Thanks, yeah I also remember `te.scan` only supports scanning along the first
axis. I think `te.extern` is good as a first step.
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How is this RFC going ? Are there any following pull requests?
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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.
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I'll make them down into several prs, than send them directly. Thank you.
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cc @anijain2305 @ramana-arm @FrozenGene (we had this discussion before)
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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
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
Merged #6719 into main.
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Is there some code examples for this topic?
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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.
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I think we could just send pr directly. Of course, we could make them be
several prs, not one big pr.
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