pytorch/docs
Edward Z. Yang b4597fffce Try to reuse old symbol name rather than new symbol name when renaming (#124782)
Previously, unbacked SymInts would gradually get larger and larger as we kept rebinding them.  Now, we do the replacement to preserve the old symbol.

Actually doing this is a bit tricky.  Here’s the order things happen when retracing data dependent:

1. Run fake tensor prop: allocate new unbacked SymInt
2. Run proxy tensor mode, calculate bindings and associate them with FX node
3. Run PropagateUnbackedSymInts, rename unbacked bindings to their old ones so they are consistent

So the problem is when we calculate bindings in step (2), we don't know
what the original names are yet, we only find out later at (3).  But by
the time (3) runs, we've already stuffed some new bindings in
meta["unbacked_bindings"] and we don't know how to update them!  To fix
this, I introduce resolve_unbacked_bindings which post facto applies any
of the renamings we discovered in (3).

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124782
Approved by: https://github.com/lezcano
ghstack dependencies: #124310, #124314, #124316, #124394, #124739
2024-04-25 14:02:42 +00:00
..
caffe2 Enable UFMT on a bunch of low traffic Python files outside of main files (#106052) 2023-07-27 01:01:17 +00:00
cpp export XPUStream to doc (#121398) 2024-03-30 00:36:26 +00:00
source Try to reuse old symbol name rather than new symbol name when renaming (#124782) 2024-04-25 14:02:42 +00:00
.gitignore
libtorch.rst Replace master with main in links and docs/conf.py (#100176) 2023-05-02 18:20:32 +00:00
make.bat
Makefile Refactor torch.onnx documentation (#108379) 2023-09-08 18:23:48 +00:00
README.md
requirements.txt update requirements.txt in /docs (#101092) 2023-05-12 03:19:36 +00:00

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