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1// RUN: mlir-opt %s --sparsifier="enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"2 3#MAT_D_C = #sparse_tensor.encoding<{4 map = (d0, d1) -> (d0 : dense, d1 : compressed)5}>6 7#MAT_C_C_P = #sparse_tensor.encoding<{8 map = (d0, d1) -> (d1 : compressed, d0 : compressed)9}>10 11#MAT_C_D_P = #sparse_tensor.encoding<{12 map = (d0, d1) -> (d1 : compressed, d0 : dense)13}>14 15//16// Ensures only last loop is vectorized17// (vectorizing the others would crash).18//19// CHECK-LABEL: llvm.func @foo20// CHECK: llvm.intr.masked.load21// CHECK: llvm.intr.masked.scatter22//23func.func @foo(%arg0: tensor<2x4xf64, #MAT_C_C_P>,24 %arg1: tensor<3x4xf64, #MAT_C_D_P>,25 %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {26 %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}27 : tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>28 return %0 : tensor<9x4xf64>29}30