From CPython bytecode up to function objects (in brief)
Python bytecode is the low level heart of (C)Python; it's what the CPython interpreter actually processes in order to run your Python code. The dis module is the heart of information on examining bytecode and on the bytecodes themselves. But CPython doesn't just run bytecode in isolation. In practice bytecode is always part of some other object, partly because bytecode by itself is not self-contained; it relies on various other things for context.
Bytecode by itself looks like this:
>>> fred.func_code.co_code '|\x00\x00G|\x01\x00GHd\x00\x00S'
(That's authentic bytecode; you can feed it to dis.dis()
to see
what it means in isolation.)
I believe that Python bytecode is always found embedded in a code
object. Code objects have two sorts of additional attributes; attributes
which provide the necessary surrounding context that the bytecode itself
needs, and attributes that just have information about the code that's
useful for debugging. Examples of context attributes are co_consts
,
a tuple of constants used in the bytecode, and co_nlocals
,
the number of local variables that the code uses. Examples of
information attributes are co_filename
, co_firstlineno
, and even
co_varnames
(which tells you what local variable N is called).
Note that the context attributes are absolutely essential; bytecode
is not self-contained and cannot be run in isolation without them.
Many bytecodes simply do things like, say 'load constant 0'; if you
don't know what constant 0 is, you're not going to get far with the
bytecode. It is the code object that tells you this necessary stuff.
Most code objects are embedded in function objects (as the func_code
attribute). Function objects supply some additional context
attributes that are specific to using a piece of code as a function,
as well as another collection of information about the function
(most prominently func_doc
, the function's docstring if
any). As it happens, all of the special function attributes are
documented reasonably well in the official Python data model, along with code
objects and much more.
(Because I just looked it up, the mysterious func_dict
property is
another name for a function's __dict__
attribute, which is used
to allow you to add arbitrary properties to a function. See PEP 232. Note that functions don't
actually have a dictionary object attached to func_dict
until you
look at it or otherwise need it.)
Function objects themselves are frequently found embedded in instance method objects, which are used for methods on classes (whether bound to an object that's an instance of the class or unbound). But that's as far up the stack as I want to go today and anyways, instance method objects only have three attributes and they're all pretty obvious.
(If you have a class A
with a method function fred
, A.fred
is
actually an (unbound) instance method object. The fred
function itself
is A.fred.im_func
, or if you want, A.__dict__["fred"]
.)
Note that not all code objects are embedded in function objects. For
example, if you call compile()
what you get back is a bare code
object. I suspect that module level code winds up as a code object
before getting run by the interpreter, but I haven't looked at the
interpreter source to see so don't quote me on that.
(This entry was inspired by reading this introduction to the CPython interpreter (via Hacker News), which goes at things from the other direction.)
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