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// RUN: mlir-opt %s --sparsification-and-bufferization | FileCheck %s
#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#sparse = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 : compressed)
}>
//
// Make sure a simple ReLU passes the sparsifier
//
// CHECK-LABEL: func.func @relu
// CHECK: scf.for
// CHECK: scf.for
// CHECK: scf.for
// CHECK: arith.cmpf ugt
// CHECK: arith.select
//
func.func @relu(%arg0: tensor<10x20x30xf64, #sparse>) -> tensor<10x20x30xf64, #sparse> {
%cst = arith.constant 0.000000e+00 : f64
%0 = tensor.empty() : tensor<10x20x30xf64>
%1 = linalg.generic {
indexing_maps = [#map, #map],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0 : tensor<10x20x30xf64, #sparse>)
outs(%0 : tensor<10x20x30xf64>) {
^bb0(%in: f64, %out: f64):
%2 = arith.cmpf ugt, %in, %cst : f64
%3 = arith.select %2, %in, %cst : f64
linalg.yield %3 : f64
} -> tensor<10x20x30xf64>
%cast = tensor.cast %1 : tensor<10x20x30xf64> to tensor<10x20x30xf64, #sparse>
return %cast : tensor<10x20x30xf64, #sparse>
}
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