#143063 was missing handling a couple UCS cases as well as had some bugs in the way it dealt with errors.
- Fix all the UCS handling (and make some of the common code more common)
- Make sure all the error paths return `nullptr`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147436
Approved by: https://github.com/jansel
This PR and the previous:
- Moves parts of `eval_frame.c` to C++.
- Reduces code duplication in `dynamo__custom_eval_frame` and makes the control flow more clear.
- Enables `convert_frame` to signal to `eval_frame.cpp` in a general manner how to evaluate this frame, recursive frames, and future frames with the same code object (default/compile, skip, run-only). e.g. this will allow us to change skipping/cache limit hit eval_frame behavior directly from convert_frame without requiring changes to C/C++.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146355
Approved by: https://github.com/jansel
ghstack dependencies: #145603
Implements https://github.com/pytorch/pytorch/issues/93753 - move frame local guard accessors to C++.
Before, we used dict accessors on a Python dict representing the frame's fastlocals that we manually build. We move this accessor to C++ and additionally use the fastlocal index whenever possible.
Some implementation notes:
- `FrameLocalsMapping` is now initialized as a C++ vector of `PyObject`s. We do not just use the frame's localsplus/fastlocals buffer because we also unbox cells.
- `FrameLocalsMapping` can still be converted into a Python dict representing the frame's fastlocals, but it is done lazily.
- We update `LeafGuard`, `GuardAccessor`, and `GuardManager`'s `check_nopybind` methods to accept `FrameLocalsMapping`. By default, we convert the `FrameLocalsMapping` to a Python dict and run the original `check_nopybind` on it, but in some cases, conversion is not needed.
- We add a new guard accessor `FrameLocalsGuardAccessor`, which is similar to `DictGetItemGuardAccessor` but has special handling for `FrameLocalsMapping`. We create a separate class to emphasize different use cases, but we could probably combine these two (can do in a follow up)
dynamo_guard_eval.py microbenchmark update:
- 713.2us -> 630.0us (3.10)
- 598.8us -> 530.7us (3.12)
Other followups:
- Add `FrameLocalsMapping` version for `check_verbose_nopybind` in order to match behavior between `check_nopybind` and `check_verbose_nopybind`. This can prevent difficult debugging situations where guards fail (`check_nopybind` returns false) but no guard error message is generated (`check_verbose_nopybind` succeeds).
- Rewrite the `SHAPE_ENV` guard into C++ - it is a fairly common guard that results in `FrameLocalsMapping` needing to convert to a dict
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140063
Approved by: https://github.com/jansel
ghstack dependencies: #142117, #142430
Summary: In D60803317, we added CompileContext (trace_id) information to Kineto traces using caching when a CompileContext exits. As pointed out by some users, this gives innaccurate IDs because we are not getting the context that we is being looked up within the eval_frame. For this reason, we decided to revert that change, and go with an approach that involves getting the trace_id associated with a given CacheEntry. To do this, we add a trace_id to the GuardedCode so that it can be passed onto a CacheEntry. Then, we change the lookup function to return said trace_id alongside the code so that we can pass both into our eval function. Once we get to a Torch-Compiled Region, we can just append the context information to the name of the annotation thus bypassing any need for kwargs.
Test Plan: Added more comprehensive unit test. Saw that all the trace_ids appeared within the graph.
Differential Revision: D63138786
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136460
Approved by: https://github.com/ezyang
----
- 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
