Shift+Scale and Input+Shift+Scale pattern for PT (#1989)

### Changes

Introduced Shift + Scale fused pattern and Input+Shift+Scale pattern to
insert Fake Quantize operations optimally for CPU.

### Reason for changes

Customer has a model that is quantized not optimally:

![image](https://github.com/openvinotoolkit/nncf/assets/4014476/59a228fd-1336-4e91-80c4-b67f76febcb8)

FakeQuantize between subtraction and division is redundant and
introduces additional cost in runtime.
FakeQuantize between input and pre-processing is not needed in case of
single edge from input, because pre-processing can be fused to the FQ
after pre-processing.

![image](https://github.com/openvinotoolkit/nncf/assets/4014476/2037597e-cf0d-45f3-b36b-83c1fa7f0de2)
When there are multiple edges from input and one edge is going to
pre-processing, it's optimal to have a common fake quantize for all
edges.

![image](https://github.com/openvinotoolkit/nncf/assets/4014476/a150a4b2-1e34-461a-9683-46955abb6ffc)


If pre-processing represented via normalize op from torchvision (e.g.
like here
https://github.com/PeterL1n/RobustVideoMatting/blob/master/model/mobilenetv3.py#L37),
NNCF doesn't insert FQ between subtraction and division and between
input and pre-processing.
It happens because pre-processing is implemented via in-place
operations, and since FQ is not in-place it can't be applied (see more
details here: #1565)

![image](https://github.com/openvinotoolkit/nncf/assets/4014476/17ecd154-a7d8-468c-95d0-31d99ca3185f)


### Related tickets

112934

### Tests

synthetic tests for pre-processing

nncf/common/graph/patterns/patterns.py

@@ -296,6 +296,7 @@ class HWFusedPatternNames(Enum):
     MVN_SCALE_SHIFT = PatternDesc("mvn_scale_shift")
     NORMALIZE_L2_MULTIPLY = PatternDesc("normalize_l2_multiply")
     SCALE_SHIFT = PatternDesc("scale_shift")
+    SHIFT_SCALE = PatternDesc("shift_scale")
     SE_BLOCK = PatternDesc("se_block")
     SOFTMAX_DIV = PatternDesc("softmax_div")
 

nncf/onnx/hardware/fused_patterns.py

@@ -44,6 +44,26 @@ def create_scale_shift() -> GraphPattern:
     return pattern
 
 
+@ONNX_HW_FUSED_PATTERNS.register(HWFusedPatternNames.SHIFT_SCALE)
+def create_shift_scale() -> GraphPattern:
+    pattern = GraphPattern()
+    add_node = pattern.add_node(
+        **{
+            GraphPattern.LABEL_ATTR: "ADD, SUBTRACT",
+            GraphPattern.METATYPE_ATTR: [om.ONNXAddLayerMetatype, om.ONNXSubMetatype],
+        }
+    )
+    mul_node = pattern.add_node(
+        **{
+            GraphPattern.LABEL_ATTR: "MULTIPLY, DIV",
+            GraphPattern.METATYPE_ATTR: [om.ONNXMulLayerMetatype, om.ONNXDivLayerMetatype],
+        }
+    )
+
+    pattern.add_edge(add_node, mul_node)
+    return pattern
+
+
 @ONNX_HW_FUSED_PATTERNS.register(HWFusedPatternNames.SWISH_WITH_SIGMOID)
 def create_swish_with_sigmoid() -> GraphPattern:
     pattern = GraphPattern()
@@ -113,24 +133,23 @@ def create_hswish() -> GraphPattern:
 # INPUT PROCESSING
 
 
+@ONNX_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_SCALE_SHIFT)
+def create_input_scale_shift() -> GraphPattern:
+    pattern = GraphPattern()
+    pattern.add_node(**{GraphPattern.LABEL_ATTR: "MODEL_INPUT", GraphPattern.METATYPE_ATTR: InputNoopMetatype})
+    scale_shift = create_scale_shift()
+
+    pattern.join_patterns(scale_shift)
+    return pattern
+
+
 @ONNX_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_SHIFT_SCALE)
 def create_input_shift_scale() -> GraphPattern:
     pattern = GraphPattern()
-    input_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "MODEL_INPUT", GraphPattern.METATYPE_ATTR: InputNoopMetatype}
-    )
-    add_node = pattern.add_node(
-        **{
-            GraphPattern.LABEL_ATTR: "ADD, SUBTRACT",
-            GraphPattern.METATYPE_ATTR: [om.ONNXAddLayerMetatype, om.ONNXSubMetatype],
-        }
-    )
-    multiply_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "MULTIPLY", GraphPattern.METATYPE_ATTR: om.ONNXMulLayerMetatype}
-    )
+    pattern.add_node(**{GraphPattern.LABEL_ATTR: "MODEL_INPUT", GraphPattern.METATYPE_ATTR: InputNoopMetatype})
+    shift_scale = create_shift_scale()
 
-    pattern.add_edge(input_node, add_node)
-    pattern.add_edge(add_node, multiply_node)
+    pattern.join_patterns(shift_scale)
     return pattern
 
 
@@ -151,16 +170,6 @@ def create_input_add() -> GraphPattern:
     return pattern
 
 
-@ONNX_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_SCALE_SHIFT)
-def create_input_scale_shift() -> GraphPattern:
-    pattern = GraphPattern()
-    pattern.add_node(**{GraphPattern.LABEL_ATTR: "MODEL_INPUT", GraphPattern.METATYPE_ATTR: InputNoopMetatype})
-    scale_shift = create_scale_shift()
-
-    pattern.join_patterns(scale_shift)
-    return pattern
-
-
 # COMBINATIONS
 
 

nncf/openvino/hardware/fused_patterns.py

@@ -128,6 +128,25 @@ def create_scale_shift() -> GraphPattern:
     return pattern
 
 
+@OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.SHIFT_SCALE)
+def create_shift_scale() -> GraphPattern:
+    pattern = GraphPattern()
+    add_node = pattern.add_node(
+        **{
+            GraphPattern.LABEL_ATTR: "ADD, SUBTRACT",
+            GraphPattern.METATYPE_ATTR: [om.OVAddMetatype, om.OVSubtractMetatype],
+        }
+    )
+    mul_node = pattern.add_node(
+        **{
+            GraphPattern.LABEL_ATTR: "MULTIPLY, DIV",
+            GraphPattern.METATYPE_ATTR: [om.OVMultiplyMetatype, om.OVDivideMetatype],
+        }
+    )
+    pattern.add_edge(add_node, mul_node)
+    return pattern
+
+
 @OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.SE_BLOCK)
 def create_se_block() -> GraphPattern:
     pattern = GraphPattern()
@@ -308,27 +327,6 @@ def create_softmax() -> GraphPattern:
 # INPUT PROCESSING
 
 
-@OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_SHIFT_SCALE)
-def create_input_shift_scale() -> GraphPattern:
-    pattern = GraphPattern()
-    model_input = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "MODEL_INPUT", GraphPattern.METATYPE_ATTR: om.OVParameterMetatype}
-    )
-    add_node = pattern.add_node(
-        **{
-            GraphPattern.LABEL_ATTR: "ADD, SUBTRACT",
-            GraphPattern.METATYPE_ATTR: [om.OVAddMetatype, om.OVSubtractMetatype],
-        }
-    )
-    multiply_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "MULTIPLY", GraphPattern.METATYPE_ATTR: om.OVMultiplyMetatype}
-    )
-
-    pattern.add_edge(model_input, add_node)
-    pattern.add_edge(add_node, multiply_node)
-    return pattern
-
-
 @OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_CONVERT_TRANSPOSE_PROCESSING)
 def create_input_convert_transpose_processing() -> GraphPattern:
     input_convert_transpose = create_input_convert_transpose()
@@ -461,6 +459,16 @@ def create_input_scale_shift() -> GraphPattern:
     return pattern
 
 
+@OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_SHIFT_SCALE)
+def create_input_shift_scale() -> GraphPattern:
+    pattern = GraphPattern()
+    pattern.add_node(**{GraphPattern.LABEL_ATTR: "MODEL_INPUT", GraphPattern.METATYPE_ATTR: om.OVParameterMetatype})
+    shift_scale = create_shift_scale()
+
+    pattern.join_patterns(shift_scale)
+    return pattern
+
+
 @OPENVINO_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_TRANSPOSE_PROCESSING)
 def create_input_transpose_processing() -> GraphPattern:
     pattern = GraphPattern()

nncf/torch/hardware/fused_patterns.py

@@ -12,6 +12,7 @@
 from nncf.common.graph.patterns import GraphPattern
 from nncf.common.graph.patterns import HWFusedPatternNames
 from nncf.common.utils.registry import Registry
+from nncf.torch.graph.operator_metatypes import PTInputNoopMetatype
 from nncf.torch.graph.pattern_operations import ARITHMETIC_OPERATIONS
 from nncf.torch.graph.pattern_operations import ATOMIC_ACTIVATIONS_OPERATIONS
 from nncf.torch.graph.pattern_operations import BATCH_NORMALIZATION_OPERATIONS
@@ -49,6 +50,24 @@ def create_l2_norm_operations() -> GraphPattern:
 # COMBINATIONS
 
 
+@PT_HW_FUSED_PATTERNS.register(HWFusedPatternNames.SHIFT_SCALE)
+def create_shift_scale() -> GraphPattern:
+    pattern = GraphPattern()
+    add_node = pattern.add_node(label="ADD, SUB", type=["__add__", "__sub__"])
+    truediv_node = pattern.add_node(label="MUL, DIV", type=["__mul__", "__truediv__"])
+    pattern.add_edge(add_node, truediv_node)
+    return pattern
+
+
+@PT_HW_FUSED_PATTERNS.register(HWFusedPatternNames.INPUT_SHIFT_SCALE)
+def create_input_shift_scale() -> GraphPattern:
+    pattern = GraphPattern()
+    pattern.add_node(**{GraphPattern.LABEL_ATTR: "MODEL_INPUT", GraphPattern.METATYPE_ATTR: PTInputNoopMetatype})
+    shift_scale = create_shift_scale()
+    pattern.join_patterns(shift_scale)
+    return pattern
+
+
 @PT_HW_FUSED_PATTERNS.register(HWFusedPatternNames.LINEAR_ARITHMETIC)
 def create_linear_arithmetic_operations() -> GraphPattern:
     linear = linear_operations()

tests/torch/data/reference_graphs/quantized/synthetic_model/ShiftScale__multi_input_branch.dot

@@ -0,0 +1,23 @@
+strict digraph  {
+"0 /nncf_model_input_0" [id=0, type=nncf_model_input];
+"1 SymmetricQuantizer/symmetric_quantize_0" [id=1, type=symmetric_quantize];
+"2 ShiftScaleParametrized/__sub___0" [id=2, type=__sub__];
+"3 ShiftScaleParametrized/__truediv___0" [id=3, type=__truediv__];
+"4 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/__truediv___0|OUTPUT]/symmetric_quantize_0" [id=4, type=symmetric_quantize];
+"5 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=5, type=symmetric_quantize];
+"6 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" [id=6, type=conv2d];
+"7 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_1" [id=7, type=symmetric_quantize];
+"8 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1" [id=8, type=conv2d];
+"9 /nncf_model_output_0" [id=9, type=nncf_model_output];
+"10 /nncf_model_output_1" [id=10, type=nncf_model_output];
+"0 /nncf_model_input_0" -> "1 SymmetricQuantizer/symmetric_quantize_0";
+"1 SymmetricQuantizer/symmetric_quantize_0" -> "2 ShiftScaleParametrized/__sub___0";
+"1 SymmetricQuantizer/symmetric_quantize_0" -> "8 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1";
+"2 ShiftScaleParametrized/__sub___0" -> "3 ShiftScaleParametrized/__truediv___0";
+"3 ShiftScaleParametrized/__truediv___0" -> "4 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/__truediv___0|OUTPUT]/symmetric_quantize_0";
+"4 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/__truediv___0|OUTPUT]/symmetric_quantize_0" -> "6 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"5 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "6 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"6 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" -> "9 /nncf_model_output_0";
+"7 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_1" -> "8 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1";
+"8 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1" -> "10 /nncf_model_output_1";
+}

tests/torch/data/reference_graphs/quantized/synthetic_model/ShiftScale__normalize__multi_input_branch.dot

@@ -0,0 +1,27 @@
+strict digraph  {
+"0 /nncf_model_input_0" [id=0, type=nncf_model_input];
+"1 ShiftScaleParametrized/is_floating_point_0" [id=1, type=is_floating_point];
+"2 ShiftScaleParametrized/clone_0" [id=2, type=clone];
+"3 ShiftScaleParametrized/sub__0" [id=3, type=sub_];
+"4 ShiftScaleParametrized/div__0" [id=4, type=div_];
+"5 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/div__0|OUTPUT]/symmetric_quantize_0" [id=5, type=symmetric_quantize];
+"6 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=6, type=symmetric_quantize];
+"7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" [id=7, type=conv2d];
+"8 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_1" [id=8, type=symmetric_quantize];
+"9 ShiftScaleParametrized/NNCFConv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" [id=9, type=symmetric_quantize];
+"10 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1" [id=10, type=conv2d];
+"11 /nncf_model_output_0" [id=11, type=nncf_model_output];
+"12 /nncf_model_output_1" [id=12, type=nncf_model_output];
+"0 /nncf_model_input_0" -> "1 ShiftScaleParametrized/is_floating_point_0";
+"0 /nncf_model_input_0" -> "2 ShiftScaleParametrized/clone_0";
+"0 /nncf_model_input_0" -> "9 ShiftScaleParametrized/NNCFConv2d[conv]/SymmetricQuantizer/symmetric_quantize_0";
+"2 ShiftScaleParametrized/clone_0" -> "3 ShiftScaleParametrized/sub__0";
+"3 ShiftScaleParametrized/sub__0" -> "4 ShiftScaleParametrized/div__0";
+"4 ShiftScaleParametrized/div__0" -> "5 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/div__0|OUTPUT]/symmetric_quantize_0";
+"5 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/div__0|OUTPUT]/symmetric_quantize_0" -> "7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"6 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" -> "11 /nncf_model_output_0";
+"8 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_1" -> "10 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1";
+"9 ShiftScaleParametrized/NNCFConv2d[conv]/SymmetricQuantizer/symmetric_quantize_0" -> "10 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1";
+"10 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_1" -> "12 /nncf_model_output_1";
+}

tests/torch/data/reference_graphs/quantized/synthetic_model/ShiftScale__normalize__single_input_branch.dot

@@ -0,0 +1,19 @@
+strict digraph  {
+"0 /nncf_model_input_0" [id=0, type=nncf_model_input];
+"1 ShiftScaleParametrized/is_floating_point_0" [id=1, type=is_floating_point];
+"2 ShiftScaleParametrized/clone_0" [id=2, type=clone];
+"3 ShiftScaleParametrized/sub__0" [id=3, type=sub_];
+"4 ShiftScaleParametrized/div__0" [id=4, type=div_];
+"5 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/div__0|OUTPUT]/symmetric_quantize_0" [id=5, type=symmetric_quantize];
+"6 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=6, type=symmetric_quantize];
+"7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" [id=7, type=conv2d];
+"8 /nncf_model_output_0" [id=8, type=nncf_model_output];
+"0 /nncf_model_input_0" -> "1 ShiftScaleParametrized/is_floating_point_0";
+"0 /nncf_model_input_0" -> "2 ShiftScaleParametrized/clone_0";
+"2 ShiftScaleParametrized/clone_0" -> "3 ShiftScaleParametrized/sub__0";
+"3 ShiftScaleParametrized/sub__0" -> "4 ShiftScaleParametrized/div__0";
+"4 ShiftScaleParametrized/div__0" -> "5 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/div__0|OUTPUT]/symmetric_quantize_0";
+"5 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/div__0|OUTPUT]/symmetric_quantize_0" -> "7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"6 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"7 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" -> "8 /nncf_model_output_0";
+}

tests/torch/data/reference_graphs/quantized/synthetic_model/ShiftScale__single_input_branch.dot

@@ -0,0 +1,15 @@
+strict digraph  {
+"0 /nncf_model_input_0" [id=0, type=nncf_model_input];
+"1 ShiftScaleParametrized/__sub___0" [id=1, type=__sub__];
+"2 ShiftScaleParametrized/__truediv___0" [id=2, type=__truediv__];
+"3 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/__truediv___0|OUTPUT]/symmetric_quantize_0" [id=3, type=symmetric_quantize];
+"4 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" [id=4, type=symmetric_quantize];
+"5 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" [id=5, type=conv2d];
+"6 /nncf_model_output_0" [id=6, type=nncf_model_output];
+"0 /nncf_model_input_0" -> "1 ShiftScaleParametrized/__sub___0";
+"1 ShiftScaleParametrized/__sub___0" -> "2 ShiftScaleParametrized/__truediv___0";
+"2 ShiftScaleParametrized/__truediv___0" -> "3 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/__truediv___0|OUTPUT]/symmetric_quantize_0";
+"3 ShiftScaleParametrized/NNCFNetworkInterface[_nncf]/ModuleDict[external_quantizers]/SymmetricQuantizer[ShiftScaleParametrized/__truediv___0|OUTPUT]/symmetric_quantize_0" -> "5 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"4 ShiftScaleParametrized/NNCFConv2d[conv]/ModuleDict[pre_ops]/UpdateWeight[0]/SymmetricQuantizer[op]/symmetric_quantize_0" -> "5 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0";
+"5 ShiftScaleParametrized/NNCFConv2d[conv]/conv2d_0" -> "6 /nncf_model_output_0";
+}

tests/torch/test_compressed_graph.py

@@ -9,6 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import itertools
 import os
 from abc import ABC
 from abc import abstractmethod
@@ -68,6 +69,7 @@
 from tests.torch.test_models.synthetic import MultiOutputSameTensorModel
 from tests.torch.test_models.synthetic import PoolUnPool
 from tests.torch.test_models.synthetic import ReshapeModel
+from tests.torch.test_models.synthetic import ShiftScaleParametrized
 from tests.torch.test_models.synthetic import TransposeModel
 
 
@@ -575,6 +577,21 @@ def forward(self, x):
         return TestModel(self.tensor_method, **self.model_kwargs)
 
 
+shift_scale_models = []
+params_combinations = list(itertools.product([True, False], repeat=2))
+
+
+for pair in params_combinations:
+    names = ["is_single_input", "use_normalize"]
+    kwargs = dict(zip(names, pair))
+    desc = GeneralModelDesc(
+        model_name=ShiftScaleParametrized.get_name(**kwargs),
+        model_builder=partial(ShiftScaleParametrized, **kwargs),
+        input_sample_sizes=(ShiftScaleParametrized.INPUT_SIZES),
+    )
+    shift_scale_models.append(desc)
+
+
 TWO_INT_INPUTS_INFO = [{"sample_size": [1], "type": "long"}, {"sample_size": [1], "type": "long"}]
 SYNTHETIC_MODEL_DESC_LIST = [
     SingleLayerModelDesc(layer=nn.Conv1d(1, 1, 1), input_sample_sizes=[1, 1, 1]),
@@ -732,6 +749,7 @@ def forward(self, x):
         wrap_inputs_fn=partial(n_inputs_fn, nargs=3),
     ),
     GeneralModelDesc(model_builder=MHA_single_input, input_sample_sizes=(MHA_single_input.INPUT_SIZES,)),
+    *shift_scale_models,
 ]
 
 

tests/torch/test_models/synthetic.py

@@ -16,6 +16,7 @@
 from torch.nn import BatchNorm2d
 from torch.nn import Dropout
 from torch.nn import Parameter
+from torchvision.transforms.functional import normalize
 
 from nncf.torch import register_module
 from tests.torch.helpers import create_conv
@@ -332,3 +333,33 @@ def __init__(self):
 
     def forward(self, x):
         return self.mha(x, x, x)
+
+
+class ShiftScaleParametrized(torch.nn.Module):
+    NUM_CHANNELS = 3
+    INPUT_SIZES = [1, NUM_CHANNELS, 2, 2]
+
+    def __init__(self, is_single_input: bool, use_normalize: bool):
+        super().__init__()
+        self.conv = create_conv(self.NUM_CHANNELS, 1, 1)
+        self.is_single_input = is_single_input
+        self.use_normalize = use_normalize
+
+    @classmethod
+    def get_name(cls, is_single_input: bool, use_normalize: bool):
+        suffix_1 = "single" if is_single_input else "multi"
+        suffix_2 = "__normalize" if use_normalize else ""
+        return f"ShiftScale{suffix_2}__{suffix_1}_input_branch"
+
+    def forward(self, x):
+        values = [1] * self.NUM_CHANNELS
+        if self.use_normalize:
+            pre_proc = normalize(x, values, values, inplace=False)
+        else:
+            vector = torch.Tensor(values).unsqueeze(dim=0).unsqueeze(dim=2).unsqueeze(dim=3)
+            pre_proc = (x - vector) / vector
+
+        output = self.conv(pre_proc)
+        if self.is_single_input:
+            return output
+        return output, self.conv(x)

tests/torch/test_pattern_manager.py

@@ -39,7 +39,6 @@
     HWFusedPatternNames.INPUT_REVERSE_ADD: "Not relevant for Torch.",
     HWFusedPatternNames.INPUT_REVERSE_SCALE_SHIFT: "Not relevant for Torch.",
     HWFusedPatternNames.INPUT_SCALE_SHIFT: "Not relevant for Torch.",
-    HWFusedPatternNames.INPUT_SHIFT_SCALE: "Not relevant for Torch.",
     HWFusedPatternNames.INPUT_TRANSPOSE_PROCESSING: "Not relevant for Torch.",
     HWFusedPatternNames.INPUT_TRANSPOSE_REVERSE_ADD: "Not relevant for Torch.",
     HWFusedPatternNames.INPUT_TRANSPOSE_SCALE_SHIFT: "Not relevant for Torch.",