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1// RUN: mlir-opt %s --sparse-assembler -split-input-file | FileCheck %s2 3// -----4 5// CHECK-LABEL: func.func @nop(6// CHECK-SAME: %[[A:.*]]: tensor<100xf32>) -> tensor<100xf32> {7// CHECK: return %[[A]] : tensor<100xf32>8// CHECK: }9func.func @nop(%arg0: tensor<100xf32>) -> tensor<100xf32> {10 return %arg0 : tensor<100xf32>11}12 13// -----14 15// CHECK-LABEL: func.func @sparse_in(16// CHECK-SAME: %[[B:.*0]]: tensor<?xindex>,17// CHECK-SAME: %[[C:.*1]]: tensor<?xindex>,18// CHECK-SAME: %[[A:.*]]: tensor<?xf32>) -> tensor<64x64xf32> {19// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]]), %[[A]]20// CHECK: %[[F:.*]] = call @_internal_sparse_in(%[[I]])21// CHECK: return %[[F]] : tensor<64x64xf32>22// CHECK: }23// CHECK: func.func private @_internal_sparse_in24#sparse = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>25func.func @sparse_in(%arg0: tensor<64x64xf32, #sparse>) -> tensor<64x64xf32> {26 %0 = sparse_tensor.convert %arg0 : tensor<64x64xf32, #sparse> to tensor<64x64xf32>27 return %0 : tensor<64x64xf32>28}29 30// -----31 32// CHECK-LABEL: func.func @sparse_in2(33// CHECK-SAME: %[[X:.*0]]: tensor<100xf32>,34// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,35// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,36// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>) -> tensor<64x64xf32> {37// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]]), %[[A]]38// CHECK: %[[F:.*]] = call @_internal_sparse_in2(%[[X]], %[[I]])39// CHECK: return %[[F]] : tensor<64x64xf32>40// CHECK: }41// CHECK: func.func private @_internal_sparse_in242#sparse = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>43func.func @sparse_in2(%arg0: tensor<100xf32>, %arg1: tensor<64x64xf32, #sparse>) -> tensor<64x64xf32> {44 %0 = sparse_tensor.convert %arg1 : tensor<64x64xf32, #sparse> to tensor<64x64xf32>45 return %0 : tensor<64x64xf32>46}47 48// -----49 50// CHECK-LABEL: func.func @sparse_out(51// CHECK-SAME: %[[X:.*0]]: tensor<64x64xf32>,52// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,53// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,54// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>)55// CHECK: %[[F:.*]] = call @_internal_sparse_out(%[[X]])56// CHECK: sparse_tensor.disassemble %[[F]]57// CHECK: return58// CHECK: }59// CHECK: func.func private @_internal_sparse_out60#sparse = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>61func.func @sparse_out(%arg0: tensor<64x64xf32>) -> tensor<64x64xf32, #sparse> {62 %0 = sparse_tensor.convert %arg0 : tensor<64x64xf32> to tensor<64x64xf32, #sparse>63 return %0 : tensor<64x64xf32, #sparse>64}65 66// -----67 68// CHECK-LABEL: func.func @sparse_out2(69// CHECK-SAME: %[[X:.*0]]: tensor<64x64xf32>,70// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,71// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,72// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>)73// CHECK: %[[F:.*]]:2 = call @_internal_sparse_out2(%[[X]])74// CHECK: sparse_tensor.disassemble %[[F]]#175// CHECK: return %[[F]]#076// CHECK: }77// CHECK: func.func private @_internal_sparse_out278#sparse = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>79func.func @sparse_out2(%arg0: tensor<64x64xf32>) -> (tensor<64x64xf32>, tensor<64x64xf32, #sparse>) {80 %0 = sparse_tensor.convert %arg0 : tensor<64x64xf32> to tensor<64x64xf32, #sparse>81 return %arg0, %0 : tensor<64x64xf32>, tensor<64x64xf32, #sparse>82}83 84// -----85 86// CHECK-LABEL: func.func @sparse_inout(87// CHECK-SAME: %[[B:.*0]]: tensor<?xindex>,88// CHECK-SAME: %[[C:.*1]]: tensor<?xindex>,89// CHECK-SAME: %[[A:.*2]]: tensor<?xf32>,90// CHECK-SAME: %[[E:.*3]]: tensor<?xindex>,91// CHECK-SAME: %[[F:.*4]]: tensor<?xindex>,92// CHECK-SAME: %[[D:.*5]]: tensor<?xf32>)93// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]]), %[[A]]94// CHECK: %[[F:.*]] = call @_internal_sparse_inout(%[[I]])95// CHECK: sparse_tensor.disassemble %[[F]]96// CHECK: return97// CHECK: }98// CHECK: func.func private @_internal_sparse_inout99#sparse = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>100func.func @sparse_inout(%arg0: tensor<64x64xf32, #sparse>) -> tensor<64x64xf32, #sparse> {101 return %arg0 : tensor<64x64xf32, #sparse>102}103 104// -----105 106// CHECK-LABEL: func.func @sparse_inout_coo_soa(107// CHECK-SAME: %[[B:.*0]]: tensor<?xindex>,108// CHECK-SAME: %[[C:.*1]]: tensor<?xindex>,109// CHECK-SAME: %[[D:.*2]]: tensor<?xindex>,110// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>,111// CHECK-SAME: %[[F:.*4]]: tensor<?xindex>,112// CHECK-SAME: %[[G:.*5]]: tensor<?xindex>,113// CHECK-SAME: %[[H:.*6]]: tensor<?xindex>,114// CHECK-SAME: %[[E:.*7]]: tensor<?xf32>)115// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]], %[[D]]), %[[A]]116// CHECK: %[[F:.*]] = call @_internal_sparse_inout_coo_soa(%[[I]])117// CHECK: sparse_tensor.disassemble %[[F]]118// CHECK: return119// CHECK: }120// CHECK: func.func private @_internal_sparse_inout121#sparse = #sparse_tensor.encoding<{122 map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa))123}>124func.func @sparse_inout_coo_soa(%arg0: tensor<64x64xf32, #sparse>) -> tensor<64x64xf32, #sparse> {125 return %arg0 : tensor<64x64xf32, #sparse>126}127