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// RUN: mlir-opt %s -generate-runtime-verification | FileCheck %s
// Most of the tests for linalg runtime-verification are implemented as integration tests.
#identity = affine_map<(d0) -> (d0)>
// CHECK-LABEL: @static_dims
func.func @static_dims(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> (tensor<5xf32>) {
// CHECK: %[[TRUE:.*]] = index.bool.constant true
// CHECK: cf.assert %[[TRUE]]
%result = tensor.empty() : tensor<5xf32>
%0 = linalg.generic {
indexing_maps = [#identity, #identity, #identity],
iterator_types = ["parallel"]
} ins(%arg0, %arg1 : tensor<5xf32>, tensor<5xf32>)
outs(%result : tensor<5xf32>) {
^bb0(%gen_arg1: f32, %gen_arg2: f32, %out: f32) :
%tmp1 = arith.addf %gen_arg1, %gen_arg2 : f32
linalg.yield %tmp1 : f32
} -> tensor<5xf32>
return %0 : tensor<5xf32>
}
// -----
#map = affine_map<() -> ()>
// CHECK-LABEL: @scalars
func.func @scalars(%arg0: tensor<f32>, %arg1: tensor<f32>) -> (tensor<f32>) {
// No runtime checks are required if the operands are all scalars
// CHECK-NOT: cf.assert
%result = tensor.empty() : tensor<f32>
%0 = linalg.generic {
indexing_maps = [#map, #map, #map],
iterator_types = []
} ins(%arg0, %arg1 : tensor<f32>, tensor<f32>)
outs(%result : tensor<f32>) {
^bb0(%gen_arg1: f32, %gen_arg2: f32, %out: f32) :
%tmp1 = arith.addf %gen_arg1, %gen_arg2 : f32
linalg.yield %tmp1 : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
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