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// RUN: mlir-opt %s -split-input-file -allow-unregistered-dialect -linalg-detensorize | FileCheck %s
#map0 = affine_map<() -> ()>
#attrs = {
indexing_maps = [#map0, #map0, #map0],
iterator_types = []
}
func @main() -> (tensor<i32>) attributes {} {
%c0 = constant 0 : i32
%0 = tensor.from_elements %c0 : tensor<1xi32>
%reshaped0 = linalg.tensor_collapse_shape %0 [] : tensor<1xi32> into tensor<i32>
%c10 = constant 10 : i32
%1 = tensor.from_elements %c10 : tensor<1xi32>
%reshaped1 = linalg.tensor_collapse_shape %1 [] : tensor<1xi32> into tensor<i32>
br ^bb1(%reshaped0 : tensor<i32>)
^bb1(%2: tensor<i32>): // 2 preds: ^bb0, ^bb2
%3 = linalg.init_tensor [] : tensor<i1>
%4 = linalg.generic #attrs
ins(%2, %reshaped1 : tensor<i32>, tensor<i32>)
outs(%3 : tensor<i1>) {
^bb0(%arg0: i32, %arg1: i32, %arg2: i1): // no predecessors
%8 = cmpi slt, %arg0, %arg1 : i32
linalg.yield %8 : i1
} -> tensor<i1>
%5 = tensor.extract %4[] : tensor<i1>
cond_br %5, ^bb2(%2 : tensor<i32>), ^bb3(%2 : tensor<i32>)
^bb2(%6: tensor<i32>): // pred: ^bb1
%7 = linalg.init_tensor [] : tensor<i32>
%8 = linalg.generic #attrs
ins(%6, %6 : tensor<i32>, tensor<i32>)
outs(%7 : tensor<i32>) {
^bb0(%arg0: i32, %arg1: i32, %arg2: i32): // no predecessors
%9 = addi %arg0, %arg1 : i32
linalg.yield %9 : i32
} -> tensor<i32>
br ^bb3(%8 : tensor<i32>)
^bb3(%10: tensor<i32>): // pred: ^bb1
return %10 : tensor<i32>
}
// CHECK-LABEL: func @main()
// CHECK-NEXT: constant 0
// CHECK-NEXT: constant 10
// CHECK-NEXT: br ^[[bb1:.*]](%{{.*}}: i32)
// CHECK-NEXT: ^[[bb1]](%{{.*}}: i32):
// CHECK-NEXT: cmpi slt, %{{.*}}, %{{.*}}
// CHECK-NEXT: cond_br %{{.*}}, ^[[bb2:.*]](%{{.*}} : i32), ^bb3(%{{.*}} : i32)
// CHECK-NEXT: ^[[bb2]](%{{.*}}: i32)
// CHECK-NEXT: addi %{{.*}}, %{{.*}}
// CHECK-NEXT: br ^[[bb3:.*]](%{{.*}} : i32)
// CHECK-NEXT: ^[[bb3]](%{{.*}}: i32)
// CHECK-NEXT: tensor.from_elements %{{.*}} : tensor<1xi32>
// CHECK-NEXT: linalg.tensor_collapse_shape %{{.*}} [] : tensor<1xi32> into tensor<i32>
// CHECK-NEXT: return %{{.*}}
// CHECK-NEXT: }
// -----
// Similar to the above test with one change: one of the block after the
// if-condition passes/forwards its tensor argument to another block.
#map0 = affine_map<() -> ()>
#attrs = {
indexing_maps = [#map0, #map0, #map0],
iterator_types = []
}
func @main() -> (tensor<i32>) attributes {} {
%c0 = constant 0 : i32
%0 = tensor.from_elements %c0 : tensor<1xi32>
%reshaped0 = linalg.tensor_collapse_shape %0 [] : tensor<1xi32> into tensor<i32>
%c10 = constant 10 : i32
%1 = tensor.from_elements %c10 : tensor<1xi32>
%reshaped1 = linalg.tensor_collapse_shape %1 [] : tensor<1xi32> into tensor<i32>
br ^bb1(%reshaped0 : tensor<i32>)
^bb1(%2: tensor<i32>): // 2 preds: ^bb0, ^bb2
%3 = linalg.init_tensor [] : tensor<i1>
%4 = linalg.generic #attrs
ins(%2, %reshaped1 : tensor<i32>, tensor<i32>)
outs(%3 : tensor<i1>) {
^bb0(%arg0: i32, %arg1: i32, %arg2: i1): // no predecessors
%8 = cmpi slt, %arg0, %arg1 : i32
linalg.yield %8 : i1
} -> tensor<i1>
%5 = tensor.extract %4[] : tensor<i1>
cond_br %5, ^bb2(%2 : tensor<i32>), ^bb3(%2 : tensor<i32>)
^bb2(%6: tensor<i32>): // pred: ^bb1
%7 = linalg.init_tensor [] : tensor<i32>
%8 = linalg.generic #attrs
ins(%6, %6 : tensor<i32>, tensor<i32>)
outs(%7 : tensor<i32>) {
^bb0(%arg0: i32, %arg1: i32, %arg2: i32): // no predecessors
%9 = addi %arg0, %arg1 : i32
linalg.yield %9 : i32
} -> tensor<i32>
br ^bb3(%8 : tensor<i32>)
^bb3(%10: tensor<i32>): // pred: ^bb1
br ^bb4(%10 : tensor<i32>)
^bb4(%11: tensor<i32>): // pred: ^bb1
return %11 : tensor<i32>
}
// CHECK-LABEL: func @main()
// CHECK-NEXT: constant 0
// CHECK-NEXT: constant 10
// CHECK-NEXT: br ^[[bb1:.*]](%{{.*}}: i32)
// CHECK-NEXT: ^[[bb1]](%{{.*}}: i32):
// CHECK-NEXT: cmpi slt, %{{.*}}, %{{.*}}
// CHECK-NEXT: cond_br %{{.*}}, ^[[bb2:.*]](%{{.*}} : i32), ^bb3(%{{.*}} : i32)
// CHECK-NEXT: ^[[bb2]](%{{.*}}: i32)
// CHECK-NEXT: addi %{{.*}}, %{{.*}}
// CHECK-NEXT: br ^[[bb3:.*]](%{{.*}} : i32)
// CHECK-NEXT: ^[[bb3]](%{{.*}}: i32)
// CHECK-NEXT: br ^[[bb4:.*]](%{{.*}} : i32)
// CHECK-NEXT: ^[[bb4]](%{{.*}}: i32)
// CHECK-NEXT: tensor.from_elements %{{.*}} : tensor<1xi32>
// CHECK-NEXT: linalg.tensor_collapse_shape %{{.*}} [] : tensor<1xi32> into tensor<i32>
// CHECK-NEXT: return %{{.*}}
// CHECK-NEXT: }
// -----
#map0 = affine_map<() -> ()>
#attrs = {
indexing_maps = [#map0, #map0, #map0],
iterator_types = []
}
func @main() -> (tensor<i32>) attributes {} {
%c0 = constant 0 : i32
%0 = tensor.from_elements %c0 : tensor<1xi32>
%reshaped0 = linalg.tensor_collapse_shape %0 [] : tensor<1xi32> into tensor<i32>
%c10 = constant 10 : i32
%1 = tensor.from_elements %c10 : tensor<1xi32>
%reshaped1 = linalg.tensor_collapse_shape %1 [] : tensor<1xi32> into tensor<i32>
br ^bb1(%reshaped0 : tensor<i32>)
^bb1(%2: tensor<i32>): // 2 preds: ^bb0, ^bb2
%3 = linalg.init_tensor [] : tensor<i1>
%4 = linalg.generic #attrs
ins(%2, %reshaped1 : tensor<i32>, tensor<i32>)
outs(%3 : tensor<i1>) {
^bb0(%arg0: i32, %arg1: i32, %arg2: i1): // no predecessors
%8 = cmpi slt, %arg0, %arg1 : i32
linalg.yield %8 : i1
} -> tensor<i1>
%5 = tensor.extract %4[] : tensor<i1>
// This cond_br intentionally has bb2 as it's target for both branches. This
// is to make sure that the "forward phase" of the cost-model correctly adds
// the users of a block argument (in this case bb2's argument) to the work
// list.
cond_br %5, ^bb2(%2 : tensor<i32>), ^bb2(%2 : tensor<i32>)
^bb2(%6: tensor<i32>): // pred: ^bb1
%12 = tensor.from_elements %c10 : tensor<1xi32>
%reshaped12 = linalg.tensor_collapse_shape %12 [] : tensor<1xi32> into tensor<i32>
%7 = linalg.init_tensor [] : tensor<i32>
%8 = linalg.generic #attrs
ins(%6, %reshaped12 : tensor<i32>, tensor<i32>)
outs(%7 : tensor<i32>) {
^bb0(%arg0: i32, %arg1: i32, %arg2: i32): // no predecessors
%9 = addi %arg0, %arg1 : i32
linalg.yield %9 : i32
} -> tensor<i32>
br ^bb3(%8 : tensor<i32>)
^bb3(%10: tensor<i32>): // pred: ^bb1
return %10 : tensor<i32>
}
// CHECK-LABEL: func @main()
// CHECK-NEXT: constant 0
// CHECK-NEXT: constant 10
// CHECK-NEXT: br ^[[bb1:.*]](%{{.*}}: i32)
// CHECK-NEXT: ^[[bb1]](%{{.*}}: i32):
// CHECK-NEXT: cmpi slt, %{{.*}}, %{{.*}}
// CHECK-NEXT: cond_br %{{.*}}, ^[[bb2:.*]](%{{.*}} : i32), ^bb2(%{{.*}} : i32)
// CHECK-NEXT: ^[[bb2]](%{{.*}}: i32)
// CHECK-NEXT: addi %{{.*}}, %{{.*}}
// CHECK-NEXT: br ^[[bb3:.*]](%{{.*}} : i32)
// CHECK-NEXT: ^[[bb3]](%{{.*}}: i32)
// CHECK-NEXT: tensor.from_elements %{{.*}} : tensor<1xi32>
// CHECK-NEXT: linalg.tensor_collapse_shape %{{.*}} [] : tensor<1xi32> into tensor<i32>
// CHECK-NEXT: return %{{.*}}
// CHECK-NEXT: }
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