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Handle NotPSDError and hitting maxiter in fit_gpytorch_model #1007

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Handle NotPSDError and hitting maxiter in fit_gpytorch_model #1007

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9efc5a9
handle notPSD and maxiter in fit_gpytorch
saitcakmak Dec 1, 2021
b6ef79d
lint flake8
saitcakmak Dec 1, 2021
4c47c62
address comments
saitcakmak Dec 2, 2021
9fd8ed4
Fix coverage
saitcakmak Dec 2, 2021
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20 changes: 17 additions & 3 deletions botorch/fit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
from botorch.optim.utils import sample_all_priors
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
from gpytorch.utils.errors import NotPSDError


FAILED_CONVERSION_MSG = (
Expand Down Expand Up @@ -80,7 +81,6 @@ def fit_gpytorch_model(
tf = mll.model.outcome_transform
mll.model.outcome_transform = None
model_list = batched_to_model_list(mll.model)
model_ = model_list_to_batched(model_list)
mll_ = SumMarginalLogLikelihood(model_list.likelihood, model_list)
fit_gpytorch_model(
mll=mll_,
Expand Down Expand Up @@ -121,12 +121,26 @@ def fit_gpytorch_model(
if retry > 0: # use normal initial conditions on first try
mll.model.load_state_dict(original_state_dict)
sample_all_priors(mll.model)
mll, _ = optimizer(mll, track_iterations=False, **kwargs)
try:
mll, _ = optimizer(mll, track_iterations=False, **kwargs)
except NotPSDError:
retry += 1
logging.log(
logging.DEBUG,
f"Fitting failed on try {retry} due to a NotPSDError.",
)
continue
has_optwarning = False
for w in ws:
# Do not count reaching `maxiter` as an optimization failure.
if "ITERATIONS REACHED LIMIT" in str(w.message):
logging.log(
logging.DEBUG,
"Fitting ended early due to reaching the iteration limit.",
)
continue
has_optwarning |= issubclass(w.category, OptimizationWarning)
warnings.warn(w.message, w.category)
# TODO: this counts hitting `maxiter` as an optimization failure!
if not has_optwarning:
mll.eval()
return mll
Expand Down
111 changes: 58 additions & 53 deletions test/test_fit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,9 +97,7 @@ def test_fit_gpytorch_model(self, optimizer=fit_gpytorch_scipy):
self.assertTrue(
any(issubclass(w.category, OptimizationWarning)) for w in ws
)
self.assertEqual(
sum(1 for w in ws if MAX_RETRY_MSG in str(w.message)), 1
)
self.assertFalse(any(MAX_RETRY_MSG in str(w.message) for w in ws))
model = mll.model
# Make sure all of the parameters changed
self.assertGreater(model.likelihood.raw_noise.abs().item(), 1e-3)
Expand All @@ -111,21 +109,13 @@ def test_fit_gpytorch_model(self, optimizer=fit_gpytorch_scipy):

# test overriding the default bounds with user supplied bounds
mll = self._getModel(double=double)
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
mll = fit_gpytorch_model(
mll,
optimizer=optimizer,
options=options,
max_retries=1,
bounds={"likelihood.noise_covar.raw_noise": (1e-1, None)},
)
if optimizer == fit_gpytorch_scipy:
self.assertTrue(
any(issubclass(w.category, OptimizationWarning)) for w in ws
)
self.assertEqual(
sum(1 for w in ws if MAX_RETRY_MSG in str(w.message)), 1
)
mll = fit_gpytorch_model(
mll,
optimizer=optimizer,
options=options,
max_retries=1,
bounds={"likelihood.noise_covar.raw_noise": (1e-1, None)},
)

model = mll.model
self.assertGreaterEqual(model.likelihood.raw_noise.abs().item(), 1e-1)
Expand Down Expand Up @@ -175,14 +165,9 @@ def test_fit_gpytorch_model(self, optimizer=fit_gpytorch_scipy):
)
),
)
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
mll = fit_gpytorch_model(
mll, optimizer=optimizer, options=options, max_retries=1
)
if optimizer == fit_gpytorch_scipy:
self.assertEqual(
sum(1 for w in ws if MAX_RETRY_MSG in str(w.message)), 1
)
mll = fit_gpytorch_model(
mll, optimizer=optimizer, options=options, max_retries=1
)
self.assertTrue(mll.dummy_param.grad is None)

# test excluding a parameter
Expand All @@ -193,17 +178,9 @@ def test_fit_gpytorch_model(self, optimizer=fit_gpytorch_scipy):
"model.mean_module.constant",
"likelihood.noise_covar.raw_noise",
]
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
mll = fit_gpytorch_model(
mll, optimizer=optimizer, options=options, max_retries=1
)
if optimizer == fit_gpytorch_scipy:
self.assertTrue(
any(issubclass(w.category, OptimizationWarning)) for w in ws
)
self.assertEqual(
sum(1 for w in ws if MAX_RETRY_MSG in str(w.message)), 1
)
mll = fit_gpytorch_model(
mll, optimizer=optimizer, options=options, max_retries=1
)
model = mll.model
# Make excluded params did not change
self.assertEqual(
Expand All @@ -221,21 +198,13 @@ def test_fit_gpytorch_model(self, optimizer=fit_gpytorch_scipy):
# test non-default setting for approximate MLL computation
is_scipy = optimizer == fit_gpytorch_scipy
mll = self._getModel(double=double)
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
mll = fit_gpytorch_model(
mll,
optimizer=optimizer,
options=options,
max_retries=1,
approx_mll=is_scipy,
)
if is_scipy:
self.assertTrue(
any(issubclass(w.category, OptimizationWarning)) for w in ws
)
self.assertEqual(
sum(1 for w in ws if MAX_RETRY_MSG in str(w.message)), 1
)
mll = fit_gpytorch_model(
mll,
optimizer=optimizer,
options=options,
max_retries=1,
approx_mll=is_scipy,
)
model = mll.model
# Make sure all of the parameters changed
self.assertGreater(model.likelihood.raw_noise.abs().item(), 1e-3)
Expand Down Expand Up @@ -268,8 +237,9 @@ def test_fit_gpytorch_model_singular(self):
)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.to(device=self.device, dtype=dtype)
with self.assertRaises(NotPSDError):
with self.assertLogs(level="DEBUG") as logs:
fit_gpytorch_model(mll, options=options, max_retries=2)
self.assertTrue(any("NotPSDError" in log for log in logs.output))
# ensure we can handle NaNErrors in the optimizer
with mock.patch.object(SingleTaskGP, "__call__", side_effect=NanError):
gp = SingleTaskGP(X_train, Y_train, likelihood=test_likelihood)
Expand All @@ -278,6 +248,32 @@ def test_fit_gpytorch_model_singular(self):
fit_gpytorch_model(
mll, options={"disp": False, "maxiter": 1}, max_retries=1
)
# ensure we catch NotPSDErrors
with mock.patch.object(SingleTaskGP, "__call__", side_effect=NotPSDError):
mll = self._getModel()
with self.assertLogs(level="DEBUG") as logs:
fit_gpytorch_model(mll, max_retries=2)
for retry in [1, 2]:
self.assertTrue(
any(
f"Fitting failed on try {retry} due to a NotPSDError."
in log
for log in logs.output
)
)

# Failure due to optimization warning

def optimize_w_warning(mll, **kwargs):
warnings.warn("Dummy warning.", OptimizationWarning)
return mll, None

mll = self._getModel()
with self.assertLogs(level="DEBUG") as logs, settings.debug(True):
fit_gpytorch_model(mll, optimizer=optimize_w_warning, max_retries=2)
self.assertTrue(
any("Fitting failed on try 1." in log for log in logs.output)
)

def test_fit_gpytorch_model_torch(self):
self.test_fit_gpytorch_model(optimizer=fit_gpytorch_torch)
Expand Down Expand Up @@ -317,3 +313,12 @@ def test_fit_gpytorch_model_sequential(self):
for w in ws
)
)

def test_fit_w_maxiter(self):
options = {"maxiter": 1}
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
mll = self._getModel()
fit_gpytorch_model(mll, options=options, max_retries=3)
mll = self._getBatchedModel()
fit_gpytorch_model(mll, options=options, max_retries=3)
self.assertFalse(any("ITERATIONS REACHED LIMIT" in str(w.message) for w in ws))