| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s |
| |
| #DenseMatrix = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : dense, d1 : dense) |
| }> |
| |
| #SparseMatrix = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : compressed, d1 : compressed) |
| }> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = A(i,j) * i * j" |
| } |
| |
| // CHECK-LABEL: func.func @dense_index( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_5:.*]] = tensor.empty(%[[VAL_3]], %[[VAL_4]]) : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_2]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_24:.*]] = sparse_tensor.lvl %[[VAL_5]], %[[VAL_2]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_5]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_1]] to %[[VAL_7]] step %[[VAL_2]] { |
| // CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_8]] : index |
| // CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_10]], %[[VAL_24]] : index |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_1]] to %[[VAL_8]] step %[[VAL_2]] { |
| // CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_12]] : index |
| // CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_11]], %[[VAL_14]] : index |
| // CHECK: %[[VAL_16:.*]] = arith.index_cast %[[VAL_11]] : index to i64 |
| // CHECK: %[[VAL_17:.*]] = arith.index_cast %[[VAL_10]] : index to i64 |
| // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xi64> |
| // CHECK: %[[VAL_19:.*]] = arith.muli %[[VAL_17]], %[[VAL_18]] : i64 |
| // CHECK: %[[VAL_20:.*]] = arith.muli %[[VAL_16]], %[[VAL_19]] : i64 |
| // CHECK: memref.store %[[VAL_20]], %[[VAL_9]]{{\[}}%[[VAL_15]]] : memref<?xi64> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_21:.*]] = sparse_tensor.load %[[VAL_5]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK: return %[[VAL_21]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK: } |
| func.func @dense_index(%arga: tensor<?x?xi64, #DenseMatrix>) |
| -> tensor<?x?xi64, #DenseMatrix> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 0 : index |
| %0 = sparse_tensor.lvl %arga, %c0 : tensor<?x?xi64, #DenseMatrix> |
| %1 = sparse_tensor.lvl %arga, %c1 : tensor<?x?xi64, #DenseMatrix> |
| %init = tensor.empty(%0, %1) : tensor<?x?xi64, #DenseMatrix> |
| %r = linalg.generic #trait |
| ins(%arga: tensor<?x?xi64, #DenseMatrix>) |
| outs(%init: tensor<?x?xi64, #DenseMatrix>) { |
| ^bb(%a: i64, %x: i64): |
| %i = linalg.index 0 : index |
| %j = linalg.index 1 : index |
| %ii = arith.index_cast %i : index to i64 |
| %jj = arith.index_cast %j : index to i64 |
| %m1 = arith.muli %ii, %a : i64 |
| %m2 = arith.muli %jj, %m1 : i64 |
| linalg.yield %m2 : i64 |
| } -> tensor<?x?xi64, #DenseMatrix> |
| return %r : tensor<?x?xi64, #DenseMatrix> |
| } |
| |
| |
| // CHECK-LABEL: func.func @sparse_index( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_5:.*]] = tensor.empty(%[[VAL_3]], %[[VAL_4]]) : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_1]]] : memref<?xindex> |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK: %[[T:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_2]] {{.*}} { |
| // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex> |
| // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<?xindex> |
| // CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index |
| // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> |
| // CHECK: %[[L:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_2]] {{.*}} { |
| // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex> |
| // CHECK: %[[VAL_20:.*]] = arith.index_cast %[[VAL_19]] : index to i64 |
| // CHECK: %[[VAL_21:.*]] = arith.index_cast %[[VAL_14]] : index to i64 |
| // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref<?xi64> |
| // CHECK: %[[VAL_23:.*]] = arith.muli %[[VAL_21]], %[[VAL_22]] : i64 |
| // CHECK: %[[VAL_24:.*]] = arith.muli %[[VAL_20]], %[[VAL_23]] : i64 |
| // CHECK: %[[Y:.*]] = tensor.insert %[[VAL_24]] into %{{.*}}[%[[VAL_14]], %[[VAL_19]]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK: scf.yield %[[Y]] |
| // CHECK: } |
| // CHECK: scf.yield %[[L]] |
| // CHECK: } |
| // CHECK: %[[VAL_25:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK: return %[[VAL_25]] : tensor<?x?xi64, #sparse{{[0-9]*}}> |
| // CHECK: } |
| func.func @sparse_index(%arga: tensor<?x?xi64, #SparseMatrix>) |
| -> tensor<?x?xi64, #SparseMatrix> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 0 : index |
| %0 = sparse_tensor.lvl %arga, %c0 : tensor<?x?xi64, #SparseMatrix> |
| %1 = sparse_tensor.lvl %arga, %c1 : tensor<?x?xi64, #SparseMatrix> |
| %init = tensor.empty(%0, %1) : tensor<?x?xi64, #SparseMatrix> |
| %r = linalg.generic #trait |
| ins(%arga: tensor<?x?xi64, #SparseMatrix>) |
| outs(%init: tensor<?x?xi64, #SparseMatrix>) { |
| ^bb(%a: i64, %x: i64): |
| %i = linalg.index 0 : index |
| %j = linalg.index 1 : index |
| %ii = arith.index_cast %i : index to i64 |
| %jj = arith.index_cast %j : index to i64 |
| %m1 = arith.muli %ii, %a : i64 |
| %m2 = arith.muli %jj, %m1 : i64 |
| linalg.yield %m2 : i64 |
| } -> tensor<?x?xi64, #SparseMatrix> |
| return %r : tensor<?x?xi64, #SparseMatrix> |
| } |
