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Enable gen fallback model spec for GenerationNode (#3209)
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Summary:

Allowing for gen fallback (default to sobol) upon running into specified error in GenerationNode.gen()

Reviewed By: saitcakmak

Differential Revision: D67232696
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@ItsMrLin @facebook-github-bot
ItsMrLin authored and facebook-github-bot committed Feb 12, 2025
1 parent da08472 commit e433d5a
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Showing 4 changed files with 160 additions and 99 deletions.
130 changes: 89 additions & 41 deletions ax/generation_strategy/generation_node.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,6 @@
from ax.exceptions.generation_strategy import GenerationStrategyRepeatedPoints
from ax.generation_strategy.best_model_selector import BestModelSelector


if TYPE_CHECKING:
from ax.generation_strategy.generation_node_input_constructors import (
InputConstructorPurpose,
Expand All @@ -43,7 +42,11 @@
TrialBasedCriterion,
)
from ax.modelbridge.base import Adapter
from ax.modelbridge.registry import _extract_model_state_after_gen, ModelRegistryBase
from ax.modelbridge.registry import (
_extract_model_state_after_gen,
Generators,
ModelRegistryBase,
)
from ax.utils.common.base import SortableBase
from ax.utils.common.constants import Keys
from ax.utils.common.logger import get_logger
Expand All @@ -60,12 +63,17 @@
"the `BestModelSelector` will be used to select the `GeneratorSpec` to "
"use for candidate generation."
)
MAX_GEN_DRAWS = 5
MAX_GEN_DRAWS_EXCEEDED_MESSAGE = (
f"GenerationStrategy exceeded `MAX_GEN_DRAWS` of {MAX_GEN_DRAWS} while trying to "
"generate a unique parameterization. This indicates that the search space has "
"likely been fully explored, or that the sweep has converged."
MAX_GEN_ATTEMPTS = 5
MAX_GEN_ATTEMPTS_EXCEEDED_MESSAGE = (
f"GenerationStrategy exceeded `MAX_GEN_ATTEMPTS` of {MAX_GEN_ATTEMPTS} while "
"trying to generate a unique parameterization. This indicates that the search "
"space has likely been fully explored, or that the sweep has converged."
)
DEFAULT_FALLBACK = {
GenerationStrategyRepeatedPoints: GeneratorSpec(
model_enum=Generators.SOBOL, model_key_override="Fallback_Sobol"
)
}


class GenerationNode(SerializationMixin, SortableBase):
Expand Down Expand Up @@ -105,6 +113,8 @@ class GenerationNode(SerializationMixin, SortableBase):
store the most recent previous ``GenerationNode`` name.
should_skip: Whether to skip this node during generation time. Defaults to
False, and can only currently be set to True via ``NodeInputConstructors``
fallback_specs: Optional dict mapping expected exception types to `ModelSpec`
fallbacks used when gen fails.
Note for developers: by "model" here we really mean an Ax Adapter object, which
contains an Ax Model under the hood. We call it "model" here to simplify and focus
Expand Down Expand Up @@ -150,6 +160,7 @@ def __init__(
previous_node_name: str | None = None,
trial_type: str | None = None,
should_skip: bool = False,
fallback_specs: dict[type[Exception], GeneratorSpec] | None = None,
) -> None:
self._node_name = node_name
# Check that the model specs have unique model keys.
Expand Down Expand Up @@ -179,6 +190,10 @@ def __init__(
self._previous_node_name = previous_node_name
self._trial_type = trial_type
self._should_skip = should_skip
# pyre-fixme[8]: Incompatible attribute type
self.fallback_specs: dict[type[Exception], GeneratorSpec] = (
fallback_specs if fallback_specs is not None else DEFAULT_FALLBACK
)

@property
def node_name(self) -> str:
Expand Down Expand Up @@ -374,7 +389,7 @@ def gen(
self,
n: int | None = None,
pending_observations: dict[str, list[ObservationFeatures]] | None = None,
max_gen_draws_for_deduplication: int = MAX_GEN_DRAWS,
max_gen_attempts_for_deduplication: int = MAX_GEN_ATTEMPTS,
arms_by_signature_for_deduplication: dict[str, Arm] | None = None,
**model_gen_kwargs: Any,
) -> GeneratorRun:
Expand All @@ -389,54 +404,87 @@ def gen(
Args:
n: Optional integer representing how many arms should be in the generator
run produced by this method. When this is ``None``, ``n`` will be
determined by the ``GeneratorSpec`` that we are generating from.
determined by the ``ModelSpec`` that we are generating from.
pending_observations: A map from metric name to pending
observations for that metric, used by some models to avoid
resuggesting points that are currently being evaluated.
max_gen_draws_for_deduplication: Maximum number of attempts for generating
new candidates without duplicates. If non-duplicate candidates are not
generated with these attempts, a ``GenerationStrategyRepeatedPoints``
exception will be raised.
max_gen_attempts_for_deduplication: Maximum number of attempts for
generating new candidates without duplicates. If non-duplicate
candidates are not generated with these attempts, a
``GenerationStrategyRepeatedPoints`` exception will be raised.
arms_by_signature_for_deduplication: A dictionary mapping arm signatures to
the arms, to be used for deduplicating newly generated arms.
model_gen_kwargs: Keyword arguments, passed through to
``GeneratorSpec.gen``; these override any pre-specified in
``GeneratorSpec.model_gen_kwargs``.
``ModelSpec.gen``; these override any pre-specified in
``ModelSpec.model_gen_kwargs``.
Returns:
A ``GeneratorRun`` containing the newly generated candidates.
"""
should_generate_run = True
generator_run = None
n_gen_draws = 0
# Keep generating until each of `generator_run.arms` is not a duplicate
# of a previous arm, if `should_deduplicate is True`
while should_generate_run:
try:
# Keep generating until each of `generator_run.arms` is not a duplicate
# of a previous arm, if `should_deduplicate is True`
while n_gen_draws < max_gen_attempts_for_deduplication:
n_gen_draws += 1
generator_run = self._gen(
n=n,
pending_observations=pending_observations,
**model_gen_kwargs,
)
if not (
self.should_deduplicate
and arms_by_signature_for_deduplication
and any(
arm.signature in arms_by_signature_for_deduplication
for arm in generator_run.arms
)
): # Not deduplicating or generated a non-duplicate arm.
break

logger.info(
"The generator run produced duplicate arms. Re-running the "
"generation step in an attempt to deduplicate. Candidates "
f"produced in the last generator run: {generator_run.arms}."
)

if n_gen_draws >= max_gen_attempts_for_deduplication:
raise GenerationStrategyRepeatedPoints(
MAX_GEN_ATTEMPTS_EXCEEDED_MESSAGE
)
except Exception as e:
error_type = type(e)
if error_type not in self.fallback_specs:
raise e

# identify fallback model to use
fallback_model = self.fallback_specs[error_type]
logger.warning(
f"gen failed with error {e}, "
"switching to fallback model with model_enum "
f"{fallback_model.model_enum}"
)

# fit fallback model using information from `self.experiment`
# as ground truth
fallback_model.fit(
experiment=self.experiment,
data=self.experiment.lookup_data(),
search_space=self.experiment.search_space,
optimization_config=self.experiment.optimization_config,
**self._get_model_state_from_last_generator_run(
model_spec=fallback_model
),
)
# Switch _model_spec_to_gen_from to a fallback spec
self._model_spec_to_gen_from = fallback_model
generator_run = self._gen(
n=n,
pending_observations=pending_observations,
**model_gen_kwargs,
)
should_generate_run = (
self.should_deduplicate
and arms_by_signature_for_deduplication
and any(
arm.signature in arms_by_signature_for_deduplication
for arm in generator_run.arms
)
)
n_gen_draws += 1
if should_generate_run:
if n_gen_draws > max_gen_draws_for_deduplication:
raise GenerationStrategyRepeatedPoints(
MAX_GEN_DRAWS_EXCEEDED_MESSAGE
)
else:
logger.info(
"The generator run produced duplicate arms. Re-running the "
"generation step in an attempt to deduplicate. Candidates "
f"produced in the last generator run: {generator_run.arms}."
)

assert generator_run is not None, (
"The GeneratorRun is None which is an unexpected state of this"
" GenerationStrategy. This occurred on GenerationNode: {self.node_name}."
Expand Down Expand Up @@ -929,14 +977,14 @@ def gen(
self,
n: int | None = None,
pending_observations: dict[str, list[ObservationFeatures]] | None = None,
max_gen_draws_for_deduplication: int = MAX_GEN_DRAWS,
max_gen_attempts_for_deduplication: int = MAX_GEN_ATTEMPTS,
arms_by_signature_for_deduplication: dict[str, Arm] | None = None,
**model_gen_kwargs: Any,
) -> GeneratorRun:
gr = super().gen(
n=n,
pending_observations=pending_observations,
max_gen_draws_for_deduplication=max_gen_draws_for_deduplication,
max_gen_attempts_for_deduplication=max_gen_attempts_for_deduplication,
arms_by_signature_for_deduplication=arms_by_signature_for_deduplication,
**model_gen_kwargs,
)
Expand Down
6 changes: 0 additions & 6 deletions ax/generation_strategy/generation_strategy.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -43,12 +43,6 @@


MAX_CONDITIONS_GENERATED = 10000
MAX_GEN_DRAWS = 5
MAX_GEN_DRAWS_EXCEEDED_MESSAGE = (
f"GenerationStrategy exceeded `MAX_GEN_DRAWS` of {MAX_GEN_DRAWS} while trying to "
"generate a unique parameterization. This indicates that the search space has "
"likely been fully explored, or that the sweep has converged."
)
T = TypeVar("T")


Expand Down
117 changes: 68 additions & 49 deletions ax/generation_strategy/tests/test_generation_strategy.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,7 @@ def test_with_model_selection(self, mock_model_state: Mock) -> None:
# Model state is not extracted since there is no past GR.
mock_model_state.assert_not_called()
exp.new_trial(gs.gen(experiment=exp))
# Model state is extracted since there is a past GR.
# Model state is extracted for the model since there is a past GR.
mock_model_state.assert_called_once()
mock_model_state.reset_mock()
# Gen with MBM/BO_MIXED.
Expand Down Expand Up @@ -770,51 +770,69 @@ def test_max_parallelism_reached(self) -> None:
with self.assertRaises(MaxParallelismReachedException):
sobol_generation_strategy.gen(experiment=exp)

def test_deduplication(self) -> None:
tiny_parameters = [
FixedParameter(
name="x1",
parameter_type=ParameterType.FLOAT,
value=1.0,
),
ChoiceParameter(
name="x2",
parameter_type=ParameterType.FLOAT,
values=[float(x) for x in range(2)],
),
]
tiny_search_space = SearchSpace(
parameters=cast(list[Parameter], tiny_parameters)
)
exp = get_branin_experiment(search_space=tiny_search_space)
sobol = GenerationStrategy(
name="Sobol",
steps=[
GenerationStep(
model=Generators.SOBOL,
num_trials=-1,
# Disable model-level deduplication.
model_kwargs={"deduplicate": False},
should_deduplicate=True,
def test_deduplication_and_fallback(self) -> None:
# None uses default fallback, which catches
# GenerationStrategyRepeatedPoints and re-generate with sobol
# {} will not have a fallback model and will raise the exception
for fallback_specs in [{}, None]:
tiny_parameters = [
FixedParameter(
name="x1",
parameter_type=ParameterType.FLOAT,
value=1.0,
),
],
)
for _ in range(2):
g = sobol.gen(exp)
exp.new_trial(generator_run=g).run()

self.assertEqual(len(exp.arms_by_signature), 2)

with self.assertRaisesRegex(
GenerationStrategyRepeatedPoints, "exceeded `MAX_GEN_DRAWS`"
), mock.patch(
"ax.generation_strategy.generation_node.logger.info"
) as mock_logger:
g = sobol.gen(exp)
self.assertEqual(mock_logger.call_count, 5)
self.assertIn(
"The generator run produced duplicate arms.", mock_logger.call_args[0][0]
)
ChoiceParameter(
name="x2",
parameter_type=ParameterType.FLOAT,
values=[float(x) for x in range(2)],
),
]
tiny_search_space = SearchSpace(
parameters=cast(list[Parameter], tiny_parameters)
)
exp = get_branin_experiment(search_space=tiny_search_space)
sobol = GenerationStrategy(
name="Sobol",
nodes=[
GenerationNode(
node_name="sobol",
model_specs=[
GeneratorSpec(
model_enum=Generators.SOBOL,
model_kwargs={"deduplicate": False},
)
],
# Disable model-level deduplication.
should_deduplicate=True,
fallback_specs=fallback_specs,
),
],
)
for _ in range(2):
g = sobol.gen(exp)
exp.new_trial(generator_run=g).run()

self.assertEqual(len(exp.arms_by_signature), 2)

if fallback_specs is not None:
with self.assertRaisesRegex(
GenerationStrategyRepeatedPoints, "exceeded `MAX_GEN_ATTEMPTS`"
), mock.patch(
"ax.generation_strategy.generation_node.logger.info"
) as mock_logger:
g = sobol.gen(exp)
else:
# generation with a fallback model
with self.assertLogs(GenerationNode.__module__, logging.WARNING) as cm:
g = sobol.gen(exp)
self.assertTrue(
any("gen failed with error" in msg for msg in cm.output)
)
self.assertEqual(mock_logger.call_count, 5)
self.assertIn(
"The generator run produced duplicate arms.",
mock_logger.call_args[0][0],
)

def test_current_generator_run_limit(self) -> None:
NUM_INIT_TRIALS = 5
Expand Down Expand Up @@ -904,7 +922,8 @@ def test_hierarchical_search_space(self) -> None:
RandomAdapter, "gen"
):
self.sobol_GS.gen(experiment=experiment)
mock_model_fit.assert_called_once()
# We should only fit once for each model
self.assertEqual(mock_model_fit.call_count, 1)
observations = mock_model_fit.call_args[1].get("observations")
all_parameter_names = assert_is_instance(
experiment.search_space, HierarchicalSearchSpace
Expand Down Expand Up @@ -952,9 +971,9 @@ def test_gen_multiple(self) -> None:
# first four become trials.
grs = sobol_MBM_gs._gen_multiple(experiment=exp, num_generator_runs=3)
self.assertEqual(len(grs), 3)
# We should only fit once; refitting for each `gen` would be
# wasteful as there is no new data.
model_spec_fit_mock.assert_called_once()
# We should only fit once for each model
# refitting for each `gen` would be wasteful as there is no new data.
self.assertEqual(model_spec_fit_mock.call_count, 1)
self.assertEqual(model_spec_gen_mock.call_count, 3)
pending_in_each_gen = enumerate(
args_and_kwargs.kwargs.get("pending_observations")
Expand Down
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