The Kitchin Research Group

First, we look at an uncached version of a function that uses a global variable.

a = 3

def f3(x=1.0):
    return a*x**2

f3(2)

12

We can change a and see the change.

a=0
f3(2)

0

Now we look at a cached version.

a = 3
@memory.cache
def f4(x=1.0):
    print('If you see this, go get a coffee while it runs 🐌.')
    return a*x**2

(f4(2), f4(2), f4(2))

If you see this, go get a coffee while it runs 🐌.

Changing the global variable does not change the cache though. uh oh. This is just wrong. The answers should clearly be 0. Incorrect cache invalidation strikes.

a = 0
(f4(2), f4(2), f4(2))

Using mutable arguments is a recipe for trouble and unanticipated problems, but it is easy to unintentionally do, and not always obvious, as I show here.

from ase.build import bulk
from ase.calculators.emt import EMT

atoms = bulk('Pd')
atoms.set_calculator(EMT())

@memory.cache
def f(atoms):
    print('If you see this, go get a coffee.')
    return atoms.get_potential_energy()

(f(atoms), f(atoms))

If you see this, go get a coffee. If you see this, go get a coffee.

You can see this ran twice. The reason is that the atoms object was mutated by adding data onto it. Here is how I know:

import joblib
atoms = bulk('Pd')
atoms.set_calculator(EMT())
joblib.hash(atoms), atoms._calc.results

Here you can see that simply running the get energy function the hash changes because the results dictionary on the calculator changes. That means subsequent uses of the atoms object will have a different hash, and you cannot rely on that to look up the results. In this case the results should not change the output of the function, but since they are included in the hash, it incorrectly invalidates the hash.

atoms.get_potential_energy()
joblib.hash(atoms), atoms._calc.results

Suffice to say, this is non-obvious, but having seen it, not a surprise; mutable arguments are frequently a source of problems.

Some aspects of this are specific to org-mode and how scripts are run in it. Here we have to use an absolute path to make sure we use the right cache. That still doesn't solve the problem though as we will see.

from joblib import Memory
location = '/Users/jkitchin/Dropbox/emacs/journal/2023/02/01/joblib_cache/joblib_cache'
memory = Memory(location, verbose=0)

a = 3
@memory.cache
def f4(x=1.0):
    print('If you see this, go get a coffee while it runs')
    return a*x**2

print((f4(2), f4(2), f4(2)))

The issue is that joblib uses the file name it thinks the function is from in the path it saves the results. The filename is different

joblib uses the function source in its hash. That means any change to the source, including the function name, renaming variables, whitespace, comments or docstring changes invalidates the hash even though they may have no change in the output. That is an overabundance of caution, but simple to implement.

@memory.cache
def f4(x=1.0):
    'add a ds.'
    # comment
    print('If you see this, go get a coffee while it runs')
    return a*x**2

print((f4(2), f4(2), f4(2)))

If you see this, go get a coffee while it runs (0, 0, 0)

a = 3
@hashcache
def h4(x=1.0):
    print('If you see this, go get a coffee while it runs')
    return a*x**2

(h4(2), h4(2), h4(2))

If you see this, go get a coffee while it runs

a=0
(h4(2), h4(2), h4(2))

Whew!!! we got the right answers. hashcache does a better job detecting the external change.

hashcache does not solve the mutable argument problem, but, it does warn you it detected it.

from ase.build import bulk
from ase.calculators.emt import EMT

atoms = bulk('Pd')
atoms.set_calculator(EMT())

@hashcache
def h(atoms):
    print('If you see this, go get a coffee.')
    return atoms.get_potential_energy()

(h(atoms), h(atoms), h(atoms))

hashcache uses the same cache at the function and function environment level, so it avoids reruns even from different places. It is a judgement call by you to say if this is the right thing to do.

print(h1(2), h1(2))

4 4

from pycse.hashcache import hashcache
hashcache.location = "/Users/jkitchin/Dropbox/emacs/journal/2023/02/01/cache"

@hashcache
def h1(x):
    print('This runs soo slow... Go get a coffee')
    return x**2

print(h1(2), h1(2))

Instead of hashing the source of the function, in hashcache I hash the bytecode instead. This is certainly less sensitive to unimportant changes like docstrings, comments or whitespace. I do use the function name in the hash, so even though that does not affect the output, I thought it might be confusing in the future.

Here, small changes like comments, docstrings, etc, don't affect the hash.

a = 3
@hashcache
def h4(x=1.0):
    'doc string'
    # comments
    print('If you see this, go get a coffee while it runs')
    return a*x**2    

(h4(2), h4(2), h4(2))