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model_utils.py
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model_utils.py
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import fnmatch
import os
from functools import partial
from pickle import UnpicklingError
from typing import Optional, Union
import multiprocess as mp
import wget
from loguru import logger
from data_juicer import cuda_device_count, is_cuda_available
from .cache_utils import DATA_JUICER_MODELS_CACHE as DJMC
MODEL_ZOO = {}
# Default cached models links for downloading
MODEL_LINKS = 'https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/' \
'data_juicer/models/'
# Backup cached models links for downloading
BACKUP_MODEL_LINKS = {
# language identification model from fasttext
'lid.176.bin':
'https://dl.fbaipublicfiles.com/fasttext/supervised-models/',
# tokenizer and language model for English from sentencepiece and KenLM
'*.sp.model':
'https://huggingface.co/edugp/kenlm/resolve/main/wikipedia/',
'*.arpa.bin':
'https://huggingface.co/edugp/kenlm/resolve/main/wikipedia/',
# sentence split model from nltk punkt
'punkt.*.pickle':
'https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/'
'data_juicer/models/',
}
def get_backup_model_link(model_name):
for pattern, url in BACKUP_MODEL_LINKS.items():
if fnmatch.fnmatch(model_name, pattern):
return url
return None
def check_model(model_name, force=False):
"""
Check whether a model exists in DATA_JUICER_MODELS_CACHE.
If exists, return its full path.
Else, download it from cached models links.
:param model_name: a specified model name
:param force: Whether to download model forcefully or not, Sometimes
the model file maybe incomplete for some reason, so need to
download again forcefully.
"""
# check for local model
if os.path.exists(model_name):
return model_name
if not os.path.exists(DJMC):
os.makedirs(DJMC)
# check if the specified model exists. If it does not exist, download it
cached_model_path = os.path.join(DJMC, model_name)
if force:
if os.path.exists(cached_model_path):
os.remove(cached_model_path)
logger.info(
f'Model [{cached_model_path}] invalid, force to downloading...'
)
else:
logger.info(
f'Model [{cached_model_path}] not found . Downloading...')
try:
model_link = os.path.join(MODEL_LINKS, model_name)
wget.download(model_link, cached_model_path, bar=None)
except: # noqa: E722
try:
backup_model_link = os.path.join(
get_backup_model_link(model_name), model_name)
wget.download(backup_model_link, cached_model_path, bar=None)
except: # noqa: E722
logger.error(
f'Downloading model [{model_name}] error. '
f'Please retry later or download it into {DJMC} '
f'manually from {model_link} or {backup_model_link} ')
exit(1)
return cached_model_path
def prepare_fasttext_model(model_name='lid.176.bin'):
"""
Prepare and load a fasttext model.
:param model_name: input model name
:return: model instance.
"""
import fasttext
logger.info('Loading fasttext language identification model...')
try:
ft_model = fasttext.load_model(check_model(model_name))
except: # noqa: E722
ft_model = fasttext.load_model(check_model(model_name, force=True))
return ft_model
def prepare_sentencepiece_model(model_path):
"""
Prepare and load a sentencepiece model.
:param model_path: input model path
:return: model instance
"""
import sentencepiece
logger.info('Loading sentencepiece model...')
sentencepiece_model = sentencepiece.SentencePieceProcessor()
try:
sentencepiece_model.load(check_model(model_path))
except: # noqa: E722
sentencepiece_model.load(check_model(model_path, force=True))
return sentencepiece_model
def prepare_sentencepiece_for_lang(lang, name_pattern='{}.sp.model'):
"""
Prepare and load a sentencepiece model for specific langauge.
:param lang: language to render model name
:param name_pattern: pattern to render the model name
:return: model instance.
"""
model_name = name_pattern.format(lang)
return prepare_sentencepiece_model(model_name)
def prepare_kenlm_model(lang, name_pattern='{}.arpa.bin'):
"""
Prepare and load a kenlm model.
:param model_name: input model name in formatting syntax.
:param lang: language to render model name
:return: model instance.
"""
import kenlm
model_name = name_pattern.format(lang)
logger.info('Loading kenlm language model...')
try:
kenlm_model = kenlm.Model(check_model(model_name))
except: # noqa: E722
kenlm_model = kenlm.Model(check_model(model_name, force=True))
return kenlm_model
def prepare_nltk_model(lang, name_pattern='punkt.{}.pickle'):
"""
Prepare and load a nltk punkt model.
:param model_name: input model name in formatting syntax
:param lang: language to render model name
:return: model instance.
"""
from nltk.data import load
nltk_to_punkt = {
'en': 'english',
'fr': 'french',
'pt': 'portuguese',
'es': 'spanish'
}
assert lang in nltk_to_punkt.keys(
), 'lang must be one of the following: {}'.format(
list(nltk_to_punkt.keys()))
model_name = name_pattern.format(nltk_to_punkt[lang])
logger.info('Loading nltk punkt split model...')
try:
nltk_model = load(check_model(model_name))
except: # noqa: E722
nltk_model = load(check_model(model_name, force=True))
return nltk_model
def prepare_video_blip_model(pretrained_model_name_or_path,
return_model=True,
trust_remote_code=False):
"""
Prepare and load a video-clip model with the correspoding processor.
:param pretrained_model_name_or_path: model name or path
:param return_model: return model or not
:param trust_remote_code: passed to transformers
:return: a tuple (model, input processor) if `return_model` is True;
otherwise, only the processor is returned.
"""
import torch
import torch.nn as nn
from transformers import (AutoModelForCausalLM, AutoModelForSeq2SeqLM,
Blip2Config, Blip2ForConditionalGeneration,
Blip2QFormerModel, Blip2VisionModel)
from transformers.modeling_outputs import BaseModelOutputWithPooling
class VideoBlipVisionModel(Blip2VisionModel):
"""A simple, augmented version of Blip2VisionModel to handle
videos."""
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithPooling]:
"""Flatten `pixel_values` along the batch and time dimension,
pass it through the original vision model,
then unflatten it back.
:param pixel_values: a tensor of shape
(batch, channel, time, height, width)
:returns:
last_hidden_state: a tensor of shape
(batch, time * seq_len, hidden_size)
pooler_output: a tensor of shape
(batch, time, hidden_size)
hidden_states:
a tuple of tensors of shape
(batch, time * seq_len, hidden_size),
one for the output of the embeddings +
one for each layer
attentions:
a tuple of tensors of shape
(batch, time, num_heads, seq_len, seq_len),
one for each layer
"""
if pixel_values is None:
raise ValueError('You have to specify pixel_values')
batch, _, time, _, _ = pixel_values.size()
# flatten along the batch and time dimension to create a
# tensor of shape
# (batch * time, channel, height, width)
flat_pixel_values = pixel_values.permute(0, 2, 1, 3,
4).flatten(end_dim=1)
vision_outputs: BaseModelOutputWithPooling = super().forward(
pixel_values=flat_pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
# now restore the original dimensions
# vision_outputs.last_hidden_state is of shape
# (batch * time, seq_len, hidden_size)
seq_len = vision_outputs.last_hidden_state.size(1)
last_hidden_state = vision_outputs.last_hidden_state.view(
batch, time * seq_len, -1)
# vision_outputs.pooler_output is of shape
# (batch * time, hidden_size)
pooler_output = vision_outputs.pooler_output.view(batch, time, -1)
# hidden_states is a tuple of tensors of shape
# (batch * time, seq_len, hidden_size)
hidden_states = (tuple(
hidden.view(batch, time * seq_len, -1)
for hidden in vision_outputs.hidden_states)
if vision_outputs.hidden_states is not None else
None)
# attentions is a tuple of tensors of shape
# (batch * time, num_heads, seq_len, seq_len)
attentions = (tuple(
hidden.view(batch, time, -1, seq_len, seq_len)
for hidden in vision_outputs.attentions)
if vision_outputs.attentions is not None else None)
if return_dict:
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooler_output,
hidden_states=hidden_states,
attentions=attentions,
)
return (last_hidden_state, pooler_output, hidden_states,
attentions)
class VideoBlipForConditionalGeneration(Blip2ForConditionalGeneration):
def __init__(self, config: Blip2Config) -> None:
# HACK: we call the grandparent super().__init__() to bypass
# Blip2ForConditionalGeneration.__init__() so we can replace
# self.vision_model
super(Blip2ForConditionalGeneration, self).__init__(config)
self.vision_model = VideoBlipVisionModel(config.vision_config)
self.query_tokens = nn.Parameter(
torch.zeros(1, config.num_query_tokens,
config.qformer_config.hidden_size))
self.qformer = Blip2QFormerModel(config.qformer_config)
self.language_projection = nn.Linear(
config.qformer_config.hidden_size,
config.text_config.hidden_size)
if config.use_decoder_only_language_model:
language_model = AutoModelForCausalLM.from_config(
config.text_config)
else:
language_model = AutoModelForSeq2SeqLM.from_config(
config.text_config)
self.language_model = language_model
# Initialize weights and apply final processing
self.post_init()
from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
if return_model:
model_class = VideoBlipForConditionalGeneration
model = model_class.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
return (model, processor) if return_model else processor
def prepare_simple_aesthetics_model(pretrained_model_name_or_path,
return_model=True,
trust_remote_code=False):
"""
Prepare and load a simple aesthetics model.
:param pretrained_model_name_or_path: model name or path
:param return_model: return model or not
:return: a tuple (model, input processor) if `return_model` is True;
otherwise, only the processor is returned.
"""
from aesthetics_predictor import (AestheticsPredictorV1,
AestheticsPredictorV2Linear,
AestheticsPredictorV2ReLU)
from transformers import CLIPProcessor
processor = CLIPProcessor.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
if not return_model:
return processor
else:
if 'v1' in pretrained_model_name_or_path:
model = AestheticsPredictorV1.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code)
elif ('v2' in pretrained_model_name_or_path
and 'linear' in pretrained_model_name_or_path):
model = AestheticsPredictorV2Linear.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code)
elif ('v2' in pretrained_model_name_or_path
and 'relu' in pretrained_model_name_or_path):
model = AestheticsPredictorV2ReLU.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code)
else:
raise ValueError(
'Not support {}'.format(pretrained_model_name_or_path))
return (model, processor)
def prepare_huggingface_model(pretrained_model_name_or_path,
return_model=True,
trust_remote_code=False):
"""
Prepare and load a HuggingFace model with the correspoding processor.
:param pretrained_model_name_or_path: model name or path
:param return_model: return model or not
:param trust_remote_code: passed to transformers
:return: a tuple (model, input processor) if `return_model` is True;
otherwise, only the processor is returned.
"""
import transformers
from transformers import AutoConfig, AutoProcessor
processor = AutoProcessor.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
if return_model:
config = AutoConfig.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
if hasattr(config, 'auto_map'):
class_name = next(
(k for k in config.auto_map if k.startswith('AutoModel')),
'AutoModel')
else:
# TODO: What happens if more than one
class_name = config.architectures[0]
model_class = getattr(transformers, class_name)
model = model_class.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code)
return (model, processor) if return_model else processor
def prepare_spacy_model(lang, name_pattern='{}_core_web_md-3.5.0'):
"""
Prepare spacy model for specific language.
:param lang: language of sapcy model. Should be one of ["zh",
"en"]
:return: corresponding spacy model
"""
import spacy
assert lang in ['zh', 'en'], 'Diversity only support zh and en'
model_name = name_pattern.format(lang)
logger.info(f'Loading spacy model [{model_name}]...')
compressed_model = '{}.zip'.format(model_name)
# decompress the compressed model if it's not decompressed
def decompress_model(compressed_model_path):
decompressed_model_path = compressed_model_path.replace('.zip', '')
if os.path.exists(decompressed_model_path) \
and os.path.isdir(decompressed_model_path):
return decompressed_model_path
import zipfile
with zipfile.ZipFile(compressed_model_path) as zf:
zf.extractall(DJMC)
return decompressed_model_path
try:
diversity_model = spacy.load(
decompress_model(check_model(compressed_model)))
except: # noqa: E722
diversity_model = spacy.load(
decompress_model(check_model(compressed_model, force=True)))
return diversity_model
def prepare_diffusion_model(pretrained_model_name_or_path,
diffusion_type,
torch_dtype='fp32',
revision='main',
trust_remote_code=False):
"""
Prepare and load an Diffusion model from HuggingFace.
:param pretrained_model_name_or_path: input Diffusion model name
or local path to the model
:param diffusion_type: the use of the diffusion model. It can be
'image2image', 'text2image', 'inpainting'
:param torch_dtype: the floating point to load the diffusion
model. Can be one of ['fp32', 'fp16', 'bf16']
:param revision: The specific model version to use. It can be a
branch name, a tag name, a commit id, or any identifier allowed
by Git.
:return: a Diffusion model.
"""
import torch
from diffusers import (AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image)
diffusion_type_to_pipeline = {
'image2image': AutoPipelineForImage2Image,
'text2image': AutoPipelineForText2Image,
'inpainting': AutoPipelineForInpainting
}
if diffusion_type not in diffusion_type_to_pipeline.keys():
raise ValueError(
f'Not support {diffusion_type} diffusion_type for diffusion '
'model. Can only be one of '
'["image2image", "text2image", "inpainting"].')
if torch_dtype not in ['fp32', 'fp16', 'bf16']:
raise ValueError(
f'Not support {torch_dtype} torch_dtype for diffusion '
'model. Can only be one of '
'["fp32", "fp16", "bf16"].')
if not is_cuda_available() and (torch_dtype == 'fp16'
or torch_dtype == 'bf16'):
raise ValueError(
'In cpu mode, only fp32 torch_dtype can be used for diffusion'
' model.')
pipeline = diffusion_type_to_pipeline[diffusion_type]
if torch_dtype == 'bf16':
torch_dtype = torch.bfloat16
elif torch_dtype == 'fp16':
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
model = pipeline.from_pretrained(pretrained_model_name_or_path,
revision=revision,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code)
return model
def prepare_recognizeAnything_model(
pretrained_model_name_or_path='ram_plus_swin_large_14m.pth',
input_size=384):
"""
Prepare and load recognizeAnything model.
:param model_name: input model name.
:param input_size: the input size of the model.
"""
from ram.models import ram_plus
logger.info('Loading recognizeAnything model...')
try:
model = ram_plus(pretrained=check_model(pretrained_model_name_or_path),
image_size=input_size,
vit='swin_l')
except (RuntimeError, UnpicklingError) as e: # noqa: E722
logger.warning(e)
model = ram_plus(pretrained=check_model(pretrained_model_name_or_path,
force=True),
image_size=input_size,
vit='swin_l')
model.eval()
return model
def prepare_opencv_classifier(model_path):
import cv2
model = cv2.CascadeClassifier(model_path)
return model
MODEL_FUNCTION_MAPPING = {
'fasttext': prepare_fasttext_model,
'sentencepiece': prepare_sentencepiece_for_lang,
'kenlm': prepare_kenlm_model,
'nltk': prepare_nltk_model,
'huggingface': prepare_huggingface_model,
'simple_aesthetics': prepare_simple_aesthetics_model,
'spacy': prepare_spacy_model,
'diffusion': prepare_diffusion_model,
'video_blip': prepare_video_blip_model,
'recognizeAnything': prepare_recognizeAnything_model,
'opencv_classifier': prepare_opencv_classifier,
}
def prepare_model(model_type, **model_kwargs):
assert (model_type in MODEL_FUNCTION_MAPPING.keys()
), 'model_type must be one of the following: {}'.format(
list(MODEL_FUNCTION_MAPPING.keys()))
global MODEL_ZOO
model_func = MODEL_FUNCTION_MAPPING[model_type]
model_key = partial(model_func, **model_kwargs)
return model_key
def move_to_cuda(model, rank):
# Assuming model can be either a single module or a tuple of modules
if not isinstance(model, tuple):
model = (model, )
for module in model:
if callable(getattr(module, 'to', None)):
logger.debug(
f'Moving {module.__class__.__name__} to CUDA device {rank}')
module.to(f'cuda:{rank}')
def get_model(model_key=None, rank=None, use_cuda=False):
if model_key is None:
return None
global MODEL_ZOO
if model_key not in MODEL_ZOO:
logger.debug(
f'{model_key} not found in MODEL_ZOO ({mp.current_process().name})'
)
MODEL_ZOO[model_key] = model_key()
if use_cuda:
rank = 0 if rank is None else rank
rank = rank % cuda_device_count()
move_to_cuda(MODEL_ZOO[model_key], rank)
return MODEL_ZOO[model_key]