File size: 6,870 Bytes
b5beb60 |
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 |
import re
import logging
import base64
from io import BytesIO
import os
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from vllm import LLM, SamplingParams
def encode_image_to_base64(image):
buffered = BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
return img_str
def create_message(sample):
query = sample['query']
all_contents = []
matches = re.findall(r"<(image_\d+)>", query)
split_text = re.split(r"<image_\d+>", query)
for i, fragment in enumerate(split_text):
if fragment.strip():
all_contents.extend([
{"type": "text", "text": fragment}
])
if i < len(matches):
if sample[matches[i]]:
img_base64 = encode_image_to_base64(sample[matches[i]])
all_contents.extend([
{
"type": "image",
"image": f"data:image/png;base64,{img_base64}"
}
])
else:
logging.error(
f"The image token {matches[i]} is in the query, but there is no corresponding image provided by the data")
messages = [
{
"role": "user",
"content": all_contents
}
]
return messages
class Qwen_Model:
def __init__(
self,
model_path,
temperature=0,
max_tokens=1024
):
self.model_path = model_path
self.temperature = temperature
self.max_tokens = max_tokens
self.model = Qwen2VLForConditionalGeneration.from_pretrained(self.model_path, torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto", )
self.processor = AutoProcessor.from_pretrained(self.model_path)
def get_response(self, sample):
model = self.model
processor = self.processor
try:
messages = create_message(sample)
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, add_vision_id=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=self.max_tokens, temperature=self.temperature)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
response = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return response[0]
except Exception as e:
print(e)
return None
class Qwen2_5_Model:
def __init__(
self,
model_path="Qwen/Qwen2.5-VL-72B-Instruct",
temperature=0,
max_tokens=1024
):
self.model_path = model_path
self.temperature = temperature
self.max_tokens = max_tokens
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
self.model_path,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto"
)
self.processor = AutoProcessor.from_pretrained(self.model_path)
def get_response(self, sample):
model = self.model
processor = self.processor
try:
messages = create_message(sample)
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, add_vision_id=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=self.max_tokens, temperature=self.temperature)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
response = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return response[0]
except Exception as e:
print(e)
return None
class Qwen_vllm_Model:
def __init__(
self,
model_path,
greedy=1,
max_tokens=1024,
parallel=1,
seed=42,
device=0
):
self.model_path = model_path
self.max_tokens = max_tokens
self.model = LLM(
model=model_path,
enable_prefix_caching=True,
trust_remote_code=True,
limit_mm_per_prompt={"image": 8, "video": 1},
tensor_parallel_size=parallel,
device=device
)
self.sampling_params = SamplingParams(
temperature=0 if greedy else 1,
top_p=0.001 if greedy else 1,
top_k=1 if greedy else -1,
repetition_penalty=1,
max_tokens=max_tokens,
stop_token_ids=[],
seed=seed
)
self.processor = AutoProcessor.from_pretrained(self.model_path)
def get_response(self, sample):
try:
messages = create_message(sample)
text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, _ = process_vision_info([messages])
inputs = {
"prompt": text,
"multi_modal_data": {'image': image_inputs},
}
out = self.model.generate(
inputs,
sampling_params=self.sampling_params,
use_tqdm=False
)
response = out[0].outputs[0].text
return response
except Exception as e:
print(e)
return None |