In this quick start, we use multi-trial NAS as an example to show how to construct and explore a model space. There are mainly three crucial components for a neural architecture search task, namely,
Model search space that defines a set of models to explore.
A proper strategy as the method to explore this model space.
A model evaluator that reports the performance of every model in the space.
The tutorial for One-shot NAS can be found here.
Currently, PyTorch is the only supported framework by Retiarii, and we have only tested PyTorch 1.7 to 1.10. This documentation assumes PyTorch context but it should also apply to other frameworks, which is in our future plan.
Model space is defined by users to express a set of models that users want to explore, which contains potentially good-performing models. In this framework, a model space is defined with two parts: a base model and possible mutations on the base model.
Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model. Usually, you only need to replace the code
import torch.nn as nn with
import nni.retiarii.nn.pytorch as nn to use our wrapped PyTorch modules.
Below is a very simple example of defining a base model.
import torch import torch.nn.functional as F import nni.retiarii.nn.pytorch as nn from nni.retiarii import model_wrapper @model_wrapper # this decorator should be put on the out most class Net(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = F.relu(self.conv1(x)) x = F.max_pool2d(self.conv2(x), 2) x = torch.flatten(self.dropout1(x), 1) x = self.fc2(self.dropout2(F.relu(self.fc1(x)))) output = F.log_softmax(x, dim=1) return output
Always keep in mind that you should use
import nni.retiarii.nn.pytorch as nn and
nni.retiarii.model_wrapper(). Many mistakes are a result of forgetting one of those. Also, please use
torch.nn for submodules of
torch.nn.init instead of
A base model is only one concrete model not a model space. We provide APIs and primitives for users to express how the base model can be mutated. That is, to build a model space which includes many models.
Based on the above base model, we can define a model space as below.
import torch import torch.nn.functional as F import nni.retiarii.nn.pytorch as nn from nni.retiarii import model_wrapper @model_wrapper class Net(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) - self.conv2 = nn.Conv2d(32, 64, 3, 1) + self.conv2 = nn.LayerChoice([ + nn.Conv2d(32, 64, 3, 1), + DepthwiseSeparableConv(32, 64) + ]) - self.dropout1 = nn.Dropout(0.25) + self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75])) self.dropout2 = nn.Dropout(0.5) - self.fc1 = nn.Linear(9216, 128) - self.fc2 = nn.Linear(128, 10) + feature = nn.ValueChoice([64, 128, 256]) + self.fc1 = nn.Linear(9216, feature) + self.fc2 = nn.Linear(feature, 10) def forward(self, x): x = F.relu(self.conv1(x)) x = F.max_pool2d(self.conv2(x), 2) x = torch.flatten(self.dropout1(x), 1) x = self.fc2(self.dropout2(F.relu(self.fc1(x)))) output = F.log_softmax(x, dim=1) return output
This example uses two mutation APIs,
nn.LayerChoice takes a list of candidate modules (two in this example), one will be chosen for each sampled model. It can be used like normal PyTorch module.
nn.ValueChoice takes a list of candidate values, one will be chosen to take effect for each sampled model.
More detailed API description and usage can be found here .
We are actively enriching the mutation APIs, to facilitate easy construction of model space. If the currently supported mutation APIs cannot express your model space, please refer to this doc for customizing mutators.
There are basically two exploration approaches: (1) search by evaluating each sampled model independently, which is the search approach in multi-trial NAS and (2) one-shot weight-sharing based search, which is used in one-shot NAS. We demonstrate the first approach in this tutorial. Users can refer to here for the second approach.
First, users need to pick a proper exploration strategy to explore the defined model space. Second, users need to pick or customize a model evaluator to evaluate the performance of each explored model.
Retiarii supports many exploration strategies.
Simply choosing (i.e., instantiate) an exploration strategy as below.
import nni.retiarii.strategy as strategy search_strategy = strategy.Random(dedup=True) # dedup=False if deduplication is not wanted
In the exploration process, the exploration strategy repeatedly generates new models. A model evaluator is for training and validating each generated model to obtain the model’s performance. The performance is sent to the exploration strategy for the strategy to generate better models.
Retiarii has provided built-in model evaluators, but to start with, it is recommended to use
FunctionalEvaluator, that is, to wrap your own training and evaluation code with one single function. This function should receive one single model class and uses
nni.report_final_result to report the final score of this model.
An example here creates a simple evaluator that runs on MNIST dataset, trains for 2 epochs, and reports its validation accuracy.
def evaluate_model(model_cls): # "model_cls" is a class, need to instantiate model = model_cls() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True) test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64) device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') for epoch in range(3): # train the model for one epoch train_epoch(model, device, train_loader, optimizer, epoch) # test the model for one epoch accuracy = test_epoch(model, device, test_loader) # call report intermediate result. Result can be float or dict nni.report_intermediate_result(accuracy) # report final test result nni.report_final_result(accuracy) # Create the evaluator evaluator = nni.retiarii.evaluator.FunctionalEvaluator(evaluate_model)
test_epoch here can be any customized function, where users can write their own training recipe. See Github link: examples/nas/multi-trial/mnist/search.py for the full example.
It is recommended that the
evaluate_model here accepts no additional arguments other than
model_cls. However, in the advanced tutorial, we will show how to use additional arguments in case you actually need those. In future, we will support mutation on the arguments of evaluators, which is commonly called “Hyper-parmeter tuning”.
After all the above are prepared, it is time to start an experiment to do the model search. An example is shown below.
exp = RetiariiExperiment(base_model, evaluator, , search_strategy) exp_config = RetiariiExeConfig('local') exp_config.experiment_name = 'mnist_search' exp_config.trial_concurrency = 2 exp_config.max_trial_number = 20 exp_config.training_service.use_active_gpu = False exp.run(exp_config, 8081)
Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment. For example, open
localhost::8081 in your browser, 8081 is the port that you set in
exp.run. Please refer to here for details.
We support visualizing models with 3rd-party visualization engines (like Netron). This can be used by clicking
Visualization in detail panel for each trial. Note that current visualization is based on onnx , thus visualization is not feasible if the model cannot be exported into onnx. Built-in evaluators (e.g., Classification) will automatically export the model into a file. For your own evaluator, you need to save your file into
$NNI_OUTPUT_DIR/model.onnx to make this work.
Users can export top models after the exploration is done using
for model_code in exp.export_top_models(formatter='dict'): print(model_code)
The output is json object which records the mutation actions of the top model. If users want to output source code of the top model, they can use graph-based execution engine for the experiment, by simply adding the following two lines.
exp_config.execution_engine = 'base' export_formatter = 'code'