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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 | |
| 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 | |
| from transformers import AutoConfig | |
| from transformers.pipelines.base import infer_framework_load_model | |
| 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. | |
| """ | |
| class AlreadyExists(Exception): | |
| pass | |
| def shared_pointers(tensors): | |
| ptrs = defaultdict(list) | |
| for k, v in tensors.items(): | |
| ptrs[v.data_ptr()].append(k) | |
| failing = [] | |
| for ptr, names in ptrs.items(): | |
| if len(names) > 1: | |
| failing.append(names) | |
| return failing | |
| def check_file_size(sf_filename: str, pt_filename: str): | |
| sf_size = os.stat(sf_filename).st_size | |
| pt_size = os.stat(pt_filename).st_size | |
| if (sf_size - pt_size) / pt_size > 0.01: | |
| raise RuntimeError( | |
| f"""The file size different is more than 1%: | |
| - {sf_filename}: {sf_size} | |
| - {pt_filename}: {pt_size} | |
| """ | |
| ) | |
| 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) -> List["CommitOperationAdd"]: | |
| 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()) | |
| local_filenames = [] | |
| 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) | |
| local_filenames.append(sf_filename) | |
| 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) | |
| local_filenames.append(index) | |
| operations = [ | |
| CommitOperationAdd(path_in_repo=local.split("/")[-1], path_or_fileobj=local) for local in local_filenames | |
| ] | |
| return operations | |
| def convert_single(model_id: str, folder: str) -> List["CommitOperationAdd"]: | |
| 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) | |
| operations = [CommitOperationAdd(path_in_repo=sf_name, path_or_fileobj=sf_filename)] | |
| return operations | |
| 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"] | |
| shared = shared_pointers(loaded) | |
| for shared_weights in shared: | |
| for name in shared_weights[1:]: | |
| loaded.pop(name) | |
| # 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={"format": "pt"}) | |
| check_file_size(sf_filename, pt_filename) | |
| 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 check_final_model(model_id: str, folder: str): | |
| config = hf_hub_download(repo_id=model_id, filename="config.json") | |
| shutil.copy(config, os.path.join(folder, "config.json")) | |
| config = AutoConfig.from_pretrained(folder) | |
| _, (pt_model, pt_infos) = infer_framework_load_model(model_id, config, output_loading_info=True) | |
| _, (sf_model, sf_infos) = infer_framework_load_model(folder, config, output_loading_info=True) | |
| if pt_infos != sf_infos: | |
| error_string = create_diff(pt_infos, sf_infos) | |
| raise ValueError(f"Different infos when reloading the model: {error_string}") | |
| pt_params = pt_model.state_dict() | |
| sf_params = sf_model.state_dict() | |
| pt_shared = shared_pointers(pt_params) | |
| sf_shared = shared_pointers(sf_params) | |
| if pt_shared != sf_shared: | |
| raise RuntimeError("The reconstructed model is wrong, shared tensors are different {shared_pt} != {shared_tf}") | |
| sig = signature(pt_model.forward) | |
| input_ids = torch.arange(10).unsqueeze(0) | |
| pixel_values = torch.randn(1, 3, 224, 224) | |
| input_values = torch.arange(1000).float().unsqueeze(0) | |
| kwargs = {} | |
| if "input_ids" in sig.parameters: | |
| kwargs["input_ids"] = input_ids | |
| if "decoder_input_ids" in sig.parameters: | |
| kwargs["decoder_input_ids"] = input_ids | |
| if "pixel_values" in sig.parameters: | |
| kwargs["pixel_values"] = pixel_values | |
| if "input_values" in sig.parameters: | |
| kwargs["input_values"] = input_values | |
| if "bbox" in sig.parameters: | |
| kwargs["bbox"] = torch.zeros((1, 10, 4)).long() | |
| if "image" in sig.parameters: | |
| kwargs["image"] = pixel_values | |
| if torch.cuda.is_available(): | |
| pt_model = pt_model.cuda() | |
| sf_model = sf_model.cuda() | |
| kwargs = {k: v.cuda() for k, v in kwargs.items()} | |
| pt_logits = pt_model(**kwargs)[0] | |
| sf_logits = sf_model(**kwargs)[0] | |
| torch.testing.assert_close(sf_logits, pt_logits) | |
| print(f"Model {model_id} is ok !") | |
| def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]: | |
| try: | |
| 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: | |
| details = api.get_discussion_details(repo_id=model_id, discussion_num=discussion.num) | |
| if details.target_branch == "refs/heads/main": | |
| return discussion | |
| def convert_generic(model_id: str, folder: str, filenames: Set[str]) -> List["CommitOperationAdd"]: | |
| operations = [] | |
| extensions = set([".bin", ".ckpt"]) | |
| 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) | |
| convert_file(pt_filename, sf_filename) | |
| operations.append(CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename)) | |
| return operations | |
| def convert(api: "HfApi", model_id: str, force: bool = False) -> Optional["CommitInfo"]: | |
| pr_title = "Adding `safetensors` variant of this model" | |
| 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: | |
| operations = convert_single(model_id, folder) | |
| elif "pytorch_model.bin.index.json" in filenames: | |
| operations = convert_multi(model_id, folder) | |
| else: | |
| raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert") | |
| check_final_model(model_id, folder) | |
| else: | |
| operations = convert_generic(model_id, folder, filenames) | |
| if operations: | |
| new_pr = api.create_commit( | |
| repo_id=model_id, | |
| operations=operations, | |
| commit_message=pr_title, | |
| commit_description=COMMIT_DESCRIPTION, | |
| create_pr=True, | |
| ) | |
| print(f"Pr created at {new_pr.pr_url}") | |
| else: | |
| print("No files to convert") | |
| finally: | |
| shutil.rmtree(folder) | |
| return new_pr | |
| 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.", | |
| ) | |
| args = parser.parse_args() | |
| model_id = args.model_id | |
| api = HfApi() | |
| convert(api, model_id, force=args.force) | |