Open
Description
Thanks to ToennisStef for raising this in #2393.
🐛 Bug
I'm looking at an example with a SingleTaskMultiFidelityGP, evaluating acquisition values where both the x and the objective are at fidelities other than the highest fidelity. This produces NaN acquisition values and causes optimize_acqf to error out. While optimizing for a fidelity other than the highest may not make sense, this also happens when optimizing qMultiFidelityKnowledgeGradient for the highest fidelity. I'm seeing the following behavior:
- The posterior variance is sometimes initially computed as negative and then clipped to 1e-10. When computing it, I get
gpytorch/distributions/multivariate_normal.py:319: NumericalWarning: Negative variance values detected. This is likely due to numerical instabilities. Rounding negative variances up to 1e-10. - qLogEI returns a NaN at the same locations as the posterior variance can be negative.
- optimize_acqf with a
FixedFeatureAcquisitionFunction, fixing the fidelity to 0 and using qLogEI, errors out. - Following the setup of the multi-fidelity tutorial, optimizing
qMultiFidelityKnowledgeGradientfor the highest fidelity.
What the posterior looks like:
acqf values if we were to just work with fidelity=0:
To reproduce
See gist for full code. It ends with
candidates, _ = optimize_acqf_mixed(
acq_function=mfkg_acqf,
bounds=bounds_x,
fixed_features_list=[{1: 0}],
q=1,
num_restarts=5,
raw_samples=128,
# batch_initial_conditions=X_init,
options={"batch_limit": 5, "maxiter": 200},
)Alternatively, skipping the cost function setup, the same error can be produced more simply with
acq_func = FixedFeatureAcquisitionFunction(
acq_function=qLogExpectedImprovement(model=model, best_f=train_y[train_x[:,1]==3].max()),
d=1+1,
columns=[1],
values=[0],
)
candidates, _ = optimize_acqf(
acq_function=acq_func,
bounds=torch.tensor([[0.], [1.]], dtype=torch.float64),
q=1,
num_restarts=20,
raw_samples=512,
)** Stack trace/error message **
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[12], line 1
----> 1 candidates, _ = optimize_acqf_mixed(
2 acq_function=mfkg_acqf,
3 bounds=bounds_x,
4 # fixed_features_list=[{1: i} for i in range(3)],
5 fixed_features_list=[{1: 0}],
6 q=1,
7 num_restarts=5,
8 raw_samples=128,
9 # batch_initial_conditions=X_init,
10 options={"batch_limit": 5, "maxiter": 200},
11 )
File ~/botorch/botorch/optim/optimize.py:926, in optimize_acqf_mixed(acq_function, bounds, q, num_restarts, fixed_features_list, raw_samples, options, inequality_constraints, equality_constraints, nonlinear_inequality_constraints, post_processing_func, batch_initial_conditions, ic_generator, ic_gen_kwargs)
924 ff_candidate_list, ff_acq_value_list = [], []
925 for fixed_features in fixed_features_list:
--> 926 candidate, acq_value = optimize_acqf(
927 acq_function=acq_function,
928 bounds=bounds,
929 q=q,
930 num_restarts=num_restarts,
931 raw_samples=raw_samples,
932 options=options or {},
933 inequality_constraints=inequality_constraints,
934 equality_constraints=equality_constraints,
935 nonlinear_inequality_constraints=nonlinear_inequality_constraints,
936 fixed_features=fixed_features,
937 post_processing_func=post_processing_func,
938 batch_initial_conditions=batch_initial_conditions,
939 ic_generator=ic_generator,
940 return_best_only=True,
941 **ic_gen_kwargs,
942 )
943 ff_candidate_list.append(candidate)
944 ff_acq_value_list.append(acq_value)
File ~/botorch/botorch/optim/optimize.py:543, in optimize_acqf(acq_function, bounds, q, num_restarts, raw_samples, options, inequality_constraints, equality_constraints, nonlinear_inequality_constraints, fixed_features, post_processing_func, batch_initial_conditions, return_best_only, gen_candidates, sequential, ic_generator, timeout_sec, return_full_tree, retry_on_optimization_warning, **ic_gen_kwargs)
520 gen_candidates = gen_candidates_scipy
521 opt_acqf_inputs = OptimizeAcqfInputs(
522 acq_function=acq_function,
523 bounds=bounds,
(...)
541 ic_gen_kwargs=ic_gen_kwargs,
542 )
--> 543 return _optimize_acqf(opt_acqf_inputs)
File ~/botorch/botorch/optim/optimize.py:564, in _optimize_acqf(opt_inputs)
561 return _optimize_acqf_sequential_q(opt_inputs=opt_inputs)
563 # Batch optimization (including the case q=1)
--> 564 return _optimize_acqf_batch(opt_inputs=opt_inputs)
File ~/botorch/botorch/optim/optimize.py:255, in _optimize_acqf_batch(opt_inputs)
252 batch_initial_conditions = opt_inputs.batch_initial_conditions
253 else:
254 # pyre-ignore[28]: Unexpected keyword argument `acq_function` to anonymous call.
--> 255 batch_initial_conditions = opt_inputs.get_ic_generator()(
256 acq_function=opt_inputs.acq_function,
257 bounds=opt_inputs.bounds,
258 q=opt_inputs.q,
259 num_restarts=opt_inputs.num_restarts,
260 raw_samples=opt_inputs.raw_samples,
261 fixed_features=opt_inputs.fixed_features,
262 options=options,
263 inequality_constraints=opt_inputs.inequality_constraints,
264 equality_constraints=opt_inputs.equality_constraints,
265 **opt_inputs.ic_gen_kwargs,
266 )
268 batch_limit: int = options.get(
269 "batch_limit",
270 (
(...)
274 ),
275 )
277 def _optimize_batch_candidates() -> Tuple[Tensor, Tensor, List[Warning]]:
File ~/botorch/botorch/optim/initializers.py:515, in gen_one_shot_kg_initial_conditions(acq_function, bounds, q, num_restarts, raw_samples, fixed_features, options, inequality_constraints, equality_constraints)
512 q_aug = acq_function.get_augmented_q_batch_size(q=q)
514 # TODO: Avoid unnecessary computation by not generating all candidates
--> 515 ics = gen_batch_initial_conditions(
516 acq_function=acq_function,
517 bounds=bounds,
518 q=q_aug,
519 num_restarts=num_restarts,
520 raw_samples=raw_samples,
521 fixed_features=fixed_features,
522 options=options,
523 inequality_constraints=inequality_constraints,
524 equality_constraints=equality_constraints,
525 )
527 # compute maximizer of the value function
528 value_function = _get_value_function(
529 model=acq_function.model,
530 objective=acq_function.objective,
(...)
533 project=getattr(acq_function, "project", None),
534 )
File ~/botorch/botorch/optim/initializers.py:424, in gen_batch_initial_conditions(acq_function, bounds, q, num_restarts, raw_samples, fixed_features, options, inequality_constraints, equality_constraints, generator, fixed_X_fantasies)
422 start_idx += batch_limit
423 Y_rnd = torch.cat(Y_rnd_list)
--> 424 batch_initial_conditions = init_func(
425 X=X_rnd, Y=Y_rnd, n=num_restarts, **init_kwargs
426 ).to(device=device)
427 if not any(issubclass(w.category, BadInitialCandidatesWarning) for w in ws):
428 return batch_initial_conditions
File ~/botorch/botorch/optim/initializers.py:952, in initialize_q_batch(X, Y, n, eta)
950 weights = torch.exp(etaZ)
951 if batch_shape == torch.Size():
--> 952 idcs = torch.multinomial(weights, n)
953 else:
954 idcs = batched_multinomial(
955 weights=weights.permute(*range(1, len(batch_shape) + 1), 0), num_samples=n
956 ).permute(-1, *range(len(batch_shape)))
RuntimeError: probability tensor contains either `inf`, `nan` or element < 0
Expected Behavior
Numerical inaccuracy is not uncommon in optimization; however, this typically should not lead to exceptions, since multi-restart optimization may allow for finding an optimum nonetheless. In this case, it is clear there is an optimum, so optimize_acqf should find it.
System information
Please complete the following information:
- BoTorch Version--> 0.11.2.dev4+g80ac43eda.d20240614
- GPyTorch Version --> 1.11
- PyTorch Version --> 2.2.1
- OS X