There are several hooks in the
pypyjit module that may help you with
understanding what pypy’s JIT is doing while running your program:
Set a compiling hook that will be called each time a loop is compiled.
The callable will be called with the
pypyjit.JitLoopInfoobject. Refer to it’s documentation for details.
Note that jit hook is not reentrant. It means that if the code inside the jit hook is itself jitted, it will get compiled, but the jit hook won’t be called for that.
if operations=False, no list of operations will be available. Useful if the hook is supposed to be very lighweight.
Set a hook (callable) that will be called each time there is tracing aborted due to some reason.
The hook will be invoked with the siagnture:
hook(jitdriver_name, greenkey, reason, oplist)
Reason is a string, the meaning of other arguments is the same as attributes on JitLoopInfo object
Start recording debugging counters for
Stop recording debugging counters for
Get the jit status in the specific moment in time. Note that this is eager - the attribute access is not lazy, if you need new stats you need to call this function again. You might want to call
enable_debugto get more information. It returns an instance of
A class describing current snapshot. Usable attributes:
counters- internal JIT integer counters
counter_times- internal JIT float counters, notably time spent TRACING and in the JIT BACKEND
loop_run_times- counters for number of times loops are run, only works when
A class containing information about the compiled loop. Usable attributes:
operations- list of operations, if requested
jitdriver_name- the name of jitdriver associated with this loop
greenkey- a key at which the loop got compiled (e.g. code position, is_being_profiled, pycode tuple for python jitdriver)
loop_no- loop cardinal number
bridge_no- id of the fail descr
type- “entry bridge”, “loop” or “bridge”
asmaddr- an address in raw memory where assembler resides
asmlen- length of raw memory with assembler associated
Resetting the JIT¶
Marks all current machine code objects as ready to release. They will be released at the next GC (unless they are currently in use in the stack of one of the threads). Doing
pypyjit.releaseall(); gc.collect()is a heavy hammer that forces the JIT roughly back to the state of a newly started PyPy.