onnx--onnx/docs/ONNXTypes.md

ONNX Types

Optional Type

An optional type represents a reference to either an element (could be Tensor, Sequence, Map, or Sparse Tensor) or a null value. The optional type appears in model inputs, outputs, as well as intermediate values.

Use-cases

Optional type enables users to represent more dynamic typing scenarios in ONNX. Similar to Optional[X] type hint in Python typing which is equivalent to Union[None, X], Optional types in ONNX may reference a single element, or null.

Examples in PyTorch

Optional type only appears in TorchScript graphs generated by jit script compiler. Scripting a model captures dynamic types where an optional value can be assigned either None or a value.

• Example 1

    class Model(torch.nn.Module):
        def forward(self, x, y:Optional[Tensor]=None):
            if y is not None:
                return x + y
            return x
  

  Corresponding TorchScript graph:

    Graph(
        %self : __torch__.Model,
        %x.1 : Tensor,
        %y.1 : Tensor?
    ):
        %11 : int = prim::Constant[value=1]()
        %4 : None = prim::Constant()
        %5 : bool = aten::__isnot__(%y.1, %4)
        %6 : Tensor = prim::If(%5)
            block0():
                %y.4 : Tensor = prim::unchecked_cast(%y.1)
                %12 : Tensor = aten::add(%x.1, %y.4, %11)
            -> (%12)
            block1():
            -> (%x.1)
        return (%6)
  

  ONNX graph:

    Graph(
        %x.1 : Float(2, 3),
        %y.1 : Float(2, 3)
    ):
        %2 : Bool(1) = onnx::OptionalHasElement(%y.1)
        %5 : Float(2, 3) = onnx::If(%2)
            block0():
                %3 : Float(2, 3) = onnx::OptionalGetElement(%y.1)
                %4 : Float(2, 3) = onnx::Add(%x.1, %3)
            -> (%4)
            block1():
                %x.2 : Float(2, 3) = onnx::Identity(%x.1)
            -> (%x.2)
        return (%5)
  

• Example 2

    class Model(torch.nn.Module):
        def forward(
                self,
                src_tokens,
                return_all_hiddens=torch.tensor([False]),
        ):
            encoder_states: Optional[Tensor] = None
            if return_all_hiddens:
                encoder_states = src_tokens
  
            return src_tokens, encoder_states
  

  Corresponding TorchScript graph:

    Graph(
        %src_tokens.1 : Float(3, 2, 4,),
        %return_all_hiddens.1 : Bool(1)
    ):
        %3 : None = prim::Constant()
        %encoder_states : Tensor? = prim::If(%return_all_hiddens.1)
            block0():
            -> (%src_tokens.1)
            block1():
            -> (%3)
        return (%src_tokens.1, %encoder_states)
  

  ONNX graph:

    Graph(
        %src_tokens.1 : Float(3, 2, 4),
        %return_all_hiddens.1 : Bool(1)
    ):
        %2 : Float(3, 2, 4) = onnx::Optional[type=tensor(float)]()
        %3 : Float(3, 2, 4) = onnx::If(%return_all_hiddens.1)
            block0():
            -> (%src_tokens.1)
            block1():
            -> (%2)
        return (%3)