Why would IExecutionContext:: enqueue(V2/V3) return false?

AI & Data Science Deep Learning (Training & Inference) TensorRT
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Description

We successfully run inference with our model and observe some stability issues using the configuration mentioned below. After hours / days of runtime IExecutionContext:: enqueue(V2/V3) suddenly starts returning false and does not recover any more. Before digging deeper into this, what are the causes of the “enqueue” methods returning false in the first place? The documentation does not mention what this actually means and how such situation should be handled.

Thanks.

Environment

TensorRT Version: 8.6.1.6
GPU Type: Ada Lovelace A4500
Nvidia Driver Version: December 2024
CUDA Version: 11.8
CUDNN Version: 8.9.1.23
Operating System + Version: Windows Server 2022

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When the IExecutionContext::enqueue method returns false during model inference operations, it signals that there was an issue with enqueuing the inference task. Here are potential causes for this occurrence and suggestions on how to handle such situations:

Possible Causes:

  1. Resource Availability:

    • Insufficient resources such as GPU memory or CUDA stream resources could prevent the inference from being enqueued.
    • Solution: Monitor GPU memory utilization and other system resources to ensure availability. Optimize usage patterns if resources are constrained.

  2. Synchronization Issues:

    • Long delays in CUDA kernel execution may cause synchronization bottlenecks, preventing timely enqueuing.
    • Solution: Enhance synchronization strategies and CUDA stream handling to alleviate latency issues.

  3. Input Data Problems:

    • Incorrect dimensions, data type mismatches, or empty input tensors can lead to enqueue failures.
    • Solution: Validate that the input data is correctly formatted and conforms to the expected specifications of the model.

  4. Error Handling:

    • Lack of robust error handling can exacerbate issues when enqueue fails.
    • Solution: Implement comprehensive error logging and handling strategies. Consider retrying the operation with exponential back-off or reverting to a stable state if failures persist.

  5. Debugging and Logging:

    • Insufficient logs may hinder diagnosing the underlying issue causing the enqueue failures.
    • Solution: Integrate detailed logging to track the execution flow and identify where failures occur.

  6. Consult Documentation:

    • The TensorRT documentation may outline additional best practices or common pitfalls related to the enqueue method.
    • Solution: Refer to the official TensorRT documentation for insights on expected behavior and guidance for best practices.

By addressing these causes and implementing the suggested solutions, you can better manage situations where the IExecutionContext::enqueue method fails, ultimately leading to more stable inference operations over extended runtimes.

5

Hi AakankshaS,
thanks for your valuable hints. However, to the best of our knowledge, no of the causes you mentioned should apply in our case.

For example, all resources like streams or memory are allocated before the inference loop is entered, so there should be no way of running out of memory or streams (confirmed by regular resource checks). Likewise, input tensors are created before entering the inference loop, so no dimension mismatch can happen.

We used the CUDA Compute Sanitizer to confirm the inference loop is healthy. We also don’t see any other errors coming from CUDA or TRT functions until executeV3 returns false after some days of runtime without any prior errors anywhere.

We’ve upgraded CUDA to 12.6, TensorRT to 10.7 (and cuDNN to 9.6) and used the latest driver. However, after some days of runtime the same situation arises.

Finally, the server runs 2x GPU A4500 and the problem arises in both (otherwise independent) inference loops (so on both GPUs) in the exact same second which, in my eyes, somehow points at the driver.

Are there any other tools / strategies / ways of narrowing down the possible causes?

Thank you.