text-generation-webui/download-model.py

341 lines
14 KiB
Python

'''
Downloads models from Hugging Face to models/username_modelname.
Example:
python download-model.py facebook/opt-1.3b
'''
import argparse
import base64
import datetime
import hashlib
import json
import os
import re
import sys
from pathlib import Path
from time import sleep
import requests
import tqdm
from requests.adapters import HTTPAdapter
from requests.exceptions import ConnectionError, RequestException, Timeout
from tqdm.contrib.concurrent import thread_map
base = os.environ.get("HF_ENDPOINT") or "https://huggingface.co"
class ModelDownloader:
def __init__(self, max_retries=5):
self.max_retries = max_retries
def get_session(self):
session = requests.Session()
if self.max_retries:
session.mount('https://cdn-lfs.huggingface.co', HTTPAdapter(max_retries=self.max_retries))
session.mount('https://huggingface.co', HTTPAdapter(max_retries=self.max_retries))
if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
session.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
try:
from huggingface_hub import get_token
token = get_token()
except ImportError:
token = os.getenv("HF_TOKEN")
if token is not None:
session.headers = {'authorization': f'Bearer {token}'}
return session
def sanitize_model_and_branch_names(self, model, branch):
if model[-1] == '/':
model = model[:-1]
if model.startswith(base + '/'):
model = model[len(base) + 1:]
model_parts = model.split(":")
model = model_parts[0] if len(model_parts) > 0 else model
branch = model_parts[1] if len(model_parts) > 1 else branch
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
def get_download_links_from_huggingface(self, model, branch, text_only=False, specific_file=None):
session = self.get_session()
page = f"/api/models/{model}/tree/{branch}"
cursor = b""
links = []
sha256 = []
classifications = []
has_pytorch = False
has_pt = False
has_gguf = False
has_safetensors = False
is_lora = False
while True:
url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
r = session.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 specific_file not in [None, ''] and fname != specific_file:
continue
if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
is_lora = True
is_pytorch = re.match(r"(pytorch|adapter|gptq)_model.*\.bin", fname)
is_safetensors = re.match(r".*\.safetensors", fname)
is_pt = re.match(r".*\.pt", fname)
is_gguf = re.match(r'.*\.gguf', fname)
is_tiktoken = re.match(r".*\.tiktoken", fname)
is_tokenizer = re.match(r"(tokenizer|ice|spiece).*\.model", fname) or is_tiktoken
is_text = re.match(r".*\.(txt|json|py|md)", fname) or is_tokenizer
if any((is_pytorch, is_safetensors, is_pt, is_gguf, is_tokenizer, is_text)):
if 'lfs' in dict[i]:
sha256.append([fname, dict[i]['lfs']['oid']])
if is_text:
links.append(f"{base}/{model}/resolve/{branch}/{fname}")
classifications.append('text')
continue
if not text_only:
links.append(f"{base}/{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_gguf:
has_gguf = True
classifications.append('gguf')
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)
# For GGUF, try to download only the Q4_K_M if no specific file is specified.
# If not present, exclude all GGUFs, as that's likely a repository with both
# GGUF and fp16 files.
if has_gguf and specific_file is None:
has_q4km = False
for i in range(len(classifications) - 1, -1, -1):
if 'q4_k_m' in links[i].lower():
has_q4km = True
if has_q4km:
for i in range(len(classifications) - 1, -1, -1):
if 'q4_k_m' not in links[i].lower():
links.pop(i)
else:
for i in range(len(classifications) - 1, -1, -1):
if links[i].lower().endswith('.gguf'):
links.pop(i)
is_llamacpp = has_gguf and specific_file is not None
return links, sha256, is_lora, is_llamacpp
def get_output_folder(self, model, branch, is_lora, is_llamacpp=False):
base_folder = 'models' if not is_lora else 'loras'
# If the model is of type GGUF, save directly in the base_folder
if is_llamacpp:
return Path(base_folder)
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 get_single_file(self, url, output_folder, start_from_scratch=False):
filename = Path(url.rsplit('/', 1)[1])
output_path = output_folder / filename
max_retries = 7
attempt = 0
while attempt < max_retries:
attempt += 1
session = self.get_session()
headers = {}
mode = 'wb'
if output_path.exists() and not start_from_scratch:
# Resume download
r = session.get(url, stream=True, timeout=20)
total_size = int(r.headers.get('content-length', 0))
if output_path.stat().st_size >= total_size:
return
headers = {'Range': f'bytes={output_path.stat().st_size}-'}
mode = 'ab'
try:
with session.get(url, stream=True, headers=headers, timeout=30) as r:
r.raise_for_status() # If status is not 2xx, raise an error
total_size = int(r.headers.get('content-length', 0))
block_size = 1024 * 1024 # 1MB
tqdm_kwargs = {
'total': total_size,
'unit': 'iB',
'unit_scale': True,
'bar_format': '{l_bar}{bar}| {n_fmt}/{total_fmt} {rate_fmt}'
}
if 'COLAB_GPU' in os.environ:
tqdm_kwargs.update({
'position': 0,
'leave': True
})
with open(output_path, mode) as f:
with tqdm.tqdm(**tqdm_kwargs) as t:
count = 0
for data in r.iter_content(block_size):
f.write(data)
t.update(len(data))
if total_size != 0 and self.progress_bar is not None:
count += len(data)
self.progress_bar(float(count) / float(total_size), f"{filename}")
break # Exit loop if successful
except (RequestException, ConnectionError, Timeout) as e:
print(f"Error downloading {filename}: {e}.")
print(f"That was attempt {attempt}/{max_retries}.", end=' ')
if attempt < max_retries:
print(f"Retry begins in {2 ** attempt} seconds.")
sleep(2 ** attempt)
else:
print("Failed to download after the maximum number of attempts.")
def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=4):
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, progress_bar=None, start_from_scratch=False, threads=4, specific_file=None, is_llamacpp=False):
self.progress_bar = progress_bar
# Create the folder and writing the metadata
output_folder.mkdir(parents=True, exist_ok=True)
if not is_llamacpp:
metadata = f'url: https://huggingface.co/{model}\n' \
f'branch: {branch}\n' \
f'download date: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n'
sha256_str = '\n'.join([f' {item[1]} {item[0]}' for item in sha256])
if sha256_str:
metadata += f'sha256sum:\n{sha256_str}'
metadata += '\n'
(output_folder / 'huggingface-metadata.txt').write_text(metadata)
if specific_file:
print(f"Downloading {specific_file} to {output_folder}")
else:
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)):
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]}')
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__':
parser = argparse.ArgumentParser()
parser.add_argument('MODEL', type=str, default=None, nargs='?')
parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
parser.add_argument('--threads', type=int, default=4, help='Number of files to download simultaneously.')
parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
parser.add_argument('--specific-file', type=str, default=None, help='Name of the specific file to download (if not provided, downloads all).')
parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
parser.add_argument('--max-retries', type=int, default=5, help='Max retries count when get error in download time.')
args = parser.parse_args()
branch = args.branch
model = args.MODEL
specific_file = args.specific_file
if model is None:
print("Error: Please specify the model you'd like to download (e.g. 'python download-model.py facebook/opt-1.3b').")
sys.exit()
downloader = ModelDownloader(max_retries=args.max_retries)
# Clean up the model/branch names
try:
model, branch = downloader.sanitize_model_and_branch_names(model, branch)
except ValueError as err_branch:
print(f"Error: {err_branch}")
sys.exit()
# Get the download links from Hugging Face
links, sha256, is_lora, is_llamacpp = downloader.get_download_links_from_huggingface(model, branch, text_only=args.text_only, specific_file=specific_file)
# Get the output folder
if args.output:
output_folder = Path(args.output)
else:
output_folder = downloader.get_output_folder(model, branch, is_lora, is_llamacpp=is_llamacpp)
if args.check:
# Check previously downloaded files
downloader.check_model_files(model, branch, links, sha256, output_folder)
else:
# Download files
downloader.download_model_files(model, branch, links, sha256, output_folder, specific_file=specific_file, threads=args.threads, is_llamacpp=is_llamacpp)