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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""This module contains validators related to SageMaker JumpStart."""
from __future__ import absolute_import
from typing import Any, Dict, List, Optional
from sagemaker.jumpstart.constants import JUMPSTART_DEFAULT_REGION_NAME
from sagemaker.jumpstart.enums import HyperparameterValidationMode, VariableScope, VariableTypes
from sagemaker.jumpstart import accessors as jumpstart_accessors
from sagemaker.jumpstart.exceptions import JumpStartHyperparametersError
from sagemaker.jumpstart.types import JumpStartHyperparameter
def _validate_hyperparameter(
hyperparameter_name: str,
hyperparameter_value: Any,
hyperparameter_specs: List[JumpStartHyperparameter],
) -> None:
"""Perform low-level hyperparameter validation on single parameter.
Args:
hyperparameter_name (str): The name of the hyperparameter to validate.
hyperparameter_value (Any): The value of the hyperparemter to validate.
hyperparameter_specs (List[JumpStartHyperparameter]): List of ``JumpStartHyperparameter`` to
use when validating the hyperparameter.
Raises:
JumpStartHyperparametersError: If the hyperparameter is not formatted correctly,
according to its specs in the model metadata.
"""
hyperparameter_spec = [
spec for spec in hyperparameter_specs if spec.name == hyperparameter_name
]
if len(hyperparameter_spec) == 0:
raise JumpStartHyperparametersError(
f"Unable to perform validation -- cannot find hyperparameter '{hyperparameter_name}' "
"in model specs."
)
if len(hyperparameter_spec) > 1:
raise JumpStartHyperparametersError(
"Unable to perform validation -- found multiple hyperparameter "
f"'{hyperparameter_name}' in model specs."
)
hyperparameter_spec = hyperparameter_spec[0]
if hyperparameter_spec.type == VariableTypes.BOOL.value:
if isinstance(hyperparameter_value, bool):
return
if not isinstance(hyperparameter_value, str):
raise JumpStartHyperparametersError(
f"Expecting boolean valued hyperparameter, but got '{str(hyperparameter_value)}'."
)
if str(hyperparameter_value).lower() not in ["true", "false"]:
raise JumpStartHyperparametersError(
f"Expecting boolean valued hyperparameter, but got '{str(hyperparameter_value)}'."
)
elif hyperparameter_spec.type == VariableTypes.TEXT.value:
if not isinstance(hyperparameter_value, str):
raise JumpStartHyperparametersError(
"Expecting text valued hyperparameter to have string type."
)
if hasattr(hyperparameter_spec, "options"):
if hyperparameter_value not in hyperparameter_spec.options:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must have one of the following "
f"values: {', '.join(hyperparameter_spec.options)}."
)
if hasattr(hyperparameter_spec, "min"):
if len(hyperparameter_value) < hyperparameter_spec.min:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must have length no less than "
f"{hyperparameter_spec.min}."
)
if hasattr(hyperparameter_spec, "exclusive_min"):
if len(hyperparameter_value) <= hyperparameter_spec.exclusive_min:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must have length greater than "
f"{hyperparameter_spec.exclusive_min}."
)
if hasattr(hyperparameter_spec, "max"):
if len(hyperparameter_value) > hyperparameter_spec.max:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must have length no greater than "
f"{hyperparameter_spec.max}."
)
if hasattr(hyperparameter_spec, "exclusive_max"):
if len(hyperparameter_value) >= hyperparameter_spec.exclusive_max:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must have length less than "
f"{hyperparameter_spec.exclusive_max}."
)
# validate numeric types
elif hyperparameter_spec.type in [VariableTypes.INT.value, VariableTypes.FLOAT.value]:
try:
numeric_hyperparam_value = float(hyperparameter_value)
except ValueError:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must be numeric type "
f"('{hyperparameter_value}')."
)
if hyperparameter_spec.type == VariableTypes.INT.value:
hyperparameter_value_str = str(hyperparameter_value)
start_index = 0
if hyperparameter_value_str[0] in ["+", "-"]:
start_index = 1
if not hyperparameter_value_str[start_index:].isdigit():
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must be integer type "
f"('{hyperparameter_value}')."
)
if hasattr(hyperparameter_spec, "min"):
if numeric_hyperparam_value < hyperparameter_spec.min:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' can be no less than "
f"{hyperparameter_spec.min}."
)
if hasattr(hyperparameter_spec, "max"):
if numeric_hyperparam_value > hyperparameter_spec.max:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' can be no greater than "
f"{hyperparameter_spec.max}."
)
if hasattr(hyperparameter_spec, "exclusive_min"):
if numeric_hyperparam_value <= hyperparameter_spec.exclusive_min:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must be greater than "
f"{hyperparameter_spec.exclusive_min}."
)
if hasattr(hyperparameter_spec, "exclusive_max"):
if numeric_hyperparam_value >= hyperparameter_spec.exclusive_max:
raise JumpStartHyperparametersError(
f"Hyperparameter '{hyperparameter_name}' must be less than "
f"{hyperparameter_spec.exclusive_max}."
)
def validate_hyperparameters(
model_id: str,
model_version: str,
hyperparameters: Dict[str, Any],
validation_mode: HyperparameterValidationMode = HyperparameterValidationMode.VALIDATE_PROVIDED,
region: Optional[str] = JUMPSTART_DEFAULT_REGION_NAME,
) -> None:
"""Validate hyperparameters for JumpStart models.
Args:
model_id (str): Model ID of the model for which to validate hyperparameters.
model_version (str): Version of the model for which to validate hyperparameters.
hyperparameters (dict): Hyperparameters to validate.
validation_mode (HyperparameterValidationMode): Method of validation to use with
hyperparameters. If set to ``VALIDATE_PROVIDED``, only hyperparameters provided
to this function will be validated, the missing hyperparameters will be ignored.
If set to``VALIDATE_ALGORITHM``, all algorithm hyperparameters will be validated.
If set to ``VALIDATE_ALL``, all hyperparameters for the model will be validated.
region (str): Region for which to validate hyperparameters. (Default: JumpStart
default region).
Raises:
JumpStartHyperparametersError: If the hyperparameters are not formatted correctly,
according to their metadata specs.
"""
if validation_mode is None:
validation_mode = HyperparameterValidationMode.VALIDATE_PROVIDED
if region is None:
region = JUMPSTART_DEFAULT_REGION_NAME
model_specs = jumpstart_accessors.JumpStartModelsAccessor.get_model_specs(
region=region, model_id=model_id, version=model_version
)
hyperparameters_specs = model_specs.hyperparameters
if validation_mode == HyperparameterValidationMode.VALIDATE_PROVIDED:
for hyperparam_name, hyperparam_value in hyperparameters.items():
_validate_hyperparameter(hyperparam_name, hyperparam_value, hyperparameters_specs)
elif validation_mode == HyperparameterValidationMode.VALIDATE_ALGORITHM:
for hyperparam in hyperparameters_specs:
if hyperparam.scope == VariableScope.ALGORITHM:
if hyperparam.name not in hyperparameters:
raise JumpStartHyperparametersError(
f"Cannot find algorithm hyperparameter for '{hyperparam.name}'."
)
_validate_hyperparameter(
hyperparam.name, hyperparameters[hyperparam.name], hyperparameters_specs
)
elif validation_mode == HyperparameterValidationMode.VALIDATE_ALL:
for hyperparam in hyperparameters_specs:
if hyperparam.name not in hyperparameters:
raise JumpStartHyperparametersError(
f"Cannot find hyperparameter for '{hyperparam.name}'."
)
_validate_hyperparameter(
hyperparam.name, hyperparameters[hyperparam.name], hyperparameters_specs
)
else:
raise NotImplementedError(
f"Unable to handle validation for the mode '{validation_mode.value}'."
)
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