480 lines · plain
1// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s2 3func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous(4 %src: tensor<80x16xf32>,5 %output : tensor<1x3xf32>,6 %idx: index) -> tensor<1x3xf32> {7 8 %c79 = arith.constant 79 : index9 %1 = linalg.generic {10 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],11 iterator_types = ["parallel", "parallel"]12 } outs(%output : tensor<1x3xf32>) {13 ^bb0(%out: f32):14 %2 = linalg.index 1 : index15 %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %idx)16 %extracted = tensor.extract %src[%c79, %3] : tensor<80x16xf32>17 linalg.yield %extracted : f3218 } -> tensor<1x3xf32>19 return %1 : tensor<1x3xf32>20}21 22// CHECK-LABEL: func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous23// CHECK-SAME: %[[SRC:.*]]: tensor<80x16xf32>,24// CHECK-SAME: %[[OUTPUT:.*]]: tensor<1x3xf32>,25// CHECK-SAME: %[[IDX_IN:.*]]: index) -> tensor<1x3xf32> {26 27/// Create the mask28// CHECK-DAG: %[[DIM_0:.*]] = arith.constant 1 : index29// CHECK-DAG: %[[DIM_1:.*]] = arith.constant 3 : index30// CHECK-DAG: %[[C79:.*]] = arith.constant 79 : index31// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM_0]], %[[DIM_1]] : vector<1x4xi1>32 33/// TODO: This transfer_read is redundant - remove34// CHECK: vector.mask %[[MASK]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<1x3xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>35 36/// Caluclate the index vector37// CHECK: %[[STEP:.*]] = vector.step : vector<4xindex>38// CHECK: %[[IDX_BC:.*]] = vector.broadcast %[[IDX_IN]] : index to vector<4xindex>39// CHECK: %[[IDX_VEC:.*]] = arith.addi %[[STEP]], %[[IDX_BC]] : vector<4xindex>40// CHECK: %[[SC:.*]] = vector.shape_cast %[[IDX_VEC]] : vector<4xindex> to vector<4xindex>41 42/// Extract the starting point from the index vector43// CHECK: %[[IDX_START:.*]] = vector.extract %[[SC]][0] : index from vector<4xindex>44 45// Final read and write46// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC]]{{\[}}%[[C79]], %[[IDX_START]]], {{.*}} {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>47// CHECK: %[[C0_1:.*]] = arith.constant 0 : index48// CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[OUTPUT]]{{\[}}%[[C0_1]], %[[C0_1]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32>49 50module attributes {transform.with_named_sequence} {51 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {52 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op53 transform.structured.vectorize %0 vector_sizes [1, 4] {vectorize_nd_extract} : !transform.any_op54 transform.yield55 }56}57 58// -----59 60// Identical to the above, but with scalable vectors.61 62func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous_scalable(63 %src: tensor<80x16xf32>,64 %output : tensor<1x3xf32>,65 %idx: index) -> tensor<1x3xf32> {66 67 %c79 = arith.constant 79 : index68 %1 = linalg.generic {69 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],70 iterator_types = ["parallel", "parallel"]71 } outs(%output : tensor<1x3xf32>) {72 ^bb0(%out: f32):73 %2 = linalg.index 1 : index74 %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %idx)75 %extracted = tensor.extract %src[%c79, %3] : tensor<80x16xf32>76 linalg.yield %extracted : f3277 } -> tensor<1x3xf32>78 79 return %1 : tensor<1x3xf32>80}81 82// CHECK-LABEL: func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous_scalable83// CHECK-SAME: %[[SRC:.*]]: tensor<80x16xf32>,84// CHECK-SAME: %[[OUTPUT:.*]]: tensor<1x3xf32>,85// CHECK-SAME: %[[IDX_IN:.*]]: index) -> tensor<1x3xf32> {86 87/// Create the mask88// CHECK-DAG: %[[DIM_0:.*]] = arith.constant 1 : index89// CHECK-DAG: %[[DIM_1:.*]] = arith.constant 3 : index90// CHECK-DAG: %[[C79:.*]] = arith.constant 79 : index91// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM_0]], %[[DIM_1]] : vector<1x[4]xi1>92 93/// TODO: This transfer_read is redundant - remove94// CHECK: vector.mask %[[MASK]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<1x3xf32>, vector<1x[4]xf32> } : vector<1x[4]xi1> -> vector<1x[4]xf32>95 96/// Caluclate the index vector97// CHECK: %[[STEP:.*]] = vector.step : vector<[4]xindex>98// CHECK: %[[IDX_BC:.*]] = vector.broadcast %[[IDX_IN]] : index to vector<[4]xindex>99// CHECK: %[[IDX_VEC:.*]] = arith.addi %[[STEP]], %[[IDX_BC]] : vector<[4]xindex>100// CHECK: %[[SC:.*]] = vector.shape_cast %[[IDX_VEC]] : vector<[4]xindex> to vector<[4]xindex>101 102/// Extract the starting point from the index vector103// CHECK: %[[IDX_START:.*]] = vector.extract %[[SC]][0] : index from vector<[4]xindex>104 105// Final read and write106// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC]]{{\[}}%[[C79]], %[[IDX_START]]], {{.*}} {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x[4]xf32> } : vector<1x[4]xi1> -> vector<1x[4]xf32>107// CHECK: %[[C0_1:.*]] = arith.constant 0 : index108// CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[OUTPUT]]{{\[}}%[[C0_1]], %[[C0_1]]] {in_bounds = [true, true]} : vector<1x[4]xf32>, tensor<1x3xf32> } : vector<1x[4]xi1> -> tensor<1x3xf32>109 110 111module attributes {transform.with_named_sequence} {112 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {113 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op114 transform.structured.vectorize %0 vector_sizes [1, [4]] {vectorize_nd_extract} : !transform.any_op115 transform.yield116 }117}118 119// -----120 121func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous(122 %src: tensor<?x?xf32>,123 %output : tensor<?x?xf32>,124 %idx: index) -> tensor<?x?xf32> {125 126 %c79 = arith.constant 79 : index127 %1 = linalg.generic {128 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],129 iterator_types = ["parallel", "parallel"]130 } outs(%output : tensor<?x?xf32>) {131 ^bb0(%out: f32):132 %2 = linalg.index 1 : index133 %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %idx)134 %extracted = tensor.extract %src[%c79, %3] : tensor<?x?xf32>135 linalg.yield %extracted : f32136 } -> tensor<?x?xf32>137 return %1 : tensor<?x?xf32>138}139 140// CHECK-LABEL: func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous(141// CHECK-SAME: %[[SRC:[a-zA-Z0-9]*]]: tensor<?x?xf32>,142// CHECK-SAME: %[[OUTPUT:[a-zA-Z0-9]*]]: tensor<?x?xf32>,143// CHECK-SAME: %[[IDX:.*]]: index)144 145/// Create the mask146// CHECK: %[[C79:.*]] = arith.constant 79 : index147// CHECK: %[[DIM_0_IDX:.*]] = arith.constant 0 : index148// CHECK: %[[DIM_0:.*]] = tensor.dim %[[OUTPUT]], %[[DIM_0_IDX]] : tensor<?x?xf32>149// CHECK: %[[DIM_1_IDX:.*]] = arith.constant 1 : index150// CHECK: %[[DIM_1:.*]] = tensor.dim %[[OUTPUT]], %[[DIM_1_IDX]] : tensor<?x?xf32>151// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM_0]], %[[DIM_1]] : vector<1x4xi1>152 153/// TODO: This transfer_read is redundant - remove154// CHECK: vector.mask %[[MASK]] { vector.transfer_read %[[OUTPUT]]{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>155 156/// Caluclate the index vector157// CHECK: %[[STEP:.*]] = vector.step : vector<4xindex>158// CHECK: %[[IDX_BC:.*]] = vector.broadcast %[[IDX]] : index to vector<4xindex>159// CHECK: %[[IDX_VEC:.*]] = arith.addi %[[STEP]], %[[IDX_BC]] : vector<4xindex>160// CHECK: %[[SC:.*]] = vector.shape_cast %[[IDX_VEC]] : vector<4xindex> to vector<4xindex>161 162/// Extract the starting point from the index vector163// CHECK: %[[IDX_START:.*]] = vector.extract %[[SC]][0] : index from vector<4xindex>164 165// Final read and write166// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC]]{{\[}}%[[C79]], %[[IDX_START]]], {{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>167// CHECK: %[[VAL_24:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[OUTPUT]]{{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<?x?xf32> } : vector<1x4xi1> -> tensor<?x?xf32>168 169module attributes {transform.with_named_sequence} {170 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {171 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op172 transform.structured.vectorize %0 vector_sizes [1, 4] {vectorize_nd_extract} : !transform.any_op173 transform.yield174 }175}176 177// -----178 179func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous_scalable(180 %src: tensor<?x?xf32>,181 %output : tensor<?x?xf32>,182 %idx: index) -> tensor<?x?xf32> {183 184 %c79 = arith.constant 79 : index185 %1 = linalg.generic {186 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],187 iterator_types = ["parallel", "parallel"]188 } outs(%output : tensor<?x?xf32>) {189 ^bb0(%out: f32):190 %2 = linalg.index 1 : index191 %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %idx)192 %extracted = tensor.extract %src[%c79, %3] : tensor<?x?xf32>193 linalg.yield %extracted : f32194 } -> tensor<?x?xf32>195 return %1 : tensor<?x?xf32>196}197 198// CHECK-LABEL: func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous_scalable(199// CHECK-SAME: %[[SRC:[a-zA-Z0-9]*]]: tensor<?x?xf32>,200// CHECK-SAME: %[[OUTPUT:[a-zA-Z0-9]*]]: tensor<?x?xf32>,201// CHECK-SAME: %[[IDX:.*]]: index)202 203/// Create the mask204// CHECK: %[[C79:.*]] = arith.constant 79 : index205// CHECK: %[[DIM_0_IDX:.*]] = arith.constant 0 : index206// CHECK: %[[DIM_0:.*]] = tensor.dim %[[OUTPUT]], %[[DIM_0_IDX]] : tensor<?x?xf32>207// CHECK: %[[DIM_1_IDX:.*]] = arith.constant 1 : index208// CHECK: %[[DIM_1:.*]] = tensor.dim %[[OUTPUT]], %[[DIM_1_IDX]] : tensor<?x?xf32>209// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM_0]], %[[DIM_1]] : vector<1x[4]xi1>210 211/// TODO: This transfer_read is redundant - remove212// CHECK: vector.mask %[[MASK]] { vector.transfer_read %[[OUTPUT]]{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x[4]xf32> } : vector<1x[4]xi1> -> vector<1x[4]xf32>213 214/// Caluclate the index vector215// CHECK: %[[STEP:.*]] = vector.step : vector<[4]xindex>216// CHECK: %[[IDX_BC:.*]] = vector.broadcast %[[IDX]] : index to vector<[4]xindex>217// CHECK: %[[IDX_VEC:.*]] = arith.addi %[[STEP]], %[[IDX_BC]] : vector<[4]xindex>218// CHECK: %[[SC:.*]] = vector.shape_cast %[[IDX_VEC]] : vector<[4]xindex> to vector<[4]xindex>219 220/// Extract the starting point from the index vector221// CHECK: %[[IDX_START:.*]] = vector.extract %[[SC]][0] : index from vector<[4]xindex>222 223// Final read and write224// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC]]{{\[}}%[[C79]], %[[IDX_START]]], {{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x[4]xf32> } : vector<1x[4]xi1> -> vector<1x[4]xf32>225// CHECK: %[[VAL_24:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[OUTPUT]]{{.*}} {in_bounds = [true, true]} : vector<1x[4]xf32>, tensor<?x?xf32> } : vector<1x[4]xi1> -> tensor<?x?xf32>226 227module attributes {transform.with_named_sequence} {228 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {229 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op230 transform.structured.vectorize %0 vector_sizes [1, [4]] {vectorize_nd_extract} : !transform.any_op231 transform.yield232 }233}234 235// -----236 237func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<80x16xf32>, %arg0: index, %extracted_slice : tensor<1x3xf32>) -> tensor<1x3xf32> {238 %c16 = arith.constant 16 : index239 %1 = linalg.generic {240 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],241 iterator_types = ["parallel", "parallel"]242 } outs(%extracted_slice : tensor<1x3xf32>) {243 ^bb0(%out: f32):244 %2 = linalg.index 1 : index245 %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0)246 %extracted = tensor.extract %6[%3, %c16] : tensor<80x16xf32>247 linalg.yield %extracted : f32248 } -> tensor<1x3xf32>249 return %1 : tensor<1x3xf32>250}251 252// CHECK-LABEL: func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather253// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index254// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 3 : index255// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_5]] : vector<1x4xi1>256// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<1x3xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>257// CHECK: %[[VAL_11:.*]] = vector.broadcast {{.*}} : index to vector<4xindex>258// CHECK: %[[VAL_12:.*]] = arith.addi {{.*}} : vector<4xindex>259// CHECK: %[[VAL_16:.*]] = vector.broadcast {{.*}} : vector<4xindex> to vector<1x4xindex>260// CHECK: %[[VAL_18:.*]] = tensor.dim {{.*}} : tensor<80x16xf32>261// CHECK: %[[VAL_19:.*]] = vector.broadcast {{.*}} : index to vector<1x4xindex>262// CHECK: %[[VAL_20:.*]] = arith.muli {{.*}} : vector<1x4xindex>263// CHECK: %[[VAL_22:.*]] = arith.addi {{.*}} : vector<1x4xindex>264// CHECK: %[[VAL_23:.*]] = vector.mask %[[VAL_8]] { vector.gather {{.*}} : tensor<80x16xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>265// CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32>266 267module attributes {transform.with_named_sequence} {268 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {269 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op270 transform.structured.vectorize %0 vector_sizes [1, 4] {vectorize_nd_extract} : !transform.any_op271 transform.yield272 }273}274 275 // -----276 277func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<?x?xf32>, %arg0: index, %extracted_slice : tensor<?x?xf32>) -> tensor<?x?xf32> {278 %c16 = arith.constant 16 : index279 %1 = linalg.generic {280 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],281 iterator_types = ["parallel", "parallel"]282 } outs(%extracted_slice : tensor<?x?xf32>) {283 ^bb0(%out: f32):284 %2 = linalg.index 1 : index285 %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0)286 %extracted = tensor.extract %6[%3, %c16] : tensor<?x?xf32>287 linalg.yield %extracted : f32288 } -> tensor<?x?xf32>289 return %1 : tensor<?x?xf32>290}291 292// CHECK-LABEL: func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(293// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,294// CHECK-SAME: %[[VAL_1:.*]]: index,295// CHECK-SAME: %[[VAL_2:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {296// CHECK: %[[VAL_3:.*]] = arith.constant 16 : index297// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index298// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32>299// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index300// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32>301// CHECK: %[[VAL_8:.*]] = arith.constant 0 : index302// CHECK: %[[VAL_9:.*]] = ub.poison : f32303// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<1x4xi1>304// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_2]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>305// CHECK: %[[VAL_12:.*]] = vector.step : vector<4xindex>306// CHECK: %[[VAL_13:.*]] = vector.broadcast %[[VAL_1]] : index to vector<4xindex>307// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : vector<4xindex>308// CHECK: %[[VAL_15:.*]] = arith.constant dense<true> : vector<1x4xi1>309// CHECK: %[[VAL_16:.*]] = arith.constant dense<0.000000e+00> : vector<1x4xf32>310// CHECK: %[[VAL_17:.*]] = arith.constant 0 : index311// CHECK: %[[VAL_18:.*]] = vector.broadcast %[[VAL_14]] : vector<4xindex> to vector<1x4xindex>312// CHECK: %[[VAL_19:.*]] = arith.constant 1 : index313// CHECK: %[[VAL_20:.*]] = tensor.dim %[[VAL_0]], %[[VAL_19]] : tensor<?x?xf32>314// CHECK: %[[VAL_21:.*]] = vector.broadcast %[[VAL_20]] : index to vector<1x4xindex>315// CHECK: %[[VAL_22:.*]] = arith.muli %[[VAL_18]], %[[VAL_21]] : vector<1x4xindex>316// CHECK: %[[VAL_23:.*]] = arith.constant dense<16> : vector<1x4xindex>317// CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : vector<1x4xindex>318// CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_10]] { vector.gather %[[VAL_0]]{{\[}}%[[VAL_17]], %[[VAL_17]]] {{\[}}%[[VAL_24]]], %[[VAL_15]], %[[VAL_16]] : tensor<?x?xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>319// CHECK: %[[VAL_26:.*]] = arith.constant 0 : index320// CHECK: %[[VAL_27:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_25]], %[[VAL_2]]{{\[}}%[[VAL_26]], %[[VAL_26]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<?x?xf32> } : vector<1x4xi1> -> tensor<?x?xf32>321// CHECK: return %[[VAL_27]] : tensor<?x?xf32>322// CHECK: }323 324module attributes {transform.with_named_sequence} {325 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {326 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op327 transform.structured.vectorize %0 vector_sizes [1, 4] {vectorize_nd_extract} : !transform.any_op328 transform.yield329 }330}331 332// -----333 334#map1 = affine_map<(d0, d1) -> (d0, d1)>335func.func @extract_masked_vectorize(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {336 %c0 = arith.constant 1 : index337 %c1 = arith.constant 2 : index338 %2 = linalg.generic {339 indexing_maps = [#map1],340 iterator_types = ["parallel", "parallel"]341 } outs(%arg1 : tensor<?x?xf32>) {342 ^bb0(%arg3: f32):343 %7 = tensor.extract %arg0[%c0, %c1] : tensor<?x?xf32>344 linalg.yield %7 : f32345 } -> tensor<?x?xf32>346 return %2 : tensor<?x?xf32>347}348 349// CHECK-LABEL: func.func @extract_masked_vectorize(350// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,351// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {352// CHECK: %[[VAL_2:.*]] = arith.constant 1 : index353// CHECK: %[[VAL_3:.*]] = arith.constant 2 : index354// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index355// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?xf32>356// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index357// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_6]] : tensor<?x?xf32>358// CHECK: %[[VAL_8:.*]] = arith.constant 0 : index359// CHECK: %[[VAL_9:.*]] = ub.poison : f32360// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<3x3xi1>361// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_1]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<3x3xf32> } : vector<3x3xi1> -> vector<3x3xf32>362// CHECK: %[[VAL_12:.*]] = arith.constant dense<true> : vector<3x3xi1>363// CHECK: %[[VAL_13:.*]] = arith.constant dense<0.000000e+00> : vector<3x3xf32>364// CHECK: %[[VAL_14:.*]] = arith.constant 0 : index365// CHECK: %[[VAL_15:.*]] = arith.constant dense<1> : vector<3x3xindex>366// CHECK: %[[VAL_16:.*]] = arith.constant 1 : index367// CHECK: %[[VAL_17:.*]] = tensor.dim %[[VAL_0]], %[[VAL_16]] : tensor<?x?xf32>368// CHECK: %[[VAL_18:.*]] = vector.broadcast %[[VAL_17]] : index to vector<3x3xindex>369// CHECK: %[[VAL_19:.*]] = arith.muli %[[VAL_15]], %[[VAL_18]] : vector<3x3xindex>370// CHECK: %[[VAL_20:.*]] = arith.constant dense<2> : vector<3x3xindex>371// CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_20]], %[[VAL_19]] : vector<3x3xindex>372// CHECK: %[[VAL_22:.*]] = vector.mask %[[VAL_10]] { vector.gather %[[VAL_0]]{{\[}}%[[VAL_14]], %[[VAL_14]]] {{\[}}%[[VAL_21]]], %[[VAL_12]], %[[VAL_13]] : tensor<?x?xf32>, vector<3x3xindex>, vector<3x3xi1>, vector<3x3xf32> into vector<3x3xf32> } : vector<3x3xi1> -> vector<3x3xf32>373// CHECK: %[[VAL_23:.*]] = arith.constant 0 : index374// CHECK: %[[VAL_24:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_22]], %[[VAL_1]]{{\[}}%[[VAL_23]], %[[VAL_23]]] {in_bounds = [true, true]} : vector<3x3xf32>, tensor<?x?xf32> } : vector<3x3xi1> -> tensor<?x?xf32>375 376module attributes {transform.with_named_sequence} {377 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {378 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op379 transform.structured.vectorize %0 vector_sizes [3, 3] {vectorize_nd_extract} : !transform.any_op380 transform.yield381 }382}383 384// -----385 386#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>387func.func @tensor_extract_dynamic_shape(%arg1: tensor<123x321xf32>, %arg2: tensor<1x?x8xf32>) -> tensor<1x?x8xf32> {388 %c0 = arith.constant 1 : index389 %c1 = arith.constant 2 : index390 %2 = linalg.generic {391 indexing_maps = [#map1],392 iterator_types = ["parallel", "parallel", "parallel"]393 } outs(%arg2 : tensor<1x?x8xf32>)394 {395 ^bb0(%arg3: f32):396 %idx_0 = linalg.index 0 : index397 %idx_1 = linalg.index 1 : index398 %idx = arith.addi %idx_0, %idx_1 : index399 %7 = tensor.extract %arg1[%c0, %idx] : tensor<123x321xf32>400 linalg.yield %7 : f32401 } -> tensor<1x?x8xf32>402 return %2 : tensor<1x?x8xf32>403} 404 405// TODO: Make sure that this is vectorized as "scalar broadcast" when only406// vectorising the 2nd dimension.407// CHECK-LABEL: func.func @tensor_extract_dynamic_shape(408// CHECK-SAME: %[[ARG_1:.*]]: tensor<123x321xf32>,409// CHECK-SAME: %[[ARG_2:.*]]: tensor<1x?x8xf32>) -> tensor<1x?x8xf32> {410// CHECK: %[[C2:.*]] = arith.constant 2 : index411// CHECK: %[[C1_1:.*]] = arith.constant 1 : index412// CHECK: %[[C1_2:.*]] = arith.constant 1 : index413// CHECK: %[[DIM:.*]] = tensor.dim %[[ARG_2]], %[[C1_2]] : tensor<1x?x8xf32>414// CHECK: %[[C8:.*]] = arith.constant 8 : index415// CHECK: %[[MASK:.*]] = vector.create_mask %[[C1_1]], %[[DIM]], %[[C8]] : vector<1x3x8xi1>416// CHECK: %[[MASK_2:.*]] = arith.constant dense<true> : vector<1x3x8xi1>417// CHECK: %[[FALLTHROUGH:.*]] = arith.constant dense<0.000000e+00> : vector<1x3x8xf32>418// CHECK: %[[C0_1:.*]] = arith.constant 0 : index419// CHECK: vector.mask %[[MASK]] { vector.gather %[[ARG_1]][%[[C0_1]], %[[C0_1]]] [%{{.*}}], %[[MASK_2]], %[[FALLTHROUGH]] : tensor<123x321xf32>, vector<1x3x8xindex>, vector<1x3x8xi1>, vector<1x3x8xf32> into vector<1x3x8xf32> } : vector<1x3x8xi1> -> vector<1x3x8xf32>420 421module attributes {transform.with_named_sequence} {422 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {423 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op424 transform.structured.vectorize %0 vector_sizes [1, 3, 8] {vectorize_nd_extract} : !transform.any_op425 transform.yield426 }427}428 429// -----430 431#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>432func.func @scalar_broadcast(%init : tensor<1x1x3xi32>, %src: tensor<1x3x2x4xi32>, %idx :index) -> tensor<1x1x3xi32> {433 434 %c0 = arith.constant 0 :index435 436 %res = linalg.generic {437 indexing_maps = [#map],438 iterator_types = ["parallel", "parallel", "parallel"]}439 outs(%init : tensor<1x1x3xi32>) {440 ^bb0(%out: i32):441 %val = tensor.extract %src[%idx, %idx, %idx, %idx] : tensor<1x3x2x4xi32>442 linalg.yield %val : i32443 } -> tensor<1x1x3xi32>444 445 return %res : tensor<1x1x3xi32>446}447 448// CHECK: #[[$MAP:.+]] = affine_map<(d0, d1, d2, d3) -> (0, 0, 0)>449// CHECK-LABEL: func.func @scalar_broadcast(450// CHECK-SAME: %[[INIT:.*]]: tensor<1x1x3xi32>,451// CHECK-SAME: %[[SRC:.*]]: tensor<1x3x2x4xi32>,452// CHECK-SAME: %[[IDX:.*]]: index) -> tensor<1x1x3xi32> {453 454/// Compute the mask for saving the final result455// CHECK: %[[C1:.*]] = arith.constant 1 : index456// CHECK: %[[C1_2:.*]] = arith.constant 1 : index457// CHECK: %[[C3:.*]] = arith.constant 3 : index458// CHECK: %[[MASK_RES:.*]] = vector.create_mask %[[C1]], %[[C1_2]], %[[C3]] : vector<1x1x4xi1>459 460/// Read and broadcast the scalar461// CHECK: %[[PAD:.*]] = ub.poison : i32462// CHECK: %[[MASK_READ:.*]] = vector.constant_mask [1] : vector<1xi1>463// CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {464// CHECK-SAME: vector.transfer_read %[[SRC]]{{\[}}%[[IDX]], %[[IDX]], %[[IDX]], %[[IDX]]], %[[PAD]]465// CHECK-SAME: {in_bounds = [true, true, true], permutation_map = #[[$MAP]]} : tensor<1x3x2x4xi32>, vector<1x1x4xi32>466// CHECK-SAME: } : vector<1xi1> -> vector<1x1x4xi32>467 468/// Save the result in the output tensor469// CHECK: vector.mask %[[MASK_RES]] {470// CHECK-SAME: vector.transfer_write %[[READ]], %[[INIT]]{{.*}} {in_bounds = [true, true, true]} : vector<1x1x4xi32>, tensor<1x1x3xi32>471// CHECK-SAME: } : vector<1x1x4xi1> -> tensor<1x1x3xi32>472 473module attributes {transform.with_named_sequence} {474 transform.named_sequence @__transform_main(%module: !transform.any_op {transform.readonly}) {475 %0 = transform.structured.match ops{["linalg.generic"]} in %module : (!transform.any_op) -> !transform.any_op476 transform.structured.vectorize %0 vector_sizes [1, 1, 4] {vectorize_nd_extract} : !transform.any_op477 transform.yield478 }479}480