SQ model with shared nodes used in tests

tests/openvino/native/test_smooth_quant.py

@@ -25,6 +25,7 @@
 from nncf.quantization.algorithms.smooth_quant.openvino_backend import OVSmoothQuantAlgoBackend
 from tests.post_training.test_templates.helpers import ConvTestModel
 from tests.post_training.test_templates.helpers import LinearMultiShapeModel
+from tests.post_training.test_templates.helpers import ShareWeghtsConvAndShareLinearModel
 from tests.post_training.test_templates.test_smooth_quant import TemplateTestSQAlgorithm
 
 OV_LINEAR_MODEL_MM_OP_MAP = {
@@ -80,6 +81,8 @@ def get_node_name_map(self, model_cls) -> Dict[str, str]:
             return OV_LINEAR_MODEL_MM_OP_MAP
         if model_cls is ConvTestModel:
             return OV_CONV_MODEL_MM_OP_MAP
+        if model_cls is ShareWeghtsConvAndShareLinearModel:
+            return {}
         raise NotImplementedError
 
     @staticmethod

tests/post_training/test_templates/helpers.py

@@ -338,3 +338,21 @@ def forward(self, x):
         x = self.embedding(x)
         x = self.matmul(x)
         return x
+
+
+class ShareWeghtsConvAndShareLinearModel(nn.Module):
+    INPUT_SIZE = [1, 1, 4, 4]
+
+    def __init__(self):
+        super().__init__()
+        with set_torch_seed():
+            self.conv = create_conv(1, 1, 1)
+            self.linear = nn.Linear(4, 4)
+            self.linear.weight.data = torch.randn((4, 4), dtype=torch.float32)
+            self.linear.bias.data = torch.randn((1, 4), dtype=torch.float32)
+
+    def forward(self, x):
+        for _ in range(2):
+            x = self.conv(x)
+            x = self.linear(x)
+        return x

tests/post_training/test_templates/test_smooth_quant.py

@@ -27,9 +27,11 @@
 from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.quantization.algorithms.smooth_quant.algorithm import SmoothQuant
 from nncf.quantization.algorithms.smooth_quant.backend import SmoothQuantAlgoBackend
+from nncf.quantization.algorithms.smooth_quant.openvino_backend import OVSmoothQuantAlgoBackend
 from tests.post_training.test_templates.helpers import ConvTestModel
 from tests.post_training.test_templates.helpers import LinearMultiShapeModel
 from tests.post_training.test_templates.helpers import NonZeroLinearModel
+from tests.post_training.test_templates.helpers import ShareWeghtsConvAndShareLinearModel
 from tests.post_training.test_templates.helpers import get_static_dataset
 
 TModel = TypeVar("TModel")
@@ -203,12 +205,16 @@ def test_get_abs_max_channel_collector(self, inplace_statistics: bool):
                 ],
             ),
             (ConvTestModel, [("Conv1", 0)]),
+            (ShareWeghtsConvAndShareLinearModel, []),
         ),
     )
     def test__get_nodes_to_smooth_data(self, model_cls, references, tmpdir):
         model = self.backend_specific_model(model_cls(), tmpdir)
         nncf_graph = NNCFGraphFactory.create(model)
 
+        if isinstance(self.get_backend(), OVSmoothQuantAlgoBackend) and model_cls is ShareWeghtsConvAndShareLinearModel:
+            pytest.xfail("Matmuls don't share one weight in OV ir for some reason")
+
         algo = SmoothQuant()
         algo._set_backend_entity(model)
         alpha_map = algo._get_alpha_map()

tests/torch/ptq/test_smooth_quant.py

@@ -26,6 +26,7 @@
 from nncf.torch.nncf_network import ExtraCompressionModuleType
 from tests.post_training.test_templates.helpers import ConvTestModel
 from tests.post_training.test_templates.helpers import LinearMultiShapeModel
+from tests.post_training.test_templates.helpers import ShareWeghtsConvAndShareLinearModel
 from tests.post_training.test_templates.test_smooth_quant import TemplateTestSQAlgorithm
 
 PT_LINEAR_MODEL_SQ_MAP = {
@@ -60,6 +61,8 @@ def get_node_name_map(self, model_cls) -> Dict[str, str]:
             return PT_LINEAR_MODEL_MM_MAP
         if model_cls is ConvTestModel:
             return PT_CONV_MODEL_MM_MAP
+        if model_cls is ShareWeghtsConvAndShareLinearModel:
+            return {}
         raise NotImplementedError
 
     @staticmethod