[NNCF] Experimental 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_bias_shape ->
get_bias_shape_legacy(layer_attributes: WeightedLayerAttributes)

Locations in /nncf/experimental/torch/sparsity/movement/ folder
  - layers.py
  - structured_mask_handler.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/experimental/torch/sparsity/movement/layers.py

@@ -18,6 +18,8 @@
 
 import nncf
 from nncf.common.graph import NNCFNode
+from nncf.common.graph.utils import get_weight_shape_legacy
+from nncf.common.graph.utils import get_bias_shape_legacy
 from nncf.experimental.torch.sparsity.movement.functions import binary_mask_by_threshold
 from nncf.torch.layer_utils import COMPRESSION_MODULES
 from nncf.torch.layer_utils import CompressionParameter
@@ -167,7 +169,7 @@ def __init__(
         self._importance_threshold = -math.inf
         self._importance_regularization_factor = 0.0
 
-        weight_shape: List[int] = target_module_node.layer_attributes.get_weight_shape()
+        weight_shape: List[int] = get_weight_shape_legacy(target_module_node.layer_attributes)
         assert len(weight_shape) == 2, "Unsupported module with weight shape not in 2D."
         self.weight_ctx = BinaryMask(weight_shape)
         self.sparse_factors = self._get_sparse_factors(weight_shape, sparse_cfg)
@@ -185,7 +187,7 @@ def __init__(
         self.weight_ctx.binary_mask = self._calc_training_binary_mask()
 
         if self.prune_bias:
-            bias_shape = target_module_node.layer_attributes.get_bias_shape()
+            bias_shape = get_bias_shape_legacy(target_module_node.layer_attributes)
             self.bias_ctx = BinaryMask(bias_shape)
             bias_importance_shape = weight_importance_shape[0]
             self.bias_importance = CompressionParameter(

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

@@ -18,6 +18,8 @@
 
 from nncf.common.graph.graph import NNCFNodeName
 from nncf.common.graph.layer_attributes import LinearLayerAttributes
+from nncf.common.graph.utils import get_weight_shape_legacy
+from nncf.common.graph.utils import get_bias_shape_legacy
 from nncf.common.logging import nncf_logger
 from nncf.experimental.common.pruning.nodes_grouping import get_pruning_groups
 from nncf.experimental.common.pruning.nodes_grouping import select_largest_groups
@@ -209,9 +211,9 @@ def gather_statistics_from_operand(self) -> StructuredMaskContextStatistics:
         """
         node = self.sparsifier_operand.target_module_node
         assert isinstance(node.layer_attributes, tuple(EXPECTED_NODE_LAYER_ATTRS))
-        weight_shape: Tuple[int, int] = tuple(node.layer_attributes.get_weight_shape())
+        weight_shape: Tuple[int, int] = tuple(get_weight_shape_legacy(node.layer_attributes))
         bias_shape: Tuple[int] = (
-            (node.layer_attributes.get_bias_shape(),) if self.sparsifier_operand.prune_bias else (0,)
+            (get_bias_shape_legacy(node.layer_attributes),) if self.sparsifier_operand.prune_bias else (0,)
         )
 
         pruned_weight_shape = list(weight_shape)