Question About Eval_scanrefer

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Introduction

When conducting scanrefer inference, an error may be reported when the inference runs 1/4. This issue can be frustrating, especially when multiple solutions have been tried without resolving the problem. In this article, we will explore the possible causes of this issue and provide potential solutions.

Error Messages

The error messages reported during the inference process are as follows:

Traceback (most recent call last):
  File "/data/Video-3D-LLM/llava/eval/model_scanrefer.py", line 363, in <module>
    ret = ray.get(features)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/auto_init_hook.py", line 21, in auto_init_wrapper
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/worker.py", line 2782, in get
    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/worker.py", line 929, in get_objects
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(RuntimeError): ray::eval_model() (pid=790928, ip=172.17.0.3)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 431, in __repr__
    return torch._tensor_str._str(self, tensor_contents=tensor_contents)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 664, in _str
    return _str_intern(self, tensor_contents=tensor_contents)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 595, in _str_intern
    tensor_str = _tensor_str(self, indent)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 347, in _tensor_str
    formatter = _Formatter(get_summarized_data(self) if summarize else self)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 137, in __init__
    nonzero_finite_vals = torch.masked_select(
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.


During handling of the above exception, another exception occurred:

ray::eval_model() (pid=790928, ip=172.17.0.3)
  File "/data/Video-3D-LLM/llava/eval/model_scanrefer.py", line 301, in eval_model
    _, pred_id = torch.max(scores[:-1], dim=0)      # remove the zero-target
RuntimeError: CUDA error: device-side triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.`

Reasoning on CPU

When reasoning only on CPU, the following errors are encountered:

/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:896: 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( 2025-05-08 17:04:54,480 INFO worker.py:1852 -- Started a local Ray instance. /opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, /opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide ret = ret.dtype.type(ret / rcount) 
time: nan

Potential Causes

Based on the error messages, the potential causes of this issue are:

  1. Data and Model Dtype Issue: The data and model dtype may not be compatible, leading to errors during inference.
  2. Input Data Issue: The input data may be empty or corrupted, causing errors during inference.
  3. CUDA Error: The CUDA error may be triggered due to device-side assertions or kernel errors.

Solutions

To resolve this issue, the following solutions can be tried:

  1. Set Both Model and Data to BF16: Set both the model and data to BF16 to ensure compatibility.
  2. Check Input Data: Check the input data for emptiness or corruption.
  3. Reasoning on Single GPU: Reasoning on a single GPU may help to identify the issue.
  4. Pass CUDA_LAUNCH_BLOCKING=1: Pass CUDA_LAUNCH_BLOCKING=1 to enable device-side assertions.
  5. Compile with TORCH_USE_CUDA_DSA: Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Conclusion

In conclusion, the issue with eval_scanrefer inference can be caused by various factors, including data and model dtype issues, input data issues, and CUDA errors. By trying the above solutions, you can resolve this issue and ensure successful inference.

Future Work

Future work can include:

  1. Investigating CUDA Errors: Investigate CUDA errors in more detail to identify the root cause.
  2. Optimizing Model and Data: Optimize the model and data to ensure compatibility and reduce errors.
  3. Implementing Error Handling: Implement error handling mechanisms to catch and handle errors during inference.

Introduction

In our previous article, we explored the potential causes of issues with eval_scanrefer inference and provided solutions to resolve these issues. In this article, we will provide a Q&A section to address common questions and concerns related to eval_scanrefer inference.

Q: What are the common causes of issues with eval_scanrefer inference?

A: The common causes of issues with eval_scanrefer inference include data and model dtype issues, input data issues, and CUDA errors.

Q: How can I resolve data and model dtype issues?

A: To resolve data and model dtype issues, you can set both the model and data to BF16 to ensure compatibility.

Q: What are the potential causes of CUDA errors?

A: The potential causes of CUDA errors include device-side assertions and kernel errors.

Q: How can I resolve CUDA errors?

A: To resolve CUDA errors, you can pass CUDA_LAUNCH_BLOCKING=1 to enable device-side assertions and compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Q: What are the potential causes of input data issues?

A: The potential causes of input data issues include emptiness or corruption of the input data.

Q: How can I resolve input data issues?

A: To resolve input data issues, you can check the input data for emptiness or corruption and ensure that it is properly formatted.

Q: What are the benefits of reasoning on a single GPU?

A: Reasoning on a single GPU can help to identify the issue and reduce the complexity of the problem.

Q: How can I implement error handling mechanisms for eval_scanrefer inference?

A: You can implement error handling mechanisms by catching and handling errors during inference using try-except blocks.

Q: What are the potential benefits of optimizing the model and data for eval_scanrefer inference?

A: Optimizing the model and data can improve the performance and reduce the errors during inference.

Q: How can I optimize the model and data for eval_scanrefer inference?

A: You can optimize the model and data by reducing the complexity of the model, using data augmentation techniques, and ensuring that the data is properly formatted.

Conclusion

In conclusion, the Q&A section provides answers to common questions and concerns related to eval_scanrefer inference. By following the solutions and tips provided in this article, you can resolve issues with eval_scanrefer inference and ensure successful inference.

Future Work

Future work can include:

  1. Investigating CUDA Errors: Investigate CUDA errors in more detail to identify the root cause.
  2. Optimizing Model and Data: Optimize the model and data to ensure compatibility and reduce errors.
  3. Implementing Error Handling: Implement error handling mechanisms to catch and handle errors during inference.

By following these steps, you can resolve the issue with eval_scanrefer inference and ensure successful inference.