[Python-Dev] Re: Annotating pure functions to improve inline caching/optimization

[Eric V. Smith via Python-Dev]

You should bring this up on discuss.python.org. It's not going to see 
much if any discussion here.


Eric

On 9/14/2022 10:05 AM, Philipp Burch wrote:

Hello everyone,

the docs on the upcoming 3.11 release state

> This [specializing adaptive interpreter] also brings in another 
concept called inline caching, where Python caches the results of 
expensive operations directly in the bytecode.


I wonder how this caching works, given that the dynamic nature means 
that virtually every operation could have side effects, causing wrong 
behaviour when cached. The only mitigation for this that I can imagine 
is that caching just occurs for basic operations defined in the 
standard library, where it is known that they are free of side effects 
or "pure".


A web search did reveal some discussions[1,2] and a module related to 
dealing with pure functions, but, as far as I see, not related to 
optimization.


As an example, consider a code like this:

```py
@pure
def rot_factor(angle_deg: float) -> complex:
    # This could also be a much more expensive calculation.
    return cmath.exp(angle_deg / 180 * cmath.pi * 1j)

# ...

res: List[Tuple(complex, complex, complex, float)] = []
for x in many:
    res.append((
    x * rot_factor(90),
    x * rot_factor(45),
    x * rot_factor(-45),
    x * math.sin(math.pi/8),
    ))
```

The problem with this code is obvious, every loop iteration calls 
`rot_factor()` with 90, 45 and -45 and will get exactly the same set 
of results. The last factor might already be inline cached by the 
interpreter, since it probably knows that `math.pi` is a constant and 
`math.sin()` is a pure function. Optimizing this by hand (not 
considering a list comprehension or other more sophisticated 
improvements) is easy, but not very pretty:


```py
f_p90 = rot_factor(90)
f_p45 = rot_factor(45)
f_m45 = rot_factor(-45)
f_sin = math.sin(math.pi / 8)
res: List[Tuple(complex, complex, complex, float)] = []
for x in many:
    res.append((
    x * f_p90,
    x * f_p45,
    x * f_m45,
    x * f_sin,
    ))
```

I actually find myself often factoring such data out of loops in 
Python, whereas in C I would just leave that to the optimizer/compiler.


An available option would be to use `@lru_cache` for `rot_factor()`, 
but this will still cause the same dictionary lookups in every 
iteration and it may not work at all in case the function argument(s) 
is/are not hashable.


Now, if the interpreter understood the `@pure` decorator for 
`rot_factor()` indicated above would give it the same opportunity to 
cache the three results throughout the loop, basically creating the 
manually-optimized code above. For these completely static values, it 
could even precompute the results and integrate them into the bytecode.



Has anything like this been considered already, or is the interpreter 
itself capable to perform such optimizations?



Thanks and best regards,
Philipp



[1] 'pure' type annotation for mypy: 
https://github.com/python/mypy/issues/4468


[2] pure-func module: https://pypi.org/project/pure-func/
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[Python-Dev] Re: Retiring this mailing list ?

[Eric V. Smith via Python-Dev]

On 11/14/2023 1:21 PM, Steve Holden wrote:

On Mon, 13 Nov 2023 at 10:18, Marc-Andre Lemburg  wrote:

[...]

Question: Should we retire and archive this mailing list ?
(I'm asking as one of the maintainers of the ML)

[...]

Hi Marc-Andre,

Maybe just require senders to be members of the python.org 
 domain, and retain the release announcements?


I think the python-announce list serves that purpose. Any time there's 
an announcement here, I also see a separate copy of it on 
python-announce (where I'm a moderator).


Eric
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