# Sharing Intermediates

If you want to compute multiple similar contractions with common terms, you can embed them in a opt_einsum.shared_intermediates context. Computations of subexpressions in this context will be memoized, and will be garbage collected when the contexts exits.

For example, suppose we want to compute marginals at each point in a factor chain:

inputs = 'ab,bc,cd,de,ef'
factors = [np.random.rand(1000, 1000) for _ in range(5)]

%%timeit
marginals = {output: contract('{}->{}'.format(inputs, output), *factors)
for output in 'abcdef'}
#> 1 loop, best of 3: 5.82 s per loop


To share this computation, we can perform all contractions in a shared context:

%%timeit
with shared_intermediates():
marginals = {output: contract('{}->{}'.format(inputs, output), *factors)
for output in 'abcdef'}
#> 1 loop, best of 3: 1.55 s per loop


If it is difficult to fit your code into a context, you can instead save the sharing cache for later reuse.

with shared_intermediates() as cache:  # create a cache
pass
marginals = {}
for output in 'abcdef':
with shared_intermediates(cache):  # reuse a common cache
marginals[output] = contract('{}->{}'.format(inputs, output), *factors)
del cache  # garbage collect intermediates


Note that sharing contexts can be nested, so it is safe to to use opt_einsum.shared_intermediates in library code without leaking intermediates into user caches.

Note

By default a cache is thread safe, to share intermediates between threads explicitly pass the same cache to each thread.