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PEP 288, Generators Attributes and Exceptions, Hettinger

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SR 244 The `directive' Statement von Loewis
SR 259 Omit printing newline after newline van Rossum
SR 271 Prefixing sys.path by command line option Giacometti
SD 288 Generators Attributes and Exceptions Hettinger
SR 666 Reject Foolish Indentation Creighton
@ -263,6 +264,7 @@ Numerical Index
S 285 Adding a bool type van Rossum
S 286 Enhanced Argument Tuples von Loewis
S 287 reStructuredText Standard Docstring Format Goodger
SD 288 Generators Attributes and Exceptions Hettinger
SR 666 Reject Foolish Indentation Creighton

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PEP: 288
Title: Generators Attributes and Exceptions
Version: $Revision$
Last-Modified: $Date$
Author: python@rcn.com (Raymond D. Hettinger)
Status: Deferred
Type: Standards Track
Created: 21-Mar-2002
Python-Version: 2.4
Post-History:
Abstract
This PEP introduces ideas for enhancing the generators introduced
in Python version 2.2 [1]. The goal is to increase the
convenience, utility, and power of generators by providing a
mechanism for passing data into a generator and for triggering
exceptions inside a generator.
These mechanisms were first proposed along with two other
generator tools in PEP 279 [7]. They were split-off to this
separate PEP to allow the ideas more time to mature and for
alternatives to be considered.
Rationale
Python 2.2 introduced the concept of an iterable interface as
proposed in PEP 234 [2]. The iter() factory function was provided
as common calling convention and deep changes were made to use
iterators as a unifying theme throughout Python. The unification
came in the form of establishing a common iterable interface for
mappings, sequences, and file objects.
Generators, as proposed in PEP 255 [1], were introduced as a means for
making it easier to create iterators, especially ones with complex
internal execution or variable states.
The next step in the evolution of generators is to extend the
syntax of the 'yield' keyword to enable generator parameter
passing. The resulting increase in power simplifies the creation
of consumer streams which have a complex execution state and/or
variable state.
A better alternative being considered is to allow generators to
accept attribute assignments. This allows data to be passed in a
standard Python fashion.
A related evolutionary step is to add a generator method to enable
exceptions to be passed to a generator. Currently, there is no
clean method for triggering exceptions from outside the generator.
Also, generator exception passing helps mitigate the try/finally
prohibition for generators.
These suggestions are designed to take advantage of the existing
implementation and require little additional effort to
incorporate. They are backwards compatible and require no new
keywords. They are being recommended for Python version 2.4.
Reference Implementation
There is not currently a CPython implementation; however, a
simulation module written in pure Python is available on
SourceForge [5]. The simulation is meant to allow direct
experimentation with the proposal.
There is also a module [6] with working source code for all of the
examples used in this PEP. It serves as a test suite for the
simulator and it documents how each of the feature works in
practice.
The authors and implementers of PEP 255 [1] were contacted to
provide their assessment of whether the enhancement was going to
be straight-forward to implement and require only minor
modification of the existing generator code. Neil felt the
assertion was correct. Ka-Ping thought so also. GvR said he
could believe that it was true. Tim did not have an opportunity
to give an assessment.
Specification for Generator Parameter Passing
1. Allow 'yield' to assign a value as in:
def mygen():
while 1:
x = yield None
print x
2. Let the .next() method take a value to pass to the generator as in:
g = mygen()
g.next() # runs the generator until the first 'yield'
g.next(1) # '1' is bound to 'x' in mygen(), then printed
g.next(2) # '2' is bound to 'x' in mygen(), then printed
The control flow of 'yield' and 'next' is unchanged by this
proposal. The only change is that a value can be sent into the
generator. By analogy, consider the quality improvement from
GOSUB (which had no argument passing mechanism) to modern
procedure calls (which can pass in arguments and return values).
Most of the underlying machinery is already in place, only the
communication needs to be added by modifying the parse syntax to
accept the new 'x = yield expr' syntax and by allowing the .next()
method to accept an optional argument.
Yield is more than just a simple iterator creator. It does
something else truly wonderful -- it suspends execution and saves
state. It is good for a lot more than writing iterators. This
proposal further expands its capability by making it easier to
share data with the generator.
The .next(arg) mechanism is especially useful for:
1. Sending data to any generator
2. Writing lazy consumers with complex execution states
3. Writing co-routines (as demonstrated in Dr. Mertz's articles [3])
The proposal is a clear improvement over the existing alternative
of passing data via global variables. It is also much simpler,
more readable and easier to debug than an approach involving the
threading module with its attendant mutexes, semaphores, and data
queues. A class-based approach competes well when there are no
complex execution states or variable states. However, when the
complexity increases, generators with parameter passing are much
simpler because they automatically save state (unlike classes
which must explicitly save the variable and execution state in
instance variables).
Note A: This proposal changes 'yield' from a statement to an
expression with binding and precedence similar to lambda.
Examples
Example of a Complex Consumer
The encoder for arithmetic compression sends a series of
fractional values to a complex, lazy consumer. That consumer
makes computations based on previous inputs and only writes out
when certain conditions have been met. After the last fraction is
received, it has a procedure for flushing any unwritten data.
Example of a Consumer Stream
def filelike(packagename, appendOrOverwrite):
cum = []
if appendOrOverwrite == 'w+':
cum.extend(packages[packagename])
try:
while 1:
dat = yield None
cum.append(dat)
except FlushStream:
packages[packagename] = cum
ostream = filelike('mydest','w') # Analogous to file.open(name,flag)
ostream.next() # Advance to the first yield
ostream.next(firstdat) # Analogous to file.write(dat)
ostream.next(seconddat)
ostream.throw(FlushStream) # Throw is proposed below
Example of a Complex Consumer
Loop over the picture files in a directory, shrink them one at a
time to thumbnail size using PIL [4], and send them to a lazy
consumer. That consumer is responsible for creating a large blank
image, accepting thumbnails one at a time and placing them in a 5
by 3 grid format onto the blank image. Whenever the grid is full,
it writes-out the large image as an index print. A FlushStream
exception indicates that no more thumbnails are available and that
the partial index print should be written out if there are one or
more thumbnails on it.
Example of a Producer and Consumer Used Together in a Pipe-like Fashion
'Analogy to Linux style pipes: source | upper | sink'
sink = sinkgen()
sink.next()
for word in source():
sink.next(word.upper())
Comments
Comments from GvR: We discussed this at length when we were hashing
out generators and coroutines, and found that there's always a
problem with this: the argument to the first next() call has
to be thrown away, because it doesn't correspond to a yield
statement. This looks ugly (note that the example code has a
dummy call to next() to get the generator going). But there
may be useful examples that can only be programmed (elegantly)
with this feature, so I'm reserving judgment. I can believe
that it's easy to implement.
Comments from Ka-Ping Yee: I also think there is a lot of power to be
gained from generator argument passing.
Comments from Neil Schemenauer: I like the idea of being able to pass
values back into a generator. I originally pitched this idea
to Guido but in the end we decided against it (at least for
the initial implementation). There was a few issues to work
out but I can't seem to remember what they were. My feeling
is that we need to wait until the Python community has more
experience with generators before adding this feature. Maybe
for 2.4 but not for 2.3. In the mean time you can work around
this limitation by making your generator a method. Values can
be passed back by mutating the instance.
Comments for Magnus Lie Hetland: I like the generator parameter
passing mechanism. Although I see no need to defer it,
deferral seems to be the most likely scenario, and in the
meantime I guess the functionality can be emulated either by
implementing the generator as a method, or by passing a
parameter with the exception passing mechanism.
Author response: Okay, consider this proposal deferred until version 2.4
so the idea can fully mature. I am currently teasing out two
alternatives which may eliminate the issue with the initial
next() call not having a corresponding yield.
Alternative 1: Submit
Instead of next(arg), use a separate method, submit(arg).
Submit would behave just like next() except that on the first
call, it will call next() twice. The word 'submit' has the
further advantage of being explicit in its intent. It also
allows checking for the proper number of arguments (next
always has zero and submit always has one). Using this
alternative, the call to the consumer stream looks like this:
ostream = filelike('mydest','w')
ostream.submit(firstdat) # No call to next is needed
ostream.submit(seconddat)
ostream.throw(FlushStream) # Throw is proposed below
Alternative 2: Generator Attributes
Instead of generator parameter passing, enable writable
generator attributes: g.data=firstdat; g.next(). The code on
the receiving end is written knowing that the attribute is set
from the very beginning. This solves the problem because the
first next call does not need to be associated with a yield
statement.
This solution uses a standard Python tool, object attributes,
in a standard way. It is also explicit in its intention and
provides some error checking (the receiving code raises an
AttributeError if the expected field has not be set before the
call).
The one unclean part of this approach is that the generator
needs some way to reference itself (something parallel to the
use of the function name in a recursive function or to the use
of 'self' in a method). The only way I can think of is to
introduce a new system variable, __self__, in any function
that employs a yield statement. Using this alternative, the
code for the consumer stream looks like this:
def filelike(packagename, appendOrOverwrite):
cum = []
if appendOrOverwrite == 'w+':
cum.extend(packages[packagename])
try:
while 1:
cum.append(__self__.dat)
yield None
except FlushStream:
packages[packagename] = cum
ostream = filelike('mydest','w')
ostream.dat = firstdat; ostream.next()
ostream.dat = firstdat; ostream.next()
ostream.throw(FlushStream) # Throw is proposed in PEP 279
Specification for Generator Exception Passing:
Add a .throw(exception) method to the generator interface:
def logger():
start = time.time()
log = []
try:
while 1:
log.append( time.time() - start )
yield log[-1]
except WriteLog:
writelog(log)
g = logger()
for i in [10,20,40,80,160]:
testsuite(i)
g.next()
g.throw(WriteLog)
There is no existing work-around for triggering an exception
inside a generator. This is a true deficiency. It is the only
case in Python where active code cannot be excepted to or through.
Generator exception passing also helps address an intrinsic
limitation on generators, the prohibition against their using
try/finally to trigger clean-up code [1]. Without .throw(), the
current work-around forces the resolution or clean-up code to be
moved outside the generator.
Note A: The name of the throw method was selected for several
reasons. Raise is a keyword and so cannot be used as a method
name. Unlike raise which immediately raises an exception from the
current execution point, throw will first return to the generator
and then raise the exception. The word throw is suggestive of
putting the exception in another location. The word throw is
already associated with exceptions in other languages.
Alternative method names were considered: resolve(), signal(),
genraise(), raiseinto(), and flush(). None of these seem to fit
as well as throw().
Note B: The throw syntax should exactly match raise's syntax:
throw([expression, [expression, [expression]]])
Accordingly, it should be implemented to handle all of the following:
raise string g.throw(string)
raise string, data g.throw(string,data)
raise class, instance g.throw(class,instance)
raise instance g.throw(instance)
raise g.throw()
Comments from GvR: I'm not convinced that the cleanup problem that
this is trying to solve exists in practice. I've never felt
the need to put yield inside a try/except. I think the PEP
doesn't make enough of a case that this is useful.
This one gets a big fat -1 until there's a good motivational
section.
Comments from Ka-Ping Yee: I agree that the exception issue needs to
be resolved and [that] you have suggested a fine solution.
Comments from Neil Schemenauer: The exception passing idea is one I
hadn't thought of before and looks interesting. If we enable
the passing of values back, then we should add this feature
too.
Comments for Magnus Lie Hetland: Even though I cannot speak for the
ease of implementation, I vote +1 for the exception passing
mechanism.
Comments from the Community: The response has been mostly favorable. One
negative comment from GvR is shown above. The other was from
Martin von Loewis who was concerned that it could be difficult
to implement and is withholding his support until a working
patch is available. To probe Martin's comment, I checked with
the implementers of the original generator PEP for an opinion
on the ease of implementation. They felt that implementation
would be straight-forward and could be grafted onto the
existing implementation without disturbing its internals.
Author response: When the sole use of generators is to simplify writing
iterators for lazy producers, then the odds of needing
generator exception passing are slim. If, on the other hand,
generators are used to write lazy consumers, create
coroutines, generate output streams, or simply for their
marvelous capability for restarting a previously frozen state,
THEN the need to raise exceptions will come up frequently.
I'm no judge of what is truly Pythonic, but am still
astonished that there can exist blocks of code that can't be
excepted to or through, that the try/finally combination is
blocked, and that the only work-around is to rewrite as a
class and move the exception code out of the function or
method being excepted.
References
[1] PEP 255 Simple Generators
http://python.sourceforge.net/peps/pep-0255.html
[2] PEP 234 Iterators
http://python.sourceforge.net/peps/pep-0234.html
[3] Dr. David Mertz's draft column for Charming Python.
http://gnosis.cx/publish/programming/charming_python_b5.txt
http://gnosis.cx/publish/programming/charming_python_b7.txt
[4] PIL, the Python Imaging Library can be found at:
http://www.pythonware.com/products/pil/
[5] A pure Python simulation of every feature in this PEP is at:
http://sourceforge.net/tracker/download.php?group_id=5470&atid=305470&file_id=17348&aid=513752
[6] The full, working source code for each of the examples in this PEP
along with other examples and tests is at:
http://sourceforge.net/tracker/download.php?group_id=5470&atid=305470&file_id=17412&aid=513756
[7] PEP 279 Enhanced Generators
http://python.sourceforge.net/peps/pep-0279.html
Copyright
This document has been placed in the public domain.
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