I confirmed that the tracing was correct i.e. NamedTupleVariable had the correct dynamic attribute added to it.
The problem was that NamedTupleVariable was always marked as immutable. This does not reflect the behavior of namedtuple.
Subclasses of namedtuple may be mutable, so when a NamedTupleVariable is derived from a subclass that is mutable, I made NamedTupleVariable mutable as well. Then side_effects correctly updates the returned object.
Fixes#161610
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161645
Approved by: https://github.com/anijain2305, https://github.com/StrongerXi
Summary:
[reland]
Since `allow_complex_guards_as_runtime_asserts` is now sync'd with `prefer_deferred_runtime_asserts_over_guards`, we can kill the former (especially since it was a export-only concept).
Test Plan:
updated tests
Rollback Plan:
Differential Revision: D81334984
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161794
Approved by: https://github.com/zhxchen17
Summary: Since `allow_complex_guards_as_runtime_asserts` is now sync'd with `prefer_deferred_runtime_asserts_over_guards`, we can kill the former (especially since it was a export-only concept).
Test Plan:
updated tests
Rollback Plan:
Differential Revision: D79903317
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160198
Approved by: https://github.com/ezyang
Summary: Since `allow_complex_guards_as_runtime_asserts` is now sync'd with `prefer_deferred_runtime_asserts_over_guards`, we can kill the former (especially since it was a export-only concept).
Test Plan:
updated tests
Rollback Plan:
Differential Revision: D79903317
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160198
Approved by: https://github.com/ezyang
Summary:
convert_frame.compile_frame used to take a callback transform function which will capture the frame object it has, but the frame information is not passed directly into compile_frame function.
This PR changes the signature of compile_frame so that frame information is directly passed in the function without taking a callback. This makes it easier to build fullgraph capture API on top of compile_frame.
Test Plan:
CI
Rollback Plan:
Differential Revision: D81041296
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161514
Approved by: https://github.com/tugsbayasgalan
This PR replaces "guard_serialization_mode" into `save_guards`. All cases where we care about whether or not we're *loading* guards can be inferred automatically from the existing inputs.
The only case that's special here is whether or not to check guards. We don't want to check guards on guard load in CheckFnManager, because these guards have already been checked on save. Therefore, we put the setting in OutputGraphGuardsState, so that when we save, we bypass the guards check.
Because of this change, it is *technically* possible to do a load and a save in the *same* CheckFunctionManager.__init__() by passing all the necessary parts, and also passing `save_guards=True`. This should just work out of the box, but so far no callsites need it, so not super important.
Next up, we'll work on removing save_guards from GuardBuilder, and putting it into its own phase.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160531
Approved by: https://github.com/zhxchen17
Changes:
(1) Replace UserDefinedSetVariable by UserDefinedObjectVariable in all binop calls
Test plan:
(1) The three tests from CPython `test_collections.py` ensures that Dynamo can trace through a dunder method (e.g. __add__, __ixor__, etc) defined in a user defined class
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159865
Approved by: https://github.com/mlazos
ghstack dependencies: #159365, #159366, #159368, #159483, #159902, #159864
Dynamo was aggressively specializing on lazy VTs over `set_name_hint` in
`STORE_FAST`, etc., and `isinstance` in `LOAD_FAST_CHECK`. This causes
regional `torch.compile` from optimizing ComfyUI GGUF + LoRA to either
(1). exceed the recompialtion limit of 8, which results in suboptimal
performance, and (2). even if recompilation limit is increased, the
compilation time gets unnecessarily high (180s v.s. 20s for Flux).
This patch fixes the recompilation issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156891
Approved by: https://github.com/williamwen42, https://github.com/mlazos
Currently, every time we construct a GLOBAL_STATE guard, we always create a fresh guard based on the current global state. For precompile, we want to create a GLOBAL_STATE guard always based on some external sources, e.g. serialized global states. This can also be applied with the normal case where we just pass in the global state guard from Python.
Differential Revision: [D77400988](https://our.internmc.facebook.com/intern/diff/D77400988/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157285
Approved by: https://github.com/jansel
adding more information to the error message for debugging.
example error message:
```
Detected recompile when torch.compile stance is 'fail_on_recompile'. filename: 'caffe2/test/dynamo/test_misc.py', function name: 'fn', line number: 0
Failed on the following precompiled guards:
TREE_GUARD_MANAGER:
+- RootGuardManager
| +- LAMBDA_GUARD: isinstance(L['x'], bool)
GuardDebugInfo(
result=0,
verbose_code_parts=["isinstance(L['x'], bool)"],
num_guards_executed=1)
```
Differential Revision: [D76987126](https://our.internmc.facebook.com/intern/diff/D76987126/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156433
Approved by: https://github.com/jamesjwu
In practice `bool(...)` is either constant folded by Dynamo or used for
branching (so most of its emulation logic lived in
`InstructionTranslator.generic_jump`.
This patch adds a dedicated `bool` hanlder (only for symbolic
bool/int/float for now), and fixes#136075.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155863
Approved by: https://github.com/williamwen42
Summary:
This PR adds support for torch.cuda.FloatTensor and friends in Dynamo.
These are indeed legacy APIs, but that doesn't stop us from adding
support for them in torch.compile.
I add support for these in the same way that we support torch.Tensor:
these APIs can be safely put into the Dynamo graph.
Fixes#130722
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156107
Approved by: https://github.com/williamwen42
