fix: replace bare except clauses with except Exception

auto_round/calib_dataset.py

@@ -74,7 +74,7 @@ def apply_chat_template_to_samples(samples, tokenizer, seqlen, system_prompt=Non
                 tokenize=False,
                 add_generation_prompt=True,
             )
-        except:
+        except Exception:
             logger.warning("Failed to apply chat template. removing the system role in chat history.")
             message_modified = [msg for msg in message if msg["role"] != "system"]
             chat_templated = tokenizer.apply_chat_template(
@@ -706,7 +706,7 @@ def get_dataset_len(dataset):
     try:
         dataset_len = len(dataset)
         return dataset_len
-    except:
+    except Exception:
         cnt = 0
         for _ in dataset:
             cnt += 1
@@ -748,7 +748,7 @@ def select_dataset(dataset, indices):
     """
     try:
         return dataset.select(indices)
-    except:
+    except Exception:
         list_data = list(select(dataset, indices))
         import pandas as pd
 

auto_round/inference/backend.py

@@ -1192,7 +1192,7 @@ def build_pip_commands(gptq_req, other_reqs):
     for req in requirements:
         try:
             require_version(req)
-        except:
+        except Exception:
             missing_requirements.append(req)
 
     gptq_req = next((f'"{req}"' for req in missing_requirements if "gptqmodel" in req), None)

auto_round/inference/convert_model.py

@@ -65,7 +65,7 @@ def skip_not_convert_modules(model, quantization_config, layer_names, layer_conf
     modules_to_not_convert = getattr(quantization_config, "modules_to_not_convert", [])
     try:  # transformers new api
         modules_to_not_convert = get_modules_to_not_convert(model, modules_to_not_convert, add_default_skips=True)
-    except:
+    except Exception:
         modules_to_not_convert = _get_modules_to_not_convert(model, modules_to_not_convert)
     if modules_to_not_convert:
         for layer_name in layer_names:
@@ -155,7 +155,7 @@ def _get_modules_to_not_convert(
     from transformers.quantizers.base import HfQuantizer
 
     get_modules_to_not_convert = HfQuantizer.get_modules_to_not_convert
-except:
+except Exception:
     get_modules_to_not_convert = _get_modules_to_not_convert
 
 
@@ -492,7 +492,7 @@ def _import_exllamav2_kernels():
     """Attempts to import ExLlamaV2 kernels for performance optimization."""
     try:
         from exllamav2_kernels import gemm_half_q_half, make_q_matrix  # pylint: disable=E0611, E0401
-    except:
+    except Exception:
         logger.warning_once(
             "AutoGPTQ ExLlamaV2 has not been installed, Please install it using the following command: "
             "`pip install git+https://github.com/AutoGPTQ/AutoGPTQ.git@b8b4127`"

auto_round/modeling/unfused_moe/__init__.py

@@ -178,7 +178,7 @@ def pre_check_config(model_name: str | torch.nn.Module, trust_remote_code: bool
             for key in model_keys:
                 if "gate_up_proj" in key:
                     return False
-    except:
+    except Exception:
         return True
     return True
 

auto_round/utils/common.py

@@ -98,7 +98,7 @@ def __getattr__(self, name):
         try:
             self.module = importlib.import_module(self.module_name)
             mod = getattr(self.module, name)
-        except:
+        except Exception:
             spec = importlib.util.find_spec(str(self.module_name + "." + name))
             mod = importlib.util.module_from_spec(spec)
             spec.loader.exec_module(mod)

auto_round/utils/device.py

@@ -313,7 +313,7 @@ def set_cuda_visible_devices(device: str):
             indices = [int(device) for device in devices]
             try:
                 pick_device = [current_visible_devices[i] for i in indices]
-            except:
+            except Exception:
                 raise ValueError(
                     "Invalid '--device' value: It must be smaller than the number of available devices."
                     " For example, with CUDA_VISIBLE_DEVICES=4,5, "

auto_round/utils/device_manager.py

@@ -394,7 +394,7 @@ def empty_cache(self) -> None:
         if callable(fn):
             try:
                 fn()  # pylint: disable=E1102 # mps has issues
-            except:
+            except Exception:
                 pass
 
     def device_index(self, index: int):

auto_round/utils/model.py

@@ -301,7 +301,7 @@ def _is_mxfp4_model(model_path, trust_remote_code=True):
 
     try:  # in case of config loading failure for new models
         config = AutoConfig.from_pretrained(model_path, trust_remote_code=trust_remote_code)
-    except:
+    except Exception:
         return False
 
     model_type = getattr(config, "model_type", "")
@@ -844,7 +844,7 @@ def diffusion_load_model(
         from transformers import AutoConfig
 
         config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
-    except:
+    except Exception:
         config = None
 
     model_type = getattr(config, "model_type", "")
@@ -1043,7 +1043,7 @@ def is_diffusion_model(model_or_path: Union[str, object], trust_remote_code: boo
             # A special case for NextStep
             if model_type == "nextstep":
                 return True
-        except:
+        except Exception:
             logger.warning(
                 f"Failed to load config for {model_or_path}, trying to check model_index.json for diffusion pipeline."
             )
@@ -1436,7 +1436,7 @@ def _to_model_dtype(model, model_dtype):
                 model = model.to(torch.bfloat16)
             elif model_dtype == "float32" or model_dtype == "fp32" and model.dtype != torch.bfloat32:
                 model = model.to(torch.float32)
-        except:
+        except Exception:
             logger.error("please use more device to fit the device or just use one device")
             exit()
     return model