[mlir][sparse] avoid tensor to memref conversion in sparse tensor rewri… #69362
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PeimingLiu
requested review from
aartbik and
yinying-lisa-li
and removed request for
aartbik
aartbik
approved these changes
Oct 17, 2023
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@llvm/pr-subscribers-mlir-sparse @llvm/pr-subscribers-mlir Author: Peiming Liu (PeimingLiu) Changes…ting rules. Patch is 27.11 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/69362.diff 3 Files Affected:
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
index 1bfee3aa1d7ee8e..e50b14975e83d63 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
@@ -829,47 +829,40 @@ struct ReshapeRewriter : public OpRewritePattern<ReshapeOp> {
}
};
+// A trivial wrapper to help generate different operations for dense/sparse
+// tensors.
struct TensorLike {
TensorLike(OpBuilder &builder, Location loc, RankedTensorType rtt,
- ValueRange sizes)
- : isSparse(rtt.getEncoding() != nullptr) {
+ ValueRange sizes) {
SmallVector<Value> dynSzs;
getDynamicSizes(rtt, sizes, dynSzs);
- if (isSparse)
- val = builder.create<AllocTensorOp>(loc, rtt, dynSzs);
- else
- val = allocDenseTensor(builder, loc, rtt, sizes);
- };
-
- void insertOrStore(OpBuilder &builder, Location loc, Value v,
- ValueRange crds) {
- if (isSparse)
- val = builder.create<InsertOp>(loc, v, val, crds);
- else
- builder.create<memref::StoreOp>(loc, v, val, crds);
+ val = builder.create<AllocTensorOp>(loc, rtt, dynSzs);
+ if (!isSparse()) {
+ Value c0 = constantZero(builder, loc, rtt.getElementType());
+ val = builder.create<linalg::FillOp>(loc, c0, val).getResult(0);
+ }
}
- Value getSSA() const {
- // We don't need to maintain the SSA chain for a memref value.
- return isSparse ? val : nullptr;
+ void insert(OpBuilder &builder, Location loc, Value v, ValueRange crds) {
+ // TODO: Unify these two.
+ if (isSparse())
+ val = builder.create<sparse_tensor::InsertOp>(loc, v, val, crds);
+ else
+ val = builder.create<tensor::InsertOp>(loc, v, val, crds);
}
Value finalize(OpBuilder &builder, Location loc, RankedTensorType rtp) const {
- if (isSparse)
+ if (isSparse())
return builder.create<LoadOp>(loc, val, true);
- return builder.create<bufferization::ToTensorOp>(loc, rtp, val);
+ return val;
}
- void updateSSA(Value v) {
- // Dense memref is a non-SSA value.
- assert(isSparse);
- val = v;
+ bool isSparse() const {
+ return getSparseTensorEncoding(val.getType()) != nullptr;
}
-private:
- bool isSparse;
- Value val; // either a memref (for dense tensor) or a sparse tensor.
+ Value val;
};
struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
@@ -901,14 +894,14 @@ struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
TensorLike dstBuf(rewriter, loc, dstTp.getRankedTensorType(), sizes);
Value offset = constantIndex(rewriter, loc, 0);
- Value iterArg = dstBuf.getSSA();
+ Value iterArg = dstBuf.val;
ForeachOp foreachOp;
for (Value input : op.getInputs()) {
// Builds a for op for each input tensor to append new values into the
// output tensor.
foreachOp = rewriter.create<ForeachOp>(
- loc, input, iterArg ? ValueRange{iterArg} : ValueRange{},
+ loc, input, iterArg,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
SmallVector<Value> dstLcvs(dstTp.getLvlRank());
@@ -920,32 +913,26 @@ struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
// FIXME: `toStoredDim` is deprecated
dstLcvs[toStoredDim(dstTp.getEncoding(), d)] = crd;
}
-
- if (!reduc.empty())
- dstBuf.updateSSA(reduc.front());
-
+ // Enters foreach, updates the SSA chain.
+ dstBuf.val = reduc.front();
if (!dstTp.isAllDense()) {
Value cond = genIsNonzero(builder, loc, v);
auto ifOp = builder.create<scf::IfOp>(loc, reduc.getTypes(), cond,
/*else*/ true);
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
- builder.create<scf::YieldOp>(loc, dstBuf.getSSA());
+ builder.create<scf::YieldOp>(loc, dstBuf.val);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
- dstBuf.insertOrStore(builder, loc, v, dstLcvs);
- builder.create<scf::YieldOp>(loc, dstBuf.getSSA());
+ dstBuf.insert(builder, loc, v, dstLcvs);
+ builder.create<scf::YieldOp>(loc, dstBuf.val);
// Exits the ifOp, update the sparse tensor SSA value.
builder.setInsertionPointAfter(ifOp);
- assert(!reduc.empty());
- dstBuf.updateSSA(ifOp.getResult(0));
+ dstBuf.val = ifOp.getResult(0);
} else {
- dstBuf.insertOrStore(builder, loc, v, dstLcvs);
+ dstBuf.insert(builder, loc, v, dstLcvs);
}
- if (reduc.empty())
- builder.create<sparse_tensor::YieldOp>(loc);
- else
- builder.create<sparse_tensor::YieldOp>(loc, dstBuf.getSSA());
+ builder.create<sparse_tensor::YieldOp>(loc, dstBuf.val);
});
// Accumulates the offset. Note that only static-shaped inputs are allowed
// by concatenate op verifier, which saves us from computing the offset
@@ -955,15 +942,11 @@ struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
offset = rewriter.create<arith::AddIOp>(
loc, offset, constantIndex(rewriter, loc, *sh));
- if (!foreachOp.getResults().empty()) {
- iterArg = foreachOp.getResult(0);
- dstBuf.updateSSA(iterArg);
- }
+ iterArg = foreachOp.getResult(0);
+ dstBuf.val = iterArg;
}
- if (!foreachOp.getResults().empty())
- dstBuf.updateSSA(iterArg);
-
+ dstBuf.val = iterArg;
Value ret = dstBuf.finalize(rewriter, loc, dstTp.getRankedTensorType());
rewriter.replaceOp(op, ret);
return success();
@@ -1010,15 +993,12 @@ struct DirectConvertRewriter : public OpRewritePattern<ConvertOp> {
ValueRange vs;
TensorLike dstBuf(rewriter, loc, dstStt.getRankedTensorType(), sizes);
- Value iterArg = dstBuf.getSSA();
auto foreachOp = rewriter.create<ForeachOp>(
- loc, src, iterArg ? ValueRange{iterArg} : ValueRange{}, foreachOrder,
+ loc, src, dstBuf.val, foreachOrder,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
// Enters the loop, update the SSA value for insertion chain.
- if (!reduc.empty())
- dstBuf.updateSSA(reduc.front());
-
+ dstBuf.val = reduc.front();
const Dimension dimRank = dstStt.getDimRank();
const Level lvlRank = dstStt.getLvlRank();
SmallVector<Value> lcvs(lvlRank);
@@ -1028,34 +1008,29 @@ struct DirectConvertRewriter : public OpRewritePattern<ConvertOp> {
}
if (!skipZeroCheck) {
- assert(!reduc.empty());
Value cond = genIsNonzero(builder, loc, v);
auto ifOp = builder.create<scf::IfOp>(loc, reduc.getTypes(), cond,
/*else*/ true);
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
- builder.create<scf::YieldOp>(loc, dstBuf.getSSA());
+ builder.create<scf::YieldOp>(loc, dstBuf.val);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
- dstBuf.insertOrStore(builder, loc, v, lcvs);
- builder.create<scf::YieldOp>(loc, dstBuf.getSSA());
+ dstBuf.insert(builder, loc, v, lcvs);
+ builder.create<scf::YieldOp>(loc, dstBuf.val);
// Exits the ifOp, update the sparse tensor SSA value.
builder.setInsertionPointAfter(ifOp);
- dstBuf.updateSSA(ifOp.getResult(0));
+ dstBuf.val = ifOp.getResult(0);
} else {
- dstBuf.insertOrStore(builder, loc, v, lcvs);
+ dstBuf.insert(builder, loc, v, lcvs);
}
- if (reduc.empty())
- builder.create<sparse_tensor::YieldOp>(loc);
- else
- builder.create<sparse_tensor::YieldOp>(loc, dstBuf.getSSA());
+ builder.create<sparse_tensor::YieldOp>(loc, dstBuf.val);
});
rewriter.setInsertionPointAfter(foreachOp);
// Exits the for loop, links the SSA chain.
- if (!foreachOp.getResults().empty())
- dstBuf.updateSSA(foreachOp.getResult(0));
+ dstBuf.val = foreachOp.getResult(0);
Value ret = dstBuf.finalize(rewriter, loc, dstStt.getRankedTensorType());
rewriter.replaceOp(op, ret);
diff --git a/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir b/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir
index c22f051a0d5854d..e2dcb068e11851e 100644
--- a/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir
+++ b/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir
@@ -14,11 +14,10 @@
// CHECK-LABEL: func.func @sparse_convert_1d
// CHECK-NOT: sparse_tensor.reorder_coo
-// CHECK: memref.alloc
+// CHECK: bufferization.alloc_tensor
// CHECK: linalg.fill
// CHECK: sparse_tensor.foreach
-// CHECK: memref.store
-// CHECK: bufferization.to_tensor
+// CHECK: tensor.insert
func.func @sparse_convert_1d(%arg0: tensor<13xi32, #SparseVector>) -> tensor<13xi32> {
%0 = sparse_tensor.convert %arg0 : tensor<13xi32, #SparseVector> to tensor<13xi32>
return %0 : tensor<13xi32>
@@ -26,11 +25,10 @@ func.func @sparse_convert_1d(%arg0: tensor<13xi32, #SparseVector>) -> tensor<13x
// CHECK-LABEL: func.func @sparse_convert_1d_dyn
// CHECK-NOT: sparse_tensor.reorder_coo
-// CHECK: memref.alloc
+// CHECK: bufferization.alloc_tensor
// CHECK: linalg.fill
// CHECK: sparse_tensor.foreach
-// CHECK: memref.store
-// CHECK: bufferization.to_tensor
+// CHECK: tensor.insert
func.func @sparse_convert_1d_dyn(%arg0: tensor<?xi32, #SparseVector>) -> tensor<?xi32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xi32, #SparseVector> to tensor<?xi32>
return %0 : tensor<?xi32>
@@ -38,11 +36,10 @@ func.func @sparse_convert_1d_dyn(%arg0: tensor<?xi32, #SparseVector>) -> tensor<
// CHECK-LABEL: func.func @sparse_convert_2d
// CHECK-NOT: sparse_tensor.reorder_coo
-// CHECK: memref.alloc
+// CHECK: bufferization.alloc_tensor
// CHECK: linalg.fill
// CHECK: sparse_tensor.foreach
-// CHECK: memref.store
-// CHECK: bufferization.to_tensor
+// CHECK: tensor.insert
func.func @sparse_convert_2d(%arg0: tensor<2x4xf64, #SparseMatrix>) -> tensor<2x4xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<2x4xf64, #SparseMatrix> to tensor<2x4xf64>
return %0 : tensor<2x4xf64>
@@ -50,11 +47,10 @@ func.func @sparse_convert_2d(%arg0: tensor<2x4xf64, #SparseMatrix>) -> tensor<2x
// CHECK-LABEL: func.func @sparse_convert_2d_dyn
// CHECK-NOT: sparse_tensor.reorder_coo
-// CHECK: memref.alloc
+// CHECK: bufferization.alloc_tensor
// CHECK: linalg.fill
// CHECK: sparse_tensor.foreach
-// CHECK: memref.store
-// CHECK: bufferization.to_tensor
+// CHECK: tensor.insert
func.func @sparse_convert_2d_dyn0(%arg0: tensor<?x4xf64, #SparseMatrix>) -> tensor<?x4xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<?x4xf64, #SparseMatrix> to tensor<?x4xf64>
return %0 : tensor<?x4xf64>
@@ -62,11 +58,10 @@ func.func @sparse_convert_2d_dyn0(%arg0: tensor<?x4xf64, #SparseMatrix>) -> tens
// CHECK-LABEL: func.func @sparse_convert_2d_dyn1
// CHECK-NOT: sparse_tensor.reorder_coo
-// CHECK: memref.alloc
+// CHECK: bufferization.alloc_tensor
// CHECK: linalg.fill
// CHECK: sparse_tensor.foreach
-// CHECK: memref.store
-// CHECK: bufferization.to_tensor
+// CHECK: tensor.insert
func.func @sparse_convert_2d_dyn1(%arg0: tensor<2x?xf64, #SparseMatrix>) -> tensor<2x?xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<2x?xf64, #SparseMatrix> to tensor<2x?xf64>
return %0 : tensor<2x?xf64>
@@ -74,11 +69,10 @@ func.func @sparse_convert_2d_dyn1(%arg0: tensor<2x?xf64, #SparseMatrix>) -> tens
// CHECK-LABEL: func.func @sparse_convert_2d_dyn2
// CHECK-NOT: sparse_tensor.reorder_coo
-// CHECK: memref.alloc
+// CHECK: bufferization.alloc_tensor
// CHECK: linalg.fill
// CHECK: sparse_tensor.foreach
-// CHECK: memref.store
-// CHECK: bufferization.to_tensor
+// CHECK: tensor.insert
func.func @sparse_convert_2d_dyn2(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<?x?xf64, #SparseMatrix> to tensor<?x?xf64>
return %0 : tensor<?x?xf64>
@@ -86,11 +80,10 @@ func.func @sparse_convert_2d_dyn2(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tens
// CHECK-LABEL: func.func @sparse_convert_3d
// CHECK-NOT: sparse_tensor.reorder_coo
-// CHECK: memref.alloc
+// CHECK: bufferization.alloc_tensor
// CHECK: linalg.fill
// CHECK: sparse_tensor.foreach
-// CHECK: memref.store
-// CHECK: bufferization.to_tensor
+// CHECK: tensor.insert
func.func @sparse_convert_3d(%arg0: tensor<2x3x4xf64, #SparseTensor>) -> tensor<2x3x4xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<2x3x4xf64, #SparseTensor> to tensor<2x3x4xf64>
return %0 : tensor<2x3x4xf64>
diff --git a/mlir/test/Dialect/SparseTensor/sparse_concat.mlir b/mlir/test/Dialect/SparseTensor/sparse_concat.mlir
index bdfab54dc6daeb5..f3d3dd28563e891 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_concat.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_concat.mlir
@@ -176,77 +176,83 @@ func.func @concat_sparse_sparse_dynamic(%arg0: tensor<2x4xf64, #DCSR>,
return %0 : tensor<?x?xf64, #DCSR>
}
-// CHECK-LABEL: @concat_sparse_sparse_dense(
-// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<2x4xf64, #sparse_tensor
-// CHECK-SAME: %[[TMP_arg1:.*]]: tensor<3x4xf64, #sparse_tensor
-// CHECK-SAME: %[[TMP_arg2:.*]]: tensor<4x4xf64, #sparse_tensor
-// CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[TMP_c5:.*]] = arith.constant 5 : index
-// CHECK-DAG: %[[TMP_c2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[TMP_c9:.*]] = arith.constant 9 : index
-// CHECK-DAG: %[[TMP_c4:.*]] = arith.constant 4 : index
-// CHECK-DAG: %[[TMP_d0:.*]] = arith.constant 0.000000e+00 : f64
-// CHECK: %[[A:.*]] = memref.alloc(%[[TMP_c9]], %[[TMP_c4]]) : memref<?x?xf64>
-// CHECK: linalg.fill ins(%[[TMP_d0]] : f64) outs(%[[A]] : memref<?x?xf64>)
-// CHECK: %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
-// CHECK: %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
-// CHECK: %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
-// CHECK: %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
-// CHECK: %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
-// CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
-// CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
-// CHECK: scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]]
-// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
-// CHECK-DAG: %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
-// CHECK-DAG: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
-// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
-// CHECK: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
-// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
-// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
-// CHECK: memref.store %[[TMP_28]], %[[A]]{{\[}}%[[TMP_23]], %[[TMP_27]]] : memref<?x?xf64>
-// CHECK: }
-// CHECK: }
-// CHECK: %[[TMP_8:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
-// CHECK: %[[TMP_9:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
-// CHECK: %[[TMP_10:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
-// CHECK: %[[TMP_11:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
-// CHECK: %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
-// CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
-// CHECK: %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
-// CHECK: scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]]
-// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
-// CHECK-DAG: %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
-// CHECK-DAG: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
-// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
-// CHECK: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
-// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
-// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
-// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
-// CHECK: memref.store %[[TMP_28]], %[[A]]{{\[}}%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
-// CHECK: }
-// CHECK: }
-// CHECK: %[[TMP_15:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
-// CHECK: %[[TMP_16:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
-// CHECK: %[[TMP_17:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
-// CHECK: %[[TMP_18:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
-// CHECK: %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
-// CHECK: %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
-// CHECK: %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
-// CHECK: scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]]
-// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
-// CHECK: %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
-// CHECK: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
-// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>
-// CHECK: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
-// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
-// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
-// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
-// CHECK: memref.store %[[TMP_28]], %[[A]]{{\[}}%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
-// CHECK: }
-// CHECK: }
-// CHECK: %[[R:.*]] = bufferization.to_tensor %[[A]] : memref<?x?xf64>
-// CHECK: return %[[R]] : tensor<?x?xf64>
+// CHECK-LABEL: func.func @concat_sparse_sparse_dense(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<2x4xf64, #sparse_tensor
+// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x4xf64, #sparse_tensor
+// CHECK-SAME: %[[VAL_2...
[truncated]
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…ting rules.