[AFQ] Optimize tensor_flatten for runtime #1114
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ghstack-source-id: f028ae1d0afb3aaea9a4afebe29b114de80b5d9e Pull Request resolved: #1114
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1114
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit d44ddab with merge base 3475aed ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Stack from ghstack (oldest at bottom):
tensor_flatten is called at runtime when inputs are subclasses e.g. AffineQuantizedTensor
In case of using
float8_dynamic_activation_float8_weightquantization, the activations will also be AQT.There was external complain, that unwrap_tensor_subclasses sometimes exceeds in duration that compiled region execution.
Profiling shows that tensor_flatten attribute access will go through torch_function handling (AQT has it).
If to remove this torch_function dispatch for each getattr at runtime - tensor_flatten becomes (in my measurements x9 faster)