The risk of a race with this state is relatively low, but we play it safe anyway. We do avoid using the lock in performance-sensitive cases where the risk of a race is very, very low.
This avoids the problematic race in drop_gil() by skipping the FORCE_SWITCHING code there for finalizing threads.
(The idea for this approach came out of discussions with @markshannon.)
Remove functions in the C API:
* PyEval_AcquireLock()
* PyEval_ReleaseLock()
* PyEval_InitThreads()
* PyEval_ThreadsInitialized()
But keep these functions in the stable ABI.
Mention "make regen-limited-abi" in "make regen-all".
Remove the following old functions to configure the Python
initialization, deprecated in Python 3.11:
* PySys_AddWarnOptionUnicode()
* PySys_AddWarnOption()
* PySys_AddXOption()
* PySys_HasWarnOptions()
* PySys_SetArgvEx()
* PySys_SetArgv()
* PySys_SetPath()
* Py_SetPath()
* Py_SetProgramName()
* Py_SetPythonHome()
* Py_SetStandardStreamEncoding()
* _Py_SetProgramFullPath()
Most of these functions are kept in the stable ABI, except:
* Py_SetStandardStreamEncoding()
* _Py_SetProgramFullPath()
Update Doc/extending/embedding.rst and Doc/extending/extending.rst to
use the new PyConfig API.
_testembed.c:
* check_stdio_details() now sets stdio_encoding and stdio_errors
of PyConfig.
* Add definitions of functions removed from the API but kept in the
stable ABI.
* test_init_from_config() and test_init_read_set() now use
PyConfig_SetString() instead of PyConfig_SetBytesString().
Remove _Py_ClearStandardStreamEncoding() internal function.
Deprecate the old Py_UNICODE and PY_UNICODE_TYPE types in the C API:
use wchar_t instead.
Replace Py_UNICODE with wchar_t in multiple C files.
Co-authored-by: Inada Naoki <songofacandy@gmail.com>
* Remove the Lib/test/imghdrdata/ directory.
* Copy 5 pictures (gif, png, ppm, pgm, xbm) from removed
Lib/test/imghdrdata/ to a new Lib/test/tkinterdata/ directory.
* Update Sphinx from 4.5 to 6.2 in Doc/requirements.txt.
* socket_helper.transient_internet() no longer imports nntplib to
catch nntplib.NNTPTemporaryError.
* ssltests.py no longer runs test_nntplib.
* "make quicktest" no longer runs test_nntplib.
* WASM: remove nntplib from OMIT_NETWORKING_FILES.
* Remove mentions to nntplib in the email documentation.
This commit replaces the Python implementation of the tokenize module with an implementation
that reuses the real C tokenizer via a private extension module. The tokenize module now implements
a compatibility layer that transforms tokens from the C tokenizer into Python tokenize tokens for backward
compatibility.
As the C tokenizer does not emit some tokens that the Python tokenizer provides (such as comments and non-semantic newlines), a new special mode has been added to the C tokenizer mode that currently is only used via
the extension module that exposes it to the Python layer. This new mode forces the C tokenizer to emit these new extra tokens and add the appropriate metadata that is needed to match the old Python implementation.
Co-authored-by: Pablo Galindo <pablogsal@gmail.com>
This implements PEP 695, Type Parameter Syntax. It adds support for:
- Generic functions (def func[T](): ...)
- Generic classes (class X[T](): ...)
- Type aliases (type X = ...)
- New scoping when the new syntax is used within a class body
- Compiler and interpreter changes to support the new syntax and scoping rules
Co-authored-by: Marc Mueller <30130371+cdce8p@users.noreply.github.com>
Co-authored-by: Eric Traut <eric@traut.com>
Co-authored-by: Larry Hastings <larry@hastings.org>
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
When monitoring LINE events, instrument all instructions that can have a predecessor on a different line.
Then check that the a new line has been hit in the instrumentation code.
This brings the behavior closer to that of 3.11, simplifying implementation and porting of tools.
This PR removes `_Py_dg_stdnan` and `_Py_dg_infinity` in favour of
using the standard `NAN` and `INFINITY` macros provided by C99.
This change has the side-effect of fixing a bug on MIPS where the
hard-coded value used by `_Py_dg_stdnan` gave a signalling NaN
rather than a quiet NaN.
---------
Co-authored-by: Mark Dickinson <dickinsm@gmail.com>
This is the culmination of PEP 684 (and of my 8-year long multi-core Python project)!
Each subinterpreter may now be created with its own GIL (via Py_NewInterpreterFromConfig()). If not so configured then the interpreter will share with the main interpreter--the status quo since subinterpreters were added decades ago. The main interpreter always has its own GIL and subinterpreters from Py_NewInterpreter() will always share with the main interpreter.
We also add PyInterpreterState.ceval.own_gil to record if the interpreter actually has its own GIL.
Note that for now we don't actually respect own_gil; all interpreters still share the one GIL. However, PyInterpreterState.ceval.own_gil does reflect PyInterpreterConfig.own_gil. That lie is a temporary one that we will fix when the GIL really becomes per-interpreter.
Here we are doing no more than adding the value for Py_mod_multiple_interpreters and using it for stdlib modules. We will start checking for it in gh-104206 (once PyInterpreterState.ceval.own_gil is added in gh-104204).
In preparation for a per-interpreter GIL, we add PyInterpreterState.ceval.gil, set it to the shared GIL for each interpreter, and use that rather than using _PyRuntime.ceval.gil directly. Note that _PyRuntime.ceval.gil is still the actual GIL.
This function no longer makes sense, since its runtime parameter is
no longer used. Use directly _PyThreadState_GET() and
_PyInterpreterState_GET() instead.
This breaks the tests, but we are keeping it as a separate commit so
that the move operation and editing of the moved files are separate, for
a cleaner history.
We also expose PyInterpreterConfig. This is part of the PEP 684 (per-interpreter GIL) implementation. We will add docs as soon as we can.
FYI, I'm adding the new config field for per-interpreter GIL in gh-99114.
This is strictly about moving the "obmalloc" runtime state from
`_PyRuntimeState` to `PyInterpreterState`. Doing so improves isolation
between interpreters, specifically most of the memory (incl. objects)
allocated for each interpreter's use. This is important for a
per-interpreter GIL, but such isolation is valuable even without it.
FWIW, a per-interpreter obmalloc is the proverbial
canary-in-the-coalmine when it comes to the isolation of objects between
interpreters. Any object that leaks (unintentionally) to another
interpreter is highly likely to cause a crash (on debug builds at
least). That's a useful thing to know, relative to interpreter
isolation.
This speeds up `super()` (by around 85%, for a simple one-level
`super().meth()` microbenchmark) by avoiding allocation of a new
single-use `super()` object on each use.
Deep-frozen code objects are cannot be shared (currently) by
interpreters, due to how adaptive specialization can modify the
bytecodes. We work around this by only using the deep-frozen objects in
the main interpreter. This does incur a performance penalty for
subinterpreters, which we may be able to resolve later.
We replace _PyRuntime.tstate_current with a thread-local variable. As part of this change, we add a _Py_thread_local macro in pyport.h (only for the core runtime) to smooth out the compiler differences. The main motivation here is in support of a per-interpreter GIL, but this change also provides some performance improvement opportunities.
Note that we do not provide a fallback to the thread-local, either falling back to the old tstate_current or to thread-specific storage (PyThread_tss_*()). If that proves problematic then we can circle back. I consider it unlikely, but will run the buildbots to double-check.
Also note that this does not change any of the code related to the GILState API, where it uses a thread state stored in thread-specific storage. I suspect we can combine that with _Py_tss_tstate (from here). However, that can be addressed separately and is not urgent (nor critical).
(While this change was mostly done independently, I did take some inspiration from earlier (~2020) work by @markshannon (main...markshannon:threadstate_in_tls) and @vstinner (#23976).)
This is the implementation of PEP683
Motivation:
The PR introduces the ability to immortalize instances in CPython which bypasses reference counting. Tagging objects as immortal allows up to skip certain operations when we know that the object will be around for the entire execution of the runtime.
Note that this by itself will bring a performance regression to the runtime due to the extra reference count checks. However, this brings the ability of having truly immutable objects that are useful in other contexts such as immutable data sharing between sub-interpreters.
* The majority of the monitoring code is in instrumentation.c
* The new instrumentation bytecodes are in bytecodes.c
* legacy_tracing.c adapts the new API to the old sys.setrace and sys.setprofile APIs
Erlend E. Aasland