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// RUN: mlir-opt %s -split-input-file --linalg-specialize-generic-ops | FileCheck %s
#map = affine_map<(d0, d1, d2) -> (d1, d0)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// This test checks that linalg.generic does not get incorrectly specialized to transform or broadcast.
// CHECK-LABEL: @transpose_and_broadcast
// CHECK: linalg.generic
func.func @transpose_and_broadcast(%arg0: tensor<7x8xf32>, %arg1: tensor<8x7x9xf32>) -> tensor<8x7x9xf32> {
%res = linalg.generic {
indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel"]
} ins(%arg0 : tensor<7x8xf32>) outs(%arg1 : tensor<8x7x9xf32>) {
^bb0(%in: f32, %out: f32):
linalg.yield %in : f32
} -> tensor<8x7x9xf32>
return %res : tensor<8x7x9xf32>
}
// -----
#map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: @neither_permutation_nor_broadcast
// CHECK: linalg.generic
func.func @neither_permutation_nor_broadcast(%init : tensor<8xi32>) -> tensor<8xi32> {
%res = linalg.generic {
indexing_maps = [#map], iterator_types = ["parallel"]
} outs(%init: tensor<8xi32>) {
^bb0(%out: i32):
linalg.yield %out: i32
} -> tensor<8xi32>
return %res : tensor<8xi32>
}
// -----
#map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: func @not_copy
// CHECK-NOT: linalg.copy
// CHECK: linalg.generic
func.func @not_copy(%input: tensor<8xi32>, %init: tensor<8xi32>) -> tensor<8xi32> {
%c0_i32 = arith.constant 0 : i32
%res = linalg.generic {
indexing_maps = [#map, #map], iterator_types = ["parallel"]
} ins(%input: tensor<8xi32>) outs(%init: tensor<8xi32>) {
^bb0(%in: i32, %out: i32):
linalg.yield %c0_i32 : i32
} -> tensor<8xi32>
return %res : tensor<8xi32>
}
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