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d39bdefb33a19e407c352df27fb04127f4fe8a1d by Peter Hawkins <phawkins@google.com>:
Migrate more tests from jtu.cases_from_list to jtu.sample_product.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/jax/pull/12717 from hawkinsp:sampletest d39bdefb33a19e407c352df27fb04127f4fe8a1d
PiperOrigin-RevId: 480136538
* trailing-whitespace
* dangerous-default-value. None of these appear to be bugs in practice, but the potential for accidentally mutating the default value is there, and the cost of avoiding the problem is small.
* invalid-envvar-default. Pass strings as getenv() defaults.
* unnecessary-semicolon. Use tuples instead for this one-liner.
* invalid-hash-returned. Raise an exception rather than asserting false.
* pointless-string-statement. Use comments instead.
* unreachable. Use @unittest.skip() decorator rather than raising as first line in test.
* logging-not-lazy. Make the logging lazy.
* bad-format-string-type. Use f-string instead.
* subprocess-run-check. Pass check=...
PiperOrigin-RevId: 400858477
* Use a whitelist to restrict visibility in top-level jax namespace.
The goal of this change is to capture the way the world is (i.e., not break users), and separately we will work on fixing users to avoid accidentally-exported APIs.
* Improve JAX test PRNG APIs to fix correlations between test cases.
In #2863, we observed that we were missing gradient problems because the random test cases being generated were too similar because they were formed with identically seeded PRNGs. This change updates the test_util.rand_...() functions to take an explicit numpy.random.RandomState, and adds a rng() method to JaxTestCase to form a RandomState seeded on the test case name.
This gives the following properties:
* different test cases receive different seeds
* PRNG seeding is deterministic and independent of execution order and sharding.
* PRNG seeding is deterministic across runs.
* Fix some failing tests.
* Fix more test failures.
Simplify ediff1d implementation and make it more permissive when casting.
* Relax test tolerance of laplace CDF test.
* Implement np.roots.
* Expose jit-compatible variant of np.roots.
General np.roots implementation has a value dependent output shape.
If the input coefficients are guaranteed to have no leading zeros,
output shape is independent of values. Skip checking for leading
zeros by setting a keyword argument.
* Fix typo.
* Make roots jit-argument keyword only.
Co-Authored-By: Stephan Hoyer <shoyer@google.com>
* Format docstring to enable parsing.
Co-Authored-By: Stephan Hoyer <shoyer@google.com>
* Add np.roots function to documentation.
* Add more tests for np.roots function.
- Include length 0 polynomial coefficients
- Test strip_zeros=False argument
- Test jit compiled version (only on cpu due to eigvals)
- Confirm that adding leading zeros while skipping check
for them results in nan's (expected behavior)
* Fix bug in np.roots test.
The polynomial with coefficents [0] never fails because the number of
roots is 0.
* Avoid bug in eigvals and adjust test accuracy.
The parameters of the test that was changed are non-essential
since they test for how the code behaves given invalid inputs.
The accuracy in comparing to the numpy result is changed because
the algorithm in those cases is slightly changed with respect to
the original numpy algorithm (to allow jit).
Co-authored-by: Stephan Hoyer <shoyer@google.com>
