Class ExecutorchBackendConfig

exir/capture/_config.py:57–125

@compatibility(is_backward_compatible=False)
@dataclass
class ExecutorchBackendConfig:
    passes: List[PassType] = field(default_factory=list)

    # A single memory planning pass can be defined for all the programs in the
    # EdgeProgramManager or can be defined per program.
    memory_planning_pass: Union[PassType, Dict[str, PassType]] = MemoryPlanningPass()
    to_out_var_pass: PassType = ToOutVarPass(ignore_to_out_var_failure=False)
    dynamic_memory_planning_mode: DynamicMemoryPlanningMode = (
        DynamicMemoryPlanningMode.UPPER_BOUND
    )
    emit_stacktrace: bool = False

    # Whether to move delegate data blobs from the Program into separate
    # segments, rather than encoding those blobs in the flatbuffer data.
    # This makes it possible to free those blobs at runtime.
    extract_delegate_segments: bool = True

    # When extracting segments, the starting offset of each segment will be
    # aligned to this value (in bytes). Must be a power of two.
    segment_alignment: int = 128

    # If provided, the minimum alignment of tensor buffers in the program. Must
    # be a power of 2. If not provided, uses the value in the schema file.
    constant_tensor_alignment: Optional[int] = None

    # If provided, the minimum alignment of delegate data in the program. Must
    # be a power of 2. If not provided, uses the value in the schema file.
    delegate_alignment: Optional[int] = None

    # A single sym shape eval pass can be defined for all the programs in the
    # EdgeProgramManager or can be defined per program.
    sym_shape_eval_pass: Union[PassType, Dict[str, PassType]] = (
        ConstraintBasedSymShapeEvalPass()
    )

    # If set to true, view_copy operations will be converted to lightweight
    # view operations in the ET runtime
    # Moreover, static views will be elided from the ExecuTorch graph
    remove_view_copy: bool = True

    # Bool: if True, all constant tensors will be stored in a separate file. If False,
    #       all constant tensors will be stored in the PTE file.
    # Callable: a function from torch.fx.Node to Optional[str]. This will be called for each
    #       placeholder (constant tensor) node, and if it returns a string, that node will be
    #       tagged with the string. If None, the constant tensor is stored in the PTE file.
    #       Otherwise, it is stored in a file named by the string. E.g., a function
    #       lambda x: "model_weights" will save all constants into a file "model_weights.ptd".
    external_constants: Union[bool, Callable[[torch.fx.Node], Optional[str]]] = False

    # If set to true, all trainable weights will be stored in a separate file,
    # external to the PTE file.
    external_mutable_weights: bool = False

    # If set to true, all mutable buffers will have their fully qualified names
    # serialized in the PTE file. Its value is ignored if mutable buffers are not
    # memory planned as the names must be serialized in that case.
    emit_mutable_buffer_names: bool = False