ENH Add common Array API tests and estimator tag by betatim · Pull Request #26372 · scikit-learn/scikit-learn

betatim · 2023-05-15T08:57:17Z

Reference Issues/PRs

What does this implement/fix? Explain your changes.

This adds a "Array API support" estimator tag that allows the common estimator checks to test if an estimator's Array API support full fills basic requirements.

The idea is to have these tests as common tests instead of having them duplicated in the tests for each estimator (#26243, #26315, #22554, #25956).

Any other comments?

What would be a good way to have an additional dtype (float16) in the torch test? We can't just add it because some estimators (like LinearDiscriminantAnalysis) don't support it. But some others do. So we need a way to detect if an estimator should work or not with float16s.

adrinjalali

we also need tests to test the new added common checks

adrinjalali · 2023-05-15T10:10:57Z

+    """Check that the array_api Array gives the same results as ndarrays."""
+    xp = pytest.importorskip(array_namespace)
+
+    X, y = make_classification(random_state=42)


don't we use load_iris here instead?

I don't understand the question.

the rest of the common tests use the iris dataset, we don't seem to use make_classification anywhere else. Here we could use iris too.

Since we repeat that everywhere, I wanted to see the impact in terms of computational resources and both options are cheap enough.

In [1]: from sklearn.datasets import load_iris In [2]: %timeit load_iris() 215 µs ± 1.96 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each) In [3]: from sklearn.datasets import make_classification In [4]: %timeit make_classification(random_state=42) 164 µs ± 721 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

We could switch to load_iris for the sake of consistency but no strong opinion.

ogrisel · 2023-05-16T12:48:47Z

Would it be possible to consolidate the two check_* functions together to avoid introducing a PyTorch specific function?

It should be possible to call the standard some_xp_array.to_device(ns_specific_device_name_or_object) for namespaces that support multiple devices or alternatively rely on asarray(data, device=ns_specific_device_name_or_object).

EDIT: you might need https://github.com/data-apis/array-api-compat to expose torch as a regular array namespace to make the code of the checker library agnostic beyond the namespace name to create the arrays and get an asarray constructor:

>>> import array_api_compat.torch as xp
>>> xp.asarray(np.arange(10), dtype=xp.float32, device="cpu")
tensor([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])

Micky774 · 2023-05-16T15:21:17Z

+
+        est_xp_param_np = _convert_to_numpy(est_xp_param, xp=xp)
+        assert_allclose(
+            attribute, est_xp_param_np, err_msg=f"{key} not the same", atol=1e-3


I'm actually a bit surprised to see atol=1e-3. Is there a particular reason why we have such a large tolerance? I know it is inherited from the existing tests, but I don't know the reasoning for it originally either. It just seems especially large compared to our tolerances on many other algorithms and implementations.

This is comparing the output of results from a GPU and CPU, which can result in bigger differences. For example, PyTorch sets atol=1e-3 and rtol=1e-3 when comparing CPU and GPU operations: https://github.com/pytorch/pytorch/blob/c618093681bfbb7fca6159de5b73ffceb5b4e0f5/test/test_ops.py#L287

Is this a good enough explanation @Micky774 or should we dig a bit more?

Worth adding Thomas' answer as a comment for people from the future who have the same question?

After seeing Olivier's change to 100 * finfo(dtype).eps, should we still add a comment?

It wouldn't hurt to leave a comment about how these error bounds are tighter than PyTorch which is a strong precedence.

ogrisel · 2023-05-30T13:53:31Z

I went ahead and fixed the tests for cupy and removed the now redundant pytorch test.

I also changed the atol value to be 100 x finfo(dtype).eps and the tests pass on a machine with a cuda GPU (both cupy and pytorch):

$ pytest -k check_array_api_input sklearn/tests/test_common.py  -vx
============================================================================================== test session starts ===============================================================================================
platform linux -- Python 3.11.3, pytest-7.3.1, pluggy-1.0.0 -- /data/parietal/store3/work/ogrisel/mambaforge/envs/cuda/bin/python3.11
cachedir: .pytest_cache
rootdir: /home/parietal/ogrisel/code/scikit-learn
configfile: setup.cfg
collected 9857 items / 9851 deselected / 6 selected                                                                                                                                                              

sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_array_api_input(array_namespace=numpy.array_api)] PASSED                                                                  [ 16%]
sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_array_api_input(array_namespace=cupy.array_api)] PASSED                                                                   [ 33%]
sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_array_api_input(array_namespace=torch,dtype=float64,device=cpu)] PASSED                                                   [ 50%]
sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_array_api_input(array_namespace=torch,dtype=float32,device=cpu)] PASSED                                                   [ 66%]
sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_array_api_input(array_namespace=torch,dtype=float64,device=cuda)] PASSED                                                  [ 83%]
sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_array_api_input(array_namespace=torch,dtype=float32,device=cuda)] PASSED                                                  [100%]

================================================================================ 6 passed, 9851 deselected, 77 warnings in 6.27s =================================================================================

ogrisel

Assuming CI is green, LGTM.

ogrisel · 2023-05-30T16:18:34Z

CI is not green when array-api-compat is not installed. Will update the tests.

ogrisel · 2023-05-30T16:38:30Z

@betatim I also made sure that estimator checks can be executed without the dependency on pytest by raising SkipTest manually instead of relying on pytest.skip or pytest.importorskip.

ogrisel · 2023-05-31T14:46:27Z

I missed some pytest imports in sklearn/utils/tests/test_estimator_checks.py, hopefully fixed in the last commit.

adrinjalali · 2023-06-01T10:40:32Z

Shouldn't we have a place where we explain how an environment can be created where these tests are not skipped? We're adding quite some environment complexity here with a few dependencies, it'd be nice to have them documented. Otherwise this LGTM.

…on-test-array-api

ogrisel · 2023-06-01T12:39:28Z

Shouldn't we have a place where we explain how an environment can be created where these tests are not skipped

As soon as the optional dependencies (array-api-compat and pytorch or cupy) are installed and the matching hardware is available (e.g. cuda for torch gpu and cupy), the relevant tests are not skipped anymore. The skip tests messages seem explicit enough to know what to install, no? I am worried that adding doc on how to install such extra packages will be redundant with the installation instructions of said packages and I am worried that our doc might not stay up to date.

Or maybe we could add a list of links to the installation instructions of the afore mentioned packages at the bottom of the Array API doc page in scikit-learn?

betatim · 2023-06-01T12:44:35Z

I added a short section to develop.rst about the dependencies you need, that there are common checks and that things get skipped if not available.

adrinjalali · 2023-06-01T12:55:30Z

The skip tests messages seem explicit enough to know what to install, no?

When I write tests, there are hundreds of skipped tests and tons of xfailed ones, it's not reasonable to expect that one can easily find steps to make all the tests run. But it's also not super crucial maybe.

I just don't understand where we're testing the GPU code here in our CI. If we have a job doing that, I'd be happier with having the PR merged.

thomasjpfan · 2023-06-01T13:49:07Z

I just don't understand where we're testing the GPU code here in our CI. If we have a job doing that, I'd be happier with having the PR merged.

On the CI, we are only testing Array API support using PyTorch on CPU and NumPy's Array API implementation. For coverage, PyTorch on CPU will test the same code paths as a GPU array. The only thing we are missing from the CI is the correctness of the GPU result.

thomasjpfan

Overall, I think this looks good.

betatim · 2023-06-01T14:47:40Z

Comments addressed.

Testing on an actual GPU in CI would be nice. Even if we only do it once a week or so. However, it is proving hard to find a CI with a GPU :-/

aktech · 2023-06-01T18:31:19Z

it is proving hard to find a CI with a GPU :-/

Hey @betatim not anymore. You can try https://cirun.io it supports GPU runners for GitHub Actions, spun up on a cloud provider like say AWS.

Here is an example of a sample run: https://github.com/pystatgen/sgkit/actions/runs/5142554146/jobs/9256432281

Here is an example configuration:

https://github.com/pystatgen/sgkit/blob/main/.cirun.yml

betatim · 2023-06-02T12:17:41Z

My comment about hardness might have factored in the hardness of finding someone who pays for the costs of running CI on GPUs ;)

But while I have an expert here: what alternatives/competitors exist to cirun.io?

aktech · 2023-06-02T12:37:30Z

My comment about hardness might have factored in the hardness of finding someone who pays for the costs of running CI on GPUs ;)

Yeah, that's the tricky bit.

But while I have an expert here: what alternatives/competitors exist to cirun.io?

None that I'm aware of in the GPU space, I read somewhere that CircleCI supports GPUs as well on enterprise plans, but have never tried it. There is another called buildjet in non-gpu space.

rgommers · 2023-06-02T14:26:34Z

Cost isn't really the problem, unless you want to run it by default on PRs. Above you (@betatim) said "Even if we only do it once a week or so." - that'd be like $100/yr. It's admin'ing the integration and cloud account that's the painful part.

None that I'm aware of in the GPU space, I read somewhere that CircleCI supports GPUs as well on enterprise plans, but have never tried it.

PyTorch CI used to run completely on CircleCI; it is very good, but also very expensive.

aktech · 2023-06-02T14:35:09Z

It's admin'ing the integration and cloud account that's the painful part.

That's something I can help with, I already do that with some projects, if I can get a cloud account.

thomasjpfan

I tested the changes on a local machine with CuPy and left some suggestions to get the common test working with CuPy.

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

The namespace is not exactly the same as the module.

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

betatim · 2023-06-06T08:14:56Z

Addressed the latest rounds of comments from Thomas

ogrisel · 2023-06-13T15:26:17Z

@thomasjpfan I think your comments were taken into account. Merge?

thomasjpfan

Thanks for the update! LGTM

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

betatim added 2 commits May 15, 2023 10:44

Add common Array API tests and estimator tag

abf3299

Remove dedicated test for LinearDiscriminantAnalysis

32cb962

github-actions Bot added the module:utils label May 15, 2023

adrinjalali reviewed May 15, 2023

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betatim added 2 commits May 15, 2023 15:42

Lower case estimator tag

9fa5b66

Add tests for Array API common tests

059ff21

ogrisel added the No Changelog Needed label May 16, 2023

Micky774 reviewed May 16, 2023

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More tests

4e12f4c

ogrisel reviewed May 25, 2023

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Comment thread sklearn/utils/estimator_checks.py

ogrisel added 3 commits May 30, 2023 15:16

FIX use device=None to make common tests pass with cupy

e27bc4a

Remove redundant check_array_api_input_torch

32f3edb

Use a dtype specific atol in check_array_api_input

873a463

ogrisel approved these changes May 30, 2023

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ogrisel added 2 commits May 30, 2023 18:36

Make estimator checks runnable without pytest

7cb40b4

Skip test_check_array_api_input if array_api_compat is not installed

176fa43

FIX do not import pytest in sklearn/utils/tests/test_estimator_checks.py

a5d60de

betatim added 2 commits June 1, 2023 14:37

Add documentation about running common checks

94c52dc

Merge remote-tracking branch 'origin/common-test-array-api' into comm…

443071f

…on-test-array-api

thomasjpfan reviewed Jun 1, 2023

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Comment thread sklearn/utils/estimator_checks.py Outdated

Comment thread sklearn/utils/estimator_checks.py Outdated

Comment thread sklearn/utils/estimator_checks.py

Comment thread sklearn/utils/estimator_checks.py Outdated

Comment thread sklearn/utils/tests/test_estimator_checks.py Outdated

Implement nits

c902dcd

Remove unused import

a375fe2

thomasjpfan reviewed Jun 2, 2023

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Comment thread sklearn/utils/estimator_checks.py Outdated

Comment thread sklearn/utils/estimator_checks.py

Comment thread sklearn/utils/estimator_checks.py Outdated

Comment thread sklearn/utils/tests/test_estimator_checks.py Outdated

Comment thread sklearn/utils/estimator_checks.py Outdated

ogrisel and others added 5 commits June 4, 2023 17:32

Merge branch 'main' into common-test-array-api

ec25e97

Improve error messages

9b6d6f5

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

Get namespace from a dummy array

951f8d2

The namespace is not exactly the same as the module.

Avoid copies where possible

5b65968

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

Skip CUDA tests with cupy

da8141e

thomasjpfan approved these changes Jun 13, 2023

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thomasjpfan changed the title ~~Add common Array API tests and estimator tag~~ ENH Add common Array API tests and estimator tag Jun 13, 2023

thomasjpfan merged commit d29f78e into scikit-learn:main Jun 13, 2023

This was referenced Jun 14, 2023

ENH Array API support for PCA #26315

Merged

Add common tests for estimators that support the Array API #26348

Closed

betatim deleted the common-test-array-api branch June 21, 2023 15:03

valeriupredoi mentioned this pull request Jul 3, 2023

Fix failing mlr diagnostic test ESMValGroup/ESMValTool#3271

Closed

5 tasks

thomasjpfan added the Array API label Jul 3, 2023

REDVM pushed a commit to REDVM/scikit-learn that referenced this pull request Nov 16, 2023

ENH Add common Array API tests and estimator tag (scikit-learn#26372)

d93be08

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

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betatim commented May 15, 2023

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What does this implement/fix? Explain your changes.

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