f248d0b28d
As a side effect of the change, it also unifies the convertOp implementation between lib/codegen path.
90 lines
3.7 KiB
MLIR
90 lines
3.7 KiB
MLIR
// RUN: mlir-opt %s --stage-sparse-ops --post-sparsification-rewrite="enable-foreach=false" --canonicalize --cse | FileCheck %s
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#SparseVector64 = #sparse_tensor.encoding<{
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map = (d0) -> (d0 : compressed),
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posWidth = 64,
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crdWidth = 64
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}>
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#SparseVector32 = #sparse_tensor.encoding<{
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map = (d0) -> (d0 : compressed),
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posWidth = 32,
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crdWidth = 32
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}>
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#SparseVector = #sparse_tensor.encoding<{
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map = (d0) -> (d0 : compressed)
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}>
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#SortedCOO2D = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton),
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}>
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#SortedCOO3D = #sparse_tensor.encoding<{
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map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton(nonunique), d2 : singleton)
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}>
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#TsssPermuted = #sparse_tensor.encoding<{
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map = (d0, d1, d2) -> (d2 : compressed, d0 : compressed, d1 : compressed)
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}>
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#COOSlice = #sparse_tensor.encoding<{
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map = (d0 : #sparse_tensor<slice(2, 2, 1)>, d1 : #sparse_tensor<slice(12, 13, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
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}>
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// CHECK-LABEL: func.func @sparse_nop_convert
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// CHECK-NEXT: return
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func.func @sparse_nop_convert(%arg0: tensor<64xf32, #SparseVector>) -> tensor<64xf32, #SparseVector> {
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%0 = sparse_tensor.convert %arg0 : tensor<64xf32, #SparseVector> to tensor<64xf32, #SparseVector>
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return %0 : tensor<64xf32, #SparseVector>
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}
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// CHECK-LABEL: func.func @sparse_hidden_nop_cast
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// TODO: The following convert should be a cast instead.
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// CHECK: sparse_tensor.convert
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// CHECK: return
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func.func @sparse_hidden_nop_cast(%arg0: tensor<32xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> {
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%0 = sparse_tensor.convert %arg0 : tensor<32xf32, #SparseVector> to tensor<?xf32, #SparseVector>
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return %0 : tensor<?xf32, #SparseVector>
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}
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// CHECK-LABEL: func.func @sparse_convert_1d_ss(
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// TODO: libgen path need to support efficient format conversion (e.g., 32 bit pos -> 64 bit pos).
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// Maybe we should use a different operator as well to be clear.
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func.func @sparse_convert_1d_ss(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
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%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
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return %0 : tensor<?xf32, #SparseVector32>
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}
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// CHECK-LABEL: func.func @sparse_convert(
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// TODO: libgen path need to support efficient format conversion (e.g., 32 bit pos -> 64 bit pos).
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// Maybe we should use a different operator as well to be clear.
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func.func @sparse_convert(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
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%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
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return %0 : tensor<?xf32, #SparseVector32>
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}
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// CHECK-LABEL: func.func @sparse_convert_permuted
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// CHECK: sparse_tensor.foreach
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// CHECK: sparse_tensor.insert
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// CHECK: sparse_tensor.load
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// CHECK: sparse_tensor.reorder_coo
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// CHECK: sparse_tensor.foreach
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// CHECK: sparse_tensor.insert
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// CHECK: sparse_tensor.load
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func.func @sparse_convert_permuted(%arg0: tensor<?x?x?xf32, #SortedCOO3D>) -> tensor<?x?x?xf32, #TsssPermuted> {
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%0 = sparse_tensor.convert %arg0 : tensor<?x?x?xf32, #SortedCOO3D> to tensor<?x?x?xf32, #TsssPermuted>
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return %0 : tensor<?x?x?xf32, #TsssPermuted>
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}
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// CHECK-LABEL: func.func @sparse_convert_slice
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// CHECK: sparse_tensor.foreach
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// CHECK: sparse_tensor.insert
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// CHECK: sparse_tensor.load
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// CHECK-NOT: sparse_tensor.reorder_coo
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func.func @sparse_convert_slice(%arg0: tensor<2x13xi32, #COOSlice>) -> (tensor<2x13xi32, #SortedCOO2D>) {
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%0 = sparse_tensor.convert %arg0 : tensor<2x13xi32, #COOSlice> to tensor<2x13xi32, #SortedCOO2D>
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return %0 : tensor<2x13xi32, #SortedCOO2D>
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}
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