❓ [Question] Is there support for optional arguments in model's `forward()`?

[lhai37]

❓ Question

Is there support for optional arguments in model's forward()? For example, I have the following: def forward(self, x, y: Optional[Tensor] = None): where y is an optional tensor. The return result is x + y if y is provided, otherwise just x.

What you have already tried

I added a second torch_tensorrt.Input() in the input spec, then at inference time got the error:
Expected dimension specifications for all input tensors, but found 1 input tensors and 2 dimension specs

I then removed the Optional annotation and just pass in None or the actual tensor for y. When None is passed in, I got the error: RuntimeError: forward() Expected a value of type 'Tensor' for argument 'input_1' but instead found type 'NoneType'.

I also tried passing in just 1 argument for x, and got:
RuntimeError: forward() is missing value for argument 'input_1'

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• PyTorch Version (e.g., 1.0): 1.10.0+cu113
• CPU Architecture:
• OS (e.g., Linux): Ubuntu 18.04
• How you installed PyTorch (conda, pip, libtorch, source): pip
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version: 3.7.11
• CUDA version: 11.1
• GPU models and configuration: Tesla V100 with 32GB memory
• Any other relevant information:

Additional context

[peri044]

@lhai37 I don't think we support optional tensors at the moment. cc @narendasan. We expect inputs and outputs of a module to be torch::Tensors. Can you share how your torchscript model looks like ? I tried to convert the following to TS but torch.jit.script fails on this

class Optional(torch.nn.Module):

    def __init__(self):
        super(Optional, self).__init__()

    def forward(self, x, y: Optional[torch.Tensor] = None):
        return x + y

model = Optional()
scripted_model = torch.jit.script(model)

[ProGamerGov]

@peri044 Your code fails because you are doing x + None. If you add an if statement to prevent that, then it will work in TorchScript.

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[narendasan]

This is not currently supported and requires the next phase collections feature (#629). The issue is we need to be able to generate the torchscript code to manage mapping from function input to tensorrt input when potentially any arbitrary input could be None.

[github-actions]

This issue has not seen activity for 90 days, Remove stale label or comment or this will be closed in 10 days

[github-actions]

This issue has not seen activity for 90 days, Remove stale label or comment or this will be closed in 10 days