Move is_float method

nncf/quantization/algorithms/weight_compression/algorithm.py

@@ -43,7 +43,6 @@
 from nncf.scopes import IgnoredScope
 from nncf.scopes import get_ignored_node_names_from_ignored_scope
 from nncf.tensor import Tensor
-from nncf.tensor.definitions import TensorDataType
 
 TModel = TypeVar("TModel")
 TTensor = TypeVar("TTensor")
@@ -322,21 +321,6 @@ def _get_bitwidth_distribution_str(
  pretty_string = f"Statistics of the bitwidth distribution:\n{table}"
  return pretty_string
 
- @staticmethod
- def _is_float(dtype):
- """
- Check if the given data type is a floating-point type.
-
- :param dtype: The data type to check.
- :return: True if the data type is one of the floating-point types, False otherwise.
- """
- return dtype in [
- TensorDataType.float16,
- TensorDataType.bfloat16,
- TensorDataType.float32,
- TensorDataType.float64,
- ]
-
  def _get_ignored_scope_weight_statistics(self, model: TModel, graph: NNCFGraph) -> List[int]:
  """
  Collect the weight statistics for nodes in the ignored scope.
@@ -359,7 +343,7 @@ def _get_ignored_scope_weight_statistics(self, model: TModel, graph: NNCFGraph)
  continue
  for _, weight_port_id in self._backend_entity.get_weight_names_and_port_ids(node, graph):
  weight_dtype = self._backend_entity.get_weight_dtype(node, weight_port_id, model, graph)
- if not WeightCompression._is_float(weight_dtype):
+ if weight_dtype.is_float():
  continue
  weight_shape = self._backend_entity.get_weight_shape(node, weight_port_id, graph)
  weight_size = reduce(operator.mul, weight_shape, 1)
@@ -391,7 +375,7 @@ def apply(
  continue
 
  weight_dtype = self._backend_entity.get_weight_dtype(node, weight_port_id, model, graph)
- if not WeightCompression._is_float(weight_dtype):
+ if not weight_dtype.is_float():
  continue
  weight_shape = self._backend_entity.get_weight_shape(node, weight_port_id, graph)
  weight_size = reduce(operator.mul, weight_shape, 1)

nncf/tensor/definitions.py

@@ -25,7 +25,7 @@ class TensorBackendType(Enum):
 
 class TensorDataType(Enum):
  """
- Enum representing the different tensor data types.
+ The class represents the different tensor data types.
  """
 
  float16 = auto()
@@ -37,6 +37,12 @@ class TensorDataType(Enum):
  int64 = auto()
  uint8 = auto()
 
+ def is_float(self):
+ """
+ :return: True if the tensor data type is a floating-point type, else False.
+ """
+ return self in [TensorDataType.float16, TensorDataType.bfloat16, TensorDataType.float32, TensorDataType.float64]
+
 
 class TensorDeviceType(Enum):
  """