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// RUN: mlir-opt %s --linalg-fuse-elementwise-ops | FileCheck %s
#SV = #sparse_tensor.encoding<{ lvlTypes = ["compressed"] }>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // A
affine_map<(i) -> (i)> // B (out)
],
iterator_types = ["parallel"],
doc = "B(i) = OP A(i)"
}
// CHECK-LABEL: func @sparse_fusion
// CHECK: linalg.generic
// CHECK: arith.addf
// CHECK: linalg.generic
// CHECK: math.exp
// CHECK: arith.maxf
// CHECK-NOT: linalg.generic
// CHECK: return
func.func @sparse_fusion(%argA: tensor<100xf64, #SV>) -> tensor<100xf64> {
%c1 = arith.constant 1.0 : f64
%c100 = arith.constant 100.0 : f64
//
// Densifying op.
// Should not be fused with subsequent dense ops.
//
%t0 = tensor.empty() : tensor<100xf64>
%l0 = linalg.generic #trait
ins(%argA: tensor<100xf64, #SV>) outs(%t0: tensor<100xf64>) {
^bb0(%in0: f64, %out0: f64):
%b0 = arith.addf %in0, %c1 : f64
linalg.yield %b0 : f64
} -> tensor<100xf64>
//
// Two following dense ops.
// Should be fused, but not with above.
//
%t1 = tensor.empty() : tensor<100xf64>
%l1 = linalg.generic #trait
ins(%l0: tensor<100xf64>) outs(%t1: tensor<100xf64>) {
^bb0(%in1: f64, %out1: f64):
%b1 = math.exp %in1 : f64
linalg.yield %b1 : f64
} -> tensor<100xf64>
%t2 = tensor.empty() : tensor<100xf64>
%l2 = linalg.generic #trait
ins(%l1: tensor<100xf64>) outs(%t2: tensor<100xf64>) {
^bb0(%in2: f64, %out2: f64):
%b2 = arith.maxf %in2, %c100 : f64
linalg.yield %b2 : f64
} -> tensor<100xf64>
return %l2 : tensor<100xf64>
}
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