This PR is part of a series attempting to re-submit https://github.com/pytorch/pytorch/pull/134592 as smaller PRs.
In jit tests:
- Add and use a common raise_on_run_directly method for when a user runs a test file directly which should not be run this way. Print the file which the user should have run.
- Raise a RuntimeError on tests which have been disabled (not run)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154725
Approved by: https://github.com/clee2000
Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.
I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69776
If we have an node output which is an optional type, but both if blocks produce a non-optional value, we can try to refine the if output type, which can open up further optimization opportunities.
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D33515235
Pulled By: eellison
fbshipit-source-id: 34f6ab94ac4238498f9db36a1b673c5d165e832e
Summary:
Some of the "no-ops" are not actually no-ops because they can change the dtype
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67688
Reviewed By: davidberard98
Differential Revision: D32104601
Pulled By: eellison
fbshipit-source-id: ccb99179a4b30fd20b5a9228374584f2cdc8ec21
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65860
Re-enable peepholes like `x + 0 == x`. These were at one point enabled, and then disabled because they did not properly account for aliasing, and then re-enabled with reconstructing the alias db everytime which is slow - O(n^2). I've added correctness conditions, and I've also made it so that we avoid using stale aliasing properties for either the input or output of nodes we optimize.
Some of the other code that we have written to avoid re-instantiating the alias db involves internally mutating it, however this is tricky to reason about and we probably have to add some extra invariants...
cc navahgar relevant to graph opts and d1jang alias analysis relevant here
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D31352382
Pulled By: eellison
fbshipit-source-id: 441a27f17dc623d6c24538d1d43cba0412c3c482
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65014
ghstack-source-id: 138656948
Test Plan:
```
(pytorch) [maxren@devvm3115.atn0 ~/pytorch] python3 test/test_jit.py TestPeephole
CUDA not available, skipping tests
monkeytype is not installed. Skipping tests for Profile-Directed Typing
........s......................
----------------------------------------------------------------------
Ran 31 tests in 0.393s
OK (skipped=1)
(pytorch) [maxren@devvm3115.atn0 ~/pytorch] python3 test/test_jit.py TestPeephole.test_normalized_rsub
CUDA not available, skipping tests
monkeytype is not installed. Skipping tests for Profile-Directed Typing
.
----------------------------------------------------------------------
Ran 1 test in 0.015s
OK
```
Reviewed By: eellison
Differential Revision: D30941389
fbshipit-source-id: 03f0416d99090845c9bfb1e5fcf771d5f1d7a050
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65341
The changes in D30231044 (babd449978) were removed due to a downstream issue in glow. Now that the issue has been fixed by D30849396, we can safely re-introduce the changes.
Test Plan:
`buck test //caffe2/test:jit -- TestPeephole`
Glow test:
* `buck test //glow/fb/torch_glow/tests:unfuse_glow_ops_test`
* qxy11 confirmed that the problematic glow model now loads correctly with these changes
Reviewed By: eellison
Differential Revision: D31056878
fbshipit-source-id: 049903ee04ba88885cc9d1a91427af0f1f44f681
Summary:
Original commit changeset: d12ee39f6828
build-break
overriding_review_checks_triggers_an_audit_and_retroactive_review
Oncall Short Name: dskhudia
Test Plan: Local run succeeds
Differential Revision: D30633990
fbshipit-source-id: 91cf7cc0ad7e47d919347c2a1527688e062e0c62
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63049
Given a graph produced from a function like this:
```
def foo():
li = [1, 2, 3, 4, 5, 6]
return li[0:2]
```
This pass produces a graph like this:
```
def foo():
li = [1, 2]
return li
```
These changes are mostly adapted from https://github.com/pytorch/pytorch/pull/62297/
Test Plan: `buck test //caffe2/test:jit -- TestPeephole`
Reviewed By: eellison
Differential Revision: D30231044
fbshipit-source-id: d12ee39f68289a574f533041a5adb38b2f000dd5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62429
Introduce a new pass to eliminate calls to `prim::DictConstruct/aten::__getitem__`. Given a graph like this:
```
%2 : Dict = prim::DictConstruct(%key, %value)
%3 : Tensor = aten::__getitem__(%2, %key)
%4 : Tensor = op(%3)
```
This pass produces a graph like this (after dead code elimination):
```
%4 : Tensor = op(%value)
```
This optimization is applied in the static runtime.
Test Plan:
`buck test //caffe2/test:jit -- TestPeephole`
**local.forward performance summary**
About 3% runtime benefit. All `DictConstruct` calls optimized out, `__getitem__` calls reduced significantly (~50% of them are cut out)
P438354810
**local_request_only.forward performance summary**
About 14% runtime benefit. Again, all `DictConstruct` calls optimized out, 50% `__getitem__` calls removed.
P438359742
There is some variance with runtime measurements, so take these numbers with a grain of salt. Also note that the benefit does not exist in the shrunk model since there are no `DictConstruct` calls
Reviewed By: hlu1
Differential Revision: D29995087
fbshipit-source-id: f376376a46ff808115afd2d60446e5db8f6f752f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55978
This is needed for broadcasting two of the same symbolic shape
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb
Differential Revision: D27755328
Pulled By: eellison
fbshipit-source-id: d38d9458a9e28d31558f0bc55206516b78131032
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55926
This is necessary for code like conv2d where we wish to share a generic convolution shape function logic with that of conv2d but for conv2d always infer the output is dimension 4. I'm also hoping the refinement algorithm here could be refactored out and used to support refining tensor types from user annotations. i have a length comment explaining how this works, and the logic outside of data structures is pretty small and contained. Additionally, you might check out https://fb.quip.com/X7EVAdQ99Zzm for a very similar description of how to refine values based on comparison operators.
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D27750997
Pulled By: eellison
fbshipit-source-id: d962415af519ac37ebc9de88f2e1ea60a1374f7c
Summary:
normalizing `__is__` to `eq`, and `__isnot__` to `ne` in the case of bools.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57862
Test Plan:
```
python test/test_jit.py TestPeephole
```
11 Tests, 1 skipped, no failures
Fixes https://github.com/pytorch/pytorch/issues/57387
Reviewed By: eellison
Differential Revision: D28335646
Pulled By: Gamrix
fbshipit-source-id: c9f885044b32897ba35483091bcf7037759b7517
