[GitHub] [tvm-rfcs] tkonolige commented on pull request #79: [RFC] TVMScript Metaprogramming

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tkonolige commented on PR #79:
URL: https://github.com/apache/tvm-rfcs/pull/79#issuecomment-1204583687

   Ok, I think I understand things a little bit better now. Thanks for the explanation! I can see how if the IRBuilder handles all state then there is not really much of a point to having classes for the parser. It might be worthwhile to mention having a stateful parser class as an alternative in the Alternatives section (with the mentioned disadvantages around duplicating logic).
   
   Two more questions:
   
   Given that IRBuilder maintains all state, how does it determine what context to use for evaluating expressions. Say I have
   ```python
   @T.prim_func
   def f(a: T.Buffer[2, "float32"]):
       ...
   ```
   And I want this buffer to be a `TIRBuffer` when the language is tir and a `RelaxBuffer` when the language is relax. Is this possible? From my reading, the parser and IRBuilder are only called for statements and for function calls. Is this correct? Or can you register a parser for any node in the ast (instead of just statements)?
   
   
   Anther question is how does this IRBuilder approach handle error catching and reporting? Lets say we have
   ```python
   @T.prim_func
   def f(a: T.handle):
       with T.block(...):
           A = T.match_buffer(a, ["a string", 1], "float32")
           #                      ^^^^^^^^^^
           # An error should be reported here because the second arguments to match_buffer should be a `Sequence[Union[int, T.Var]]`
   ```
   If I understand correctly, the `T.match_buffer` call is `eval`uated within a python context. This context calls `match_buffer` on the IRBuilder. Then the IRBuilder constructs a `MatchBuffer` object which in turn throws an error because its second argument has the wrong type. How is this error and error location then bubbled back up to the user?


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