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[mlir][sparse] fix bugs when convert coo to coo but with different di…
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…m ordering

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D145723
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@PeimingLiu
PeimingLiu committed Mar 9, 2023
1 parent 4c82050 commit 41089f8
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Showing 3 changed files with 47 additions and 42 deletions.
12 changes: 9 additions & 3 deletions mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
View file
Expand Up @@ -681,14 +681,21 @@ struct ConvertRewriter : public OpRewritePattern<ConvertOp> {
// COO tensor.
// TODO: enhance foreachOp to take ordering to remove the need of a
// temporary COO tensor here.
const RankedTensorType bufferTp = dstTp.isIdentity()
const RankedTensorType bufferTp = dstTp.isIdentity() || fromSparseConst
? dstTp.getRankedTensorType()
: getUnorderedCOOFromTypeWithOrdering(
dstTp, dstTp.getDimToLvlMap());
// Only imposes foreach order on dense constant (which will be statically
// sorted by the sparse compiler), otherwise the rotated loop sequence
// results to bad cache locality.
AffineMapAttr foreachOrder = nullptr;
if (encDst.getDimOrdering() && fromSparseConst)
foreachOrder = AffineMapAttr::get(encDst.getDimOrdering());

auto buffer =
rewriter.create<AllocTensorOp>(loc, bufferTp, dynSizes).getResult();
auto foreachOp = rewriter.create<ForeachOp>(
loc, src, buffer,
loc, src, buffer, foreachOrder,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
Value input = reduc.front();
Expand Down Expand Up @@ -795,7 +802,6 @@ struct ConvertRewriter : public OpRewritePattern<ConvertOp> {
// tensor (e.g., src tensor is not ordered or src tensor haves a different
// dimOrdering).
if (const SparseTensorType srcTp(srcRTT);
!isUniqueCOOType(srcRTT) &&
!(srcTp.isAllOrdered() && srcTp.hasSameDimToLvlMap(dstTp))) {
// Construct a COO tensor from the src tensor.
// TODO: there may be cases for which more efficiently without
Expand Down
35 changes: 11 additions & 24 deletions mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir
View file
Expand Up @@ -183,30 +183,17 @@ func.func @sparse_constant() -> tensor<8x7xf32, #CSR>{
return %1 : tensor<8x7xf32, #CSR>
}

// CHECK-RWT-LABEL: func.func @sparse_constant_csc() -> tensor<8x7xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> {
// CHECK-RWT: %[[F0:.*]] = arith.constant sparse<{{\[\[}}0, 0], [1, 6]], [1.000000e+00, 5.000000e+00]> : tensor<8x7xf32>
// CHECK-RWT: %[[T0:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[T1:.*]] = sparse_tensor.foreach in %[[F0]] init(%[[T0]])
// CHECK-RWT: ^bb0(%[[L0I0:.*]]: index, %[[L0I1:.*]]: index, %[[L0V:.*]]: f32, %[[L0T:.*]]: tensor
// CHECK-RWT: %[[L0T2:.*]] = sparse_tensor.insert %[[L0V]] into %[[L0T]]{{\[}}%[[L0I1]], %[[L0I0]]]
// CHECK-RWT: sparse_tensor.yield %[[L0T2]]
// CHECK-RWT: }
// CHECK-RWT: %[[COO:.*]] = sparse_tensor.load %[[T1]] hasInserts
// CHECK-RWT: %[[NSE:.*]] = sparse_tensor.number_of_entries %[[COO]]
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[COO]]
// CHECK-RWT: %[[I:.*]] = sparse_tensor.coordinates_buffer %[[COO]]
// CHECK-RWT: sparse_tensor.sort_coo hybrid_quick_sort %[[NSE]], %[[I]] jointly %[[V]] {nx = 2 : index, ny = 0 : index}
// CHECK-RWT: %[[T3:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[T4:.*]] = sparse_tensor.foreach in %[[COO]] init(%[[T3]])
// CHECK-RWT: ^bb0(%[[L1I0:.*]]: index, %[[L1I1:.*]]: index, %[[L1V:.*]]: f32, %[[L1T:.*]]: tensor
// CHECK-RWT: %[[L1T2:.*]] = sparse_tensor.insert %[[L1V]] into %[[L1T]]{{\[}}%[[L1I1]], %[[L1I0]]]
// CHECK-RWT: sparse_tensor.yield %[[L1T2]]
// CHECK-RWT: }
// CHECK-RWT: %[[T5:.*]] = sparse_tensor.load %[[T4]] hasInserts
// CHECK-RWT: %[[T6:.*]] = sparse_tensor.convert %[[T5]]
// CHECK-RWT: bufferization.dealloc_tensor %[[COO]]
// CHECK-RWT: return %[[T6]]
// CHECK-RWT: }
// CHECK-RWT-LABEL: func.func @sparse_constant_csc() -> tensor<8x7xf32,
// CHECK-RWT: %[[VAL_0:.*]] = arith.constant sparse<{{\[\[}}0, 0], [1, 6]], [1.000000e+00, 5.000000e+00]> : tensor<8x7xf32>
// CHECK-RWT: %[[VAL_1:.*]] = bufferization.alloc_tensor() :
// CHECK-RWT: %[[VAL_2:.*]] = sparse_tensor.foreach in %[[VAL_0]] init(%[[VAL_1]]) {order = #map} : tensor<8x7xf32>,
// CHECK-RWT: ^bb0(%[[VAL_3:.*]]: index, %[[VAL_4:.*]]: index, %[[VAL_5:.*]]: f32, %[[VAL_6:.*]]: tensor
// CHECK-RWT: %[[VAL_7:.*]] = sparse_tensor.insert %[[VAL_5]] into %[[VAL_6]]{{\[}}%[[VAL_4]], %[[VAL_3]]] :
// CHECK-RWT: sparse_tensor.yield %[[VAL_7]] :
// CHECK-RWT: }
// CHECK-RWT: %[[VAL_8:.*]] = sparse_tensor.load %[[VAL_9:.*]] hasInserts :
// CHECK-RWT: return %[[VAL_8]] :
// CHECK-RWT: }
func.func @sparse_constant_csc() -> tensor<8x7xf32, #CSC>{
// Initialize a tensor.
%0 = arith.constant sparse<[[0, 0], [1, 6]], [1.0, 5.0]> : tensor<8x7xf32>
Expand Down
42 changes: 27 additions & 15 deletions mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
View file
Expand Up @@ -153,22 +153,34 @@ func.func @sparse_convert_singleton(%arg0: tensor<?xf32, #SparseSingleton64>) ->
}

// CHECK-RWT-LABEL: func.func @sparse_convert_permuted(
// CHECK-RWT-SAME: %[[COO:.*]]:
// CHECK-RWT-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-RWT-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-RWT-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-RWT: %[[D0:.*]] = tensor.dim %[[COO]], %[[C0]]
// CHECK-RWT: %[[D1:.*]] = tensor.dim %[[COO]], %[[C1]]
// CHECK-RWT: %[[D2:.*]] = tensor.dim %[[COO]], %[[C2]]
// CHECK-RWT: %[[T1:.*]] = bufferization.alloc_tensor(%[[D0]], %[[D1]], %[[D2]])
// CHECK-RWT: %[[T2:.*]] = sparse_tensor.foreach in %[[COO]] init(%[[T1]])
// CHECK-RWT: ^bb0(%[[LI0:.*]]: index, %[[LI1:.*]]: index, %[[LI2:.*]]: index, %[[LV:.*]]: f32, %[[LT1:.*]]: tensor<?x?x?xf32,
// CHECK-RWT: %[[LT2:.*]] = sparse_tensor.insert %[[LV]] into %[[LT1]]{{\[}}%[[LI2]], %[[LI0]], %[[LI1]]]
// CHECK-RWT: sparse_tensor.yield %[[LT2]]
// CHECK-RWT-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #{{.*}}>>) -> tensor<?x?x?xf32, #{{.*}}>> {
// CHECK-RWT-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-RWT-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-RWT-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-RWT-DAG: %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]]
// CHECK-RWT-DAG: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]]
// CHECK-RWT-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]]
// CHECK-RWT-DAG: %[[VAL_7:.*]] = sparse_tensor.number_of_entries %[[VAL_0]]
// CHECK-RWT: %[[VAL_8:.*]] = bufferization.alloc_tensor(%[[VAL_4]], %[[VAL_5]], %[[VAL_6]]) size_hint=%[[VAL_7]]
// CHECK-RWT: %[[VAL_9:.*]] = sparse_tensor.foreach in %[[VAL_0]] init(%[[VAL_8]])
// CHECK-RWT: ^bb0(%[[VAL_10:.*]]: index, %[[VAL_11:.*]]: index, %[[VAL_12:.*]]: index, %[[VAL_13:.*]]: f32, %[[VAL_14:.*]]: tensor<?x?x?xf32, #{{.*}}>>):
// CHECK-RWT: %[[VAL_15:.*]] = sparse_tensor.insert %[[VAL_13]] into %[[VAL_14]]{{\[}}%[[VAL_12]], %[[VAL_10]], %[[VAL_11]]]
// CHECK-RWT: sparse_tensor.yield %[[VAL_15]] : tensor<?x?x?xf32, #{{.*}}>>
// CHECK-RWT: }
// CHECK-RWT: %[[T3:.*]] = sparse_tensor.load %[[T2:.*]] hasInserts
// CHECK-RWT: %[[T4:.*]] = sparse_tensor.convert %[[T3]]
// CHECK-RWT: return %[[T4]]
// CHECK-RWT: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_17:.*]] hasInserts : tensor<?x?x?xf32, #{{.*}}>>
// CHECK-RWT: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_16]] : tensor<?x?x?xf32, #{{.*}}>> to memref<?xf32>
// CHECK-RWT: %[[VAL_19:.*]] = sparse_tensor.coordinates_buffer %[[VAL_16]] : tensor<?x?x?xf32, #{{.*}}>> to memref<?xindex>
// CHECK-RWT: sparse_tensor.sort_coo hybrid_quick_sort %[[VAL_7]], %[[VAL_19]] jointly %[[VAL_18]] {nx = 3 : index, ny = 0 : index}
// CHECK-RWT: %[[VAL_20:.*]] = bufferization.alloc_tensor(%[[VAL_4]], %[[VAL_5]], %[[VAL_6]]) size_hint=%[[VAL_7]]
// CHECK-RWT: %[[VAL_21:.*]] = sparse_tensor.foreach in %[[VAL_16]] init(%[[VAL_20]])
// CHECK-RWT: ^bb0(%[[VAL_22:.*]]: index, %[[VAL_23:.*]]: index, %[[VAL_24:.*]]: index, %[[VAL_25:.*]]: f32, %[[VAL_26:.*]]: tensor<?x?x?xf32, #{{.*}}>>):
// CHECK-RWT: %[[VAL_27:.*]] = sparse_tensor.insert %[[VAL_25]] into %[[VAL_26]]{{\[}}%[[VAL_24]], %[[VAL_22]], %[[VAL_23]]]
// CHECK-RWT: sparse_tensor.yield %[[VAL_27]]
// CHECK-RWT: }
// CHECK-RWT: bufferization.dealloc_tensor %[[VAL_16]]
// CHECK-RWT: %[[VAL_28:.*]] = sparse_tensor.load %[[VAL_29:.*]] hasInserts
// CHECK-RWT: %[[VAL_30:.*]] = sparse_tensor.convert %[[VAL_28]]
// CHECK-RWT: return %[[VAL_30]]
func.func @sparse_convert_permuted(%arg0: tensor<?x?x?xf32, #SortedCOO3D>) -> tensor<?x?x?xf32, #TsssPermuted> {
%0 = sparse_tensor.convert %arg0 : tensor<?x?x?xf32, #SortedCOO3D> to tensor<?x?x?xf32, #TsssPermuted>
return %0 : tensor<?x?x?xf32, #TsssPermuted>
Expand Down

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