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// RUN: mlir-opt %s -test-linalg-elementwise-fusion-patterns=fuse-generic-ops-control -split-input-file | FileCheck %s
#map0 = affine_map<(d0, d1) -> (d0, d1)>
#binary2Dpointwise = {
indexing_maps = [#map0, #map0, #map0],
iterator_types = ["parallel", "parallel"]
}
#ternary2Dpointwise = {
indexing_maps = [#map0, #map0, #map0, #map0],
iterator_types = ["parallel", "parallel"]
}
func.func @test_fusion_limit(
%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>,
%arg3 : tensor<?x?xf32>, %arg4 : tensor<?x?xf32>, %arg5 : tensor<?x?xf32>)
-> tensor<?x?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%d1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
%init = tensor.empty(%d0, %d1) : tensor<?x?xf32>
%0 = linalg.generic #binary2Dpointwise
ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%init : tensor<?x?xf32>) {
^bb0(%arg6 : f32, %arg7 : f32, %arg8 : f32):
%1 = arith.mulf %arg6, %arg7 : f32
linalg.yield %1 : f32
} -> tensor<?x?xf32>
%2 = linalg.generic #binary2Dpointwise
ins(%arg2, %arg3 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%init : tensor<?x?xf32>) {
^bb0(%arg6 : f32, %arg7 : f32, %arg8 : f32):
%3 = arith.mulf %arg6, %arg7 : f32
linalg.yield %3 : f32
} -> tensor<?x?xf32>
%4 = linalg.generic #binary2Dpointwise
ins(%arg4, %arg5 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%init : tensor<?x?xf32>) {
^bb0(%arg6 : f32, %arg7 : f32, %arg8 : f32):
%5 = arith.mulf %arg6, %arg7 : f32
linalg.yield %5 : f32
} -> tensor<?x?xf32>
%6 = linalg.generic #ternary2Dpointwise
ins(%0, %2, %4 : tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>)
outs(%init : tensor<?x?xf32>) {
^bb0(%arg6 : f32, %arg7 : f32, %arg8 : f32, %arg9 : f32):
%7 = arith.addf %arg6, %arg7 : f32
%8 = arith.addf %7, %arg8 : f32
linalg.yield %8 : f32
} -> tensor<?x?xf32>
return %6 : tensor<?x?xf32>
}
// CHECK-LABEL: func @test_fusion_limit
// 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-SAME: %[[ARG5:[a-zA-z0-9_]+]]: tensor<?x?xf32>
// CHECK: %[[OP1:.+]] = linalg.generic {{.+}} ins(%[[ARG2]], %[[ARG3]]
// CHECK: %[[OP2:.+]] = linalg.generic {{.+}} ins(%[[ARG4]], %[[ARG5]]
// CHECK: %[[OP3:.+]] = linalg.generic {{.+}} ins(%[[ARG0]], %[[ARG1]], %[[OP1]], %[[OP2]]
// CHECK: return %[[OP3]]
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