for now, delete the float8-all-gather-only functionality from float8 … #1451
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1451
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…training Summary: In #1093 we added a config option, off by default, to use only float8 all-gather for training and do the matrix multiply in high precision. This seems generally useful for communication bound workloads, but we can probably think of a cleaner way to add this functionality (such as a weight wrapper tensor subclass). The current implementation adds non-trivial complexity and doesn't jive well with where we want to take this codebase. Since no one is using this internally or externally yet and we haven't talked about it in the release notes, I think we should do a BC-breaking delete as a one-off. However, if people have concerns - let me know and we can talk about less aggressive options. Test Plan: ``` ./test/float8/test_everything.sh ``` Reviewers: Subscribers: Tasks: Tags:
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#1451) for now, delete the float8-all-gather-only functionality from float8 training Summary: In #1093 we added a config option, off by default, to use only float8 all-gather for training and do the matrix multiply in high precision. This seems generally useful for communication bound workloads, but we can probably think of a cleaner way to add this functionality (such as a weight wrapper tensor subclass). The current implementation adds non-trivial complexity and doesn't jive well with where we want to take this codebase. Since no one is using this internally or externally yet and we haven't talked about it in the release notes, I think we should do a BC-breaking delete as a one-off. However, if people have concerns - let me know and we can talk about less aggressive options. Test Plan: ``` ./test/float8/test_everything.sh ``` Reviewers: Subscribers: Tasks: Tags:
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…training
Summary:
In #1093 we added a config option, off by default, to use only float8 all-gather for training and do the matrix multiply in high precision.
This seems generally useful for communication bound workloads, but we can probably think of a cleaner way to add this functionality (such as a weight wrapper tensor subclass). The current implementation adds non-trivial complexity and doesn't jive well with where we want to take this codebase.
Since no one is using this internally or externally yet and we haven't talked about it in the release notes, I think we should do a BC-breaking delete as a one-off. However, if people have concerns - let me know and we can talk about less aggressive options.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags: