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// RUN: mlir-opt %s -sparsification | FileCheck %s
#SM = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
#trait = {
indexing_maps = [
affine_map<(i,j) -> (i,j)> // A
],
iterator_types = ["parallel", "parallel"],
doc = "A(i,j) += 2.0 where A(i,j) != 0"
}
module {
// Example of a semi-ring operation that only adds a
// constant at stored values (something that would
// typically not sparsify since it would densify the
// implicit zeros in the normal case). The sparse
// compiler should see that this is a "simply dynamic"
// operation, and the values can be change "in-place".
//
// CHECK-LABEL: func.func @add_only_where_nonzero(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2.000000e+00 : f64
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
// CHECK: scf.for %[[VAL_7:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] {
// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_9:.*]] = arith.addi %[[VAL_7]], %[[VAL_3]] : index
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_9]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_10]] step %[[VAL_3]] {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xf64>
// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_12]], %[[VAL_4]] : f64
// CHECK: memref.store %[[VAL_13]], %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xf64>
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: %[[VAL_14:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_14]] : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
func.func @add_only_where_nonzero(%argA: tensor<8x8xf64, #SM>) -> tensor<8x8xf64, #SM> {
%c = arith.constant 2.0 : f64
%result = linalg.generic #trait
outs(%argA: tensor<8x8xf64, #SM>) {
^bb(%a: f64):
%u = sparse_tensor.unary %a : f64 to f64
present={
^bb0(%p: f64):
%add = arith.addf %p, %c : f64
sparse_tensor.yield %add : f64
}
absent={}
linalg.yield %u : f64
} -> tensor<8x8xf64, #SM>
return %result : tensor<8x8xf64, #SM>
}
}
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