gh-126220: Adapt _lsprof to Argument Clinic

[sobolevn]

I decided to go with the AC, because:

1. It is supported in this module
2. It solves the problem
3. It fixes multiple signatures

But, I believe it will be slower. If we really want to preserve speed in this case, I can add manual size checks for args.

Refs #103534

• Issue: _lsprof.Profiler._creturn_callback() segfaults #126220

[ZeroIntensity]

LGTM. I'm always happy with fixes via AC, because they're much more scalable.

[gaogaotiantian]

What's the perf impact here?

[sobolevn]

@gaogaotiantian I've never had experience with profiling cProfile. Do you have any links / suggestions on how to do that?

[gaogaotiantian]

Just do a quick fib() with cprofile and compare it with the previous implementation would do. Maybe also with one without cprofile.

[sobolevn]

@gaogaotiantian maybe I did something wrong, but looks like we also got a speedup from my numbers 😅 (I have little idea about how cProfile works).

# Test code: ex.py
def test():
    def fib():
        start, nextn = 0, 1
        while True:
            yield start
            start, nextn = nextn, start + nextn

    gen = fib()
    for _ in range(100000):
        next(gen)

import cProfile
cProfile.run(test.__code__)

Running it on main:

» ./python.exe ex.py
         200003 function calls in 0.188 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.017    0.017    0.188    0.188 ex.py:1(test)
   100000    0.147    0.000    0.147    0.000 ex.py:2(fib)
        1    0.000    0.000    0.188    0.188 {built-in method builtins.exec}
   100000    0.024    0.000    0.171    0.000 {built-in method builtins.next}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

Running on this PR:

» ./python.exe ex.py                                                     
         200003 function calls in 0.185 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.016    0.016    0.185    0.185 ex.py:1(test)
   100000    0.145    0.000    0.145    0.000 ex.py:2(fib)
        1    0.000    0.000    0.185    0.185 {built-in method builtins.exec}
   100000    0.024    0.000    0.168    0.000 {built-in method builtins.next}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

Am I missing something?

[gaogaotiantian]

Sorry I meant a recursive fib().

import time
def fib(n):
    if n <= 1:
        return 1
    return fib(n - 1) + fib(n - 2)
start = time.time()
# enable profiler
fib(24)
# disable profiler
print(time.time() - start)

We need to consider the worst case for cprofile, which is when the code has a lot of function calls. We need to know the overhead of the profiler, and how that is compared with before.

You can also refer to #103533, there's a profiling code listed. That might give some extra information.

Moving to sys.monitoring gave us a 20%+ improvement on overhead, just want to check what's the regression with clinic and whether it's acceptable. Profiler is a bit sensitive to performance because the more overhead there is, the less useful it is.

[sobolevn]

It still gives me a perf boost for some reason 🤔

import time
import cProfile

profiler = cProfile.Profile()

def fib(n):
    if n <= 1:
        return 1
    return fib(n - 1) + fib(n - 2)
start = time.perf_counter()
profiler.enable()
fib(30)  # 100th
profiler.disable()
print(time.perf_counter() - start)
# Old: 0.9349823750017094
# New: 0.9253051670020795
# 1% faster 

[gaogaotiantian]

Is the boost stable(reproducible)?

[sobolevn]

Yes, it is pretty much the same on m2 macos:

(.venv) ~/Desktop/cpython2  main ✗                                                        
» ./python.exe ex.py                                                     
0.9349823750017094
                                                                                           
(.venv) ~/Desktop/cpython2  main ✗                                                        
» ./python.exe ex.py
0.9357958749969839
                                                                                           
(.venv) ~/Desktop/cpython2  main ✗                                                        
» ./python.exe ex.py
0.9352227920026053
                                                                                           
(.venv) ~/Desktop/cpython2  main ✗                                                        
» ./python.exe ex.py
0.924946416002058
                                                                                           
(.venv) ~/Desktop/cpython2  main ✗                                                        
» ./python.exe ex.py
0.926337541997782
                                                                                           
(.venv) ~/Desktop/cpython2  main ✗                                                        
» ./python.exe ex.py
0.9347994170000311

(.venv) ~/Desktop/cpython2  issue-126220 ✗  
» ./python.exe ex.py
0.9370929580036318
                                                                                           
(.venv) ~/Desktop/cpython2  issue-126220 ✗                                                
» ./python.exe ex.py
0.9262957079990883
                                                                                           
(.venv) ~/Desktop/cpython2  issue-126220 ✗                                                
» ./python.exe ex.py
0.9328266249940498
                                                                                           
(.venv) ~/Desktop/cpython2  issue-126220 ✗                                                
» ./python.exe ex.py
0.9382220000011148
                                                                                           
(.venv) ~/Desktop/cpython2  issue-126220 ✗                                                
» ./python.exe ex.py
0.9285237919984502
                                                                                           
(.venv) ~/Desktop/cpython2  issue-126220 ✗                                                
» ./python.exe ex.py
0.9296539580027456

[gaogaotiantian]

Yeah from the result I don't think there's an observable boost. On the other hand, that's good, because no observable regression either.

[gaogaotiantian]

Just a side note - this needs to be backported to 3.12 as well :) I added the tags so you should be able to just merge it.

[sobolevn]

@gaogaotiantian sorry, I forgot to highlight it initially. Please, double check params names that I introduced. Are they correct?

[gaogaotiantian]

Actually, I have some doubts about the changes for functions that are not crashing. This is a bug fix, which would be backported. I don't think we should mix in the code polish to the PR. Even though I think changing this in main would be fine, I would prefer having only the crash fix (could be with AC) in one PR, and the rest in the other. One thing that bothers me immediately is that I can't quickly convince myself the changes to __init__ is purely equivalent.

As for the argument name, the obj in the callbacks should be either unused or instruction_offset(which is what it is).

[gaogaotiantian]

Can we split this PR into 2? One with the changes to only the 4 callbacks, and the other with all the other methods?

[sobolevn]

Fair enough, I would split this PR later! 👍
Thank you!

[gaogaotiantian]

LGTM!

[sobolevn]

@erlend-aasland maybe you would be interested in double checking the AC part? :)

[erlend-aasland]

Please amend the title to reflect the actual change :)

[sobolevn]

Done!

[miss-islington-app]

Thanks @sobolevn for the PR 🌮🎉.. I'm working now to backport this PR to: 3.12, 3.13.
🐍🍒⛏🤖

[sobolevn]

Thanks everyone!

[miss-islington-app]

Sorry, @sobolevn, I could not cleanly backport this to 3.12 due to a conflict.
Please backport using cherry_picker on command line.

cherry_picker c806cd5af677c385470001efc68da38a32919196 3.12

[bedevere-app]

GH-126402 is a backport of this pull request to the 3.13 branch.

[sobolevn]

Oups, backports are not needed anymore. Closing them.