| // RUN: mlir-opt --transform-interpreter --cse --split-input-file %s | FileCheck %s |
| |
| func.func @gemm_fill_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %cst = arith.constant 0.0 : f32 |
| %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32> |
| %d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32> |
| %init = tensor.empty(%d0, %d1) : tensor<?x?xf32> |
| %fill = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32> |
| %gemm = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) |
| outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32> |
| return %gemm : tensor<?x?xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) { |
| %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| %a, %b = transform.test.fuse_using_forall %matmul [10, 20] |
| : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| // CHECK: func.func @gemm_fill_fusion( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>) |
| // CHECK: %[[INIT:.+]] = tensor.empty |
| // CHECK: scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) = |
| // CHECK-SAME: shared_outs(%[[ITERARG0:.+]] = %[[INIT]]) |
| // CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], 0] |
| // CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV1]]] |
| // CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[ITERARG0]][%[[IV0]], %[[IV1]]] |
| // CHECK: %[[FILL_TILE:.+]] = linalg.fill |
| // CHECK-SAME: outs(%[[INIT_TILE]] : |
| // CHECK: %[[GEMM_TILE:.+]] = linalg.matmul |
| // CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] : |
| // CHECK-SAME: outs(%[[FILL_TILE]] : |
| // CHECK: scf.forall.in_parallel { |
| // CHECK: tensor.parallel_insert_slice %[[GEMM_TILE]] into %[[ITERARG0]][%[[IV0]], %[[IV1]]] |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @gemm_generic_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, |
| %arg2 : tensor<?xf32>) -> tensor<?x?xf32> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %cst = arith.constant 0.0 : f32 |
| %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32> |
| %d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32> |
| %init = tensor.empty(%d0, %d1) : tensor<?x?xf32> |
| %fill = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32> |
| %gemm = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) |
| outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32> |
| %generic = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"]} |
| ins(%gemm, %arg2 : tensor<?x?xf32>, tensor<?xf32>) outs(%init : tensor<?x?xf32>) { |
| ^bb0(%b0 : f32, %b1 : f32, %b2 : f32): |
| %add = arith.addf %b0, %b1 : f32 |
| linalg.yield %add : f32 |
| } -> tensor<?x?xf32> |
| return %generic : tensor<?x?xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) { |
| %generic = transform.structured.match ops{["linalg.generic"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| %a, %b = transform.test.fuse_using_forall %generic [10, 20] |
| : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| // CHECK: func.func @gemm_generic_fusion( |
| // 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<?xf32>) |
| // CHECK: %[[INIT:.+]] = tensor.empty |
| // CHECK: scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) = |
| // CHECK-SAME: shared_outs(%[[ITERARG0:.+]] = %[[INIT]]) |
| // CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], 0] |
| // CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV1]]] |
| // CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT]][%[[IV0]], %[[IV1]]] |
| // CHECK: %[[FILL_TILE:.+]] = linalg.fill |
| // CHECK-SAME: outs(%[[INIT_TILE]] : |
| // CHECK: %[[GEMM_TILE:.+]] = linalg.matmul |
| // CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] : |
| // CHECK-SAME: outs(%[[FILL_TILE]] : |
| // CHECK-DAG: %[[BIAS_TILE:.+]] = tensor.extract_slice %[[ARG2]][%[[IV1]]] |
| // CHECK-DAG: %[[OUTS_TILE:.+]] = tensor.extract_slice %[[ITERARG0]][%[[IV0]], %[[IV1]]] |
| // CHECK: %[[GENERIC_TILE:.+]] = linalg.generic |
| // CHECK-SAME: ins(%[[GEMM_TILE]], %[[BIAS_TILE]] : |
| // CHECK-SAME: outs(%[[OUTS_TILE]] : |
| // CHECK: scf.forall.in_parallel { |
| // CHECK: tensor.parallel_insert_slice %[[GENERIC_TILE]] into %[[ITERARG0]][%[[IV0]], %[[IV1]]] |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @reduction_sequence(%arg0: tensor<30x3xf32>) -> tensor<30x3xf32> { |
| %cst = arith.constant 0.000000e+00 : f32 |
| %cst_0 = arith.constant 0xFF800000 : f32 |
| %0 = tensor.empty() : tensor<30xf32> |
| %1 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<30xf32>) -> tensor<30xf32> |
| %2 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>], |
| iterator_types = ["parallel", "reduction"]} |
| ins(%arg0 : tensor<30x3xf32>) outs(%1 : tensor<30xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32): |
| %8 = arith.maximumf %arg2, %arg1 : f32 |
| linalg.yield %8 : f32 |
| } -> tensor<30xf32> |
| %3 = tensor.empty() : tensor<30x3xf32> |
| %4 = linalg.fill ins(%cst : f32) outs(%0 : tensor<30xf32>) -> tensor<30xf32> |
| %5:2 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>, |
| affine_map<(d0, d1) -> (d0)>, affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "reduction"]} |
| ins(%arg0, %2 : tensor<30x3xf32>, tensor<30xf32>) outs(%4, %3 : tensor<30xf32>, tensor<30x3xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32, %arg3: f32, %arg4: f32): |
| %8 = arith.subf %arg1, %arg2 : f32 |
| %9 = math.exp %8 : f32 |
| %10 = arith.addf %arg3, %9 : f32 |
| linalg.yield %10, %9 : f32, f32 |
| } -> (tensor<30xf32>, tensor<30x3xf32>) |
| %6 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>, |
| affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"]} |
| ins(%5#1, %5#0 : tensor<30x3xf32>, tensor<30xf32>) outs(%3 : tensor<30x3xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32, %arg3: f32): |
| %8 = arith.divf %arg1, %arg2 : f32 |
| linalg.yield %8 : f32 |
| } -> tensor<30x3xf32> |
| return %6 : tensor<30x3xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) { |
| %generics = transform.structured.match ops{["linalg.generic"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| %generic1, %generic2, %generic3 = transform.split_handle %generics |
| : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op) |
| %a, %b = transform.test.fuse_using_forall %generic3 [10] |
| : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| // CHECK: func @reduction_sequence(%[[ARG0:.+]]: tensor<30x3xf32>) |
| // CHECK-DAG: %[[INIT0:.+]] = tensor.empty() : tensor<30xf32> |
| // CHECK-DAG: %[[INIT1:.+]] = tensor.empty() : tensor<30x3xf32> |
| // CHECK: %[[RESULT:[a-zA-Z0-9]+]] = scf.forall (%[[IV:[a-zA-Z0-9]+]]) |
| // CHECK-SAME: shared_outs(%[[ITERARG0:[a-zA-Z0-9]+]] = %[[INIT1]]) |
| // CHECK-DAG: %[[ARG0_SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV]], 0] |
| // CHECK-DAG: %[[INIT0_SLICE:.+]] = tensor.extract_slice %[[INIT0]][%[[IV]]] |
| // CHECK: %[[FILL0:.+]] = linalg.fill |
| // CHECK-SAME: outs(%[[INIT0_SLICE]] : |
| // CHECK: %[[GENERIC0:.+]] = linalg.generic |
| // CHECK-SAME: ins(%[[ARG0_SLICE]] : |
| // CHECK-SAME: outs(%[[FILL0]] : |
| // CHECK: %[[FILL1:.+]] = linalg.fill |
| // CHECK-SAME: outs(%[[INIT0_SLICE]] : |
| // CHECK: %[[INIT1_SLICE:.+]] = tensor.extract_slice %[[INIT1]][%[[IV]], 0] |
| // CHECK: %[[GENERIC1:.+]]:2 = linalg.generic |
| // CHECK-SAME: ins(%[[ARG0_SLICE]], %[[GENERIC0]] : |
| // CHECK-SAME: outs(%[[FILL1]], %[[INIT1_SLICE]] : |
| // CHECK: %[[ITERARG0_SLICE:.+]] = tensor.extract_slice %[[ITERARG0]][%[[IV]], 0] |
| // CHECK: %[[GENERIC2:.+]] = linalg.generic |
| // CHECK-SAME: ins(%[[GENERIC1]]#1, %[[GENERIC1]]#0 : |
| // CHECK-SAME: outs(%[[ITERARG0_SLICE]] : |
| // CHECK: scf.forall.in_parallel { |
| // CHECK: tensor.parallel_insert_slice %[[GENERIC2]] into %[[ITERARG0]][%[[IV]], 0] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
