1// RUN: mlir-opt -test-linalg-elementwise-fusion-patterns=fuse-multiuse-producer -split-input-file %s | FileCheck %s 2 3#map = affine_map<(d0, d1) -> (d0, d1)> 4func.func @multi_use_producer(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, 5 %arg2 : tensor<?x?xf32>, %arg3 : tensor<?x?xf32>, %arg4 : tensor<?x?xf32>) 6 -> (tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>) { 7 %0:2 = linalg.generic { 8 indexing_maps = [#map, #map, #map], 9 iterator_types = ["parallel", "parallel"]} 10 ins(%arg0 : tensor<?x?xf32>) 11 outs(%arg1, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>) { 12 ^bb0(%b0: f32, %b1 : f32, %b2 : f32): 13 %1 = arith.addf %b0, %b1 : f32 14 linalg.yield %1, %1 : f32, f32 15 } -> (tensor<?x?xf32>, tensor<?x?xf32>) 16 %2 = linalg.generic { 17 indexing_maps = [#map, #map, #map], 18 iterator_types = ["parallel", "parallel"]} 19 ins(%0#1, %arg3 : tensor<?x?xf32>, tensor<?x?xf32>) 20 outs(%arg4 : tensor<?x?xf32>) { 21 ^bb0(%b0 : f32, %b1 : f32, %b2 : f32): 22 %3 = arith.mulf %b0, %b1 : f32 23 linalg.yield %3 : f32 24 } -> tensor<?x?xf32> 25 return %0#0, %0#1, %2 : tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32> 26} 27// CHECK: func @multi_use_producer( 28// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32> 29// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32> 30// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32> 31// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: tensor<?x?xf32> 32// CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: tensor<?x?xf32>) 33// CHECK: %[[RESULT:.+]]:3 = linalg.generic 34// CHECK: return %[[RESULT]]#0, %[[RESULT]]#1, %[[RESULT]]#2 35