Module Numpy Conflict Error

[jongwoopark7978]

Hi team,

I have been successfully running llava-next video, but it suddenly stopped working after I pulled the latest changes from the repo two days ago. I have been trying to resolve the issue but haven't been able to fix it yet. When I run video_demo.py with the command below, I encounter the following error. Apologies for the lengthy output, but I have included everything to get some guidance on what to do next.

CUDA_VISIBLE_DEVICES=0 python playground/demo/old/video_demo.py --video_path playground/demo/xU25MMA2N4aVtYay.mp4 --output_dir /efs/users/jongwp/ModerationScienceVideoUnderstanding/LLaVA-NeXT/playground/result --model-path lmms-lab/LLaVA-NeXT-Video-7B-DPO --conv-mode vicuna_v1 --for_get_frames_num 4 --output_name testest

 module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/efs/users/jongwp/ModerationScienceVideoUnderstanding/LLaVA-NeXT/playground/demo/old/video_demo_new.py", line 2, in <module>
    import torch
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/torch/__init__.py", line 1382, in <module>
    from .functional import *  # noqa: F403
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/torch/functional.py", line 7, in <module>
    import torch.nn.functional as F
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/torch/nn/__init__.py", line 1, in <module>
    from .modules import *  # noqa: F403
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/torch/nn/modules/__init__.py", line 35, in <module>
    from .transformer import TransformerEncoder, TransformerDecoder, \
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 20, in <module>
    device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),
/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.)
  device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),
/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/transformers/models/llava/configuration_llava.py:103: FutureWarning: The `vocab_size` argument is deprecated and will be removed in v4.42, since it can be inferred from the `text_config`. Passing this argument has no effect
  warnings.warn(
Overwriting config with {'mm_resampler_type': 'spatial_pool', 'mm_spatial_pool_stride': 4, 'mm_spatial_pool_out_channels': 1024, 'mm_spatial_pool_mode': 'average', 'patchify_video_feature': False}
/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/transformers/models/llava/configuration_llava.py:143: FutureWarning: The `vocab_size` attribute is deprecated and will be removed in v4.42, Please use `text_config.vocab_size` instead.
  warnings.warn(
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.30s/it]
Some weights of the model checkpoint at lmms-lab/LLaVA-NeXT-Video-7B-DPO were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.embeddings.patch_embedding.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.position_embedding.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc2.bias', 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- 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).
/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()
Traceback (most recent call last):
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 182, in convert_to_tensors
    tensor = as_tensor(value)
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 141, in as_tensor
    return torch.tensor(value)
RuntimeError: Could not infer dtype of numpy.float32

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/efs/users/jongwp/ModerationScienceVideoUnderstanding/LLaVA-NeXT/playground/demo/old/video_demo_new.py", line 183, in <module>
    run_inference(args)
  File "/efs/users/jongwp/ModerationScienceVideoUnderstanding/LLaVA-NeXT/playground/demo/old/video_demo_new.py", line 131, in run_inference
    video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].half().cuda()
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/transformers/models/clip/image_processing_clip.py", line 346, in preprocess
    return BatchFeature(data=data, tensor_type=return_tensors)
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 78, in __init__
    self.convert_to_tensors(tensor_type=tensor_type)
  File "/efs/users/jongwp/envs/lavnext2/lib/python3.10/site-packages/transformers/feature_extraction_utils.py", line 188, in convert_to_tensors
    raise ValueError(
ValueError: Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.

[jongwoopark7978]

I downgraded the numpy version to 1.22 and it works again.