Python’s Innards: Code Objects

This article, part of a series of articles about Python’s internals, will continue our preparation to engage the machinery of code evaluation by discussing Code Objects. To those of you who just now joined in and didn’t even read the introduction (but why?!), please note an important disclaimer: while the series as a whole is CPython 3.x centric and might not ‘apply cleanly’ to other Python implementations, matters of bytecode and evaluation (like this article discusses) are even more likely to deviate between implementations. So some of what I say in this post may apply to other implementations, some not – I’m not even checking at the moment; if and when we’ll discuss implementations like PyPy, Jython, IronPython, etc, I’ll highlight some of the differences. With this disclaimer in mind, we can get back to the plot: Code Objects. The compilation of Python source code emits Python bytecode, which is evaluated at runtime to produce whatever behaviour the programmer implemented. I guess you can think of bytecode as ‘machine code for the Python virtual machine’, and indeed if you look at some binary x86 machine code (like this one: 0x55 0x89 0xe5 0xb8 0x2a 0x0 0x0 0x0 0x5d) and some Python bytecode (like that one: 0x64 0x1 0x0 0x53) they look more or less like the same sort of gibberish. Along with the actual bytecode, Python’s compiler emits additional fields, most of them must be coupled with the bytecode (otherwise it would be meaningless). The bytecode and these fields are lumped together in an object called a code object, our subject for this article.

You might initially confuse function objects with code objects, but shouldn’t. Functions are higher level creatures that execute code by relying on a lower level primitive, the code object, but adding more functionality on top of that (in other words, every function has precisely one code object directly associated with it, this is the function’s __code__ attribute, or f_code in Python 2.x). For example, among other things, a function keeps a reference to the global namespace (remember that?) in which it was originally defined, and knows the default values of arguments it receives. You can sometimes execute a code objects without a function (see eval and exec), but then you will have to provide it with a namespace or two to work in. Finally, just for accuracy’s sake, please note that tp_call of a function object isn’t exactly like exec or eval; the latter don’t pass in arguments or provide free argument binding (more below on these). If this doesn’t sit well with you yet, don’t panic, it just means functions’ code objects won’t necessarily be executable using eval or exec. I hope we have that settled.

Let’s see when code objects are created. Code objects are created whenever a block of Python code is compiled. We have mentioned blocks briefly before, the fine material defines them as “a piece of Python program text that is executed as a unit. The following are blocks: a module, a function body, and a class definition.” (the fine material also lists other but less-interesting-to-us code blocks, like every command in the interactive interpreter, the string passed to Python’s executable’s -c switch, etc). As usual, I don’t want to dig too deeply into compilation, but basically when a code block is encountered, it has to be successfully transformed into an AST (which requires mostly that its syntax will be correct), which is then passed to ./Python/compile.c: PyAST_Compile, the entry point into Python’s compilation machinary. A kind comment in ./Python/compile.c explains the general execution flow of this function.

Next, let’s discuss what is in a code object; I said it has stuff other than bytecode, but what? To whet our appetite about the various fields of a code object, we can look at the compiled Python sample from the first paragraph and disassemble it ourselves; it’s easier if we know beforehand that both samples implement a function which simply returns the value 42. Unlike the x86 machine code sample, which is self-contained and should be ready to run (<cough>assuming I didn’t botch it</cough>) the Python bytecode sample doesn’t include the constant value 42 in it at all. You absolutely can’t run this code meaningfully without its constants, and indeed 42 is referred to by one of the extra fields of the code object. We will best see the interaction between the actual bytecode and the accompanying fields as we do a manual disassembly.

From the interpreter (as usual, slight editing for readability):

# the opcode module has a mapping of opcode
# byte values to their symbolic names
>>> import opcode
>>> def return42(): return 42

# this is the function’s code object
>>> return42.__code__
<code object return42 … >
# this is the actual bytecode
>>> return42.__code__.co_code
# this is the field holding constants
>>> return42.__code__.co_consts
(None, 42)
# the first opcode is LOAD_CONST
>>> opcode.opname[return42.__code__.co_code[0]]
# LOAD_CONST has one word as an operand
# let’s get its value
>>> return42.__code__.co_code[1] + \
… 256 * return42.__code__.co_code[2]
# and which constant can we find in offset 1?
>>> return42.__code__.co_consts[1]
# finally, the next opcode
>>> opcode.opname[return42.__code__.co_code[3]]

I hope this was educational, albeit doing it all the time could get boring. Fortunately, we have dis to do this work for us (>>> from dis import dis, you already saw I aliased and augmented it as diss). In addition to dis, the function show_code from the same module is useful to look at code objects (I aliased and augmented a bit as ssc). So let’s look at return42 with diss and ssc:

>>> diss(return42)
1 0 LOAD_CONST 1 (42)
>>> ssc(return42)
Name: return42
Filename: <stdin>
Argument count: 0
Kw-only arguments: 0
Number of locals: 0
Stack size: 1
0: None
1: 42

We see diss and ssc generally agree with our disassembly, though ssc further parsed all sorts of other fields of the code object which we didn’t handle so far (you can run dir on a code object to see them yourself). We have also seen that our value of 42 is indeed referred to by a field of the code object, rather than somehow be encoded in the bytecode.

Code objects are immutable and their fields don’t hold any references (directly or indirectly) to mutable objects. This immutability is useful in simplifying many things, one of which is the handling of nested code blocks. An example of a nested code block is a class with two methods: the class is built using a code block, and this code block nests two inner code blocks, one for each method. This situation is recursively handled by creating the innermost code objects first and treating them as constants for the enclosing code object (much like an integer or a string literal would be treated). You may be wondering how mutable object literals (a = [1, 2, 3]) are represented in a code object, and the answer is that rather than referring to the mutable object with the code object, the ‘recipe’ to prepare it is kept (try >>> import dis; dis.dis(compile("a=(1,2,[3,4,{5:6}])", "string", "exec")) to make this immediately clear).

Now that we have seen the relation between the bytecode and a code object field (co_consts), let’s take a look at the myriad of other fields in a code object. To be honest, I’m not sure this list would be particularly exciting. Many of these fields are just integer counters or tuples of strings representing how many or which variables of various sorts are used in a code object. But looking to the horizon where ceval.c and frame object evaluation is waiting for us, I can tell you that we need an immediate and crisp understanding of all these fields and their exact meaning, subtleties included. So I’ll (tediously?) list and categorize them all, building on the rather terse description you can find in the standard type hierarchy. If this seems to boring right now, you best skim it now but keep it as a reference for later posts; trust me, it’s useful.

Identity or origin (strings)
A name (a string) for this code object; for a function this would be the function’s name, for a class this would be the class’ name, etc. The compile builtin doesn’t let you specify this, so all code objects generated with it carry the name <module>.
The filename from which the code was compiled. Will be <stdin> for code entered in the interactive interpreter or whatever name is given as the second argument to compile for code objects created with compile.
Different types of names (string tuples)
A tuple containing the names of the local variables (including arguments). To parse this tuple properly you need to look at co_flags and the counter fields listed below, so you’ll know which item in the tuple is what kind of variable. In the ‘richest’ case, co_varnames contains (in order): positional argument names (including optional ones), keyword only argument names (again, both required and optional), varargs argument name (i.e., *args), kwds argument name (i.e., **kwargs), and then any other local variable names. So you need to look at co_argcount, co_kwonlyargcount and co_flags to fully interpret this tuple.
A tuple containing the names of local variables that are stored in cells (discussed in the previous article) because they are referenced by lexically nested functions.
A tuple containing the names of free variables. Generally, a free variable means a variable which is referenced by an expression but isn’t defined in it. In our case, it means a variable that is referenced in this code object but was defined and will be dereferenced to a cell in another code object (also see co_cellvars above and, again, the previous article).
A tuple containing the names which aren’t covered by any of the other fields (they are not local variables, they are not free variables, etc) used by the bytecode. This includes names deemed to be in the global or builtin namespace as well as attributes (i.e., if you do in a function, bar will be listed in its code object’s names).
Counters and indexes (integers)
The number of positional arguments the code object expects to receive, including those with default values. For example, def foo(a, b, c=3): pass would have a code object with this value set to three. The code object of classes accept one argument which we will explore when we discuss class creation.
The number of keyword arguments the code object can receive.
The number of local variables used in the code object (including arguments).
The line offset where the code object’s source code began, relative to the module it was defined in, starting from one. In this (and some but not all other regards), each input line typed in the interactive interpreter is a module of its own.
The maximum size required of the value stack when running this object. This size is statically computed by the compiler (./Python/compile.c: stackdepth when the code object is created, by looking at all possible flow paths searching for the one that requires the deepest value stack. To illustrate this, look at the diss and ssc outputs for a = 1 and a = [1,2,3]. The former has at most one value on the value stack at a time, the latter has three, because it needs to put all three integer literals on the stack before building the list.
Other stuff (various)
A string representing the sequence of bytecode instructions, contains a stream of opcodes and their operands (or rather, indexes which are used with other code object fields to represent their operands, as we saw above).
A tuple containing the literals used by the bytecode. Remember everything in a code object must be immutable, running diss and ssc on the code snippets a=(1,2,3) versus [1,2,3] and yet again versus a=(1,2,3,[4,5,6]) recommended to dig this field.
A string encoding the mapping from bytecode offsets to line numbers. If you happen to really care how this is encoded you can either look at ./Python/compile.c or ./Lib/ findlinestarts.
An integer encoding a number of flags regarding the way this code object was created (which says something about how it should be evaluated). The list of possible flags is listed in ./Include/code.h, as a small example I can give CO_NESTED, which marks a code object which was compiled from a lexically nested function. Flags also have an important role in the implementation of the __future__ mechanism, which is still unused in Python 3.1 at the time of this writing, as no “future syntax” exists in Python 3.1. However, even when thinking in Python 3.x terms co_flags is still important as it facilitates the migration from the 2.x branch. In 2.x, __future__ is used when enabling Python 3.x like behaviour (i.e., from __future__ import print_function in Python 2.7 will disable the print statement and add a print function to the builtins module, just like in Python 3.x). If we come across flags from now on (in future posts), I’ll try to mention their relevance in the particular scenario.
This field of the PyCodeObject struct is not exposed in the Python object; it (optionally) points to a stack frame object. This can aid performance by maintaining an association between a code object and a stack frame object, so as to avoid reallocation of frames by recycling the frame object used for a code object. There’s a detailed comment in ./Objects/frameobject.c explaining zombie frames and their reanimation, we may mention this issue again when we discuss stack frames.

Phew! This is everything in a code object. In making this list I’ve compiled quite a few code blocks, looking how changes in the Python source changes the resulting code object. I recommend you do something similar, and I actually bothered to make it ultra-easy for you to look into how various code blocks affect these fields: in the Mercurial repository I have for this series I created a directory called code_objects, within it you can find a self-explaining little utility that can facilitate looking at a few sample code blocks I wrote alongside with their disassembly and show_code output. Not all fields are necessarily covered in the sample code blocks I provided, you should be able to add a few more samples (if anything intrigues you) yourself and see them disassembled/analyzed. Also, I’m sorry, I’m totally a *NIX bigot (and will erase all flame or even flame-ish comments about that) and this toy might not run on Windows. There’s no good reason for that, I just wanted to use less for pagination, etc, and couldn’t be bothered with achieving the same effect on Windows.

This pretty much sums up what I have to say about code objects at the moment. Time permitting, I sincerely hope we’ll soon reach the next article, where we’ll tackle the final frontier before ./Python/ceval.c: PyEval_EvalFrameEx itself: frame objects. ¡Olé!

I would like to thank Nick Coghlan for reviewing this article; any mistakes that slipped through are my own.


5 responses to “Python’s Innards: Code Objects”

  1. Cool article. Just wanted to point out that PyPy fully mirrors CPython’s code objects and uses mostly the same bytecode. The only difference to your examples is that PyPy only implements Python 2.5, so functions still have the “func_code” attribute instead of the “__code__” attribute.

  2. Great aticle, many thanks!

    I was looking for a help on code objects and this was exactly what I needed.

  3. i loved this article, but an example application where such code objects can be used would be really helpful

    1. Ugh, Code Objects are largely a low-level implementation detail, under usual circumstances you shouldn’t really use them for anything. A simple (and effective) rule of thumb is “if you’re not certain what you need them for and know there’s no other way, you don’t need them”. More than anything, this is to understand how Python works and how to do advanced debugging/optimizations.

  4. Thank you for sharing these technical details