searcher — Model Optimizer 0.31.0 (original) (raw)
Standard interface to implement a searcher algorithm.
A searcher is useful whenever we want to search/optimize over a set of hyperparameters in the model. Searchers are usually used in conjunction with a mode, which can define a search space via its entrypoints, i.e., convert the model into a search space. The searcher then optimizes over this search space.
Classes
| BaseSearcher | A basic search interface that can be used to search/optimize a model. |
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class BaseSearcher
Bases: ABC
A basic search interface that can be used to search/optimize a model.
The base interface supports basic features like setting up a search, checkpointing, and loading logic and defines a minimal workflow to follow.
final __init__()
We don’t allow to override __init__ method.
Return type:
None
after_search()
Optional post-processing steps after the search.
Return type:
None
before_search()
Optional pre-processing steps before the search.
Return type:
None
config_: dict[str, Any]_
constraints_: dict[str, str | float | dict | None]_
construct_forward_loop(silent=True, progress_bar_msg=None, max_iter_data_loader=None, post_process_fn=False)
Get runnable forward loop on the model using the provided configs.
Return type:
_Callable_[[_Module_], _None_] | None
property default_search_config_: dict[str, Any]_
Get the default config for the searcher.
abstract property default_state_dict_: dict[str, Any]_
Return default state dict.
deployment_: dict[str, str] | None_
dummy_input_: Any | tuple_
eval_score(silent=True)
Optionally silent evaluation of the score function.
Return type:
float
forward_loop_: Callable[[Module], None] | None_
property has_score_: bool_
Check if the model has a score function.
load_search_checkpoint()
Load function for search checkpoint returning indicator whether checkpoint was loaded.
Return type:
bool
model_: Module_
reset_search()
Reset search at the beginning.
Return type:
None
abstract run_search()
Run actual search.
Return type:
None
sanitize_search_config(config)
Sanitize the search config dict.
Parameters:
config (dict [ str , Any ] | None)
Return type:
_dict_[str, _Any_]
save_search_checkpoint()
Save function for search checkpoint.
Return type:
None
final search(model, constraints, dummy_input=None, config=None)
Search a given prunable model for the best sub-net and return the search model.
The best sub-net maximizes the score given by score_func while satisfying theconstraints.
Parameters:
- model (Module) – The converted model to be searched.
- constraints (dict [ str , str | float | dict | None ]) – The dictionary from constraint name to upper bound the searched model has to satisfy.
- dummy_input (Any | tuple | None) – Arguments of
model.forward(). This is used for exporting and calculating inference-based metrics, such as latency/FLOPs. The format ofdummy_inputsfollows the convention of theargsargument intorch.onnx.export. - config (dict [ str , Any ] | None) – Additional optional arguments to configure the search.
Return type:
_dict_[str, _Any_]
Returns: A tuple (subnet, state_dict) where
subnet is the searched subnet (nn.Module), which can be used for subsequent tasks like fine-tuning, state_dict contains the history and detailed stats of the search procedure.
final state_dict()
The state dictionary that can be stored/loaded.
Return type:
_dict_[str, _Any_]