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[mlir][sparse] Change tests to use new syntax for ELL and slice #67569

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Sep 27, 2023
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[mlir][sparse] Change tests to use new syntax for ELL and slice #67569

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0f1db40
[mlir][sparse] Change tests to use new syntax for ELL and slice
yinying-lisa-li Sep 27, 2023
b815f82
Merge branch 'llvm:main' into update_tests
yinying-lisa-li Sep 27, 2023
80e188b
updated sparse tensor attr description
yinying-lisa-li Sep 27, 2023
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26 changes: 24 additions & 2 deletions mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td
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Expand Up @@ -237,11 +237,33 @@ def SparseTensorEncodingAttr : SparseTensor_Attr<"SparseTensorEncoding",
}>
... tensor<20x30xf32, #BSR_explicit> ...

// ELL format.
// In the simple format for matrix, one array stores values and another
// array stores column indices. The arrays have the same number of rows
// as the original matrix, but only have as many columns as
// the maximum number of nonzeros on a row of the original matrix.
// There are many variants for ELL such as jagged diagonal scheme.
// To implement ELL, map provides a notion of "counting a
// dimension", where every stored element with the same coordinate
// is mapped to a new slice. For instance, ELL storage of a 2-d
// tensor can be defined with the mapping (i, j) -> (#i, i, j)
// using the notation of [Chou20]. Lacking the # symbol in MLIR's
// affine mapping, we use a free symbol c to define such counting,
// together with a constant that denotes the number of resulting
// slices. For example, the mapping [c](i, j) -> (c * 3 * i, i, j)
// with the level-types ["dense", "dense", "compressed"] denotes ELL
// storage with three jagged diagonals that count the dimension i.
#ELL = #sparse_tensor.encoding<{
map = [c](i, j) -> (c * 3 * i : dense, i : dense, j : compressed)
}>
... tensor<?x?xf64, #ELL> ...

// CSR slice (offset = 0, size = 4, stride = 1 on the first dimension;
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Can we also add the #ELL here, so we can explain the "free" symbol (for now, since we may implement this very differently)

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Done!

// offset = 0, size = 8, and a dynamic stride on the second dimension).
#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (0, 4, 1), (0, 8, ?) ]
map = (i : #sparse_tensor<slice(0, 4, 1)>,
j : #sparse_tensor<slice(0, 8, ?)>) ->
(i : dense, j : compressed)
}>
... tensor<?x?xf64, #CSC_SLICE> ...

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3 changes: 1 addition & 2 deletions mlir/test/CAPI/sparse_tensor.c
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Expand Up @@ -25,8 +25,7 @@ static int testRoundtripEncoding(MlirContext ctx) {
// clang-format off
const char *originalAsm =
"#sparse_tensor.encoding<{ "
"lvlTypes = [ \"dense\", \"compressed\", \"compressed\"], "
"dimToLvl = affine_map<(d0, d1)[s0] -> (s0, d0, d1)>, "
"map = [s0](d0, d1) -> (s0 : dense, d0 : compressed, d1 : compressed), "
"posWidth = 32, crdWidth = 64 }>";
// clang-format on
MlirAttribute originalAttr =
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3 changes: 1 addition & 2 deletions mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
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Expand Up @@ -39,8 +39,7 @@
}>

#COOSlice = #sparse_tensor.encoding<{
lvlTypes = [ "compressed_nu", "singleton" ],
dimSlices = [ (2, 2, 1), (12, 13, 1) ]
map = (d0 : #sparse_tensor<slice(2, 2, 1)>, d1 : #sparse_tensor<slice(12, 13, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>

// CHECK-LABEL: func @sparse_nop_convert(
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9 changes: 3 additions & 6 deletions mlir/test/Dialect/SparseTensor/invalid.mlir
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Expand Up @@ -201,8 +201,7 @@ func.func @mismatch_values_types(%arg0: tensor<?xf64, #SparseVector>) -> memref<
// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
Expand All @@ -214,8 +213,7 @@ func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

func.func @sparse_slice_stride(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
Expand Down Expand Up @@ -400,8 +398,7 @@ func.func @invalid_out_dense(%arg0: tensor<10xf64>, %arg1: !llvm.ptr<i8>) {
// -----

#CSR = #sparse_tensor.encoding<{
lvlTypes = ["dense", "compressed"],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

func.func @sparse_convert_to_slice(%arg0: tensor<10x?xf32>) -> tensor<10x10xf32, #CSR> {
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3 changes: 1 addition & 2 deletions mlir/test/Dialect/SparseTensor/invalid_encoding.mlir
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Expand Up @@ -218,8 +218,7 @@ func.func private @tensor_invalid_key(%arg0: tensor<16x32xf32, #a>) -> ()
// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (-1, ?, 1), (?, 4, 2) ] // expected-error{{expect positive value or ? for slice offset/size/stride}}
map = (d0 : #sparse_tensor<slice(-1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed)// expected-error{{expect positive value or ? for slice offset/size/stride}}
}>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)

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3 changes: 1 addition & 2 deletions mlir/test/Dialect/SparseTensor/pre_rewriting.mlir
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Expand Up @@ -13,8 +13,7 @@
}>

#Slice = #sparse_tensor.encoding<{
lvlTypes = [ "compressed_nu", "singleton" ],
dimSlices = [ (?, 1, 1), (?, 3, 1) ]
map = (d0 : #sparse_tensor<slice(?, 1, 1)>, d1 : #sparse_tensor<slice(?, 3, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>

#sel_trait = {
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15 changes: 5 additions & 10 deletions mlir/test/Dialect/SparseTensor/roundtrip.mlir
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Expand Up @@ -143,8 +143,7 @@ func.func @sparse_values(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xf64>
// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

// CHECK-LABEL: func @sparse_slice_offset(
Expand All @@ -159,8 +158,7 @@ func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

// CHECK-LABEL: func @sparse_slice_stride(
Expand Down Expand Up @@ -188,8 +186,7 @@ func.func @sparse_metadata_init() -> !sparse_tensor.storage_specifier<#SparseVec

#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#SparseVector_Slice = #sparse_tensor.encoding<{
lvlTypes = ["compressed"],
dimSlices = [ (?, ?, ?) ]
map = (d0 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed)
}>

// CHECK-LABEL: func @sparse_metadata_init(
Expand Down Expand Up @@ -220,8 +217,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----

#SparseVector_Slice = #sparse_tensor.encoding<{
lvlTypes = ["compressed"],
dimSlices = [ (?, ?, ?) ]
map = (d0 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed)
}>

// CHECK-LABEL: func @sparse_get_md(
Expand All @@ -237,8 +233,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector_S
// -----

#SparseVector = #sparse_tensor.encoding<{
lvlTypes = ["compressed"],
dimSlices = [ (?, ?, ?) ]
map = (d0 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed)
}>

// CHECK-LABEL: func @sparse_get_md(
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20 changes: 3 additions & 17 deletions mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
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Expand Up @@ -101,8 +101,7 @@ func.func private @sparse_bcsr(tensor<10x60xf64, #BCSR>)
// -----

#ELL = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "dense", "compressed" ],
dimToLvl = affine_map<(i,j)[c] -> (c*4*i, i, j)>
map = [s0](d0, d1) -> (d0 * (s0 * 4) : dense, d0 : dense, d1 : compressed)
}>

// CHECK-LABEL: func private @sparse_ell(
Expand All @@ -112,8 +111,7 @@ func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

// CHECK-LABEL: func private @sparse_slice(
Expand All @@ -123,19 +121,7 @@ func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
}>

// CHECK-LABEL: func private @sparse_slice(
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimSlices = [ (1, 4, 1), (1, 4, 2) ] }>>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)

// -----

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, ?, 1), (?, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

// CHECK-LABEL: func private @sparse_slice(
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3 changes: 1 addition & 2 deletions mlir/test/Dialect/SparseTensor/sparse_extract_slice.mlir
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Expand Up @@ -5,8 +5,7 @@
}>

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (0, 4, 1), (0, 8, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(0, 8, 1)>) -> (d0 : dense, d1 : compressed)
}>

// CHECK-LABEL: func.func @sparse_slice(
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6 changes: 2 additions & 4 deletions mlir/test/Dialect/SparseTensor/sparse_foreach.mlir
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Expand Up @@ -29,13 +29,11 @@ func.func @sparse_foreach_constant() -> () {
}

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimSlices = [ (0, 4, 1), (2, 4, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(2, 4, 1)>) -> (d0 : compressed, d1 : compressed)
}>

#CSR_SLICE_DYN = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimSlices = [ (?, ?, ?), (?, ?, ?) ]
map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed, d1 : compressed)
}>


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12 changes: 4 additions & 8 deletions mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_foreach_slices.mlir
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Expand Up @@ -28,27 +28,23 @@
}>

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>

#CSR_SLICE_DYN = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (?, ?, ?), (?, ?, ?) ]
map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : dense, d1 : compressed)
}>

#COO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
}>

#COO_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "compressed_nu", "singleton" ],
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>

#COO_SLICE_DYN = #sparse_tensor.encoding<{
lvlTypes = [ "compressed_nu", "singleton" ],
dimSlices = [ (?, ?, ?), (?, ?, ?) ]
map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>


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24 changes: 8 additions & 16 deletions mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul_slice.mlir
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Expand Up @@ -27,51 +27,43 @@
}>

#DCSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimSlices = [ (0, 4, 1), (0, 8, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(0, 8, 1)>) -> (d0 : compressed, d1 : compressed)
}>

#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed)
}>

#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (0, 4, 1), (0, 8, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(0, 8, 1)>) -> (d0 : dense, d1 : compressed)
}>

#COO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
}>

#CSR_SLICE_1 = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (0, 4, 2), (0, 4, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(0, 4, 1)>) -> (d0 : dense, d1 : compressed)
}>

#DCSR_SLICE_1 = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimSlices = [ (0, 4, 2), (1, 4, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(1, 4, 1)>) -> (d0 : compressed, d1 : compressed)
}>

#COO_SLICE_1 = #sparse_tensor.encoding<{
lvlTypes = [ "compressed_nu", "singleton" ],
dimSlices = [ (0, 4, 2), (0, 4, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(0, 4, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>

#COO_SLICE_2 = #sparse_tensor.encoding<{
lvlTypes = [ "compressed_nu", "singleton" ],
dimSlices = [ (0, 4, 2), (1, 4, 1) ]
map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(1, 4, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>

#CSR_SLICE_dyn = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimSlices = [ (?, 4, ?), (?, 4, ?) ]
map = (d0 : #sparse_tensor<slice(?, 4, ?)>, d1 : #sparse_tensor<slice(?, 4, ?)>) -> (d0 : dense, d1 : compressed)
}>

#DCSR_SLICE_dyn = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimSlices = [ (?, 4, ?), (?, 4, ?) ]
map = (d0 : #sparse_tensor<slice(?, 4, ?)>, d1 : #sparse_tensor<slice(?, 4, ?)>) -> (d0 : compressed, d1 : compressed)
}>

module {
Expand Down