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Currently, TracedONNXFunction does not support prmoting python constant to tensors when inputs and attributes enters the function. The challenge arises when dynamic shapes is involved, since dynamic shapes could potentially turn python constants to torch.SymInt (torch.fx.Node), which makes writing torchlib more complicated. The solution would be let the well-defined (the function has op signature) TracedONNXFunction be able to promote python constants to tensors before entering the function, while the function without good annotations staying fully tracing.
The text was updated successfully, but these errors were encountered:
Currently, TracedONNXFunction does not support prmoting python constant to tensors when inputs and attributes enters the function. The challenge arises when dynamic shapes is involved, since dynamic shapes could potentially turn python constants to torch.SymInt (torch.fx.Node), which makes writing torchlib more complicated. The solution would be let the well-defined (the function has op signature) TracedONNXFunction be able to promote python constants to tensors before entering the function, while the function without good annotations staying fully tracing.
The text was updated successfully, but these errors were encountered: