1// RUN: mlir-opt %s --sparsification-and-bufferization | FileCheck %s --check-prefix=CHECK-NOPARA 2// RUN: mlir-opt %s --sparsification-and-bufferization="parallelization-strategy=any-storage-any-loop" | FileCheck %s --check-prefix=CHECK-PARA 3 4// Test to ensure we can pass parallelization flags into 5// the mini sparsification and bufferization pipeline. 6 7#SparseMatrix = #sparse_tensor.encoding<{ 8 map = (d0, d1) -> (d0 : compressed, d1 : compressed) 9}> 10 11#trait_ss = { 12 indexing_maps = [ 13 affine_map<(i,j) -> (i,j)>, // A 14 affine_map<(i,j) -> (i,j)> // X (out) 15 ], 16 iterator_types = ["parallel", "parallel"], 17 doc = "X(i,j) = A(i,j) * SCALE" 18} 19 20// 21// CHECK-NOPARA-LABEL: func.func @scale_ss 22// CHECK-NOPARA: scf.for 23// 24// CHECK-PARA-LABEL: func.func @scale_ss 25// CHECK-PARA: scf.parallel 26// 27func.func @scale_ss(%scale: f32, 28 %arga: tensor<?x?xf32, #SparseMatrix>, 29 %argx: tensor<?x?xf32>) -> tensor<?x?xf32> { 30 %0 = linalg.generic #trait_ss 31 ins(%arga: tensor<?x?xf32, #SparseMatrix>) 32 outs(%argx: tensor<?x?xf32>) { 33 ^bb(%a: f32, %x: f32): 34 %0 = arith.mulf %a, %scale : f32 35 linalg.yield %0 : f32 36 } -> tensor<?x?xf32> 37 return %0 : tensor<?x?xf32> 38} 39