Halve model loading time for llama demo#4032
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/4032
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 4d922b4 with merge base 3eec95a ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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This pull request was exported from Phabricator. Differential Revision: D58826044 |
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Summary: Pull Request resolved: pytorch#4032 mmap is not recommended for large sequential workloads -- you have to take a bunch of page faults. Surprisingly, this doesn't seem to hurt reported peak memory usage. Differential Revision: D58826044
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Summary: Pull Request resolved: pytorch#4032 mmap is not recommended for large sequential workloads -- you have to take a bunch of page faults. Surprisingly, this doesn't seem to hurt reported peak memory usage. Differential Revision: D58826044
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Summary: Pull Request resolved: pytorch#4032 mmap is not recommended for large sequential workloads -- you have to take a bunch of page faults. I originally assumed this would hurt peak memory usage (we read all the weights into memory at once and then pack them; packing is basically copying them), but it doesn't. In retrospect, this makes sense because we actually operate on one weights tensor at a time, and the individual tensors aren't gigantic, there are just a lot of them. Reviewed By: larryliu0820 Differential Revision: D58826044
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This pull request has been merged in 8740c69. |
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Summary:
mmap is not recommended for large sequential workloads -- you
have to take a bunch of page faults. Surprisingly, this doesn't seem
to hurt reported peak memory usage.
Differential Revision: D58826044