test_public_bindings should be run on anything that changes the public API - need to figure out in the future what is part of the public api, currently I'm using anything in torch/
flex_attention should be run on anything involving autograd
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130397
Approved by: https://github.com/malfet
test_public_bindings should be run on anything that changes the public API - need to figure out in the future what is part of the public api, currently I'm using anything in torch/
flex_attention should be run on anything involving autograd
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130397
Approved by: https://github.com/malfet
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
yolo
iirc the a10g/sm86 runners have ~21 GB of space, so we can increase parallelism on it to 3. This results in about 6GB CUDA mem per proc. The previous calculation + 2 procs resulted in about 8 GB
Also fixes the the calc for per proc memory, assuming that CUDA context + anything else take about a little under 1GB of space (previous calc was .11 on about 7.5 - 8 GB <= .9GB)
Times on main are about 1.9-2.5hr per shard
This commit is around 1.6-2hr per shard
Risks: increase in flaky tests due to OOM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125598
Approved by: https://github.com/huydhn
yolo
Also
* Ensure that at least 1 test always gets run (`//` does truncation which results in 0 if you have too few tests discovered)
* Don't run test removal on slow tests - I'm not touching that yet
I am avoid everything other than pull + trunk workflows, so not doing this on windows CUDA, which runs on periodic
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125049
Approved by: https://github.com/huydhn, https://github.com/ZainRizvi
A better query for the base commit of a PR.
Some ghstack PRs are not connected to main so git merge-base doesn't work. Instead, use the Github API to query for the base of the PR, which should be more accurate
Sanity checked on one of Ed's ghstack PRs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122214
Approved by: https://github.com/seemethere
Test the generic torch.Stream/Event with fake device gurad and hooks. Since we added a fake device backend, it is mutual exclusive to other backends. Tests will be skipped if TEST_CUDA or TEST_ROCM is true.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123614
Approved by: https://github.com/albanD
ghstack dependencies: #123611, #123612
A better query for the base commit of a PR.
Some ghstack PRs are not connected to main so git merge-base doesn't work. Instead, use the Github API to query for the base of the PR, which should be more accurate
Sanity checked on one of Ed's ghstack PRs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122214
Approved by: https://github.com/seemethere
Fix round robin sharding when there are no test times and sort_by_time=False
Adds more tests to test_test_selections for sort_by_time=False
Adds more checks to test_split_shards_random for serial/parallel ordering + ordering of tests
Refactoring of dup code
Tested locally by running `python test/run_test.py --shard 3 5` with no test times downloaded and checked that it wasn't an empty list.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121022
Approved by: https://github.com/huydhn, https://github.com/osalpekar
Fix round robin sharding when there are no test times and sort_by_time=False
Adds more tests to test_test_selections for sort_by_time=False
Adds more checks to test_split_shards_random for serial/parallel ordering + ordering of tests
Refactoring of dup code
Tested locally by running `python test/run_test.py --shard 3 5` with no test times downloaded and checked that it wasn't an empty list.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121022
Approved by: https://github.com/huydhn, https://github.com/osalpekar
Move tests that are mentioned in PR body or commit message to front. Also attempts to find any issues/PRs mentioned in the PR body and search for those too (ex if you link a disable issue and that issue contains the test file that it was failing on)
looking for: dynamo/test_export_mutations
Also removes some printed information in TD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120621
Approved by: https://github.com/osalpekar
Give TD it's own job so that each shard can get the results from this one job artifact and they will always be in sync with each other/no longer need to worry about consistently issues
* Move test discovery to its own file that is not dependent on torch so it can be run without building torch
* Cannot do cpp test discovery before building pytorch
* Move TD calculation to own file that will create a json file with the final results
* TD is now job/build env agnostic
* TD will rank all tests, including those that test jobs may not want to run (ex it will rank distributed tests along with default tests, even though these tests are never run on the same machine together)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118250
Approved by: https://github.com/huydhn
Current threshold is to cut the bottom 75% of test files, which results in 13 min of tests getting cut.
test_ops, functorch/test_ops, and test_decomp and other really long running test files are not getting cut and make the top 25% to take really long (still 90+ min)
The original plan was to test on rocm but I'm worried about queuing given that cutting 75% of test files only cuts off 13 min, and crossref is rarely referenced by others and people keep talking about getting rid of it, so it's a good alternative
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119426
Approved by: https://github.com/huydhn
Convert from a list/bucket based TD system to just a numbers based TD system. Looks like a massive change but a decent amount of it is tests and removing code.
Main file of interest is interface.py, which Github is collapsing by default due to size
The test files pretty much got rewritten entirely since a lot of the old tests are no longer relevant.
Other notable changes:
* Use Frozenset to make TestRun hashable
* Adds tools/test/heuristics/__init__.py to ensure that unittest can discover the tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119901
Approved by: https://github.com/osalpekar, https://github.com/huydhn
Changes sharding to attempt to put all serial tests on as few shards as possible. Parallel tests are then distributed across all shards, with most of which likely ending up on the non serial shards
Example: 8 minutes of serial tests, 20 minutes of parallel tests, 2 proc per machine, 6 machines
-> 8 + 20/2 = 18 total minutes of tests
-> 18 / 6 machines = 3 min per machine
-> all serial tests should fit on 3 machines (3min, 3 min, 2min)
-> majority of parallel tests should go on last 4 machines, one of which is shared with the serial tests
Move serial tests to run first
If I want to move to a purely numbers based sharding, this ensures that parallel tests are run with parallel tests as much as possible instead of interleaving serial + parallel tests, which decreases effectiveness of parallelization, while also ensuring that test reordering is still mostly effective.
See 73e816ee80 for example logs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119078
Approved by: https://github.com/huydhn
Changes sharding to attempt to put all serial tests on as few shards as possible. Parallel tests are then distributed across all shards, with most of which likely ending up on the non serial shards
Example: 8 minutes of serial tests, 20 minutes of parallel tests, 2 proc per machine, 6 machines
-> 8 + 20/2 = 18 total minutes of tests
-> 18 / 6 machines = 3 min per machine
-> all serial tests should fit on 3 machines (3min, 3 min, 2min)
-> majority of parallel tests should go on last 4 machines, one of which is shared with the serial tests
Move serial tests to run first
If I want to move to a purely numbers based sharding, this ensures that parallel tests are run with parallel tests as much as possible instead of interleaving serial + parallel tests, which decreases effectiveness of parallelization, while also ensuring that test reordering is still mostly effective.
See 73e816ee80 for example logs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119078
Approved by: https://github.com/huydhn
Everyday I move closer and closer to just using numbers
* number of heuristics that marked it as high, probable, low, none etc
* order of heuristics in the `__init__` file as well as how the heuristic ordered the tests
* put heuristics historical edited files and profiling as not trial mode
* briefly sanity checked that all shards of the larger test files (ex test_ops) exist and there are no dups
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118029
Approved by: https://github.com/huydhn
Moves test discovery into a file that doesn't have import torch so test listing can be done without having torch installed.
Helpful when you don't have torch installed (aka me when I'm feeling lazy)
I want to move TD into it's own job that doesn't need to wait for build to finish, so this is part of that.
The first commit is a nothing more than a copy paste of the selected functions/vars into a new file, the second commit has various changes that should be checked.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118574
Approved by: https://github.com/huydhn
This replaces a bunch of unnecessary lambdas with the operator package. This is semantically equivalent, but the operator package is faster, and arguably more readable. When the FURB rules are taken out of preview, I will enable it as a ruff check.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116027
Approved by: https://github.com/malfet
