Backend-Agnostic Refactor Using Array API Compatibility

[amalia-k510]

This PR refactors the gen_adata function in benchmarks/utils.py to support backend-agnostic generation of AnnData objects, using the array-api-compat interface to infer and operate with the correct array namespace (e.g., NumPy or JAX). The goal is to allow tests and benchmarking utilities to seamlessly run across supported array backends without manual rewrites. The function now detects the namespace from a real array object instead of relying on a global fallback and uses backend-specific RNG to generate observation and variable metadata. I also updated the related test to parameterize over NumPy and JAX backends. However, I'm currently running into an issue where passing a scipy.sparse.csr_matrix into get_namespace() raises a TypeError, since sparse matrices are not supported by array-api-compat. As a workaround, I’m generating a dense array first to infer the namespace, and only falling back to sparse when explicitly requested in the attribute set. This works fine for NumPy but breaks for JAX, as sparse support isn’t available there. Still trying to figure out whether to raise, skip, or handle this more gracefully depending on the backend. Would appreciate any thoughts on best practices here.

[ilan-gold]

3 things to simplify things:

1. Ignore the benchmarks for now, including the gen_adata there. There is another gen_adata in https://github.com/scverse/anndata/blob/main/src/anndata/tests/helpers.py that you can add a jax type to much more easily (i.e., X_type or or DEFAULT_KEY_TYPES), and then you will get the entire test suit for free running against jax. Specifically, I would add it as a type for obsm, varm, X, and layers while ignoring the otehrs. It doesn't make sense to use it in obs or var, for example
2. No need to use the array_api where it's not needed (like for sparse as you point out). I would remove it from all the other subsetting functions and only have it in the default single_dispatch case
3. The test file you have is great! I would start there if you feel overwhelmed by the full test suite

[ilan-gold]

No need to worry bout jax for this! I don't even think strings are in the array api: https://data-apis.org/array-api/latest/API_specification/data_types.html#data-types

[codecov]

Codecov Report

Attention: Patch coverage is 38.07829% with 174 lines in your changes missing coverage. Please review.

Project coverage is 78.09%. Comparing base (a51582d) to head (f5c387c).
Report is 2 commits behind head on main.

Files with missing lines	Patch %	Lines
src/anndata/_core/index_modified.py	19.28%	159 Missing ⚠️
src/anndata/tests/helpers.py	82.14%	15 Missing ⚠️

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Flag	BASE (a51582d)	HEAD (f5c387c)
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Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2019      +/-   ##
==========================================
- Coverage   87.47%   78.09%   -9.38%     
==========================================
  Files          46       47       +1     
  Lines        7057     7332     +275     
==========================================
- Hits         6173     5726     -447     
- Misses        884     1606     +722     

Files with missing lines	Coverage Δ
src/anndata/tests/helpers.py	79.42% <82.14%> (-13.65%)	⬇️
src/anndata/_core/index_modified.py	19.28% <19.28%> (ø)

... and 20 files with indirect coverage changes

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[ilan-gold]

Chekc type of subset_idx against what is required from https://data-apis.org/array-api/latest/API_specification/indexing.html#indexing