File size: 10,221 Bytes
eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa 06f6212 eb202aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
import os
import os.path as osp
from collections import defaultdict
from typing import List, Union
from transformers import AutoConfig, AutoImageProcessor, AutoModel, AutoProcessor, AutoTokenizer
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, VideoInput
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import logging
from .constants import DEFAULT_IMAGE_TOKEN, MEDIA_TOKENS
from .media import Image, Video, extract_media
from .mm_utils import process_image, process_images
from .tokenizer_utils import tokenize_conversation
class VILAProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
}
class VILAProcessor(ProcessorMixin):
# attributes = ["image_processor", "tokenizer"]
attributes = []
# valid_kwargs = ["chat_template"]
valid_kwargs = []
# image_processor_class = "VILAImageProcessor"
# tokenizer_class = ("VILATokenizer", "VILATokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, chat_template=None, config=None, **kwargs):
# self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
# self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
self.image_token = MEDIA_TOKENS["image"]
self.video_token = MEDIA_TOKENS["video"]
self.config = config
self.image_processor = image_processor
self.tokenizer = tokenizer
super().__init__(image_processor, tokenizer, chat_template=chat_template)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
if os.path.isdir(pretrained_model_name_or_path):
pretrained_model_name_or_path = pretrained_model_name_or_path
else:
print(f"pretrained_model_name_or_path {pretrained_model_name_or_path} is not a directory, downloading")
from huggingface_hub import HfApi, snapshot_download
pretrained_model_name_or_path = snapshot_download(pretrained_model_name_or_path)
image_processor = AutoImageProcessor.from_pretrained(
osp.join(pretrained_model_name_or_path, "vision_tower"), trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
osp.join(pretrained_model_name_or_path, "llm"), trust_remote_code=True
)
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)
return cls(image_processor=image_processor, tokenizer=tokenizer, config=config)
def __repr__(self):
return (
f"VILAProcessor(image_processor={self.image_processor}, tokenizer={self.tokenizer}, config={self.config})"
)
def __call__(
self,
conversation,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
videos: VideoInput = None,
**kwargs: Unpack[VILAProcessorKwargs],
) -> BatchFeature:
# TODO: should be merged with llava_arch.py/generate_content()
# TODO (extract and preprocess should be done together, as the preprocess of image and video can be different, i.e. when dynamic res is used)
media = extract_media(conversation, self.config)
# Process media
media_config = defaultdict(dict)
for name in media:
if name == "image":
if len(media["image"]) == 1 and self.config.image_aspect_ratio in ["dynamic", "dynamic_s2"]:
self.config.image_processor = self.image_processor
if self.config.image_aspect_ratio == "dynamic":
images = process_image(media["image"][0], self.config, None, enable_dynamic_res=True).half()
conversation[0]["value"] = conversation[0]["value"].replace(
DEFAULT_IMAGE_TOKEN, f"{DEFAULT_IMAGE_TOKEN}\n" * images.shape[0]
)
else:
if type(self.config.s2_scales) is str:
self.config.s2_scales = list(map(int, self.config.s2_scales.split(",")))
images, block_sizes = process_image(
media["image"][0], self.config, None, enable_dynamic_s2=True
)
images = images.half()
media_config[name]["block_sizes"] = [block_sizes]
else:
images = process_images(media["image"], self.vision_tower.image_processor, self.config).half()
media[name] = [image for image in images]
elif name == "video":
media[name] = [
process_images(images, self.vision_tower.image_processor, self.config).half()
for images in media[name]
]
else:
raise ValueError(f"Unsupported media type: {name}")
input_ids = tokenize_conversation(conversation, self.tokenizer, add_generation_prompt=True).cuda().unsqueeze(0)
# Set up the generation config
# print(input_ids.shape); print(media); input()
return BatchFeature(data={"input_ids": input_ids, **media})
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def post_process_image_text_to_text(self, generated_outputs):
"""
Post-process the output of the model to decode the text.
Args:
generated_outputs (`torch.Tensor` or `np.ndarray`):
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
or `(sequence_length,)`.
Returns:
`List[str]`: The decoded text.
"""
return self.tokenizer.batch_decode(
generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
# inputs = processor(conversation=llavaconv, padding=True, return_tensors="pt")
def apply_chat_template(self, conversation, add_generation_prompt=True, **kwargs):
vila_conv = []
for chat in conversation:
vila_chat = {"from": "", "value": []}
if chat["role"] == "user":
# user allows to input image and text
vila_chat["from"] = "human"
for content in chat["content"]:
if content["type"] == "image":
vila_chat["value"].append(Image(content["path"]))
elif content["type"] == "text":
vila_chat["value"].append(content["text"])
else:
raise ValueError(f"Unsupported content type: {content['type']}")
elif chat["role"] == "assistant":
vila_chat["from"] = "gpt"
for content in chat["content"]:
assert content["type"] == "text", f"Unsupported content type: {content['type']}"
vila_chat["value"].append(content["text"])
vila_conv.append(vila_chat)
return self(vila_conv)
if __name__ == "__main__":
# gpt style: user, assistant
# vila style: human, gpt
gpt_conv = [
{
"role": "user",
"content": [
{"type": "image", "path": "demo_images/demo_img_1.png"},
{"type": "text", "text": "Describe this image."},
],
}
]
llavaconv = [
{
"from": "human",
"value": [
PIL.Image.open("demo_images/demo_img_1.png"),
"Describe this image.",
],
}
]
processor = AutoProcessor.from_pretrained(output_dir, trust_remote_code=True)
inputs = processor.apply_chat_template(conversation=gpt_conv, padding=True, return_tensors="pt")
# model = llava.load("Efficient-Large-Model/qwen25_2B_3x3-sft").cuda()
# print(model)
model_path = "NVILA-Lite-2B-hf-preview"
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, device_map="auto")
# res = model.generate_content(["how are you today?"])
# print(model.config)
# print(model.tokenizer)
# print(res)
# exit(0)
processor = VILAProcessor(
config=model.config,
image_processor=model.vision_tower.image_processor,
tokenizer=model.tokenizer,
)
# TODO: add padding, return_tensors,
inputs = processor(conversation=llavaconv, padding=True, return_tensors="pt")
print(inputs.keys(), inputs.input_ids.shape, [_.shape for _ in inputs.image])
print("vila conv pass")
inputs = processor.apply_chat_template(conversation=gpt_conv, padding=True, return_tensors="pt")
print(inputs.keys(), inputs.input_ids.shape, [_.shape for _ in inputs.image])
print("gpt conv pass")
output_ids = model.generate(
input_ids=inputs.input_ids,
media={
"image": inputs.image,
},
media_config={"image": {}},
generation_config=model.generation_config,
max_new_tokens=100,
)
print(output_ids)
|