[dtensor] fix simplefsdp mixed-precision training bugs #154975
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/154975
Note: Links to docs will display an error until the docs builds have been completed. ⏳ 1 Pending, 1 Unrelated FailureAs of commit c172c07 with merge base a7e496a ( UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:
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qingyi-yan
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Jun 3, 2025
This is a follow-up on the previous dtensor redistribute PR: pytorch#150740, which enables SimpleFSDP's mixed-precision training. In the most recent integration in TorchTitan: pytorch/torchtitan#1250, we found some discrepancies between SimpleFSDP's `fully_shard` and `replicate` modes when MPT is enabled. After debugging, I found the problem is in dtensor redistribute --`local_tensor` is taken out again from the original `input`. Thus, the dtensor used for communication has its original precision instead of using `forward_dtype`. This PR fixes this issue and corrects previously added test cases. After fixing the bug, the loss curves of `fully_shard` and `replicate` mode match perfectly.  Pull Request resolved: pytorch#154975 Approved by: https://github.com/tianyu-l
iupaikov-amd
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Jun 4, 2025
This is a follow-up on the previous dtensor redistribute PR: pytorch#150740, which enables SimpleFSDP's mixed-precision training. In the most recent integration in TorchTitan: pytorch/torchtitan#1250, we found some discrepancies between SimpleFSDP's `fully_shard` and `replicate` modes when MPT is enabled. After debugging, I found the problem is in dtensor redistribute --`local_tensor` is taken out again from the original `input`. Thus, the dtensor used for communication has its original precision instead of using `forward_dtype`. This PR fixes this issue and corrects previously added test cases. After fixing the bug, the loss curves of `fully_shard` and `replicate` mode match perfectly.  Pull Request resolved: pytorch#154975 Approved by: https://github.com/tianyu-l
angelayi
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Jun 5, 2025
This is a follow-up on the previous dtensor redistribute PR: pytorch#150740, which enables SimpleFSDP's mixed-precision training. In the most recent integration in TorchTitan: pytorch/torchtitan#1250, we found some discrepancies between SimpleFSDP's `fully_shard` and `replicate` modes when MPT is enabled. After debugging, I found the problem is in dtensor redistribute --`local_tensor` is taken out again from the original `input`. Thus, the dtensor used for communication has its original precision instead of using `forward_dtype`. This PR fixes this issue and corrects previously added test cases. After fixing the bug, the loss curves of `fully_shard` and `replicate` mode match perfectly.  Pull Request resolved: pytorch#154975 Approved by: https://github.com/tianyu-l
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This is a follow-up on the previous dtensor redistribute PR: #150740, which enables SimpleFSDP's mixed-precision training.
In the most recent integration in TorchTitan: pytorch/torchtitan#1250, we found some discrepancies between SimpleFSDP's
fully_shardandreplicatemodes when MPT is enabled. After debugging, I found the problem is in dtensor redistribute --local_tensoris taken out again from the originalinput. Thus, the dtensor used for communication has its original precision instead of usingforward_dtype.This PR fixes this issue and corrects previously added test cases.
After fixing the bug, the loss curves of
fully_shardandreplicatemode match perfectly.cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k