1// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR 2// 3// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification --sparse-tensor-conversion --canonicalize | \ 4// RUN: FileCheck %s --check-prefix=CHECK-MIR 5 6#X = #sparse_tensor.encoding<{ 7 map = (d0, d1, d2) -> (d2 : dense, d0 : dense, d1 : dense) 8}> 9 10#trait = { 11 indexing_maps = [ 12 affine_map<(i,j,k) -> (k,i,j)>, // A (in) 13 affine_map<(i,j,k) -> ()> // X (out) 14 ], 15 iterator_types = ["reduction", "reduction", "reduction"] 16} 17 18// CHECK-HIR-LABEL: func @sparse_dynamic_dims( 19// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse{{[0-9]*}}>, 20// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { 21// CHECK-HIR-DAG: %[[VAL_2:.*]] = arith.constant 1 : index 22// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index 23// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index 24// CHECK-HIR: %[[DEMAP:. *]] = sparse_tensor.reinterpret_map %[[VAL_0]] 25// CHECK-HIR-DAG: %[[VAL_5:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> 26// CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> 27// CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> 28// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[DEMAP]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> 29// CHECK-HIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : tensor<f32> to memref<f32> 30// CHECK-HIR: %[[VAL_11:.*]] = tensor.extract %[[VAL_1]][] : tensor<f32> 31// CHECK-HIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) { 32// CHECK-HIR: %[[VAL_18:.*]] = arith.muli %[[VAL_13]], %[[VAL_6]] : index 33// CHECK-HIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_2]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) { 34// CHECK-HIR: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_18]] : index 35// CHECK-HIR: %[[VAL_23:.*]] = arith.muli %[[VAL_19]], %[[VAL_7]] : index 36// CHECK-HIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_2]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) { 37// CHECK-HIR: %[[VAL_24:.*]] = arith.addi %[[VAL_21]], %[[VAL_23]] : index 38// CHECK-HIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32> 39// CHECK-HIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32 40// CHECK-HIR: scf.yield %[[VAL_26]] : f32 41// CHECK-HIR: } 42// CHECK-HIR: scf.yield %[[VAL_20]] : f32 43// CHECK-HIR: } 44// CHECK-HIR: scf.yield %[[VAL_15]] : f32 45// CHECK-HIR: } 46// CHECK-HIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32> 47// CHECK-HIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32> 48// CHECK-HIR: return %[[VAL_30]] : tensor<f32> 49// CHECK-HIR: } 50// 51// CHECK-MIR-LABEL: func @sparse_dynamic_dims( 52// CHECK-MIR-SAME: %[[ARGA:.*]]: !llvm.ptr, 53// CHECK-MIR-SAME: %[[ARGX:.*]]: tensor<f32>) -> tensor<f32> { 54// CHECK-MIR-DAG: %[[I0:.*]] = arith.constant 0 : index 55// CHECK-MIR-DAG: %[[I1:.*]] = arith.constant 1 : index 56// CHECK-MIR-DAG: %[[I2:.*]] = arith.constant 2 : index 57// CHECK-MIR-DAG: %[[DimSize0:.*]] = call @sparseLvlSize(%[[ARGA]], %[[I0]]) 58// CHECK-MIR-DAG: %[[DimSize1:.*]] = call @sparseLvlSize(%[[ARGA]], %[[I1]]) 59// CHECK-MIR-DAG: %[[DimSize2:.*]] = call @sparseLvlSize(%[[ARGA]], %[[I2]]) 60// CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF32(%[[ARGA]]) : (!llvm.ptr) -> memref<?xf32> 61// CHECK-MIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[ARGX]] : tensor<f32> to memref<f32> 62// CHECK-MIR: %[[VAL_11:.*]] = tensor.extract %[[ARGX]][] : tensor<f32> 63// CHECK-MIR: %[[VAL_12:.*]] = scf.for %[[D2:.*]] = %[[I0]] to %[[DimSize0]] step %[[I1]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) { 64// CHECK-MIR: %[[VAL_18:.*]] = arith.muli %[[D2]], %[[DimSize1]] : index 65// CHECK-MIR: %[[VAL_15:.*]] = scf.for %[[D0:.*]] = %[[I0]] to %[[DimSize1]] step %[[I1]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) { 66// CHECK-MIR: %[[VAL_19:.*]] = arith.addi %[[D0]], %[[VAL_18]] : index 67// CHECK-MIR: %[[VAL_23:.*]] = arith.muli %[[VAL_19]], %[[DimSize2]] : index 68// CHECK-MIR: %[[VAL_20:.*]] = scf.for %[[D1:.*]] = %[[I0]] to %[[DimSize2]] step %[[I1]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) { 69// CHECK-MIR: %[[VAL_24:.*]] = arith.addi %[[D1]], %[[VAL_23]] : index 70// CHECK-MIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32> 71// CHECK-MIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32 72// CHECK-MIR: scf.yield %[[VAL_26]] : f32 73// CHECK-MIR: } 74// CHECK-MIR: scf.yield %[[VAL_20]] : f32 75// CHECK-MIR: } 76// CHECK-MIR: scf.yield %[[VAL_15]] : f32 77// CHECK-MIR: } 78// CHECK-MIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32> 79// CHECK-MIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32> 80// CHECK-MIR: return %[[VAL_30]] : tensor<f32> 81// CHECK-MIR: } 82func.func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>, 83 %argx: tensor<f32>) -> tensor<f32> { 84 %0 = linalg.generic #trait 85 ins(%arga: tensor<?x?x?xf32, #X>) 86 outs(%argx: tensor<f32>) { 87 ^bb(%a : f32, %x: f32): 88 %0 = arith.addf %x, %a : f32 89 linalg.yield %0 : f32 90 } -> tensor<f32> 91 return %0 : tensor<f32> 92} 93