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1// RUN: mlir-opt %s -split-input-file | mlir-opt -split-input-file | FileCheck %s2 3#SV = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>4 5// CHECK: #[[$SV:.*]] = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>6// CHECK-LABEL: func private @sparse_1d_tensor(7// CHECK-SAME: tensor<32xf64, #[[$SV]]>)8func.func private @sparse_1d_tensor(tensor<32xf64, #SV>)9 10// -----11 12#CSR = #sparse_tensor.encoding<{13 map = (d0, d1) -> (d0 : dense, d1 : compressed),14 posWidth = 64,15 crdWidth = 6416}>17 18// CHECK: #[[$CSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), posWidth = 64, crdWidth = 64 }>19// CHECK-LABEL: func private @sparse_csr(20// CHECK-SAME: tensor<?x?xf32, #[[$CSR]]>)21func.func private @sparse_csr(tensor<?x?xf32, #CSR>)22 23// -----24 25#CSR_OnlyOnes = #sparse_tensor.encoding<{26 map = (d0, d1) -> (d0 : dense, d1 : compressed),27 posWidth = 64,28 crdWidth = 64,29 explicitVal = 1.0 : f32,30 implicitVal = 0.0 : f3231}>32 33// CHECK: #[[$CSR_OnlyOnes:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), posWidth = 64, crdWidth = 64, explicitVal = 1.000000e+00 : f32, implicitVal = 0.000000e+00 : f32 }>34// CHECK-LABEL: func private @sparse_csr(35// CHECK-SAME: tensor<?x?xf32, #[[$CSR_OnlyOnes]]>)36func.func private @sparse_csr(tensor<?x?xf32, #CSR_OnlyOnes>)37 38// -----39 40#CSR_OnlyOnes = #sparse_tensor.encoding<{41 map = (d0, d1) -> (d0 : dense, d1 : compressed),42 explicitVal = 1.0 : f64,43 implicitVal = 0.0 : f6444}>45 46// CHECK: #[[$CSR_OnlyOnes:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), explicitVal = 1.000000e+00 : f64, implicitVal = 0.000000e+00 : f64 }>47// CHECK-LABEL: func private @sparse_csr(48// CHECK-SAME: tensor<?x?xf64, #[[$CSR_OnlyOnes]]>)49func.func private @sparse_csr(tensor<?x?xf64, #CSR_OnlyOnes>)50 51// -----52 53#CSR_OnlyOnes = #sparse_tensor.encoding<{54 map = (d0, d1) -> (d0 : dense, d1 : compressed),55 posWidth = 64,56 crdWidth = 64,57 explicitVal = 1 : i32,58 implicitVal = 0 : i3259}>60 61// CHECK: #[[$CSR_OnlyOnes:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), posWidth = 64, crdWidth = 64, explicitVal = 1 : i32, implicitVal = 0 : i32 }>62// CHECK-LABEL: func private @sparse_csr(63// CHECK-SAME: tensor<?x?xi32, #[[$CSR_OnlyOnes]]>)64func.func private @sparse_csr(tensor<?x?xi32, #CSR_OnlyOnes>)65 66// -----67 68#CSR_OnlyOnes = #sparse_tensor.encoding<{69 map = (d0, d1) -> (d0 : dense, d1 : compressed),70 posWidth = 64,71 crdWidth = 64,72 explicitVal = 1 : i64,73 implicitVal = 0 : i6474}>75 76// CHECK: #[[$CSR_OnlyOnes:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), posWidth = 64, crdWidth = 64, explicitVal = 1 : i64, implicitVal = 0 : i64 }>77// CHECK-LABEL: func private @sparse_csr(78// CHECK-SAME: tensor<?x?xi64, #[[$CSR_OnlyOnes]]>)79func.func private @sparse_csr(tensor<?x?xi64, #CSR_OnlyOnes>)80 81// -----82 83#CSR_OnlyOnes = #sparse_tensor.encoding<{84 map = (d0, d1) -> (d0 : dense, d1 : compressed),85 posWidth = 64,86 crdWidth = 64,87 explicitVal = #complex.number<:f32 1.0, 0.0>,88 implicitVal = #complex.number<:f32 0.0, 0.0>89}>90 91// CHECK: #[[$CSR_OnlyOnes:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), posWidth = 64, crdWidth = 64, explicitVal = #complex.number<:f32 1.000000e+00, 0.000000e+00> : complex<f32>, implicitVal = #complex.number<:f32 0.000000e+00, 0.000000e+00> : complex<f32> }>92// CHECK-LABEL: func private @sparse_csr(93// CHECK-SAME: tensor<?x?xcomplex<f32>, #[[$CSR_OnlyOnes]]>)94func.func private @sparse_csr(tensor<?x?xcomplex<f32>, #CSR_OnlyOnes>)95 96// -----97 98#BCSR = #sparse_tensor.encoding<{99 map = (d0, d1, d2) -> (d0 : batch, d1: dense, d2 : compressed),100}>101 102// CHECK: #[[$BCSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : batch, d1 : dense, d2 : compressed) }>103// CHECK-LABEL: func private @sparse_bcsr(104// CHECK-SAME: tensor<?x?x?xf32, #[[$BCSR]]>)105func.func private @sparse_bcsr(tensor<?x?x?xf32, #BCSR>)106 107// -----108 109#CSR_explicit = #sparse_tensor.encoding<{110 map = {l0, l1} (d0 = l0, d1 = l1) -> (l0 = d0 : dense, l1 = d1 : compressed)111}>112 113// CHECK: #[[$CSR_EXPLICIT:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>114// CHECK-LABEL: func private @CSR_explicit(115// CHECK-SAME: tensor<?x?xf64, #[[$CSR_EXPLICIT]]>116func.func private @CSR_explicit(%arg0: tensor<?x?xf64, #CSR_explicit>) {117 return118}119 120// -----121 122#CSC = #sparse_tensor.encoding<{123 map = (d0, d1) -> (d1 : dense, d0 : compressed),124 posWidth = 0,125 crdWidth = 0126}>127 128// CHECK-DAG: #[[$CSC:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed) }>129// CHECK-LABEL: func private @sparse_csc(130// CHECK-SAME: tensor<?x?xf32, #[[$CSC]]>)131func.func private @sparse_csc(tensor<?x?xf32, #CSC>)132 133// -----134 135#DCSC = #sparse_tensor.encoding<{136 map = (d0, d1) -> (d1 : compressed, d0 : compressed),137 posWidth = 0,138 crdWidth = 64139}>140 141// CHECK-DAG: #[[$DCSC:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : compressed, d0 : compressed), crdWidth = 64 }>142// CHECK-LABEL: func private @sparse_dcsc(143// CHECK-SAME: tensor<?x?xf32, #[[$DCSC]]>)144func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)145 146// -----147 148#COO = #sparse_tensor.encoding<{149 map = (d0, d1) -> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered))150}>151 152// CHECK-DAG: #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered)) }>153// CHECK-LABEL: func private @sparse_coo(154// CHECK-SAME: tensor<?x?xf32, #[[$COO]]>)155func.func private @sparse_coo(tensor<?x?xf32, #COO>)156 157// -----158 159#COO_DENSE = #sparse_tensor.encoding<{160 map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton, d2: dense)161}>162 163// CHECK-DAG: #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton, d2 : dense) }>164// CHECK-LABEL: func private @sparse_coo_trailing_dense(165// CHECK-SAME: tensor<?x?x1xf32, #[[$COO]]>)166func.func private @sparse_coo_trailing_dense(tensor<?x?x1xf32, #COO_DENSE>)167 168// -----169 170#BCOO = #sparse_tensor.encoding<{171 map = (d0, d1, d2) -> (d0 : dense, d1 : loose_compressed(nonunique), d2 : singleton)172}>173 174// CHECK-DAG: #[[$BCOO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d1 : loose_compressed(nonunique), d2 : singleton) }>175// CHECK-LABEL: func private @sparse_bcoo(176// CHECK-SAME: tensor<?x?x?xf32, #[[$BCOO]]>)177func.func private @sparse_bcoo(tensor<?x?x?xf32, #BCOO>)178 179// -----180 181#SortedCOO = #sparse_tensor.encoding<{182 map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)183}>184 185// CHECK-DAG: #[[$SortedCOO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton) }>186// CHECK-LABEL: func private @sparse_sorted_coo(187// CHECK-SAME: tensor<10x10xf64, #[[$SortedCOO]]>)188func.func private @sparse_sorted_coo(tensor<10x10xf64, #SortedCOO>)189 190// -----191 192#COO_SoA = #sparse_tensor.encoding<{193 map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa))194}>195 196// CHECK-DAG: #[[$COO_SoA:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa)) }>197// CHECK-LABEL: func private @sparse_coo(198// CHECK-SAME: tensor<?x?xf32, #[[$COO_SoA]]>)199func.func private @sparse_coo(tensor<?x?xf32, #COO_SoA>)200 201// -----202 203#BSR = #sparse_tensor.encoding<{204 map = ( i, j ) ->205 ( i floordiv 2 : dense,206 j floordiv 3 : compressed,207 i mod 2 : dense,208 j mod 3 : dense209 )210}>211 212// CHECK-DAG: #[[$BSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : dense, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>213// CHECK-LABEL: func private @sparse_bsr(214// CHECK-SAME: tensor<10x60xf64, #[[$BSR]]>215func.func private @sparse_bsr(tensor<10x60xf64, #BSR>)216 217 218// -----219 220#ELL = #sparse_tensor.encoding<{221 map = [s0](d0, d1) -> (d0 * (s0 * 4) : dense, d0 : dense, d1 : compressed)222}>223 224// CHECK-DAG: #[[$ELL:.*]] = #sparse_tensor.encoding<{ map = [s0](d0, d1) -> (d0 * (s0 * 4) : dense, d0 : dense, d1 : compressed) }>225// CHECK-LABEL: func private @sparse_ell(226// CHECK-SAME: tensor<?x?xf64, #[[$ELL]]>227func.func private @sparse_ell(tensor<?x?xf64, #ELL>)228 229// -----230 231#CSR_SLICE = #sparse_tensor.encoding<{232 map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)233}>234 235// CHECK-DAG: #[[$CSR_SLICE:.*]] = #sparse_tensor.encoding<{ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed) }>236// CHECK-LABEL: func private @sparse_slice(237// CHECK-SAME: tensor<?x?xf64, #[[$CSR_SLICE]]>238func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)239 240// -----241 242#CSR_SLICE = #sparse_tensor.encoding<{243 map = (d0 : #sparse_tensor<slice(1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed)244}>245 246// CHECK-DAG: #[[$CSR_SLICE:.*]] = #sparse_tensor.encoding<{ map = (d0 : #sparse_tensor<slice(1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed) }>247// CHECK-LABEL: func private @sparse_slice(248// CHECK-SAME: tensor<?x?xf64, #[[$CSR_SLICE]]>249func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)250 251// -----252 253#BSR = #sparse_tensor.encoding<{254 map = ( i, j ) ->255 ( i floordiv 2 : dense,256 j floordiv 3 : compressed,257 i mod 2 : dense,258 j mod 3 : dense259 )260}>261 262// CHECK-DAG: #[[$BSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : dense, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>263// CHECK-LABEL: func private @BSR(264// CHECK-SAME: tensor<?x?xf64, #[[$BSR]]>265func.func private @BSR(%arg0: tensor<?x?xf64, #BSR>) {266 return267}268 269// -----270 271#BCSR = #sparse_tensor.encoding<{272 map = ( i, j, k ) ->273 ( i floordiv 2 : dense,274 j floordiv 3 : dense,275 k floordiv 4 : compressed,276 i mod 2 : dense,277 j mod 3 : dense,278 k mod 4 : dense279 )280}>281 282// CHECK-DAG: #[[$BCSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 floordiv 2 : dense, d1 floordiv 3 : dense, d2 floordiv 4 : compressed, d0 mod 2 : dense, d1 mod 3 : dense, d2 mod 4 : dense) }>283// CHECK-LABEL: func private @BCSR(284// CHECK-SAME: tensor<?x?x?xf64, #[[$BCSR]]>285func.func private @BCSR(%arg0: tensor<?x?x?xf64, #BCSR>) {286 return287}288// -----289 290#BSR_explicit = #sparse_tensor.encoding<{291 map =292 {il, jl, ii, jj}293 ( i = il * 2 + ii,294 j = jl * 3 + jj295 ) ->296 ( il = i floordiv 2 : dense,297 jl = j floordiv 3 : compressed,298 ii = i mod 2 : dense,299 jj = j mod 3 : dense300 )301}>302 303// CHECK-DAG: #[[$BSR_explicit:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : dense, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>304// CHECK-LABEL: func private @BSR_explicit(305// CHECK-SAME: tensor<?x?xf64, #[[$BSR_explicit]]>306func.func private @BSR_explicit(%arg0: tensor<?x?xf64, #BSR_explicit>) {307 return308}309 310// -----311 312#NV_24 = #sparse_tensor.encoding<{313 map = ( i, j ) ->314 ( i : dense,315 j floordiv 4 : dense,316 j mod 4 : structured[2, 4]317 ),318 crdWidth = 8 // we would even like just 2-bits319}>320 321// CHECK-DAG: #[[$NV_24:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 floordiv 4 : dense, d1 mod 4 : structured[2, 4]), crdWidth = 8 }>322// CHECK-LABEL: func private @NV_24(323// CHECK-SAME: tensor<?x?xf64, #[[$NV_24]]>324func.func private @NV_24(%arg0: tensor<?x?xf64, #NV_24>) {325 return326}327 328// -----329 330#NV_24 = #sparse_tensor.encoding<{331 map = ( i, j, k ) ->332 ( i : dense,333 j : dense,334 k floordiv 4 : dense,335 k mod 4 : structured[2, 4]336 )337}>338 339// CHECK-DAG: #[[$NV_24:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 floordiv 4 : dense, d2 mod 4 : structured[2, 4]) }>340// CHECK-LABEL: func private @NV_24(341// CHECK-SAME: tensor<?x?x?xf64, #[[$NV_24]]>342func.func private @NV_24(%arg0: tensor<?x?x?xf64, #NV_24>) {343 return344}345 346// -----347 348#NV_24 = #sparse_tensor.encoding<{349 map = ( i, j, k ) ->350 ( i : dense,351 k floordiv 4 : dense,352 j : dense,353 k mod 4 : structured[2, 4]354 )355}>356 357// CHECK-DAG: #[[$NV_24:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d2 floordiv 4 : dense, d1 : dense, d2 mod 4 : structured[2, 4]) }>358// CHECK-LABEL: func private @NV_24(359// CHECK-SAME: tensor<?x?x?xf64, #[[$NV_24]]>360func.func private @NV_24(%arg0: tensor<?x?x?xf64, #NV_24>) {361 return362}363 364// -----365 366#NOutOfM = #sparse_tensor.encoding<{367 map = ( i, j, k ) ->368 ( i : dense,369 k floordiv 8 : dense,370 j : dense,371 k mod 8 : structured[5, 8]372 )373}>374 375// CHECK-DAG: #[[$NOutOfM:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d2 floordiv 8 : dense, d1 : dense, d2 mod 8 : structured[5, 8]) }>376// CHECK-LABEL: func private @NOutOfM(377// CHECK-SAME: tensor<?x?x?xf64, #[[$NOutOfM]]>378func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {379 return380}381