Method __init__

torch/fx/node.py:157–233

Instantiate an instance of ``Node``. Note: most often, you want to use the Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather than instantiating a ``Node`` directly. Args: graph (Graph): The ``Graph`` to which this ``Node`` should

(self, graph: 'Graph', name: str, op: str, target: 'Target',
                 args: Tuple['Argument', ...], kwargs: Dict[str, 'Argument'],
                 return_type : Optional[Any] = None)

    @compatibility(is_backward_compatible=True)
    def __init__(self, graph: 'Graph', name: str, op: str, target: 'Target',
                 args: Tuple['Argument', ...], kwargs: Dict[str, 'Argument'],
                 return_type : Optional[Any] = None) -> None:
        """
        Instantiate an instance of ``Node``. Note: most often, you want to use the
        Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather
        than instantiating a ``Node`` directly.

        Args:
            graph (Graph): The ``Graph`` to which this ``Node`` should belong.

            name (str): The name to which the output of this ``Node`` should be assigned

            op (str): The opcode for this ``Node``. Can be one of 'placeholder',
                'call_method', 'call_module', 'call_function', 'get_attr',
                'output'

            target ('Target'): The target this op should call. See the broader
                ``Node`` docstring for more details.

            args (Tuple['Argument']): The args to be passed to ``target``

            kwargs (Dict[str, 'Argument']): The kwargs to be passed to ``target``

            return_type (Optional[Any]): The python type expression representing the
                type of the output of this node. This field can be used for
                annotation of values in the generated code or for other types
                of analyses.
        """
        self.graph = graph
        self.name = name  # unique name of value being created
        assert op in ['placeholder', 'call_method', 'call_module', 'call_function', 'get_attr', 'output', 'root']
        self.op = op  # the kind of operation = placeholder|call_method|call_module|call_function|get_attr
        if op == 'call_function':
            if not callable(target):
                raise ValueError(f'Node [graph = {graph}, name = \'{name}\'] target {target} has type {torch.typename(target)} '
                                 'but a Callable is expected')
        else:
            if not isinstance(target, str):
                raise ValueError(f'Node [graph = {graph}, name = \'{name}\'] target {target} has type {torch.typename(target)} '
                                 'but a str is expected')
        self.target = target  # for method/module/function, the name of the method/module/function/attr
        # being invoked, e.g add, layer1, or torch.add

        # All `Node`-valued inputs. Key is the Node, value is don't-care.
        # The public API for this is `all_input_nodes`, this private attribute
        # should not be accessed directly.
        self._input_nodes : Dict[Node, None] = {}
        self.__update_args_kwargs(map_arg(args, lambda x: x), map_arg(kwargs, lambda x: x))  # type: ignore[arg-type]

        # All of the nodes that use the value produced by this Node
        # Note one user may correspond to several uses, e.g. the node fo ``x + x``
        # would appear once here, but represents two uses.
        #
        # Is a dict to act as an "ordered set". Keys are significant, value dont-care
        self.users : Dict[Node, None] = {}
        # Type expression representing the output value of this node.
        # This should contain the same class of Type objects that would appear