[AMDAIEInsertIntoCores] minor simplification

[newling]

The arith ops are nested inside linalg ops, so they'll get hoisted into cores without explicitly listing them here

[jtuyls]

Not necessarily, see matmul + truncf:

// input ${M}x${K}x${TYPE1}
// input ${K}x${N}x${TYPE1}

func.func @matmul_truncf(%arg0: tensor<${M}x${K}x${TYPE1}>, %arg1: tensor<${K}x${N}x${TYPE1}>) -> tensor<${M}x${N}x${TYPE1}>
{
  %cst = arith.constant ${ZERO} : ${TYPE2}
  %0 = tensor.empty() : tensor<${M}x${N}x${TYPE2}>
  %1 = linalg.fill ins(%cst : ${TYPE2}) outs(%0 : tensor<${M}x${N}x${TYPE2}>) -> tensor<${M}x${N}x${TYPE2}>
  %2 = linalg.matmul ins(%arg0, %arg1 : tensor<${M}x${K}x${TYPE1}>, tensor<${K}x${N}x${TYPE1}>)
    outs(%1: tensor<${M}x${N}x${TYPE2}>) -> tensor<${M}x${N}x${TYPE2}>
  %3 = arith.truncf %2 : tensor<${M}x${N}x${TYPE2}> to tensor<${M}x${N}x${TYPE1}>
  return %3: tensor<${M}x${N}x${TYPE1}>
}

Not sure how this PR is passing though...

[newling]

In one of the very early passes (convert-elementwise-to-linalg) It gets converted into a linalg.generic.

[jtuyls]

I see, thanks, that's great! Then we can assume all computational ops will be linalg ops, which makes these checks a lot less fragile and improves maintainability.

[newling]

Yes!

[jtuyls]

LGTM