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aria2_hook now uses new cache format if you have transformers 4.22

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vfbd 2022-09-15 16:50:43 -04:00
parent 7bf6c9a23f
commit 463bf86bcc
2 changed files with 267 additions and 49 deletions
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8
aiserver.py
View File

@ -1713,11 +1713,13 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
import transformers.configuration_utils
import transformers.modeling_utils
import transformers.file_utils
import huggingface_hub
legacy = packaging.version.parse(transformers_version) < packaging.version.parse("4.22.0.dev0")
# Save the config.json
shutil.move(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, transformers.configuration_utils.CONFIG_NAME, revision=vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME))
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(vars.model, transformers.configuration_utils.CONFIG_NAME, revision=vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME))
if(utils.num_shards is None):
# Save the pytorch_model.bin of an unsharded model
shutil.move(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME))
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME))
else:
with open(utils.from_pretrained_index_filename) as f:
map_data = json.load(f)
@ -1726,7 +1728,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
shutil.move(utils.from_pretrained_index_filename, os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_INDEX_NAME))
# Then save the pytorch_model-#####-of-#####.bin files
for filename in filenames:
shutil.move(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, filename, revision=vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(vars.model.replace('/', '_')), filename))
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(vars.model, filename, revision=vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(vars.model.replace('/', '_')), filename))
shutil.rmtree("cache/")
if(vars.hascuda):

308
utils.py
View File

@ -4,6 +4,7 @@ import shutil
import json
import subprocess
import tempfile
from urllib.error import HTTPError
import requests
import requests.adapters
import time
@ -12,6 +13,8 @@ import os
import itertools
import hashlib
import huggingface_hub
import packaging.version
from pathlib import Path
from typing import Optional
vars = None
@ -161,6 +164,262 @@ def num_layers(config):
#==================================================================#
# Downloads huggingface checkpoints using aria2c if possible
#==================================================================#
def _download_with_aria2(aria2_config: str, total_length: int, directory: str = ".", user_agent=None, force_download=False, use_auth_token=None):
import transformers
lengths = {}
s = requests.Session()
s.mount("http://", requests.adapters.HTTPAdapter(max_retries=requests.adapters.Retry(total=120, backoff_factor=1)))
bar = None
done = False
secret = os.urandom(17).hex()
try:
with tempfile.NamedTemporaryFile("w+b", delete=False) as f:
f.write(aria2_config)
f.flush()
p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(vars.aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-d", directory, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {use_auth_token}'"] if use_auth_token else []), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
while p.poll() is None:
r = s.post(f"http://localhost:{vars.aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"]
if not r:
s.close()
if bar is not None:
bar.n = bar.total
bar.close()
p.terminate()
done = True
break
if bar is None:
bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000)
visited = set()
for x in r:
filename = x["files"][0]["path"]
lengths[filename] = (int(x["completedLength"]), int(x["totalLength"]))
visited.add(filename)
for k, v in lengths.items():
if k not in visited:
lengths[k] = (v[1], v[1])
bar.n = sum(v[0] for v in lengths.values())
bar.update()
time.sleep(0.1)
path = f.name
except Exception as e:
p.terminate()
raise e
finally:
try:
os.remove(path)
except OSError:
pass
code = p.wait()
if not done and code:
raise OSError(f"aria2 exited with exit code {code}")
def _transformers22_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.modeling_utils
from huggingface_hub import HfFolder
if use_auth_token:
if isinstance(use_auth_token, str):
token = use_auth_token
else:
token = HfFolder.get_token()
if token is None:
raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
_cache_dir = str(cache_dir) if cache_dir is not None else transformers.TRANSFORMERS_CACHE
_revision = revision if revision is not None else huggingface_hub.constants.DEFAULT_REVISION
sharded = False
headers = {"user-agent": transformers.file_utils.http_user_agent(user_agent)}
if use_auth_token:
headers["authorization"] = f"Bearer {use_auth_token}"
storage_folder = os.path.join(_cache_dir, huggingface_hub.file_download.repo_folder_name(repo_id=pretrained_model_name_or_path, repo_type="model"))
os.makedirs(storage_folder, exist_ok=True)
def is_cached(filename):
try:
huggingface_hub.hf_hub_download(pretrained_model_name_or_path, filename, cache_dir=cache_dir, local_files_only=True)
except ValueError:
return False
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
try:
filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
except AttributeError:
return
url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=revision)
if is_cached(filename) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
break
if sharded:
return
else:
sharded = True
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]
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 = huggingface_hub.hf_hub_download(pretrained_model_name_or_path, filename, 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:
map_data = json.load(f)
filenames = set(map_data["weight_map"].values())
urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=revision) for n in filenames]
if not force_download:
urls = [u for u, n in zip(urls, filenames) if not is_cached(n)]
if not urls:
return
blob_paths = []
# This section is a modified version of hf_hub_download from huggingface_hub
# See https://github.com/huggingface/huggingface_hub/blob/main/LICENSE for license
for u, n in zip(urls, filenames):
relative_filename = os.path.join(*n.split("/"))
if not local_files_only:
try:
r = huggingface_hub.file_download._request_wrapper(
method="HEAD",
url=u,
headers=headers,
allow_redirects=False,
follow_relative_redirects=True,
proxies=proxies,
timeout=10,
)
try:
r.raise_for_status()
except HTTPError as e:
error_code = r.headers.get("X-Error-Code")
if error_code != "EntryNotFound":
raise RuntimeError(f"HEAD {u} failed with error code {r.status_code}")
commit_hash = r.headers.get(huggingface_hub.file_download.HUGGINGFACE_HEADER_X_REPO_COMMIT)
if commit_hash is not None:
no_exist_file_path = (
Path(storage_folder)
/ ".no_exist"
/ commit_hash
/ relative_filename
)
no_exist_file_path.parent.mkdir(parents=True, exist_ok=True)
no_exist_file_path.touch()
huggingface_hub.file_download._cache_commit_hash_for_specific_revision(
storage_folder, _revision, commit_hash
)
raise
commit_hash = r.headers[huggingface_hub.file_download.HUGGINGFACE_HEADER_X_REPO_COMMIT]
if commit_hash is None:
raise OSError(
"Distant resource does not seem to be on huggingface.co (missing"
" commit header)."
)
etag = r.headers.get(huggingface_hub.file_download.HUGGINGFACE_HEADER_X_LINKED_ETAG) or r.headers.get(
"ETag"
)
# We favor a custom header indicating the etag of the linked resource, and
# we fallback to the regular etag header.
# If we don't have any of those, raise an error.
if etag is None:
raise OSError(
"Distant resource does not have an ETag, we won't be able to"
" reliably ensure reproducibility."
)
etag = huggingface_hub.file_download._normalize_etag(etag)
# In case of a redirect, save an extra redirect on the request.get call,
# and ensure we download the exact atomic version even if it changed
# between the HEAD and the GET (unlikely, but hey).
# Useful for lfs blobs that are stored on a CDN.
if 300 <= r.status_code <= 399:
url_to_download = r.headers["Location"]
if (
"lfs.huggingface.co" in url_to_download
or "lfs-staging.huggingface.co" in url_to_download
):
# Remove authorization header when downloading a LFS blob
headers.pop("authorization", None)
except (requests.exceptions.SSLError, requests.exceptions.ProxyError):
# Actually raise for those subclasses of ConnectionError
raise
except (
requests.exceptions.ConnectionError,
requests.exceptions.Timeout,
huggingface_hub.file_download.OfflineModeIsEnabled,
):
# Otherwise, our Internet connection is down.
# etag is None
pass
if etag is None:
# In those cases, we cannot force download.
if force_download:
raise ValueError(
"We have no connection or you passed local_files_only, so"
" force_download is not an accepted option."
)
if huggingface_hub.file_download.REGEX_COMMIT_HASH.match(_revision):
commit_hash = _revision
else:
ref_path = os.path.join(storage_folder, "refs", _revision)
with open(ref_path) as f:
commit_hash = f.read()
pointer_path = os.path.join(
storage_folder, "snapshots", commit_hash, relative_filename
)
if os.path.exists(pointer_path):
return pointer_path
# If we couldn't find an appropriate file on disk,
# raise an error.
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise huggingface_hub.file_download.LocalEntryNotFoundError(
"Cannot find the requested files in the disk cache and"
" outgoing traffic has been disabled. To enable hf.co look-ups"
" and downloads online, set 'local_files_only' to False."
)
else:
raise huggingface_hub.file_download.LocalEntryNotFoundError(
"Connection error, and we cannot find the requested files in"
" the disk cache. Please try again or make sure your Internet"
" connection is on."
)
# From now on, etag and commit_hash are not None.
blob_path = os.path.join(storage_folder, "blobs", etag)
pointer_path = os.path.join(
storage_folder, "snapshots", commit_hash, relative_filename
)
os.makedirs(os.path.dirname(blob_path), exist_ok=True)
os.makedirs(os.path.dirname(pointer_path), exist_ok=True)
# if passed revision is not identical to commit_hash
# then revision has to be a branch name or tag name.
# In that case store a ref.
huggingface_hub.file_download._cache_commit_hash_for_specific_revision(storage_folder, _revision, commit_hash)
if os.path.exists(pointer_path) and not force_download:
return pointer_path
if os.path.exists(blob_path) and not force_download:
# we have the blob already, but not the pointer
huggingface_hub.file_download.logger.info("creating pointer to %s from %s", blob_path, pointer_path)
huggingface_hub.file_download._create_relative_symlink(blob_path, pointer_path)
return pointer_path
# Some Windows versions do not allow for paths longer than 255 characters.
# In this case, we must specify it is an extended path by using the "\\?\" prefix.
if os.name == "nt" and len(os.path.abspath(blob_path)) > 255:
blob_path = "\\\\?\\" + os.path.abspath(blob_path)
blob_paths.append(blob_path)
filenames = blob_paths
headers = [requests.head(u, headers=headers, allow_redirects=True, proxies=proxies, timeout=10).headers for u in urls]
for n in filenames:
prefix, suffix = n.rsplit("/", 1)
path = os.path.join(prefix, "kai-tempfile." + suffix + ".aria2")
if os.path.exists(path):
os.remove(path)
path = os.path.join(prefix, "kai-tempfile." + suffix)
if os.path.exists(path):
os.remove(path)
total_length = sum(int(h["Content-Length"]) for h in headers)
aria2_config = "\n".join(f"{u}\n out={os.path.join(prefix, 'kai-tempfile.' + suffix)}" for u, n in zip(urls, filenames) for prefix, suffix in [n.rsplit("/", 1)]).encode()
_download_with_aria2(aria2_config, total_length, use_auth_token=token if use_auth_token else None, user_agent=user_agent, force_download=force_download)
for u, n in zip(urls, filenames):
prefix, suffix = n.rsplit("/", 1)
os.rename(os.path.join(prefix, "kai-tempfile." + suffix), os.path.join(prefix, suffix))
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.modeling_utils
@ -174,6 +433,8 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
if proxies:
print("WARNING: KoboldAI does not support using aria2 to download models from huggingface.co through a proxy. Disabling aria2 download mode.")
return
if packaging.version.parse(transformers.__version__) >= packaging.version.parse("4.22.0.dev0"):
return _transformers22_aria2_hook(pretrained_model_name_or_path, force_download=force_download, cache_dir=cache_dir, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, **kwargs)
if use_auth_token:
if isinstance(use_auth_token, str):
token = use_auth_token
@ -234,53 +495,8 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
if os.path.exists(path):
os.remove(path)
total_length = sum(int(h["Content-Length"]) for h in headers)
lengths = {}
aria2_config = "\n".join(f"{u}\n out=kai-tempfile.{n}" for u, n in zip(urls, filenames)).encode()
s = requests.Session()
s.mount("http://", requests.adapters.HTTPAdapter(max_retries=requests.adapters.Retry(total=120, backoff_factor=1)))
bar = None
done = False
secret = os.urandom(17).hex()
try:
with tempfile.NamedTemporaryFile("w+b", delete=False) as f:
f.write(aria2_config)
f.flush()
p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(vars.aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-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.DEVNULL, stderr=subprocess.DEVNULL)
while p.poll() is None:
r = s.post(f"http://localhost:{vars.aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"]
if not r:
s.close()
if bar is not None:
bar.n = bar.total
bar.close()
p.terminate()
done = True
break
if bar is None:
bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000)
visited = set()
for x in r:
filename = x["files"][0]["path"]
lengths[filename] = (int(x["completedLength"]), int(x["totalLength"]))
visited.add(filename)
for k, v in lengths.items():
if k not in visited:
lengths[k] = (v[1], v[1])
bar.n = sum(v[0] for v in lengths.values())
bar.update()
time.sleep(0.1)
path = f.name
except Exception as e:
p.terminate()
raise e
finally:
try:
os.remove(path)
except OSError:
pass
code = p.wait()
if not done and code:
raise OSError(f"aria2 exited with exit code {code}")
_download_with_aria2(aria2_config, total_length, directory=_cache_dir, use_auth_token=token if use_auth_token else None, user_agent=user_agent, force_download=force_download)
for u, t, n in zip(urls, etags, filenames):
os.rename(os.path.join(_cache_dir, "kai-tempfile." + n), os.path.join(_cache_dir, n))
with open(os.path.join(_cache_dir, n + ".json"), "w") as f: