Merge branch 'develop' into ad/pt_graph_const_minmax

.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

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"],
+                )
             ],
         ),
     )

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/quantization/advanced_parameters.py

@@ -59,7 +59,7 @@ class OverflowFix(StrEnum):
 
 
 @api()
-class FP8Type(Enum):
+class FP8Type(StrEnum):
     """
     Defines FP8 special types (https://arxiv.org/pdf/2209.05433.pdf).
 

nncf/quantization/algorithms/min_max/algorithm.py

@@ -863,25 +863,29 @@ def filter_func(point: StatisticPoint) -> bool:
                     group_statistics.append(statistics)
 
             unified_values = self._backend_entity.unify_statistics(group_statistics)
-            for quantization_target_point in unified_scale_group:
-                qconfig = quantization_target_points[quantization_target_point]
-                q_group = QuantizerGroup.ACTIVATIONS
-                narrow_range = get_quantizer_narrow_range(qconfig, q_group)
-                if self._mode is not None:
-                    destination_type = self._quantization_params[q_group].destination_type
-                    parameters = calculate_convert_parameters(
-                        unified_values, is_per_channel=qconfig.per_channel, destination_type=destination_type
-                    )
-                    command = self._backend_entity.create_convert_insertion_command(
-                        quantization_target_point, parameters
-                    )
-                else:
-                    parameters = calculate_quantizer_parameters(unified_values, qconfig, q_group, narrow_range)
-                    command = self._backend_entity.create_quantizer_insertion_command(
-                        graph, quantization_target_point, qconfig, parameters
+            qconfigs = [quantization_target_points[qtp] for qtp in unified_scale_group]
+            if any(qconfigs[0] != qconfig for qconfig in qconfigs[1:]):
+                raise nncf.InternalError(f"QConfigs for unified scale group {unified_scale_group} are not equal")
+            qconfig = qconfigs[0]
+            q_group = QuantizerGroup.ACTIVATIONS
+            narrow_range = get_quantizer_narrow_range(qconfig, q_group)
+            if self._mode is not None:
+                destination_type = self._quantization_params[q_group].destination_type
+                parameters = calculate_convert_parameters(
+                    unified_values, is_per_channel=qconfig.per_channel, destination_type=destination_type
+                )
+                for quantization_target_point in unified_scale_group:
+                    transformation_layout.register(
+                        self._backend_entity.create_convert_insertion_command(quantization_target_point, parameters)
                     )
+                continue
+            parameters = calculate_quantizer_parameters(unified_values, qconfig, q_group, narrow_range)
+            commands = self._backend_entity.create_unified_scales_quantizers_insertion_commands(
+                graph, unified_scale_group, qconfig, parameters
+            )
+            for command in commands:
                 transformation_layout.register(command)
-                unified_ops_list.add(quantization_target_point)
+            unified_ops_list.update(unified_scale_group)
 
         for quantization_target_point, qconfig in quantization_target_points.items():
             if quantization_target_point in unified_ops_list:

nncf/quantization/algorithms/min_max/backend.py

@@ -141,12 +141,31 @@ def create_quantizer_insertion_command(
         Returns backend-specific quantizer insertion command.
 
         :param nncf_graph: NNCFGraph to get input/output shapes for the target point.
-        :param target_point: Target location for the correction.
+        :param target_point: Target location for the quantizer insertion.
         :param quantizer_config: QuantizerConfig instance for the current layer.
         :param parameters: FakeQuantizeParameters to calculate activation quantization parameters.
         :return: Backend-specific TransformationCommand for the quantizer insertion operation.
         """
 
+    @staticmethod
+    @abstractmethod
+    def create_unified_scales_quantizers_insertion_commands(
+        nncf_graph: NNCFGraph,
+        target_points: List[TargetPoint],
+        quantizer_config: QuantizerConfig,
+        parameters: FakeQuantizeParameters,
+    ) -> List[TransformationCommand]:
+        """
+        Returns backend-specific unified scales quantizers insertion commands.
+
+        :param nncf_graph: NNCFGraph to get input/output shapes for the target point.
+        :param target_points: List of target locations for the quantizers insertion.
+        :param quantizer_config: QuantizerConfig instance for the current layer.
+        :param parameters: FakeQuantizeParameters to calculate activation quantization parameters.
+        :return: List of backend-specific TransformationCommands
+            for the quantizers with unified scales insertion operations.
+        """
+
     @staticmethod
     @abstractmethod
     def create_convert_insertion_command(

nncf/quantization/algorithms/min_max/onnx_backend.py

@@ -118,7 +118,7 @@ def create_quantizer_insertion_command(
         target_point: ONNXTargetPoint,
         quantizer_config: QuantizerConfig,
         parameters: FakeQuantizeParameters,
-    ):
+    ) -> ONNXQuantizerInsertionCommand:
         tensor_type = np.int8 if np.any(parameters.input_low.data < 0) else np.uint8
         is_weight = target_point.is_weight_target_point()
         if is_weight:
@@ -131,6 +131,20 @@ def create_quantizer_insertion_command(
         onnx_parameters = convert_fq_params_to_onnx_params(parameters, quantizer_config.num_bits, tensor_type, axis)
         return ONNXQuantizerInsertionCommand(target_point, nncf_input_node_next_nodes, onnx_parameters)
 
+    @staticmethod
+    def create_unified_scales_quantizers_insertion_commands(
+        nncf_graph: NNCFGraph,
+        target_points: List[ONNXTargetPoint],
+        quantizer_config: QuantizerConfig,
+        parameters: FakeQuantizeParameters,
+    ) -> List[ONNXQuantizerInsertionCommand]:
+        return [
+            ONNXMinMaxAlgoBackend.create_quantizer_insertion_command(
+                nncf_graph, target_point, quantizer_config, parameters
+            )
+            for target_point in target_points
+        ]
+
     @staticmethod
     def create_convert_insertion_command(
         target_point: ONNXTargetPoint,

nncf/quantization/algorithms/min_max/openvino_backend.py

@@ -120,6 +120,15 @@ def create_quantizer_insertion_command(
     ) -> OVQuantizerInsertionCommand:
         return OVQuantizerInsertionCommand(target_point, parameters)
 
+    @staticmethod
+    def create_unified_scales_quantizers_insertion_commands(
+        nncf_graph: NNCFGraph,
+        target_points: List[OVTargetPoint],
+        quantizer_config: QuantizerConfig,
+        parameters: FakeQuantizeParameters,
+    ) -> List[OVQuantizerInsertionCommand]:
+        return [OVQuantizerInsertionCommand(target_point, parameters) for target_point in target_points]
+
     @staticmethod
     def create_convert_insertion_command(
         target_point: OVTargetPoint,

nncf/quantization/algorithms/min_max/torch_backend.py

@@ -37,6 +37,7 @@
 from nncf.torch.graph.graph import PTNNCFGraph
 from nncf.torch.graph.graph import PTTargetPoint
 from nncf.torch.graph.transformations.command_creation import create_quantizer_insertion_command
+from nncf.torch.graph.transformations.command_creation import create_shared_quantizer_insertion_command
 from nncf.torch.graph.transformations.commands import PTInsertionCommand
 from nncf.torch.graph.transformations.commands import PTSharedFnInsertionCommand
 from nncf.torch.hardware.config import PTHWConfig
@@ -296,6 +297,22 @@ def create_quantizer_insertion_command(
         )
         return create_quantizer_insertion_command(target_point, quantizer)
 
+    @staticmethod
+    def create_unified_scales_quantizers_insertion_commands(
+        nncf_graph: NNCFGraph,
+        target_points: List[PTTargetPoint],
+        quantizer_config: QuantizerConfig,
+        parameters: FakeQuantizeParameters,
+    ) -> List[PTSharedFnInsertionCommand]:
+        _, scale_shape, _ = PTMinMaxAlgoBackend._get_input_scale_shape(
+            nncf_graph, target_points[0], quantizer_config.per_channel
+        )
+
+        quantizer = PTMinMaxAlgoBackend._create_quantizer(
+            quantizer_config, scale_shape, parameters, target_points[0].target_type
+        )
+        return [create_shared_quantizer_insertion_command(target_points, quantizer)]
+
     @staticmethod
     def get_ignored_metatypes(model_type: ModelType, device: TargetDevice) -> List[OperatorMetatype]:
         types = []

nncf/torch/graph/transformations/command_creation.py

@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Union
+from typing import List, Union
 
 from torch import Tensor
 
@@ -62,3 +62,20 @@ def create_quantizer_insertion_command(
         compression_module_type=ExtraCompressionModuleType.EXTERNAL_QUANTIZER,
         priority=TransformationPriority.QUANTIZATION_PRIORITY,
     )
+
+
+def create_shared_quantizer_insertion_command(
+    target_points: List[PTTargetPoint], quantizer: BaseQuantizer
+) -> PTSharedFnInsertionCommand:
+    quantizers_ids = []
+    for target_point in target_points:
+        quantizers_ids.append(NonWeightQuantizerId(target_point.target_node_name, target_point.input_port_id))
+
+    storage_key = ";".join(str(quantizer_id) for quantizer_id in sorted(quantizers_ids, key=str))
+    return PTSharedFnInsertionCommand(
+        target_points=target_points,
+        fn=quantizer,
+        op_unique_name=storage_key,
+        compression_module_type=ExtraCompressionModuleType.EXTERNAL_QUANTIZER,
+        priority=TransformationPriority.QUANTIZATION_PRIORITY,
+    )

nncf/torch/graph/transformations/commands.py

@@ -57,6 +57,7 @@ def __eq__(self, other: "PTTargetPoint"):
             isinstance(other, PTTargetPoint)
             and self.target_type == other.target_type
             and self.target_node_name == other.target_node_name
+            and self.input_port_id == other.input_port_id
         )
 
     def __str__(self):