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 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) 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") 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: 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) _, 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 = "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) 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 info.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, 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)