import contextlib import functools import hashlib import inspect import os import gc import pathlib import pickle 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" elif torch.backends.mps.is_built(): device = "mps" 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 try: 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 except AttributeError: pass # 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 (KeyError, 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 = '' if prompt is None else prompt output = '' 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 """%s""" % ( source, source_name) else: return """%s""" % ( 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): import platform if platform.system() in ['Darwin', 'Windows']: return func(*args, **kwargs) 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) def get_local_ip(): import socket s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) try: # doesn't even have to be reachable s.connect(('10.255.255.255', 1)) IP = s.getsockname()[0] except Exception: IP = '127.0.0.1' finally: s.close() return IP try: assert pkg_resources.get_distribution('langchain') is not None have_langchain = True except (pkg_resources.DistributionNotFound, AssertionError): have_langchain = False import distutils.spawn have_tesseract = distutils.spawn.find_executable("tesseract") have_libreoffice = distutils.spawn.find_executable("libreoffice") import pkg_resources try: assert pkg_resources.get_distribution('arxiv') is not None assert pkg_resources.get_distribution('pymupdf') is not None have_arxiv = True except (pkg_resources.DistributionNotFound, AssertionError): have_arxiv = False try: assert pkg_resources.get_distribution('pymupdf') is not None have_pymupdf = True except (pkg_resources.DistributionNotFound, AssertionError): have_pymupdf = False try: assert pkg_resources.get_distribution('selenium') is not None have_selenium = True except (pkg_resources.DistributionNotFound, AssertionError): have_selenium = False try: assert pkg_resources.get_distribution('playwright') is not None have_playwright = True except (pkg_resources.DistributionNotFound, AssertionError): have_playwright = False # disable, hangs too often have_playwright = False def set_openai(inference_server): if inference_server.startswith('vllm'): import openai_vllm openai_vllm.api_key = "EMPTY" inf_type = inference_server.split(':')[0] ip_vllm = inference_server.split(':')[1] port_vllm = inference_server.split(':')[2] openai_vllm.api_base = f"http://{ip_vllm}:{port_vllm}/v1" return openai_vllm, inf_type else: import openai openai.api_key = os.getenv("OPENAI_API_KEY") openai.api_base = os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1") inf_type = inference_server return openai, inf_type visible_langchain_modes_file = 'visible_langchain_modes.pkl' def save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode, db1s): """ extra controls if UserData type of MyData type """ # use first default MyData hash as general user hash to maintain file # if user moves MyData from langchain modes, db will still survive, so can still use hash scratch_collection_names = list(db1s.keys()) user_hash = db1s.get(LangChainMode.MY_DATA.value, '')[1] llms = ['ChatLLM', 'LLM', 'Disabled'] scratch_langchain_modes = [x for x in langchain_modes if x in scratch_collection_names] scratch_visible_langchain_modes = [x for x in visible_langchain_modes if x in scratch_collection_names] scratch_langchain_mode_paths = {k: v for k, v in langchain_mode_paths.items() if k in scratch_collection_names and k not in llms} user_langchain_modes = [x for x in langchain_modes if x not in scratch_collection_names] user_visible_langchain_modes = [x for x in visible_langchain_modes if x not in scratch_collection_names] user_langchain_mode_paths = {k: v for k, v in langchain_mode_paths.items() if k not in scratch_collection_names and k not in llms} base_path = 'locks' makedirs(base_path) # user extra = '' file = "%s%s" % (visible_langchain_modes_file, extra) with filelock.FileLock(os.path.join(base_path, "%s.lock" % file)): with open(file, 'wb') as f: pickle.dump((user_langchain_modes, user_visible_langchain_modes, user_langchain_mode_paths), f) # scratch extra = user_hash file = "%s%s" % (visible_langchain_modes_file, extra) with filelock.FileLock(os.path.join(base_path, "%s.lock" % file)): with open(file, 'wb') as f: pickle.dump((scratch_langchain_modes, scratch_visible_langchain_modes, scratch_langchain_mode_paths), f) def load_collection_enum(extra): """ extra controls if UserData type of MyData type """ file = "%s%s" % (visible_langchain_modes_file, extra) langchain_modes_from_file = [] visible_langchain_modes_from_file = [] langchain_mode_paths_from_file = {} if os.path.isfile(visible_langchain_modes_file): try: with filelock.FileLock("%s.lock" % file): with open(file, 'rb') as f: langchain_modes_from_file, visible_langchain_modes_from_file, langchain_mode_paths_from_file = pickle.load( f) except BaseException as e: print("Cannot load %s, ignoring error: %s" % (file, str(e)), flush=True) for k, v in langchain_mode_paths_from_file.items(): if v is not None and not os.path.isdir(v) and isinstance(v, str): # assume was deleted, but need to make again to avoid extra code elsewhere makedirs(v) return langchain_modes_from_file, visible_langchain_modes_from_file, langchain_mode_paths_from_file def remove_collection_enum(): remove(visible_langchain_modes_file)