[PT] add metatype for torch2

nncf/experimental/torch2/function_hook/nncf_graph/nncf_graph_builder.py

@@ -11,13 +11,14 @@
 
 
 from collections import defaultdict
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union, cast
 
 import networkx as nx  # type: ignore
 import torch
 from torch import nn
 
 import nncf
+import nncf.torch.graph.operator_metatypes as om
 from nncf.common.graph.graph import NNCFGraph
 from nncf.common.graph.graph import NNCFNode
 from nncf.common.graph.layer_attributes import Dtype
@@ -76,6 +77,20 @@ def get_dtype(dtype: torch.dtype) -> Dtype:
     return Dtype.INTEGER
 
 
+def get_meta_type(node_type: str, meta: Union[ConstMeta, FunctionMeta, InOutMeta]) -> om.PTOperatorMetatype:
+    """
+    Converts the node type and metadata into a PTOperatorMetatype object.
+    :param node_type: The type of the node.
+    :param meta: The metadata associated with the node.
+    :return: The PTOperatorMetatype object.
+    """
+    node_metatype = cast(om.PTOperatorMetatype, om.PT_OPERATOR_METATYPES.get_operator_metatype_by_op_name(node_type))
+    node_sub_meta_type: Optional[om.PTOperatorMetatype] = None
+    if node_metatype.get_subtypes() and isinstance(meta, FunctionMeta):
+        node_sub_meta_type = node_metatype.determine_subtype(function_args=meta.args, functions_kwargs=meta.kwargs)
+    return node_sub_meta_type or node_metatype
+
+
 def convert_to_nncf_graph(nx_graph: nx.MultiDiGraph) -> NNCFGraph:
     """
     Converts a graph to an NNCFGraph.
@@ -88,12 +103,14 @@ def convert_to_nncf_graph(nx_graph: nx.MultiDiGraph) -> NNCFGraph:
     map_nx_node_to_nncf_node: Dict[int, NNCFNode] = {}
     for node, data in nx_graph.nodes(data=True):
         meta: Union[ConstMeta, FunctionMeta, InOutMeta] = data["meta"]
+        node_name = get_name_of_node(meta)
         node_type = get_node_type(data["type"], meta)
-        node_metatype = None  # TODO(AlexanderDokuchaev): add node_metatype
+        meta_type = get_meta_type(node_type, meta)
+
         nncf_node = nncf_graph.add_nncf_node(
-            node_name=get_name_of_node(meta),
+            node_name=node_name,
             node_type=node_type,
-            node_metatype=node_metatype,  # type: ignore[arg-type]
+            node_metatype=meta_type,  # type: ignore[arg-type]
         )
         map_nx_node_to_nncf_node[node] = nncf_node
 

nncf/torch/__init__.py

@@ -14,6 +14,7 @@
 Base subpackage for NNCF PyTorch functionality.
 """
 
+import os
 from nncf import nncf_logger
 from nncf.common.logging.logger import warn_bkc_version_mismatch
 
@@ -76,4 +77,5 @@
 if torch.__version__ >= "2.5.0":
     from torch._dynamo.polyfills import loader
 
-patch_torch_operators()
+if os.getenv("NNCF_EXPERIMENTAL_TORCH_TRACING") is None:
+    patch_torch_operators()

nncf/torch/graph/operator_metatypes.py

@@ -148,6 +148,13 @@ class PTDepthwiseConvOperatorSubtype(PTOperatorSubtype):
     def matches(
         cls, layer_attributes: Optional[BaseLayerAttributes] = None, function_args=None, functions_kwargs=None
     ) -> bool:
+        if layer_attributes is None and function_args is not None and functions_kwargs is not None:
+            # Used for torch2
+            weight_meta = functions_kwargs.get("weight", function_args[0])
+            in_channels = weight_meta.shape[1]
+            groups = functions_kwargs.get("groups", function_args[6] if len(function_args) > 6 else 1)
+            return in_channels > 1 and groups == in_channels
+
         if _is_called_inside_nncf_module(functions_kwargs):
             return False
         if not isinstance(layer_attributes, ConvolutionLayerAttributes):

tests/torch2/conftest.py

@@ -54,6 +54,9 @@ def pytest_configure(config: Config) -> None:
     if nncf_debug:
         set_log_level(logging.DEBUG)
 
+    # Disable patching of torch functions
+    os.environ["NNCF_EXPERIMENTAL_TORCH_TRACING"] = "1"
+
 
 @pytest.fixture
 def regen_ref_data(request: FixtureRequest):

tests/torch2/data/function_hook/nncf_graph/convert_to_nncf_graph.dot

@@ -1,17 +1,17 @@
 strict digraph  {
-"0 x" [id=0, type=nncf_model_input];
-"1 conv.weight" [id=1, type=nncf_model_const];
-"2 conv.bias" [id=2, type=nncf_model_const];
-"3 conv/conv2d/0" [id=3, type=conv2d];
-"4 __nncf_hooks.post_hooks.conv/conv2d/0__0.0.w" [id=4, type=nncf_model_const];
-"5 conv/post_hook__conv-conv2d-0__0[0]/add/0" [id=5, type=add];
-"6 /relu/0" [id=6, type=relu];
-"7 output" [id=7, type=nncf_model_output];
-"0 x" -> "3 conv/conv2d/0"  [label="(1, 1, 3, 3)", style=solid];
-"1 conv.weight" -> "3 conv/conv2d/0"  [label="(1, 1, 1, 1)", style=solid];
-"2 conv.bias" -> "3 conv/conv2d/0"  [label="(1,)", style=solid];
-"3 conv/conv2d/0" -> "5 conv/post_hook__conv-conv2d-0__0[0]/add/0"  [label="(1, 1, 3, 3)", style=solid];
-"4 __nncf_hooks.post_hooks.conv/conv2d/0__0.0.w" -> "5 conv/post_hook__conv-conv2d-0__0[0]/add/0"  [label="(1,)", style=solid];
-"5 conv/post_hook__conv-conv2d-0__0[0]/add/0" -> "6 /relu/0"  [label="(1, 1, 3, 3)", style=solid];
-"6 /relu/0" -> "7 output"  [label="(1, 1, 3, 3)", style=solid];
+x [id=0, metatype=PTInputNoopMetatype, type=nncf_model_input];
+"conv.weight" [id=1, metatype=PTConstNoopMetatype, type=nncf_model_const];
+"conv.bias" [id=2, metatype=PTConstNoopMetatype, type=nncf_model_const];
+"conv/conv2d/0" [id=3, metatype=PTConv2dMetatype, type=conv2d];
+"__nncf_hooks.post_hooks.conv/conv2d/0__0.0.w" [id=4, metatype=PTConstNoopMetatype, type=nncf_model_const];
+"conv/post_hook__conv-conv2d-0__0[0]/add/0" [id=5, metatype=PTAddMetatype, type=add];
+"/relu/0" [id=6, metatype=PTRELUMetatype, type=relu];
+output [id=7, metatype=PTOutputNoopMetatype, type=nncf_model_output];
+x -> "conv/conv2d/0"  [dtype=float, shape="(1, 1, 3, 3)"];
+"conv.weight" -> "conv/conv2d/0"  [dtype=float, shape="(1, 1, 1, 1)"];
+"conv.bias" -> "conv/conv2d/0"  [dtype=float, shape="(1,)"];
+"conv/conv2d/0" -> "conv/post_hook__conv-conv2d-0__0[0]/add/0"  [dtype=float, shape="(1, 1, 3, 3)"];
+"__nncf_hooks.post_hooks.conv/conv2d/0__0.0.w" -> "conv/post_hook__conv-conv2d-0__0[0]/add/0"  [dtype=float, shape="(1,)"];
+"conv/post_hook__conv-conv2d-0__0[0]/add/0" -> "/relu/0"  [dtype=float, shape="(1, 1, 3, 3)"];
+"/relu/0" -> output  [dtype=float, shape="(1, 1, 3, 3)"];
 }

tests/torch2/data/function_hook/nncf_graph/convert_to_nncf_graph_multi_edges.dot

@@ -1,7 +1,7 @@
 strict digraph  {
-"0 x" [id=0, type=nncf_model_input];
-"1 /add/0" [id=1, type=add];
-"2 output" [id=2, type=nncf_model_output];
-"0 x" -> "1 /add/0"  [label="parallel_input_port_ids [1], shape (1, 1)", style=solid];
-"1 /add/0" -> "2 output"  [label="(1, 1)", style=solid];
+x [id=0, metatype=PTInputNoopMetatype, type=nncf_model_input];
+"/add/0" [id=1, metatype=PTAddMetatype, type=add];
+output [id=2, metatype=PTOutputNoopMetatype, type=nncf_model_output];
+x -> "/add/0"  [dtype=float, parallel_input_port_ids="[1]", shape="(1, 1)"];
+"/add/0" -> output  [dtype=float, shape="(1, 1)"];
 }