ANSHUMAN87 commented on pull request #6889:
URL: https://github.com/apache/incubator-tvm/pull/6889#issuecomment-729671304
> @tqchen : Tristan is helping here with his valuable review efforts. But i
think we need a third opinion here (possibly an official reviewer or committer)
to help
ANSHUMAN87 commented on pull request #6889:
URL: https://github.com/apache/incubator-tvm/pull/6889#issuecomment-728244556
> But maybe someone else can chime in.
Thanks @tkonolige for your feedback. I believe the performance stats are
quite clear to opt for a new Op in the case.
ANSHUMAN87 commented on pull request #6889:
URL: https://github.com/apache/incubator-tvm/pull/6889#issuecomment-727237614
Gentle ping @tkonolige !!!
I am not too sure who else from TVM official reviewer or committer
interested in sparse. If you are aware of anyone please feel free to
ANSHUMAN87 commented on pull request #6889:
URL: https://github.com/apache/incubator-tvm/pull/6889#issuecomment-726801407
> Just to check, you're only transposing the dense matrix? Also, what is the
density of the sparse matrix?
>
> I'm curious, could you do a benchmark with a
ANSHUMAN87 commented on pull request #6889:
URL: https://github.com/apache/incubator-tvm/pull/6889#issuecomment-725492811
Hi @tkonolige , below is the benchmark data i have obtained for 4 different
input dimensions.
NOTE: Here with Transpose means using existing sparse_dense Op with
ANSHUMAN87 commented on pull request #6889:
URL: https://github.com/apache/incubator-tvm/pull/6889#issuecomment-724235751
Sure I will check on how much overhead added in case of transpose with
existing Op case.
This is an
ANSHUMAN87 commented on pull request #6889:
URL: https://github.com/apache/incubator-tvm/pull/6889#issuecomment-724198770
> I'm not sure that the correct course of action is to add a flag to
`sparse_dense` to support AB^T with B sparse. This makes all the
implementations of `sparse_dense`
