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

I’m not sure I make the connection here between hidden fields and API deprecation. You seem to be asking two vaguely related questions:

  1. Should we have deprecated field access in the first place
  2. Does our api deprecation mechanism need work

I think a more substantial problem statement is needed for 2, so I’m only going to respond to 1 here.

Hiding fields seems to me to match the CPython model of things, where your public api is PyArray<thing>_SomeGetter(thing).
If you look at the cpython source code, they only expose the underlying struct fields if you don’t define Py_LIMITED_API, ie if you as a consumer volunteer to be broken by upstream changes in minor versions. People (like us) are willing to produce separate builds for each python versions, so often do not define this.

We could add a similar PyArray_LIMITED_API that allows field access under a similar guarantee - the question is, are many downstream consumers willing to produce builds against multiple numpy versions? (especially if they also do so against multiple python versions)

Also, for example, cython has a mechanism to transpile python code into C, mapping slow python attribute lookup to fast C struct field access

How does this work for builtin types? Does cython deliberately not define Py_LIMITED_API? Or are you just forced to use PyTuple_GetItem(t) if you want the fast path.

Eric

On Tue, 30 Oct 2018 at 02:04 Matti Picus <matti.picus@gmail.com> 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.


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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