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[c10d] Ensure collectives are called with the same dtype for all tensor params. #84664
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/84664
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Thanks for addressing the issue. This would be a great QoL update |
…or params. While passing tensors with different dtypes don't crash, they don't produce sensible results. We see data tearing instead of casting. It's not clear we want to support transparent casting so, for now, we fail when such input is presented. Fixes pytorch#84525
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LGTM overall, but I guess it is technically BC breaking?
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…or params. (#84664) While passing tensors with different dtypes don't crash, they don't produce sensible results. We see data tearing instead of casting. It's not clear we want to support transparent casting so, for now, we fail when such input is presented. Fixes #84525 Fixes #ISSUE_NUMBER Pull Request resolved: #84664 Approved by: https://github.com/rohan-varma
…or params. (#84664) While passing tensors with different dtypes don't crash, they don't produce sensible results. We see data tearing instead of casting. It's not clear we want to support transparent casting so, for now, we fail when such input is presented. Fixes #84525 Fixes #ISSUE_NUMBER Pull Request resolved: #84664 Approved by: https://github.com/rohan-varma
…or params. (pytorch#84664) While passing tensors with different dtypes don't crash, they don't produce sensible results. We see data tearing instead of casting. It's not clear we want to support transparent casting so, for now, we fail when such input is presented. Fixes pytorch#84525 Fixes #ISSUE_NUMBER Pull Request resolved: pytorch#84664 Approved by: https://github.com/rohan-varma
While passing tensors with different dtypes don't crash, they don't produce sensible results.
We see data tearing instead of casting.
It's not clear we want to support transparent casting so, for now, we fail when such input is presented.
Fixes #84525
Fixes #ISSUE_NUMBER