Spaces:
Running
Running
File size: 11,264 Bytes
569f484 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 |
import os
import torch
import json
from PIL import Image
import base64
import io
from accelerate import load_checkpoint_and_dispatch, init_empty_weights
from transformers import AutoTokenizer, AutoModel
from omnilmm.utils import disable_torch_init
from omnilmm.model.omnilmm import OmniLMMForCausalLM
from omnilmm.model.utils import build_transform
from omnilmm.train.train_utils import omni_preprocess
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
def init_omni_lmm(model_path):
torch.backends.cuda.matmul.allow_tf32 = True
disable_torch_init()
model_name = os.path.expanduser(model_path)
print(f'Load omni_lmm model and tokenizer from {model_name}')
tokenizer = AutoTokenizer.from_pretrained(
model_name, model_max_length=2048)
if False:
# model on multiple devices for small size gpu memory (Nvidia 3090 24G x2)
with init_empty_weights():
model = OmniLMMForCausalLM.from_pretrained(model_name, tune_clip=True, torch_dtype=torch.bfloat16)
model = load_checkpoint_and_dispatch(model, model_name, dtype=torch.bfloat16,
device_map="auto", no_split_module_classes=['Eva','MistralDecoderLayer', 'ModuleList', 'Resampler']
)
else:
model = OmniLMMForCausalLM.from_pretrained(
model_name, tune_clip=True, torch_dtype=torch.bfloat16
).to(device='cuda', dtype=torch.bfloat16)
image_processor = build_transform(
is_train=False, input_size=model.model.config.image_size, std_mode='OPENAI_CLIP')
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
assert mm_use_im_start_end
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN], special_tokens=True)
vision_config = model.model.vision_config
vision_config.im_patch_token = tokenizer.convert_tokens_to_ids(
[DEFAULT_IMAGE_PATCH_TOKEN])[0]
vision_config.use_im_start_end = mm_use_im_start_end
vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids(
[DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
image_token_len = model.model.config.num_query
return model, image_processor, image_token_len, tokenizer
def expand_question_into_multimodal(question_text, image_token_len, im_st_token, im_ed_token, im_patch_token):
if '<image>' in question_text[0]['content']:
question_text[0]['content'] = question_text[0]['content'].replace(
'<image>', im_st_token + im_patch_token * image_token_len + im_ed_token)
else:
question_text[0]['content'] = im_st_token + im_patch_token * \
image_token_len + im_ed_token + '\n' + question_text[0]['content']
return question_text
def wrap_question_for_omni_lmm(question, image_token_len, tokenizer):
question = expand_question_into_multimodal(
question, image_token_len, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN)
conversation = question
data_dict = omni_preprocess(sources=[conversation],
tokenizer=tokenizer,
generation=True)
data_dict = dict(input_ids=data_dict["input_ids"][0],
labels=data_dict["labels"][0])
return data_dict
class OmniLMM12B:
def __init__(self, model_path) -> None:
model, img_processor, image_token_len, tokenizer = init_omni_lmm(model_path)
self.model = model
self.image_token_len = image_token_len
self.image_transform = img_processor
self.tokenizer = tokenizer
self.model.eval()
def decode(self, image, input_ids):
with torch.inference_mode():
output = self.model.generate_vllm(
input_ids=input_ids.unsqueeze(0).cuda(),
images=image.unsqueeze(0).half().cuda(),
temperature=0.6,
max_new_tokens=1024,
# num_beams=num_beams,
do_sample=True,
output_scores=True,
return_dict_in_generate=True,
repetition_penalty=1.1,
top_k=30,
top_p=0.9,
)
response = self.tokenizer.decode(
output.sequences[0], skip_special_tokens=True)
response = response.strip()
return response
def chat(self, input):
try:
image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
except Exception as e:
return "Image decode error"
msgs = json.loads(input['question'])
input_ids = wrap_question_for_omni_lmm(
msgs, self.image_token_len, self.tokenizer)['input_ids']
input_ids = torch.as_tensor(input_ids)
#print('input_ids', input_ids)
image = self.image_transform(image)
out = self.decode(image, input_ids)
return out
def img2base64(file_name):
with open(file_name, 'rb') as f:
encoded_string = base64.b64encode(f.read())
return encoded_string
class MiniCPMV:
def __init__(self, model_path) -> None:
self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.bfloat16)
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
self.model.eval().cuda()
def chat(self, input):
try:
image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
except Exception as e:
return "Image decode error"
msgs = json.loads(input['question'])
answer, context, _ = self.model.chat(
image=image,
msgs=msgs,
context=None,
tokenizer=self.tokenizer,
sampling=True,
temperature=0.7
)
return answer
class MiniCPMV2_5:
def __init__(self, model_path) -> None:
self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.float16)
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
self.model.eval().cuda()
def chat(self, input):
try:
image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
except Exception as e:
return "Image decode error"
msgs = json.loads(input['question'])
answer = self.model.chat(
image=image,
msgs=msgs,
tokenizer=self.tokenizer,
sampling=True,
temperature=0.7
)
return answer
class MiniCPMV2_6:
def __init__(self, model_path, multi_gpus=False) -> None:
print('torch_version:', torch.__version__)
if multi_gpus: # inference on multi-gpus
from accelerate import load_checkpoint_and_dispatch, init_empty_weights, infer_auto_device_map
with init_empty_weights():
model = AutoModel.from_pretrained(model_path, trust_remote_code=True,
attn_implementation='sdpa', torch_dtype=torch.bfloat16)
device_map = infer_auto_device_map(model, max_memory={0: "10GB", 1: "10GB"},
no_split_module_classes=['SiglipVisionTransformer', 'Qwen2DecoderLayer'])
device_id = device_map["llm.model.embed_tokens"]
device_map["llm.lm_head"] = device_id # first and last layer of llm should be in the same device
device_map["vpm"] = device_id
device_map["resampler"] = device_id
device_id2 = device_map["llm.model.layers.26"]
device_map["llm.model.layers.8"] = device_id2
device_map["llm.model.layers.9"] = device_id2
device_map["llm.model.layers.10"] = device_id2
device_map["llm.model.layers.11"] = device_id2
device_map["llm.model.layers.12"] = device_id2
device_map["llm.model.layers.13"] = device_id2
device_map["llm.model.layers.14"] = device_id2
device_map["llm.model.layers.15"] = device_id2
device_map["llm.model.layers.16"] = device_id2
print(device_map)
self.model = load_checkpoint_and_dispatch(model, model_path, dtype=torch.bfloat16, device_map=device_map)
self.model.eval()
else:
self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True,
attn_implementation='sdpa', torch_dtype=torch.bfloat16)
self.model.eval().cuda()
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def chat(self, input):
image = None
if "image" in input and len(input["image"]) > 10: # legacy API
try:
image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
except Exception as e:
return "Image decode error"
msgs = json.loads(input["question"])
for msg in msgs:
contents = msg.pop('content') # support str or List[Dict]
if isinstance(contents, str):
contents = [contents]
new_cnts = []
for c in contents:
if isinstance(c, dict):
if c['type'] == 'text':
c = c['pairs']
elif c['type'] == 'image':
c = Image.open(io.BytesIO(base64.b64decode(c["pairs"]))).convert('RGB')
else:
raise ValueError("content type only support text and image.")
new_cnts.append(c)
msg['content'] = new_cnts
print(f'msgs: {str(msgs)}')
answer = self.model.chat(
image=image,
msgs=msgs,
tokenizer=self.tokenizer,
)
return answer
class MiniCPMVChat:
def __init__(self, model_path, multi_gpus=False) -> None:
if '12B' in model_path:
self.model = OmniLMM12B(model_path)
elif 'MiniCPM-Llama3-V' in model_path:
self.model = MiniCPMV2_5(model_path)
elif 'MiniCPM-V-2_6' in model_path:
self.model = MiniCPMV2_6(model_path, multi_gpus)
else:
self.model = MiniCPMV(model_path)
def chat(self, input):
return self.model.chat(input)
if __name__ == '__main__':
model_path = 'openbmb/OmniLMM-12B'
chat_model = MiniCPMVChat(model_path)
im_64 = img2base64('./assets/worldmap_ck.jpg')
# first round chat
msgs = [{"role": "user", "content": "What is interesting about this image?"}]
input = {"image": im_64, "question": json.dumps(msgs, ensure_ascii=True)}
answer = chat_model.chat(input)
print(msgs[-1]["content"]+'\n', answer)
# second round chat
msgs.append({"role": "assistant", "content": answer})
msgs.append({"role": "user", "content": "Where is China in the image"})
input = {"image": im_64,"question": json.dumps(msgs, ensure_ascii=True)}
answer = chat_model.chat(input)
print(msgs[-1]["content"]+'\n', answer)
|