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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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/
_______________________________________________
Python-Dev mailing list -- python-dev@python.org
To unsubscribe send an email to python-dev-leave@python.org
https://mail.python.org/mailman3/lists/python-dev.python.org/
Message archived at https://mail.python.org/archives/list/python-dev@python.org/message/XDYZRC6L7LBPE3T6RO6S5IVY3J6IMRSJ/
Code of Conduct: http://python.org/psf/codeofconduct/