# This file is modified from https://github.com/haotian-liu/LLaVA/
import datetime
import time
import logging
import logging.handlers
import os
import sys
import requests
import torch
import transformers
from transformers.integrations import is_deepspeed_zero3_enabled
from llava.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:
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, encoding='UTF-8')
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':
self.logger.log(self.log_level, line.rstrip())
else:
self.linebuf += line
def flush(self):
if self.linebuf != '':
self.logger.log(self.log_level, self.linebuf.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
def pretty_print_semaphore(semaphore):
if semaphore is None:
return "None"
return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"
@torch.no_grad()
def load_state_dict_into_model(model_to_load, state_dict, start_prefix=""):
# copied and altered from:
# https://github.com/huggingface/transformers/blob/9d35edbb30625489bf286a9b15aed0c5a3119c1c/src/transformers/modeling_utils.py#L650
# https://github.com/baaivision/EVA/blob/2ca37a8c0d82b9496754f3fa9c3966b4caa54d75/EVA-CLIP-18B/shinji/eva_clip/factory.py#L168
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, "_metadata", None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
error_msgs = []
# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
# so we need to apply the function recursively.
def load(module: torch.nn.Module, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
# Parameters of module and children will start with prefix. We can exit early if there are none in this state_dict
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(list(module.parameters(recurse=False)), modifier_rank=0):
if torch.distributed.get_rank() == 0:
module._load_from_state_dict(*args)
else:
module._load_from_state_dict(*args)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
load(model_to_load, prefix=start_prefix)
# Delete `state_dict` so it could be collected by GC earlier. Note that `state_dict` is a copy of the argument, so
# it's safe to delete it.
del state_dict
return error_msgs
class Timer:
def __init__(self):
self.start_time = None
self.elapsed_time = 0
def start(self):
self.start_time = time.time()
def reset(self):
self.start_time = None
self.elapsed_time = 0
def get_elapsed_time(self):
if self.start_time is not None:
return self.elapsed_time + (time.time() - self.start_time)
class TimeoutTerminateCallback(transformers.TrainerCallback):
def __init__(self, args, total_time_limit=240, pre_terminate_time=10):
self.training_args = args
self.total_time_limit = total_time_limit
self.pre_terminate_time = pre_terminate_time
self.timer = Timer()
self.timer.start()
if args.local_rank == 0:
print(f"Timer for terminate callback has been set.\nTotal limit: {total_time_limit}min\nPre terminate time: {pre_terminate_time}min")
self.time_to_kill = (total_time_limit - pre_terminate_time) * 60
def on_step_end(self, args, state, control, model, **kwargs):
elapsed_time = self.timer.get_elapsed_time()
if elapsed_time > self.time_to_kill:
if args.local_rank == 0:
print("Timeout, start to save checkpoint....")
control.should_save = True
control.should_training_stop = True
return control