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import argparse
import json
import os
import shutil
from tempfile import TemporaryDirectory
from collections import defaultdict
from inspect import signature
from typing import Optional, List
import torch
from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download, get_repo_discussions
from huggingface_hub.file_download import repo_folder_name
from transformers import AutoConfig
from transformers.pipelines.base import infer_framework_load_model
from safetensors.torch import save_file
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:
local = pt_filename.replace(".bin", ".safetensors")
local = local.replace("pytorch_model", "model")
return local
def convert_multi(model_id: 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())
for filename in filenames:
cached_filename = hf_hub_download(repo_id=model_id, filename=filename)
loaded = torch.load(cached_filename)
sf_filename = rename(filename)
local = os.path.join(folder, sf_filename)
save_file(loaded, local, metadata={"format": "pt"})
check_file_size(local, cached_filename)
local_filenames.append(local)
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"]:
sf_filename = "model.safetensors"
filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin")
loaded = torch.load(filename)
local = os.path.join(folder, sf_filename)
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()}
save_file(loaded, local, metadata={"format": "pt"})
check_file_size(local, filename)
operations = [CommitOperationAdd(path_in_repo=sf_filename, path_or_fileobj=local)]
return operations
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 = infer_framework_load_model(model_id, config)
_, sf_model = infer_framework_load_model(folder, config)
pt_model = pt_model
sf_model = sf_model
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(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 ("model.safetensors" in filenames or "model_index.safetensors.index.json" 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 "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")
if operations:
check_final_model(model_id, folder)
new_pr = api.create_commit(
repo_id=model_id,
operations=operations,
commit_message=pr_title,
create_pr=True,
)
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)
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