_bind() materializes a Python list[float] on every evaluation, including inside the functionals

Aperta
  • Qualcosa non va?
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Valutazione

Difficoltà
3/5
Tempo stimato
1-2 giorni
Idoneità per principianti
57/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Tranquilla
Stack tecnologico
cpp, numpy, python
Ambito
backend, performance

Direzione di ricerca

Read src/monoprop/monomial_propagator.py:602-612 and trace how _bind feeds the functional closures; then check the evaluation signatures in src/monoprop/bindings/binder.h to confirm the proposed sequence remains compatible. Run just bench serial and compare benches/results/REPORT.md, especially functional-driven benchmarks, then run just test to verify lists, tuples, and Circuit inputs still work.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

performance

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What

MonomialPropagator._bind builds a new Python list[float] on every call.

src/monoprop/monomial_propagator.py:602-612

def _bind(self, parameters: ParameterValues) -> list[float]:
    if isinstance(parameters, Circuit):
        parameters = parameters.parameters
    if parameters is None:
        return []
    return [float(v) for v in parameters]

Why this is a problem

_bind is on every evaluation entry point — expectation_value, expectation_value_and_gradient,
gradient, contract_partially, evolved_operator, and, most importantly, inside the closures
returned by expectation_value_functional and expectation_value_and_gradient_functional:

fn = self._simulator.expectation_value_functional(pare_threshold)
return lambda parameters=None: fn(self._bind(parameters))

Those functionals exist precisely to be called in an optimizer loop. So each iteration allocates N
PyFloat objects plus a list, then throws them away — for a parameter vector that the caller almost
always already holds as a contiguous float64 ndarray.

Suggested fix

Pass a contiguous float64 ndarray straight through when the caller supplied one, and widen the
return annotation from list[float] to Sequence[float] | np.ndarray:

if isinstance(parameters, np.ndarray):
    return np.ascontiguousarray(parameters, dtype=np.float64)
return [float(v) for v in parameters]

nanobind's stl/vector.h caster accepts any Python sequence, so the C++ signature
(const VecD &) is unchanged. To be clear about the size of the win: this removes the intermediate
Python objects, not the element-by-element conversion on the C++ side — list_caster still
iterates.

If the measured gain is small and the path matters, the larger version is to add an
nb::ndarray<const double, nb::ndim<1>, nb::c_contig> overload for the evaluation entry points in
src/monoprop/bindings/binder.h, which removes the per-element conversion too. That is a bigger
change to the binding surface and probably wants its own issue.

Verification

  • just bench serial before/after, comparing benches/results/REPORT.md; the functional-driven
    benchmarks are the ones to watch.
  • just test — in particular the paths that pass lists, tuples and Circuit objects rather than
    arrays, which must keep working unchanged.

Found by a code-reading review of the repository at 29a8050. No build tree was available, so the
analysis is from source inspection and should be confirmed by measurement.

Lingua principale
C++
Stelle
52
Fork
2
Merge medio
2g 2h
PR unite (30g)
39

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