Fix tests

nncf/common/tensor_statistics/collectors.py

@@ -324,18 +324,17 @@ def no_outliers_map(
         """
 
     @classmethod
+    @abstractmethod
     def masked_map(cls, x: NNCFTensor, fn: MaskedReduceFN, filter_fn) -> NNCFTensor:
         """ """
 
     @classmethod
+    @abstractmethod
     def sub(cls, a: NNCFTensor, b: NNCFTensor) -> NNCFTensor:
         """"""
 
     @classmethod
-    def filter_by_fn(cls, x: NNCFTensor, filter_fn) -> NNCFTensor:
-        """ """
-
-    @classmethod
+    @abstractmethod
     def non_zero_elements(cls, x: NNCFTensor) -> NNCFTensor:
         """ """
 

nncf/onnx/statistics/collectors.py

@@ -15,6 +15,7 @@
 
 from nncf.common.tensor import NNCFTensor
 from nncf.common.tensor import TensorElementsType
+from nncf.common.tensor_statistics.collectors import MaskedReduceFN
 from nncf.common.tensor_statistics.collectors import MeanMinMaxStatisticCollector
 from nncf.common.tensor_statistics.collectors import MeanStatisticCollector
 from nncf.common.tensor_statistics.collectors import MinMaxStatisticCollector
@@ -106,6 +107,10 @@ def stack(x: Union[List[NNCFTensor], Deque[NNCFTensor]], axis: int = 0) -> NNCFT
     def unstack(x: NNCFTensor, axis: int = 0) -> List[NNCFTensor]:
         return [ONNXNNCFTensor(np.squeeze(e, axis)) for e in np.split(x.tensor, x.tensor.shape[axis], axis=axis)]
 
+    @staticmethod
+    def squeeze(x: NNCFTensor, dim: Optional[int] = None) -> NNCFTensor:
+        raise NotImplementedError()
+
     @staticmethod
     def sum(tensor: NNCFTensor) -> TensorElementsType:
         return np.sum(tensor.tensor)
@@ -129,11 +134,23 @@ def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
     def batch_mean(x: NNCFTensor) -> NNCFTensor:
         return ONNXNNCFTensor(np.mean(x.tensor, axis=0, keepdims=True))
 
+    @classmethod
+    def masked_map(cls, x: NNCFTensor, fn: MaskedReduceFN, filter_fn) -> NNCFTensor:
+        raise NotImplementedError()
+
+    @classmethod
+    def sub(cls, a: NNCFTensor, b: NNCFTensor) -> NNCFTensor:
+        raise NotImplementedError()
+
+    @classmethod
+    def non_zero_elements(cls, x: NNCFTensor) -> NNCFTensor:
+        raise NotImplementedError()
+
 
 class ONNXMinMaxStatisticCollector(MinMaxStatisticCollector):
     @staticmethod
     def _get_processor() -> NNCFCollectorTensorProcessor:
-        return ONNXNNCFCollectorTensorProcessor()
+        return ONNXNNCFCollectorTensorProcessor
 
     def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)
@@ -145,7 +162,7 @@ def _get_statistics(self) -> ONNXMinMaxTensorStatistic:
 class ONNXMeanMinMaxStatisticCollector(MeanMinMaxStatisticCollector):
     @staticmethod
     def _get_processor() -> NNCFCollectorTensorProcessor:
-        return ONNXNNCFCollectorTensorProcessor()
+        return ONNXNNCFCollectorTensorProcessor
 
     def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)
@@ -157,7 +174,7 @@ def _get_statistics(self) -> ONNXMinMaxTensorStatistic:
 class ONNXMeanStatisticCollector(MeanStatisticCollector):
     @staticmethod
     def _get_processor() -> NNCFCollectorTensorProcessor:
-        return ONNXNNCFCollectorTensorProcessor()
+        return ONNXNNCFCollectorTensorProcessor
 
     def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)
@@ -169,7 +186,7 @@ def _get_statistics(self) -> ONNXMeanTensorStatistic:
 class ONNXRawStatisticCollector(RawStatisticCollector):
     @staticmethod
     def _get_processor() -> NNCFCollectorTensorProcessor:
-        return ONNXNNCFCollectorTensorProcessor()
+        return ONNXNNCFCollectorTensorProcessor
 
     def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)

nncf/openvino/statistics/collectors.py

@@ -15,6 +15,7 @@
 
 from nncf.common.tensor import NNCFTensor
 from nncf.common.tensor import TensorElementsType
+from nncf.common.tensor_statistics.collectors import MaskedReduceFN
 from nncf.common.tensor_statistics.collectors import NNCFCollectorTensorProcessor
 from nncf.experimental.common.tensor_statistics.collectors import AbsMaxReducer
 from nncf.experimental.common.tensor_statistics.collectors import AbsQuantileReducer
@@ -135,6 +136,10 @@ def stack(x: Union[List[NNCFTensor], Deque[NNCFTensor]], axis: int = 0) -> NNCFT
     def unstack(x: NNCFTensor, axis: int = 0) -> List[NNCFTensor]:
         return [OVNNCFTensor(np.squeeze(e, axis)) for e in np.split(x.tensor, x.tensor.shape[axis], axis=axis)]
 
+    @staticmethod
+    def squeeze(x: NNCFTensor, dim: Optional[int] = None) -> NNCFTensor:
+        raise NotImplementedError()
+
     @staticmethod
     def sum(tensor: NNCFTensor) -> TensorElementsType:
         return np.sum(tensor.tensor)
@@ -146,6 +151,18 @@ def quantile(
         result = np.quantile(tensor.tensor, quantile, axis, keepdims=keepdims)
         return [OVNNCFTensor(x) for x in result]
 
+    @classmethod
+    def masked_map(cls, x: NNCFTensor, fn: MaskedReduceFN, filter_fn) -> NNCFTensor:
+        raise NotImplemented()
+
+    @classmethod
+    def sub(cls, a: NNCFTensor, b: NNCFTensor) -> NNCFTensor:
+        raise NotImplemented()
+
+    @classmethod
+    def non_zero_elements(cls, x: NNCFTensor) -> NNCFTensor:
+        raise NotImplemented()
+
 
 class OVNoopReducer(NoopReducer):
     def get_output_names(self, target_node_name: str, port_id: int) -> List[str]:

nncf/quantization/algorithms/bias_correction/onnx_backend.py

@@ -43,7 +43,7 @@
 class ONNXBiasCorrectionAlgoBackend(BiasCorrectionAlgoBackend):
     @property
     def tensor_processor(self) -> ONNXNNCFCollectorTensorProcessor:
-        return ONNXNNCFCollectorTensorProcessor()
+        return ONNXNNCFCollectorTensorProcessor
 
     @property
     def types_to_insert_bias(self):

nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py

@@ -42,7 +42,7 @@ def types_to_insert_bias(self):
 
     @property
     def tensor_processor(self) -> ONNXNNCFCollectorTensorProcessor:
-        return ONNXNNCFCollectorTensorProcessor()
+        return ONNXNNCFCollectorTensorProcessor
 
     @staticmethod
     def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> ONNXTargetPoint:

nncf/quantization/algorithms/smooth_quant/openvino_backend.py

@@ -69,8 +69,8 @@ def get_abs_max_channel_collector(
         num_samples: int, stats_reduction_shape: Tuple[int], inplace: bool, branch_key: str
     ) -> TensorCollector:
         collector = TensorCollector()
-        reducer = OVAbsMaxReducer(stats_reduction_shape, inplace)
-        aggregator = MaxAggregator(OVNNCFCollectorTensorProcessor, num_samples)
+        reducer = OVAbsMaxReducer(reduction_axes=stats_reduction_shape, inplace=inplace)
+        aggregator = MaxAggregator(tensor_processor=OVNNCFCollectorTensorProcessor, num_samples=num_samples)
         collector.register_statistic_branch(branch_key, reducer, aggregator)
         return collector
 

nncf/tensorflow/tensor_statistics/collectors.py

@@ -85,6 +85,10 @@ def unstack(x: NNCFTensor, axis: int = 0) -> List[NNCFTensor]:
         tensor_list = tf.unstack(tensor, axis=axis)
         return [TFNNCFTensor(t) for t in tensor_list]
 
+    @staticmethod
+    def squeeze(x: NNCFTensor, dim: Optional[int] = None) -> NNCFTensor:
+        raise NotImplementedError()
+
     @staticmethod
     def sum(tensor: NNCFTensor) -> TensorElementsType:
         return tf.reduce_sum(tensor.tensor).numpy()
@@ -105,6 +109,18 @@ def no_outliers_map(
     ):
         raise NotImplementedError()
 
+    @classmethod
+    def masked_map(cls, x: NNCFTensor, fn: MaskedReduceFN, filter_fn) -> NNCFTensor:
+        raise NotImplementedError()
+
+    @classmethod
+    def sub(cls, a: NNCFTensor, b: NNCFTensor) -> NNCFTensor:
+        raise NotImplementedError()
+
+    @classmethod
+    def non_zero_elements(cls, x: NNCFTensor) -> NNCFTensor:
+        raise NotImplementedError()
+
 
 class TFMinMaxStatisticCollector(MinMaxStatisticCollector):
     @staticmethod

tests/post_training/test_templates/test_calculate_quantizer_parameters.py

@@ -19,6 +19,7 @@
 from nncf.common.quantization.structs import QuantizationMode
 from nncf.common.quantization.structs import QuantizerConfig
 from nncf.common.quantization.structs import QuantizerGroup
+from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic
 from nncf.quantization.fake_quantize import FakeQuantizeParameters
 from nncf.quantization.fake_quantize import calculate_quantizer_parameters
 from tests.post_training.conftest import FQ_CALCULATED_PARAMETERS_PATH
@@ -213,7 +214,9 @@ def test_calculate_quantizer_parameters(self, case_to_test):
         else:
             max_values = np.amax(data, axis=axes, keepdims=q_config.per_channel)
 
-        statistics = self.tensor_statistic(max_values=max_values, min_values=min_values)
+        statistics = self.tensor_statistic(
+            {MinMaxTensorStatistic.MIN_STAT: max_values, MinMaxTensorStatistic.MAX_STAT: min_values}
+        )
 
         if not case_to_test.should_fail:
             fq_params = calculate_quantizer_parameters(statistics, q_config, quant_group, narrow_range, half_range)

tests/post_training/test_templates/test_channel_alignment.py

@@ -496,4 +496,5 @@ def test_statistic_collectors(self, inplace_ref, q_ref):
         for aggr in statistic_collector.aggregators.values():
             assert isinstance(aggr, MedianAggregator)
             assert aggr.num_samples == num_samples_ref
-            assert not aggr._use_per_sample_stats
+            assert not aggr._keepdims
+            assert aggr._aggregation_axes == (0,)

tests/torch/ptq/helpers.py

@@ -94,7 +94,7 @@ def mock_collect_statistics(mocker):
     min_, max_ = 0.0, 1.0
     min_, max_ = torch.tensor(min_), torch.tensor(max_)
     _ = mocker.patch(
-        "nncf.common.tensor_statistics.collectors.TensorStatisticCollectorBase.get_statistics",
+        "nncf.experimental.common.tensor_statistics.collectors.TensorCollector.get_statistics",
         return_value=PTMinMaxTensorStatistic(
             {PTMinMaxTensorStatistic.MIN_STAT: min_, PTMinMaxTensorStatistic.MAX_STAT: max_}
         ),