Comments

nncf/experimental/quantization/algorithms/post_training/algorithm.py

@@ -17,7 +17,7 @@
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.common.utils.backend import BackendType
 from nncf.experimental.quantization.algorithms.post_training.pipeline import experimental_create_ptq_pipeline
-from nncf.experimental.quantization.quantizer.quantizer import Quantizer as NNCFQuantizer
+from nncf.experimental.quantization.quantizers.quantizer import Quantizer
 from nncf.quantization.advanced_parameters import AdvancedBiasCorrectionParameters
 from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters
 from nncf.quantization.advanced_parameters import RangeEstimatorParameters
@@ -37,7 +37,7 @@ class ExperimentalPostTrainingQuantization(Algorithm):
 
     def __init__(
         self,
-        quantizer: NNCFQuantizer,
+        quantizer: Quantizer,
         subset_size: int = 300,
         fast_bias_correction: Optional[bool] = True,
         smooth_quant: bool = False,
@@ -48,7 +48,7 @@ def __init__(
         batchwise_statistics: bool = False,
     ):
         """
-        :param quantizer: NNCFQuantizer to use in MiMaxRangeInit algorithm.
+        :param quantizer: Quantizer to use in MiMaxRangeInit algorithm.
         :param subset_size: Size of a subset to calculate activations
             statistics used for quantization.
         :param fast_bias_correction: Setting this option to `False` enables a different

nncf/experimental/quantization/algorithms/post_training/pipeline.py

@@ -12,7 +12,7 @@
 from typing import Optional, TypeVar
 
 from nncf.experimental.quantization.algorithms.range_estimator.algorithm import MinMaxRangeEstimator
-from nncf.experimental.quantization.quantizer.quantizer import Quantizer as NNCFQuantizer
+from nncf.experimental.quantization.quantizers.quantizer import Quantizer
 from nncf.quantization.advanced_parameters import AdvancedBiasCorrectionParameters
 from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters
 from nncf.quantization.advanced_parameters import RangeEstimatorParameters
@@ -27,7 +27,7 @@
 
 
 def experimental_create_ptq_pipeline(
-    quantizer: NNCFQuantizer,
+    quantizer: Quantizer,
     subset_size: int = 300,
     fast_bias_correction: Optional[bool] = True,
     smooth_quant: bool = False,
@@ -45,7 +45,7 @@ def experimental_create_ptq_pipeline(
         2) MinMaxRangeInit
         3) FastBiasCorrection or BiasCorrection
 
-    :param quantizer: NNCFQuantizer to use in MiMaxRangeInit algorithm.
+    :param quantizer: Quantizer to use in MiMaxRangeInit algorithm.
     :param subset_size: Size of a subset to calculate activations
         statistics used for quantization.
     :param fast_bias_correction: Setting this option to `False` enables a different

nncf/experimental/quantization/algorithms/range_estimator/algorithm.py

@@ -15,7 +15,7 @@
 from nncf.common.graph.graph import NNCFGraph
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.common.utils.backend import BackendType
-from nncf.experimental.quantization.quantizer.quantizer import Quantizer as NNCFQuantizer
+from nncf.experimental.quantization.quantizers.quantizer import Quantizer
 from nncf.quantization.algorithms.algorithm import Algorithm
 from nncf.quantization.algorithms.min_max.algorithm import MinMaxQuantization
 from nncf.quantization.range_estimator import RangeEstimatorParameters
@@ -26,15 +26,15 @@
 class MinMaxRangeEstimator(Algorithm):
     def __init__(
         self,
-        quantizer: NNCFQuantizer,
+        quantizer: Quantizer,
         subset_size: int = 300,
         inplace_statistics: bool = True,
         batchwise_statistics: bool = False,
         activations_range_estimator_params: Optional[RangeEstimatorParameters] = None,
         weights_range_estimator_params: Optional[RangeEstimatorParameters] = None,
     ):
         """
-        :param quantizer: Instance of NNCFQuantizer to retrieve a quantization config
+        :param quantizer: Instance of Quantizer to retrieve a quantization config
             for the given model.
         :param subset_size: Size of a subset to calculate activations statistics used
             for quantization, defaults to 300.

nncf/experimental/quantization/quantizer/torch_ao_adapter.py → nncf/experimental/quantization/quantizers/torch_ao_adapter.py

@@ -16,7 +16,7 @@
 
 import torch
 import torch.fx
-from torch.ao.quantization.quantizer import Quantizer
+from torch.ao.quantization.quantizer import Quantizer as TorchAOQuantizer
 from torch.ao.quantization.quantizer.quantizer import QuantizationSpec
 from torch.ao.quantization.quantizer.quantizer import QuantizationSpecBase
 from torch.ao.quantization.quantizer.quantizer import SharedQuantizationSpec
@@ -31,18 +31,18 @@
 from nncf.common.quantization.quantizer_setup import WeightQuantizationInsertionPoint
 from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode
 from nncf.common.quantization.structs import QuantizerConfig
-from nncf.experimental.quantization.quantizer.quantizer import Quantizer as NNCFQuantizer
+from nncf.experimental.quantization.quantizers.quantizer import Quantizer
 from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter
 
 EdgeOrNode = Union[Tuple[torch.fx.Node, torch.fx.Node]]
 
 
-class TorchAOQuantizerAdapter(NNCFQuantizer):
+class TorchAOQuantizerAdapter(Quantizer):
     """
     Implementation of the NNCF Quantizer interface for any given torch.ao quantizer.
     """
 
-    def __init__(self, quantizer: Quantizer):
+    def __init__(self, quantizer: TorchAOQuantizer):
         self._quantizer = quantizer
 
     def get_quantization_setup(self, model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> SingleConfigQuantizerSetup:

nncf/experimental/torch/fx/quantization/quantize_pt2e.py

@@ -27,7 +27,7 @@
 from nncf.common.logging import nncf_logger
 from nncf.data import Dataset
 from nncf.experimental.quantization.algorithms.post_training.algorithm import ExperimentalPostTrainingQuantization
-from nncf.experimental.quantization.quantizer.torch_ao_adapter import TorchAOQuantizerAdapter
+from nncf.experimental.quantization.quantizers.torch_ao_adapter import TorchAOQuantizerAdapter
 from nncf.experimental.torch.fx.constant_folding import constant_fold
 from nncf.experimental.torch.fx.transformations import QUANTIZE_NODE_TARGETS
 from nncf.quantization.advanced_parameters import AdvancedBiasCorrectionParameters