# Adapted from PixArt
#
# Copyright (C) 2023 PixArt-alpha/PixArt-alpha
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# PixArt: https://github.com/PixArt-alpha/PixArt-alpha
# T5: https://github.com/google-research/text-to-text-transfer-transformer
# --------------------------------------------------------
import html
import os
import re
import urllib.parse as ul
import ftfy
import torch
from bs4 import BeautifulSoup
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, T5EncoderModel
from opensora.registry import MODELS
class T5Embedder:
available_models = ["t5-v1_1-xxl"]
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
def __init__(
self,
device,
dir_or_name="t5-v1_1-xxl",
*,
local_cache=False,
cache_dir=None,
hf_token=None,
use_text_preprocessing=True,
t5_model_kwargs=None,
torch_dtype=None,
use_offload_folder=None,
model_max_length=120,
):
self.device = torch.device(device)
self.torch_dtype = torch_dtype or torch.bfloat16
if t5_model_kwargs is None:
t5_model_kwargs = {"low_cpu_mem_usage": True, "torch_dtype": self.torch_dtype}
if use_offload_folder is not None:
t5_model_kwargs["offload_folder"] = use_offload_folder
t5_model_kwargs["device_map"] = {
"shared": self.device,
"encoder.embed_tokens": self.device,
"encoder.block.0": self.device,
"encoder.block.1": self.device,
"encoder.block.2": self.device,
"encoder.block.3": self.device,
"encoder.block.4": self.device,
"encoder.block.5": self.device,
"encoder.block.6": self.device,
"encoder.block.7": self.device,
"encoder.block.8": self.device,
"encoder.block.9": self.device,
"encoder.block.10": self.device,
"encoder.block.11": self.device,
"encoder.block.12": "disk",
"encoder.block.13": "disk",
"encoder.block.14": "disk",
"encoder.block.15": "disk",
"encoder.block.16": "disk",
"encoder.block.17": "disk",
"encoder.block.18": "disk",
"encoder.block.19": "disk",
"encoder.block.20": "disk",
"encoder.block.21": "disk",
"encoder.block.22": "disk",
"encoder.block.23": "disk",
"encoder.final_layer_norm": "disk",
"encoder.dropout": "disk",
}
else:
t5_model_kwargs["device_map"] = {"shared": self.device, "encoder": self.device}
self.use_text_preprocessing = use_text_preprocessing
self.hf_token = hf_token
self.cache_dir = cache_dir or os.path.expanduser("~/.cache/IF_")
self.dir_or_name = dir_or_name
tokenizer_path, path = dir_or_name, dir_or_name
if local_cache:
cache_dir = os.path.join(self.cache_dir, dir_or_name)
tokenizer_path, path = cache_dir, cache_dir
elif dir_or_name in self.available_models:
cache_dir = os.path.join(self.cache_dir, dir_or_name)
for filename in [
"config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
"pytorch_model.bin.index.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
]:
hf_hub_download(
repo_id=f"DeepFloyd/{dir_or_name}",
filename=filename,
cache_dir=cache_dir,
force_filename=filename,
token=self.hf_token,
)
tokenizer_path, path = cache_dir, cache_dir
else:
cache_dir = os.path.join(self.cache_dir, "t5-v1_1-xxl")
for filename in [
"config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json",
]:
hf_hub_download(
repo_id="DeepFloyd/t5-v1_1-xxl",
filename=filename,
cache_dir=cache_dir,
force_filename=filename,
token=self.hf_token,
)
tokenizer_path = cache_dir
print(tokenizer_path)
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
self.model = T5EncoderModel.from_pretrained(path, **t5_model_kwargs).eval()
self.model_max_length = model_max_length
def get_text_embeddings(self, texts):
texts = [self.text_preprocessing(text) for text in texts]
text_tokens_and_mask = self.tokenizer(
texts,
max_length=self.model_max_length,
padding="max_length",
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
text_tokens_and_mask["input_ids"] = text_tokens_and_mask["input_ids"]
text_tokens_and_mask["attention_mask"] = text_tokens_and_mask["attention_mask"]
with torch.no_grad():
text_encoder_embs = self.model(
input_ids=text_tokens_and_mask["input_ids"].to(self.device),
attention_mask=text_tokens_and_mask["attention_mask"].to(self.device),
)["last_hidden_state"].detach()
return text_encoder_embs, text_tokens_and_mask["attention_mask"].to(self.device)
def text_preprocessing(self, text):
if self.use_text_preprocessing:
# The exact text cleaning as was in the training stage:
text = self.clean_caption(text)
text = self.clean_caption(text)
return text
else:
return text.lower().strip()
@staticmethod
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
def clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = self.basic_clean(caption)
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
@MODELS.register_module("t5")
class T5Encoder:
def __init__(
self,
from_pretrained=None,
model_max_length=120,
device="cuda",
dtype=torch.float,
local_cache=True,
shardformer=False,
):
assert from_pretrained is not None, "Please specify the path to the T5 model"
self.t5 = T5Embedder(
device=device,
torch_dtype=dtype,
local_cache=local_cache,
cache_dir=from_pretrained,
model_max_length=model_max_length,
)
self.t5.model.to(dtype=dtype)
self.y_embedder = None
self.model_max_length = model_max_length
self.output_dim = self.t5.model.config.d_model
if shardformer:
self.shardformer_t5()
def shardformer_t5(self):
from colossalai.shardformer import ShardConfig, ShardFormer
from opensora.acceleration.shardformer.policy.t5_encoder import T5EncoderPolicy
from opensora.utils.misc import requires_grad
shard_config = ShardConfig(
tensor_parallel_process_group=None,
pipeline_stage_manager=None,
enable_tensor_parallelism=False,
enable_fused_normalization=False,
enable_flash_attention=False,
enable_jit_fused=True,
enable_sequence_parallelism=False,
enable_sequence_overlap=False,
)
shard_former = ShardFormer(shard_config=shard_config)
optim_model, _ = shard_former.optimize(self.t5.model, policy=T5EncoderPolicy())
self.t5.model = optim_model.half()
# ensure the weights are frozen
requires_grad(self.t5.model, False)
def encode(self, text):
caption_embs, emb_masks = self.t5.get_text_embeddings(text)
caption_embs = caption_embs[:, None]
return dict(y=caption_embs, mask=emb_masks)
def null(self, n):
null_y = self.y_embedder.y_embedding[None].repeat(n, 1, 1)[:, None]
return null_y