replied to comments

Author: alexsu52

nncf/quantization/algorithms/weight_compression/algorithm.py

@@ -45,6 +45,7 @@
 from nncf.quantization.algorithms.weight_compression.weight_lowering import WeightCompressionConfig
 from nncf.scopes import IgnoredScope
 from nncf.scopes import get_ignored_node_names_from_ignored_scope
+from nncf.tensor.definitions import TensorDataType
 
 TModel = TypeVar("TModel")
 TTensor = TypeVar("TTensor")
@@ -56,6 +57,12 @@
     CompressWeightsMode.NF4,
     CompressWeightsMode.E2M1,
 ]
+SUPPORTED_DATA_TYPES = [
+    TensorDataType.float16,
+    TensorDataType.bfloat16,
+    TensorDataType.float32,
+    TensorDataType.float64,
+]
 
 
 def get_weight_compression_configuration(
@@ -489,7 +496,7 @@ 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 weight_dtype.is_float():
+                if weight_dtype in SUPPORTED_DATA_TYPES:
                     continue
                 weight_shape = self._backend_entity.get_weight_shape(node, weight_port_id, graph)
                 weight_size = reduce(operator.mul, weight_shape, 1)
@@ -535,7 +542,7 @@ def apply(
                     continue
 
                 weight_dtype = self._backend_entity.get_weight_dtype(node, weight_port_id, model, graph)
-                if not weight_dtype.is_float():
+                if weight_dtype not in SUPPORTED_DATA_TYPES:
                     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

@@ -53,7 +53,15 @@ def is_float(self) -> bool:
         """
         :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]
+        return self in [
+            TensorDataType.float16,
+            TensorDataType.bfloat16,
+            TensorDataType.float32,
+            TensorDataType.float64,
+            TensorDataType.f8e4m3,
+            TensorDataType.f8e5m2,
+            TensorDataType.nf4,
+        ]
 
 
 class TensorDeviceType(Enum):