python-peps/pep-0523.txt

PEP: 523
Title: Adding a frame evaluation API to CPython
Version: $Revision$
Last-Modified: $Date$
Author: Brett Cannon <brett@python.org>,
        Dino Viehland <dinov@microsoft.com>
Status: Accepted
Type: Standards Track
Content-Type: text/x-rst
Created: 16-May-2016
Post-History: 16-May-2016
Resolution: https://mail.python.org/pipermail/python-dev/2016-August/145937.html


Abstract
========

This PEP proposes to expand CPython's C API [#c-api]_ to allow for
the specification of a per-interpreter function pointer to handle the
evaluation of frames [#pyeval_evalframeex]_. This proposal also
suggests adding a new field to code objects [#pycodeobject]_ to store
arbitrary data for use by the frame evaluation function.


Rationale
=========

One place where flexibility has been lacking in Python is in the direct
execution of Python code. While CPython's C API [#c-api]_ allows for
constructing the data going into a frame object and then evaluating it
via ``PyEval_EvalFrameEx()`` [#pyeval_evalframeex]_, control over the
execution of Python code comes down to individual objects instead of a
holistic control of execution at the frame level.

While wanting to have influence over frame evaluation may seem a bit
too low-level, it does open the possibility for things such as a
method-level JIT to be introduced into CPython without CPython itself
having to provide one. By allowing external C code to control frame
evaluation, a JIT can participate in the execution of Python code at
the key point where evaluation occurs. This then allows for a JIT to
conditionally recompile Python bytecode to machine code as desired
while still allowing for executing regular CPython bytecode when
running the JIT is not desired. This can be accomplished by allowing
interpreters to specify what function to call to evaluate a frame. And
by placing the API at the frame evaluation level it allows for a
complete view of the execution environment of the code for the JIT.

This ability to specify a frame evaluation function also allows for
other use-cases beyond just opening CPython up to a JIT. For instance,
it would not be difficult to implement a tracing or profiling function
at the call level with this API. While CPython does provide the
ability to set a tracing or profiling function at the Python level,
this would be able to match the data collection of the profiler and
quite possibly be faster for tracing by simply skipping per-line
tracing support.

It also opens up the possibility of debugging where the frame
evaluation function only performs special debugging work when it
detects it is about to execute a specific code object. In that
instance the bytecode could be theoretically rewritten in-place to
inject a breakpoint function call at the proper point for help in
debugging while not having to do a heavy-handed approach as
required by ``sys.settrace()``.

To help facilitate these use-cases, we are also proposing the adding
of a "scratch space" on code objects via a new field. This will allow
per-code object data to be stored with the code object itself for easy
retrieval by the frame evaluation function as necessary. The field
itself will simply be a ``PyObject *`` type so that any data stored in
the field will participate in normal object memory management.


Proposal
========

All proposed C API changes below will not be part of the stable ABI.


Expanding ``PyCodeObject``
--------------------------

One field is to be added to the ``PyCodeObject`` struct
[#pycodeobject]_::

  typedef struct {
     ...
     PyObject *co_extra;  /* "Scratch space" for the code object. */
  } PyCodeObject;

The ``co_extra`` will be ``NULL`` by default and will not be used by
CPython itself. Third-party code is free to use the field as desired.
Values stored in the field are expected to not be required in order
for the code object to function, allowing the loss of the data of the
field to be acceptable. The field will be freed like all other fields
on ``PyCodeObject`` during deallocation using ``Py_XDECREF()``.

Code using the field is expected to always store a tuple in the field.
This allows for multiple users of the field to not trample over each
other while being as performant as possible. Typical usage of the
field is expected to roughly follow the following pseudo-code::

  if co_extra is None:
    data = DataClass()
    co_extra = (data,)
  else:
    assert isinstance(co_extra, tuple)
    for x in co_extra:
        if isinstance(x, DataClass):
            data = x
            break
    else:
        data = DataClass()
        co_extra += (data,)

Using a list was considered but was found to be less performant, and
with a key use-case being JIT usage the performance consideration it
was deemed more important to use a tuple than a list. A tuple also
makes more sense semantically as the objects stored in the tuple will
be heterogeneous.

A dict was also considered, but once again performance was more
important. While a dict will have constant overhead in looking up
data, the overhead for the common case of a single object being stored
in the data structure leads to a tuple having better performance
characteristics (i.e. iterating a tuple of length 1 is faster than
the overhead of hashing and looking up an object in a dict).


Expanding ``PyInterpreterState``
--------------------------------

The entrypoint for the frame evaluation function is per-interpreter::

  // Same type signature as PyEval_EvalFrameEx().
  typedef PyObject* (__stdcall *PyFrameEvalFunction)(PyFrameObject*, int);

  typedef struct {
      ...
      PyFrameEvalFunction eval_frame;
  } PyInterpreterState;

By default, the ``eval_frame`` field will be initialized to a function
pointer that represents what ``PyEval_EvalFrameEx()`` currently is
(called ``PyEval_EvalFrameDefault()``, discussed later in this PEP).
Third-party code may then set their own frame evaluation function
instead to control the execution of Python code. A pointer comparison
can be used to detect if the field is set to
``PyEval_EvalFrameDefault()`` and thus has not been mutated yet.


Changes to ``Python/ceval.c``
-----------------------------

``PyEval_EvalFrameEx()`` [#pyeval_evalframeex]_ as it currently stands
will be renamed to ``PyEval_EvalFrameDefault()``. The new
``PyEval_EvalFrameEx()`` will then become::

    PyObject *
    PyEval_EvalFrameEx(PyFrameObject *frame, int throwflag)
    {
        PyThreadState *tstate = PyThreadState_GET();
        return tstate->interp->eval_frame(frame, throwflag);
    }

This allows third-party code to place themselves directly in the path
of Python code execution while being backwards-compatible with code
already using the pre-existing C API.


Updating ``python-gdb.py``
--------------------------

The generated ``python-gdb.py`` file used for Python support in GDB
makes some hard-coded assumptions about ``PyEval_EvalFrameEx()``, e.g.
the names of local variables. It will need to be updated to work with
the proposed changes.


Performance impact
==================

As this PEP is proposing an API to add pluggability, performance
impact is considered only in the case where no third-party code has
made any changes.

Several runs of pybench [#pybench]_ consistently showed no performance
cost from the API change alone.

A run of the Python benchmark suite [#py-benchmarks]_ showed no
measurable cost in performance.

In terms of memory impact, since there are typically not many CPython
interpreters executing in a single process that means the impact of
``co_extra`` being added to ``PyCodeObject`` is the only worry.
According to [#code-object-count]_, a run of the Python test suite
results in about 72,395 code objects being created. On a 64-bit
CPU that would result in 579,160 bytes of extra memory being used if
all code objects were alive at once and had nothing set in their
``co_extra`` fields.


Example Usage
=============

A JIT for CPython
-----------------

Pyjion
''''''

The Pyjion project [#pyjion]_ has used this proposed API to implement
a JIT for CPython using the CoreCLR's JIT [#coreclr]_. Each code
object has its ``co_extra`` field set to a ``PyjionJittedCode`` object
which stores four pieces of information:

1. Execution count
2. A boolean representing whether a previous attempt to JIT failed
3. A function pointer to a trampoline (which can be type tracing or not)
4. A void pointer to any JIT-compiled machine code

The frame evaluation function has (roughly) the following algorithm::

    def eval_frame(frame, throw_flag):
        pyjion_code = frame.code.co_extra
        if not pyjion_code:
            frame.code.co_extra = PyjionJittedCode()
        elif not pyjion_code.jit_failed:
            if not pyjion_code.jit_code:
                return pyjion_code.eval(pyjion_code.jit_code, frame)
            elif pyjion_code.exec_count > 20_000:
                if jit_compile(frame):
                    return pyjion_code.eval(pyjion_code.jit_code, frame)
                else:
                    pyjion_code.jit_failed = True
        pyjion_code.exec_count += 1
        return PyEval_EvalFrameDefault(frame, throw_flag)

The key point, though, is that all of this work and logic is separate
from CPython and yet with the proposed API changes it is able to
provide a JIT that is compliant with Python semantics (as of this
writing, performance is almost equivalent to CPython without the new
API). This means there's nothing technically preventing others from
implementing their own JITs for CPython by utilizing the proposed API.


Other JITs
''''''''''

It should be mentioned that the Pyston team was consulted on an
earlier version of this PEP that was more JIT-specific and they were
not interested in utilizing the changes proposed because they want
control over memory layout they had no interest in directly supporting
CPython itself. An informal discusion with a developer on the PyPy
team led to a similar comment.

Numba [#numba]_, on the other hand, suggested that they would be
interested in the proposed change in a post-1.0 future for
themselves [#numba-interest]_.

The experimental Coconut JIT [#coconut]_ could have benefitted from
this PEP. In private conversations with Coconut's creator we were told
that our API was probably superior to the one they developed for
Coconut to add JIT support to CPython.


Debugging
---------

In conversations with the Python Tools for Visual Studio team (PTVS)
[#ptvs]_, they thought they would find these API changes useful for
implementing more performant debugging. As mentioned in the Rationale_
section, this API would allow for switching on debugging functionality
only in frames where it is needed. This could allow for either
skipping information that ``sys.settrace()`` normally provides and
even go as far as to dynamically rewrite bytecode prior to execution
to inject e.g. breakpoints in the bytecode.

It also turns out that Google has provided a very similar API
internally for years. It has been used for performant debugging
purposes.


Implementation
==============

A set of patches implementing the proposed API is available through
the Pyjion project [#pyjion]_. In its current form it has more
changes to CPython than just this proposed API, but that is for ease
of development instead of strict requirements to accomplish its goals.


Open Issues
===========

Allow ``eval_frame`` to be ``NULL``
-----------------------------------

Currently the frame evaluation function is expected to always be set.
It could very easily simply default to ``NULL`` instead which would
signal to use ``PyEval_EvalFrameDefault()``. The current proposal of
not special-casing the field seemed the most straightforward, but it
does require that the field not accidentally be cleared, else a crash
may occur.


Rejected Ideas
==============

A JIT-specific C API
--------------------

Originally this PEP was going to propose a much larger API change
which was more JIT-specific. After soliciting feedback from the Numba
team [#numba]_, though, it became clear that the API was unnecessarily
large. The realization was made that all that was truly needed was the
opportunity to provide a trampoline function to handle execution of
Python code that had been JIT-compiled and a way to attach that
compiled machine code along with other critical data to the
corresponding Python code object. Once it was shown that there was no
loss in functionality or in performance while minimizing the API
changes required, the proposal was changed to its current form.


Is co_extra needed?
-------------------

While discussing this PEP at PyCon US 2016, some core developers
expressed their worry of the ``co_extra`` field making code objects
mutable. The thinking seemed to be that having a field that was
mutated after the creation of the code object made the object seem
mutable, even though no other aspect of code objects changed.

The view of this PEP is that the `co_extra` field doesn't change the
fact that code objects are immutable. The field is specified in this
PEP to not contain information required to make the code object
usable, making it more of a caching field. It could be viewed as
similar to the UTF-8 cache that string objects have internally;
strings are still considered immutable even though they have a field
that is conditionally set.

Performance measurements were also made where the field was not
available for JIT workloads. The loss of the field was deemed too
costly to performance when using an unordered map from C++ or Python's
dict to associated a code object with JIT-specific data objects.


References
==========

.. [#pyjion] Pyjion project
   (https://github.com/microsoft/pyjion)

.. [#c-api] CPython's C API
   (https://docs.python.org/3/c-api/index.html)

.. [#pycodeobject] ``PyCodeObject``
   (https://docs.python.org/3/c-api/code.html#c.PyCodeObject)

.. [#coreclr] .NET Core Runtime (CoreCLR)
   (https://github.com/dotnet/coreclr)

.. [#pyeval_evalframeex] ``PyEval_EvalFrameEx()``
   (https://docs.python.org/3/c-api/veryhigh.html?highlight=pyframeobject#c.PyEval_EvalFrameEx)

.. [#numba] Numba
   (http://numba.pydata.org/)

.. [#numba-interest]  numba-users mailing list:
   "Would the C API for a JIT entrypoint being proposed by Pyjion help out Numba?"
   (https://groups.google.com/a/continuum.io/forum/#!topic/numba-users/yRl_0t8-m1g)

.. [#code-object-count] [Python-Dev] Opcode cache in ceval loop
   (https://mail.python.org/pipermail/python-dev/2016-February/143025.html)

.. [#py-benchmarks] Python benchmark suite
   (https://hg.python.org/benchmarks)

.. [#pyston] Pyston
   (http://pyston.org)

.. [#pypy] PyPy
   (http://pypy.org/)

.. [#ptvs] Python Tools for Visual Studio
   (http://microsoft.github.io/PTVS/)

.. [#coconut] Coconut
   (https://github.com/davidmalcolm/coconut)

.. [#pybench] pybench
   (https://hg.python.org/cpython/file/default/Tools/pybench)


Copyright
=========

This document has been placed in the public domain.



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