Improve gradient performance #53
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Should improve performance by factor of ~2-5 in most cases. For now, there are two working solutions: one vectorized and one where one time axis is looped over, similar to the calculation of the filter function.
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## master #53 +/- ##
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- Coverage 97.21% 97.04% -0.17%
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Files 9 9
Lines 2187 2203 +16
Branches 502 499 -3
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- Misses 25 29 +4
Partials 36 36
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Looped calculation is faster in almost all cases.
liouville_derivative and control_matrix_at_timestep_derivative do not really make sense on their own.
Used in both Basis and PulseSequence constructors
This was referenced Mar 2, 2021
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This is a refactor meant to improve the performance of the gradient calculation and align the API to the main modules.
Currently, there are two solutions implemented for testing:
gradient.calculate_derivative_of_control_matrix_from_scratch, which then usesgradient.control_matrix_at_timestep_derivative. Python loops are mostly avoided in favor of closedeinsumexpressions.gradient.calculate_derivative_of_control_matrix_from_scratch_looploops over one time dimension in a Python loop (similar to the calculation of the control matrix), usesgradient.control_matrix_at_timestep_derivative_loop.Benchmarks indicate that as soon as the dimension
d > 2, the loop version is faster because theeinsumexpressions become too large: