecision});
} }
NetworkHelper::copyInfo(convolution, relaxedNewConvolution); NetworkHelper::copyInfo(convolution, relaxedNewConvolution);
newMultiplyAfter = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( newMultiplyAfter = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<element::Type>{ deqPrecision, deqPrecision }, std::vector<element::Type>{ deqPrecision, deqPrecision },
std::vector<element::Type>{ dequantization.multiply->get_output_element_type(0) }, std::vector<element::Type>{ dequantization.multiply->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(relaxedNewConvolution, deqPrecision).get(), ov::op::TemporaryReplaceOutputType(relaxedNewConvolution, deqPrecision).get(),
@ -192,7 +192,7 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
NetworkHelper::insertDequantizationAfter(convolution, newMultiplyAfter, relaxedNewConvolution); NetworkHelper::insertDequantizationAfter(convolution, newMultiplyAfter, relaxedNewConvolution);
convolution = newMultiplyAfter->input_value(0).get_node_shared_ptr(); convolution = newMultiplyAfter->input_value(0).get_node_shared_ptr();
if (ov::is_type<opset1::Convert>(convolution->get_input_node_ptr(0))) { if (ov::is_type<ov::opset1::Convert>(convolution->get_input_node_ptr(0))) {
auto newConvolution = convolution->clone_with_new_inputs({ auto newConvolution = convolution->clone_with_new_inputs({
convolution->get_input_node_ptr(0)->input_value(0), convolution->get_input_node_ptr(0)->input_value(0),
convolution->input_value(1)}); convolution->input_value(1)});
@ -208,27 +208,27 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
return false; return false;
} }
std::shared_ptr<opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<opset1::Reshape>(convolution->get_input_node_shared_ptr(1)); std::shared_ptr<ov::opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<ov::opset1::Reshape>(convolution->get_input_node_shared_ptr(1));
dequantization = reshapeFromWeights == nullptr ? dequantization = reshapeFromWeights == nullptr ?
NetworkHelper::getDequantization(convolution, defaultPrecisions, 1ul) : NetworkHelper::getDequantization(convolution, defaultPrecisions, 1ul) :
NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions); NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions);
assert(!dequantization.empty()); assert(!dequantization.empty());
if (const auto fq = ov::as_type_ptr<opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr())) { if (const auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr())) {
const auto newFQ = NetworkHelper::fold_fake_quantize(fq, true); const auto newFQ = NetworkHelper::fold_fake_quantize(fq, true);
NetworkHelper::copyInfo(fq, newFQ); NetworkHelper::copyInfo(fq, newFQ);
replace_node(fq, newFQ); replace_node(fq, newFQ);
} }
std::shared_ptr<opset1::Multiply> multiplyFromWeights = ov::as_type_ptr<opset1::Multiply>( std::shared_ptr<ov::opset1::Multiply> multiplyFromWeights = ov::as_type_ptr<ov::opset1::Multiply>(
reshapeFromWeights == nullptr ? reshapeFromWeights == nullptr ?
convolution->get_input_node_shared_ptr(1) : convolution->get_input_node_shared_ptr(1) :
convolution->get_input_node_ptr(1)->get_input_node_shared_ptr(0)); convolution->get_input_node_ptr(1)->get_input_node_shared_ptr(0));
std::shared_ptr<opset1::Subtract> subtractFromWeights = ov::as_type_ptr<opset1::Subtract>(multiplyFromWeights->get_input_node_shared_ptr(0)); std::shared_ptr<ov::opset1::Subtract> subtractFromWeights = ov::as_type_ptr<ov::opset1::Subtract>(multiplyFromWeights->get_input_node_shared_ptr(0));
{ {
if (reshapeFromWeights != nullptr) { if (reshapeFromWeights != nullptr) {
reshapeFromWeights = ov::as_type_ptr<opset1::Reshape>(reshapeFromWeights->clone_with_new_inputs({ reshapeFromWeights = ov::as_type_ptr<ov::opset1::Reshape>(reshapeFromWeights->clone_with_new_inputs({
multiplyFromWeights->input_value(0), multiplyFromWeights->input_value(0),
reshapeFromWeights->input_value(1) })); reshapeFromWeights->input_value(1) }));
} }
@ -251,12 +251,12 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
return new_shape; return new_shape;
}(); }();
newMultiplyAfter = std::make_shared<opset1::Multiply>( newMultiplyAfter = std::make_shared<ov::opset1::Multiply>(
newConvolution, newConvolution,
foldConvert( foldConvert(
fold_reshape<opset1::Reshape>( fold_reshape<ov::opset1::Reshape>(
multiplyFromWeights->input_value(1), multiplyFromWeights->input_value(1),
std::make_shared<opset1::Constant>(element::i32, Shape{ newScaleShape.size() }, newScaleShape), std::make_shared<ov::opset1::Constant>(element::i32, Shape{ newScaleShape.size() }, newScaleShape),
false), false),
convolution->get_output_element_type(0))); convolution->get_output_element_type(0)));
NetworkHelper::insertDequantizationAfter(convolution, newMultiplyAfter, newConvolution); NetworkHelper::insertDequantizationAfter(convolution, newMultiplyAfter, newConvolution);
@ -271,7 +271,7 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
if (optimizedSubtract == nullptr) { if (optimizedSubtract == nullptr) {
subtractFromWeights = nullptr; subtractFromWeights = nullptr;
} else { } else {
subtractFromWeights = ov::as_type_ptr<opset1::Subtract>(optimizedSubtract); subtractFromWeights = ov::as_type_ptr<ov::opset1::Subtract>(optimizedSubtract);
const auto weightsPShape = subtractFromWeights->get_input_partial_shape(0); const auto weightsPShape = subtractFromWeights->get_input_partial_shape(0);
assert(weightsPShape.is_static()); assert(weightsPShape.is_static());
@ -285,15 +285,15 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
zeroPointShape[1] = static_cast<size_t>(weightsPShape[1].get_length()); zeroPointShape[1] = static_cast<size_t>(weightsPShape[1].get_length());
} }
auto zeroPointConstant = fold<opset1::Broadcast>( auto zeroPointConstant = fold<ov::opset1::Broadcast>(
subtractFromWeights->input_value(1), subtractFromWeights->input_value(1),
std::make_shared<opset1::Constant>(element::i32, Shape{ zeroPointShape.size() }, zeroPointShape)); std::make_shared<ov::opset1::Constant>(element::i32, Shape{ zeroPointShape.size() }, zeroPointShape));
NetworkHelper::copyInfo(subtractFromWeights->get_input_node_shared_ptr(1), zeroPointConstant); NetworkHelper::copyInfo(subtractFromWeights->get_input_node_shared_ptr(1), zeroPointConstant);
replace_node(subtractFromWeights->get_input_node_shared_ptr(1), zeroPointConstant); replace_node(subtractFromWeights->get_input_node_shared_ptr(1), zeroPointConstant);
} }
} }
std::shared_ptr<opset1::Convert> convertFromWeights = ov::as_type_ptr<opset1::Convert>(subtractFromWeights == nullptr ? std::shared_ptr<ov::opset1::Convert> convertFromWeights = ov::as_type_ptr<ov::opset1::Convert>(subtractFromWeights == nullptr ?
multiplyFromWeights->get_input_node_shared_ptr(0) : multiplyFromWeights->get_input_node_shared_ptr(0) :
subtractFromWeights->get_input_node_shared_ptr(0)); subtractFromWeights->get_input_node_shared_ptr(0));
if (convertFromWeights != nullptr) { if (convertFromWeights != nullptr) {
@ -310,10 +310,10 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
convolution = newConvolution; convolution = newConvolution;
} }
reshapeFromWeights = ov::as_type_ptr<opset1::Reshape>(convolution->get_input_node_shared_ptr(1)); reshapeFromWeights = ov::as_type_ptr<ov::opset1::Reshape>(convolution->get_input_node_shared_ptr(1));
if (reshapeFromWeights != nullptr) { if (reshapeFromWeights != nullptr) {
// remove Reshape on weights // remove Reshape on weights
const std::shared_ptr<Node> newWeights = fold_reshape<opset1::Reshape>( const std::shared_ptr<Node> newWeights = fold_reshape<ov::opset1::Reshape>(
reshapeFromWeights->input_value(0), reshapeFromWeights->input_value(0),
reshapeFromWeights->input_value(1), reshapeFromWeights->input_value(1),
false); false);
@ -323,15 +323,15 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
} }
const auto finalDequantization = NetworkHelper::optimizeMultipliesAfter(newMultiplyAfter); const auto finalDequantization = NetworkHelper::optimizeMultipliesAfter(newMultiplyAfter);
ngraph::copy_runtime_info({ convolution, finalDequantization }, finalDequantization); ov::copy_runtime_info({ convolution, finalDequantization }, finalDequantization);
updateOutput(context, finalDequantization, convolution); updateOutput(context, finalDequantization, convolution);
auto onWeights = convolution->get_input_node_shared_ptr(1); auto onWeights = convolution->get_input_node_shared_ptr(1);
if (ov::is_type<opset1::Reshape>(onWeights)) { if (ov::is_type<ov::opset1::Reshape>(onWeights)) {
onWeights = onWeights->get_input_node_shared_ptr(0); onWeights = onWeights->get_input_node_shared_ptr(0);
} }
if (ov::is_type<opset1::Subtract>(onWeights)) { if (ov::is_type<ov::opset1::Subtract>(onWeights)) {
ov::disable_constant_folding(onWeights); ov::disable_constant_folding(onWeights);
} }
return true; return true;

72
src/common/low_precision_transformations/src/convolution_backprop_data.cpp
View File

@ -23,27 +23,27 @@ namespace low_precision {
ConvolutionBackpropDataTransformation::ConvolutionBackpropDataTransformation(const Params& params) : WeightableLayerTransformation(params) { ConvolutionBackpropDataTransformation::ConvolutionBackpropDataTransformation(const Params& params) : WeightableLayerTransformation(params) {
MATCHER_SCOPE(ConvolutionBackpropDataTransformation); MATCHER_SCOPE(ConvolutionBackpropDataTransformation);
auto matcher = std::make_shared<pattern::op::Or>(OutputVector{ auto matcher = std::make_shared<pattern::op::Or>(OutputVector{
pattern::wrap_type<opset1::ConvolutionBackpropData>({ pattern::wrap_type<ov::opset1::ConvolutionBackpropData>({
pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<ov::opset1::Multiply>(),
pattern::wrap_type<opset1::Multiply>() pattern::wrap_type<ov::opset1::Multiply>()
}), }),
ngraph::pattern::wrap_type<opset1::ConvolutionBackpropData>({ ngraph::pattern::wrap_type<ov::opset1::ConvolutionBackpropData>({
pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<ov::opset1::Multiply>(),
pattern::wrap_type<opset1::FakeQuantize>() pattern::wrap_type<ov::opset1::FakeQuantize>()
}), }),
ngraph::pattern::wrap_type<opset1::ConvolutionBackpropData>({ ngraph::pattern::wrap_type<ov::opset1::ConvolutionBackpropData>({
pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<ov::opset1::Multiply>(),
pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<ov::opset1::Multiply>(),
pattern::wrap_type<opset1::Constant>() pattern::wrap_type<ov::opset1::Constant>()
}), }),
ngraph::pattern::wrap_type<opset1::ConvolutionBackpropData>({ ngraph::pattern::wrap_type<ov::opset1::ConvolutionBackpropData>({
pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<ov::opset1::Multiply>(),
pattern::wrap_type<opset1::FakeQuantize>(), pattern::wrap_type<ov::opset1::FakeQuantize>(),
pattern::wrap_type<opset1::Constant>() pattern::wrap_type<ov::opset1::Constant>()
}), }),
}); });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -76,7 +76,7 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
if (!canBeTransformed(context, convolutionBackpropData)) { if (!canBeTransformed(context, convolutionBackpropData)) {
auto weightsInput = convolutionBackpropData->get_input_node_shared_ptr(1); auto weightsInput = convolutionBackpropData->get_input_node_shared_ptr(1);
std::shared_ptr<opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<opset1::Reshape>(weightsInput); std::shared_ptr<ov::opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<ov::opset1::Reshape>(weightsInput);
FakeQuantizeDequantization dequantization = reshapeFromWeights == nullptr ? FakeQuantizeDequantization dequantization = reshapeFromWeights == nullptr ?
NetworkHelper::getDequantization(convolutionBackpropData, defaultPrecisions, 1ul) : NetworkHelper::getDequantization(convolutionBackpropData, defaultPrecisions, 1ul) :
NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions); NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions);
@ -89,12 +89,12 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
auto resultConstant = NetworkHelper::fold_fake_quantize(fqOnWeights, false); auto resultConstant = NetworkHelper::fold_fake_quantize(fqOnWeights, false);
if (reshapeFromWeights != nullptr) { if (reshapeFromWeights != nullptr) {
resultConstant = fold_reshape<opset1::Reshape>( resultConstant = fold_reshape<ov::opset1::Reshape>(
resultConstant, resultConstant,
reshapeFromWeights->input_value(1), reshapeFromWeights->input_value(1),
false); false);
} }
if (ov::is_type<opset1::Constant>(resultConstant)) { if (ov::is_type<ov::opset1::Constant>(resultConstant)) {
replace_node(weightsInput, resultConstant); replace_node(weightsInput, resultConstant);
} }
} else { } else {
@ -111,7 +111,7 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
NetworkHelper::optimizeSubtract(dequantization.subtract); NetworkHelper::optimizeSubtract(dequantization.subtract);
} }
std::shared_ptr<Node> newMultiplyAfterConst = std::make_shared<opset1::Constant>( std::shared_ptr<Node> newMultiplyAfterConst = std::make_shared<ov::opset1::Constant>(
dequantization.multiplyConstant->get_element_type(), dequantization.multiplyConstant->get_element_type(),
Shape{ 1 }, Shape{ 1 },
dequantization.multiplyConstant->cast_vector<float>()[0]); dequantization.multiplyConstant->cast_vector<float>()[0]);
@ -119,12 +119,12 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
inputs[0] = dequantization.multiply->input_value(0); inputs[0] = dequantization.multiply->input_value(0);
const auto copyNode = convolutionBackpropData->clone_with_new_inputs(inputs); const auto copyNode = convolutionBackpropData->clone_with_new_inputs(inputs);
const auto relaxedConvolutionBackpropData = std::make_shared<ov::op::TypeRelaxed<opset1::ConvolutionBackpropData>>( const auto relaxedConvolutionBackpropData = std::make_shared<ov::op::TypeRelaxed<ov::opset1::ConvolutionBackpropData>>(
*ov::as_type_ptr<opset1::ConvolutionBackpropData>(copyNode), *ov::as_type_ptr<ov::opset1::ConvolutionBackpropData>(copyNode),
std::vector<element::Type>{deqPrecision, deqPrecision}, std::vector<element::Type>{deqPrecision, deqPrecision},
std::vector<element::Type>{deqPrecision}); std::vector<element::Type>{deqPrecision});
newMultiplyAfter = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( newMultiplyAfter = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<element::Type>{ deqPrecision, deqPrecision }, std::vector<element::Type>{ deqPrecision, deqPrecision },
std::vector<element::Type>{ dequantization.multiply->get_output_element_type(0) }, std::vector<element::Type>{ dequantization.multiply->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(relaxedConvolutionBackpropData, deqPrecision).get(), ov::op::TemporaryReplaceOutputType(relaxedConvolutionBackpropData, deqPrecision).get(),
@ -133,7 +133,7 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
convolutionBackpropData = newMultiplyAfter->get_input_node_shared_ptr(0); convolutionBackpropData = newMultiplyAfter->get_input_node_shared_ptr(0);
inputs[0] = convolutionBackpropData->get_input_node_ptr(0)->input_value(0); inputs[0] = convolutionBackpropData->get_input_node_ptr(0)->input_value(0);
if (ov::is_type<opset1::Convert>(convolutionBackpropData->get_input_node_ptr(0))) { if (ov::is_type<ov::opset1::Convert>(convolutionBackpropData->get_input_node_ptr(0))) {
auto newConvolution = convolutionBackpropData->clone_with_new_inputs(inputs); auto newConvolution = convolutionBackpropData->clone_with_new_inputs(inputs);
replace_node(convolutionBackpropData, newConvolution); replace_node(convolutionBackpropData, newConvolution);
convolutionBackpropData = newConvolution; convolutionBackpropData = newConvolution;
@ -144,14 +144,14 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
decomposeFakeQuantizeForWeightsPath(convolutionBackpropData, 1ul); decomposeFakeQuantizeForWeightsPath(convolutionBackpropData, 1ul);
dequantization = NetworkHelper::getDequantization(convolutionBackpropData, defaultPrecisions, 1ul); dequantization = NetworkHelper::getDequantization(convolutionBackpropData, defaultPrecisions, 1ul);
if (const auto fq = ov::as_type_ptr<opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr())) { if (const auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr())) {
const auto newFQ = NetworkHelper::fold_fake_quantize(fq, true); const auto newFQ = NetworkHelper::fold_fake_quantize(fq, true);
NetworkHelper::copyInfo(fq, newFQ); NetworkHelper::copyInfo(fq, newFQ);
replace_node(fq, newFQ); replace_node(fq, newFQ);
} }
const auto multiplyFromWeights = convolutionBackpropData->get_input_node_shared_ptr(1); const auto multiplyFromWeights = convolutionBackpropData->get_input_node_shared_ptr(1);
auto subtractFromWeights = ov::as_type_ptr<opset1::Subtract>(multiplyFromWeights->get_input_node_shared_ptr(0)); auto subtractFromWeights = ov::as_type_ptr<ov::opset1::Subtract>(multiplyFromWeights->get_input_node_shared_ptr(0));
{ {
const auto newScalePShape = multiplyFromWeights->get_input_partial_shape(1); const auto newScalePShape = multiplyFromWeights->get_input_partial_shape(1);
@ -162,12 +162,12 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
inputs[1] = multiplyFromWeights->input_value(0); inputs[1] = multiplyFromWeights->input_value(0);
const auto newconvolutionBackpropData = convolutionBackpropData->copy_with_new_inputs(inputs); const auto newconvolutionBackpropData = convolutionBackpropData->copy_with_new_inputs(inputs);
newMultiplyAfter = std::make_shared<opset1::Multiply>( newMultiplyAfter = std::make_shared<ov::opset1::Multiply>(
newconvolutionBackpropData, newconvolutionBackpropData,
foldConvert( foldConvert(
fold_reshape<opset1::Reshape>( fold_reshape<ov::opset1::Reshape>(
multiplyFromWeights->input_value(1), multiplyFromWeights->input_value(1),
std::make_shared<opset1::Constant>(element::u64, Shape{ newScaleShape.size() }, newScaleShape), std::make_shared<ov::opset1::Constant>(element::u64, Shape{ newScaleShape.size() }, newScaleShape),
false), false),
convolutionBackpropData->get_output_element_type(0))); convolutionBackpropData->get_output_element_type(0)));
NetworkHelper::insertDequantizationAfter(convolutionBackpropData, newMultiplyAfter, newconvolutionBackpropData); NetworkHelper::insertDequantizationAfter(convolutionBackpropData, newMultiplyAfter, newconvolutionBackpropData);
@ -180,7 +180,7 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
if (optimizedSubtract == nullptr) { if (optimizedSubtract == nullptr) {
subtractFromWeights = nullptr; subtractFromWeights = nullptr;
} else { } else {
subtractFromWeights = ov::as_type_ptr<opset1::Subtract>(optimizedSubtract); subtractFromWeights = ov::as_type_ptr<ov::opset1::Subtract>(optimizedSubtract);
const auto weightsPShape = subtractFromWeights->get_input_partial_shape(0); const auto weightsPShape = subtractFromWeights->get_input_partial_shape(0);
assert(weightsPShape.is_static()); assert(weightsPShape.is_static());
@ -189,15 +189,15 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
Shape zeroPointShape(weightsRankValue, 1ul); Shape zeroPointShape(weightsRankValue, 1ul);
zeroPointShape[1] = static_cast<size_t>(weightsPShape[1].get_length()); zeroPointShape[1] = static_cast<size_t>(weightsPShape[1].get_length());
auto zeroPointConstant = fold<opset1::Broadcast>( auto zeroPointConstant = fold<ov::opset1::Broadcast>(
subtractFromWeights->input_value(1), subtractFromWeights->input_value(1),
std::make_shared<opset1::Constant>(element::i32, Shape{zeroPointShape.size()}, zeroPointShape)); std::make_shared<ov::opset1::Constant>(element::i32, Shape{zeroPointShape.size()}, zeroPointShape));
replace_node(subtractFromWeights->get_input_node_shared_ptr(1), zeroPointConstant); replace_node(subtractFromWeights->get_input_node_shared_ptr(1), zeroPointConstant);
} }
} }
std::shared_ptr<opset1::Convert> convertFromWeights = std::shared_ptr<ov::opset1::Convert> convertFromWeights =
ov::as_type_ptr<opset1::Convert>( ov::as_type_ptr<ov::opset1::Convert>(
subtractFromWeights == nullptr ? subtractFromWeights == nullptr ?
multiplyFromWeights->get_input_node_shared_ptr(0) : multiplyFromWeights->get_input_node_shared_ptr(0) :
subtractFromWeights->get_input_node_shared_ptr(0)); subtractFromWeights->get_input_node_shared_ptr(0));
@ -212,15 +212,15 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
} }
const auto finalDequantization = NetworkHelper::optimizeMultipliesAfter(newMultiplyAfter); const auto finalDequantization = NetworkHelper::optimizeMultipliesAfter(newMultiplyAfter);
ngraph::copy_runtime_info({ convolutionBackpropData, finalDequantization }, finalDequantization); ov::copy_runtime_info({ convolutionBackpropData, finalDequantization }, finalDequantization);
updateOutput(context, finalDequantization, convolutionBackpropData); updateOutput(context, finalDequantization, convolutionBackpropData);
auto onWeights = convolutionBackpropData->get_input_node_shared_ptr(1); auto onWeights = convolutionBackpropData->get_input_node_shared_ptr(1);
if (ov::is_type<opset1::Reshape>(onWeights)) { if (ov::is_type<ov::opset1::Reshape>(onWeights)) {
onWeights = onWeights->get_input_node_shared_ptr(0); onWeights = onWeights->get_input_node_shared_ptr(0);
} }
if (ov::is_type<opset1::Subtract>(onWeights)) { if (ov::is_type<ov::opset1::Subtract>(onWeights)) {
ov::disable_constant_folding(onWeights); ov::disable_constant_folding(onWeights);
} }

6
src/common/low_precision_transformations/src/depth_to_space.cpp
View File

@ -9,13 +9,11 @@
#include "low_precision/network_helper.hpp" #include "low_precision/network_helper.hpp"
#include "itt.hpp" #include "itt.hpp"
using namespace ngraph;
using namespace ngraph::pass;
using namespace ngraph::pass::low_precision; using namespace ngraph::pass::low_precision;
DepthToSpaceTransformation::DepthToSpaceTransformation(const Params& params) : TransparentBaseTransformation(params) { DepthToSpaceTransformation::DepthToSpaceTransformation(const Params& params) : TransparentBaseTransformation(params) {
MATCHER_SCOPE(DepthToSpaceTransformation); MATCHER_SCOPE(DepthToSpaceTransformation);
auto matcher = pattern::wrap_type<opset1::DepthToSpace>({ pattern::wrap_type<opset1::Multiply>() }); auto matcher = pattern::wrap_type<ov::opset1::DepthToSpace>({ pattern::wrap_type<ov::opset1::Multiply>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -29,7 +27,7 @@ DepthToSpaceTransformation::DepthToSpaceTransformation(const Params& params) : T
this->register_matcher(m, callback); this->register_matcher(m, callback);
} }
bool DepthToSpaceTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> layer) const { bool DepthToSpaceTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<ov::Node> layer) const {
if (!LayerTransformation::canBeTransformed(context, layer)) { if (!LayerTransformation::canBeTransformed(context, layer)) {
return false; return false;
} }

34
src/common/low_precision_transformations/src/eliminate_fake_quantize.cpp
View File

@ -17,12 +17,12 @@ namespace low_precision {
EliminateFakeQuantizeTransformation::EliminateFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) { EliminateFakeQuantizeTransformation::EliminateFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(FuseMultiplyToFakeQuantizeTransformation); MATCHER_SCOPE(FuseMultiplyToFakeQuantizeTransformation);
const auto matcher = pattern::wrap_type<opset1::FakeQuantize>({ const auto matcher = pattern::wrap_type<ov::opset1::FakeQuantize>({
pattern::any_input(), pattern::any_input(),
pattern::wrap_type<opset1::Constant>(), pattern::wrap_type<ov::opset1::Constant>(),
pattern::wrap_type<opset1::Constant>(), pattern::wrap_type<ov::opset1::Constant>(),
pattern::wrap_type<opset1::Constant>(), pattern::wrap_type<ov::opset1::Constant>(),
pattern::wrap_type<opset1::Constant>() pattern::wrap_type<ov::opset1::Constant>()
}); });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
@ -47,8 +47,8 @@ bool EliminateFakeQuantizeTransformation::transform(TransformationContext& conte
} }
namespace { namespace {
bool check_interval(const std::shared_ptr<opset1::FakeQuantize>& fq, bool check_interval(const std::shared_ptr<ov::opset1::FakeQuantize>& fq,
const std::shared_ptr<opset1::Constant>& constant, const std::shared_ptr<ov::opset1::Constant>& constant,
const float value, const float value,
const float max_diff, const float max_diff,
const bool exact_comparison) noexcept { const bool exact_comparison) noexcept {
@ -67,14 +67,14 @@ bool check_interval(const std::shared_ptr<opset1::FakeQuantize>& fq,
} }
if (need_to_check_intervals) { if (need_to_check_intervals) {
auto tmp_fq = as_type_ptr<opset1::FakeQuantize>(fq->clone_with_new_inputs({ auto tmp_fq = as_type_ptr<ov::opset1::FakeQuantize>(fq->clone_with_new_inputs({
constant, constant,
fq->get_input_node_shared_ptr(1), fq->get_input_node_shared_ptr(1),
fq->get_input_node_shared_ptr(2), fq->get_input_node_shared_ptr(2),
fq->get_input_node_shared_ptr(3), fq->get_input_node_shared_ptr(3),
fq->get_input_node_shared_ptr(4)})); fq->get_input_node_shared_ptr(4)}));
auto result = NetworkHelper::fold_fake_quantize(tmp_fq, false); auto result = NetworkHelper::fold_fake_quantize(tmp_fq, false);
const auto result_constant = as_type_ptr<opset1::Constant>(result); const auto result_constant = as_type_ptr<ov::opset1::Constant>(result);
if (result_constant == nullptr) { if (result_constant == nullptr) {
return false; return false;
} }
@ -90,7 +90,7 @@ bool check_interval(const std::shared_ptr<opset1::FakeQuantize>& fq,
return true; return true;
} }
bool check_intervals(const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize) { bool check_intervals(const std::shared_ptr<ov::opset1::FakeQuantize>& fakeQuantize) {
const auto& element_type = fakeQuantize->get_output_element_type(0); const auto& element_type = fakeQuantize->get_output_element_type(0);
const auto levels = fakeQuantize->get_levels(); const auto levels = fakeQuantize->get_levels();
const auto min_value = DataPrecision::getMinValue(element_type, levels); const auto min_value = DataPrecision::getMinValue(element_type, levels);
@ -100,15 +100,19 @@ bool check_intervals(const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize)
const auto exact_comparison = !element_type.is_integral(); const auto exact_comparison = !element_type.is_integral();
return return
check_interval(fakeQuantize, ov::as_type_ptr<opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(1)), min_value, max_diff, exact_comparison) && check_interval(fakeQuantize, ov::as_type_ptr<ov::opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(1)),
check_interval(fakeQuantize, ov::as_type_ptr<opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(2)), max_value, max_diff, exact_comparison) && min_value, max_diff, exact_comparison) &&
check_interval(fakeQuantize, ov::as_type_ptr<opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(3)), min_value, max_diff, true) && check_interval(fakeQuantize, ov::as_type_ptr<ov::opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(2)),
check_interval(fakeQuantize, ov::as_type_ptr<opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(4)), max_value, max_diff, true); max_value, max_diff, exact_comparison) &&
check_interval(fakeQuantize, ov::as_type_ptr<ov::opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(3)),
min_value, max_diff, true) &&
check_interval(fakeQuantize, ov::as_type_ptr<ov::opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(4)),
max_value, max_diff, true);
} }
} // namespace } // namespace
bool EliminateFakeQuantizeTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const { bool EliminateFakeQuantizeTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const {
const auto fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(operation); const auto fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(operation);
OPENVINO_ASSERT(fakeQuantize != nullptr, "unexpected operation type"); OPENVINO_ASSERT(fakeQuantize != nullptr, "unexpected operation type");
const auto& input_type = fakeQuantize->get_input_element_type(0); const auto& input_type = fakeQuantize->get_input_element_type(0);

48
src/common/low_precision_transformations/src/eltwise_base_transformation.cpp
View File

@ -60,14 +60,14 @@ bool EltwiseBaseTransformation::canBeTransformed(const TransformationContext& co
} }
static bool isTargetType(const std::shared_ptr<Node> node) { static bool isTargetType(const std::shared_ptr<Node> node) {
return node != nullptr && (ov::is_type<opset1::Convolution>(node) || return node != nullptr && (ov::is_type<ov::opset1::Convolution>(node) ||
ov::is_type<opset1::GroupConvolution>(node) || ov::is_type<ov::opset1::GroupConvolution>(node) ||
ov::is_type<opset1::MatMul>(node)); ov::is_type<ov::opset1::MatMul>(node));
} }
static std::shared_ptr<Node> getDataParent(const std::shared_ptr<Node> branchData) { static std::shared_ptr<Node> getDataParent(const std::shared_ptr<Node> branchData) {
std::shared_ptr<Node> parent = branchData; std::shared_ptr<Node> parent = branchData;
while (ov::is_type<opset1::FakeQuantize>(parent)) { while (ov::is_type<ov::opset1::FakeQuantize>(parent)) {
parent = parent->get_input_node_shared_ptr(0); parent = parent->get_input_node_shared_ptr(0);
} }
@ -101,7 +101,7 @@ static bool isBranchHaveMultipleConsumers(const std::shared_ptr<Node> branchData
return true; return true;
} }
const auto new_parent = parent->get_input_node_shared_ptr(0); const auto new_parent = parent->get_input_node_shared_ptr(0);
parent = !ov::is_type<opset1::Constant>(new_parent) ? new_parent : parent->get_input_node_shared_ptr(1); parent = !ov::is_type<ov::opset1::Constant>(new_parent) ? new_parent : parent->get_input_node_shared_ptr(1);
} }
return several_consumers(parent); return several_consumers(parent);
} }
@ -109,12 +109,12 @@ static bool isBranchHaveMultipleConsumers(const std::shared_ptr<Node> branchData
// return branch index with FP32 precision after eltwise transformation // return branch index with FP32 precision after eltwise transformation
int EltwiseBaseTransformation::getNotEmpty(const std::shared_ptr<Node>& eltwise) const { int EltwiseBaseTransformation::getNotEmpty(const std::shared_ptr<Node>& eltwise) const {
const FakeQuantizeDequantization dequantization1 = pass::low_precision::NetworkHelper::getDequantization(eltwise, defaultPrecisions, 0ul); const FakeQuantizeDequantization dequantization1 = pass::low_precision::NetworkHelper::getDequantization(eltwise, defaultPrecisions, 0ul);
if (ov::as_type<opset1::Constant>(dequantization1.data.get_node())) { if (ov::as_type<ov::opset1::Constant>(dequantization1.data.get_node())) {
return -1; return -1;
} }
const FakeQuantizeDequantization dequantization2 = pass::low_precision::NetworkHelper::getDequantization(eltwise, defaultPrecisions, 1ul); const FakeQuantizeDequantization dequantization2 = pass::low_precision::NetworkHelper::getDequantization(eltwise, defaultPrecisions, 1ul);
if (ov::as_type<opset1::Constant>(dequantization2.data.get_node())) { if (ov::as_type<ov::opset1::Constant>(dequantization2.data.get_node())) {
return -1; return -1;
} }
@ -142,10 +142,10 @@ int EltwiseBaseTransformation::getNotEmpty(const std::shared_ptr<Node>& eltwise)
} }
} }
const std::shared_ptr<opset1::FakeQuantize> fakeQuantize1 = const std::shared_ptr<ov::opset1::FakeQuantize> fakeQuantize1 =
ov::as_type_ptr<opset1::FakeQuantize>(dequantization1.data.get_node_shared_ptr()); ov::as_type_ptr<ov::opset1::FakeQuantize>(dequantization1.data.get_node_shared_ptr());
const std::shared_ptr<opset1::FakeQuantize> fakeQuantize2 = const std::shared_ptr<ov::opset1::FakeQuantize> fakeQuantize2 =
ov::as_type_ptr<opset1::FakeQuantize>(dequantization2.data.get_node_shared_ptr()); ov::as_type_ptr<ov::opset1::FakeQuantize>(dequantization2.data.get_node_shared_ptr());
if (fakeQuantize1 && !fakeQuantize2) { if (fakeQuantize1 && !fakeQuantize2) {
return 0; return 0;
@ -164,11 +164,11 @@ int EltwiseBaseTransformation::getNotEmpty(const std::shared_ptr<Node>& eltwise)
return 1; return 1;
} }
if (ov::is_type<opset1::Constant>(dequantization1.data.get_node())) { if (ov::is_type<ov::opset1::Constant>(dequantization1.data.get_node())) {
return 0; return 0;
} }
if (ov::is_type<opset1::Constant>(dequantization2.data.get_node())) { if (ov::is_type<ov::opset1::Constant>(dequantization2.data.get_node())) {
return 1; return 1;
} }
@ -211,18 +211,18 @@ std::pair<int, int> EltwiseBaseTransformation::getMultiplyConstBranch(const std:
const std::shared_ptr<Node> parent2 = eltwise->get_input_node_shared_ptr(1); const std::shared_ptr<Node> parent2 = eltwise->get_input_node_shared_ptr(1);
const auto dequantization2 = NetworkHelper::getDequantization(eltwise, defaultPrecisions, 1); const auto dequantization2 = NetworkHelper::getDequantization(eltwise, defaultPrecisions, 1);
std::shared_ptr<opset1::Constant> constParent = dequantization1.empty() ? std::shared_ptr<ov::opset1::Constant> constParent = dequantization1.empty() ?
ov::as_type_ptr<opset1::Constant>(parent1) : ov::as_type_ptr<ov::opset1::Constant>(parent1) :
ov::as_type_ptr<opset1::Constant>(dequantization1.data.get_node_shared_ptr()); ov::as_type_ptr<ov::opset1::Constant>(dequantization1.data.get_node_shared_ptr());
std::shared_ptr<opset1::Multiply> multiplyParent = ov::as_type_ptr<opset1::Multiply>(parent2); std::shared_ptr<ov::opset1::Multiply> multiplyParent = ov::as_type_ptr<ov::opset1::Multiply>(parent2);
int multiplyBranch = 1; int multiplyBranch = 1;
if (constParent == nullptr || multiplyParent == nullptr) { if (constParent == nullptr || multiplyParent == nullptr) {
constParent = dequantization2.empty() ? constParent = dequantization2.empty() ?
ov::as_type_ptr<opset1::Constant>(parent2) : ov::as_type_ptr<ov::opset1::Constant>(parent2) :
ov::as_type_ptr<opset1::Constant>(dequantization2.data.get_node_shared_ptr()); ov::as_type_ptr<ov::opset1::Constant>(dequantization2.data.get_node_shared_ptr());
multiplyParent = ov::as_type_ptr<opset1::Multiply>(parent1); multiplyParent = ov::as_type_ptr<ov::opset1::Multiply>(parent1);
multiplyBranch = 0; multiplyBranch = 0;
} }
@ -233,14 +233,14 @@ std::pair<int, int> EltwiseBaseTransformation::getMultiplyConstBranch(const std:
auto multiplyParentParent1 = multiplyParent->get_input_node_shared_ptr(0); auto multiplyParentParent1 = multiplyParent->get_input_node_shared_ptr(0);
auto multiplyParentParent2 = multiplyParent->get_input_node_shared_ptr(1); auto multiplyParentParent2 = multiplyParent->get_input_node_shared_ptr(1);
auto multiplyParentParent = ov::as_type_ptr<opset1::Multiply>(multiplyParentParent1); auto multiplyParentParent = ov::as_type_ptr<ov::opset1::Multiply>(multiplyParentParent1);
auto multiplyParentConst = ov::as_type_ptr<opset1::Constant>(multiplyParentParent2); auto multiplyParentConst = ov::as_type_ptr<ov::opset1::Constant>(multiplyParentParent2);
int multiplyActBranch = 0; int multiplyActBranch = 0;
if (multiplyParentConst == nullptr) { if (multiplyParentConst == nullptr) {
multiplyParentParent = ov::as_type_ptr<opset1::Multiply>(multiplyParentParent2); multiplyParentParent = ov::as_type_ptr<ov::opset1::Multiply>(multiplyParentParent2);
multiplyParentConst = ov::as_type_ptr<opset1::Constant>(multiplyParentParent1); multiplyParentConst = ov::as_type_ptr<ov::opset1::Constant>(multiplyParentParent1);
multiplyActBranch = 1; multiplyActBranch = 1;
} }

4
src/common/low_precision_transformations/src/fake_quantize.cpp
View File

@ -21,7 +21,7 @@ FakeQuantizeTransformation::FakeQuantizeTransformation(const Params& params) : L
MATCHER_SCOPE(FakeQuantizeTransformation); MATCHER_SCOPE(FakeQuantizeTransformation);
auto matcher = pattern::wrap_type<opset1::FakeQuantize>(); auto matcher = pattern::wrap_type<opset1::FakeQuantize>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -220,7 +220,7 @@ std::shared_ptr<opset1::FakeQuantize> FakeQuantizeTransformation::fuseElementwis
matcherPass->register_new_node(newFakeQuantize); matcherPass->register_new_node(newFakeQuantize);
replace_node(fakeQuantize, newFakeQuantize); replace_node(fakeQuantize, newFakeQuantize);
ngraph::copy_runtime_info({ fakeQuantize, eltwise }, newFakeQuantize); ov::copy_runtime_info({ fakeQuantize, eltwise }, newFakeQuantize);
newFakeQuantize->set_friendly_name(fakeQuantize->get_friendly_name()); newFakeQuantize->set_friendly_name(fakeQuantize->get_friendly_name());
return newFakeQuantize; return newFakeQuantize;
} }

14
src/common/low_precision_transformations/src/fold_convert.cpp
View File

@ -16,7 +16,7 @@ namespace low_precision {
FoldConvertTransformation::FoldConvertTransformation(const Params& params) : LayerTransformation(params) { FoldConvertTransformation::FoldConvertTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(FoldConvertTransformation); MATCHER_SCOPE(FoldConvertTransformation);
auto subtract = pattern::wrap_type<opset1::Subtract>(); auto subtract = pattern::wrap_type<ov::opset1::Subtract>();
auto matcher = std::make_shared<ngraph::pattern::Matcher>(subtract, matcher_name); auto matcher = std::make_shared<ngraph::pattern::Matcher>(subtract, matcher_name);
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
@ -38,12 +38,12 @@ bool FoldConvertTransformation::transform(TransformationContext& context, ngraph
auto foldConvert = [&](const size_t branch) { auto foldConvert = [&](const size_t branch) {
const auto convert = subtract->get_input_node_shared_ptr(branch); const auto convert = subtract->get_input_node_shared_ptr(branch);
if (!ov::is_type<opset1::Convert>(convert) || !ov::is_type<opset1::Constant>(convert->get_input_node_shared_ptr(0))) { if (!ov::is_type<ov::opset1::Convert>(convert) || !ov::is_type<ov::opset1::Constant>(convert->get_input_node_shared_ptr(0))) {
return; return;
} }
const auto resultConstant = ngraph::pass::low_precision::foldConvert(convert->input_value(0), convert->get_output_element_type(0)); const auto resultConstant = ngraph::pass::low_precision::foldConvert(convert->input_value(0), convert->get_output_element_type(0));
assert(ov::is_type<opset1::Constant>(resultConstant)); assert(ov::is_type<ov::opset1::Constant>(resultConstant));
replace_node(convert, resultConstant); replace_node(convert, resultConstant);
updateOutput(context, resultConstant, convert); updateOutput(context, resultConstant, convert);
@ -57,10 +57,10 @@ bool FoldConvertTransformation::transform(TransformationContext& context, ngraph
bool FoldConvertTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const { bool FoldConvertTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const {
return return
(ov::is_type<opset1::Convert>(operation->get_input_node_ptr(1)) && (ov::is_type<ov::opset1::Convert>(operation->get_input_node_ptr(1)) &&
ov::is_type<opset1::Constant>(operation->get_input_node_ptr(1)->get_input_node_ptr(0))) || ov::is_type<ov::opset1::Constant>(operation->get_input_node_ptr(1)->get_input_node_ptr(0))) ||
(ov::is_type<opset1::Convert>(operation->get_input_node_ptr(0)) && (ov::is_type<ov::opset1::Convert>(operation->get_input_node_ptr(0)) &&
ov::is_type<opset1::Constant>(operation->get_input_node_ptr(0)->get_input_node_ptr(0))); ov::is_type<ov::opset1::Constant>(operation->get_input_node_ptr(0)->get_input_node_ptr(0)));
} }
bool FoldConvertTransformation::isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept { bool FoldConvertTransformation::isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept {

14
src/common/low_precision_transformations/src/fold_fake_quantize.cpp
View File

@ -19,7 +19,7 @@ namespace low_precision {
FoldFakeQuantizeTransformation::FoldFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) { FoldFakeQuantizeTransformation::FoldFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(FoldFakeQuantizeTransformation); MATCHER_SCOPE(FoldFakeQuantizeTransformation);
auto fakeQuantize = pattern::wrap_type<opset1::FakeQuantize>(); auto fakeQuantize = pattern::wrap_type<ov::opset1::FakeQuantize>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -34,7 +34,7 @@ FoldFakeQuantizeTransformation::FoldFakeQuantizeTransformation(const Params& par
} }
bool FoldFakeQuantizeTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) { bool FoldFakeQuantizeTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) {
const auto fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(m.get_match_root()); const auto fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(m.get_match_root());
if (fakeQuantize == nullptr) { if (fakeQuantize == nullptr) {
return false; return false;
} }
@ -49,7 +49,7 @@ bool FoldFakeQuantizeTransformation::transform(TransformationContext& context, n
} }
const auto resultConstant = NetworkHelper::fold_fake_quantize(fakeQuantize, false); const auto resultConstant = NetworkHelper::fold_fake_quantize(fakeQuantize, false);
if (ov::is_type<opset1::Constant>(resultConstant)) { if (ov::is_type<ov::opset1::Constant>(resultConstant)) {
replace_node(fakeQuantize, resultConstant); replace_node(fakeQuantize, resultConstant);
return true; return true;
} }
@ -58,13 +58,13 @@ bool FoldFakeQuantizeTransformation::transform(TransformationContext& context, n
} }
bool FoldFakeQuantizeTransformation::isConstantOutput(std::shared_ptr<ngraph::Node> node) const { bool FoldFakeQuantizeTransformation::isConstantOutput(std::shared_ptr<ngraph::Node> node) const {
const auto fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(node); const auto fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(node);
if (!fakeQuantize) { if (!fakeQuantize) {
return false; return false;
} }
const auto outputLow = as_type_ptr<opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(3)); const auto outputLow = as_type_ptr<ov::opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(3));
const auto outputHigh = as_type_ptr<opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(4)); const auto outputHigh = as_type_ptr<ov::opset1::Constant>(fakeQuantize->get_input_node_shared_ptr(4));
if (outputLow == nullptr || outputHigh == nullptr) { if (outputLow == nullptr || outputHigh == nullptr) {
return false; return false;
@ -81,7 +81,7 @@ bool FoldFakeQuantizeTransformation::canBeTransformed(const TransformationContex
return false; return false;
} }
const auto fq = ov::as_type_ptr<opset1::FakeQuantize>(op); const auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(op);
if (!fq) { if (!fq) {
return false; return false;
} }

34
src/common/low_precision_transformations/src/fuse_convert.cpp
View File

@ -21,11 +21,11 @@ namespace low_precision {
FuseConvertTransformation::FuseConvertTransformation(const Params& params) : LayerTransformation(params) { FuseConvertTransformation::FuseConvertTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(FuseConvertTransformation); MATCHER_SCOPE(FuseConvertTransformation);
auto multiply = pattern::wrap_type<opset1::Multiply>({ pattern::wrap_type<opset1::Convert>(), pattern::wrap_type<opset1::Constant>() }); auto multiply = pattern::wrap_type<ov::opset1::Multiply>({ pattern::wrap_type<ov::opset1::Convert>(), pattern::wrap_type<ov::opset1::Constant>() });
auto subtract = pattern::wrap_type<opset1::Subtract>({ pattern::wrap_type<opset1::Convert>(), pattern::wrap_type<opset1::Constant>() }); auto subtract = pattern::wrap_type<ov::opset1::Subtract>({ pattern::wrap_type<ov::opset1::Convert>(), pattern::wrap_type<ov::opset1::Constant>() });
auto add = pattern::wrap_type<opset1::Add>({ pattern::wrap_type<opset1::Convert>(), pattern::wrap_type<opset1::Constant>() }); auto add = pattern::wrap_type<ov::opset1::Add>({ pattern::wrap_type<ov::opset1::Convert>(), pattern::wrap_type<ov::opset1::Constant>() });
auto fakeQuantize = pattern::wrap_type<opset1::FakeQuantize>({ auto fakeQuantize = pattern::wrap_type<ov::opset1::FakeQuantize>({
pattern::wrap_type<opset1::Convert>({pattern::wrap_type<opset1::Constant>()}), pattern::wrap_type<ov::opset1::Convert>({pattern::wrap_type<ov::opset1::Constant>()}),
pattern::any_input(), pattern::any_input(),
pattern::any_input(), pattern::any_input(),
pattern::any_input(), pattern::any_input(),
@ -48,13 +48,13 @@ FuseConvertTransformation::FuseConvertTransformation(const Params& params) : Lay
namespace { namespace {
std::shared_ptr<Node> removeConvertIfPossibleForSubtract( std::shared_ptr<Node> removeConvertIfPossibleForSubtract(
const std::shared_ptr<opset1::Convert>& convert, const std::shared_ptr<ov::opset1::Convert>& convert,
const std::shared_ptr<opset1::Subtract>& subtract) { const std::shared_ptr<ov::opset1::Subtract>& subtract) {
std::shared_ptr<Node> newSubtract; std::shared_ptr<Node> newSubtract;
const element::Type precisionBeforeConvert = convert->input(0).get_element_type(); const element::Type precisionBeforeConvert = convert->input(0).get_element_type();
if (NetworkHelper::checkConstantValuePrecision(precisionBeforeConvert, subtract->get_input_node_shared_ptr(1))) { if (NetworkHelper::checkConstantValuePrecision(precisionBeforeConvert, subtract->get_input_node_shared_ptr(1))) {
newSubtract = std::make_shared<ov::op::TypeRelaxed<opset1::Subtract>>( newSubtract = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Subtract>>(
std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{}, std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{},
ov::op::TemporaryReplaceOutputType(convert->input_value(0), element::f32).get(), ov::op::TemporaryReplaceOutputType(convert->input_value(0), element::f32).get(),
ov::op::TemporaryReplaceOutputType(subtract->input_value(1), element::f32).get()); ov::op::TemporaryReplaceOutputType(subtract->input_value(1), element::f32).get());
@ -73,27 +73,27 @@ bool FuseConvertTransformation::transform(TransformationContext& context, ngraph
return false; return false;
} }
const auto convert = ov::as_type_ptr<opset1::Convert>(op->get_input_node_shared_ptr(0)); const auto convert = ov::as_type_ptr<ov::opset1::Convert>(op->get_input_node_shared_ptr(0));
auto parent = convert->input_value(0); auto parent = convert->input_value(0);
if (ov::is_type<opset1::Constant>(parent.get_node_shared_ptr())) { if (ov::is_type<ov::opset1::Constant>(parent.get_node_shared_ptr())) {
auto convertedConstant = foldConvert(parent, convert->get_convert_element_type()); auto convertedConstant = foldConvert(parent, convert->get_convert_element_type());
NetworkHelper::copyInfo(parent.get_node_shared_ptr(), convertedConstant); NetworkHelper::copyInfo(parent.get_node_shared_ptr(), convertedConstant);
replace_node(convert, convertedConstant); replace_node(convert, convertedConstant);
} else { } else {
std::shared_ptr<Node> newOp; std::shared_ptr<Node> newOp;
if (ov::is_type<opset1::Subtract>(op)) { if (ov::is_type<ov::opset1::Subtract>(op)) {
auto subtract = ov::as_type_ptr<opset1::Subtract>(op); auto subtract = ov::as_type_ptr<ov::opset1::Subtract>(op);
newOp = removeConvertIfPossibleForSubtract(convert, subtract); newOp = removeConvertIfPossibleForSubtract(convert, subtract);
} else if (ov::is_type<opset1::Multiply>(op)) { } else if (ov::is_type<ov::opset1::Multiply>(op)) {
newOp = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( newOp = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{}, std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{},
ov::op::TemporaryReplaceOutputType(convert->input_value(0), element::f32).get(), ov::op::TemporaryReplaceOutputType(convert->input_value(0), element::f32).get(),
ov::op::TemporaryReplaceOutputType(op->input_value(1), element::f32).get()); ov::op::TemporaryReplaceOutputType(op->input_value(1), element::f32).get());
NetworkHelper::setOutDataPrecisionForTypeRelaxed(newOp, op->get_output_element_type(0)); NetworkHelper::setOutDataPrecisionForTypeRelaxed(newOp, op->get_output_element_type(0));
replace_node(op, newOp); replace_node(op, newOp);
} else if (ov::is_type<opset1::Add>(op)) { } else if (ov::is_type<ov::opset1::Add>(op)) {
newOp = std::make_shared<ov::op::TypeRelaxed<opset1::Add>>( newOp = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Add>>(
std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{}, std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{},
ov::op::TemporaryReplaceOutputType(convert->input_value(0), element::f32).get(), ov::op::TemporaryReplaceOutputType(convert->input_value(0), element::f32).get(),
ov::op::TemporaryReplaceOutputType(op->input_value(1), element::f32).get()); ov::op::TemporaryReplaceOutputType(op->input_value(1), element::f32).get());
@ -118,7 +118,7 @@ bool FuseConvertTransformation::canBeTransformed(const TransformationContext& co
return false; return false;
} }
const auto convert = ov::as_type_ptr<opset1::Convert>(op->get_input_node_shared_ptr(0)); const auto convert = ov::as_type_ptr<ov::opset1::Convert>(op->get_input_node_shared_ptr(0));
// issue #40395 // issue #40395
if (convert == nullptr) { if (convert == nullptr) {
return false; return false;

26
src/common/low_precision_transformations/src/fuse_multiply_to_fake_quantize.cpp
View File

@ -18,7 +18,7 @@ namespace low_precision {
FuseMultiplyToFakeQuantizeTransformation::FuseMultiplyToFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) { FuseMultiplyToFakeQuantizeTransformation::FuseMultiplyToFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(FuseMultiplyToFakeQuantizeTransformation); MATCHER_SCOPE(FuseMultiplyToFakeQuantizeTransformation);
auto matcher = pattern::wrap_type<opset1::Multiply>(); auto matcher = pattern::wrap_type<ov::opset1::Multiply>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -39,15 +39,15 @@ bool FuseMultiplyToFakeQuantizeTransformation::transform(TransformationContext&
} }
const auto parent = multiply->get_input_node_shared_ptr(0); const auto parent = multiply->get_input_node_shared_ptr(0);
auto fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(parent); auto fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(parent);
const auto convert = ov::as_type_ptr<opset1::Convert>(parent); const auto convert = ov::as_type_ptr<ov::opset1::Convert>(parent);
if (convert) { if (convert) {
fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0)); fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0));
} }
const auto multiplyConstant = multiply->get_input_node_shared_ptr(1); const auto multiplyConstant = multiply->get_input_node_shared_ptr(1);
if (!ov::is_type<opset1::Constant>(multiplyConstant)) { if (!ov::is_type<ov::opset1::Constant>(multiplyConstant)) {
return false; return false;
} }
@ -58,8 +58,8 @@ bool FuseMultiplyToFakeQuantizeTransformation::transform(TransformationContext&
multiplyConstant : multiplyConstant :
foldConvert(multiplyConstant, deqPrecision); foldConvert(multiplyConstant, deqPrecision);
outputLowConst_f32 = fold<opset1::Multiply>(outputLowConst_f32, value); outputLowConst_f32 = fold<ov::opset1::Multiply>(outputLowConst_f32, value);
outputHighConst_f32 = fold<opset1::Multiply>(outputHighConst_f32, value); outputHighConst_f32 = fold<ov::opset1::Multiply>(outputHighConst_f32, value);
const auto inputLow = foldConvert(fakeQuantize->input_value(1), deqPrecision); const auto inputLow = foldConvert(fakeQuantize->input_value(1), deqPrecision);
const auto inputHigh = foldConvert(fakeQuantize->input_value(2), deqPrecision); const auto inputHigh = foldConvert(fakeQuantize->input_value(2), deqPrecision);
@ -68,8 +68,8 @@ bool FuseMultiplyToFakeQuantizeTransformation::transform(TransformationContext&
NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(3), outputLowConst_f32); NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(3), outputLowConst_f32);
NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(4), outputHighConst_f32); NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(4), outputHighConst_f32);
auto newFakeQuantize = std::make_shared<ov::op::TypeRelaxed<opset1::FakeQuantize>>( auto newFakeQuantize = std::make_shared<ov::op::TypeRelaxed<ov::opset1::FakeQuantize>>(
opset1::FakeQuantize( ov::opset1::FakeQuantize(
fakeQuantize->input_value(0), fakeQuantize->input_value(0),
inputLow, inputLow,
inputHigh, inputHigh,
@ -91,7 +91,7 @@ bool FuseMultiplyToFakeQuantizeTransformation::transform(TransformationContext&
} }
bool FuseMultiplyToFakeQuantizeTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const { bool FuseMultiplyToFakeQuantizeTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const {
if (!ov::is_type<opset1::Constant>(operation->get_input_node_shared_ptr(1))) { if (!ov::is_type<ov::opset1::Constant>(operation->get_input_node_shared_ptr(1))) {
return false; return false;
} }
@ -104,11 +104,11 @@ bool FuseMultiplyToFakeQuantizeTransformation::canBeTransformed(const Transforma
} }
const auto parent = operation->get_input_node_shared_ptr(0); const auto parent = operation->get_input_node_shared_ptr(0);
auto fq = ov::as_type_ptr<opset1::FakeQuantize>(parent); auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(parent);
const auto convert = ov::as_type_ptr<opset1::Convert>(parent); const auto convert = ov::as_type_ptr<ov::opset1::Convert>(parent);
if (convert) { if (convert) {
fq = ov::as_type_ptr<opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0)); fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0));
} }
if (!fq) { if (!fq) {

32
src/common/low_precision_transformations/src/fuse_subtract_to_fake_quantize.cpp
View File

@ -17,7 +17,7 @@ namespace low_precision {
FuseSubtractToFakeQuantizeTransformation::FuseSubtractToFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) { FuseSubtractToFakeQuantizeTransformation::FuseSubtractToFakeQuantizeTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(FuseSubtractToFakeQuantizeTransformation); MATCHER_SCOPE(FuseSubtractToFakeQuantizeTransformation);
auto matcher = pattern::wrap_type<opset1::Subtract>(); auto matcher = pattern::wrap_type<ov::opset1::Subtract>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -38,15 +38,15 @@ bool FuseSubtractToFakeQuantizeTransformation::transform(TransformationContext&
} }
const auto parent = subtract->get_input_node_shared_ptr(0); const auto parent = subtract->get_input_node_shared_ptr(0);
auto fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(parent); auto fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(parent);
const auto convert = ov::as_type_ptr<opset1::Convert>(parent); const auto convert = ov::as_type_ptr<ov::opset1::Convert>(parent);
if (convert) { if (convert) {
fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0)); fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0));
} }
const auto subtractConstant = subtract->get_input_node_shared_ptr(1); const auto subtractConstant = subtract->get_input_node_shared_ptr(1);
if (!ov::is_type<opset1::Constant>(subtractConstant)) { if (!ov::is_type<ov::opset1::Constant>(subtractConstant)) {
return false; return false;
} }
@ -57,8 +57,8 @@ bool FuseSubtractToFakeQuantizeTransformation::transform(TransformationContext&
subtractConstant : subtractConstant :
foldConvert(subtractConstant, deqPrecision); foldConvert(subtractConstant, deqPrecision);
outputLowConst_f32 = fold<opset1::Subtract>(outputLowConst_f32, value); outputLowConst_f32 = fold<ov::opset1::Subtract>(outputLowConst_f32, value);
outputHighConst_f32 = fold<opset1::Subtract>(outputHighConst_f32, value); outputHighConst_f32 = fold<ov::opset1::Subtract>(outputHighConst_f32, value);
const auto inputLow = foldConvert(fakeQuantize->input_value(1), deqPrecision); const auto inputLow = foldConvert(fakeQuantize->input_value(1), deqPrecision);
const auto inputHigh = foldConvert(fakeQuantize->input_value(2), deqPrecision); const auto inputHigh = foldConvert(fakeQuantize->input_value(2), deqPrecision);
@ -67,8 +67,8 @@ bool FuseSubtractToFakeQuantizeTransformation::transform(TransformationContext&
NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(3), outputLowConst_f32); NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(3), outputLowConst_f32);
NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(4), outputHighConst_f32); NetworkHelper::copyInfo(fakeQuantize->get_input_node_shared_ptr(4), outputHighConst_f32);
auto newFakeQuantize = std::make_shared<ov::op::TypeRelaxed<opset1::FakeQuantize>>( auto newFakeQuantize = std::make_shared<ov::op::TypeRelaxed<ov::opset1::FakeQuantize>>(
opset1::FakeQuantize( ov::opset1::FakeQuantize(
fakeQuantize->input_value(0), fakeQuantize->input_value(0),
inputLow, inputLow,
inputHigh, inputHigh,
@ -85,7 +85,7 @@ bool FuseSubtractToFakeQuantizeTransformation::transform(TransformationContext&
} }
bool FuseSubtractToFakeQuantizeTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const { bool FuseSubtractToFakeQuantizeTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const {
if (!ov::is_type<opset1::Constant>(operation->get_input_node_shared_ptr(1))) { if (!ov::is_type<ov::opset1::Constant>(operation->get_input_node_shared_ptr(1))) {
return false; return false;
} }
@ -100,20 +100,20 @@ bool FuseSubtractToFakeQuantizeTransformation::canBeTransformed(const Transforma
const auto children = operation->get_output_target_inputs(0); const auto children = operation->get_output_target_inputs(0);
for (const auto& target : children) { for (const auto& target : children) {
const auto convolution = ov::is_type<opset1::Convolution>(target.get_node()); const auto convolution = ov::is_type<ov::opset1::Convolution>(target.get_node());
const auto groupConvolution = ov::is_type<opset1::GroupConvolution>(target.get_node()); const auto groupConvolution = ov::is_type<ov::opset1::GroupConvolution>(target.get_node());
const auto convolutionBackpropData = ov::is_type<opset1::ConvolutionBackpropData>(target.get_node()); const auto convolutionBackpropData = ov::is_type<ov::opset1::ConvolutionBackpropData>(target.get_node());
if (convolution || groupConvolution || convolutionBackpropData) { if (convolution || groupConvolution || convolutionBackpropData) {
return false; return false;
} }
} }
const auto parent = operation->get_input_node_shared_ptr(0); const auto parent = operation->get_input_node_shared_ptr(0);
auto fq = ov::as_type_ptr<opset1::FakeQuantize>(parent); auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(parent);
const auto convert = ov::as_type_ptr<opset1::Convert>(parent); const auto convert = ov::as_type_ptr<ov::opset1::Convert>(parent);
if (convert) { if (convert) {
fq = ov::as_type_ptr<opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0)); fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(convert->get_input_node_shared_ptr(0));
} }
if (!fq) { if (!fq) {

2
src/common/low_precision_transformations/src/group_convolution.cpp
View File

@ -18,7 +18,7 @@ namespace low_precision {
GroupConvolutionTransformation::GroupConvolutionTransformation(const Params& params) : ConvolutionTransformation(params) { GroupConvolutionTransformation::GroupConvolutionTransformation(const Params& params) : ConvolutionTransformation(params) {
MATCHER_SCOPE(GroupConvolutionTransformation); MATCHER_SCOPE(GroupConvolutionTransformation);
auto matcher = pattern::wrap_type<opset1::GroupConvolution>(); auto matcher = pattern::wrap_type<ov::opset1::GroupConvolution>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();

17
src/common/low_precision_transformations/src/layer_transformation.cpp
View File

@ -16,6 +16,7 @@
#include <vector> #include <vector>
#include <queue> #include <queue>
#include "itt.hpp" #include "itt.hpp"
#include "openvino/util/log.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -139,9 +140,9 @@ bool LayerTransformation::canSubtractBeHandled(const std::shared_ptr<Node>& op,
const auto parent = dequantization.subtract->input_value(1).get_node_shared_ptr(); const auto parent = dequantization.subtract->input_value(1).get_node_shared_ptr();
if (ov::is_type<opset1::Constant>(parent)) { if (ov::is_type<ov::opset1::Constant>(parent)) {
return true; return true;
} else if (ov::is_type<opset1::Convert>(parent) && ov::is_type<opset1::Constant>(parent->get_input_node_shared_ptr(0))) { } else if (ov::is_type<ov::opset1::Convert>(parent) && ov::is_type<ov::opset1::Constant>(parent->get_input_node_shared_ptr(0))) {
const auto constant = parent->get_input_node_shared_ptr(0); const auto constant = parent->get_input_node_shared_ptr(0);
const auto constantType = constant->output(0).get_element_type(); const auto constantType = constant->output(0).get_element_type();
return operationType == constantType; return operationType == constantType;
@ -403,7 +404,7 @@ void LayerTransformation::updateOutput(
// TODO: not tested!!! // TODO: not tested!!!
for (auto output : lastNode->outputs()) { for (auto output : lastNode->outputs()) {
for (auto input : output.get_target_inputs()) { for (auto input : output.get_target_inputs()) {
if (ov::is_type<ngraph::opset1::Result>(input.get_node())) { if (ov::is_type<ov::opset1::Result>(input.get_node())) {
const std::string originalName = originalNode->get_friendly_name(); const std::string originalName = originalNode->get_friendly_name();
originalNode->set_friendly_name(originalName + LayerTransformation::originalLayerPostfix); originalNode->set_friendly_name(originalName + LayerTransformation::originalLayerPostfix);
lastNode->set_friendly_name(originalName); lastNode->set_friendly_name(originalName);
@ -428,9 +429,9 @@ void LayerTransformation::updateOutput(
} }
} }
void LayerTransformation::addPattern(ngraph::pass::GraphRewrite& pass, TransformationContext& context, std::shared_ptr<Node> patternRoot) { void LayerTransformation::addPattern(ov::pass::GraphRewrite& pass, TransformationContext& context, std::shared_ptr<Node> patternRoot) {
MATCHER_SCOPE(SingleNodeMatcher); MATCHER_SCOPE(SingleNodeMatcher);
ngraph::graph_rewrite_callback internal_callback = [this, &context](ngraph::pattern::Matcher &m) { ov::graph_rewrite_callback internal_callback = [this, &context](ov::pass::pattern::Matcher &m) {
const bool result = transform(context, m); const bool result = transform(context, m);
(void)result; (void)result;
#ifdef LPT_DISPLAY_PRECISION #ifdef LPT_DISPLAY_PRECISION
@ -446,15 +447,15 @@ void LayerTransformation::addPattern(ngraph::pass::GraphRewrite& pass, Transform
return false; return false;
}; };
// TODO: better name for matcher? required? // TODO: better name for matcher? required?
auto m = std::make_shared<ngraph::pattern::Matcher>(patternRoot, matcher_name); auto m = std::make_shared<ov::pass::pattern::Matcher>(patternRoot, matcher_name);
auto match_pass = std::make_shared<ov::pass::MatcherPass>( auto match_pass = std::make_shared<ov::pass::MatcherPass>(
m->get_name(), m->get_name(),
m, m,
[m, internal_callback](const std::shared_ptr<Node>& node) -> bool { [m, internal_callback](const std::shared_ptr<Node>& node) -> bool {
NGRAPH_DEBUG << "Running matcher " << m->get_name() << " on " << node; OPENVINO_DEBUG << "Running matcher " << m->get_name() << " on " << node;
OV_PASS_CALLBACK(m); OV_PASS_CALLBACK(m);
if (std::dynamic_pointer_cast<ov::pass::pattern::Matcher>(m)->match(node->output(0))) { if (std::dynamic_pointer_cast<ov::pass::pattern::Matcher>(m)->match(node->output(0))) {
NGRAPH_DEBUG << "Matcher " << m->get_name() << " matched " << node; OPENVINO_DEBUG << "Matcher " << m->get_name() << " matched " << node;
bool status = internal_callback(*m.get()); bool status = internal_callback(*m.get());
// explicitly clear Matcher state because it holds pointers to matched nodes // explicitly clear Matcher state because it holds pointers to matched nodes
m->clear_state(); m->clear_state();

5
src/common/low_precision_transformations/src/low_precision.cpp
View File

@ -27,6 +27,7 @@
#include "low_precision/propagate_precisions.hpp" #include "low_precision/propagate_precisions.hpp"
#include "low_precision/align_quantization_parameters.hpp" #include "low_precision/align_quantization_parameters.hpp"
#include "openvino/util/log.hpp"
#include "transformations/common_optimizations/lin_op_sequence_fusion.hpp" #include "transformations/common_optimizations/lin_op_sequence_fusion.hpp"
#include "low_precision/fold_convert.hpp" #include "low_precision/fold_convert.hpp"
#include "low_precision/pull_reshape_through_dequantization.hpp" #include "low_precision/pull_reshape_through_dequantization.hpp"
@ -139,9 +140,9 @@ void make_matcher_type_relaxed(ngraph::pass::GraphRewrite* transformation) {
m->get_name(), m->get_name(),
m, m,
[m, callback](const std::shared_ptr<Node>& node) -> bool { [m, callback](const std::shared_ptr<Node>& node) -> bool {
NGRAPH_DEBUG << "Running matcher " << m->get_name() << " on " << node; OPENVINO_DEBUG << "Running matcher " << m->get_name() << " on " << node;
if (std::dynamic_pointer_cast<ov::pass::pattern::Matcher>(m)->match(node->output(0))) { if (std::dynamic_pointer_cast<ov::pass::pattern::Matcher>(m)->match(node->output(0))) {
NGRAPH_DEBUG << "Matcher " << m->get_name() << " matched " << node; OPENVINO_DEBUG << "Matcher " << m->get_name() << " matched " << node;
OV_PASS_CALLBACK(m); OV_PASS_CALLBACK(m);
bool status = callback(*m.get()); bool status = callback(*m.get());
// explicitly clear Matcher state because it holds pointers to matched nodes // explicitly clear Matcher state because it holds pointers to matched nodes

3
src/common/low_precision_transformations/src/markup_avg_pool_precision_preserved.cpp
View File

@ -10,6 +10,7 @@
#include "low_precision/propagate_through_precision_preserved.hpp" #include "low_precision/propagate_through_precision_preserved.hpp"
#include "low_precision/update_shared_precision_preserved.hpp" #include "low_precision/update_shared_precision_preserved.hpp"
#include "itt.hpp" #include "itt.hpp"
#include "openvino/pass/manager.hpp"
using namespace ngraph; using namespace ngraph;
@ -18,7 +19,7 @@ ngraph::pass::low_precision::MarkupAvgPoolPrecisionPreserved::MarkupAvgPoolPreci
bool ngraph::pass::low_precision::MarkupAvgPoolPrecisionPreserved::run_on_model(const std::shared_ptr<ngraph::Function>& f) { bool ngraph::pass::low_precision::MarkupAvgPoolPrecisionPreserved::run_on_model(const std::shared_ptr<ngraph::Function>& f) {
RUN_ON_FUNCTION_SCOPE(MarkupAvgPoolPrecisionPreserved); RUN_ON_FUNCTION_SCOPE(MarkupAvgPoolPrecisionPreserved);
ngraph::pass::Manager manager; ov::pass::Manager manager;
manager.set_per_pass_validation(false); manager.set_per_pass_validation(false);
std::shared_ptr<ngraph::pass::GraphRewrite> markupAvgPoolPrecision = manager.register_pass<ngraph::pass::GraphRewrite>(); std::shared_ptr<ngraph::pass::GraphRewrite> markupAvgPoolPrecision = manager.register_pass<ngraph::pass::GraphRewrite>();
markupAvgPoolPrecision->add_matcher<low_precision::CreatePrecisionsDependentAttribute<AvgPoolPrecisionPreservedAttribute, opset1::AvgPool>>(); markupAvgPoolPrecision->add_matcher<low_precision::CreatePrecisionsDependentAttribute<AvgPoolPrecisionPreservedAttribute, opset1::AvgPool>>();

2
src/common/low_precision_transformations/src/markup_quantization_granularity.cpp
View File

@ -57,7 +57,7 @@ bool ngraph::pass::low_precision::MarkupQuantizationGranularity::run_on_model(co
continue; continue;
} }
if (const auto multiSubGraph = ov::as_type_ptr<ngraph::op::util::MultiSubGraphOp>(node)) { if (const auto multiSubGraph = ov::as_type_ptr<ov::op::util::MultiSubGraphOp>(node)) {
for (size_t i = 0; i < multiSubGraph->get_internal_subgraphs_size(); i++) for (size_t i = 0; i < multiSubGraph->get_internal_subgraphs_size(); i++)
run_on_model(multiSubGraph->get_function(i)); run_on_model(multiSubGraph->get_function(i));
continue; continue;

52
src/common/low_precision_transformations/src/mat_mul.cpp
View File

@ -21,12 +21,12 @@ using namespace ngraph::pass::low_precision;
MatMulTransformation::MatMulTransformation(const Params& params) : LayerTransformation(params) { MatMulTransformation::MatMulTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(MatMulTransformation); MATCHER_SCOPE(MatMulTransformation);
auto mul1 = pattern::wrap_type<opset1::Multiply>(); auto mul1 = pattern::wrap_type<ov::opset1::Multiply>();
auto mul2 = pattern::wrap_type<opset1::Multiply>(); auto mul2 = pattern::wrap_type<ov::opset1::Multiply>();
auto fq2 = pattern::wrap_type<opset1::FakeQuantize>(); auto fq2 = pattern::wrap_type<ov::opset1::FakeQuantize>();
auto matcher = pattern::wrap_type<opset1::MatMul>({ mul1, std::make_shared<pattern::op::Or>(OutputVector{ mul2, fq2 })}); auto matcher = pattern::wrap_type<ov::opset1::MatMul>({ mul1, std::make_shared<pattern::op::Or>(OutputVector{ mul2, fq2 })});
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -39,18 +39,18 @@ MatMulTransformation::MatMulTransformation(const Params& params) : LayerTransfor
} }
bool MatMulTransformation::transform(TransformationContext &context, ngraph::pattern::Matcher &m) { bool MatMulTransformation::transform(TransformationContext &context, ngraph::pattern::Matcher &m) {
std::shared_ptr<opset1::MatMul> matMul = ov::as_type_ptr<opset1::MatMul>(m.get_match_root()); std::shared_ptr<ov::opset1::MatMul> matMul = ov::as_type_ptr<ov::opset1::MatMul>(m.get_match_root());
if ((matMul == nullptr) || !canBeTransformed(context, matMul)) { if ((matMul == nullptr) || !canBeTransformed(context, matMul)) {
return false; return false;
} }
matMul = ov::as_type_ptr<opset1::MatMul>(NetworkHelper::separateInStandaloneBranch(matMul, defaultPrecisions)); matMul = ov::as_type_ptr<ov::opset1::MatMul>(NetworkHelper::separateInStandaloneBranch(matMul, defaultPrecisions));
const auto dequantization1 = NetworkHelper::getDequantization(matMul, defaultPrecisions, 0); const auto dequantization1 = NetworkHelper::getDequantization(matMul, defaultPrecisions, 0);
auto dequantization2 = NetworkHelper::getDequantization(matMul, defaultPrecisions, 1); auto dequantization2 = NetworkHelper::getDequantization(matMul, defaultPrecisions, 1);
if (dequantization2.empty()) { if (dequantization2.empty()) {
const std::shared_ptr<opset1::FakeQuantize> fakeQuantize = const std::shared_ptr<ov::opset1::FakeQuantize> fakeQuantize =
ov::as_type_ptr<opset1::FakeQuantize>(dequantization2.data.get_node_shared_ptr()); ov::as_type_ptr<ov::opset1::FakeQuantize>(dequantization2.data.get_node_shared_ptr());
if (fakeQuantize != nullptr) { if (fakeQuantize != nullptr) {
const QuantizationDetails quantizationDetails = QuantizationDetails::getDetails(fakeQuantize); const QuantizationDetails quantizationDetails = QuantizationDetails::getDetails(fakeQuantize);
@ -80,7 +80,7 @@ bool MatMulTransformation::transform(TransformationContext &context, ngraph::pat
dequantization2 = NetworkHelper::getDequantization(matMul, defaultPrecisions, 1); dequantization2 = NetworkHelper::getDequantization(matMul, defaultPrecisions, 1);
} }
const std::shared_ptr<opset1::MatMul> newMatMul = std::make_shared<ov::op::TypeRelaxed<opset1::MatMul>>( const std::shared_ptr<ov::opset1::MatMul> newMatMul = std::make_shared<ov::op::TypeRelaxed<ov::opset1::MatMul>>(
std::vector<element::Type>({ deqPrecision, deqPrecision }), std::vector<element::Type>({ deqPrecision }), std::vector<element::Type>({ deqPrecision, deqPrecision }), std::vector<element::Type>({ deqPrecision }),
ov::op::TemporaryReplaceOutputType(dequantization1.data, deqPrecision).get(), ov::op::TemporaryReplaceOutputType(dequantization1.data, deqPrecision).get(),
ov::op::TemporaryReplaceOutputType(dequantization2.data, deqPrecision).get(), ov::op::TemporaryReplaceOutputType(dequantization2.data, deqPrecision).get(),
@ -92,7 +92,7 @@ bool MatMulTransformation::transform(TransformationContext &context, ngraph::pat
// dequantization with subtract on activations & constant weights // dequantization with subtract on activations & constant weights
if (dequantization1.subtract) { if (dequantization1.subtract) {
auto broadcastShape = NetworkHelper::isScalarLike(ov::as_type_ptr<opset1::Constant>(dequantization1.subtractConstant)) ? auto broadcastShape = NetworkHelper::isScalarLike(ov::as_type_ptr<ov::opset1::Constant>(dequantization1.subtractConstant)) ?
Shape(dequantization1.subtract->get_output_partial_shape(0).rank().get_length(), 1) : Shape(dequantization1.subtract->get_output_partial_shape(0).rank().get_length(), 1) :
dequantization1.subtractConstant->get_shape(); dequantization1.subtractConstant->get_shape();
@ -105,25 +105,25 @@ bool MatMulTransformation::transform(TransformationContext &context, ngraph::pat
broadcastShape[lastDataIdx] = weightsShape[firstWeightsIdx]; broadcastShape[lastDataIdx] = weightsShape[firstWeightsIdx];
// broadcasted sub const to form [1, ..., 1, Y] // broadcasted sub const to form [1, ..., 1, Y]
const auto broadcastedConst = fold<opset1::Broadcast>( const auto broadcastedConst = fold<ov::opset1::Broadcast>(
dequantization1.subtractConstant, dequantization1.subtractConstant,
opset1::Constant::create(ngraph::element::i32, { broadcastShape.size() }, broadcastShape)); ov::opset1::Constant::create(ngraph::element::i32, { broadcastShape.size() }, broadcastShape));
// multiply by weights: [1, ..., 1, Y] x [Y, Z] => [1, ..., 1, Z] // multiply by weights: [1, ..., 1, Y] x [Y, Z] => [1, ..., 1, Z]
const auto newSubConst = NetworkHelper::toScalarIfPossible(fold<opset1::MatMul>( const auto newSubConst = NetworkHelper::toScalarIfPossible(fold<ov::opset1::MatMul>(
foldConvert(broadcastedConst, newMatMul->get_element_type()), foldConvert(broadcastedConst, newMatMul->get_element_type()),
foldConvert(newMatMul->input_value(1), newMatMul->get_element_type()), foldConvert(newMatMul->input_value(1), newMatMul->get_element_type()),
newMatMul->get_transpose_a(), newMatMul->get_transpose_a(),
newMatMul->get_transpose_b())); newMatMul->get_transpose_b()));
const auto newSubtract = std::make_shared<opset1::Subtract>(newMatMul, newSubConst); const auto newSubtract = std::make_shared<ov::opset1::Subtract>(newMatMul, newSubConst);
newSubtract->set_friendly_name(newMatMul->get_friendly_name() + "/DequantizationSubtract"); newSubtract->set_friendly_name(newMatMul->get_friendly_name() + "/DequantizationSubtract");
copy_runtime_info({ newSubtract, matMul }, newSubtract); copy_runtime_info({ newSubtract, matMul }, newSubtract);
parent = newSubtract; parent = newSubtract;
} }
auto transpose = [](const std::shared_ptr<opset1::Constant>& node) -> std::shared_ptr<Node> { auto transpose = [](const std::shared_ptr<ov::opset1::Constant>& node) -> std::shared_ptr<Node> {
const Shape outputShape = node->get_shape(); const Shape outputShape = node->get_shape();
if (outputShape.size() < 2ul) { if (outputShape.size() < 2ul) {
return node; return node;
@ -133,16 +133,16 @@ bool MatMulTransformation::transform(TransformationContext &context, ngraph::pat
std::iota(transposeConstant.begin(), transposeConstant.end(), 0); std::iota(transposeConstant.begin(), transposeConstant.end(), 0);
std::swap(*(transposeConstant.end() - 1), *(transposeConstant.end() - 2)); std::swap(*(transposeConstant.end() - 1), *(transposeConstant.end() - 2));
auto order = opset1::Constant::create(element::u32, Shape{ transposeConstant.size() }, transposeConstant); auto order = ov::opset1::Constant::create(element::u32, Shape{ transposeConstant.size() }, transposeConstant);
std::shared_ptr<Node> transposedConstant = fold<opset1::Transpose>(node, order); std::shared_ptr<Node> transposedConstant = fold<ov::opset1::Transpose>(node, order);
return transposedConstant; return transposedConstant;
}; };
const auto mulConst1 = matMul->get_transpose_a() ? transpose(dequantization1.multiplyConstant) : dequantization1.multiplyConstant; const auto mulConst1 = matMul->get_transpose_a() ? transpose(dequantization1.multiplyConstant) : dequantization1.multiplyConstant;
auto mulConst2 = matMul->get_transpose_b() ? transpose(dequantization2.multiplyConstant) : dequantization2.multiplyConstant; auto mulConst2 = matMul->get_transpose_b() ? transpose(dequantization2.multiplyConstant) : dequantization2.multiplyConstant;
if (NetworkHelper::isScalarLike(ov::as_type_ptr<opset1::Constant>(mulConst2))) { if (NetworkHelper::isScalarLike(ov::as_type_ptr<ov::opset1::Constant>(mulConst2))) {
mulConst2 = NetworkHelper::toScalar(ov::as_type_ptr<opset1::Constant>(mulConst2)); mulConst2 = NetworkHelper::toScalar(ov::as_type_ptr<ov::opset1::Constant>(mulConst2));
} else { } else {
const auto constShape = mulConst2->get_shape(); const auto constShape = mulConst2->get_shape();
const size_t inputRank = matMul->get_input_partial_shape(0).rank().get_length(); const size_t inputRank = matMul->get_input_partial_shape(0).rank().get_length();
@ -152,17 +152,17 @@ bool MatMulTransformation::transform(TransformationContext &context, ngraph::pat
Shape unsqueezeConstantShape(inputRank - constShape.size()); Shape unsqueezeConstantShape(inputRank - constShape.size());
std::iota(unsqueezeConstantShape.begin(), unsqueezeConstantShape.end(), 0ul); std::iota(unsqueezeConstantShape.begin(), unsqueezeConstantShape.end(), 0ul);
mulConst2 = fold<opset1::Unsqueeze>( mulConst2 = fold<ov::opset1::Unsqueeze>(
mulConst2, mulConst2,
op::Constant::create(element::i32, Shape{ unsqueezeConstantShape.size() }, unsqueezeConstantShape)); ov::op::v0::Constant::create(element::i32, Shape{ unsqueezeConstantShape.size() }, unsqueezeConstantShape));
} }
} }
const auto newMulConst = NetworkHelper::toScalarIfPossible(fold<opset1::Multiply>( const auto newMulConst = NetworkHelper::toScalarIfPossible(fold<ov::opset1::Multiply>(
mulConst1, mulConst1,
foldConvert(mulConst2, element::f32))); foldConvert(mulConst2, element::f32)));
const auto newMultiply = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( const auto newMultiply = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<element::Type>{ deqPrecision, deqPrecision }, std::vector<element::Type>{ deqPrecision, deqPrecision },
std::vector<element::Type>{ dequantization1.multiply->get_output_element_type(0) }, std::vector<element::Type>{ dequantization1.multiply->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(parent, deqPrecision).get(), ov::op::TemporaryReplaceOutputType(parent, deqPrecision).get(),
@ -187,7 +187,7 @@ bool MatMulTransformation::canBeTransformed(const TransformationContext& context
return false; return false;
} }
std::shared_ptr<opset1::MatMul> matMul = ov::as_type_ptr<opset1::MatMul>(layer); std::shared_ptr<ov::opset1::MatMul> matMul = ov::as_type_ptr<ov::opset1::MatMul>(layer);
if (matMul == nullptr) { if (matMul == nullptr) {
return false; return false;
} }
@ -245,7 +245,7 @@ bool MatMulTransformation::canBeTransformed(const TransformationContext& context
} }
} }
const auto fakeQuantize = ov::as_type_ptr<opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1)); const auto fakeQuantize = ov::as_type_ptr<ov::opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1));
if (fakeQuantize) { if (fakeQuantize) {
if (!QuantizationDetails::outputLayoutIsSupported(fakeQuantize)) { if (!QuantizationDetails::outputLayoutIsSupported(fakeQuantize)) {
return false; return false;

20
src/common/low_precision_transformations/src/multiply.cpp
View File

@ -24,7 +24,7 @@ namespace low_precision {
MultiplyTransformation::MultiplyTransformation(const Params& params) : EltwiseBaseTransformation(params) { MultiplyTransformation::MultiplyTransformation(const Params& params) : EltwiseBaseTransformation(params) {
MATCHER_SCOPE(MultiplyTransformation); MATCHER_SCOPE(MultiplyTransformation);
auto matcher = pattern::wrap_type<opset1::Multiply>(); auto matcher = pattern::wrap_type<ov::opset1::Multiply>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -51,10 +51,10 @@ bool MultiplyTransformation::transform(TransformationContext& context, ngraph::p
auto newMultiply = multiply; auto newMultiply = multiply;
auto fold_fake_quantizes = [](std::shared_ptr<Node>& multiply, const size_t index) { auto fold_fake_quantizes = [](std::shared_ptr<Node>& multiply, const size_t index) {
auto fakeQuantizeOnWeights = ov::as_type_ptr<opset1::FakeQuantize>(multiply->get_input_node_shared_ptr(index)); auto fakeQuantizeOnWeights = ov::as_type_ptr<ov::opset1::FakeQuantize>(multiply->get_input_node_shared_ptr(index));
if (fakeQuantizeOnWeights != nullptr) { if (fakeQuantizeOnWeights != nullptr) {
auto result = NetworkHelper::fold_fake_quantize(fakeQuantizeOnWeights); auto result = NetworkHelper::fold_fake_quantize(fakeQuantizeOnWeights);
if (ov::is_type<opset1::Constant>(result)) { if (ov::is_type<ov::opset1::Constant>(result)) {
replace_node(fakeQuantizeOnWeights, result); replace_node(fakeQuantizeOnWeights, result);
} }
} }
@ -82,12 +82,12 @@ bool MultiplyTransformation::transform(TransformationContext& context, ngraph::p
auto multiplyParentParent = multiplyParent.get_node_shared_ptr()->input_value(multiplyBranch.second); auto multiplyParentParent = multiplyParent.get_node_shared_ptr()->input_value(multiplyBranch.second);
auto multiplyParentConst = multiplyParent.get_node_shared_ptr()->input_value(multiplyBranch.second == 0 ? 1 : 0); auto multiplyParentConst = multiplyParent.get_node_shared_ptr()->input_value(multiplyBranch.second == 0 ? 1 : 0);
newMultiply = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( newMultiply = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{ element::f32, element::f32 },
std::vector<ngraph::element::Type>{ multiply->get_output_element_type(0) }, std::vector<ngraph::element::Type>{ multiply->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(multiplyParentParent, element::f32).get(), ov::op::TemporaryReplaceOutputType(multiplyParentParent, element::f32).get(),
ov::op::TemporaryReplaceOutputType( ov::op::TemporaryReplaceOutputType(
fold<opset1::Multiply>( fold<ov::opset1::Multiply>(
foldConvert(multiplyParentConst, element::f32), foldConvert(multiplyParentConst, element::f32),
foldConvert(constParent, element::f32)), foldConvert(constParent, element::f32)),
element::f32).get()); element::f32).get());
@ -123,17 +123,17 @@ bool MultiplyTransformation::transform(TransformationContext& context, ngraph::p
// before: Y = (SC1 * (X1 - SH1)) * (SC2 * X2) // before: Y = (SC1 * (X1 - SH1)) * (SC2 * X2)
// after : Y = (SC1' * (X1 - SH1)) * (X2) , where : // after : Y = (SC1' * (X1 - SH1)) * (X2) , where :
// SC1' = SC1 * SC2 // SC1' = SC1 * SC2
auto newMultiplyValuesFullPath = fold<opset1::Multiply>(multiplyValuesEmptyPath, multiplyValuesFullPath); auto newMultiplyValuesFullPath = fold<ov::opset1::Multiply>(multiplyValuesEmptyPath, multiplyValuesFullPath);
OutputVector inputs{ {}, {} }; OutputVector inputs{ {}, {} };
inputs[emptyPathIndex] = dequantizationEmptyPath.data; inputs[emptyPathIndex] = dequantizationEmptyPath.data;
inputs[fullPathIndex] = std::make_shared<opset1::Multiply>( inputs[fullPathIndex] = std::make_shared<ov::opset1::Multiply>(
dequantizationFullPath.subtract == nullptr ? dequantizationFullPath.subtract == nullptr ?
(dequantizationFullPath.convert == nullptr ? (dequantizationFullPath.convert == nullptr ?
dequantizationFullPath.data : dequantizationFullPath.convert) : dequantizationFullPath.data : dequantizationFullPath.convert) :
dequantizationFullPath.subtract, dequantizationFullPath.subtract,
newMultiplyValuesFullPath); newMultiplyValuesFullPath);
newMultiply = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( newMultiply = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{element::f32, element::f32},
std::vector<element::Type>{ multiply->get_output_element_type(0) }, std::vector<element::Type>{ multiply->get_output_element_type(0) },
ov::op::TemporaryReplaceOutputType(inputs[0], element::f32).get(), ov::op::TemporaryReplaceOutputType(inputs[0], element::f32).get(),
@ -159,8 +159,8 @@ bool MultiplyTransformation::canBeTransformed(const TransformationContext& conte
return false; return false;
} }
const bool nonConstantData = !ov::is_type<opset1::Constant>(dequantization1.data.get_node_shared_ptr()) && const bool nonConstantData = !ov::is_type<ov::opset1::Constant>(dequantization1.data.get_node_shared_ptr()) &&
!ov::is_type<opset1::Constant>(dequantization2.data.get_node_shared_ptr()); !ov::is_type<ov::opset1::Constant>(dequantization2.data.get_node_shared_ptr());
if (((dequantization1.empty() || dequantization2.empty()) && nonConstantData)) { if (((dequantization1.empty() || dequantization2.empty()) && nonConstantData)) {
return false; return false;

28
src/common/low_precision_transformations/src/multiply_to_group_convolution.cpp
View File

@ -17,7 +17,7 @@ MultiplyToGroupConvolutionTransformation::MultiplyToGroupConvolutionTransformati
const Params& params, const Params& params,
const PrecisionsRestriction::PrecisionsByPorts& restrictions) : LayerTransformation(params), restrictions(restrictions), groupSize(1ul) { const PrecisionsRestriction::PrecisionsByPorts& restrictions) : LayerTransformation(params), restrictions(restrictions), groupSize(1ul) {
MATCHER_SCOPE(MultiplyToGroupConvolutionTransformation); MATCHER_SCOPE(MultiplyToGroupConvolutionTransformation);
auto matcher = pattern::wrap_type<opset1::Multiply>(); auto matcher = pattern::wrap_type<ov::opset1::Multiply>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -40,7 +40,7 @@ bool MultiplyToGroupConvolutionTransformation::transform(TransformationContext&
auto input = multiply->get_input_node_shared_ptr(0); auto input = multiply->get_input_node_shared_ptr(0);
auto constant = multiply->get_input_node_shared_ptr(1); auto constant = multiply->get_input_node_shared_ptr(1);
auto inputIndex = 0; auto inputIndex = 0;
if (!ov::is_type<opset1::Constant>(constant)) { if (!ov::is_type<ov::opset1::Constant>(constant)) {
input = multiply->get_input_node_shared_ptr(1); input = multiply->get_input_node_shared_ptr(1);
constant = multiply->get_input_node_shared_ptr(0); constant = multiply->get_input_node_shared_ptr(0);
inputIndex = 1; inputIndex = 1;
@ -108,14 +108,14 @@ bool MultiplyToGroupConvolutionTransformation::transform(TransformationContext&
weightsShape[0] = group; weightsShape[0] = group;
weightsShape[1] = outputChannelsCount / group; weightsShape[1] = outputChannelsCount / group;
weightsShape[2] = inputChannelsCount / group; weightsShape[2] = inputChannelsCount / group;
const auto weightsNode = std::make_shared<opset1::Constant>(weightsPrecision, weightsShape, weightsBuffer); const auto weightsNode = std::make_shared<ov::opset1::Constant>(weightsPrecision, weightsShape, weightsBuffer);
const size_t spatialDimsSize = pShape.rank().get_length() - 2; const size_t spatialDimsSize = pShape.rank().get_length() - 2;
ngraph::Strides strides(spatialDimsSize, 1ul); ngraph::Strides strides(spatialDimsSize, 1ul);
ngraph::CoordinateDiff pads(spatialDimsSize, 0ul); ngraph::CoordinateDiff pads(spatialDimsSize, 0ul);
ngraph::Strides dilations(spatialDimsSize, 1ul); ngraph::Strides dilations(spatialDimsSize, 1ul);
const auto convolution = std::make_shared<ov::op::TypeRelaxed<opset1::GroupConvolution>>( const auto convolution = std::make_shared<ov::op::TypeRelaxed<ov::opset1::GroupConvolution>>(
std::vector<element::Type>{ element::f32, element::f32 }, std::vector<element::Type>{ element::f32, element::f32 },
std::vector<element::Type>{ element::f32 }, std::vector<element::Type>{ element::f32 },
ov::op::TemporaryReplaceOutputType(dequantization.data, element::f32).get(), ov::op::TemporaryReplaceOutputType(dequantization.data, element::f32).get(),
@ -128,9 +128,9 @@ bool MultiplyToGroupConvolutionTransformation::transform(TransformationContext&
std::shared_ptr<Node> lastNode = convolution; std::shared_ptr<Node> lastNode = convolution;
if (dequantization.subtract != nullptr) { if (dequantization.subtract != nullptr) {
lastNode = std::make_shared<opset1::Add>( lastNode = std::make_shared<ov::opset1::Add>(
convolution, convolution,
fold<opset1::Negative>(foldConvert(dequantization.subtractConstant, element::f32))); fold<ov::opset1::Negative>(foldConvert(dequantization.subtractConstant, element::f32)));
lastNode->set_friendly_name(convolution->get_friendly_name() + "/Add"); lastNode->set_friendly_name(convolution->get_friendly_name() + "/Add");
} }
@ -164,15 +164,15 @@ bool MultiplyToGroupConvolutionTransformation::canBeTransformed(const Transforma
Shape constShape; Shape constShape;
int inputIndex; int inputIndex;
if (const auto constant = ov::as_type_ptr<opset1::Constant>(operation->get_input_node_shared_ptr(1))) { if (const auto constant = ov::as_type_ptr<ov::opset1::Constant>(operation->get_input_node_shared_ptr(1))) {
inputIndex = 0; inputIndex = 0;
constShape = constant->get_shape(); constShape = constant->get_shape();
if (ov::is_type<opset1::Constant>(operation->get_input_node_shared_ptr(0)) || if (ov::is_type<ov::opset1::Constant>(operation->get_input_node_shared_ptr(0)) ||
(ov::is_type<opset1::Subtract>(operation->get_input_node_shared_ptr(0)) && (ov::is_type<ov::opset1::Subtract>(operation->get_input_node_shared_ptr(0)) &&
ov::is_type<opset1::Constant>(operation->get_input_node_shared_ptr(0)->get_input_node_shared_ptr(0)))) { ov::is_type<ov::opset1::Constant>(operation->get_input_node_shared_ptr(0)->get_input_node_shared_ptr(0)))) {
return false; return false;
} }
} else if (const auto constant = ov::as_type_ptr<opset1::Constant>(operation->get_input_node_shared_ptr(0))) { } else if (const auto constant = ov::as_type_ptr<ov::opset1::Constant>(operation->get_input_node_shared_ptr(0))) {
inputIndex = 1; inputIndex = 1;
constShape = constant->get_shape(); constShape = constant->get_shape();
} else { } else {
@ -210,7 +210,7 @@ bool MultiplyToGroupConvolutionTransformation::canBeTransformedToGroupConvolutio
const auto parent0 = layer->get_input_node_shared_ptr(0); const auto parent0 = layer->get_input_node_shared_ptr(0);
const auto parent1 = layer->get_input_node_shared_ptr(1); const auto parent1 = layer->get_input_node_shared_ptr(1);
if (!ov::is_type<opset1::Constant>(parent0) && !ov::is_type<opset1::Constant>(parent1)) { if (!ov::is_type<ov::opset1::Constant>(parent0) && !ov::is_type<ov::opset1::Constant>(parent1)) {
return false; return false;
} }
@ -225,10 +225,10 @@ bool MultiplyToGroupConvolutionTransformation::canBeTransformedToGroupConvolutio
bool MultiplyToGroupConvolutionTransformation::isDynamicOrScalar(const std::shared_ptr<const Node>& node) { bool MultiplyToGroupConvolutionTransformation::isDynamicOrScalar(const std::shared_ptr<const Node>& node) {
auto getConstantIndex = [](const std::shared_ptr<const Node>& node) -> int { auto getConstantIndex = [](const std::shared_ptr<const Node>& node) -> int {
if (ov::is_type<opset1::Constant>(node->get_input_node_shared_ptr(1))) { if (ov::is_type<ov::opset1::Constant>(node->get_input_node_shared_ptr(1))) {
return 1; return 1;
} }
if (ov::is_type<opset1::Constant>(node->get_input_node_shared_ptr(0))) { if (ov::is_type<ov::opset1::Constant>(node->get_input_node_shared_ptr(0))) {
return 0; return 0;
} }
return -1; return -1;

14
src/common/low_precision_transformations/src/mvn.cpp
View File

@ -44,11 +44,11 @@ std::shared_ptr<ngraph::op::Constant> createNewScalesConst(const ngraph::op::Con
MVNTransformation::MVNTransformation(const Params& params) : LayerTransformation(params) { MVNTransformation::MVNTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(MVNTransformation); MATCHER_SCOPE(MVNTransformation);
auto matcher = std::make_shared<pattern::op::Or>(OutputVector{ auto matcher = std::make_shared<pattern::op::Or>(OutputVector{
pattern::wrap_type<ngraph::op::MVN>({ pattern::wrap_type<ngraph::opset1::Multiply>() }), pattern::wrap_type<ngraph::op::MVN>({ pattern::wrap_type<ov::opset1::Multiply>() }),
pattern::wrap_type<ngraph::opset6::MVN>({ pattern::wrap_type<ngraph::opset1::Multiply>(), pattern::wrap_type<ngraph::opset1::Constant>() }) pattern::wrap_type<ngraph::opset6::MVN>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() })
}); });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -84,7 +84,7 @@ bool MVNTransformation::canBeTransformed(const TransformationContext& context, s
} else { } else {
// MVN-6 allows negative values in reduction axes: [-r, r-1] // MVN-6 allows negative values in reduction axes: [-r, r-1]
// given static rank of input data of MVN node, we can recover the exact axis number // given static rank of input data of MVN node, we can recover the exact axis number
auto axis_set = ov::as_type_ptr<opset1::Constant>(mvn->get_input_node_shared_ptr(1))->cast_vector<int64_t>(); auto axis_set = ov::as_type_ptr<ov::opset1::Constant>(mvn->get_input_node_shared_ptr(1))->cast_vector<int64_t>();
Dimension::value_type ndims = 0; Dimension::value_type ndims = 0;
if (std::any_of(axis_set.begin(), axis_set.end(), [](int64_t v) { return v < 0; })) { if (std::any_of(axis_set.begin(), axis_set.end(), [](int64_t v) { return v < 0; })) {
@ -166,10 +166,10 @@ bool MVNTransformation::transform(TransformationContext &context, ngraph::patter
NetworkHelper::setOutDataPrecisionForTypeRelaxed(newMVN, deqPrecision); NetworkHelper::setOutDataPrecisionForTypeRelaxed(newMVN, deqPrecision);
NetworkHelper::copyInfo(mvn, newMVN); NetworkHelper::copyInfo(mvn, newMVN);
auto newMultiply = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( auto newMultiply = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
opset1::Multiply(newMVN, newScalesConst), ov::opset1::Multiply(newMVN, newScalesConst),
mvn->get_output_element_type(0)); mvn->get_output_element_type(0));
ngraph::copy_runtime_info({ mvn, newMultiply }, newMultiply); ov::copy_runtime_info({ mvn, newMultiply }, newMultiply);
NetworkHelper::insertDequantizationAfter(mvn, newMultiply, newMVN); NetworkHelper::insertDequantizationAfter(mvn, newMultiply, newMVN);

393
src/common/low_precision_transformations/src/network_helper.cpp
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File diff suppressed because it is too large Load Diff

26
src/common/low_precision_transformations/src/normalize_l2.cpp
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@ -23,7 +23,7 @@ using namespace ngraph::pass::low_precision;
namespace normalize_l2 { namespace normalize_l2 {
template<typename T> template<typename T>
std::shared_ptr<ngraph::op::Constant> createNewScalesConst(const ngraph::op::Constant& originalConst) { std::shared_ptr<ov::opset1::Constant> createNewScalesConst(const ov::opset1::Constant& originalConst) {
std::vector<T> source = originalConst.cast_vector<T>(); std::vector<T> source = originalConst.cast_vector<T>();
std::vector<T> newData(source.size()); std::vector<T> newData(source.size());
@ -32,16 +32,16 @@ std::shared_ptr<ngraph::op::Constant> createNewScalesConst(const ngraph::op::Con
} }
const ngraph::element::Type type = originalConst.get_output_element_type(0); const ngraph::element::Type type = originalConst.get_output_element_type(0);
return ngraph::op::Constant::create(type, originalConst.get_shape(), newData); return ov::opset1::Constant::create(type, originalConst.get_shape(), newData);
} }
} // namespace normalize_l2 } // namespace normalize_l2
NormalizeL2Transformation::NormalizeL2Transformation(const Params& params) : LayerTransformation(params) { NormalizeL2Transformation::NormalizeL2Transformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(NormalizeL2Transformation); MATCHER_SCOPE(NormalizeL2Transformation);
auto matcher = pattern::wrap_type<opset1::NormalizeL2>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::NormalizeL2>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -69,7 +69,7 @@ bool NormalizeL2Transformation::canBeTransformed(const TransformationContext& co
} }
// TODO: Expand transformation for all cases of axes values // TODO: Expand transformation for all cases of axes values
const auto axes = ov::as_type_ptr<opset1::Constant>(operation->get_input_node_shared_ptr(1)); const auto axes = ov::as_type_ptr<ov::opset1::Constant>(operation->get_input_node_shared_ptr(1));
const std::vector<int64_t> axesAcrossSpatial = { 1 }; const std::vector<int64_t> axesAcrossSpatial = { 1 };
const std::vector<int64_t> axesByChannels = { 1, 2, 3 }; const std::vector<int64_t> axesByChannels = { 1, 2, 3 };
@ -102,16 +102,16 @@ bool NormalizeL2Transformation::transform(TransformationContext &context, ngraph
return false; return false;
} }
auto normalize = ov::as_type_ptr<opset1::NormalizeL2>(NetworkHelper::separateInStandaloneBranch(operation, defaultPrecisions)); auto normalize = ov::as_type_ptr<ov::opset1::NormalizeL2>(NetworkHelper::separateInStandaloneBranch(operation, defaultPrecisions));
const auto axes = ov::as_type_ptr<opset1::Constant>(normalize->get_input_node_shared_ptr(1)); const auto axes = ov::as_type_ptr<ov::opset1::Constant>(normalize->get_input_node_shared_ptr(1));
FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(normalize, defaultPrecisions); FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(normalize, defaultPrecisions);
auto scalesConst = ov::as_type_ptr<opset1::Constant>(dequantization.multiply->get_input_node_shared_ptr(1)); auto scalesConst = ov::as_type_ptr<ov::opset1::Constant>(dequantization.multiply->get_input_node_shared_ptr(1));
if (scalesConst == nullptr) { if (scalesConst == nullptr) {
scalesConst = ov::as_type_ptr<opset1::Constant>(dequantization.multiply->get_input_node_shared_ptr(0)); scalesConst = ov::as_type_ptr<ov::opset1::Constant>(dequantization.multiply->get_input_node_shared_ptr(0));
} }
std::shared_ptr<opset1::Constant> newScalesConst; std::shared_ptr<ov::opset1::Constant> newScalesConst;
const auto type = scalesConst->get_output_element_type(0); const auto type = scalesConst->get_output_element_type(0);
switch (type) { switch (type) {
case ngraph::element::Type_t::f16: { case ngraph::element::Type_t::f16: {
@ -127,7 +127,7 @@ bool NormalizeL2Transformation::transform(TransformationContext &context, ngraph
} }
} }
auto newNormalize = std::make_shared<ov::op::TypeRelaxed<opset1::NormalizeL2>>( auto newNormalize = std::make_shared<ov::op::TypeRelaxed<ov::opset1::NormalizeL2>>(
std::vector<ngraph::element::Type>{ element::f32, axes->output(0).get_element_type() }, std::vector<ngraph::element::Type>{ element::f32, axes->output(0).get_element_type() },
std::vector<ngraph::element::Type>{deqPrecision}, std::vector<ngraph::element::Type>{deqPrecision},
ov::op::TemporaryReplaceOutputType(dequantization.subtract == nullptr ? dequantization.data : dequantization.subtract, element::f32).get(), ov::op::TemporaryReplaceOutputType(dequantization.subtract == nullptr ? dequantization.data : dequantization.subtract, element::f32).get(),
@ -136,14 +136,14 @@ bool NormalizeL2Transformation::transform(TransformationContext &context, ngraph
normalize->get_eps_mode()); normalize->get_eps_mode());
NetworkHelper::copyInfo(normalize, newNormalize); NetworkHelper::copyInfo(normalize, newNormalize);
auto newMultiply = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>( auto newMultiply = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(
std::vector<ngraph::element::Type>{ element::f32, element::f32 }, std::vector<ngraph::element::Type>{ element::f32, element::f32 },
std::vector<ngraph::element::Type>{normalize->get_output_element_type(0)}, std::vector<ngraph::element::Type>{normalize->get_output_element_type(0)},
ov::op::TemporaryReplaceOutputType(newNormalize, element::f32).get(), ov::op::TemporaryReplaceOutputType(newNormalize, element::f32).get(),
ov::op::TemporaryReplaceOutputType(newScalesConst, element::f32).get()); ov::op::TemporaryReplaceOutputType(newScalesConst, element::f32).get());
NetworkHelper::insertDequantizationAfter(normalize, newMultiply, newNormalize); NetworkHelper::insertDequantizationAfter(normalize, newMultiply, newNormalize);
ngraph::copy_runtime_info({ normalize, newMultiply }, newMultiply); ov::copy_runtime_info({ normalize, newMultiply }, newMultiply);
updateOutput(context, newMultiply, newNormalize); updateOutput(context, newMultiply, newNormalize);
return true; return true;

42
src/common/low_precision_transformations/src/pad.cpp
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@ -17,11 +17,11 @@ namespace low_precision {
PadTransformation::PadTransformation(const Params& params) : LayerTransformation(params) { PadTransformation::PadTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(PadTransformation); MATCHER_SCOPE(PadTransformation);
auto mul = pattern::wrap_type<opset1::Multiply>(); auto mul = pattern::wrap_type<ov::opset1::Multiply>();
auto padsBegin = pattern::wrap_type<opset1::Constant>(); auto padsBegin = pattern::wrap_type<ov::opset1::Constant>();
auto padsEnd = pattern::wrap_type<opset1::Constant>(); auto padsEnd = pattern::wrap_type<ov::opset1::Constant>();
auto padsValue = pattern::wrap_type<opset1::Constant>(); auto padsValue = pattern::wrap_type<ov::opset1::Constant>();
auto matcher = pattern::wrap_type<opset1::Pad>({ mul, padsBegin, padsEnd, padsValue }); auto matcher = pattern::wrap_type<ov::opset1::Pad>({ mul, padsBegin, padsEnd, padsValue });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -40,8 +40,8 @@ bool PadTransformation::transform(TransformationContext& context, ngraph::patter
return false; return false;
} }
const auto pad = ov::as_type_ptr<opset1::Pad>(NetworkHelper::separateInStandaloneBranch(m.get_match_root(), defaultPrecisions)); const auto pad = ov::as_type_ptr<ov::opset1::Pad>(NetworkHelper::separateInStandaloneBranch(m.get_match_root(), defaultPrecisions));
const auto padConstant = ov::as_type_ptr<opset1::Constant>(pad->get_input_node_shared_ptr(3)); const auto padConstant = ov::as_type_ptr<ov::opset1::Constant>(pad->get_input_node_shared_ptr(3));
const auto padConstantValue = padConstant->cast_vector<float>()[0]; const auto padConstantValue = padConstant->cast_vector<float>()[0];
const auto padsBegin = pad->get_pads_begin(); const auto padsBegin = pad->get_pads_begin();
@ -51,7 +51,7 @@ bool PadTransformation::transform(TransformationContext& context, ngraph::patter
auto dequantization = NetworkHelper::getDequantization(pad, defaultPrecisions); auto dequantization = NetworkHelper::getDequantization(pad, defaultPrecisions);
if (padMode == op::PadMode::CONSTANT) { if (padMode == op::PadMode::CONSTANT) {
auto bcastConstant = [&](const std::shared_ptr<opset1::Constant> &constant) { auto bcastConstant = [&](const std::shared_ptr<ov::opset1::Constant> &constant) {
size_t padIdx = 0; size_t padIdx = 0;
for (size_t i = 0; i < padsBegin.size(); ++i) { for (size_t i = 0; i < padsBegin.size(); ++i) {
if (padsBegin[i] != 0 || padsEnd[i] != 0) { if (padsBegin[i] != 0 || padsEnd[i] != 0) {
@ -66,8 +66,8 @@ bool PadTransformation::transform(TransformationContext& context, ngraph::patter
auto bcastedShape = Shape(inputPShape.rank().get_length(), 1ul); auto bcastedShape = Shape(inputPShape.rank().get_length(), 1ul);
bcastedShape[padIdx] = inputPShape[padIdx].get_length(); bcastedShape[padIdx] = inputPShape[padIdx].get_length();
const auto bCastConst = opset1::Constant::create(element::i32, Shape{bcastedShape.size()}, bcastedShape); const auto bCastConst = ov::opset1::Constant::create(element::i32, Shape{bcastedShape.size()}, bcastedShape);
return ov::as_type_ptr<opset1::Constant>(fold<opset1::Broadcast>(constant, bCastConst)); return ov::as_type_ptr<ov::opset1::Constant>(fold<ov::opset1::Broadcast>(constant, bCastConst));
}; };
if (dequantization.subtract && shape_size(dequantization.subtractConstant->get_shape()) == 1ul) { if (dequantization.subtract && shape_size(dequantization.subtractConstant->get_shape()) == 1ul) {
@ -84,8 +84,8 @@ bool PadTransformation::transform(TransformationContext& context, ngraph::patter
} }
auto foldConstantIfNecessary = [&padMode, &padsBegin, &padsEnd]( auto foldConstantIfNecessary = [&padMode, &padsBegin, &padsEnd](
const std::shared_ptr<opset1::Constant>& constant, const std::shared_ptr<ov::opset1::Constant>& constant,
const std::shared_ptr<opset1::Pad>& pad, const std::shared_ptr<ov::opset1::Pad>& pad,
float padVal) { float padVal) {
const auto constantShape = constant->get_shape(); const auto constantShape = constant->get_shape();
if (shape_size(constantShape) == 1ul) { if (shape_size(constantShape) == 1ul) {
@ -110,11 +110,11 @@ bool PadTransformation::transform(TransformationContext& context, ngraph::patter
} }
if (foldingIsNecessary) { if (foldingIsNecessary) {
const auto beginConst = opset1::Constant::create(element::u32, { padsForConstantBegin.size() }, padsForConstantBegin); const auto beginConst = ov::opset1::Constant::create(element::u32, { padsForConstantBegin.size() }, padsForConstantBegin);
const auto endConst = opset1::Constant::create(element::u32, { padsForConstantEnd.size() }, padsForConstantEnd); const auto endConst = ov::opset1::Constant::create(element::u32, { padsForConstantEnd.size() }, padsForConstantEnd);
const auto padValueConstant = opset1::Constant::create(constant->get_element_type(), Shape{}, { padVal }); const auto padValueConstant = ov::opset1::Constant::create(constant->get_element_type(), Shape{}, { padVal });
const auto foldedConstant = fold<opset1::Pad>(constant, beginConst, endConst, padValueConstant, padMode); const auto foldedConstant = fold<ov::opset1::Pad>(constant, beginConst, endConst, padValueConstant, padMode);
return ov::as_type_ptr<opset1::Constant>(foldedConstant); return ov::as_type_ptr<ov::opset1::Constant>(foldedConstant);
} else { } else {
return constant; return constant;
} }
@ -145,7 +145,7 @@ bool PadTransformation::transform(TransformationContext& context, ngraph::patter
} }
// we must convert pad value in low precision // we must convert pad value in low precision
const auto convertedZero = opset1::Constant::create(dequantization.data.get_element_type(), Shape{}, { padConstantValue }); const auto convertedZero = ov::opset1::Constant::create(dequantization.data.get_element_type(), Shape{}, { padConstantValue });
pad->set_argument(3, convertedZero); pad->set_argument(3, convertedZero);
moveDequantizationAfter(context, pad, dequantization, true); moveDequantizationAfter(context, pad, dequantization, true);
@ -157,7 +157,7 @@ bool PadTransformation::canBeTransformed(const TransformationContext& context, s
return false; return false;
} }
const auto pad = ov::as_type_ptr<opset1::Pad>(op); const auto pad = ov::as_type_ptr<ov::opset1::Pad>(op);
if (!pad) { if (!pad) {
return false; return false;
} }
@ -169,7 +169,7 @@ bool PadTransformation::canBeTransformed(const TransformationContext& context, s
const auto mode = pad->get_pad_mode(); const auto mode = pad->get_pad_mode();
if (mode == op::PadMode::CONSTANT) { if (mode == op::PadMode::CONSTANT) {
auto padAndDqByTheSameDimension = [&](const std::shared_ptr<opset1::Constant>& deqConst) { auto padAndDqByTheSameDimension = [&](const std::shared_ptr<ov::opset1::Constant>& deqConst) {
const auto padsBegin = pad->get_pads_begin(); const auto padsBegin = pad->get_pads_begin();
const auto padsEnd = pad->get_pads_end(); const auto padsEnd = pad->get_pads_end();
@ -231,7 +231,7 @@ bool PadTransformation::canBeTransformed(const TransformationContext& context, s
return false; return false;
} }
const auto constant = ov::as_type_ptr<opset1::Constant>(pad->get_input_node_shared_ptr(3)); const auto constant = ov::as_type_ptr<ov::opset1::Constant>(pad->get_input_node_shared_ptr(3));
const auto constantValue = constant->cast_vector<float>()[0]; const auto constantValue = constant->cast_vector<float>()[0];
if (constantValue != 0.f && !padAndDqByTheSameDimension(dequantization.multiplyConstant)) { if (constantValue != 0.f && !padAndDqByTheSameDimension(dequantization.multiplyConstant)) {
return false; return false;

2
src/common/low_precision_transformations/src/prelu.cpp
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@ -20,7 +20,7 @@ namespace low_precision {
PReluTransformation::PReluTransformation(const Params& params) : LayerTransformation(params) { PReluTransformation::PReluTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(PReluTransformation); MATCHER_SCOPE(PReluTransformation);
auto matcher = pattern::wrap_type<opset1::PRelu>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::PRelu>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();

3
src/common/low_precision_transformations/src/propagate_precisions.cpp
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@ -12,6 +12,7 @@
#include "low_precision/propagate_through_precision_preserved.hpp" #include "low_precision/propagate_through_precision_preserved.hpp"
#include "low_precision/propagate_to_input.hpp" #include "low_precision/propagate_to_input.hpp"
#include "itt.hpp" #include "itt.hpp"
#include "openvino/pass/manager.hpp"
using namespace ngraph; using namespace ngraph;
using namespace ngraph::pass::low_precision; using namespace ngraph::pass::low_precision;
@ -20,7 +21,7 @@ ngraph::pass::low_precision::PropagatePrecisions::PropagatePrecisions(const Attr
bool ngraph::pass::low_precision::PropagatePrecisions::run_on_model(const std::shared_ptr<ngraph::Function>& f) { bool ngraph::pass::low_precision::PropagatePrecisions::run_on_model(const std::shared_ptr<ngraph::Function>& f) {
RUN_ON_FUNCTION_SCOPE(PropagatePrecisions); RUN_ON_FUNCTION_SCOPE(PropagatePrecisions);
ngraph::pass::Manager manager; ov::pass::Manager manager;
manager.set_per_pass_validation(false); manager.set_per_pass_validation(false);
std::shared_ptr<ngraph::pass::GraphRewrite> precisionsPropagation = manager.register_pass<ngraph::pass::GraphRewrite>(); std::shared_ptr<ngraph::pass::GraphRewrite> precisionsPropagation = manager.register_pass<ngraph::pass::GraphRewrite>();
precisionsPropagation->add_matcher<low_precision::CreateAttribute<PrecisionsAttribute, opset1::FakeQuantize>>(params, AttributeSource::OutputPort); precisionsPropagation->add_matcher<low_precision::CreateAttribute<PrecisionsAttribute, opset1::FakeQuantize>>(params, AttributeSource::OutputPort);

4
src/common/low_precision_transformations/src/pull_reshape_through_dequantization.cpp
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@ -86,7 +86,7 @@ std::shared_ptr<Node> moveThroughElementwise(const std::shared_ptr<Node>& reshap
std::make_shared<opset1::Convert>(newElementwiseValues, elementwiseValuesConvert->get_destination_type()) }); std::make_shared<opset1::Convert>(newElementwiseValues, elementwiseValuesConvert->get_destination_type()) });
replace_node(reshape, newElementwise); replace_node(reshape, newElementwise);
copy_runtime_info({ elementwise, reshape }, { newReshape, newElementwise }); ov::copy_runtime_info({ elementwise, reshape }, { newReshape, newElementwise });
return newReshape; return newReshape;
} }
@ -94,7 +94,7 @@ std::shared_ptr<Node> moveThroughConvert(const std::shared_ptr<Node>& reshape, c
const auto newReshape = reshape->clone_with_new_inputs({ convert->input_value(0), reshape->input_value(1) }); const auto newReshape = reshape->clone_with_new_inputs({ convert->input_value(0), reshape->input_value(1) });
const auto newConvert = convert->clone_with_new_inputs({ newReshape }); const auto newConvert = convert->clone_with_new_inputs({ newReshape });
replace_node(reshape, newConvert); replace_node(reshape, newConvert);
copy_runtime_info({ convert, reshape }, { newReshape, newConvert }); ov::copy_runtime_info({ convert, reshape }, { newReshape, newConvert });
return newReshape; return newReshape;
} }

4
src/common/low_precision_transformations/src/pull_transpose_through_dequantization.cpp
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@ -62,7 +62,7 @@ std::shared_ptr<Node> moveThroughElementwise(const std::shared_ptr<Node>& transp
std::make_shared<opset1::Convert>(newElementwiseValues, elementwiseValuesConvert->get_destination_type()) }); std::make_shared<opset1::Convert>(newElementwiseValues, elementwiseValuesConvert->get_destination_type()) });
replace_node(transpose, newElementwise); replace_node(transpose, newElementwise);
copy_runtime_info({ elementwise, transpose }, { newTranspose, newElementwise }); ov::copy_runtime_info({ elementwise, transpose }, { newTranspose, newElementwise });
return newTranspose; return newTranspose;
} }
@ -71,7 +71,7 @@ std::shared_ptr<Node> moveThroughConvert(const std::shared_ptr<Node>& transpose,
const auto newTranspose = transpose->clone_with_new_inputs({convert->input_value(0), transpose->input_value(1) }); const auto newTranspose = transpose->clone_with_new_inputs({convert->input_value(0), transpose->input_value(1) });
const auto newConvert = convert->clone_with_new_inputs({ newTranspose }); const auto newConvert = convert->clone_with_new_inputs({ newTranspose });
replace_node(transpose, newConvert); replace_node(transpose, newConvert);
copy_runtime_info({ convert, transpose }, { newTranspose, newConvert }); ov::copy_runtime_info({ convert, transpose }, { newTranspose, newConvert });
return newTranspose; return newTranspose;
} }

30
src/common/low_precision_transformations/src/quantization_details.cpp
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@ -49,13 +49,13 @@ QuantizationDetails::QuantizationDetails(const size_t levels, const std::vector<
outputLowValues(outputLowValues), outputLowValues(outputLowValues),
outputHighValues(outputHighValues) {} outputHighValues(outputHighValues) {}
bool QuantizationDetails::outputLayoutIsSupported(std::shared_ptr<opset1::FakeQuantize> quantize, bool isConvertExpected) { bool QuantizationDetails::outputLayoutIsSupported(std::shared_ptr<ov::opset1::FakeQuantize> quantize, bool isConvertExpected) {
const auto inputs = quantize->inputs(); const auto inputs = quantize->inputs();
for (size_t i = 1; i < inputs.size(); ++i) { for (size_t i = 1; i < inputs.size(); ++i) {
const auto node = inputs[i].get_source_output().get_node_shared_ptr(); const auto node = inputs[i].get_source_output().get_node_shared_ptr();
bool supported = ov::is_type<opset1::Constant>(node); bool supported = ov::is_type<ov::opset1::Constant>(node);
if (!supported && isConvertExpected) { if (!supported && isConvertExpected) {
supported = ov::is_type<op::Convert>(node) && ov::is_type<opset1::Constant>(node->get_input_node_ptr(0)); supported = ov::is_type<ov::opset1::Convert>(node) && ov::is_type<ov::opset1::Constant>(node->get_input_node_ptr(0));
} }
if (!supported) { if (!supported) {
return false; return false;
@ -65,14 +65,14 @@ bool QuantizationDetails::outputLayoutIsSupported(std::shared_ptr<opset1::FakeQu
} }
void QuantizationDetails::getInputIntervals( void QuantizationDetails::getInputIntervals(
std::shared_ptr<opset1::FakeQuantize> quantize, std::shared_ptr<ov::opset1::FakeQuantize> quantize,
std::vector<float>& inputLowValues, std::vector<float>& inputLowValues,
std::vector<float>& inputHighValues) { std::vector<float>& inputHighValues) {
std::shared_ptr<opset1::Constant> inputLowLayer = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(1)); std::shared_ptr<ov::opset1::Constant> inputLowLayer = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(1));
const std::vector<float>& inputLowBlobValues = getBlobValue(inputLowLayer); const std::vector<float>& inputLowBlobValues = getBlobValue(inputLowLayer);
inputLowValues.insert(inputLowValues.end(), inputLowBlobValues.begin(), inputLowBlobValues.end()); inputLowValues.insert(inputLowValues.end(), inputLowBlobValues.begin(), inputLowBlobValues.end());
std::shared_ptr<opset1::Constant> inputHighLayer = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(2)); std::shared_ptr<ov::opset1::Constant> inputHighLayer = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(2));
const std::vector<float> inputHighBlobValues = getBlobValue(inputHighLayer); const std::vector<float> inputHighBlobValues = getBlobValue(inputHighLayer);
inputHighValues.insert(inputHighValues.end(), inputHighBlobValues.begin(), inputHighBlobValues.end()); inputHighValues.insert(inputHighValues.end(), inputHighBlobValues.begin(), inputHighBlobValues.end());
@ -83,14 +83,14 @@ void QuantizationDetails::getInputIntervals(
void QuantizationDetails::getOutputIntervals( void QuantizationDetails::getOutputIntervals(
std::shared_ptr<opset1::FakeQuantize> quantize, std::shared_ptr<ov::opset1::FakeQuantize> quantize,
std::vector<float>& outputLowValues, std::vector<float>& outputLowValues,
std::vector<float>& outputHighValues) { std::vector<float>& outputHighValues) {
std::shared_ptr<opset1::Constant> outputLowLayer = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(3)); std::shared_ptr<ov::opset1::Constant> outputLowLayer = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(3));
const std::vector<float>& outputLowBlobValues = getBlobValue(outputLowLayer); const std::vector<float>& outputLowBlobValues = getBlobValue(outputLowLayer);
outputLowValues.insert(outputLowValues.end(), outputLowBlobValues.begin(), outputLowBlobValues.end()); outputLowValues.insert(outputLowValues.end(), outputLowBlobValues.begin(), outputLowBlobValues.end());
std::shared_ptr<opset1::Constant> outputHighLayer = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(4)); std::shared_ptr<ov::opset1::Constant> outputHighLayer = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(4));
const std::vector<float> outputHighBlobValues = getBlobValue(outputHighLayer); const std::vector<float> outputHighBlobValues = getBlobValue(outputHighLayer);
outputHighValues.insert(outputHighValues.end(), outputHighBlobValues.begin(), outputHighBlobValues.end()); outputHighValues.insert(outputHighValues.end(), outputHighBlobValues.begin(), outputHighBlobValues.end());
@ -99,16 +99,16 @@ void QuantizationDetails::getOutputIntervals(
} }
} }
QuantizationDetails QuantizationDetails::getDetails(std::shared_ptr<opset1::FakeQuantize> quantize) { QuantizationDetails QuantizationDetails::getDetails(std::shared_ptr<ov::opset1::FakeQuantize> quantize) {
if (!QuantizationDetails::outputLayoutIsSupported(quantize)) { if (!QuantizationDetails::outputLayoutIsSupported(quantize)) {
return QuantizationDetails(); return QuantizationDetails();
} }
const std::vector<float> inputLowValues = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(1))->cast_vector<float>(); const std::vector<float> inputLowValues = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(1))->cast_vector<float>();
const std::vector<float> inputHighValues = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(2))->cast_vector<float>(); const std::vector<float> inputHighValues = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(2))->cast_vector<float>();
const std::vector<float> outputLowValues = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(3))->cast_vector<float>(); const std::vector<float> outputLowValues = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(3))->cast_vector<float>();
const std::vector<float> outputHighValues = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(4))->cast_vector<float>(); const std::vector<float> outputHighValues = ov::as_type_ptr<ov::opset1::Constant>(quantize->get_input_node_shared_ptr(4))->cast_vector<float>();
return QuantizationDetails( return QuantizationDetails(
quantize->get_levels(), quantize->get_levels(),
@ -163,7 +163,7 @@ float QuantizationDetails::getOutputHighValue(const size_t index) const {
} }
std::vector<float> QuantizationDetails::getBlobValue(std::shared_ptr<Node> constantLayer) { std::vector<float> QuantizationDetails::getBlobValue(std::shared_ptr<Node> constantLayer) {
return ov::as_type_ptr<opset1::Constant>(constantLayer)->cast_vector<float>(); return ov::as_type_ptr<ov::opset1::Constant>(constantLayer)->cast_vector<float>();
} }
bool QuantizationDetails::empty() const noexcept { bool QuantizationDetails::empty() const noexcept {

2
src/common/low_precision_transformations/src/reduce_base_transformation.cpp
View File

@ -36,7 +36,7 @@ bool ReduceBaseTransformation::canBeTransformed(const TransformationContext& con
return false; return false;
} }
const auto axesConstant = ov::as_type_ptr<ngraph::opset1::Constant>(reduce->get_input_node_shared_ptr(1)); const auto axesConstant = ov::as_type_ptr<ov::opset1::Constant>(reduce->get_input_node_shared_ptr(1));
if (axesConstant == nullptr) { if (axesConstant == nullptr) {
return false; return false;
} }

6
src/common/low_precision_transformations/src/reduce_max.cpp
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@ -16,7 +16,7 @@ namespace low_precision {
ReduceMaxTransformation::ReduceMaxTransformation(const Params& params) : ReduceBaseTransformation(params) { ReduceMaxTransformation::ReduceMaxTransformation(const Params& params) : ReduceBaseTransformation(params) {
MATCHER_SCOPE(ReduceMaxTransformation); MATCHER_SCOPE(ReduceMaxTransformation);
auto matcher = pattern::wrap_type<opset1::ReduceMax>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::ReduceMax>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -31,7 +31,7 @@ ReduceMaxTransformation::ReduceMaxTransformation(const Params& params) : ReduceB
} }
bool ReduceMaxTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const { bool ReduceMaxTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const {
if (!ov::is_type<opset1::ReduceMax>(reduce)) { if (!ov::is_type<ov::opset1::ReduceMax>(reduce)) {
return false; return false;
} }
@ -40,7 +40,7 @@ bool ReduceMaxTransformation::canBeTransformed(const TransformationContext& cont
} }
const auto dequantization = NetworkHelper::getDequantization(reduce, defaultPrecisions); const auto dequantization = NetworkHelper::getDequantization(reduce, defaultPrecisions);
const std::vector<float> scales = ov::as_type_ptr<opset1::Constant>(dequantization.multiplyConstant)->cast_vector<float>(); const std::vector<float> scales = ov::as_type_ptr<ov::opset1::Constant>(dequantization.multiplyConstant)->cast_vector<float>();
if (std::any_of(scales.begin(), scales.end(), [](const float value) { return value < 0.0; })) { if (std::any_of(scales.begin(), scales.end(), [](const float value) { return value < 0.0; })) {
return false; return false;
} }

4
src/common/low_precision_transformations/src/reduce_mean.cpp
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@ -16,7 +16,7 @@ namespace low_precision {
ReduceMeanTransformation::ReduceMeanTransformation(const Params& params) : ReduceBaseTransformation(params) { ReduceMeanTransformation::ReduceMeanTransformation(const Params& params) : ReduceBaseTransformation(params) {
MATCHER_SCOPE(ReduceMeanTransformation); MATCHER_SCOPE(ReduceMeanTransformation);
auto matcher = pattern::wrap_type<opset1::ReduceMean>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::ReduceMean>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -31,7 +31,7 @@ ReduceMeanTransformation::ReduceMeanTransformation(const Params& params) : Reduc
} }
bool ReduceMeanTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const { bool ReduceMeanTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const {
return ov::is_type<opset1::ReduceMean>(reduce) ? ReduceBaseTransformation::canBeTransformed(context, reduce) : false; return ov::is_type<ov::opset1::ReduceMean>(reduce) ? ReduceBaseTransformation::canBeTransformed(context, reduce) : false;
} }
bool ReduceMeanTransformation::isPrecisionPreserved(std::shared_ptr<Node> reduce) const noexcept { bool ReduceMeanTransformation::isPrecisionPreserved(std::shared_ptr<Node> reduce) const noexcept {

6
src/common/low_precision_transformations/src/reduce_min.cpp
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@ -16,7 +16,7 @@ namespace low_precision {
ReduceMinTransformation::ReduceMinTransformation(const Params& params) : ReduceBaseTransformation(params) { ReduceMinTransformation::ReduceMinTransformation(const Params& params) : ReduceBaseTransformation(params) {
MATCHER_SCOPE(ReduceMinTransformation); MATCHER_SCOPE(ReduceMinTransformation);
auto matcher = pattern::wrap_type<opset1::ReduceMin>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::ReduceMin>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -31,7 +31,7 @@ ReduceMinTransformation::ReduceMinTransformation(const Params& params) : ReduceB
} }
bool ReduceMinTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const { bool ReduceMinTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const {
if (!ov::is_type<opset1::ReduceMin>(reduce)) { if (!ov::is_type<ov::opset1::ReduceMin>(reduce)) {
return false; return false;
} }
@ -40,7 +40,7 @@ bool ReduceMinTransformation::canBeTransformed(const TransformationContext& cont
} }
const auto dequantization = NetworkHelper::getDequantization(reduce, defaultPrecisions); const auto dequantization = NetworkHelper::getDequantization(reduce, defaultPrecisions);
const std::vector<float> scales = ov::as_type_ptr<opset1::Constant>(dequantization.multiplyConstant)->cast_vector<float>(); const std::vector<float> scales = ov::as_type_ptr<ov::opset1::Constant>(dequantization.multiplyConstant)->cast_vector<float>();
if (std::any_of(scales.begin(), scales.end(), [](const float value) { return value < 0.0; })) { if (std::any_of(scales.begin(), scales.end(), [](const float value) { return value < 0.0; })) {
return false; return false;
} }

12
src/common/low_precision_transformations/src/reduce_sum.cpp
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@ -16,7 +16,7 @@ namespace low_precision {
ReduceSumTransformation::ReduceSumTransformation(const Params& params) : ReduceBaseTransformation(params) { ReduceSumTransformation::ReduceSumTransformation(const Params& params) : ReduceBaseTransformation(params) {
MATCHER_SCOPE(ReduceSumTransformation); MATCHER_SCOPE(ReduceSumTransformation);
auto matcher = pattern::wrap_type<opset1::ReduceSum>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::ReduceSum>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -31,7 +31,7 @@ ReduceSumTransformation::ReduceSumTransformation(const Params& params) : ReduceB
} }
bool ReduceSumTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const { bool ReduceSumTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const {
const auto reduceSum = ov::as_type_ptr<opset1::ReduceSum>(reduce); const auto reduceSum = ov::as_type_ptr<ov::opset1::ReduceSum>(reduce);
if (!reduceSum || !ReduceBaseTransformation::canBeTransformed(context, reduceSum)) { if (!reduceSum || !ReduceBaseTransformation::canBeTransformed(context, reduceSum)) {
return false; return false;
} }
@ -57,7 +57,7 @@ void ReduceSumTransformation::changeDequantizationValues(
ReduceBaseTransformation::changeDequantizationValues(reduce, dequantization); ReduceBaseTransformation::changeDequantizationValues(reduce, dequantization);
if (dequantization.subtract) { if (dequantization.subtract) {
const auto reduceSum = ov::as_type_ptr<opset1::ReduceSum>(reduce); const auto reduceSum = ov::as_type_ptr<ov::opset1::ReduceSum>(reduce);
const auto reductionAxes = reduceSum->get_reduction_axes(); const auto reductionAxes = reduceSum->get_reduction_axes();
const auto inputShape = reduceSum->get_input_partial_shape(0); const auto inputShape = reduceSum->get_input_partial_shape(0);
@ -68,11 +68,11 @@ void ReduceSumTransformation::changeDequantizationValues(
} }
// (a1 - s) + (a2 - s) + ... + (an - s) = (a1 + a2 + ... + an) - n * s // (a1 - s) + (a2 - s) + ... + (an - s) = (a1 + a2 + ... + an) - n * s
const auto reductionSizeConstant = opset1::Constant::create(deqPrecision, Shape{}, { static_cast<float>(reductionSize) }); const auto reductionSizeConstant = ov::opset1::Constant::create(deqPrecision, Shape{}, { static_cast<float>(reductionSize) });
const auto result = fold<opset1::Multiply>(dequantization.subtractConstant, reductionSizeConstant); const auto result = fold<ov::opset1::Multiply>(dequantization.subtractConstant, reductionSizeConstant);
replace_node(dequantization.subtractConstant, result); replace_node(dequantization.subtractConstant, result);
dequantization.subtractConstant = ov::as_type_ptr<opset1::Constant>(result); dequantization.subtractConstant = ov::as_type_ptr<ov::opset1::Constant>(result);
} }
} }

2
src/common/low_precision_transformations/src/relu.cpp
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@ -20,7 +20,7 @@ namespace low_precision {
ReluTransformation::ReluTransformation(const Params& params) : LayerTransformation(params) { ReluTransformation::ReluTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(ReluTransformation); MATCHER_SCOPE(ReluTransformation);
auto matcher = pattern::wrap_type<opset1::Relu>({ pattern::wrap_type<opset1::Multiply>() }); auto matcher = pattern::wrap_type<ov::opset1::Relu>({ pattern::wrap_type<ov::opset1::Multiply>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();

36
src/common/low_precision_transformations/src/reshape.cpp
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@ -24,15 +24,15 @@ namespace low_precision {
ReshapeTransformation::ReshapeTransformation(const Params& params) : LayerTransformation(params) { ReshapeTransformation::ReshapeTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(ReshapeTransformation); MATCHER_SCOPE(ReshapeTransformation);
auto input = pattern::any_input(); auto input = ov::pass::pattern::any_input();
auto mul_const_m = pattern::wrap_type<opset1::Constant>(); auto mul_const_m = pattern::wrap_type<ov::opset1::Constant>();
auto mul_m = pattern::wrap_type<opset1::Multiply>({ input, mul_const_m }); auto mul_m = pattern::wrap_type<ov::opset1::Multiply>({ input, mul_const_m });
auto reshape_pattern_const = pattern::wrap_type<opset1::Constant>(); auto reshape_pattern_const = pattern::wrap_type<ov::opset1::Constant>();
auto reshape_pattern_nonconst = pattern::any_input(); auto reshape_pattern_nonconst = ov::pass::pattern::any_input();
auto reshape_pattern = std::make_shared<pattern::op::Or>(OutputVector{ reshape_pattern_const, reshape_pattern_nonconst }); auto reshape_pattern = std::make_shared<pattern::op::Or>(OutputVector{ reshape_pattern_const, reshape_pattern_nonconst });
auto matcher = pattern::wrap_type<opset1::Reshape>({ mul_m, reshape_pattern }); auto matcher = pattern::wrap_type<ov::opset1::Reshape>({ mul_m, reshape_pattern });
ngraph::graph_rewrite_callback callback = [=](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [=](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -41,7 +41,7 @@ ReshapeTransformation::ReshapeTransformation(const Params& params) : LayerTransf
// we can propagate only per-tensor dq through reshape with non-const reshape_pattern // we can propagate only per-tensor dq through reshape with non-const reshape_pattern
const auto& pattern_map = m.get_pattern_value_map(); const auto& pattern_map = m.get_pattern_value_map();
if (pattern_map.count(reshape_pattern_nonconst)) { if (pattern_map.count(reshape_pattern_nonconst)) {
const auto mul_const = as_type_ptr<opset1::Constant>(pattern_map.at(mul_const_m).get_node_shared_ptr()); const auto mul_const = as_type_ptr<ov::opset1::Constant>(pattern_map.at(mul_const_m).get_node_shared_ptr());
if (!mul_const || ngraph::shape_size(mul_const->get_shape()) != 1) { if (!mul_const || ngraph::shape_size(mul_const->get_shape()) != 1) {
return false; return false;
} }
@ -56,14 +56,14 @@ ReshapeTransformation::ReshapeTransformation(const Params& params) : LayerTransf
namespace { namespace {
void reshapeDequantizationConstant(const std::shared_ptr<opset1::Reshape>& reshape, const std::vector<ngraph::element::Type>& defaultPrecisions) { void reshapeDequantizationConstant(const std::shared_ptr<ov::opset1::Reshape>& reshape, const std::vector<ngraph::element::Type>& defaultPrecisions) {
// Reshape dequantization operation Constant. // Reshape dequantization operation Constant.
// 1. Calculate result dequantization Constant shape for broadcast based on original dequantization Constant shape and Reshape output. // 1. Calculate result dequantization Constant shape for broadcast based on original dequantization Constant shape and Reshape output.
// For example: dequantization shape {1, 3, 1, 1}, output Reshape shape {1, 12, 3, 3}, result for broadcast: {1, 3, 4, 1}, // For example: dequantization shape {1, 3, 1, 1}, output Reshape shape {1, 12, 3, 3}, result for broadcast: {1, 3, 4, 1},
// where '4' calculated for temporary broadcast before reshape. // where '4' calculated for temporary broadcast before reshape.
// 2. Broadcast dequantization Constant, if channels are changed // 2. Broadcast dequantization Constant, if channels are changed
// 3. Reshape and replace // 3. Reshape and replace
auto replaceConstant = [](const std::shared_ptr<opset1::Reshape>& reshape, const std::shared_ptr<opset1::Constant>& originalConstant) { auto replaceConstant = [](const std::shared_ptr<ov::opset1::Reshape>& reshape, const std::shared_ptr<ov::opset1::Constant>& originalConstant) {
// reshape for element-wise constant is not required // reshape for element-wise constant is not required
auto constantShape = originalConstant->get_shape(); auto constantShape = originalConstant->get_shape();
if (NetworkHelper::isScalarLike(originalConstant)) { if (NetworkHelper::isScalarLike(originalConstant)) {
@ -94,7 +94,7 @@ void reshapeDequantizationConstant(const std::shared_ptr<opset1::Reshape>& resha
return; return;
} }
auto getBCastedConst = [](const std::shared_ptr<opset1::Constant>& constant, size_t dimensionsToBroadcast) -> std::shared_ptr<Node> { auto getBCastedConst = [](const std::shared_ptr<ov::opset1::Constant>& constant, size_t dimensionsToBroadcast) -> std::shared_ptr<Node> {
if (dimensionsToBroadcast == 1ul) { if (dimensionsToBroadcast == 1ul) {
return constant; return constant;
} }
@ -107,24 +107,24 @@ void reshapeDequantizationConstant(const std::shared_ptr<opset1::Reshape>& resha
newOperationConstantBroadcastedShape[2] = dimensionsToBroadcast; newOperationConstantBroadcastedShape[2] = dimensionsToBroadcast;
} }
const auto targetShapeConstant = opset1::Constant::create( const auto targetShapeConstant = ov::opset1::Constant::create(
element::i32, element::i32,
Shape{ newOperationConstantBroadcastedShape.size() }, Shape{ newOperationConstantBroadcastedShape.size() },
newOperationConstantBroadcastedShape); newOperationConstantBroadcastedShape);
return fold<opset1::Broadcast>(constant, targetShapeConstant); return fold<ov::opset1::Broadcast>(constant, targetShapeConstant);
}; };
const std::shared_ptr<Node> broadcastedConstant = getBCastedConst(originalConstant, dimensionsToBroadcast); const std::shared_ptr<Node> broadcastedConstant = getBCastedConst(originalConstant, dimensionsToBroadcast);
std::vector<int> newReshapeConstValues(reshapeOutputRank.get_length(), 1ul); std::vector<int> newReshapeConstValues(reshapeOutputRank.get_length(), 1ul);
newReshapeConstValues[1] = static_cast<int>(reshapeOutputPShape[1].get_length()); newReshapeConstValues[1] = static_cast<int>(reshapeOutputPShape[1].get_length());
const std::shared_ptr<opset1::Constant> newReshapeConstant = std::make_shared<opset1::Constant>( const std::shared_ptr<ov::opset1::Constant> newReshapeConstant = std::make_shared<ov::opset1::Constant>(
element::i32, element::i32,
Shape({ newReshapeConstValues.size() }), Shape({ newReshapeConstValues.size() }),
newReshapeConstValues); newReshapeConstValues);
const std::shared_ptr<Node> resultConstant = fold<opset1::Reshape>( const std::shared_ptr<Node> resultConstant = fold<ov::opset1::Reshape>(
broadcastedConstant, broadcastedConstant,
newReshapeConstant, newReshapeConstant,
reshape->get_special_zero()); reshape->get_special_zero());
@ -146,7 +146,7 @@ void reshapeDequantizationConstant(const std::shared_ptr<opset1::Reshape>& resha
} // namespace } // namespace
bool ReshapeTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) { bool ReshapeTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) {
std::shared_ptr<opset1::Reshape> reshape = ov::as_type_ptr<opset1::Reshape>(m.get_match_root()); std::shared_ptr<ov::opset1::Reshape> reshape = ov::as_type_ptr<ov::opset1::Reshape>(m.get_match_root());
if (NetworkHelper::isConstantPath(reshape)) { if (NetworkHelper::isConstantPath(reshape)) {
return false; return false;
} }
@ -155,7 +155,7 @@ bool ReshapeTransformation::transform(TransformationContext& context, ngraph::pa
return false; return false;
} }
reshape = ov::as_type_ptr<opset1::Reshape>(NetworkHelper::separateInStandaloneBranch(reshape, defaultPrecisions)); reshape = ov::as_type_ptr<ov::opset1::Reshape>(NetworkHelper::separateInStandaloneBranch(reshape, defaultPrecisions));
reshapeDequantizationConstant(reshape, defaultPrecisions); reshapeDequantizationConstant(reshape, defaultPrecisions);
moveDequantizationAfter(context, reshape, NetworkHelper::getDequantization(reshape, defaultPrecisions, 0), false); moveDequantizationAfter(context, reshape, NetworkHelper::getDequantization(reshape, defaultPrecisions, 0), false);
return true; return true;
@ -200,7 +200,7 @@ bool ReshapeTransformation::canBeTransformed(const TransformationContext& contex
const auto inputs = op->get_output_target_inputs(0); const auto inputs = op->get_output_target_inputs(0);
if (inputs.size() == 1ul) { if (inputs.size() == 1ul) {
const auto consumer = inputs.begin()->get_node(); const auto consumer = inputs.begin()->get_node();
ignorePerTensorQuantizationCheck = ngraph::as_type<ngraph::opset1::MatMul>(consumer) != nullptr; ignorePerTensorQuantizationCheck = ngraph::as_type<ov::opset1::MatMul>(consumer) != nullptr;
} }
} }

17
src/common/low_precision_transformations/src/split.cpp
View File

@ -9,6 +9,7 @@
#include "low_precision/network_helper.hpp" #include "low_precision/network_helper.hpp"
#include "itt.hpp" #include "itt.hpp"
#include "ngraph/validation_util.hpp"
namespace ngraph { namespace ngraph {
namespace pass { namespace pass {
@ -16,9 +17,9 @@ namespace low_precision {
SplitTransformation::SplitTransformation(const Params& params) : LayerTransformation(params) { SplitTransformation::SplitTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(SplitTransformation); MATCHER_SCOPE(SplitTransformation);
auto matcher = pattern::wrap_type<opset1::Split>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::Split>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;
@ -43,11 +44,11 @@ bool SplitTransformation::transform(TransformationContext& context, ngraph::patt
const auto newSplit = split->clone_with_new_inputs(inputs); const auto newSplit = split->clone_with_new_inputs(inputs);
newSplit->set_friendly_name(split->get_friendly_name()); newSplit->set_friendly_name(split->get_friendly_name());
ngraph::copy_runtime_info(split, newSplit); ov::copy_runtime_info(split, newSplit);
const int64_t axis = ov::as_type_ptr<opset1::Constant>(split->get_input_node_shared_ptr(1))->cast_vector<int64_t>()[0]; const int64_t axis = ov::as_type_ptr<ov::opset1::Constant>(split->get_input_node_shared_ptr(1))->cast_vector<int64_t>()[0];
OPENVINO_SUPPRESS_DEPRECATED_START OPENVINO_SUPPRESS_DEPRECATED_START
const size_t normalizedAxis = normalize_axis(split->get_friendly_name(), axis, split->get_input_partial_shape(0).rank()); const size_t normalizedAxis = ngraph::normalize_axis(split->get_friendly_name(), axis, split->get_input_partial_shape(0).rank());
OPENVINO_SUPPRESS_DEPRECATED_END OPENVINO_SUPPRESS_DEPRECATED_END
const size_t outputSize = newSplit->get_output_size(); const size_t outputSize = newSplit->get_output_size();
@ -97,7 +98,7 @@ bool SplitTransformation::transform(TransformationContext& context, ngraph::patt
parent = subtract; parent = subtract;
} }
const auto multiply = std::make_shared<ov::op::TypeRelaxed<opset1::Multiply>>(parent, splitedMul[i]); const auto multiply = std::make_shared<ov::op::TypeRelaxed<ov::opset1::Multiply>>(parent, splitedMul[i]);
NetworkHelper::setOutDataPrecisionForTypeRelaxed(multiply, dequantization.multiply->get_output_element_type(0)); NetworkHelper::setOutDataPrecisionForTypeRelaxed(multiply, dequantization.multiply->get_output_element_type(0));
copy_runtime_info({ newSplit, multiply }, multiply); copy_runtime_info({ newSplit, multiply }, multiply);
@ -114,7 +115,7 @@ bool SplitTransformation::transform(TransformationContext& context, ngraph::patt
// We do it to avoid dequantization propagation to the shapeOf subgraphs // We do it to avoid dequantization propagation to the shapeOf subgraphs
for (size_t i = 0; i < replacement.size(); ++i) { for (size_t i = 0; i < replacement.size(); ++i) {
for (const auto& input : replacement[i].get_target_inputs()) { for (const auto& input : replacement[i].get_target_inputs()) {
if (const auto shapeOf = as_type_ptr<opset1::ShapeOf>(input.get_node()->shared_from_this())) { if (const auto shapeOf = as_type_ptr<ov::opset1::ShapeOf>(input.get_node()->shared_from_this())) {
const auto newShapeOf = shapeOf->clone_with_new_inputs({ newSplit->output(i) }); const auto newShapeOf = shapeOf->clone_with_new_inputs({ newSplit->output(i) });
replace_node_update_name(shapeOf, newShapeOf); replace_node_update_name(shapeOf, newShapeOf);
} }
@ -139,7 +140,7 @@ void SplitTransformation::updateOutputs(
const auto lastNode = lastNodes[i]; const auto lastNode = lastNodes[i];
for (auto output : lastNodes[i]->outputs()) { for (auto output : lastNodes[i]->outputs()) {
for (auto input : output.get_target_inputs()) { for (auto input : output.get_target_inputs()) {
if (ov::is_type<ngraph::opset1::Result>(input.get_node())) { if (ov::is_type<ov::opset1::Result>(input.get_node())) {
originalNode->set_friendly_name(originalName + LayerTransformation::originalLayerPostfix); originalNode->set_friendly_name(originalName + LayerTransformation::originalLayerPostfix);
lastNode->set_friendly_name(originalName + "." + std::to_string(i)); lastNode->set_friendly_name(originalName + "." + std::to_string(i));
break; break;

20
src/common/low_precision_transformations/src/strided_slice.cpp
View File

@ -17,10 +17,10 @@ namespace low_precision {
namespace { namespace {
std::shared_ptr<opset1::Constant> stridedSliceDeqConstant( std::shared_ptr<ov::opset1::Constant> stridedSliceDeqConstant(
const std::shared_ptr<ngraph::Node> strSlice, const std::shared_ptr<ngraph::Node> strSlice,
const std::shared_ptr<ngraph::Node> dequantizaitonConstant) { const std::shared_ptr<ngraph::Node> dequantizaitonConstant) {
auto constant = ov::as_type_ptr<ngraph::opset1::Constant>(dequantizaitonConstant); auto constant = ov::as_type_ptr<ov::opset1::Constant>(dequantizaitonConstant);
auto constantShape = constant->get_shape(); auto constantShape = constant->get_shape();
if (shape_size(constantShape) == 1ul) { if (shape_size(constantShape) == 1ul) {
return NetworkHelper::toScalar(constant); return NetworkHelper::toScalar(constant);
@ -43,13 +43,13 @@ std::shared_ptr<opset1::Constant> stridedSliceDeqConstant(
} }
constantShape = newConstantShape; constantShape = newConstantShape;
const auto newConstant = fold<ngraph::opset1::Broadcast>( const auto newConstant = fold<ov::opset1::Broadcast>(
constant, constant,
ngraph::opset1::Constant::create(ngraph::element::i32, { newConstantShape.size() }, newConstantShape)); ov::opset1::Constant::create(ngraph::element::i32, { newConstantShape.size() }, newConstantShape));
constant = ov::as_type_ptr<ngraph::opset1::Constant>(newConstant); constant = ov::as_type_ptr<ov::opset1::Constant>(newConstant);
} }
const auto stridedSlice = ov::as_type_ptr<ngraph::opset1::StridedSlice>(strSlice); const auto stridedSlice = ov::as_type_ptr<ov::opset1::StridedSlice>(strSlice);
auto beginMask = stridedSlice->get_begin_mask(); auto beginMask = stridedSlice->get_begin_mask();
auto endMask = stridedSlice->get_end_mask(); auto endMask = stridedSlice->get_end_mask();
@ -64,7 +64,7 @@ std::shared_ptr<opset1::Constant> stridedSliceDeqConstant(
} }
} }
const auto result = fold<ngraph::opset1::StridedSlice>( const auto result = fold<ov::opset1::StridedSlice>(
constant, constant,
stridedSlice->input_value(1), stridedSlice->input_value(1),
stridedSlice->input_value(2), stridedSlice->input_value(2),
@ -75,14 +75,14 @@ std::shared_ptr<opset1::Constant> stridedSliceDeqConstant(
stridedSlice->get_shrink_axis_mask(), stridedSlice->get_shrink_axis_mask(),
stridedSlice->get_ellipsis_mask()); stridedSlice->get_ellipsis_mask());
return ov::as_type_ptr<opset1::Constant>(NetworkHelper::toScalarIfPossible(result)); return ov::as_type_ptr<ov::opset1::Constant>(NetworkHelper::toScalarIfPossible(result));
} }
} // namespace } // namespace
StridedSliceTransformation::StridedSliceTransformation(const Params& params) : LayerTransformation(params) { StridedSliceTransformation::StridedSliceTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(StridedSliceTransformation); MATCHER_SCOPE(StridedSliceTransformation);
auto matcher = ngraph::pattern::wrap_type<opset1::StridedSlice>(); auto matcher = ngraph::pattern::wrap_type<ov::opset1::StridedSlice>();
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -119,7 +119,7 @@ bool StridedSliceTransformation::transform(TransformationContext& context, ngrap
} }
bool StridedSliceTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const { bool StridedSliceTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> operation) const {
if (!ov::is_type<ngraph::opset1::StridedSlice>(operation)) { if (!ov::is_type<ov::opset1::StridedSlice>(operation)) {
return false; return false;
} }

18
src/common/low_precision_transformations/src/subtract.cpp
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@ -24,10 +24,10 @@ namespace low_precision {
SubtractTransformation::SubtractTransformation(const Params& params) : LayerTransformation(params) { SubtractTransformation::SubtractTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(SubtractTransformation); MATCHER_SCOPE(SubtractTransformation);
auto convert = pattern::wrap_type<opset1::Convert>(); auto convert = pattern::wrap_type<ov::opset1::Convert>();
auto multiply = pattern::wrap_type<opset1::Multiply>(); auto multiply = pattern::wrap_type<ov::opset1::Multiply>();
auto subParent = std::make_shared<pattern::op::Or>(OutputVector{ convert, multiply }); auto subParent = std::make_shared<pattern::op::Or>(OutputVector{ convert, multiply });
auto subtract = pattern::wrap_type<opset1::Subtract>({ subParent, pattern::wrap_type<opset1::Constant>() }); auto subtract = pattern::wrap_type<ov::opset1::Subtract>({ subParent, pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -42,7 +42,7 @@ SubtractTransformation::SubtractTransformation(const Params& params) : LayerTran
} }
bool SubtractTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) { bool SubtractTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher &m) {
std::shared_ptr<opset1::Subtract> subtract = ov::as_type_ptr<opset1::Subtract>(m.get_match_root()); std::shared_ptr<ov::opset1::Subtract> subtract = ov::as_type_ptr<ov::opset1::Subtract>(m.get_match_root());
if (!canBeTransformed(context, subtract)) { if (!canBeTransformed(context, subtract)) {
return false; return false;
} }
@ -54,9 +54,9 @@ bool SubtractTransformation::transform(TransformationContext& context, ngraph::p
// before: Y = X * SC - SH, after: Y = (X - SH') * SC // before: Y = X * SC - SH, after: Y = (X - SH') * SC
// X * SC - SH = X * SC - SH' * SC // X * SC - SH = X * SC - SH' * SC
// SH' = SH / SC // SH' = SH / SC
std::shared_ptr<opset1::Subtract> newSubtract = ov::as_type_ptr<opset1::Subtract>(subtract->clone_with_new_inputs({ std::shared_ptr<ov::opset1::Subtract> newSubtract = ov::as_type_ptr<ov::opset1::Subtract>(subtract->clone_with_new_inputs({
dequantization.multiply->input_value(0), dequantization.multiply->input_value(0),
ngraph::pass::low_precision::fold<ngraph::opset1::Divide>( ngraph::pass::low_precision::fold<ov::opset1::Divide>(
subtract->input_value(1), subtract->input_value(1),
dequantization.multiply->input_value(1)) dequantization.multiply->input_value(1))
})); }));
@ -71,9 +71,9 @@ bool SubtractTransformation::transform(TransformationContext& context, ngraph::p
} }
if (dequantization.subtract != nullptr) { if (dequantization.subtract != nullptr) {
std::shared_ptr<opset1::Subtract> newSubtract = ov::as_type_ptr<opset1::Subtract>(subtract->clone_with_new_inputs({ std::shared_ptr<ov::opset1::Subtract> newSubtract = ov::as_type_ptr<ov::opset1::Subtract>(subtract->clone_with_new_inputs({
dequantization.subtract->input_value(0), dequantization.subtract->input_value(0),
fold<ngraph::opset1::Add>(subtract->input_value(1), dequantization.subtractConstant) fold<ov::opset1::Add>(subtract->input_value(1), dequantization.subtractConstant)
})); }));
replace_node(subtract, newSubtract); replace_node(subtract, newSubtract);
@ -85,7 +85,7 @@ bool SubtractTransformation::transform(TransformationContext& context, ngraph::p
// std::shared_ptr<Node> newSubtract = NetworkHelper::optimizeElementwise(subtract); // std::shared_ptr<Node> newSubtract = NetworkHelper::optimizeElementwise(subtract);
subtract->set_output_type(0, originalPrecision, subtract->get_output_partial_shape(0)); subtract->set_output_type(0, originalPrecision, subtract->get_output_partial_shape(0));
replace_node(subtract, std::make_shared<ov::op::TypeRelaxed<opset1::Subtract>>( replace_node(subtract, std::make_shared<ov::op::TypeRelaxed<ov::opset1::Subtract>>(
subtract->input_value(0), subtract->input_value(0),
subtract->input_value(1))); subtract->input_value(1)));
} }

16
src/common/low_precision_transformations/src/transpose.cpp
View File

@ -19,7 +19,7 @@ namespace low_precision {
TransposeTransformation::TransposeTransformation(const Params& params) : LayerTransformation(params) { TransposeTransformation::TransposeTransformation(const Params& params) : LayerTransformation(params) {
MATCHER_SCOPE(TransposeTransformation); MATCHER_SCOPE(TransposeTransformation);
auto matcher = pattern::wrap_type<opset1::Transpose>({ pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<opset1::Constant>() }); auto matcher = pattern::wrap_type<ov::opset1::Transpose>({ pattern::wrap_type<ov::opset1::Multiply>(), pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
@ -45,7 +45,7 @@ void transposeDequantizationConstant(std::shared_ptr<Node>& transpose, const std
} }
auto transposeDeqConstant = []( auto transposeDeqConstant = [](
const std::shared_ptr<opset1::Constant>& dequantizationConstant, const std::shared_ptr<ov::opset1::Constant>& dequantizationConstant,
const PartialShape& transposeOutputPShape, const PartialShape& transposeOutputPShape,
const std::shared_ptr<Node>& transposeConstant) -> std::shared_ptr<Node> { const std::shared_ptr<Node>& transposeConstant) -> std::shared_ptr<Node> {
const auto constantShape = dequantizationConstant->get_shape(); const auto constantShape = dequantizationConstant->get_shape();
@ -56,11 +56,11 @@ void transposeDequantizationConstant(std::shared_ptr<Node>& transpose, const std
assert(transposeOutputPShape.rank().is_static()); assert(transposeOutputPShape.rank().is_static());
const size_t transposeOutRank = transposeOutputPShape.rank().get_length(); const size_t transposeOutRank = transposeOutputPShape.rank().get_length();
if (constantShape.size() != transposeOutRank) { if (constantShape.size() != transposeOutRank) {
const auto unsqueezeConst = opset1::Constant::create(element::i32, Shape{ 1 }, std::vector<size_t>{ 0 }); const auto unsqueezeConst = ov::opset1::Constant::create(element::i32, Shape{ 1 }, std::vector<size_t>{ 0 });
const auto deqConstantWithBatch = fold<opset1::Unsqueeze>(dequantizationConstant, unsqueezeConst); const auto deqConstantWithBatch = fold<ov::opset1::Unsqueeze>(dequantizationConstant, unsqueezeConst);
return fold<opset1::Transpose>(deqConstantWithBatch, transposeConstant); return fold<ov::opset1::Transpose>(deqConstantWithBatch, transposeConstant);
} else { } else {
return fold<opset1::Transpose>(dequantizationConstant, transposeConstant); return fold<ov::opset1::Transpose>(dequantizationConstant, transposeConstant);
} }
}; };
@ -104,7 +104,7 @@ bool TransposeTransformation::canBeTransformed(const TransformationContext& cont
return false; return false;
} }
const std::shared_ptr<opset1::Constant> constant = ov::as_type_ptr<opset1::Constant>(op->get_input_node_shared_ptr(1)); const std::shared_ptr<ov::opset1::Constant> constant = ov::as_type_ptr<ov::opset1::Constant>(op->get_input_node_shared_ptr(1));
if (constant == nullptr) { if (constant == nullptr) {
return false; return false;
} }
@ -133,7 +133,7 @@ bool TransposeTransformation::canBeTransformed(const TransformationContext& cont
} }
} }
auto checkShape = [](const std::shared_ptr<opset1::Constant>& dequantizationConstant, const PartialShape& transposeOutputShape) -> bool { auto checkShape = [](const std::shared_ptr<ov::opset1::Constant>& dequantizationConstant, const PartialShape& transposeOutputShape) -> bool {
const auto dequantizationShape = dequantizationConstant->get_shape(); const auto dequantizationShape = dequantizationConstant->get_shape();
const auto rank = transposeOutputShape.rank(); const auto rank = transposeOutputShape.rank();
if (rank.is_dynamic()) { if (rank.is_dynamic()) {

10
src/common/low_precision_transformations/src/variadic_split.cpp
View File

@ -16,12 +16,12 @@ namespace low_precision {
VariadicSplitTransformation::VariadicSplitTransformation(const Params& params) : SplitTransformation(params) { VariadicSplitTransformation::VariadicSplitTransformation(const Params& params) : SplitTransformation(params) {
MATCHER_SCOPE(VariadicSplitTransformation); MATCHER_SCOPE(VariadicSplitTransformation);
auto matcher = pattern::wrap_type<opset1::VariadicSplit>({ auto matcher = pattern::wrap_type<ov::opset1::VariadicSplit>({
pattern::wrap_type<opset1::Multiply>(), pattern::wrap_type<ov::opset1::Multiply>(),
pattern::wrap_type<opset1::Constant>(), pattern::wrap_type<ov::opset1::Constant>(),
pattern::wrap_type<opset1::Constant>() }); pattern::wrap_type<ov::opset1::Constant>() });
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) { ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
auto op = m.get_match_root(); auto op = m.get_match_root();
if (transformation_callback(op)) { if (transformation_callback(op)) {
return false; return false;

56
src/common/low_precision_transformations/src/weightable_layer_transformation.cpp
View File

@ -16,19 +16,19 @@ namespace low_precision {
namespace { namespace {
std::vector<size_t> getWeightsDequantizationIdces(const std::shared_ptr<const Node> weightableLayer) { std::vector<size_t> getWeightsDequantizationIdces(const std::shared_ptr<const Node> weightableLayer) {
if (ov::is_type<opset1::Convolution>(weightableLayer)) { if (ov::is_type<ov::opset1::Convolution>(weightableLayer)) {
return std::vector<size_t>{0}; return std::vector<size_t>{0};
} else if (ov::is_type<opset1::ConvolutionBackpropData>(weightableLayer)) { } else if (ov::is_type<ov::opset1::ConvolutionBackpropData>(weightableLayer)) {
return std::vector<size_t>{1}; return std::vector<size_t>{1};
} else if (ov::is_type<opset1::GroupConvolution>(weightableLayer)) { } else if (ov::is_type<ov::opset1::GroupConvolution>(weightableLayer)) {
return ov::is_type<opset1::Reshape>(weightableLayer->get_input_node_shared_ptr(1)) ? std::vector<size_t>{0} return ov::is_type<ov::opset1::Reshape>(weightableLayer->get_input_node_shared_ptr(1)) ? std::vector<size_t>{0}
: std::vector<size_t>{0, 1}; : std::vector<size_t>{0, 1};
} else { } else {
THROW_IE_LPT_EXCEPTION(*weightableLayer) << "getWeightsDequantizationIdces is called for unexpected layer"; THROW_IE_LPT_EXCEPTION(*weightableLayer) << "getWeightsDequantizationIdces is called for unexpected layer";
} }
} }
bool checkConstShape(const std::vector<size_t>& idcesToCheck, const std::shared_ptr<opset1::Constant> constant) { bool checkConstShape(const std::vector<size_t>& idcesToCheck, const std::shared_ptr<ov::opset1::Constant> constant) {
const auto& shape = constant->get_shape(); const auto& shape = constant->get_shape();
if (shape_size(shape) == 1) { if (shape_size(shape) == 1) {
return true; return true;
@ -62,7 +62,7 @@ bool WeightableLayerTransformation::canConvolutionBeTransformed(const Transforma
return false; return false;
} }
std::shared_ptr<opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<opset1::Reshape>(layer->get_input_node_shared_ptr(1)); std::shared_ptr<ov::opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<ov::opset1::Reshape>(layer->get_input_node_shared_ptr(1));
dequantization = reshapeFromWeights == nullptr ? dequantization = reshapeFromWeights == nullptr ?
NetworkHelper::getDequantization(layer, defaultPrecisions, 1ul) : NetworkHelper::getDequantization(layer, defaultPrecisions, 1ul) :
NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions); NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions);
@ -162,20 +162,20 @@ bool WeightableLayerTransformation::canBeTransformed(const TransformationContext
// TODO Implement similar checks in other weightable operaitons // TODO Implement similar checks in other weightable operaitons
const std::shared_ptr<opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<opset1::Reshape>(layer->get_input_node_shared_ptr(1)); const std::shared_ptr<ov::opset1::Reshape> reshapeFromWeights = ov::as_type_ptr<ov::opset1::Reshape>(layer->get_input_node_shared_ptr(1));
std::shared_ptr<opset1::FakeQuantize> fqFromWeights; std::shared_ptr<ov::opset1::FakeQuantize> fqFromWeights;
if (reshapeFromWeights == nullptr) { if (reshapeFromWeights == nullptr) {
fqFromWeights = ov::as_type_ptr<opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1)); fqFromWeights = ov::as_type_ptr<ov::opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1));
if (fqFromWeights == nullptr) { if (fqFromWeights == nullptr) {
const FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(layer, defaultPrecisions, 1ul); const FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(layer, defaultPrecisions, 1ul);
fqFromWeights = ov::as_type_ptr<opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr()); fqFromWeights = ov::as_type_ptr<ov::opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr());
} }
} else { } else {
fqFromWeights = ov::as_type_ptr<opset1::FakeQuantize>(reshapeFromWeights->get_input_node_shared_ptr(0)); fqFromWeights = ov::as_type_ptr<ov::opset1::FakeQuantize>(reshapeFromWeights->get_input_node_shared_ptr(0));
if (fqFromWeights == nullptr) { if (fqFromWeights == nullptr) {
const FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions, 0ul); const FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions, 0ul);
fqFromWeights = ov::as_type_ptr<opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr()); fqFromWeights = ov::as_type_ptr<ov::opset1::FakeQuantize>(dequantization.data.get_node_shared_ptr());
} }
} }
@ -225,7 +225,7 @@ bool WeightableLayerTransformation::canBeTransformed(const TransformationContext
return false; return false;
} }
const auto weightsData = ov::as_type_ptr<opset1::Constant>(dequantizationOnWeights.data.get_node_shared_ptr()); const auto weightsData = ov::as_type_ptr<ov::opset1::Constant>(dequantizationOnWeights.data.get_node_shared_ptr());
if (weightsData == nullptr) { if (weightsData == nullptr) {
return false; return false;
} }
@ -258,18 +258,18 @@ bool WeightableLayerTransformation::isQuantizedStatic(const std::shared_ptr<cons
FakeQuantizeDequantization dequantizationOnWeights; FakeQuantizeDequantization dequantizationOnWeights;
if (reshapeIsRequired) { if (reshapeIsRequired) {
const auto reshape = layer->get_input_node_shared_ptr(1); const auto reshape = layer->get_input_node_shared_ptr(1);
std::shared_ptr<Node> parent = ov::is_type<opset1::Reshape>(reshape) ? std::shared_ptr<Node> parent = ov::is_type<ov::opset1::Reshape>(reshape) ?
reshape->get_input_node_shared_ptr(0) : reshape->get_input_node_shared_ptr(0) :
reshape; reshape;
const auto fq = ov::as_type_ptr<opset1::FakeQuantize>(parent); const auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(parent);
if (fq != nullptr) { if (fq != nullptr) {
return NetworkHelper::isQuantizeSupported(fq); return NetworkHelper::isQuantizeSupported(fq);
} }
dequantizationOnWeights = NetworkHelper::getDequantization(parent, defaultPrecisions, 0, true); dequantizationOnWeights = NetworkHelper::getDequantization(parent, defaultPrecisions, 0, true);
} else if (ov::is_type<opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1))) { } else if (ov::is_type<ov::opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1))) {
const std::shared_ptr<opset1::FakeQuantize> fq = ov::as_type_ptr<opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1)); const std::shared_ptr<ov::opset1::FakeQuantize> fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(layer->get_input_node_shared_ptr(1));
return NetworkHelper::isQuantizeSupported(fq); return NetworkHelper::isQuantizeSupported(fq);
} else { } else {
// TODO: update NetworkHelper API later // TODO: update NetworkHelper API later
@ -289,11 +289,11 @@ bool WeightableLayerTransformation::isQuantizedStatic(const std::shared_ptr<cons
auto deqData = dequantizationOnWeights.data.get_node_shared_ptr(); auto deqData = dequantizationOnWeights.data.get_node_shared_ptr();
// Quantize/Dequantize case // Quantize/Dequantize case
if (ov::is_type<ngraph::opset1::Convert>(deqData)) { if (ov::is_type<ov::opset1::Convert>(deqData)) {
deqData = deqData->get_input_node_shared_ptr(0); deqData = deqData->get_input_node_shared_ptr(0);
} }
// TODO: LPT: is it possible to share with canBeTransformed? // TODO: LPT: is it possible to share with canBeTransformed?
if (ov::is_type<opset1::Constant>(deqData)) { if (ov::is_type<ov::opset1::Constant>(deqData)) {
const ngraph::element::Type weightsDataPrecision = dequantizationOnWeights.data.get_element_type(); const ngraph::element::Type weightsDataPrecision = dequantizationOnWeights.data.get_element_type();
if (!DataPrecision::isSupported(weightsDataPrecision)) { if (!DataPrecision::isSupported(weightsDataPrecision)) {
return false; return false;
@ -306,9 +306,9 @@ bool WeightableLayerTransformation::isQuantizedStatic(const std::shared_ptr<cons
} }
} }
return true; return true;
} else if (auto fq = ov::as_type_ptr<opset1::FakeQuantize>(deqData)) { } else if (auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(deqData)) {
for (size_t i = 1; i < fq->get_input_size(); ++i) { for (size_t i = 1; i < fq->get_input_size(); ++i) {
if (auto constant = ov::as_type_ptr<ngraph::opset1::Constant>(fq->get_input_node_shared_ptr(i))) { if (auto constant = ov::as_type_ptr<ov::opset1::Constant>(fq->get_input_node_shared_ptr(i))) {
if (!checkConstShape(dqIdces, constant)) { if (!checkConstShape(dqIdces, constant)) {
return false; return false;
} }
@ -358,7 +358,7 @@ bool WeightableLayerTransformation::decomposeFakeQuantizeForWeightsPath(const st
return false; return false;
} }
if (ov::as_type_ptr<ngraph::opset1::Constant>(fqOnWeights) == nullptr) { if (ov::as_type_ptr<ov::opset1::Constant>(fqOnWeights) == nullptr) {
THROW_IE_LPT_EXCEPTION(*fqOnWeights) << "FakeQuantize on weights was not folded to constant"; THROW_IE_LPT_EXCEPTION(*fqOnWeights) << "FakeQuantize on weights was not folded to constant";
} }
@ -366,7 +366,7 @@ bool WeightableLayerTransformation::decomposeFakeQuantizeForWeightsPath(const st
} }
bool WeightableLayerTransformation::isGroup(const std::shared_ptr<Node>& layer) { bool WeightableLayerTransformation::isGroup(const std::shared_ptr<Node>& layer) {
if (!ov::is_type<opset1::Convolution>(layer) && !ov::is_type<opset1::GroupConvolution>(layer)) { if (!ov::is_type<ov::opset1::Convolution>(layer) && !ov::is_type<ov::opset1::GroupConvolution>(layer)) {
return false; return false;
} }
@ -375,7 +375,7 @@ bool WeightableLayerTransformation::isGroup(const std::shared_ptr<Node>& layer)
} }
bool WeightableLayerTransformation::isDepthwise(const std::shared_ptr<Node>& layer) { bool WeightableLayerTransformation::isDepthwise(const std::shared_ptr<Node>& layer) {
if (!ov::as_type_ptr<opset1::Convolution>(layer) && !ov::as_type_ptr<opset1::GroupConvolution>(layer)) { if (!ov::as_type_ptr<ov::opset1::Convolution>(layer) && !ov::as_type_ptr<ov::opset1::GroupConvolution>(layer)) {
return false; return false;
} }
@ -385,11 +385,11 @@ bool WeightableLayerTransformation::isDepthwise(const std::shared_ptr<Node>& lay
return (group == inputChannelsCount) && (inputChannelsCount == outputChannelsCount); return (group == inputChannelsCount) && (inputChannelsCount == outputChannelsCount);
} }
std::shared_ptr<opset1::FakeQuantize> WeightableLayerTransformation::getFakeQuantizeOnWeights(const std::shared_ptr<Node>& node) { std::shared_ptr<ov::opset1::FakeQuantize> WeightableLayerTransformation::getFakeQuantizeOnWeights(const std::shared_ptr<Node>& node) {
auto fq = ov::as_type_ptr<opset1::FakeQuantize>(node->get_input_node_shared_ptr(1)); auto fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(node->get_input_node_shared_ptr(1));
// TODO: temporary workaround // TODO: temporary workaround
if (fq == nullptr) { if (fq == nullptr) {
fq = ov::as_type_ptr<opset1::FakeQuantize>(node->get_input_node_ptr(1)->get_input_node_shared_ptr(0)); fq = ov::as_type_ptr<ov::opset1::FakeQuantize>(node->get_input_node_ptr(1)->get_input_node_shared_ptr(0));
} }
return fq; return fq;
@ -417,7 +417,7 @@ bool WeightableLayerTransformation::isAsymmetricOnWeights(
const std::vector<ngraph::element::Type>& defaultPrecisions) { const std::vector<ngraph::element::Type>& defaultPrecisions) {
const auto n = const_cast<ngraph::Node*>(node.get())->shared_from_this(); const auto n = const_cast<ngraph::Node*>(node.get())->shared_from_this();
const auto reshapeFromWeights = ngraph::as_type_ptr<ngraph::opset1::Reshape>(n->get_input_node_shared_ptr(1)); const auto reshapeFromWeights = ngraph::as_type_ptr<ov::opset1::Reshape>(n->get_input_node_shared_ptr(1));
const auto dequantization = reshapeFromWeights == nullptr ? const auto dequantization = reshapeFromWeights == nullptr ?
NetworkHelper::getDequantization(n, defaultPrecisions, 1ul) : NetworkHelper::getDequantization(n, defaultPrecisions, 1ul) :
NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions); NetworkHelper::getDequantization(reshapeFromWeights, defaultPrecisions);

18
src/common/low_precision_transformations/tests/concat_with_neighbors_transformation_with_convolution.cpp
View File

@ -105,7 +105,7 @@ public:
testValues.actual.dequantization3); testValues.actual.dequantization3);
auto supportedPrecisionsOnActivation = std::vector<ngraph::pass::low_precision::PrecisionsRestriction>({ auto supportedPrecisionsOnActivation = std::vector<ngraph::pass::low_precision::PrecisionsRestriction>({
ngraph::pass::low_precision::PrecisionsRestriction::create<ngraph::opset1::Convolution>({ ngraph::pass::low_precision::PrecisionsRestriction::create<ov::opset1::Convolution>({
{{0}, {ngraph::element::u8}}, {{0}, {ngraph::element::u8}},
{{1}, {ngraph::element::i8}} {{1}, {ngraph::element::i8}}
}) })
@ -114,14 +114,14 @@ public:
auto quantizationRestrictions = testValues.multiChannels ? auto quantizationRestrictions = testValues.multiChannels ?
std::vector<ngraph::pass::low_precision::QuantizationGranularityRestriction>() : std::vector<ngraph::pass::low_precision::QuantizationGranularityRestriction>() :
std::vector<ngraph::pass::low_precision::QuantizationGranularityRestriction>({ std::vector<ngraph::pass::low_precision::QuantizationGranularityRestriction>({
ngraph::pass::low_precision::QuantizationGranularityRestriction::create<ngraph::opset1::Convolution>({0}) ngraph::pass::low_precision::QuantizationGranularityRestriction::create<ov::opset1::Convolution>({0})
}); });
SimpleLowPrecisionTransformer transform(supportedPrecisionsOnActivation, quantizationRestrictions); SimpleLowPrecisionTransformer transform(supportedPrecisionsOnActivation, quantizationRestrictions);
transform.add<ngraph::pass::low_precision::ConcatTransformation, ngraph::opset1::Concat>(testValues.params); transform.add<ngraph::pass::low_precision::ConcatTransformation, ov::opset1::Concat>(testValues.params);
transform.add<ngraph::pass::low_precision::ConvolutionTransformation, ngraph::opset1::Convolution>(testValues.params); transform.add<ngraph::pass::low_precision::ConvolutionTransformation, ov::opset1::Convolution>(testValues.params);
transform.add<ngraph::pass::low_precision::FakeQuantizeDecompositionTransformation, ngraph::opset1::FakeQuantize>(testValues.params); transform.add<ngraph::pass::low_precision::FakeQuantizeDecompositionTransformation, ov::opset1::FakeQuantize>(testValues.params);
transform.add<ngraph::pass::low_precision::MaxPoolTransformation, ngraph::opset1::MaxPool>(testValues.params); transform.add<ngraph::pass::low_precision::MaxPoolTransformation, ov::opset1::MaxPool>(testValues.params);
transform.transform(actualFunction); transform.transform(actualFunction);
referenceFunction = ngraph::builder::subgraph::PrecisionPropagationFunction::getReferenceWithNeighbors( referenceFunction = ngraph::builder::subgraph::PrecisionPropagationFunction::getReferenceWithNeighbors(
@ -157,13 +157,13 @@ TEST_P(ConcatWithNeighborsWithConvolutionTransformation, CompareFunctions) {
//auto res = compare_functions(actualFunction, referenceFunction, true, false, false); //auto res = compare_functions(actualFunction, referenceFunction, true, false, false);
//ASSERT_TRUE(res.first) << res.second; //ASSERT_TRUE(res.first) << res.second;
auto actualFakeQuantizes = LayerTransformation::get<opset1::FakeQuantize>(actualFunction); auto actualFakeQuantizes = LayerTransformation::get<ov::opset1::FakeQuantize>(actualFunction);
ASSERT_EQ(3ul, actualFakeQuantizes.size()) << "unexpected FakeQuantize operations count " << actualFakeQuantizes.size(); ASSERT_EQ(3ul, actualFakeQuantizes.size()) << "unexpected FakeQuantize operations count " << actualFakeQuantizes.size();
ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<PrecisionsAttribute>(actualFakeQuantizes)) << ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<PrecisionsAttribute>(actualFakeQuantizes)) <<
"PrecisionsAttribute shared values are not the same"; "PrecisionsAttribute shared values are not the same";
auto actualConcatOperations = LayerTransformation::get<opset1::Concat>(actualFunction); auto actualConcatOperations = LayerTransformation::get<ov::opset1::Concat>(actualFunction);
ASSERT_EQ(2ul, actualConcatOperations.size()) << "unexpected concat operations"; ASSERT_EQ(2ul, actualConcatOperations.size()) << "unexpected concat operations";
ASSERT_FALSE(ngraph::pass::low_precision::getAttribute<QuantizationAlignmentAttribute>(actualConcatOperations[0]).empty()); ASSERT_FALSE(ngraph::pass::low_precision::getAttribute<QuantizationAlignmentAttribute>(actualConcatOperations[0]).empty());
ASSERT_FALSE(ngraph::pass::low_precision::getAttribute<QuantizationAlignmentAttribute>(actualConcatOperations[1]).empty()); ASSERT_FALSE(ngraph::pass::low_precision::getAttribute<QuantizationAlignmentAttribute>(actualConcatOperations[1]).empty());
@ -172,7 +172,7 @@ TEST_P(ConcatWithNeighborsWithConvolutionTransformation, CompareFunctions) {
ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<IntervalsAlignmentAttribute>(actualConcatOperations)) << ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<IntervalsAlignmentAttribute>(actualConcatOperations)) <<
"IntervalsAlignmentAttribute shared values are not the same"; "IntervalsAlignmentAttribute shared values are not the same";
auto convolutions = LayerTransformation::get<opset1::Convolution>(actualFunction); auto convolutions = LayerTransformation::get<ov::opset1::Convolution>(actualFunction);
ASSERT_EQ(1ul, convolutions.size()) << "unexpected convolution operations"; ASSERT_EQ(1ul, convolutions.size()) << "unexpected convolution operations";
ASSERT_EQ(2ul, convolutions[0]->input(0).get_rt_info().size()) << ASSERT_EQ(2ul, convolutions[0]->input(0).get_rt_info().size()) <<
"unexpected input 0 attributes count: LowPrecision::PerTensorQuantization & LowPrecision::Precisions"; "unexpected input 0 attributes count: LowPrecision::PerTensorQuantization & LowPrecision::Precisions";

4
src/common/low_precision_transformations/tests/is_function_quantized_transformation.cpp
View File

@ -33,7 +33,7 @@ public:
void SetUp() override { void SetUp() override {
const auto testValues = GetParam(); const auto testValues = GetParam();
const auto input = std::make_shared<ngraph::opset1::Parameter>(testValues.precision, ngraph::Shape(testValues.shape)); const auto input = std::make_shared<ov::opset1::Parameter>(testValues.precision, ngraph::Shape(testValues.shape));
const auto fakeQuantize = ngraph::builder::subgraph::makeFakeQuantize( const auto fakeQuantize = ngraph::builder::subgraph::makeFakeQuantize(
input, input,
testValues.precision, testValues.precision,
@ -44,7 +44,7 @@ public:
replace_node(fakeQuantize->get_input_node_shared_ptr(3), input); replace_node(fakeQuantize->get_input_node_shared_ptr(3), input);
} }
ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(fakeQuantize) }; ngraph::ResultVector results{ std::make_shared<ov::opset1::Result>(fakeQuantize) };
function = std::make_shared<ngraph::Function>(results, ngraph::ParameterVector{ input }, "IsFunctionQuantizedFunction"); function = std::make_shared<ngraph::Function>(results, ngraph::ParameterVector{ input }, "IsFunctionQuantizedFunction");
function->validate_nodes_and_infer_types(); function->validate_nodes_and_infer_types();
} }

6
src/common/low_precision_transformations/tests/markup_avg_pool_precisions_transformation.cpp
View File

@ -83,7 +83,7 @@ public:
pass.run_on_model(actualFunction); pass.run_on_model(actualFunction);
auto supportedPrecisionsOnActivation = std::vector<ngraph::pass::low_precision::PrecisionsRestriction>( auto supportedPrecisionsOnActivation = std::vector<ngraph::pass::low_precision::PrecisionsRestriction>(
{ngraph::pass::low_precision::PrecisionsRestriction::create<ngraph::opset1::Convolution>( {ngraph::pass::low_precision::PrecisionsRestriction::create<ov::opset1::Convolution>(
{{{0}, {ngraph::element::u8}}, {{1}, {ngraph::element::i8}}})}); {{{0}, {ngraph::element::u8}}, {{1}, {ngraph::element::i8}}})});
SimpleLowPrecisionTransformer transform(supportedPrecisionsOnActivation); SimpleLowPrecisionTransformer transform(supportedPrecisionsOnActivation);
@ -132,7 +132,7 @@ TEST_P(MarkupAvgPoolPrecisionsTransformation, CompareFunctions) {
ov::pass::InitNodeInfo().run_on_model(actualFunction); ov::pass::InitNodeInfo().run_on_model(actualFunction);
actualFunction->validate_nodes_and_infer_types(); actualFunction->validate_nodes_and_infer_types();
const auto avgPoolOperations = LayerTransformation::get<opset1::AvgPool>(actualFunction); const auto avgPoolOperations = LayerTransformation::get<ov::opset1::AvgPool>(actualFunction);
ASSERT_EQ(1ul, avgPoolOperations.size()) << "unexpected avgPoolOperations size: " << avgPoolOperations.size(); ASSERT_EQ(1ul, avgPoolOperations.size()) << "unexpected avgPoolOperations size: " << avgPoolOperations.size();
{ {
@ -142,7 +142,7 @@ TEST_P(MarkupAvgPoolPrecisionsTransformation, CompareFunctions) {
ASSERT_EQ(true, avgPoolPrecisioinPreservedAttribute.as<AvgPoolPrecisionPreservedAttribute>().value()); ASSERT_EQ(true, avgPoolPrecisioinPreservedAttribute.as<AvgPoolPrecisionPreservedAttribute>().value());
} }
const auto precisionPreserved = LayerTransformation::get<opset1::MaxPool>(actualFunction); const auto precisionPreserved = LayerTransformation::get<ov::opset1::MaxPool>(actualFunction);
ASSERT_TRUE(checkIfAttributesAreTheSame<AvgPoolPrecisionPreservedAttribute>(precisionPreserved)) ASSERT_TRUE(checkIfAttributesAreTheSame<AvgPoolPrecisionPreservedAttribute>(precisionPreserved))
<< "AvgPoolPrecisionPreservedAttribute are not the same"; << "AvgPoolPrecisionPreservedAttribute are not the same";

4
src/common/offline_transformations/include/mask_attribute.hpp
View File

@ -18,6 +18,8 @@
#include <set> #include <set>
#include <string> #include <string>
#include "openvino/util/log.hpp"
namespace ngraph { namespace ngraph {
/** /**
@ -211,7 +213,7 @@ public:
bool add_callback(const std::function<bool(Mask::Ptr)>& receive_callback, Mask::Ptr mask) { bool add_callback(const std::function<bool(Mask::Ptr)>& receive_callback, Mask::Ptr mask) {
if (m_callbacks.find(mask.get()) != m_callbacks.end()) if (m_callbacks.find(mask.get()) != m_callbacks.end())
NGRAPH_DEBUG << "Attempt to rewrite callback, could lead to unexpected behaviour"; OPENVINO_DEBUG << "Attempt to rewrite callback, could lead to unexpected behaviour";
m_callbacks[mask.get()] = receive_callback; m_callbacks[mask.get()] = receive_callback;
m_dependencies.push_back(mask.get()); m_dependencies.push_back(mask.get());

7
src/common/offline_transformations/src/pruning/init_const_mask.cpp
View File

@ -28,8 +28,9 @@ ngraph::pass::InitConstMask::InitConstMask(const ngraph::AxisSet& dims,
for (const auto& dim : dims) { for (const auto& dim : dims) {
if (dim >= shape.size()) { if (dim >= shape.size()) {
NGRAPH_DEBUG << "[WARNING] Attemt to initialize masks on " << dim << " dimension which is out of shape " OPENVINO_DEBUG << "[WARNING] Attemt to initialize masks on " << dim
<< shape << " for node (" << const_node->get_friendly_name() << ")"; << " dimension which is out of shape " << shape << " for node ("
<< const_node->get_friendly_name() << ")";
continue; continue;
} }
@ -61,7 +62,7 @@ ngraph::pass::InitConstMask::InitConstMask(const ngraph::AxisSet& dims,
setInitMask(const_node, mask); setInitMask(const_node, mask);
#endif #endif
if (!mask->all_dims_are_empty()) { if (!mask->all_dims_are_empty()) {
NGRAPH_DEBUG << "MASK (" << const_node->get_friendly_name() << ") " << *mask << std::endl; OPENVINO_DEBUG << "MASK (" << const_node->get_friendly_name() << ") " << *mask << std::endl;
} }
return false; return false;

15
src/common/offline_transformations/src/pruning/init_masks.cpp
View File

@ -42,8 +42,8 @@ public:
cur_node = cur_node->get_input_node_shared_ptr(0); cur_node = cur_node->get_input_node_shared_ptr(0);
} }
if (!ngraph::is_type<opset6::Constant>(cur_node)) { if (!ngraph::is_type<opset6::Constant>(cur_node)) {
NGRAPH_DEBUG << "Can't find Constant weights for Convolution: " OPENVINO_DEBUG << "Can't find Constant weights for Convolution: "
<< m_output.get_node()->get_friendly_name() << std::endl; << m_output.get_node()->get_friendly_name() << std::endl;
return false; return false;
} }
@ -101,16 +101,17 @@ public:
dim_order = new_order; dim_order = new_order;
} else { } else {
if (ngraph::is_type<opset6::Reshape>(cur_node) || ngraph::is_type<opset6::MatMul>(cur_node)) { if (ngraph::is_type<opset6::Reshape>(cur_node) || ngraph::is_type<opset6::MatMul>(cur_node)) {
NGRAPH_DEBUG << "Can't init mask for MatMul: " << matmul->get_friendly_name() OPENVINO_DEBUG << "Can't init mask for MatMul: " << matmul->get_friendly_name()
<< " because of node " << cur_node->get_friendly_name() << " because of node " << cur_node->get_friendly_name()
<< " in the way from weights to Matmul" << std::endl; << " in the way from weights to Matmul" << std::endl;
return false; return false;
} }
} }
cur_node = cur_node->get_input_node_shared_ptr(0); cur_node = cur_node->get_input_node_shared_ptr(0);
} }
if (!ngraph::is_type<opset6::Constant>(cur_node)) { if (!ngraph::is_type<opset6::Constant>(cur_node)) {
NGRAPH_DEBUG << "Can't find Constant weights for MatMul: " << matmul->get_friendly_name() << std::endl; OPENVINO_DEBUG << "Can't find Constant weights for MatMul: " << matmul->get_friendly_name()
<< std::endl;
return false; return false;
} }
// 2. Get constant rank to set mask on last dimension // 2. Get constant rank to set mask on last dimension
@ -118,7 +119,7 @@ public:
const auto shape_rank = const_op->get_shape().size(); const auto shape_rank = const_op->get_shape().size();
const size_t shift = (matmul->get_transpose_b()) ? 2 : 1; const size_t shift = (matmul->get_transpose_b()) ? 2 : 1;
if (shape_rank < shift) { if (shape_rank < shift) {
NGRAPH_DEBUG << "Can't init mask for MatMul: " << matmul->get_friendly_name() << std::endl; OPENVINO_DEBUG << "Can't init mask for MatMul: " << matmul->get_friendly_name() << std::endl;
return false; return false;
} }
const auto idx = shape_rank - shift; const auto idx = shape_rank - shift;

49
src/common/offline_transformations/src/pruning/propagate_masks.cpp
View File

@ -17,6 +17,7 @@
#include <ngraph/validation_util.hpp> #include <ngraph/validation_util.hpp>
#include "mask_attribute.hpp" #include "mask_attribute.hpp"
#include "openvino/util/log.hpp"
#include "pruning.hpp" #include "pruning.hpp"
namespace ngraph { namespace ngraph {
@ -78,17 +79,17 @@ public:
auto b_mask = getMask(m_b); auto b_mask = getMask(m_b);
if (!a_mask && !b_mask) { if (!a_mask && !b_mask) {
NGRAPH_DEBUG << "No mask for any input of " << m_matmul.get_node()->get_friendly_name() << "\n"; OPENVINO_DEBUG << "No mask for any input of " << m_matmul.get_node()->get_friendly_name() << "\n";
return false; return false;
} }
if (!b_mask) { if (!b_mask) {
NGRAPH_DEBUG << "No mask for input b of " << m_matmul.get_node()->get_friendly_name() << "\n"; OPENVINO_DEBUG << "No mask for input b of " << m_matmul.get_node()->get_friendly_name() << "\n";
return false; return false;
} }
const auto matmul_range = m_matmul.get_shape().size(); const auto matmul_range = m_matmul.get_shape().size();
if (matmul_range < 2) { if (matmul_range < 2) {
NGRAPH_DEBUG << "Matmul operation with rank = 1 is not supported by pruning algo by now\n"; OPENVINO_DEBUG << "Matmul operation with rank = 1 is not supported by pruning algo by now\n";
return false; return false;
} }
@ -216,7 +217,7 @@ public:
// Weights mask for convolution should be initialized in the InitMasks pass (and propagate after it). // Weights mask for convolution should be initialized in the InitMasks pass (and propagate after it).
// If mask isn't initialized - this weights (and hence all convolution) can't be pruned for some reason. // If mask isn't initialized - this weights (and hence all convolution) can't be pruned for some reason.
if (!weights_mask) { if (!weights_mask) {
NGRAPH_DEBUG << "No weights mask for " << m_output.get_node()->get_friendly_name() << "\n"; OPENVINO_DEBUG << "No weights mask for " << m_output.get_node()->get_friendly_name() << "\n";
return false; return false;
} }
auto weights_mask_row = weights_mask.get(); auto weights_mask_row = weights_mask.get();
@ -309,8 +310,8 @@ public:
weights_mask = std::make_shared<Mask>(weights_shape.size()); weights_mask = std::make_shared<Mask>(weights_shape.size());
setMask(m_weights, weights_mask); setMask(m_weights, weights_mask);
} else { } else {
NGRAPH_DEBUG << "GroupConvolution: No weights mask and weights aren't constant for " OPENVINO_DEBUG << "GroupConvolution: No weights mask and weights aren't constant for "
<< *m_output.get_node() << "\n"; << *m_output.get_node() << "\n";
return false; return false;
} }
} }
@ -401,7 +402,7 @@ public:
const auto constant = get_constant_from_source(m_shape.get_node_shared_ptr()); const auto constant = get_constant_from_source(m_shape.get_node_shared_ptr());
OPENVINO_SUPPRESS_DEPRECATED_END OPENVINO_SUPPRESS_DEPRECATED_END
if (!constant) { if (!constant) {
NGRAPH_DEBUG << "Can't get constant from source node " << m_shape.get_node()->get_friendly_name(); OPENVINO_DEBUG << "Can't get constant from source node " << m_shape.get_node()->get_friendly_name();
return false; return false;
} }
auto input_mask_row = input_mask.get(); auto input_mask_row = input_mask.get();
@ -479,8 +480,8 @@ public:
return false; return false;
if (m_output.get_node_shared_ptr()->get_autob() != op::AutoBroadcastType::NUMPY) { if (m_output.get_node_shared_ptr()->get_autob() != op::AutoBroadcastType::NUMPY) {
NGRAPH_DEBUG << "Can't propagate mask through " << m_output.get_node()->get_friendly_name() OPENVINO_DEBUG << "Can't propagate mask through " << m_output.get_node()->get_friendly_name()
<< " because node is using unsupported broadcast mode." << std::endl; << " because node is using unsupported broadcast mode." << std::endl;
return false; return false;
} }
// Case when input masks should be united instead of intersection // Case when input masks should be united instead of intersection
@ -541,8 +542,8 @@ public:
// Prevent case when input_shape and weights_shape both has broadcasted dims // Prevent case when input_shape and weights_shape both has broadcasted dims
if (input_shape_broadcasted_dims.size() && weights_shape_broadcasted_dims.size()) { if (input_shape_broadcasted_dims.size() && weights_shape_broadcasted_dims.size()) {
NGRAPH_DEBUG << "Can't propagate mask through " << m_output.get_node()->get_friendly_name() OPENVINO_DEBUG << "Can't propagate mask through " << m_output.get_node()->get_friendly_name()
<< " because both input shapes contains broadcasted dims." << std::endl; << " because both input shapes contains broadcasted dims." << std::endl;
return false; return false;
} }
@ -562,14 +563,14 @@ public:
} }
if (!input_mask) { if (!input_mask) {
NGRAPH_DEBUG << "No input mask for: " << m_output.get_node()->get_friendly_name() << std::endl; OPENVINO_DEBUG << "No input mask for: " << m_output.get_node()->get_friendly_name() << std::endl;
return false; return false;
} }
if (!weights_mask) { if (!weights_mask) {
// Set dummy mask to weight input in case this input has no mask // Set dummy mask to weight input in case this input has no mask
// and has broadcastable dimentions // and has broadcastable dimentions
if (!weights_shape_broadcasted_dims.size()) { if (!weights_shape_broadcasted_dims.size()) {
NGRAPH_DEBUG << "No weights mask for: " << m_output.get_node()->get_friendly_name() << std::endl; OPENVINO_DEBUG << "No weights mask for: " << m_output.get_node()->get_friendly_name() << std::endl;
return false; return false;
} }
weights_mask = std::make_shared<Mask>(m_weights.get_partial_shape().rank().get_length()); weights_mask = std::make_shared<Mask>(m_weights.get_partial_shape().rank().get_length());
@ -664,7 +665,7 @@ public:
// Input mask is the only source of pruning in FQ // Input mask is the only source of pruning in FQ
if (!input_mask) { if (!input_mask) {
NGRAPH_DEBUG << "FakeQuantize: No input mask for " << *m_output.get_node() << "\n"; OPENVINO_DEBUG << "FakeQuantize: No input mask for " << *m_output.get_node() << "\n";
return false; return false;
} }
@ -1138,9 +1139,9 @@ public:
constant = get_constant_from_source(m_weights.get_node_shared_ptr()); constant = get_constant_from_source(m_weights.get_node_shared_ptr());
OPENVINO_SUPPRESS_DEPRECATED_END OPENVINO_SUPPRESS_DEPRECATED_END
if (!constant) { if (!constant) {
NGRAPH_DEBUG << "Can't process reshape node " << m_output.get_node()->get_friendly_name() OPENVINO_DEBUG << "Can't process reshape node " << m_output.get_node()->get_friendly_name()
<< " with no constant node " << m_weights.get_node()->get_friendly_name() << " with no constant node " << m_weights.get_node()->get_friendly_name()
<< " as shape input."; << " as shape input.";
return false; return false;
} }
} }
@ -1385,19 +1386,19 @@ public:
const auto input_order_node = get_constant_from_source(m_weights.get_node_shared_ptr()); const auto input_order_node = get_constant_from_source(m_weights.get_node_shared_ptr());
OPENVINO_SUPPRESS_DEPRECATED_END OPENVINO_SUPPRESS_DEPRECATED_END
if (!input_order_node) { if (!input_order_node) {
NGRAPH_DEBUG << "Can't process transpose node " << m_output.get_node()->get_friendly_name() OPENVINO_DEBUG << "Can't process transpose node " << m_output.get_node()->get_friendly_name()
<< " with no constant node " << m_weights.get_node()->get_friendly_name() << " with no constant node " << m_weights.get_node()->get_friendly_name()
<< " as input_order input."; << " as input_order input.";
return false; return false;
} }
const auto input_mask = getMask(m_input); const auto input_mask = getMask(m_input);
if (!input_mask) { if (!input_mask) {
NGRAPH_DEBUG << "No input mask for: " << m_output.get_node()->get_friendly_name() << std::endl; OPENVINO_DEBUG << "No input mask for: " << m_output.get_node()->get_friendly_name() << std::endl;
return false; return false;
} }
if (static_cast<int64_t>(input_mask->size()) != m_output.get_partial_shape().rank().get_length()) { if (static_cast<int64_t>(input_mask->size()) != m_output.get_partial_shape().rank().get_length()) {
NGRAPH_DEBUG << "Transpose which change tensor rank is not supported yet."; OPENVINO_DEBUG << "Transpose which change tensor rank is not supported yet.";
return false; return false;
} }
@ -1627,8 +1628,8 @@ public:
// Invalidate current mask and its parent masks // Invalidate current mask and its parent masks
output_mask->apply_callback(input_mask); output_mask->apply_callback(input_mask);
NGRAPH_DEBUG << "Invalidate masks for " << *input.get_node() << " because " << node OPENVINO_DEBUG << "Invalidate masks for " << *input.get_node() << " because " << node
<< " is in scope of stop ops.\n"; << " is in scope of stop ops.\n";
any_input_with_masks = true; any_input_with_masks = true;
} }
} }

30
src/common/offline_transformations/src/pruning/shrink_weights.cpp
View File

@ -57,7 +57,7 @@ static bool maybe_adopt_reshape_node(std::shared_ptr<ov::Node> reshape, ngraph::
const auto shape = reshape->input_value(1); const auto shape = reshape->input_value(1);
const auto consumers = shape.get_node()->get_output_target_inputs(0); const auto consumers = shape.get_node()->get_output_target_inputs(0);
if (shape.get_node()->outputs().size() != 1 || consumers.size() != 1) { if (shape.get_node()->outputs().size() != 1 || consumers.size() != 1) {
NGRAPH_DEBUG << "Adoptation for node " << shape.get_node()->get_friendly_name() << " is not supported."; OPENVINO_DEBUG << "Adoptation for node " << shape.get_node()->get_friendly_name() << " is not supported.";
return false; return false;
} }
@ -88,8 +88,8 @@ static bool maybe_adopt_reshape_node(std::shared_ptr<ov::Node> reshape, ngraph::
consumers.begin()->replace_source_output(sub); consumers.begin()->replace_source_output(sub);
copy_runtime_info(shape.get_node_shared_ptr(), {sub_const, sub}); copy_runtime_info(shape.get_node_shared_ptr(), {sub_const, sub});
NGRAPH_DEBUG << "Adopting values in (" << shape.get_node()->get_friendly_name() << ")" OPENVINO_DEBUG << "Adopting values in (" << shape.get_node()->get_friendly_name() << ")"
<< " by substracting " << vec_to_str(sub_const_vector); << " by substracting " << vec_to_str(sub_const_vector);
return true; return true;
} }
@ -200,7 +200,7 @@ bool ngraph::pass::ShrinkWeights::run_on_model(const std::shared_ptr<ngraph::Fun
#ifdef ENABLE_OPENVINO_DEBUG #ifdef ENABLE_OPENVINO_DEBUG
auto init_mask = getInitMask(node->output(0)); auto init_mask = getInitMask(node->output(0));
if (!mask && init_mask) if (!mask && init_mask)
NGRAPH_DEBUG << "Mask was ruined for node:" << node->get_friendly_name() << "\nInit mask: " << *init_mask; OPENVINO_DEBUG << "Mask was ruined for node:" << node->get_friendly_name() << "\nInit mask: " << *init_mask;
#endif #endif
if (is_static_reshape_op(node) && not_empty_mask(mask) && if (is_static_reshape_op(node) && not_empty_mask(mask) &&
!ov::op::util::is_constant(node->get_input_node_ptr(1))) !ov::op::util::is_constant(node->get_input_node_ptr(1)))
@ -239,8 +239,8 @@ bool ngraph::pass::ShrinkWeights::run_on_model(const std::shared_ptr<ngraph::Fun
dim_current_set.end(), dim_current_set.end(),
dim_init_set.begin(), dim_init_set.begin(),
dim_init_set.end())) { dim_init_set.end())) {
NGRAPH_DEBUG << "Mask was ruined for node:" << const_node->get_friendly_name() OPENVINO_DEBUG << "Mask was ruined for node:" << const_node->get_friendly_name()
<< "\nInit mask: " << *init_mask << "\nCurrent mask: " << *mask; << "\nInit mask: " << *init_mask << "\nCurrent mask: " << *mask;
break; break;
} }
} }
@ -262,8 +262,8 @@ bool ngraph::pass::ShrinkWeights::run_on_model(const std::shared_ptr<ngraph::Fun
ngraph::copy_runtime_info(const_node, new_const); ngraph::copy_runtime_info(const_node, new_const);
ngraph::replace_node(const_node, new_const); ngraph::replace_node(const_node, new_const);
NGRAPH_DEBUG << "Adjust value in (" << const_node->get_friendly_name() << "): " << vec_to_str(value) OPENVINO_DEBUG << "Adjust value in (" << const_node->get_friendly_name() << "): " << vec_to_str(value)
<< " to " << vec_to_str(new_const_value); << " to " << vec_to_str(new_const_value);
continue; continue;
} }
auto last_output = const_node->output(0); auto last_output = const_node->output(0);
@ -282,8 +282,8 @@ bool ngraph::pass::ShrinkWeights::run_on_model(const std::shared_ptr<ngraph::Fun
auto new_const = opset6::Constant::create(const_node->get_element_type(), Shape{res.size()}, res); auto new_const = opset6::Constant::create(const_node->get_element_type(), Shape{res.size()}, res);
replace_node(const_node, new_const); replace_node(const_node, new_const);
copy_runtime_info(const_node, new_const); copy_runtime_info(const_node, new_const);
NGRAPH_DEBUG << "Transform shape like (" << last_output.get_node()->get_friendly_name() OPENVINO_DEBUG << "Transform shape like (" << last_output.get_node()->get_friendly_name()
<< "): " << const_node->get_shape_val() << " to " << new_const->get_shape_val() << std::endl; << "): " << const_node->get_shape_val() << " to " << new_const->get_shape_val() << std::endl;
new_const->set_friendly_name(const_node->get_friendly_name()); new_const->set_friendly_name(const_node->get_friendly_name());
} else { } else {
for (size_t dim = 0; dim < mask->size(); ++dim) { for (size_t dim = 0; dim < mask->size(); ++dim) {
@ -308,13 +308,13 @@ bool ngraph::pass::ShrinkWeights::run_on_model(const std::shared_ptr<ngraph::Fun
last_output, last_output,
opset6::Constant::create(element::i64, Shape{dims_to_keep.size()}, dims_to_keep), opset6::Constant::create(element::i64, Shape{dims_to_keep.size()}, dims_to_keep),
opset6::Constant::create(element::i64, Shape{}, {dim})); opset6::Constant::create(element::i64, Shape{}, {dim}));
NGRAPH_DEBUG << "Transform(" << prev_name << "): " << prev_shape << " to " OPENVINO_DEBUG << "Transform(" << prev_name << "): " << prev_shape << " to "
<< last_output.get_partial_shape(); << last_output.get_partial_shape();
if (prev_shape.is_static() && last_output.get_partial_shape().is_static()) { if (prev_shape.is_static() && last_output.get_partial_shape().is_static()) {
reduced_weights_count += shape_size(prev_shape.get_shape()) - shape_size(last_output.get_shape()); reduced_weights_count += shape_size(prev_shape.get_shape()) - shape_size(last_output.get_shape());
} else { } else {
NGRAPH_DEBUG << "[ WARNING ] Can not find the number of reduced elements due to dynamic shapes."; OPENVINO_DEBUG << "[ WARNING ] Can not find the number of reduced elements due to dynamic shapes.";
} }
} }
// Trying to fold sequence of Gather ops to avoid additional constant folding. // Trying to fold sequence of Gather ops to avoid additional constant folding.
@ -331,7 +331,7 @@ bool ngraph::pass::ShrinkWeights::run_on_model(const std::shared_ptr<ngraph::Fun
copy_runtime_info(const_node, last_output.get_node_shared_ptr()); copy_runtime_info(const_node, last_output.get_node_shared_ptr());
} }
} }
NGRAPH_DEBUG << "[ INFO ] TOTAL WEIGHTS: " << total_weights_count << std::endl; OPENVINO_DEBUG << "[ INFO ] TOTAL WEIGHTS: " << total_weights_count << std::endl;
NGRAPH_DEBUG << "[ INFO ] REDUCED WEIGHTS: " << reduced_weights_count << std::endl; OPENVINO_DEBUG << "[ INFO ] REDUCED WEIGHTS: " << reduced_weights_count << std::endl;
return true; return true;
} }

2
src/common/snippets/src/lowered/linear_ir.cpp
View File

@ -91,7 +91,9 @@ LinearIR::container LinearIR::deep_copy_range(LinearIR::container::const_iterato
for (auto it = begin; it != end; it++) for (auto it = begin; it != end; it++)
original_nodes.push_back((*it)->get_node()); original_nodes.push_back((*it)->get_node());
ngraph::NodeMap node_map; ngraph::NodeMap node_map;
OPENVINO_SUPPRESS_DEPRECATED_START
ngraph::clone_nodes(original_nodes, node_map); ngraph::clone_nodes(original_nodes, node_map);
OPENVINO_SUPPRESS_DEPRECATED_END
for (auto it = begin; it != end; it++) { for (auto it = begin; it != end; it++) {
// copy by value, so result shared_pointer point to new objects // copy by value, so result shared_pointer point to new objects
Expression new_expr = **it; Expression new_expr = **it;

5
src/common/transformations/src/transformations/common_optimizations/nop_elimination.cpp
View File

@ -19,6 +19,7 @@
#include "compare.hpp" #include "compare.hpp"
#include "itt.hpp" #include "itt.hpp"
#include "openvino/util/log.hpp"
using namespace std; using namespace std;
using namespace ov; using namespace ov;
@ -38,7 +39,7 @@ static bool simplify_gather(shared_ptr<Node> node) {
auto axis = gather->get_axis(); auto axis = gather->get_axis();
if (axis == opset3::Gather::AXIS_NOT_SET_VALUE) { if (axis == opset3::Gather::AXIS_NOT_SET_VALUE) {
NGRAPH_DEBUG << "axis value not set"; OPENVINO_DEBUG << "axis value not set";
return false; return false;
} }
@ -111,7 +112,7 @@ static bool eliminate_reshape_v1(const shared_ptr<Node>& node) {
// check if reshape is not identity op // check if reshape is not identity op
if (input.get_partial_shape().is_dynamic() || node->get_output_partial_shape(0).is_dynamic()) { if (input.get_partial_shape().is_dynamic() || node->get_output_partial_shape(0).is_dynamic()) {
NGRAPH_DEBUG << node << " has dynamic shapes."; OPENVINO_DEBUG << node << " has dynamic shapes.";
return false; return false;
} }
// remove identity op // remove identity op

1
src/common/transformations/src/transformations/common_optimizations/optimize_strided_slice.cpp
View File

@ -43,6 +43,7 @@ bool ov::pass::UselessStridedSliceEraser::run_on_model(const std::shared_ptr<ngr
return rewritten; return rewritten;
} }
OPENVINO_SUPPRESS_DEPRECATED_START
namespace { namespace {
ngraph::SlicePlan get_slice_plan(std::shared_ptr<opset1::StridedSlice> slice) { ngraph::SlicePlan get_slice_plan(std::shared_ptr<opset1::StridedSlice> slice) {

9
src/common/transformations/src/transformations/common_optimizations/ric_fusion.cpp
View File

@ -18,6 +18,7 @@
#include <vector> #include <vector>
#include "itt.hpp" #include "itt.hpp"
#include "openvino/util/log.hpp"
#include "transformations/utils/utils.hpp" #include "transformations/utils/utils.hpp"
namespace ov { namespace ov {
@ -69,18 +70,18 @@ public:
const auto& input_pshape = input.get_partial_shape(); const auto& input_pshape = input.get_partial_shape();
const auto input_rank = input_pshape.rank(); const auto input_rank = input_pshape.rank();
if (input_rank.is_dynamic()) { if (input_rank.is_dynamic()) {
NGRAPH_DEBUG << "Axis calculated to materialize RIC on input: input rank is dynamic"; OPENVINO_DEBUG << "Axis calculated to materialize RIC on input: input rank is dynamic";
return; return;
} }
const auto axis = get_axis(); const auto axis = get_axis();
// Despite of m_axis is signed integer this transformartion does not handle negative axes values // Despite of m_axis is signed integer this transformartion does not handle negative axes values
if (axis < 0 || axis >= static_cast<int64_t>(input_pshape.size())) { if (axis < 0 || axis >= static_cast<int64_t>(input_pshape.size())) {
NGRAPH_DEBUG << "Axis calculated to materialize RIC on input: " << input << " is out of range"; OPENVINO_DEBUG << "Axis calculated to materialize RIC on input: " << input << " is out of range";
return; return;
} }
const auto& axis_dim = input_pshape[axis]; const auto& axis_dim = input_pshape[axis];
if (axis_dim.is_dynamic()) { if (axis_dim.is_dynamic()) {
NGRAPH_DEBUG << "Axis calculated to materialize RIC on input: " << input << " is dynamic"; OPENVINO_DEBUG << "Axis calculated to materialize RIC on input: " << input << " is dynamic";
return; return;
} }
auto output = input.get_source_output(); auto output = input.get_source_output();
@ -572,7 +573,7 @@ public:
continue; continue;
} }
ric.set_can_be_fused(false); ric.set_can_be_fused(false);
NGRAPH_DEBUG << "Node is unsupported by RIC Fusion: " << *m.get_match_root() << std::endl; OPENVINO_DEBUG << "Node is unsupported by RIC Fusion: " << *m.get_match_root() << std::endl;
} }
} }
return true; return true;

2
src/common/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
View File

@ -50,7 +50,9 @@ std::shared_ptr<opset6::Constant> get_reduced_order_constant(const std::shared_p
std::shared_ptr<opset6::Constant> get_reversed_order_constant(const std::shared_ptr<opset6::Constant>& order_const) { std::shared_ptr<opset6::Constant> get_reversed_order_constant(const std::shared_ptr<opset6::Constant>& order_const) {
const auto& order = order_const->cast_vector<size_t>(); const auto& order = order_const->cast_vector<size_t>();
const auto& rank = order.size(); const auto& rank = order.size();
OPENVINO_SUPPRESS_DEPRECATED_START
const auto& default_order = ngraph::get_default_order(rank); const auto& default_order = ngraph::get_default_order(rank);
OPENVINO_SUPPRESS_DEPRECATED_END
std::vector<size_t> reverse_order(rank); std::vector<size_t> reverse_order(rank);
for (size_t i = 0; i < rank; ++i) for (size_t i = 0; i < rank; ++i)
reverse_order[order[i]] = default_order[i]; reverse_order[order[i]] = default_order[i];

2
src/common/transformations/src/transformations/control_flow/unroll_tensor_iterator.cpp
View File

@ -35,7 +35,7 @@ bool ov::pass::UnrollTensorIterator::run_on_model(const std::shared_ptr<ngraph::
// Assign names to the created layers. // Assign names to the created layers.
std::vector<std::shared_ptr<ngraph::Function>> body_functions(num_iter); std::vector<std::shared_ptr<ngraph::Function>> body_functions(num_iter);
for (int64_t idx = 0; idx < num_iter; ++idx) { for (int64_t idx = 0; idx < num_iter; ++idx) {
body_functions[idx] = ngraph::clone_function(*function); body_functions[idx] = function->clone();
for (auto& node : body_functions[idx]->get_ops()) { for (auto& node : body_functions[idx]->get_ops()) {
node->set_friendly_name(sub_graph_op->get_friendly_name() + "/" + std::to_string(idx + 1) + "/" + node->set_friendly_name(sub_graph_op->get_friendly_name() + "/" + std::to_string(idx + 1) + "/" +
node->get_friendly_name()); node->get_friendly_name());

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