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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 | |
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) | |
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) | |
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 ("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 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) | |