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import logging
import logging.handlers
import os
import sys
import json
import warnings
import platform
import requests
import torch
from fastchat.constants import LOGDIR
server_error_msg = (
"**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
)
moderation_msg = (
"YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN."
)
handler = None
def build_logger(logger_name, logger_filename):
global handler
formatter = logging.Formatter(
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
# Set the format of root handlers
if not logging.getLogger().handlers:
if sys.version_info[1] >= 9:
# This is for windows
logging.basicConfig(level=logging.INFO, encoding="utf-8")
else:
if platform.system() == "Windows":
warnings.warn("If you are running on Windows, "
"we recommend you use Python >= 3.9 for UTF-8 encoding.")
logging.basicConfig(level=logging.INFO)
logging.getLogger().handlers[0].setFormatter(formatter)
# Redirect stdout and stderr to loggers
stdout_logger = logging.getLogger("stdout")
stdout_logger.setLevel(logging.INFO)
sl = StreamToLogger(stdout_logger, logging.INFO)
sys.stdout = sl
stderr_logger = logging.getLogger("stderr")
stderr_logger.setLevel(logging.ERROR)
sl = StreamToLogger(stderr_logger, logging.ERROR)
sys.stderr = sl
# Get logger
logger = logging.getLogger(logger_name)
logger.setLevel(logging.INFO)
# Add a file handler for all loggers
if handler is None:
os.makedirs(LOGDIR, exist_ok=True)
filename = os.path.join(LOGDIR, logger_filename)
handler = logging.handlers.TimedRotatingFileHandler(
filename, when="D", utc=True
)
handler.setFormatter(formatter)
for name, item in logging.root.manager.loggerDict.items():
if isinstance(item, logging.Logger):
item.addHandler(handler)
return logger
class StreamToLogger(object):
"""
Fake file-like stream object that redirects writes to a logger instance.
"""
def __init__(self, logger, log_level=logging.INFO):
self.terminal = sys.stdout
self.logger = logger
self.log_level = log_level
self.linebuf = ""
def __getattr__(self, attr):
return getattr(self.terminal, attr)
def write(self, buf):
temp_linebuf = self.linebuf + buf
self.linebuf = ""
for line in temp_linebuf.splitlines(True):
# From the io.TextIOWrapper docs:
# On output, if newline is None, any '\n' characters written
# are translated to the system default line separator.
# By default sys.stdout.write() expects '\n' newlines and then
# translates them so this is still cross platform.
if line[-1] == "\n":
encoded_message = line.encode("utf-8", "ignore").decode("utf-8")
self.logger.log(self.log_level, encoded_message.rstrip())
else:
self.linebuf += line
def flush(self):
if self.linebuf != "":
encoded_message = self.linebuf.encode("utf-8", "ignore").decode("utf-8")
self.logger.log(self.log_level, encoded_message.rstrip())
self.linebuf = ""
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
import torch
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
def violates_moderation(text):
"""
Check whether the text violates OpenAI moderation API.
"""
url = "https://api.openai.com/v1/moderations"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer " + os.environ["OPENAI_API_KEY"],
}
text = text.replace("\n", "")
data = "{" + '"input": ' + f'"{text}"' + "}"
data = data.encode("utf-8")
try:
ret = requests.post(url, headers=headers, data=data, timeout=5)
flagged = ret.json()["results"][0]["flagged"]
except requests.exceptions.RequestException as e:
flagged = False
except KeyError as e:
flagged = False
return flagged
# Flan-t5 trained with HF+FSDP saves corrupted weights for shared embeddings,
# Use this function to make sure it can be correctly loaded.
def clean_flant5_ckpt(ckpt_path):
index_file = os.path.join(ckpt_path, "pytorch_model.bin.index.json")
index_json = json.load(open(index_file, "r"))
weightmap = index_json["weight_map"]
share_weight_file = weightmap["shared.weight"]
share_weight = torch.load(os.path.join(ckpt_path, share_weight_file))[
"shared.weight"
]
for weight_name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]:
weight_file = weightmap[weight_name]
weight = torch.load(os.path.join(ckpt_path, weight_file))
weight[weight_name] = share_weight
torch.save(weight, os.path.join(ckpt_path, weight_file))
def pretty_print_semaphore(semaphore):
if semaphore is None:
return "None"
return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"
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