Class ETRecord

devtools/etrecord/_etrecord.py:60–518

class ETRecord:
    def __init__(
        self,
        exported_program: Optional[ExportedProgram] = None,
        export_graph_id: Optional[int] = None,
        edge_dialect_program: Optional[
            Union[ExportedProgram, Dict[str, ExportedProgram]]
        ] = None,
        graph_map: Optional[Dict[str, ExportedProgram]] = None,
        _debug_handle_map: Optional[Dict[int, Union[int, List[int]]]] = None,
        _delegate_map: Optional[
            Dict[str, Dict[int, Dict[str, Union[str, _DelegateDebugIdentifierMap]]]]
        ] = None,
        _instruction_id_to_num_outs_map: Optional[
            Dict[str, Dict[int, Union[int, List[int]]]]
        ] = None,
        _reference_outputs: Optional[Dict[str, List[ProgramOutput]]] = None,
        _representative_inputs: Optional[List[ProgramInput]] = None,
    ):
        """
        Please do not construct an ETRecord object directly.

        If you want to create an ETRecord for logging AOT information to further analysis, please mark `generate_etrecord`
        as True in your export api, and get the ETRecord object from the `ExecutorchProgramManager`.
        For exmaple:
        ```python
            exported_program = torch.export.export(model, inputs)
            edge_program = to_edge_transform_and_lower(exported_program, generate_etrecord=True)
            executorch_program = edge_program.to_executorch()
            etrecord = executorch_program.get_etrecord()
        ```

        If user need to create an ETRecord manually, please use the `create_etrecord` function.

        **EXPERIMENTAL**: This API supports multiple methods. For example:
        ```python
        lowered_and_edge = to_edge_transform_and_lower(
            {
                "vision_encoder": vision_encoder_ep,
                "token_embedding": token_embedding_ep,
                "text_decoder": causal_llm_ep,
            },
            partitioner={
                "vision_encoder": [XnnpackPartitioner()],
                "token_embedding": [XnnpackPartitioner()],
                "text_decoder": [
                    XnnpackPartitioner(
                        config_precisions=ConfigPrecisionType.DYNAMIC_QUANT,
                        per_op_mode=True,
                    ),
                    XnnpackPartitioner(),
                ],
            },
            compile_config=EdgeCompileConfig(_check_ir_validity=False),
            constant_methods=manager.metadata,
            generate_etrecord=True,  # Enable ETRecord generation for all 3 methods
        )
        ```