[mlir][sparse] rename lex_insert into insert

[aartbik]

Summary

This change goes not impact any semantics yet, but it
is in preparation for implementing the unordered and not-unique
properties. Changing lex_insert to insert is a first step.

[aartbik]

ops

[Peiming]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

208–218

Is it still the case if out-of-order insertion is allowed?

228

I think memref for array of indices make sense when we are using library.

Would an array<rank x index> (or even variadic inputs) be more straight forwards for codegen purpose?

[Peiming]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

208–218

Is it still the case if out-of-order insertion is allowed?

Or even more aggressively, if user can guarantee two insert sessions happen in lex order, which prevents them from being able to append value to an already materialized tensor?

[Peiming]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

208–218

Is it still the case if out-of-order insertion is allowed?

Or even more aggressively, if user can guarantee two insert sessions happen in lex order, which prevents them from being able to append value to an already materialized tensor?

Sorry, I mean what prevents them from being able to append value to an already materialized tensor...

[Peiming]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

228

Say sparse_tensor.insert %t[%1, %2], %v : tensor<2x2xf64>, f64

[aartbik]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

208–218

Agreed we may relax to allow such in-place "fill-in" at some point, but for now I like to conservatively keep the "fresh output" behavior that is inherent to TACO's index expressions and relax the requirements when the cross that bridge

228

Yes, I was thinking of that too. We may benefit from the

store i, memref[0]
load j, memref[0] // rewrites i -> j

optimization in the codegen, but expressing this as SSA values will make this behavior much more clear

[Peiming]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

228

So, will it be in the next patch? I believe it would be more than just changing test cases.

If so, the patch LGTM.

[aartbik]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

228

yeah that change will be much more involved; for now I really just wanted to remove the "lex" from "lex_insert". I changed the title to make that more clear.

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorBase.td

let description = [{
     constructed for the tensor expression for every index in topological
     order. Each iteration lattice point consists of a conjunction of tensor
     indices together with a tensor (sub)expression that needs to be evaluated
     for that conjunction.  Within the lattice, iteration points are ordered
     according to the way indices are exhausted. As such these iteration
     lattices drive actual sparse code generation, which consists of a
     relatively straightforward one-to-one mapping from iteration lattices
     to combinations of for-loops, while-loops, and if-statements. Sparse
-    tensor outputs that materialize uninitialized are handled with
-    insertions in pure lexicographical index order if all parallel loops
-    are outermost or using a 1-dimensional access pattern expansion
+    tensor outputs that materialize uninitialized are handled with direct
+    insertions if all parallel loops are outermost or insertions that
+    indirectly go through a 1-dimensional access pattern expansion
     (a.k.a. workspace) where feasible [Gustavson72,Bik96,Kjolstad19].
 
     * [Bik96] Aart J.C. Bik. Compiler Support for Sparse Matrix Computations.
     PhD thesis, Leiden University, May 1996.
     * [Biketal22] Aart J.C. Bik, Penporn Koanantakool, Tatiana Shpeisman,
     Nicolas Vasilache, Bixia Zheng, and Fredrik Kjolstad. Compiler Support
     for Sparse Tensor Computations in MLIR. ACM Transactions on Architecture
     and Code Optimization, June, 2022. See: https://dl.acm.org/doi/10.1145/3544559

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td

 // Sparse Tensor Management Operations. These operations are "impure" in the
 // sense that they do not properly operate on SSA values. Instead, the behavior
 // is solely defined by side-effects. These operations provide a bridge between
 // "sparsification" on one hand and a support library or actual code generation
 // on the other hand. The semantics of these operations may be refined over time
 // as our sparse abstractions evolve.
 //===----------------------------------------------------------------------===//
 
-def SparseTensor_LexInsertOp : SparseTensor_Op<"lex_insert", []>,
+def SparseTensor_InsertOp : SparseTensor_Op<"insert", []>,
     Arguments<(ins AnySparseTensor:$tensor,
                StridedMemRefRankOf<[Index], [1]>:$indices,
                AnyType:$value)> {
-  string summary = "Inserts a value into given sparse tensor in lexicographical index order";
+  string summary = "Inserts a value into given sparse tensor";
   string description = [{
     Inserts the given value at given indices into the underlying sparse
     storage format of the given tensor with the given indices. This
     operation can only be applied when a tensor materializes unintialized
-    with a `bufferization.alloc_tensor` operation, the insertions occur in
-    strict lexicographical index order, and the final tensor is constructed
-    with a `load` operation that has the `hasInserts` attribute set.
+    with a `bufferization.alloc_tensor` operation and the final tensor
+    is constructed with a `load` operation that has the `hasInserts`
+    attribute set.
+
+    Properties in the sparse tensor type fully describe what kind
+    of insertion order is allowed. When all dimensions have "unique"
+    and "ordered" properties, for example, insertions should occur in
+    strict lexicographical index order. Other properties define
+    different insertion regimens. Inserting in a way contrary to
+    these properties results in undefined behavior.
 
     Note that this operation is "impure" in the sense that its behavior
     is solely defined by side-effects and not SSA values. The semantics
     may be refined over time as our sparse abstractions evolve.
 
     Example:
 
     ```mlir
-    sparse_tensor.lex_insert %tensor, %indices, %val
+    sparse_tensor.insert %tensor, %indices, %val
       : tensor<1024x1024xf64, #CSR>, memref<?xindex>, memref<f64>
     ```
   }];
   let assemblyFormat = "$tensor `,` $indices `,` $value attr-dict `:`"
                        " type($tensor) `,` type($indices) `,` type($value)";
 }
 
 def SparseTensor_ExpandOp : SparseTensor_Op<"expand", []>,
     Arguments<(ins AnySparseTensor:$tensor)>,

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp

if (op.getHasInserts()) {
       createFuncCall(rewriter, op->getLoc(), name, {}, adaptor.getOperands(),
                      EmitCInterface::Off);
     }
     rewriter.replaceOp(op, adaptor.getOperands());
     return success();
   }
 };
 
-/// Sparse conversion rule for inserting in lexicographic index order.
-class SparseTensorLexInsertConverter : public OpConversionPattern<LexInsertOp> {
+/// Sparse conversion rule for the insertion operator.
+class SparseTensorInsertConverter : public OpConversionPattern<InsertOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(LexInsertOp op, OpAdaptor adaptor,
+  matchAndRewrite(InsertOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
+    // Note that the current regime only allows for strict lexicographic
+    // index order.
     Type elemTp = op.getTensor().getType().cast<ShapedType>().getElementType();
     SmallString<12> name{"lexInsert", primaryTypeFunctionSuffix(elemTp)};
     replaceOpWithFuncCall(rewriter, op, name, {}, adaptor.getOperands(),
                           EmitCInterface::On);
     return success();
   }
 };
 
void mlir::populateSparseTensorConversionPatterns(
     const SparseTensorConversionOptions &options) {
   patterns.add<SparseReturnConverter, SparseTensorToDimSizeConverter,
                SparseCastConverter, SparseTensorNewConverter,
                SparseReshapeConverter<tensor::ExpandShapeOp>,
                SparseReshapeConverter<tensor::CollapseShapeOp>,
                SparseTensorConcatConverter, SparseTensorAllocConverter,
                SparseTensorDeallocConverter, SparseTensorToPointersConverter,
                SparseTensorToIndicesConverter, SparseTensorToValuesConverter,
-               SparseTensorLoadConverter, SparseTensorLexInsertConverter,
+               SparseTensorLoadConverter, SparseTensorInsertConverter,
                SparseTensorExpandConverter, SparseTensorCompressConverter,
                SparseTensorOutConverter>(typeConverter, patterns.getContext());
 
   patterns.add<SparseTensorConvertConverter>(typeConverter,
                                              patterns.getContext(), options);
 }

mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp

 
 /// Generates insertion code to implement dynamic tensor store.
 static void genInsertionStore(CodeGen &codegen, OpBuilder &builder,
                               linalg::GenericOp op, OpOperand *t, Value rhs) {
   Location loc = op.getLoc();
   // Direct insertion in lexicographic index order.
   if (!codegen.expValues) {
     builder.create<memref::StoreOp>(loc, rhs, codegen.lexVal);
-    builder.create<LexInsertOp>(loc, t->get(), codegen.lexIdx, codegen.lexVal);
+    builder.create<InsertOp>(loc, t->get(), codegen.lexIdx, codegen.lexVal);
     return;
   }
   // Generates insertion code along expanded access pattern.
   //   if (!expFilled[i]) then
   //     expFilled[i] = true
   //     expAdded[inserts++] = i
   //   endif
   //   values[i] = rhs

mlir/test/Dialect/SparseTensor/conversion.mlir

 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>,
 //  CHECK-SAME: %[[B:.*]]: memref<?xindex>,
 //  CHECK-SAME: %[[C:.*]]: memref<f32>) {
 //       CHECK: call @lexInsertF32(%[[A]], %[[B]], %[[C]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f32>) -> ()
 //       CHECK: return
 func.func @sparse_insert(%arg0: tensor<128xf32, #SparseVector>,
                     %arg1: memref<?xindex>,
                     %arg2: memref<f32>) {
-  sparse_tensor.lex_insert %arg0, %arg1, %arg2 : tensor<128xf32, #SparseVector>, memref<?xindex>, memref<f32>
+  sparse_tensor.insert %arg0, %arg1, %arg2 : tensor<128xf32, #SparseVector>, memref<?xindex>, memref<f32>
   return
 }
 
 // CHECK-LABEL: func @sparse_expansion1()
 //       CHECK: %[[N:.*]] = call @newSparseTensor
 //       CHECK: %[[A:.*]] = memref.alloc() : memref<8xf64>
 //       CHECK: %[[B:.*]] = memref.alloc() : memref<8xi1>
 //       CHECK: %[[C:.*]] = memref.alloc() : memref<8xindex>

mlir/test/Dialect/SparseTensor/invalid.mlir

func.func @sparse_unannotated_load(%arg0: tensor<16x32xf64>) -> tensor<16x32xf64> {
   // expected-error@+1 {{'sparse_tensor.load' op operand #0 must be sparse tensor of any type values, but got 'tensor<16x32xf64>'}}
   %0 = sparse_tensor.load %arg0 : tensor<16x32xf64>
   return %0 : tensor<16x32xf64>
 }
 
 // -----
 
 func.func @sparse_unannotated_insert(%arg0: tensor<128xf64>, %arg1: memref<?xindex>, %arg2: f64) {
-  // expected-error@+1 {{'sparse_tensor.lex_insert' op operand #0 must be sparse tensor of any type values, but got 'tensor<128xf64>'}}
-  sparse_tensor.lex_insert %arg0, %arg1, %arg2 : tensor<128xf64>, memref<?xindex>, f64
+  // expected-error@+1 {{'sparse_tensor.insert' op operand #0 must be sparse tensor of any type values, but got 'tensor<128xf64>'}}
+  sparse_tensor.insert %arg0, %arg1, %arg2 : tensor<128xf64>, memref<?xindex>, f64
   return
 }
 
 // -----
 
 func.func @sparse_unannotated_expansion(%arg0: tensor<128xf64>) {
   // expected-error@+1 {{'sparse_tensor.expand' op operand #0 must be sparse tensor of any type values, but got 'tensor<128xf64>'}}
   %values, %filled, %added, %count = sparse_tensor.expand %arg0

mlir/test/Dialect/SparseTensor/roundtrip.mlir

 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_insert(
 //  CHECK-SAME: %[[A:.*]]: tensor<128xf64, #sparse_tensor.encoding<{{.*}}>>,
 //  CHECK-SAME: %[[B:.*]]: memref<?xindex>,
 //  CHECK-SAME: %[[C:.*]]: f64) {
-//       CHECK: sparse_tensor.lex_insert %[[A]], %[[B]], %[[C]] : tensor<128xf64, #{{.*}}>, memref<?xindex>, f64
+//       CHECK: sparse_tensor.insert %[[A]], %[[B]], %[[C]] : tensor<128xf64, #{{.*}}>, memref<?xindex>, f64
 //       CHECK: return
 func.func @sparse_insert(%arg0: tensor<128xf64, #SparseVector>, %arg1: memref<?xindex>, %arg2: f64) {
-  sparse_tensor.lex_insert %arg0, %arg1, %arg2 : tensor<128xf64, #SparseVector>, memref<?xindex>, f64
+  sparse_tensor.insert %arg0, %arg1, %arg2 : tensor<128xf64, #SparseVector>, memref<?xindex>, f64
   return
 }
 
 // -----
 
 #SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
 
 // CHECK-LABEL: func @sparse_expansion(

mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir

 // CHECK:           %[[VAL_15:.*]] = math.ceil %[[VAL_14]] : f64
 // CHECK:           %[[VAL_16:.*]] = math.floor %[[VAL_15]] : f64
 // CHECK:           %[[VAL_17:.*]] = math.sqrt %[[VAL_16]] : f64
 // CHECK:           %[[VAL_18:.*]] = math.expm1 %[[VAL_17]] : f64
 // CHECK:           %[[VAL_19:.*]] = math.log1p %[[VAL_18]] : f64
 // CHECK:           %[[VAL_20:.*]] = math.sin %[[VAL_19]] : f64
 // CHECK:           %[[VAL_21:.*]] = math.tanh %[[VAL_20]] : f64
 // CHECK:           memref.store %[[VAL_21]], %[[BUF]][] : memref<f64>
-// CHECK:           sparse_tensor.lex_insert %[[VAL_4]], %[[VAL_8]], %[[BUF]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>, memref<?xindex>, memref<f64>
+// CHECK:           sparse_tensor.insert %[[VAL_4]], %[[VAL_8]], %[[BUF]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>, memref<?xindex>, memref<f64>
 // CHECK:         }
 // CHECK:         %[[VAL_22:.*]] = sparse_tensor.load %[[VAL_4]] hasInserts : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         return %[[VAL_22]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:       }
 func.func @zero_preserving_math(%arga: tensor<32xf64, #SV>) -> tensor<32xf64, #SV> {
   %c32 = arith.constant 32 : index
   %xinp = bufferization.alloc_tensor() : tensor<32xf64, #SV>
   %0 = linalg.generic #trait1

 // CHECK:         %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:         %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] {
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK:           memref.store %[[VAL_13]], %[[VAL_8]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xcomplex<f64>>
 // CHECK:           %[[VAL_15:.*]] = complex.div %[[VAL_14]], %[[VAL_3]] : complex<f64>
 // CHECK:           memref.store %[[VAL_15]], %[[VAL_9]][] : memref<complex<f64>>
-// CHECK:           sparse_tensor.lex_insert %[[VAL_4]], %[[VAL_8]], %[[VAL_9]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{{.*}}>>, memref<?xindex>, memref<complex<f64>>
+// CHECK:           sparse_tensor.insert %[[VAL_4]], %[[VAL_8]], %[[VAL_9]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{{.*}}>>, memref<?xindex>, memref<complex<f64>>
 // CHECK:         }
 // CHECK:         %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_4]] hasInserts : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:         return %[[VAL_16]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:       }
 func.func @complex_divbyc(%arg0: tensor<32xcomplex<f64>, #SV>) -> tensor<32xcomplex<f64>, #SV> {
   %c = complex.constant [0.0, 1.0] : complex<f64>
   %init = bufferization.alloc_tensor() : tensor<32xcomplex<f64>, #SV>
   %0 = linalg.generic #traitc
      ins(%arg0: tensor<32xcomplex<f64>, #SV>)
     outs(%init: tensor<32xcomplex<f64>, #SV>) {
       ^bb(%a: complex<f64>, %x: complex<f64>):
         %0 = complex.div %a, %c : complex<f64>
         linalg.yield %0 : complex<f64>
   } -> tensor<32xcomplex<f64>, #SV>
   return %0 : tensor<32xcomplex<f64>, #SV>
 }

mlir/test/Dialect/SparseTensor/sparse_index.mlir

 // CHECK:               %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex>
 // CHECK:               memref.store %[[VAL_21]], %[[VAL_12]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:               %[[VAL_22:.*]] = arith.index_cast %[[VAL_21]] : index to i64
 // CHECK:               %[[VAL_23:.*]] = arith.index_cast %[[VAL_16]] : index to i64
 // CHECK:               %[[VAL_24:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_20]]] : memref<?xi64>
 // CHECK:               %[[VAL_25:.*]] = arith.muli %[[VAL_23]], %[[VAL_24]] : i64
 // CHECK:               %[[VAL_26:.*]] = arith.muli %[[VAL_22]], %[[VAL_25]] : i64
 // CHECK:               memref.store %[[VAL_26]], %[[BUF]][] : memref<i64>
-// CHECK:               sparse_tensor.lex_insert %[[VAL_6]], %[[VAL_12]], %[[BUF]] : tensor<?x?xi64, #sparse_tensor.encoding
+// CHECK:               sparse_tensor.insert %[[VAL_6]], %[[VAL_12]], %[[BUF]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK:             }
 // CHECK:           }
 // CHECK:           %[[VAL_27:.*]] = sparse_tensor.load %[[VAL_6]] hasInserts : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK:           return %[[VAL_27]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK:         }
 func.func @sparse_index(%arga: tensor<?x?xi64, #SparseMatrix>)
                        -> tensor<?x?xi64, #SparseMatrix> {
   %c0 = arith.constant 0 : index

mlir/test/Dialect/SparseTensor/sparse_out.mlir

 // CHECK:             %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_4]] : index
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_16:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_4]] {
 // CHECK:               %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK:               memref.store %[[VAL_17]], %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:               %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_16]]] : memref<?xf32>
 // CHECK:               %[[VAL_19:.*]] = arith.mulf %[[VAL_18]], %[[VAL_1]] : f32
 // CHECK:               memref.store %[[VAL_19]], %[[BUF]][] : memref<f32>
-// CHECK:               sparse_tensor.lex_insert %[[VAL_7]], %[[VAL_11]], %[[BUF]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
+// CHECK:               sparse_tensor.insert %[[VAL_7]], %[[VAL_11]], %[[BUF]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:             }
 // CHECK:           }
 // CHECK:           %[[VAL_20:.*]] = sparse_tensor.load %[[VAL_7]] hasInserts : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:           return %[[VAL_20]] : tensor<10x20xf32, #sparse_tensor.encoding<{
 // CHECK:         }
 func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20xf32, #DCSR> {
   %s = arith.constant 2.0 : f32
   %xm = bufferization.alloc_tensor() : tensor<10x20xf32, #DCSR>

 // CHECK:                     %[[VAL_93:.*]] = arith.addi %[[VAL_77]], %[[VAL_3]] : index
 // CHECK:                     %[[VAL_94:.*]] = arith.select %[[VAL_92]], %[[VAL_93]], %[[VAL_77]] : index
 // CHECK:                     %[[VAL_95:.*]] = arith.cmpi eq, %[[VAL_81]], %[[VAL_83]] : index
 // CHECK:                     %[[VAL_96:.*]] = arith.addi %[[VAL_78]], %[[VAL_3]] : index
 // CHECK:                     %[[VAL_97:.*]] = arith.select %[[VAL_95]], %[[VAL_96]], %[[VAL_78]] : index
 // CHECK:                     scf.yield %[[VAL_94]], %[[VAL_97]], %[[VAL_98:.*]] : index, index, i32
 // CHECK:                   }
 // CHECK:                   memref.store %[[VAL_70]]#2, %[[BUF]][] : memref<i32>
-// CHECK:                   sparse_tensor.lex_insert %[[VAL_8]], %[[VAL_23]], %[[BUF]] : tensor<?x?xi32, #{{.*}}>, memref<?xindex>, memref<i32>
+// CHECK:                   sparse_tensor.insert %[[VAL_8]], %[[VAL_23]], %[[BUF]] : tensor<?x?xi32, #{{.*}}>, memref<?xindex>, memref<i32>
 // CHECK:                 } else {
 // CHECK:                 }
 // CHECK:                 %[[VAL_100:.*]] = arith.cmpi eq, %[[VAL_57]], %[[VAL_60]] : index
 // CHECK:                 %[[VAL_101:.*]] = arith.addi %[[VAL_55]], %[[VAL_3]] : index
 // CHECK:                 %[[VAL_102:.*]] = arith.select %[[VAL_100]], %[[VAL_101]], %[[VAL_55]] : index
 // CHECK:                 %[[VAL_103:.*]] = arith.cmpi eq, %[[VAL_58]], %[[VAL_60]] : index
 // CHECK:                 %[[VAL_104:.*]] = arith.addi %[[VAL_56]], %[[VAL_3]] : index
 // CHECK:                 %[[VAL_105:.*]] = arith.select %[[VAL_103]], %[[VAL_104]], %[[VAL_56]] : index

mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir

 // CHECK:               %[[VAL_33:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]], %[[VAL_31]]] : memref<8x8xf64>
 // CHECK:               %[[VAL_34:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_31]], %[[VAL_28]]] : memref<8x8xf64>
 // CHECK:               %[[VAL_35:.*]] = arith.mulf %[[VAL_33]], %[[VAL_34]] : f64
 // CHECK:               %[[VAL_36:.*]] = arith.mulf %[[VAL_35]], %[[VAL_29]] : f64
 // CHECK:               %[[VAL_37:.*]] = arith.addf %[[VAL_32]], %[[VAL_36]] : f64
 // CHECK:               scf.yield %[[VAL_37]] : f64
 // CHECK:             }
 // CHECK:             memref.store %[[VAL_30:.*]], %[[VAL_19]][] : memref<f64>
-// CHECK:             sparse_tensor.lex_insert %[[VAL_10]], %[[VAL_18]], %[[VAL_19]] : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>>, memref<?xindex>, memref<f64>
+// CHECK:             sparse_tensor.insert %[[VAL_10]], %[[VAL_18]], %[[VAL_19]] : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>>, memref<?xindex>, memref<f64>
 // CHECK:           }
 // CHECK:         }
 // CHECK:         %[[VAL_39:.*]] = sparse_tensor.load %[[VAL_10]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:         return %[[VAL_39]] : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:       }
 func.func @sparse_sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
                                      %arga: tensor<8x8xf64>,
                                      %argb: tensor<8x8xf64>) -> tensor<8x8xf64, #SM> {

mlir/test/Dialect/SparseTensor/sparse_transpose.mlir

 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK:             %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_2]] : index
 // CHECK:             %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_19]] step %[[VAL_2]] {
 // CHECK:               %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
 // CHECK:               memref.store %[[VAL_21]], %[[VAL_11]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:               %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xf64>
 // CHECK:               memref.store %[[VAL_22]], %[[VAL_12]][] : memref<f64>
-// CHECK:               sparse_tensor.lex_insert %[[VAL_4]], %[[VAL_11]], %[[VAL_12]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>, memref<?xindex>, memref<f64>
+// CHECK:               sparse_tensor.insert %[[VAL_4]], %[[VAL_11]], %[[VAL_12]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>, memref<?xindex>, memref<f64>
 // CHECK:             }
 // CHECK:           }
 // CHECK:           %[[VAL_23:.*]] = sparse_tensor.load %[[VAL_4]] hasInserts : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
 // CHECK:           bufferization.dealloc_tensor %[[VAL_5]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
 // CHECK:           return %[[VAL_23]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
 // CHECK:         }
 func.func @sparse_transpose_auto(%arga: tensor<3x4xf64, #DCSR>)
                                      -> tensor<4x3xf64, #DCSR> {