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1// RUN: mlir-opt %s -eliminate-empty-tensors -empty-tensor-to-alloc-tensor -one-shot-bufferize="bufferize-function-boundaries" -cse -canonicalize -split-input-file | FileCheck %s2// RUN: mlir-opt %s -eliminate-empty-tensors | FileCheck %s --check-prefix=CHECK-ELIM3 4// CHECK: func @buffer_forwarding_conflict(5// CHECK-SAME: %[[FUNC_ARG:[0-9a-zA-Z]*]]: memref<?xf32>6// CHECK-SAME: %[[sz:[0-9a-zA-Z]*]]: index7func.func @buffer_forwarding_conflict(8 %t: tensor<?xf32> {bufferization.buffer_layout = affine_map<(d0) -> (d0)>, bufferization.writable = true},9 %sz: index)10 -> (tensor<?xf32>, tensor<?xf32>)11{12 %f0 = arith.constant 0.0: f3213 14 // CHECK: %[[EXTRACT_SLICE_ALLOC:.*]] = memref.alloc(%[[sz]])15 // CHECK: linalg.fill ins({{.*}} : f32) outs(%[[EXTRACT_SLICE_ALLOC]] : memref<?xf32>)16 // Alloc is needed for the **first** insert_slice (due to backward traversal during analysis).17 // CHECK: %[[DIM:.*]] = memref.dim %[[FUNC_ARG]]18 // This allocs the whole dim to allow for a full clone of t.19 // CHECK: %[[ALLOC:.*]] = memref.alloc(%[[DIM]])20 // tensor.empty itself does not alloc but forwards to the **second**21 // insert_slice. The pass replaces the tensor.empty with an out-of-place22 // extract_slice.23 %a = tensor.empty(%sz) : tensor<?xf32>24 %f = linalg.fill ins(%f0 : f32) outs(%a : tensor<?xf32>) -> tensor<?xf32>25 26 // CHECK: memref.copy %[[FUNC_ARG]], %[[ALLOC]] : memref<?xf32> to memref<?xf32>27 // CHECK: %[[SV0_ALLOC:.*]] = memref.subview %[[ALLOC]][0] [%[[sz]]] [1] : memref<?xf32> to memref<?xf32, strided<[1]>>28 // CHECK: memref.copy %[[EXTRACT_SLICE_ALLOC]], %[[SV0_ALLOC]] : memref<?xf32> to memref<?xf32, strided<[1]>>29 %r0 = tensor.insert_slice %f into %t[0][%sz][1]: tensor<?xf32> into tensor<?xf32>30 31 // CHECK: %[[T_SUBVIEW:.*]] = memref.subview %[[FUNC_ARG]][42] [%[[sz]]] [1]32 // CHECK: memref.copy %[[EXTRACT_SLICE_ALLOC]], %[[T_SUBVIEW]]33 %r1 = tensor.insert_slice %f into %t[42][%sz][1]: tensor<?xf32> into tensor<?xf32>34 35 return %r0, %r1: tensor<?xf32>, tensor<?xf32>36}37 38// -----39 40// CHECK: func @buffer_forwarding_no_conflict(41// CHECK-SAME: %[[FUNC_ARG:[0-9a-zA-Z]*]]: memref<?xf32>42// CHECK-SAME: %[[sz:[0-9a-zA-Z]*]]: index43func.func @buffer_forwarding_no_conflict(44 %t: tensor<?xf32> {bufferization.buffer_layout = affine_map<(d0) -> (d0)>, bufferization.writable = true},45 %sz: index)46 -> (tensor<?xf32>)47{48 %f0 = arith.constant 0.0: f3249 50 // tensor.empty itself does not alloc but forwards to the insert_slice.51 // EmptyTensorOpElimination replaces the tensor.empty with an inplace52 // extract_slice.53 // CHECK: %[[T_SUBVIEW:.*]] = memref.subview %[[FUNC_ARG]][42] [%[[sz]]] [1]54 %a = tensor.empty(%sz) : tensor<?xf32>55 56 // CHECK: linalg.fill ins({{.*}} : f32) outs(%[[T_SUBVIEW]] : memref<?xf3257 %f = linalg.fill ins(%f0 : f32) outs(%a : tensor<?xf32>) -> tensor<?xf32>58 59 // Self-copy canonicalizes away later.60 %r1 = tensor.insert_slice %f into %t[42][%sz][1]: tensor<?xf32> into tensor<?xf32>61 62 return %r1: tensor<?xf32>63}64 65// -----66 67// CHECK: func @insertion_point_inside_loop(68// CHECK-SAME: %[[t:.*]]: memref<?xf32, strided{{.*}}>, %[[sz:.*]]: index)69func.func @insertion_point_inside_loop(%t : tensor<?xf32>, %sz : index) -> (tensor<?xf32>) {70 %c0 = arith.constant 0 : index71 %c1 = arith.constant 1 : index72 %c5 = arith.constant 5 : index73 74 // CHECK-NOT: memref.alloc75 %blank = tensor.empty() : tensor<5xf32>76 77 // CHECK: scf.for %[[iv:.*]] = %{{.*}} to %[[sz]] step %{{.*}} {78 %r = scf.for %iv = %c0 to %sz step %c5 iter_args(%bb = %t) -> (tensor<?xf32>) {79 // CHECK: %[[subview:.*]] = memref.subview %[[t]][%[[iv]]] [5] [1]80 %iv_i32 = arith.index_cast %iv : index to i3281 %f = arith.sitofp %iv_i32 : i32 to f3282 83 // CHECK: linalg.fill ins(%{{.*}}{{.*}}outs(%[[subview]]84 %filled = linalg.fill ins(%f : f32) outs(%blank : tensor<5xf32>) -> tensor<5xf32>85 86 // CHECK-NOT: memref.copy87 %inserted = tensor.insert_slice %filled into %bb[%iv][5][1] : tensor<5xf32> into tensor<?xf32>88 scf.yield %inserted : tensor<?xf32>89 }90 91 return %r : tensor<?xf32>92}93 94// -----95 96// CHECK: func @insertion_point_outside_loop(97// CHECK-SAME: %[[t:.*]]: memref<?xf32, strided{{.*}}>, %[[sz:.*]]: index, %[[idx:.*]]: index)98func.func @insertion_point_outside_loop(%t : tensor<?xf32>, %sz : index,99 %idx : index) -> (tensor<?xf32>) {100 %c0 = arith.constant 0 : index101 %c1 = arith.constant 1 : index102 %c5 = arith.constant 5 : index103 104 // CHECK-NOT: memref.alloc105 %blank = tensor.empty() : tensor<5xf32>106 107 // CHECK: scf.for %[[iv:.*]] = %{{.*}} to %[[sz]] step %{{.*}} {108 %r = scf.for %iv = %c0 to %sz step %c5 iter_args(%bb = %t) -> (tensor<?xf32>) {109 %iv_i32 = arith.index_cast %iv : index to i32110 %f = arith.sitofp %iv_i32 : i32 to f32111 112 // CHECK: %[[subview:.*]] = memref.subview %[[t]][%[[idx]]] [5] [1]113 // CHECK: linalg.fill ins(%{{.*}}{{.*}}outs(%[[subview]]114 %filled = linalg.fill ins(%f : f32) outs(%blank : tensor<5xf32>) -> tensor<5xf32>115 116 // CHECK-NOT: memref.copy117 %inserted = tensor.insert_slice %filled into %bb[%idx][5][1] : tensor<5xf32> into tensor<?xf32>118 scf.yield %inserted : tensor<?xf32>119 }120 121 return %r : tensor<?xf32>122}123 124// -----125 126// EmptyTensorElimination does not currently apply to chains where the type is127// changing. (Casts are supported.) This test just ensures that we do not crash128// or generate IR that does not verify.129 130// CHECK-LABEL: func @shape_mismatch131func.func @shape_mismatch(%t: tensor<5x6x128xf32>) -> tensor<5x6x128xf32> {132 %cst = arith.constant 8.0 : f32133 %0 = tensor.empty() : tensor<128xf32>134 %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<128xf32>) -> tensor<128xf32>135 %2 = tensor.expand_shape %1 [[0, 1, 2]] output_shape [1, 1, 128]136 : tensor<128xf32> into tensor<1x1x128xf32>137 %3 = tensor.insert_slice %2 into %t[2, 3, 0][1, 1, 128][1, 1, 1]138 : tensor<1x1x128xf32> into tensor<5x6x128xf32>139 return %3 : tensor<5x6x128xf32>140}141 142// -----143 144// CHECK-LABEL: func @cast(145// CHECK-SAME: %[[t:.*]]: memref<256xf32,146// CHECK: %[[sv:.*]] = memref.subview %[[t]]147// CHECK: linalg.fill {{.*}} outs(%[[sv]]148// CHECK: return %[[t]]149func.func @cast(%t: tensor<256xf32>) -> tensor<256xf32> {150 %cst = arith.constant 8.0 : f32151 %c128 = arith.constant 128 : index152 %0 = tensor.empty(%c128) : tensor<?xf32>153 %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?xf32>) -> tensor<?xf32>154 %2 = tensor.cast %1 : tensor<?xf32> to tensor<128xf32>155 %3 = tensor.insert_slice %2 into %t[2][128][1]156 : tensor<128xf32> into tensor<256xf32>157 return %3 : tensor<256xf32>158}159 160// -----161 162// CHECK: func @parallel_insert_slice(163// CHECK-SAME: %[[FUNC_ARG:[0-9a-zA-Z]*]]: memref<?xf32>164// CHECK-SAME: %[[sz:[0-9a-zA-Z]*]]: index165func.func @parallel_insert_slice(166 %t: tensor<?xf32> {bufferization.buffer_layout = affine_map<(d0) -> (d0)>, bufferization.writable = true},167 %sz: index)168 -> (tensor<?xf32>)169{170 %f0 = arith.constant 0.0: f32171 %c512 = arith.constant 512 : index172 173 %r1 = scf.forall (%iv) in (%c512) shared_outs(%o = %t) -> (tensor<?xf32>) {174 // tensor.empty itself does not alloc but forwards to the insert_slice.175 // EmptyTensorOpElimination replaces the tensor.empty with an inplace176 // extract_slice.177 // CHECK: %[[T_SUBVIEW:.*]] = memref.subview %[[FUNC_ARG]][42] [%[[sz]]] [1]178 %a = tensor.empty(%sz) : tensor<?xf32>179 180 // CHECK: linalg.fill ins({{.*}} : f32) outs(%[[T_SUBVIEW]] : memref<?xf32181 %f = linalg.fill ins(%f0 : f32) outs(%a : tensor<?xf32>) -> tensor<?xf32>182 183 // Self-copy canonicalizes away later.184 scf.forall.in_parallel {185 tensor.parallel_insert_slice %f into %o[42][%sz][1]: tensor<?xf32> into tensor<?xf32>186 }187 }188 189 return %r1: tensor<?xf32>190}191 192// -----193 194// CHECK-LABEL: func @eleminate_multiple_ops(195// CHECK-SAME: %[[FUNC_ARG:[0-9a-zA-Z]*]]: memref<?xf32>196// CHECK-SAME: %[[sz:[0-9a-zA-Z]*]]: index197func.func @eleminate_multiple_ops(%t: tensor<?xf32> {bufferization.buffer_layout = affine_map<(d0) -> (d0)>}, %sz: index, %c: i1)198 -> (tensor<?xf32>)199{200 %cst1 = arith.constant 0.0: f32201 %cst2 = arith.constant 1.0: f32202 203 // CHECK: %[[r:.*]] = scf.if %{{.*}} -> (memref204 %if = scf.if %c -> tensor<?xf32> {205 // CHECK: %[[T_SUBVIEW_1:.*]] = memref.subview %[[FUNC_ARG]][42] [%[[sz]]] [1]206 %a1 = tensor.empty(%sz) : tensor<?xf32>207 // CHECK: linalg.fill ins({{.*}} : f32) outs(%[[T_SUBVIEW_1]] : memref<?xf32208 %f1 = linalg.fill ins(%cst1 : f32) outs(%a1 : tensor<?xf32>) -> tensor<?xf32>209 // CHECK: scf.yield %[[T_SUBVIEW_1]]210 scf.yield %f1 : tensor<?xf32>211 } else {212 // CHECK: %[[T_SUBVIEW_2:.*]] = memref.subview %[[FUNC_ARG]][42] [%[[sz]]] [1]213 %a2 = tensor.empty(%sz) : tensor<?xf32>214 // CHECK: linalg.fill ins({{.*}} : f32) outs(%[[T_SUBVIEW_2]] : memref<?xf32215 %f2 = linalg.fill ins(%cst2 : f32) outs(%a2 : tensor<?xf32>) -> tensor<?xf32>216 // CHECK: scf.yield %[[T_SUBVIEW_2]]217 scf.yield %f2 : tensor<?xf32>218 }219 220 // Self-copy could canonicalize away later.221 // CHECK: %[[T_SUBVIEW_3:.*]] = memref.subview %[[FUNC_ARG]][42] [%[[sz]]] [1]222 // CHECK: memref.copy %[[r]], %[[T_SUBVIEW_3]]223 %r1 = tensor.insert_slice %if into %t[42][%sz][1]: tensor<?xf32> into tensor<?xf32>224 return %r1: tensor<?xf32>225}226 227// -----228 229// This is a regression test. Make sure that the tensor.extract_slice is not230// eliminated.231 232// CHECK-LABEL: func.func @regression_do_not_eliminate_non_empty(233// CHECK: memref.subview234// CHECK: memref.subview235// CHECK: memref.copy236func.func @regression_do_not_eliminate_non_empty(237 %t: tensor<10xf32>, %t2: tensor<10xf32>) -> tensor<10xf32> {238 %1 = tensor.extract_slice %t[0] [5] [1] : tensor<10xf32> to tensor<5xf32>239 %2 = tensor.insert_slice %1 into %t2[1] [5] [1]240 : tensor<5xf32> into tensor<10xf32>241 return %2 : tensor<10xf32>242}243 244// -----245 246// This is a regression test. Make sure that there is no crash.247 248// CHECK-LABEL: func.func @regression_insert_of_bbarg(249func.func @regression_insert_of_bbarg(%t0: tensor<5xf32>, %t1: tensor<10xf32>) -> tensor<10xf32> {250 %0 = tensor.insert_slice %t0 into %t1 [2] [5] [1] : tensor<5xf32> into tensor<10xf32>251 return %0 : tensor<10xf32>252}253 254// -----255 256// This is a regression test. Make sure that there is no crash.257 258// CHECK-LABEL: func.func @regression_eliminate_equivalent_only(259func.func @regression_eliminate_equivalent_only(%sz: index, %p: index, %t0: tensor<?x16xi8>) -> tensor<?x16xi8> {260 %c0 = arith.constant 0 : index261 %c8 = arith.constant 8 : index262 %c16 = arith.constant 16 : index263 %27 = tensor.empty(%sz) : tensor<?x8xi32>264 %extracted_slice = tensor.extract_slice %27[0, 0] [%p, 8] [1, 1] : tensor<?x8xi32> to tensor<?x8xi32>265 %28 = scf.for %arg4 = %c0 to %c16 step %c8 iter_args(%arg5 = %t0) -> (tensor<?x16xi8>) {266 %inserted_slice = tensor.insert_slice %extracted_slice into %27[0, 0] [%sz, 8] [1, 1] : tensor<?x8xi32> into tensor<?x8xi32>267 %extracted_slice_2 = tensor.extract_slice %arg5[%p, %p] [%sz, 8] [1, 1] : tensor<?x16xi8> to tensor<?x8xi8>268 %32 = linalg.generic269 {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],270 iterator_types = ["parallel", "parallel"]}271 ins(%inserted_slice : tensor<?x8xi32>) outs(%extracted_slice_2 : tensor<?x8xi8>) {272 ^bb0(%in: i32, %out: i8):273 %tr = arith.trunci %in : i32 to i8274 linalg.yield %tr : i8275 } -> tensor<?x8xi8>276 %inserted_slice_3 = tensor.insert_slice %32 into %arg5[%p, %arg4] [%sz, 8] [1, 1] : tensor<?x8xi8> into tensor<?x16xi8>277 scf.yield %inserted_slice_3 : tensor<?x16xi8>278 }279 func.return %28 : tensor<?x16xi8>280}281 282// -----283 284// CHECK-LABEL: func.func @regression_multiple_insertion_points(285// CHECK-NOT: memref.alloc286func.func @regression_multiple_insertion_points(%t1: tensor<?x?xf32>) -> tensor<?x?xf32> {287 %empty = tensor.empty() : tensor<2x5xf32>288 %f0 = arith.constant 5.5 : f32289 %0 = "test.foo"() : () -> (index)290 %1 = "test.bar"() : () -> (index)291 %filled = linalg.fill ins(%f0 : f32) outs(%empty : tensor<2x5xf32>) -> tensor<2x5xf32>292 %2 = tensor.insert_slice %filled into %t1 [%0, %1] [2, 5] [1, 1] : tensor<2x5xf32> into tensor<?x?xf32>293 return %2 : tensor<?x?xf32>294}295 296// -----297 298// CHECK-LABEL: func @materialize_in_destination(299// CHECK-SAME: %[[m:.*]]: memref<5xf32, strided<[?], offset: ?>>,300// CHECK: linalg.fill {{.*}} outs(%[[m]]301// CHECK: return %[[m]]302func.func @materialize_in_destination(%t: tensor<5xf32>, %f: f32) -> tensor<5xf32> {303 %0 = tensor.empty() : tensor<5xf32>304 %filled = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>305 %1 = bufferization.materialize_in_destination %filled in %t : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>306 return %1 : tensor<5xf32>307}308 309// -----310 311// CHECK-LABEL: func @materialize_in_destination_buffer(312// CHECK-SAME: %[[m:.*]]: memref<5xf32>,313// CHECK-NEXT: linalg.fill {{.*}} outs(%[[m]]314// CHECK-NEXT: return315func.func @materialize_in_destination_buffer(%m: memref<5xf32>, %f: f32) {316 %0 = tensor.empty() : tensor<5xf32>317 %filled = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>318 bufferization.materialize_in_destination %filled in restrict writable %m : (tensor<5xf32>, memref<5xf32>) -> ()319 return320}321 322// -----323 324// CHECK-LABEL: func @linalg_copy(325// CHECK-SAME: %[[m:.*]]: memref<5xf32, strided<[?], offset: ?>>,326// CHECK: linalg.fill {{.*}} outs(%[[m]]327// CHECK: return %[[m]]328func.func @linalg_copy(%t: tensor<5xf32>, %f: f32) -> tensor<5xf32> {329 %0 = tensor.empty() : tensor<5xf32>330 %filled = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>331 %1 = linalg.copy ins(%filled : tensor<5xf32>) outs(%t : tensor<5xf32>) -> tensor<5xf32>332 return %1 : tensor<5xf32>333}334 335// -----336 337// CHECK-LABEL: func @linalg_copy_empty(338// CHECK: %[[ret:.*]] = memref.alloc()339// CHECK-NEXT: return %[[ret]]340func.func @linalg_copy_empty() -> tensor<26xi32> {341 %0 = tensor.empty() : tensor<26xi32>342 %1 = linalg.copy ins(%0 : tensor<26xi32>) outs(%0 : tensor<26xi32>) -> tensor<26xi32>343 return %1 : tensor<26xi32>344}345 346// -----347 348// CHECK-ELIM-LABEL: func @multiple_materialize_in_destination_buffer(349// CHECK-ELIM-SAME: %[[m:.*]]: memref<5xf32>350// CHECK-ELIM: tensor.empty351// CHECK-ELIM: bufferization.to_tensor %[[m]] restrict writable352// CHECK-ELIM: bufferization.materialize_in_destination {{.*}} in writable %[[m]]353func.func @multiple_materialize_in_destination_buffer(%m: memref<5xf32>, %f: f32, %f2: f32, %c: i1) {354 %0 = tensor.empty() : tensor<5xf32>355 %filled = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>356 357 %1 = tensor.empty() : tensor<5xf32>358 %filled2 = linalg.fill ins(%f2 : f32) outs(%1 : tensor<5xf32>) -> tensor<5xf32>359 360 %selected = scf.if %c -> tensor<5xf32> {361 scf.yield %filled : tensor<5xf32>362 } else {363 scf.yield %filled2 : tensor<5xf32>364 }365 bufferization.materialize_in_destination %selected in restrict writable %m : (tensor<5xf32>, memref<5xf32>) -> ()366 return367}368 369// -----370 371// `EmptyTensorElimination` fails to find a valid insertion372// point for the new injected `SubsetExtraction`.373// CHECK-LABEL: func.func @fail_to_eliminate_any_empty_tensors374func.func @fail_to_eliminate_any_empty_tensors() -> tensor<5x6x128xf32> {375 %cst_1 = arith.constant 1.0 : f32376 %cst_2 = arith.constant 2.0 : f32377 // CHECK: memref.alloc378 // CHECK: memref.alloc379 // CHECK: memref.alloc380 %empty_1 = tensor.empty() : tensor<5x6x64xf32>381 %res_1 = linalg.fill ins(%cst_1 : f32) outs(%empty_1 : tensor<5x6x64xf32>) -> tensor<5x6x64xf32>382 %empty_2 = tensor.empty() : tensor<5x6x64xf32>383 %res_2 = linalg.fill ins(%cst_2 : f32) outs(%empty_2 : tensor<5x6x64xf32>) -> tensor<5x6x64xf32>384 %cancatenated_empty = tensor.empty() : tensor<5x6x128xf32>385 // CHECK: memref.copy386 %inserted_slice_1 = tensor.insert_slice %res_1 into %cancatenated_empty[0, 0, 0][5, 6, 64][1, 1, 1]387 : tensor<5x6x64xf32> into tensor<5x6x128xf32>388 %inserted_slice_2 = tensor.insert_slice %res_2 into %inserted_slice_1[0, 0, 64][5, 6, 64][1, 1, 1]389 : tensor<5x6x64xf32> into tensor<5x6x128xf32>390 return %inserted_slice_2 : tensor<5x6x128xf32>391}392 393// -----394 395// CHECK-LABEL: func.func @succeed_to_eliminate_one_empty_tensor396func.func @succeed_to_eliminate_one_empty_tensor() -> tensor<5x6x128xf32> {397 %cst_1 = arith.constant 1.0 : f32398 %cst_2 = arith.constant 2.0 : f32399 // CHECK: memref.alloc() {alignment = 64 : i64} : memref<5x6x128xf32>400 // CHECK: memref.alloc401 // CHECK-NOT: memref.alloc402 %cancatenated_empty = tensor.empty() : tensor<5x6x128xf32>403 %empty_1 = tensor.empty() : tensor<5x6x64xf32>404 %res_1 = linalg.fill ins(%cst_1 : f32) outs(%empty_1 : tensor<5x6x64xf32>) -> tensor<5x6x64xf32>405 %empty_2 = tensor.empty() : tensor<5x6x64xf32>406 %res_2 = linalg.fill ins(%cst_2 : f32) outs(%empty_2 : tensor<5x6x64xf32>) -> tensor<5x6x64xf32>407 // CHECK: memref.copy408 %inserted_slice_1 = tensor.insert_slice %res_1 into %cancatenated_empty[0, 0, 0][5, 6, 64][1, 1, 1]409 : tensor<5x6x64xf32> into tensor<5x6x128xf32>410 %inserted_slice_2 = tensor.insert_slice %res_2 into %inserted_slice_1[0, 0, 64][5, 6, 64][1, 1, 1]411 : tensor<5x6x64xf32> into tensor<5x6x128xf32>412 return %inserted_slice_2 : tensor<5x6x128xf32>413}414 415// -----416 417// `EmptyTensorElimination` will replace the specific use of the tensor418// empty with the new injected `SubsetExtraction`, i.e. the specific use419// which has been tracked.420 421// CHECK-ELIM-LABEL: func.func @multi_use_of_the_same_tensor_empty422// CHECK-LABEL: func.func @multi_use_of_the_same_tensor_empty423func.func @multi_use_of_the_same_tensor_empty() -> tensor<5x6x128xf32> {424 %cst_1 = arith.constant 1.0 : f32425 %cst_2 = arith.constant 2.0 : f32426 %cancatenated_empty = tensor.empty() : tensor<5x6x128xf32>427 %empty_1 = tensor.empty() : tensor<5x6x64xf32>428 // CHECK-ELIM: %[[VAL_3:.*]] = tensor.extract_slice429 // CHECK-ELIM: linalg.fill ins(%[[VAL_0:.*]] : f32) outs(%[[VAL_3]]430 // CHECK-ELIM-NOT: linalg.fill ins(%[[VAL_1:.*]] : f32) outs(%[[VAL_3]]431 %res_1 = linalg.fill ins(%cst_1 : f32) outs(%empty_1 : tensor<5x6x64xf32>) -> tensor<5x6x64xf32>432 %res_2 = linalg.fill ins(%cst_2 : f32) outs(%empty_1 : tensor<5x6x64xf32>) -> tensor<5x6x64xf32>433 // CHECK: memref.copy434 %inserted_slice_1 = tensor.insert_slice %res_1 into %cancatenated_empty[0, 0, 0][5, 6, 64][1, 1, 1]435 : tensor<5x6x64xf32> into tensor<5x6x128xf32>436 // CHECK-NOT: memref.copy437 %inserted_slice_2 = tensor.insert_slice %res_2 into %inserted_slice_1[0, 0, 64][5, 6, 64][1, 1, 1]438 : tensor<5x6x64xf32> into tensor<5x6x128xf32>439 return %inserted_slice_2 : tensor<5x6x128xf32>440}441 442// -----443 444// CHECK-LABEL: func.func @multi_use_of_the_same_tensor_empty_creates_non_existent_read445// CHECK-ELIM-LABEL: func.func @multi_use_of_the_same_tensor_empty_creates_non_existent_read446func.func @multi_use_of_the_same_tensor_empty_creates_non_existent_read(%arg1: tensor<5x6x128xf32> , %arg2: tensor<5x6x64xf32>)447 -> (tensor<5x6x128xf32>, tensor<5x6x64xf32>) {448 %cst_1 = arith.constant 1.0 : f32449 %empty_1 = tensor.empty() : tensor<5x6x64xf32>450 // CHECK: memref.alloc() {alignment = 64 : i64} : memref<5x6x64xf32>451 // CHECK-NOT: memref.alloc452 %res_1 = linalg.fill ins(%cst_1 : f32) outs(%empty_1 : tensor<5x6x64xf32>) -> tensor<5x6x64xf32>453 %res_2 = linalg.generic{454 indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],455 iterator_types = ["parallel", "parallel", "parallel"]456 }457 ins(%empty_1 : tensor<5x6x64xf32>)458 outs(%arg2 :tensor<5x6x64xf32>) {459 ^bb0(%in: f32, %out: f32):460 %res = arith.addf %in, %in : f32461 linalg.yield %res : f32462 } -> tensor<5x6x64xf32>463 // CHECK-NOT: memref.copy464 %inserted_slice_1 = tensor.insert_slice %res_1 into %arg1[0, 0, 0][5, 6, 64][1, 1, 1]465 : tensor<5x6x64xf32> into tensor<5x6x128xf32>466 return %inserted_slice_1, %res_2 : tensor<5x6x128xf32>, tensor<5x6x64xf32>467}468 469// -----470 471// CHECK-LABEL: func.func @direct_use_of_tensor_empty472func.func @direct_use_of_tensor_empty(%arg0: tensor<5x6x128xf32>) -> tensor<5x6x128xf32> {473 // CHECK-NOT: memref.alloc474 %empty_1 = tensor.empty() : tensor<5x6x64xf32>475 %inserted_slice_1 = tensor.insert_slice %empty_1 into %arg0[0, 0, 0][5, 6, 64][1, 1, 1]476 : tensor<5x6x64xf32> into tensor<5x6x128xf32>477 return %inserted_slice_1 : tensor<5x6x128xf32>478}479