Fix docstrings in neural_compressor directory

Co-authored-by: chensuyue <51692656+chensuyue@users.noreply.github.com>

Author: Copilot

neural_compressor/common/base_tuning.py

@@ -70,7 +70,7 @@ class Evaluator:
         def eval_acc(model):
             ...
 
-        def eval_perf(molde):
+        def eval_perf(model):
             ...
 
         # Usage
@@ -525,6 +525,10 @@ def init_tuning(tuning_config: TuningConfig) -> Tuple[ConfigLoader, TuningLogger
 
     Args:
         tuning_config (TuningConfig): The configuration for the tuning process.
+
+    Returns:
+        Tuple[ConfigLoader, TuningLogger, TuningMonitor]: A tuple containing the config loader,
+            tuning logger, and tuning monitor.
     """
     config_loader = ConfigLoader(config_set=tuning_config.config_set, sampler=tuning_config.sampler)
     tuning_logger = TuningLogger()

neural_compressor/torch/quantization/autotune.py

@@ -55,6 +55,14 @@ def get_all_config_set() -> Union[BaseConfig, List[BaseConfig]]:
 
 
 def _deepcopy_warp(model):
+    """Create a deep copy of the model while preserving specific attributes.
+
+    Args:
+        model (torch.nn.Module): The model to deep copy.
+
+    Returns:
+        torch.nn.Module: A deep copy of the model with preserved attributes.
+    """
     additional_attr_lst = ["_exported", "dynamic_shapes"]
     original_attr = {key: getattr(model, key, None) for key in additional_attr_lst}
     new_model = deepcopy(model)
@@ -64,7 +72,15 @@ def _deepcopy_warp(model):
 
 
 def _preprocess_model_quant_config(model, quant_config):
-    """Preprocess model and quant config before quantization."""
+    """Preprocess model and quant config before quantization.
+
+    Args:
+        model (torch.nn.Module): The model to be quantized.
+        quant_config (TuningConfig): The quantization configuration to preprocess.
+
+    Returns:
+        Tuple[torch.nn.Module, TuningConfig]: The preprocessed model and quantization configuration.
+    """
     for config in quant_config.config_set:
         # handle tokenizer attribute in AutoRoundConfig
         if isinstance(config, AutoRoundConfig):
@@ -88,8 +104,8 @@ def autotune(
     """The main entry of auto-tune.
 
     Args:
-        model (torch.nn.Module): _description_
-        tune_config (TuningConfig): _description_
+        model (torch.nn.Module): The model to be quantized.
+        tune_config (TuningConfig): The configuration for the auto-tuning process.
         eval_fn (Callable): for evaluation of quantized models.
         eval_args (tuple, optional): arguments used by eval_fn. Defaults to None.
         run_fn (Callable, optional): for calibration to quantize model. Defaults to None.

neural_compressor/torch/quantization/config.py

@@ -1767,7 +1767,7 @@ def __init__(
             allowlist (dict, optional): Whether to execute fp8 quantization for specific op names or types. Defaults to {"names": [], "types": FP8_WHITE_LIST}.
             mode (str, optional): Choose the quantization mode. Defaults to "AUTO".
             scale_method (str or dict, optional): Select method used to generate scale from calibration info. Can be a string or a dict. Defaults to "maxabs_hw".
-            scale_params (dict, optional): _description_. Defaults to {}.
+            scale_params (dict, optional): Scaling parameters that override the default ones for specific modules. Defaults to {}.
             observer (str, optional): Params of scales. Defaults to "maxabs".
             mod_dict (dict, optional): The dict of modules to quantize. Defaults to {}.
             measure_exclude (str, optional): Select INPUT/OUTPUT to be exculded by measurement. Defaults to "OUTPUT".

neural_compressor/torch/utils/llm_utility.py

@@ -64,7 +64,7 @@ def get_default_llm_dataloader(tokenizer, dataset_name="NeelNanda/pile-10k", bs=
 
     Args:
         tokenizer (obj): tokenizer object.
-        seq_len (int, optional): _description_. Defaults to 128.
+        seq_len (int, optional): the sequence length of the input tokens. Defaults to 128.
         dataset_name (str, optional): dataset name. Defaults to "NeelNanda/pile-10k".
         seed (int, optional): random seed. Defaults to 42.
         bs (int, optional): batch size. Defaults to 8.