Cleanup

nncf/experimental/tensor/functions.py

@@ -340,10 +340,7 @@ def stack(x: List[Tensor], axis: int = 0) -> Tensor:
     :return: Stacked Tensor.
     """
     if isinstance(x, List):
-        unwrapped_x = [i.data for i in x]
-        # singledispatch cannot dispatch function by element in a list
-        res = stack.dispatch(type(unwrapped_x[0]))(unwrapped_x, axis=axis)
-        return Tensor(res)
+        return Tensor(_dispatch_list(stack, x, axis=axis))
     raise NotImplementedError(f"Function `stack` is not implemented for {type(x)}")
 
 

nncf/torch/nncf_network.py

@@ -376,9 +376,6 @@ def get_modules_in_nncf_modules_by_type(self, class_names: List[str]) -> Dict[Sc
                 retval[nncf_module_scope + relative_scope] = target_module
         return retval
 
-    def update_model_ref(self, model: torch.nn.Module) -> None:
-        object.__setattr__(self, "__model_ref", model)
-
     def temporary_insert_at_point(self, point: PTInsertionPoint, fn_list: List[Callable]):
         hook_addresses = self.insert_at_point(point, fn_list)
         self._temprorary_hooks_adresses.append(hook_addresses)

nncf/torch/quantization/external_quantizer.py

@@ -35,5 +35,5 @@ def __init__(
 
     def __call__(self, *args, **kwargs):
         if self.debug_interface is not None:
-            self.debug_interface.register_activation_quantize_call(str(self.quantizer_storage_key))
+            self.debug_interface.register_activation_quantize_call(str(self._storage_key))
         return super().__call__(*args, **kwargs)

nncf/torch/statistics/aggregator.py

@@ -9,7 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from copy import deepcopy
 from typing import Dict
 
 import numpy as np
@@ -27,41 +26,9 @@
 from nncf.torch.tensor_statistics.algo import create_register_input_hook
 
 
-class ModelView:
-    def __init__(self, model: NNCFNetwork):
-        self.model = model
-        self.nncf_module_additions = self.model.nncf.save_nncf_module_additions()
-
-    def __enter__(self):
-        # Model ref removed to prevent copying
-        self.model.nncf.update_model_ref(None)
-
-        # nncf_replaced_models removed to prevent copying
-        replaced_modules = self.model.nncf._nncf_replaced_modules
-        self.model.nncf._nncf_replaced_modules = None
-
-        self.nncf_interface = deepcopy(self.model.nncf)
-
-        # Model ref is recovering
-        self.model.nncf.update_model_ref(self.model)
-        self.nncf_interface.update_model_ref(self.model)
-
-        # nncf_replaced_models is recovering
-        self.model.nncf._nncf_replaced_modules = replaced_modules
-        self.nncf_interface._nncf_replaced_modules = replaced_modules
-        return self.model
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.model._nncf = self.nncf_interface
-        self.model.nncf.reset_nncf_modules()
-        self.model.nncf.load_nncf_module_additions(self.nncf_module_additions)
-
-
 class PTStatisticsAggregator(StatisticsAggregator):
     def collect_statistics(self, model: NNCFNetwork, graph: NNCFGraph) -> None:
         with torch.no_grad():
-            # with ModelView(model) as intermediate_model:
-            #    super().collect_statistics(intermediate_model, graph)
             super().collect_statistics(model, graph)
             model.nncf.remove_temporary_ops()
 

tests/common/experimental/test_statistic_collector.py

@@ -329,7 +329,7 @@ class BadStatContainer:
 
 class TemplateTestStatisticCollector:
     @abstractmethod
-    def get_nncf_tensor_cls(self):
+    def get_nncf_tensor(self, value: np.ndarray) -> NNCFTensor:
         pass
 
     @abstractmethod
@@ -366,10 +366,10 @@ def test_empty_tensors_register(self, inplace, any_not_empty):
         collector.register_statistic_branch("A", reducer, aggregator)
         input_name = "input_name"
         full_inputs = TensorCollector.get_tensor_collector_inputs(
-            {input_name: self.get_nncf_tensor_cls()(np.array([100]))}, [(hash(reducer), [input_name])]
+            {input_name: self.get_nncf_tensor(np.array([100]))}, [(hash(reducer), [input_name])]
         )
         empty_inputs = TensorCollector.get_tensor_collector_inputs(
-            {input_name: self.get_nncf_tensor_cls()(np.array([]))}, [(hash(reducer), [input_name])]
+            {input_name: self.get_nncf_tensor(np.array([]))}, [(hash(reducer), [input_name])]
         )
 
         stats = collector.get_statistics()
@@ -385,7 +385,7 @@ def test_empty_tensors_register(self, inplace, any_not_empty):
             assert aggregator._collected_samples == 2
             stats = collector.get_statistics()
             assert len(stats) == 1
-            assert stats["A"] == self.get_nncf_tensor_cls()([100])
+            assert stats["A"] == self.get_nncf_tensor([100])
             return
 
         assert len(aggregator._container) == 0

tests/openvino/native/test_statistic_collector.py

@@ -11,8 +11,10 @@
 
 from typing import Type
 
+import numpy as np
 import pytest
 
+from nncf.common.tensor import NNCFTensor
 from nncf.common.tensor_statistics.statistics import MeanTensorStatistic
 from nncf.common.tensor_statistics.statistics import MedianMADTensorStatistic
 from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic
@@ -26,8 +28,8 @@
 
 
 class TestOVStatisticCollector(TemplateTestStatisticCollector):
-    def get_nncf_tensor_cls(self):
-        return OVNNCFTensor
+    def get_nncf_tensor(self, value: np.ndarray) -> NNCFTensor:
+        return OVNNCFTensor(value)
 
     @pytest.fixture
     def min_max_statistic_cls(self) -> Type[MinMaxTensorStatistic]:

tests/torch/ptq/test_statistic_collector.py

@@ -11,8 +11,11 @@
 
 from typing import Type
 
+import numpy as np
 import pytest
+import torch
 
+from nncf.common.tensor import NNCFTensor
 from nncf.common.tensor_statistics.statistics import MeanTensorStatistic
 from nncf.common.tensor_statistics.statistics import MedianMADTensorStatistic
 from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic
@@ -26,9 +29,9 @@
 from tests.common.experimental.test_statistic_collector import TemplateTestStatisticCollector
 
 
-class TestOVStatisticCollector(TemplateTestStatisticCollector):
-    def get_nncf_tensor_cls(self):
-        return PTNNCFTensor
+class TestPTStatisticCollector(TemplateTestStatisticCollector):
+    def get_nncf_tensor(self, value: np.ndarray) -> NNCFTensor:
+        return PTNNCFTensor(torch.tensor(value))
 
     @pytest.fixture
     def min_max_statistic_cls(self) -> Type[MinMaxTensorStatistic]: