Inference print logs with warning or errors?

[AlphaNext]

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.

[gyxxyg]

我也遇到过这个问题，这个是因为inference.py没传attention mask和pad token id。参考videollama2/__init__.py里面的infer函数可以改成：

    input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_index, return_tensors='pt').unsqueeze(0).cuda()
    attention_masks = input_ids.ne(tokenizer.pad_token_id).long().cuda()

    # 3. generate response according to visual signals and prompts. 
    stop_str = conv.sep if conv.sep_style in [SeparatorStyle.SINGLE] else conv.sep2
    # keywords = ["<s>", "</s>"]
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
    do_sample=True

    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            attention_mask=attention_masks,
            images_or_videos=tensor,
            modal_list=modals,
            do_sample=do_sample,
            temperature=0.2 if do_sample else 0.0,
            max_new_tokens=1024,
            use_cache=True,
            stopping_criteria=[stopping_criteria],
            pad_token_id=tokenizer.eos_token_id,
        )

我自己这边测试是有效的