LLaVA-NeXT demo code froze while running

[FrankFcc]

Trying to deploy and run demo on a 4 A6000 cluster but it seemed that the runtime froze without any exceptions... Could there be any possible problems? Sorry for asking a naive question and thanks for help...

Here is the cmd output: `bash scripts/video/demo/video_demo.sh lmms-lab/LLaVA-NeXT-Video-7B-DPO vicuna_v1 32 2 average after no_token True playground/demo/xU25MMA2N4aVtYay.mp4 Overwriting config with {'mm_spatial_pool_mode': 'average', 'mm_spatial_pool_stride': 2, 'mm_pooling_position': 'after', 'mm_newline_position': 'no_token', 'max_sequence_length': 8192, 'tokenizer_model_max_length': 8192} Delay load: True Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.23it/s] Some weights of the model checkpoint at lmms-lab/LLaVA-NeXT-Video-7B-DPO were not used when initializing LlavaLlamaForCausalLM: ['model.vision_resampler.mm_projector.0.bias', 'model.vision_resampler.mm_projector.0.weight', 'model.vision_resampler.mm_projector.2.bias', 'model.vision_resampler.mm_projector.2.weight', 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'model.vision_tower.vision_tower.vision_model.post_layernorm.weight', 'model.vision_tower.vision_tower.vision_model.pre_layrnorm.bias', 'model.vision_tower.vision_tower.vision_model.pre_layrnorm.weight']

• This IS expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).`

[ZhangYuanhan-AI]

Don't worry about this warning, it is because of the "Delay load: True", which forces model to load vision model weights later.

[FrankFcc]

Don't worry about this warning, it is because of the "Delay load: True", which forces model to load vision model weights later.

Thanks for the quick reply and I really appreciate that. I also assume the process is normal. However, the demo code runtime continued for nearly 30 mins the last printout and didn't return anything in pred.json, so I try to debug what's going on with the process.

[FrankFcc]

It seems that the problem arises when

output_ids = model.generate(inputs=input_ids, images=video, attention_mask=attention_masks, modalities="video", do_sample=False, temperature=0.0, max_new_tokens=1024, top_p=0.1,num_beams=1,use_cache=True) #, stopping_criteria=[stopping_criteria])

in video_demo.py line 229 while dropping in the while loop

while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):

It continues remain in this while loop by using greedy search

if generation_mode == GenerationMode.GREEDY_SEARCH:
            # 11. run greedy search
            print("Run greedy search: ")
            result = self._greedy_search(
                input_ids,
                logits_processor=prepared_logits_processor,
                stopping_criteria=prepared_stopping_criteria,
                pad_token_id=generation_config.pad_token_id,
                eos_token_id=generation_config.eos_token_id,
                output_scores=generation_config.output_scores,
                output_logits=generation_config.output_logits,
                return_dict_in_generate=generation_config.return_dict_in_generate,
                synced_gpus=synced_gpus,
                streamer=streamer,
                **model_kwargs,
            )