Refine funcs

auto_round/calib_dataset.py

@@ -150,7 +150,7 @@ def get_pile_val_dataset(tokenizer, seqlen, dataset_name="swift/pile-val-backup"
 
 
 @register_dataset("BAAI/CCI3-HQ")
-def get_CCI3_HQ_dataset(tokenizer, seqlen, dataset_name="BAAI/CCI3-HQ", split=None, seed=42, apply_chat_template=False):
+def get_cci3_hq_dataset(tokenizer, seqlen, dataset_name="BAAI/CCI3-HQ", split=None, seed=42, apply_chat_template=False):
     """Returns a dataloader for the specified dataset and split.
 
     Args:
@@ -235,18 +235,19 @@ def get_new_chinese_title_dataset(
         seed=42,
         apply_chat_template=False
 ):
-    """Returns a dataloader for the specified dataset and split.
+    """
+    Returns a tokenized dataset for the specified parameters.
 
     Args:
-    tokenizer: The tokenizer to be used for tokenization.
-    seqlen: The maximum sequence length.
-    data_name: The name of the dataset.
-    split: The data split to be used (e.g., "train", "test").
-    seed: The random seed for shuffling the dataset.
-    apply_chat_template: Whether to apply chat template in tokenization.
+        tokenizer: The tokenizer to use.
+        seqlen: Maximum sequence length.
+        dataset_name: Name of the dataset to load.
+        split: Which split of the dataset to use.
+        seed: Random seed for shuffling.
+        apply_template: Whether to apply a template to the data.
 
     Returns:
-    A dataloader for the specified dataset and split, using the provided tokenizer and sequence length.
+        A tokenized and shuffled dataset.
     """
 
     def get_tokenizer_function(tokenizer, seqlen, apply_chat_template=apply_chat_template):
@@ -639,3 +640,4 @@ def collate_batch(batch):
 
     calib_dataloader = DataLoader(dataset_final, batch_size=bs, shuffle=False, collate_fn=collate_batch)
     return calib_dataloader
+

auto_round/export/export_to_autoround/export.py

@@ -50,7 +50,7 @@ def check_neq_config(config, data_type, bits, group_size, sym):
     return [key for key, expected_value in expected_config.items() if config.get(key) != expected_value]
 
 
-def dynamic_import_quantLinear_for_packing(backend, bits, group_size, sym):
+def dynamic_import_quant_linear_for_packing(backend, bits, group_size, sym):
     """
     Dynamically imports and returns the appropriate QuantLinear class based on the specified backend and parameters.
 
@@ -97,7 +97,7 @@ def pack_layer(name, model, layer_config, backend, pbar):
         layer = get_module(model, name)
         device = layer.weight.device
 
-        QuantLinear = dynamic_import_quantLinear_for_packing(backend, bits, group_size, sym)
+        QuantLinear = dynamic_import_quant_linear_for_packing(backend, bits, group_size, sym)
 
         if isinstance(layer, nn.Linear):
             in_features = layer.in_features
@@ -286,3 +286,4 @@ def save(model: nn.Module, save_dir: str, max_shard_size: str = "5GB", safe_seri
     if hasattr(model, "config") and hasattr(model.config, "quantization_config"):
         with open(os.path.join(save_dir, config_file), "w", encoding="utf-8") as f:
             json.dump(model.config.quantization_config, f, indent=2)
+

auto_round/low_cpu_mem/utils.py

@@ -78,7 +78,7 @@ def get_named_children(model, pre=[]):
     return module_list
 
 
-def dowload_hf_model(repo_id, cache_dir=None, repo_type=None, revision=None):
+def download_hf_model(repo_id, cache_dir=None, repo_type=None, revision=None):
     """Download hugging face model from hf hub."""
     from huggingface_hub.constants import DEFAULT_REVISION, HUGGINGFACE_HUB_CACHE
     from huggingface_hub.file_download import REGEX_COMMIT_HASH, repo_folder_name
@@ -116,7 +116,7 @@ def load_empty_model(pretrained_model_name_or_path, cls=AutoModelForCausalLM, sa
     if is_local:  # pragma: no cover
         path = pretrained_model_name_or_path
     else:
-        path = dowload_hf_model(pretrained_model_name_or_path)
+        path = download_hf_model(pretrained_model_name_or_path)
     torch_dtype = kwargs.pop("torch_dtype", None)
     if cls.__base__ == _BaseAutoModelClass:
         config = AutoConfig.from_pretrained(path, **kwargs)
@@ -258,7 +258,7 @@ def _get_path(pretrained_model_name_or_path):
     if is_local:  # pragma: no cover
         path = pretrained_model_name_or_path
     else:
-        path = dowload_hf_model(pretrained_model_name_or_path)
+        path = download_hf_model(pretrained_model_name_or_path)
     return path
 
 
@@ -471,3 +471,4 @@ def layer_wise_load(path):
                 d = pickle.loads(d)
                 state_dict.update(d)
     return state_dict
+

auto_round/mllm/processor.py

@@ -20,15 +20,15 @@
 PROCESSORS = {}
 
 
-def regist_processor(name):
+def register_processor(name):
     def register(processor):
         PROCESSORS[name] = processor
         return processor
 
     return register
 
 
-@regist_processor("basic")
+@register_processor("basic")
 class BasicProcessor:
     def __init__(self):
         pass
@@ -111,7 +111,7 @@ def squeeze_result(ret):
         return ret
 
 
-@regist_processor("qwen2_vl")
+@register_processor("qwen2_vl")
 class Qwen2VLProcessor(BasicProcessor):
     @staticmethod
     def squeeze_result(ret):
@@ -122,7 +122,7 @@ def squeeze_result(ret):
         return ret
 
 
-@regist_processor("cogvlm2")
+@register_processor("cogvlm2")
 class CogVLM2Processor(BasicProcessor):
     def get_input(
             self, text, images, truncation=False,
@@ -205,7 +205,7 @@ def default_image_processor(image_path_or_url):
 llava_train = LazyImport("llava.train.train")
 
 
-@regist_processor("llava")
+@register_processor("llava")
 class LlavaProcessor(BasicProcessor):
     def post_init(self, model, tokenizer, image_processor=None, **kwargs):
         self.model = model
@@ -245,3 +245,4 @@ class DataArgs:
 
     def data_collator(self, batch):
         return self.collator_func(batch)
+