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[BugFix] Fix invalid CUDA ID error when loading Bounded variables across devices #2421

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merged 2 commits into from
Sep 5, 2024

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cbhua
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@cbhua cbhua commented Sep 4, 2024

Description

This pull request resolves an invalid CUDA ID error that occurs when transferring a Bounded variable between servers with different numbers of GPUs.

In the current implementation, when changing the device of a Bounded variable, there is:

low=self.space.low.to(dest),
high=self.space.high.to(dest),

This operation first attempts to move self.space._low to its self.space.device before transferring to the target device (dest):

@property
def low(self):
return self._low.to(self.device)
@property
def high(self):
return self._high.to(self.device)

This process leads to errors when a variable previously on cuda:7 (in an 8 GPU server) is loaded on a server with only one GPU, as it incorrectly attempts to access cuda:7.

Motivation and Context

The issue was identified when a model was trained and saved on a multi-GPU cluster and subsequently loaded on a local server equipped with fewer GPUs. The model’s saved state includes device information specific to the original multi-GPU environment. When attempting to assign the model to a device available on the current server, the discrepancy in device IDs between the environments leads to this bug.

This PR fixes #2420 .

  • I have raised an issue to propose this change (required for new features and bug fixes)

Types of changes

  • Bug fix (non-breaking change which fixes an issue)

To resolve this bug, I have adjusted the approach to device assignment. Instead of calling the saved device information from the previous cluster, we directly update the device information to match the current server’s available hardware.

In the meantime, I think there is repeat function code here:

@low.setter
def low(self, value):
self.device = value.device
self._low = value.cpu()
@high.setter
def high(self, value):
self.device = value.device
self._high = value.cpu()
@low.setter
def low(self, value):
self.device = value.device
self._low = value.cpu()
@high.setter
def high(self, value):
self.device = value.device
self._high = value.cpu()

Checklist

  • I have read the CONTRIBUTION guide (required)
  • I have updated the tests accordingly (required for a bug fix or a new feature).

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pytorch-bot bot commented Sep 4, 2024

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/rl/2421

Note: Links to docs will display an error until the docs builds have been completed.

❌ 3 New Failures, 1 Cancelled Job, 2 Pending, 6 Unrelated Failures

As of commit 9b22619 with merge base df4fa78 (image):

NEW FAILURES - The following jobs have failed:

CANCELLED JOB - The following job was cancelled. Please retry:

FLAKY - The following jobs failed but were likely due to flakiness present on trunk:

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

This comment was automatically generated by Dr. CI and updates every 15 minutes.

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@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 4, 2024
@cbhua cbhua marked this pull request as draft September 5, 2024 06:34
@vmoens vmoens added the bug Something isn't working label Sep 5, 2024
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LGTM thanks!

@vmoens
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vmoens commented Sep 5, 2024

Feel free to mark is as ready for review for me to merge it

@cbhua
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cbhua commented Sep 5, 2024

Thanks for your review! @vmoens, I saw some unpassed pytest checks, so I made this PR draft. It seems those are from GitHub action settings or dependency packages, so should be fine. I will make it ready to merge!

@cbhua cbhua marked this pull request as ready for review September 5, 2024 10:37
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vmoens commented Sep 5, 2024

Errors in CI are not related to this PR, happy to merge it

@vmoens vmoens merged commit 57f0580 into pytorch:main Sep 5, 2024
64 of 74 checks passed
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[BUG] Invalid CUDA ID error when loading Bounded variables across devices
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