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⚠️ Nightly upstream-dev CI failed ⚠️ #22251

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github-actions bot opened this issue Jul 3, 2024 · 1 comment · Fixed by #22257
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⚠️ Nightly upstream-dev CI failed ⚠️ #22251

github-actions bot opened this issue Jul 3, 2024 · 1 comment · Fixed by #22257
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github-actions bot commented Jul 3, 2024

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Summary of Failures 
tests/lax_numpy_reducers_test.py::JaxNumpyReducerTests::testCumulativeSum0: AssertionError: 
Not equal to tolerance rtol=1e-07, atol=0

Mismatched elements: 2 / 3 (66.7%)
Max absolute difference among violations: 1
Max relative difference among violations: 1.
 ACTUAL: array([[1],
       [0],
       [0]], dtype=uint8)
 DESIRED: array([[1],
       [1],
       [1]], dtype=uint8)
tests/lax_numpy_test.py::NumpySignaturesTest::testWrappedSignaturesMatch: AssertionError: {'clip': {'np_params': ['a', 'a_min', 'a_max', 'min', 'max'], 'jnp_params': ['a', 'a_min', 'a_max']}, 'cumulative_sum': {'np_params': ['x', 'axis', 'dtype', 'out', 'include_initial'], 'jnp_params': ['x', 'axis', 'dtype', 'include_initial']}} != {}
Missing entries:
'clip': {'np_params': ['a', 'a_min', 'a_max', 'min', 'max'], 'jnp_params': ['a', 'a_min', 'a_max']}
'cumulative_sum': {'np_params': ['x', 'axis', 'dtype', 'out', 'include_initial'], 'jnp_params': ['x', 'axis', 'dtype', 'include_initial']}
@github-actions github-actions bot added the CI label Jul 3, 2024
@dfm dfm self-assigned this Jul 3, 2024
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dfm commented Jul 3, 2024

I can reproduce the issues locally with the nightly numpy - I will investigate!

dfm added a commit to dfm/jax that referenced this issue Jul 3, 2024
@dfm
Fix compatibility with nightly numpy
1b4c4a5 
Numpy recently merged support for the 2023.12 revision of the Array API:
numpy/numpy#26724

This breaks two of our tests and I've chosen to skip those tests for
now:

1. The first breakage was caused by differences in how numpy and JAX
   cast negative floats to `uint8`. Specifically
   `np.float32(-1).astype(np.uint8)` returns `np.uint8(255)` whereas
   `jnp.float32(-1).astype(jnp.uint8)` produces `Array(0, dtype=uint8)`.
   We don't make any promises about consistency with casting floats to
   ints, noting that this can even be backend dependent. I don't believe
   this failure is identifying any unexpected behavior, and we test many
   other dtypes properly so I'm not concerned about skipping this test.

2. The second failure was caused by the fact that the approach we took
   in jax-ml#20550 to support backwards compatibility and the Array API for
   `clip` differs from the one used in numpy/numpy#26724. Again, the
   behavior is consistent, but it produces a different signature. I've
   skipped checking `clip`'s signature, but we should revisit it once
   the `a_min` and `a_max` parameters have been removed from JAX.

Fixes jax-ml#22251
@dfm dfm mentioned this issue Jul 3, 2024
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Fix compatibility with nightly numpy dfm/jax
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