Move is_weight_compression_needed function to common

Author: aobolensk

src/nncf/experimental/torch/fx/quantization/backend_parameters.py

@@ -25,7 +25,6 @@
 from nncf.common.logging import nncf_logger
 from nncf.common.quantization.structs import QuantizationPreset
 from nncf.data import Dataset
-from nncf.experimental.torch.fx.quantization.backend_parameters import is_weight_compression_needed
 from nncf.experimental.torch.fx.transformations import DuplicateDQPassNoAnnotations
 from nncf.experimental.torch.fx.transformations import apply_quantization_transformations
 from nncf.experimental.torch.fx.transformations import compress_post_quantize_transformation
@@ -39,6 +38,7 @@
 from nncf.parameters import TargetDevice
 from nncf.quantization.advanced_parameters import AdvancedCompressionParameters
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
+from nncf.quantization.advanced_parameters import is_weight_compression_needed
 from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression
 from nncf.scopes import IgnoredScope

src/nncf/experimental/torch/fx/quantization/quantize_model.py

@@ -27,8 +27,6 @@
 from nncf.openvino.graph.model_utils import remove_friendly_name_duplicates
 from nncf.openvino.graph.nncf_graph_builder import GraphConverter
 from nncf.openvino.graph.node_utils import get_number_if_op
-from nncf.openvino.quantization.backend_parameters import BackendParameters
-from nncf.openvino.quantization.backend_parameters import is_weight_compression_needed
 from nncf.openvino.quantization.quantize_ifmodel import apply_algorithm_if_bodies
 from nncf.openvino.rt_info import dump_parameters
 from nncf.parameters import BackupMode
@@ -42,7 +40,9 @@
 from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters
 from nncf.quantization.advanced_parameters import AdvancedCompressionParameters
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
+from nncf.quantization.advanced_parameters import BackendParameters
 from nncf.quantization.advanced_parameters import convert_to_dict_recursively
+from nncf.quantization.advanced_parameters import is_weight_compression_needed
 from nncf.quantization.algorithms.accuracy_control.algorithm import QuantizationAccuracyRestorer
 from nncf.quantization.algorithms.accuracy_control.algorithm import calculate_accuracy_drop
 from nncf.quantization.algorithms.accuracy_control.evaluator import Evaluator

src/nncf/openvino/quantization/backend_parameters.py

@@ -287,6 +287,30 @@ class AdvancedQuantizationParameters:
     backend_params: dict[str, Any] = field(default_factory=dict)
 
 
+class BackendParameters:
+    COMPRESS_WEIGHTS = "compress_weights"
+    STAT_REQUESTS_NUMBER = "stat_requests_number"
+    EVAL_REQUESTS_NUMBER = "eval_requests_number"
+    ACTIVATIONS = "activations"
+    WEIGHTS = "weights"
+    LEVEL_LOW = "level_low"
+    LEVEL_HIGH = "level_high"
+
+
+def is_weight_compression_needed(advanced_parameters: Optional["AdvancedQuantizationParameters"]) -> bool:
+    """
+    Determine whether weight compression is needed based on advanced quantization parameters.
+
+    If `advanced_parameters` or its `backend_params` are not provided, defaults to True.
+
+    :param advanced_parameters: Advanced quantization parameters.
+    :return: True if weight compression is needed, False otherwise.
+    """
+    if advanced_parameters is not None and advanced_parameters.backend_params is not None:
+        return bool(advanced_parameters.backend_params.get(BackendParameters.COMPRESS_WEIGHTS, True))
+    return True
+
+
 @api()
 @dataclass
 class AdvancedAWQParameters:

src/nncf/openvino/quantization/quantize_model.py

@@ -33,10 +33,10 @@
 from nncf.common.utils.os import safe_open
 from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter
 from nncf.experimental.torch.fx.node_utils import get_tensor_constant_from_node
-from nncf.experimental.torch.fx.quantization.backend_parameters import FXBackendParameters
 from nncf.experimental.torch.fx.transformations import DEQUANTIZE_NODE_TARGETS
 from nncf.experimental.torch.fx.transformations import _get_node_inputs
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
+from nncf.quantization.advanced_parameters import BackendParameters
 from tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference
 from tests.cross_fw.shared.paths import TEST_ROOT
 from tests.torch import test_models
@@ -219,7 +219,7 @@ def transform_fn(data_item):
     calibration_dataset = nncf.Dataset([example_input], transform_fn)
 
     quantization_parameters["advanced_parameters"] = AdvancedQuantizationParameters(
-        disable_bias_correction=True, backend_params={FXBackendParameters.COMPRESS_WEIGHTS: compress_weights}
+        disable_bias_correction=True, backend_params={BackendParameters.COMPRESS_WEIGHTS: compress_weights}
     )
     quantization_parameters["subset_size"] = 1