h2ogpt-chatbot / utils.py
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import contextlib
import functools
import hashlib
import inspect
import os
import gc
import pathlib
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
from datetime import datetime
import filelock
import requests, uuid
from typing import Tuple, Callable, Dict
from tqdm.auto import tqdm
from joblib import Parallel
from concurrent.futures import ProcessPoolExecutor
import numpy as np
import pandas as pd
def set_seed(seed: int):
"""
Sets the seed of the entire notebook so results are the same every time we run.
This is for REPRODUCIBILITY.
"""
import torch
np.random.seed(seed)
random_state = np.random.RandomState(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ['PYTHONHASHSEED'] = str(seed)
return random_state
def flatten_list(lis):
"""Given a list, possibly nested to any level, return it flattened."""
new_lis = []
for item in lis:
if type(item) == type([]):
new_lis.extend(flatten_list(item))
else:
new_lis.append(item)
return new_lis
def clear_torch_cache():
import torch
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
gc.collect()
def ping():
try:
print('Ping: %s' % str(datetime.now()), flush=True)
except AttributeError:
# some programs wrap print and will fail with flush passed
pass
def ping_gpu():
try:
print('Ping_GPU: %s %s' % (str(datetime.now()), system_info()), flush=True)
except AttributeError:
# some programs wrap print and will fail with flush passed
pass
try:
ping_gpu_memory()
except Exception as e:
print('Ping_GPU memory failure: %s' % str(e), flush=True)
def ping_gpu_memory():
from models.gpu_mem_track import MemTracker
gpu_tracker = MemTracker() # define a GPU tracker
from torch.cuda import memory_summary
gpu_tracker.track()
def get_torch_allocated():
import torch
return torch.cuda.memory_allocated()
def get_device():
import torch
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
return device
def system_info():
import psutil
system = {}
# https://stackoverflow.com/questions/48951136/plot-multiple-graphs-in-one-plot-using-tensorboard
# https://arshren.medium.com/monitoring-your-devices-in-python-5191d672f749
temps = psutil.sensors_temperatures(fahrenheit=False)
if 'coretemp' in temps:
coretemp = temps['coretemp']
temp_dict = {k.label: k.current for k in coretemp}
for k, v in temp_dict.items():
system['CPU_C/%s' % k] = v
# https://github.com/gpuopenanalytics/pynvml/blob/master/help_query_gpu.txt
try:
from pynvml.smi import nvidia_smi
nvsmi = nvidia_smi.getInstance()
gpu_power_dict = {'W_gpu%d' % i: x['power_readings']['power_draw'] for i, x in
enumerate(nvsmi.DeviceQuery('power.draw')['gpu'])}
for k, v in gpu_power_dict.items():
system['GPU_W/%s' % k] = v
gpu_temp_dict = {'C_gpu%d' % i: x['temperature']['gpu_temp'] for i, x in
enumerate(nvsmi.DeviceQuery('temperature.gpu')['gpu'])}
for k, v in gpu_temp_dict.items():
system['GPU_C/%s' % k] = v
gpu_memory_free_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['free'] for i, x in
enumerate(nvsmi.DeviceQuery('memory.free')['gpu'])}
gpu_memory_total_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['total'] for i, x in
enumerate(nvsmi.DeviceQuery('memory.total')['gpu'])}
gpu_memory_frac_dict = {k: gpu_memory_free_dict[k] / gpu_memory_total_dict[k] for k in gpu_memory_total_dict}
for k, v in gpu_memory_frac_dict.items():
system[f'GPU_M/%s' % k] = v
except ModuleNotFoundError:
pass
system['hash'] = get_githash()
return system
def system_info_print():
try:
df = pd.DataFrame.from_dict(system_info(), orient='index')
# avoid slamming GPUs
time.sleep(1)
return df.to_markdown()
except Exception as e:
return "Error: %s" % str(e)
def zip_data(root_dirs=None, zip_file=None, base_dir='./', fail_any_exception=False):
try:
return _zip_data(zip_file=zip_file, base_dir=base_dir, root_dirs=root_dirs)
except Exception as e:
traceback.print_exc()
print('Exception in zipping: %s' % str(e))
if not fail_any_exception:
raise
def _zip_data(root_dirs=None, zip_file=None, base_dir='./'):
if isinstance(root_dirs, str):
root_dirs = [root_dirs]
if zip_file is None:
datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
host_name = os.getenv('HF_HOSTNAME', 'emptyhost')
zip_file = "data_%s_%s.zip" % (datetime_str, host_name)
assert root_dirs is not None
if not os.path.isdir(os.path.dirname(zip_file)) and os.path.dirname(zip_file):
os.makedirs(os.path.dirname(zip_file), exist_ok=True)
with zipfile.ZipFile(zip_file, "w") as expt_zip:
for root_dir in root_dirs:
if root_dir is None:
continue
for root, d, files in os.walk(root_dir):
for file in files:
file_to_archive = os.path.join(root, file)
assert os.path.exists(file_to_archive)
path_to_archive = os.path.relpath(file_to_archive, base_dir)
expt_zip.write(filename=file_to_archive, arcname=path_to_archive)
return zip_file, zip_file
def save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from',
extra_dict={}):
try:
return _save_generate_output(prompt=prompt, output=output, base_model=base_model, save_dir=save_dir,
where_from=where_from, extra_dict=extra_dict)
except Exception as e:
traceback.print_exc()
print('Exception in saving: %s' % str(e))
def _save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from',
extra_dict={}):
"""
Save conversation to .json, row by row.
json_file_path is path to final JSON file. If not in ., then will attempt to make directories.
Appends if file exists
"""
prompt = '<not set>' if prompt is None else prompt
output = '<not set>' if output is None else output
assert save_dir, "save_dir must be provided"
if os.path.exists(save_dir) and not os.path.isdir(save_dir):
raise RuntimeError("save_dir already exists and is not a directory!")
os.makedirs(save_dir, exist_ok=True)
import json
dict_to_save = dict(prompt=prompt, text=output, time=time.ctime(), base_model=base_model, where_from=where_from)
dict_to_save.update(extra_dict)
with filelock.FileLock("save_dir.lock"):
# lock logging in case have concurrency
with open(os.path.join(save_dir, "history.json"), "a") as f:
# just add [ at start, and ] at end, and have proper JSON dataset
f.write(
" " + json.dumps(
dict_to_save
) + ",\n"
)
def s3up(filename):
try:
return _s3up(filename)
except Exception as e:
traceback.print_exc()
print('Exception for file %s in s3up: %s' % (filename, str(e)))
return "Failed to upload %s: Error: %s" % (filename, str(e))
def _s3up(filename):
import boto3
aws_access_key_id = os.getenv('AWS_SERVER_PUBLIC_KEY')
aws_secret_access_key = os.getenv('AWS_SERVER_SECRET_KEY')
bucket = os.getenv('AWS_BUCKET')
assert aws_access_key_id, "Set AWS key"
assert aws_secret_access_key, "Set AWS secret"
assert bucket, "Set AWS Bucket"
s3 = boto3.client('s3',
aws_access_key_id=os.getenv('AWS_SERVER_PUBLIC_KEY'),
aws_secret_access_key=os.getenv('AWS_SERVER_SECRET_KEY'),
)
ret = s3.upload_file(
Filename=filename,
Bucket=os.getenv('AWS_BUCKET'),
Key=filename,
)
if ret in [None, '']:
return "Successfully uploaded %s" % filename
def get_githash():
try:
githash = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE).stdout.decode('utf-8')[0:-1]
except:
githash = ''
return githash
def copy_code(run_id):
"""
copy code to track changes
:param run_id:
:return:
"""
rnd_num = str(random.randint(0, 2 ** 31))
run_id = 'run_' + str(run_id)
os.makedirs(run_id, exist_ok=True)
me_full = os.path.join(pathlib.Path(__file__).parent.resolve(), __file__)
me_file = os.path.basename(__file__)
new_me = os.path.join(run_id, me_file + '_' + get_githash())
if os.path.isfile(new_me):
new_me = os.path.join(run_id, me_file + '_' + get_githash() + '_' + rnd_num)
shutil.copy(me_full, new_me)
else:
shutil.copy(me_full, new_me)
class NullContext(threading.local):
"""No-op context manager, executes block without doing any additional processing.
Used as a stand-in if a particular block of code is only sometimes
used with a normal context manager:
"""
def __init__(self, *args, **kwargs):
pass
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
self.finally_act()
def finally_act(self):
pass
def wrapped_partial(func, *args, **kwargs):
"""
Give partial properties of normal function, like __name__ attribute etc.
:param func:
:param args:
:param kwargs:
:return:
"""
partial_func = functools.partial(func, *args, **kwargs)
functools.update_wrapper(partial_func, func)
return partial_func
class ThreadException(Exception):
pass
class EThread(threading.Thread):
# Function that raises the custom exception
def __init__(self, group=None, target=None, name=None,
args=(), kwargs=None, *, daemon=None, streamer=None, bucket=None):
self.bucket = bucket
self.streamer = streamer
self.exc = None
self._return = None
super().__init__(group=group, target=target, name=name, args=args, kwargs=kwargs, daemon=daemon)
def run(self):
# Variable that stores the exception, if raised by someFunction
try:
if self._target is not None:
self._return = self._target(*self._args, **self._kwargs)
except BaseException as e:
print("thread exception: %s" % str(sys.exc_info()))
self.bucket.put(sys.exc_info())
self.exc = e
if self.streamer:
print("make stop: %s" % str(sys.exc_info()), flush=True)
self.streamer.do_stop = True
finally:
# Avoid a refcycle if the thread is running a function with
# an argument that has a member that points to the thread.
del self._target, self._args, self._kwargs
def join(self, timeout=None):
threading.Thread.join(self)
# Since join() returns in caller thread
# we re-raise the caught exception
# if any was caught
if self.exc:
raise self.exc
return self._return
def import_matplotlib():
import matplotlib
matplotlib.use('agg')
# KEEP THESE HERE! START
import matplotlib.pyplot as plt
import pandas as pd
# to avoid dlopen deadlock in fork
import pandas.core.computation.expressions as pd_expressions
import pandas._libs.groupby as pd_libgroupby
import pandas._libs.reduction as pd_libreduction
import pandas.core.algorithms as pd_algorithms
import pandas.core.common as pd_com
import numpy as np
# KEEP THESE HERE! END
def get_sha(value):
return hashlib.md5(str(value).encode('utf-8')).hexdigest()
def sanitize_filename(name):
"""
Sanitize file *base* names.
:param name: name to sanitize
:return:
"""
bad_chars = ['[', ']', ',', '/', '\\', '\\w', '\\s', '-', '+', '\"', '\'', '>', '<', ' ', '=', ')', '(', ':', '^']
for char in bad_chars:
name = name.replace(char, "_")
length = len(name)
file_length_limit = 250 # bit smaller than 256 for safety
sha_length = 32
real_length_limit = file_length_limit - (sha_length + 2)
if length > file_length_limit:
sha = get_sha(name)
half_real_length_limit = max(1, int(real_length_limit / 2))
name = name[0:half_real_length_limit] + "_" + sha + "_" + name[length - half_real_length_limit:length]
return name
def shutil_rmtree(*args, **kwargs):
return shutil.rmtree(*args, **kwargs)
def remove(path: str):
try:
if path is not None and os.path.exists(path):
if os.path.isdir(path):
shutil_rmtree(path, ignore_errors=True)
else:
with contextlib.suppress(FileNotFoundError):
os.remove(path)
except:
pass
def makedirs(path, exist_ok=True):
"""
Avoid some inefficiency in os.makedirs()
:param path:
:param exist_ok:
:return:
"""
if os.path.isdir(path) and os.path.exists(path):
assert exist_ok, "Path already exists"
return path
os.makedirs(path, exist_ok=exist_ok)
def atomic_move_simple(src, dst):
try:
shutil.move(src, dst)
except (shutil.Error, FileExistsError):
pass
remove(src)
def download_simple(url, dest=None, print_func=None):
if print_func is not None:
print_func("BEGIN get url %s" % str(url))
if url.startswith("file://"):
from requests_file import FileAdapter
s = requests.Session()
s.mount('file://', FileAdapter())
url_data = s.get(url, stream=True)
else:
url_data = requests.get(url, stream=True)
if dest is None:
dest = os.path.basename(url)
if url_data.status_code != requests.codes.ok:
msg = "Cannot get url %s, code: %s, reason: %s" % (
str(url),
str(url_data.status_code),
str(url_data.reason),
)
raise requests.exceptions.RequestException(msg)
url_data.raw.decode_content = True
makedirs(os.path.dirname(dest), exist_ok=True)
uuid_tmp = str(uuid.uuid4())[:6]
dest_tmp = dest + "_dl_" + uuid_tmp + ".tmp"
with open(dest_tmp, "wb") as f:
shutil.copyfileobj(url_data.raw, f)
atomic_move_simple(dest_tmp, dest)
if print_func is not None:
print_func("END get url %s" % str(url))
def download(url, dest=None, dest_path=None):
if dest_path is not None:
dest = os.path.join(dest_path, os.path.basename(url))
if os.path.isfile(dest):
print("already downloaded %s -> %s" % (url, dest))
return dest
elif dest is not None:
if os.path.exists(dest):
print("already downloaded %s -> %s" % (url, dest))
return dest
else:
uuid_tmp = "dl2_" + str(uuid.uuid4())[:6]
dest = uuid_tmp + os.path.basename(url)
print("downloading %s to %s" % (url, dest))
if url.startswith("file://"):
from requests_file import FileAdapter
s = requests.Session()
s.mount('file://', FileAdapter())
url_data = s.get(url, stream=True)
else:
url_data = requests.get(url, stream=True)
if url_data.status_code != requests.codes.ok:
msg = "Cannot get url %s, code: %s, reason: %s" % (
str(url), str(url_data.status_code), str(url_data.reason))
raise requests.exceptions.RequestException(msg)
url_data.raw.decode_content = True
dirname = os.path.dirname(dest)
if dirname != "" and not os.path.isdir(dirname):
makedirs(os.path.dirname(dest), exist_ok=True)
uuid_tmp = "dl3_" + str(uuid.uuid4())[:6]
dest_tmp = dest + "_" + uuid_tmp + ".tmp"
with open(dest_tmp, 'wb') as f:
shutil.copyfileobj(url_data.raw, f)
try:
shutil.move(dest_tmp, dest)
except FileExistsError:
pass
remove(dest_tmp)
return dest
def get_url(x, from_str=False, short_name=False):
if not from_str:
source = x.metadata['source']
else:
source = x
if short_name:
source_name = get_short_name(source)
else:
source_name = source
if source.startswith('http://') or source.startswith('https://'):
return """<a href="%s" target="_blank" rel="noopener noreferrer">%s</a>""" % (
source, source_name)
else:
return """<a href="file/%s" target="_blank" rel="noopener noreferrer">%s</a>""" % (
source, source_name)
def get_short_name(name, maxl=50):
if name is None:
return ''
length = len(name)
if length > maxl:
allow_length = maxl - 3
half_allowed = max(1, int(allow_length / 2))
name = name[0:half_allowed] + "..." + name[length - half_allowed:length]
return name
def cuda_vis_check(total_gpus):
"""Helper function to count GPUs by environment variable
Stolen from Jon's h2o4gpu utils
"""
cudavis = os.getenv("CUDA_VISIBLE_DEVICES")
which_gpus = []
if cudavis is not None:
# prune away white-space, non-numerics,
# except commas for simple checking
cudavis = "".join(cudavis.split())
import re
cudavis = re.sub("[^0-9,]", "", cudavis)
lencudavis = len(cudavis)
if lencudavis == 0:
total_gpus = 0
else:
total_gpus = min(
total_gpus,
os.getenv("CUDA_VISIBLE_DEVICES").count(",") + 1)
which_gpus = os.getenv("CUDA_VISIBLE_DEVICES").split(",")
which_gpus = [int(x) for x in which_gpus]
else:
which_gpus = list(range(0, total_gpus))
return total_gpus, which_gpus
def get_ngpus_vis(raise_if_exception=True):
ngpus_vis1 = 0
shell = False
if shell:
cmd = "nvidia-smi -L 2> /dev/null"
else:
cmd = ["nvidia-smi", "-L"]
try:
timeout = 5 * 3
o = subprocess.check_output(cmd, shell=shell, timeout=timeout)
lines = o.decode("utf-8").splitlines()
ngpus_vis1 = 0
for line in lines:
if 'Failed to initialize NVML' not in line:
ngpus_vis1 += 1
except (FileNotFoundError, subprocess.CalledProcessError, OSError):
# GPU systems might not have nvidia-smi, so can't fail
pass
except subprocess.TimeoutExpired as e:
print('Failed get_ngpus_vis: %s' % str(e))
if raise_if_exception:
raise
ngpus_vis1, which_gpus = cuda_vis_check(ngpus_vis1)
return ngpus_vis1
def get_mem_gpus(raise_if_exception=True, ngpus=None):
totalmem_gpus1 = 0
usedmem_gpus1 = 0
freemem_gpus1 = 0
if ngpus == 0:
return totalmem_gpus1, usedmem_gpus1, freemem_gpus1
try:
cmd = "nvidia-smi -q 2> /dev/null | grep -A 3 'FB Memory Usage'"
o = subprocess.check_output(cmd, shell=True, timeout=15)
lines = o.decode("utf-8").splitlines()
for line in lines:
if 'Total' in line:
totalmem_gpus1 += int(line.split()[2]) * 1024 ** 2
if 'Used' in line:
usedmem_gpus1 += int(line.split()[2]) * 1024 ** 2
if 'Free' in line:
freemem_gpus1 += int(line.split()[2]) * 1024 ** 2
except (FileNotFoundError, subprocess.CalledProcessError, OSError):
# GPU systems might not have nvidia-smi, so can't fail
pass
except subprocess.TimeoutExpired as e:
print('Failed get_mem_gpus: %s' % str(e))
if raise_if_exception:
raise
return totalmem_gpus1, usedmem_gpus1, freemem_gpus1
class ForkContext(threading.local):
"""
Set context for forking
Ensures state is returned once done
"""
def __init__(self, args=None, kwargs=None, forkdata_capable=True):
"""
:param args:
:param kwargs:
:param forkdata_capable: whether fork is forkdata capable and will use copy-on-write forking of args/kwargs
"""
self.forkdata_capable = forkdata_capable
if self.forkdata_capable:
self.has_args = args is not None
self.has_kwargs = kwargs is not None
forkdatacontext.args = args
forkdatacontext.kwargs = kwargs
else:
self.has_args = False
self.has_kwargs = False
def __enter__(self):
try:
# flush all outputs so doesn't happen during fork -- don't print/log inside ForkContext contexts!
sys.stdout.flush()
sys.stderr.flush()
except BaseException as e:
# exit not called if exception, and don't want to leave forkdatacontext filled in that case
print("ForkContext failure on enter: %s" % str(e))
self.finally_act()
raise
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
self.finally_act()
def finally_act(self):
"""
Done when exception hit or exit is reached in context
first reset forkdatacontext as crucial to have reset even if later 2 calls fail
:return: None
"""
if self.forkdata_capable and (self.has_args or self.has_kwargs):
forkdatacontext._reset()
class _ForkDataContext(threading.local):
def __init__(
self,
args=None,
kwargs=None,
):
"""
Global context for fork to carry data to subprocess instead of relying upon copy/pickle/serialization
:param args: args
:param kwargs: kwargs
"""
assert isinstance(args, (tuple, type(None)))
assert isinstance(kwargs, (dict, type(None)))
self.__args = args
self.__kwargs = kwargs
@property
def args(self) -> Tuple:
"""returns args"""
return self.__args
@args.setter
def args(self, args):
if self.__args is not None:
raise AttributeError(
"args cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs))
)
self.__args = args
@property
def kwargs(self) -> Dict:
"""returns kwargs"""
return self.__kwargs
@kwargs.setter
def kwargs(self, kwargs):
if self.__kwargs is not None:
raise AttributeError(
"kwargs cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs))
)
self.__kwargs = kwargs
def _reset(self):
"""Reset fork arg-kwarg context to default values"""
self.__args = None
self.__kwargs = None
def get_args_kwargs(self, func, args, kwargs) -> Tuple[Callable, Tuple, Dict]:
if self.__args:
args = self.__args[1:]
if not func:
assert len(self.__args) > 0, "if have no func, must have in args"
func = self.__args[0] # should always be there
if self.__kwargs:
kwargs = self.__kwargs
try:
return func, args, kwargs
finally:
forkdatacontext._reset()
@staticmethod
def get_args_kwargs_for_traced_func(func, args, kwargs):
"""
Return args/kwargs out of forkdatacontext when using copy-on-write way of passing args/kwargs
:param func: actual function ran by _traced_func, which itself is directly what mppool treats as function
:param args:
:param kwargs:
:return: func, args, kwargs from forkdatacontext if used, else originals
"""
# first 3 lines are debug
func_was_None = func is None
args_was_None_or_empty = args is None or len(args) == 0
kwargs_was_None_or_empty = kwargs is None or len(kwargs) == 0
forkdatacontext_args_was_None = forkdatacontext.args is None
forkdatacontext_kwargs_was_None = forkdatacontext.kwargs is None
func, args, kwargs = forkdatacontext.get_args_kwargs(func, args, kwargs)
using_forkdatacontext = func_was_None and func is not None # pulled func out of forkdatacontext.__args[0]
assert forkdatacontext.args is None, "forkdatacontext.args should be None after get_args_kwargs"
assert forkdatacontext.kwargs is None, "forkdatacontext.kwargs should be None after get_args_kwargs"
proc_type = kwargs.get('proc_type', 'SUBPROCESS')
if using_forkdatacontext:
assert proc_type == "SUBPROCESS" or proc_type == "SUBPROCESS"
if proc_type == "NORMAL":
assert forkdatacontext_args_was_None, "if no fork, expect forkdatacontext.args None entering _traced_func"
assert forkdatacontext_kwargs_was_None, "if no fork, expect forkdatacontext.kwargs None entering _traced_func"
assert func is not None, "function should not be None, indicates original args[0] was None or args was None"
return func, args, kwargs
forkdatacontext = _ForkDataContext()
def _traced_func(func, *args, **kwargs):
func, args, kwargs = forkdatacontext.get_args_kwargs_for_traced_func(func, args, kwargs)
return func(*args, **kwargs)
def call_subprocess_onetask(func, args=None, kwargs=None):
if isinstance(args, list):
args = tuple(args)
if args is None:
args = ()
if kwargs is None:
kwargs = {}
args = list(args)
args = [func] + args
args = tuple(args)
with ForkContext(args=args, kwargs=kwargs):
args = (None,)
kwargs = {}
with ProcessPoolExecutor(max_workers=1) as executor:
future = executor.submit(_traced_func, *args, **kwargs)
return future.result()
class ProgressParallel(Parallel):
def __init__(self, use_tqdm=True, total=None, *args, **kwargs):
self._use_tqdm = use_tqdm
self._total = total
super().__init__(*args, **kwargs)
def __call__(self, *args, **kwargs):
with tqdm(disable=not self._use_tqdm, total=self._total) as self._pbar:
return Parallel.__call__(self, *args, **kwargs)
def print_progress(self):
if self._total is None:
self._pbar.total = self.n_dispatched_tasks
self._pbar.n = self.n_completed_tasks
self._pbar.refresh()
def get_kwargs(func, exclude_names=None, **kwargs):
func_names = list(inspect.signature(func).parameters)
missing_kwargs = [x for x in func_names if x not in kwargs]
if exclude_names:
for k in exclude_names:
if k in missing_kwargs:
missing_kwargs.remove(k)
if k in func_names:
func_names.remove(k)
assert not missing_kwargs, "Missing %s" % missing_kwargs
kwargs = {k: v for k, v in kwargs.items() if k in func_names}
return kwargs
import pkg_resources
have_faiss = False
try:
assert pkg_resources.get_distribution('faiss') is not None
have_faiss = True
except (pkg_resources.DistributionNotFound, AssertionError):
pass
try:
assert pkg_resources.get_distribution('faiss_gpu') is not None
have_faiss = True
except (pkg_resources.DistributionNotFound, AssertionError):
pass
try:
assert pkg_resources.get_distribution('faiss_cpu') is not None
have_faiss = True
except (pkg_resources.DistributionNotFound, AssertionError):
pass
def hash_file(file):
try:
import hashlib
# BUF_SIZE is totally arbitrary, change for your app!
BUF_SIZE = 65536 # lets read stuff in 64kb chunks!
md5 = hashlib.md5()
# sha1 = hashlib.sha1()
with open(file, 'rb') as f:
while True:
data = f.read(BUF_SIZE)
if not data:
break
md5.update(data)
# sha1.update(data)
except BaseException as e:
print("Cannot hash %s due to %s" % (file, str(e)))
traceback.print_exc()
md5 = None
return md5.hexdigest()
def start_faulthandler():
# If hit server or any subprocess with signal SIGUSR1, it'll print out all threads stack trace, but wont't quit or coredump
# If more than one fork tries to write at same time, then looks corrupted.
import faulthandler
# SIGUSR1 in h2oai/__init__.py as well
faulthandler.enable()
if hasattr(faulthandler, 'register'):
# windows/mac
import signal
faulthandler.register(signal.SIGUSR1)
def get_hf_server(inference_server):
inf_split = inference_server.split(" ")
assert len(inf_split) == 1 or len(inf_split) == 3
inference_server = inf_split[0]
if len(inf_split) == 3:
headers = {"authorization": "%s %s" % (inf_split[1], inf_split[2])}
else:
headers = None
return inference_server, headers
class FakeTokenizer:
"""
1) For keeping track of model_max_length
2) For when model doesn't directly expose tokenizer but need to count tokens
"""
def __init__(self, model_max_length=2048, encoding_name="cl100k_base"):
# dont' push limit, since if using fake tokenizer, only estimate, and seen underestimates by order 250
self.model_max_length = model_max_length - 250
self.encoding_name = encoding_name
# The first time this runs, it will require an internet connection to download. Later runs won't need an internet connection.
import tiktoken
self.encoding = tiktoken.get_encoding(self.encoding_name)
def encode(self, x, *args, return_tensors="pt", **kwargs):
input_ids = self.encoding.encode(x, disallowed_special=())
if return_tensors == 'pt' and isinstance(input_ids, list):
import torch
input_ids = torch.tensor(input_ids)
return dict(input_ids=input_ids)
def decode(self, x, *args, **kwargs):
# input is input_ids[0] form
return self.encoding.decode(x)
def num_tokens_from_string(self, prompt: str) -> int:
"""Returns the number of tokens in a text string."""
num_tokens = len(self.encoding.encode(prompt))
return num_tokens
def __call__(self, x, *args, **kwargs):
return self.encode(x, *args, **kwargs)