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// RUN: mlir-opt %s --canonicalize --cse | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
// CHECK-LABEL: func @sparse_nop_dense2dense_convert(
// CHECK-SAME: %[[A:.*]]: tensor<64xf32>)
// CHECK-NOT: sparse_tensor.convert
// CHECK: return %[[A]] : tensor<64xf32>
func.func @sparse_nop_dense2dense_convert(%arg0: tensor<64xf32>) -> tensor<64xf32> {
%0 = sparse_tensor.convert %arg0 : tensor<64xf32> to tensor<64xf32>
return %0 : tensor<64xf32>
}
// CHECK-LABEL: func @sparse_dce_convert(
// CHECK-SAME: %[[A:.*]]: tensor<64xf32>)
// CHECK-NOT: sparse_tensor.convert
// CHECK: return
func.func @sparse_dce_convert(%arg0: tensor<64xf32>) {
%0 = sparse_tensor.convert %arg0 : tensor<64xf32> to tensor<64xf32, #SparseVector>
return
}
// CHECK-LABEL: func @sparse_dce_getters(
// CHECK-SAME: %[[A:.*]]: tensor<64xf32, #sparse{{[0-9]*}}>)
// CHECK-NOT: sparse_tensor.positions
// CHECK-NOT: sparse_tensor.coordinates
// CHECK-NOT: sparse_tensor.values
// CHECK: return
func.func @sparse_dce_getters(%arg0: tensor<64xf32, #SparseVector>) {
%0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex>
%1 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex>
%2 = sparse_tensor.values %arg0 : tensor<64xf32, #SparseVector> to memref<?xf32>
return
}
// CHECK-LABEL: func @sparse_concat_dce(
// CHECK-NOT: sparse_tensor.concatenate
// CHECK: return
func.func @sparse_concat_dce(%arg0: tensor<2xf64, #SparseVector>,
%arg1: tensor<3xf64, #SparseVector>,
%arg2: tensor<4xf64, #SparseVector>) {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2xf64, #SparseVector>,
tensor<3xf64, #SparseVector>,
tensor<4xf64, #SparseVector> to tensor<9xf64, #SparseVector>
return
}
// CHECK-LABEL: func @sparse_get_specifier_dce_fold(
// CHECK-SAME: %[[A0:.*]]: !sparse_tensor.storage_specifier
// CHECK-SAME: %[[A1:.*]]: index,
// CHECK-SAME: %[[A2:.*]]: index)
// CHECK-NOT: sparse_tensor.storage_specifier.set
// CHECK-NOT: sparse_tensor.storage_specifier.get
// CHECK: return %[[A1]]
func.func @sparse_get_specifier_dce_fold(%arg0: !sparse_tensor.storage_specifier<#SparseVector>, %arg1: index, %arg2: index) -> index {
%0 = sparse_tensor.storage_specifier.set %arg0 lvl_sz at 0 with %arg1
: !sparse_tensor.storage_specifier<#SparseVector>
%1 = sparse_tensor.storage_specifier.set %0 pos_mem_sz at 0 with %arg2
: !sparse_tensor.storage_specifier<#SparseVector>
%2 = sparse_tensor.storage_specifier.get %1 lvl_sz at 0
: !sparse_tensor.storage_specifier<#SparseVector>
return %2 : index
}
#COO = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)}>
// CHECK-LABEL: func @sparse_reorder_coo(
// CHECK-SAME: %[[A:.*]]: tensor<?x?xf32, #sparse{{[0-9]*}}>
// CHECK-NOT: %[[R:.*]] = sparse_tensor.reorder_coo
// CHECK: return %[[A]]
func.func @sparse_reorder_coo(%arg0 : tensor<?x?xf32, #COO>) -> tensor<?x?xf32, #COO> {
%ret = sparse_tensor.reorder_coo quick_sort %arg0 : tensor<?x?xf32, #COO> to tensor<?x?xf32, #COO>
return %ret : tensor<?x?xf32, #COO>
}
#BSR = #sparse_tensor.encoding<{
map = ( i, j ) ->
( i floordiv 2 : dense,
j floordiv 3 : compressed,
i mod 2 : dense,
j mod 3 : dense
)
}>
// CHECK-LABEL: func @sparse_crd_translate(
// CHECK-NOT: sparse_tensor.crd_translate
func.func @sparse_crd_translate(%arg0: index, %arg1: index) -> (index, index) {
%l0, %l1, %l2, %l3 = sparse_tensor.crd_translate dim_to_lvl [%arg0, %arg1] as #BSR : index, index, index, index
%d0, %d1 = sparse_tensor.crd_translate lvl_to_dim [%l0, %l1, %l2, %l3] as #BSR : index, index
return %d0, %d1 : index, index
}
// CHECK-LABEL: func.func @sparse_lvl_0(
// CHECK: %[[C5:.*]] = arith.constant 5 : index
// CHECK: return %[[C5]] : index
func.func @sparse_lvl_0(%t : tensor<10x?xi32, #BSR>) -> index {
%lvl = arith.constant 0 : index
%l0 = sparse_tensor.lvl %t, %lvl : tensor<10x?xi32, #BSR>
return %l0 : index
}
// CHECK-LABEL: func.func @sparse_lvl_3(
// CHECK: %[[C3:.*]] = arith.constant 3 : index
// CHECK: return %[[C3]] : index
func.func @sparse_lvl_3(%t : tensor<?x?xi32, #BSR>) -> index {
%lvl = arith.constant 3 : index
%l0 = sparse_tensor.lvl %t, %lvl : tensor<?x?xi32, #BSR>
return %l0 : index
}
#DSDD = #sparse_tensor.encoding<{
map = (i, j, k, l) -> (i: dense, j: compressed, k: dense, l: dense)
}>
// CHECK-LABEL: func.func @sparse_reinterpret_map(
// CHECK-NOT: sparse_tensor.reinterpret_map
func.func @sparse_reinterpret_map(%t0 : tensor<6x12xi32, #BSR>) -> tensor<6x12xi32, #BSR> {
%t1 = sparse_tensor.reinterpret_map %t0 : tensor<6x12xi32, #BSR>
to tensor<3x4x2x3xi32, #DSDD>
%t2 = sparse_tensor.reinterpret_map %t1 : tensor<3x4x2x3xi32, #DSDD>
to tensor<6x12xi32, #BSR>
return %t2 : tensor<6x12xi32, #BSR>
}
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