Add transformation pipline for POT (#5328)

inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api.pyx

@@ -5,10 +5,14 @@ from .cimport offline_transformations_api_impl_defs as C
 from ..inference_engine.ie_api cimport IENetwork
 
 from libcpp cimport bool
+from libcpp.string cimport string
 
 def ApplyMOCTransformations(IENetwork network, bool cf):
     C.ApplyMOCTransformations(network.impl, cf)
 
+def ApplyPOTTransformations(IENetwork network, string device):
+    C.ApplyPOTTransformations(network.impl, device)
+
 def ApplyLowLatencyTransformation(IENetwork network):
     C.ApplyLowLatencyTransformation(network.impl)
 

inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.cpp

@@ -5,6 +5,7 @@
 #include "offline_transformations_api_impl.hpp"
 
 #include <moc_transformations.hpp>
+#include <pot_transformations.hpp>
 #include <pruning.hpp>
 
 #include <transformations/control_flow/unroll_tensor_iterator.hpp>
@@ -21,6 +22,12 @@ void InferenceEnginePython::ApplyMOCTransformations(InferenceEnginePython::IENet
     manager.run_passes(network.actual->getFunction());
 }
 
+void InferenceEnginePython::ApplyPOTTransformations(InferenceEnginePython::IENetwork network, std::string device) {
+    ngraph::pass::Manager manager;
+    manager.register_pass<ngraph::pass::POTTransformations>(std::move(device));
+    manager.run_passes(network.actual->getFunction());
+}
+
 void InferenceEnginePython::ApplyLowLatencyTransformation(InferenceEnginePython::IENetwork network) {
     ngraph::pass::Manager manager;
     manager.register_pass<ngraph::pass::LowLatency>();

inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.hpp

@@ -4,6 +4,8 @@
 
 #pragma once
 
+#include <string>
+
 #include "Python.h"
 #include "ie_api_impl.hpp"
 
@@ -11,6 +13,8 @@ namespace InferenceEnginePython {
 
 void ApplyMOCTransformations(InferenceEnginePython::IENetwork network, bool cf);
 
+void ApplyPOTTransformations(InferenceEnginePython::IENetwork network, std::string device);
+
 void ApplyLowLatencyTransformation(InferenceEnginePython::IENetwork network);
 
 void ApplyPruningTransformation(InferenceEnginePython::IENetwork network);

inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl_defs.pxd

@@ -2,11 +2,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from libcpp cimport bool
+from libcpp.string cimport string
+
 from ..inference_engine.ie_api_impl_defs cimport IENetwork
 
 cdef extern from "offline_transformations_api_impl.hpp" namespace "InferenceEnginePython":
     cdef void ApplyMOCTransformations(IENetwork network, bool cf)
 
+    cdef void ApplyPOTTransformations(IENetwork network, string device)
+
     cdef void ApplyLowLatencyTransformation(IENetwork network)
 
     cdef void ApplyPruningTransformation(IENetwork network)

inference-engine/src/offline_transformations/include/pot_transformations.hpp

@@ -0,0 +1,33 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <string>
+
+#include <ngraph/pass/graph_rewrite.hpp>
+
+namespace ngraph {
+namespace pass {
+
+class POTTransformations;
+
+}  // namespace pass
+}  // namespace ngraph
+
+/**
+ * @brief This transformation is an entry point for nGraph transformations that will be
+ * executed inside POT.
+ */
+
+class ngraph::pass::POTTransformations: public ngraph::pass::FunctionPass {
+    std::string m_device;
+
+public:
+    NGRAPH_RTTI_DECLARATION;
+    explicit POTTransformations(std::string device) : m_device(std::move(device)) {}
+
+    bool run_on_function(std::shared_ptr<ngraph::Function>) override;
+};

inference-engine/src/offline_transformations/src/pot_transformations.cpp

@@ -0,0 +1,25 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <memory>
+
+#include <ngraph/pass/manager.hpp>
+
+#include <transformations/op_conversions/lstm_cell_decomposition.hpp>
+
+#include "pot_transformations.hpp"
+
+NGRAPH_RTTI_DEFINITION(ngraph::pass::POTTransformations, "POTTransformations", 0);
+
+bool ngraph::pass::POTTransformations::run_on_function(std::shared_ptr<ngraph::Function> f) {
+    ngraph::pass::Manager manager(get_pass_config());
+    if (m_device == "CPU") {
+        // TODO: register CPU passes
+        // manager.register_pass<ngraph::pass::LSTMCellDecomposition>();
+    } else {
+        throw ngraph_error("Device name is unsupported");
+    }
+    manager.run_passes(f);
+    return false;
+}