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pypy / pypy / doc / jit / pyjitpl5.rst

PyJitPl5

This document describes the fifth generation of PyPy's JIT.

Implementation of the JIT

The JIT's theory is great in principle, but the actual code is a different story. This section tries to give a high level overview of how PyPy's JIT is implemented. It's helpful to have an understanding of how the RPython translation toolchain works before digging into the sources.

Almost all JIT specific code is found in rpython/jit subdirectories. Translation time code is in the codewriter directory. The metainterp directory holds platform independent code including the the tracer and the optimizer. Code in the backend directory is responsible for generating machine code.

JIT hints

To add a JIT to an interpreter, PyPy only requires that two hints be added to the target interpreter. These are jit_merge_point and can_enter_jit. jit_merge_point is supposed to go at the start of opcode dispatch. It allows the JIT to bail back to the interpreter in case running machine code is no longer suitable. can_enter_jit goes at the end of a application level loop. In the Python interpreter, this is the JUMP_ABSOLUTE bytecode. The Python interpreter defines its hints in pypy/module/pypyjit/interp_jit.py in a few overridden methods of the default interpreter loop.

An interpreter wishing to use the PyPy's JIT must define a list of green variables and a list of red variables. The green variables are loop constants. They are used to identify the current loop. Red variables are for everything else used in the execution loop. For example, the Python interpreter passes the code object and the instruction pointer as greens and the frame object and execution context as reds. These objects are passed to the JIT at the location of the JIT hints.

JIT Generation

After the RTyping phase of translation, where high level Python operations are turned into low-level ones for the backend, the translation driver calls apply_jit() in metainterp/warmspot.py to add a JIT compiler to the currently translating interpreter. apply_jit() decides what assembler backend to use then delegates the rest of the work to the WarmRunnerDesc class. WarmRunnerDesc finds the two JIT hints in the function graphs. It rewrites the graph containing the jit_merge_point hint, called the portal graph, to be able to handle special JIT exceptions, which indicate special conditions to the interpreter upon exiting from the JIT. The location of the can_enter_jit hint is replaced with a call to a function, maybe_compile_and_run in warmstate.py, that checks if current loop is "hot" and should be compiled.

Next, starting with the portal graph, codewriter/*.py converts the graphs of the interpreter into JIT bytecode. Since this bytecode is stored in the final binary, it's designed to be concise rather than fast. The bytecode codewriter doesn't "see" (what it sees is defined by the JIT's policy) every part of the interpreter. In these cases, it simply inserts an opaque call.

Finally, translation finishes, including the bytecode of the interpreter in the final binary, and interpreter is ready to use the runtime component of the JIT.

Tracing

Application code running on the JIT-enabled interpreter starts normally; it is interpreted on top of the usual evaluation loop. When an application loop is closed (where the can_enter_jit hint was), the interpreter calls the maybe_compile_and_run() method of WarmEnterState. This method increments a counter associated with the current green variables. When this counter reaches a certain level, usually indicating the application loop has been run many times, the JIT enters tracing mode.

Tracing is where JIT interprets the bytecode, generated at translation time, of the interpreter interpreting the application level code. This allows it to see the exact operations that make up the application level loop. Tracing is performed by MetaInterp and MIFrame classes in metainterp/pyjitpl.py. maybe_compile_and_run() creates a MetaInterp and calls its compile_and_run_once() method. This initializes the MIFrame for the input arguments of the loop, the red and green variables passed from the jit_merge_point hint, and sets it to start interpreting the bytecode of the portal graph.

Before starting the interpretation, the loop input arguments are wrapped in a box. Boxes (defined in metainterp/history.py) wrap the value and type of a value in the program the JIT is interpreting. There are two main varieties of boxes: constant boxes and normal boxes. Constant boxes are used for values assumed to be known during tracing. These are not necessarily compile time constants. All values which are "promoted", assumed to be constant by the JIT for optimization purposes, are also stored in constant boxes. Normal boxes contain values that may change during the running of a loop. There are three kinds of normal boxes: BoxInt, BoxPtr, and BoxFloat, and four kinds of constant boxes: ConstInt, ConstPtr, ConstFloat, and ConstAddr. (ConstAddr is only used to get around a limitation in the translation toolchain.)

The meta-interpreter starts interpreting the JIT bytecode. Each operation is executed and then recorded in a list of operations, called the trace. Operations can have a list of boxes they operate on, arguments. Some operations (like GETFIELD and GETARRAYITEM) also have special objects that describe how their arguments are laid out in memory. All possible operations generated by tracing are listed in metainterp/resoperation.py. When a (interpreter-level) call to a function the JIT has bytecode for occurs during tracing, another MIFrame is added to the stack and the tracing continues with the same history. This flattens the list of operations over calls. Most importantly, it unrolls the opcode dispatch loop. Interpretation continues until the can_enter_jit hint is seen. At this point, a whole iteration of the application level loop has been seen and recorded.

Because only one iteration has been recorded the JIT only knows about one codepath in the loop. For example, if there's a if statement construct like this:

if x:
    do_something_exciting()
else:
    do_something_else()

and x is true when the JIT does tracing, only the codepath do_something_exciting will be added to the trace. In future runs, to ensure that this path is still valid, a special operation called a guard operation is added to the trace. A guard is a small test that checks if assumptions the JIT makes during tracing are still true. In the example above, a GUARD_TRUE guard will be generated for x before running do_something_exciting.

Once the meta-interpreter has verified that it has traced a loop, it decides how to compile what it has. There is an optional optimization phase between these actions which is covered future down this page. The backend converts the trace operations into assembly for the particular machine. It then hands the compiled loop back to the frontend. The next time the loop is seen in application code, the optimized assembly can be run instead of the normal interpreter.

Optimizations

The JIT employs several techniques, old and new, to make machine code run faster.

Virtuals and Virtualizables

A virtual value is an array, struct, or RPython level instance that is created during the loop and does not escape from it via calls or longevity past the loop. Since it is only used by the JIT, it can be "optimized out"; the value doesn't have to be allocated at all and its fields can be stored as first class values instead of deferencing them in memory. Virtuals allow temporary objects in the interpreter to be unwrapped. For example, a W_IntObject in the PyPy can be unwrapped to just be its integer value as long as the object is known not to escape the machine code.

A virtualizable is similar to a virtual in that its structure is optimized out in the machine code. Virtualizables, however, can escape from JIT controlled code.

Other optimizations

Most of the JIT's optimizer is contained in the subdirectory metainterp/optimizeopt/. Refer to it for more details.

More resources

More documentation about the current JIT is available as a first published article:

Chapters 5 and 6 of Antonio Cuni's PhD thesis contain an overview of how Tracing JITs work in general and more informations about the concrete case of PyPy's JIT.

The blog posts with the JIT tag might also contain additional information.

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