Closures bind late

This post is originally shared on my personal blog. If you have trouble reading the code examples, please make sure to check out my blog. I have syntax highlighting there.

“Closure is a turtle carrying its shell,”

My favorite explaination to closures, quote by Raymond Hettinger. With closures, lots of great features are possible in Python like higher order functions and decorators.

I came across this StackOverflow post — How can I return a function that uses the value of a variable?, which helped me bridging the gap in my knowledge, the gap that I sort-of understood it but couldn’t explain it very well. The bridge is just a simple sentence, “closures bind late.” What does “closures bind late” mean exactly? Consider a function generator that generates a series of multiplication functions starting from 0 to max:

def mult_function_generator(max):
    for i in range(max):
        yield lambda x: x * i
mult_functions = list(mult_function_generator(3))
print([func(3) for func in mult_functions])
# Oh crap, it’s [6, 6, 6], not [0, 3, 6]

Seems like all the i‘s in the generated functions are assigned to 2, which is the last value of i after all functions were being generated. Let’s take a closer look at what’s behind the scene. First of all, how does a closure represent in Python, or, how the turtle shell looks like:

mult_functions = mult_function_generator(3)
mult_zero = next(mult_functions)
mult_zero
# <function …>

To look at the closure, there’s a special attribute in each function called __closure__ (quite obvious, huh?)

mult_zero.__closure__
# (<cell at 0x…, int object at 0x…>,)

Okay, we are getting closer. The __closure__ attribute is a tuple of cell objects. Let’s see what’s inside the cell, which has an attribute cell_contents to help us:

cell_zero = mult_zero.__closure__[0]
cell_zero.cell_contents
# 0

Bingo! This is the int object i that trapped inside the function’s closure. (I haven’t figured out how Python find the reference of i so I won’t go any further. For more discussion please refer to the Appendix section.)

The interesting thing is, the same cell object is used across our multiplication functions. Thus, when we get the second function:

mult_one = next(mult_functions)
cell_one = mult_one.__closure__[0]
cell_one.cell_contents
# 1
# So far so good. But…
cell_zero.cell_contents
# 1
# Crap!
cell_zero is cell_one
# True
# Okay fine

The same cell object means the same reference to i. This explains what happened in this example. Or, if you like, you could explain all this by saying, “closures bind late.” They are both correct but just with different mental models.

Now we understand the problem that introduced by late binding. Let’s see how to bind the variable during function declaration. The idea is to have i also be a argument passed into the lambda function and then we assign the value immediately:

def mult_function_generator(max):
    for i in range(max):
        yield (lambda i: lambda x: x * i)(i)

Or if you like partial functions:

import functools
def mult_function_generator(max):
    for i in range(max):
        yield functools.partial(lambda x, i: x * i, i=i)

Or my favorite one:

def mult_function_generator(max):
    for i in range(max):
        yield lambda x, i=i: x * i

The last one works because default arguments is assigned when the function is defined.

Bonus

My favorite example for taking advantage of the late binding closure is from 20 Python libraries you aren’t using (but should):

from time import perf_counter
from array import array
from contextlib import contextmanager

@contextmanager
def timing(label: str):
    t0 = perf_counter()
    yield lambda: (label, t1 — t0)
    t1 = perf_counter()

with timing(‘Array tests’) as total:
    with timing(‘Array creation innermul’) as inner:
        x = array(‘d’, [0] * 1000000)
    with timing(‘Array creation outermul’) as outer:
        x = array(‘d’, [0]) * 1000000

print(‘Total [%s]: %.6f s’ % total())
print(‘  Timing [%s]: %.6f s’ % inner())
print(‘  Timing [%s]: %.6f s’ % outer())
# Total [Array tests]: 0.064896 s
#   Timing [Array creation innermul]: 0.064195 s
#   Timing [Array creation outermul]: 0.000659 s

Appendix — discussion on loading a dereferenced variable

Take the mult_zero function for example. The disambled code looks:

3 0 LOAD_FAST 0 (x)
  2 LOAD_DEREF 0 (i)
  4 BINARY_MULTIPLY
  6 RETURN_VALUE

The second line calls the LOAD_DEREF op code in Python. My guess is that the name i was never dereferenced because the cell object still holds a reference on it. However, i must be treated specially since i isn’t visible to the scope outside the closure functions. My next move will be looking into the CPython code to see how this op code works exactly.