1// RUN: mlir-opt -transform-interpreter -cse -split-input-file %s | FileCheck %s 2 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 : index 7 %c1 = arith.constant 1 : index 8 %cst = arith.constant 0.0 : f32 9 %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.matmul 13 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.matmul 17 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 %arg1 24 : (!transform.any_op) -> !transform.any_op 25 %mm1, %mm2 = transform.split_handle %matmuls 26 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 27 %a, %b = transform.test.fuse_and_yield %mm2 [10] use_forall true 28 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 29 transform.yield 30 } 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 : index 39// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index 40// 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.fill 46// CHECK-SAME: outs(%[[INIT0_TILE]] : 47// CHECK: %[[GEMM0_TILE:.+]] = linalg.matmul 48// 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.fill 53// CHECK-SAME: outs(%[[INIT1_TILE]] : 54// CHECK: %[[GEMM1_TILE:.+]] = linalg.matmul 55// 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]]#0 61