On Tue, Aug 21, 2018 at 12:21 AM Nathaniel Smith <njs@pobox.com> wrote:

On Wed, Aug 15, 2018 at 9:45 AM, Stephan Hoyer <shoyer@gmail.com> wrote:

> This avoids a classic subclassing problem that has plagued NumPy for years,

> where overriding the behavior of method A causes apparently unrelated method

> B to break, because it relied on method A internally. In NumPy, this

> constrained our implementation of np.median(), because it needed to call

> np.mean() in order for subclasses implementing units to work properly.

I don't think I follow... if B uses A internally, then overriding A

shouldn't cause B to break, unless the overridden A is buggy.

Let me try another example with arrays with units. My understanding of the contract provided by unit implementations is their behavior should never deviate from NumPy unless an operation raises an error. (This is more explicit for arrays with units because they raise errors for operations with incompatible units, but practically speaking almost all duck arrays will have at least some unsupported operations in NumPy's giant API.)

It is quite possible that NumPy functions could be (re)written in a way that is incompatible with some unit implementations but is perfectly valid for "full" duck arrays. We actually see this even within NumPy already -- for example, see this recent PR adding support for the datetime64 dtype to percentile:

A lesser case of this are changes in NumPy causing performance issues for users of duck arrays, which is basically inevitable if we share implementations.

I don't think it's possible to anticipate all of these cases, and I don't want NumPy to be unduly constrained in its internal design. I want our user support answer to be simple: if you care about performance for a particular array operations on your type of arrays, you should implement it yourself (i.e., with __array_function__).

This definitely doesn't preclude the careful, systematic overriding approach. But I think we'll almost always want NumPy's external API to be overridable.

And when we fix a bug in row_stack, this means we also have to fix it

in all the copy-paste versions, which won't happen, so np.row_stack

has different semantics on different objects, even if they started out

matching. The NDArrayOperatorsMixin reduces the number of duplicate

copies of the same code that need to be updated, but 2 copies is still

a lot worse than 1 copy :-).

I see your point, but in all seriousness if encounter a bug in np.row_stack at this point we might just call it a feature instead.

> 1. The details of how NumPy implements a high-level function in terms of overloaded functions now becomes an implicit part of NumPy’s public API. For example, refactoring stack to use np.block() instead of np.concatenate() internally would now become a breaking change.

The way I'm imagining this would work is, we guarantee not to take a

function that used to be implemented in terms of overridable

operations, and refactor it so it's implemented in terms of

overridable operations. So long as people have correct implementations

of __array_concatenate__ and __array_block__, they shouldn't care

which one we use. In the interim period where we have

__array_concatenate__ but there's no such thing as __array_block__,

then that refactoring would indeed break things, so we shouldn't do

that :-). But we could fix that by adding __array_block__.

""we guarantee not to take a function that used to be implemented in terms of overridable operations, and refactor it so it's implemented in terms of overridable operations"

Did you miss a "not" in here somewhere, e.g., "refactor it so it's NOT implemented"?

If we ever tried to do something like this, I'm pretty sure that it just wouldn't happen -- unless we also change NumPy's extremely conservative approach to breaking third-party code. np.block() is much more complex to implement than np.concatenate(), and users would resist being forced to handle that complexity if they don't need it. (Example: TensorFlow has a concatenate function, but not block.)

> 2. Array libraries may prefer to implement high level functions differently than NumPy. For example, a library might prefer to implement a fundamental operations like mean() directly rather than relying on sum() followed by division. More generally, it’s not clear yet what exactly qualifies as core functionality, and figuring this out could be a large project.

True. And this is a very general problem... for example, the

appropriate way to implement logistic regression is very different

in-core versus out-of-core. You're never going to be able to take code

written for ndarray, drop in an arbitrary new array object, and get

optimal results in all cases -- that's just way too ambitious to hope

for. There will be cases where reducing to operations like sum() and

division is fine. There will be cases where you have a high-level

operation like logistic regression, where reducing to sum() and

division doesn't work, but reducing to slightly-higher-level

operations like np.mean also doesn't work, because you need to redo

the whole high-level operation. And then there will be cases where

sum() and division are too low-level, but mean() is high-level enough

to make the critical difference. It's that last one where it's

important to be able to override mean() directly. Are there a lot of

cases like this?

mean() is not entirely hypothetical. TensorFlow and Eigen actually do implement mean separately from sum, though to be honest it's not entirely clear to me why:

I do think this probably will come up with some frequency for other operations, but the bigger answer here really is consistency -- it allows projects and their users to have very clearly defined dependencies on NumPy's API. They don't need to worry about any implementation details from NumPy leaking into their override of a function.

> 3. We don’t yet have an overloading system for attributes and methods on array objects, e.g., for accessing .dtype and .shape. This should be the subject of a future NEP, but until then we should be reluctant to rely on these properties.

This one I don't understand. If you have a duck-array object, and you

want to access its .dtype or .shape attributes, you just... write

myobj.dtype or myobj.shape? That doesn't need a NEP though so I must

be missing something :-).

We don't have np.asduckarray() yet or whatever we'll end up calling our proposed casting function from NEP 22, so we don't have a fully fleshed out mechanism for NumPy to declare "this object needs to support .shape and .dtype, or I'm going to cast it into something that does".

More comments on the environment variable and the interface to come in my next email...

Cheers,

Stephan