David Mertz, Ph.D.
Essence Preceder, Gnosis Software, Inc.
Object oriented programming gains much of its versatility through polymorphism. Objects of different kinds can behave in similar ways, given the right contexts. But most OOP programming is single dispatch; that is, just one designated object determines which code path is taken. Conceptually, a more general technique is to allow all the arguments to a function/method to determine its specialization. This article presents an implementation of multiple dispatch in Python, and shows examples where this makes for better programs.
Most programmers--in Python or other object oriented programming languages--who utilize polymorphism, do so in a rather practical and concrete way. Perhaps the most common application of polymorphism is in creating a family of object that follow a common protocol. In Python, this is usually simply a matter of ad hoc polymorphism; in other languages, formal interfaces are more often declared and/or these families share a common ancestor.
For example, there are many functions that operate on "file-like"
objects, where file-like is defined simply by supporting a few
.readlines(), and maybe
read_app_data() might take an argument
src--when we call the function, we might decide to pass it a
local file, a
urllib object, a
cStringIO object, or some
custom object that lets the function call
object type is interchangeable from the point of view of how it
Let us step back a bit to think about what is really happening here. At heart, what we are concerned about is choosing the right code path to execute within a context; old-fashioned procedural code can make equivalent decisions, OOP merely adds some elegance. For example, a fragment of procedural (pseudo-)code might look like:
...bind 'src' in some manner... if <<src is a file object>>: read_from_file(src) elif <<src is a urllib object>>: read_from_url(src) elif <<src is a stringio object>>: read_from_stringio(src) ...etc...
By arranging for objects of different types to implement common
methods, we move the dispatch decision into the objects, and
out of an explict conditional block. A given
knows what block of code it needs to call by looking through
its inheritance tree. There is still an implicit switch going
on, but it is on the type of the object
src is privileged over any arguments passed to its
methods. OOP syntax makes this privileging seem inevitable,
but it is not really. Procedural switching is simply pushed
into the method bodies of classes in many cases. For example
we might implement protocol-compatible classes
as follows (in pseudo-Python):
class Foo: def meth(self, arg): if <<arg is a Foo>>: ...FooFoo code block... elif <<arg is a Bar>>: ...FooBar code block... class Bar: def meth(self, arg): if <<arg is a Foo>>: ...BarFoo code block... elif <<arg is a Bar>>: ...BarBar code block... # Function to utilize Foo/Bar single-dispatch polymorphism def x_with_y(x, y): if <<x is Foo or Bar>> and <<y is Foo or Bar>>: x.meth(y) else: raise TypeError,"x, y must be either Foo's or Bar's"
There are five distinct code paths/blocks that might get
x_with_y() is called. If the types of
y are not suitable, an exception is raised (you could also do
somethig different, of course). But assuming the types are OK,
the code path is chosen first by a polymorphic dispatch, and
second by procedural switch. Moreover, the switches within
the definitions of
Bar.meth() are largely
equivalent. Polymorphism--of the single-dispatch
variety--only goes half way.
In single-dispatch polymorphism, the object that "owns" a method is singled out. Syntactically, it is singled out in Python by being named before the dot--everything following the dot, method name, and left parenthesis is just an argument. But semantically, the object is also special in utilizing an inheritence tree for method resolution.
What if we did not treat just one object in a special fashion, but allowed every object involved in a code block to help choose the correct code path? For example, we might express our five-way switch in a more symmetric fashion:
x_with_y = Dispatch([((object, object), <<exception block>>)]) x_with_y.add_rule((Foo,Foo), <<FooFoo block>>) x_with_y.add_rule((Foo,Bar), <<FooBar block>>) x_with_y.add_rule((Bar,Foo), <<BarFoo block>>) x_with_y.add_rule((Bar,Bar), <<BarBar block>>) #...call the function x_with_y() using some arguments... x_with_y(something, otherthing)
I think this symmetry in polymorphic dispatch on multiple arguments is much more elegant than is the prior style. As well, the style helps document the equal role of the two objects involved in determining the appropriate code path to take.
Standard Python does not let you configure this type of
multiple dispatch; but fortunately, you can do so using the
multimethods that I have written. See Resources to
download the module by itself, or as part of Gnosis Utilities.
All you need to do once you have installed
include the following line at the top of your application:
from multimethods import Dispatch
"Multimethods" is generally a synonym for multiple dispatch; but the name multimethod suggests the concrete function/object handling the more abstract concept of multiple dispatch.
An instance of
Dispatch is a callable object, and can be
configured with as many rules as you wish. The method
Dispatch.remove_rule() can be used to delete rules as well,
which makes multiple dispatch using
multimethods a bit more
dynamic than is a static class hierarchy (but you can also do
some arcane things with Python classes at runtime). Note also
Dispatch instance can accept a variable number of
arguments, matching is done first on number of arguments, then on
their types. If a
Dispatch instance is called with any
pattern that is not defined in a rule, a
TypeError is raised.
The initialization of
x_with_y() with a fallback
(object,object) pattern is not necessary if you simply want
undefined cases to raise an exception.
(pattern,function) tuple that is listed in the
initialization call to
Dispatch is simply passed on to the
.add_rule() method; it is solely a matter of programmer
convenience whether to establish rules on initialization or at
a later point (you can mix-and-match, as in the prior example).
When a function is called from the dispatcher, it is passed the
arguments used in the call to the dispatcher; you need to make
sure the function you use can accept the number of arguments it
is matched against. For example, the following are equivalent:
# Define function, classes, objects def func(a,b): print "The X is", a, "the Y is", b class X(object): pass class Y(object): pass x, y = X(), Y() # Explicit call to func with args func(x,y) # Dispatched call to func on args from multimethods import Dispatch dispatch = Dispatch() dispatch.add_rule((X,Y), func) dispatch(x,y) # resolves to 'func(x,y)'
Obviously, if you alredy know the types of
design time, the machinery of setting up a dispatcher is just
overhead. But then, the same limitation is true of
polymorphism--it is only helpful when you cannot constrain an
object to a single type for every execution path.
Multiple dispatch does not merely generalize polymorphism, it also provides a more flexible alternative to inheritence in many contexts. An example is illustrative here. Suppose you are programming a drawing or CAD program that deals with a variety of shapes; in particular, you want to be able to combine two shapes in a way that depends on both of the shapes involved. Moreover, the collection of shapes to consider will be extended by derived applications or plugins. Extending a collection of shape classes provides a clumsy technique for enhancement, e.g.:
# Base classes class Circle(Shape): def combine_with_circle(self, circle): ... def combine_with_square(self, square): ... class Square(Shape): def combine_with_circle(self, circle): ... def combine_with_square(self, square): ... # Enhancing base with triangle shape class Triangle(Shape): def combine_with_circle(self, circle): ... def combine_with_square(self, square): ... def combine_with_triangle(self, triangle): ... class NewCircle(Circle): def combine_with_triangle(self, triangle): ... class NewSquare(Square): def combine_with_triangle(self, triangle): ... # Can optionally use original class names in new context Circle, Square = NewCircle, NewSquare # Use the classes in application c, t, s = Circle(...), Triangle(...), Square(...) newshape1 = c.combine_with_triangle(t) newshape2 = s.combine_with_circle(c) # discover 'x' of unknown type, then combine with 't' if isinstance(x, Triangle): new3 = t.combine_with_triangle(x) elif isinstance(x, Square): new3 = t.combine_with_square(x) elif isinstance(x, Circle): new3 = t.combine_with_circle(x)
In particular, each existing shape class has to add capabilities in a descendent, which runs into combinatorial complexities, and difficulties in maintenance.
In contrast, a multiple dispatch technique is more straightforward:
# Base rules (stipulate combination is order independent) class Circle(Shape): pass class Square(Shape): pass def circle_with_square(circle, square): ... def circle_with_circle(circle, circle): ... def square_with_square(square, square): ... combine = Dispatch() combine.add_rule((Circle, Square), circle_with_square) combine.add_rule((Circle, Circle), circle_with_circle) combine.add_rule((Square, Square), square_with_square) combine.add_rule((Square, Circle), lambda s,c: circle_with_square(c,s)) # Enhancing base with triangle shape class Triangle(Shape): pass def triangle_with_circle(triangle_with_circle): ... def triangle_with_square(triangle_with_square): ... combine.add_rule((Triangle,Circle), triangle_with_circle) combine.add_rule((Triangle,Square), triangle_with_square) combine.add_rule((Circle,Triangle), lambda c,t: triangle_with_circle(t,c)) combine.add_rule((Square,Triangle), lambda s,t: triangle_with_square(t,s)) # Use the rules in application c, t, s = Circle(...), Triangle(...), Square(...) newshape1 = combine(c, t) newshape2 = combine(s, c) # discover 'x' of unknown type, then combine with 't' newshape3 = combine(t, x)
The definition of new rules (and support functions/methods) is
largely equivalent. But the huge advantage of the multiple
dispatch style is in the seamlessness with which you can
combine shapes of unknown types. Rather than revert back to
explicit (and lengthy) conditional blocks, the rule definitions
take care of matters automatically. Even better, all
combinining is done with a single
combine() callable, rather
than with a menagarie of distinct combinations methods.
Without needing to think about dispatch further, the
multimethods.Dispatch class will select the "best fit" for a
given call to a dispatcher. However, it is sometimes worth
noticing that "best" is not "only." That is, a call to
dispatch(foo,bar) might be close fit with a defined rule
(Foo,Bar)'-but it might also be a loose (rather than non) fit
with '(FooParent,BarParent). Just as you sometimes want to call
on superclass methods in an inherited method, you also sometimes
want to call on less specific rules within a dispatcher.
multimethods module gives you both a quick way of calling
less specific rules, and a more fine-tuned way. At a rough
level, you usually just want to automatically call a less
specific rule at either the start or the end of execution of a
code block. Likewise, you almost always call a superclass
method at either the start or end of a descendent method body.
For a generic start/end call to less specific methods, you can
just specify that as part of the rule. For example:
class General(object): pass class Between(General): pass class Specific(Between): pass dispatch = Dispatch() dispatch.add_rule((General,), lambda _:"Gen", AT_END) dispatch.add_rule((Between,), lambda _:"Betw", AT_END) dispatch.add_rule((Specific,), lambda _:"Specif", AT_END) dispatch(General()) # Result: ['Gen'] dispatch(Specific()) # Result: ['Specif', 'Betw', 'Gen']
Of course, in some cases (like the
(General) rule), there is
nothing less specific available in the defined rules. For
uniformity, however, every call to a dispatcher returns a list of
return values from all functions that control was propagated to.
AT_START is specified in the rules, no
propagation occurs (and the returned list has length one). This
indexing that probably looked mysterious in
the shape example.
The fine-tuned way of propagating control is with the
.next_method() method of a dispatcher. In order to utilize
manual propagation, you should define rules using the
.add_dispatchable() method rather than the
method. As well, the dispatched functions themselves should
dispatch argument. The call to the dispatcher
either needs a dispatch argument, or you can use the
.with_dispatch() convenience method. For example:
def do_between(x, dispatch): print "do some initial stuff" val = dispatch.next_method() # return simple value of up-call print "do some followup stuff" return "My return value" foo = Foo() import multimethods multi = multimethods.Dispatch() multi.add_dispatchable((Foo,), do_between) multi.with_dispatch(foo) # Or: multi(foo, multi)
Manual propagation to less specific multimethods can get tricky
in many of the same ways that calls to superclass methods can get
tricky. To make things tractable, calls to
always return the simple return value of the up-call--if you want
to assemble such return values into a list like the
argument does, you will need to append and manipulate the values
as you think appropriate. The most common "use case," however, is
where a series of related initializations are peformed; in this
case, the return values are usually irrelevant.
A quick interjection is worthwhile lest readers run into a problem. Because of the stateful way propagation tracks which (successively less specific) rules have been called, a dispatcher is not thread safe. If you wish to use a dispatcher in multiple threads, you should "clone" it for each thread. Doing so is inexpensive in memory and CPU resources, so there is no significant penalty for cloning dispatchers. For example, suppose a function might be called across thread; you can write:
def threadable_dispatch(dispatcher, other, arguments) dispatcher = dispatcher.clone() #...do setup activities... dispatcher(some, rule, pattern) #...do other stuff...
If no new threads are spawned within
You may obtain
multimethods either as a standalone module or
as part of the Gnosis Utilities package. By itself, download
Gnosis Utilities as a whole comes as a Python
package. You may obtain it from:
Other languages have implemented multiple dispatch, either within the language itself, or in libraries. For example, MultiJava is a superset of Java that implements multiple dispatch:
CLOS and Dylan both use multiple dispatch as the basic foundation of their OOP systems. A discussion of Dylan's mechanism is at:
Perl has a module called
Class::Multimethods to implement
multiple dispatch (and apparently Perl 6 is slated to build the
concept more deeply into the language). Damian Conway
discusses his module at:
David Mertz feels that programmers with multiple personality syndrome will want all of their functions to be generic. David may be reached at email@example.com; his life pored over at http://gnosis.cx/publish/. Suggestions and recommendations on this, past, or future, columns are welcomed.