b30c1d8 over 1 year ago

9.31 kB

import datetime
import logging
import logging.handlers
import os
import sys

import requests

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()})"




# Modified from github.com/openai/CLIP
import gzip
import html
import os
from functools import lru_cache

import ftfy
import regex as re
import torch


@lru_cache()
def default_bpe():
    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")


@lru_cache()
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8+n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """Return set of symbol pairs in a word.
    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r'\s+', ' ', text)
    text = text.strip()
    return text


class SimpleTokenizer(object):
    def __init__(self, bpe_path: str = default_bpe()):
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
        merges = merges[1:49152-256-2+1]
        merges = [tuple(merge.split()) for merge in merges]
        vocab = list(bytes_to_unicode().values())
        vocab = vocab + [v+'</w>' for v in vocab]
        for merge in merges:
            vocab.append(''.join(merge))
        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
        self.encoder = dict(zip(vocab, range(len(vocab))))
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
        pairs = get_pairs(word)

        if not pairs:
            return token+'</w>'

        while True:
            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                    new_word.extend(word[i:j])
                    i = j
                except:
                    new_word.extend(word[i:])
                    break

                if word[i] == first and i < len(word)-1 and word[i+1] == second:
                    new_word.append(first+second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = ' '.join(word)
        self.cache[token] = word
        return word

    def encode(self, text):
        bpe_tokens = []
        text = whitespace_clean(basic_clean(text)).lower()
        for token in re.findall(self.pat, text):
            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
        return bpe_tokens

    def decode(self, tokens):
        text = ''.join([self.decoder[token] for token in tokens])
        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
        return text

    def __call__(self, texts, context_length=77):
        if isinstance(texts, str):
            texts = [texts]

        sot_token = self.encoder["<|startoftext|>"]
        eot_token = self.encoder["<|endoftext|>"]
        all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)

        for i, tokens in enumerate(all_tokens):
            tokens = tokens[:context_length]
            result[i, :len(tokens)] = torch.tensor(tokens)

        if len(result) == 1:
            return result[0]
        return result

	import datetime
	import logging
	import logging.handlers
	import os
	import sys

	import requests

	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()})"




	# Modified from github.com/openai/CLIP
	import gzip
	import html
	import os
	from functools import lru_cache

	import ftfy
	import regex as re
	import torch


	@lru_cache()
	def default_bpe():
	return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")


	@lru_cache()
	def bytes_to_unicode():
	"""
	Returns list of utf-8 byte and a corresponding list of unicode strings.
	The reversible bpe codes work on unicode strings.
	This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
	When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
	This is a signficant percentage of your normal, say, 32K bpe vocab.
	To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
	And avoids mapping to whitespace/control characters the bpe code barfs on.
	"""
	bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
	cs = bs[:]
	n = 0
	for b in range(2**8):
	if b not in bs:
	bs.append(b)
	cs.append(2**8+n)
	n += 1
	cs = [chr(n) for n in cs]
	return dict(zip(bs, cs))


	def get_pairs(word):
	"""Return set of symbol pairs in a word.
	Word is represented as tuple of symbols (symbols being variable-length strings).
	"""
	pairs = set()
	prev_char = word[0]
	for char in word[1:]:
	pairs.add((prev_char, char))
	prev_char = char
	return pairs


	def basic_clean(text):
	text = ftfy.fix_text(text)
	text = html.unescape(html.unescape(text))
	return text.strip()


	def whitespace_clean(text):
	text = re.sub(r'\s+', ' ', text)
	text = text.strip()
	return text


	class SimpleTokenizer(object):
	def __init__(self, bpe_path: str = default_bpe()):
	self.byte_encoder = bytes_to_unicode()
	self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
	merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
	merges = merges[1:49152-256-2+1]
	merges = [tuple(merge.split()) for merge in merges]
	vocab = list(bytes_to_unicode().values())
	vocab = vocab + [v+'</w>' for v in vocab]
	for merge in merges:
	vocab.append(''.join(merge))
	vocab.extend(['<\|startoftext\|>', '<\|endoftext\|>'])
	self.encoder = dict(zip(vocab, range(len(vocab))))
	self.decoder = {v: k for k, v in self.encoder.items()}
	self.bpe_ranks = dict(zip(merges, range(len(merges))))
	self.cache = {'<\|startoftext\|>': '<\|startoftext\|>', '<\|endoftext\|>': '<\|endoftext\|>'}
	self.pat = re.compile(r"""<\\|startoftext\\|>\|<\\|endoftext\\|>\|'s\|'t\|'re\|'ve\|'m\|'ll\|'d\|[\p{L}]+\|[\p{N}]\|[^\s\p{L}\p{N}]+""", re.IGNORECASE)

	def bpe(self, token):
	if token in self.cache:
	return self.cache[token]
	word = tuple(token[:-1]) + ( token[-1] + '</w>',)
	pairs = get_pairs(word)

	if not pairs:
	return token+'</w>'

	while True:
	bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
	if bigram not in self.bpe_ranks:
	break
	first, second = bigram
	new_word = []
	i = 0
	while i < len(word):
	try:
	j = word.index(first, i)
	new_word.extend(word[i:j])
	i = j
	except:
	new_word.extend(word[i:])
	break

	if word[i] == first and i < len(word)-1 and word[i+1] == second:
	new_word.append(first+second)
	i += 2
	else:
	new_word.append(word[i])
	i += 1
	new_word = tuple(new_word)
	word = new_word
	if len(word) == 1:
	break
	else:
	pairs = get_pairs(word)
	word = ' '.join(word)
	self.cache[token] = word
	return word

	def encode(self, text):
	bpe_tokens = []
	text = whitespace_clean(basic_clean(text)).lower()
	for token in re.findall(self.pat, text):
	token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
	bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
	return bpe_tokens

	def decode(self, tokens):
	text = ''.join([self.decoder[token] for token in tokens])
	text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
	return text

	def __call__(self, texts, context_length=77):
	if isinstance(texts, str):
	texts = [texts]

	sot_token = self.encoder["<\|startoftext\|>"]
	eot_token = self.encoder["<\|endoftext\|>"]
	all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
	result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)

	for i, tokens in enumerate(all_tokens):
	tokens = tokens[:context_length]
	result[i, :len(tokens)] = torch.tensor(tokens)

	if len(result) == 1:
	return result[0]
	return result