This combines most _PyStackRef functions and macros between the free
threaded and default builds.
- Remove Py_TAG_DEFERRED (same as Py_TAG_REFCNT)
- Remove PyStackRef_IsDeferred (same as !PyStackRef_RefcountOnObject)
This roughly follows what was done for dictobject to make a lock-free
lookup operation. With this change, the set contains operation scales much
better when used from multiple-threads. The frozenset contains performance
seems unchanged (as already lock-free).
Summary of changes:
* refactor set_lookkey() into set_do_lookup() which now takes a function
pointer that does the entry comparison. This is similar to dictobject and
do_lookup(). In an optimized build, the comparison function is inlined and
there should be no performance cost to this.
* change set_do_lookup() to return a status separately from the entry value
* add set_compare_frozenset() and use if the object is a frozenset. For the
free-threaded build, this avoids some overhead (locking, atomic operations,
incref/decref on key)
* use FT_ATOMIC_* macros as needed for atomic loads and stores
* use a deferred free on the set table array, if shared (only on free-threaded
build, normal build always does an immediate free)
* for free-threaded build, use explicit for loop to zero the table, rather than memcpy()
* when mutating the set, assign so->table to NULL while the change is a
happening. Assign the real table array after the change is done.
There are places we use "relaxed" loads where C11 requires "consume" or
stronger. Unfortunately, compilers don't really implement "consume" so
fake it for our use in a way that avoids upsetting TSan.
On m68k, an fmove instruction accessing %fpcr may only move from
or to a data register or a memory operand. The constraint "g" also
permits the use of address registers, which is invalid. The correct
constraint is "dm". Beginning with GCC 15, the register allocator
picks an address register in the code which causes SIGILL during
runtime.
Co-authored-by: Michael Karcher <github@mkarcher.dialup.fu-berlin.de>
This PR implements frame caching in the RemoteUnwinder class to significantly reduce memory reads when profiling remote processes with deep call stacks.
When cache_frames=True, the unwinder stores the frame chain from each sample and reuses unchanged portions in subsequent samples. Since most profiling samples capture similar call stacks (especially the parent frames), this optimization avoids repeatedly reading the same frame data from the target process.
The implementation adds a last_profiled_frame field to the thread state that tracks where the previous sample stopped. On the next sample, if the current frame chain reaches this marker, the cached frames from that point onward are reused instead of being re-read from remote memory.
The sampling profiler now enables frame caching by default.
* Factor out bodies of the largest uops, to reduce jit code size.
* Factor out common assert, also reducing jit code size.
* Limit size of jitted code for a single executor to 1MB.
Added atomic operations to `scanner_begin()` and `scanner_end()` to prevent
race conditions on the `executing` flag in free-threaded builds. Also added
tests for concurrent usage of the `re` module.
Without the atomic operations, `test_scanner_concurrent_access()` triggers
`assert(self->executing)` failures, or a thread sanitizer run emits errors.
Only raises if the stack pointer is both below the limit *and* above the stack base.
This prevents false positives for user-space threads, as the stack pointer will be outside those bounds
if the stack has been swapped.
* Promote _PyObject_Dump() as a public function.
* Keep _PyObject_Dump() alias to PyUnstable_Object_Dump()
for backward compatibility.
* Replace _PyObject_Dump() with PyUnstable_Object_Dump().
Co-authored-by: Peter Bierma <zintensitydev@gmail.com>
Co-authored-by: Kumar Aditya <kumaraditya@python.org>
Co-authored-by: Petr Viktorin <encukou@gmail.com>
Adapted from a patch for Python 3.14 submitted to the Debian BTS by John
https://bugs.debian.org/1105111#20
Co-authored-by: John David Anglin <dave.anglin@bell.net>
- Introduce a new field in the GC state to store the frame that initiated garbage collection.
- Update RemoteUnwinder to include options for including "<native>" and "<GC>" frames in the stack trace.
- Modify the sampling profiler to accept parameters for controlling the inclusion of native and GC frames.
- Enhance the stack collector to properly format and append these frames during profiling.
- Add tests to verify the correct behavior of the profiler with respect to native and GC frames, including options to exclude them.
Co-authored-by: Pablo Galindo Salgado <pablogsal@gmail.com>
Replace code that directly accesses PyASCIIObject.hash with
PyUnstable_Unicode_GET_CACHED_HASH().
Remove redundant "assert(PyUnicode_Check(op))" from
PyUnstable_Unicode_GET_CACHED_HASH(), _PyASCIIObject_CAST() already
implements the check.
This needs a single bit, but was stored as a void* in the module
struct. This didn't matter due to packing, but now that there's
another bool in the struct, we can save a bit of memory by
making md_gil a bool.
Variables that changed type are renamed, to detect conflicts.
This PR changes the current JIT model from trace projection to trace recording. Benchmarking: better pyperformance (about 1.7% overall) geomean versus current https://raw.githubusercontent.com/facebookexperimental/free-threading-benchmarking/refs/heads/main/results/bm-20251108-3.15.0a1%2B-7e2bc1d-JIT/bm-20251108-vultr-x86_64-Fidget%252dSpinner-tracing_jit-3.15.0a1%2B-7e2bc1d-vs-base.svg, 100% faster Richards on the most improved benchmark versus the current JIT. Slowdown of about 10-15% on the worst benchmark versus the current JIT. **Note: the fastest version isn't the one merged, as it relies on fixing bugs in the specializing interpreter, which is left to another PR**. The speedup in the merged version is about 1.1%. https://raw.githubusercontent.com/facebookexperimental/free-threading-benchmarking/refs/heads/main/results/bm-20251112-3.15.0a1%2B-f8a764a-JIT/bm-20251112-vultr-x86_64-Fidget%252dSpinner-tracing_jit-3.15.0a1%2B-f8a764a-vs-base.svg
Stats: 50% more uops executed, 30% more traces entered the last time we ran them. It also suggests our trace lengths for a real trace recording JIT are too short, as a lot of trace too long aborts https://github.com/facebookexperimental/free-threading-benchmarking/blob/main/results/bm-20251023-3.15.0a1%2B-eb73378-CLANG%2CJIT/bm-20251023-vultr-x86_64-Fidget%252dSpinner-tracing_jit-3.15.0a1%2B-eb73378-pystats-vs-base.md .
This new JIT frontend is already able to record/execute significantly more instructions than the previous JIT frontend. In this PR, we are now able to record through custom dunders, simple object creation, generators, etc. None of these were done by the old JIT frontend. Some custom dunders uops were discovered to be broken as part of this work gh-140277
The optimizer stack space check is disabled, as it's no longer valid to deal with underflow.
Pros:
* Ignoring the generated tracer code as it's automatically created, this is only additional 1k lines of code. The maintenance burden is handled by the DSL and code generator.
* `optimizer.c` is now significantly simpler, as we don't have to do strange things to recover the bytecode from a trace.
* The new JIT frontend is able to handle a lot more control-flow than the old one.
* Tracing is very low overhead. We use the tail calling interpreter/computed goto interpreter to switch between tracing mode and non-tracing mode. I call this mechanism dual dispatch, as we have two dispatch tables dispatching to each other. Specialization is still enabled while tracing.
* Better handling of polymorphism. We leverage the specializing interpreter for this.
Cons:
* (For now) requires tail calling interpreter or computed gotos. This means no Windows JIT for now :(. Not to fret, tail calling is coming soon to Windows though https://github.com/python/cpython/pull/139962
Design:
* After each instruction, the `record_previous_inst` function/label is executed. This does as the name suggests.
* The tracing interpreter lowers bytecode to uops directly so that it can obtain "fresh" values at the point of lowering.
* The tracing version behaves nearly identical to the normal interpreter, in fact it even has specialization! This allows it to run without much of a slowdown when tracing. The actual cost of tracing is only a function call and writes to memory.
* The tracing interpreter uses the specializing interpreter's deopt to naturally form the side exit chains. This allows it to side exit chain effectively, without repeating much code. We force a re-specializing when tracing a deopt.
* The tracing interpreter can even handle goto errors/exceptions, but I chose to disable them for now as it's not tested.
* Because we do not share interpreter dispatch, there is should be no significant slowdown to the original specializing interpreter on tailcall and computed got with JIT disabled. With JIT enabled, there might be a slowdown in the form of the JIT trying to trace.
* Things that could have dynamic instruction pointer effects are guarded on. The guard deopts to a new instruction --- `_DYNAMIC_EXIT`.
