rixty
/
KoboldAI-Client
mirror of https://github.com/KoboldAI/KoboldAI-Client.git
1
1
Fork 0
Code Issues Projects Releases Wiki Activity

Fix error in aria2_hook when transformers version is at least 4.22.0 

Some of the transformers.file_utils functions that were removed in
transformers v4.22.0 have equivalents in the huggingface_hub module.
This commit is contained in:
vfbd 2022-09-15 13:37:50 -04:00
parent aac999c073
commit 551565c5ac
3 changed files with 14 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
requirements.txt
View File

@ -1,4 +1,4 @@
transformers==4.21.3 transformers>=4.20.1
Flask Flask
Flask-SocketIO Flask-SocketIO
requests requests

2
requirements_mtj.txt
View File

@ -6,7 +6,7 @@ optax >= 0.0.5, <= 0.0.9
dm-haiku == 0.0.5 dm-haiku == 0.0.5
jax == 0.2.21 jax == 0.2.21
jaxlib >= 0.1.69, <= 0.3.7 jaxlib >= 0.1.69, <= 0.3.7
transformers == 4.21.3 transformers >= 4.20.1
progressbar2 progressbar2
git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck
flask flask

20
utils.py
View File

@ -10,6 +10,8 @@ import time
from tqdm.auto import tqdm from tqdm.auto import tqdm
import os import os
import itertools import itertools
import hashlib
import huggingface_hub
from typing import Optional from typing import Optional
vars = None vars = None
@ -159,7 +161,7 @@ def num_layers(config):
#==================================================================# #==================================================================#
# Downloads huggingface checkpoints using aria2c if possible # Downloads huggingface checkpoints using aria2c if possible
#==================================================================# #==================================================================#
def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_dir=None, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, mirror=None, **kwargs): def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_dir=None, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, **kwargs):
import transformers import transformers
import transformers.modeling_utils import transformers.modeling_utils
from huggingface_hub import HfFolder from huggingface_hub import HfFolder
@ -186,8 +188,8 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
headers["authorization"] = f"Bearer {use_auth_token}" headers["authorization"] = f"Bearer {use_auth_token}"
def is_cached(url): def is_cached(url):
try: try:
transformers.file_utils.get_from_cache(url, cache_dir=cache_dir, local_files_only=True) huggingface_hub.cached_download(url, cache_dir=cache_dir, local_files_only=True)
except (FileNotFoundError, transformers.file_utils.EntryNotFoundError): except ValueError:
return False return False
return True return True
while True: # Try to get the huggingface.co URL of the model's pytorch_model.bin or pytorch_model.bin.index.json file while True: # Try to get the huggingface.co URL of the model's pytorch_model.bin or pytorch_model.bin.index.json file
@ -195,7 +197,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
except AttributeError: except AttributeError:
return return
url = transformers.file_utils.hf_bucket_url(pretrained_model_name_or_path, filename, revision=revision, mirror=mirror) url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=revision)
if is_cached(url) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers): if is_cached(url) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
break break
if sharded: if sharded:
@ -205,18 +207,18 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
if not sharded: # If the model has a pytorch_model.bin file, that's the only file to download if not sharded: # If the model has a pytorch_model.bin file, that's the only file to download
filenames = [transformers.modeling_utils.WEIGHTS_NAME] filenames = [transformers.modeling_utils.WEIGHTS_NAME]
else: # 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, use_auth_token=use_auth_token, user_agent=user_agent) map_filename = huggingface_hub.cached_download(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, 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)
filenames = set(map_data["weight_map"].values()) filenames = set(map_data["weight_map"].values())
urls = [transformers.file_utils.hf_bucket_url(pretrained_model_name_or_path, n, revision=revision, mirror=mirror) for n in filenames] urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=revision) for n in filenames]
if not force_download: if not force_download:
urls = [u for u in urls if not is_cached(u)] urls = [u for u in urls if not is_cached(u)]
if not urls: if not urls:
return return
etags = [h.get("X-Linked-Etag") or h.get("ETag") for u in urls for h in [requests.head(u, headers=headers, allow_redirects=False, proxies=proxies, timeout=10).headers]] etags = [h.get("X-Linked-Etag") or h.get("ETag") for u in urls for h in [requests.head(u, headers=headers, allow_redirects=False, proxies=proxies, timeout=10).headers]]
headers = [requests.head(u, headers=headers, allow_redirects=True, proxies=proxies, timeout=10).headers for u in urls] headers = [requests.head(u, headers=headers, allow_redirects=True, proxies=proxies, timeout=10).headers for u in urls]
filenames = [transformers.file_utils.url_to_filename(u, t) for u, t in zip(urls, etags)] filenames = [hashlib.sha256(u.encode("utf-8")).hexdigest() + "." + hashlib.sha256(t.encode("utf-8")).hexdigest() for u, t in zip(urls, etags)]
for n in filenames: for n in filenames:
path = os.path.join(_cache_dir, "kai-tempfile." + n + ".aria2") path = os.path.join(_cache_dir, "kai-tempfile." + n + ".aria2")
if os.path.exists(path): if os.path.exists(path):
@ -298,8 +300,8 @@ def get_num_shards(filename):
# pytorch_model.bin.index.json, returns a list of weight names in the # pytorch_model.bin.index.json, returns a list of weight names in the
# sharded model. Requires lazy loader to be enabled to work properl # sharded model. Requires lazy loader to be enabled to work properl
#==================================================================# #==================================================================#
def get_sharded_checkpoint_num_tensors(pretrained_model_name_or_path, filename, cache_dir=None, force_download=False, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, mirror=None, **kwargs): def get_sharded_checkpoint_num_tensors(pretrained_model_name_or_path, filename, cache_dir=None, force_download=False, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, **kwargs):
import transformers.modeling_utils import transformers.modeling_utils
import torch import torch
shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, 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, revision=revision, mirror=mirror) shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, 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, revision=revision)
return list(itertools.chain(*(torch.load(p, map_location="cpu").keys() for p in shard_paths))) return list(itertools.chain(*(torch.load(p, map_location="cpu").keys() for p in shard_paths)))