76647fce13
This is a major step along the way towards the new STEA design. While a great deal of this patch is simple renaming, there are several significant changes as well. I've done my best to ensure that this patch retains the previous behavior and error-conditions, even though those are at odds with the eventual intended semantics of the `dimToLvl` mapping. Since the majority of the compiler does not yet support non-permutations, I've also added explicit assertions in places that previously had implicitly assumed it was dealing with permutations. Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D151505
32 lines
1.0 KiB
MLIR
32 lines
1.0 KiB
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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lvlTypes = ["dense", "compressed"]
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}>
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#MAT_C_C_P = #sparse_tensor.encoding<{
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lvlTypes = [ "compressed", "compressed" ],
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dimToLvl = affine_map<(i,j) -> (j,i)>
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}>
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#MAT_C_D_P = #sparse_tensor.encoding<{
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lvlTypes = [ "compressed", "dense" ],
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dimToLvl = affine_map<(i,j) -> (j,i)>
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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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