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Make it possible to download protected HF models from the command line. (#2408)

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Morgan Schweers 2023-05-31 20:11:21 -07:00 committed by GitHub
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commit 1aed2b9e52
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3 changed files with 172 additions and 157 deletions
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4
README.md
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@ -156,7 +156,9 @@ For example:
python download-model.py facebook/opt-1.3b
If you want to download a model manually, note that all you need are the json, txt, and pytorch\*.bin (or model*.safetensors) files. The remaining files are not necessary.
* If you want to download a model manually, note that all you need are the json, txt, and pytorch\*.bin (or model*.safetensors) files. The remaining files are not necessary.
* If you want to download a protected model (one gated behind accepting a license or otherwise private, like `bigcode/starcoder`) you can set the environment variables `HF_USER` to your huggingface username and `HF_PASS` to your password or (_as a better option_) to a [User Access Token](https://huggingface.co/settings/tokens). Note that you will need to accept the model terms on the Hugging Face website before starting the download.
#### GGML models

320
download-model.py
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@ -12,6 +12,7 @@ import datetime
import hashlib
import json
import re
import os
import sys
from pathlib import Path
@ -70,173 +71,183 @@ EleutherAI/pythia-1.4b-deduped
return model, branch
def sanitize_model_and_branch_names(model, branch):
if model[-1] == '/':
model = model[:-1]
if branch is None:
branch = "main"
else:
pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
if not pattern.match(branch):
raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
return model, branch
class ModelDownloader:
def __init__(self):
self.s = requests.Session()
if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
self.s.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
def get_download_links_from_huggingface(model, branch, text_only=False):
base = "https://huggingface.co"
page = f"/api/models/{model}/tree/{branch}"
cursor = b""
def sanitize_model_and_branch_names(self, model, branch):
if model[-1] == '/':
model = model[:-1]
links = []
sha256 = []
classifications = []
has_pytorch = False
has_pt = False
has_ggml = False
has_safetensors = False
is_lora = False
while True:
url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
r = requests.get(url, timeout=10)
r.raise_for_status()
content = r.content
if branch is None:
branch = "main"
else:
pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
if not pattern.match(branch):
raise ValueError(
"Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
dict = json.loads(content)
if len(dict) == 0:
break
for i in range(len(dict)):
fname = dict[i]['path']
if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
is_lora = True
is_pytorch = re.match("(pytorch|adapter|gptq)_model.*\.bin", fname)
is_safetensors = re.match(".*\.safetensors", fname)
is_pt = re.match(".*\.pt", fname)
is_ggml = re.match(".*ggml.*\.bin", fname)
is_tokenizer = re.match("(tokenizer|ice).*\.model", fname)
is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)):
if 'lfs' in dict[i]:
sha256.append([fname, dict[i]['lfs']['oid']])
if is_text:
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
classifications.append('text')
continue
if not text_only:
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
if is_safetensors:
has_safetensors = True
classifications.append('safetensors')
elif is_pytorch:
has_pytorch = True
classifications.append('pytorch')
elif is_pt:
has_pt = True
classifications.append('pt')
elif is_ggml:
has_ggml = True
classifications.append('ggml')
cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
cursor = base64.b64encode(cursor)
cursor = cursor.replace(b'=', b'%3D')
# If both pytorch and safetensors are available, download safetensors only
if (has_pytorch or has_pt) and has_safetensors:
for i in range(len(classifications) - 1, -1, -1):
if classifications[i] in ['pytorch', 'pt']:
links.pop(i)
return links, sha256, is_lora
return model, branch
def get_output_folder(model, branch, is_lora, base_folder=None):
if base_folder is None:
base_folder = 'models' if not is_lora else 'loras'
def get_download_links_from_huggingface(self, model, branch, text_only=False):
base = "https://huggingface.co"
page = f"/api/models/{model}/tree/{branch}"
cursor = b""
output_folder = f"{'_'.join(model.split('/')[-2:])}"
if branch != 'main':
output_folder += f'_{branch}'
output_folder = Path(base_folder) / output_folder
return output_folder
links = []
sha256 = []
classifications = []
has_pytorch = False
has_pt = False
has_ggml = False
has_safetensors = False
is_lora = False
while True:
url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
r = self.s.get(url, timeout=10)
r.raise_for_status()
content = r.content
dict = json.loads(content)
if len(dict) == 0:
break
for i in range(len(dict)):
fname = dict[i]['path']
if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
is_lora = True
is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname)
is_safetensors = re.match(".*\.safetensors", fname)
is_pt = re.match(".*\.pt", fname)
is_ggml = re.match(".*ggml.*\.bin", fname)
is_tokenizer = re.match("(tokenizer|ice).*\.model", fname)
is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)):
if 'lfs' in dict[i]:
sha256.append([fname, dict[i]['lfs']['oid']])
if is_text:
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
classifications.append('text')
continue
if not text_only:
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
if is_safetensors:
has_safetensors = True
classifications.append('safetensors')
elif is_pytorch:
has_pytorch = True
classifications.append('pytorch')
elif is_pt:
has_pt = True
classifications.append('pt')
elif is_ggml:
has_ggml = True
classifications.append('ggml')
cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
cursor = base64.b64encode(cursor)
cursor = cursor.replace(b'=', b'%3D')
# If both pytorch and safetensors are available, download safetensors only
if (has_pytorch or has_pt) and has_safetensors:
for i in range(len(classifications) - 1, -1, -1):
if classifications[i] in ['pytorch', 'pt']:
links.pop(i)
return links, sha256, is_lora
def get_single_file(url, output_folder, start_from_scratch=False):
filename = Path(url.rsplit('/', 1)[1])
output_path = output_folder / filename
if output_path.exists() and not start_from_scratch:
# Check if the file has already been downloaded completely
r = requests.get(url, stream=True, timeout=10)
total_size = int(r.headers.get('content-length', 0))
if output_path.stat().st_size >= total_size:
return
# Otherwise, resume the download from where it left off
headers = {'Range': f'bytes={output_path.stat().st_size}-'}
mode = 'ab'
else:
headers = {}
mode = 'wb'
def get_output_folder(self, model, branch, is_lora, base_folder=None):
if base_folder is None:
base_folder = 'models' if not is_lora else 'loras'
r = requests.get(url, stream=True, headers=headers, timeout=10)
with open(output_path, mode) as f:
total_size = int(r.headers.get('content-length', 0))
block_size = 1024
with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
for data in r.iter_content(block_size):
t.update(len(data))
f.write(data)
output_folder = f"{'_'.join(model.split('/')[-2:])}"
if branch != 'main':
output_folder += f'_{branch}'
output_folder = Path(base_folder) / output_folder
return output_folder
def start_download_threads(file_list, output_folder, start_from_scratch=False, threads=1):
thread_map(lambda url: get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
def get_single_file(self, url, output_folder, start_from_scratch=False):
filename = Path(url.rsplit('/', 1)[1])
output_path = output_folder / filename
if output_path.exists() and not start_from_scratch:
# Check if the file has already been downloaded completely
r = self.s.get(url, stream=True, timeout=10)
total_size = int(r.headers.get('content-length', 0))
if output_path.stat().st_size >= total_size:
return
# Otherwise, resume the download from where it left off
headers = {'Range': f'bytes={output_path.stat().st_size}-'}
mode = 'ab'
else:
headers = {}
mode = 'wb'
r = self.s.get(url, stream=True, headers=headers, timeout=10)
with open(output_path, mode) as f:
total_size = int(r.headers.get('content-length', 0))
block_size = 1024
with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
for data in r.iter_content(block_size):
t.update(len(data))
f.write(data)
def download_model_files(model, branch, links, sha256, output_folder, start_from_scratch=False, threads=1):
# Creating the folder and writing the metadata
if not output_folder.exists():
output_folder.mkdir(parents=True, exist_ok=True)
with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
f.write(f'url: https://huggingface.co/{model}\n')
f.write(f'branch: {branch}\n')
f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
sha256_str = ''
def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
def download_model_files(self, model, branch, links, sha256, output_folder, start_from_scratch=False, threads=1):
# Creating the folder and writing the metadata
if not output_folder.exists():
output_folder.mkdir(parents=True, exist_ok=True)
with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
f.write(f'url: https://huggingface.co/{model}\n')
f.write(f'branch: {branch}\n')
f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
sha256_str = ''
for i in range(len(sha256)):
sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n'
if sha256_str != '':
f.write(f'sha256sum:\n{sha256_str}')
# Downloading the files
print(f"Downloading the model to {output_folder}")
self.start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
def check_model_files(self, model, branch, links, sha256, output_folder):
# Validate the checksums
validated = True
for i in range(len(sha256)):
sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n'
if sha256_str != '':
f.write(f'sha256sum:\n{sha256_str}')
fpath = (output_folder / sha256[i][0])
# Downloading the files
print(f"Downloading the model to {output_folder}")
start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
def check_model_files(model, branch, links, sha256, output_folder):
# Validate the checksums
validated = True
for i in range(len(sha256)):
fpath = (output_folder / sha256[i][0])
if not fpath.exists():
print(f"The following file is missing: {fpath}")
validated = False
continue
with open(output_folder / sha256[i][0], "rb") as f:
bytes = f.read()
file_hash = hashlib.sha256(bytes).hexdigest()
if file_hash != sha256[i][1]:
print(f'Checksum failed: {sha256[i][0]} {sha256[i][1]}')
if not fpath.exists():
print(f"The following file is missing: {fpath}")
validated = False
else:
print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}')
continue
if validated:
print('[+] Validated checksums of all model files!')
else:
print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
with open(output_folder / sha256[i][0], "rb") as f:
bytes = f.read()
file_hash = hashlib.sha256(bytes).hexdigest()
if file_hash != sha256[i][1]:
print(f'Checksum failed: {sha256[i][0]} {sha256[i][1]}')
validated = False
else:
print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}')
if validated:
print('[+] Validated checksums of all model files!')
else:
print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
if __name__ == '__main__':
@ -256,22 +267,23 @@ if __name__ == '__main__':
if model is None:
model, branch = select_model_from_default_options()
downloader = ModelDownloader()
# Cleaning up the model/branch names
try:
model, branch = sanitize_model_and_branch_names(model, branch)
model, branch = downloader.sanitize_model_and_branch_names(model, branch)
except ValueError as err_branch:
print(f"Error: {err_branch}")
sys.exit()
# Getting the download links from Hugging Face
links, sha256, is_lora = get_download_links_from_huggingface(model, branch, text_only=args.text_only)
links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=args.text_only)
# Getting the output folder
output_folder = get_output_folder(model, branch, is_lora, base_folder=args.output)
output_folder = downloader.get_output_folder(model, branch, is_lora, base_folder=args.output)
if args.check:
# Check previously downloaded files
check_model_files(model, branch, links, sha256, output_folder)
downloader.check_model_files(model, branch, links, sha256, output_folder)
else:
# Download files
download_model_files(model, branch, links, sha256, output_folder, threads=args.threads)
downloader.download_model_files(model, branch, links, sha256, output_folder, threads=args.threads)

5
server.py
View File

@ -184,7 +184,8 @@ def count_tokens(text):
def download_model_wrapper(repo_id):
try:
downloader = importlib.import_module("download-model")
downloader_module = importlib.import_module("download-model")
downloader = downloader_module.ModelDownloader()
repo_id_parts = repo_id.split(":")
model = repo_id_parts[0] if len(repo_id_parts) > 0 else repo_id
branch = repo_id_parts[1] if len(repo_id_parts) > 1 else "main"
@ -369,7 +370,7 @@ def create_model_menus():
shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.')
with gr.Box():
gr.Markdown('Transformers 4-bit')
with gr.Row():