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Co-authored-by: Kumar Aditya <59607654+kumaraditya303@users.noreply.github.com>
Co-authored-by: Victor Stinner <vstinner@python.org>
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(#104460)
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(GH-104387)
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.
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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.
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Co-authored-by: Mark Dickinson <dickinsm@gmail.com>
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Co-authored-by: Irit Katriel <1055913+iritkatriel@users.noreply.github.com>
Co-authored-by: Erlend E. Aasland <erlend.aasland@protonmail.com>
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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.
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This is a cleanup overlooked in PR #104033.
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Co-authored-by: Jelle Zijlstra <jelle.zijlstra@gmail.com>
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This is a pre-requisite for a per-interpreter GIL. Without it this change isn't strictly necessary. However, there is no real downside otherwise.
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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.
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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).
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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.
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for users of PEP 523. (GH-96849)
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This function no longer makes sense, since its runtime parameter is
no longer used. Use directly _PyThreadState_GET() and
_PyInterpreterState_GET() instead.
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Co-authored-by: Oleg Iarygin <oleg@arhadthedev.net>
Co-authored-by: Erlend E. Aasland <erlend.aasland@protonmail.com>
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back (GH-103851)
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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.
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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.
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This makes it much cleaner to move more PyTypeObject fields to PyInterpreterState.
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Interpreter (gh-104072)
Until now, we haven't been initializing nor finalizing the per-interpreter state properly.
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This also does some cleanup.
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There were cases where we do unnecessary work for builtin static types. This also simplifies some work necessary for a per-interpreter GIL.
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(gh-103460)
The lock is unnecessary as long as there's a GIL, but completely
necessary with a per-interpreter GIL.
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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.
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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.
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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.
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