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Proposal for Supporting various parameter distributions in Katib

Summary

The goal of this project is to enhance the existing Katib Experiment APIs to support various parameter distributions such as uniform, log-uniform, and qlog-uniform. Then extend the suggestion services to be able to configure distributions for search space using libraries provided in each framework.

Motivation

Currently, Katib is limited to supporting only uniform distribution for integer, float, and categorical hyperparameters. By introducing additional distributions, Katib will become more flexible and powerful in conducting hyperparameter optimization tasks.

A Data Scientist requires Katib to support multiple hyperparameter distributions, such as log-uniform, normal, and log-normal, in addition to the existing uniform distribution. This enhancement is crucial for more flexible and precise hyperparameter optimization. For instance, learning rates often benefit from a log-uniform distribution because small values can significantly impact performance. Similarly, normal distributions are useful for parameters that are expected to vary around a central value.

Goals

  • Add Distribution field to FeasibleSpace alongside ParameterType.
  • Support for the log-uniform, normal, and log-normal Distributions.
  • Update the Experiment and gRPC API to support Distribution.
  • Update logic to handle the new parameter distributions for each suggestion service (e.g., Optuna, Hyperopt).
  • Extend the Python SDK to support the new Distribution field.

Non-Goals

  • This proposal do not aim to create new version for CRD APIs.
  • This proposal do not aim to make the necessary Katib UI changes.
  • No changes will be made to the core optimization algorithms beyond supporting new distributions.

Proposal

Parameter Distribution Comparison Table

Distribution Type Hyperopt Optuna Ray Tune Nevergrad
Uniform Continuous hp.uniform FloatDistribution tune.uniform p.Scalar with uniform transformation
Quantized Uniform hp.quniform DiscreteUniformDistribution (deprecated) tune.quniform p.Scalar with uniform and step specified
Log Uniform hp.loguniform LogUniformDistribution (deprecated) tune.loguniform p.Log with uniform transformation
Uniform Integer hp.randint or quantized distributions with step size q set to 1 IntDistribution tune.randint p.Scalar with integer transformation
Categorical hp.choice CategoricalDistribution tune.choice p.Choice
Quantized Log Uniform hp.qloguniform Custom Implementation tune.qloguniform p.Log with uniform and step specified
Normal hp.normal (Not directly supported) tune.randn (Not directly supported)
Quantized Normal hp.qnormal (Not directly supported) tune.qrandn (Not directly supported)
Log Normal hp.lognormal (Not directly supported) (Use custom transformation in tune.randn) (Not directly supported)
Quantized Log Normal hp.qlognormal (Not directly supported) (Use custom transformation in tune.qrandn) (Not directly supported)
Quantized Integer hp.quniformint IntUniformDistribution (deprecated) p.Scalar with integer and step specified
Log Integer IntLogUniformDistribution (deprecated) tune.lograndint p.Scalar with log-integer transformation
  • Note: In Nevergrad, parameter types like p.Scalar, p.Log, and p.Choice are mapped to corresponding Hyperopt search space definitions like hp.uniform, hp.loguniform, and hp.choice using internal functions to convert parameter bounds and distributions.

API Design

FeasibleSpace

Feasible space for optimization. Int and Double type use Max/Min. Discrete and Categorical type use List.

Field Type Label Description
max string Max Value
min string Minimum Value
list string repeated List of Values.
step string Step for double or int parameter or q for quantization
distribution Distribution Type of the Distribution.

Distribution

  • Types of value for HyperParameter Distributions.
  • We add the distribution field to represent the hyperparameters search space rather than ParameterType.
  • The distribution allows users to configure more granular search space customizations.
  • In this enhancement, we would propose the following 4 distributions:
Name Number Description
UNIFORM 0 Continuous uniform distribution. Samples values evenly between a minimum and maximum value. Use "Max/Min". Use "Step" for q.
LOGUNIFORM 1 Samples values such that their logarithm is uniformly distributed. Use "Max/Min". Use "Step" for q.
NORMAL 2 Normal (Gaussian) distribution type. Samples values according to a normal distribution characterized by a mean and standard deviation. Use "Max/Min". Use "Step" for q.
LOGNORMAL 3 Log-normal distribution type. Samples values such that their logarithm is normally distributed. Use "Max/Min". Use "Step" for q.

Experiment API changes

Scope: pkg/apis/controller/experiments/v1beta1/experiment_types.go

type ParameterSpec struct {
	Name          string        `json:"name,omitempty"`
	ParameterType ParameterType `json:"parameterType,omitempty"`
	FeasibleSpace FeasibleSpace `json:"feasibleSpace,omitempty"`
}

  • Adding new field Distribution to FeasibleSpace

  • The Step field can be used to define quantization steps for uniform or log-uniform distributions, effectively covering q-quantization requirements.

Updated FeasibleSpace struct

type FeasibleSpace struct {
	Max           string        `json:"max,omitempty"`
	Min           string        `json:"min,omitempty"`
	List          []string      `json:"list,omitempty"`
	Step          string        `json:"step,omitempty"` // Step can be used to define q-quantization
+       Distribution  Distribution  `json:"distribution,omitempty"` // Added Distribution field
}

  • New Field Description: Distribution

  • Type: Distribution

  • Description: The Distribution field specifies the type of statistical distribution to be applied to the parameter. This allows the definition of various distributions, such as uniform, log-uniform, or other supported types.

  • Defining Distribution type

type Distribution string

const (
	DistributionUniform    Distribution = "uniform"
	DistributionLogUniform Distribution = "logUniform"
	DistributionNormal     Distribution = "normal"
	DistributionLogNormal  Distribution = "logNormal"
)

gRPC API changes

Scope: pkg/apis/manager/v1beta1/api.proto

  • Add the Distribution field to the FeasibleSpace message
/**
 * Feasible space for optimization.
 * Int and Double type use Max/Min.
 * Discrete and Categorical type use List.
 */
message FeasibleSpace {
    string max = 1; /// Max Value
    string min = 2; /// Minimum Value
    repeated string list = 3; /// List of Values.
    string step = 4; /// Step for double or int parameter
+   Distribution distribution = 4; // Distribution of the parameter.
}
  • Define the Distribution enum
/**
 * Distribution types for HyperParameter.
 */
enum Distribution {
    UNIFORM = 0;
    LOG_UNIFORM = 1;
    NORMAL = 2;
    LOG_NORMAL = 3;
}

Suggestion Service Logic

  • For each suggestion service (e.g., Optuna, Hyperopt), the logic will be updated to handle the new parameter distributions.
  • This involves modifying the conversion functions to map Katib distributions to the corresponding framework-specific distributions.

Optuna

ref: https://optuna.readthedocs.io/en/stable/reference/distributions.html

For example:

  • Update the _get_optuna_search_space for new Distributions. scope: pkg/suggestion/v1beta1/optuna/base_service.py

Goptuna

ref: https://github.com/c-bata/goptuna/blob/2245ddd9e8d1edba750839893c8a618f852bc1cf/distribution.go

Hyperopt

ref: http://hyperopt.github.io/hyperopt/getting-started/search_spaces/#parameter-expressions

Ray-tune

ref: https://docs.ray.io/en/latest/tune/api/search_space.html

Python SDK

Extend the Python SDK to support the new Distribution field.