i think get_reordered_tests broken since master -> main switch
add typing for some functions
checked for `prioritized` in the logs
limited testing because I only care about one very small part of the log thats near the beginning
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100752
Approved by: https://github.com/huydhn
Sharing code between the code that handles test results in parallel vs serial mode.
Note that the original version of this code had an inconsistency between the two versions where it would execute `print_to_stderr(err_message)` on every test that ran in parallel, but for serial tests it would only invoke `print_to_stderr(err_message)` if `continue_on_error` was also specified. By sharing code, this PR changes that behavior to be consistent between the two modes.
Also adding some comments.
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 029342c</samp>
> _Sing, O Muse, of the skillful coder who refined_
> _The PyTorch testing script, `run_test.py`, and shined_
> _A light on its obscure logic, with docstrings and comments_
> _And made it run more smoothly, with better error contents_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99467
Approved by: https://github.com/huydhn, https://github.com/malfet
Fixes#90940. This PR revamps how tests are run in parallel as well as device visibility at the docker container and within the run_test.py test runner.
First, running multiple test modules concurrently on the same GPU was causing instability for ROCm runners manifesting as timeouts. ROCm runners have at least 1 GPU each, but often 2 or more. This PR allows NUM_PROCS to be set equal to the number of devices available, but also takes care to set HIP_VISIBLE_DEVICES to avoid oversubscribing any GPU.
Second, we had introduced env vars `-e ROCR_VISIBLE_DEVICES` (#91031) to prepare for two GHA runners per CI node, to split up the GPU visibility at the docker level between the two runners. This effort wasn't fully realized; to date, we haven't had more than one runner per CI host. We abandon this effort in favor of all GPUs being visible to a single runner and managing GPU resources as stated above.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91137
Approved by: https://github.com/kit1980, https://github.com/huydhn, https://github.com/pruthvistony
tbh at this point it might be easier to make a new workflow and copy the relevant jobs...
Changes:
* Disable cuda mem leak check except for on scheduled workflows
* Make pull and trunk run on a schedule which will run the memory leak check
* Periodic will always run the memory leak check -> periodic does not have parallelization anymore
* Concurrency check changed to be slightly more generous
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88373
Approved by: https://github.com/ZainRizvi, https://github.com/huydhn
Sometimes you want to query the small element of a set of elements and use `sorted(elements)[0]` without a second thought. However, this is not optimal, since the entire list must be sorted first `O(n log n)`. It would be better to use the `min(elements)` method provided for this purpose `O(n)`.
Furthermore `sorted(elements)[::-1]` is not very efficient, because it would be better to use `sorted(elements, reverse=True)` to save the slice operation.
**TLDR: using `sorted(elements)[0]` is slow and can be replaced with `min(elements)`.**
I stumbled across these code snippets while playing around with CodeQL (see https://lgtm.com/query/4148064474379348546/).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86995
Approved by: https://github.com/jansel
run tests in parallel at the test file granularity
runs 3 files in parallel using multiprocessing pool, output goes to a file, which is then printed when the test finishes. Some tests cannot be run in parallel (usually due to lacking memory), so we run those after. Sharding is changed to attempt to mask large files with other large files/run them on the same shard.
test_ops* gets a custom handler to run it because it is simply too big (2hrs on windows) and linalg_cholesky fails (I would really like a solution to this if possible, but until then we use the custom handler).
reduces cuda tests by a lot, reduces total windows test time by ~1hr
Ref. https://github.com/pytorch/pytorch/issues/82894
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84961
Approved by: https://github.com/huydhn
Fix use-dict-literal pylint suggestions by changing `dict()` to `{}`. This PR should do the change for every Python file except test/jit/test_list_dict.py, where I think the intent is to test the constructor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83718
Approved by: https://github.com/albanD
With ufmt in place https://github.com/pytorch/pytorch/pull/81157, we can now use it to gradually format all files. I'm breaking this down into multiple smaller batches to avoid too many merge conflicts later on.
This batch (as copied from the current BLACK linter config):
* `tools/**/*.py`
Upcoming batchs:
* `torchgen/**/*.py`
* `torch/package/**/*.py`
* `torch/onnx/**/*.py`
* `torch/_refs/**/*.py`
* `torch/_prims/**/*.py`
* `torch/_meta_registrations.py`
* `torch/_decomp/**/*.py`
* `test/onnx/**/*.py`
Once they are all formatted, BLACK linter will be removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81285
Approved by: https://github.com/suo
`JOB_BASE_NAME` was a holdover from jenkins compatibility. Eventually,
it morphed to be always set to the build enviroment + `-test` or
`-build`, and we used it to detect whether we were in a build or test.
That's sort of pointless, so removing and fixing up the few remaining
use cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80046
Approved by: https://github.com/malfet, https://github.com/janeyx99
`JOB_BASE_NAME` was a holdover from jenkins compatibility. Eventually,
it morphed to be always set to the build enviroment + `-test` or
`-build`, and we used it to detect whether we were in a build or test.
That's sort of pointless, so removing and fixing up the few remaining
use cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80046
Approved by: https://github.com/malfet
In the case of target determination, this is just removing comments that
refer to non-existent code.
In the case of the test specification code; this removes (what I believe
to be) an unused feature. If we're using this somehow let me know and I
can revise the PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79372
Approved by: https://github.com/janeyx99
Fixes #ISSUE_NUMBER
Sharding for linux-bionic-py3.7-clang9 previously included slow test times in the calculation for how long a test takes, causing the sharding to be uneven:
| Duration | Count | Name|
| ----------- | ----------- | ---|
| 11.2m | 221 |linux-bionic-py3.7-clang9 / test (default, 1, 2, linux.2xlarge)|
| 1.1h | 218 | linux-bionic-py3.7-clang9 / test (default, 2, 2, linux.2xlarge)|
Numbers taken from https://hud.pytorch.org/metrics from 04/10/2022 12:20 PM to 04/17/2022 12:20 PM.
The duration of these jobs on this PR are 39m and 38m.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75918
Approved by: https://github.com/seemethere, https://github.com/janeyx99
Summary:
and into tools/ folder
Currently run_tests.py invokes tools/test_selections.py
1. download and analyze what test_file to run
2. download and parse S3 stats and pass the info to local files.
3. common_utils.py uses download S3 stats to determine what test cases to run.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61479
Reviewed By: janeyx99
Differential Revision: D29661986
Pulled By: walterddr
fbshipit-source-id: bebd8c474bcc2444e135bfd2fa4bdd1eefafe595
Summary:
run_test.py currently does lots of downloading and test file/suite/case parsing. It doesn't work well outside of the CI environment
Restructured the run_test.py and created tools/test/test_selections.py and move all test selection logic (reordering, categorizing slow test, creating shards)
Follow up PRs should:
- refactor those file read/write logic entangled inside test_selections.py into stats/ folder
- restructure and add network independent test logics to test_test_selections.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61124
Test Plan:
- tools/test
- CI
Related PR:
This follows the refactoring example in: https://github.com/pytorch/pytorch/issues/60373
Reviewed By: malfet
Differential Revision: D29558981
Pulled By: walterddr
fbshipit-source-id: 7f0fd9b4720a918d82918766c002295e8df04169
Summary:
Changes including:
- introduced `linter/`, `testing/`, `stats/` folders in `tools/`
- move appropriate scripts into these folders
- change grepped references in the pytorch/pytorch repo
Next step
- introduce `build/` folder for build scripts
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60473
Test Plan:
- CI (this is important b/c pytorch/test-infra also rely on some script reference.
- tools/tests/
Reviewed By: albanD
Differential Revision: D29352716
Pulled By: walterddr
fbshipit-source-id: bad40b5ce130b35dfd9e59b8af34f9025f3285fd
