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NNI Annotation

Overview

To improve user experience and reduce user effort, we design an annotation grammar. Using NNI annotation, users can adapt their code to NNI just by adding some standalone annotating strings, which does not affect the execution of the original code.

Below is an example:

'''@nni.variable(nni.choice(0.1, 0.01, 0.001), name=learning_rate)'''
learning_rate = 0.1

The meaning of this example is that NNI will choose one of several values (0.1, 0.01, 0.001) to assign to the learning_rate variable. Specifically, this first line is an NNI annotation, which is a single string. Following is an assignment statement. What nni does here is to replace the right value of this assignment statement according to the information provided by the annotation line.

In this way, users could either run the python code directly or launch NNI to tune hyper-parameter in this code, without changing any codes.

Types of Annotation:

In NNI, there are mainly four types of annotation:

1. Annotate variables

'''@nni.variable(sampling_algo, name)'''

@nni.variable is used in NNI to annotate a variable.

Arguments

  • sampling_algo: Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an nni. identification and a search space type specified in SearchSpaceSpec such as choice or uniform.
  • name: The name of the variable that the selected value will be assigned to. Note that this argument should be the same as the left value of the following assignment statement.

There are 10 types to express your search space as follows:

  • @nni.variable(nni.choice(option1,option2,...,optionN),name=variable) Which means the variable value is one of the options, which should be a list The elements of options can themselves be stochastic expressions
  • @nni.variable(nni.randint(lower, upper),name=variable) Which means the variable value is a value like round(uniform(low, high)). For now, the type of chosen value is float. If you want to use integer value, please convert it explicitly.
  • @nni.variable(nni.uniform(low, high),name=variable) Which means the variable value is a value uniformly between low and high.
  • @nni.variable(nni.quniform(low, high, q),name=variable) Which means the variable value is a value like clip(round(uniform(low, high) / q) * q, low, high), where the clip operation is used to constraint the generated value in the bound.
  • @nni.variable(nni.loguniform(low, high),name=variable) Which means the variable value is a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.
  • @nni.variable(nni.qloguniform(low, high, q),name=variable) Which means the variable value is a value like clip(round(loguniform(low, high) / q) * q, low, high), where the clip operation is used to constraint the generated value in the bound.
  • @nni.variable(nni.normal(mu, sigma),name=variable) Which means the variable value is a real value that's normally-distributed with mean mu and standard deviation sigma.
  • @nni.variable(nni.qnormal(mu, sigma, q),name=variable) Which means the variable value is a value like round(normal(mu, sigma) / q) * q
  • @nni.variable(nni.lognormal(mu, sigma),name=variable) Which means the variable value is a value drawn according to exp(normal(mu, sigma))
  • @nni.variable(nni.qlognormal(mu, sigma, q),name=variable) Which means the variable value is a value like round(exp(normal(mu, sigma)) / q) * q

Below is an example:

'''@nni.variable(nni.choice(0.1, 0.01, 0.001), name=learning_rate)'''
learning_rate = 0.1

2. Annotate functions

'''@nni.function_choice(*functions, name)'''

@nni.function_choice is used to choose one from several functions.

Arguments

  • functions: Several functions that are waiting to be selected from. Note that it should be a complete function call with arguments. Such as max_pool(hidden_layer, pool_size).
  • name: The name of the function that will be replaced in the following assignment statement.

An example here is:

"""@nni.function_choice(max_pool(hidden_layer, pool_size), avg_pool(hidden_layer, pool_size), name=max_pool)"""
h_pooling = max_pool(hidden_layer, pool_size)

3. Annotate intermediate result

'''@nni.report_intermediate_result(metrics)'''

@nni.report_intermediate_result is used to report intermediate result, whose usage is the same as nni.report_intermediate_result in the doc of Write a trial run on NNI

4. Annotate final result

'''@nni.report_final_result(metrics)'''

@nni.report_final_result is used to report the final result of the current trial, whose usage is the same as nni.report_final_result in the doc of Write a trial run on NNI