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[mlir][sparse] add test cases for sparse tensor slices on COO tensors

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Reviewed By: aartbik, bixia

Differential Revision: https://reviews.llvm.org/D146074
This commit is contained in:
Peiming Liu 2023-03-14 18:18:20 +00:00
parent 60ebe901eb
commit 5da8f30d63
1 changed files with 71 additions and 2 deletions
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73
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_foreach_slices.mlir
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@ -9,6 +9,7 @@
//
// TODO: support slices on lib path
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ]
}>
@ -23,6 +24,21 @@
slice = [ (?, ?, ?), (?, ?, ?) ]
}>
#COO = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ]
}>
#COO_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
#COO_SLICE_DYN = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ],
slice = [ (?, ?, ?), (?, ?, ?) ]
}>
module {
func.func @foreach_print_non_slice(%A: tensor<4x4xf64, #CSR>) {
@ -55,6 +71,26 @@ module {
return
}
func.func @foreach_print_slice_coo(%A: tensor<4x4xf64, #COO_SLICE>) {
sparse_tensor.foreach in %A : tensor<4x4xf64, #COO_SLICE> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
func.func @foreach_print_slice_coo_dyn(%A: tensor<?x?xf64, #COO_SLICE_DYN>) {
sparse_tensor.foreach in %A : tensor<?x?xf64, #COO_SLICE_DYN> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
func.func @entry() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
@ -76,6 +112,10 @@ module {
%tmp = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #CSR>
%a = tensor.extract_slice %tmp[1, 1][4, 4][1, 2] : tensor<8x8xf64, #CSR> to
tensor<4x4xf64, #CSR_SLICE>
%tmp_coo = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #COO>
%a_coo = tensor.extract_slice %tmp_coo[1, 1][4, 4][1, 2] : tensor<8x8xf64, #COO> to
tensor<4x4xf64, #COO_SLICE>
// Foreach on sparse tensor slices directly
//
// CHECK: 1
@ -89,6 +129,18 @@ module {
// CHECK-NEXT: 2.1
//
call @foreach_print_slice(%a) : (tensor<4x4xf64, #CSR_SLICE>) -> ()
// Same results for COO
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 2.3
// CHECK-NEXT: 2
// CHECK-NEXT: 3
// CHECK-NEXT: 1
// CHECK-NEXT: 3
// CHECK-NEXT: 2
// CHECK-NEXT: 2.1
//
call @foreach_print_slice_coo(%a_coo) : (tensor<4x4xf64, #COO_SLICE>) -> ()
%dense = tensor.extract_slice %sa[1, 1][4, 4][1, 2] : tensor<8x8xf64> to
tensor<4x4xf64>
@ -111,8 +163,12 @@ module {
// TODO: Investigates why reusing the same %tmp above would cause bufferization
// errors.
%tmp1 = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #CSR>
%a_dyn = tensor.extract_slice %tmp1[%c1, %c1][%c4, %c4][%c1, %c2] :
tensor<8x8xf64, #CSR> to tensor<?x?xf64, #CSR_SLICE_DYN>
%a_dyn = tensor.extract_slice %tmp1[%c1, %c1][%c4, %c4][%c1, %c2] : tensor<8x8xf64, #CSR> to
tensor<?x?xf64, #CSR_SLICE_DYN>
%tmp1_coo = sparse_tensor.convert %sa : tensor<8x8xf64> to tensor<8x8xf64, #COO>
%a_dyn_coo = tensor.extract_slice %tmp1_coo[%c1, %c1][%c4, %c4][%c1, %c2] : tensor<8x8xf64, #COO> to
tensor<?x?xf64, #COO_SLICE_DYN>
//
// CHECK-NEXT: 1
// CHECK-NEXT: 0
@ -125,9 +181,22 @@ module {
// CHECK-NEXT: 2.1
//
call @foreach_print_slice_dyn(%a_dyn) : (tensor<?x?xf64, #CSR_SLICE_DYN>) -> ()
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 2.3
// CHECK-NEXT: 2
// CHECK-NEXT: 3
// CHECK-NEXT: 1
// CHECK-NEXT: 3
// CHECK-NEXT: 2
// CHECK-NEXT: 2.1
//
call @foreach_print_slice_coo_dyn(%a_dyn_coo) : (tensor<?x?xf64, #COO_SLICE_DYN>) -> ()
bufferization.dealloc_tensor %tmp : tensor<8x8xf64, #CSR>
bufferization.dealloc_tensor %tmp1 : tensor<8x8xf64, #CSR>
bufferization.dealloc_tensor %tmp_coo : tensor<8x8xf64, #COO>
bufferization.dealloc_tensor %tmp1_coo : tensor<8x8xf64, #COO>
bufferization.dealloc_tensor %b : tensor<4x4xf64, #CSR>
return
}