Method program

exir/lowered_backend_module.py:181–349

(
        self,
        emit_stacktrace: bool = False,
        memory_planning: MemoryPlanningPass = None,  # pyre-fixme[9]
    )

    # TODO(chenlai): re-consider recapture instead of manually constructing the program because
    # the meta data construction is done manually.
    def program(
        self,
        emit_stacktrace: bool = False,
        memory_planning: MemoryPlanningPass = None,  # pyre-fixme[9]
    ) -> Program:
        # Fix autodpes introuces cyclic dependencies:
        # program -> verifier -> lowered_backend_module -> program
        # @manual
        from executorch.exir.program._program import (
            _get_updated_graph_signature,
            _transform,
        )

        """
        Returns the object that represents the ExecuTorch binary before serialization.
        """
        # Creates a new module based on the original module. The original module will
        # look something like following:
        #
        # opcode         name                 target            args                                        kwargs
        # -------------  -------------------  ----------------  ------------------------------------------  --------
        # placeholder    arg0_1               arg0_1            ()                                          {}
        # placeholder    arg1_1               arg1_1            ()                                          {}
        # call_function  aten_repeat_default  *                 (arg1_1, [4, 1])                            {}
        # call_function  aten_mul_tensor      *                 (aten_repeat_default, aten_repeat_default)  {}
        # call_function  aten_add_tensor      *                 (arg1_1, arg1_1)                            {}
        # output         output               output            ([aten_mul_tensor, aten_add_tensor],)       {}
        #
        # if the whole module is lowered, the resulting lowered module look like
        #
        # opcode         name                      target                       args                                kwargs
        # -------------  ------------------------  ---------------------------  ----------------------------------  --------
        # placeholder    arg0_1                    arg0_1                       ()                                  {}
        # placeholder    arg1_1                    arg1_1                       ()                                  {}
        # get_attr       lowered_module_0          lowered_module_0             ()                                  {}
        # call_function  executorch_call_delegate  executorch_call_delegate     (lowered_module_0, arg0_1, arg1_1)  {}
        # call_function  getitem                   <built-in function getitem>  (executorch_call_delegate, 0)       {}
        # call_function  getitem_1                 <built-in function getitem>  (executorch_call_delegate, 1)       {}
        # output         output_1                  output                       ([getitem, getitem_1],)             {}
        #
        # We'll remove all call_function nodes, insert an call_delegate node, inserting getitems nodes to get the result for call_delegate node
        # and return the list of getitems as the output

        lowered_exported_program = copy.deepcopy(self._original_exported_program)

        # Cache these properties to avoid rebuilding the dict on each access.
        sig = lowered_exported_program.graph_signature
        params_map = sig.inputs_to_parameters
        buffers_map = sig.inputs_to_buffers

        # The real input nodes are the ones not buffer or parameter
        all_input_nodes = [
            node
            for node in lowered_exported_program.graph.nodes
            if (
                node.op == "placeholder"
                and node.name not in buffers_map
                and node.name not in params_map