hmm. after a bit of thinking, doesnt this pass force visit to all children,
even though that dont necessarily need it? will making the right hand side a
lazy value make more sense?
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writing trampoline by hand is probably the way to go for those passes.
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@junrushao1994 there is no plan to make relay dynamically typed. However, you
can still do the same by roughly:
data Any =
| AnyIntScalar (Tensor[(), Int])
| AnyFunction (Any -> Any)
| AnyTuple (Any, Any)
...
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@junrushao1994 there are PR to add mutual recursion into relay. In general, the
only reason a relay program cant typecheck is because it is wrong, thus we want
to catch this as early as possible. For case 1/2, it is still possible to call
the type inference function yourself. What is your use
@Ruinhuang see https://github.com/dmlc/tvm/pull/3729.
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@Ruinhuang I fixed it in add_grad, will upstream rn.
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> I’m wondering whether it has any problem such as what @junrushao1994
> mentioned.
If there are unknown alias/unknown add_to in other places of the code, it
cannot be model as option 1. Let's hope it doesnt happend.
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If I understand correctly, `add_to(a, b)` increment a, with b. During this
process, the value of a will be changed.
The second approach is a, imho, RED FLAG idea, that I dont think we should do.
If add_to is implemented as above, it will greatly complicate Operator Fusion,
Gradient, Partial Eva
I know how to fix it, and I will in a few days.
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