[SymmetricMemoryOps] use float32 as the accumulator type when accumulating bfloat16 with multimem.ld_reduce #137529
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…ating bfloat16 with multimem.ld_reduce This provides better accuracy without additional cost. [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/137529
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 0603ed5 with merge base 9b2e453 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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- Previously the detection would fail before user calling APIs such as `torch.cuda.set_device()`. This is because the detection logic requires nvml initialization. In this PR, we added explicit nvml initialization (which idempotent). - Previously any nvml issue occurred in the detection logic would result in fatal error. Now we issue an informative warning and return a topology assuming no NVLink connectivity. Pull Request resolved: #137530 Approved by: https://github.com/Chillee ghstack dependencies: #137471, #137472, #137473, #137474, #137475, #137529
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…ating bfloat16 with multimem.ld_reduce This provides better accuracy without additional cost. ghstack-source-id: 7b8c55d Pull Request resolved: pytorch#137529
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Stack from ghstack (oldest at bottom):
This provides better accuracy without additional cost.
Also added documentation to
multimem_one_shot_all_reduceto note the numerical caveats.cc @XilunWu @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o