----
- We now record on CacheEntry what the compile id that populated it was, so now we can say why a specific frame was rejected
- Add structured log for recompiles under name artifact "recompile_reasons". As it stands, it's not terribly structured, but this was the easiest thing I could do to start
- Slightly reformat multi-reason printing; since we only report one guard failure seems better to have it as a single line
Example output:
```
V0703 10:34:13.273000 140345997743104 torch/_dynamo/guards.py:2590] [0/1] [__recompiles] Recompiling function f in /data/users/ezyang/a/pytorch/b.py:3
V0703 10:34:13.273000 140345997743104 torch/_dynamo/guards.py:2590] [0/1] [__recompiles] triggered by the following guard failure(s):
V0703 10:34:13.273000 140345997743104 torch/_dynamo/guards.py:2590] [0/1] [__recompiles] - 0/0: tensor 'L['x']' size mismatch at index 0. expected 4, actual 5
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130043
Approved by: https://github.com/anijain2305
Attempt #2 for https://github.com/pytorch/pytorch/pull/117875 to fix https://github.com/pytorch/pytorch/issues/112090.
Summary of changes:
- ~Changed CacheEntry linked list into a doubly-linked list structure to support deletion.~ (done by C++ refactor)
- Added CacheEntry and ExtraState borrowed references to GuardFn so that GuardFn can tell ExtraState to delete CacheEntry when the GuardFn is invalidated.
- ~Added ExtraState raw reference to CacheEntry so that we can get ExtraState to correctly point to the first CacheEntry if it gets deleted.~ (done by C++ refactor)
- CacheEntry destructor needs to reset GuardFn refs to ExtraState/CacheEntry in order to prevent use-after-free.
- code_context values that are nn.GraphModules need to be weakrefs in order to prevent circular references.
- Added tests that check for memory leaks and cache deletion operations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119107
Approved by: https://github.com/jansel
This branch:
1) converts the autograd tape into an FX graph
2) caches that conversion using a "shadow" graph
3) compiles and runs the generated FX graph instead of the normal autograd
What works currently:
1) Caching, capture, and initial integration
2) Backwards hooks
3) Inlining AotAutograd generated subgraphs
4) torch.compiling the generated FX graph
5) Auto-detecting dynamic shapes based on changes
Future work
1) Larger scale testing
1) Boxed calling convention, so memory can be freed incrementally
1) Support hooks on SavedTensor
1) Additional testing by running eager autograd tests under compiled_autograd.enable()
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103822
Approved by: https://github.com/ezyang, https://github.com/albanD
