2a07f0fd40
**Dense** `lvlTypes = [ "dense", "dense" ]` to `map = (d0, d1) -> (d0 : dense, d1 : dense)` `lvlTypes = [ "dense", "dense" ], dimToLvl = affine_map<(i,j) -> (j,i)>` to `map = (d0, d1) -> (d1 : dense, d0 : dense)` **DCSR** `lvlTypes = [ "compressed", "compressed" ]` to `map = (d0, d1) -> (d0 : compressed, d1 : compressed)` **DCSC** `lvlTypes = [ "compressed", "compressed" ], dimToLvl = affine_map<(i,j) -> (j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : compressed)` **Block Row** `lvlTypes = [ "compressed", "dense" ]` to `map = (d0, d1) -> (d0 : compressed, d1 : dense)` **Block Column** `lvlTypes = [ "compressed", "dense" ], dimToLvl = affine_map<(i,j) -> (j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : dense)` This is an ongoing effort: #66146, #66309
30 lines
1009 B
MLIR
30 lines
1009 B
MLIR
// RUN: mlir-opt %s --sparse-compiler="enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
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#MAT_D_C = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : dense, d1 : compressed)
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}>
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#MAT_C_C_P = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d1 : compressed, d0 : compressed)
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}>
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#MAT_C_D_P = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d1 : compressed, d0 : dense)
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}>
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//
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// Ensures only last loop is vectorized
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// (vectorizing the others would crash).
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//
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// CHECK-LABEL: llvm.func @foo
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// CHECK: llvm.intr.masked.load
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// CHECK: llvm.intr.masked.scatter
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//
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func.func @foo(%arg0: tensor<2x4xf64, #MAT_C_C_P>,
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%arg1: tensor<3x4xf64, #MAT_C_D_P>,
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%arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {
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%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
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: tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>
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return %0 : tensor<9x4xf64>
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}
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