Closed #8976.
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The release candidate v0.8.rc0 is approved:
* Voting thread: https://github.com/apache/tvm/issues/9504
* Voting result: https://github.com/apache/tvm/issues/9566
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When would 0.8 official release be? Estimatedly, days, weeks, months?
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Blocker #9486
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The release is cut and is available for test in
https://github.com/apache/tvm/tree/v0.8
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Blocker: Conv2dTranspose currently does not support `groups`. But `groups` is
popularly used in recent efficient models and Conv2dTranspose is an important
operator in GAN related applications.
* `groups` support: https://github.com/apache/tvm/issues/8182
https://github.com/apache/tvm/pull/879
Blocker: We need this bugfix in to address a regression
https://github.com/apache/tvm/pull/9421
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Also, if there is any bug/issue blocking the release, please don't hesitate to
let us know in this thread :-)
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Hi all, we cut a v0.8 release branch for Apache TVM:
https://github.com/apache/tvm/tree/v0.8. Please find:
- The release note (candidate): https://github.com/apache/tvm/issues/9416
- The full changelog (candidate):
https://gist.github.com/junrushao1994/c669905dbc41edc2e691316df49d8562
There have
@Mousius Thanks for asking!
> does this mean that 0.8 will go out with half finished implementations for
> things, such as library integrations (i.e. CMSIS-NN) and tvmc arguments (tvmc
> is not yet stable as there's breaking changes incoming)
Yes, we directly cut main into the v0.8 branch:
htt
@jiangjiajun Sure! We will list experimental paddlepaddle frontend support as a
separate category and a highlight of this release
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Hi, @junrushao1994
I think we could add paddlepaddle frontend as a new feature in the release note
of v0.8
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@junrushao1994 does this mean that 0.8 will go out with half finished
implementations for things, such as library integrations (i.e. CMSIS-NN) and
tvmc arguments (tvmc is not yet stable as there's breaking changes incoming)?
If the process is just to take `main` and tag it, can we rapidly move t
@areusch thanks for asking! sorry I was in vacation by the time of the post.
@vinx13 and I are actively drafting a release note, and will cut a release
candidate by next Monday (Nov 1, 2021). If there is any small commit after
Monday that needs to be included in the RC, please let us know in thi
@junrushao1994 what's the status of the release? is there a list of issues that
need to be resolved before we cut?
also cc @denise-k @Mousius @mbs-octoml
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@vinx13 and I spent the previous 2 weeks categorizing the 69 pages of
changelogs via git commits:
https://docs.google.com/document/d/1D2rig32yZ4H48_tXHb_svnPnt4-FRx3JUbBxKyrW_-I/
Below are the categories we are using:
- TE & TIR & TIR Scheduling & TVM Script
- Relay IR
- AutoTVM & Auto Schedule
Just checking--by "stable" for the "command line driver interface," does that
mean "someone should write a script and presume we won't change the command
syntax?"
For µTVM we could add:
- microTVM Project API -> initial support
- microTVM AOT executor -> initial support
- microTVM platforms: Zep
Hexagon support still needs more work.
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> Agree with @leandron that we could firstly refer to the items there. Many
> "initial" features in v0.7 are now stable. For example:
>
> * Initial automatic scheduling support -> stable.
>
> * Initial command line driver interface -> stable.
>
> * Intial Hexagon support -> stable.
Expecting the new release!
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I wanted to propose as a highlight the TE-level auto-differentiation work, lead
by @yzhliu, which unlocks TE-level training capability in the TVM stack
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Agree with @leandron that we could firstly refer to the items there. Many
"initial" features in v0.7 are now stable. For example:
* Initial automatic scheduling support -> stable.
* Initial command line driver interface -> stable.
* Intial Hexagon support -> stable.
* Bring your own codegen (BYOC
Thanks for the work. I believe v0.8 is a good chance to land TensorIR
scheduling (https://github.com/apache/tvm/issues/7527). Also, I will try my
best to contribute some initial TensorIR tutorials and documentations before
the v0.8 release.
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Thanks for doing this work! There were some items listed on #7434, perhaps we
can start with those?
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Thanks to everyone who contributed in the past cycle! I would love to use this
thread to plan for the v0.8 release. The community has always been focusing on
high quality releases.
One of the major highlights in the past cycle is TensorIR scheduling, the new
low-level intermediate representatio
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