cpython/InternalDocs/jit.md

# The JIT

The [adaptive interpreter](interpreter.md) consists of a main loop that
executes the bytecode instructions generated by the
[bytecode compiler](compiler.md) and their
[specializations](interpreter.md#Specialization). Runtime optimization in
this interpreter can only be done for one instruction at a time. The JIT
is based on a mechanism to replace an entire sequence of bytecode instructions,
and this enables optimizations that span multiple instructions.

Historically, the adaptive interpreter was referred to as `tier 1` and
the JIT as `tier 2`. You will see remnants of this in the code.

## The Optimizer and Executors

The program begins running on the adaptive interpreter, until a `JUMP_BACKWARD`
instruction determines that it is "hot" because the counter in its
[inline cache](interpreter.md#inline-cache-entries) indicates that it
executed more than some threshold number of times (see
[`backoff_counter_triggers`](../Include/internal/pycore_backoff.h)).
It then calls the function `_PyOptimizer_Optimize()` in
[`Python/optimizer.c`](../Python/optimizer.c), passing it the current
[frame](frames.md) and instruction pointer. `_PyOptimizer_Optimize()`
constructs an object of type
[`_PyExecutorObject`](Include/internal/pycore_optimizer.h) which implements
an optimized version of the instruction trace beginning at this jump.

The optimizer determines where the trace ends, and the executor is set up
to either return to the adaptive interpreter and resume execution, or
transfer control to another executor (see `_PyExitData` in
Include/internal/pycore_optimizer.h).

The executor is stored on the [`code object`](code_objects.md) of the frame,
in the `co_executors` field which is an array of executors. The start
instruction of the trace (the `JUMP_BACKWARD`) is replaced by an
`ENTER_EXECUTOR` instruction whose `oparg` is equal to the index of the
executor in `co_executors`.

## The micro-op optimizer

The optimizer that `_PyOptimizer_Optimize()` runs is configurable via the
`_Py_SetTier2Optimizer()` function (this is used in test via
`_testinternalcapi.set_optimizer()`.)

The micro-op (abbreviated `uop` to approximate `μop`) optimizer is defined in
[`Python/optimizer.c`](../Python/optimizer.c) as the type `_PyUOpOptimizer_Type`.
It translates an instruction trace into a sequence of micro-ops by replacing
each bytecode by an equivalent sequence of micro-ops (see
`_PyOpcode_macro_expansion` in
[pycore_opcode_metadata.h](../Include/internal/pycore_opcode_metadata.h)
which is generated from [`Python/bytecodes.c`](../Python/bytecodes.c)).
The micro-op sequence is then optimized by
`_Py_uop_analyze_and_optimize` in
[`Python/optimizer_analysis.c`](../Python/optimizer_analysis.c)
and an instance of `_PyUOpExecutor_Type` is created to contain it.

## The JIT interpreter

After a `JUMP_BACKWARD` instruction invokes the uop optimizer to create a uop
executor, it transfers control to this executor via the `GOTO_TIER_TWO` macro.

CPython implements two executors. Here we describe the JIT interpreter,
which is the simpler of them and is therefore useful for debugging and analyzing
the uops generation and optimization stages. To run it, we configure the
JIT to run on its interpreter (i.e., python is configured with
[`--enable-experimental-jit=interpreter`](https://docs.python.org/dev/using/configure.html#cmdoption-enable-experimental-jit)).

When invoked, the executor jumps to the `tier2_dispatch:` label in
[`Python/ceval.c`](../Python/ceval.c), where there is a loop that
executes the micro-ops. The body of this loop is a switch statement over
the uops IDs, resembling the one used in the adaptive interpreter.

The swtich implementing the uops is in [`Python/executor_cases.c.h`](../Python/executor_cases.c.h),
which is generated by the build script
[`Tools/cases_generator/tier2_generator.py`](../Tools/cases_generator/tier2_generator.py)
from the bytecode definitions in
[`Python/bytecodes.c`](../Python/bytecodes.c).

When an `_EXIT_TRACE` or `_DEOPT` uop is reached, the uop interpreter exits
and execution returns to the adaptive interpreter.

## Invalidating Executors

In addition to being stored on the code object, each executor is also
inserted into a list of all executors, which is stored in the interpreter
state's `executor_list_head` field. This list is used when it is necessary
to invalidate executors because values they used in their construction may
have changed.

## The JIT

When the full jit is enabled (python was configured with
[`--enable-experimental-jit`](https://docs.python.org/dev/using/configure.html#cmdoption-enable-experimental-jit),
the uop executor's `jit_code` field is populated with a pointer to a compiled
C function that implements the executor logic. This function's signature is
defined by `jit_func` in [`pycore_jit.h`](Include/internal/pycore_jit.h).
When the executor is invoked by `ENTER_EXECUTOR`, instead of jumping to
the uop interpreter at `tier2_dispatch`, the executor runs the function
that `jit_code` points to. This function returns the instruction pointer
of the next Tier 1 instruction that needs to execute.

The generation of the jitted functions uses the copy-and-patch technique
which is described in
[Haoran Xu's article](https://sillycross.github.io/2023/05/12/2023-05-12/).
At its core are statically generated `stencils` for the implementation
of the micro ops, which are completed with runtime information while
the jitted code is constructed for an executor by
[`_PyJIT_Compile`](../Python/jit.c).

The stencils are generated at build time under the Makefile target `regen-jit`
by the scripts in [`/Tools/jit`](/Tools/jit). This script reads
[`Python/executor_cases.c.h`](../Python/executor_cases.c.h) (which is
generated from [`Python/bytecodes.c`](../Python/bytecodes.c)). For
each opcode, it constructs a `.c` file that contains a function for
implementing this opcode, with some runtime information injected.
This is done by replacing `CASE` by the bytecode definition in the
template file [`Tools/jit/template.c`](../Tools/jit/template.c).

Each of the `.c` files is compiled by LLVM, to produce an object file
that contains a function that executes the opcode. These compiled
functions are used to generate the file
[`jit_stencils.h`](../jit_stencils.h), which contains the functions
that the JIT can use to emit code for each of the bytecodes.

For Python maintainers this means that changes to the bytecodes and
their implementations do not require changes related to the stencils,
because everything is automatically generated from
[`Python/bytecodes.c`](../Python/bytecodes.c) at build time.

See Also:

* [Copy-and-Patch Compilation: A fast compilation algorithm for high-level languages and bytecode](https://arxiv.org/abs/2011.13127)

* [PyCon 2024: Building a JIT compiler for CPython](https://www.youtube.com/watch?v=kMO3Ju0QCDo)