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// RUN: mlir-opt %s -test-transform-dialect-interpreter -split-input-file -verify-diagnostics | FileCheck %s
// CHECK: #[[$MAP:.*]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK-LABEL: @interchange_generic
func.func @interchange_generic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[$MAP]], #[[$MAP]]
%0 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]
} ins(%arg0 : tensor<?x?xf32>) outs(%arg1 : tensor<?x?xf32>) {
^bb0(%arg2: f32, %arg3: f32):
%1 = math.exp %arg2 : f32
linalg.yield %1 : f32
} -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !pdl.operation):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1
transform.structured.interchange %0 iterator_interchange = [1, 0]
}
// -----
func.func @interchange_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
// expected-note @below {{when applied to this op}}
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !pdl.operation):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1
// expected-error @below {{transform applied to the wrong op kind}}
transform.structured.interchange %0 iterator_interchange = [1, 0]
}
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