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PEP: 511
Title: API for AST transformers
Version: $Revision$
Last-Modified: $Date$
Author: Victor Stinner <victor.stinner@gmail.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 4-January-2016
Python-Version: 3.6
Abstract
========
Propose an API to support AST transformers. Add also ``-o OPTIM_TAG``
command line option to change ``.pyc`` filenames. Raise an
``ImportError`` exception on import if the ``.pyc`` file is missing and
the AST transformers required to transform the code are missing.
AST transformers are not needed code transformed ahead of time (loaded
from ``.pyc`` files).
Rationale
=========
Python does not provide a standard way to transform the code. Projects
transforming the code use various hooks. The MacroPy project uses an
import hook: it adds its own module finder in ``sys.meta_path`` to
hook its AST transformer. Another option is to monkey-patch the
builtin ``compile()`` function. There are even more options to
hook a code transformer.
Python 3.4 added a ``compile_source()`` method to
``importlib.abc.SourceLoader``. But code transformation is wider than just
importing modules, see described use cases below.
Writing an optimizer or a preprocessor is out of the scope of this PEP.
Usage 1: AST optimizer
----------------------
Python 3.6 optimizes the code using a peephole optimizer. By
definition, a peephole optimizer has a narrow view of the code and so
can only implement basic optimizations. The optimizer rewrites the
bytecode. It is difficult to enhance it, because it written in C.
Transforming an Abstract Syntax Tree (AST) is a convenient
way to implement an optimizer. It's easier to work on the AST than
working on the bytecode, AST contains more information and is more high
level.
Example of optimizations which can be implemented with an AST optimizer:
* `Copy propagation
<https://en.wikipedia.org/wiki/Copy_propagation>`_:
replace ``x=1; y=x`` with ``x=1; y=1``
* `Constant folding
<https://en.wikipedia.org/wiki/Constant_folding>`_:
replace ``1+1`` with ``2``
* `Dead code elimination
<https://en.wikipedia.org/wiki/Dead_code_elimination>`_
Using guards (see the `PEP 510
<https://www.python.org/dev/peps/pep-0510/>`_), it is possible to
implement a much wider choice of optimizations. Examples:
* Simplify iterable: replace ``range(3)`` with ``(0, 1, 2)`` when used
as iterable
* `Loop unrolling <https://en.wikipedia.org/wiki/Loop_unrolling>`_
* Call pure builtins: replace ``len("abc")`` with ``3``
* Copy used builtin symbols to constants
* See also `optimizations implemented in fatoptimizer
<https://fatoptimizer.readthedocs.org/en/latest/optimizations.html>`_,
a static optimizer for Python 3.6.
The following issues can be implemented with an AST optimizer:
* `Issue #1346238
<https://bugs.python.org/issue1346238>`_: A constant folding
optimization pass for the AST
* `Issue #2181 <http://bugs.python.org/issue2181>`_:
optimize out local variables at end of function
* `Issue #2499 <http://bugs.python.org/issue2499>`_:
Fold unary + and not on constants
* `Issue #4264 <http://bugs.python.org/issue4264>`_:
Patch: optimize code to use LIST_APPEND instead of calling list.append
* `Issue #7682 <http://bugs.python.org/issue7682>`_:
Optimisation of if with constant expression
* `Issue #10399 <https://bugs.python.org/issue10399>`_: AST
Optimization: inlining of function calls
* `Issue #11549 <http://bugs.python.org/issue11549>`_:
Build-out an AST optimizer, moving some functionality out of the
peephole optimizer
* `Issue #17068 <http://bugs.python.org/issue17068>`_:
peephole optimization for constant strings
* `Issue #17430 <http://bugs.python.org/issue17430>`_:
missed peephole optimization
Usage 2: Preprocessor
---------------------
A preprocessor can be easily implemented with an AST transformer. A
preprocessor has various and different usages. Examples:
* Remove debug code like assertions and logs to make the code faster to run
it for production.
* `Tail-call Optimization <https://en.wikipedia.org/wiki/Tail_call>`_
* Add profiling code
* `Lazy evaluation <https://en.wikipedia.org/wiki/Lazy_evaluation>`_:
see `lazy_python <https://github.com/llllllllll/lazy_python>`_
(bytecode transformer) and `lazy macro of MacroPy
<https://github.com/lihaoyi/macropy#lazy>`_ (AST transformer)
* Change dictionary literals into collection.OrderedDict instances
* Declare constants: see `@asconstants of codetransformer
<https://pypi.python.org/pypi/codetransformer>`_
* Domain Specific Language (DSL) like SQL queries. The
Python language itself doesn't need to be modified. Previous attempts to
implement DSL for SQL like `PEP 335 - Overloadable Boolean Operators
<https://www.python.org/dev/peps/pep-0335/>`_ was rejected.
* Pattern Matching of functional languages
* String Interpolation, but `PEP 498 -- Literal String Interpolation
<https://www.python.org/dev/peps/pep-0498/>`_ was merged into Python 3.6.
`MacroPy <https://github.com/lihaoyi/macropy>`_ has a long list of
examples and use cases.
See also `PyXfuscator <https://bitbucket.org/namn/pyxfuscator>`_: Python
obfuscator, deobfuscator, and user-assisted decompiler.
Use Cases
=========
This section give examples of use cases explaining when and how AST
transformers will be used.
Interactive interpreter
-----------------------
It will be possible to use AST transformers with the interactive
interpreter which is popular in Python and commonly used to demonstrate
Python.
The code is transformed at runtime and so the interpreter can be slower
when expensive AST transformers are used.
Build a transformed package
---------------------------
It will be possible to build a package of the transformed code.
A transformer can have a configuration. The configuration is not stored
in the package.
All ``.pyc`` files of the package must be transformed with the same AST
transformers and the same transformers configuration.
It is possible to build different ``.pyc`` files using different
optimizer tags. Example: ``fat`` for the default configuration and
``fat_inline`` for a different configuration with function inlining
enabled.
A package can contain ``.pyc`` files with different optimizer tags.
Install a package containing transformed .pyc files
---------------------------------------------------
It will be possible to install a package which contains transformed
``.pyc`` files.
All ``.pyc`` files with any optimizer tag contained in the package are
installed, not only for the current optimizer tag.
Build .pyc files when installing a package
------------------------------------------
If a package does not contain any ``.pyc`` files of the current
optimizer tag (or some ``.pyc`` files are missing), the ``.pyc`` are
created during the installation.
AST transformers of the optimizer tag are required. Otherwise, the
installation fails with an error.
Execute transformed code
------------------------
It will be possible to execute transformed code.
Raise an ``ImportError`` exception on import if the ``.pyc`` file of the
current optimizer tag is missing and the AST transformers required to
transform the code are missing.
The interesting point here is that AST transformers are not needed to
execute the transformed code if all required ``.pyc`` files are already
available.
Changes
=======
This PEP proposes to add an API to register AST transformers.
The transformation can done ahead of time. It allows to implement
powerful but expensive transformations.
API for AST transformers
------------------------
Add new functions to register AST transformers:
* ``sys.set_ast_transformers(transformers)``: set the list of AST
transformers
* ``sys.get_ast_transformers()``: get the list of AST
transformers.
The order of AST transformers matter. Running transformer A and then
transformer B can give a different output than running transformer B an
then transformer A.
API of an AST transformer:
* An AST transformer is a callable object with the prototype::
def ast_transformer(tree, context):
...
return tree
where *tree* is an AST tree and *context* is an object with a
``filename`` attribute (``str``). New attributes may be added to
*context* in the future.
* It must return an AST tree.
* It must have a ``name`` attribute (``str``): short string used to identify an
optimizer. The name must not contain ``.`` (dot) nor ``-`` (dash) characters:
``.`` is used to separated fields in a ``.pyc`` filename and ``-`` is used
to join AST transformer names to build the optimizer tag.
* The transformer is called after the creation of the AST by the parser
and before the compilation to bytecode
* It can modify the AST tree in place, or create a new AST tree.
.. note::
It would be nice to pass the fully qualified name of a module in the
*context* when an AST transformer is used to transform a module, but
it looks like the information is not available in
``PyParser_ASTFromStringObject()``.
Optimizer tag
-------------
Changes:
* Add ``sys.implementation.optim_tag`` (``str``): optimization tag.
The default optimization tag is ``'opt'``.
* Add a new ``-o OPTIM_TAG`` command line option to set
``sys.implementation.optim_tag``
Changes on ``importlib``:
* ``importlib`` uses ``sys.implementation.optim_tag`` to build the
``.pyc`` filename to importing modules, instead of always using
``opt``. Remove also the special case for the optimizer level ``0``
with the default optimizer tag ``'opt'`` to simplify the code.
* When loading a module, if the ``.pyc`` file is missing but the ``.py``
is available, the ``.py`` is only used if AST optimizers have the same
optimizer tag than the current tag, otherwise an ``ImportError``
exception is raised.
Pseudo-code of a ``use_py()`` function to decide if a ``.py`` file can
be compiled to import a module::
def get_ast_optim_tag():
transformers = sys.get_ast_transformers()
if not transformers:
return 'opt'
return '-'.join(transformer.name for transformer in transformers)
def use_py():
return (get_ast_transformers() == sys.implementation.optim_tag)
The order of ``sys.get_ast_transformers()`` matter. For example, the
``fat`` transformer followed by the ``pythran`` transformer gives the
optimizer tag ``fat-pythran``.
The behaviour of the ``importlib`` module is unchanged with the default
optimizer tag (``'opt'``).
AST enhancements
----------------
Enhancements to simplify the implementation of AST transformers:
* Add a new compiler flag ``PyCF_TRANSFORMED_AST`` to get the
transformed AST. ``PyCF_ONLY_AST`` returns the AST before the
transformers.
* Add ``ast.Constant``: this type is not emited by the compiler, but
can be used in an AST transformer to simplify the code. It does not
contain line number and column offset informations on tuple or
frozenset items.
* ``PyCodeObject.co_lnotab``: line number delta becomes signed to
support moving instructions (note: need to modify MAGIC_NUMBER in
importlib). Implemented in the `issue #26107
<https://bugs.python.org/issue26107>`_
* Enhance the bytecode compiler to support ``tuple`` and ``frozenset``
constants. Currently, ``tuple`` and ``frozenset`` constants are
created by the peephole transformer, after the bytecode compilation.
* ``marshal`` module: fix serialization of the empty frozenset singleton
* update ``Tools/parser/unparse.py`` to support the new ``ast.Constant``
node type
Example
=======
.pyc filenames
--------------
Example of ``.pyc`` filenames of the ``os`` module.
With the default optimizer tag ``'opt'``:
=========================== ==================
.pyc filename Optimization level
=========================== ==================
``os.cpython-36.opt-0.pyc`` 0
``os.cpython-36.opt-1.pyc`` 1
``os.cpython-36.opt-2.pyc`` 2
=========================== ==================
With the ``'fat'`` optimizer tag:
=========================== ==================
.pyc filename Optimization level
=========================== ==================
``os.cpython-36.fat-0.pyc`` 0
``os.cpython-36.fat-1.pyc`` 1
``os.cpython-36.fat-2.pyc`` 2
=========================== ==================
AST transformer
----------------
Scary AST transformer replacing all strings with ``"Ni! Ni! Ni!"``::
import ast
import sys
class KnightsWhoSayNi(ast.NodeTransformer):
def visit_Str(self, node):
node.s = 'Ni! Ni! Ni!'
return node
class ASTTransformer:
name = "knights_who_say_ni"
def __init__(self):
self.transformer = KnightsWhoSayNi()
def __call__(self, tree, context):
self.transformer.visit(tree)
return tree
# register the AST transformer
sys.set_ast_transformers([ASTTransformer()])
# execute code which will be transformed by ast_transformer()
exec("print('Hello World!')")
Output::
Ni! Ni! Ni!
Other Python implementations
============================
The PEP 511 should be implemented be all Python implementation. The AST
emited by the parser is not specified.
By the way, even between minor version of CPython, there are changes on
the AST API. There are differences, but only minor differences. It is
quite easy to write an AST transformer which works on Python 2.7 and
Python 3.5 for example.
Discussion
==========
* `[Python-Dev] AST optimizer implemented in Python
<https://mail.python.org/pipermail/python-dev/2012-August/121286.html>`_
(August 2012)
Prior Art
=========
AST optimizers
--------------
In 2011, Eugene Toder proposed to rewrite some peephole optimizations in
a new AST optimizer: issue #11549, `Build-out an AST optimizer, moving
some functionality out of the peephole optimizer
<https://bugs.python.org/issue11549>`_. The patch adds ``ast.Lit`` (it
was proposed to rename it to ``ast.Literal``).
In 2012, Victor Stinner wrote the `astoptimizer
<https://bitbucket.org/haypo/astoptimizer/>`_ project, an AST optimizer
implementing various optimizations. Most interesting optimizations break
the Python semantics since no guard is used to disable optimization if
something changes.
Issue #17515: `Add sys.setasthook() to allow to use a custom AST
optimizer <https://bugs.python.org/issue17515>`_.
Python Preprocessors
--------------------
* `MacroPy <https://github.com/lihaoyi/macropy>`_: MacroPy is an
implementation of Syntactic Macros in the Python Programming Language.
MacroPy provides a mechanism for user-defined functions (macros) to
perform transformations on the abstract syntax tree (AST) of a Python
program at import time.
* `pypreprocessor <https://code.google.com/p/pypreprocessor/>`_: C-style
preprocessor directives in Python, like ``#define`` and ``#ifdef``
Modify the bytecode
-------------------
* `codetransformer <https://pypi.python.org/pypi/codetransformer>`_:
Bytecode transformers for CPython inspired by the ``ast`` module’s
``NodeTransformer``.
* `byteplay <http://code.google.com/p/byteplay/>`_: Byteplay lets you
convert Python code objects into equivalent objects which are easy to
play with, and lets you convert those objects back into living Python
code objects. It's useful for applying crazy transformations on Python
functions, and is also useful in learning Python byte code
intricacies. See `byteplay documentation
<http://wiki.python.org/moin/ByteplayDoc>`_.
See also:
* `BytecodeAssembler <http://pypi.python.org/pypi/BytecodeAssembler>`_
* `Issue #2506 <https://bugs.python.org/issue2506>`_: Add mechanism to
disable optimizations
* `[Python-ideas] Disable all peephole optimizations
<https://mail.python.org/pipermail/python-ideas/2014-May/027893.html>`_
Copyright
=========
This document has been placed in the public domain.
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