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1// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py2// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s3 4#X = #sparse_tensor.encoding<{5 map = (d0, d1, d2) -> (d2 : dense, d0 : dense, d1 : dense)6}>7 8#trait = {9 indexing_maps = [10 affine_map<(i,j,k) -> (k,i,j)>, // A (in)11 affine_map<(i,j,k) -> (i,j,k)> // X (out)12 ],13 iterator_types = ["parallel", "parallel", "parallel"]14}15 16// CHECK-LABEL: func @sparse_static_dims(17// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse{{[0-9]*}}>,18// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {19// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f3220// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 20 : index21// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index22// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index23// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index24// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index25// CHECK: %[[DEMAP:.*]] = sparse_tensor.reinterpret_map %[[VAL_0]]26// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[DEMAP]] : tensor<30x10x20xf32, #sparse{{[0-9]*}}>27// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_buffer %[[VAL_1]] : tensor<20x30x10xf32> to memref<20x30x10xf32>28// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_9]] : memref<20x30x10xf32>)29// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {30// CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index31// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {32// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_12]] : index33// CHECK: %[[VAL_15:.*]] = arith.muli %[[VAL_13]], %[[VAL_2]] : index34// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] {35// CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_15]] : index36// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xf32>37// CHECK: memref.store %[[VAL_17]], %[[VAL_9]]{{\[}}%[[VAL_14]], %[[VAL_10]], %[[VAL_11]]] : memref<20x30x10xf32>38// CHECK: }39// CHECK: }40// CHECK: }41// CHECK: %[[VAL_18:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<20x30x10xf32>42// CHECK: return %[[VAL_18]] : tensor<20x30x10xf32>43// CHECK: }44func.func @sparse_static_dims(%arga: tensor<10x20x30xf32, #X>,45 %argx: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {46 %0 = linalg.generic #trait47 ins(%arga: tensor<10x20x30xf32, #X>)48 outs(%argx: tensor<20x30x10xf32>) {49 ^bb(%a : f32, %x: f32):50 linalg.yield %a : f3251 } -> tensor<20x30x10xf32>52 return %0 : tensor<20x30x10xf32>53}54 55// CHECK-LABEL: func @sparse_dynamic_dims(56// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse{{[0-9]*}}>,57// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {58// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f3259// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index60// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index61// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index62// CHECK: %[[DEMAP:.*]] = sparse_tensor.reinterpret_map %[[VAL_0]]63// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[DEMAP]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>64// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>65// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>66// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>67// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_buffer %[[VAL_1]] : tensor<?x?x?xf32> to memref<?x?x?xf32>68// CHECK-DAG: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_10]] : memref<?x?x?xf32>)69// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] {70// CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_11]], %[[VAL_8]] : index71// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] {72// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : index73// CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_14]], %[[VAL_6]] : index74// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_4]] {75// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_16]] : index76// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_17]]] : memref<?xf32>77// CHECK: memref.store %[[VAL_18]], %[[VAL_10]]{{\[}}%[[VAL_15]], %[[VAL_11]], %[[VAL_12]]] : memref<?x?x?xf32>78// CHECK: }79// CHECK: }80// CHECK: }81// CHECK: %[[VAL_19:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<?x?x?xf32>82// CHECK: return %[[VAL_19]] : tensor<?x?x?xf32>83// CHECK: }84func.func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,85 %argx: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {86 %0 = linalg.generic #trait87 ins(%arga: tensor<?x?x?xf32, #X>)88 outs(%argx: tensor<?x?x?xf32>) {89 ^bb(%a : f32, %x: f32):90 linalg.yield %a : f3291 } -> tensor<?x?x?xf32>92 return %0 : tensor<?x?x?xf32>93}94