Customize Exploration Strategy

Customize Multi-trial Strategy

If users want to innovate a new exploration strategy, they can easily customize a new one following the interface provided by NNI. Specifically, users should inherit the base strategy class nni.retiarii.strategy.BaseStrategy, then implement the member function run. This member function takes base_model and applied_mutators as its input arguments. It can simply apply the user specified mutators in applied_mutators onto base_model to generate a new model. When a mutator is applied, it should be bound with a sampler (e.g., RandomSampler). Every sampler implements the choice function which chooses value(s) from candidate values. The choice functions invoked in mutators are executed with the sampler.

Below is a very simple random strategy, which makes the choices completely random.

from nni.retiarii import Sampler

class RandomSampler(Sampler):
    def choice(self, candidates, mutator, model, index):
        return random.choice(candidates)

class RandomStrategy(BaseStrategy):
    def __init__(self):
        self.random_sampler = RandomSampler()

    def run(self, base_model, applied_mutators):'stargety start...')
        while True:
            avail_resource = query_available_resources()
            if avail_resource > 0:
                model = base_model
      'apply mutators...')
      'mutators: %s', str(applied_mutators))
                for mutator in applied_mutators:
                    model = mutator.apply(model)
                # run models

You can find that this strategy does not know the search space beforehand, it passively makes decisions every time choice is invoked from mutators. If a strategy wants to know the whole search space before making any decision (e.g., TPE, SMAC), it can use dry_run function provided by Mutator to obtain the space. An example strategy can be found here.

After generating a new model, the strategy can use our provided APIs (e.g., nni.retiarii.execution.submit_models(), nni.retiarii.execution.is_stopped_exec()) to submit the model and get its reported results.

Customize a New One-shot Trainer (legacy)

One-shot trainers should inherit nni.retiarii.oneshot.BaseOneShotTrainer, and need to implement fit() (used to conduct the fitting and searching process) and export() method (used to return the searched best architecture).

Writing a one-shot trainer is very different to single-arch evaluator. First of all, there are no more restrictions on init method arguments, any Python arguments are acceptable. Secondly, the model fed into one-shot trainers might be a model with Retiarii-specific modules, such as LayerChoice and InputChoice. Such model cannot directly forward-propagate and trainers need to decide how to handle those modules.

A typical example is DartsTrainer, where learnable-parameters are used to combine multiple choices in LayerChoice. Retiarii provides ease-to-use utility functions for module-replace purposes, namely nni.retiarii.oneshot.pytorch.utils.replace_layer_choice(), nni.retiarii.oneshot.pytorch.utils.replace_input_choice(). A simplified example is as follows:

from nni.retiarii.oneshot import BaseOneShotTrainer
from nni.retiarii.oneshot.pytorch.utils import replace_layer_choice, replace_input_choice

class DartsLayerChoice(nn.Module):
    def __init__(self, layer_choice):
        super(DartsLayerChoice, self).__init__() = layer_choice.label
        self.op_choices = nn.ModuleDict(layer_choice.named_children())
        self.alpha = nn.Parameter(torch.randn(len(self.op_choices)) * 1e-3)

    def forward(self, *args, **kwargs):
        op_results = torch.stack([op(*args, **kwargs) for op in self.op_choices.values()])
        alpha_shape = [-1] + [1] * (len(op_results.size()) - 1)
        return torch.sum(op_results * F.softmax(self.alpha, -1).view(*alpha_shape), 0)

class DartsTrainer(BaseOneShotTrainer):

    def __init__(self, model, loss, metrics, optimizer):
        self.model = model
        self.loss = loss
        self.metrics = metrics
        self.num_epochs = 10

        self.nas_modules = []
        replace_layer_choice(self.model, DartsLayerChoice, self.nas_modules)

        ... # init dataloaders and optimizers

    def fit(self):
        for i in range(self.num_epochs):
            for (trn_X, trn_y), (val_X, val_y) in zip(self.train_loader, self.valid_loader):
                self.train_architecture(val_X, val_y)
                self.train_model_weight(trn_X, trn_y)

    def export(self):
        result = dict()
        for name, module in self.nas_modules:
            if name not in result:
                result[name] = select_best_of_module(module)
        return result

The full code of DartsTrainer is available to Retiarii source code. Please have a check at DartsTrainer.