add hasattr check for torch fp8 dtype (#1985)

Signed-off-by: xin3he <xin3.he@intel.com>

Author: xin3he

neural_compressor/torch/algorithms/weight_only/utility.py

@@ -93,12 +93,18 @@
 
 FLOAT_MAPPING = {"nf4": NF4, "fp4": FP4_BNB, "fp4_e2m1_bnb": FP4_BNB, "fp4_e2m1": FP4_E2M1}
 INT_MAPPING = {"nf4": NF4_BIT, "fp4": FP4_BNB_BIT, "fp4_e2m1_bnb": FP4_BNB_BIT, "fp4_e2m1": FP4_E2M1_BIT}
-FP8_MAPPING = {
-    "fp8_e5m2": torch.float8_e5m2,
-    "fp8_e5m2fnuz": torch.float8_e5m2fnuz,
-    "fp8_e4m3fn": torch.float8_e4m3fn,
-    "fp8_e4m3fnuz": torch.float8_e4m3fnuz,
-}
+if hasattr(torch, "float8_e5m2") and hasattr(torch, "float8_e4m3fn"):
+    FP8_MAPPING = {
+        "fp8_e5m2": torch.float8_e5m2,
+        "fp8_e4m3fn": torch.float8_e4m3fn,
+    }
+if hasattr(torch, "float8_e5m2fnuz") and hasattr(torch, "float8_e4m3fnuz"):
+    FP8_MAPPING = {
+        "fp8_e5m2": torch.float8_e5m2,
+        "fp8_e4m3fn": torch.float8_e4m3fn,
+        "fp8_e5m2fnuz": torch.float8_e5m2fnuz,
+        "fp8_e4m3fnuz": torch.float8_e4m3fnuz,
+    }
 
 
 def quantize_4bit(tensor, quantile=1.0, dtype="nf4", return_int=False, **kwargs):