SearchSpaceSpec.md

Search Space

Overview

In NNI, tuner will sample parameters/architecture according to the search space, which is defined as a json file.

To define a search space, users should define the name of the variable, the type of sampling strategy and its parameters.

• An example of a search space definition is as follow:

{
    "dropout_rate": {"_type": "uniform", "_value": [0.1, 0.5]},
    "conv_size": {"_type": "choice", "_value": [2, 3, 5, 7]},
    "hidden_size": {"_type": "choice", "_value": [124, 512, 1024]},
    "batch_size": {"_type": "choice", "_value": [50, 250, 500]},
    "learning_rate": {"_type": "uniform", "_value": [0.0001, 0.1]}
}

Take the first line as an example. dropout_rate is defined as a variable whose priori distribution is a uniform distribution with a range from 0.1 to 0.5.

Note that the available sampling strategies within a search space depend on the tuner you want to use. We list the supported types for each builtin tuner below. For a customized tuner, you don't have to follow our convention and you will have the flexibility to define any type you want.

Types

All types of sampling strategies and their parameter are listed here:

• {"_type": "choice", "_value": options}

  • The variable's value is one of the options. Here options should be a list of numbers or a list of strings. Using arbitrary objects as members of this list (like sublists, a mixture of numbers and strings, or null values) should work in most cases, but may trigger undefined behaviors.
  • options can also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space can be seen as conditional variables. Here is an simple example of nested search space definition. If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a _name key in this dict, which helps you to identify which element is chosen. Accordingly, here is a sample which users can get from nni with nested search space definition. See the table below for the tuners which support nested search spaces.

• {"_type": "randint", "_value": [lower, upper]}

  • Choosing a random integer between lower (inclusive) and upper (exclusive).
  • Note: Different tuners may interpret randint differently. Some (e.g., TPE, GridSearch) treat integers from lower to upper as unordered ones, while others respect the ordering (e.g., SMAC). If you want all the tuners to respect the ordering, please use quniform with q=1.

• {"_type": "uniform", "_value": [low, high]}

  • The variable value is uniformly sampled between low and high.
  • When optimizing, this variable is constrained to a two-sided interval.

• {"_type": "quniform", "_value": [low, high, q]}

  • The variable value is determined using clip(round(uniform(low, high) / q) * q, low, high), where the clip operation is used to constrain the generated value within the bounds. For example, for _value specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For _value specified as [2, 10, 5], possible values are [2, 5, 10].
  • Suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below. If you want to uniformly choose an integer from a range [low, high], you can write _value like this: [low, high, 1].

• {"_type": "loguniform", "_value": [low, high]}

  • The variable value is drawn from a range [low, high] according to a loguniform distribution like exp(uniform(log(low), log(high))), so that the logarithm of the return value is uniformly distributed.
  • When optimizing, this variable is constrained to be positive.

• {"_type": "qloguniform", "_value": [low, high, q]}

  • The variable value is determined using clip(round(loguniform(low, high) / q) * q, low, high), where the clip operation is used to constrain the generated value within the bounds.
  • Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.

• {"_type": "normal", "_value": [mu, sigma]}

  • The variable value is a real value that's normally-distributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.

• {"_type": "qnormal", "_value": [mu, sigma, q]}

  • The variable value is determined using round(normal(mu, sigma) / q) * q
  • Suitable for a discrete variable that probably takes a value around mu, but is fundamentally unbounded.

• {"_type": "lognormal", "_value": [mu, sigma]}

  • The variable value is drawn according to exp(normal(mu, sigma)) so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.

• {"_type": "qlognormal", "_value": [mu, sigma, q]}

  • The variable value is determined using round(exp(normal(mu, sigma)) / q) * q
  • Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.

Search Space Types Supported by Each Tuner

	choice	choice(nested)	randint	uniform	quniform	loguniform	qloguniform	normal	qnormal	lognormal	qlognormal
TPE Tuner	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Random Search Tuner	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Anneal Tuner	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Evolution Tuner	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
SMAC Tuner	✓		✓	✓	✓	✓
Batch Tuner	✓
Grid Search Tuner	✓		✓		✓
Hyperband Advisor	✓		✓	✓	✓	✓	✓	✓	✓	✓	✓
Metis Tuner	✓		✓	✓	✓
GP Tuner	✓		✓	✓	✓	✓	✓

Known Limitations:

• GP Tuner and Metis Tuner support only numerical values in search space (choice type values can be no-numerical with other tuners, e.g. string values). Both GP Tuner and Metis Tuner use Gaussian Process Regressor(GPR). GPR make predictions based on a kernel function and the 'distance' between different points, it's hard to get the true distance between no-numerical values.

• Note that for nested search space:

  • Only Random Search/TPE/Anneal/Evolution tuner supports nested search space