Thanks for the review! I'll continue working on it tomorrow (Monday) 🙂

I just merged#135761 in. Sorry but you'll have to merge the changes in, as I did not ultimately add_SWAP_FAST.

I will do some microbenchmarking to see if this does anything. Give me a sec.

On this script:

import pyperfdef func():    x = 1    for _ in range(120000000):        type(x)if __name__ == "__main__":    runner = pyperf.Runner()    runner.bench_func('micro', func)Mean +- std dev: [no-opt] 918 ms +- 17 ms -> [opt] 897 ms +- 21 ms: 1.02x faster

So it's slightly faster. However, note that this optimization won't fully pay off till we get#135465 merged. So let's wait for now till then. Thanks!

Basically right now there's the extra cost of reading and writing to the stack due to the extra _POP_TOP. We're eliminating 1 reference count check in return. This seems to be barely worth it, but I crunched the numbers before and once the stack becomes registers, it becomes very worth it.

If you're interested, I benchmarked my own branch with this (higher is better):

CPython base JIT:Number of long_calcul per second: 10.23 ± 0.13CPython TOS caching:Number of long_calcul per second: 9.60 ± 0.37CPython decref binary op int elimination:Number of long_calcul per second: 10.14 ± 0.07CPython TOS + decref binary op int elimination:Number of long_calcul per second: 11.45 ± 0.10

So we should expect to see an around ~10% speedup once TOS caching is merged, and we start doing this!

@tomasr8 congrats!!! I decreased the loop count, rebased your PR on top of Mark's TOS caching PR, and did a micro bench with the script above.

TOS Caching vs TOS Caching + This PRMean +- std dev: [no-opt] 210 ms +- 3 ms -> [opt] 195 ms +- 1 ms: 1.08x faster

So 8% improvement if we have TOS caching enabled ontype operations that load a local variable!

Awesome stuff! And a pretty nice speedup for a relatively simple optimization too!
I can try and have a look at Mark's PR so that we can get it merged sooner :)