numpy.einsum_path — NumPy v2.2 Manual (original) (raw)

numpy.einsum_path(subscripts, *operands, optimize='greedy')[source]#

Evaluates the lowest cost contraction order for an einsum expression by considering the creation of intermediate arrays.

Parameters:

subscriptsstr

Specifies the subscripts for summation.

*operandslist of array_like

These are the arrays for the operation.

optimize{bool, list, tuple, ‘greedy’, ‘optimal’}

Choose the type of path. If a tuple is provided, the second argument is assumed to be the maximum intermediate size created. If only a single argument is provided the largest input or output array size is used as a maximum intermediate size.

• if a list is given that starts with einsum_path, uses this as the contraction path
• if False no optimization is taken
• if True defaults to the ‘greedy’ algorithm
• ‘optimal’ An algorithm that combinatorially explores all possible ways of contracting the listed tensors and chooses the least costly path. Scales exponentially with the number of terms in the contraction.
• ‘greedy’ An algorithm that chooses the best pair contraction at each step. Effectively, this algorithm searches the largest inner, Hadamard, and then outer products at each step. Scales cubically with the number of terms in the contraction. Equivalent to the ‘optimal’ path for most contractions.

Default is ‘greedy’.

Returns:

pathlist of tuples

A list representation of the einsum path.

string_reprstr

A printable representation of the einsum path.

Notes

The resulting path indicates which terms of the input contraction should be contracted first, the result of this contraction is then appended to the end of the contraction list. This list can then be iterated over until all intermediate contractions are complete.

Examples

We can begin with a chain dot example. In this case, it is optimal to contract the b and c tensors first as represented by the first element of the path (1, 2). The resulting tensor is added to the end of the contraction and the remaining contraction (0, 1) is then completed.

np.random.seed(123) a = np.random.rand(2, 2) b = np.random.rand(2, 5) c = np.random.rand(5, 2) path_info = np.einsum_path('ij,jk,kl->il', a, b, c, optimize='greedy') print(path_info[0]) ['einsum_path', (1, 2), (0, 1)] print(path_info[1]) Complete contraction: ij,jk,kl->il # may vary Naive scaling: 4 Optimized scaling: 3 Naive FLOP count: 1.600e+02 Optimized FLOP count: 5.600e+01 Theoretical speedup: 2.857 Largest intermediate: 4.000e+00 elements

scaling current remaining

3 kl,jk->jl ij,jl->il 3 jl,ij->il il->il

A more complex index transformation example.

I = np.random.rand(10, 10, 10, 10) C = np.random.rand(10, 10) path_info = np.einsum_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C, ... optimize='greedy')

print(path_info[0]) ['einsum_path', (0, 2), (0, 3), (0, 2), (0, 1)] print(path_info[1]) Complete contraction: ea,fb,abcd,gc,hd->efgh # may vary Naive scaling: 8 Optimized scaling: 5 Naive FLOP count: 8.000e+08 Optimized FLOP count: 8.000e+05 Theoretical speedup: 1000.000 Largest intermediate: 1.000e+04 elements

scaling current remaining

5 abcd,ea->bcde fb,gc,hd,bcde->efgh 5 bcde,fb->cdef gc,hd,cdef->efgh 5 cdef,gc->defg hd,defg->efgh 5 defg,hd->efgh efgh->efgh