Test finishing

Cleanup

nncf/common/graph/transformations/commands.py

@@ -35,7 +35,6 @@ class TransformationPriority(IntEnum):
     FP32_TENSOR_STATISTICS_OBSERVATION = 1
     PRUNING_PRIORITY = 2
     SPARSIFICATION_PRIORITY = 3
-    OP_INSERTION_PRIORITY = 4
     QUANTIZATION_PRIORITY = 11
 
 

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/dynamic_graph/context.py

@@ -95,7 +95,7 @@ def __init__(self):
 
         self._post_hooks = defaultdict(OrderedDict)
         self._pre_hooks: Dict[PreHookId, List[Callable]] = defaultdict(OrderedDict)
-        self._hooks_counter = 0
+        self._hooks_counter = -1
 
         self._threading = CopySafeThreadingVars()
 

nncf/torch/graph/transformations/command_creation.py

@@ -16,7 +16,6 @@
 
 from nncf.common.graph.graph import NNCFNode
 from nncf.common.graph.transformations.commands import TargetType
-from nncf.common.graph.transformations.commands import TransformationPriority
 from nncf.torch.graph.transformations.commands import PTBiasCorrectionCommand
 from nncf.torch.graph.transformations.commands import PTInsertionCommand
 from nncf.torch.graph.transformations.commands import PTSharedFnInsertionCommand
@@ -60,9 +59,10 @@ def forward(self, x):
 def multiply_insertion_command(
     target_nodes: List[NNCFNode], scale_value: Tensor, scale_node_name: str, input_port_id: int
 ) -> PTInsertionCommand:
-    commands = []
+    target_points = []
     for target_node in target_nodes:
-        target_point = PTTargetPoint(TargetType.OPERATOR_PRE_HOOK, target_node.node_name, input_port_id=input_port_id)
-        commands.append(PTInsertionCommand(target_point, None, priority=TransformationPriority.OP_INSERTION_PRIORITY))
+        target_points.append(
+            PTTargetPoint(TargetType.OPERATOR_PRE_HOOK, target_node.node_name, input_port_id=input_port_id)
+        )
 
-    return PTSharedFnInsertionCommand(commands, SQMultiply(scale_value), scale_node_name)
+    return PTSharedFnInsertionCommand(target_points, SQMultiply(scale_value), scale_node_name)

nncf/torch/nncf_network.py

@@ -17,7 +17,7 @@
 from copy import deepcopy
 from enum import Enum
 from enum import IntEnum
-from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, TypeVar
+from typing import Callable, Dict, Iterator, List, Optional, Tuple, TypeVar
 
 import torch
 from torch import nn
@@ -345,25 +345,6 @@ def reset_nncf_modules(self):
             module = self.get_module_by_scope(some_scope)
             module.reset()
 
-    def get_shallow_copy(self) -> "NNCFNetwork":
-        from nncf.torch.utils import load_module_state
-        from nncf.torch.utils import save_module_state
-
-        saved_state = save_module_state(self._model_ref)
-        new_interface = NNCFNetworkInterface(
-            self._model_ref,
-            self._input_infos,
-            self._user_dummy_forward_fn,
-            self._wrap_inputs_fn,
-            self._scopes_without_shape_matching,
-            self._ignored_scopes,
-            self._target_scopes,
-            wrap_outputs_fn=self._wrap_outputs_fn,
-        )
-        self._model_ref._nncf = new_interface
-        load_module_state(self._model_ref, saved_state)
-        return self._model_ref
-
     def get_clean_shallow_copy(self) -> "NNCFNetwork":
         # WARNING: Will reset pre- and post-ops of the underlying model. Use save_nncf_module_additions
         # and load_nncf_module_additions to preserve these, or temporary_clean_view().
@@ -395,9 +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)
@@ -802,14 +780,6 @@ def strip(self, do_copy: bool = True) -> "NNCFNetwork":
         return self.compression_controller.strip(do_copy)
 
 
-class TemporaryOp:
-    def __init__(self, op: Callable) -> None:
-        self._op = op
-
-    def __call__(self, *args: Any, **kwargs: Any) -> Any:
-        return self._op(*args, **kwargs)
-
-
 class NNCFNetworkMeta(type):
     """
     Metaclass for the NNCFNetwork mixin. Has magic methods defined so that the original model object could be

nncf/torch/quantization/external_quantizer.py

@@ -14,7 +14,6 @@
 from nncf.torch.quantization.debug_interface import QuantizationDebugInterface
 
 EXTERNAL_QUANTIZERS_STORAGE_NAME = "external_quantizers"
-EXTERNAL_OP_STORAGE_NAME = "external_op"
 EXTERNAL_QUANTIZERS_STORAGE_PREFIX = "_nncf." + EXTERNAL_QUANTIZERS_STORAGE_NAME
 
 

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/common/test_statistics_aggregator.py

@@ -49,8 +49,9 @@ class BCStatsCollectors(Enum):
 
 
 class TemplateTestStatisticsAggregator:
+    @classmethod
     @abstractmethod
-    def get_min_max_algo_backend_cls(self) -> Type[MinMaxAlgoBackend]:
+    def get_min_max_algo_backend_cls(cls) -> Type[MinMaxAlgoBackend]:
         pass
 
     @abstractmethod
@@ -73,6 +74,7 @@ def get_statistics_aggregator(self, dataset):
     def get_dataset(self, samples):
         pass
 
+    @staticmethod
     @abstractmethod
     def get_target_point(self, target_type: TargetType) -> TargetPoint:
         pass
@@ -631,10 +633,11 @@ def filter_func(point):
                     assert ref.shape == val.shape
                 assert np.allclose(val, ref)
 
+    @classmethod
     def create_statistics_point(
-        self, model, q_config, target_point, subset_size, algorithm_name, inplace_statistics, range_estimator
+        cls, model, q_config, target_point, subset_size, algorithm_name, inplace_statistics, range_estimator
     ):
-        algo_backend = self.get_min_max_algo_backend_cls()
+        algo_backend = cls.get_min_max_algo_backend_cls()
         nncf_graph = NNCFGraphFactory.create(model)
         tensor_collector = algo_backend.get_statistic_collector(
             range_estimator,

tests/onnx/test_statistics_aggregator.py

@@ -32,7 +32,8 @@
 
 
 class TestStatisticsAggregator(TemplateTestStatisticsAggregator):
-    def get_min_max_algo_backend_cls(self) -> Type[ONNXMinMaxAlgoBackend]:
+    @classmethod
+    def get_min_max_algo_backend_cls(cls) -> Type[ONNXMinMaxAlgoBackend]:
         return ONNXMinMaxAlgoBackend
 
     def get_bias_correction_algo_backend_cls(self) -> Type[ONNXBiasCorrectionAlgoBackend]:
@@ -65,7 +66,8 @@ def transform_fn(data_item):
 
         return Dataset(samples, transform_fn)
 
-    def get_target_point(self, target_type: TargetType):
+    @staticmethod
+    def get_target_point(target_type: TargetType):
         target_node_name = IDENTITY_NODE_NAME
         port_id = 0
         if target_type == TargetType.OPERATION_WITH_WEIGHTS:

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/openvino/native/test_statistics_aggregator.py

@@ -50,7 +50,8 @@ def get_StatisticAgregatorTestModel(input_shape, kernel):
 
 
 class TestStatisticsAggregator(TemplateTestStatisticsAggregator):
-    def get_min_max_algo_backend_cls(self) -> Type[OVMinMaxAlgoBackend]:
+    @classmethod
+    def get_min_max_algo_backend_cls(cls) -> Type[OVMinMaxAlgoBackend]:
         return OVMinMaxAlgoBackend
 
     def get_bias_correction_algo_backend_cls(self) -> Type[OVBiasCorrectionAlgoBackend]:
@@ -82,7 +83,8 @@ def get_target_point_cls(self):
     def get_dataset(self, samples):
         return Dataset(samples, lambda data: {INPUT_NAME: data})
 
-    def get_target_point(self, target_type: TargetType) -> TargetPoint:
+    @staticmethod
+    def get_target_point(target_type: TargetType) -> TargetPoint:
         target_node_name = INPUT_NAME
         port_id = 0
         if target_type == TargetType.OPERATION_WITH_WEIGHTS:

tests/torch/helpers.py

@@ -549,6 +549,12 @@ def check_with_reference(self):
         hooks = self._target_model.nncf._compressed_context._post_hooks
         self._check_pre_post_hooks(hooks, self._ref_hooks[TargetType.OPERATOR_POST_HOOK])
 
+    def clear(self):
+        """
+        Removes all recorded references.
+        """
+        self._ref_hooks.clear()
+
     @staticmethod
     def _check_weight_update_hooks(ref_hooks):
         for target_module, ref_hooks_per_module in ref_hooks.items():

tests/torch/ptq/test_smooth_quant.py

@@ -58,7 +58,7 @@ def get_node_name_map(self) -> Dict[str, str]:
     @staticmethod
     def get_target_node_name(command: TransformationCommand):
         if isinstance(command, PTSharedFnInsertionCommand):
-            return command.target_commands[0].target_point.target_node_name
+            return command.target_points[0].target_node_name
         return command.target_point.target_node_name
 
     @staticmethod

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]: