Python Enhancement Proposals

PEP 523 – Adding a frame evaluation API to CPython

Adding a frame evaluation API to CPython
Brett Cannon <brett at>, Dino Viehland <dinov at>
Standards Track



This PEP proposes to expand CPython’s C API [2] to allow for the specification of a per-interpreter function pointer to handle the evaluation of frames [5]. This proposal also suggests adding a new field to code objects [3] to store arbitrary data for use by the frame evaluation function.


One place where flexibility has been lacking in Python is in the direct execution of Python code. While CPython’s C API [2] allows for constructing the data going into a frame object and then evaluating it via PyEval_EvalFrameEx() [5], 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.


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 [3]:

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

The co_extra will be NULL by default and only filled in as needed. 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.

A private API has been introduced to work with the field:

PyAPI_FUNC(Py_ssize_t) _PyEval_RequestCodeExtraIndex(freefunc);
PyAPI_FUNC(int) _PyCode_GetExtra(PyObject *code, Py_ssize_t index,
                                 void **extra);
PyAPI_FUNC(int) _PyCode_SetExtra(PyObject *code, Py_ssize_t index,
                                 void *extra);

Users of the field are expected to call _PyEval_RequestCodeExtraIndex() to receive (what should be considered) an opaque index value to adding data into co-extra. With that index, users can set data using _PyCode_SetExtra() and later retrieve the data with _PyCode_GetExtra(). The API is purposefully listed as private to communicate the fact that there are no semantic guarantees of the API between Python releases.

Using a list and tuple were considered but was found to be less performant, and with a key use-case being JIT usage the performance consideration won out for using a custom struct instead of a Python object.

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* (*_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() [5] 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.


The generated 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 [14] consistently showed no performance cost from the API change alone.

A run of the Python benchmark suite [9] 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 [8], 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


The Pyjion project [1] has used this proposed API to implement a JIT for CPython using the CoreCLR’s JIT [4]. 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)
                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 discussion with a developer on the PyPy team led to a similar comment.

Numba [6], on the other hand, suggested that they would be interested in the proposed change in a post-1.0 future for themselves [7].

The experimental Coconut JIT [13] 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.


In conversations with the Python Tools for Visual Studio team (PTVS) [12], 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 provides a very similar API internally. It has been used for performant debugging purposes.


A set of patches implementing the proposed API is available through the Pyjion project [1]. 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 [6], 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.


[1] (1, 2)
Pyjion project (
[2] (1, 2)
CPython’s C API (
[3] (1, 2)
PyCodeObject (
.NET Core Runtime (CoreCLR) (
[5] (1, 2, 3)
PyEval_EvalFrameEx() (
[6] (1, 2)
Numba (
numba-users mailing list: “Would the C API for a JIT entrypoint being proposed by Pyjion help out Numba?” (!topic/numba-users/yRl_0t8-m1g)
[Python-Dev] Opcode cache in ceval loop (
Python benchmark suite (
Pyston (
PyPy (
Python Tools for Visual Studio (
Coconut (
pybench (


Last modified: 2021-09-17 18:18:24 GMT