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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.")
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