Those are all great questions @liangfu.
Question 3 is an interesting one w.r.t to what kinds of scheduling primitives
we'll need for sparse operators. One easy workaround is to apply vectorization
along a dense tensor dimension if there is one. For many practical examples,
tensors won't be spa
@FrozenGene Thanks. Fixed just now. Also we fixed some license problem per
IPMC's comments. RC1:
https://github.com/apache/incubator-tvm/releases/tag/0.6.0.rc1
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one typo... [RUTNIME] -> [RUNTIME]
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https://github.com/apache/incubator-tvm/issues/4406#issuecomment-557994453
Shall the `basic primitives` implemented in the runtime? Or it could be
designed with either hybrid script or ir_builder, I mean the primitives could
be generated.
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Thanks @ZihengJiang for bringing up the RFC, especially the in-depth thinking
to bring the representation in TACO.
I think we shall also address some detailed issues to deal with sparse tensors.
1. How shall we implement `SparsePlaceholder` with varying length in the `idx`
and `val` variables?
2
# New Features
### Relay in Production
Relay is a functional, differentiable programming language designed to be an
expressive intermediate representation for machine learning systems. Relay
supports algebraic data types, closures, control flow, and recursion, allowing
it to directly represent
@hsaputra , I remember the release artifact needs to be uploaded to apache dist
svn https://dist.apache.org/repos/dist/dev/incubator/ , is it still required? I
don't see tvm under the repository, shall we create one?
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