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1// RUN: mlir-opt -transform-interpreter -cse -split-input-file %s | FileCheck %s2 3func.func @gemm_gemm_fusion_yield_both(%lhs0 : tensor<?x?xf32>, %rhs0 : tensor<?x?xf32>, %rhs1 : tensor<?x?xf32>,4 %init0 : tensor<?x?xf32>, %init1 : tensor<?x?xf32>)5 -> (tensor<?x?xf32>, tensor<?x?xf32>) {6 %c0 = arith.constant 0 : index7 %c1 = arith.constant 1 : index8 %cst = arith.constant 0.0 : f329 %d0 = tensor.dim %lhs0, %c0 : tensor<?x?xf32>10 %d1 = tensor.dim %rhs0, %c1 : tensor<?x?xf32>11 %fill0 = linalg.fill ins(%cst : f32) outs(%init0 : tensor<?x?xf32>) -> tensor<?x?xf32>12 %gemm0 = linalg.matmul13 ins(%lhs0, %rhs0 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%fill0 : tensor<?x?xf32>) -> tensor<?x?xf32>14 %d2 = tensor.dim %rhs1, %c1 : tensor<?x?xf32>15 %fill1 = linalg.fill ins(%cst : f32) outs(%init1 : tensor<?x?xf32>) -> tensor<?x?xf32>16 %gemm1 = linalg.matmul17 ins(%gemm0, %rhs1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%fill1 : tensor<?x?xf32>) -> tensor<?x?xf32>18 return %gemm0, %gemm1 : tensor<?x?xf32>, tensor<?x?xf32>19}20 21module attributes {transform.with_named_sequence} {22 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {23 %matmuls = transform.structured.match ops{["linalg.matmul"]} in %arg124 : (!transform.any_op) -> !transform.any_op25 %mm1, %mm2 = transform.split_handle %matmuls26 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)27 %a, %b = transform.test.fuse_and_yield %mm2 [10] use_forall true28 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)29 transform.yield30 }31}32// CHECK: func.func @gemm_gemm_fusion_yield_both(33// CHECK-SAME: %[[LHS0:[a-zA-Z0-9]+]]: tensor<?x?xf32>34// CHECK-SAME: %[[RHS0:[a-zA-Z0-9]+]]: tensor<?x?xf32>,35// CHECK-SAME: %[[RHS1:[a-zA-Z0-9]+]]: tensor<?x?xf32>,36// CHECK-SAME: %[[INIT0:[a-zA-Z0-9]+]]: tensor<?x?xf32>,37// CHECK-SAME: %[[INIT1:[a-zA-Z0-9]+]]: tensor<?x?xf32>)38// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index39// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index40// CHECK: %[[RESULT:.+]]:2 = scf.forall (%[[IV:[a-zA-Z0-9]+]]) =41// CHECK-SAME: shared_outs(%[[ITERARG0:[a-zA-Z0-9]+]] = %[[INIT1]], %[[ITERARG1:[a-zA-Z0-9]+]] = %[[INIT0]])42// CHECK-DAG: %[[LHS0_TILE:.+]] = tensor.extract_slice %[[LHS0]][%[[IV]], 0]43// CHECK-DAG: %[[RHS0_TILE:.+]] = tensor.extract_slice %[[RHS0]][0, 0]44// CHECK-DAG: %[[INIT0_TILE:.+]] = tensor.extract_slice %[[ITERARG1]][%[[IV]], 0]45// CHECK: %[[FILL0_TILE:.+]] = linalg.fill46// CHECK-SAME: outs(%[[INIT0_TILE]] :47// CHECK: %[[GEMM0_TILE:.+]] = linalg.matmul48// CHECK-SAME: ins(%[[LHS0_TILE]], %[[RHS0_TILE]] :49// CHECK-SAME: outs(%[[FILL0_TILE]] :50// CHECK-DAG: %[[RHS1_TILE:.+]] = tensor.extract_slice %[[RHS1]][0, 0]51// CHECK-DAG: %[[INIT1_TILE:.+]] = tensor.extract_slice %[[ITERARG0]][%[[IV]], 0]52// CHECK: %[[FILL1_TILE:.+]] = linalg.fill53// CHECK-SAME: outs(%[[INIT1_TILE]] :54// CHECK: %[[GEMM1_TILE:.+]] = linalg.matmul55// CHECK-SAME: ins(%[[GEMM0_TILE]], %[[RHS1_TILE]] :56// CHECK-SAME: outs(%[[FILL1_TILE]] :57// CHECK: scf.forall.in_parallel {58// CHECK: tensor.parallel_insert_slice %[[GEMM1_TILE]] into %[[ITERARG0]][%[[IV]], 0]59// CHECK: tensor.parallel_insert_slice %[[GEMM0_TILE]] into %[[ITERARG1]][%[[IV]], 0]60// CHECK: return %[[RESULT]]#1, %[[RESULT]]#061