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https://github.com/scientific-python/spatch

Scientific Python diSPATCHing
https://github.com/scientific-python/spatch

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Scientific Python diSPATCHing

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README 

          
# spatch



**`spatch` is still exploratory and design/API may change significantly

based on feedback.**



Spatch is a dispatching tool with a focus on scientific python libraries.

It integrates two forms of dispatching into a single backend system:



- Type dispatching for the main type used by a library.

  In the scientific python world, this is often the array object.

- Backend selection to offer alternative implementations to users.

  These may be faster or less precise, but using them should typically

  not change code behavior drastically.



Spatch is opinionated about these points being two different, but related,

use-cases.

It thus combines machinery to do both, but keeps the related choices

separate for the user (and library/backend authors).



## Type dispatching



Many libraries are written for NumPy, but users may wish to use them

with other types, for example because code needs to scale from an experiment

to a large scale deployment.



Unfortunately, providing code for a host of such types isn't easy

and the original library authors usually have neither the bandwidth nor

expertise to do it. Additionally, such layers would have to be optional

components of the library.



`spatch` allows for a solution to this dilemma by allowing a third party

to enable library functions to work with alternative types.



It should be noted that spatch is not a generic multiple dispatching

library. It is opinionated about being strictly typed (we can and probably

will support subclasses in the future, though).

It also considers all arguments identically. I.e. if a function takes

two inputs (of the kind we dispatch for), there is no distinction for

their order.

Besides these two things, `spatch` is however a typical type dispatching

library.



Type dispatching mostly _extends_ functionality to cover function inputs

that it did not cover before.

Users may choose to work with a specific type explicitly but in many cases

the input types decide the use here.



## Backend selection



The type dispatching functionality extends a library to support new types.

Another use-case is to not change which types we work with, but to

provide alternative implementations.



For example, we may have a faster algorithm that is parallelized while the

old one was not. Or an implementation that dispatches to the GPU but still

returns NumPy arrays (as the library always did).



Backend selection _modifies_ behavior rather than extending it. In some

cases those modifications may be small (maybe it is really only faster).

For the user, backend _selection_ often means that they should explicitly

select a preferred backend (e.g. over the default implementation).

This could be for example via a context manager:



```python

with backend_opts(prioritize="gpu_backend"):

    library.function()  # now running on the GPU

```



# Development status



(Please bear in mind that this section may get outdated)



`spatch` is functional but not complete at this point and

it should be considered a prototype when it comes to API stability.



Some examples for missing things we are still working on:



- No way to conveniently see which backends may be used when calling a

  function (rather than actually calling it). And probably more inspection

  utilities.

- We have implemented the ability for a backend to defer and not run,

  but not the ability to run anyway if there is no alternative.

- The main library implementation currently can't distinguish fallback

  and default path easily. It should be easy to do this (two functions,

  `should_run`, or just via `uses_context`).

- `spatch` is very much designed to be fast but that doesn't mean it

  is particularly fast yet. We may need to optimize parts (potentially

  lowering parts to a compiled language).

- We have not implemented tools to test backends, e.g. against parts

  of the original library. We expect that "spatch" actually includes most

  tools to do this. For example, we could define a `convert` function

  that backends can implement to convert arguments in tests as needed.



There are also many smaller or bigger open questions and those include whether

the API proposed here is actually quite what we want.

Other things are for example whether we want API like:



- `dispatchable.invoke(type=, backend=)`.

- Maybe libraries should use `like=` in functions that take no dispatchable

  arguments.

- How do we do classes such as scikit-learn estimators. A simple solution might

  a `get_backend(...)` dispatching explicitly once. But we could use more involved

  schemes, rather remembering the dispatching state of the `.fit()`.



We can also see many small conveniences, for example:



- Extract the dispatchable arguments from type annotations.

- Support a magic `Defer` return, rather than the `should_run` call.



# Usage examples



Please see the example readme for a (very minimal) usage example.



# For library and backend authors



Please see our small example for how to use `spatch` in a library and how to

implement backends for a library that uses `spatch`.



We would like to note that while `spatch` is designed in a way that tries

to ensure that simply installing a backend will not modify library behavior,

in practice it must be the backend author who ensures this.



Thus, backend authors must review library documentation and when in doubt

contact the library authors about acceptable behavior.