[PTQ][Torch][KQV self attention] Align FQ placement between OV and Torch backend

nncf/common/graph/graph.py

@@ -597,12 +597,22 @@ def get_graph_for_structure_analysis(self, extended: bool = False) -> nx.DiGraph
             attrs_edge = {}
             u = u.replace(__RESERVED_DOT_CHARACTER, __CHARACTER_REPLACE_TO)
             v = v.replace(__RESERVED_DOT_CHARACTER, __CHARACTER_REPLACE_TO)
+            label = {}
+            if edge[NNCFGraph.PARALLEL_INPUT_PORT_IDS_ATTR]:
+                label["parallel_input_port_ids"] = edge[NNCFGraph.PARALLEL_INPUT_PORT_IDS_ATTR]
+
             if extended:
                 if edge[NNCFGraph.DTYPE_EDGE_ATTR] is Dtype.INTEGER:
                     attrs_edge["style"] = "dashed"
                 else:
                     attrs_edge["style"] = "solid"
-                attrs_edge["label"] = edge[NNCFGraph.ACTIVATION_SHAPE_EDGE_ATTR]
+                label["shape"] = edge[NNCFGraph.ACTIVATION_SHAPE_EDGE_ATTR]
+
+            if label:
+                if "shape" in label and len(label) == 1:
+                    attrs_edge["label"] = label["shape"]
+                else:
+                    attrs_edge["label"] = ", ".join((f"{k}:{v}" for k, v in label.items()))
             out_graph.add_edge(u, v, **attrs_edge)
         return out_graph
 

nncf/quantization/algorithms/min_max/algorithm.py

@@ -505,7 +505,10 @@ def _get_quantization_target_points(
         hw_patterns = PatternsManager.get_full_hw_pattern_graph(backend=backend, device=device, model_type=model_type)
 
         inference_nncf_graph = transform_to_inference_graph(
-            deepcopy(nncf_graph), self._backend_entity.shapeof_metatypes, self._backend_entity.read_variable_metatypes
+            deepcopy(nncf_graph),
+            self._backend_entity.shapeof_metatypes,
+            self._backend_entity.dropout_metatypes,
+            self._backend_entity.read_variable_metatypes,
         )
 
         quantizer_setup = self._get_quantizer_setup(nncf_graph, inference_nncf_graph, hw_patterns, ignored_patterns)

nncf/quantization/algorithms/min_max/backend.py

@@ -51,23 +51,23 @@ def post_processing_metatypes(self) -> List[OperatorMetatype]:
 
     @property
     @abstractmethod
-    def shapeof_metatypes(self) -> List[OperatorMetatype]:
+    def conv_metatypes(self) -> List[OperatorMetatype]:
         """
-        Property for the backend-specific ShapeOf metatypes.
+        Property for the backend-specific Convolution metatypes.
         """
 
     @property
     @abstractmethod
-    def conv_metatypes(self) -> List[OperatorMetatype]:
+    def shapeof_metatypes(self) -> List[OperatorMetatype]:
         """
-        Property for the backend-specific Convolution metatypes.
+        Property for the backend-specific ShapeOf metatypes.
         """
 
     @property
     @abstractmethod
-    def overflow_fix_metatypes(self) -> List[OperatorMetatype]:
+    def dropout_metatypes(self) -> List[OperatorMetatype]:
         """
-        Property for the backend-specific metatypes for which overflow_fix is applicable.
+        Property for the backend-specific Dropout metatypes.
         """
 
     @property
@@ -77,6 +77,13 @@ def read_variable_metatypes(self) -> List[OperatorMetatype]:
         Property for the backend-specific metatypes that also can be interpreted as inputs (ReadValue).
         """
 
+    @property
+    @abstractmethod
+    def overflow_fix_metatypes(self) -> List[OperatorMetatype]:
+        """
+        Property for the backend-specific metatypes for which overflow_fix is applicable.
+        """
+
     @property
     @abstractmethod
     def add_metatypes(self) -> List[OperatorMetatype]:

nncf/quantization/algorithms/min_max/onnx_backend.py

@@ -56,10 +56,6 @@ def mat_mul_metatypes(self) -> List[OperatorMetatype]:
     def post_processing_metatypes(self) -> List[OperatorMetatype]:
         return [om.ONNXTopKMetatype, om.ONNXNonMaxSuppressionMetatype]
 
-    @property
-    def shapeof_metatypes(self) -> List[OperatorMetatype]:
-        return [om.ONNXShapeMetatype]
-
     @property
     def conv_metatypes(self) -> List[OperatorMetatype]:
         return [om.ONNXConvolutionMetatype]
@@ -68,10 +64,6 @@ def conv_metatypes(self) -> List[OperatorMetatype]:
     def overflow_fix_metatypes(self) -> List[OperatorMetatype]:
         return [om.ONNXConvolutionMetatype, om.ONNXConvolutionTransposeMetatype, *MATMUL_METATYPES]
 
-    @property
-    def read_variable_metatypes(self) -> List[OperatorMetatype]:
-        return []
-
     @property
     def add_metatypes(self) -> List[OperatorMetatype]:
         return [om.ONNXAddLayerMetatype]
@@ -80,6 +72,18 @@ def add_metatypes(self) -> List[OperatorMetatype]:
     def group_conv_metatypes(self) -> List[OperatorMetatype]:
         return self.conv_metatypes
 
+    @property
+    def shapeof_metatypes(self) -> List[OperatorMetatype]:
+        return [om.ONNXShapeMetatype]
+
+    @property
+    def dropout_metatypes(self) -> List[OperatorMetatype]:
+        return []
+
+    @property
+    def read_variable_metatypes(self) -> List[OperatorMetatype]:
+        return []
+
     @property
     def scales_unification_map(self) -> Dict[OperatorMetatype, OperatorMetatype]:
         return {om.ONNXConcatMetatype: self.overflow_fix_metatypes}

nncf/quantization/algorithms/min_max/openvino_backend.py

@@ -56,10 +56,6 @@ def mat_mul_metatypes(self) -> List[OperatorMetatype]:
     def post_processing_metatypes(self) -> List[OperatorMetatype]:
         return [om.OVTopKMetatype, om.OVNonMaxSuppressionMetatype]
 
-    @property
-    def shapeof_metatypes(self) -> List[OperatorMetatype]:
-        return [om.OVShapeOfMetatype]
-
     @property
     def conv_metatypes(self) -> List[OperatorMetatype]:
         return [om.OVConvolutionMetatype]
@@ -74,10 +70,6 @@ def overflow_fix_metatypes(self) -> List[OperatorMetatype]:
             om.OVMatMulMetatype,
         ]
 
-    @property
-    def read_variable_metatypes(self) -> List[OperatorMetatype]:
-        return [om.OVReadValueMetatype]
-
     @property
     def add_metatypes(self) -> List[OperatorMetatype]:
         return [om.OVAddMetatype]
@@ -86,6 +78,18 @@ def add_metatypes(self) -> List[OperatorMetatype]:
     def group_conv_metatypes(self) -> List[OperatorMetatype]:
         return [om.OVGroupConvolutionMetatype]
 
+    @property
+    def shapeof_metatypes(self) -> List[OperatorMetatype]:
+        return [om.OVShapeOfMetatype]
+
+    @property
+    def dropout_metatypes(self) -> List[OperatorMetatype]:
+        return []
+
+    @property
+    def read_variable_metatypes(self) -> List[OperatorMetatype]:
+        return [om.OVReadValueMetatype]
+
     @property
     def scales_unification_map(self) -> Dict[OperatorMetatype, OperatorMetatype]:
         return {om.OVConcatMetatype: self.overflow_fix_metatypes}

nncf/quantization/algorithms/min_max/torch_backend.py

@@ -69,6 +69,14 @@ def post_processing_metatypes(self) -> List[OperatorMetatype]:
     def shapeof_metatypes(self) -> List[OperatorMetatype]:
         return []
 
+    @property
+    def dropout_metatypes(self) -> List[OperatorMetatype]:
+        return [om.PTDropoutMetatype]
+
+    @property
+    def read_variable_metatypes(self) -> List[OperatorMetatype]:
+        return []
+
     @property
     def conv_metatypes(self) -> List[OperatorMetatype]:
         return [om.PTModuleConv1dMetatype, om.PTModuleConv2dMetatype, om.PTModuleConv3dMetatype]
@@ -85,10 +93,6 @@ def overflow_fix_metatypes(self) -> List[OperatorMetatype]:
             om.PTModuleConvTranspose3dMetatype,
         ]
 
-    @property
-    def read_variable_metatypes(self) -> List[OperatorMetatype]:
-        return []
-
     @property
     def add_metatypes(self) -> List[OperatorMetatype]:
         return [om.PTAddMetatype]
@@ -307,6 +311,10 @@ def get_ignored_metatypes(model_type: ModelType, device: TargetDevice) -> List[O
                 om.PTDivMetatype,
                 om.PTMaxMetatype,
                 om.PTSqueezeMetatype,
+                om.PTLayerNormMetatype,
+                om.PTModuleLayerNormMetatype,
+                om.PTGroupNormMetatype,
+                om.PTModuleGroupNormMetatype,
             ]
             if device != TargetDevice.CPU_SPR:
                 types.append(om.PTMulMetatype)

nncf/quantization/passes.py

@@ -23,20 +23,23 @@
 def transform_to_inference_graph(
     nncf_graph: NNCFGraph,
     shapeof_metatypes: List[OperatorMetatype],
+    dropout_metatypes: List[OperatorMetatype],
     read_variable_metatypes: Optional[List[OperatorMetatype]] = None,
 ) -> NNCFGraph:
     """
-    This method contains pipeline of the passes that uses to provide inference graph without constant flows.
+    This method contains inplace pipeline of the passes that uses to provide inference graph without constant flows.
 
     :param nncf_graph: NNCFGraph instance for the transformation.
     :param shapeof_metatypes: List of backend-specific ShapeOf metatypes.
+    :param dropout_metatypes: List of backend-specific Dropout metatypes.
     :param read_variable_metatypes: List of backend-specific metatypes
         that also can be interpreted as inputs (ReadValue).
     :return: NNCFGraph in the inference style.
     """
-    inference_nncf_graph = remove_shapeof_subgraphs(nncf_graph, shapeof_metatypes, read_variable_metatypes)
-    inference_nncf_graph = filter_constant_nodes(nncf_graph, read_variable_metatypes)
-    return inference_nncf_graph
+    remove_shapeof_subgraphs(nncf_graph, shapeof_metatypes, read_variable_metatypes)
+    remove_nodes_and_reconnect_graph(nncf_graph, dropout_metatypes)
+    filter_constant_nodes(nncf_graph, read_variable_metatypes)
+    return nncf_graph
 
 
 def remove_shapeof_subgraphs(
@@ -45,7 +48,7 @@ def remove_shapeof_subgraphs(
     read_variable_metatypes: Optional[List[OperatorMetatype]] = None,
 ) -> NNCFGraph:
     """
-    Removes the ShapeOf subgraphs from the provided NNCFGraph instance.
+    Removes the ShapeOf subgraphs from the provided NNCFGraph instance inplace.
 
     :param nncf_graph: NNCFGraph instance for the transformation.
     :param shapeof_metatypes: List of backend-specific ShapeOf metatypes.
@@ -88,11 +91,61 @@ def remove_shapeof_subgraphs(
     return nncf_graph
 
 
+def remove_nodes_and_reconnect_graph(
+    nncf_graph: NNCFGraph,
+    metatypes: List[OperatorMetatype],
+) -> NNCFGraph:
+    """
+    Removes nodes with metatypes specified by `metatypes` parameter from
+    the provided NNCFGraph instance and connects previous node of a matched node
+    with next nodes of a matched node inplace for each matched node.
+    Matched nodes should have only one input node and only one output port.
+
+    :param nncf_graph: NNCFGraph instance for the transformation.
+    :param metatypes: List of backend-specific metatypes.
+    :return: Resulting NNCFGraph.
+    """
+    if not metatypes:
+        return nncf_graph
+
+    nodes_to_drop = []
+    for node in nncf_graph.get_nodes_by_metatypes(metatypes):
+        if node.metatype in metatypes:
+            nodes_to_drop.append(node)
+
+            prev_nodes = nncf_graph.get_previous_nodes(node)
+            input_edges = nncf_graph.get_input_edges(node)
+            assert len(prev_nodes) == len(input_edges) == 1
+            prev_node = prev_nodes[0]
+            input_edge = input_edges[0]
+            assert not input_edge.parallel_input_port_ids
+
+            # nncf_graph.get_next_edges is not used to preserve
+            # parallel_input_port_ids
+            for output_node in nncf_graph.get_next_nodes(node):
+                output_edge = nncf_graph.get_edge(node, output_node)
+                # Connects previous node with all next nodes
+                # to keep NNCFGraph connected.
+                assert input_edge.dtype == output_edge.dtype
+                assert input_edge.tensor_shape == output_edge.tensor_shape
+                nncf_graph.add_edge_between_nncf_nodes(
+                    from_node_id=prev_node.node_id,
+                    to_node_id=output_edge.to_node.node_id,
+                    tensor_shape=input_edge.tensor_shape,
+                    input_port_id=output_edge.input_port_id,
+                    output_port_id=input_edge.output_port_id,
+                    dtype=input_edge.dtype,
+                    parallel_input_port_ids=output_edge.parallel_input_port_ids,
+                )
+    nncf_graph.remove_nodes_from(nodes_to_drop)
+    return nncf_graph
+
+
 def filter_constant_nodes(
     nncf_graph: NNCFGraph, read_variable_metatypes: Optional[List[OperatorMetatype]] = None
 ) -> NNCFGraph:
     """
-    Removes all Constant nodes from NNCFGraph, making it inference graph.
+    Removes all Constant nodes from NNCFGraph inplace, making it inference graph.
     The traversing starts from the input nodes and nodes with weights.
 
     :param nncf_graph: NNCFGraph instance for the transformation.

nncf/torch/quantization/ignored_patterns.py

@@ -67,8 +67,15 @@ def _add_softmax_reshape_matmul(
 
 @PT_IGNORED_PATTERNS.register(IgnoredPatternNames.MULTIHEAD_ATTENTION_OUTPUT)
 def create_multihead_attention_output() -> GraphPattern:
-    matmul_aliases = ["linear", "addmm", "matmul", "bmm", "mm", "baddbmm"]
-    reshape_squeeze_aliases = ["reshape", "view", "flatten", "squeeze", "unsqueeze", "squeeze", "flatten", "unsqueeze"]
+    matmul_aliases = ["linear", "addmm", "matmul", "bmm", "mm", "baddbmm", "__matmul__"]
+    reshape_squeeze_aliases = [
+        "reshape",
+        "view",
+        "flatten",
+        "unsqueeze",
+        "squeeze",
+        "unbind",
+    ]
     gather_aliases = ["gather", "index_select", "where", "index_select", "__getitem__"]
     transpose_aliases = ["transpose", "permute", "transpose_"]
 

tests/common/data/reference_graphs/passes/dropout_synthetic_model_after.dot

@@ -0,0 +1,19 @@
+strict digraph  {
+"0 /Input_1_0" [id=0, type=Input_1];
+"1 /Split_1_0" [id=1, type=Split_1];
+"5 /Output_1_0" [id=5, type=Output_1];
+"6 /Output_2_1_0" [id=6, type=Output_2_1];
+"7 /Output_2_2_0" [id=7, type=Output_2_2];
+"8 /Output_2_3_0" [id=8, type=Output_2_3];
+"9 /Output_3_0" [id=9, type=Output_3];
+"10 /Output_2_4_0" [id=10, type=output];
+"11 /Output_3_1_0" [id=11, type=output];
+"0 /Input_1_0" -> "1 /Split_1_0";
+"1 /Split_1_0" -> "5 /Output_1_0";
+"1 /Split_1_0" -> "6 /Output_2_1_0";
+"1 /Split_1_0" -> "7 /Output_2_2_0";
+"1 /Split_1_0" -> "8 /Output_2_3_0";
+"1 /Split_1_0" -> "9 /Output_3_0";
+"1 /Split_1_0" -> "10 /Output_2_4_0";
+"1 /Split_1_0" -> "11 /Output_3_1_0"  [label="parallel_input_port_ids:[2, 3, 4, 5, 6, 7, 8, 9]"];
+}

tests/common/data/reference_graphs/passes/dropout_synthetic_model_before.dot

@@ -0,0 +1,25 @@
+strict digraph  {
+"0 /Input_1_0" [id=0, type=Input_1];
+"1 /Split_1_0" [id=1, type=Split_1];
+"2 /Dropout_1_0" [id=2, type=Dropout_1];
+"3 /Dropout_2_0" [id=3, type=Dropout_2];
+"4 /Dropout_3_0" [id=4, type=Dropout_3];
+"5 /Output_1_0" [id=5, type=Output_1];
+"6 /Output_2_1_0" [id=6, type=Output_2_1];
+"7 /Output_2_2_0" [id=7, type=Output_2_2];
+"8 /Output_2_3_0" [id=8, type=Output_2_3];
+"9 /Output_3_0" [id=9, type=Output_3];
+"10 /Output_2_4_0" [id=10, type=output];
+"11 /Output_3_1_0" [id=11, type=output];
+"0 /Input_1_0" -> "1 /Split_1_0";
+"1 /Split_1_0" -> "2 /Dropout_1_0";
+"1 /Split_1_0" -> "3 /Dropout_2_0";
+"1 /Split_1_0" -> "4 /Dropout_3_0";
+"2 /Dropout_1_0" -> "5 /Output_1_0";
+"3 /Dropout_2_0" -> "6 /Output_2_1_0";
+"3 /Dropout_2_0" -> "7 /Output_2_2_0";
+"3 /Dropout_2_0" -> "8 /Output_2_3_0";
+"3 /Dropout_2_0" -> "10 /Output_2_4_0";
+"4 /Dropout_3_0" -> "9 /Output_3_0";
+"4 /Dropout_3_0" -> "11 /Output_3_1_0"  [label="parallel_input_port_ids:[2, 3, 4, 5, 6, 7, 8, 9]"];
+}