[Numpy-discussion] Attribute hiding APIs for PyArrayObject

Allan Haldane allanhaldane at gmail.com
Wed Oct 31 17:59:29 EDT 2018

On 10/30/18 5:04 AM, Matti Picus wrote:
> TL;DR - should we revert the attribute-hiding constructs in
> ndarraytypes.h and unify PyArrayObject_fields with PyArrayObject?
> Background
> NumPy 1.8 deprecated direct access to PyArrayObject fields. It made
> PyArrayObject "opaque", and hid the fields behind a PyArrayObject_fields
> structure
> https://github.com/numpy/numpy/blob/v1.15.3/numpy/core/include/numpy/ndarraytypes.h#L659
> with a comment about moving this to a private header. In order to access
> the fields, users are supposed to use PyArray_FIELDNAME functions, like
> PyArray_DATA and PyArray_NDIM. It seems there were thoughts at the time
> that numpy might move away from a C-struct based
> underlying data structure. Other changes were also made to enum names,
> but those are relatively painless to find-and-replace.
> NumPy has a mechanism to manage deprecating APIs, C users define
> NPY_NO_DEPRICATED_API to a desired level, say NPY_1_8_API_VERSION, and
> can then access the API "as if" they were using NumPy 1.8. Users who do
> not define NPY_NO_DEPRICATED_API get a warning when compiling, and
> default to the pre-1.8 API (aliasing of PyArrayObject to
> PyArrayObject_fields and direct access to the C struct fields). This is
> convenient for downstream users, both since the new API does not provide
> much added value, and it is much easier to write a->nd than
> PyArray_NDIM(a). For instance, pandas uses direct assignment to the data
> field for fast json parsing
> https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/src/ujson/python/JSONtoObj.c#L203
> via chunks. Working around the new API in pandas would require more
> engineering. Also, for example, cython has a mechanism to transpile
> python code into C, mapping slow python attribute lookup to fast C
> struct field access
> https://cython.readthedocs.io/en/latest/src/userguide/extension_types.html#external-extension-types
> In a parallel but not really related universe, cython recently upgraded
> the object mapping so that we can quiet the annoying "size changed"
> runtime warning https://github.com/numpy/numpy/issues/11788 without
> requiring warning filters, but that requires updating the numpy.pxd file
> provided with cython, and it was proposed that NumPy actually vendor its
> own file rather than depending on the cython one
> (https://github.com/numpy/numpy/issues/11803).
> The problem
> We have now made further changes to our API. In NumPy 1.14 we changed
> UPDATEIFCOPY to WRITEBACKIFCOPY, and in 1.16 we would like to deprecate
> PyArray_SetNumericOps and PyArray_GetNumericOps. The strange warning
> when NPY_NO_DEPRICATED_API is annoying. The new API cannot be supported
> by cython without some deep surgery
> (https://github.com/cython/cython/pull/2640). When I tried dogfooding an
> updated numpy.pxd for the only cython code in NumPy, mtrand.pxy, I came
> across some of these issues (https://github.com/numpy/numpy/pull/12284).
> Forcing the new API will require downstream users to refactor code or
> re-engineer constructs, as in the pandas example above.

I haven't understood the cython issue, but just want to mention that for
optimization purposes it's nice to be able to modify the fields, like in
the pandas/json example above.

In particular, PyArray_ConcatenateArrays uses some tricks which
temporarily clobber the data pointer and shape of an array to
concatenate arrays efficiently. It seems fairly safe to me. These tricks
would be nice to re-use in a C port of the new block code we merged

Those optimizations aren't possible if only using PyArray_Object.


> The question
> Is the attribute-hiding effort worth it? Should we give up, revert the
> PyArrayObject/PyArrayObject_fields division and allow direct access from
> C to the numpy internals? Is there another path forward that is less
> painful?
> Matti
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