[Torch] NNCFNetwork.transformation_layout (#2595)

On top of #2584 

### Changes

`NNCFNetwork.transformation_layout` method is introduced

### Reason for changes

To make it possible to collect applied transformation commands from a
modified `NNCFNetwork`

### Related tickets

129586

### Tests

TestGetAppliedModificationCommands

nncf/torch/graph/graph.py

@@ -53,14 +53,35 @@ def get_input_shape_for_insertion_point(self, insertion_point: PTTargetPoint) ->
         return quantizer_input_shape
 
     def get_op_nodes_in_scope(self, scope: Scope) -> List[NNCFNode]:
+        """
+        Returns all NNCFNodes inside the given scope.
+
+        :param scope: Given scope.
+        :return: All NNCFNodes inside the given scope.
+        """
         matching_graph_op_nodes = []
         for scope_str, nodes_in_module in self._layer_name_vs_shared_nodes.items():
             module_scope = Scope.from_str(scope_str)
             if module_scope in scope:
                 matching_graph_op_nodes.extend(nodes_in_module)
         return matching_graph_op_nodes
 
+    def get_op_nodes_with_scope(self, scope: Scope) -> List[NNCFNode]:
+        """
+        Returns all NNCFNodes which share the given scope.
+
+        :param scope: Given scope.
+        :return: All NNCFNodes which share the given scope.
+        """
+        return self._layer_name_vs_shared_nodes[str(scope)]
+
     def get_scope_by_node_name(self, node_name: NNCFNodeName) -> Scope:
+        """
+        Returns a scope which corresponds to the given NNCF node name.
+
+        :param node_name: Given node name.
+        :return: A scope which corresponds to the given NNCF node name.
+        """
         matches = []
         for node_id, scope_str in self._node_ids_vs_layer_names.items():
             node = self.get_node_by_id(node_id)

nncf/torch/graph/transformations/command_creation.py

@@ -9,8 +9,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List, Union
+from typing import List, Optional, Union
 
+import torch
 from torch import Tensor
 
 from nncf.common.graph.graph import NNCFNode
@@ -79,3 +80,26 @@ def create_shared_quantizer_insertion_command(
         compression_module_type=ExtraCompressionModuleType.EXTERNAL_QUANTIZER,
         priority=TransformationPriority.QUANTIZATION_PRIORITY,
     )
+
+
+def create_pt_insertion_command(
+    module: torch.nn.Module,
+    target_type: TargetType,
+    target_node_name: str,
+    priority: int,
+    input_port_id: Optional[int],
+) -> PTInsertionCommand:
+    """
+    Creates a PTInsertionCommand.
+
+    :param module: Torch module to insert.
+    :param target_type: Insertion command target type.
+    :param target_name: Insertion command target name.
+    :param priority: Insertion command priority.
+    :param input_port_id: Insertion command input port id.
+    :return: A PTInsertionCommand
+    """
+    target_point = PTTargetPoint(
+        target_type=target_type, target_node_name=target_node_name, input_port_id=input_port_id
+    )
+    return PTInsertionCommand(point=target_point, fn=module, priority=priority)

nncf/torch/graph/transformations/commands.py

@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from enum import Enum
-from typing import Any, Callable, Dict, List
+from typing import Any, Callable, Dict, List, Union
 
 import torch
 
@@ -139,7 +139,7 @@ def __init__(
         self,
         point: PTTargetPoint,
         fn: Callable,
-        priority: TransformationPriority = TransformationPriority.DEFAULT_PRIORITY,
+        priority: Union[TransformationPriority, int] = TransformationPriority.DEFAULT_PRIORITY,
         hooks_group_name: str = DEFAULT_HOOKS_GROUP_NAME,
     ):
         super().__init__(TransformationType.INSERT, point)
@@ -164,7 +164,7 @@ def __init__(
         fn: Callable,
         op_unique_name: str,
         compression_module_type: ExtraCompressionModuleType = ExtraCompressionModuleType.EXTERNAL_OP,
-        priority: TransformationPriority = TransformationPriority.DEFAULT_PRIORITY,
+        priority: Union[TransformationPriority, int] = TransformationPriority.DEFAULT_PRIORITY,
         hooks_group_name: str = DEFAULT_HOOKS_GROUP_NAME,
     ):
         super().__init__(TransformationType.INSERT, None)

nncf/torch/nncf_network.py

@@ -41,6 +41,7 @@
 from nncf.common.utils.debug import is_debug
 from nncf.torch.debug import CombinedDebugInterface
 from nncf.torch.debug import debuggable_forward
+from nncf.torch.dynamic_graph.context import PreHookId
 from nncf.torch.dynamic_graph.context import TracingContext
 from nncf.torch.dynamic_graph.graph import DynamicGraph
 from nncf.torch.dynamic_graph.graph import ShapeIgnoringTensorMetaComparator
@@ -60,16 +61,21 @@
 from nncf.torch.dynamic_graph.wrappers import wrap_module_call
 from nncf.torch.dynamic_graph.wrappers import wrap_parameters
 from nncf.torch.external_hook import EXTERNAL_OP_STORAGE_NAME
+from nncf.torch.external_hook import ExternalOpCallHook
 from nncf.torch.graph.graph import PTNNCFGraph
 from nncf.torch.graph.graph_builder import GraphBuilder
 from nncf.torch.graph.graph_builder import GraphConverter
 from nncf.torch.graph.operator_metatypes import OPERATORS_WITH_WEIGHTS_METATYPES
 from nncf.torch.graph.operator_metatypes import PTSplitMetatype
+from nncf.torch.graph.transformations.command_creation import create_pt_insertion_command
 from nncf.torch.graph.transformations.commands import DEFAULT_HOOKS_GROUP_NAME
 from nncf.torch.graph.transformations.commands import ExtraCompressionModuleType
+from nncf.torch.graph.transformations.commands import PTSharedFnInsertionCommand
 from nncf.torch.graph.transformations.commands import PTTargetPoint
+from nncf.torch.graph.transformations.layout import PTTransformationLayout
 from nncf.torch.knowledge_distillation.knowledge_distillation_handler import KnowledgeDistillationLossHandler
 from nncf.torch.layer_utils import _NNCFModuleMixin
+from nncf.torch.module_operations import UpdateWeight
 from nncf.torch.nncf_module_replacement import replace_modules_by_nncf_modules
 from nncf.torch.quantization.external_quantizer import EXTERNAL_QUANTIZERS_STORAGE_NAME
 from nncf.torch.utils import compute_FLOPs_hook
@@ -778,6 +784,127 @@ def _collect_eval_op_scopes(self, model: nn.Module, dummy_forward_fn: Callable)
                 result.append(scope_in_model)
         return result
 
+    def transformation_layout(self) -> PTTransformationLayout:
+        """
+        Collects all hooks applied to the NNCFNetwork, converts them to insertion commands
+        and returns in PTTransformationLayout format. Default hooks group name is used in
+        recovered commands, so hooks group names specified during the model modification
+        become outdated.
+
+        :return: Transformation layout with all commands applied to the NNCFNetwork.
+        """
+
+        def _check_external_call_hook_is_valid(hook: ExternalOpCallHook, info: str):
+            """
+            Check given external op call hook reference is correct.
+
+            :param hook: External op call hook to check correctness.
+            :param info: Info to log in case op call hook references are broken.
+            """
+            assert hasattr(
+                self, hook._storage_name
+            ), f"Storage name {hook._storage_name} is not registered. Info: {info}"
+            assert hook._storage_key in getattr(
+                self, hook._storage_name
+            ), f"Key {hook._storage_key} is not registered in {hook._storage_name}. Info: {info}"
+
+        context_hooks = defaultdict(lambda: defaultdict(list))
+        transformation_layout = PTTransformationLayout()
+        nncf_graph = self.get_graph()
+        nncf_node_names_map = self.get_op_address_to_op_name_map()
+
+        # Collect pre/post layer and op with weights insertion commands
+        for nncf_module, module_scope in self.get_nncf_modules().items():
+            for ops, target_type in (
+                (nncf_module.pre_ops, TargetType.PRE_LAYER_OPERATION),
+                (nncf_module.post_ops, TargetType.POST_LAYER_OPERATION),
+            ):
+                for priority, module in enumerate(ops.values()):
+                    nodes_in_scope = nncf_graph.get_op_nodes_with_scope(module_scope)
+                    # Several NNCFNodes means that current NNCFModule was called
+                    # several times. Only one insertion command is required to
+                    # call hook as much times as the current NNCFModule, therefore
+                    # we use first correspondent NNCFNode.
+                    nncf_node = nodes_in_scope[0]
+                    command_target_type = target_type
+                    if isinstance(module, UpdateWeight):
+                        command_target_type = TargetType.OPERATION_WITH_WEIGHTS
+                        module = module.op
+                    if not isinstance(module, ExternalOpCallHook):
+                        command = create_pt_insertion_command(
+                            module, command_target_type, nncf_node.node_name, priority, None
+                        )
+                        transformation_layout.register(command)
+                        continue
+
+                    info = (
+                        f"TargetType: {command_target_type}, nncf node name: {nncf_node.node_name},"
+                        f" priority: {priority}, fn: {module}"
+                    )
+                    _check_external_call_hook_is_valid(module, info)
+
+                    context_hooks[module._storage_name][module._storage_key].append(
+                        (command_target_type, nncf_node.node_name, priority, module, None)
+                    )
+
+        # Collect all pre/post hooks commands
+        for ops, target_type in (
+            (self._compressed_context._pre_hooks, TargetType.OPERATOR_PRE_HOOK),
+            (self._compressed_context._post_hooks, TargetType.OPERATOR_POST_HOOK),
+        ):
+            for op_address, hooks in ops.items():
+                if isinstance(op_address, PreHookId):
+                    input_port_id = op_address.input_port_id
+                    op_address = op_address.op_address
+                else:
+                    input_port_id = None
+                for priority, fn in enumerate(hooks.values()):
+                    target_node_names = nncf_node_names_map[op_address]
+                    # Operation address is unique for each module call
+                    assert len(target_node_names) == 1
+                    target_node_name = target_node_names[0]
+
+                    if not isinstance(fn, ExternalOpCallHook):
+                        command = create_pt_insertion_command(
+                            fn, target_type, target_node_name, priority, input_port_id
+                        )
+                        transformation_layout.register(command)
+                        continue
+
+                    info = f"TargetType: {target_type}, op_address: {op_address}, priority: {priority}, fn: {fn}"
+                    _check_external_call_hook_is_valid(fn, info)
+
+                    context_hooks[fn._storage_name][fn._storage_key].append(
+                        (target_type, target_node_name, priority, fn, input_port_id)
+                    )
+
+        # Create shared fn insertion commands according to external hooks collected from
+        # pre/post layer, pre/post hooks and op with weights target points.
+        for module_type_name, storage in context_hooks.items():
+            for storage_key, call_hook_list_info in storage.items():
+                compression_module = getattr(self, module_type_name)[storage_key]
+                target_points = []
+                for target_type, target_node_name, priority, fn, input_port_id in call_hook_list_info:
+                    target_points.append(PTTargetPoint(target_type, target_node_name, input_port_id=input_port_id))
+
+                if module_type_name == EXTERNAL_QUANTIZERS_STORAGE_NAME:
+                    module_type = ExtraCompressionModuleType.EXTERNAL_QUANTIZER
+                elif module_type_name == EXTERNAL_OP_STORAGE_NAME:
+                    module_type = ExtraCompressionModuleType.EXTERNAL_OP
+                else:
+                    raise RuntimeError(f"Module type {module_type_name} is not supported")
+
+                command = PTSharedFnInsertionCommand(
+                    target_points=target_points,
+                    fn=compression_module,
+                    op_unique_name=storage_key,
+                    compression_module_type=module_type,
+                    priority=priority,
+                )
+                transformation_layout.register(command)
+
+        return transformation_layout
+
     def get_node_to_op_address_mapping(self) -> Dict[NNCFNodeName, OperationAddress]:
         """
         Returns map of NNCFGraph node names vs DynamicGraph operation addresses.
@@ -796,6 +923,17 @@ def get_node_to_op_address_mapping(self) -> Dict[NNCFNodeName, OperationAddress]
             retval[nncf_node.node_name] = op_address
         return retval
 
+    def get_op_address_to_op_name_map(self) -> Dict[OperationAddress, NNCFNodeName]:
+        """
+        Returns map of DynamicGraph operation addresses vs NNCFGraph node names.
+
+        :return: DynamicGraph operation addresses vs NNCFGraph node names.
+        """
+        retval = defaultdict(list)
+        for nncf_node_name, op_address in self.get_node_to_op_address_mapping().items():
+            retval[op_address].append(nncf_node_name)
+        return retval
+
     def set_compression_controller(self, ctrl: CompressionAlgorithmController):
         self.compression_controller = ctrl