> I am not sure if tensorize is a good way to suport VNNI:
>
> 1. VNNI is not true tensorization, though reduction dimension is introduced.
> It still operates on 1-D inputs. Due to the design of `tensorization`
> interface, you need to provide the declared intrin the shape of tensors
> offload
I can run the correct result locally. Also updated the summary part for this PR
to report the performance results.
However, I had the same issue as https://github.com/dmlc/tvm/issues/3598 for
the OSS compilation error
(http://ci.tvm.ai:8080/blue/organizations/jenkins/tvm/detail/PR-3388/11/pipel
Merged #3673 into master.
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This PR adds @slyubomirsky to the reviewer list of tvm. He has been
contributing to many core features in Relay.
- [Commits](https://github.com/dmlc/tvm/commits?author=slyubomirsky)
- [Code
Review](https://github.com/dmlc/tvm/pulls?utf8=%E2%9C%93&q=reviewed-by%3Aslyubomirsky)
- [Community Engage
This is the follow-up issue for
https://discuss.tvm.ai/t/rfc-functionality-of-alteroplayout-and-possible-refactoring/
To enhance the AlterOpLayout pass, I would like to propose 4 more passes to
replace current AlterOpLayout pass,
- [ ] Layout inference pass
To infer the layout of each layer.
I would appreciate an inivitation as well for t...@beamnet.de (t-vi on github).
Thank you.
Thomas
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