Tracing through `__init__` is important because it initializes (calls STORE_ATTR) on members. By doing that, we kick in the mutation tracking for these objects. So, things like mutating `_modules` etc is tracked automatically.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126578
Approved by: https://github.com/jansel
ghstack dependencies: #128001
While there are some similarities, they are also quite different (one
handles Numpy numbers while the other handles ints. I am also going to
add a wrap_symfloat soon which will do even more different behavior.
So split these out for clarity.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125483
Approved by: https://github.com/lezcano
ghstack dependencies: #125395, #125419
We guard on key order
1) When a key is a non-constant object
2) When we actually need key order - like .values, .items etc
For dicts/OrderedDicts that do not require key order guarding, we just rely on usual `GuardManger + DictGetItemGuardAccessor`. This is faster than going through the `list(d.keys())` based design for OrderedDicts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124779
Approved by: https://github.com/jansel
FSDP2 has this pattern of using user-defined object instance method as hook, and it will throw this error under compile:
`torch._dynamo.exc.Unsupported: call_function UserDefinedObjectVariable(_pre_forward) [FSDPManagedNNModuleVariable(), TupleVariable(), ConstDictVariable()] {}`
This PR adds support for it by always allowing to trace into a UserDefinedObjectVariable that's an instance method (i.e. `MethodType`).
Supersedes https://github.com/pytorch/pytorch/pull/123320.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123399
Approved by: https://github.com/jansel
I was originally trying to solve https://github.com/pytorch/pytorch/issues/120799 but got sidetracked along the way.
This PR contains a couple fixes. Let me know if you want me to split them up!
- Properly handle invalid user code when "super()" is called from non-method/classmethod. It will now properly raise the same error as CPython
- Fix base VariableTracker `__str__` method shadowing all `__repr__` methods defined in subclasses
- Fix accessing a classmethod on a user object to bind "cls" and not "self"
- Fix custom class handling of super() call to properly handle mixed regular/class/static methods
Locally , test_repros.py -k test_batch_norm_act still fails where the generated graph module is:
```
Call using an FX-traced Module, line 8 of the traced Module's generated forward function:
x = self.forward(l_x_); self = l_x_ = None
x_1 = self.L__self___act(x); x = None
```
note that "self" is being unset on the first line even though it is used on the second one.
For reference, this is the test c268ce4a6d/test/dynamo/test_repros.py (L1368-L1369)
I cannot figure out where the generated forward function comes from though, any hint would be welcome!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121365
Approved by: https://github.com/jansel
When building guards which went through a property we were analyzing the property using getattr_static but the guard wasn't built using getattr_static so if the property was "unusual" it generated misbehaved code which referenced a non-existent `__closure__` field.
Fixes#118786
Note that after this change some of the referenced tests are still failing with a different error - but getting further.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119719
Approved by: https://github.com/oulgen
Finally we have this PR to merge allow_in_graph/inline/skip trace rules into ```trace_rules.lookup_inner```, where we can define and lookup trace rules at both function level and file level. Going forward, this is the central place that we define and consulte Dynamo trace rule for any function.
* ```trace_rules.looup``` is the API can return allow_in_graph, inline or skip.
* ```skipfiles.check``` is the API can return inline or skip, since we have multiple places that only do inline/skip check.
* I'll move ```skipfiles.check``` to ```trace_rules.check``` as one of the follow-ups.
* Both functions consulte ```trace_rules.lookup_inner``` to get the tracing rule.
To avoid a single big PR, I left a few items as the follow-ups:
* Remove ```skipfiles.py``` and merge the code into ```trace_rules.py```.
* We do double check in ```symbolic_convert.check_inlineable```, will refactor and simplify it. We should only do inline/skip check before generating ```SkipFilesVariable``` and ```UserFunctionVariable```.
* Rename ```SkipFilesVariable``` as ```SkipFunctionVariable```, since we only handle functions.
* The inline/skip reasons are not logged for some cases, since the new lookup framework doesn't always return inline/skip reasons. I'll refactor loggings to record the inline/skip reason in next step.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118971
Approved by: https://github.com/jansel
Fix https://github.com/pytorch/pytorch/issues/118787
In the compiled function, calls to random() are replaced with a single function call
to a function that generates all the random variables .
The random calls encountered during compilation used to be tracked inside a variable
stored inside the instruction translator. And when there are nested translators, the tracked
calls used to get lost when the inner instructions translator popped out.
This diff fixes that by moving the tracked calla to the output graph which is shared across translators that are generating the same function.
More details about the issue and why this solution is picked are in the github issue above.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119218
Approved by: https://github.com/jansel, https://github.com/anijain2305
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.
Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
