2023-08-16 02:04:17 +08:00
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#ifndef Py_INTERNAL_OPTIMIZER_H
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#define Py_INTERNAL_OPTIMIZER_H
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#ifdef __cplusplus
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extern "C" {
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#endif
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#ifndef Py_BUILD_CORE
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# error "this header requires Py_BUILD_CORE define"
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#endif
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2025-03-19 15:23:32 +01:00
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#include "pycore_typedefs.h" // _PyInterpreterFrame
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2025-09-17 18:50:16 +01:00
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#include "pycore_uop.h" // _PyUOpInstruction
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2024-02-13 21:24:48 +08:00
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#include "pycore_uop_ids.h"
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2025-06-27 19:37:44 +08:00
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#include "pycore_stackref.h" // _PyStackRef
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2026-01-09 03:38:21 +08:00
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#include "pycore_optimizer_types.h"
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2024-02-27 10:51:26 +00:00
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#include <stdbool.h>
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2024-02-13 21:24:48 +08:00
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2024-06-26 13:54:03 +02:00
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typedef struct _PyExecutorLinkListNode {
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struct _PyExecutorObject *next;
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struct _PyExecutorObject *previous;
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} _PyExecutorLinkListNode;
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typedef struct {
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uint8_t opcode;
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uint8_t oparg;
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2025-12-11 10:32:52 +00:00
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uint8_t valid;
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uint8_t chain_depth; // Must be big enough for MAX_CHAIN_DEPTH - 1.
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2026-01-14 18:27:33 +08:00
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bool cold;
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2025-12-23 17:19:34 +00:00
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uint8_t pending_deletion;
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2025-12-11 10:32:52 +00:00
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int32_t index; // Index of ENTER_EXECUTOR (if code isn't NULL, below).
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2024-06-26 13:54:03 +02:00
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_PyBloomFilter bloom;
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_PyExecutorLinkListNode links;
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PyCodeObject *code; // Weak (NULL if no corresponding ENTER_EXECUTOR).
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} _PyVMData;
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2025-08-01 16:26:07 +01:00
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typedef struct _PyExitData {
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2024-06-26 13:54:03 +02:00
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uint32_t target;
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2025-12-11 10:32:52 +00:00
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uint16_t index:12;
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uint16_t stack_cache:2;
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gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
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uint16_t is_dynamic:1;
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uint16_t is_control_flow:1;
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2024-06-26 13:54:03 +02:00
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_Py_BackoffCounter temperature;
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2025-05-04 10:05:35 +01:00
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struct _PyExecutorObject *executor;
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2024-06-26 13:54:03 +02:00
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} _PyExitData;
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typedef struct _PyExecutorObject {
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PyObject_VAR_HEAD
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const _PyUOpInstruction *trace;
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_PyVMData vm_data; /* Used by the VM, but opaque to the optimizer */
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uint32_t exit_count;
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uint32_t code_size;
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size_t jit_size;
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void *jit_code;
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_PyExitData exits[1];
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} _PyExecutorObject;
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// Export for '_opcode' shared extension (JIT compiler).
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PyAPI_FUNC(_PyExecutorObject*) _Py_GetExecutor(PyCodeObject *code, int offset);
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void _Py_ExecutorInit(_PyExecutorObject *, const _PyBloomFilter *);
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void _Py_ExecutorDetach(_PyExecutorObject *);
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void _Py_BloomFilter_Init(_PyBloomFilter *);
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void _Py_BloomFilter_Add(_PyBloomFilter *bloom, void *obj);
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PyAPI_FUNC(void) _Py_Executor_DependsOn(_PyExecutorObject *executor, void *obj);
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#define _Py_MAX_ALLOWED_BUILTINS_MODIFICATIONS 3
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#define _Py_MAX_ALLOWED_GLOBALS_MODIFICATIONS 6
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#ifdef _Py_TIER2
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PyAPI_FUNC(void) _Py_Executors_InvalidateDependency(PyInterpreterState *interp, void *obj, int is_invalidation);
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PyAPI_FUNC(void) _Py_Executors_InvalidateAll(PyInterpreterState *interp, int is_invalidation);
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2024-09-26 17:35:42 -07:00
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PyAPI_FUNC(void) _Py_Executors_InvalidateCold(PyInterpreterState *interp);
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2024-06-26 13:54:03 +02:00
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#else
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# define _Py_Executors_InvalidateDependency(A, B, C) ((void)0)
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# define _Py_Executors_InvalidateAll(A, B) ((void)0)
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2024-09-26 17:35:42 -07:00
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2024-06-26 13:54:03 +02:00
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#endif
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2024-09-26 17:35:42 -07:00
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// Used as the threshold to trigger executor invalidation when
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2025-10-27 18:37:37 +02:00
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// executor_creation_counter is greater than this value.
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// This value is arbitrary and was not optimized.
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#define JIT_CLEANUP_THRESHOLD 1000
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2024-06-26 13:54:03 +02:00
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gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
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int _Py_uop_analyze_and_optimize(
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2026-01-09 03:38:21 +08:00
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_PyThreadStateImpl *tstate,
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2024-02-02 12:14:34 +00:00
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_PyUOpInstruction *trace, int trace_len, int curr_stackentries,
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_PyBloomFilter *dependencies);
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2023-08-16 02:04:17 +08:00
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2023-10-29 13:53:25 -07:00
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extern PyTypeObject _PyUOpExecutor_Type;
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2023-08-16 02:04:17 +08:00
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2024-02-27 10:51:26 +00:00
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2024-05-10 18:20:12 +02:00
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#define UOP_FORMAT_TARGET 0
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2024-07-01 13:17:40 -07:00
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#define UOP_FORMAT_JUMP 1
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2024-05-10 18:20:12 +02:00
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static inline uint32_t uop_get_target(const _PyUOpInstruction *inst)
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{
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assert(inst->format == UOP_FORMAT_TARGET);
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return inst->target;
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}
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static inline uint16_t uop_get_jump_target(const _PyUOpInstruction *inst)
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{
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assert(inst->format == UOP_FORMAT_JUMP);
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return inst->jump_target;
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}
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static inline uint16_t uop_get_error_target(const _PyUOpInstruction *inst)
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{
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assert(inst->format != UOP_FORMAT_TARGET);
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return inst->error_target;
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}
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2025-06-17 23:25:53 +08:00
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#define REF_IS_BORROWED 1
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2026-01-17 23:20:35 +08:00
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#define REF_IS_INVALID 2
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#define REF_TAG_BITS 3
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2025-06-17 23:25:53 +08:00
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2026-01-17 23:20:35 +08:00
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#define JIT_BITS_TO_PTR_MASKED(REF) ((JitOptSymbol *)(((REF).bits) & (~REF_TAG_BITS)))
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2025-06-17 23:25:53 +08:00
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static inline JitOptSymbol *
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PyJitRef_Unwrap(JitOptRef ref)
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{
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return JIT_BITS_TO_PTR_MASKED(ref);
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}
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bool _Py_uop_symbol_is_immortal(JitOptSymbol *sym);
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static inline JitOptRef
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PyJitRef_Wrap(JitOptSymbol *sym)
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{
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return (JitOptRef){.bits=(uintptr_t)sym};
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}
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2026-01-17 23:20:35 +08:00
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static inline JitOptRef
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PyJitRef_WrapInvalid(void *ptr)
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{
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return (JitOptRef){.bits=(uintptr_t)ptr | REF_IS_INVALID};
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}
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static inline bool
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PyJitRef_IsInvalid(JitOptRef ref)
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{
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return (ref.bits & REF_IS_INVALID) == REF_IS_INVALID;
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}
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2025-09-04 02:05:06 +08:00
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static inline JitOptRef
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PyJitRef_StripReferenceInfo(JitOptRef ref)
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{
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return PyJitRef_Wrap(PyJitRef_Unwrap(ref));
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}
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2025-06-17 23:25:53 +08:00
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static inline JitOptRef
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PyJitRef_Borrow(JitOptRef ref)
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{
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return (JitOptRef){ .bits = ref.bits | REF_IS_BORROWED };
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}
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static const JitOptRef PyJitRef_NULL = {.bits = REF_IS_BORROWED};
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static inline bool
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PyJitRef_IsNull(JitOptRef ref)
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{
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return ref.bits == PyJitRef_NULL.bits;
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}
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static inline int
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PyJitRef_IsBorrowed(JitOptRef ref)
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{
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return (ref.bits & REF_IS_BORROWED) == REF_IS_BORROWED;
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}
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2024-02-27 10:51:26 +00:00
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2025-06-17 23:25:53 +08:00
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extern bool _Py_uop_sym_is_null(JitOptRef sym);
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extern bool _Py_uop_sym_is_not_null(JitOptRef sym);
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extern bool _Py_uop_sym_is_const(JitOptContext *ctx, JitOptRef sym);
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extern PyObject *_Py_uop_sym_get_const(JitOptContext *ctx, JitOptRef sym);
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extern JitOptRef _Py_uop_sym_new_unknown(JitOptContext *ctx);
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extern JitOptRef _Py_uop_sym_new_not_null(JitOptContext *ctx);
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extern JitOptRef _Py_uop_sym_new_type(
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2025-01-20 15:49:15 +00:00
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JitOptContext *ctx, PyTypeObject *typ);
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2025-06-19 11:10:29 +01:00
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2025-06-17 23:25:53 +08:00
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extern JitOptRef _Py_uop_sym_new_const(JitOptContext *ctx, PyObject *const_val);
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2025-06-27 19:37:44 +08:00
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extern JitOptRef _Py_uop_sym_new_const_steal(JitOptContext *ctx, PyObject *const_val);
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bool _Py_uop_sym_is_safe_const(JitOptContext *ctx, JitOptRef sym);
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_PyStackRef _Py_uop_sym_get_const_as_stackref(JitOptContext *ctx, JitOptRef sym);
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2025-06-17 23:25:53 +08:00
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extern JitOptRef _Py_uop_sym_new_null(JitOptContext *ctx);
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extern bool _Py_uop_sym_has_type(JitOptRef sym);
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extern bool _Py_uop_sym_matches_type(JitOptRef sym, PyTypeObject *typ);
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extern bool _Py_uop_sym_matches_type_version(JitOptRef sym, unsigned int version);
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extern void _Py_uop_sym_set_null(JitOptContext *ctx, JitOptRef sym);
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extern void _Py_uop_sym_set_non_null(JitOptContext *ctx, JitOptRef sym);
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extern void _Py_uop_sym_set_type(JitOptContext *ctx, JitOptRef sym, PyTypeObject *typ);
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extern bool _Py_uop_sym_set_type_version(JitOptContext *ctx, JitOptRef sym, unsigned int version);
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extern void _Py_uop_sym_set_const(JitOptContext *ctx, JitOptRef sym, PyObject *const_val);
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extern bool _Py_uop_sym_is_bottom(JitOptRef sym);
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extern int _Py_uop_sym_truthiness(JitOptContext *ctx, JitOptRef sym);
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extern PyTypeObject *_Py_uop_sym_get_type(JitOptRef sym);
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extern JitOptRef _Py_uop_sym_new_tuple(JitOptContext *ctx, int size, JitOptRef *args);
|
2025-09-03 23:42:26 +09:00
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extern JitOptRef _Py_uop_sym_tuple_getitem(JitOptContext *ctx, JitOptRef sym, Py_ssize_t item);
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extern Py_ssize_t _Py_uop_sym_tuple_length(JitOptRef sym);
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2025-06-17 23:25:53 +08:00
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extern JitOptRef _Py_uop_sym_new_truthiness(JitOptContext *ctx, JitOptRef value, bool truthy);
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2025-06-19 11:10:29 +01:00
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extern bool _Py_uop_sym_is_compact_int(JitOptRef sym);
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extern JitOptRef _Py_uop_sym_new_compact_int(JitOptContext *ctx);
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extern void _Py_uop_sym_set_compact_int(JitOptContext *ctx, JitOptRef sym);
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2025-01-20 15:49:15 +00:00
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extern void _Py_uop_abstractcontext_init(JitOptContext *ctx);
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extern void _Py_uop_abstractcontext_fini(JitOptContext *ctx);
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2024-02-27 13:25:02 +00:00
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extern _Py_UOpsAbstractFrame *_Py_uop_frame_new(
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2025-01-20 15:49:15 +00:00
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JitOptContext *ctx,
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2024-02-27 10:51:26 +00:00
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|
PyCodeObject *co,
|
2024-06-08 05:41:45 -04:00
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int curr_stackentries,
|
2025-06-17 23:25:53 +08:00
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JitOptRef *args,
|
2024-06-08 05:41:45 -04:00
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|
int arg_len);
|
gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
|
|
|
extern int _Py_uop_frame_pop(JitOptContext *ctx, PyCodeObject *co, int curr_stackentries);
|
2024-02-27 10:51:26 +00:00
|
|
|
|
2024-02-27 13:25:02 +00:00
|
|
|
PyAPI_FUNC(PyObject *) _Py_uop_symbols_test(PyObject *self, PyObject *ignored);
|
2024-02-27 10:51:26 +00:00
|
|
|
|
gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
|
|
|
PyAPI_FUNC(int) _PyOptimizer_Optimize(_PyInterpreterFrame *frame, PyThreadState *tstate);
|
2024-02-29 08:11:28 -08:00
|
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|
|
2025-08-01 16:26:07 +01:00
|
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|
static inline _PyExecutorObject *_PyExecutor_FromExit(_PyExitData *exit)
|
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|
|
|
{
|
|
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|
|
_PyExitData *exit0 = exit - exit->index;
|
|
|
|
|
return (_PyExecutorObject *)(((char *)exit0) - offsetof(_PyExecutorObject, exits));
|
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|
|
|
}
|
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|
|
|
|
|
|
extern _PyExecutorObject *_PyExecutor_GetColdExecutor(void);
|
gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
|
|
|
extern _PyExecutorObject *_PyExecutor_GetColdDynamicExecutor(void);
|
2025-08-01 16:26:07 +01:00
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|
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|
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|
PyAPI_FUNC(void) _PyExecutor_ClearExit(_PyExitData *exit);
|
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|
|
|
|
|
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|
|
extern void _PyExecutor_Free(_PyExecutorObject *self);
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|
|
|
|
|
2024-12-13 11:00:00 +00:00
|
|
|
PyAPI_FUNC(int) _PyDumpExecutors(FILE *out);
|
2025-05-04 10:05:35 +01:00
|
|
|
#ifdef _Py_TIER2
|
2026-01-10 19:15:48 +08:00
|
|
|
PyAPI_FUNC(void) _Py_ClearExecutorDeletionList(PyInterpreterState *interp);
|
2025-05-04 10:05:35 +01:00
|
|
|
#endif
|
2024-12-13 11:00:00 +00:00
|
|
|
|
2026-01-17 21:31:38 +08:00
|
|
|
PyAPI_FUNC(int) _PyJit_translate_single_bytecode_to_trace(PyThreadState *tstate, _PyInterpreterFrame *frame, _Py_CODEUNIT *next_instr, int stop_tracing_opcode);
|
gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
|
|
|
|
2025-11-18 13:31:48 +00:00
|
|
|
PyAPI_FUNC(int)
|
gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
|
|
|
_PyJit_TryInitializeTracing(PyThreadState *tstate, _PyInterpreterFrame *frame,
|
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|
|
|
_Py_CODEUNIT *curr_instr, _Py_CODEUNIT *start_instr,
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|
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|
|
_Py_CODEUNIT *close_loop_instr, int curr_stackdepth, int chain_depth, _PyExitData *exit,
|
2026-01-10 19:15:48 +08:00
|
|
|
int oparg, _PyExecutorObject *current_executor);
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gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
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2026-01-17 21:31:38 +08:00
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PyAPI_FUNC(void) _PyJit_FinalizeTracing(PyThreadState *tstate, int err);
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2026-01-10 03:00:49 +08:00
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void _PyJit_TracerFree(_PyThreadStateImpl *_tstate);
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gh-139109: A new tracing JIT compiler frontend for CPython (GH-140310)
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`.
2025-11-14 02:08:32 +08:00
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void _PyJit_Tracer_InvalidateDependency(PyThreadState *old_tstate, void *obj);
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2023-08-16 02:04:17 +08:00
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#ifdef __cplusplus
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}
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#endif
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#endif /* !Py_INTERNAL_OPTIMIZER_H */
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