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@yelite It's a great RFC，and this is what we need right now.
the requirements we need:
1) For compute fusion. With TE compute, it's easy to concate TE computes with
producer-comsuer relation to get a fused compute. for example, conv + elemwise
ops fusion. We should have similar function in TVM s
This is a great disscussion here. Actually, we are supporting a DSA with TVM,
let me share my practice.
1, We only re-use some of tvm relay or tir passes, less than 10 passes, such
storage flatten, we don't need most of tvm passes, keep them in our flow means
wasting compilation time.
2, We dev
[quote="hogepodge, post:1, topic:10305"]
What platforms are you using TVM for?
* [ ] X86 CPU
* [ ] ARM CPU
* [ ] Other CPU
* [ ] NVidia GPU
* [ ] AMD GPU
* [ ] Other GPU
* [ ] Embedded Platform
[/quote]
We are using TVM for DSA NPU, can you add one option, thanks!
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One issue in old schedule ops is we can not get the accurate bouds with
inferbound, what will it be like in new schedule system? thanks.
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@junrushao1994 It's better to know loops can be vectoried, permutable or
distributied, isl can provide these information，so we can do loop optimization
and tensorization/vectorization automatically.
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Is Fusion in Ansor based on tir?
For other transforms, you may checkout here, that's what we've done in AKG. I
can explain some if you are intrested.
https://github.com/mindspore-ai/akg/blob/master/src/codegen/build_module.cc#L439
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This is the right way to go. However I have two concern,
1) How to fuse ops as much as possible? Basically fusion is copy propagation
optimization in compilers, which is based on data flow analysis, but still lack
of programming analysis in TVM now.
2) TE tensorize can not handle some complex p
we do support ascend310 op codegen on AKG side, but not in MindSpore for now.
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We have a poly + tvm solution for Davinci, which will be released soon, maybe
in the next week.
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Do we support round trip ir? which can parser a readable ir and construct ir
objects as inputs for compiler.
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@tqchen That's great!
BTW I notice you delete ir dump in recently pr, but this is very very important
utility for compiler development in HW projects, do we have other alternatives
in tvm?
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@tqchen do we have abstractions in TVM’s unfied IR infra?
1, multi-stage ir for relay::Function:
```
c = IRModule A(a, b){
a = a + 1;
b = b + 1;
return a+b;
}
e = IRModule B(c, d){
c = c + 1;
d = d + 1;
return c+d;
}
```
With this abstraction, we can express complex/big ops with l
@tqchen, what's your suggestion? IMO, low level IR has been there for a while,
and we've had experience and understanding in low level ir. the post of unified
ir to me is just a high level proposal, details needs to be discussed further,
such as,
The most valuable thing to me is we can make op
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@tqchen Thanks. Both ok for us, as long as we can get a release in one or two
month, is that possible?
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Are we going to release 0.6 in new repo? @tqchen
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When will we have 0.6 release ? thanks
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My take is,
MLIR is a replacement of HalideIR. 1) compiler infra support, like cfg/dfa/ssa,
with these, we can avoid pattern matching style pass on Halide, which is not
good for maintaining, 2) other better utilities, like text ir; 3) unified IR
for multi-level, graph and tensor.
I agree the
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