-
Notifications
You must be signed in to change notification settings - Fork 7k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Remove extraneous device conversions in raft #8500
base: main
Are you sure you want to change the base?
Conversation
By default, these calls were allocating tensors on the CPU, to then convert to the device used in the rest of the model. It's faster to allocate them directly on the correct device.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/vision/8500
Note: Links to docs will display an error until the docs builds have been completed. ❌ 3 New Failures, 12 Unrelated FailuresAs of commit 0714fdb with merge base 3e60dbd (): NEW FAILURES - The following jobs have failed:
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. |
Hi @vbarrielle! Thank you for your pull request and welcome to our community. Action RequiredIn order to merge any pull request (code, docs, etc.), we require contributors to sign our Contributor License Agreement, and we don't seem to have one on file for you. ProcessIn order for us to review and merge your suggested changes, please sign at https://code.facebook.com/cla. If you are contributing on behalf of someone else (eg your employer), the individual CLA may not be sufficient and your employer may need to sign the corporate CLA. Once the CLA is signed, our tooling will perform checks and validations. Afterwards, the pull request will be tagged with If you have received this in error or have any questions, please contact us at [email protected]. Thanks! |
Thanks for the PR @vbarrielle . PR looks good. Please make sure to sign the CLA for us to be able to merge your contribution! |
By default, these calls were allocating tensors on the CPU, to then convert to the device used in the rest of the model. It's faster to allocate them directly on the correct device.
I have measured these changes on my machine to reduce the runtime of the forward pass by 25%.