62 lines · plain
1// RUN: mlir-opt -slice-analysis-test -split-input-file %s | FileCheck %s2 3func.func @slicing_linalg_op(%arg0 : index, %arg1 : index, %arg2 : index) {4 %a = memref.alloc(%arg0, %arg2) : memref<?x?xf32>5 %b = memref.alloc(%arg2, %arg1) : memref<?x?xf32>6 %c = memref.alloc(%arg0, %arg1) : memref<?x?xf32>7 %d = memref.alloc(%arg0, %arg1) : memref<?x?xf32>8 linalg.matmul ins(%a, %b : memref<?x?xf32>, memref<?x?xf32>)9 outs(%c : memref<?x?xf32>)10 linalg.matmul ins(%a, %b : memref<?x?xf32>, memref<?x?xf32>)11 outs(%d : memref<?x?xf32>)12 memref.dealloc %c : memref<?x?xf32>13 memref.dealloc %b : memref<?x?xf32>14 memref.dealloc %a : memref<?x?xf32>15 memref.dealloc %d : memref<?x?xf32>16 return17}18 19// CHECK-LABEL: func @slicing_linalg_op__backward_slice__020// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: index21// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: index22// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index23// CHECK-DAG: %[[A:.+]] = memref.alloc(%[[ARG0]], %[[ARG2]]) : memref<?x?xf32>24// CHECK-DAG: %[[B:.+]] = memref.alloc(%[[ARG2]], %[[ARG1]]) : memref<?x?xf32>25// CHECK-DAG: %[[C:.+]] = memref.alloc(%[[ARG0]], %[[ARG1]]) : memref<?x?xf32>26// CHECK: return27 28// CHECK-LABEL: func @slicing_linalg_op__backward_slice__129// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: index30// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: index31// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index32// CHECK-DAG: %[[A:.+]] = memref.alloc(%[[ARG0]], %[[ARG2]]) : memref<?x?xf32>33// CHECK-DAG: %[[B:.+]] = memref.alloc(%[[ARG2]], %[[ARG1]]) : memref<?x?xf32>34// CHECK-DAG: %[[C:.+]] = memref.alloc(%[[ARG0]], %[[ARG1]]) : memref<?x?xf32>35// CHECK: return36 37// -----38 39#map = affine_map<(d0, d1) -> (d0, d1)>40func.func @slice_use_from_above(%arg0: tensor<5x5xf32>, %arg1: tensor<5x5xf32>) {41 %0 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<5x5xf32>) outs(%arg1 : tensor<5x5xf32>) {42 ^bb0(%in: f32, %out: f32):43 %2 = arith.addf %in, %in : f3244 linalg.yield %2 : f3245 } -> tensor<5x5xf32>46 %collapsed = tensor.collapse_shape %0 [[0, 1]] : tensor<5x5xf32> into tensor<25xf32>47 %1 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%0 : tensor<5x5xf32>) outs(%arg1 : tensor<5x5xf32>) {48 ^bb0(%in: f32, %out: f32):49 %c2 = arith.constant 2 : index50 %extracted = tensor.extract %collapsed[%c2] : tensor<25xf32>51 %2 = arith.addf %extracted, %extracted : f3252 linalg.yield %2 : f3253 } -> tensor<5x5xf32>54 return55}56 57// CHECK-LABEL: func @slice_use_from_above__backward_slice__058// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor 59// CHECK: %[[A:.+]] = linalg.generic {{.*}} ins(%[[ARG0]]60// CHECK: %[[B:.+]] = tensor.collapse_shape %[[A]]61// CHECK: return62