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Migrate transforms and utils to RangeParameter.step_size #5222

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f7d711c
Migrate TL adapter utils tests to non-fb location and drop shims
saitcakmak Jun 3, 2026
f786271
Add native step_size support to RangeParameter
saitcakmak Jun 3, 2026
4f19839
Add storage support for RangeParameter.step_size (JSON + SQA)
saitcakmak Jun 3, 2026
6c023bc
Migrate transforms and utils to RangeParameter.step_size
saitcakmak Jun 4, 2026
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384 changes: 384 additions & 0 deletions ax/adapter/transfer_learning/tests/test_utils.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# pyre-strict

from ax.adapter.transfer_learning.utils import (
get_joint_search_space,
merge_dependents,
merge_parameters,
)
from ax.core.auxiliary_source import AuxiliarySource
from ax.core.experiment import Experiment
from ax.core.parameter import (
ChoiceParameter,
DerivedParameter,
FixedParameter,
Parameter,
ParameterType,
RangeParameter,
)
from ax.core.search_space import SearchSpace
from ax.utils.common.testutils import TestCase
from pyre_extensions import assert_is_instance, none_throws


class AxFbCoreUtilsTest(TestCase):
def test_get_joint_search_space(self) -> None:
parameters: list[Parameter] = [
RangeParameter(f"x{i}", parameter_type=ParameterType.INT, lower=0, upper=5)
for i in range(3)
]
exp1 = Experiment(
search_space=SearchSpace(parameters=parameters[:2]), name="test1"
)
exp2 = Experiment(
search_space=SearchSpace(parameters=parameters[:2]), name="test2"
)
exp3 = Experiment(
search_space=SearchSpace(parameters=parameters[1:]), name="test3"
)
aux_2 = AuxiliarySource(experiment=exp2)
aux_3 = AuxiliarySource(experiment=exp3)
aux_4 = AuxiliarySource(experiment=exp3, transfer_param_config={"x0": "x2"})
for exp, aux_srcs, expected_params in (
(exp1, [aux_2], {"x0", "x1"}),
(exp1, [aux_2, aux_3], {"x0", "x1", "x2"}),
(exp1, [aux_2, aux_4], {"x0", "x1"}),
):
self.assertEqual(
set(
get_joint_search_space(
search_space=exp.search_space, auxiliary_sources=aux_srcs
).parameters.keys()
),
expected_params,
)

def test_get_joint_search_space_update_fixed_params(self) -> None:
# test update fixed params
range_param = RangeParameter(
"x", parameter_type=ParameterType.INT, lower=0, upper=5
)
fixed_param1 = FixedParameter("y", parameter_type=ParameterType.INT, value=1)
fixed_param2 = FixedParameter("y", parameter_type=ParameterType.INT, value=2)
exp = Experiment(
search_space=SearchSpace(parameters=[range_param, fixed_param1]),
name="test1",
)
exp2 = Experiment(
search_space=SearchSpace(parameters=[range_param, fixed_param2]),
name="test2",
)
for update_fixed_params in [True, False]:
aux2 = AuxiliarySource(
experiment=exp2, update_fixed_params=update_fixed_params
)
ss_params = get_joint_search_space(
search_space=exp.search_space, auxiliary_sources=[aux2]
).parameters
self.assertEqual(
assert_is_instance(ss_params["y"], FixedParameter).value, 1
)
self.assertIn("x", ss_params)

def test_get_joint_search_space_with_hss_and_choice(self) -> None:
ss1 = SearchSpace(
parameters=[
FixedParameter(
"root",
parameter_type=ParameterType.INT,
value=1,
dependents={1: ["learning_rate", "optimizer", "method"]},
),
ChoiceParameter(
"learning_rate",
parameter_type=ParameterType.FLOAT,
values=[0.01, 0.05],
),
ChoiceParameter(
"optimizer",
parameter_type=ParameterType.STRING,
values=["Adam", "SGD", "AdaGrad"],
),
ChoiceParameter(
"method",
parameter_type=ParameterType.STRING,
values=["train", "eval"],
),
]
)
ss2 = SearchSpace(
parameters=[
FixedParameter(
"root2",
parameter_type=ParameterType.INT,
value=1,
dependents={1: ["lr", "optimizer"]},
),
ChoiceParameter(
"lr", parameter_type=ParameterType.FLOAT, values=[0.01, 0.1]
),
ChoiceParameter(
"optimizer",
parameter_type=ParameterType.STRING,
values=["Adam", "SGD"],
),
]
)
aux_src = AuxiliarySource(
experiment=Experiment(search_space=ss2, name="test"),
transfer_param_config={"learning_rate": "lr", "root": "root2"},
update_fixed_params=False,
)
joint_ss = get_joint_search_space(search_space=ss1, auxiliary_sources=[aux_src])
self.assertEqual(
set(joint_ss.parameters.keys()),
{"root", "learning_rate", "optimizer", "method"},
)
self.assertEqual(
set(joint_ss["root"].dependents[1]),
{"learning_rate", "optimizer", "method"},
)
self.assertEqual(
assert_is_instance(
joint_ss.parameters["learning_rate"], ChoiceParameter
).values,
[0.01, 0.05, 0.1],
)
self.assertEqual(
set(
assert_is_instance(
joint_ss.parameters["optimizer"], ChoiceParameter
).values
),
{"Adam", "SGD", "AdaGrad"},
)

def test_merge_dependents(self) -> None:
p_no_dependents = FixedParameter(
"p", parameter_type=ParameterType.BOOL, value=True
)
# No dependents returns None.
self.assertIsNone(
merge_dependents(
p1=p_no_dependents, p2=p_no_dependents, reverse_param_config={}
)
)
p_dependents_1 = FixedParameter(
"p1", parameter_type=ParameterType.INT, value=1, dependents={1: ["q"]}
)
p_dependents_2 = FixedParameter(
"p2", parameter_type=ParameterType.INT, value=1, dependents={1: ["z"]}
)
# p1 dependents do not get renamed.
self.assertEqual(
merge_dependents(
p1=p_dependents_1, p2=p_no_dependents, reverse_param_config={"q": "w"}
),
{1: ["q"]},
)
# p2 dependents get renamed.
self.assertEqual(
merge_dependents(
p1=p_no_dependents, p2=p_dependents_1, reverse_param_config={"q": "w"}
),
{1: ["w"]},
)
# Merge p1 & p2 dependents with renaming for p2 only.
self.assertEqual(
set(
none_throws(
merge_dependents(
p1=p_dependents_1,
p2=p_dependents_2,
reverse_param_config={"q": "w", "z": "v"},
)
)[1]
),
{"q", "v"},
)

def test_merge_parameters(self) -> None:
p_fixed = FixedParameter(
name="fixed", parameter_type=ParameterType.BOOL, value=True
)
p_fixed_2 = FixedParameter(name="f2", parameter_type=ParameterType.INT, value=1)
p_fixed_3 = FixedParameter(name="f3", parameter_type=ParameterType.INT, value=2)
p_fixed_4 = FixedParameter(
name="f4", parameter_type=ParameterType.INT, value=1, dependents={1: ["a"]}
)
with self.assertRaisesRegex(ValueError, "different names"):
merge_parameters(p1=p_fixed, p2=p_fixed_2, reverse_param_config={})
with self.assertRaisesRegex(ValueError, "different types"):
merge_parameters(
p1=p_fixed, p2=p_fixed_2, reverse_param_config={"f2": "fixed"}
)
# Check that it works with both values of update_fixed_params.
for update_fixed_params in [True, False]:
self.assertEqual(
merge_parameters(
p1=p_fixed_2,
p2=p_fixed_3,
reverse_param_config={"f3": "f2"},
update_fixed_params=update_fixed_params,
),
FixedParameter(
name="f2",
parameter_type=ParameterType.INT,
value=1,
),
)
self.assertEqual(
merge_parameters(
p1=p_fixed_2, p2=p_fixed_4, reverse_param_config={"f4": "f2"}
),
FixedParameter(
name="f2",
parameter_type=ParameterType.INT,
value=1,
dependents={1: ["a"]},
),
)
p_range_1 = RangeParameter(
name="p", parameter_type=ParameterType.INT, lower=1, upper=3
)
p_range_2 = RangeParameter(
name="p", parameter_type=ParameterType.INT, lower=0, upper=2
)
self.assertEqual(
merge_parameters(p1=p_range_1, p2=p_range_2, reverse_param_config={}),
RangeParameter(
name="p", parameter_type=ParameterType.INT, lower=0, upper=3
),
)
# The grid spec (step_size, or legacy digits) is forwarded from p1, even
# when the merged bounds are widened by p2 (as long as they stay on p1's
# grid).
p_range_step = RangeParameter(
name="p",
parameter_type=ParameterType.FLOAT,
lower=0.0,
upper=1.0,
step_size=0.1,
)
p_range_wide = RangeParameter(
name="p", parameter_type=ParameterType.FLOAT, lower=0.0, upper=2.0
)
merged_step = assert_is_instance(
merge_parameters(p1=p_range_step, p2=p_range_wide, reverse_param_config={}),
RangeParameter,
)
self.assertEqual(merged_step.upper, 2.0)
self.assertEqual(merged_step.step_size, 0.1)
self.assertIsNone(merged_step.digits)
p_range_digits = RangeParameter(
name="p",
parameter_type=ParameterType.FLOAT,
lower=0.0,
upper=1.0,
digits=2,
)
merged_digits = assert_is_instance(
merge_parameters(
p1=p_range_digits, p2=p_range_wide, reverse_param_config={}
),
RangeParameter,
)
self.assertEqual(merged_digits.digits, 2)
self.assertIsNone(merged_digits.step_size)
p_choice_1 = ChoiceParameter(
name="p",
parameter_type=ParameterType.STRING,
values=["a", "b", "c"],
dependents={"a": ["p1"], "c": ["p2"]},
)
p_choice_2 = ChoiceParameter(
name="p", parameter_type=ParameterType.STRING, values=["a", "b", "d"]
)
self.assertEqual(
merge_parameters(p1=p_choice_1, p2=p_choice_2, reverse_param_config={}),
ChoiceParameter(
name="p",
parameter_type=ParameterType.STRING,
values=["a", "b", "c", "d"],
dependents={"a": ["p1"], "c": ["p2"]},
),
)

# FixedParameter + ChoiceParameter: fixed value already in choices.
p_fixed_str = FixedParameter(
name="p", parameter_type=ParameterType.STRING, value="a"
)
merged_fc = merge_parameters(
p1=p_fixed_str, p2=p_choice_1, reverse_param_config={}
)
self.assertIsInstance(merged_fc, ChoiceParameter)
merged_fc_choice = assert_is_instance(merged_fc, ChoiceParameter)
self.assertEqual(set(merged_fc_choice.values), {"a", "b", "c"})
# Dependents from the choice parameter are preserved.
self.assertEqual(merged_fc_choice.dependents, {"a": ["p1"], "c": ["p2"]})

# FixedParameter + ChoiceParameter: fixed value NOT in choices.
p_fixed_str_new = FixedParameter(
name="p", parameter_type=ParameterType.STRING, value="z"
)
merged_fc2 = merge_parameters(
p1=p_fixed_str_new, p2=p_choice_1, reverse_param_config={}
)
self.assertEqual(
set(assert_is_instance(merged_fc2, ChoiceParameter).values),
{"a", "b", "c", "z"},
)

# Reversed order: ChoiceParameter as p1, FixedParameter as p2.
merged_cf = merge_parameters(
p1=p_choice_1, p2=p_fixed_str_new, reverse_param_config={}
)
self.assertEqual(
set(assert_is_instance(merged_cf, ChoiceParameter).values),
{"a", "b", "c", "z"},
)

# DerivedParameter: same expression succeeds.
p_derived_1 = DerivedParameter(
name="d",
parameter_type=ParameterType.FLOAT,
expression_str="0.5 * x + 0.3 * y",
)
p_derived_2 = DerivedParameter(
name="d",
parameter_type=ParameterType.FLOAT,
expression_str="0.5 * x + 0.3 * y",
)
merged = merge_parameters(
p1=p_derived_1, p2=p_derived_2, reverse_param_config={}
)
self.assertIsInstance(merged, DerivedParameter)
self.assertEqual(
assert_is_instance(merged, DerivedParameter).expression_str,
"0.5 * x + 0.3 * y",
)
self.assertEqual(merged.name, "d")

# DerivedParameter: different expressions raises ValueError.
p_derived_3 = DerivedParameter(
name="d",
parameter_type=ParameterType.FLOAT,
expression_str="0.7 * x + 0.1 * y",
)
with self.assertRaisesRegex(ValueError, "different expressions"):
merge_parameters(p1=p_derived_1, p2=p_derived_3, reverse_param_config={})

# DerivedParameter vs FixedParameter raises ValueError (type mismatch).
p_fixed_float = FixedParameter(
name="d", parameter_type=ParameterType.FLOAT, value=1.0
)
with self.assertRaisesRegex(ValueError, "different types"):
merge_parameters(
p1=p_derived_1,
p2=p_fixed_float,
reverse_param_config={},
)
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