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1// RUN: mlir-opt %s -pre-sparsification-rewrite | FileCheck %s2 3#SparseVector = #sparse_tensor.encoding<{4 map = (d0) -> (d0 : compressed)5}>6 7#SortedCOO = #sparse_tensor.encoding<{8 map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)9}>10 11#DCSR = #sparse_tensor.encoding<{12 map = (d0, d1) -> (d0 : compressed, d1 : compressed)13}>14 15#Slice = #sparse_tensor.encoding<{16 map = (d0 : #sparse_tensor<slice(?, 1, 1)>, d1 : #sparse_tensor<slice(?, 3, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)17}>18 19#sel_trait = {20 indexing_maps = [21 affine_map<(i,j) -> (i,j)>, // C (in)22 affine_map<(i,j) -> (i,j)>, // L (in)23 affine_map<(i,j) -> (i,j)>, // R (in)24 affine_map<(i,j) -> (i,j)> // X (out)25 ],26 iterator_types = ["parallel", "parallel"]27}28 29// CHECK-LABEL: func @sparse_nop_cast(30// CHECK-SAME: %[[A:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>)31// CHECK: return %[[A]] : tensor<?xf32, #sparse{{[0-9]*}}>32func.func @sparse_nop_cast(%a : tensor<?xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> {33 %0 = tensor.cast %a : tensor<?xf32, #SparseVector> to tensor<?xf32, #SparseVector>34 %1 = tensor.cast %0 : tensor<?xf32, #SparseVector> to tensor<?xf32, #SparseVector>35 %2 = tensor.cast %1 : tensor<?xf32, #SparseVector> to tensor<?xf32, #SparseVector>36 return %2 : tensor<?xf32, #SparseVector>37}38 39// CHECK-LABEL: func @sparse_repair_cast(40// CHECK-SAME: %[[A:.*]]: tensor<?xf32>)41// CHECK: %[[C:.*]] = sparse_tensor.convert %[[A]] : tensor<?xf32> to tensor<?xf32, #sparse{{[0-9]*}}>42// CHECK: return %[[C]] : tensor<?xf32, #sparse{{[0-9]*}}>43func.func @sparse_repair_cast(%a : tensor<?xf32>) -> tensor<?xf32, #SparseVector> {44 %0 = tensor.cast %a : tensor<?xf32> to tensor<?xf32, #SparseVector>45 return %0 : tensor<?xf32, #SparseVector>46}47 48// CHECK-LABEL: func @sparse_fuse_slice(49// CHECK-SAME: %[[A:.*]]: tensor<2x3xi64, #sparse{{[0-9]*}}>)50// CHECK: %[[E:.*]] = tensor.extract_slice %[[A]][1, 0] [1, 3] [1, 1] : tensor<2x3xi64, #sparse{{[0-9]*}}> to tensor<1x3xi64, #sparse{{[0-9]*}}>51// CHECK: %[[C:.*]] = sparse_tensor.convert %[[E]] : tensor<1x3xi64, #sparse{{[0-9]*}}> to tensor<1x3xi64, #sparse{{[0-9]*}}>52// CHECK: return %[[C]] : tensor<1x3xi64, #sparse{{[0-9]*}}>53func.func @sparse_fuse_slice(%a : tensor<2x3xi64, #SortedCOO>) -> tensor<1x3xi64, #SortedCOO> {54 %extracted_slice = tensor.extract_slice %a[1, 0] [1, 3] [1, 1] : tensor<2x3xi64, #SortedCOO> to tensor<1x3xi64>55 %cast = tensor.cast %extracted_slice : tensor<1x3xi64> to tensor<1x3xi64, #Slice>56 %0 = sparse_tensor.convert %cast : tensor<1x3xi64, #Slice> to tensor<1x3xi64, #SortedCOO>57 return %0 : tensor<1x3xi64, #SortedCOO>58}59 60// CHECK-LABEL: func.func @sparse_select(61// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x4xi1>,62// CHECK-SAME: %[[VAL_1:.*]]: tensor<4x4xf64, #sparse{{[0-9]*}}>,63// CHECK-SAME: %[[VAL_2:.*]]: tensor<4x4xf64, #sparse{{[0-9]*}}>) -> tensor<4x4xf64, #sparse{{[0-9]*}}> {64// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f6465// CHECK-DAG: %[[VAL_4:.*]] = tensor.empty() : tensor<4x4xf64, #sparse{{[0-9]*}}>66// CHECK-NEXT: %[[VAL_5:.*]] = linalg.generic {indexing_maps = [#map, #map, #map, #map], iterator_types = ["parallel", "parallel"]}67// CHECK-SAME: ins(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]68// CHECK-NEXT: ^bb0(%[[VAL_6:.*]]: i1, %[[VAL_7:.*]]: f64, %[[VAL_8:.*]]: f64, %[[VAL_9:.*]]: f64):69// CHECK-NEXT: %[[VAL_10:.*]] = sparse_tensor.binary %[[VAL_7]], %[[VAL_8]] : f64, f64 to f6470// CHECK-NEXT: overlap = {71// CHECK-NEXT: ^bb0(%[[VAL_11:.*]]: f64, %[[VAL_12:.*]]: f64):72// CHECK-NEXT: %[[VAL_13:.*]] = arith.select %[[VAL_6]], %[[VAL_11]], %[[VAL_12]] : f6473// CHECK-NEXT: sparse_tensor.yield %[[VAL_13]] : f6474// CHECK-NEXT: }75// CHECK-NEXT: left = {76// CHECK-NEXT: ^bb0(%[[VAL_14:.*]]: f64):77// CHECK-NEXT: %[[VAL_15:.*]] = arith.select %[[VAL_6]], %[[VAL_14]], %[[VAL_3]] : f6478// CHECK-NEXT: sparse_tensor.yield %[[VAL_15]] : f6479// CHECK-NEXT: }80// CHECK-NEXT: right = {81// CHECK-NEXT: ^bb0(%[[VAL_16:.*]]: f64):82// CHECK-NEXT: %[[VAL_17:.*]] = arith.select %[[VAL_6]], %[[VAL_3]], %[[VAL_16]] : f6483// CHECK-NEXT: sparse_tensor.yield %[[VAL_17]] : f6484// CHECK-NEXT: }85// CHECK-NEXT: linalg.yield %[[VAL_10]] : f6486// CHECK-NEXT: } -> tensor<4x4xf64, #sparse{{[0-9]*}}>87// CHECK-NEXT: return %[[VAL_18:.*]] : tensor<4x4xf64, #sparse{{[0-9]*}}>88// CHECK-NEXT: }89func.func @sparse_select(%cond: tensor<4x4xi1>,90 %arga: tensor<4x4xf64, #DCSR>,91 %argb: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {92 %xv = tensor.empty() : tensor<4x4xf64, #DCSR>93 %0 = linalg.generic #sel_trait94 ins(%cond, %arga, %argb: tensor<4x4xi1>, tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>)95 outs(%xv: tensor<4x4xf64, #DCSR>) {96 ^bb(%c: i1, %a: f64, %b: f64, %x: f64):97 %1 = arith.select %c, %a, %b : f6498 linalg.yield %1 : f6499 } -> tensor<4x4xf64, #DCSR>100 return %0 : tensor<4x4xf64, #DCSR>101}102