capi-sig July 2020

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2 weeks, 2 days

Pre-PEP: Isolating Extension Modules

by Petr Viktorin

Hello, Several people asked me to put the long-term effort re. PEPs 489 and 573 in words, so I've written a document that I'd like to submit as an informational PEP and/or a HOWTO. A rendered version is available at: https://hackmd.io/@encukou/module-state It's in Markdown: I've gotten so used to MD that translating to ReST once sounds easier than drafting in ReST. If you have a HackMD account and would like to co-author or fix typos directly, I can give you access. Isolating Extension Modules =========================== Abstract -------- Traditionally, state of Python extension modules was kept in C `static` variables, which have process-wide scope. This document describes problems of such per-process state, and efforts to make per-module state, a better default, possible and easy to use. The document also describes how to switch to per-module state where possible. The switch involves allocating space for that state, switching from static types to heap types, and—perhaps most importantly—accessing per-module state from code. About this document ------------------- This document does not introduce any changes: those should be done in their own PEPs (or issues, if small enough). Rather, it covers the motivation behind an effort that spans multiple releases. Once support is reasonably complete, the text can be moved to a [HOWTO](https://docs.python.org/3/howto/index.html) in the documentation. Meanwhile, in the spirit of documentation-driven development, gaps in the text show where to focus the effort. Whenever the document mentions *extension modules*, the advice also applies to *built-in* modules, such as the C parts of the standard library. The standard library is expected to switch to per-module state early. PEPs related to this effort are: * PEP 384 -- *Defining a Stable ABI*, which added C API for creating heap types * PEP 489 -- *Multi-phase extension module initialization* * PEP 573 -- *Module State Access from C Extension Methods* This document is concerned with Python's public C API. Nothing is specific to CPython. Motivation ---------- An *interpreter* is the context in which Python code runs. It contains configuration (e.g. the import path) and runtime state (e.g. the set of imported modules). Python supports running multiple interpreters in one process. There are two cases to think about. Users may run interpreters: * in sequence, with several `Py_InitializeEx`/`Py_FinalizeEx` cycles, and * in parallel, managing “sub-interpreters” using `Py_NewInterpreter`/`Py_EndInterpreter`. Both cases (and combinations of them) would be most useful when embedding Python within a library. Libraries generally shouldn't make assumptions about the application that uses them, which includes assumptions about a process-wide “main Python interpreter”. Currently, CPython doesn't handle this use case well. Many extension modules (and even some stdlib modules) use *per-process* global state, because C `static` variables are extremely easy to use. Thus, data that should be specific to an interpreter ends up being shared between interpreters. Unless the extension developer is careful, it is very easy to introduce edge cases that lead to crashes when a module is loaded in more than one interpreter. Unfortunately, *per-interpreter* state is not easy to achieve: extension authors tend to not keep multiple interpreters in mind when developing, and it is currently cumbersome to test the behavior. Rationale for Per-module State ------------------------------ Instead of focusing on per-interpreter state, Python's C API is evolving to better support the more granular *per-module* state. By default, C-level data will be attached to a *module object*. Each interpreter will then create its own module object, keeping data separate. For testing the isolation, multiple module objects can even be loaded in a single interpreter. Per-module state provides an easy way to think about lifetime and resource ownership: the extension module author will set up when a module object is created, and clean up when it's freed. In this regard, a module is just like any other `PyObject *`; there are no “on interpreter shutdown” hooks to think about (or forget about). ### Easy-to-use Module State as a Goal It is currently cumbersome or impossible to do everything the C API offers while keeping modules isolated. Enabled by PEP 384, changes in PEPs 489 and 573 (and future planned ones) aim to first make it *possible* to build modules this way, and then to make it *easy* to write new modules this way and to convert old ones, so that it can become a natural default. Even if per-module state becomes the default, there will be use cases for different levels of encapsulation: per-process, per-interpreter, per-thread or per-task state. The goal is to treat these as exceptional cases: extension authors will need to think more carefully about them. ### Non-goals: Speedups and the GIL There is some effort to speed up CPython by making the GIL per-interpreter. While isolating interpreters helps that effort, defaulting to per-module state will be beneficial even if no speed-up is achieved. How to make modules safe with multiple interpreters --------------------------------------------------- There are many ways to correctly support multiple interpreters in extension modules. The rest of this text describes the preferred way to write such a module, or convert an existing module. Note that support is a work in progress; the API for some features your module needs might not yet be ready. A full example module is (XXX currently available in [a fork on GitHub](https://github.com/encukou/cpython/blob/xxlimited-facelift/Modules/…; later it should be in the CPython source tree). ### Isolated Module Objects The key point to keep in mind when developing an extension module is that several module objects can be created from a single shared library. For example: ```pycon >>> import sys >>> import binascii >>> old_binascii = binascii >>> del sys.modules['binascii'] >>> import binascii # create a new module object >>> old_binascii == binascii False ``` As a rule of thumb, the two modules should be completely independent. All objects and state specific to the module should be encapsulated within the module object, not shared with other module objects, and cleaned up when the module object is deallocated. Exceptions are possible (see “Managing global state” below) but they will need more thought and attention to edge cases than code that follows this rule of thumb. While some modules could do with less stringent restrictions, isolated modules make it easier to set clear expectations (and guidelines) that work across a variety of use cases. ### Surprising Edge Cases Note that isolated modules do create some surprising edge cases. Most notably, each module object will typically not share its classes and exceptions with other similar modules. Continuing from the example above, note that `old_binascii.Error` and `binascii.Error` are separate objects. In the following code, the exception is *not* caught: ```pycon >>> old_binascii.Error == binascii.Error False >>> try: ... old_binascii.unhexlify(b'qwertyuiop') ... except binascii.Error: ... print('boo') ... Traceback (most recent call last): File "<stdin>", line 2, in <module> binascii.Error: Non-hexadecimal digit found ``` This is expected. Notice that pure-Python modules behave the same way: it is a part of how Python works. The goal is to make extension modules safe at the C level, not to make hacks (like mutating `sys.modules` or sharing objects across interpreters) behave intuitively. ### Managing Global State Sometimes, state of a Python module is not specific to that module, but to the entire process (or something else “more global” than a module). For example: * The `readline` module provides access to *the* terminal. * A module running on a circuit board wants to control *the* on-board LED. In these cases, the Python module should provide *access* to the global state, rather than *own* it. If possible, write the module so that multiple copies of it can access the state independently (along with other libraries, whether for Python or other languages). If that is not possible, consider explicit locking. If it is necessary to use process-global state, the simplest way to avoid issues with multiple interpreters is to explicitly prevent a module from being loaded more than once per process—see “An Opt-Out Method” below. ### Managing Per-module state To use per-module state, use [multi-phase extension module initialization](https://docs.python.org/3/c-api/module.html#multi-phase-ini… introduced in PEP 489. This signals that your module supports multiple interpreters correctly. Set `PyModuleDef.m_size` to a positive number to request that many bytes of storage local to the module. Usually, this will be set to the size of some module-specific `struct`, which can store all of the module's C-level state. In particular, it is where you should put pointers to classes (including exceptions) and settings (e.g. `csv.field_size_limit`) which the C code needs to function. > [Note] > Another option is to store state in the module's `__dict__`, but you must avoid crashing when users modify `__dict__` from Python code. > This means error- and type-checking at the C level, which is easy to get wrong and hard to test sufficiently. If the module state includes `PyObject` pointers, the module object must hold references to those objects and implement module-level hooks `m_traverse`, `m_clear`, `m_free`. These work like `tp_traverse`, `tp_clear`, `tp_free` of a class. Adding them will require some work and make the code longer; this is the price for modules which can be unloaded cleanly. An example of a module with per-module state is (XXX currently available in [a fork on GitHub](https://github.com/encukou/cpython/blob/xxlimited-facelift/Modules/…; later it should be in the CPython source tree), with module initialization is at the bottom of the file. ### Opt-Out: Limiting to One Module Object per Process A non-negative `PyModuleDef.m_size` signals that a module supports multiple interpreters correctly. If this is not yet the case for your module, you can explicitly make your module loadable only once per process. Raising an exception is better than mysteriously crashing in C code! For example: ```c static int loaded = 0; static int exec_module(PyObject* module) { if (loaded) { PyErr_SetString(PyExc_ImportError, "cannot load module more than once per process"); return -1; } loaded = 1; ... // rest of initialization } ``` When your module becomes ready, you can remove this check. ### Module state access from functions Accessing the state from module-level functions is straightforward. Functions get the module object as their first argument; for extracting the state there is `PyModule_GetState`: ```c static PyObject * func(PyObject *module, PyObject *args) { my_struct *state = (my_struct*)PyModule_GetState(module); if (state == NULL) { return NULL; } ... // rest of logic } ``` (Note that `PyModule_GetState` may return NULL without seting an exception if there is no module state, i.e. `PyModuleDef.m_size` was zero. In your own module, you're in control of `m_size`, so this is easy to prevent.) ### Heap types Traditionally, types defined in C code were static, that is, `static PyTypeObject` structures defined directly in code and initialized using `PyType_Ready()`. Such types are necessarily shared across the process. Sharing them between module objects requires paying attention to any state they own or access. To limit the possible issues, static types are immutable at the Python level: for example, you can't set `str.myattribute = 123`. > [Note] > Sharing truly immutable objects between interpreters is fine, as long as they don't provide access to mutable objects. > But, every Python object has a mutable implementation detail: the reference count. > Changes to the refcount are guarded by the GIL. > Thus, code that shares any Python objects across interpreters implicitly depends on CPython's current, process-wide GIL. An alternative to static types is *heap-allocated types*, or heap types for short. These correspond more closely to classes created by Python’s `class` statement. Heap types can be created by filling a `PyType_Spec` structure, a description or “blueprint” of a class, and calling `PyType_FromModuleAndSpec()` to construct a new class object. > [note] > Other functions, like `PyType_FromSpec()`, can also create heap types, > but `PyType_FromModuleAndSpec()` associates the module with the class, granting access > to the module state to methods. The class should generally be stored in *both* the module state (for safe access from C) and the module's `__dict__` (for access from Python code). ### Module State Access from Classes If you have a type object defined with `PyType_FromModuleAndSpec()`, call `PyType_GetModule` to get the associated module, then `PyModule_GetState` to get the module's state. These steps can be combined with `PyType_GetModuleState` to save typing some tedious error-handling boilerplate: ```c my_struct *state = (my_struct*)PyType_GetModuleState(type); if (state === NULL) { return NULL; } ``` ### Module State Access from Regular Methods Accessing the module-level state from methods of a class is somewhat more complicated, but possible thanks to changes introduced in PEP 573. To get the state, you need to first get the *defining class*, and then get the module state from it. The largest roadblock is getting *the class a method was defined in*, or that method's “defining class” for short. The defining class can have a reference to the module it is part of. It is important to not confuse the defining class with `Py_TYPE(self)`. If the method is called on a *subclass* of your type, `Py_TYPE(self)` will refer to that subclass, which may be defined in different module than yours. In the following Python example, the defining class of the method `Base.get_defining_class` is `Base`, even if `type(self) == Sub`: ```python class Base: def get_defining_class(self): return __class__ class Sub(Base): pass ``` To get its “defining class”, a method must use the `METH_METHOD | METH_FASTCALL | METH_KEYWORDS` [calling convention](https://docs.python.org/3.9/c-api/structures.html?highlight=met… and the corresponding [PyCMethod signature](https://docs.python.org/3.9/c-api/structures.html#c.PyCMethod): ```c PyObject *PyCMethod( PyObject *self, // object the method was called on PyTypeObject *defining_class, // defining class PyObject *const *args, // C array of arguments Py_ssize_t nargs, // length of "args" PyObject *kwnames) // NULL, or dict of keyword arguments ``` Once you have the defining class, call `PyType_GetModuleState` to get the state of its associated module. For example: ```c static PyObject * example_method(PyObject *self, PyTypeObject *defining_class, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames) { my_struct *state = (my_struct*)PyType_GetModuleState(defining_class); if (state === NULL) { return NULL; } ... // rest of logic } PyDoc_STRVAR(example_method_doc, "..."); static PyMethodDef my_methods[] = { {"example_method", (PyCFunction)(void(*)(void))example_method, METH_METHOD|METH_FASTCALL|METH_KEYWORDS, example_method_doc} {NULL}, } ``` Open Issues ----------- Several issues around per-module state and heap types are still open. Discussions about improving the situation are best held on the [capi-sig mailing list](https://mail.python.org/mailman3/lists/capi-sig.python.org/). ### Module State Access from Slot Methods, Getters and Setters Currently (as of Python 3.9), there is no API to access the module state from: * slot methods (meaning type slots, such as `tp_new`, `nb_add` or `tp_iternext`) * getters and setters defined with `tp_getset` ### Type Checking Currently (as of Python 3.9), there is no good API to write `Py*_Check` functions (like `PyUnicode_Check` exists for `str`, a static type) for heap types. This check ensures whether instances have a particular C layout. ### Metaclasses Currently (as of Python 3.9), there is no good API to specify the *metaclass* of a heap type, that is, the `ob_type` field of the type object. ### Per-Class scope It is also not possible to attach state to *types*. While `PyHeapTypeObject` is a variable-size object (`PyVarObject`), but its variable-size storage is currently consumed by slots. There will also be issues if several classes in an inheritance hierarchy need state. Copyright --------- This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

7 months, 1 week

Module state access from slot methods: _PyType_GetModuleByDef

by Petr Viktorin

Hello, Now that PEP 573 (Module State Access from C Extension Methods) is in, it's time to address the most important part missing from it: module state access from slot methods (like tp_init or nb_add). This has long history; see e.g. a thread I started in 2015: https://mail.python.org/pipermail/import-sig/2015-July/thread.html#1037 Most recently, a possible solution, an MRO walker, was one of the last things removed from PEP 573 before acceptance, because the proposed solution was broken: see https://mail.python.org/archives/list/python-dev@python.org/message/PFBAQWQ… Here's the removed text: > Slot methods > ------------ > > To allow access to `per-module state`_ from slot methods, an MRO walker > will be implemented:: > > PyTypeObject *PyType_DefiningTypeFromSlotFunc(PyTypeObject *type, > int slot, void *func) > > The walker will go through bases of heap-allocated ``type`` > and search for class that defines ``func`` at its ``slot``. > > The ``func`` does not need to be inherited by ``type`` (i.e. it may have been > overridden in a subclass). The only requirement for the walker to find the > defining class is that the defining class must be heap-allocated. > > On failure, exception is set and NULL is returned. and the last iteration of the implementation is here: https://github.com/Dormouse759/cpython/blob/41fe314c5124fa9434d56169868dfc0… A MRO walker is not efficient. The other viable options I'm aware of are: - putting a pointer in the class object (which would need reliable class-level storage, see https://mail.python.org/archives/list/capi-sig@python.org/thread/BWVQLET4L3… ) - solutions that aren't fully general, like context variables or putting a pointer in each instance (thorny because constructors/initializers are themselves slots) - "__typeslots__" mentioned in https://mail.python.org/pipermail/import-sig/2015-July/001035.html -- which are, IMO, quite invasive Given that, I believe that some kind of MRO walker is still the best immediate solution, mainly because it's easiest to implement. If done well, it can only use current public API (limiting the maintenance burden you'd have with something wired into the internals, and allowing it to be copied to 3rd party code to support older Python versions), and should be easy to replace with a more performant solution once that comes out. And if it's added, more modules could switch to the stable ABI and eschew process-global state, so we get more exposure and experience with the remaining problems. Now, the main problem with "PyType_DefiningTypeFromSlotFunc" is that it looked slot functions, which can be set from Python code, and doing so can easily introduce C-level failures. To solve this, my next thought is instead looking for something only available in C: the PyModuleDef. Here's an implementation of such an MRO walker, which I call "_PyType_GetModuleByDef": https://github.com/encukou/cpython/blob/98dd889575cf7d1688495983ba791e14894… My thinking is to start using this in 3.10 as internal API to get a feel for its strengths and weaknesses. It should be easy to rewrite the function to only use public API, and added to another project (like Cython). It should also be easy to make it public once we're sure it's the way forward. What are your thoughts?

10 months, 1 week

Question about Advanced Debugger Support C-API

by Andrew Pashkin

Hello! There is Advanced Debugger Support <https://docs.python.org/dev/c-api/init.html#advanced-debugger-support> API in CPython. I see that in CPython source code objects on which it operates are operated on while HEAD_LOCK() is acquired (1 <https://github.com/python/cpython/blob/71a3522ef85df06a3acc718107360e37e411…>, 2 <https://github.com/python/cpython/blob/71a3522ef85df06a3acc718107360e37e411…>, 3 <https://github.com/python/cpython/blob/71a3522ef85df06a3acc718107360e37e411…>, 4 <https://github.com/python/cpython/blob/71a3522ef85df06a3acc718107360e37e411…>). But HEAD_LOCK() is not part of public C-API. So I wonder - is Advanced Debugger Support API really complete because it seems like it can't be used withotu HEAD_LOCK()/HEAD_UNLOCK(). Could someone please enlighten? -- With kind regards, Andrew Pashkin. cell phone - +375 (44) 492-16-85 Skype - waves_in_fluids e-mail - andrew.pashkin(a)gmx.co.uk

12 months

Python: Different native runtime state tiers.

by William Pickard

NOTE: This is copied straight from Python-Ideas mailing list. CPython at the very least has 2 different type of native states: Interpreter & Module state. Unfortunately, the multi-phase initialization has a weakness when it comes to Module states. You can't access the module state without a pointer to the module. PyState_GetModule from a standpoint looks to be the obvious answer to use, but it's documentation states it's unfit for multi-phase initialization. I'm proposing an idea here for discussion on a new state system for at least CPython. Tier 1: Core state - This state lives within CPython's core binary and exists the entire lifetime of the binary. - The data held within this state is available to the main interpreter and subsequent sub-interpreters. (Example: sys.executable) Tier 2-1: Interpreter state (Branches from Core state) - This state lives within CPython's core binary and is tied to a specific interpreter. - The data held within this state is only available to the interpreter it's tied to. (Example: Modules loaded into memory) Tier 2-2: Extension state (Branches from Core state) - This state lives within any CPython's core binary EXCEPT it's size and structure is defined by the extension CPython has loaded. - The purpose of this state is to allow an extension to hold data that can't be tied to a specific module. (Examples can be: Windows WSA, MySQL) Tier 3-1: Thread state (Branches from Interpreter State) - This state lives within CPython's core binary and is tied to a specific Python thread (IE: Threading library threads), - The data held within this state is only available to the thread it's tied to. (No known examples available) Tier 3-2: Module state (Branches from Interpreter State) - This type of state is already available in CPython, explaining it is not required.

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