| |
|
|
| import importlib.util |
| import os |
| from contextlib import contextmanager |
| from pathlib import Path |
| from typing import List, Optional, Tuple |
|
|
| from lightning_utilities.core.imports import RequirementCache |
|
|
| from litgpt.config import configs |
| from litgpt.scripts.convert_hf_checkpoint import convert_hf_checkpoint |
|
|
| _SAFETENSORS_AVAILABLE = RequirementCache("safetensors") |
| _HF_TRANSFER_AVAILABLE = RequirementCache("hf_transfer") |
|
|
|
|
| def download_from_hub( |
| repo_id: str, |
| access_token: Optional[str] = os.getenv("HF_TOKEN"), |
| tokenizer_only: bool = False, |
| convert_checkpoint: bool = True, |
| dtype: Optional[str] = None, |
| checkpoint_dir: Path = Path("checkpoints"), |
| model_name: Optional[str] = None, |
| ) -> None: |
| """Download weights or tokenizer data from the Hugging Face Hub. |
| |
| Arguments: |
| repo_id: The repository ID in the format ``org/name`` or ``user/name`` as shown in Hugging Face. |
| If "list" is provided as input, a list of the currently supported models in LitGPT and quits. |
| access_token: Optional API token to access models with restrictions. |
| tokenizer_only: Whether to download only the tokenizer files. |
| convert_checkpoint: Whether to convert the checkpoint files to the LitGPT format after downloading. |
| dtype: The data type to convert the checkpoint files to. If not specified, the weights will remain in the |
| dtype they are downloaded in. |
| checkpoint_dir: Where to save the downloaded files. |
| model_name: The existing config name to use for this repo_id. This is useful to download alternative weights of |
| existing architectures. |
| """ |
| options = [f"{config['hf_config']['org']}/{config['hf_config']['name']}" for config in configs] |
|
|
| if repo_id == "list": |
| print("Please specify --repo_id <repo_id>. Available values:") |
| print("\n".join(sorted(options, key=lambda x: x.lower()))) |
| return |
|
|
| if model_name is None and repo_id not in options: |
| print( |
| f"Unsupported `repo_id`: {repo_id}." |
| "\nIf you are trying to download alternative " |
| "weights for a supported model, please specify the corresponding model via the `--model_name` option, " |
| "for example, `litgpt download NousResearch/Hermes-2-Pro-Llama-3-8B --model_name Llama-3-8B`." |
| "\nAlternatively, please choose a valid `repo_id` from the list of supported models, which can be obtained via " |
| "`litgpt download list`." |
| ) |
| return |
|
|
| from huggingface_hub import snapshot_download |
|
|
| if importlib.util.find_spec("hf_transfer") is None: |
| print( |
| "It is recommended to install hf_transfer for faster checkpoint download speeds: `pip install hf_transfer`" |
| ) |
|
|
| download_files = ["tokenizer*", "generation_config.json", "config.json"] |
| if not tokenizer_only: |
| bins, safetensors = find_weight_files(repo_id, access_token) |
| if bins: |
| |
| download_files.append("*.bin*") |
| elif safetensors: |
| if not _SAFETENSORS_AVAILABLE: |
| raise ModuleNotFoundError(str(_SAFETENSORS_AVAILABLE)) |
| download_files.append("*.safetensors*") |
| else: |
| raise ValueError(f"Couldn't find weight files for {repo_id}") |
|
|
| import huggingface_hub._snapshot_download as download |
| import huggingface_hub.constants as constants |
|
|
| previous = constants.HF_HUB_ENABLE_HF_TRANSFER |
| if _HF_TRANSFER_AVAILABLE and not previous: |
| print("Setting HF_HUB_ENABLE_HF_TRANSFER=1") |
| constants.HF_HUB_ENABLE_HF_TRANSFER = True |
| download.HF_HUB_ENABLE_HF_TRANSFER = True |
|
|
| directory = checkpoint_dir / repo_id |
| with gated_repo_catcher(repo_id, access_token): |
| snapshot_download( |
| repo_id, |
| local_dir=directory, |
| allow_patterns=download_files, |
| token=access_token, |
| ) |
|
|
| constants.HF_HUB_ENABLE_HF_TRANSFER = previous |
| download.HF_HUB_ENABLE_HF_TRANSFER = previous |
|
|
| if convert_checkpoint and not tokenizer_only: |
| print("Converting checkpoint files to LitGPT format.") |
| convert_hf_checkpoint(checkpoint_dir=directory, dtype=dtype, model_name=model_name) |
|
|
|
|
| def find_weight_files(repo_id: str, access_token: Optional[str]) -> Tuple[List[str], List[str]]: |
| from huggingface_hub import repo_info |
| from huggingface_hub.utils import filter_repo_objects |
|
|
| with gated_repo_catcher(repo_id, access_token): |
| info = repo_info(repo_id, token=access_token) |
| filenames = [f.rfilename for f in info.siblings] |
| bins = list(filter_repo_objects(items=filenames, allow_patterns=["*model*.bin*"])) |
| safetensors = list(filter_repo_objects(items=filenames, allow_patterns=["*.safetensors*"])) |
| return bins, safetensors |
|
|
|
|
| @contextmanager |
| def gated_repo_catcher(repo_id: str, access_token: Optional[str]): |
| try: |
| yield |
| except OSError as e: |
| err_msg = str(e) |
| if "Repository Not Found" in err_msg: |
| raise ValueError( |
| f"Repository at https://huggingface.co/api/models/{repo_id} not found." |
| " Please make sure you specified the correct `repo_id`." |
| ) from None |
| elif "gated repo" in err_msg: |
| if not access_token: |
| raise ValueError( |
| f"https://huggingface.co/{repo_id} requires authentication, please set the `HF_TOKEN=your_token`" |
| " environment variable or pass `--access_token=your_token`. You can find your token by visiting" |
| " https://huggingface.co/settings/tokens." |
| ) from None |
| else: |
| raise ValueError( |
| f"https://huggingface.co/{repo_id} requires authentication. The access token provided by `HF_TOKEN=your_token`" |
| " environment variable or `--access_token=your_token` may not have sufficient access rights. Please" |
| f" visit https://huggingface.co/{repo_id} for more information." |
| ) from None |
| raise e from None |
|
|
