`dynamo.explain()` was updated to return a structure but the docs weren't updated to match.
- Update the docs to use the new API
- Remove some dead code left when `explain` was updated.
- Drive-by: Fix some `nopython` uses that I noticed
- Drive-by: I noticed an ignored error coming from CleanupHook on shutdown - make it check the global before setting it.
Fixes#122573
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122745
Approved by: https://github.com/jansel
List of changes:
- Replace JVP_NESTING by torch._C._functorch.maybe_current_level()
- Remove all increment nesting functions from wrap_fx_proxy_cls
- fwAD.make_dual receives the dual_level as keyword argument
- Add jvp_increment_nesting, set_fwd_grad_enabled and dual_level context managers to dynamo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119926
Approved by: https://github.com/zou3519
`unimplemented` is a function that raises an error, so
`raise unimplemented(...)` never reaches the `raise`.
Another related issue is that `raise unimplemented(...) from e`
doesn't attach the exception cause correctly. I fix this by adding
a `from_exc` argument to `unimplemented`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122136
Approved by: https://github.com/lezcano
List of changes:
- Replace JVP_NESTING by torch._C._functorch.maybe_current_level()
- Remove all increment nesting functions from wrap_fx_proxy_cls
- fwAD.make_dual receives the dual_level as keyword argument
- Add jvp_increment_nesting, set_fwd_grad_enabled and dual_level context managers to dynamo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119926
Approved by: https://github.com/zou3519
List of changes:
- Replace JVP_NESTING by torch._C._functorch.maybe_current_level()
- Remove all increment nesting functions from wrap_fx_proxy_cls
- fwAD.make_dual receives the dual_level as keyword argument
- Add jvp_increment_nesting, set_fwd_grad_enabled and dual_level context managers to dynamo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119926
Approved by: https://github.com/zou3519
Putting this PR as an RFC since I have resorted to some horrible hacks in order to make this work.
```
(Pdb) p triton.language.float32
triton.language.fp32
(Pdb) p str(triton.language.float32)
'fp32'
(Pdb) p repr(triton.language.float32)
'triton.language.fp32'
```
This means that we need to "rewrite" them for fx graph and inductor execution.
This PR allows Mamba2 to work with `torch.compile`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121690
Approved by: https://github.com/Skylion007
Add these log to debug the regress of accuracy test for dm_nfnet_f0 model for training.
With these extra log when the accuracy check fail, we can verify if it's close to succeed or not. If yes that indicates there is no real issue but just flaky and we probably can tune the tolerance to fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121656
Approved by: https://github.com/jansel, https://github.com/Skylion007
Reduces the torch.compile(backend="eager") for this code by 1-2 seconds.
~~~
def fn(x):
for _ in range(10000):
# x = torch.sin(x)
x = torch.ops.aten.sin(x)
# x = sin(x)
return x
~~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121052
Approved by: https://github.com/jansel
ghstack dependencies: #121053
1. right now we double increment the profile counter. The PR avoid that so we don't end up with profile_0, profile_2, profile_4 ...
2. log the latency to run the passed in function with profiling on so we can easily skip those _compile call which returns quickly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120100
Approved by: https://github.com/eellison
Fix: https://github.com/pytorch/pytorch/issues/119779 by properly graph breaking a proper fix is to handle quantized tensors for full complete solution.
if when generating a fake tensor, UnsupportedFakeTensorException is thrown, then its handled and converted into a
Unimplemented in inside wrap_fake_exception which is then translated to a graph break.
However run_node used to convert UnsupportedFakeTensorException into a runtime error, creating runtime
errors instead of graph breaks whenever generating a fake tensor for a quantized tensor fails.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120026
Approved by: https://github.com/jansel
partially address https://github.com/pytorch/pytorch/issues/118785
This diff fixes three things:
1. add get_function to FunctoolsPartialVariable note that it will be available only if all args constant otherwise,
it would throw unimplemented in the call to asPythonConstant.
2. NamedTupleVariable takes args dispatched not as list ex: NamedTuple(a, b, c) vs NamedTuple([a, b, c]),
hence fix that by specializing asProxy.
3. A call to create_arg from within create_proxy, changes a python NamedTuple to a function call node without
associating an example value! Updated get_fake_values_from_nodes to handle such case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119435
Approved by: https://github.com/jansel, https://github.com/anijain2305
ghstack dependencies: #119314
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
I was just playing around with improving the typing of symbolic_shapes. The PR is not "complete" but I in particular wanted to get feedback on whether or not people liked making ValueRanges Generic; it seems that distinguishing if you have an Expr ValueRange or a SympyBoolean ValueRange is a lot of trouble for downstream. Using TypeGuard, we can perform refinements on the generic parameter inside methods, although we still have to cast back to ValueRange[T] due to https://github.com/python/mypy/issues/14425#issuecomment-1914852707
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118529
Approved by: https://github.com/Skylion007
I was just playing around with improving the typing of symbolic_shapes. The PR is not "complete" but I in particular wanted to get feedback on whether or not people liked making ValueRanges Generic; it seems that distinguishing if you have an Expr ValueRange or a SympyBoolean ValueRange is a lot of trouble for downstream. Using TypeGuard, we can perform refinements on the generic parameter inside methods, although we still have to cast back to ValueRange[T] due to https://github.com/python/mypy/issues/14425#issuecomment-1914852707
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118529
Approved by: https://github.com/Skylion007
