pre-commit

docs/api/source/conf.py

@@ -5,8 +5,7 @@
 import sys
 from dataclasses import dataclass
 from dataclasses import field
-from typing import Any
-from typing import Dict, List
+from typing import Any, Dict, List
 
 from sphinx.ext.autodoc import mock
 
@@ -17,9 +16,7 @@
 author = "Intel Corporation"
 release = "v2.4.0"
 
-extensions = ["autoapi.extension",
-              "sphinx.ext.autodoc",
-              "sphinx.ext.linkcode"]
+extensions = ["autoapi.extension", "sphinx.ext.autodoc", "sphinx.ext.linkcode"]
 
 # The below line in conjunction with specifying the 'sphinx.ext.autodoc' as extension
 # makes the type hints from the function signature disappear from the signature in the HTML and instead
@@ -30,11 +27,7 @@
 autodoc_typehints = "description"
 
 autoapi_dirs = ["../../../nncf"]
-autoapi_options = ["members",
-                   "show-inheritance",
-                   "show-module-summary",
-                   "special-members",
-                   "imported-members"]
+autoapi_options = ["members", "show-inheritance", "show-module-summary", "special-members", "imported-members"]
 
 autoapi_template_dir = "_autoapi_templates"
 autoapi_keep_files = True
@@ -161,21 +154,21 @@ def linkcode_resolve(domain, info):
     # sphinx.ext.linkcode interface; will link to Github here.
     target_ref = "develop"
     base_url = f"https://github.com/openvinotoolkit/nncf/blob/{target_ref}/"
-    if not info['module']:
+    if not info["module"]:
         return None
-    fullname = info["module"] + '.' + info["fullname"]
+    fullname = info["module"] + "." + info["fullname"]
 
     if fullname not in api_info.api_names_vs_obj_dict:
         # Got a method/property description, info["fullname"] contained class.method_name
-        fullname = fullname.rpartition('.')[0]
+        fullname = fullname.rpartition(".")[0]
 
     if fullname in api_info.canonical_name_vs_fqn:
         fullname = api_info.canonical_name_vs_fqn[fullname]
-        module_name = fullname.rpartition('.')[0]
+        module_name = fullname.rpartition(".")[0]
     else:
         module_name = info["module"]
-    filename = module_name.replace('.', '/')
-    complete_url = base_url + filename + '.py'
+    filename = module_name.replace(".", "/")
+    complete_url = base_url + filename + ".py"
     return complete_url
 
 

docs/styleguide/PyGuide.md

@@ -771,10 +771,11 @@ if __name__ == "__main__":
 <a id="5-api-doc-rules"></a>
 <a id="api-doc-rules"></a>
 ## 5 API documentation rules
-All functions and classes that belong to NNCF API should be documented.
+All functions and classes that belong to NNCF API should be documented. 
+The documentation should utilize the reStructuredText (.rst) format for specifying parameters, return types and otherwise formatting the docstring, since the docstring is used as a source for generating the HTML API documentation with Sphinx.
 
 Argument descriptions for `__init__(...)` methods of API classes should be located in the docstring of the class itself, not the docstring of the `__init__(...)` method.
 This is required so that the autogenerated API documentation is rendered properly.
 
 If the autogenerated API documentation does not show type hints for certain arguments despite the fact that the type hints are present in the object's implementation code, 
-or if the type hints do not refer to the API symbol's canonical alias, then the type hint should be explicitly declared in the docstring using the `:type:` directive:
+or if the type hints do not refer to the API symbol's canonical alias, then the type hint should be explicitly declared in the docstring using the `:type *param_name*:` directive (or `:rtype:` for return types).

nncf/api/compression.py

@@ -326,6 +326,7 @@ def maximal_compression_rate(self) -> float:
         Returns the maximal model compression rate supported by the compression controller.
         """
 
+
 @api()
 class CompressionAlgorithmBuilder(ABC):
     """

nncf/common/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-agnostic or backend-common NNCF functionality.
-"""
+"""

nncf/common/accuracy_aware_training/__init__.py

@@ -8,9 +8,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""
+Accuracy Aware Training functionality.
+"""
 
 from nncf.common.accuracy_aware_training.training_loop import AccuracyAwareTrainingMode
 from nncf.common.accuracy_aware_training.training_loop import create_accuracy_aware_training_loop
-"""
-Accuracy Aware Training functionality.
-"""

nncf/common/accuracy_aware_training/training_loop.py

@@ -15,10 +15,7 @@
 from abc import ABC
 from abc import abstractmethod
 from functools import partial
-from typing import Callable
-from typing import Optional
-from typing import TypeVar
-from typing import Union
+from typing import Callable, Optional, TypeVar, Union
 
 import numpy as np
 from scipy.interpolate import interp1d
@@ -91,23 +88,24 @@ class BaseEarlyExitCompressionTrainingLoop(TrainingLoop, ABC):
     """
     Base class to generalize functionality of derived training loop classes.
     """
+
     def __init__(self, compression_controller: CompressionAlgorithmController):
         self.runner = None  # type: BaseAccuracyAwareTrainingRunner
         self.compression_controller = compression_controller
         self._current_compression_rate = None
 
     def run(
-            self,
-            model: TModel,
-            train_epoch_fn: Callable,
-            validate_fn: Callable,
-            configure_optimizers_fn: Callable = None,
-            dump_checkpoint_fn: Callable = None,
-            load_checkpoint_fn: Callable = None,
-            early_stopping_fn: Callable = None,
-            tensorboard_writer: Optional[TensorboardWriterType] = None,
-            log_dir: Union[pathlib.Path, str] = None,
-            update_learning_rate_fn: Callable = None,
+        self,
+        model: TModel,
+        train_epoch_fn: Callable,
+        validate_fn: Callable,
+        configure_optimizers_fn: Callable = None,
+        dump_checkpoint_fn: Callable = None,
+        load_checkpoint_fn: Callable = None,
+        early_stopping_fn: Callable = None,
+        tensorboard_writer: Optional[TensorboardWriterType] = None,
+        log_dir: Union[pathlib.Path, str] = None,
+        update_learning_rate_fn: Callable = None,
     ):
         self.runner.initialize_training_loop_fns(
             train_epoch_fn,
@@ -275,7 +273,7 @@ def __init__(
         verbose: bool = True,
         minimal_compression_rate: float = 0.0,
         maximal_compression_rate: float = 0.95,
-        dump_checkpoints: bool = True
+        dump_checkpoints: bool = True,
     ):
         super().__init__(compression_controller)
         self.adaptive_controller = self._get_adaptive_compression_ctrl(compression_controller)
@@ -334,17 +332,17 @@ def remove_registry_prefix(algo_name):
         )
 
     def run(
-            self,
-            model: TModel,
-            train_epoch_fn: Callable,
-            validate_fn: Callable,
-            configure_optimizers_fn: Callable = None,
-            dump_checkpoint_fn: Callable = None,
-            load_checkpoint_fn: Callable = None,
-            early_stopping_fn: Callable = None,
-            tensorboard_writer: Optional[TensorboardWriterType] = None,
-            log_dir: Union[pathlib.Path, str] = None,
-            update_learning_rate_fn: Callable = None,
+        self,
+        model: TModel,
+        train_epoch_fn: Callable,
+        validate_fn: Callable,
+        configure_optimizers_fn: Callable = None,
+        dump_checkpoint_fn: Callable = None,
+        load_checkpoint_fn: Callable = None,
+        early_stopping_fn: Callable = None,
+        tensorboard_writer: Optional[TensorboardWriterType] = None,
+        log_dir: Union[pathlib.Path, str] = None,
+        update_learning_rate_fn: Callable = None,
     ):
         self.runner.initialize_training_loop_fns(
             train_epoch_fn,

nncf/common/initialization/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Functions and classes utilized during the user dataset-driven initialization of the compression algorithms.
-"""
+"""

nncf/common/quantization/structs.py

@@ -28,6 +28,7 @@ class QuantizationMode:
     :param SYMMETRIC:
     :param ASYMMETRIC:
     """
+
     SYMMETRIC = "symmetric"
     ASYMMETRIC = "asymmetric"
 
@@ -321,6 +322,7 @@ class QuantizationPreset(Enum):
     """
     An enum with values corresponding to the available quantization presets.
     """
+
     PERFORMANCE = "performance"
     MIXED = "mixed"
 

nncf/config/config.py

@@ -11,8 +11,7 @@
 
 from copy import deepcopy
 from pathlib import Path
-from typing import Dict
-from typing import List, Optional, Type
+from typing import Dict, List, Optional, Type
 
 import jsonschema
 import jstyleson as json

nncf/config/structures.py

@@ -90,6 +90,7 @@ class ModelEvaluationArgs(NNCFExtraConfigStruct):
     :param eval_fn: A function accepting a single argument - the model object - and returning the model's metric on
         the evaluation split of the dataset corresponding to the model.
     """
+
     def __init__(self, eval_fn: Callable):
         self.eval_fn = eval_fn
 

nncf/onnx/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Base subpackage for NNCF ONNX functionality.
-"""
+"""

nncf/openvino/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Base subpackage for NNCF OpenVINO functionality.
-"""
+"""

nncf/tensorflow/helpers/callback_creation.py

@@ -19,10 +19,12 @@
 
 
 @api(canonical_alias="nncf.tensorflow.create_compression_callbacks")
-def create_compression_callbacks(compression_ctrl: CompressionAlgorithmController,
-                                 log_tensorboard: bool = True,
-                                 log_text: bool = True,
-                                 log_dir: bool = None):
+def create_compression_callbacks(
+    compression_ctrl: CompressionAlgorithmController,
+    log_tensorboard: bool = True,
+    log_text: bool = True,
+    log_dir: bool = None,
+):
     compression_controllers = (
         compression_ctrl.child_ctrls
         if isinstance(compression_ctrl, CompositeCompressionAlgorithmController)

nncf/tensorflow/pruning/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of pruning algorithms.
-"""
+"""

nncf/tensorflow/pruning/filter_pruning/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of filter-wise pruning.
-"""
+"""

nncf/tensorflow/quantization/algorithm.py

@@ -696,6 +696,7 @@ class QuantizationController(BaseCompressionAlgorithmController):
     """
     Controller for the quantization algorithm in TensorFlow.
     """
+
     def __init__(self, target_model, config: NNCFConfig, op_names: List[str]):
         super().__init__(target_model)
         self._scheduler = BaseCompressionScheduler()

nncf/tensorflow/sparsity/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of sparsity algorithms.
-"""
+"""

nncf/tensorflow/sparsity/magnitude/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of magnitude sparsity.
-"""
+"""

nncf/tensorflow/sparsity/rb/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of regularization-based (RB) sparsity.
-"""
+"""

nncf/tensorflow/sparsity/rb/algorithm.py

@@ -113,6 +113,7 @@ class RBSparsityController(BaseSparsityController):
     """
     Controller class for regularization-based (RB) sparsity in TF.
     """
+
     def __init__(self, target_model, config: NNCFConfig, op_names: List[str]):
         super().__init__(target_model, op_names)
         algo_config = extract_algo_specific_config(config, "rb_sparsity")

nncf/torch/knowledge_distillation/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of knowledge distillation algorithms.
-"""
+"""

nncf/torch/knowledge_distillation/algo.py

@@ -61,6 +61,7 @@ class KnowledgeDistillationController(PTCompressionAlgorithmController):
     """
     Controller for the knowledge distillation in PT.
     """
+
     def __init__(
         self, target_model: NNCFNetwork, original_model: nn.Module, kd_type: str, scale: float, temperature: float
     ):

nncf/torch/pruning/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of pruning algorithms.
-"""
+"""

nncf/torch/pruning/filter_pruning/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of filter-wise pruning.
-"""
+"""

nncf/torch/quantization/algo.py

@@ -1290,6 +1290,7 @@ class QuantizationControllerBase(PTCompressionAlgorithmController):
     """
     Base controller class for the quantization controllers in PT.
     """
+
     def enable_activation_quantization(self):
         raise NotImplementedError
 
@@ -1311,6 +1312,7 @@ class QuantizationController(QuantizationControllerBase):
     """
     Controller for the quantization algorithm in PT.
     """
+
     def __init__(
         self,
         target_model: NNCFNetwork,

nncf/torch/sparsity/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementations of sparsity algorithms.
-"""
+"""

nncf/torch/sparsity/base_algo.py

@@ -100,6 +100,7 @@ class BaseSparsityAlgoController(PTCompressionAlgorithmController, SparsityContr
     """
     Base class for sparsity algorithm controllers in PT.
     """
+
     def __init__(self, target_model: NNCFNetwork, sparsified_module_info: List[SparseModuleInfo]):
         super().__init__(target_model)
         self._loss = ZeroCompressionLoss(get_model_device(target_model))

nncf/torch/sparsity/const/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementation of the auxiliary "constant" sparsity algorithm.
-"""
+"""

nncf/torch/sparsity/const/algo.py

@@ -40,6 +40,7 @@ class ConstSparsityController(BaseSparsityAlgoController):
     """
     Controller for the auxiliary constant sparsity algorithm in PT.
     """
+
     def freeze(self):
         pass
 

nncf/torch/sparsity/magnitude/__init__.py

@@ -10,4 +10,4 @@
 # limitations under the License.
 """
 Backend-specific implementation of magnitude sparsity algorithm.
-"""
+"""