The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64721
There are a couple PRs where distributed CI did not runa nd we expect
it to. Examples:
https://github.com/pytorch/pytorch/pull/64513/checks?check_run_id=3539190960,
https://github.com/pytorch/pytorch/pull/64113. All distributed tests should've
been run on these PRs, but we can see they were not:
```
Determination is skipping distributed/test_c10d_common
Determination is skipping distributed/test_c10d_gloo
Determination is skipping distributed/test_c10d_nccl
Determination is skipping distributed/test_c10d_spawn_gloo
Determination is skipping distributed/test_c10d_spawn_nccl
Running distributed/test_data_parallel without determination
Determination is skipping distributed/test_distributed_spawn
Determination is skipping distributed/test_jit_c10d
```
Since it is important to run distributed tests on PRs that touch distributed,
exclude distributed from target_det_list for now.
ghstack-source-id: 137654015
Test Plan: CI
Reviewed By: driazati, mrshenli
Differential Revision: D30830455
fbshipit-source-id: 8b0fdf5b57c2c647b0d82c48e2bb8e2bdbe4d307
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64102
We sort this list so that we may add comments to indicate the absence
of a file right where that file would need to be put. This makes it
difficult to wrongly add such a file.
The sorting itself was done programmatically to ensure that no entries
were inadvertently removed.
I printed the sorted list with:
```
for p in sorted(TARGET_DET_LIST):
print(f' "{p}",')
```
Then copied it back into the file.
Test Plan: Imported from OSS
Reviewed By: driazati
Differential Revision: D30625076
Pulled By: dagitses
fbshipit-source-id: cf36fcb3e53e274b76d1f4aae83da1f53c03f9ed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63809
This moves out the modulefinder determinator to `tools/testing` since it is supposed to be CI-only. This also simplifies run_test.py a little bit.
Test Plan: Imported from OSS
Reviewed By: malfet, seemethere, janeyx99
Differential Revision: D30497438
Pulled By: driazati
fbshipit-source-id: 1d203037af5af6a20c1e7812da935e7cbb5cd82f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59007
Create folders for each test category and move the tests.
Will follow-up with a cleanup of test_quantization.py
Test Plan:
python test/test_quantization.py
Imported from OSS
Reviewed By: HDCharles
Differential Revision: D28718742
fbshipit-source-id: 4c2dbbf36db35d289df9708565b7e88e2381ff04
Summary:
This PR:
1. refactors the logic for S3 stats gathering.
2. Renames SLOW_TESTS to TARGET_DET_LIST to disambiguate and remove confusion with slowTest
2. detects slow tests (tests with time > 5min) to add to the TARGET_DET_LIST based on results in S3 from the previous nightly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53549
Test Plan:
Set CIRCLE_JOB to your favorite CI job (like `pytorch_linux_bionic_py3_8_gcc9_coverage_test1`).
Run `python test/run_test.py --determine-from=<your fave pytorch files>`
e.g., `python test/run_test.py --determine-from=test/run_test.py`
Reviewed By: mrshenli
Differential Revision: D26904478
Pulled By: janeyx99
fbshipit-source-id: 9576b34f4fee09291d60e36ff2631753a3925094
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42932
Follow up from https://github.com/pytorch/pytorch/pull/41769, rename `test_distributed` to `test_distributed_fork` to make it explicit that it forks.
New command to run test:
`python test/run_test.py -i distributed/test_distributed_fork -v`
ghstack-source-id: 111632568
Test Plan: `python test/run_test.py -i distributed/test_distributed_fork -v`
Reviewed By: izdeby
Differential Revision: D23072201
fbshipit-source-id: 48581688b6c5193a309e803c3de38e70be980872
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40818
Summary of the entire stack:
--
This diff is part of an attempt to refactor the RPC tests. They currently suffer from several problems:
- Several ways to specify the agent to use: there exists one "generic" fixture that uses the global variable TEST_CONFIG to look up the agent name, and is used for process group and Thrift, and then there are separate fixtures for the flaky agent and the TensorPipe one.
- These two ways lead to having two separate decorators (`requires_process_group_agent` and `@_skip_if_tensorpipe_agent`) which must both be specified, making it unclear what the effect of each of them is and what happens if only one is given.
- Thrift must override the TEST_CONFIG global variable before any other import (in order for the `requires_process_group_agent` decorator to work correctly) and for that it must use a "trap" file, which makes it even harder to track which agent is being used, and which is specific to Buck, and thus cannot be used in OSS by other agents.
- Even if the TensorPipe fixture doesn't use TEST_CONFIG, it still needs to set it to the right value for other parts of the code to work. (This is done in `dist_init`).
- There are a few functions in dist_utils.py that return some properties of the agent (e.g., a regexp to match against the error it returns in case of shutdown). These functions are effectively chained if/elses on the various agents, which has the effect of "leaking" some part of the Thrift agent into OSS.
- Each test suite (RPC, dist autograd/dist optimizer, their JIT versions, remote module, ...) must be run on each agent (or almost; the faulty one is an exception) in both fork and spawn mode. Each of these combinations is a separate file, which leads to a proliferation of scripts.
- There is no "master list" of what combinations make sense and should be run. Therefore it has happened that when adding new tests or new agents we forgot to enroll them into the right tests. (TensorPipe is still missing a few tests, it turns out).
- All of these tiny "entry point" files contain almost the same duplicated boilerplate. This makes it very easy to get the wrong content into one of them due to a bad copy-paste.
This refactoring aims to address these problems by:
- Avoiding global state, defaults/override, traps, if/elses, ... and have a single way to specify the agent, based on an abstract base class and several concrete subclasses which can be "mixed in" to any test suite.
- Instead of enabling/disabling tests using decorators, the tests that are specific to a certain agent are now in a separate class (which is a subclass of the "generic" test suite) so that they are only picked up by the agent they apply to.
- Instead of having one separate entry point script for each combination, it uses one entry point for each agent, and in that script it provides a list of all the test suites it wants to run on that agent. And it does that by trying to deduplicate the boilerplate as much as possible. (In fact, the various agent-suite combinations could be grouped in any way, not necessarily by agent as I did here).
It provides further advantages:
- It puts all the agents on equal standing, by not having any of them be the default, making it thus easier to migrate from process group to TensorPipe.
- It will make it easier to add more versions of the TensorPipe tests (e.g., one that disables the same-machine backends in order to test the TCP-based ones) without a further duplication of entry points, of boilerplate, ...
Summary of this commit
--
This diff does the changes described above for the process group agent. It defines a fixture for it (instead of using the generic fixture in its default behavior) and then merges all the entry points into a single script. Note that after this change there won't be anymore a "vanilla" RPC test: all test scripts now specify what agent they are using. This puts all agents on equal standing.
ghstack-source-id: 109229474
Test Plan: Sandcastle and CircleCI
Reviewed By: pritamdamania87
Differential Revision: D22283182
fbshipit-source-id: 7e3626bbbf37d88b892077a03725f0598576b370
Summary:
Should be a no-op, just makes the intent a bit cleaner
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39068
Differential Revision: D21829464
Pulled By: malfet
fbshipit-source-id: dc174a3d7da3701bd9d31c366dfa9d24044ef27a
Summary:
This makes it a proper python package, therefore `ModuleFinder` will parse dependencies from this module. (see https://docs.python.org/3/tutorial/modules.html )
As result, changes to `torch/testing/_internal/common_quantization` or `test/quantization/*.py` would be considered affecting `test_quantization.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37934
Test Plan: CI
Differential Revision: D21432413
Pulled By: malfet
fbshipit-source-id: acff6cee69a1dfd5535e33978f826ed1f6a70821
Summary:
The original behavior of pytorch c10d only supports built-in c10d backends, such as
nccl/gloo/mpi. This patch is used to extend the c10d capability to support dynamically
loading 3rd party communication libraries which are derived from ProcessGroup base class.
related RFC is in: https://github.com/pytorch/pytorch/issues/27955
Through this way, user just need specify a 3rd party c10d backend name when invoking
torch.distributed.init_process_group(). The proposed logic will try to load corresponding
c10d backend cpp extension automatically. as for how to develop a new 3rd party c10d backend
through cpp extension, pls refer to test/cpp_extensions/cpp_c10d_extension.cpp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28068
Differential Revision: D19174838
Pulled By: agolynski
fbshipit-source-id: 3409a504a43ce7260e6f9d1207c00e87471fac62
