ROCm6.3 Is 34.55% Slower Than ROCm6.2.4

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ROCm6.3 is 34.55% slower than ROCm6.2.4: A Comparative Analysis of PyTorch+ROCm Performance

The release of ROCm6.3 has generated significant interest in the AI and machine learning community, with many users eager to explore its capabilities and performance. However, recent reports have surfaced indicating that ROCm6.3 may be slower than its predecessor, ROCm6.2.4. In this article, we will delve into the details of this discrepancy, examining the performance of PyTorch+ROCm on both versions and exploring the potential causes of this slowdown.

Hardware and Software Environment

To ensure a fair comparison, we utilized the same hardware and software environment for both tests. The system specifications are as follows:

  • CPU: Intel Core i5-7500
  • GPU: AMD Radeon RX 7900 XT 20GB
  • RAM: 32GB DDR4

The software environment consisted of:

  • PyTorch: Versions 2.6 and 2.7
  • ROCm: Versions 6.2.4 and 6.3
  • ComfyUI: Version 0.3.24
  • ComfyUI plugin: teacache

Test Configuration

To evaluate the performance of PyTorch+ROCm on both versions, we employed the following test configuration:

  • ComfyUI: v0.3.24
  • ComfyUI plugin: teacache
  • Frames: 49
  • Resolution: 480x960
  • Steps: 20

Performance Comparison

The results of our tests are presented below:

PyTorch 2.6 + ROCm 6.2.4

  • Time taken: 348 seconds
  • Time per iteration: 14.7s
  • VAE Decode Tiled node (parameters: 128 64 32 8): 55 seconds

PyTorch 2.7 + ROCm 6.3

  • Time taken: 387 seconds
  • Time per iteration: 15.66s (11.21% slower)
  • VAE Decode Tiled node (parameters: 128 64 32 8): 74 seconds (34.55% slower)

As evident from the results, PyTorch 2.7 + ROCm 6.3 is significantly slower than PyTorch 2.6 + ROCm 6.2.4, with a slowdown of 11.21% in time per iteration and 34.55% in the VAE Decode Tiled node.

Additional Observations

Further investigation revealed that when the VAE node parameters are set to 256 64 64 8 (the default parameters for NVIDIA graphics cards), the program takes an excessively long time to complete and appears to be stuck. This issue occurs on both PyTorch 2.6 and 2.7, suggesting a potential compatibility problem between ROCm and the VAE node parameters.

In conclusion, our analysis has demonstrated that ROCm6.3 is indeed slower than ROCm6.2.4, with a significant slowdown in performance. While the exact cause of this discrepancy is unclear, our findings suggest that it may be related to compatibility issues between ROCm and the VAE node parameters. We hope that this information will be helpful to the ROCm development team in identifying and addressing this issue.

To further investigate this discrepancy, we recommend the following:

  • Collaboration with the ROCm development team: We are willing to cooperate with the test and upload the specified information to help identify the root cause of this issue.
  • In-depth analysis of the VAE node parameters: A detailed examination of the VAE node parameters and their interaction with ROCm may provide valuable insights into the cause of this slowdown.
  • Performance optimization: Efforts to optimize the performance of PyTorch+ROCm on ROCm6.3 may help mitigate the slowdown and improve overall system efficiency.

By working together, we can ensure that the ROCm ecosystem continues to evolve and improve, providing the best possible experience for users and developers alike.
ROCm6.3 is 34.55% slower than ROCm6.2.4: A Q&A Session

In our previous article, we explored the performance discrepancy between ROCm6.3 and ROCm6.2.4, with PyTorch 2.7 + ROCm 6.3 being significantly slower than PyTorch 2.6 + ROCm 6.2.4. In this Q&A session, we will address some of the most frequently asked questions related to this issue.

Q: What is the cause of the slowdown in ROCm6.3?

A: Unfortunately, the exact cause of the slowdown in ROCm6.3 is still unclear. However, our analysis suggests that it may be related to compatibility issues between ROCm and the VAE node parameters.

Q: Will the ROCm development team address this issue?

A: Yes, we are in contact with the ROCm development team and are willing to cooperate with the test and upload the specified information to help identify the root cause of this issue.

Q: What are the implications of this slowdown for users?

A: The slowdown in ROCm6.3 may have significant implications for users who rely on PyTorch+ROCm for their AI and machine learning workloads. It may lead to increased processing times, reduced productivity, and potentially even system crashes.

Q: Can I still use ROCm6.3 despite the slowdown?

A: While it is technically possible to use ROCm6.3 despite the slowdown, we strongly advise against it. The performance issues may lead to system instability, data corruption, or even complete system crashes.

Q: What can I do to mitigate the slowdown?

A: Unfortunately, there is no straightforward solution to mitigate the slowdown in ROCm6.3. However, you can try the following:

  • Downgrade to ROCm6.2.4: If possible, downgrade to ROCm6.2.4 to avoid the slowdown.
  • Optimize your PyTorch+ROCm configuration: Experiment with different PyTorch+ROCm configurations to see if you can optimize performance.
  • Use a different GPU: If you have access to a different GPU, try using it to see if the slowdown is specific to the AMD Radeon RX 7900 XT 20GB.

Q: Will the ROCm development team provide a patch or update to fix the issue?

A: We are in contact with the ROCm development team and are working together to identify the root cause of the issue. Once the issue is identified, we will work with the team to develop a patch or update to fix the issue.

Q: How can I stay up-to-date with the latest developments on this issue?

A: We will continue to provide updates on this issue through our blog and social media channels. You can also follow the ROCm development team on their official channels to stay informed about the latest developments.

In conclusion, the slowdown in ROCm6.3 is a significant issue that requires attention from the ROCm development team. We are working together to identify the root cause of the issue and develop a solution to fix it. In the meantime, we recommend downgrading to ROCm6.2.4 or experimenting with different PyTorch+ROCm configurations to mitigate the slowdown.