mirror of
https://github.com/oobabooga/text-generation-webui.git
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447569e31a
* Show download progress on the model screen. * In case of error, mark as done to clear progress bar. * Increase the iteration block size to reduce overhead.
297 lines
12 KiB
Python
297 lines
12 KiB
Python
'''
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Downloads models from Hugging Face to models/model-name.
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Example:
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python download-model.py facebook/opt-1.3b
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'''
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import argparse
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import base64
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import datetime
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import hashlib
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import json
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import re
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import os
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import sys
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from pathlib import Path
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import requests
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import tqdm
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from tqdm.contrib.concurrent import thread_map
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def select_model_from_default_options():
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models = {
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"OPT 6.7B": ("facebook", "opt-6.7b", "main"),
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"OPT 2.7B": ("facebook", "opt-2.7b", "main"),
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"OPT 1.3B": ("facebook", "opt-1.3b", "main"),
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"OPT 350M": ("facebook", "opt-350m", "main"),
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"GALACTICA 6.7B": ("facebook", "galactica-6.7b", "main"),
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"GALACTICA 1.3B": ("facebook", "galactica-1.3b", "main"),
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"GALACTICA 125M": ("facebook", "galactica-125m", "main"),
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"Pythia-6.9B-deduped": ("EleutherAI", "pythia-6.9b-deduped", "main"),
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"Pythia-2.8B-deduped": ("EleutherAI", "pythia-2.8b-deduped", "main"),
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"Pythia-1.4B-deduped": ("EleutherAI", "pythia-1.4b-deduped", "main"),
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"Pythia-410M-deduped": ("EleutherAI", "pythia-410m-deduped", "main"),
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}
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choices = {}
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print("Select the model that you want to download:\n")
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for i, name in enumerate(models):
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char = chr(ord('A') + i)
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choices[char] = name
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print(f"{char}) {name}")
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char_hugging = chr(ord('A') + len(models))
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print(f"{char_hugging}) Manually specify a Hugging Face model")
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char_exit = chr(ord('A') + len(models) + 1)
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print(f"{char_exit}) Do not download a model")
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print()
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print("Input> ", end='')
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choice = input()[0].strip().upper()
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if choice == char_exit:
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exit()
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elif choice == char_hugging:
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print("""\nType the name of your desired Hugging Face model in the format organization/name.
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Examples:
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facebook/opt-1.3b
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EleutherAI/pythia-1.4b-deduped
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""")
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print("Input> ", end='')
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model = input()
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branch = "main"
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else:
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arr = models[choices[choice]]
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model = f"{arr[0]}/{arr[1]}"
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branch = arr[2]
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return model, branch
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class ModelDownloader:
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def __init__(self):
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self.s = requests.Session()
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if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
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self.s.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
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def sanitize_model_and_branch_names(self, model, branch):
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if model[-1] == '/':
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model = model[:-1]
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if branch is None:
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branch = "main"
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else:
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pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
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if not pattern.match(branch):
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raise ValueError(
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"Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
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return model, branch
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def get_download_links_from_huggingface(self, model, branch, text_only=False):
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base = "https://huggingface.co"
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page = f"/api/models/{model}/tree/{branch}"
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cursor = b""
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links = []
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sha256 = []
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classifications = []
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has_pytorch = False
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has_pt = False
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# has_ggml = False
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has_safetensors = False
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is_lora = False
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while True:
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url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
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r = self.s.get(url, timeout=20)
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r.raise_for_status()
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content = r.content
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dict = json.loads(content)
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if len(dict) == 0:
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break
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for i in range(len(dict)):
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fname = dict[i]['path']
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if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
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is_lora = True
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is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname)
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is_safetensors = re.match(".*\.safetensors", fname)
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is_pt = re.match(".*\.pt", fname)
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is_ggml = re.match(".*ggml.*\.bin", fname)
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is_tokenizer = re.match("(tokenizer|ice).*\.model", fname)
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is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
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if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)):
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if 'lfs' in dict[i]:
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sha256.append([fname, dict[i]['lfs']['oid']])
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if is_text:
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links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
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classifications.append('text')
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continue
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if not text_only:
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links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
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if is_safetensors:
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has_safetensors = True
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classifications.append('safetensors')
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elif is_pytorch:
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has_pytorch = True
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classifications.append('pytorch')
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elif is_pt:
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has_pt = True
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classifications.append('pt')
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elif is_ggml:
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# has_ggml = True
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classifications.append('ggml')
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cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
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cursor = base64.b64encode(cursor)
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cursor = cursor.replace(b'=', b'%3D')
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# If both pytorch and safetensors are available, download safetensors only
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if (has_pytorch or has_pt) and has_safetensors:
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for i in range(len(classifications) - 1, -1, -1):
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if classifications[i] in ['pytorch', 'pt']:
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links.pop(i)
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return links, sha256, is_lora
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def get_output_folder(self, model, branch, is_lora, base_folder=None):
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if base_folder is None:
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base_folder = 'models' if not is_lora else 'loras'
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output_folder = f"{'_'.join(model.split('/')[-2:])}"
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if branch != 'main':
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output_folder += f'_{branch}'
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output_folder = Path(base_folder) / output_folder
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return output_folder
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def get_single_file(self, url, output_folder, start_from_scratch=False):
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filename = Path(url.rsplit('/', 1)[1])
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output_path = output_folder / filename
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if output_path.exists() and not start_from_scratch:
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# Check if the file has already been downloaded completely
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r = self.s.get(url, stream=True, timeout=20)
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total_size = int(r.headers.get('content-length', 0))
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if output_path.stat().st_size >= total_size:
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return
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# Otherwise, resume the download from where it left off
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headers = {'Range': f'bytes={output_path.stat().st_size}-'}
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mode = 'ab'
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else:
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headers = {}
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mode = 'wb'
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r = self.s.get(url, stream=True, headers=headers, timeout=20)
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with open(output_path, mode) as f:
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total_size = int(r.headers.get('content-length', 0))
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# Every 4MB we report an update
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block_size = 4*1024*1024
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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:
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count = 0
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for data in r.iter_content(block_size):
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t.update(len(data))
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f.write(data)
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if self.progress_bar is not None:
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count += len(data)
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self.progress_bar(float(count)/float(total_size), f"Downloading {filename}")
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def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
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thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
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def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar = None, start_from_scratch=False, threads=1):
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self.progress_bar = progress_bar
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# Creating the folder and writing the metadata
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if not output_folder.exists():
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output_folder.mkdir(parents=True, exist_ok=True)
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with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
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f.write(f'url: https://huggingface.co/{model}\n')
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f.write(f'branch: {branch}\n')
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f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
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sha256_str = ''
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for i in range(len(sha256)):
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sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n'
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if sha256_str != '':
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f.write(f'sha256sum:\n{sha256_str}')
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# Downloading the files
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print(f"Downloading the model to {output_folder}")
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self.start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
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def check_model_files(self, model, branch, links, sha256, output_folder):
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# Validate the checksums
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validated = True
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for i in range(len(sha256)):
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fpath = (output_folder / sha256[i][0])
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if not fpath.exists():
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print(f"The following file is missing: {fpath}")
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validated = False
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continue
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with open(output_folder / sha256[i][0], "rb") as f:
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bytes = f.read()
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file_hash = hashlib.sha256(bytes).hexdigest()
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if file_hash != sha256[i][1]:
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print(f'Checksum failed: {sha256[i][0]} {sha256[i][1]}')
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validated = False
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else:
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print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}')
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if validated:
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print('[+] Validated checksums of all model files!')
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else:
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print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('MODEL', type=str, default=None, nargs='?')
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parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
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parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
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parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
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parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
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parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
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parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
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args = parser.parse_args()
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branch = args.branch
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model = args.MODEL
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if model is None:
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model, branch = select_model_from_default_options()
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downloader = ModelDownloader()
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# Cleaning up the model/branch names
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try:
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model, branch = downloader.sanitize_model_and_branch_names(model, branch)
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except ValueError as err_branch:
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print(f"Error: {err_branch}")
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sys.exit()
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# Getting the download links from Hugging Face
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links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=args.text_only)
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# Getting the output folder
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output_folder = downloader.get_output_folder(model, branch, is_lora, base_folder=args.output)
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if args.check:
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# Check previously downloaded files
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downloader.check_model_files(model, branch, links, sha256, output_folder)
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else:
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# Download files
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downloader.download_model_files(model, branch, links, sha256, output_folder, threads=args.threads)
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