mirror of https://github.com/python/cpython
586 lines
20 KiB
HTML
586 lines
20 KiB
HTML
<HTML>
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<HEAD>
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<TITLE>Metaprogramming in Python 1.5</TITLE>
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</HEAD>
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<BODY BGCOLOR="FFFFFF">
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<H1>Metaprogramming in Python 1.5 (DRAFT)</H1>
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<H4>XXX This is very much a work in progress.</H4>
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<P>While Python 1.5 is only out as a <A
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HREF="http://grail.cnri.reston.va.us/python/1.5a3/">restricted alpha
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release</A>, its metaprogramming feature is worth mentioning.
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<P>In previous Python releases (and still in 1.5), there is something
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called the ``Don Beaudry hook'', after its inventor and champion.
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This allows C extensions to provide alternate class behavior, thereby
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allowing the Python class syntax to be used to define other class-like
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entities. Don Beaudry has used this in his infamous <A
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HREF="http://maigret.cog.brown.edu/pyutil/">MESS</A> package; Jim
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Fulton has used it in his <A
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HREF="http://www.digicool.com/papers/ExtensionClass.html">Extension
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Classes</A> package. (It has also been referred to as the ``Don
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Beaudry <i>hack</i>,'' but that's a misnomer. There's nothing hackish
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about it -- in fact, it is rather elegant and deep, even though
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there's something dark to it.)
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<P>Documentation of the Don Beaudry hook has purposefully been kept
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minimal, since it is a feature of incredible power, and is easily
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abused. Basically, it checks whether the <b>type of the base
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class</b> is callable, and if so, it is called to create the new
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class.
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<P>Note the two indirection levels. Take a simple example:
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<PRE>
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class B:
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pass
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class C(B):
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pass
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</PRE>
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Take a look at the second class definition, and try to fathom ``the
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type of the base class is callable.''
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<P>(Types are not classes, by the way. See questions 4.2, 4.19 and in
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particular 6.22 in the <A
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HREF="http://grail.cnri.reston.va.us/cgi-bin/faqw.py" >Python FAQ</A>
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for more on this topic.)
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<P>
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<UL>
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<LI>The <b>base class</b> is B; this one's easy.<P>
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<LI>Since B is a class, its type is ``class''; so the <b>type of the
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base class</b> is the type ``class''. This is also known as
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types.ClassType, assuming the standard module <code>types</code> has
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been imported.<P>
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<LI>Now is the type ``class'' <b>callable</b>? No, because types (in
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core Python) are never callable. Classes are callable (calling a
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class creates a new instance) but types aren't.<P>
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</UL>
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<P>So our conclusion is that in our example, the type of the base
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class (of C) is not callable. So the Don Beaudry hook does not apply,
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and the default class creation mechanism is used (which is also used
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when there is no base class). In fact, the Don Beaudry hook never
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applies when using only core Python, since the type of a core object
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is never callable.
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<P>So what do Don and Jim do in order to use Don's hook? Write an
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extension that defines at least two new Python object types. The
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first would be the type for ``class-like'' objects usable as a base
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class, to trigger Don's hook. This type must be made callable.
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That's why we need a second type. Whether an object is callable
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depends on its type. So whether a type object is callable depends on
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<i>its</i> type, which is a <i>meta-type</i>. (In core Python there
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is only one meta-type, the type ``type'' (types.TypeType), which is
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the type of all type objects, even itself.) A new meta-type must
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be defined that makes the type of the class-like objects callable.
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(Normally, a third type would also be needed, the new ``instance''
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type, but this is not an absolute requirement -- the new class type
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could return an object of some existing type when invoked to create an
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instance.)
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<P>Still confused? Here's a simple device due to Don himself to
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explain metaclasses. Take a simple class definition; assume B is a
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special class that triggers Don's hook:
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<PRE>
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class C(B):
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a = 1
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b = 2
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</PRE>
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This can be though of as equivalent to:
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<PRE>
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C = type(B)('C', (B,), {'a': 1, 'b': 2})
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</PRE>
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If that's too dense for you, here's the same thing written out using
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temporary variables:
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<PRE>
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creator = type(B) # The type of the base class
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name = 'C' # The name of the new class
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bases = (B,) # A tuple containing the base class(es)
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namespace = {'a': 1, 'b': 2} # The namespace of the class statement
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C = creator(name, bases, namespace)
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</PRE>
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This is analogous to what happens without the Don Beaudry hook, except
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that in that case the creator function is set to the default class
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creator.
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<P>In either case, the creator is called with three arguments. The
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first one, <i>name</i>, is the name of the new class (as given at the
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top of the class statement). The <i>bases</i> argument is a tuple of
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base classes (a singleton tuple if there's only one base class, like
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the example). Finally, <i>namespace</i> is a dictionary containing
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the local variables collected during execution of the class statement.
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<P>Note that the contents of the namespace dictionary is simply
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whatever names were defined in the class statement. A little-known
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fact is that when Python executes a class statement, it enters a new
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local namespace, and all assignments and function definitions take
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place in this namespace. Thus, after executing the following class
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statement:
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<PRE>
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class C:
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a = 1
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def f(s): pass
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</PRE>
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the class namespace's contents would be {'a': 1, 'f': <function f
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...>}.
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<P>But enough already about Python metaprogramming in C; read the
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documentation of <A
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HREF="http://maigret.cog.brown.edu/pyutil/">MESS</A> or <A
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HREF="http://www.digicool.com/papers/ExtensionClass.html" >Extension
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Classes</A> for more information.
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<H2>Writing Metaclasses in Python</H2>
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<P>In Python 1.5, the requirement to write a C extension in order to
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engage in metaprogramming has been dropped (though you can still do
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it, of course). In addition to the check ``is the type of the base
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class callable,'' there's a check ``does the base class have a
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__class__ attribute.'' If so, it is assumed that the __class__
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attribute refers to a class.
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<P>Let's repeat our simple example from above:
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<PRE>
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class C(B):
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a = 1
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b = 2
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</PRE>
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Assuming B has a __class__ attribute, this translates into:
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<PRE>
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C = B.__class__('C', (B,), {'a': 1, 'b': 2})
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</PRE>
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This is exactly the same as before except that instead of type(B),
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B.__class__ is invoked. If you have read <A HREF=
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"http://grail.cnri.reston.va.us/cgi-bin/faqw.py?req=show&file=faq06.022.htp"
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>FAQ question 6.22</A> you will understand that while there is a big
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technical difference between type(B) and B.__class__, they play the
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same role at different abstraction levels. And perhaps at some point
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in the future they will really be the same thing (at which point you
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would be able to derive subclasses from built-in types).
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<P>At this point it may be worth mentioning that C.__class__ is the
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same object as B.__class__, i.e., C's metaclass is the same as B's
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metaclass. In other words, subclassing an existing class creates a
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new (meta)inststance of the base class's metaclass.
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<P>Going back to the example, the class B.__class__ is instantiated,
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passing its constructor the same three arguments that are passed to
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the default class constructor or to an extension's metaprogramming
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code: <i>name</i>, <i>bases</i>, and <i>namespace</i>.
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<P>It is easy to be confused by what exactly happens when using a
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metaclass, because we lose the absolute distinction between classes
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and instances: a class is an instance of a metaclass (a
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``metainstance''), but technically (i.e. in the eyes of the python
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runtime system), the metaclass is just a class, and the metainstance
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is just an instance. At the end of the class statement, the metaclass
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whose metainstance is used as a base class is instantiated, yielding a
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second metainstance (of the same metaclass). This metainstance is
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then used as a (normal, non-meta) class; instantiation of the class
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means calling the metainstance, and this will return a real instance.
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And what class is that an instance of? Conceptually, it is of course
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an instance of our metainstance; but in most cases the Python runtime
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system will see it as an instance of a a helper class used by the
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metaclass to implement its (non-meta) instances...
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<P>Hopefully an example will make things clearer. Let's presume we
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have a metaclass MetaClass1. It's helper class (for non-meta
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instances) is callled HelperClass1. We now (manually) instantiate
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MetaClass1 once to get an empty special base class:
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<PRE>
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BaseClass1 = MetaClass1("BaseClass1", (), {})
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</PRE>
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We can now use BaseClass1 as a base class in a class statement:
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<PRE>
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class MySpecialClass(BaseClass1):
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i = 1
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def f(s): pass
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</PRE>
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At this point, MySpecialClass is defined; it is a metainstance of
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MetaClass1 just like BaseClass1, and in fact the expression
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``BaseClass1.__class__ == MySpecialClass.__class__ == MetaClass1''
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yields true.
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<P>We are now ready to create instances of MySpecialClass. Let's
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assume that no constructor arguments are required:
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<PRE>
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x = MySpecialClass()
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y = MySpecialClass()
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print x.__class__, y.__class__
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</PRE>
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The print statement shows that x and y are instances of HelperClass1.
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How did this happen? MySpecialClass is an instance of MetaClass1
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(``meta'' is irrelevant here); when an instance is called, its
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__call__ method is invoked, and presumably the __call__ method defined
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by MetaClass1 returns an instance of HelperClass1.
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<P>Now let's see how we could use metaprogramming -- what can we do
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with metaclasses that we can't easily do without them? Here's one
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idea: a metaclass could automatically insert trace calls for all
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method calls. Let's first develop a simplified example, without
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support for inheritance or other ``advanced'' Python features (we'll
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add those later).
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<PRE>
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import types
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class Tracing:
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def __init__(self, name, bases, namespace):
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"""Create a new class."""
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self.__name__ = name
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self.__bases__ = bases
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self.__namespace__ = namespace
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def __call__(self):
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"""Create a new instance."""
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return Instance(self)
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class Instance:
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def __init__(self, klass):
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self.__klass__ = klass
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def __getattr__(self, name):
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try:
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value = self.__klass__.__namespace__[name]
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except KeyError:
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raise AttributeError, name
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if type(value) is not types.FunctionType:
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return value
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return BoundMethod(value, self)
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class BoundMethod:
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def __init__(self, function, instance):
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self.function = function
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self.instance = instance
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def __call__(self, *args):
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print "calling", self.function, "for", self.instance, "with", args
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return apply(self.function, (self.instance,) + args)
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Trace = Tracing('Trace', (), {})
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class MyTracedClass(Trace):
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def method1(self, a):
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self.a = a
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def method2(self):
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return self.a
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aninstance = MyTracedClass()
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aninstance.method1(10)
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print "the answer is %d" % aninstance.method2()
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</PRE>
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Confused already? The intention is to read this from top down. The
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Tracing class is the metaclass we're defining. Its structure is
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really simple.
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<P>
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<UL>
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<LI>The __init__ method is invoked when a new Tracing instance is
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created, e.g. the definition of class MyTracedClass later in the
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example. It simply saves the class name, base classes and namespace
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as instance variables.<P>
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<LI>The __call__ method is invoked when a Tracing instance is called,
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e.g. the creation of aninstance later in the example. It returns an
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instance of the class Instance, which is defined next.<P>
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</UL>
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<P>The class Instance is the class used for all instances of classes
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built using the Tracing metaclass, e.g. aninstance. It has two
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methods:
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<P>
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<UL>
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<LI>The __init__ method is invoked from the Tracing.__call__ method
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above to initialize a new instance. It saves the class reference as
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an instance variable. It uses a funny name because the user's
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instance variables (e.g. self.a later in the example) live in the same
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namespace.<P>
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<LI>The __getattr__ method is invoked whenever the user code
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references an attribute of the instance that is not an instance
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variable (nor a class variable; but except for __init__ and
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__getattr__ there are no class variables). It will be called, for
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example, when aninstance.method1 is referenced in the example, with
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self set to aninstance and name set to the string "method1".<P>
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</UL>
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<P>The __getattr__ method looks the name up in the __namespace__
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dictionary. If it isn't found, it raises an AttributeError exception.
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(In a more realistic example, it would first have to look through the
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base classes as well.) If it is found, there are two possibilities:
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it's either a function or it isn't. If it's not a function, it is
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assumed to be a class variable, and its value is returned. If it's a
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function, we have to ``wrap'' it in instance of yet another helper
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class, BoundMethod.
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<P>The BoundMethod class is needed to implement a familiar feature:
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when a method is defined, it has an initial argument, self, which is
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automatically bound to the relevant instance when it is called. For
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example, aninstance.method1(10) is equivalent to method1(aninstance,
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10). In the example if this call, first a temporary BoundMethod
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instance is created with the following constructor call: temp =
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BoundMethod(method1, aninstance); then this instance is called as
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temp(10). After the call, the temporary instance is discarded.
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<P>
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<UL>
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<LI>The __init__ method is invoked for the constructor call
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BoundMethod(method1, aninstance). It simply saves away its
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arguments.<P>
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<LI>The __call__ method is invoked when the bound method instance is
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called, as in temp(10). It needs to call method1(aninstance, 10).
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However, even though self.function is now method1 and self.instance is
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aninstance, it can't call self.function(self.instance, args) directly,
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because it should work regardless of the number of arguments passed.
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(For simplicity, support for keyword arguments has been omitted.)<P>
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</UL>
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<P>In order to be able to support arbitrary argument lists, the
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__call__ method first constructs a new argument tuple. Conveniently,
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because of the notation *args in __call__'s own argument list, the
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arguments to __call__ (except for self) are placed in the tuple args.
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To construct the desired argument list, we concatenate a singleton
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tuple containing the instance with the args tuple: (self.instance,) +
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args. (Note the trailing comma used to construct the singleton
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tuple.) In our example, the resulting argument tuple is (aninstance,
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10).
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<P>The intrinsic function apply() takes a function and an argument
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tuple and calls the function for it. In our example, we are calling
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apply(method1, (aninstance, 10)) which is equivalent to calling
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method(aninstance, 10).
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<P>From here on, things should come together quite easily. The output
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of the example code is something like this:
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<PRE>
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calling <function method1 at ae8d8> for <Instance instance at 95ab0> with (10,)
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calling <function method2 at ae900> for <Instance instance at 95ab0> with ()
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the answer is 10
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</PRE>
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<P>That was about the shortest meaningful example that I could come up
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with. A real tracing metaclass (for example, <A
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HREF="#Trace">Trace.py</A> discussed below) needs to be more
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complicated in two dimensions.
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<P>First, it needs to support more advanced Python features such as
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class variables, inheritance, __init__ methods, and keyword arguments.
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<P>Second, it needs to provide a more flexible way to handle the
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actual tracing information; perhaps it should be possible to write
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your own tracing function that gets called, perhaps it should be
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possible to enable and disable tracing on a per-class or per-instance
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basis, and perhaps a filter so that only interesting calls are traced;
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it should also be able to trace the return value of the call (or the
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exception it raised if an error occurs). Even the Trace.py example
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doesn't support all these features yet.
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<P>
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<HR>
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<H1>Real-life Examples</H1>
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<P>Have a look at some very preliminary examples that I coded up to
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teach myself how to use metaprogramming:
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<DL>
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<DT><A HREF="Enum.py">Enum.py</A>
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<DD>This (ab)uses the class syntax as an elegant way to define
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enumerated types. The resulting classes are never instantiated --
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rather, their class attributes are the enumerated values. For
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example:
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<PRE>
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class Color(Enum):
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red = 1
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green = 2
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blue = 3
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print Color.red
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</PRE>
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will print the string ``Color.red'', while ``Color.red==1'' is true,
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and ``Color.red + 1'' raise a TypeError exception.
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<P>
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<DT><A NAME=Trace></A><A HREF="Trace.py">Trace.py</A>
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<DD>The resulting classes work much like standard
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classes, but by setting a special class or instance attribute
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__trace_output__ to point to a file, all calls to the class's methods
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are traced. It was a bit of a struggle to get this right. This
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should probably redone using the generic metaclass below.
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<P>
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<DT><A HREF="Meta.py">Meta.py</A>
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<DD>A generic metaclass. This is an attempt at finding out how much
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standard class behavior can be mimicked by a metaclass. The
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preliminary answer appears to be that everything's fine as long as the
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class (or its clients) don't look at the instance's __class__
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attribute, nor at the class's __dict__ attribute. The use of
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__getattr__ internally makes the classic implementation of __getattr__
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hooks tough; we provide a similar hook _getattr_ instead.
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(__setattr__ and __delattr__ are not affected.)
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(XXX Hm. Could detect presence of __getattr__ and rename it.)
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<P>
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<DT><A HREF="Eiffel.py">Eiffel.py</A>
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<DD>Uses the above generic metaclass to implement Eiffel style
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pre-conditions and post-conditions.
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<P>
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<DT><A HREF="Synch.py">Synch.py</A>
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<DD>Uses the above generic metaclass to implement synchronized
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methods.
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<P>
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<DT><A HREF="Simple.py">Simple.py</A>
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<DD>The example module used above.
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<P>
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</DL>
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<P>A pattern seems to be emerging: almost all these uses of
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metaclasses (except for Enum, which is probably more cute than useful)
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mostly work by placing wrappers around method calls. An obvious
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|
problem with that is that it's not easy to combine the features of
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|
different metaclasses, while this would actually be quite useful: for
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|
example, I wouldn't mind getting a trace from the test run of the
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Synch module, and it would be interesting to add preconditions to it
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|
as well. This needs more research. Perhaps a metaclass could be
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|
provided that allows stackable wrappers...
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<HR>
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<H2>Things You Could Do With Metaclasses</H2>
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<P>There are lots of things you could do with metaclasses. Most of
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|
these can also be done with creative use of __getattr__, but
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|
metaclasses make it easier to modify the attribute lookup behavior of
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|
classes. Here's a partial list.
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|
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|
<P>
|
|
|
|
<UL>
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|
|
|
<LI>Enforce different inheritance semantics, e.g. automatically call
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|
base class methods when a derived class overrides<P>
|
|
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|
<LI>Implement class methods (e.g. if the first argument is not named
|
|
'self')<P>
|
|
|
|
<LI>Implement that each instance is initialized with <b>copies</b> of
|
|
all class variables<P>
|
|
|
|
<LI>Implement a different way to store instance variables (e.g. in a
|
|
list kept outside the the instance but indexed by the instance's id())<P>
|
|
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|
<LI>Automatically wrap or trap all or certain methods
|
|
|
|
<UL>
|
|
|
|
<LI>for tracing
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|
|
|
<LI>for precondition and postcondition checking
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|
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|
<LI>for synchronized methods
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|
|
|
<LI>for automatic value caching
|
|
|
|
</UL>
|
|
<P>
|
|
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|
<LI>When an attribute is a parameterless function, call it on
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|
reference (to mimic it being an instance variable); same on assignment<P>
|
|
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|
<LI>Instrumentation: see how many times various attributes are used<P>
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|
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|
<LI>Different semantics for __setattr__ and __getattr__ (e.g. disable
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|
them when they are being used recursively)<P>
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|
|
|
<LI>Abuse class syntax for other things<P>
|
|
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|
<LI>Experiment with automatic type checking<P>
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|
|
|
<LI>Delegation (or acquisition)<P>
|
|
|
|
<LI>Dynamic inheritance patterns<P>
|
|
|
|
<LI>Automatic caching of methods<P>
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|
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|
</UL>
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|
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<P>
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|
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<HR>
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|
<H4>Credits</H4>
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|
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|
<P>Many thanks to David Ascher and Donald Beaudry for their comments
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|
on earlier draft of this paper. Also thanks to Matt Conway and Tommy
|
|
Burnette for putting a seed for the idea of metaclasses in my
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|
mind, nearly three years ago, even though at the time my response was
|
|
``you can do that with __getattr__ hooks...'' :-)
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<P>
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