llava/utils.py

# 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