Thank you for your comments!
So there are two parts:
- What do we represent in Relay,
- what will be the function in the runtime.
I guess I'm reading agreement here that TorchScript functions are a reasonably
good fit for the runtime.
If we determine this function during the (currently trivia
I don't think serialization would necessarily preclude you from having a 1-1
relation between C++ and Python objects -- deserializing would create a new C++
object so I would not expect that you get back the same Python object. Now I
can see how we would not serialize all stuff people attach t
I made this, in the end I stayed in the PyTorch frontend and solved it locally
given that inplace things don't look like TVM that much.
https://github.com/apache/incubator-tvm/pull/6900
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I don't think guarding against specific use cases is more a hack. In the end,
you'll have many funny effects with this type of logic. If you add something to
a set, do you expect the exact object to be in the set afterwards?
In the end, one would need to think about whether having the Python in
The difficulty with the map is that you need to pass the map around or make it
global state.
My usecase is the PyTorch frontend (which could be made object-oriented instead
of made purely functional to keep track of the map and prelude which is
currently kept track of by indirection). Concrete
I'm working on this, but I'd still need feedback on whether to implement an own
IR node or try to squeeze into using a call node somehow.
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You are receiving this because
Hi,
I've been implementing graph transformations in Python and sometimes it is
handy to add annotations to nodes. Now, running these through an ExprMutator
will give me all new nodes and they're gone. But actually, they're gone ealier
than that:
```python
x = tvm.relay.var('x', shape=(1,1))
x
