I think we could just send pr directly. Of course, we could make them be
several prs, not one big pr.
---
[Visit
Topic](https://discuss.tvm.apache.org/t/add-some-new-tensorflow-ops/8217/3) to
respond.
You are receiving this because you enabled mailing list mode.
To unsubscribe from thes
I'm curious, how did you implement cumsum? I also wanted cumsum op a while
back, but for me it was not clear how it can be implemented efficiently in TVM.
---
[Visit
Topic](https://discuss.tvm.apache.org/t/add-some-new-tensorflow-ops/8217/4) to
respond.
You are receiving this because you
Is there some code examples for this topic?
---
[Visit
Topic](https://discuss.tvm.apache.org/t/rfc-add-tensorflow-custom-op-to-embed-tvm-runtime-in-tensorflow-graph-and-session/4601/14)
to respond.
You are receiving this because you enabled mailing list mode.
To unsubscribe from these em
Merged #6719 into main.
--
You are receiving this because you are subscribed to this thread.
Reply to this email directly or view it on GitHub:
https://github.com/apache/incubator-tvm/pull/6719#event-3910196704
We are catching up on upstream development in our local repo, and we ran into
an issue: pointer annotation on buffer variables blocks buffer type casting.
See https://discuss.tvm.apache.org/t/type-casting-a-buffer/4035 for more
details. In short, given a buffer of one data type, we want to
Hi all,
I am trying to improve quantized performance for memory bound operators (e.g.,
depthwise or 1x1 convolutions with small shapes).
### Bottom line question
Is there any way we can know the strategy picked by the autotuner during the
legalization pass of a quantized convolution (qnn.co
cc @anijain2305 @ramana-arm @FrozenGene (we had this discussion before)
---
[Visit
Topic](https://discuss.tvm.apache.org/t/quantized-models-and-legalization-pass/8253/2)
to respond.
You are receiving this because you enabled mailing list mode.
To unsubscribe from these emails, [click
he
I'll make them down into several prs, than send them directly. Thank you.
---
[Visit
Topic](https://discuss.tvm.apache.org/t/add-some-new-tensorflow-ops/8217/5) to
respond.
You are receiving this because you enabled mailing list mode.
To unsubscribe from these emails, [click
here](https
We have tried using te.scan and te.compute to implement cumsum. But it seems
that there is no way to use the same formula to adapt to all situations.
Finally, we implement it with te.extern.
---
[Visit
Topic](https://discuss.tvm.apache.org/t/add-some-new-tensorflow-ops/8217/6) to
respond
How is this RFC going ? Are there any following pull requests?
--
You are receiving this because you are subscribed to this thread.
Reply to this email directly or view it on GitHub:
https://github.com/apache/incubator-tvm/issues/4118#issuecomment-714889811
Thanks, yeah I also remember `te.scan` only supports scanning along the first
axis. I think `te.extern` is good as a first step.
---
[Visit
Topic](https://discuss.tvm.apache.org/t/add-some-new-tensorflow-ops/8217/7) to
respond.
You are receiving this because you enabled mailing list mode
11 matches
Mail list logo
