DeadlineExceeded failures in test_eye/test_linspace

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#214 opened on Nov 19, 2023 3 comments 1 reaction 0 assignees View on GitHub

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Assessment

Difficulty: 2/5
Estimated time: 1-3 hours
Newbie friendliness: 45/100

Issue type: Bug
Clarity: Mostly clear
Activity status: Stale
Tech stack: python
Domain: testing

Research direction

Run the listed pytest command and inspect array_api_tests/test_creation_functions.py, focusing on test_eye and test_linspace. Trace the generated dimension sizes and apply the issue's proposed restriction on the maximum size. Done means both tests complete without Hypothesis DeadlineExceeded failures.

Written by the indexing model from the issue text.

Description

bug performance

Running array-api-tests suite on dpctl.tensor (https://github.com/IntelPython/dpctl):

ONEAPI_DEVICE_SELECTOR=*:cpu ARRAY_API_TESTS_MODULE=dpctl.tensor python -m pytest array_api_tests/test_creation_functions.py

two test new failures consistently occur (after updating from f82c7bc8627cc2c3a44fa3e425f53a253a609aa8 to recent tip of main, i.e. 9d7777bc60d45a35124b0de084ff8edbd284c06b), e.g.:

FAILED array_api_tests/test_creation_functions.py::test_eye - hypothesis.errors.DeadlineExceeded: Test took 1470.80ms, which exceeds the deadline of 800.00ms
FAILED array_api_tests/test_creation_functions.py::test_linspace - hypothesis.errors.DeadlineExceeded: Test took 864.40ms, which exceeds the deadline of 800.00ms

Here is my understanding of what happens.

Hypothesis would generate a sufficiently large array, i.e. eye(47, 100) but testing of the output correctness is is done one element at the time (suboptimal usage for offloading libraries as it triggers 4700 submissions of small kernels) and this testing would take much longer than the creation of the array itself:

In [7]: import dpctl, dpctl.tensor as dpt

In [8]: %time eye_m = dpt.eye(47,100)
CPU times: user 3.23 ms, sys: 0 ns, total: 3.23 ms
Wall time: 2.76 ms

In [9]: %time all(eye_m[i, j] == (1 if i == j else 0) for i in range(eye_m.shape[0]) for j in range(eye_m.shape[1]))
CPU times: user 6.68 s, sys: 1.98 s, total: 8.67 s
Wall time: 2.09 s
Out[9]: True

The remedy would be to restrict the maximal dimension size.

Dominant language: Python
Stars: 74
Forks: 54
Avg merge: 4h 35m
Merged PRs (30d): 4

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