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Also enable aria2 downloading for non-sharded checkpoints

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Gnome Ann 2022-05-10 22:43:41 -04:00
parent e115bb68e4
commit c1ef20bcff
2 changed files with 9 additions and 9 deletions
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2
colabkobold.sh
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@ -186,7 +186,7 @@ fi
#Download routine for Aria2c scripts #Download routine for Aria2c scripts
if [ ! -z ${aria2+x} ]; then if [ ! -z ${aria2+x} ]; then
curl -L $aria2 | aria2c -c -i- -d$dloc --user-agent=KoboldAI --file-allocation=none curl -L $aria2 | aria2c -x 10 -s 10 -j 10 -c -i- -d$dloc --user-agent=KoboldAI --file-allocation=none
fi fi
#Extract the model with 7z #Extract the model with 7z

8
utils.py
View File

@ -183,9 +183,9 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
sharded = True sharded = True
else: else:
break break
if not sharded: # If the model has a pytorch_model.bin file, that's the only large file to download so it's probably more efficient to just let transformers download it if not sharded: # If the model has a pytorch_model.bin file, that's the only file to download
return filenames = [transformers.modeling_utils.WEIGHTS_NAME]
# Otherwise download the pytorch_model.bin.index.json and then let aria2 download all the pytorch_model-#####-of-#####.bin files mentioned inside it else: # Otherwise download the pytorch_model.bin.index.json and then let aria2 download all the pytorch_model-#####-of-#####.bin files mentioned inside it
map_filename = transformers.file_utils.cached_path(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent) map_filename = transformers.file_utils.cached_path(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent)
with open(map_filename) as f: with open(map_filename) as f:
map_data = json.load(f) map_data = json.load(f)
@ -210,7 +210,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
aria2_config = "\n".join(f"{u}\n out={n}" for u, n in zip(urls, filenames)).encode() aria2_config = "\n".join(f"{u}\n out={n}" for u, n in zip(urls, filenames)).encode()
with tempfile.NamedTemporaryFile("w+b", delete=True) as f: with tempfile.NamedTemporaryFile("w+b", delete=True) as f:
f.write(aria2_config) f.write(aria2_config)
p = subprocess.Popen(["aria2c", "-d", _cache_dir, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {token}'"] if use_auth_token else []), stdout=subprocess.PIPE, stderr=subprocess.STDOUT) p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "-d", _cache_dir, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {token}'"] if use_auth_token else []), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
for line in p.stdout: for line in p.stdout:
print(line.decode(), end="", flush=True) print(line.decode(), end="", flush=True)
for u, t, n in zip(urls, etags, filenames): for u, t, n in zip(urls, etags, filenames):