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// RUN: mlir-opt %s -sparsification | FileCheck %s
//
// A SDDMM implementation with "spy" function and
// in-place update of the sampling sparse matrix.
//
#SM = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
#trait_sampled_dense_dense = {
indexing_maps = [
affine_map<(i,j,k) -> (i,k)>, // A
affine_map<(i,j,k) -> (k,j)>, // B
affine_map<(i,j,k) -> (i,j)> // S
],
iterator_types = ["parallel", "parallel", "reduction"],
doc = "S(i,j) += spy[S(i,j)] x SUM_k A(i,k) B(k,j)"
}
// CHECK-LABEL: func.func @sparse_sampled_dd(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<8x8xf64>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<8x8xf64>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xf64>
// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_11]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_11]], %[[VAL_5]] : index
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_16]]] : memref<?xf64>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]], %[[VAL_12]]] : memref<8x8xf64>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]], %[[VAL_17]]] : memref<8x8xf64>
// CHECK: %[[VAL_21:.*]] = arith.mulf %[[VAL_19]], %[[VAL_20]] : f64
// CHECK: %[[VAL_22:.*]] = arith.addf %[[VAL_18]], %[[VAL_21]] : f64
// CHECK: memref.store %[[VAL_22]], %[[VAL_10]]{{\[}}%[[VAL_16]]] : memref<?xf64>
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: %[[VAL_23:.*]] = sparse_tensor.load %[[VAL_2]] : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_23]] : tensor<8x8xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
func.func @sparse_sampled_dd(%argA: tensor<8x8xf64>,
%argB: tensor<8x8xf64>,
%argS: tensor<8x8xf64, #SM>) -> tensor<8x8xf64, #SM> {
%f0 = arith.constant 0.0 : f64
%result = linalg.generic #trait_sampled_dense_dense
ins(%argA, %argB: tensor<8x8xf64>, tensor<8x8xf64>) outs(%argS: tensor<8x8xf64, #SM>) {
^bb(%a: f64, %b: f64, %s: f64):
%u = sparse_tensor.unary %s : f64 to f64
present={
^bb0(%p: f64):
%mul = arith.mulf %a, %b : f64
sparse_tensor.yield %mul : f64
}
absent={}
%r = sparse_tensor.reduce %s, %u, %f0 : f64 {
^bb0(%p: f64, %q: f64):
%add = arith.addf %p, %q : f64
sparse_tensor.yield %add : f64
}
linalg.yield %r : f64
} -> tensor<8x8xf64, #SM>
return %result : tensor<8x8xf64, #SM>
}
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