Fix onnx

nncf/common/tensor_statistics/statistic_point.py

@@ -38,7 +38,7 @@ def __eq__(self, other):
     def register_tensor(self, x: TensorType):
         for tensor_collectors in self.algorithm_to_tensor_collectors.values():
             for tensor_collector in tensor_collectors:
-                tensor_collector.register_unnamed_inputs(x)
+                tensor_collector.register_inputs(x)
 
 
 class StatisticPointsContainer(UserDict):

nncf/onnx/statistics/collectors.py

@@ -156,7 +156,12 @@ def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)
 
     def _get_statistics(self) -> ONNXMinMaxTensorStatistic:
-        return ONNXMinMaxTensorStatistic(self._min_values.tensor, self._max_values.tensor)
+        return ONNXMinMaxTensorStatistic(
+            {
+                ONNXMinMaxTensorStatistic.MIN_STAT: self._min_values.tensor,
+                ONNXMinMaxTensorStatistic.MAX_STAT: self._max_values.tensor,
+            }
+        )
 
 
 class ONNXMeanMinMaxStatisticCollector(MeanMinMaxStatisticCollector):
@@ -168,7 +173,12 @@ def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)
 
     def _get_statistics(self) -> ONNXMinMaxTensorStatistic:
-        return ONNXMinMaxTensorStatistic(self._min_aggregate().tensor, self._max_aggregate().tensor)
+        return ONNXMinMaxTensorStatistic(
+            {
+                ONNXMinMaxTensorStatistic.MIN_STAT: self._min_aggregate().tensor,
+                ONNXMinMaxTensorStatistic.MAX_STAT: self._max_aggregate().tensor,
+            }
+        )
 
 
 class ONNXMeanStatisticCollector(MeanStatisticCollector):
@@ -180,7 +190,12 @@ def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)
 
     def _get_statistics(self) -> ONNXMeanTensorStatistic:
-        return ONNXMeanTensorStatistic(self._mean_aggregate().tensor, self._shape())
+        return ONNXMeanTensorStatistic(
+            {
+                ONNXMeanTensorStatistic.MEAN_STAT: self._mean_aggregate().tensor,
+                ONNXMeanTensorStatistic.SHAPE_STAT: self._shape(),
+            }
+        )
 
 
 class ONNXRawStatisticCollector(RawStatisticCollector):
@@ -192,4 +207,4 @@ def _register_input(self, x: ONNXNNCFTensor):
         self._register_input_common(x)
 
     def _get_statistics(self) -> ONNXRawTensorStatistic:
-        return ONNXRawTensorStatistic(self._all_values)
+        return ONNXRawTensorStatistic({ONNXRawTensorStatistic.VALUES_STATS: self._all_values})

nncf/onnx/statistics/statistics.py

@@ -17,18 +17,27 @@
 
 
 class ONNXMinMaxTensorStatistic(MinMaxTensorStatistic):
+    def __init__(self, tensor_collector_output):
+        super().__init__(tensor_collector_output[self.MIN_STAT], tensor_collector_output[self.MAX_STAT])
+
     @staticmethod
     def tensor_eq(tensor1: np.ndarray, tensor2: np.ndarray, rtol=1e-6) -> bool:
         return bool(np.allclose(tensor1, tensor2, rtol=rtol))
 
 
 class ONNXMeanTensorStatistic(MeanTensorStatistic):
+    def __init__(self, tensor_collector_output):
+        super().__init__(tensor_collector_output[self.MEAN_STAT], tensor_collector_output[self.SHAPE_STAT])
+
     @staticmethod
     def tensor_eq(tensor: np.ndarray, rtol=1e-6) -> bool:
         return bool(np.all(tensor, rtol=rtol))
 
 
 class ONNXRawTensorStatistic(RawTensorStatistic):
+    def __init__(self, tensor_collector_output):
+        super().__init__(tensor_collector_output[self.VALUES_STATS])
+
     @staticmethod
     def tensor_eq(tensor: np.ndarray, rtol=1e-6) -> bool:
         return bool(np.all(tensor, rtol=rtol))

nncf/quantization/algorithms/min_max/onnx_backend.py

@@ -120,7 +120,9 @@ def unify_statistics(statistics: List[ONNXMinMaxTensorStatistic]) -> ONNXMinMaxT
             min_values.append(np.array(statistic.min_values).flatten())
         max_values = np.max(max_values, axis=0)
         min_values = np.min(min_values, axis=0)
-        return ONNXMinMaxTensorStatistic(min_values=min_values, max_values=max_values)
+        return ONNXMinMaxTensorStatistic(
+            {ONNXMinMaxTensorStatistic.MIN_STAT: min_values, ONNXMinMaxTensorStatistic.MAX_STAT: max_values}
+        )
 
     @staticmethod
     def _get_input_edges_mapping(nncf_graph: NNCFGraph):

nncf/tensorflow/tensor_statistics/collectors.py

@@ -16,6 +16,7 @@
 
 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 MeanPercentileStatisticCollector
 from nncf.common.tensor_statistics.collectors import MedianMADStatisticCollector

tests/onnx/quantization/common.py

@@ -32,7 +32,9 @@
 
 
 def mock_collect_statistics(mocker):
-    get_statistics_value = ONNXMinMaxTensorStatistic(min_values=-1, max_values=1)
+    get_statistics_value = ONNXMinMaxTensorStatistic(
+        {ONNXMinMaxTensorStatistic.MIN_STAT: -1, ONNXMinMaxTensorStatistic.MAX_STAT: 1}
+    )
     _ = mocker.patch(
         "nncf.quantization.fake_quantize.calculate_quantizer_parameters",
         return_value=FakeQuantizeParameters(np.array(0), np.array(0), np.array(0), np.array(0), 256),

tests/onnx/quantization/test_quantizer_config.py

@@ -9,9 +9,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Tuple
+
 import pytest
 
 from nncf.common.graph.transformations.commands import TargetType
+from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase
 from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXAddLayerMetatype
 from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXConvolutionMetatype
 from nncf.onnx.graph.metatypes.onnx_metatypes import ONNXDepthwiseConvolutionMetatype
@@ -27,6 +30,9 @@
 ParamsCls = TemplateTestQuantizerConfig.TestGetStatisticsCollectorParameters
 
 
+# pylint: disable=protected-access
+
+
 class TestQuantizerConfig(TemplateTestQuantizerConfig):
     def get_algo_backend(self):
         return ONNXMinMaxAlgoBackend()
@@ -37,6 +43,9 @@ def check_is_min_max_statistic_collector(self, tensor_collector):
     def check_is_mean_min_max_statistic_collector(self, tensor_collector):
         assert isinstance(tensor_collector, ONNXMeanMinMaxStatisticCollector)
 
+    def get_reduction_axes(self, reducer: TensorStatisticCollectorBase) -> Tuple[int, ...]:
+        return reducer._reduction_shape
+
     @pytest.fixture(
         params=[
             pytest.param(

tests/openvino/native/quantization/test_quantizer_config.py

@@ -9,13 +9,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Tuple
+
 import pytest
 
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator
 from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator
 from nncf.experimental.common.tensor_statistics.collectors import MinAggregator
 from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
+from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase
 from nncf.openvino.graph.layer_attributes import OVLayerAttributes
 from nncf.openvino.graph.metatypes.openvino_metatypes import OVConvolutionMetatype
 from nncf.openvino.graph.metatypes.openvino_metatypes import OVDepthwiseConvolutionMetatype
@@ -45,6 +48,9 @@ def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorColl
         assert MeanAggregator in aggrs
         assert aggrs[0].__class__ == aggrs[1].__class__
 
+    def get_reduction_axes(self, reducer: TensorReducerBase) -> Tuple[int, ...]:
+        return reducer._reduction_axes
+
     @pytest.fixture(
         params=[
             pytest.param(

tests/post_training/test_templates/test_quantizer_config.py

@@ -12,7 +12,7 @@
 from abc import abstractmethod
 from copy import deepcopy
 from dataclasses import dataclass
-from typing import List
+from typing import List, Tuple
 
 import pytest
 
@@ -53,6 +53,10 @@ def check_is_min_max_statistic_collector(self, tensor_collector):
     def check_is_mean_min_max_statistic_collector(self, tensor_collector):
         pass
 
+    @abstractmethod
+    def get_reduction_axes(self, reducer) -> Tuple[int, ...]:
+        pass
+
     @abstractmethod
     @pytest.fixture
     def single_conv_nncf_graph(self) -> NNCFGraphToTest:
@@ -278,8 +282,8 @@ def test_get_stat_collector(
 
         for reducer in reducers:
             if q_config_per_channel:
-                assert reducer._reduction_axes == params.ref_per_ch_reduction_shape
+                assert self.get_reduction_axes(reducer) == params.ref_per_ch_reduction_shape
             else:
-                assert reducer._reduction_axes == params.ref_per_tensor_reduction_shape
+                assert self.get_reduction_axes(reducer) == params.ref_per_tensor_reduction_shape
 
         assert tensor_collector.num_samples == num_samples

tests/torch/ptq/test_quantizer_config.py

@@ -9,13 +9,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Tuple
+
 import pytest
 
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.experimental.common.tensor_statistics.collectors import MaxAggregator
 from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator
 from nncf.experimental.common.tensor_statistics.collectors import MinAggregator
 from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
+from nncf.experimental.common.tensor_statistics.collectors import TensorReducerBase
 from nncf.quantization.algorithms.min_max.torch_backend import PTMinMaxAlgoBackend
 from tests.post_training.test_templates.models import NNCFGraphToTest
 from tests.post_training.test_templates.models import NNCFGraphToTestDepthwiseConv
@@ -44,6 +47,9 @@ def check_is_mean_min_max_statistic_collector(self, tensor_collector: TensorColl
         assert MeanAggregator in aggrs
         assert aggrs[0].__class__ == aggrs[1].__class__
 
+    def get_reduction_axes(self, reducer: TensorReducerBase) -> Tuple[int, ...]:
+        return reducer._reduction_axes
+
     @pytest.fixture(
         params=[
             (TargetType.PRE_LAYER_OPERATION, "/Sum_1_0", (0, 2), (0, 1, 2)),