Narsil HF staff commited on
Commit
cbdc5ec
1 Parent(s): d49670b

Revert "Update app.py"

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This reverts commit d49670bbab0c38913b141c315347c62c919a9d9f.

Files changed (1) hide show
  1. app.py +84 -275
app.py CHANGED
@@ -1,285 +1,94 @@
1
- import argparse
2
- import json
3
  import os
4
- import shutil
5
- from collections import defaultdict
6
- from inspect import signature
7
- from tempfile import TemporaryDirectory
8
- from typing import Dict, List, Optional, Set
9
 
10
- import torch
 
11
 
12
- from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download
13
- from huggingface_hub.file_download import repo_folder_name
14
- from safetensors.torch import load_file, save_file
15
- from transformers import AutoConfig
16
- from transformers.pipelines.base import infer_framework_load_model
17
 
 
 
 
18
 
19
- class AlreadyExists(Exception):
20
- pass
21
 
 
 
 
22
 
23
- def shared_pointers(tensors):
24
- ptrs = defaultdict(list)
25
- for k, v in tensors.items():
26
- ptrs[v.data_ptr()].append(k)
27
- failing = []
28
- for ptr, names in ptrs.items():
29
- if len(names) > 1:
30
- failing.append(names)
31
- return failing
32
 
33
-
34
- def check_file_size(sf_filename: str, pt_filename: str):
35
- sf_size = os.stat(sf_filename).st_size
36
- pt_size = os.stat(pt_filename).st_size
37
-
38
- if (sf_size - pt_size) / pt_size > 0.01:
39
- raise RuntimeError(
40
- f"""The file size different is more than 1%:
41
- - {sf_filename}: {sf_size}
42
- - {pt_filename}: {pt_size}
43
- """
44
- )
45
-
46
-
47
- def rename(pt_filename: str) -> str:
48
- filename, ext = os.path.splitext(pt_filename)
49
- local = f"{filename}.safetensors"
50
- local = local.replace("pytorch_model", "model")
51
- return local
52
-
53
-
54
- def convert_multi(model_id: str, folder: str) -> List["CommitOperationAdd"]:
55
- filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin.index.json")
56
- with open(filename, "r") as f:
57
- data = json.load(f)
58
-
59
- filenames = set(data["weight_map"].values())
60
- local_filenames = []
61
- for filename in filenames:
62
- pt_filename = hf_hub_download(repo_id=model_id, filename=filename)
63
-
64
- sf_filename = rename(pt_filename)
65
- sf_filename = os.path.join(folder, sf_filename)
66
- convert_file(pt_filename, sf_filename)
67
- local_filenames.append(sf_filename)
68
-
69
- index = os.path.join(folder, "model.safetensors.index.json")
70
- with open(index, "w") as f:
71
- newdata = {k: v for k, v in data.items()}
72
- newmap = {k: rename(v) for k, v in data["weight_map"].items()}
73
- newdata["weight_map"] = newmap
74
- json.dump(newdata, f, indent=4)
75
- local_filenames.append(index)
76
-
77
- operations = [
78
- CommitOperationAdd(path_in_repo=local.split("/")[-1], path_or_fileobj=local) for local in local_filenames
79
- ]
80
-
81
- return operations
82
-
83
-
84
- def convert_single(model_id: str, folder: str) -> List["CommitOperationAdd"]:
85
- pt_filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin")
86
-
87
- sf_name = "model.safetensors"
88
- sf_filename = os.path.join(folder, sf_name)
89
- convert_file(pt_filename, sf_filename)
90
- operations = [CommitOperationAdd(path_in_repo=sf_name, path_or_fileobj=sf_filename)]
91
- return operations
92
-
93
-
94
- def convert_file(
95
- pt_filename: str,
96
- sf_filename: str,
97
- ):
98
- loaded = torch.load(pt_filename, map_location="cpu")
99
- if "state_dict" in loaded:
100
- loaded = loaded["state_dict"]
101
- shared = shared_pointers(loaded)
102
- for shared_weights in shared:
103
- for name in shared_weights[1:]:
104
- loaded.pop(name)
105
-
106
- # For tensors to be contiguous
107
- loaded = {k: v.contiguous() for k, v in loaded.items()}
108
-
109
- dirname = os.path.dirname(sf_filename)
110
- os.makedirs(dirname, exist_ok=True)
111
- save_file(loaded, sf_filename, metadata={"format": "pt"})
112
- check_file_size(sf_filename, pt_filename)
113
- reloaded = load_file(sf_filename)
114
- for k in loaded:
115
- pt_tensor = loaded[k]
116
- sf_tensor = reloaded[k]
117
- if not torch.equal(pt_tensor, sf_tensor):
118
- raise RuntimeError(f"The output tensors do not match for key {k}")
119
-
120
-
121
- def create_diff(pt_infos: Dict[str, List[str]], sf_infos: Dict[str, List[str]]) -> str:
122
- errors = []
123
- for key in ["missing_keys", "mismatched_keys", "unexpected_keys"]:
124
- pt_set = set(pt_infos[key])
125
- sf_set = set(sf_infos[key])
126
-
127
- pt_only = pt_set - sf_set
128
- sf_only = sf_set - pt_set
129
-
130
- if pt_only:
131
- errors.append(f"{key} : PT warnings contain {pt_only} which are not present in SF warnings")
132
- if sf_only:
133
- errors.append(f"{key} : SF warnings contain {sf_only} which are not present in PT warnings")
134
- return "\n".join(errors)
135
-
136
-
137
- def check_final_model(model_id: str, folder: str):
138
- config = hf_hub_download(repo_id=model_id, filename="config.json")
139
- shutil.copy(config, os.path.join(folder, "config.json"))
140
- config = AutoConfig.from_pretrained(folder)
141
-
142
- _, (pt_model, pt_infos) = infer_framework_load_model(model_id, config, output_loading_info=True)
143
- _, (sf_model, sf_infos) = infer_framework_load_model(folder, config, output_loading_info=True)
144
-
145
- if pt_infos != sf_infos:
146
- error_string = create_diff(pt_infos, sf_infos)
147
- raise ValueError(f"Different infos when reloading the model: {error_string}")
148
-
149
- pt_params = pt_model.state_dict()
150
- sf_params = sf_model.state_dict()
151
-
152
- pt_shared = shared_pointers(pt_params)
153
- sf_shared = shared_pointers(sf_params)
154
- if pt_shared != sf_shared:
155
- raise RuntimeError("The reconstructed model is wrong, shared tensors are different {shared_pt} != {shared_tf}")
156
-
157
- sig = signature(pt_model.forward)
158
- input_ids = torch.arange(10).unsqueeze(0)
159
- pixel_values = torch.randn(1, 3, 224, 224)
160
- input_values = torch.arange(1000).float().unsqueeze(0)
161
- kwargs = {}
162
- if "input_ids" in sig.parameters:
163
- kwargs["input_ids"] = input_ids
164
- if "decoder_input_ids" in sig.parameters:
165
- kwargs["decoder_input_ids"] = input_ids
166
- if "pixel_values" in sig.parameters:
167
- kwargs["pixel_values"] = pixel_values
168
- if "input_values" in sig.parameters:
169
- kwargs["input_values"] = input_values
170
- if "bbox" in sig.parameters:
171
- kwargs["bbox"] = torch.zeros((1, 10, 4)).long()
172
- if "image" in sig.parameters:
173
- kwargs["image"] = pixel_values
174
-
175
- if torch.cuda.is_available():
176
- pt_model = pt_model.cuda()
177
- sf_model = sf_model.cuda()
178
- kwargs = {k: v.cuda() for k, v in kwargs.items()}
179
-
180
- pt_logits = pt_model(**kwargs)[0]
181
- sf_logits = sf_model(**kwargs)[0]
182
-
183
- torch.testing.assert_close(sf_logits, pt_logits)
184
- print(f"Model {model_id} is ok !")
185
-
186
-
187
- def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
188
  try:
189
- discussions = api.get_repo_discussions(repo_id=model_id)
190
- except Exception:
191
- return None
192
- for discussion in discussions:
193
- if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
194
- return discussion
195
-
196
-
197
- def convert_generic(model_id: str, folder: str, filenames: Set[str]) -> List["CommitOperationAdd"]:
198
- operations = []
199
-
200
- extensions = set([".bin", ".ckpt"])
201
- for filename in filenames:
202
- prefix, ext = os.path.splitext(filename)
203
- if ext in extensions:
204
- pt_filename = hf_hub_download(model_id, filename=filename)
205
- _, raw_filename = os.path.split(filename)
206
- if raw_filename == "pytorch_model.bin":
207
- # XXX: This is a special case to handle `transformers` and the
208
- # `transformers` part of the model which is actually loaded by `transformers`.
209
- sf_in_repo = "model.safetensors"
210
- else:
211
- sf_in_repo = f"{prefix}.safetensors"
212
- sf_filename = os.path.join(folder, sf_in_repo)
213
- convert_file(pt_filename, sf_filename)
214
- operations.append(CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename))
215
- return operations
216
-
217
-
218
- def convert(api: "HfApi", model_id: str, force: bool = False) -> Optional["CommitInfo"]:
219
- pr_title = "Adding `safetensors` variant of this model"
220
- info = api.model_info(model_id)
221
- filenames = set(s.rfilename for s in info.siblings)
222
-
223
- with TemporaryDirectory() as d:
224
- folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
225
- os.makedirs(folder)
226
- new_pr = None
227
- try:
228
- operations = None
229
- pr = previous_pr(api, model_id, pr_title)
230
-
231
- library_name = getattr(info, "library_name", None)
232
- if any(filename.endswith(".safetensors") for filename in filenames) and not force:
233
- raise AlreadyExists(f"Model {model_id} is already converted, skipping..")
234
- elif pr is not None and not force:
235
- url = f"https://huggingface.co/{model_id}/discussions/{pr.num}"
236
- new_pr = pr
237
- raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}")
238
- elif library_name == "transformers":
239
- if "pytorch_model.bin" in filenames:
240
- operations = convert_single(model_id, folder)
241
- elif "pytorch_model.bin.index.json" in filenames:
242
- operations = convert_multi(model_id, folder)
243
- else:
244
- raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert")
245
- check_final_model(model_id, folder)
246
- else:
247
- operations = convert_generic(model_id, folder, filenames)
248
-
249
- if operations:
250
- new_pr = api.create_commit(
251
- repo_id=model_id,
252
- operations=operations,
253
- commit_message=pr_title,
254
- create_pr=True,
255
  )
256
- print(f"Pr created at {new_pr.pr_url}")
257
- else:
258
- print("No files to convert")
259
- finally:
260
- shutil.rmtree(folder)
261
- return new_pr
262
-
263
-
264
- if __name__ == "__main__":
265
- DESCRIPTION = """
266
- Simple utility tool to convert automatically some weights on the hub to `safetensors` format.
267
- It is PyTorch exclusive for now.
268
- It works by downloading the weights (PT), converting them locally, and uploading them back
269
- as a PR on the hub.
270
- """
271
- parser = argparse.ArgumentParser(description=DESCRIPTION)
272
- parser.add_argument(
273
- "model_id",
274
- type=str,
275
- help="The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`",
276
- )
277
- parser.add_argument(
278
- "--force",
279
- action="store_true",
280
- help="Create the PR even if it already exists of if the model was already converted.",
281
- )
282
- args = parser.parse_args()
283
- model_id = args.model_id
284
- api = HfApi()
285
- convert(api, model_id, force=args.force)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import csv
2
+ from datetime import datetime
3
  import os
4
+ from typing import Optional
5
+ import gradio as gr
 
 
 
6
 
7
+ from convert import convert
8
+ from huggingface_hub import HfApi, Repository
9
 
 
 
 
 
 
10
 
11
+ DATASET_REPO_URL = "https://huggingface.co/datasets/safetensors/conversions"
12
+ DATA_FILENAME = "data.csv"
13
+ DATA_FILE = os.path.join("data", DATA_FILENAME)
14
 
15
+ HF_TOKEN = os.environ.get("HF_TOKEN")
 
16
 
17
+ repo: Optional[Repository] = None
18
+ if HF_TOKEN:
19
+ repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, token=HF_TOKEN)
20
 
 
 
 
 
 
 
 
 
 
21
 
22
+ def run(token: str, model_id: str) -> str:
23
+ if token == "" or model_id == "":
24
+ return """
25
+ ### Invalid input 🐞
26
+
27
+ Please fill a token and model_id.
28
+ """
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
29
  try:
30
+ api = HfApi(token=token)
31
+ is_private = api.model_info(repo_id=model_id).private
32
+ print("is_private", is_private)
33
+
34
+ commit_info = convert(api=api, model_id=model_id)
35
+ print("[commit_info]", commit_info)
36
+
37
+ # save in a (public) dataset:
38
+ if repo is not None and not is_private:
39
+ repo.git_pull(rebase=True)
40
+ print("pulled")
41
+ with open(DATA_FILE, "a") as csvfile:
42
+ writer = csv.DictWriter(
43
+ csvfile, fieldnames=["model_id", "pr_url", "time"]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
44
  )
45
+ writer.writerow(
46
+ {
47
+ "model_id": model_id,
48
+ "pr_url": commit_info.pr_url,
49
+ "time": str(datetime.now()),
50
+ }
51
+ )
52
+ commit_url = repo.push_to_hub()
53
+ print("[dataset]", commit_url)
54
+
55
+ return f"""
56
+ ### Success 🔥
57
+
58
+ Yay! This model was successfully converted and a PR was open using your token, here:
59
+
60
+ [{commit_info.pr_url}]({commit_info.pr_url})
61
+ """
62
+ except Exception as e:
63
+ return f"""
64
+ ### Error 😢😢😢
65
+
66
+ {e}
67
+ """
68
+
69
+
70
+ DESCRIPTION = """
71
+ The steps are the following:
72
+
73
+ - Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
74
+ - Input a model id from the Hub
75
+ - Click "Submit"
76
+ - That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥
77
+
78
+ ⚠️ For now only `pytorch_model.bin` files are supported but we'll extend in the future.
79
+ """
80
+
81
+ demo = gr.Interface(
82
+ title="Convert any model to Safetensors and open a PR",
83
+ description=DESCRIPTION,
84
+ allow_flagging="never",
85
+ article="Check out the [Safetensors repo on GitHub](https://github.com/huggingface/safetensors)",
86
+ inputs=[
87
+ gr.Text(max_lines=1, label="your_hf_token"),
88
+ gr.Text(max_lines=1, label="model_id"),
89
+ ],
90
+ outputs=[gr.Markdown(label="output")],
91
+ fn=run,
92
+ )
93
+
94
+ demo.launch()