The “interpreters” Module

The interpreters module will provide a high-level interface to the multiple interpreter functionality, and wrap a new low-level _interpreters (in the same way as the threading module). See the Examples section for concrete usage and use cases.

Along with exposing the existing (in CPython) multiple interpreter support, the module will also support a very basic mechanism for passing data between interpreters. That involves setting simple objects in the __main__ module of a target subinterpreter. If one end of an os.pipe() is passed this way then that pipe can be used to send bytes between the two interpreters.

Note that objects are not shared between interpreters since they are tied to the interpreter in which they were created. Instead, the objects’ data is passed between interpreters. See the Shared Data and API For Sharing Data sections for more details about sharing/communicating between interpreters.

API summary for interpreters module

Here is a summary of the API for the interpreters module. For a more in-depth explanation of the proposed classes and functions, see the “interpreters” Module API section below.

For creating and using interpreters:

Asynchronous results:

For sharing data between interpreters:

Help for Extension Module Maintainers

In practice, an extension that implements multi-phase init (PEP 489) is considered isolated and thus compatible with multiple interpreters. Otherwise it is “incompatible”.

Many extension modules are still incompatible. The maintainers and users of such extension modules will both benefit when they are updated to support multiple interpreters. In the meantime, users may become confused by failures when using multiple interpreters, which could negatively impact extension maintainers. See Concerns below.

To mitigate that impact and accelerate compatibility, we will do the following:

• be clear that extension modules are not required to support use in multiple interpreters
• raise ImportError when an incompatible module is imported in a subinterpreter
• provide resources (e.g. docs) to help maintainers reach compatibility
• reach out to the maintainers of Cython and of the most used extension modules (on PyPI) to get feedback and possibly provide assistance

Run isolated code

interp = interpreters.create()
print('before')
interp.run('print("during")').wait()
print('after')

Pre-populate an interpreter

interp = interpreters.create()
st = interp.run(tw.dedent("""
    import some_lib
    import an_expensive_module
    some_lib.set_up()
    """))
wait_for_request()
st.wait()
interp.run(tw.dedent("""
    some_lib.handle_request()
    """))

Handling an exception

interp = interpreters.create()
try:
    interp.run(tw.dedent("""
        raise KeyError
        """)).wait()
except interpreters.RunFailedError as exc:
    print(f"got the error from the subinterpreter: {exc}")

Re-raising an exception

interp = interpreters.create()
try:
    try:
        interp.run(tw.dedent("""
            raise KeyError
            """)).wait()
    except interpreters.RunFailedError as exc:
        raise exc.__cause__
except KeyError:
    print("got a KeyError from the subinterpreter")

Note that this pattern is a candidate for later improvement.

Synchronize using an OS pipe

interp = interpreters.create()
r, s = os.pipe()
print('before')
interp.run(tw.dedent("""
        import os
        os.read(reader, 1)
        print("during")
        """),
        shared=dict(
            reader=r,
            ),
        )
print('after')
os.write(s, '')

Sharing a file descriptor

interp = interpreters.create()
r1, s1 = os.pipe()
r2, s2 = os.pipe()
interp.run(tw.dedent("""
        import os
        fd = int.from_bytes(
                os.read(reader, 10), 'big')
        for line in os.fdopen(fd):
            print(line)
        os.write(writer, b'')
        """),
        shared=dict(
            reader=r1,
            writer=s2,
            ),
        )
with open('spamspamspam') as infile:
    fd = infile.fileno().to_bytes(1, 'big')
    os.write(s1, fd)
    os.read(r2, 1)

Passing objects via pickle

interp = interpreters.create()
r, s = os.pipe()
interp.run(tw.dedent("""
    import os
    import pickle
    """),
    shared=dict(
        reader=r,
        ),
    ).wait()
interp.run(tw.dedent("""
        data = b''
        c = os.read(reader, 1)
        while c != b'\x00':
            while c != b'\x00':
                data += c
                c = os.read(reader, 1)
            obj = pickle.loads(data)
            do_something(obj)
            c = os.read(reader, 1)
        """))
for obj in input:
    data = pickle.dumps(obj)
    os.write(s, data)
    os.write(s, b'\x00')
os.write(s, b'\x00')

Running a module

interp = interpreters.create()
main_module = mod_name
interp.run(f'import runpy; runpy.run_module({main_module!r})')

Running as script (including zip archives & directories)

interp = interpreters.create()
main_script = path_name
interp.run(f"import runpy; runpy.run_path({main_script!r})")

Concerns

• “subinterpreters are not worth the trouble”

Some have argued that subinterpreters do not add sufficient benefit to justify making them an official part of Python. Adding features to the language (or stdlib) has a cost in increasing the size of the language. So an addition must pay for itself.

In this case, multiple interpreter support provide a novel concurrency model focused on isolated threads of execution. Furthermore, they provide an opportunity for changes in CPython that will allow simultaneous use of multiple CPU cores (currently prevented by the GIL–see PEP 684).

Alternatives to subinterpreters include threading, async, and multiprocessing. Threading is limited by the GIL and async isn’t the right solution for every problem (nor for every person). Multiprocessing is likewise valuable in some but not all situations. Direct IPC (rather than via the multiprocessing module) provides similar benefits but with the same caveat.

Notably, subinterpreters are not intended as a replacement for any of the above. Certainly they overlap in some areas, but the benefits of subinterpreters include isolation and (potentially) performance. In particular, subinterpreters provide a direct route to an alternate concurrency model (e.g. CSP) which has found success elsewhere and will appeal to some Python users. That is the core value that the interpreters module will provide.

• “stdlib support for multiple interpreters adds extra burden on C extension authors”

In the Interpreter Isolation section below we identify ways in which isolation in CPython’s subinterpreters is incomplete. Most notable is extension modules that use C globals to store internal state. PEP 3121 and PEP 489 provide a solution for most of the problem, but one still remains. [petr-c-ext] Until that is resolved (see PEP 573), C extension authors will face extra difficulty to support subinterpreters.

Consequently, projects that publish extension modules may face an increased maintenance burden as their users start using subinterpreters, where their modules may break. This situation is limited to modules that use C globals (or use libraries that use C globals) to store internal state. For numpy, the reported-bug rate is one every 6 months. [bug-rate]

Ultimately this comes down to a question of how often it will be a problem in practice: how many projects would be affected, how often their users will be affected, what the additional maintenance burden will be for projects, and what the overall benefit of subinterpreters is to offset those costs. The position of this PEP is that the actual extra maintenance burden will be small and well below the threshold at which subinterpreters are worth it.

• “creating a new concurrency API deserves much more thought and experimentation, so the new module shouldn’t go into the stdlib right away, if ever”

Introducing an API for a new concurrency model, like happened with asyncio, is an extremely large project that requires a lot of careful consideration. It is not something that can be done a simply as this PEP proposes and likely deserves significant time on PyPI to mature. (See Nathaniel’s post on python-dev.)

However, this PEP does not propose any new concurrency API. At most it exposes minimal tools (e.g. subinterpreters, simple “sharing”) which may be used to write code that follows patterns associated with (relatively) new-to-Python concurrency models. Those tools could also be used as the basis for APIs for such concurrency models. Again, this PEP does not propose any such API.

• “there is no point to exposing subinterpreters if they still share the GIL”
• “the effort to make the GIL per-interpreter is disruptive and risky”

A common misconception is that this PEP also includes a promise that interpreters will no longer share the GIL. When that is clarified, the next question is “what is the point?”. This is already answered at length in this PEP. Just to be clear, the value lies in:

* increase exposure of the existing feature, which helps improve
  the code health of the entire CPython runtime
* expose the (mostly) isolated execution of interpreters
* preparation for per-interpreter GIL
* encourage experimentation

• “data sharing can have a negative impact on cache performance in multi-core scenarios”

(See [cache-line-ping-pong].)

This shouldn’t be a problem for now as we have no immediate plans to actually share data between interpreters, instead focusing on copying.

Concurrency

Concurrency is a challenging area of software development. Decades of research and practice have led to a wide variety of concurrency models, each with different goals. Most center on correctness and usability.

One class of concurrency models focuses on isolated threads of execution that interoperate through some message passing scheme. A notable example is Communicating Sequential Processes [CSP] (upon which Go’s concurrency is roughly based). The inteded isolation inherent to CPython’s interpreters makes them well-suited to this approach.

Shared Data

CPython’s interpreters are inherently isolated (with caveats explained below), in contrast to threads. So the same communicate-via-shared-memory approach doesn’t work. Without an alternative, effective use of concurrency via multiple interpreters is significantly limited.

The key challenge here is that sharing objects between interpreters faces complexity due to various constraints on object ownership, visibility, and mutability. At a conceptual level it’s easier to reason about concurrency when objects only exist in one interpreter at a time. At a technical level, CPython’s current memory model limits how Python objects may be shared safely between interpreters; effectively, objects are bound to the interpreter in which they were created. Furthermore, the complexity of object sharing increases as interpreters become more isolated, e.g. after GIL removal (though this is mitigated somewhat for some “immortal” objects (see PEP 683).

Consequently,the mechanism for sharing needs to be carefully considered. There are a number of valid solutions, several of which may be appropriate to support in Python. Earlier versions of this proposal included a basic capability (“channels”), though most of the options were quite similar.

Note that the implementation of Interpreter.run() will be done in a way that allows for may of these solutions to be implemented independently and to coexist, but doing so is not technically a part of the proposal here.

The fundamental enabling feature for communication is that most objects can be converted to some encoding of underlying raw data, which is safe to be passed between interpreters. For example, an int object can be turned into a C long value, send to another interpreter, and turned back into an int object there.

Regardless, the effort to determine the best way forward here is outside the scope of this PEP. In the meantime, this proposal provides a basic interim solution, described in API For Sharing Data below.

Interpreter Isolation

CPython’s interpreters are intended to be strictly isolated from each other. Each interpreter has its own copy of all modules, classes, functions, and variables. The same applies to state in C, including in extension modules. The CPython C-API docs explain more. [caveats]

However, there are ways in which interpreters share some state. First of all, some process-global state remains shared:

• file descriptors
• builtin types (e.g. dict, bytes)
• singletons (e.g. None)
• underlying static module data (e.g. functions) for builtin/extension/frozen modules

There are no plans to change this.

Second, some isolation is faulty due to bugs or implementations that did not take subinterpreters into account. This includes things like extension modules that rely on C globals. [cryptography] In these cases bugs should be opened (some are already):

• readline module hook functions (http://bugs.python.org/issue4202)
• memory leaks on re-init (http://bugs.python.org/issue21387)

Finally, some potential isolation is missing due to the current design of CPython. Improvements are currently going on to address gaps in this area:

• GC is not run per-interpreter [global-gc]
• at-exit handlers are not run per-interpreter [global-atexit]
• extensions using the PyGILState_* API are incompatible [gilstate]
• interpreters share memory management (e.g. allocators, gc)
• interpreters share the GIL

Existing Usage

Multiple interpreter support is not a widely used feature. In fact, the only documented cases of widespread usage are mod_wsgi, OpenStack Ceph, and JEP. On the one hand, these cases provide confidence that existing multiple interpreter support is relatively stable. On the other hand, there isn’t much of a sample size from which to judge the utility of the feature.

Uncaught Exceptions

Regarding uncaught exceptions in Interpreter.run(), we noted that they are exposed via the returned Status object. To prevent leaking exceptions (and tracebacks) between interpreters, we create a surrogate of the exception and its traceback (see traceback.TracebackException). This is returned by Status.exception(). Status.wait() set it to __cause__ on a new RunFailedError, and raise that.

Raising (a proxy of) the exception directly is problematic since it’s harder to distinguish between an error in the wait() call and an uncaught exception from the subinterpreter.

API For Sharing Data

As discussed in Shared Data above, multiple interpreter support is less useful without a mechanism for sharing data (communicating) between them. Sharing actual Python objects between interpreters, however, has enough potential problems that we are avoiding support for that in this proposal. Nor, as mentioned earlier, are we adding anything more than the most minimal mechanism for communication.

That very basic mechanism, using pipes (see os.pipe()), will allow users to send data (bytes) from one interpreter to another. We’ll take a closer look in a moment. Fundamentally, it’s a simple application of the underlying sharing capability proposed here.

The various aspects of the approach, including keeping the API minimal, helps us avoid further exposing any underlying complexity to Python users.

is_shareable(obj) -> bool:

   Return True if the object may be shared between interpreters.
   This does not necessarily mean that the actual objects will be
   shared.  Insead, it means that the objects' underlying data will
   be shared in a cross-interpreter way, whether via a proxy, a
   copy, or some other means.

Interpreter.call()

It would be convenient to run existing functions in subinterpreters directly. Interpreter.run() could be adjusted to support this or a call() method could be added:

Interpreter.call(f, *args, **kwargs)

This suffers from the same problem as sharing objects between interpreters via queues. The minimal solution (running a source string) is sufficient for us to get the feature out where it can be explored.

Interpreter.run_in_thread()

This method would make a run() call for you in a thread. Doing this using only threading.Thread and run() is relatively trivial so we’ve left it out.

Synchronization Primitives

The threading module provides a number of synchronization primitives for coordinating concurrent operations. This is especially necessary due to the shared-state nature of threading. In contrast, interpreters do not share state. Data sharing is restricted to the runtime’s shareable objects capability, which does away with the need for explicit synchronization. If any sort of opt-in shared state support is added to CPython’s interpreters in the future, that same effort can introduce synchronization primitives to meet that need.

CSP Library

A csp module would not be a large step away from the functionality provided by this PEP. However, adding such a module is outside the minimalist goals of this proposal.

Syntactic Support

The Go language provides a concurrency model based on CSP, so it’s similar to the concurrency model that multiple interpreters support. However, Go also provides syntactic support, as well as several builtin concurrency primitives, to make concurrency a first-class feature. Conceivably, similar syntactic (and builtin) support could be added to Python using interpreters. However, that is way outside the scope of this PEP!

Multiprocessing

The multiprocessing module could support interpreters in the same way it supports threads and processes. In fact, the module’s maintainer, Davin Potts, has indicated this is a reasonable feature request. However, it is outside the narrow scope of this PEP.

C-extension opt-in/opt-out

By using the PyModuleDef_Slot introduced by PEP 489, we could easily add a mechanism by which C-extension modules could opt out of multiple interpreter support. Then the import machinery, when operating in a subinterpreter, would need to check the module for support. It would raise an ImportError if unsupported.

Alternately we could support opting in to multiple interpreters support. However, that would probably exclude many more modules (unnecessarily) than the opt-out approach. Also, note that PEP 489 defined that an extension’s use of the PEP’s machinery implies multiple interpreters support.

The scope of adding the ModuleDef slot and fixing up the import machinery is non-trivial, but could be worth it. It all depends on how many extension modules break under subinterpreters. Given that there are relatively few cases we know of through mod_wsgi, we can leave this for later.

Resetting __main__

As proposed, every call to Interpreter.run() will execute in the namespace of the interpreter’s existing __main__ module. This means that data persists there between run() calls. Sometimes this isn’t desirable and you want to execute in a fresh __main__. Also, you don’t necessarily want to leak objects there that you aren’t using any more.

Note that the following won’t work right because it will clear too much (e.g. __name__ and the other “__dunder__” attributes:

interp.run('globals().clear()')

Possible solutions include:

• a create() arg to indicate resetting __main__ after each run call
• an Interpreter.reset_main flag to support opting in or out after the fact
• an Interpreter.reset_main() method to opt in when desired
• importlib.util.reset_globals() [reset_globals]

Also note that resetting __main__ does nothing about state stored in other modules. So any solution would have to be clear about the scope of what is being reset. Conceivably we could invent a mechanism by which any (or every) module could be reset, unlike reload() which does not clear the module before loading into it. Regardless, since __main__ is the execution namespace of the interpreter, resetting it has a much more direct correlation to interpreters and their dynamic state than does resetting other modules. So a more generic module reset mechanism may prove unnecessary.

This isn’t a critical feature initially. It can wait until later if desirable.

Resetting an interpreter’s state

It may be nice to re-use an existing subinterpreter instead of spinning up a new one. Since an interpreter has substantially more state than just the __main__ module, it isn’t so easy to put an interpreter back into a pristine/fresh state. In fact, there may be parts of the state that cannot be reset from Python code.

A possible solution is to add an Interpreter.reset() method. This would put the interpreter back into the state it was in when newly created. If called on a running interpreter it would fail (hence the main interpreter could never be reset). This would likely be more efficient than creating a new interpreter, though that depends on what optimizations will be made later to interpreter creation.

While this would potentially provide functionality that is not otherwise available from Python code, it isn’t a fundamental functionality. So in the spirit of minimalism here, this can wait. Regardless, I doubt it would be controversial to add it post-PEP.

Shareable file descriptors and sockets

Given that file descriptors and sockets are process-global resources, making them shareable is a reasonable idea. They would be a good candidate for the first effort at expanding the supported shareable types. They aren’t strictly necessary for the initial API.

Integration with async

Per Antoine Pitrou [async]:

Has any thought been given to how FIFOs could integrate with async
code driven by an event loop (e.g. asyncio)?  I think the model of
executing several asyncio (or Tornado) applications each in their
own subinterpreter may prove quite interesting to reconcile multi-
core concurrency with ease of programming.  That would require the
FIFOs to be able to synchronize on something an event loop can wait
on (probably a file descriptor?).

The basic functionality of multiple interpreters support does not depend on async and can be added later.

channels

We could introduce some relatively efficient, native data types for passing data between interpreters, to use instead of OS pipes. Earlier versions of this PEP introduced one such mechanism, called “channels”. This can be pursued later.

Pipes and Queues

With the proposed object passing mechanism of “os.pipe()”, other similar basic types aren’t strictly required to achieve the minimal useful functionality of multiple interpreters. Such types include pipes (like unbuffered channels, but one-to-one) and queues (like channels, but more generic). See below in Rejected Ideas for more information.

Even though these types aren’t part of this proposal, they may still be useful in the context of concurrency. Adding them later is entirely reasonable. The could be trivially implemented as wrappers around channels. Alternatively they could be implemented for efficiency at the same low level as channels.

Support inheriting settings (and more?)

Folks might find it useful, when creating a new interpreter, to be able to indicate that they would like some things “inherited” by the new interpreter. The mechanism could be a strict copy or it could be copy-on-write. The motivating example is with the warnings module (e.g. copy the filters).

The feature isn’t critical, nor would it be widely useful, so it can wait until there’s interest. Notably, both suggested solutions will require significant work, especially when it comes to complex objects and most especially for mutable containers of mutable complex objects.

Make exceptions shareable

Exceptions are propagated out of run() calls, so it isn’t a big leap to make them shareable. However, as noted elsewhere, it isn’t essential or (particularly common) so we can wait on doing that.

Make RunFailedError.__cause__ lazy

An uncaught exception in a subinterpreter (from run()) is copied to the calling interpreter and set as __cause__ on a RunFailedError which is then raised. That copying part involves some sort of deserialization in the calling interpreter, which can be expensive (e.g. due to imports) yet is not always necessary.

So it may be useful to use an ExceptionProxy type to wrap the serialized exception and only deserialize it when needed. That could be via ExceptionProxy__getattribute__() or perhaps through RunFailedError.resolve() (which would raise the deserialized exception and set RunFailedError.__cause__ to the exception.

It may also make sense to have RunFailedError.__cause__ be a descriptor that does the lazy deserialization (and set __cause__) on the RunFailedError instance.

Make everything shareable through serialization

We could use pickle (or marshal) to serialize everything and thus make them shareable. Doing this is potentially inefficient, but it may be a matter of convenience in the end. We can add it later, but trying to remove it later would be significantly more painful.

Return a value from run()

Currently run() always returns None. One idea is to return the return value from whatever the subinterpreter ran. However, for now it doesn’t make sense. The only thing folks can run is a string of code (i.e. a script). This is equivalent to PyRun_StringFlags(), exec(), or a module body. None of those “return” anything. We can revisit this once run() supports functions, etc.

Add a “tp_share” type slot

This would replace the current global registry for shareable types.

Add a shareable synchronization primitive

This would be _threading.Lock (or something like it) where interpreters would actually share the underlying mutex. The main concern is that locks and isolated interpreters may not mix well (as learned in Go).

We can add this later if it proves desirable without much trouble.

Propagate SystemExit and KeyboardInterrupt Differently

The exception types that inherit from BaseException (aside from Exception) are usually treated specially. These types are: KeyboardInterrupt, SystemExit, and GeneratorExit. It may make sense to treat them specially when it comes to propagation from run(). Here are some options:

* propagate like normal via RunFailedError
* do not propagate (handle them somehow in the subinterpreter)
* propagate them directly (avoid RunFailedError)
* propagate them directly (set RunFailedError as __cause__)

We aren’t going to worry about handling them differently. Threads already ignore SystemExit, so for now we will follow that pattern.

Auto-run in a thread

The PEP proposes a hard separation between interpreters and threads: if you want to run in a thread you must create the thread yourself and call run() in it. However, it might be convenient if run() could do that for you, meaning there would be less boilerplate.

Furthermore, we anticipate that users will want to run in a thread much more often than not. So it would make sense to make this the default behavior. We would add a kw-only param “threaded” (default True) to run() to allow the run-in-the-current-thread operation.

Use pipes instead of channels

A pipe would be a simplex FIFO between exactly two interpreters. For most use cases this would be sufficient. It could potentially simplify the implementation as well. However, it isn’t a big step to supporting a many-to-many simplex FIFO via channels. Also, with pipes the API ends up being slightly more complicated, requiring naming the pipes.

Use queues instead of channels

Queues and buffered channels are almost the same thing. The main difference is that channels have a stronger relationship with context (i.e. the associated interpreter).

The name “Channel” was used instead of “Queue” to avoid confusion with the stdlib queue.Queue.

“enumerate”

The list_all() function provides the list of all interpreters. In the threading module, which partly inspired the proposed API, the function is called enumerate(). The name is different here to avoid confusing Python users that are not already familiar with the threading API. For them “enumerate” is rather unclear, whereas “list_all” is clear.

Alternate solutions to prevent leaking exceptions across interpreters

In function calls, uncaught exceptions propagate to the calling frame. The same approach could be taken with run(). However, this would mean that exception objects would leak across the inter-interpreter boundary. Likewise, the frames in the traceback would potentially leak.

While that might not be a problem currently, it would be a problem once interpreters get better isolation relative to memory management (which is necessary to stop sharing the GIL between interpreters). We’ve resolved the semantics of how the exceptions propagate by raising a RunFailedError instead, for which __cause__ wraps a safe proxy for the original exception and traceback.

Rejected possible solutions:

• reproduce the exception and traceback in the original interpreter and raise that.
• raise a subclass of RunFailedError that proxies the original exception and traceback.
• raise RuntimeError instead of RunFailedError
• convert at the boundary (a la subprocess.CalledProcessError) (requires a cross-interpreter representation)
• support customization via Interpreter.excepthook (requires a cross-interpreter representation)
• wrap in a proxy at the boundary (including with support for something like err.raise() to propagate the traceback).
• return the exception (or its proxy) from run() instead of raising it
• return a result object (like subprocess does) [result-object] (unnecessary complexity?)
• throw the exception away and expect users to deal with unhandled exceptions explicitly in the script they pass to run() (they can pass error info out via os.pipe()); with threads you have to do something similar

Always associate each new interpreter with its own thread

As implemented in the C-API, an interpreter is not inherently tied to any thread. Furthermore, it will run in any existing thread, whether created by Python or not. You only have to activate one of its thread states (PyThreadState) in the thread first. This means that the same thread may run more than one interpreter (though obviously not at the same time).

The proposed module maintains this behavior. Interpreters are not tied to threads. Only calls to Interpreter.run() are. However, one of the key objectives of this PEP is to provide a more human- centric concurrency model. With that in mind, from a conceptual standpoint the module might be easier to understand if each interpreter were associated with its own thread.

That would mean interpreters.create() would create a new thread and Interpreter.run() would only execute in that thread (and nothing else would). The benefit is that users would not have to wrap Interpreter.run() calls in a new threading.Thread. Nor would they be in a position to accidentally pause the current interpreter (in the current thread) while their interpreter executes.

The idea is rejected because the benefit is small and the cost is high. The difference from the capability in the C-API would be potentially confusing. The implicit creation of threads is magical. The early creation of threads is potentially wasteful. The inability to run arbitrary interpreters in an existing thread would prevent some valid use cases, frustrating users. Tying interpreters to threads would require extra runtime modifications. It would also make the module’s implementation overly complicated. Finally, it might not even make the module easier to understand.

Add a “reraise” method to RunFailedError

While having __cause__ set on RunFailedError helps produce a more useful traceback, it’s less helpful when handling the original error. To help facilitate this, we could add RunFailedError.reraise(). This method would enable the following pattern:

try:
    try:
        interp.run(script)
    except RunFailedError as exc:
        exc.reraise()
except MyException:
    ...

This would be made even simpler if there existed a __reraise__ protocol.

All that said, this is completely unnecessary. Using __cause__ is good enough:

try:
    try:
        interp.run(script)
    except RunFailedError as exc:
        raise exc.__cause__
except MyException:
    ...

Note that in extreme cases it may require a little extra boilerplate:

try:
    try:
        interp.run(script)
    except RunFailedError as exc:
        if exc.__cause__ is not None:
            raise exc.__cause__
        raise  # re-raise
except MyException:
    ...