ONNXTypes.md

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 senarios 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)