# Fix typo errors across PyTorch codebase
This PR fixes various spelling errors throughout the PyTorch codebase to improve documentation quality and code readability.
## Changes Made
### Documentation Fixes
- Changed "seperate" to "separate" in multiple files:
- `setup.py`: Build system documentation
- `torch/_library/triton.py`: AOT compilation comments
- `torch/csrc/dynamo/compiled_autograd.h`: Node compilation documentation
- `torch/export/_unlift.py`: Pass population comments
- `torch/export/exported_program.py`: Decomposition table notes
### Code Comments and Error Messages
- Changed "occured" to "occurred" in:
- `test/mobile/test_lite_script_module.py`: Exception handling comments
- `torch/export/_draft_export.py`: Error message text
- `aten/src/ATen/native/cuda/linalg/BatchLinearAlgebra.cpp`: MAGMA bug comment
- `torch/csrc/utils/python_numbers.h`: Overflow handling comment
- `torch/csrc/jit/OVERVIEW.md`: Graph compilation documentation
- `torch/_dynamo/symbolic_convert.py`: Error explanation
### API Documentation
- Changed "fullfill" to "fulfill" in `torch/distributed/checkpoint/state_dict_loader.py`
- Changed "accross" to "across" in:
- `torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp`
- `torch/distributed/distributed_c10d.py`
## Motivation
These changes improve code readability and maintain consistent spelling throughout the codebase. No functional changes were made; this is purely a documentation and comment improvement PR.
## Test Plan
No testing required as these changes only affect comments and documentation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148262
Approved by: https://github.com/janeyx99
Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>
Summary:
This pr add a _is_script_object method to differentiate scriptModule and scriptObject, where the formal inherits from ScriptObject in C++ so they both passes the isinstance(obj, torch.ScriptObject) check.
The qualified name of ScriptObject (i.e. custom class) would starts with "__torch__.torch.classes", this has been a widely used assumption for dealing with custom class across our code base.
Test Plan: Add new test.
Differential Revision: D69685316
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147399
Approved by: https://github.com/yushangdi
Previously, the doc's suggested `from torch._library.triton import wrap_triton, triton_op` doesn't work because wrap_triton is not imported in torch/_library/__init__.py but `from torch.library import wrap_triton` works. This PR imports wrap_triton and fix the doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144882
Approved by: https://github.com/zou3519
Summary:
A reland of https://github.com/pytorch/pytorch/pull/142426.
Copying the description over here:
For torch.export (strict and non-strict), we don't do functional decomposition. Instead, we preserve the custom triton ops as custom ops. This is because we want the exported program to be high-level and serializable.
The alternative:
If we decompose the custom op to a functional hop and make it a node in exported program, we need to figure out ways of serializing the hop and its arguments, which can be triton.jited python functions and triton dtypes. This is undesireble because:
it can be tedious to maintain layer that serialize the jited function (e.g. with a string) and dtypes.
changes to triton or the serialization logic for triton arguments can be BC breaking
exported program will expose the implementation detail (i.e. triton source code) for a specific backend (GPU) to users, which mixes levels of abstraction.
Future plans:
After this PR, in the short term, we expect users to have a seperate aot_compile stage that compiles the exported program into a Cubin file on the same machine that users call export, which does autotuning and removes triton dependency and serve the model with Cubin. This guarantees that triton changes won't break BC.
In the long term, we may export multiple cubins for the triton op directly.
Test Plan: see new tests.
Differential Revision: D67879685
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144284
Approved by: https://github.com/zou3519
For torch.export (strict and non-strict), we don't do functional decomposition. Instead, we preserve the custom triton ops as custom ops. This is because we want the exported program to be high-level and serializable.
#### The alternative:
If we decompose the custom op to a functional hop and make it a node in exported program, we need to figure out ways of serializing the hop and its arguments, which can be triton.jited python functions and triton dtypes. This is undesireble because:
- it can be tedious to maintain layer that serialize the jited function (e.g. with a string) and dtypes.
- changes to triton or the serialization logic for triton arguments can be BC breaking
- exported program will expose the implementation detail (i.e. triton source code) for a specific backend (GPU) to users, which mixes levels of abstraction.
#### Future plans:
After this PR, in the short term, we expect users to have a seperate aot_compile stage that compiles the exported program into a Cubin file **on the same machine that users call export**, which does autotuning and removes triton dependency and serve the model with Cubin. This guarantees that triton changes won't break BC.
In the long term, we may export multiple cubins for the triton op directly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142426
Approved by: https://github.com/zou3519
ghstack dependencies: #142425
Over time, a large number of the existing type ignores have become irrelevant/unused/dead as a result of improvements in annotations and type checking.
Having these `# type: ignore` linger around is not ideal for two reasons:
- They are verbose/ugly syntatically.
- They could hide genuine bugs in the future, if a refactoring would actually introduce a bug but it gets hidden by the ignore.
I'm counting over 1500 unused ignores already. This is a first PR that removes some of them. Note that I haven't touched type ignores that looked "conditional" like the import challenge mentioned in https://github.com/pytorch/pytorch/pull/60006#issuecomment-2480604728. I will address these at a later point, and eventually would enable `warn_unused_ignores = True` in the mypy configuration as discussed in that comment to prevent accumulating more dead ignores going forward.
This PR should have no effect on runtime at all.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142325
Approved by: https://github.com/Skylion007, https://github.com/janeyx99
We've been using it privately for half a year and everything's been
good. This PR:
1. Makes torch.library.triton_op public
2. Renames capture_triton -> wrap_triton. We got feedback that no one
knew what "capture triton" does.
3. Makes torch.library.wrap_triton public.
triton_op is used to construct a Python custom operator that may call 1+
triton kernels. Each of those triton kernels must be annotated with
wrap_triton.
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141880
Approved by: https://github.com/albanD
ghstack dependencies: #141894
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
We don't need to do a loop over all the args, kwargs in the
AdInplaceOrView key; we just need to bump the version on the args,
kwargs that are mutable.
On the benchmark mentioned in
https://github.com/pytorch/pytorch/issues/139494
this made the time go from
```
mutate2 = 61.72943878173828
no_mutate2 = 36.89440155029297
mutate = 236.3092498779297
no_mutate = 59.31964874267578
```
to
```
mutate2 = 47.976478576660156
no_mutate2 = 38.37468719482422
mutate = 71.21315002441406
no_mutate = 59.7432975769043
```
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139513
Approved by: https://github.com/bdhirsh
ghstack dependencies: #139509
This PR changes real_tensor_prop to also infer fake kernels when the
operator doesn't have it.
We infer the fake output to be of the same properties as the real
output, with unbacked symints in the sizes and some stride order.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139213
Approved by: https://github.com/pianpwk
ghstack dependencies: #139212
When we see a custom op:
- check that its mutation annotations are correct
- check that its aliasing constraints matches our constraints for custom
ops.
Otherwise, there may be undefined behavior.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139212
Approved by: https://github.com/angelayi
Summary:
* Fixed real tensor tracing w/ torchbind objs by passing the cloned tensor obj. For now I just catch the exception and have an error message if the `_clone` fails, but up for discussion on what to do here
* Separate question, should we require people to set up FakeScriptObjects and stuff for draft mode?
* Prevent side effects from happening when we do the first pass of custom ops profiling by cloning/copying everything. Not sure if deepcopying the model will succeed in all cases... But also I guess this path can be removed once custom ops profiling turns into one pass.
Test Plan: `buck2 run @//mode/dev-nosan //scripts/angelayi/draft_export:test_draft_export`
Reviewed By: ydwu4
Differential Revision: D64124825
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138797
Approved by: https://github.com/ydwu4
if the function is
```func(a, b, c) ```
and is called as
```func(a=1, b=.., c=..)```
before this change we do not iterate on the a, b, c, since those appear in kwargs this diff fix that issue.
This function is used in _inductor/ir.py to iterate over custom op arguments and when a custom pass does changes
and pass arguments as kwargs, we do not process them.
```
for info, arg in torch._library.utils.zip_schema(schema, args, kwargs):
handle_aliasing_and_mutation(info, arg)
```
Fix https://github.com/pytorch/pytorch/issues/137057
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137311
Approved by: https://github.com/zou3519
Add a way of generating a FunctionSchema from example values because hop's schema varies even for the same hop.
We didn't use torch._C.FunctionSchema because we cannot construct the classes directly (e.g. "__init__" cannot be used for torch._C.FunctionSchema). Also extending the Basic types in c++ seems not that easy.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133521
Approved by: https://github.com/zou3519
We promise the user that these custom ops (and their kernels) are black
boxes w.r.t. torch.compile. Unfortunately Dynamo can turn itself back
on in the implementation of the custom operator, so we force it off by
disabling Dynamo
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133125
Approved by: https://github.com/ezyang
The capture_triton decorator returns a function that goes through the
triton kernel wrapper HOP. This is useful for make_fx tracing and
non-strict export. However, the HOP dispatch is slow (~1ms) and not
necessary in certain situations.
This PR skips going through the HOP dispatch for any
capture_triton-wrapped triton kernels that are registered as
implementations to a `@triton_op` custom operator. We do this by
creating a new thread-local flag that controls if the
capture_trition-wrapped triton kernel goes through HOP dispatch or not.
Test Plan:
- new test and existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132822
Approved by: https://github.com/SherlockNoMad
Summary:
Skip the warning if the fake script object doesn't implement a fake method for:
1. __obj_flatten__: for real script object only.
2. __set_state__ and __get_state__ for serialization. Don't expect it to be used during tracing.
Test Plan: Existing tests.
Reviewed By: angelayi
Differential Revision: D60478460
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132306
Approved by: https://github.com/angelayi
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
ghstack dependencies: #131777
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
