[BugFix] Model State Reload with Quantized Stubs in SparseAutoModelFo…

…rCausalLM (#2226)

* Fix bug for loading models from hf hub

* Update to download only relevant files and not the whole model repo

* Add py files to relevant suffixes

src/sparseml/transformers/sparsification/sparse_model.py

@@ -40,9 +40,7 @@
     modify_save_pretrained,
 )
 from sparseml.transformers.sparsification.modification import modify_model
-from sparseml.transformers.utils.helpers import resolve_recipe
-from sparseml.utils import download_zoo_training_dir
-from sparseml.utils.fsdp.context import main_process_first_context
+from sparseml.transformers.utils.helpers import download_model_directory, resolve_recipe
 
 
 __all__ = ["SparseAutoModel", "SparseAutoModelForCausalLM", "get_shared_tokenizer_src"]
@@ -101,15 +99,9 @@ def skip(*args, **kwargs):
             else pretrained_model_name_or_path
         )
 
-        if pretrained_model_name_or_path.startswith("zoo:"):
-            _LOGGER.debug(
-                "Passed zoo stub to SparseAutoModelForCausalLM object. "
-                "Loading model from SparseZoo training files..."
-            )
-            with main_process_first_context():
-                pretrained_model_name_or_path = download_zoo_training_dir(
-                    zoo_stub=pretrained_model_name_or_path
-                )
+        pretrained_model_name_or_path = download_model_directory(
+            pretrained_model_name_or_path, **kwargs
+        )
 
         # determine compression format, if any, from the model config
         compressor = infer_compressor_from_model_config(pretrained_model_name_or_path)

src/sparseml/transformers/utils/helpers.py

@@ -34,8 +34,10 @@
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import PaddingStrategy
 
-from huggingface_hub import HUGGINGFACE_CO_URL_HOME, hf_hub_download
+from huggingface_hub import HUGGINGFACE_CO_URL_HOME, HfFileSystem, hf_hub_download
 from sparseml.export.helpers import ONNX_MODEL_NAME
+from sparseml.utils import download_zoo_training_dir
+from sparseml.utils.fsdp.context import main_process_first_context
 from sparsezoo import Model, setup_model
 
 
@@ -52,6 +54,8 @@
     "ALL_TASK_NAMES",
     "create_fake_dataloader",
     "POSSIBLE_TOKENIZER_FILES",
+    "download_repo_from_huggingface_hub",
+    "download_model_directory",
 ]
 
 
@@ -92,6 +96,7 @@ class TaskNames(Enum):
     "special_tokens_map.json",
     "tokenizer_config.json",
 }
+RELEVANT_HF_SUFFIXES = ["json", "md", "bin", "safetensors", "yaml", "yml", "py"]
 
 
 def remove_past_key_value_support_from_config(config: AutoConfig) -> AutoConfig:
@@ -553,3 +558,94 @@ def fetch_recipe_path(target: str):
         recipe_path = hf_hub_download(repo_id=target, filename=DEFAULT_RECIPE_NAME)
 
     return recipe_path
+
+
+def download_repo_from_huggingface_hub(repo_id, **kwargs):
+    """
+    Download relevant model files from the Hugging Face Hub
+    using the huggingface_hub.hf_hub_download function
+
+    Note(s):
+    - Does not download the entire repo, only the relevant files
+    for the model, such as the model weights, tokenizer files, etc.
+    - Does not re-download files that already exist locally, unless
+    the force_download flag is set to True
+
+    :pre-condition: the repo_id must be a valid Hugging Face Hub repo id
+    :param repo_id: the repo id to download
+    :param kwargs: additional keyword arguments to pass to hf_hub_download
+    """
+    hf_filesystem = HfFileSystem()
+    files = hf_filesystem.ls(repo_id)
+
+    if not files:
+        raise ValueError(f"Could not find any files in HF repo {repo_id}")
+
+    # All file(s) from hf_filesystem have "name" key
+    # Extract the file names from the files
+    relevant_file_names = (
+        Path(file["name"]).name
+        for file in files
+        if any(file["name"].endswith(suffix) for suffix in RELEVANT_HF_SUFFIXES)
+    )
+
+    hub_kwargs_names = (
+        "subfolder",
+        "repo_type",
+        "revision",
+        "library_name",
+        "library_version",
+        "cache_dir",
+        "local_dir",
+        "local_dir_use_symlinks",
+        "user_agent",
+        "force_download",
+        "force_filename",
+        "proxies",
+        "etag_timeout",
+        "resume_download",
+        "token",
+        "local_files_only",
+        "headers",
+        "legacy_cache_layout",
+        "endpoint",
+    )
+    hub_kwargs = {name: kwargs[name] for name in hub_kwargs_names if name in kwargs}
+
+    for file_name in relevant_file_names:
+        last_file = hf_hub_download(repo_id=repo_id, filename=file_name, **hub_kwargs)
+
+    # parent directory of the last file is the model directory
+    return str(Path(last_file).parent.resolve().absolute())
+
+
+def download_model_directory(pretrained_model_name_or_path: str, **kwargs):
+    """
+    Download the model directory from the HF hub or SparseZoo if the model
+    is not found locally
+
+    :param pretrained_model_name_or_path: the name of or path to the model to load
+        can be a SparseZoo/HuggingFace model stub
+    :param kwargs: additional keyword arguments to pass to the download function
+    :return: the path to the downloaded model directory
+    """
+    pretrained_model_path: Path = Path(pretrained_model_name_or_path)
+
+    if pretrained_model_path.exists():
+        _LOGGER.debug(
+            "Model directory already exists locally.",
+        )
+        return pretrained_model_name_or_path
+
+    with main_process_first_context():
+        if pretrained_model_name_or_path.startswith("zoo:"):
+            _LOGGER.debug(
+                "Passed zoo stub to SparseAutoModelForCausalLM object. "
+                "Loading model from SparseZoo training files..."
+            )
+            return download_zoo_training_dir(zoo_stub=pretrained_model_name_or_path)
+
+        _LOGGER.debug("Downloading model from HuggingFace Hub.")
+        return download_repo_from_huggingface_hub(
+            repo_id=pretrained_model_name_or_path, **kwargs
+        )