.xoptim.repeated_optim

experimental_experiment.xoptim.repeated_optim.find_largest_repeated_pattern(onx: Sequence[NodeProto] | ModelProto | GraphProto | FunctionProto, min_freq: int = 2, verbose: int = 0, all_instances: bool = False, name: str = 'RepeatedPattern') → Tuple[List[int] | List[List[int]], OnnxEasyPatternOptimization] | None

Finds the largest repeated pattern in a graph.

Parameters:

• onx – any object containing a sequence of NodeProto

• min_freq – do not consider any frequency below that threshold

• verbose – verbosity

• all_instances – if True, returns all instances

• name – class name for the instance of OnnxEasyPatternOptimization

Returns:

list of node indices in the pattern, the pattern as a subtype of OnnxEasyPatternOptimization

The opset are correct if the input is a ModelProto.

experimental_experiment.xoptim.repeated_optim.make_function_from_nodes(nodes: List[NodeProto], name: str = 'function', domain: str = 'repeated') → FunctionProto

Creates a function from a list of nodes. Looks into inputs not created by one of the nodes, looks into unused outputs. Opset versions are all set to one.

Parameters:

• nodes – list of nodes

• name – function name

• domain – domain name

Returns:

function proto

experimental_experiment.xoptim.repeated_optim.node_type_frequency(onx: Sequence[NodeProto] | ModelProto | GraphProto | FunctionProto, min_freq: int = 2) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], int], int, List[Tuple[str, str]]]

Computes the frequency of every node type in a list.

Parameters:

• onx – any object containing a sequence of NodeProto

• min_freq – do not consider any frequency below that threshold

Returns:

4 results, the frequencies of the node types, the frequencies of the frequencies, the most frequent frequency (the estimation of the number of layers), all types having the exact same frequency as the previous one

Note

This function assumes at least one type of node is present only once in every layer.