Offline Inference Vision Language Multi Image#
Source vllm-project/vllm.
1"""
2This example shows how to use vLLM for running offline inference with
3multi-image input on vision language models, using the chat template defined
4by the model.
5"""
6from argparse import Namespace
7from typing import List, NamedTuple, Optional
8
9from PIL.Image import Image
10from transformers import AutoProcessor, AutoTokenizer
11
12from vllm import LLM, SamplingParams
13from vllm.multimodal.utils import fetch_image
14from vllm.utils import FlexibleArgumentParser
15
16QUESTION = "What is the content of each image?"
17IMAGE_URLS = [
18 "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg",
19 "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg",
20]
21
22
23class ModelRequestData(NamedTuple):
24 llm: LLM
25 prompt: str
26 stop_token_ids: Optional[List[str]]
27 image_data: List[Image]
28 chat_template: Optional[str]
29
30
31# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
32# lower-end GPUs.
33# Unless specified, these settings have been tested to work on a single L4.
34
35
36def load_qwenvl_chat(question: str, image_urls: List[str]) -> ModelRequestData:
37 model_name = "Qwen/Qwen-VL-Chat"
38 llm = LLM(
39 model=model_name,
40 trust_remote_code=True,
41 max_model_len=1024,
42 max_num_seqs=2,
43 limit_mm_per_prompt={"image": len(image_urls)},
44 )
45 placeholders = "".join(f"Picture {i}: <img></img>\n"
46 for i, _ in enumerate(image_urls, start=1))
47
48 # This model does not have a chat_template attribute on its tokenizer,
49 # so we need to explicitly pass it. We use ChatML since it's used in the
50 # generation utils of the model:
51 # https://huggingface.co/Qwen/Qwen-VL-Chat/blob/main/qwen_generation_utils.py#L265
52 tokenizer = AutoTokenizer.from_pretrained(model_name,
53 trust_remote_code=True)
54
55 # Copied from: https://huggingface.co/docs/transformers/main/en/chat_templating
56 chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" # noqa: E501
57
58 messages = [{'role': 'user', 'content': f"{placeholders}\n{question}"}]
59 prompt = tokenizer.apply_chat_template(messages,
60 tokenize=False,
61 add_generation_prompt=True,
62 chat_template=chat_template)
63
64 stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"]
65 stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
66 return ModelRequestData(
67 llm=llm,
68 prompt=prompt,
69 stop_token_ids=stop_token_ids,
70 image_data=[fetch_image(url) for url in image_urls],
71 chat_template=chat_template,
72 )
73
74
75def load_phi3v(question: str, image_urls: List[str]) -> ModelRequestData:
76 # num_crops is an override kwarg to the multimodal image processor;
77 # For some models, e.g., Phi-3.5-vision-instruct, it is recommended
78 # to use 16 for single frame scenarios, and 4 for multi-frame.
79 #
80 # Generally speaking, a larger value for num_crops results in more
81 # tokens per image instance, because it may scale the image more in
82 # the image preprocessing. Some references in the model docs and the
83 # formula for image tokens after the preprocessing
84 # transform can be found below.
85 #
86 # https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally
87 # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194
88 llm = LLM(
89 model="microsoft/Phi-3.5-vision-instruct",
90 trust_remote_code=True,
91 max_model_len=4096,
92 max_num_seqs=2,
93 limit_mm_per_prompt={"image": len(image_urls)},
94 mm_processor_kwargs={"num_crops": 4},
95 )
96 placeholders = "\n".join(f"<|image_{i}|>"
97 for i, _ in enumerate(image_urls, start=1))
98 prompt = f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n"
99 stop_token_ids = None
100
101 return ModelRequestData(
102 llm=llm,
103 prompt=prompt,
104 stop_token_ids=stop_token_ids,
105 image_data=[fetch_image(url) for url in image_urls],
106 chat_template=None,
107 )
108
109
110def load_internvl(question: str, image_urls: List[str]) -> ModelRequestData:
111 model_name = "OpenGVLab/InternVL2-2B"
112
113 llm = LLM(
114 model=model_name,
115 trust_remote_code=True,
116 max_model_len=4096,
117 limit_mm_per_prompt={"image": len(image_urls)},
118 mm_processor_kwargs={"max_dynamic_patch": 4},
119 )
120
121 placeholders = "\n".join(f"Image-{i}: <image>\n"
122 for i, _ in enumerate(image_urls, start=1))
123 messages = [{'role': 'user', 'content': f"{placeholders}\n{question}"}]
124
125 tokenizer = AutoTokenizer.from_pretrained(model_name,
126 trust_remote_code=True)
127 prompt = tokenizer.apply_chat_template(messages,
128 tokenize=False,
129 add_generation_prompt=True)
130
131 # Stop tokens for InternVL
132 # models variants may have different stop tokens
133 # please refer to the model card for the correct "stop words":
134 # https://huggingface.co/OpenGVLab/InternVL2-2B#service
135 stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|end|>"]
136 stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
137
138 return ModelRequestData(
139 llm=llm,
140 prompt=prompt,
141 stop_token_ids=stop_token_ids,
142 image_data=[fetch_image(url) for url in image_urls],
143 chat_template=None,
144 )
145
146
147def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData:
148 try:
149 from qwen_vl_utils import process_vision_info
150 except ModuleNotFoundError:
151 print('WARNING: `qwen-vl-utils` not installed, input images will not '
152 'be automatically resized. You can enable this functionality by '
153 '`pip install qwen-vl-utils`.')
154 process_vision_info = None
155
156 model_name = "Qwen/Qwen2-VL-7B-Instruct"
157
158 # Tested on L40
159 llm = LLM(
160 model=model_name,
161 max_model_len=32768 if process_vision_info is None else 4096,
162 max_num_seqs=5,
163 limit_mm_per_prompt={"image": len(image_urls)},
164 )
165
166 placeholders = [{"type": "image", "image": url} for url in image_urls]
167 messages = [{
168 "role": "system",
169 "content": "You are a helpful assistant."
170 }, {
171 "role":
172 "user",
173 "content": [
174 *placeholders,
175 {
176 "type": "text",
177 "text": question
178 },
179 ],
180 }]
181
182 processor = AutoProcessor.from_pretrained(model_name)
183
184 prompt = processor.apply_chat_template(messages,
185 tokenize=False,
186 add_generation_prompt=True)
187
188 stop_token_ids = None
189
190 if process_vision_info is None:
191 image_data = [fetch_image(url) for url in image_urls]
192 else:
193 image_data, _ = process_vision_info(messages)
194
195 return ModelRequestData(
196 llm=llm,
197 prompt=prompt,
198 stop_token_ids=stop_token_ids,
199 image_data=image_data,
200 chat_template=None,
201 )
202
203
204model_example_map = {
205 "phi3_v": load_phi3v,
206 "internvl_chat": load_internvl,
207 "qwen2_vl": load_qwen2_vl,
208 "qwen_vl_chat": load_qwenvl_chat,
209}
210
211
212def run_generate(model, question: str, image_urls: List[str]):
213 req_data = model_example_map[model](question, image_urls)
214
215 sampling_params = SamplingParams(temperature=0.0,
216 max_tokens=128,
217 stop_token_ids=req_data.stop_token_ids)
218
219 outputs = req_data.llm.generate(
220 {
221 "prompt": req_data.prompt,
222 "multi_modal_data": {
223 "image": req_data.image_data
224 },
225 },
226 sampling_params=sampling_params)
227
228 for o in outputs:
229 generated_text = o.outputs[0].text
230 print(generated_text)
231
232
233def run_chat(model: str, question: str, image_urls: List[str]):
234 req_data = model_example_map[model](question, image_urls)
235
236 sampling_params = SamplingParams(temperature=0.0,
237 max_tokens=128,
238 stop_token_ids=req_data.stop_token_ids)
239 outputs = req_data.llm.chat(
240 [{
241 "role":
242 "user",
243 "content": [
244 {
245 "type": "text",
246 "text": question,
247 },
248 *({
249 "type": "image_url",
250 "image_url": {
251 "url": image_url
252 },
253 } for image_url in image_urls),
254 ],
255 }],
256 sampling_params=sampling_params,
257 chat_template=req_data.chat_template,
258 )
259
260 for o in outputs:
261 generated_text = o.outputs[0].text
262 print(generated_text)
263
264
265def main(args: Namespace):
266 model = args.model_type
267 method = args.method
268
269 if method == "generate":
270 run_generate(model, QUESTION, IMAGE_URLS)
271 elif method == "chat":
272 run_chat(model, QUESTION, IMAGE_URLS)
273 else:
274 raise ValueError(f"Invalid method: {method}")
275
276
277if __name__ == "__main__":
278 parser = FlexibleArgumentParser(
279 description='Demo on using vLLM for offline inference with '
280 'vision language models that support multi-image input')
281 parser.add_argument('--model-type',
282 '-m',
283 type=str,
284 default="phi3_v",
285 choices=model_example_map.keys(),
286 help='Huggingface "model_type".')
287 parser.add_argument("--method",
288 type=str,
289 default="generate",
290 choices=["generate", "chat"],
291 help="The method to run in `vllm.LLM`.")
292
293 args = parser.parse_args()
294 main(args)