Record success in `specialize`
This matches the existing behavior where we increment the success
stat for the generic opcode each time we successfully specialize
an instruction.
If Python fails to start newly created thread
due to failure of underlying PyThread_start_new_thread() call,
its state should be removed from interpreter' thread states list
to avoid its double cleanup.
Co-authored-by: Serhiy Storchaka <storchaka@gmail.com>
This gets rid of the immortal check in `PyStackRef_FromPyObjectSteal()`.
Overall, this improves performance about 2% in the free threading
build.
This also renames `PyStackRef_Is()` to `PyStackRef_IsExactly()` because
the macro requires that the tag bits of the arguments match, which is
only true in certain special cases.
Add free-threaded specialization for `UNPACK_SEQUENCE` opcode.
`UNPACK_SEQUENCE_TUPLE/UNPACK_SEQUENCE_TWO_TUPLE` are already thread safe since tuples are immutable.
`UNPACK_SEQUENCE_LIST` is not thread safe because of nature of lists (there is nothing preventing another thread from adding items to or removing them the list while the instruction is executing). To achieve thread safety we add a critical section to the implementation of `UNPACK_SEQUENCE_LIST`, especially around the parts where we check the size of the list and push items onto the stack.
---------
Co-authored-by: Matt Page <mpage@meta.com>
Co-authored-by: mpage <mpage@cs.stanford.edu>
Enable specialization of LOAD_GLOBAL in free-threaded builds.
Thread-safety of specialization in free-threaded builds is provided by the following:
A critical section is held on both the globals and builtins objects during specialization. This ensures we get an atomic view of both builtins and globals during specialization.
Generation of new keys versions is made atomic in free-threaded builds.
Existing helpers are used to atomically modify the opcode.
Thread-safety of specialized instructions in free-threaded builds is provided by the following:
Relaxed atomics are used when loading and storing dict keys versions. This avoids potential data races as the dict keys versions are read without holding the dictionary's per-object lock in version guards.
Dicts keys objects are passed from keys version guards to the downstream uops. This ensures that we are loading from the correct offset in the keys object. Once a unicode key has been stored in a keys object for a combined dictionary in free-threaded builds, the offset that it is stored in will never be reused for a different key. Once the version guard passes, we know that we are reading from the correct offset.
The dictionary read fast-path is used to read values from the dictionary once we know the correct offset.
This is a precursor to the actual fix for gh-114940, where we will change these macros to use the new lock. This change is almost entirely mechanical; the exceptions are the loops in codeobject.c and ceval.c, which now hold the "head" lock. Note that almost all of the uses of _Py_FOR_EACH_TSTATE_UNLOCKED() here will change to _Py_FOR_EACH_TSTATE_BEGIN() once we add the new per-interpreter lock.
Don't take a reason in unspecialize
We only want to compute the reason if stats are enabled. Optimizing
compilers should optimize this away for us (gcc and clang do), but
it's better to be safe than sorry.
This approach eliminates the originally reported race. It also gets rid of the deadlock reported in gh-96071, so we can remove the workaround added then.
* Mark almost all reachable objects before doing collection phase
* Add stats for objects marked
* Visit new frames before each increment
* Remove lazy dict tracking
* Update docs
* Clearer calculation of work to do.
* Document that slices can be marshalled
* Deduplicate and organize the list of supported types
in docs
* Organize the type code list in marshal.c, to make
it more obvious that this is a versioned format
* Back-fill some historical info
Co-authored-by: Michael Droettboom <mdboom@gmail.com>
The PyMutex implementation supports unlocking after fork because we
clear the list of waiters in parking_lot.c. This doesn't work as well
for _PyRecursiveMutex because on some systems, such as SerenityOS, the
thread id is not preserved across fork().
These changes makes it easier to backport the _interpreters, _interpqueues, and _interpchannels modules to Python 3.12.
This involves the following:
* add the _PyXI_GET_STATE() and _PyXI_GET_GLOBAL_STATE() macros
* add _PyXIData_lookup_context_t and _PyXIData_GetLookupContext()
* add _Py_xi_state_init() and _Py_xi_state_fini()
These changes makes it easier to backport the _interpreters, _interpqueues, and _interpchannels modules to Python 3.12.
This involves the following:
* rename several structs and typedefs
* add several typedefs
* stop using the PyThreadState.state field directly in parking_lot.c
Move creation of a tuple for var-positional parameter out of
_PyArg_UnpackKeywordsWithVararg().
Merge _PyArg_UnpackKeywordsWithVararg() with _PyArg_UnpackKeywords().
Add a new parameter in _PyArg_UnpackKeywords().
The "parameters" and "converters" attributes of ParseArgsCodeGen no
longer contain the var-positional parameter. It is now available as the
"varpos" attribute. Optimize code generation for var-positional
parameter and reuse the same generating code for functions with and without
keyword parameters.
Add special converters for var-positional parameter. "tuple" represents it as
a Python tuple and "array" represents it as a continuous array of PyObject*.
"object" is a temporary alias of "tuple".
The primary objective here is to allow some later changes to be cleaner. Mostly this involves renaming things and moving a few things around.
* CrossInterpreterData -> XIData
* crossinterpdatafunc -> xidatafunc
* split out pycore_crossinterp_data_registry.h
* add _PyXIData_lookup_t
Introduce helpers for (un)specializing instructions
Consolidate the code to specialize/unspecialize instructions into
two helper functions and use them in `_Py_Specialize_BinaryOp`.
The resulting code is more concise and keeps all of the logic at
the point where we decide to specialize/unspecialize an instruction.
- The specialization logic determines the appropriate specialization using only the operand's type, which is safe to read non-atomically (changing it requires stopping the world). We are guaranteed that the type will not change in between when it is checked and when we specialize the bytecode because the types involved are immutable (you cannot assign to `__class__` for exact instances of `dict`, `set`, or `frozenset`). The bytecode is mutated atomically using helpers.
- The specialized instructions rely on the operand type not changing in between the `DEOPT_IF` checks and the calls to the appropriate type-specific helpers (e.g. `_PySet_Contains`). This is a correctness requirement in the default builds and there are no changes to the opcodes in the free-threaded builds that would invalidate this.
Each thread specializes a thread-local copy of the bytecode, created on the first RESUME, in free-threaded builds. All copies of the bytecode for a code object are stored in the co_tlbc array on the code object. Threads reserve a globally unique index identifying its copy of the bytecode in all co_tlbc arrays at thread creation and release the index at thread destruction. The first entry in every co_tlbc array always points to the "main" copy of the bytecode that is stored at the end of the code object. This ensures that no bytecode is copied for programs that do not use threads.
Thread-local bytecode can be disabled at runtime by providing either -X tlbc=0 or PYTHON_TLBC=0. Disabling thread-local bytecode also disables specialization.
Concurrent modifications to the bytecode made by the specializing interpreter and instrumentation use atomics, with specialization taking care not to overwrite an instruction that was instrumented concurrently.
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