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// RUN: mlir-opt %s --sparsification-and-bufferization | FileCheck %s --check-prefix=CHECK-NOVEC
// RUN: mlir-opt %s --sparsification-and-bufferization="vl=8" | FileCheck %s --check-prefix=CHECK-VEC
// Test to ensure we can pass optimization flags into
// the mini sparsification and bufferization pipeline.
#SV = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
#trait_sum_reduction = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> ()> // x (scalar out)
],
iterator_types = ["reduction"],
doc = "x += SUM_i a(i)"
}
//
// CHECK-NOVEC-LABEL: func.func @sum_reduction
// CHECK-NOVEC: scf.for
// CHECK-NOVEC: arith.addf %{{.*}} %{{.*}} : f32
// CHECK-NOVEC: }
//
// CHECK-VEC-LABEL: func.func @sum_reduction
// CHECK-VEC: vector.insertelement
// CHECK-VEC: scf.for
// CHECK-VEC: vector.create_mask
// CHECK-VEC: vector.maskedload
// CHECK-VEC: arith.addf %{{.*}} %{{.*}} : vector<8xf32>
// CHECK-VEC: }
// CHECK-VEC: vector.reduction <add>
//
func.func @sum_reduction(%arga: tensor<?xf32, #SV>,
%argx: tensor<f32>) -> tensor<f32> {
%0 = linalg.generic #trait_sum_reduction
ins(%arga: tensor<?xf32, #SV>)
outs(%argx: tensor<f32>) {
^bb(%a: f32, %x: f32):
%0 = arith.addf %x, %a : f32
linalg.yield %0 : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
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