jax.numpy.einsum_path#
- jax.numpy.einsum_path(subscripts: str, /, *operands: Array | ndarray | bool_ | number | bool | int | float | complex, optimize: bool | str | list[tuple[int, ...]] = 'auto') tuple[list[tuple[int, ...]], Any] [source]#
- jax.numpy.einsum_path(arr: Array | ndarray | bool_ | number | bool | int | float | complex, axes: Sequence[Any], /, *operands: ArrayLike | Sequence[Any], optimize: bool | str | list[tuple[int, ...]] = 'auto') tuple[list[tuple[int, ...]], Any]
Evaluates the optimal contraction path without evaluating the einsum.
JAX implementation of
jax.numpy.einsum_path()
. This function calls into the opt_einsum package, and makes use of its optimization routines.- Parameters:
subscripts – string containing axes names separated by commas.
*operands – sequence of one or more arrays corresponding to the subscripts.
optimize – specify how to optimize the order of computation. In JAX this defaults to
"auto"
. Other options areTrue
(same as"optimize"
),False
(unoptimized), or any string supported byopt_einsum
, which includes"optimize"
,,"greedy"
,"eager"
, and others.
- Returns:
A tuple containing the path that may be passed to
einsum()
, and a printable object representing this optimal path.
Example
>>> key1, key2, key3 = jax.random.split(jax.random.key(0), 3) >>> x = jax.random.randint(key1, minval=-5, maxval=5, shape=(2, 3)) >>> y = jax.random.randint(key2, minval=-5, maxval=5, shape=(3, 100)) >>> z = jax.random.randint(key3, minval=-5, maxval=5, shape=(100, 5)) >>> path, path_info = jnp.einsum_path("ij,jk,kl", x, y, z, optimize="optimal") >>> print(path) [(1, 2), (0, 1)] >>> print(path_info) Complete contraction: ij,jk,kl->il Naive scaling: 4 Optimized scaling: 3 Naive FLOP count: 9.000e+3 Optimized FLOP count: 3.060e+3 Theoretical speedup: 2.941e+0 Largest intermediate: 1.500e+1 elements -------------------------------------------------------------------------------- scaling BLAS current remaining -------------------------------------------------------------------------------- 3 GEMM kl,jk->lj ij,lj->il 3 GEMM lj,ij->il il->il
Use the computed path in
einsum()
:>>> jnp.einsum("ij,jk,kl", x, y, z, optimize=path) Array([[-539, 216, 95, 592, 209], [ 527, 76, 285, -436, -529]], dtype=int32)