python-peps/pep-0255.txt

PEP: 255
Title: Simple Generators
Version: $Revision$
Author: nas@python.ca (Neil Schemenauer),
    tim.one@home.com (Tim Peters),
    magnus@hetland.org (Magnus Lie Hetland)
Discussion-To: python-iterators@lists.sourceforge.net
Status: Draft
Type: Standards Track
Requires: 234
Created: 18-May-2001
Python-Version: 2.2
Post-History: 14-Jun-2001


Abstract

    This PEP introduces the concept of generators to Python, as well
    as a new statement used in conjunction with them, the "yield"
    statement.


Motivation

    When a producer function has a hard enough job that it requires
    maintaining state between values produced, most programming languages
    offer no pleasant and efficient solution beyond adding a callback
    function to the producer's argument list, to be called with each value
    produced.

    For example, tokenize.py in the standard library takes this approach:
    the caller must pass a "tokeneater" function to tokenize(), called
    whenever tokenize() finds the next token.  This allows tokenize to be
    coded in a natural way, but programs calling tokenize are typically
    convoluted by the need to remember between callbacks which token(s)
    were seen last.  The tokeneater function in tabnanny.py is a good
    example of that, maintaining a state machine in global variables, to
    remember across callbacks what it has already seen and what it hopes to
    see next.  This was difficult to get working correctly, and is still
    difficult for people to understand.  Unfortunately, that's typical of
    this approach.

    An alternative would have been for tokenize to produce an entire parse
    of the Python program at once, in a large list.  Then tokenize clients
    could be written in a natural way, using local variables and local
    control flow (such as loops and nested if statements) to keep track of
    their state.  But this isn't practical:  programs can be very large, so
    no a priori bound can be placed on the memory needed to materialize the
    whole parse; and some tokenize clients only want to see whether
    something specific appears early in the program (e.g., a future
    statement, or, as is done in IDLE, just the first indented statement),
    and then parsing the whole program first is a severe waste of time.

    Another alternative would be to make tokenize an iterator[1],
    delivering the next token whenever its .next() method is invoked.  This
    is pleasant for the caller in the same way a large list of results
    would be, but without the memory and "what if I want to get out early?"
    drawbacks.  However, this shifts the burden on tokenize to remember
    *its* state between .next() invocations, and the reader need only
    glance at tokenize.tokenize_loop() to realize what a horrid chore that
    would be.  Or picture a recursive algorithm for producing the nodes of
    a general tree structure:  to cast that into an iterator framework
    requires removing the recursion manually and maintaining the state of
    the traversal by hand.

    A fourth option is to run the producer and consumer in separate
    threads.  This allows both to maintain their states in natural ways,
    and so is pleasant for both.  Indeed, Demo/threads/Generator.py in the
    Python source distribution provides a usable synchronized-communication
    class for doing that in a general way.  This doesn't work on platforms
    without threads, though, and is very slow on platforms that do
    (compared to what is achievable without threads).

    A final option is to use the Stackless[2][3] variant implementation of
    Python instead, which supports lightweight coroutines.  This has much
    the same programmatic benefits as the thread option, but is much more
    efficient.  However, Stackless is a controversial rethinking of the
    Python core, and it may not be possible for Jython to implement the
    same semantics.  This PEP isn't the place to debate that, so suffice it
    to say here that generators provide a useful subset of Stackless
    functionality in a way that fits easily into the current CPython
    implementation, and is believed to be relatively straightforward for
    other Python implementations.

    That exhausts the current alternatives.  Some other high-level
    languages provide pleasant solutions, notably iterators in Sather[4],
    which were inspired by iterators in CLU; and generators in Icon[5], a
    novel language where every expression "is a generator".  There are
    differences among these, but the basic idea is the same:  provide a
    kind of function that can return an intermediate result ("the next
    value") to its caller, but maintaining the function's local state so
    that the function can be resumed again right where it left off.  A
    very simple example:

        def fib():
            a, b = 0, 1
            while 1:
                yield b
                a, b = b, a+b

    When fib() is first invoked, it sets a to 0 and b to 1, then yields b
    back to its caller.  The caller sees 1.  When fib is resumed, from its
    point of view the yield statement is really the same as, say, a print
    statement:  fib continues after the yield with all local state intact.
    a and b then become 1 and 1, and fib loops back to the yield, yielding
    1 to its invoker.  And so on.  From fib's point of view it's just
    delivering a sequence of results, as if via callback.  But from its
    caller's point of view, the fib invocation is an iterable object that
    can be resumed at will.  As in the thread approach, this allows both
    sides to be coded in the most natural ways; but unlike the thread
    approach, this can be done efficiently and on all platforms.  Indeed,
    resuming a generator should be no more expensive than a function call.

    The same kind of approach applies to many producer/consumer functions.
    For example, tokenize.py could yield the next token instead of invoking
    a callback function with it as argument, and tokenize clients could
    iterate over the tokens in a natural way:  a Python generator is a kind
    of Python iterator[1], but of an especially powerful kind.


Specification

    A new statement is introduced:

        yield_stmt:    "yield" expression_list

    "yield" is a new keyword, so a future statement[8] is needed to phase
    this in.  [XXX spell this out]

    The yield statement may only be used inside functions.  A function that
    contains a yield statement is called a generator function.

    When a generator function is called, the actual arguments are bound to
    function-local formal argument names in the usual way, but no code in
    the body of the function is executed.  Instead a generator-iterator
    object is returned; this conforms to the iterator protocol[6], so in
    particular can be used in for-loops in a natural way.  Note that when
    the intent is clear from context, the unqualified name "generator" may
    be used to refer either to a generator-function or a generator-
    iterator.

    Each time the .next() method of a generator-iterator is invoked, the
    code in the body of the generator-function is executed until a yield
    or return statement (see below) is encountered, or until the end of
    the body is reached.

    If a yield statement is encountered, the state of the function is
    frozen, and the value of expression_list is returned to .next()'s
    caller.  By "frozen" we mean that all local state is retained,
    including the current bindings of local variables, the instruction
    pointer, and the internal evaluation stack:  enough information is
    saved so that the next time .next() is invoked, the function can
    proceed exactly as if the yield statement were just another external
    call.

    A generator function can also contain return statements of the form:

        "return"

    Note that an expression_list is not allowed on return statements
    in the body of a generator (although, of course, they may appear in
    the bodies of non-generator functions nested within the generator).

    When a return statement is encountered, nothing is returned, but a
    StopIteration exception is raised, signalling that the iterator is
    exhausted.   The same is true if control flows off the end of the
    function.  Note that return means "I'm done, and have nothing
    interesting to return", for both generator functions and non-generator
    functions.


Generators and Exception Propagation

    If an unhandled exception-- including, but not limited to,
    StopIteration --is raised by, or passes through, a generator function,
    then the exception is passed on to the caller in the usual way, and
    subsequent attempts to resume the generator function raise
    StopIteration.  In other words, an unhandled exception terminates a
    generator's useful life.

    Example (not idiomatic but to illustrate the point):

    >>> def f():
    ...     return 1/0
    >>> def g():
    ...     yield f()  # the zero division exception propagates
    ...     yield 42   # and we'll never get here
    >>> k = g()
    >>> k.next()
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
      File "<stdin>", line 2, in g
      File "<stdin>", line 2, in f
    ZeroDivisionError: integer division or modulo by zero
    >>> k.next()  # and the generator function cannot be resumed
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    StopIteration
    >>>


Example

        # A binary tree class.
        class Tree:

            def __init__(self, label, left=None, right=None):
                self.label = label
                self.left = left
                self.right = right

            def __repr__(self, level=0, indent="    "):
                s = level*indent + `self.label`
                if self.left:
                    s = s + "\n" + self.left.__repr__(level+1, indent)
                if self.right:
                    s = s + "\n" + self.right.__repr__(level+1, indent)
                return s

            def __iter__(self):
                return inorder(self)

        # Create a Tree from a list.
        def tree(list):
            n = len(list)
            if n == 0:
                return []
            i = n / 2
            return Tree(list[i], tree(list[:i]), tree(list[i+1:]))

        # A recursive generator that generates Tree leaves in in-order.
        def inorder(t):
            if t:
                for x in inorder(t.left):
                    yield x
                yield t.label
                for x in inorder(t.right):
                    yield x

        # Show it off: create a tree.
        t = tree("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
        # Print the nodes of the tree in in-order.
        for x in t:
            print x,
        print

        # A non-recursive generator.
        def inorder(node):
            stack = []
            while node:
                while node.left:
                    stack.append(node)
                    node = node.left
                yield node.label
                while not node.right:
                    try:
                        node = stack.pop()
                    except IndexError:
                        return
                    yield node.label
                node = node.right

        # Exercise the non-recursive generator.
        for x in t:
            print x,
        print


Q & A

    Q. Why a new keyword?  Why not a builtin function instead?

    A. Control flow is much better expressed via keyword in Python, and
       yield is a control construct.  It's also believed that efficient
       implementation in Jython requires that the compiler be able to
       determine potential suspension points at compile-time, and a new
       keyword makes that easy.


Reference Implementation

    The current implementation, in a preliminary state (no docs and no
    focused tests), is part of Python's CVS development tree[9].
    Using this requires that you build Python from source.

    This was derived from an earlier patch by Neil Schemenauer[7].


Footnotes and References

    [1] PEP 234, http://python.sf.net/peps/pep-0234.html
    [2] http://www.stackless.com/
    [3] PEP 219, http://python.sf.net/peps/pep-0219.html
    [4] "Iteration Abstraction in Sather"
        Murer , Omohundro, Stoutamire and Szyperski
        http://www.icsi.berkeley.edu/~sather/Publications/toplas.html
    [5] http://www.cs.arizona.edu/icon/
    [6] The concept of iterators is described in PEP 234
        http://python.sf.net/peps/pep-0234.html
    [7] http://python.ca/nas/python/generator.diff
    [8] http://python.sf.net/peps/pep-0236.html
    [9] To experiment with this implementation, check out Python from CVS
        according to the instructions at
            http://sf.net/cvs/?group_id=5470


Copyright

    This document has been placed in the public domain.



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