Model Evaluators

A model evaluator is for training and validating each generated model. They are necessary to evaluate the performance of new explored models.

Customize Evaluator with Any Function

The simplest way to customize a new evaluator is with functional APIs, which is very easy when training code is already available. Users only need to write a fit function that wraps everything, which usually includes training, validating and testing of a single model. This function takes one positional arguments (model_cls) and possible keyword arguments. The keyword arguments (other than model_cls) are fed to FunctionEvaluator as its initialization parameters (note that they will be serialized). In this way, users get everything under their control, but expose less information to the framework and as a result, further optimizations like CGO might be not feasible. An example is as belows:

from nni.retiarii.evaluator import FunctionalEvaluator
from nni.retiarii.experiment.pytorch import RetiariiExperiment

def fit(model_cls, dataloader):
    model = model_cls()
    train(model, dataloader)
    acc = test(model, dataloader)
    nni.report_final_result(acc)

# The dataloader will be serialized, thus ``nni.trace`` is needed here.
# See serialization tutorial for more details.
evaluator = FunctionalEvaluator(fit, dataloader=nni.trace(DataLoader)(foo, bar))
experiment = RetiariiExperiment(base_model, evaluator, mutators, strategy)

Tip

When using customized evaluators, if you want to visualize models, you need to export your model and save it into $NNI_OUTPUT_DIR/model.onnx in your evaluator. An example here:

def fit(model_cls):
    model = model_cls()
    onnx_path = Path(os.environ.get('NNI_OUTPUT_DIR', '.')) / 'model.onnx'
    onnx_path.parent.mkdir(exist_ok=True)
    dummy_input = torch.randn(10, 3, 224, 224)
    torch.onnx.export(model, dummy_input, onnx_path)
    # the rest of training code here

If the conversion is successful, the model will be able to be visualized with powerful tools Netron.

Evaluators with PyTorch-Lightning

Use Built-in Evaluators

NNI provides some commonly used model evaluators for users’ convenience. These evaluators are built upon the awesome library PyTorch-Lightning.

We recommend to read the serialization tutorial before using these evaluators. A few notes to summarize the tutorial:

  1. pl.DataLoader should be used in place of torch.utils.data.DataLoader.

  2. The datasets used in data-loader should be decorated with nni.trace recursively.

For example,

import nni.retiarii.evaluator.pytorch.lightning as pl
from torchvision import transforms

transform = nni.trace(transforms.Compose, [nni.trace(transforms.ToTensor()), nni.trace(transforms.Normalize, (0.1307,), (0.3081,))])
train_dataset = nni.trace(MNIST, root='data/mnist', train=True, download=True, transform=transform)
test_dataset = nni.trace(MNIST, root='data/mnist', train=False, download=True, transform=transform)

# pl.DataLoader and pl.Classification is already traced and supports serialization.
evaluator = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
                              val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
                              max_epochs=10)
class nni.retiarii.evaluator.pytorch.lightning.Classification(criterion: torch.nn.modules.module.Module = <class 'torch.nn.modules.loss.CrossEntropyLoss'>, learning_rate: float = 0.001, weight_decay: float = 0.0, optimizer: torch.optim.optimizer.Optimizer = <class 'torch.optim.adam.Adam'>, train_dataloader: typing.Optional[torch.utils.data.dataloader.DataLoader] = None, val_dataloaders: typing.Optional[typing.Union[torch.utils.data.dataloader.DataLoader, typing.List[torch.utils.data.dataloader.DataLoader]]] = None, export_onnx: bool = True, **trainer_kwargs)[source]

Trainer that is used for classification.

Parameters

  • criterion (nn.Module) – Class for criterion module (not an instance). default: nn.CrossEntropyLoss

  • learning_rate (float) – Learning rate. default: 0.001

  • weight_decay (float) – L2 weight decay. default: 0

  • optimizer (Optimizer) – Class for optimizer (not an instance). default: Adam

  • train_dataloders (DataLoader) – Used in trainer.fit(). A PyTorch DataLoader with training samples. If the lightning_module has a predefined train_dataloader method this will be skipped.

  • val_dataloaders (DataLoader or List of DataLoader) – Used in trainer.fit(). Either a single PyTorch Dataloader or a list of them, specifying validation samples. If the lightning_module has a predefined val_dataloaders method this will be skipped.

  • export_onnx (bool) – If true, model will be exported to model.onnx before training starts. default true

  • trainer_kwargs (dict) – Optional keyword arguments passed to trainer. See Lightning documentation for details.

class nni.retiarii.evaluator.pytorch.lightning.Regression(criterion: torch.nn.modules.module.Module = <class 'torch.nn.modules.loss.MSELoss'>, learning_rate: float = 0.001, weight_decay: float = 0.0, optimizer: torch.optim.optimizer.Optimizer = <class 'torch.optim.adam.Adam'>, train_dataloader: typing.Optional[torch.utils.data.dataloader.DataLoader] = None, val_dataloaders: typing.Optional[typing.Union[torch.utils.data.dataloader.DataLoader, typing.List[torch.utils.data.dataloader.DataLoader]]] = None, export_onnx: bool = True, **trainer_kwargs)[source]

Trainer that is used for regression.

Parameters

  • criterion (nn.Module) – Class for criterion module (not an instance). default: nn.MSELoss

  • learning_rate (float) – Learning rate. default: 0.001

  • weight_decay (float) – L2 weight decay. default: 0

  • optimizer (Optimizer) – Class for optimizer (not an instance). default: Adam

  • train_dataloders (DataLoader) – Used in trainer.fit(). A PyTorch DataLoader with training samples. If the lightning_module has a predefined train_dataloader method this will be skipped.

  • val_dataloaders (DataLoader or List of DataLoader) – Used in trainer.fit(). Either a single PyTorch Dataloader or a list of them, specifying validation samples. If the lightning_module has a predefined val_dataloaders method this will be skipped.

  • export_onnx (bool) – If true, model will be exported to model.onnx before training starts. default: true

  • trainer_kwargs (dict) – Optional keyword arguments passed to trainer. See Lightning documentation for details.

Customize Evaluator with PyTorch-Lightning

Another approach is to write training code in PyTorch-Lightning style, that is, to write a LightningModule that defines all elements needed for training (e.g., loss function, optimizer) and to define a trainer that takes (optional) dataloaders to execute the training. Before that, please read the document of PyTorch-lightning to learn the basic concepts and components provided by PyTorch-lightning.

In practice, writing a new training module in Retiarii should inherit nni.retiarii.evaluator.pytorch.lightning.LightningModule, which has a set_model that will be called after __init__ to save the candidate model (generated by strategy) as self.model. The rest of the process (like training_step) should be the same as writing any other lightning module. Evaluators should also communicate with strategies via two API calls (nni.report_intermediate_result for periodical metrics and nni.report_final_result for final metrics), added in on_validation_epoch_end and teardown respectively.

An example is as follows:

from nni.retiarii.evaluator.pytorch.lightning import LightningModule  # please import this one

@nni.trace
class AutoEncoder(LightningModule):
    def __init__(self):
        super().__init__()
        self.decoder = nn.Sequential(
            nn.Linear(3, 64),
            nn.ReLU(),
            nn.Linear(64, 28*28)
        )

    def forward(self, x):
        embedding = self.model(x)  # let's search for encoder
        return embedding

    def training_step(self, batch, batch_idx):
        # training_step defined the train loop.
        # It is independent of forward
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.model(x)  # model is the one that is searched for
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        # Logging to TensorBoard by default
        self.log('train_loss', loss)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.model(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        self.log('val_loss', loss)

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

    def on_validation_epoch_end(self):
        nni.report_intermediate_result(self.trainer.callback_metrics['val_loss'].item())

    def teardown(self, stage):
        if stage == 'fit':
            nni.report_final_result(self.trainer.callback_metrics['val_loss'].item())

Then, users need to wrap everything (including LightningModule, trainer and dataloaders) into a Lightning object, and pass this object into a Retiarii experiment.

import nni.retiarii.evaluator.pytorch.lightning as pl
from nni.retiarii.experiment.pytorch import RetiariiExperiment

lightning = pl.Lightning(AutoEncoder(),
                         pl.Trainer(max_epochs=10),
                         train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
                         val_dataloaders=pl.DataLoader(test_dataset, batch_size=100))
experiment = RetiariiExperiment(base_model, lightning, mutators, strategy)