[NNCF] Torch backend: Replace (openvinotoolkit#3249)

Changes:

Calls to following class methods in
nncf/common/graph/layer_attributes.py were replaced with calls to their
corresponding self-contained legacy functions in
nncf/common/graph/utils.py:
- layer_attributes.get_weight_shape ->
get_weight_shape_legacy(layer_attributes: WeightedLayerAttributes)
- layer_attributes.get_target_dim_for_compression ->
get_target_dim_for_compression_legacy(
    layer_attributes: WeightedLayerAttributes)

Locations in /nncf/torch/quantization folder
  - algo.py
  - init_range.py

Reason for changes:

(openvinotoolkit#3249) Torch and Tensorflow backend-specific methods need to be removed
from common layer attributes and all related calls need to be replaced
by their corresponding legacy function calls

Author: shumaari

nncf/torch/quantization/algo.py

@@ -35,6 +35,8 @@
 from nncf.common.graph.patterns.manager import TargetDevice
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.common.graph.utils import get_first_nodes_of_type
+from nncf.common.graph.utils import get_weight_shape_legacy
+from nncf.common.graph.utils import get_target_dim_for_compression_legacy
 from nncf.common.hardware.config import HWConfig
 from nncf.common.hardware.config import HWConfigType
 from nncf.common.hardware.config import get_hw_config_type
@@ -773,10 +775,10 @@ def _get_quantizer_setup(self, target_model: NNCFNetwork) -> PTQuantizerSetup:
                 layer_attributes = target_node.layer_attributes
                 assert isinstance(layer_attributes, WeightedLayerAttributes)
                 scale_shape = get_scale_shape(
-                    layer_attributes.get_weight_shape(),
+                    get_weight_shape_legacy(layer_attributes),
                     is_weights=True,
                     per_channel=qconfig.per_channel,
-                    channel_idx=layer_attributes.get_target_dim_for_compression(),
+                    channel_idx=get_target_dim_for_compression_legacy(layer_attributes),
                 )
             else:
                 input_shape = target_model_graph.get_input_shape_for_insertion_point(insertion_point)
@@ -1182,7 +1184,7 @@ def is_weights(ip: PTTargetPoint) -> bool:
             )
             module_node = target_model_graph.get_node_by_name(primary_ip.target_node_name)
             layer_attributes = module_node.layer_attributes
-            input_shape = layer_attributes.get_weight_shape()
+            input_shape = get_weight_shape_legacy(layer_attributes)
             self._quantizers_input_shapes[primary_qid] = tuple(input_shape)
         else:
             primary_qid = NonWeightQuantizerId(primary_ip.target_node_name, primary_ip.input_port_id)

nncf/torch/quantization/init_range.py

@@ -18,6 +18,8 @@
 
 import nncf
 from nncf.common.graph.layer_attributes import WeightedLayerAttributes
+from nncf.common.graph.utils import get_weight_shape_legacy
+from nncf.common.graph.utils import get_target_dim_for_compression_legacy
 from nncf.common.quantization.initialization.range import RangeInitCollectorParams
 from nncf.common.quantization.initialization.range import RangeInitConfig
 from nncf.common.quantization.initialization.range import RangeInitParams
@@ -226,8 +228,8 @@ def get_all_scale_shapes_with_params(
             module_node = target_nncf_graph.get_node_by_name(qp.insertion_point.target_node_name)
             layer_attributes = module_node.layer_attributes
             assert isinstance(layer_attributes, WeightedLayerAttributes)
-            input_shape = layer_attributes.get_weight_shape()
-            channel_idx = layer_attributes.get_target_dim_for_compression()
+            input_shape = get_weight_shape_legacy(layer_attributes)
+            channel_idx = get_target_dim_for_compression_legacy(layer_attributes)
         else:
             input_shape = target_nncf_graph.get_input_shape_for_insertion_point(qp.insertion_point)
             channel_idx = 1  # channel dim for activations