This document describes the implementation of PyPy’s Bytecode Interpreter and related Virtual Machine functionalities.
PyPy’s bytecode interpreter has a structure reminiscent of CPython’s Virtual Machine: It processes code objects parsed and compiled from Python source code. It is implemented in the pypy/interpreter/ directory. People familiar with the CPython implementation will easily recognize similar concepts there. The major differences are the overall usage of the object space indirection to perform operations on objects, and the organization of the built-in modules (described here).
Code objects are a nicely preprocessed, structured representation of
source code, and their main content is bytecode. We use the same
compact bytecode format as CPython 2.7, with minor differences in the bytecode
set. Our bytecode compiler is
implemented as a chain of flexible passes (tokenizer, lexer, parser,
abstract syntax tree builder and bytecode generator). The latter passes
are based on the
compiler package from the standard library of
CPython, with various improvements and bug fixes. The bytecode compiler
(living under pypy/interpreter/astcompiler/) is now integrated and is
translated with the rest of PyPy.
Code objects contain
condensed information about their respective functions, class and
module body source codes. Interpreting such code objects means
instantiating and initializing a Frame class and then
frame.eval() method. This main entry point
initialize appropriate namespaces and then interprets each
bytecode instruction. Python’s standard library contains
the lib-python/2.7/dis.py module which allows to inspection
of the virtual machine’s bytecode instructions:
>>> import dis >>> def f(x): ... return x + 1 >>> dis.dis(f) 2 0 LOAD_FAST 0 (x) 3 LOAD_CONST 1 (1) 6 BINARY_ADD 7 RETURN_VALUE
CPython and PyPy are stack-based virtual machines, i.e.
they don’t have registers but instead push object to and pull objects
from a stack. The bytecode interpreter is only responsible
for implementing control flow and pushing and pulling black
box objects to and from this value stack. The bytecode interpreter
does not know how to perform operations on those black box
(wrapped) objects for which it delegates to the object
space. In order to implement a conditional branch in a program’s
execution, however, it needs to gain minimal knowledge about a
wrapped object. Thus, each object space has to offer a
is_true(w_obj) operation which returns an
interpreter-level boolean value.
For the understanding of the interpreter’s inner workings it
is crucial to recognize the concepts of interpreter-level and
application-level code. In short, interpreter-level is executed
directly on the machine and invoking application-level functions
leads to an bytecode interpretation indirection. However,
special care must be taken regarding exceptions because
application level exceptions are wrapped into
which are thus distinguished from plain interpreter-level exceptions.
See application level exceptions for some more information
The interpreter implementation offers mechanisms to allow a
caller to be unaware of whether a particular function invocation
leads to bytecode interpretation or is executed directly at
interpreter-level. The two basic kinds of Gateway classes
expose either an interpreter-level function to
application-level execution (
interp2app) or allow
transparent invocation of application-level helpers
app2interp) at interpreter-level.
Another task of the bytecode interpreter is to care for exposing its basic code, frame, module and function objects to application-level code. Such runtime introspection and modification abilities are implemented via interpreter descriptors (also see Raymond Hettingers how-to guide for descriptors in Python, PyPy uses this model extensively).
A significant complexity lies in function argument parsing. Python as a language offers flexible ways of providing and receiving arguments for a particular function invocation. Not only does it take special care to get this right, it also presents difficulties for the annotation pass which performs a whole-program analysis on the bytecode interpreter, argument parsing and gatewaying code in order to infer the types of all values flowing across function calls.
It is for this reason that PyPy resorts to generate specialized frame classes and functions at initialization time in order to let the annotator only see rather static program flows with homogeneous name-value assignments on function invocations.
The concept of Frames is pervasive in executing programs and on virtual machines in particular. They are sometimes called execution frame because they hold crucial information regarding the execution of a Code object, which in turn is often directly related to a Python Function. Frame instances hold the following state:
the local scope holding name-value bindings, usually implemented via a “fast scope” which is an array of wrapped objects
a blockstack containing (nested) information regarding the control flow of a function (such as
a value stack where bytecode interpretation pulls object from and puts results on. (
locals_stack_wis actually a single list containing both the local scope and the value stack.)
a reference to the globals dictionary, containing module-level name-value bindings
debugging information from which a current line-number and file location can be constructed for tracebacks
Moreover the Frame class itself has a number of methods which implement
the actual bytecodes found in a code object. The methods of the
class are added in various files:
PyPy’s code objects contain the same information found in CPython’s code objects. They differ from Function objects in that they are only immutable representations of source code and don’t contain execution state or references to the execution environment found in Frames. Frames and Functions have references to a code object. Here is a list of Code attributes:
co_flagsflags if this code object has nested scopes/generators/etc.
co_stacksizethe maximum depth the stack can reach while executing the code
co_codethe actual bytecode string
co_argcountnumber of arguments this code object expects
co_varnamesa tuple of all argument names pass to this code object
co_nlocalsnumber of local variables
co_namesa tuple of all names used in the code object
co_constsa tuple of prebuilt constant objects (“literals”) used in the code object
co_cellvarsa tuple of Cells containing values for access from nested scopes
co_freevarsa tuple of Cell names from “above” scopes
co_filenamesource file this code object was compiled from
co_firstlinenothe first linenumber of the code object in its source file
co_namename of the code object (often the function name)
co_lnotaba helper table to compute the line-numbers corresponding to bytecodes
Function class (in pypy/interpreter/function.py)
represents a Python function. A
Function carries the following
func_docthe docstring (or None)
func_namethe name of the function
func_codethe Code object representing the function source code
func_defaultsdefault values for the function (built at function definition time)
func_dictdictionary for additional (user-defined) function attributes
func_globalsreference to the globals dictionary
func_closurea tuple of Cell references
Functions classes also provide a
__get__ descriptor which creates a Method
object holding a binding to an instance or a class. Finally,
Methods both offer a
call_args() method which executes
the function given an Arguments class instance.
The Argument class (in pypy/interpreter/argument.py) is responsible for parsing arguments passed to functions. Python has rather complex argument-passing concepts:
keyword arguments specified by name
default values for positional arguments, defined at function definition time
“star args” allowing a function to accept remaining positional arguments
“star keyword args” allow a function to accept additional arbitrary name-value bindings
Moreover, a Function object can get bound to a class or instance
in which case the first argument to the underlying function becomes
the bound object. The
Arguments provides means to allow all
this argument parsing and also cares for error reporting.
Module instance represents execution state usually constructed
from executing the module’s source file. In addition to such a module’s
__dict__ dictionary it has the following application level
__doc__the docstring of the module
__file__the source filename from which this module was instantiated
__cached__the filename for the byte-compiled cache of this module
__path__state used for relative imports
Apart from the basic Module used for importing
application-level files there is a more refined
MixedModule class (see pypy/interpreter/mixedmodule.py)
which allows to define name-value bindings both at application
level and at interpreter level. See the
module’s pypy/module/__builtin__/__init__.py file for an
example and the higher level chapter on Modules in the coding
A unique PyPy property is the ability to easily cross the barrier between interpreted and machine-level code (often referred to as the difference between interpreter-level and application-level). Be aware that the according code (in pypy/interpreter/gateway.py) for crossing the barrier in both directions is somewhat involved, mostly due to the fact that the type-inferring annotator needs to keep track of the types of objects flowing across those barriers.
In order to make an interpreter-level function available at
application level, one invokes
Such a function usually takes a
space argument and any number
of positional arguments. Additionally, such functions can define
unwrap_spec telling the
interp2app logic how
application-level provided arguments should be unwrapped
before the actual interpreter-level function is invoked.
For example, interpreter descriptors such as the
method for allocating and constructing a Module instance are
defined with such code:
Module.typedef = TypeDef("module", __new__ = interp2app(Module.descr_module__new__.im_func, unwrap_spec=[ObjSpace, W_Root, Arguments]), __init__ = interp2app(Module.descr_module__init__), # module dictionaries are readonly attributes __dict__ = GetSetProperty(descr_get_dict, cls=Module), __doc__ = 'module(name[, doc])\n\nCreate a module object...' )
Module.descr_module__new__ interpreter-level method
referenced from the
__new__ keyword argument above is defined
def descr_module__new__(space, w_subtype, __args__): module = space.allocate_instance(Module, w_subtype) Module.__init__(module, space, None) return space.wrap(module)
interp2app mechanism takes care to route
an application level access or call to an internal interpreter-level
object appropriately to the descriptor, providing enough precision
and hints to keep the type-inferring annotator happy.
Application level code is often preferable. Therefore,
we often like to invoke application level code from interpreter-level.
This is done via the Gateway’s
which we usually invoke at definition time in a module.
It generates a hook which looks like an interpreter-level
function accepting a space and an arbitrary number of arguments.
When calling a function at interpreter-level the caller side
does usually not need to be aware if its invoked function
is run through the PyPy interpreter or if it will directly
execute on the machine (after translation).
Here is an example showing how we implement the Metaclass finding algorithm of the Python language in PyPy:
app = gateway.applevel(r''' def find_metaclass(bases, namespace, globals, builtin): if '__metaclass__' in namespace: return namespace['__metaclass__'] elif len(bases) > 0: base = bases if hasattr(base, '__class__'): return base.__class__ else: return type(base) elif '__metaclass__' in globals: return globals['__metaclass__'] else: try: return builtin.__metaclass__ except AttributeError: return type ''', filename=__file__) find_metaclass = app.interphook('find_metaclass')
find_metaclass interpreter-level hook is invoked
with five arguments from the
BUILD_CLASS opcode implementation
def BUILD_CLASS(f): w_methodsdict = f.valuestack.pop() w_bases = f.valuestack.pop() w_name = f.valuestack.pop() w_metaclass = find_metaclass(f.space, w_bases, w_methodsdict, f.w_globals, f.space.wrap(f.builtin)) w_newclass = f.space.call_function(w_metaclass, w_name, w_bases, w_methodsdict) f.valuestack.push(w_newclass)
Note that at a later point we can rewrite the
implementation at interpreter-level and we would not have
to modify the calling side at all.
Python traditionally has a very far-reaching introspection model
for bytecode interpreter related objects. In PyPy and in CPython read
and write accesses to such objects are routed to descriptors.
Of course, in CPython those are implemented in
C while in
PyPy they are implemented in interpreter-level Python code.
All instances of a Function, Code, Frame or Module classes
W_Root instances which means they can be represented
at application level. These days, a PyPy object space needs to
work with a basic descriptor lookup when it encounters
accesses to an interpreter-level object: an object space asks
a wrapped object for its type via a
getclass method and then
calls the type’s
lookup(name) function in order to receive a descriptor
function. Most of PyPy’s internal object descriptors are defined at the
end of pypy/interpreter/typedef.py. You can use these definitions
as a reference for the exact attributes of interpreter classes visible
at application level.