Thanks for the suggestion, @comaniac . Adding matmul operator with
implementations of all combinations of inputs' layouts seems overkill to me.
Instead, adding a target-specific relay pass to deal with such target-specific
case would be a better solution, which is lightweight and orthogonal to
This looks more like a hack, :slight_smile:
If I want to do it in the relay, I should add a version of nn.dense (say, name
it nn.dense_transposed_kernel) then register a function convert_dense(...) with
register_convert_op_layouts("nn.dense"), right?
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Hi, @comaniac . I looked into your example and did a simple experiment similar
to it.
My example network imported into relay as below:
#[version = "0.0.5"]
def @main(%input.1: Tensor[(1, 1, 32, 16), float32], %conv.0.bias:
Tensor[(1), float32], %conv.0.weight: Tensor[(1, 1, 3, 3), fl
Well, I have a special BYOC **dense** kernel that deals with kernel layout
different from default topi.nn implemenation.
The default implemenation has *weight Tensor with shape [out_dim, in_dim]*,
while I need [in_dim, out_dim].
Two questions here:
1. How can I change the default behavior o
I'm trying to create PackedFunc manually for my baremetal app. Following the
way calling below macro:
example.c
```
#include
int A_wrapper(blahblah);
TVM_DLL_EXPORT_TYPED_FUNC(A, A_wrapper_);
```
Linking the program complains**: undefined reference to `__dso_handle'.** **I
wonder where does i
First of all, I'm by no means expert in TVM. So just my two cents.
I believe the Relay-> Tir transform happens with so-called "lowering" process
in side python/tvm/relay/backend/compile_engine.py, CompileEngine::lower(blah).
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