| // RUN: mlir-opt -transform-interpreter -cse -mlir-print-local-scope -split-input-file -verify-diagnostics %s | FileCheck %s |
| |
| // Check tile+ fuse works with partial reduction outer parallel strategy. |
| |
| module{ |
| func.func @tile_and_fuse_with_partial_reduction_outer_parallel( |
| %arg0 : tensor<?x?xf32>) -> tensor<?xf32> { |
| %c0 = arith.constant 0 : index |
| %cst = arith.constant 0.0 : f32 |
| %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32> |
| %empty = tensor.empty(%d0) : tensor<?xf32> |
| %fill = linalg.fill ins(%cst : f32) outs(%empty : tensor<?xf32>) -> tensor<?xf32> |
| %generic = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>], |
| iterator_types = ["parallel", "reduction"]} |
| ins(%arg0 : tensor<?x?xf32>) outs(%fill : tensor<?xf32>) { |
| ^bb0(%b0 : f32, %b1 : f32): |
| %0 = arith.addf %b0, %b1 : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<?xf32> |
| return %generic : tensor<?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, %loop = transform.test.tile_and_fuse_outer_parallel_partial_reduction |
| %generic tile_sizes = [128] |
| : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| // CHECK-LABEL: func @tile_and_fuse_with_partial_reduction_outer_parallel( |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>) |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]] |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]] |
| // CHECK: %[[REDUCTION_NUM:.+]] = affine.apply affine_map<()[s0] -> (s0 ceildiv 128)>()[%[[D1]]] |
| // CHECK: %[[EMPTY:.+]] = tensor.empty(%[[D0]], %[[REDUCTION_NUM]]) |
| // CHECK: %[[FORALL:.+]] = scf.forall (%[[IV0:[a-zA-Z0-9]+]]) = |
| // CHECK-SAME: shared_outs(%[[ITER_ARG:.+]] = %[[EMPTY]]) |
| // CHECK-DAG: %[[TILESIZE:.+]] = affine.min affine_map<(d0)[s0] -> (-d0 + s0, 128)>(%[[IV0]])[%[[D1]]] |
| // CHECK-DAG: %[[REDUCTION_IV:.+]] = affine.apply affine_map<()[s0] -> (s0 floordiv 128)>()[%[[IV0]]] |
| // CHECK-DAG: %[[ARG0_SLICE:.+]] = tensor.extract_slice %[[ARG0]][0, %[[IV0]]] [%[[D0]], %[[TILESIZE]]] [1, 1] |
| // CHECK: %[[ITER_ARG_SLICE:.+]] = tensor.extract_slice %[[ITER_ARG]][0, %[[REDUCTION_IV]]] [%[[D0]], 1] [1, 1] |
| // CHECK: %[[FILL:.+]] = linalg.fill |
| // CHECK-SAME: outs(%[[ITER_ARG_SLICE]] : tensor<?x1xf32>) |
| // CHECK: %[[REDUCING_SLICE:.+]] = tensor.extract_slice %[[FILL]][0, 0] [%[[D0]], 1] [1, 1] : tensor<?x1xf32> to tensor<?xf32> |
| // CHECK: %[[GENERIC:.+]] = linalg.generic |
| // CHECK-SAME: ins(%[[ARG0_SLICE]] : |
| // CHECK-SAME: outs(%[[REDUCING_SLICE]] : |
| // CHECK: tensor.parallel_insert_slice %[[GENERIC]] into %[[ITER_ARG]] |
| // CHECK-SAME: [0, %[[REDUCTION_IV]]] [%[[D0]], 1] [1, 1] |
| // CHECK: %[[REDUCE:.+]] = linalg.reduce |
| // CHECK-SAME: ins(%[[FORALL]] : |
| // CHECK: return %[[REDUCE]] |
