neural_compressor.strategy.bayesian

The Bayesian tuning strategy.

Module Contents

Classes

BayesianTuneStrategy	The Bayesian tuning strategy.
TargetSpace	Holds the param-space coordinates (X) and target values (Y).
BayesianOptimization	The class for bayesian optimization.

Functions

acq_max(ac, gp, y_max, bounds, random_seed[, ...])

Find the maximum of the acquisition function parameters.

class neural_compressor.strategy.bayesian.BayesianTuneStrategy(model, conf, q_dataloader=None, q_func=None, eval_func=None, eval_dataloader=None, eval_metric=None, resume=None, q_hooks=None)

The Bayesian tuning strategy.

neural_compressor.strategy.bayesian.acq_max(ac, gp, y_max, bounds, random_seed, n_warmup=10000, n_iter=10)

Find the maximum of the acquisition function parameters.

Parameters:

• ac – The acquisition function object that return its point-wise value.

• gp – A gaussian process fitted to the relevant data.

• y_max – The current maximum known value of the target function.

• bounds – The variables bounds to limit the search of the acq max.

• random_seed – instance of np.RandomState random number generator

• n_warmup – number of times to randomly sample the acquisition function

• n_iter – number of times to run scipy.minimize

Returns:

The arg max of the acquisition function.

Return type:

x_max

class neural_compressor.strategy.bayesian.TargetSpace(pbounds, random_seed=9527)

Holds the param-space coordinates (X) and target values (Y).

Allows for constant-time appends while ensuring no duplicates are added.

class neural_compressor.strategy.bayesian.BayesianOptimization(pbounds, random_seed=9527, verbose=2)

The class for bayesian optimization.

This class takes the parameters bounds in order to find which values for the parameters yield the maximum value using bayesian optimization.