`unpack_sequence` benchmark runs slower under JIT

Bug report

Bug description:

The pyperformance unpack_sequence benchmark is the worst-performing JIT benchmark on https://www.doesjitgobrrr.com — geomean speedup 0.586× (JIT ~1.7× slower).

The benchmark is a single function with 400 inlined a,b,c,d,e,f,g,h,i,j = to_unpack statements inside a for loop. to_unpack = tuple(range(10)) is reused (refcount > 1, so _UNPACK_SEQUENCE_UNIQUE_TUPLE never fires).

Analysis of the slowdown

The JIT covers all 400 unpacks via ~22 sequential traces (~775 uops, ~19 unpacks each), linked tail-to-tail through _EXIT_TRACE → _START_EXECUTOR, totalling ~18 MB of JIT code. This is a consequence of trace length / fitness limits ([UOP_MAX_TRACE_LENGTH] https://github.com/python/cpython/blob/main/Include/internal/pycore_uop.h#L42), EXIT_QUALITY_* from gh-146073) and is arguably fine - 400 inline statements isn't realistic code. Inter-trace transition cost alone would not produce a 1.7× slowdown. Nevertheless, longer traces would help here.

Per unpack: ~33 uops vs tier 1's ~12 bytecodes — ~2.7× more dispatches. The trace recorder unconditionally emits a _CHECK_VALIDITY + _SET_IP pair before every source bytecode at Python/optimizer.c:902-906. Per unpack:

LOAD_FAST_BORROW t            (1)
UNPACK_SEQUENCE 10            (1)
STORE_FAST_STORE_FAST × 5     (5; pair-fused by the compiler)
                              ── 7 source bytecodes → 6 _CHECK_VALIDITY+_SET_IP pairs steady state

Tier 1 has no analog. Each _CHECK_VALIDITY issues a load+branch on current_executor->vm_data.valid; each _SET_IP issues a store to frame->instr_ptr. 12 such uops × 400 unpacks × 20000 iterations is the bulk of the regression.

A naive elimination pass cannot drop them because every gap contains at least one uop with HAS_ESCAPES_FLAG — typically _POP_TOP, conservatively flagged as escaping (its Py_DECREF could run __del__).

CPython versions tested on:

CPython main branch

Operating systems tested on:

Linux

Commencez par reproduire le résultat de pyperformance unpack_sequence et par lire la logique de l’enregistreur de traces dans Python/optimizer.c autour des lignes 902-906. Examinez UOP_MAX_TRACE_LENGTH dans Include/internal/pycore_uop.h et inspectez les traces générées ainsi que les vérifications de validité décrites dans le rapport. L’issue ne définit ni correctif spécifique ni test d’acceptation ; confirmez donc l’optimisation visée et les critères de réussite fondés sur des benchmarks avant de modifier le code.