Inference print logs with warning or errors?

前向使用的代码如下，只改了questions和视频路径paths，具体的questions是："Generate a brief and accurate for this video"
```
import torch
import transformers

import sys
sys.path.append('./')
from videollama2.conversation import conv_templates
from videollama2.constants import DEFAULT_MMODAL_TOKEN, MMODAL_TOKEN_INDEX
from videollama2.mm_utils import get_model_name_from_path, tokenizer_MMODAL_token, process_video, process_image
from videollama2.model.builder import load_pretrained_model

def inference():
# Video Inference
paths = ['assets/cat_and_chicken.mp4']
questions = ['What animals are in the video, what are they doing, and how does the video feel?']
# Reply:
# The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it.
modal_list = ['video']

# Image Inference
paths = ['assets/sora.png']
questions = ['What is the woman wearing, what is she doing, and how does the image feel?']
# Reply:
# The woman in the image is wearing a black coat and sunglasses, and she is walking down a rain-soaked city street. The image feels vibrant and lively, with the bright city lights reflecting off the wet pavement, creating a visually appealing atmosphere. The woman's presence adds a sense of style and confidence to the scene, as she navigates the bustling urban environment.
modal_list = ['image']

# 1. Initialize the model.
model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B'
# Base model inference (only need to replace model_path)
# model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-Base'
model_name = get_model_name_from_path(model_path)
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name)
model = model.to('cuda:0')
conv_mode = 'llama_2'

# 2. Visual preprocess (load & transform image or video).
if modal_list[0] == 'video':
tensor = process_video(paths[0], processor, model.config.image_aspect_ratio).to(dtype=torch.float16, device='cuda', non_blocking=True)
default_mm_token = DEFAULT_MMODAL_TOKEN["VIDEO"]
modal_token_index = MMODAL_TOKEN_INDEX["VIDEO"]
else:
tensor = process_image(paths[0], processor, model.config.image_aspect_ratio)[0].to(dtype=torch.float16, device='cuda', non_blocking=True)
default_mm_token = DEFAULT_MMODAL_TOKEN["IMAGE"]
modal_token_index = MMODAL_TOKEN_INDEX["IMAGE"]
tensor = [tensor]

# 3. text preprocess (tag process & generate prompt).
question = default_mm_token + "\n" + questions[0]
conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_token_index, return_tensors='pt').unsqueeze(0).to('cuda:0')

with torch.inference_mode():
output_ids = model.generate(
input_ids,
images_or_videos=tensor,
modal_list=modal_list,
do_sample=True,
temperature=0.2,
max_new_tokens=1024,
use_cache=True,
)

outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print(outputs[0])

if __name__ == "__main__":
inference()
```

频繁输出的可疑日志如下：

```
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
```