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1// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification -cse | FileCheck %s2 3#Dense = #sparse_tensor.encoding<{4 map = (d0, d1) -> (d0 : dense, d1 : dense)5}>6 7#trait_scale = {8 indexing_maps = [9 affine_map<(i,j) -> (i,j)> // X (out)10 ],11 iterator_types = ["parallel", "parallel"],12 doc = "X(i,j) = X(i,j) * 2.0"13}14 15// CHECK-LABEL: func.func @sparse_scale(16// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1xf32, #sparse{{[0-9]*}}>)17// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index18// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f3219// CHECK: %[[VAL_3:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1x1xf32, #sparse{{[0-9]*}}> to memref<?xf32>20// CHECK: %[[VAL_4:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>21// CHECK: %[[VAL_5:.*]] = arith.mulf %[[VAL_4]], %[[VAL_2]] : f3222// CHECK: memref.store %[[VAL_5]], %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>23// CHECK: %[[VAL_6:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<1x1xf32, #sparse{{[0-9]*}}>24// CHECK: return %[[VAL_6]] : tensor<1x1xf32, #sparse{{[0-9]*}}>25func.func @sparse_scale(%argx: tensor<1x1xf32, #Dense>) -> tensor<1x1xf32, #Dense> {26 %c = arith.constant 2.0 : f3227 %0 = linalg.generic #trait_scale28 outs(%argx: tensor<1x1xf32, #Dense>) {29 ^bb(%x: f32):30 %1 = arith.mulf %x, %c : f3231 linalg.yield %1 : f3232 } -> tensor<1x1xf32, #Dense>33 return %0 : tensor<1x1xf32, #Dense>34}35