File size: 3,888 Bytes
f8ba0eb |
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 |
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor, Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
from transformers import TextIteratorStreamer
from threading import Thread
import logging
import torch
import time
import pynvml
class Qwen2VL:
def __init__(self, model_id):
self.model_id = model_id
if "2.5" in model_id:
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_id, torch_dtype="float16", device_map="auto"
)
else:
self.model = Qwen2VLForConditionalGeneration.from_pretrained(
model_id, torch_dtype="float16", device_map="auto"
)
self.processor = AutoProcessor.from_pretrained(model_id)
self.handle = None
if torch.cuda.is_available():
try:
pynvml.nvmlInit()
device_id = next(self.model.parameters()).device.index
self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
except Exception as e:
logging.error(f"Failed to initialize NVML: {e}")
def __del__(self):
if hasattr(self, 'handle') and self.handle:
try:
pynvml.nvmlShutdown()
except:
pass
def generate(self, video, prompt):
start_time = time.time()
# Preparation for inference
video_paths = [f"file://{path}" for path in video]
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": video_paths,
"resized_height": 280,
"resized_width": 420,
},
{"type": "text", "text": prompt},
],
}
]
text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
logging.info(f"Prompt token length: {len(inputs.input_ids[0])}")
streamer = TextIteratorStreamer(self.processor, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=256
)
thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
thread.start()
full_response = ""
print("Response: ", end="")
first_token_time = None
for new_text in streamer:
if first_token_time is None:
first_token_time = time.time()
full_response += new_text
print(new_text, end="", flush=True)
print()
thread.join()
end_time = time.time()
if first_token_time is not None:
generation_time = end_time - first_token_time
else:
generation_time = 0
num_generated_tokens = len(self.processor.tokenizer(full_response).input_ids)
tokens_per_second = num_generated_tokens / generation_time if generation_time > 0 else 0
peak_memory_mb = 0
if self.handle:
mem_info = pynvml.nvmlDeviceGetMemoryInfo(self.handle)
peak_memory_mb = mem_info.used / (1024 * 1024)
return {
"response": full_response,
"tokens_per_second": tokens_per_second,
"peak_gpu_memory_mb": peak_memory_mb,
"num_generated_tokens": num_generated_tokens,
}
|