Yes. @yunjing_lh find that our quantized mobilenet v2 has the same issue in GCC
5, QNN lowers too many ops and has deep recursion. One workaround is to
increase stack size. However, non-recursive ast visiting should be one better
way.
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Currently Relay `gather_nd` op uses the [mxnet
semantic](https://mxnet.apache.org/api/python/docs/api/ndarray/op/index.html#mxnet.ndarray.op.gather_nd),
which each column in `indices` indicates the indices in `data`. However,
Tensorflow
[`gather_nd`](https://www.tensorflow.org/api_docs/python
I agree with @masahi, it's not obvious to me that the onnx importer will stop
working and maintaining a new frontend is a lot of ongoing work. +1 to
implementing this on top of TVM rather than inside of it if @ANSHUMAN.TRIPATHY
wants to pursue this effort.
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We're back.
https://github.com/apache/incubator-tvm/pull/5252
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Yes that's correct :slight_smile:
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>I believe your example would be taken care of using the Merge Composite pass
>before partitioning. You can imagine that after running this pass add+conv2d
>for the blue compiler would be represented by a single node.
Ok then I wasn't really sure of what passes come before and after. So you ar
Hi, @aca88 I believe your example would be taken care of using the Merge
Composite pass before partitioning. You can imagine that after running this
pass add+conv2d for the blue compiler would be represented by a single node.
The partitioning would then happen as you described. However, this w
Hi,
Thanks for the nice animation and the work put into this.
I was wondering if the subgraph partitions for the same compiler (at this
level) have any repercussions for downstream passes.
So for example assume that the last node of A1(called a1) is a Conv2d and the
first of A3 (called a3) is
