推理过程卡死，没有返回也不报错

[Zurry59]

using transformers chat, demo如下：from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

default: Load the model on the available device(s)

model = Qwen2VLForConditionalGeneration.from_pretrained(
"/home/azurengine/lzz/llm/qwen2_VL/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
)

We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.

model = Qwen2VLForConditionalGeneration.from_pretrained(

"Qwen/Qwen2-VL-7B-Instruct",

torch_dtype=torch.bfloat16,

attn_implementation="flash_attention_2",

device_map="auto",

)

default processer

processor = AutoProcessor.from_pretrained("/home/azurengine/lzz/llm/qwen2_VL/Qwen2-VL-7B-Instruct")

The default range for the number of visual tokens per image in the model is 4-16384.

You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.

min_pixels = 2562828

max_pixels = 12802828

processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)

messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]

Preparation for inference

text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=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("cpu")

Inference: Generation of the output

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)