what do you mean exaclty by "imperfect loop tiling"?
On the first issue, tensorization lets us essentially inline high-performance
code that implements a matrix-matrix or matrix-vector multiplication inner-loop
body. This is very useful when targeting special hardware intrinsics, like
perform
There is immediate int/uint/float, why we can't have immediate tensor in TVM
IR? On some special platforms, some small constant tensors, such as bias, is
prefered to be embedded into instruction to get better performance. But I found
there seems no chance to do this if we do not have constant
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 offloaded, but
esse
@jianyuh can you look into the CI error?
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Relevant QNN Dialect RFC - #3591
Some QNN operators like Requantize and Conv2D are more amenable to going
through C++ lowering pass. A couple of factors where C++ implementation seems
better is - when new operator is conceptually very different from existing
operators (Requantize), when input/
@ydy Where needs to wait dynamism is supported (a few guys are working on
dynamism actively)
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Closed #2606.
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Closed #2884.
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Duplicated with #3154
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Closed #3209.
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@weberlo eg CPU CNNs like mobilenet, resnet, etc. One thing not supported is eg
tvm.extern since we don’t support packed funcs.
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