Merge branch 'develop' into align-compression-subgraphs

.github/workflows/nightly.yml

@@ -10,4 +10,4 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
       - uses: actions/checkout@v4
-      - uses: AlexanderDokuchaev/md-dead-link-check@v0.6
+      - uses: AlexanderDokuchaev/md-dead-link-check@v0.8

.github/workflows/pre-commit-linters.yml

@@ -23,4 +23,4 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
       - uses: actions/checkout@v4
-      - uses: AlexanderDokuchaev/md-dead-link-check@v0.6
+      - uses: AlexanderDokuchaev/md-dead-link-check@v0.8

.pre-commit-config.yaml

@@ -17,7 +17,7 @@ repos:
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.1.3
+    rev: v0.3.7
     hooks:
       - id: ruff
 

README.md

@@ -374,6 +374,10 @@ pip install nncf[torch]
 
 Other viable options besides `[torch]` are `[tf]`, `[onnx]` and `[openvino]`.
 
+> [!WARNING]
+> The way to install the module package with the extra dependency like `pip install nncf[torch]` is deprecated and will be removed in a future release.
+> Instead, it is recommended to install additional dependencies separately using the pip install command (e.g., `pip install torch`) or by explicitly specifying the dependency in your requirements file.
+
 NNCF is also available via [conda](https://anaconda.org/conda-forge/nncf):
 
 ```bash
@@ -383,7 +387,7 @@ conda install -c conda-forge nncf
 ### System requirements
 
 - Ubuntu\* 18.04 or later (64-bit)
-- Python\* 3.7 or later
+- Python\* 3.8 or later
 - Supported frameworks:
   - PyTorch\* >=2.1, <2.3
   - TensorFlow\* >=2.8.4, <=2.12.1

docs/Installation.md

@@ -12,14 +12,6 @@ NNCF can be installed as a regular PyPI package via pip:
 pip install nncf
 ```
 
-If you want to install both NNCF and the supported PyTorch version in one line, you can do this by simply running:
-
-```bash
-pip install nncf[torch]
-```
-
-Other viable options besides `[torch]` are `[tf]`, `[onnx]` and `[openvino]`.
-
 ## As a package built from a checked-out repository
 
 Install the package and its dependencies by running the following command in the repository root directory:
@@ -28,20 +20,6 @@ Install the package and its dependencies by running the following command in the
 pip install .
 ```
 
-Use the same `pip install` syntax as above to install NNCF along with the backend package version in one go:
-
-```bash
-pip install .[<BACKEND>]
-```
-
-List of supported backends: `torch`, `tf`, `onnx` and `openvino`.
-
-For development purposes install extra packages by
-
-```bash
-pip install .[dev,tests]
-```
-
 _NB_: For launching example scripts in this repository, we recommend setting the `PYTHONPATH` variable to the root of the checked-out repository once the installation is completed.
 
 NNCF is also available via [conda](https://anaconda.org/conda-forge/nncf):
@@ -65,7 +43,7 @@ as well as the supported versions of Python:
 
 | NNCF      | OpenVINO   | PyTorch  | ONNX     | TensorFlow | Python |
 |-----------|------------|----------|----------|------------|--------|
-| `develop` | `2024.4.0` | `2.2.1`  | `1.13.1` | `2.12.0`   | `3.8`  |
+| `develop` | `2024.4.0` | `2.2.1`  | `1.16.0` | `2.12.0`   | `3.8`  |
 | `2.9.0`   | `2024.4.0` | `2.1.2`  | `1.13.1` | `2.12.0`   | `3.8`  |
 | `2.8.1`   | `2023.3.0` | `2.1.2`  | `1.13.1` | `2.12.0`   | `3.8`  |
 | `2.8.0`   | `2023.3.0` | `2.1.2`  | `1.13.1` | `2.12.0`   | `3.8`  |

examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py

@@ -195,20 +195,17 @@ def validation_ac(
         preset=nncf.QuantizationPreset.MIXED,
         ignored_scope=nncf.IgnoredScope(
             types=["Mul", "Sub", "Sigmoid"],  # ignore operations
-            names=[
-                "/model.22/dfl/conv/Conv",  # in the post-processing subgraph
-                "/model.22/Add",
-                "/model.22/Add_1",
-                "/model.22/Add_2",
-                "/model.22/Add_3",
-                "/model.22/Add_4",
-                "/model.22/Add_5",
-                "/model.22/Add_6",
-                "/model.22/Add_7",
-                "/model.22/Add_8",
-                "/model.22/Add_9",
-                "/model.22/Add_10",
-                "/model.22/Add_11",
+            subgraphs=[
+                nncf.Subgraph(
+                    inputs=[
+                        "/model.22/Concat_3",
+                        "/model.22/Concat_6",
+                        "/model.22/Concat_24",
+                        "/model.22/Concat_5",
+                        "/model.22/Concat_4",
+                    ],
+                    outputs=["/model.22/Concat_29"],
+                )
             ],
         ),
     )

examples/post_training_quantization/openvino/yolov8/main.py

@@ -122,20 +122,12 @@ def transform_fn(data_item: Dict):
         quantization_dataset,
         preset=nncf.QuantizationPreset.MIXED,
         ignored_scope=nncf.IgnoredScope(
-            types=["Multiply", "Subtract", "Sigmoid"],  # ignore operations
-            names=[
-                "/model.22/dfl/conv/Conv",  # in the post-processing subgraph
-                "/model.22/Add",
-                "/model.22/Add_1",
-                "/model.22/Add_2",
-                "/model.22/Add_3",
-                "/model.22/Add_4",
-                "/model.22/Add_5",
-                "/model.22/Add_6",
-                "/model.22/Add_7",
-                "/model.22/Add_8",
-                "/model.22/Add_9",
-                "/model.22/Add_10",
+            types=["Multiply", "Subtract", "Sigmoid"],
+            subgraphs=[
+                nncf.Subgraph(
+                    inputs=["/model.22/Concat", "/model.22/Concat_1", "/model.22/Concat_2"],
+                    outputs=["output0/sink_port_0"],
+                )
             ],
         ),
     )

examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py

@@ -186,20 +186,17 @@ def validation_ac(
         preset=nncf.QuantizationPreset.MIXED,
         ignored_scope=nncf.IgnoredScope(
             types=["Multiply", "Subtract", "Sigmoid"],  # ignore operations
-            names=[
-                "/model.22/dfl/conv/Conv",  # in the post-processing subgraph
-                "/model.22/Add",
-                "/model.22/Add_1",
-                "/model.22/Add_2",
-                "/model.22/Add_3",
-                "/model.22/Add_4",
-                "/model.22/Add_5",
-                "/model.22/Add_6",
-                "/model.22/Add_7",
-                "/model.22/Add_8",
-                "/model.22/Add_9",
-                "/model.22/Add_10",
-                "/model.22/Add_11",
+            subgraphs=[
+                nncf.Subgraph(
+                    inputs=[
+                        "/model.22/Concat_3",
+                        "/model.22/Concat_6",
+                        "/model.22/Concat_24",
+                        "/model.22/Concat_5",
+                        "/model.22/Concat_4",
+                    ],
+                    outputs=["output0"],
+                )
             ],
         ),
     )

examples/post_training_quantization/torch/ssd300_vgg16/main.py

@@ -70,9 +70,9 @@ def run_benchmark(model_path: str, shape=None, verbose: bool = True) -> float:
 
 class COCO128Dataset(torch.utils.data.Dataset):
     category_mapping = [
-        1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,
-        34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,
-        61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90
+        1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33,
+        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+        61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90
     ]  # fmt: skip
 
     def __init__(self, data_path: str, transform: Callable):

examples/torch/common/models/classification/mobilenet_v3_tv_092.py

@@ -305,7 +305,7 @@ def _mobilenet_v3_model(
 ):
     model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs)
     if pretrained:
-        if model_urls.get(arch, None) is None:
+        if model_urls.get(arch) is None:
             raise ValueError("No checkpoint is available for model type {}".format(arch))
         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
         model.load_state_dict(state_dict)

nncf/__init__.py

@@ -45,7 +45,9 @@
 from nncf.quantization.advanced_parameters import (
     AdvancedAccuracyRestorerParameters as AdvancedAccuracyRestorerParameters,
 )
+from nncf.quantization.advanced_parameters import AdvancedBiasCorrectionParameters as AdvancedBiasCorrectionParameters
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters as AdvancedQuantizationParameters
+from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters as AdvancedSmoothQuantParameters
 from nncf.quantization.advanced_parameters import OverflowFix as OverflowFix
 from nncf.scopes import IgnoredScope as IgnoredScope
 from nncf.scopes import Subgraph as Subgraph

nncf/common/graph/operator_metatypes.py

@@ -79,11 +79,12 @@ def __init__(self, name: str):
         super().__init__(name)
         self._op_name_to_op_meta_dict: Dict[str, Type[OperatorMetatype]] = {}
 
-    def register(self, name: Optional[str] = None) -> Callable[..., Type[OperatorMetatype]]:
+    def register(self, name: Optional[str] = None, is_subtype: bool = False) -> Callable[..., Type[OperatorMetatype]]:
         """
         Decorator for registering operator metatypes.
 
         :param name: The registration name.
+        :param is_subtype: Whether the decorated metatype is a subtype of another registered operator.
         :return: The inner function for registering operator metatypes.
         """
         name_ = name
@@ -100,15 +101,15 @@ def wrap(obj: Type[OperatorMetatype]) -> Type[OperatorMetatype]:
             if cls_name is None:
                 cls_name = obj.__name__
             super_register(obj, cls_name)
-            op_names = obj.get_all_aliases()
-            for name in op_names:
-                if name in self._op_name_to_op_meta_dict and not obj.subtype_check(self._op_name_to_op_meta_dict[name]):
-                    raise nncf.InternalError(
-                        "Inconsistent operator metatype registry - single patched "
-                        "op name maps to multiple metatypes!"
-                    )
-
-                self._op_name_to_op_meta_dict[name] = obj
+            if not is_subtype:
+                op_names = obj.get_all_aliases()
+                for name in op_names:
+                    if name in self._op_name_to_op_meta_dict:
+                        raise nncf.InternalError(
+                            "Inconsistent operator metatype registry - single patched "
+                            f"op name `{name}` maps to multiple metatypes!"
+                        )
+                    self._op_name_to_op_meta_dict[name] = obj
             return obj
 
         return wrap

nncf/common/sparsity/schedulers.py

@@ -133,7 +133,7 @@ def __init__(self, controller: SparsityController, params: Dict[str, Any]):
         self._update_per_optimizer_step = params.get(
             "update_per_optimizer_step", SPARSITY_SCHEDULER_UPDATE_PER_OPTIMIZER_STEP
         )
-        self._steps_per_epoch = params.get("steps_per_epoch", None)
+        self._steps_per_epoch = params.get("steps_per_epoch")
         self._should_skip = False
 
     def step(self, next_step: Optional[int] = None) -> None:

nncf/config/schemata/algo/filter_pruning.py

@@ -27,7 +27,11 @@
 from nncf.config.schemata.defaults import PRUNING_INTERLAYER_RANKING_TYPE
 from nncf.config.schemata.defaults import PRUNING_LEGR_GENERATIONS
 from nncf.config.schemata.defaults import PRUNING_LEGR_MAX_PRUNING
+from nncf.config.schemata.defaults import PRUNING_LEGR_MUTATE_PERCENT
+from nncf.config.schemata.defaults import PRUNING_LEGR_NUM_SAMPLES
+from nncf.config.schemata.defaults import PRUNING_LEGR_POPULATION_SIZE
 from nncf.config.schemata.defaults import PRUNING_LEGR_RANDOM_SEED
+from nncf.config.schemata.defaults import PRUNING_LEGR_SIGMA_SCALE
 from nncf.config.schemata.defaults import PRUNING_LEGR_TRAIN_STEPS
 from nncf.config.schemata.defaults import PRUNING_NUM_INIT_STEPS
 from nncf.config.schemata.defaults import PRUNING_SCHEDULE
@@ -162,6 +166,26 @@
                             description="Random seed for LeGR coefficients generation.",
                             default=PRUNING_LEGR_RANDOM_SEED,
                         ),
+                        "population_size": with_attributes(
+                            NUMBER,
+                            description="Size of population for the evolution algorithm.",
+                            default=PRUNING_LEGR_POPULATION_SIZE,
+                        ),
+                        "num_samples": with_attributes(
+                            NUMBER,
+                            description="Number of samples for the evolution algorithm.",
+                            default=PRUNING_LEGR_NUM_SAMPLES,
+                        ),
+                        "mutate_percent": with_attributes(
+                            NUMBER,
+                            description="Percent of mutate for the evolution algorithm.",
+                            default=PRUNING_LEGR_MUTATE_PERCENT,
+                        ),
+                        "scale_sigma": with_attributes(
+                            NUMBER,
+                            description="Scale sigma for the evolution algorithm.",
+                            default=PRUNING_LEGR_SIGMA_SCALE,
+                        ),
                     },
                     "additionalProperties": False,
                 },

nncf/config/schemata/defaults.py

@@ -59,6 +59,10 @@
 PRUNING_LEGR_TRAIN_STEPS = 200
 PRUNING_LEGR_MAX_PRUNING = 0.8
 PRUNING_LEGR_RANDOM_SEED = 42
+PRUNING_LEGR_POPULATION_SIZE = 64
+PRUNING_LEGR_NUM_SAMPLES = 16
+PRUNING_LEGR_MUTATE_PERCENT = 0.1
+PRUNING_LEGR_SIGMA_SCALE = 1
 
 SPARSITY_INIT = 0.0
 MAGNITUDE_SPARSITY_WEIGHT_IMPORTANCE = "normed_abs"

nncf/experimental/common/tensor_statistics/collectors.py

@@ -461,7 +461,9 @@ def __init__(self, tensor_collectors: List[TensorCollector]) -> None:
             self._aggregators[key] = unique_aggregator
 
 
-##################################################Reducers##################################################
+##################################################
+# Reducers
+##################################################
 
 
 class NoopReducer(TensorReducerBase):
@@ -578,7 +580,9 @@ def __hash__(self) -> int:
         return hash((self.__class__.__name__, self.inplace, self._reduction_axes, self._channel_axis))
 
 
-##################################################Aggregators##################################################
+##################################################
+# Aggregators
+##################################################
 
 
 class NoopAggregator(AggregatorBase):

nncf/experimental/torch/nas/bootstrapNAS/search/search.py

@@ -710,17 +710,15 @@ def _evaluate(self, x: List[float], out: Dict[str, Any], *args, **kargs) -> NoRe
 
             result = [sample]
 
-            eval_idx = 0
             bn_adaption_executed = False
-            for evaluator_handler in self._evaluator_handlers:
+            for eval_idx, evaluator_handler in enumerate(self._evaluator_handlers):
                 in_cache, value = evaluator_handler.retrieve_from_cache(tuple(x_i))
                 if not in_cache:
                     if not bn_adaption_executed and self._search.bn_adaptation is not None:
                         self._search.bn_adaptation.run(self._model)
                         bn_adaption_executed = True
                     value = evaluator_handler.evaluate_and_add_to_cache_from_pymoo(tuple(x_i))
                 evaluators_arr[eval_idx].append(value)
-                eval_idx += 1
 
                 result.append(evaluator_handler.name)
                 result.append(value)

nncf/experimental/torch/nas/bootstrapNAS/training/scheduler.py

@@ -191,12 +191,10 @@ def get_current_stage_desc(self) -> Tuple[Optional[StageDescriptor], int]:
         :return: current stage descriptor and its index in the list of all descriptors
         """
         partial_epochs = 0
-        stage_desc_idx = 0
-        for stage_desc in self.list_stage_descriptors:
+        for stage_desc_idx, stage_desc in enumerate(self.list_stage_descriptors):
             partial_epochs += stage_desc.epochs
             if self.current_epoch < partial_epochs:
                 return stage_desc, stage_desc_idx
-            stage_desc_idx += 1
         return None, -1
 
     def get_total_training_epochs(self) -> int:

nncf/experimental/torch/sparsity/movement/scheduler.py

@@ -97,16 +97,16 @@ def from_dict(cls, params: Dict[str, Any]) -> "MovementSchedulerParams":
         :param params: A dict that specifies the parameters of movement sparsity scheduler.
         :return: A `MovementSchedulerParams` object that stores the parameters from `params`.
         """
-        warmup_start_epoch: int = params.get("warmup_start_epoch", None)
-        warmup_end_epoch: int = params.get("warmup_end_epoch", None)
-        importance_regularization_factor: float = params.get("importance_regularization_factor", None)
+        warmup_start_epoch: int = params.get("warmup_start_epoch")
+        warmup_end_epoch: int = params.get("warmup_end_epoch")
+        importance_regularization_factor: float = params.get("importance_regularization_factor")
         enable_structured_masking: bool = params.get("enable_structured_masking", MOVEMENT_ENABLE_STRUCTURED_MASKING)
-        init_importance_threshold: Optional[float] = params.get("init_importance_threshold", None)
+        init_importance_threshold: Optional[float] = params.get("init_importance_threshold")
         final_importance_threshold: float = params.get(
             "final_importance_threshold", MOVEMENT_FINAL_IMPORTANCE_THRESHOLD
         )
         power: float = params.get("power", MOVEMENT_POWER)
-        steps_per_epoch: Optional[int] = params.get("steps_per_epoch", None)
+        steps_per_epoch: Optional[int] = params.get("steps_per_epoch")
 
         if None in [warmup_start_epoch, warmup_end_epoch, importance_regularization_factor]:
             raise ValueError(