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| import argparse | |
| import json | |
| import os | |
| import shutil | |
| from collections import defaultdict | |
| from inspect import signature | |
| from tempfile import TemporaryDirectory | |
| from typing import Dict, List, Optional, Set, Tuple | |
| import torch | |
| from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download | |
| from huggingface_hub.file_download import repo_folder_name | |
| from safetensors.torch import load_file, save_file, _remove_duplicate_names | |
| COMMIT_DESCRIPTION = """ | |
| This is an automated PR created with https://huggingface.co/spaces/safetensors/convert | |
| This new file is equivalent to `pytorch_model.bin` but safe in the sense that | |
| no arbitrary code can be put into it. | |
| These files also happen to load much faster than their pytorch counterpart: | |
| https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb | |
| The widgets on your model page will run using this model even if this is not merged | |
| making sure the file actually works. | |
| If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions | |
| Feel free to ignore this PR. | |
| """ | |
| PR_TITLE = "Adding `safetensors` variant of this model" | |
| ConversionResult = Tuple[List["CommitOperationAdd"], List[Tuple[str, "Exception"]]] | |
| class AlreadyExists(Exception): | |
| pass | |
| def rename(pt_filename: str) -> str: | |
| filename, ext = os.path.splitext(pt_filename) | |
| local = f"{filename}.safetensors" | |
| local = local.replace("pytorch_model", "model") | |
| return local | |
| def convert_multi(model_id: str, folder: str, api: "HfApi") -> ConversionResult: | |
| filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin.index.json") | |
| with open(filename, "r") as f: | |
| data = json.load(f) | |
| filenames = set(data["weight_map"].values()) | |
| index = os.path.join(folder, "model.safetensors.index.json") | |
| with open(index, "w") as f: | |
| newdata = {k: v for k, v in data.items()} | |
| newmap = {k: rename(v) for k, v in data["weight_map"].items()} | |
| newdata["weight_map"] = newmap | |
| json.dump(newdata, f, indent=4) | |
| new_pr = api.create_commit( | |
| repo_id=model_id, | |
| operations=[CommitOperationAdd(path_in_repo=index.split("/")[-1], path_or_fileobj=index)], | |
| commit_message=PR_TITLE, | |
| commit_description=COMMIT_DESCRIPTION, | |
| create_pr=True, | |
| ) | |
| for filename in filenames: | |
| pt_filename = hf_hub_download(repo_id=model_id, filename=filename) | |
| sf_filename = rename(pt_filename) | |
| sf_filename = os.path.join(folder, sf_filename) | |
| convert_file(pt_filename, sf_filename) | |
| api.create_commit( | |
| repo_id=model_id, | |
| commit_message=f"Adds {sf_filename}", | |
| revision=new_pr.pr_revision, | |
| operations=[CommitOperationAdd(path_in_repo=sf_filename.split("/")[-1], path_or_fileobj=sf_filename)], | |
| create_pr=False, | |
| ) | |
| os.remove(pt_filename) | |
| os.remove(sf_filename) | |
| return new_pr, [] | |
| def convert_single(model_id: str, folder: str, api: "HfApi") -> ConversionResult: | |
| pt_filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin") | |
| sf_name = "model.safetensors" | |
| sf_filename = os.path.join(folder, sf_name) | |
| convert_file(pt_filename, sf_filename) | |
| new_pr = api.create_commit( | |
| repo_id=model_id, | |
| operations=[CommitOperationAdd(path_in_repo=sf_name, path_or_fileobj=sf_filename)], | |
| commit_message=PR_TITLE, | |
| commit_description=COMMIT_DESCRIPTION, | |
| create_pr=True, | |
| ) | |
| return new_pr, [] | |
| def convert_file( | |
| pt_filename: str, | |
| sf_filename: str, | |
| ): | |
| loaded = torch.load(pt_filename, map_location="cpu") | |
| if "state_dict" in loaded: | |
| loaded = loaded["state_dict"] | |
| to_removes = _remove_duplicate_names(loaded) | |
| metadata = {"format": "pt"} | |
| for kept_name, to_remove_group in to_removes.items(): | |
| for to_remove in to_remove_group: | |
| if to_remove not in metadata: | |
| metadata[to_remove] = kept_name | |
| del loaded[to_remove] | |
| # For tensors to be contiguous | |
| loaded = {k: v.contiguous() for k, v in loaded.items()} | |
| dirname = os.path.dirname(sf_filename) | |
| os.makedirs(dirname, exist_ok=True) | |
| save_file(loaded, sf_filename, metadata=metadata) | |
| reloaded = load_file(sf_filename) | |
| for k in loaded: | |
| pt_tensor = loaded[k] | |
| sf_tensor = reloaded[k] | |
| if not torch.equal(pt_tensor, sf_tensor): | |
| raise RuntimeError(f"The output tensors do not match for key {k}") | |
| def create_diff(pt_infos: Dict[str, List[str]], sf_infos: Dict[str, List[str]]) -> str: | |
| errors = [] | |
| for key in ["missing_keys", "mismatched_keys", "unexpected_keys"]: | |
| pt_set = set(pt_infos[key]) | |
| sf_set = set(sf_infos[key]) | |
| pt_only = pt_set - sf_set | |
| sf_only = sf_set - pt_set | |
| if pt_only: | |
| errors.append(f"{key} : PT warnings contain {pt_only} which are not present in SF warnings") | |
| if sf_only: | |
| errors.append(f"{key} : SF warnings contain {sf_only} which are not present in PT warnings") | |
| return "\n".join(errors) | |
| def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]: | |
| try: | |
| main_commit = api.list_repo_commits(model_id)[0].commit_id | |
| discussions = api.get_repo_discussions(repo_id=model_id) | |
| except Exception: | |
| return None | |
| for discussion in discussions: | |
| if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title: | |
| commits = api.list_repo_commits(model_id, revision=discussion.git_reference) | |
| if main_commit == commits[1].commit_id: | |
| return discussion | |
| return None | |
| def convert_generic(model_id: str, folder: str, filenames: Set[str], api: "HfApi") -> ConversionResult: | |
| operations = [] | |
| errors = [] | |
| extensions = set([".bin", ".ckpt"]) | |
| new_pr = None | |
| for filename in filenames: | |
| prefix, ext = os.path.splitext(filename) | |
| if ext in extensions: | |
| pt_filename = hf_hub_download(model_id, filename=filename) | |
| dirname, raw_filename = os.path.split(filename) | |
| if raw_filename == "pytorch_model.bin": | |
| # XXX: This is a special case to handle `transformers` and the | |
| # `transformers` part of the model which is actually loaded by `transformers`. | |
| sf_in_repo = os.path.join(dirname, "model.safetensors") | |
| else: | |
| sf_in_repo = f"{prefix}.safetensors" | |
| sf_filename = os.path.join(folder, sf_in_repo) | |
| try: | |
| convert_file(pt_filename, sf_filename) | |
| if new_pr is None: | |
| new_pr = api.create_commit( | |
| repo_id=model_id, | |
| operations=[CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename)], | |
| commit_message=PR_TITLE, | |
| commit_description=COMMIT_DESCRIPTION, | |
| create_pr=True, | |
| ) | |
| else: | |
| api.create_commit( | |
| repo_id=model_id, | |
| commit_message=f"Adds {sf_filename}", | |
| revision=new_pr.pr_revision, | |
| operations=[CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename)], | |
| create_pr=False, | |
| ) | |
| os.remove(pt_filename) | |
| os.remove(sf_filename) | |
| except Exception as e: | |
| errors.append((pt_filename, e)) | |
| return new_pr, errors | |
| def convert(api: "HfApi", model_id: str, force: bool = False) -> Tuple["CommitInfo", List["Exception"]]: | |
| info = api.model_info(model_id) | |
| filenames = set(s.rfilename for s in info.siblings) | |
| with TemporaryDirectory() as d: | |
| folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models")) | |
| os.makedirs(folder) | |
| new_pr = None | |
| try: | |
| operations = None | |
| pr = previous_pr(api, model_id, PR_TITLE) | |
| library_name = getattr(info, "library_name", None) | |
| if any(filename.endswith(".safetensors") for filename in filenames) and not force: | |
| raise AlreadyExists(f"Model {model_id} is already converted, skipping..") | |
| elif pr is not None and not force: | |
| url = f"https://huggingface.co/{model_id}/discussions/{pr.num}" | |
| new_pr = pr | |
| raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}") | |
| elif library_name == "transformers": | |
| if "pytorch_model.bin" in filenames: | |
| new_pr, errors = convert_single(model_id, folder, api) | |
| elif "pytorch_model.bin.index.json" in filenames: | |
| new_pr, errors = convert_multi(model_id, folder, api) | |
| else: | |
| raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert") | |
| else: | |
| new_pr, errors = convert_generic(model_id, folder, filenames, api) | |
| print(f"Pr created at {new_pr.pr_url}") | |
| finally: | |
| shutil.rmtree(folder) | |
| return new_pr, errors | |
| if __name__ == "__main__": | |
| DESCRIPTION = """ | |
| Simple utility tool to convert automatically some weights on the hub to `safetensors` format. | |
| It is PyTorch exclusive for now. | |
| It works by downloading the weights (PT), converting them locally, and uploading them back | |
| as a PR on the hub. | |
| """ | |
| parser = argparse.ArgumentParser(description=DESCRIPTION) | |
| parser.add_argument( | |
| "model_id", | |
| type=str, | |
| help="The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`", | |
| ) | |
| parser.add_argument( | |
| "--force", | |
| action="store_true", | |
| help="Create the PR even if it already exists of if the model was already converted.", | |
| ) | |
| parser.add_argument( | |
| "-y", | |
| action="store_true", | |
| help="Ignore safety prompt", | |
| ) | |
| args = parser.parse_args() | |
| model_id = args.model_id | |
| api = HfApi() | |
| if args.y: | |
| txt = "y" | |
| else: | |
| txt = input( | |
| "This conversion script will unpickle a pickled file, which is inherently unsafe. If you do not trust this file, we invite you to use" | |
| " https://huggingface.co/spaces/safetensors/convert or google colab or other hosted solution to avoid potential issues with this file." | |
| " Continue [Y/n] ?" | |
| ) | |
| if txt.lower() in {"", "y"}: | |
| _commit_info, _errors = convert(api, model_id, force=args.force) | |
| else: | |
| print(f"Answer was `{txt}` aborting.") | |