Retiarii API Reference ¶

Contents

Retiarii API Reference

Inline Mutation APIs ¶

class nni.retiarii.nn.pytorch.LayerChoice(candidates: Union[Dict[str, torch.nn.modules.module.Module], List[torch.nn.modules.module.Module]], *, prior: Optional[List[float]] = None, label: Optional[str] = None, **kwargs)[source]¶

Layer choice selects one of the candidates, then apply it on inputs and return results.

Layer choice does not allow itself to be nested.

Parameters

candidates (list of nn.Module or OrderedDict) – A module list to be selected from.
prior (list of float) – Prior distribution used in random sampling.
label (str) – Identifier of the layer choice.

length¶

Deprecated. Number of ops to choose from. len(layer_choice) is recommended.

Type: int

names¶

Names of candidates.

Type: list of str

choices¶

Deprecated. A list of all candidate modules in the layer choice module. list(layer_choice) is recommended, which will serve the same purpose.

Type: list of Module

Notes

candidates can be a list of modules or a ordered dict of named modules, for example,

self.op_choice = LayerChoice(OrderedDict([
    ("conv3x3", nn.Conv2d(3, 16, 128)),
    ("conv5x5", nn.Conv2d(5, 16, 128)),
    ("conv7x7", nn.Conv2d(7, 16, 128))
]))

Elements in layer choice can be modified or deleted. Use del self.op_choice["conv5x5"] or self.op_choice[1] = nn.Conv3d(...). Adding more choices is not supported yet.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class nni.retiarii.nn.pytorch.InputChoice(n_candidates: int, n_chosen: Optional[int] = 1, reduction: str = 'sum', *, prior: Optional[List[float]] = None, label: Optional[str] = None, **kwargs)[source]¶

Input choice selects n_chosen inputs from choose_from (contains n_candidates keys). Use reduction to specify how chosen inputs are reduced into one output. A few options are:

none: do nothing and return the list directly.
sum: summing all the chosen inputs.
mean: taking the average of all chosen inputs.
concat: concatenate all chosen inputs at dimension 1.

We don’t support customizing reduction yet.

Parameters

n_candidates (int) – Number of inputs to choose from. It is required.
n_chosen (int) – Recommended inputs to choose. If None, mutator is instructed to select any.
reduction (str) – mean, concat, sum or none.
prior (list of float) – Prior distribution used in random sampling.
label (str) – Identifier of the input choice.

forward(candidate_inputs: List[torch.Tensor]) → torch.Tensor[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class nni.retiarii.nn.pytorch.ValueChoice(candidates: List[Any], *, prior: Optional[List[float]] = None, label: Optional[str] = None)[source]¶

ValueChoice is to choose one from candidates.

In most use scenarios, ValueChoice should be passed to the init parameters of a serializable module. For example,

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(3, nn.ValueChoice([32, 64]), kernel_size=nn.ValueChoice([3, 5, 7]))

    def forward(self, x):
        return self.conv(x)

In case, you want to search a parameter that is used repeatedly, this is also possible by sharing the same value choice instance. (Sharing the label should have the same effect.) For example,

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        hidden_dim = nn.ValueChoice([128, 512])
        self.fc = nn.Sequential(
            nn.Linear(64, hidden_dim),
            nn.Linear(hidden_dim, 10)
        )

        # the following code has the same effect.
        # self.fc = nn.Sequential(
        #     nn.Linear(64, nn.ValueChoice([128, 512], label='dim')),
        #     nn.Linear(nn.ValueChoice([128, 512], label='dim'), 10)
        # )

    def forward(self, x):
        return self.fc(x)

Note that ValueChoice should be used directly. Transformations like nn.Linear(32, nn.ValueChoice([64, 128]) * 2) are not supported.

Another common use case is to initialize the values to choose from in init and call the module in forward to get the chosen value. Usually, this is used to pass a mutable value to a functional API like torch.xxx or nn.functional.xxx`. For example,

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.dropout_rate = nn.ValueChoice([0., 1.])

    def forward(self, x):
        return F.dropout(x, self.dropout_rate())

Parameters

candidates (list) – List of values to choose from.
prior (list of float) – Prior distribution to sample from.
label (str) – Identifier of the value choice.

forward()[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class nni.retiarii.nn.pytorch.ChosenInputs(chosen: Union[List[int], int], reduction: str)[source]¶

A module that chooses from a tensor list and outputs a reduced tensor. The already-chosen version of InputChoice.

forward(candidate_inputs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class nni.retiarii.nn.pytorch.Repeat(blocks: Union[Callable[[], torch.nn.modules.module.Module], List[Callable[[], torch.nn.modules.module.Module]], torch.nn.modules.module.Module, List[torch.nn.modules.module.Module]], depth: Union[int, Tuple[int, int]], label: Optional[str] = None)[source]¶

Repeat a block by a variable number of times.

Parameters

blocks (function, list of function, module or list of module) – The block to be repeated. If not a list, it will be replicated into a list. If a list, it should be of length max_depth, the modules will be instantiated in order and a prefix will be taken. If a function, it will be called to instantiate a module. Otherwise the module will be deep-copied.
depth (int or tuple of int) – If one number, the block will be repeated by a fixed number of times. If a tuple, it should be (min, max), meaning that the block will be repeated at least min times and at most max times.

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class nni.retiarii.nn.pytorch.Cell(op_candidates: Union[Callable, List[torch.nn.modules.module.Module]], num_nodes: int, num_ops_per_node: int = 1, num_predecessors: int = 1, merge_op: str = 'all', label: Optional[str] = None)[source]¶

Cell structure [zophnas] [zophnasnet] that is popularly used in NAS literature.

A cell consists of multiple “nodes”. Each node is a sum of multiple operators. Each operator is chosen from op_candidates, and takes one input from previous nodes and predecessors. Predecessor means the input of cell. The output of cell is the concatenation of some of the nodes in the cell (currently all the nodes).

Parameters

op_candidates (function or list of module) – A list of modules to choose from, or a function that returns a list of modules.
num_nodes (int) – Number of nodes in the cell.
num_ops_per_node (int) – Number of operators in each node. The output of each node is the sum of all operators in the node. Default: 1.
num_predecessors (int) – Number of inputs of the cell. The input to forward should be a list of tensors. Default: 1.
merge_op (str) – Currently only all is supported, which has slight difference with that described in reference. Default: all.
label (str) – Identifier of the cell. Cell sharing the same label will semantically share the same choice.

References

zophnas: Barret Zoph, Quoc V. Le, “Neural Architecture Search with Reinforcement Learning”. https://arxiv.org/abs/1611.01578
zophnasnet: Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le, “Learning Transferable Architectures for Scalable Image Recognition”. https://arxiv.org/abs/1707.07012

forward(x: List[torch.Tensor])[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Graph Mutation APIs ¶

class nni.retiarii.Mutator(sampler: Optional[nni.retiarii.mutator.Sampler] = None, label: Optional[str] = None)[source]¶

Mutates graphs in model to generate new model. Mutator class will be used in two places:

Inherit Mutator to implement graph mutation logic.

Use Mutator subclass to implement NAS strategy.

In scenario 1, the subclass should implement Mutator.mutate() interface with Mutator.choice(). In scenario 2, strategy should use constructor or Mutator.bind_sampler() to initialize subclass, and then use Mutator.apply() to mutate model. For certain mutator subclasses, strategy or sampler can use Mutator.dry_run() to predict choice candidates. # Method names are open for discussion.

If mutator has a label, in most cases, it means that this mutator is applied to nodes with this label.

apply(model: nni.retiarii.graph.Model) → nni.retiarii.graph.Model[source]¶: Apply this mutator on a model. Returns mutated model. The model will be copied before mutation and the original model will not be modified.

bind_sampler(sampler: nni.retiarii.mutator.Sampler) → nni.retiarii.mutator.Mutator[source]¶: Set the sampler which will handle Mutator.choice calls.

choice(candidates: Iterable[Any]) → Any[source]¶: Ask sampler to make a choice.

dry_run(model: nni.retiarii.graph.Model) → Tuple[List[List[Any]], nni.retiarii.graph.Model][source]¶: Dry run mutator on a model to collect choice candidates. If you invoke this method multiple times on same or different models, it may or may not return identical results, depending on how the subclass implements Mutator.mutate().

mutate(model: nni.retiarii.graph.Model) → None[source]¶: Abstract method to be implemented by subclass. Mutate a model in place.

class nni.retiarii.Model(_internal=False)[source]¶

Represents a neural network model.

During mutation, one Model object is created for each trainable snapshot. For example, consider a mutator that insert a node at an edge for each iteration. In one iteration, the mutator invokes 4 primitives: add node, remove edge, add edge to head, add edge to tail. These 4 primitives operates in one Model object. When they are all done the model will be set to “frozen” (trainable) status and be submitted to execution engine. And then a new iteration starts, and a new Model object is created by forking last model.

python_class¶: Python class that base model is converted from.

python_init_params¶: Initialization parameters of python class.

status¶: See ModelStatus.

root_graph¶: The outermost graph which usually takes dataset as input and feeds output to loss function.

graphs¶: All graphs (subgraphs) in this model.

evaluator¶: Model evaluator

history¶: Mutation history. self is directly mutated from self.history[-1]; self.history[-1] is mutated from `self.history[-2], and so on. self.history[0] is the base graph.

metric¶: Training result of the model, or None if it’s not yet trained or has failed to train.

intermediate_metrics¶: Intermediate training metrics. If the model is not trained, it’s an empty list.

fork() → nni.retiarii.graph.Model[source]¶

Create a new model which has same topology, names, and IDs to current one.

Can only be invoked on a frozen model. The new model will be in Mutating state.

This API is used in mutator base class.

get_node_by_name(node_name: str) → nni.retiarii.graph.Node[source]¶: Traverse all the nodes to find the matched node with the given name.

get_nodes() → Iterable[nni.retiarii.graph.Node][source]¶: Traverse through all the nodes.

get_nodes_by_label(label: str) → List[nni.retiarii.graph.Node][source]¶

Traverse all the nodes to find the matched node(s) with the given label. There could be multiple nodes with the same label. Name space name can uniquely identify a graph or node.

NOTE: the implementation does not support the class abstration

get_nodes_by_type(type_name: str) → List[nni.retiarii.graph.Node][source]¶: Traverse all the nodes to find the matched node(s) with the given type.

class nni.retiarii.Graph(model: nni.retiarii.graph.Model, graph_id: int, name: Optional[str] = None, _internal: bool = False)[source]¶

Graph topology.

This class simply represents the topology, with no semantic meaning. All other information like metric, non-graph functions, mutation history, etc should go to Model.

Each graph belongs to and only belongs to one Model.

model¶: The model containing (and owning) this graph.

id¶: Unique ID in the model. If two models have graphs of identical ID, they are semantically the same graph. Typically this means one graph is mutated from another, or they are both mutated from one ancestor.

name¶: Mnemonic name of this graph. It should have an one-to-one mapping with ID.

input_names¶: Optional mnemonic names of input parameters.

output_names¶: Optional mnemonic names of output values.

input_node¶: …

output_node¶: …

hidden_nodes¶: …

nodes¶: All input/output/hidden nodes.

edges¶: …

fork() → nni.retiarii.graph.Graph[source]¶: Fork the model and returns corresponding graph in new model. This shortcut might be helpful because many algorithms only cares about “stem” subgraph instead of whole model.

get_node_by_id(node_id: int) → Optional[nni.retiarii.graph.Node][source]¶: Returns the node which has specified name; or returns None if no node has this name.

get_node_by_name(name: str) → Optional[nni.retiarii.graph.Node][source]¶: Returns the node which has specified name; or returns None if no node has this name.

get_nodes_by_type(operation_type: str) → List[nni.retiarii.graph.Node][source]¶: Returns nodes whose operation is specified typed.

class nni.retiarii.Node(graph, node_id, name, operation, _internal=False)[source]¶

An operation or an opaque subgraph inside a graph.

Each node belongs to and only belongs to one Graph. Nodes should never be created with constructor. Use Graph.add_node() instead.

The node itself is for topology only. Information of tensor calculation should all go inside operation attribute.

TODO: parameter of subgraph (cell) It’s easy to assign parameters on cell node, but it’s hard to “use” them. We need to design a way to reference stored cell parameters in inner node operations. e.g. self.fc = Linear(self.units) <- how to express self.units in IR?

graph¶: The graph containing this node.

id¶: Unique ID in the model. If two models have nodes with same ID, they are semantically the same node.

name¶: Mnemonic name. It should have an one-to-one mapping with ID.

label¶: Optional. If two nodes have the same label, they are considered same by the mutator.

operation¶: …

cell¶: Read only shortcut to get the referenced subgraph. If this node is not a subgraph (is a primitive operation), accessing cell will raise an error.

predecessors¶: Predecessor nodes of this node in the graph. This is an optional mutation helper.

successors¶: Successor nodes of this node in the graph. This is an optional mutation helper.

incoming_edges¶: Incoming edges of this node in the graph. This is an optional mutation helper.

outgoing_edges¶: Outgoing edges of this node in the graph. This is an optional mutation helper.

specialize_cell() → nni.retiarii.graph.Graph[source]¶: Only available if the operation is a cell. Duplicate the cell template and let this node reference to newly created copy.

class nni.retiarii.Edge(head: Tuple[nni.retiarii.graph.Node, Optional[int]], tail: Tuple[nni.retiarii.graph.Node, Optional[int]], _internal: bool = False)[source]¶

A tensor, or “data flow”, between two nodes.

Example forward code snippet: ` a, b, c = split(x) p = concat(a, c) q = sum(b, p) z = relu(q) `

Edges in above snippet:

head: (split, 0), tail: (concat, 0) # a in concat
head: (split, 2), tail: (concat, 1) # c in concat
head: (split, 1), tail: (sum, -1 or 0) # b in sum
head: (concat, null), tail: (sum, -1 or 1) # p in sum
head: (sum, null), tail: (relu, null) # q in relu

graph¶: …

head¶: Head node.

tail¶: Tail node.

head_slot¶: Index of outputs in head node. If the node has only one output, this should be null.

tail_slot¶: Index of inputs in tail node. If the node has only one input, this should be null. If the node does not care about order, this can be -1.

class nni.retiarii.Operation(type_name: str, parameters: Dict[str, Any] = {}, _internal: bool = False, attributes: Dict[str, Any] = {})[source]¶

Calculation logic of a graph node.

The constructor is private. Use Operation.new() to create operation object.

Operation is a naive record. Do not “mutate” its attributes or store information relate to specific node. All complex logic should be implemented in Node class.

type¶: Operation type name (e.g. Conv2D). If it starts with underscore, the “operation” is a special one (e.g. subgraph, input/output).

parameters¶: Arbitrary key-value parameters (e.g. kernel_size).

Evaluators ¶

class nni.retiarii.evaluator.FunctionalEvaluator(function, **kwargs)[source]¶

Functional evaluator that directly takes a function and thus should be general.

function¶: The full name of the function.

arguments¶: Keyword arguments for the function other than model.

class nni.retiarii.evaluator.pytorch.lightning.LightningModule(*args: Any, **kwargs: Any)[source]¶

Basic wrapper of generated model.

Lightning modules used in NNI should inherit this class.

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: Optional[torch.utils.data.dataloader.DataLoader] = None, val_dataloaders: Optional[Union[torch.utils.data.dataloader.DataLoader, 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: Optional[torch.utils.data.dataloader.DataLoader] = None, val_dataloaders: Optional[Union[torch.utils.data.dataloader.DataLoader, 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.

Oneshot Trainers ¶

class nni.retiarii.oneshot.pytorch.DartsTrainer(model, loss, metrics, optimizer, num_epochs, dataset, grad_clip=5.0, learning_rate=0.0025, batch_size=64, workers=4, device=None, log_frequency=None, arc_learning_rate=0.0003, unrolled=False)[source]¶

DARTS trainer.

Parameters

model (nn.Module) – PyTorch model to be trained.
loss (callable) – Receives logits and ground truth label, return a loss tensor.
metrics (callable) – Receives logits and ground truth label, return a dict of metrics.
optimizer (Optimizer) – The optimizer used for optimizing the model.
num_epochs (int) – Number of epochs planned for training.
dataset (Dataset) – Dataset for training. Will be split for training weights and architecture weights.
grad_clip (float) – Gradient clipping. Set to 0 to disable. Default: 5.
learning_rate (float) – Learning rate to optimize the model.
batch_size (int) – Batch size.
workers (int) – Workers for data loading.
device (torch.device) – torch.device("cpu") or torch.device("cuda").
log_frequency (int) – Step count per logging.
arc_learning_rate (float) – Learning rate of architecture parameters.
unrolled (float) – True if using second order optimization, else first order optimization.

class nni.retiarii.oneshot.pytorch.EnasTrainer(model, loss, metrics, reward_function, optimizer, num_epochs, dataset, batch_size=64, workers=4, device=None, log_frequency=None, grad_clip=5.0, entropy_weight=0.0001, skip_weight=0.8, baseline_decay=0.999, ctrl_lr=0.00035, ctrl_steps_aggregate=20, ctrl_kwargs=None)[source]¶

ENAS trainer.

Parameters

model (nn.Module) – PyTorch model to be trained.
loss (callable) – Receives logits and ground truth label, return a loss tensor.
metrics (callable) – Receives logits and ground truth label, return a dict of metrics.
reward_function (callable) – Receives logits and ground truth label, return a tensor, which will be feeded to RL controller as reward.
optimizer (Optimizer) – The optimizer used for optimizing the model.
num_epochs (int) – Number of epochs planned for training.
dataset (Dataset) – Dataset for training. Will be split for training weights and architecture weights.
batch_size (int) – Batch size.
workers (int) – Workers for data loading.
device (torch.device) – torch.device("cpu") or torch.device("cuda").
log_frequency (int) – Step count per logging.
grad_clip (float) – Gradient clipping. Set to 0 to disable. Default: 5.
entropy_weight (float) – Weight of sample entropy loss.
skip_weight (float) – Weight of skip penalty loss.
baseline_decay (float) – Decay factor of baseline. New baseline will be equal to baseline_decay * baseline_old + reward * (1 - baseline_decay).
ctrl_lr (float) – Learning rate for RL controller.
ctrl_steps_aggregate (int) – Number of steps that will be aggregated into one mini-batch for RL controller.
ctrl_steps (int) – Number of mini-batches for each epoch of RL controller learning.
ctrl_kwargs (dict) – Optional kwargs that will be passed to ReinforceController.

class nni.retiarii.oneshot.pytorch.ProxylessTrainer(model, loss, metrics, optimizer, num_epochs, dataset, warmup_epochs=0, batch_size=64, workers=4, device=None, log_frequency=None, arc_learning_rate=0.001)[source]¶

Proxyless trainer.

Parameters

model (nn.Module) – PyTorch model to be trained.
loss (callable) – Receives logits and ground truth label, return a loss tensor.
metrics (callable) – Receives logits and ground truth label, return a dict of metrics.
optimizer (Optimizer) – The optimizer used for optimizing the model.
num_epochs (int) – Number of epochs planned for training.
dataset (Dataset) – Dataset for training. Will be split for training weights and architecture weights.
warmup_epochs (int) – Number of epochs to warmup model parameters.
batch_size (int) – Batch size.
workers (int) – Workers for data loading.
device (torch.device) – torch.device("cpu") or torch.device("cuda").
log_frequency (int) – Step count per logging.
arc_learning_rate (float) – Learning rate of architecture parameters.

class nni.retiarii.oneshot.pytorch.SinglePathTrainer(model, loss, metrics, optimizer, num_epochs, dataset_train, dataset_valid, mutator=None, batch_size=64, workers=4, device=None, log_frequency=None)[source]¶

Single-path trainer. Samples a path every time and backpropagates on that path.

Parameters

model (nn.Module) – Model with mutables.
loss (callable) – Called with logits and targets. Returns a loss tensor.
metrics (callable) – Returns a dict that maps metrics keys to metrics data.
optimizer (Optimizer) – Optimizer that optimizes the model.
num_epochs (int) – Number of epochs of training.
dataset_train (Dataset) – Dataset of training.
dataset_valid (Dataset) – Dataset of validation.
batch_size (int) – Batch size.
workers (int) – Number of threads for data preprocessing. Not used for this trainer. Maybe removed in future.
device (torch.device) – Device object. Either torch.device("cuda") or torch.device("cpu"). When None, trainer will automatic detects GPU and selects GPU first.
log_frequency (int) – Number of mini-batches to log metrics.

Exploration Strategies ¶

class nni.retiarii.strategy.Random(variational=False, dedup=True, model_filter=None)[source]¶

Random search on the search space.

Parameters

variational (bool) – Do not dry run to get the full search space. Used when the search space has variational size or candidates. Default: false.
dedup (bool) – Do not try the same configuration twice. When variational is true, deduplication is not supported. Default: true.
model_filter (Callable[[Model], bool]) – Feed the model and return a bool. This will filter the models in search space and select which to submit.

class nni.retiarii.strategy.GridSearch(shuffle=True)[source]¶

Traverse the search space and try all the possible combinations one by one.

Parameters: shuffle (bool) – Shuffle the order in a candidate list, so that they are tried in a random order. Default: true.

class nni.retiarii.strategy.RegularizedEvolution(optimize_mode='maximize', population_size=100, sample_size=25, cycles=20000, mutation_prob=0.05, on_failure='ignore')[source]¶

Algorithm for regularized evolution (i.e. aging evolution). Follows “Algorithm 1” in Real et al. “Regularized Evolution for Image Classifier Architecture Search”.

Parameters

optimize_mode (str) – Can be one of “maximize” and “minimize”. Default: maximize.
population_size (int) – The number of individuals to keep in the population. Default: 100.
cycles (int) – The number of cycles (trials) the algorithm should run for. Default: 20000.
sample_size (int) – The number of individuals that should participate in each tournament. Default: 25.
mutation_prob (float) – Probability that mutation happens in each dim. Default: 0.05
on_failure (str) – Can be one of “ignore” and “worst”. If “ignore”, simply give up the model and find a new one. If “worst”, mark the model as -inf (if maximize, inf if minimize), so that the algorithm “learns” to avoid such model. Default: ignore.

class nni.retiarii.strategy.TPEStrategy[source]¶

The Tree-structured Parzen Estimator (TPE) [bergstrahpo] is a sequential model-based optimization (SMBO) approach. SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements, and then subsequently choose new hyperparameters to test based on this model.

References

bergstrahpo: Bergstra et al., “Algorithms for Hyper-Parameter Optimization”. https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf

class nni.retiarii.strategy.PolicyBasedRL(max_collect: int = 100, trial_per_collect=20, policy_fn: Optional[Callable[[nni.retiarii.strategy._rl_impl.ModelEvaluationEnv], tianshou.policy.base.BasePolicy]] = None)[source]¶

Algorithm for policy-based reinforcement learning. This is a wrapper of algorithms provided in tianshou (PPO by default), and can be easily customized with other algorithms that inherit BasePolicy (e.g., REINFORCE 1).

Parameters

max_collect (int) – How many times collector runs to collect trials for RL. Default 100.
trial_per_collect (int) – How many trials (trajectories) each time collector collects. After each collect, trainer will sample batch from replay buffer and do the update. Default: 20.
policy_fn (function) – Takes ModelEvaluationEnv as input and return a policy. See _default_policy_fn for an example.

References

1: Barret Zoph and Quoc V. Le, “Neural Architecture Search with Reinforcement Learning”. https://arxiv.org/abs/1611.01578

Retiarii Experiments ¶

CGO Execution ¶

nni.retiarii.evaluator.pytorch.cgo.evaluator.MultiModelSupervisedLearningModule(criterion: torch.nn.modules.module.Module, metrics: Dict[str, torchmetrics.metric.Metric], learning_rate: float = 0.001, weight_decay: float = 0.0, optimizer: torch.optim.optimizer.Optimizer = <class 'torch.optim.adam.Adam'>)[source]¶

Lightning Module of SupervisedLearning for Cross-Graph Optimization. Users who needs cross-graph optimization should use this module.

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

nni.retiarii.evaluator.pytorch.cgo.evaluator.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: Optional[torch.utils.data.dataloader.DataLoader] = None, val_dataloaders: Optional[Union[torch.utils.data.dataloader.DataLoader, List[torch.utils.data.dataloader.DataLoader]]] = None, **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.
trainer_kwargs (dict) – Optional keyword arguments passed to trainer. See Lightning documentation for details.

nni.retiarii.evaluator.pytorch.cgo.evaluator.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: Optional[torch.utils.data.dataloader.DataLoader] = None, val_dataloaders: Optional[Union[torch.utils.data.dataloader.DataLoader, List[torch.utils.data.dataloader.DataLoader]]] = None, **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.
trainer_kwargs (dict) – Optional keyword arguments passed to trainer. See Lightning documentation for details.

Utilities ¶

nni.retiarii.serialize(cls, *args, **kwargs)[source]¶

To create an serializable instance inline without decorator. For example,

self.op = serialize(MyCustomOp, hidden_units=128)

nni.retiarii.fixed_arch(fixed_arch: Union[str, pathlib.Path, Dict[str, Any]], verbose=True)[source]¶

Load architecture from fixed_arch and apply to model. This should be used as a context manager. For example,

with fixed_arch('/path/to/export.json'):
    model = Model(3, 224, 224)

Parameters

fixed_arc (str, Path or dict) – Path to the JSON that stores the architecture, or dict that stores the exported architecture.
verbose (bool) – Print log messages if set to True

Returns

Context manager that provides a fixed architecture when creates the model.

Return type

ContextStack