39 lines
1.2 KiB
MLIR
39 lines
1.2 KiB
MLIR
// RUN: mlir-opt %s --sparsification-and-bufferization | FileCheck %s --check-prefix=CHECK-NOPARA
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// RUN: mlir-opt %s --sparsification-and-bufferization="parallelization-strategy=any-storage-any-loop" | FileCheck %s --check-prefix=CHECK-PARA
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// Test to ensure we can pass parallelization flags into
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// the mini sparsification and bufferization pipeline.
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#SparseMatrix = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : compressed, d1 : compressed)
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}>
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#trait_ss = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = A(i,j) * SCALE"
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}
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//
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// CHECK-NOPARA-LABEL: func.func @scale_ss
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// CHECK-NOPARA: scf.for
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//
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// CHECK-PARA-LABEL: func.func @scale_ss
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// CHECK-PARA: scf.parallel
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//
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func.func @scale_ss(%scale: f32,
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%arga: tensor<?x?xf32, #SparseMatrix>,
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%argx: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = linalg.generic #trait_ss
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ins(%arga: tensor<?x?xf32, #SparseMatrix>)
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outs(%argx: tensor<?x?xf32>) {
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^bb(%a: f32, %x: f32):
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%0 = arith.mulf %a, %scale : f32
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linalg.yield %0 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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