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author | Han-Chung Wang <hanhan0912@gmail.com> | 2023-12-19 09:14:43 -0800 |
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committer | GitHub <noreply@github.com> | 2023-12-19 09:14:43 -0800 |
commit | 899c2bed9e959e822d1eccb37336981af9664e02 (patch) | |
tree | 519ca552d99782d1f1dcc049e5cf3185c2876293 /llvm/unittests/Support/UnicodeTest.cpp | |
parent | ac82c8b9257a5094ad05e79c43d007f6bd3add43 (diff) | |
download | llvm-899c2bed9e959e822d1eccb37336981af9664e02.zip llvm-899c2bed9e959e822d1eccb37336981af9664e02.tar.gz llvm-899c2bed9e959e822d1eccb37336981af9664e02.tar.bz2 |
[mlir][TilingInterface] Early return cloned ops if tile sizes are zeros. (#75410)
It is a trivial early-return case. If the cloned ops are not returned,
it will generate `extract_slice` op that extracts the whole slice.
However, it is not folded away. Early-return to avoid the case.
E.g.,
```mlir
func.func @matmul_tensors(
%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>)
-> tensor<?x?xf32> {
%0 = linalg.matmul ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2: tensor<?x?xf32>)
-> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.structured.tile_using_for %0 [0, 0, 0] : (!transform.any_op) -> (!transform.any_op)
transform.yield
}
}
```
Apply the transforms and canonicalize the IR:
```
mlir-opt --transform-interpreter -canonicalize input.mlir
```
we will get
```mlir
module {
func.func @matmul_tensors(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
%c1 = arith.constant 1 : index
%c0 = arith.constant 0 : index
%dim = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%dim_0 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
%dim_1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
%extracted_slice = tensor.extract_slice %arg0[0, 0] [%dim, %dim_0] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
%extracted_slice_2 = tensor.extract_slice %arg1[0, 0] [%dim_0, %dim_1] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
%extracted_slice_3 = tensor.extract_slice %arg2[0, 0] [%dim, %dim_1] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
%0 = linalg.matmul ins(%extracted_slice, %extracted_slice_2 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%extracted_slice_3 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
}
```
The revision early-return the case so we can get:
```mlir
func.func @matmul_tensors(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
```
Diffstat (limited to 'llvm/unittests/Support/UnicodeTest.cpp')
0 files changed, 0 insertions, 0 deletions