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// RUN: mlir-opt -test-linalg-elementwise-fusion-patterns=fuse-multiuse-producer -split-input-file %s | FileCheck %s
#map = affine_map<(d0, d1) -> (d0, d1)>
func.func @multi_use_producer(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
%arg2 : tensor<?x?xf32>, %arg3 : tensor<?x?xf32>, %arg4 : tensor<?x?xf32>)
-> (tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>) {
%0:2 = linalg.generic {
indexing_maps = [#map, #map, #map],
iterator_types = ["parallel", "parallel"]}
ins(%arg0 : tensor<?x?xf32>)
outs(%arg1, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>) {
^bb0(%b0: f32, %b1 : f32, %b2 : f32):
%1 = arith.addf %b0, %b1 : f32
linalg.yield %1, %1 : f32, f32
} -> (tensor<?x?xf32>, tensor<?x?xf32>)
%2 = linalg.generic {
indexing_maps = [#map, #map, #map],
iterator_types = ["parallel", "parallel"]}
ins(%0#1, %arg3 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg4 : tensor<?x?xf32>) {
^bb0(%b0 : f32, %b1 : f32, %b2 : f32):
%3 = arith.mulf %b0, %b1 : f32
linalg.yield %3 : f32
} -> tensor<?x?xf32>
return %0#0, %0#1, %2 : tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>
}
// CHECK: func @multi_use_producer(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: tensor<?x?xf32>)
// CHECK: %[[RESULT:.+]]:3 = linalg.generic
// CHECK: return %[[RESULT]]#0, %[[RESULT]]#1, %[[RESULT]]#2
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