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://www.python.org/peps/pep-0255.html [2] PEP 234 Iterators http://www.python.org/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://www.python.org/peps/pep-0279.html Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil fill-column: 70 End: