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1// RUN: mlir-opt %s -split-input-file -linalg-generalize-named-ops | FileCheck %s2 3// Verifies that different argument types is legal.4func.func @generalize_matmul_tensor_f16f64f32(%A : tensor<16x8xf16>, %B: tensor<8x32xf64>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {5 %0 = linalg.matmul ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)6 outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>7 return %0: tensor<16x32xf32>8}9 10// CHECK-LABEL: @generalize_matmul_tensor_f16f64f3211// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f16, %[[B_ARG:.+]]: f64, %[[C_ARG:.+]]: f32)12// Verify floating point extension and truncation.13// CHECK-NEXT: %[[A_CAST:.+]] = arith.extf %[[A_ARG]] : f16 to f3214// CHECK-NEXT: %[[B_CAST:.+]] = arith.truncf %[[B_ARG]] : f64 to f3215// CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f3216// CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f3217// CHECK-NEXT: linalg.yield %[[ADD]] : f3218// CHECK-NEXT: -> tensor<16x32xf32>19 20// -----21 22// Verifies that different argument types is legal.23func.func @generalize_matmul_tensor_i16i64i32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {24 %0 = linalg.matmul ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)25 outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>26 return %0: tensor<16x32xi32>27}28 29// CHECK-LABEL: @generalize_matmul_tensor_i16i64i3230// CHECK: ^{{.*}}(%[[A_ARG:.+]]: i16, %[[B_ARG:.+]]: i64, %[[C_ARG:.+]]: i32)31// Verify signed integer extension and truncation.32// CHECK-NEXT: %[[A_CAST:.+]] = arith.extsi %[[A_ARG]] : i16 to i3233// CHECK-NEXT: %[[B_CAST:.+]] = arith.trunci %[[B_ARG]] : i64 to i3234// CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i3235// CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i3236// CHECK-NEXT: linalg.yield %[[ADD]] : i3237// CHECK-NEXT: -> tensor<16x32xi32>38 39 40// -----41 42// Verifies that cast attributes control the cast operations used.43func.func @generalize_matmul_tensor_i16i64i32_unsigned(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {44 %0 = linalg.matmul {cast = #linalg.type_fn<cast_unsigned>}45 ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)46 outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>47 return %0: tensor<16x32xi32>48}49 50// CHECK-LABEL: @generalize_matmul_tensor_i16i64i32_unsigned51// CHECK: = arith.extui52 53// -----54 55func.func @generalize_matmul_tensor_i16i64f32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {56 %0 = linalg.matmul ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)57 outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>58 return %0: tensor<16x32xf32>59}60 61// CHECK-LABEL: @generalize_matmul_tensor_i16i64f3262// Verify signed integer to floating point cast.63// CHECK: = arith.sitofp64// CHECK: = arith.sitofp65 66// -----67 68func.func @generalize_matmul_tensor_f16f64i32(%A : tensor<16x8xf16>, %B: tensor<8x32xf64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {69 %0 = linalg.matmul ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)70 outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>71 return %0: tensor<16x32xi32>72}73 74// CHECK-LABEL: @generalize_matmul_tensor_f16f64i3275// Verify floating point to signed integer cast.76// CHECK: = arith.fptosi77// CHECK: = arith.fptosi78 79// -----80 81func.func @generalize_matmul_unsigned_tensor_i16i64i32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {82 %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }83 ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)84 outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>85 return %0: tensor<16x32xi32>86}87 88// CHECK-LABEL: @generalize_matmul_unsigned_tensor_i16i64i3289// Verify unsigned integer extension and truncation.90// CHECK: = arith.extui91// CHECK: = arith.trunci92 93// -----94 95func.func @generalize_matmul_unsigned_tensor_i16i64f32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {96 %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }97 ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)98 outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>99 return %0: tensor<16x32xf32>100}101 102// CHECK-LABEL: @generalize_matmul_unsigned_tensor_i16i64f32103// Verify unsigned integer to floating point cast.104// CHECK: = arith.uitofp105// CHECK: = arith.uitofp106 107// -----108 109func.func @generalize_matmul_unsigned_tensor_f16f64i32(%A : tensor<16x8xf16>, %B: tensor<8x32xf64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {110 %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }111 ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)112 outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>113 return %0: tensor<16x32xi32>114}115 116// CHECK-LABEL: @generalize_matmul_unsigned_tensor_f16f64i32117// Verify floating point to unsigend integer cast.118// CHECK: = arith.fptoui119// CHECK: = arith.fptoui120 121// -----122 123func.func @generalize_matmul_as_contraction_tensor_f16f64f32(124 %A: tensor<16x8xf16>,125 %B: tensor<8x32xf64>,126 %C: tensor<16x32xf32>) -> tensor<16x32xf32> {127 %0 = linalg.contract128 indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>,129 affine_map<(d0, d1, d2) -> (d2, d1)>,130 affine_map<(d0, d1, d2) -> (d0, d1)>]131 ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)132 outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>133 return %0: tensor<16x32xf32>134}135 136// CHECK-LABEL: @generalize_matmul_as_contraction_tensor_f16f64f32137// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f16, %[[B_ARG:.+]]: f64, %[[C_ARG:.+]]: f32)138// Verify floating point extension and truncation.139// CHECK-NEXT: %[[A_CAST:.+]] = arith.extf %[[A_ARG]] : f16 to f32140// CHECK-NEXT: %[[B_CAST:.+]] = arith.truncf %[[B_ARG]] : f64 to f32141// CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32142// CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32143// CHECK-NEXT: linalg.yield %[[ADD]] : f32144// CHECK-NEXT: -> tensor<16x32xf32>145 146// -----147 148func.func @generalize_matmul_as_contract_with_ext_and_trunc(149 %A: tensor<24x12xf16>,150 %B: tensor<12x25xf16>,151 %C: tensor<24x25xf32>) -> tensor<24x25xf16> {152 %0 = linalg.contract153 indexing_maps = [affine_map<(m, n, k) -> (m, k)>,154 affine_map<(m, n, k) -> (k, n)>,155 affine_map<(m, n, k) -> (m, n)>]156 ins(%A, %B : tensor<24x12xf16>, tensor<12x25xf16>)157 outs(%C : tensor<24x25xf32>) -> tensor<24x25xf32>158 %1 = arith.truncf %0 : tensor<24x25xf32> to tensor<24x25xf16>159 func.return %1 : tensor<24x25xf16>160}161 162// CHECK-LABEL: @generalize_matmul_as_contract_with_ext_and_trunc163// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f16, %[[B_ARG:.+]]: f16, %[[C_ARG:.+]]: f32)164// Verify floating point extension and truncation.165// CHECK-NEXT: %[[A_CAST:.+]] = arith.extf %[[A_ARG]] : f16 to f32166// CHECK-NEXT: %[[B_CAST:.+]] = arith.extf %[[B_ARG]] : f16 to f32167// CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32168// CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32169// CHECK-NEXT: linalg.yield %[[ADD]] : f32170// CHECK-NEXT: -> tensor<24x25xf32>171// CHECK-NEXT: %[[RES:.+]] = arith.truncf {{.*}} : tensor<24x25xf32> to tensor<24x25xf16>172 173// -----174 175func.func @generalize_pooling_nhwc_max_f32(%input : tensor<1x4x16x1xf32>, %shape: tensor<2x2xf32>, %output: tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32> {176 %0 = linalg.pooling_nhwc_max {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}177 ins(%input, %shape : tensor<1x4x16x1xf32>, tensor<2x2xf32>) outs(%output : tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>178 return %0: tensor<1x2x4x1xf32>179}180 181// CHECK-LABEL: @generalize_pooling_nhwc_max_f32182// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: f32, %[[SHAPE_ARG:.+]]: f32, %[[OUT_ARG:.+]]: f32)183// CHECK-NEXT: %[[MAX:.+]] = arith.maximumf %[[OUT_ARG]], %[[IN_ARG]] : f32184// CHECK-NEXT: linalg.yield %[[MAX]] : f32185// CHECK-NEXT: -> tensor<1x2x4x1xf32>186 187// -----188 189func.func @generalize_pooling_nwc_max_f32(%input : tensor<1x16x1xf32>, %shape: tensor<2xf32>, %output: tensor<1x4x1xf32>) -> tensor<1x4x1xf32> {190 %0 = linalg.pooling_nwc_max {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}191 ins(%input, %shape : tensor<1x16x1xf32>, tensor<2xf32>) outs(%output : tensor<1x4x1xf32>) -> tensor<1x4x1xf32>192 return %0: tensor<1x4x1xf32>193}194 195// CHECK-LABEL: @generalize_pooling_nwc_max_f32196// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: f32, %[[SHAPE_ARG:.+]]: f32, %[[OUT_ARG:.+]]: f32)197// CHECK-NEXT: %[[MAX:.+]] = arith.maximumf %[[OUT_ARG]], %[[IN_ARG]] : f32198// CHECK-NEXT: linalg.yield %[[MAX]] : f32199// CHECK-NEXT: -> tensor<1x4x1xf32>200 201// -----202 203func.func @generalize_pooling_nhwc_max_i32(%input : tensor<1x4x16x1xi32>, %shape: tensor<2x2xi32>, %output: tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32> {204 %0 = linalg.pooling_nhwc_max {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}205 ins(%input, %shape : tensor<1x4x16x1xi32>, tensor<2x2xi32>) outs(%output : tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32>206 return %0: tensor<1x2x4x1xi32>207}208 209// CHECK-LABEL: @generalize_pooling_nhwc_max_i32210// Verify signed integer maximum.211// CHECK: = arith.maxsi212 213// -----214 215func.func @generalize_pooling_nwc_max_i32(%input : tensor<1x16x1xi32>, %shape: tensor<2xi32>, %output: tensor<1x4x1xi32>) -> tensor<1x4x1xi32> {216 %0 = linalg.pooling_nwc_max {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}217 ins(%input, %shape : tensor<1x16x1xi32>, tensor<2xi32>) outs(%output : tensor<1x4x1xi32>) -> tensor<1x4x1xi32>218 return %0: tensor<1x4x1xi32>219}220 221// CHECK-LABEL: @generalize_pooling_nwc_max_i32222// Verify signed integer maximum.223// CHECK: = arith.maxsi224 225// -----226 227func.func @generalize_pooling_nhwc_max_unsigned_i32(%input : tensor<1x4x16x1xi32>, %shape: tensor<2x2xi32>, %output: tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32> {228 %0 = linalg.pooling_nhwc_max_unsigned {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}229 ins(%input, %shape : tensor<1x4x16x1xi32>, tensor<2x2xi32>) outs(%output : tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32>230 return %0: tensor<1x2x4x1xi32>231}232 233// CHECK-LABEL: @generalize_pooling_nhwc_max_unsigned_i32234// Verify unsigned integer minimum.235// CHECK: = arith.maxui236 237// -----238 239func.func @generalize_pooling_nwc_max_unsigned_i32(%input : tensor<1x16x1xi32>, %shape: tensor<2xi32>, %output: tensor<1x4x1xi32>) -> tensor<1x4x1xi32> {240 %0 = linalg.pooling_nwc_max_unsigned {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}241 ins(%input, %shape : tensor<1x16x1xi32>, tensor<2xi32>) outs(%output : tensor<1x4x1xi32>) -> tensor<1x4x1xi32>242 return %0: tensor<1x4x1xi32>243}244 245// CHECK-LABEL: @generalize_pooling_nwc_max_unsigned_i32246// Verify unsigned integer minimum.247// CHECK: = arith.maxui248 249// -----250 251func.func @generalize_pooling_nhwc_min_f32(%input : tensor<1x4x16x1xf32>, %shape: tensor<2x2xf32>, %output: tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32> {252 %0 = linalg.pooling_nhwc_min {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}253 ins(%input, %shape : tensor<1x4x16x1xf32>, tensor<2x2xf32>) outs(%output : tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>254 return %0: tensor<1x2x4x1xf32>255}256 257// CHECK-LABEL: @generalize_pooling_nhwc_min_f32258// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: f32, %[[SHAPE_ARG:.+]]: f32, %[[OUT_ARG:.+]]: f32)259// CHECK-NEXT: %[[MIN:.+]] = arith.minimumf %[[OUT_ARG]], %[[IN_ARG]] : f32260// CHECK-NEXT: linalg.yield %[[MIN]] : f32261// CHECK-NEXT: -> tensor<1x2x4x1xf32>262 263// -----264 265func.func @generalize_pooling_nwc_min_f32(%input : tensor<1x16x1xf32>, %shape: tensor<2xf32>, %output: tensor<1x4x1xf32>) -> tensor<1x4x1xf32> {266 %0 = linalg.pooling_nwc_min {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}267 ins(%input, %shape : tensor<1x16x1xf32>, tensor<2xf32>) outs(%output : tensor<1x4x1xf32>) -> tensor<1x4x1xf32>268 return %0: tensor<1x4x1xf32>269}270 271// CHECK-LABEL: @generalize_pooling_nwc_min_f32272// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: f32, %[[SHAPE_ARG:.+]]: f32, %[[OUT_ARG:.+]]: f32)273// CHECK-NEXT: %[[MIN:.+]] = arith.minimumf %[[OUT_ARG]], %[[IN_ARG]] : f32274// CHECK-NEXT: linalg.yield %[[MIN]] : f32275// CHECK-NEXT: -> tensor<1x4x1xf32>276 277// -----278 279func.func @generalize_pooling_nhwc_min_i32(%input : tensor<1x4x16x1xi32>, %shape: tensor<2x2xi32>, %output: tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32> {280 %0 = linalg.pooling_nhwc_min {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}281 ins(%input, %shape : tensor<1x4x16x1xi32>, tensor<2x2xi32>) outs(%output : tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32>282 return %0: tensor<1x2x4x1xi32>283}284 285// CHECK-LABEL: @generalize_pooling_nhwc_min_i32286// Verify signed integer minimum.287// CHECK: = arith.minsi288 289// -----290 291func.func @generalize_pooling_nwc_min_i32(%input : tensor<1x16x1xi32>, %shape: tensor<2xi32>, %output: tensor<1x4x1xi32>) -> tensor<1x4x1xi32> {292 %0 = linalg.pooling_nwc_min {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}293 ins(%input, %shape : tensor<1x16x1xi32>, tensor<2xi32>) outs(%output : tensor<1x4x1xi32>) -> tensor<1x4x1xi32>294 return %0: tensor<1x4x1xi32>295}296 297// CHECK-LABEL: @generalize_pooling_nwc_min_i32298// Verify signed integer minimum.299// CHECK: = arith.minsi300 301// -----302 303func.func @generalize_pooling_nhwc_min_unsigned_i32(%input : tensor<1x4x16x1xi32>, %shape: tensor<2x2xi32>, %output: tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32> {304 %0 = linalg.pooling_nhwc_min_unsigned {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}305 ins(%input, %shape : tensor<1x4x16x1xi32>, tensor<2x2xi32>) outs(%output : tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32>306 return %0: tensor<1x2x4x1xi32>307}308 309// CHECK-LABEL: @generalize_pooling_nhwc_min_unsigned_i32310// Verify unsigned integer minimum.311// CHECK: = arith.minui312 313// -----314 315func.func @generalize_pooling_nwc_min_unsigned_i32(%input : tensor<1x16x1xi32>, %shape: tensor<2xi32>, %output: tensor<1x4x1xi32>) -> tensor<1x4x1xi32> {316 %0 = linalg.pooling_nwc_min_unsigned {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}317 ins(%input, %shape : tensor<1x16x1xi32>, tensor<2xi32>) outs(%output : tensor<1x4x1xi32>) -> tensor<1x4x1xi32>318 return %0: tensor<1x4x1xi32>319}320 321// CHECK-LABEL: @generalize_pooling_nwc_min_unsigned_i32322// Verify unsigned integer minimum.323// CHECK: = arith.minui324 325// -----326 327func.func @generalize_pooling_nhwc_sum_f32(%input : tensor<1x4x16x1xf32>, %shape: tensor<2x2xf32>, %output: tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32> {328 %0 = linalg.pooling_nhwc_sum {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}329 ins(%input, %shape : tensor<1x4x16x1xf32>, tensor<2x2xf32>) outs(%output : tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>330 return %0: tensor<1x2x4x1xf32>331}332 333// CHECK-LABEL: @generalize_pooling_nhwc_sum_f32334// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: f32, %[[SHAPE_ARG:.+]]: f32, %[[OUT_ARG:.+]]: f32)335// CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[OUT_ARG]], %[[IN_ARG]] : f32336// CHECK-NEXT: linalg.yield %[[ADD]] : f32337// CHECK-NEXT: -> tensor<1x2x4x1xf32>338 339// -----340 341func.func @generalize_pooling_nwc_sum_f32(%input : tensor<1x16x1xf32>, %shape: tensor<2xf32>, %output: tensor<1x4x1xf32>) -> tensor<1x4x1xf32> {342 %0 = linalg.pooling_nwc_sum {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}343 ins(%input, %shape : tensor<1x16x1xf32>, tensor<2xf32>) outs(%output : tensor<1x4x1xf32>) -> tensor<1x4x1xf32>344 return %0: tensor<1x4x1xf32>345}346 347// CHECK-LABEL: @generalize_pooling_nwc_sum_f32348// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: f32, %[[SHAPE_ARG:.+]]: f32, %[[OUT_ARG:.+]]: f32)349// CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[OUT_ARG]], %[[IN_ARG]] : f32350// CHECK-NEXT: linalg.yield %[[ADD]] : f32351// CHECK-NEXT: -> tensor<1x4x1xf32>352 353// -----354 355func.func @generalize_pooling_nhwc_sum_i32(%input : tensor<1x4x16x1xi32>, %shape: tensor<2x2xi32>, %output: tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32> {356 %0 = linalg.pooling_nhwc_sum {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[2, 4]> : tensor<2xi64>}357 ins(%input, %shape : tensor<1x4x16x1xi32>, tensor<2x2xi32>) outs(%output : tensor<1x2x4x1xi32>) -> tensor<1x2x4x1xi32>358 return %0: tensor<1x2x4x1xi32>359}360 361// CHECK-LABEL: @generalize_pooling_nhwc_sum_i32362// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: i32, %[[SHAPE_ARG:.+]]: i32, %[[OUT_ARG:.+]]: i32)363// CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[OUT_ARG]], %[[IN_ARG]] : i32364// CHECK-NEXT: linalg.yield %[[ADD]] : i32365// CHECK-NEXT: -> tensor<1x2x4x1xi32>366 367// -----368 369func.func @generalize_pooling_nwc_sum_i32(%input : tensor<1x16x1xi32>, %shape: tensor<2xi32>, %output: tensor<1x4x1xi32>) -> tensor<1x4x1xi32> {370 %0 = linalg.pooling_nwc_sum {dilations = dense<[2]> : tensor<1xi64>, strides = dense<[4]> : tensor<1xi64>}371 ins(%input, %shape : tensor<1x16x1xi32>, tensor<2xi32>) outs(%output : tensor<1x4x1xi32>) -> tensor<1x4x1xi32>372 return %0: tensor<1x4x1xi32>373}374 375// CHECK-LABEL: @generalize_pooling_nwc_sum_i32376// CHECK: ^{{.*}}(%[[IN_ARG:.+]]: i32, %[[SHAPE_ARG:.+]]: i32, %[[OUT_ARG:.+]]: i32)377// CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[OUT_ARG]], %[[IN_ARG]] : i32378// CHECK-NEXT: linalg.yield %[[ADD]] : i32379// CHECK-NEXT: -> tensor<1x4x1xi32>380 381// -----382 383func.func @generalize_fill_0d(%value: f32, %O: tensor<f32>) -> tensor<f32> {384 %0 = linalg.fill ins(%value: f32) outs(%O : tensor<f32>) -> tensor<f32>385 return %0: tensor<f32>386}387 388// CHECK-DAG: #[[$MAP0:.+]] = affine_map<() -> ()>389 390// CHECK-LABEL: @generalize_fill_0d391// CHECK: linalg.generic392// CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP0]]]393// CHECK-SAME: iterator_types = []394 395// -----396 397func.func @generalize_fill_2d(%value: f32, %O: memref<16x32xf32>) {398 linalg.fill ins(%value: f32) outs(%O : memref<16x32xf32>)399 return400}401 402// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0, d1) -> ()>403// CHECK-DAG: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (d0, d1)>404 405// CHECK-LABEL: @generalize_fill406// CHECK: linalg.generic407// CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP1]]]408// CHECK-SAME: iterator_types = ["parallel", "parallel"]409 410// -----411 412func.func @generalize_index(%min: f64, %max: f64, %seed: i32, %O: tensor<16x32xf32>) -> tensor<16x32xf32> {413 %0 = linalg.fill_rng_2d ins(%min, %max, %seed: f64, f64, i32) outs(%O : tensor<16x32xf32>) -> tensor<16x32xf32>414 return %0: tensor<16x32xf32>415}416 417// CHECK-LABEL: @generalize_index418// CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index419// CHECK-DAG: %[[IDX1:.+]] = linalg.index 1 : index420// CHECK-DAG: %[[IDX0_CAST:.+]] = arith.index_cast %[[IDX0]] : index to i32421// CHECK-DAG: %[[IDX1_CAST:.+]] = arith.index_cast %[[IDX1]] : index to i32422 423// -----424 425func.func @generalize_const(%min: f64, %max: f64, %seed: i32, %O: tensor<16x32xf32>) -> tensor<16x32xf32> {426 %0 = linalg.fill_rng_2d ins(%min, %max, %seed: f64, f64, i32) outs(%O : tensor<16x32xf32>) -> tensor<16x32xf32>427 return %0: tensor<16x32xf32>428}429 430// CHECK-LABEL: @generalize_const431// CHECK-DAG: %[[CST0:.+]] = arith.constant 1103515245 : i32432// CHECK-DAG: %[[CST1:.+]] = arith.constant 12345 : i32433// CHECK-DAG: %[[CST2:.+]] = arith.constant 2.3283063999999999E-10 : f64434 435// -----436 437// Verifies the fun attribute controls the binary function used.438func.func @generalize_copy(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {439 %0 = linalg.copy ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>440 return %0: tensor<4x8xf32>441}442 443// CHECK-LABEL: @generalize_copy444// CHECK: linalg.generic445// CHECK-NEXT: ^bb0(%[[I:[0-9a-zA-Z]*]]: f32446// CHECK-NEXT: linalg.yield %[[I]]447