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Fix Mplug owl2

dev
d8ahazard 2024-02-24 14:07:35 -06:00
parent 2f728a0dc8
commit 81dcb53ccb
22 changed files with 2115 additions and 166 deletions
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41
interrogators/mplug2_interrogator.py
View File

@ -9,13 +9,15 @@ from huggingface_hub import snapshot_download
from transformers import TextStreamer
from extensions.sd_smartprocess.interrogators.interrogator import Interrogator
from extensions.sd_smartprocess.model_download import fetch_model
from extensions.sd_smartprocess.process_params import ProcessParams
from modules.paths_internal import models_path
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from mplug_owl2.conversation import conv_templates
from mplug_owl2.mm_utils import KeywordsStoppingCriteria, tokenizer_image_token, process_images, \
from extensions.sd_smartprocess.mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from extensions.sd_smartprocess.mplug_owl2.conversation import conv_templates
from extensions.sd_smartprocess.mplug_owl2.mm_utils import KeywordsStoppingCriteria, tokenizer_image_token, \
process_images, \
get_model_name_from_path
from mplug_owl2.model.builder import load_pretrained_model
from extensions.sd_smartprocess.mplug_owl2.model.builder import load_pretrained_model
# This is basically broken until we can update transformers in AUTO past the current version supported
@ -30,19 +32,13 @@ class MPLUG2Interrogator(Interrogator):
def __init__(self, params: ProcessParams):
super().__init__(params)
logger.debug("Initializing LLM model...")
pretrained_ckpt = 'MAGAer13/mplug-owl2-llama2-7b'
scripts_dir = os.path.join(models_path, "llm")
os.makedirs(scripts_dir, exist_ok=True)
model_name = "mplug-owl2-llama2-7b"
model_path = os.path.join(scripts_dir, model_name)
if not os.path.exists(model_path):
os.makedirs(model_path, exist_ok=True)
snapshot_download(pretrained_ckpt, repo_type="model", local_dir=model_path, local_dir_use_symlinks=False)
model_path = fetch_model('MAGAer13/mplug-owl2-llama2-7b', "llm")
model_name = get_model_name_from_path(model_path)
self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(model_path, None, model_name,
load_8bit=False, load_4bit=False,
device="cuda")
self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(model_path, None,
model_name,
load_8bit=False,
load_4bit=False,
device="cuda")
self._to_cpu()
logger.debug("Initialized LLM model.")
@ -51,7 +47,7 @@ class MPLUG2Interrogator(Interrogator):
self.load()
if params is None:
params = {}
query = params.get("query", "Describe the image.")
query = "Describe the image with a caption that can be used to generate a similar image."
conv = conv_templates["mplug_owl2"].copy()
roles = conv.roles
@ -67,7 +63,8 @@ class MPLUG2Interrogator(Interrogator):
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(
input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(
0).to(
self.model.device)
stop_str = conv.sep2
keywords = [stop_str]
@ -94,13 +91,13 @@ class MPLUG2Interrogator(Interrogator):
def _to_cpu(self):
self.model.to('cpu')
self.image_processor.to('cpu')
self.tokenizer.to('cpu')
#self.image_processor.to('cpu')
#self.tokenizer.to('cpu')
def _to_gpu(self):
self.model.to(self.device)
self.image_processor.to(self.device)
self.tokenizer.to(self.device)
#self.image_processor.to(self.device)
#self.tokenizer.to(self.device)
def unload(self):
self._to_cpu()

4
mplug_owl/configuration_mplug_owl.py
View File

@ -193,7 +193,7 @@ class MplugOwlConfig(PretrainedConfig):
Example:
```python
>>> from transformers import (
>>> from transformers.models.owlvit import (
... MplugOwlVisionConfig,
... MplugOwlVisualAbstractorConfig,
... OPTConfig,
@ -236,7 +236,7 @@ class MplugOwlConfig(PretrainedConfig):
if text_config is None:
# we use LLAMA 7b by default
from transformers.llama.configuration_llama import LlamaConfig
from transformers.models.llama.configuration_llama import LlamaConfig
text_config = LlamaConfig(pad_token_id=2).to_dict()
logger.info("text_config is None.")

3
mplug_owl/modeling_mplug_owl.py
View File

@ -15,6 +15,7 @@
""" PyTorch MplugOwl model."""
import math
import warnings
from typing import Any, Optional, Tuple, Union
import torch
@ -1651,7 +1652,7 @@ def bloom_forward(
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False

0
mplug_owl2/evaluate/__init__.py Normal file
View File

13
mplug_owl2/evaluate/evaluate_caption.py
View File

@ -118,13 +118,14 @@ if __name__ == '__main__':
torch.cuda.set_device(int(os.getenv('LOCAL_RANK', 0)))
prompt = 'USER: <|image|>Provide a one-sentence caption for the provided image. ASSISTANT:'
prompt = 'USER: <|image|>Provide a one-sentence caption for the provided image. ASSISTANT: '
model_path = args.checkpoint
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map="cuda", device="cuda")
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map={"":f"cuda:{os.getenv('LOCAL_RANK', '0')}"}, device="cuda")
tokenizer.padding_side = 'left'
tokenizer.pad_token_id = tokenizer.eos_token_id
if not hasattr(tokenizer, 'pad_token_id'):
tokenizer.pad_token_id = tokenizer.eos_token_id
random.seed(args.seed)
dataset = CaptionDataset(
@ -146,14 +147,14 @@ if __name__ == '__main__':
image_ids = []
captions = []
for _, (ids, image_tensor, input_ids, attention_mask) in tqdm(enumerate(coco_karpathy_test_loader)):
for _, (ids, image_tensor, input_ids, attention_mask) in enumerate(tqdm(coco_karpathy_test_loader)):
pred = model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
images=image_tensor.to(dtype=model.dtype).cuda(),
do_sample=False,
num_beams=1,
max_new_tokens=30,
max_new_tokens=60,
min_new_tokens=8,
length_penalty=0,
num_return_sequences=1,
@ -164,7 +165,7 @@ if __name__ == '__main__':
tokenizer.decode(_[input_ids.size(1):].cpu(),
skip_special_tokens=True).strip() for _ in pred
])
print(captions)
print(captions[-len(pred):])
torch.distributed.barrier()

27
mplug_owl2/evaluate/evaluate_mmbench.py
View File

@ -29,6 +29,21 @@ ds_collections = {
'annotation': 'mmbench_test_20230712.jsonl',
'max_new_tokens': 10,
},
'mmbench_test_en_20231003': {
'raw_file': 'mmbench_test_en_20231003.tsv',
'annotation': 'mmbench_test_en_20231003.jsonl',
'max_new_tokens': 10,
},
'mmbench_test_cn_20231003': {
'raw_file': 'mmbench_test_cn_20231003.tsv',
'annotation': 'mmbench_test_cn_20231003.jsonl',
'max_new_tokens': 10,
},
'ccbench_1003': {
'raw_file': 'ccbench_1003.tsv',
'annotation': 'ccbench_1003.jsonl',
'max_new_tokens': 10,
},
}
multiple_choices = ['A', 'B', 'C', 'D', 'E']
@ -45,7 +60,7 @@ def mapping_to_annotation(results, raw_annotation):
"answer": row_df.get('answer', None),
"options": [y for y in [row_df.get(x, None) for x in 'ABCD'] if isinstance(y, str)],
"prediction": prediction,
"l2-category": row_df['l2-category']
"l2-category": row_df['l2-category'] if 'l2-category' in row_df else None
}
outputs.append(output)
return outputs
@ -163,6 +178,7 @@ if __name__ == '__main__':
parser.add_argument('--seed', type=int, default=0)
args = parser.parse_args()
torch.distributed.init_process_group(
backend='nccl',
world_size=int(os.getenv('WORLD_SIZE', '1')),
@ -175,11 +191,12 @@ if __name__ == '__main__':
model_path = args.checkpoint
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map="cuda", device="cuda")
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map={"":f"cuda:{os.getenv('LOCAL_RANK', '0')}"}, device="cuda")
tokenizer.padding_side = 'left'
tokenizer.pad_token_id = tokenizer.eos_token_id
if not hasattr(tokenizer, 'pad_token_id'):
tokenizer.pad_token_id = tokenizer.eos_token_id
prompt = "USER: <|image|>{}\n{}\n{}\nAnswer with the option’s letter from the given choices directly. ASSISTANT:"
prompt = "USER: <|image|>{}\n{}\n{}\nAnswer with the option’s letter from the given choices directly. ASSISTANT: "
random.seed(args.seed)
dataset = VQADataset(
@ -199,7 +216,7 @@ if __name__ == '__main__':
)
outputs = []
for _, (image_tensor, input_ids, attention_mask, indices) in tqdm(enumerate(dataloader)):
for _, (image_tensor, input_ids, attention_mask, indices) in enumerate(tqdm(dataloader)):
pred = model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),

7
mplug_owl2/evaluate/evaluate_mme.py
View File

@ -296,11 +296,12 @@ if __name__ == '__main__':
model_path = args.checkpoint
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map="cuda", device="cuda")
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map={"":f"cuda:{os.getenv('LOCAL_RANK', '0')}"}, device="cuda")
tokenizer.padding_side = 'left'
tokenizer.pad_token_id = tokenizer.eos_token_id
if not hasattr(tokenizer, 'pad_token_id'):
tokenizer.pad_token_id = tokenizer.eos_token_id
prompt = 'USER: <|image|>{}\nAnswer the question using a single word or phrase. ASSISTANT:'
prompt = 'USER: <|image|>{}\nAnswer the question using a single word or phrase. ASSISTANT: '
random.seed(args.seed)
dataset = VQADataset(

28
mplug_owl2/evaluate/evaluate_mmmu.py
View File

@ -13,12 +13,13 @@ from PIL import Image
import pandas as pd
import re
from datasets import load_dataset
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from mplug_owl2.conversation import conv_templates, SeparatorStyle
from mplug_owl2.model.builder import load_pretrained_model
from mplug_owl2.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from pathlib import Path
from datasets import load_dataset, concatenate_datasets
DOMAIN_CAT2SUB_CAT = {
'Art and Design': ['Art', 'Art_Theory', 'Design', 'Music'],
@ -317,11 +318,9 @@ def collate_fn(batches, tokenizer):
for input_text in questions:
input_ids.append(tokenizer_image_token(input_text, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').tolist())
input_tokens_max_length = max([len(x) for x in input_ids])
pad_token_id = tokenizer.pad_token_id
input_ids = [([pad_token_id] * (input_tokens_max_length - len(_)) + _) for _ in input_ids] # pad in the left
input_ids = [([tokenizer.pad_token_id] * (input_tokens_max_length - len(_)) + _) for _ in input_ids] # pad in the left
input_ids = torch.LongTensor(input_ids)
attention_mask = 1 - input_ids.eq(pad_token_id).long()
attention_mask = 1 - input_ids.eq(tokenizer.pad_token_id).long()
image_tensor = torch.cat(image_tensor, dim=0)
return image_tensor, input_ids, attention_mask, answers, ids, origin_questions, question_types, subfields, question_splits
@ -332,7 +331,15 @@ class VQADataset(torch.utils.data.Dataset):
def __init__(self, split, image_processor, eval_split='dev'):
self.image_processor = image_processor
self.data = load_dataset("MMMU/MMMU", split)[eval_split]
# self.data = load_dataset("/nas-alinlp/qinghao.yqh/datasets/mm_chatgpt/Evaluation/MMMU/MMMU", split)[eval_split]
sub_dataset_list = []
for subject in CAT_SHORT2LONG.values():
sub_dataset = load_dataset(str(Path("/nas-alinlp/qinghao.yqh/datasets/mm_chatgpt/Evaluation/MMMU/MMMU", subject)), split=eval_split)
sub_dataset_list.append(sub_dataset)
# merge all dataset
self.data = concatenate_datasets(sub_dataset_list)
self.question_split = split
def __len__(self):
@ -352,9 +359,9 @@ class VQADataset(torch.utils.data.Dataset):
for i, c in enumerate(choices):
choice_list.append('{}. {}'.format(multiple_choices[i], c))
choice_txt = '\n'.join(choice_list)
prompt = f"USER: {question}\n{choice_txt}\nAnswer with the option’s letter from the given choices directly. ASSISTANT:"
prompt = f"USER: {question}\n{choice_txt}\nAnswer with the option’s letter from the given choices directly. ASSISTANT: "
else:
prompt = f"USER: {question}\nAnswer the question using a single word or phrase. ASSISTANT:"
prompt = f"USER: {question}\nAnswer the question using a single word or phrase. ASSISTANT: "
image_nums = re.findall(r'<image (\d+)>', prompt)
images = []
@ -430,9 +437,10 @@ if __name__ == '__main__':
model_path = args.checkpoint
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map="cuda", device="cuda")
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map={"":f"cuda:{os.getenv('LOCAL_RANK', '0')}"}, device="cuda")
if not hasattr(tokenizer, 'pad_token_id'):
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.padding_side = 'left'
tokenizer.pad_token_id = tokenizer.eos_token_id
random.seed(args.seed)

22
mplug_owl2/evaluate/evaluate_vqa.py
View File

@ -154,12 +154,13 @@ if __name__ == '__main__':
model_path = args.checkpoint
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map="cuda", device="cuda")
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=False, load_4bit=False, device_map={"":f"cuda:{os.getenv('LOCAL_RANK', '0')}"}, device="cuda")
tokenizer.padding_side = 'left'
tokenizer.pad_token_id = tokenizer.eos_token_id
prompt = 'USER: <|image|>{}\nAnswer the question using a single word or phrase. ASSISTANT:'
if not hasattr(tokenizer, 'pad_token_id'):
tokenizer.pad_token_id = tokenizer.eos_token_id
prompt = 'USER: <|image|>{} Answer the question using a single word or phrase. ASSISTANT: '
answer_processor = EvalAIAnswerProcessor()
random.seed(args.seed)
dataset = VQADataset(
train=ds_collections[args.dataset]['train'],
@ -181,13 +182,13 @@ if __name__ == '__main__':
outputs = []
for _, (question_ids, image_tensor, input_ids, attention_mask,
annotations) in tqdm(enumerate(dataloader)):
annotations) in enumerate(tqdm(dataloader)):
pred = model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
images=image_tensor.to(dtype=model.dtype).cuda(),
do_sample=False,
num_beams=5,
num_beams=1,
max_new_tokens=ds_collections[args.dataset]['max_new_tokens'],
min_new_tokens=1,
length_penalty=1,
@ -202,11 +203,16 @@ if __name__ == '__main__':
for question_id, answer, annotation in zip(question_ids, answers,
annotations):
if args.dataset in ['vqav2_val', 'vqav2_testdev', 'okvqa_val', 'textvqa_val']:
if args.dataset in ['vqav2_val', 'okvqa_val', 'textvqa_val']:
outputs.append({
'question_id': question_id,
'answer': answer,
})
elif args.dataset == 'vqav2_testdev':
outputs.append({
'question_id': question_id,
'answer': answer_processor(answer),
})
else:
raise NotImplementedError
@ -223,7 +229,7 @@ if __name__ == '__main__':
print(f"Evaluating {args.dataset} ...")
time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime())
results_file = f'{args.dataset}_{time_prefix}_fs{args.few_shot}_s{args.seed}.json'
json.dump(merged_outputs, open(results_file, 'w'), ensure_ascii=False)
json.dump(merged_outputs, open(results_file, 'w', encoding='utf-8'), ensure_ascii=False)
if ds_collections[args.dataset]['metric'] == 'vqa_score':
vqa = VQA(ds_collections[args.dataset]['annotation'],

223
mplug_owl2/evaluate/vqa_eval.py
View File

@ -327,4 +327,225 @@ class VQAEval:
'#' * block + '-' * (barLength - block), int(progress * 100),
status)
sys.stdout.write(text)
sys.stdout.flush()
sys.stdout.flush()
import re
from tqdm import tqdm
class EvalAIAnswerProcessor:
"""
Processes an answer similar to Eval AI
copied from
https://github.com/facebookresearch/mmf/blob/c46b3b3391275b4181567db80943473a89ab98ab/pythia/tasks/processors.py#L897
"""
CONTRACTIONS = {
"aint": "ain't",
"arent": "aren't",
"cant": "can't",
"couldve": "could've",
"couldnt": "couldn't",
"couldn'tve": "couldn't've",
"couldnt've": "couldn't've",
"didnt": "didn't",
"doesnt": "doesn't",
"dont": "don't",
"hadnt": "hadn't",
"hadnt've": "hadn't've",
"hadn'tve": "hadn't've",
"hasnt": "hasn't",
"havent": "haven't",
"hed": "he'd",
"hed've": "he'd've",
"he'dve": "he'd've",
"hes": "he's",
"howd": "how'd",
"howll": "how'll",
"hows": "how's",
"Id've": "I'd've",
"I'dve": "I'd've",
"Im": "I'm",
"Ive": "I've",
"isnt": "isn't",
"itd": "it'd",
"itd've": "it'd've",
"it'dve": "it'd've",
"itll": "it'll",
"let's": "let's",
"maam": "ma'am",
"mightnt": "mightn't",
"mightnt've": "mightn't've",
"mightn'tve": "mightn't've",
"mightve": "might've",
"mustnt": "mustn't",
"mustve": "must've",
"neednt": "needn't",
"notve": "not've",
"oclock": "o'clock",
"oughtnt": "oughtn't",
"ow's'at": "'ow's'at",
"'ows'at": "'ow's'at",
"'ow'sat": "'ow's'at",
"shant": "shan't",
"shed've": "she'd've",
"she'dve": "she'd've",
"she's": "she's",
"shouldve": "should've",
"shouldnt": "shouldn't",
"shouldnt've": "shouldn't've",
"shouldn'tve": "shouldn't've",
"somebody'd": "somebodyd",
"somebodyd've": "somebody'd've",
"somebody'dve": "somebody'd've",
"somebodyll": "somebody'll",
"somebodys": "somebody's",
"someoned": "someone'd",
"someoned've": "someone'd've",
"someone'dve": "someone'd've",
"someonell": "someone'll",
"someones": "someone's",
"somethingd": "something'd",
"somethingd've": "something'd've",
"something'dve": "something'd've",
"somethingll": "something'll",
"thats": "that's",
"thered": "there'd",
"thered've": "there'd've",
"there'dve": "there'd've",
"therere": "there're",
"theres": "there's",
"theyd": "they'd",
"theyd've": "they'd've",
"they'dve": "they'd've",
"theyll": "they'll",
"theyre": "they're",
"theyve": "they've",
"twas": "'twas",
"wasnt": "wasn't",
"wed've": "we'd've",
"we'dve": "we'd've",
"weve": "we've",
"werent": "weren't",
"whatll": "what'll",
"whatre": "what're",
"whats": "what's",
"whatve": "what've",
"whens": "when's",
"whered": "where'd",
"wheres": "where's",
"whereve": "where've",
"whod": "who'd",
"whod've": "who'd've",
"who'dve": "who'd've",
"wholl": "who'll",
"whos": "who's",
"whove": "who've",
"whyll": "why'll",
"whyre": "why're",
"whys": "why's",
"wont": "won't",
"wouldve": "would've",
"wouldnt": "wouldn't",
"wouldnt've": "wouldn't've",
"wouldn'tve": "wouldn't've",
"yall": "y'all",
"yall'll": "y'all'll",
"y'allll": "y'all'll",
"yall'd've": "y'all'd've",
"y'alld've": "y'all'd've",
"y'all'dve": "y'all'd've",
"youd": "you'd",
"youd've": "you'd've",
"you'dve": "you'd've",
"youll": "you'll",
"youre": "you're",
"youve": "you've",
}
NUMBER_MAP = {
"none": "0",
"zero": "0",
"one": "1",
"two": "2",
"three": "3",
"four": "4",
"five": "5",
"six": "6",
"seven": "7",
"eight": "8",
"nine": "9",
"ten": "10",
}
ARTICLES = ["a", "an", "the"]
PERIOD_STRIP = re.compile(r"(?!<=\d)(\.)(?!\d)")
COMMA_STRIP = re.compile(r"(?<=\d)(\,)+(?=\d)")
PUNCTUATIONS = [
";",
r"/",
"[",
"]",
'"',
"{",
"}",
"(",
")",
"=",
"+",
"\\",
"_",
"-",
">",
"<",
"@",
"`",
",",
"?",
"!",
]
def __init__(self, *args, **kwargs):
pass
def word_tokenize(self, word):
word = word.lower()
word = word.replace(",", "").replace("?", "").replace("'s", " 's")
return word.strip()
def process_punctuation(self, in_text):
out_text = in_text
for p in self.PUNCTUATIONS:
if (p + " " in in_text or " " + p in in_text) or (
re.search(self.COMMA_STRIP, in_text) is not None
):
out_text = out_text.replace(p, "")
else:
out_text = out_text.replace(p, " ")
out_text = self.PERIOD_STRIP.sub("", out_text, re.UNICODE)
return out_text
def process_digit_article(self, in_text):
out_text = []
temp_text = in_text.lower().split()
for word in temp_text:
word = self.NUMBER_MAP.setdefault(word, word)
if word not in self.ARTICLES:
out_text.append(word)
else:
pass
for word_id, word in enumerate(out_text):
if word in self.CONTRACTIONS:
out_text[word_id] = self.CONTRACTIONS[word]
out_text = " ".join(out_text)
return out_text
def __call__(self, item):
item = self.word_tokenize(item)
item = item.replace("\n", " ").replace("\t", " ").strip()
item = self.process_punctuation(item)
item = self.process_digit_article(item)
return item
p = EvalAIAnswerProcessor()
for line in tqdm(hfa):
line['answer'] = p(line['answer'])

4
mplug_owl2/model/__init__.py
View File

@ -1,2 +1,2 @@
from .modeling_mplug_owl2 import MPLUGOwl2LlamaForCausalLM
from .configuration_mplug_owl2 import MPLUGOwl2Config
from .modeling_mplug_owl2 import MPLUGOwl2LlamaForCausalLM, MPLUGOwl2QWenForCausalLM
from .configuration_mplug_owl2 import MPLUGOwl2Config,MPLUGOwl2QwenConfig

61
mplug_owl2/model/builder.py
View File

@ -20,10 +20,16 @@ import shutil
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
from transformers.models.clip.image_processing_clip import CLIPImageProcessor
import torch
from mplug_owl2.model import *
from extensions.sd_smartprocess.mplug_owl2.model import *
from icecream import ic
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda"):
kwargs = {"device_map": device_map}
from extensions.sd_smartprocess.mplug_owl2 import MPLUGOwl2LlamaForCausalLM
from extensions.sd_smartprocess.mplug_owl2.model import MPLUGOwl2QWenForCausalLM
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto",
device="cuda", **kwargs):
kwargs = {"device_map": device_map, "ignore_mismatched_sizes": False, **kwargs}
if device != "cuda":
kwargs['device_map'] = {"": device}
@ -43,20 +49,29 @@ def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, l
if 'mplug_owl2' in model_name.lower():
# Load LLaVA model
if 'lora' in model_name.lower() and model_base is None:
warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
warnings.warn(
'There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
if 'lora' in model_name.lower() and model_base is not None:
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
print('Loading mPLUG-Owl2 from base model...')
model = MPLUGOwl2LlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
if 'mplug_owl2_1' in model_name.lower():
model = MPLUGOwl2QWenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
config=lora_cfg_pretrained, **kwargs)
else:
model = MPLUGOwl2LlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
config=lora_cfg_pretrained, **kwargs)
token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
if model.lm_head.weight.shape[0] != token_num:
model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.lm_head.weight = torch.nn.Parameter(
torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.model.embed_tokens.weight = torch.nn.Parameter(
torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
print('Loading additional mPLUG-Owl2 weights...')
if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'),
map_location='cpu')
else:
# this is probably from HF Hub
from huggingface_hub import hf_hub_download
@ -66,10 +81,13 @@ def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, l
filename=filename,
subfolder=subfolder)
return torch.load(cache_file, map_location='cpu')
non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in
non_lora_trainables.items()}
if any(k.startswith('model.model.') for k in non_lora_trainables):
non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in
non_lora_trainables.items()}
model.load_state_dict(non_lora_trainables, strict=False)
from peft import PeftModel
@ -83,16 +101,24 @@ def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, l
print('Loading mPLUG-Owl2 from base model...')
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = MPLUGOwl2LlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
if 'mplug_owl2_1' in model_name.lower():
model = MPLUGOwl2QWenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
config=cfg_pretrained, **kwargs)
else:
model = MPLUGOwl2LlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
config=cfg_pretrained, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = MPLUGOwl2LlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)
if 'mplug_owl2_1' in model_name.lower():
model = MPLUGOwl2QWenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
else:
model = MPLUGOwl2LlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
else:
# Load language model
if model_base is not None:
# PEFT model
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs)
print(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path)
@ -102,12 +128,11 @@ def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, l
model.to(torch.float16)
else:
use_fast = False
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
vision_tower = model.get_model().vision_model
vision_tower.to(device=device, dtype=torch.float16)
# vision_tower.to(device=device, dtype=torch.float16)
image_processor = CLIPImageProcessor.from_pretrained(model_path)
if hasattr(model.config, "max_sequence_length"):
@ -115,4 +140,4 @@ def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, l
else:
context_len = 2048
return tokenizer, model, image_processor, context_len
return tokenizer, model, image_processor, context_len

21
mplug_owl2/model/configuration_mplug_owl2.py
View File

@ -10,7 +10,7 @@ from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from transformers.utils import logging
from transformers.models.auto import CONFIG_MAPPING
from .configuration_qwen import QWenConfig
class LlamaConfig(PretrainedConfig):
r"""
@ -229,9 +229,13 @@ class MplugOwlVisionConfig(PretrainedConfig):
initializer_range=0.02,
initializer_factor=1.0,
use_flash_attn=False,
use_post_layernorm=True,
use_cls_token=True,
**kwargs,
):
super().__init__(**kwargs)
self.use_cls_token=use_cls_token
self.use_post_layernorm=use_post_layernorm
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
@ -269,6 +273,8 @@ class MplugOwlVisualAbstractorConfig(PretrainedConfig):
def __init__(
self,
add_v2t_pos_emb=False,
use_cls_token=True,
num_learnable_queries=64,
hidden_size=1024,
num_hidden_layers=6,
@ -282,6 +288,8 @@ class MplugOwlVisualAbstractorConfig(PretrainedConfig):
**kwargs,
):
super().__init__(**kwargs)
self.use_cls_token=use_cls_token
self.add_v2t_pos_emb=add_v2t_pos_emb
self.hidden_size = hidden_size
self.num_learnable_queries = num_learnable_queries
self.num_hidden_layers = num_hidden_layers
@ -327,6 +335,17 @@ class MPLUGOwl2Config(LlamaConfig):
super().__init__(
**kwargs,
)
class MPLUGOwl2QwenConfig(QWenConfig):
model_type = "mplug_owl2_1"
def __init__(self, visual_config=None, **kwargs):
if visual_config is None:
self.visual_config = DEFAULT_VISUAL_CONFIG
else:
self.visual_config = visual_config
super().__init__(
**kwargs,
)
if __name__ == "__main__":
print(MplugOwlVisionConfig().to_dict())

73
mplug_owl2/model/configuration_qwen.py Normal file
View File

@ -0,0 +1,73 @@
# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from transformers import PretrainedConfig
class QWenConfig(PretrainedConfig):
model_type = "qwen"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
multiway=False,
vocab_size=151936,
hidden_size=4096,
num_hidden_layers=32,
num_attention_heads=32,
emb_dropout_prob=0.0,
attn_dropout_prob=0.0,
layer_norm_epsilon=1e-6,
initializer_range=0.02,
max_position_embeddings=8192,
scale_attn_weights=True,
use_cache=True,
bf16=False,
fp16=False,
fp32=False,
kv_channels=128,
rotary_pct=1.0,
rotary_emb_base=10000,
use_dynamic_ntk=True,
use_logn_attn=True,
use_flash_attn="auto",
intermediate_size=22016,
no_bias=True,
tie_word_embeddings=False,
use_cache_quantization=False,
use_cache_kernel=False,
softmax_in_fp32=False,
**kwargs,
):
self.multiway = multiway
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.emb_dropout_prob = emb_dropout_prob
self.attn_dropout_prob = attn_dropout_prob
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.bf16 = bf16
self.fp16 = fp16
self.fp32 = fp32
self.kv_channels = kv_channels
self.rotary_pct = rotary_pct
self.rotary_emb_base = rotary_emb_base
self.use_dynamic_ntk = use_dynamic_ntk
self.use_logn_attn = use_logn_attn
self.use_flash_attn = use_flash_attn
self.no_bias = no_bias
self.use_cache_quantization = use_cache_quantization
self.use_cache_kernel = use_cache_kernel
self.softmax_in_fp32 = softmax_in_fp32
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs
)

46
mplug_owl2/model/modeling_llama2.py
View File

@ -16,6 +16,7 @@ from transformers.utils import logging
from .modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from .configuration_mplug_owl2 import LlamaConfig
from .multiway import MultiwayNetwork
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
@ -30,33 +31,6 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class MultiwayNetwork(nn.Module):
def __init__(self, module_provider, num_multiway=2):
super(MultiwayNetwork, self).__init__()
self.multiway = torch.nn.ModuleList([module_provider() for _ in range(num_multiway)])
def forward(self, hidden_states, multiway_indices):
if len(self.multiway) == 1:
return self.multiway[0](hidden_states)
output_hidden_states = torch.empty_like(hidden_states)
for idx, subway in enumerate(self.multiway):
local_indices = multiway_indices.eq(idx).nonzero(as_tuple=True)
hidden = hidden_states[local_indices].unsqueeze(1).contiguous()
if hidden.numel():
output = subway(hidden)
if isinstance(output, tuple):
output = output[0]
output = output.squeeze(1)
output_hidden_states[local_indices] = output
return output_hidden_states.contiguous()
class LlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
@ -142,7 +116,7 @@ class LlamaAttention(nn.Module):
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len, position_ids=position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
@ -193,19 +167,15 @@ class LlamaAttention(nn.Module):
class LlamaDecoderLayer(nn.Module):
def __init__(self, config: LlamaConfig):
def __init__(self, config: LlamaConfig, annoying_param):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = LlamaAttention(config=config)
# Check if LlamaMLP takes one or three args
num_llama_args = len(inspect.signature(LlamaMLP.__init__).parameters)
if num_llama_args == 1:
self.mlp = LlamaMLP(config)
elif num_llama_args == 3:
self.mlp = LlamaMLP(config.hidden_size, config.intermediate_size, config.hidden_act)
else:
raise ValueError(f"Invalid number of arguments for LlamaMLP: {num_llama_args}")
self.mlp = LlamaMLP(config)
mlp_kwargs = {'config': config, "hidden_size": config.hidden_size,
"intermediate_size": config.intermediate_size, "hidden_act": config.hidden_act}
valid_params = set(inspect.signature(LlamaMLP.__init__).parameters.keys()) - {'self'}
mlp_kwargs = {k: v for k, v in mlp_kwargs.items() if k in valid_params}
self.mlp = LlamaMLP(**mlp_kwargs)
self.input_layernorm = MultiwayNetwork(module_provider=partial(
LlamaRMSNorm, hidden_size=config.hidden_size, eps=config.rms_norm_eps
))

231
mplug_owl2/model/modeling_mplug_owl2.py
View File

@ -18,15 +18,15 @@ from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers import AutoConfig, AutoModelForCausalLM, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig
from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
from extensions.sd_smartprocess.mplug_owl2.constants import IMAGE_TOKEN_INDEX, IGNORE_INDEX
from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig, \
MPLUGOwl2QwenConfig
from .modeling_llama2 import replace_llama_modality_adaptive
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, IGNORE_INDEX
from icecream import ic
from .modeling_qwen import QWenLMHeadModel, QWenModel
from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
class MPLUGOwl2MetaModel:
@ -67,8 +67,10 @@ class MPLUGOwl2MetaForCausalLM(ABC):
):
if images is None or input_ids.shape[1] == 1:
if past_key_values is not None and images is not None and input_ids.shape[1] == 1:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1),
dtype=attention_mask.dtype, device=attention_mask.device)
# print(attention_mask)
if attention_mask is not None:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1),
dtype=attention_mask.dtype, device=attention_mask.device)
multiway_indices = torch.zeros_like(input_ids).long().to(self.device)
return input_ids, multiway_indices, attention_mask, past_key_values, None, labels
@ -210,12 +212,82 @@ class MPLUGOwl2MetaForCausalLM(ABC):
return None, new_modality_indicators, attention_mask, past_key_values, new_input_embeds, new_labels
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat(
[torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1
)
return mask[None, None, :, :].expand(
bsz, 1, tgt_len, tgt_len + past_key_values_length
)
class MPLUGOwl2LlamaModel(MPLUGOwl2MetaModel, LlamaModel):
config_class = MPLUGOwl2Config
def __init__(self, config: MPLUGOwl2Config):
super(MPLUGOwl2LlamaModel, self).__init__(config)
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
past_key_values_length=past_key_values_length,
).to(inputs_embeds.device)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
class MPLUGOwl2QWenModel(MPLUGOwl2MetaModel, QWenModel):
config_class = MPLUGOwl2QwenConfig
def __init__(self, config: MPLUGOwl2QwenConfig):
super(MPLUGOwl2QWenModel, self).__init__(config)
class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM):
config_class = MPLUGOwl2Config
@ -229,13 +301,17 @@ class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM):
# Initialize weights and apply final processing
self.post_init()
def encode_images(self, images):
image_features = self.get_model().vision_model(images).last_hidden_state
image_features = self.get_model().visual_abstractor(encoder_hidden_states=image_features).last_hidden_state
return image_features
def get_model(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
# modality_indicators: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
@ -318,8 +394,145 @@ class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM):
return model_inputs
class MPLUGOwl2QWenForCausalLM(QWenLMHeadModel, MPLUGOwl2MetaForCausalLM):
config_class = MPLUGOwl2QwenConfig
def __init__(self, config):
super(QWenLMHeadModel, self).__init__(config)
from .modeling_qwen import SUPPORT_BF16, logger, SUPPORT_FP16, SUPPORT_CUDA, _import_flash_attn
autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0
if autoset_precision:
if SUPPORT_BF16:
logger.warn(
"The model is automatically converting to bf16 for faster inference. "
"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
)
config.bf16 = True
elif SUPPORT_FP16:
logger.warn(
"The model is automatically converting to fp16 for faster inference. "
"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
)
config.fp16 = True
else:
config.fp32 = True
if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
logger.warn(
"Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
logger.warn(
"Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
if config.fp32:
if SUPPORT_BF16:
logger.warn(
"Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
elif SUPPORT_FP16:
logger.warn(
"Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")
if config.use_flash_attn == "auto":
if config.bf16 or config.fp16:
logger.warn("Try importing flash-attention for faster inference...")
config.use_flash_attn = True
else:
config.use_flash_attn = False
if config.use_flash_attn and config.fp32:
logger.warn("Flash attention will be disabled because it does NOT support fp32.")
if config.use_flash_attn:
_import_flash_attn()
self.transformer = MPLUGOwl2QWenModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
if config.bf16:
self.transformer.bfloat16()
self.lm_head.bfloat16()
if config.fp16:
self.transformer.half()
self.lm_head.half()
self.post_init()
def get_model(self):
return self.transformer
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images=None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
input_ids, modality_indicators, attention_mask, past_key_values, inputs_embeds, labels = \
self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.transformer(
input_ids,
modality_indicators=modality_indicators,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model/pipeline parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
AutoConfig.register("mplug_owl2", MPLUGOwl2Config)
AutoModelForCausalLM.register(MPLUGOwl2Config, MPLUGOwl2LlamaForCausalLM)
AutoConfig.register("mplug_owl2_1", MPLUGOwl2QwenConfig)
AutoModelForCausalLM.register(MPLUGOwl2QwenConfig, MPLUGOwl2QWenForCausalLM)
replace_llama_modality_adaptive()

1317
mplug_owl2/model/modeling_qwen.py Normal file
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35
mplug_owl2/model/multiway.py Normal file
View File

@ -0,0 +1,35 @@
import torch
import torch.utils.checkpoint
from torch import nn
class MultiwayNetwork(nn.Module):
def __init__(self, module_provider, num_multiway=2, out_features=None):
super(MultiwayNetwork, self).__init__()
self.multiway = torch.nn.ModuleList([module_provider() for _ in range(num_multiway)])
self.out_features=out_features
def forward(self, hidden_states, multiway_indices):
if len(self.multiway) == 1:
return self.multiway[0](hidden_states)
if self.out_features:
output_hidden_states = torch.empty(
hidden_states.size(0), hidden_states.size(1), self.out_features,
dtype=hidden_states.dtype
).to(hidden_states.device)
else:
output_hidden_states = torch.empty_like(hidden_states)
for idx, subway in enumerate(self.multiway):
local_indices = multiway_indices.eq(idx).nonzero(as_tuple=True)
hidden = hidden_states[local_indices].unsqueeze(1).contiguous()
if hidden.numel():
output = subway(hidden)
if isinstance(output, tuple):
output = output[0]
output = output.squeeze(1)
output_hidden_states[local_indices] = output
return output_hidden_states.contiguous()

89
mplug_owl2/model/visual_encoder.py
View File

@ -10,7 +10,7 @@ import torch
import torch.nn as nn
import torch.utils.checkpoint
from icecream import ic
import torch.nn.functional as F
def get_abs_pos(abs_pos, tgt_size):
# abs_pos: L, C
# tgt_size: M
@ -29,6 +29,7 @@ def get_abs_pos(abs_pos, tgt_size):
else:
return abs_pos
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
@ -88,8 +89,10 @@ class MplugOwlVisionEmbeddings(nn.Module):
self.hidden_size = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size))
if config.use_cls_token:
self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size))
else:
self.cls_token = None
self.patch_embed = nn.Conv2d(
in_channels=3,
@ -99,20 +102,25 @@ class MplugOwlVisionEmbeddings(nn.Module):
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.position_embedding = nn.Parameter(torch.randn(1, self.num_patches + 1, self.hidden_size))
if self.cls_token is not None:
self.num_patches = (self.image_size // self.patch_size) ** 2
self.position_embedding = nn.Parameter(torch.randn(1, self.num_patches + 1, self.hidden_size))
else:
self.num_patches = 256
self.position_embedding = nn.Parameter(torch.randn(256, self.hidden_size))
self.pre_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
batch_size = pixel_values.size(0)
image_embeds = self.patch_embed(pixel_values)
image_embeds = image_embeds.flatten(2).transpose(1, 2)
class_embeds = self.cls_token.expand(batch_size, 1, -1).to(image_embeds.dtype)
embeddings = torch.cat([class_embeds, image_embeds], dim=1)
embeddings = embeddings + self.position_embedding[:, : embeddings.size(1)].to(image_embeds.dtype)
if self.cls_token is not None:
class_embeds = self.cls_token.expand(batch_size, 1, -1).to(image_embeds.dtype)
embeddings = torch.cat([class_embeds, image_embeds], dim=1)
embeddings = embeddings + self.position_embedding[:, : embeddings.size(1)].to(image_embeds.dtype)
else:
embeddings = image_embeds
embeddings = embeddings + get_abs_pos(self.position_embedding,embeddings.size(1))
embeddings = self.pre_layernorm(embeddings)
return embeddings
@ -221,16 +229,17 @@ class MplugOwlVisionAttention(nn.Module):
return outputs
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
# class QuickGELU(nn.Module):
# def forward(self, x: torch.Tensor):
# return x * torch.sigmoid(1.702 * x)
class MplugOwlMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = QuickGELU()
from transformers.activations import ACT2FN
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
@ -391,10 +400,20 @@ class MplugOwlVisionModel(PreTrainedModel):
self.embeddings = MplugOwlVisionEmbeddings(config)
self.encoder = MplugOwlVisionEncoder(config)
self.post_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
if config.use_post_layernorm:
self.post_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
else:
self.post_layernorm = None
self._no_split_modules = self._get_no_split_modules("")
self.post_init()
def _get_no_split_modules(self, device_map: str):
if self._no_split_modules is None:
self._no_split_modules = {
"embeddings": self.embeddings,
"encoder": self.encoder,
}
return self._no_split_modules
def forward(
self,
@ -426,10 +445,12 @@ class MplugOwlVisionModel(PreTrainedModel):
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.post_layernorm(last_hidden_state)
if self.post_layernorm:
last_hidden_state = self.post_layernorm(last_hidden_state)
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if self.post_layernorm:
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
@ -499,7 +520,7 @@ class MplugOwlVisualAbstractorMultiHeadAttention(nn.Module):
)
self.register_buffer(
'k_pos_embed',
torch.from_numpy(get_2d_sincos_pos_embed(config.hidden_size, grids, cls_token=True)).float()
torch.from_numpy(get_2d_sincos_pos_embed(config.hidden_size, grids, cls_token=config.use_cls_token)).float()
)
@ -585,7 +606,7 @@ class MplugOwlVisualAbstractorMultiHeadAttention(nn.Module):
class MplugOwlVisualAbstractorCrossOutput(nn.Module):
def __init__(self, config):
super().__init__()
dim = config.hidden_size
dim = config.encoder_hidden_size
self.out_proj = nn.Linear(dim, dim, bias=True)
self.norm2 = nn.LayerNorm(dim)
self.mlp = MplugOwlVisualAbstractorMLP(config)
@ -602,9 +623,19 @@ class MplugOwlVisualAbstractorAttention(nn.Module):
self.attention = MplugOwlVisualAbstractorMultiHeadAttention(config)
self.output = MplugOwlVisualAbstractorCrossOutput(config)
self.pruned_heads = set()
self.norm1 = nn.LayerNorm(config.hidden_size)
self.normk = nn.LayerNorm(config.hidden_size)
self.norm1 = nn.LayerNorm(config.encoder_hidden_size)
self.normk = nn.LayerNorm(config.encoder_hidden_size)
self.add_pos_embed = config.add_v2t_pos_emb
if self.add_pos_embed:
self.q_pos_embed = nn.Parameter(
torch.from_numpy(get_1d_sincos_pos_embed_from_grid(config.encoder_hidden_size, np.arange(config.num_learnable_queries, dtype=np.float32))).float()
).requires_grad_(False)
self.k_pos_embed = nn.Parameter(
torch.from_numpy(get_2d_sincos_pos_embed(config.encoder_hidden_size, config.grid_size, cls_token=config.cls_token)).float()
).requires_grad_(False)
def prune_heads(self, heads):
if len(heads) == 0:
return
@ -757,14 +788,23 @@ class MplugOwlVisualAbstractorModel(PreTrainedModel):
def __init__(self, config, language_hidden_size):
super().__init__(config)
self.config = config
self.encoder = MplugOwlVisualAbstractorEncoder(config)
self.visual_fc = torch.nn.Linear(config.hidden_size, language_hidden_size)
self._no_split_modules = self._get_no_split_modules("")
self.query_embeds = torch.nn.Parameter(torch.randn(1, config.num_learnable_queries, config.hidden_size))
self.vit_eos = torch.nn.Parameter(torch.randn(1, 1, language_hidden_size))
self.post_init()
def _get_no_split_modules(self, device_map: str):
if self._no_split_modules is None:
self._no_split_modules = {
"encoder": self.encoder,
"visual_fc": self.visual_fc,
}
return self._no_split_modules
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
@ -849,7 +889,6 @@ class MplugOwlVisualAbstractorModel(PreTrainedModel):
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
query_embeds = self.query_embeds.repeat(encoder_hidden_states.shape[0], 1, 1)
embedding_output = query_embeds
input_shape = embedding_output.size()[:-1]

2
requirements.txt
View File

@ -12,4 +12,4 @@ ruamel.yaml
markdown2
sconf
tensorboardX
transformers>=4.35.0
transformers==4.38.1

3
scripts/process_main.py
View File

@ -1,3 +1,4 @@
import os
import shutil
import sys
@ -37,7 +38,7 @@ registry = InterrogatorRegistry()
int_dict = registry.list_interrogators()
print(f"Found {len(int_dict.keys())} interrogators: {int_dict}")
natural_captioner_names = ["CLIP", "BLIP", "LLAVA"]
natural_captioner_names = ["CLIP", "BLIP", "MPLUG2"]
default_captioners = ["Swin"]
natural_captioners = {}

31
smartprocess.py
View File

@ -27,7 +27,7 @@ clip_interrogator = None
crop_clip = None
image_interrogators = {}
global_unpickler = None
image_features = None
def printi(message):
shared.state.textinfo = message
@ -77,18 +77,23 @@ def save_img_caption(image_path: str, img_caption: str, params: ProcessParams):
def list_features():
# Create buffer for pilinfo() to write into rather than stdout
buffer = StringIO()
features.pilinfo(out=buffer)
pil_features = []
# Parse and analyse lines
for line in buffer.getvalue().splitlines():
if "Extensions:" in line:
ext_list = line.split(": ")[1]
extensions = ext_list.split(", ")
for extension in extensions:
if extension not in pil_features:
pil_features.append(extension)
global image_features
if image_features is None:
# Create buffer for pilinfo() to write into rather than stdout
buffer = StringIO()
features.pilinfo(out=buffer)
pil_features = []
# Parse and analyse lines
for line in buffer.getvalue().splitlines():
if "Extensions:" in line:
ext_list = line.split(": ")[1]
extensions = ext_list.split(", ")
for extension in extensions:
if extension not in pil_features:
pil_features.append(extension)
image_features = pil_features
else:
pil_features = image_features
return pil_features