autonas

Entrypoints for AutoNAS mode.

Classes

`AutoNASPatchManager`	A class to handle the monkey patching of the model for automode.
`EvolveSearcher`	An iterative searcher that uses an evolutionary algorithm to optimize the subnet config.
`IterativeSearcher`	Base class for iterative search algorithms.
`RandomSearcher`	An iterative searcher that samples subnets randomly.

Functions

`convert_autonas_searchspace`	Convert search space for AutoNAS mode with correct patch manager.
`convert_searchspace`	Convert given model into a search space.
`export_searchspace`	Export a subnet configuration of the search space to a regular model.
`restore_autonas_searchspace`	Restore search space for AutoNAS mode with correct patch manager.
`restore_export`	Restore & export the subnet configuration of the search space to a regular model.
`restore_searchspace`	Restore a search space from the given model.
`update_autonas_metadata`	Update subnet config to current subnet config of model.

class AutoNASPatchManager

Bases: PatchManager

A class to handle the monkey patching of the model for automode.

property sample_during_training: bool: Indicates whether we should sample a new subnet during training.

class EvolveSearcher

Bases: IterativeSearcher

An iterative searcher that uses an evolutionary algorithm to optimize the subnet config.

after_step()

Update population after each iterative step.

Return type:: None

before_search()

Set the lower bound of the constraints to 0.85 * upper bound before search.

Return type:: None

before_step()

Update candidates and population before each iterative step.

Return type:: None

candidates: List[Dict[str, Any]]

property default_search_config: Dict[str, Any]: Default search config contains additional algorithm parameters.

property default_state_dict: Dict[str, Any]: Return default state dict.

population: List[Dict[str, Any]]

sample()

Sampling a new subnet involves random sampling, mutation, and crossover.

Return type:: Dict[str, Any]

class IterativeSearcher

Bases: BaseSearcher, ABC

Base class for iterative search algorithms.

after_search()

Select best model.

Return type:: None

after_step()

Run after each iterative step.

Return type:: None

before_search()

Ensure that the model is actually configurable and ready for eval.

Return type:: None

before_step()

Run before each iterative step.

Return type:: None

best: Dict[str, Any]

best_history: Dict[str, Any]

candidate: Dict[str, Any]

constraints_func: ConstraintsFunc

property default_search_config: Dict[str, Any]: Get the default config for the searcher.

property default_state_dict: Dict[str, Any]: Return default state dict.

early_stop()

Check if we should early stop the search if possible.

Return type:: bool

history: Dict[str, Any]

iter_num: int

num_satisfied: int

run_search()

Run iterative search loop.

Return type:: None

run_step()

The main routine of each iterative step.

Return type:: None

abstract sample()

Sample and select new sub-net configuration and return configuration.

Return type:: Dict[str, Any]

samples: Dict[str, Any]

sanitize_search_config(config)

Sanitize the search config dict.

Parameters:: config (Dict[str, Any] | None) –
Return type:: Dict[str, Any]

class RandomSearcher

Bases: IterativeSearcher

An iterative searcher that samples subnets randomly.

sample()

Random sample new subset during each steo.

Return type:: Dict[str, Any]

convert_autonas_searchspace(model, config)

Convert search space for AutoNAS mode with correct patch manager.

Parameters:

model (Module) –
config (ModeloptBaseConfig) –

Return type:

Tuple[Module, Dict[str, Any]]

convert_searchspace(model, config, patch_manager_type)

Convert given model into a search space.

Parameters:

model (Module) –
config (ModeloptBaseConfig) –
patch_manager_type (Type[PatchManager]) –

Return type:

Tuple[Module, Dict[str, Any]]

export_searchspace(model, config)

Export a subnet configuration of the search space to a regular model.

Parameters:

model (Module) –
config (ExportConfig) –

Return type:

Tuple[Module, Dict[str, Any]]

restore_autonas_searchspace(model, config, metadata)

Restore search space for AutoNAS mode with correct patch manager.

Parameters:

model (Module) –
config (ModeloptBaseConfig) –
metadata (Dict[str, Any]) –

Return type:

Module

restore_export(model, config, metadata)

Restore & export the subnet configuration of the search space to a regular model.

Parameters:

model (Module) –
config (ExportConfig) –
metadata (Dict[str, Any]) –

Return type:

Module

restore_searchspace(model, config, metadata, patch_manager)

Restore a search space from the given model.

Parameters:

model (Module) –
config (ModeloptBaseConfig) –
metadata (Dict[str, Any]) –
patch_manager (Type[PatchManager]) –

Return type:

Module

update_autonas_metadata(model, config, metadata)

Update subnet config to current subnet config of model.

Parameters:

model (Module) –
config (ModeloptBaseConfig) –
metadata (Dict[str, Any]) –

Return type:

None