autoray.experimental.complexity_tracing#
Functionality for tracing through an autoray.lazy computation and estimating the cost and scaling.
In the following there are cost_*
functions that estimate the total cost
of a given operation, including sub-leading factors. There are also
cost_scaling_* functions that only consider the leading factor of the cost,
so that we can prime number decompose it and extract the scaling.
Attributes#
Functions#
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Here we only care about the leading factor of the cost, which we need to |
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Module Contents#
- autoray.experimental.complexity_tracing.cost_tensordot(x)#
- autoray.experimental.complexity_tracing.cost_scaling_tensordot#
- autoray.experimental.complexity_tracing.cost_qr(x)#
- autoray.experimental.complexity_tracing.cost_svd(x)#
- autoray.experimental.complexity_tracing.cost_eigh(x)#
- autoray.experimental.complexity_tracing.cost_scaling_linalg(x)#
Here we only care about the leading factor of the cost, which we need to preserve so that we can prime number decompose it.
- autoray.experimental.complexity_tracing.cost_matmul(x)#
- autoray.experimental.complexity_tracing.cost_scaling_matmul#
- autoray.experimental.complexity_tracing.cost_einsum(x)#
- autoray.experimental.complexity_tracing.cost_scaling_einsum#
- autoray.experimental.complexity_tracing.cost_linear(x)#
- autoray.experimental.complexity_tracing.cost_nothing(x)#
- autoray.experimental.complexity_tracing.COSTS#
- autoray.experimental.complexity_tracing.cost_node(x, allow_missed=True)#
- autoray.experimental.complexity_tracing.compute_cost(z, print_missed=True)#
- autoray.experimental.complexity_tracing.COST_SCALINGS#
- autoray.experimental.complexity_tracing.prime_factors(n) list[int] #
- autoray.experimental.complexity_tracing.is_prime(n: int) bool #
- autoray.experimental.complexity_tracing.closest_prime(nt: int) int #
- autoray.experimental.complexity_tracing.frequencies(it)#
- autoray.experimental.complexity_tracing.compute_cost_scalings(z, factor_map, print_missed=True)#