ovld 0.3.2

Overloading Python functions

Ovld

Multiple dispatch in Python, with some extra features.

With ovld, you can write a version of the same function for every type signature using annotations instead of writing an awkward sequence of isinstance statements. Unlike Python singledispatch, it works for multiple arguments.

Other features of ovld:

• Multiple dispatch for methods (with metaclass=ovld.OvldMC)
• Create variants of functions
• Built-in support for extensible, stateful recursion
• Function wrappers
• Function postprocessors
• Nice stack traces

Example

Here's a function that adds lists, tuples and dictionaries:

from ovld import ovld

@ovld
def add(x: list, y: list):
    return [add(a, b) for a, b in zip(x, y)]

@ovld
def add(x: tuple, y: tuple):
    return tuple(add(a, b) for a, b in zip(x, y))

@ovld
def add(x: dict, y: dict):
    return {k: add(v, y[k]) for k, v in x.items()}

@ovld
def add(x: object, y: object):
    return x + y

Bootstrapping and variants

Now, there is another way to do this using ovld's auto-bootstrapping. Simply list self as the first argument to the function, and self will be bound to the function itself, so you can call self(x, y) for the recursion instead of add(x, y):

@ovld
def add(self, x: list, y: list):
    return [self(a, b) for a, b in zip(x, y)]

@ovld
def add(self, x: tuple, y: tuple):
    return tuple(self(a, b) for a, b in zip(x, y))

@ovld
def add(self, x: dict, y: dict):
    return {k: self(v, y[k]) for k, v in x.items()}

@ovld
def add(self, x: object, y: object):
    return x + y

Why is this useful, though? Observe:

@add.variant
def mul(self, x: object, y: object):
    return x * y

assert add([1, 2], [3, 4]) == [4, 6]
assert mul([1, 2], [3, 4]) == [3, 8]

A variant of a function is a copy which inherits all of the original's implementations but may define new ones. And because self is bound to the function that's called at the top level, the implementations for list, tuple and dict will bind self to add or mul depending on which one was called. You may also call self.super(*args) to invoke the parent implementation for that type.

State

You can pass initial_state to @ovld or variant. The initial state must be a function that takes no arguments. Its return value will be available in self.state. The state is initialized at the top level call, but recursive calls to self will preserve it.

In other words, you can do something like this:

@add.variant(initial_state=lambda: 0)
def count(self, x, y):
    self.state += 1
    return (f"#{self.state}", x + y)

assert count([1, 2, 3], [4, 5, 6]) == [("#1", 5), ("#2", 7), ("#3", 9)]

The initial_state function can return any object and you can use the state to any purpose (e.g. cache or memoization).

Custom dispatch

You can define your own dispatching function. The dispatcher's first argument is always self.

• self.resolve(x, y) to get the right function for the types of x and y
• self[type(x), type(y)] will also return the right function for these types, but it works directly with the types.

For example, here is how you might define a function such that f(x) <=> f(x, x):

@ovld.dispatch
def add_default(self, x, y=None):
    if y is None:
        y = x
    return self.resolve(x, y)(x, y)

@ovld
def add_default(x: int, y: int):
    return x + y

@ovld
def add_default(x: str, y: str):
    return x + y

@ovld
def add_default(xs: list, ys: list):
    return [add_default(x, y) for x, y in zip(xs, ys)]

assert add_default([1, 2, "alouette"]) == [2, 4, "alouettealouette"]

There are other uses for this feature, e.g. memoization.

The normal functions may also have a self, which works the same as bootstrapping, and you can give an initial_state to @ovld.dispatch as well.

Postprocess

@ovld, @ovld.dispatch, etc. take a postprocess argument which should be a function of one argument. That function will be called with the result of the call and must return the final result of the call.

Note that intermediate, bootstrapped recursive calls (recursive calls using self()) will not be postprocessed (if you want to wrap these calls, you can do so otherwise, like defining a custom dispatch). Only the result of the top level call is postprocessed.

Methods

Use the OvldMC metaclass to use multiple dispatch on methods. In this case there is no bootstrapping as described above and self is simply bound to the class instance.

from ovld import OvldMC

class Cat(metaclass=OvldMC):
    def interact(self, x: Mouse):
        return "catch"

    def interact(self, x: Food):
        return "devour"

    def interact(self, x: PricelessVase):
        return "destroy"

Subclasses of Cat will inherit the overloaded interact and it may define additional overloaded methods which will only be valid for the subclass.

Note: It is possible to use ovld.dispatch on methods, but in this case be aware that the first argument for the dispatch method will not be the usual self but an OvldCall object. The self can be retrived as ovldcall.obj. Here's an example to make it all clear:

class Stuff(metaclass=OvldMC):
    def __init__(self, mul):
        self.mul = mul

    @ovld.dispatch
    def calc(ovldcall, x):
        # Wraps every call to self.calc, but we receive ovldcall instead of self
        # ovldcall[type(x)] returns the right method to call
        # ovldcall.obj is the self (the actual instance of Stuff)
        return ovldcall[type(x)](x) * ovldcall.obj.mul

    def calc(self, x: int):
        return x + 1

    def calc(self, xs: list):
        return [self.calc(x) for x in xs]

print(Stuff(2).calc([1, 2, 3]))  # [4, 6, 8, 4, 6, 8]

Mixins in subclasses

The @extend_super decorator on a method will combine the method with the definition on the superclass:

from ovld import OvldMC, extend_super

class One(metaclass=OvldMC):
    def f(self, x: int):
        return "an integer"

class Two(One):
    @extend_super
    def f(self, x: str):
        return "a string"

assert Two().f(1) == "an integer"
assert Two().f("s") == "a string"

Ambiguous calls

The following definitions will cause a TypeError at runtime when called with two ints, because it is unclear which function is the right match:

@ovld
def ambig(x: int, y: object):
    print("io")

@ovld
def ambig(x: object, y: int):
    print("oi")

ambig(8, 8)  # ???

You may define an additional function with signature (int, int) to disambiguate:

@ovld
def ambig(x: int, y: int):
    print("ii")

Other features

meta

To test arbitrary conditions, you can use meta:

from ovld import ovld, meta

@meta
def StartsWithT(cls):
    return cls.__name__.startswith("T")

@ovld
def f(x: StartsWithT):
    return "T"

assert f(TypeError("xyz")) == "T"


# Or: a useful example, since dataclasses have no common superclass:

from dataclasses import dataclass, is_dataclass

@dataclass
class Point:
    x: int
    y: int

@ovld
def f(x: meta(is_dataclass)):
    return "dataclass"

assert f(Point(1, 2)) == "dataclass"

deferred

You may define overloads for certain classes from external packages without having to import them:

from ovld import ovld, deferred

@ovld
def f(x: deferred("numpy.ndarray")):
    return "ndarray"

# numpy is not imported
assert "numpy" not in sys.modules

# But once we import it, the ovld works:
import numpy
assert f(numpy.arange(10)) == "ndarray"

Tracebacks

ovld automagically renames functions so that the stack trace is more informative:

@add.variant
def bad(self, x: object, y: object):
    raise Exception("Bad.")

bad([1], [2])

"""
  File "/Users/breuleuo/code/ovld/ovld/core.py", line 148, in bad.entry
    res = ovc(*args, **kwargs)
  File "/Users/breuleuo/code/ovld/ovld/core.py", line 182, in bad.dispatch
    return method(self.bind_to, *args, **kwargs)
  File "example.py", line 6, in bad[list, list]
    return [self(a, b) for a, b in zip(x, y)]
  File "example.py", line 6, in <listcomp>
    return [self(a, b) for a, b in zip(x, y)]
  File "/Users/breuleuo/code/ovld/ovld/core.py", line 182, in bad.dispatch
    return method(self.bind_to, *args, **kwargs)
  File "example.py", line 26, in bad[*, *]
    raise Exception("Bad.")
  Exception: Bad.
"""

The functions on the stack have names like bad.entry, bad.dispatch, bad[list, list] and bad[*, *] (* stands for object), which lets you better understand what happened just from the stack trace.

This also means profilers will be able to differentiate between these paths and between variants, even if they share code paths.