[RFC]: Updates for float16/bfloat16 and for dtypes that are lacking full support in libraries

Comienza con la página data_types del Array API standard 2025.12 y compara sus definiciones de dtype con las referencias enlazadas de NumPy, PyTorch, JAX, CuPy, MLX, ndonnx, DPCTL y cuTile. Aclara qué documentación necesita actualizarse, reserva los nombres float16 y bfloat16 y documenta la compatibilidad únicamente de almacenamiento o parcial; un cambio terminado debe reflejar con precisión la comparación y abordar las salvedades indicadas.

This issue is meant to provide context for changes to dtype support in the next version of the standard.

Overview of data types implemented in various array libraries:

• Array API standard v2025.12: https://data-apis.org/array-api/2025.12/API_specification/data_types.html
• NumPy: https://numpy.org/devdocs/user/basics.types.html#relationship-between-numpy-data-types-and-c-data-types
• PyTorch: https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch-dtype
• JAX: dtype names in https://docs.jax.dev/en/latest/jax.numpy.html plus bfloat16 in https://docs.jax.dev/en/latest/jax.dtypes.html. See note on float64 in https://docs.jax.dev/en/latest/default_dtypes.html
• CuPy: dtype names in https://docs.cupy.dev/en/stable/reference/comparison.html, plus minimal bfloat16 support (see https://docs.cupy.dev/en/stable/upgrade.html#minimal-support-for-bfloat16)
• MLX: https://ml-explore.github.io/mlx/build/html/python/data_types.html
• ndonnx: https://ndonnx.readthedocs.io/en/latest/datatypes/datatypes.html
• DPCTL: https://intelpython.github.io/dpctl/latest/api_reference/dpctl/tensor.data_types.html
• cuTile: https://docs.nvidia.com/cuda/cutile-python/data.html#data-types
• ml_dtypes: https://github.com/jax-ml/ml_dtypes/blob/main/README.md

Summary of dtype support across array libraries

Legend: ✓ = full support, ○ = partial support, ✗ = no support

Notes:

1. PyTorch uint16/uint32/uint64 are "shell dtypes" with limited op and backend support; primarily for use with torch.compile.
2. MLX float64 only works with CPU operations; using float64 on the GPU raises an exception.
3. NumPy bfloat16 support is available via the ml_dtypes package, not natively.
4. CuPy has minimal bfloat16 support via ml_dtypes.bfloat16; some gaps remain especially in cupyx.
5. PyTorch has torch.complex32 defined, but operator coverage is limited.
6. JAX 64-bit dtypes require opt-in via jax.config.update('jax_enable_x64', True) or the JAX_ENABLE_X64 env var; disabled by default, and 64-bit values are silently truncated to 32-bit without it.
7. DPCTL float64/complex128 are device-dependent; only available when the target device's has_aspect_fp64 property is True.
8. DPCTL float16 is device-dependent; only available when the target device's has_aspect_fp16 property is True.

Conclusions

• Only a few dtypes have no caveats at all: bool, int8/int16/int32, uint8, float32
• Support for float64 is the biggest issue: very important for scientific computing and other fields that require high accuracy, not available at all or CPU-only on several deep learning-focused libraries
• Complex dtype support is generally spotty
• float16 and bfloat16 don't have universal support yet, but are consistently named

Next steps

Open a PR for discussion which brings documentation on data more in line with reality, reserves the float16/bfloat16 names, and says something about dtype support that is storage-only or partial.