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1// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR2//3// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification --sparse-tensor-conversion --canonicalize | \4// RUN: FileCheck %s --check-prefix=CHECK-MIR5 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 : index22// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index23// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index24// 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_buffer %[[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]] : index33// 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]] : index35// CHECK-HIR: %[[VAL_23:.*]] = arith.muli %[[VAL_19]], %[[VAL_7]] : index36// 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]] : index38// CHECK-HIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>39// CHECK-HIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f3240// CHECK-HIR: scf.yield %[[VAL_26]] : f3241// CHECK-HIR: }42// CHECK-HIR: scf.yield %[[VAL_20]] : f3243// CHECK-HIR: }44// CHECK-HIR: scf.yield %[[VAL_15]] : f3245// 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 : index55// CHECK-MIR-DAG: %[[I1:.*]] = arith.constant 1 : index56// CHECK-MIR-DAG: %[[I2:.*]] = arith.constant 2 : index57// 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_buffer %[[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]] : index65// 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]] : index67// CHECK-MIR: %[[VAL_23:.*]] = arith.muli %[[VAL_19]], %[[DimSize2]] : index68// 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]] : index70// CHECK-MIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>71// CHECK-MIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f3272// CHECK-MIR: scf.yield %[[VAL_26]] : f3273// CHECK-MIR: }74// CHECK-MIR: scf.yield %[[VAL_20]] : f3275// CHECK-MIR: }76// CHECK-MIR: scf.yield %[[VAL_15]] : f3277// 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 #trait85 ins(%arga: tensor<?x?x?xf32, #X>)86 outs(%argx: tensor<f32>) {87 ^bb(%a : f32, %x: f32):88 %0 = arith.addf %x, %a : f3289 linalg.yield %0 : f3290 } -> tensor<f32>91 return %0 : tensor<f32>92}93