[mlir][sparse][NFC] Switch InitOp to bufferization::AllocTensorOp

[springerm]

Summary

Now that we have an AllocTensorOp (previously InitTensorOp) in the bufferization dialect, the InitOp in the sparse dialect is no longer needed.

Depends On D126179

[aartbik]

I really like the direction this is going in, so we have one centralized mechanism to reason on allocation (and deallocation) of the buffers for tensors!
In the long run, it would be nice to have bufferization.de_alloc_tensor() as well, and have one mechanism that inserts those, independent of dense/sparse tensors.

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

57–60

can we move some of this explanation and the example into the doc of bufferization.alloc_tensor?

[springerm]

update

[springerm]

fix test

[springerm]

typo

[aartbik]

I really like the unification, since the original separation felt artificial!

Once this goes in, you will probably give a heads up to some of the other projects that depend on this, but outside this source tree.

mlir/include/mlir/Dialect/Bufferization/IR/BufferizationOps.td

37

This comment ("helper") feels a bit too strong. Right now, it is the only way to materialize a tensor into a computation. Can we just say something like, the reason for this op is ....., (something like that)

mlir/include/mlir/Dialect/Bufferization/IR/BufferizationOps.td

 //===----------------------------------------------------------------------===//
 
 def Bufferization_AllocTensorOp : Bufferization_Op<"alloc_tensor",
     [BufferizableOpInterface,
      DeclareOpInterfaceMethods<ReifyRankedShapedTypeOpInterface>]> {
   let summary = "buffer allocation in tensor land";
 
   let description = [{
-    `bufferization.alloc_tensor` is an operation that bufferizes to a buffer
-    allocation of a given shape. The shape could be dynamic or static.
-    Reading from the result of an `alloc_tensor` op yields an undefined value.
-
-    `alloc_tensor` is a helper op for bufferization. It marks the beginning of
-    a new tensor SSA use-def chain and is used to control in-place bufferization
-    decisions during One-Shot Bufferize.
+    `bufferization.alloc_tensor` materializes an uninitialized tensor with a
+    given shape (dynamic or static). It always bufferizes to a new buffer
+    allocation of the given shape. Reading from the result of an `alloc_tensor`
+    op yields an undefined value.
+
+    `alloc_tensor` is a helper op for bufferization. The operation is provided
+    as an anchor that marks the beginning of a new tensor SSA use-def chain. It
+    can be used to control in-place bufferization decisions during One-Shot
+    Bufferize: The bufferized result of a `bufferization.alloc_tensor` does not
+    alias with any other buffer, so it can be used to resolve read-after-write
+    conflicts that would have been introduced by the in-place bufferization of
+    another op.
+
+    Both dense and sparse tensor types are supported. The result of a
+    `bufferization.alloc_tensor` is a tensor value that can be used like any
+    other tensor value. In practise, it is often used as the "out" operand of
+    another op. E.g.:
+
+    ```mlir
+    %c = sparse_tensor.init_tensor [%d1, %d2] : tensor<?x?xf32, #SparseMatrix>
+    %0 = linalg.matmul
+      ins(%a, %b: tensor<?x?xf32>, tensor<?x?xf32>)
+      outs(%c: tensor<?x?xf32, #SparseMatrix>) -> tensor<?x?xf32, #SparseMatrix>
+    ```
   }];
 
   let arguments = (ins Variadic<Index>:$dynamicSizes);
 
   let results = (outs AnyTensor:$result);
 
   let assemblyFormat = "`(`$dynamicSizes`)` attr-dict `:` type($result)";
 

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

string description = [{
     ```mlir
     sparse_tensor.new %source : !Source to tensor<1024x1024xf64, #CSR>
     ```
   }];
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($result)";
   let hasVerifier = 1;
 }
 
-def SparseTensor_InitOp : SparseTensor_Op<"init", [NoSideEffect]>,
-    Arguments<(ins Variadic<Index>:$sizes)>,
-    Results<(outs AnyTensor:$result)> {
-  string summary = "Materializes an unitialized sparse tensor";
-  string description = [{
-    Materializes an uninitialized sparse tensor with given shape (either static
-    or dynamic). The operation is provided as an anchor that materializes a
-    properly typed but uninitialized sparse tensor into the output clause of
-    a subsequent operation that yields a sparse tensor as the result.
-
-    Example:
-
-    ```mlir
-    %c = sparse_tensor.init_tensor [%d1, %d2] : tensor<?x?xf32, #SparseMatrix>
-    %0 = linalg.matmul
-      ins(%a, %b: tensor<?x?xf32>, tensor<?x?xf32>)
-      outs(%c: tensor<?x?xf32, #SparseMatrix>) -> tensor<?x?xf32, #SparseMatrix>
-    ```
-  }];
-  let assemblyFormat = "`[` $sizes `]` attr-dict `:` type($result)";
-  let hasVerifier = 1;
-}
-
 def SparseTensor_ConvertOp : SparseTensor_Op<"convert",
   [NoSideEffect, SameOperandsAndResultElementType]>,
     Arguments<(ins AnyTensor:$source)>,
     Results<(outs AnyTensor:$dest)> {
   string summary = "Converts between different tensor types";
   string description = [{
     Converts one sparse or dense tensor type to another tensor type. The rank
     of the source and destination types must match exactly, and the dimension
let description = [{
       used in place of the left or right region. This token indicates that
       the return value is the input value (i.e. func(%x) => return %x).
       As a practical example, setting `left=identity` and `right=identity`
       would be equivalent to a union operation where non-overlapping values
       in the inputs are copied to the output unchanged.
 
       Example of isEqual applied to intersecting elements only.
       ```mlir
-      %C = sparse_tensor.init...
+      %C = bufferization.alloc_tensor...
       %0 = linalg.generic #trait
         ins(%A: tensor<?xf64, #SparseVec>, %B: tensor<?xf64, #SparseVec>)
         outs(%C: tensor<?xi8, #SparseVec>) {
         ^bb0(%a: f64, %b: f64, %c: i8) :
           %result = sparse_tensor.binary %a, %b : f64, f64 to i8
             overlap={
               ^bb0(%arg0: f64, %arg1: f64):
                 %cmp = arith.cmpf "oeq", %arg0, %arg1 : f64
                 %ret_i8 = arith.extui %cmp : i1 to i8
                 sparse_tensor.yield %ret_i8 : i8
             }
             left={}
             right={}
           linalg.yield %result : i8
       } -> tensor<?xi8, #SparseVec>
       ```
 
       Example of A+B in upper triangle, A-B in lower triangle
       (not working yet, but construct will be available soon).
       ```mlir
-      %C = sparse_tensor.init...
+      %C = bufferization.alloc_tensor...
       %1 = linalg.generic #trait
         ins(%A: tensor<?x?xf64, #CSR>, %B: tensor<?x?xf64, #CSR>
         outs(%C: tensor<?x?xf64, #CSR> {
         ^bb0(%a: f64, %b: f64, %c: f64) :
           %row = linalg.index 0 : index
           %col = linalg.index 1 : index
           %result = sparse_tensor.binary %a, %b : f64, f64 to f64
             overlap={
let description = [{
           linalg.yield %result : f64
       } -> tensor<?x?xf64, #CSR>
       ```
 
       Example of set difference. Returns a copy of A where its sparse structure
       is *not* overlapped by B. The element type of B can be different than A
       because we never use its values, only its sparse structure.
       ```mlir
-      %C = sparse_tensor.init...
+      %C = bufferization.alloc_tensor...
       %2 = linalg.generic #trait
         ins(%A: tensor<?x?xf64, #CSR>, %B: tensor<?x?xi32, #CSR>
         outs(%C: tensor<?x?xf64, #CSR> {
         ^bb0(%a: f64, %b: i32, %c: f64) :
           %result = sparse_tensor.binary %a, %b : f64, i32 to f64
             overlap={}
             left=identity
             right={}
let description = [{
       must match the type of `output`. The primary region's block has one
       argument, while the missing region's block has zero arguments.
 
       A region may also be declared empty (i.e. `absent={}`), indicating that the
       region does not contribute to the output.
 
       Example of A+1, restricted to existing elements:
       ```mlir
-      %C = sparse_tensor.init...
+      %C = bufferization.alloc_tensor...
       %0 = linalg.generic #trait
         ins(%A: tensor<?xf64, #SparseVec>)
         outs(%C: tensor<?xf64, #SparseVec>) {
         ^bb0(%a: f64, %c: f64) :
           %result = sparse_tensor.unary %a : f64 to f64
             present={
               ^bb0(%arg0: f64):
                 %cf1 = arith.constant 1.0 : f64

mlir/lib/Dialect/Linalg/Transforms/SparseTensorRewriting.cpp

 // Sparsity should be mostly transparent to the linalg dialect optimizations
 // (i.e., the dense and sparse take the same path). However, in some cases,
 // optimizations only make sense in the context of sparse tensors. This file
 // implements such sparsity specific rewriting rules.
 //
 //===----------------------------------------------------------------------===//
 
 #include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
+#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/Support/LLVM.h"
 
 using namespace mlir;
+using namespace mlir::bufferization;
 using namespace mlir::linalg;
 using namespace mlir::sparse_tensor;
 
 //===---------------------------------------------------------------------===//
 // Helper methods for the actual rewriting rules.
 //===---------------------------------------------------------------------===//
 
 // Helper to detect a sparse tensor type operand.
 static bool isSparseTensor(OpOperand *op) {
   if (auto enc = getSparseTensorEncoding(op->get().getType())) {
     ArrayRef<SparseTensorEncodingAttr::DimLevelType> dimTypes =
         enc.getDimLevelType();
     for (auto dimType : dimTypes)
       if (dimType == SparseTensorEncodingAttr::DimLevelType::Compressed)
         return true; // at least one compressed
   }
   return false;
 }
 
 // Helper method to find zero or empty initialization.
 static bool isEmptyInit(OpOperand *op) {
   Value val = op->get();
   return matchPattern(val, m_Zero()) || matchPattern(val, m_AnyZeroFloat()) ||
-         val.getDefiningOp<InitTensorOp>() || val.getDefiningOp<InitOp>();
+         val.getDefiningOp<InitTensorOp>() ||
+         val.getDefiningOp<AllocTensorOp>();
 }
 
 // Helper to detect sampling operation.
 static bool isSampling(GenericOp op) {
   auto yieldOp = cast<linalg::YieldOp>(op.region().front().getTerminator());
   if (auto *def = yieldOp.getOperand(0).getDefiningOp()) {
     if (isa<arith::MulFOp>(def) || isa<arith::MulIOp>(def)) {
       // Both scalar input arguments used exactly once.

mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp

 }
 
 LogicalResult NewOp::verify() {
   if (!getSparseTensorEncoding(result().getType()))
     return emitError("expected a sparse tensor result");
   return success();
 }
 
-LogicalResult InitOp::verify() {
-  if (!getSparseTensorEncoding(result().getType()))
-    return emitError("expected a sparse tensor result");
-  RankedTensorType ttp = getType().cast<RankedTensorType>();
-  unsigned rank = ttp.getRank();
-  if (rank != sizes().size())
-    return emitError("unexpected mismatch between tensor rank and sizes: ")
-           << rank << " vs. " << sizes().size();
-  auto shape = ttp.getShape();
-  for (unsigned i = 0; i < rank; i++) {
-    if (shape[i] == ShapedType::kDynamicSize)
-      continue;
-    IntegerAttr constantAttr;
-    if (!matchPattern(sizes()[i], m_Constant(&constantAttr)) ||
-        constantAttr.getInt() != shape[i]) {
-      return emitError("unexpected mismatch with static dimension size ")
-             << shape[i];
-    }
-  }
-  return success();
-}
-
 LogicalResult ConvertOp::verify() {
   if (auto tp1 = source().getType().dyn_cast<RankedTensorType>()) {
     if (auto tp2 = dest().getType().dyn_cast<RankedTensorType>()) {
       if (tp1.getRank() != tp2.getRank())
         return emitError("unexpected conversion mismatch in rank");
       auto shape1 = tp1.getShape();
       auto shape2 = tp2.getShape();
       // Accept size matches between the source and the destination type

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

matchAndRewrite(NewOp op, OpAdaptor adaptor,
     sizesFromType(rewriter, sizes, op.getLoc(), stp);
     Value ptr = adaptor.getOperands()[0];
     newParams(rewriter, params, op, stp, enc, Action::kFromFile, sizes, ptr);
     rewriter.replaceOp(op, genNewCall(rewriter, op, params));
     return success();
   }
 };
 
-/// Sparse conversion rule for the init operator.
-class SparseTensorInitConverter : public OpConversionPattern<InitOp> {
+/// Sparse conversion rule for the alloc operator.
+class SparseTensorAllocConverter
+    : public OpConversionPattern<bufferization::AllocTensorOp> {
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(InitOp op, OpAdaptor adaptor,
+  matchAndRewrite(bufferization::AllocTensorOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    Type resType = op.getType();
+    RankedTensorType resType = op.getType();
     auto enc = getSparseTensorEncoding(resType);
     if (!enc)
       return failure();
+    // Gather all dimension sizes as SSA values.
+    SmallVector<Value> sizes;
+    unsigned int operandCtr = 0;
+    for (int64_t i = 0; i < resType.getRank(); ++i) {
+      if (resType.isDynamicDim(i)) {
+        sizes.push_back(adaptor.getOperands()[operandCtr++]);
+      } else {
+        sizes.push_back(rewriter.create<arith::ConstantIndexOp>(
+            op.getLoc(), op.getStaticSize(i)));
+      }
+    }
     // Generate the call to construct empty tensor. The sizes are
-    // explicitly defined by the arguments to the init operator.
+    // explicitly defined by the arguments to the alloc operator.
     SmallVector<Value, 8> params;
     ShapedType stp = resType.cast<ShapedType>();
-    newParams(rewriter, params, op, stp, enc, Action::kEmpty,
-              adaptor.getOperands());
+    newParams(rewriter, params, op, stp, enc, Action::kEmpty, sizes);
     rewriter.replaceOp(op, genNewCall(rewriter, op, params));
     return success();
   }
 };
 
 /// Sparse conversion rule for the convert operator.
 class SparseTensorConvertConverter : public OpConversionPattern<ConvertOp> {
   /// Options to control sparse code generation.

 
 /// Populates the given patterns list with conversion rules required for
 /// the sparsification of linear algebra operations.
 void mlir::populateSparseTensorConversionPatterns(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     const SparseTensorConversionOptions &options) {
   patterns.add<SparseReturnConverter, SparseTensorToDimSizeConverter,
                SparseCastConverter, SparseTensorNewConverter,
-               SparseTensorInitConverter, SparseTensorReleaseConverter,
+               SparseTensorAllocConverter, SparseTensorReleaseConverter,
                SparseTensorToPointersConverter, SparseTensorToIndicesConverter,
                SparseTensorToValuesConverter, SparseTensorLoadConverter,
                SparseTensorLexInsertConverter, SparseTensorExpandConverter,
                SparseTensorCompressConverter, SparseTensorOutConverter>(
       typeConverter, patterns.getContext());
   patterns.add<SparseTensorConvertConverter>(typeConverter,
                                              patterns.getContext(), options);
 }

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

void runOnOperation() override {
     // rewriting rules, and are therefore marked as legal.
     target.addLegalOp<arith::CmpFOp, arith::CmpIOp, arith::ConstantOp,
                       arith::IndexCastOp, complex::ConstantOp,
                       complex::NotEqualOp, linalg::FillOp, linalg::YieldOp,
                       tensor::ExtractOp>();
     target
         .addLegalDialect<bufferization::BufferizationDialect, LLVM::LLVMDialect,
                          memref::MemRefDialect, scf::SCFDialect>();
+    target.addIllegalOp<bufferization::AllocTensorOp>();
     // Translate strategy flags to strategy options.
     SparseTensorConversionOptions options(
         sparseToSparseConversionStrategy(sparseToSparse));
     // Populate with rules and apply rewriting rules.
     populateFunctionOpInterfaceTypeConversionPattern<func::FuncOp>(patterns,
                                                                    converter);
     populateCallOpTypeConversionPattern(patterns, converter);
     populateSparseTensorConversionPatterns(converter, patterns, options);

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

if (auto funcOp = dyn_cast<func::FuncOp>(arg.getOwner()->getParentOp()))
               bufferization::BufferizableOpInterface::kInplaceableAttrName))
         return attr.getValue();
   return false;
 }
 
 /// Returns true if tensor materializes uninitialized into the computation.
 static bool isMaterializing(Value val) {
   return val.getDefiningOp<linalg::InitTensorOp>() ||
-         val.getDefiningOp<InitOp>();
+         val.getDefiningOp<bufferization::AllocTensorOp>();
 }
 
 /// Returns true when the tensor expression is admissable for codegen.
 /// Since all sparse input tensors are admissable, we just need to check
 /// whether the out tensor in the tensor expression codegen is admissable.
 /// Sets `sparseOut` to the tensor and `outerParNest` to the outer injective
 /// nesting depth when a "truly dynamic" sparse tensor output occurs.
 static bool isAdmissableTensorExp(Merger &merger, linalg::GenericOp op,

mlir/test/Dialect/SparseTensor/conversion.mlir

 //   CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<2xindex> to memref<?xindex>
 //   CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<2xindex> to memref<?xindex>
 //   CHECK-DAG: memref.store %[[I]], %[[Q]][%[[C0]]] : memref<2xindex>
 //   CHECK-DAG: memref.store %[[J]], %[[Q]][%[[C1]]] : memref<2xindex>
 //       CHECK: %[[NP:.*]] = llvm.mlir.null : !llvm.ptr<i8>
 //       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[Empty]], %[[NP]])
 //       CHECK: return %[[T]] : !llvm.ptr<i8>
 func.func @sparse_init(%arg0: index, %arg1: index) -> tensor<?x?xf64, #SparseMatrix> {
-  %0 = sparse_tensor.init [%arg0, %arg1] : tensor<?x?xf64, #SparseMatrix>
+  %0 = bufferization.alloc_tensor(%arg0, %arg1) : tensor<?x?xf64, #SparseMatrix>
   return %0 : tensor<?x?xf64, #SparseMatrix>
 }
 
 // CHECK-LABEL: func @sparse_release(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: call @delSparseTensor(%[[A]]) : (!llvm.ptr<i8>) -> ()
 //       CHECK: return
 func.func @sparse_release(%arg0: tensor<128xf64, #SparseVector>) {

 //       CHECK: %[[S:.*]] = call @sparseDimSize
 //       CHECK: %[[A:.*]] = memref.alloc(%[[S]]) : memref<?xf64>
 //       CHECK: %[[B:.*]] = memref.alloc(%[[S]]) : memref<?xi1>
 //       CHECK: %[[C:.*]] = memref.alloc(%[[S]]) : memref<?xindex>
 //   CHECK-DAG: linalg.fill ins(%{{.*}} : f64) outs(%[[A]] : memref<?xf64>)
 //   CHECK-DAG: linalg.fill ins(%{{.*}} : i1) outs(%[[B]] : memref<?xi1>)
 //       CHECK: return %[[C]] : memref<?xindex>
 func.func @sparse_expansion() -> memref<?xindex> {
-  %c = arith.constant 8 : index
-  %0 = sparse_tensor.init [%c, %c] : tensor<8x8xf64, #SparseMatrix>
+  %0 = bufferization.alloc_tensor() : tensor<8x8xf64, #SparseMatrix>
   %values, %filled, %added, %count = sparse_tensor.expand %0
     : tensor<8x8xf64, #SparseMatrix> to memref<?xf64>, memref<?xi1>, memref<?xindex>, index
   return %added : memref<?xindex>
 }
 
 // CHECK-LABEL: func @sparse_compression(
 //  CHECK-SAME: %[[A:.*0]]: !llvm.ptr<i8>,
 //  CHECK-SAME: %[[B:.*1]]: memref<?xindex>,

mlir/test/Dialect/SparseTensor/invalid.mlir

 func.func @invalid_release_dense(%arg0: tensor<4xi32>) {
   // expected-error@+1 {{expected a sparse tensor to release}}
   sparse_tensor.release %arg0 : tensor<4xi32>
   return
 }
 
 // -----
 
-func.func @invalid_init_dense(%arg0: index, %arg1: index) -> tensor<?x?xf32> {
-  // expected-error@+1 {{expected a sparse tensor result}}
-  %0 = sparse_tensor.init [%arg0, %arg1] : tensor<?x?xf32>
-  return %0 : tensor<?x?xf32>
-}
-
-// -----
-
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
-
-func.func @invalid_init_rank(%arg0: index) -> tensor<?xf32, #SparseVector> {
-  // expected-error@+1 {{unexpected mismatch between tensor rank and sizes: 1 vs. 2}}
-  %0 = sparse_tensor.init [%arg0, %arg0] : tensor<?xf32, #SparseVector>
-  return %0 : tensor<?xf32, #SparseVector>
-}
-
-// -----
-
-#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
-
-func.func @invalid_init_size() -> tensor<?x10xf32, #SparseMatrix> {
-  %c10 = arith.constant 10 : index
-  %c20 = arith.constant 20 : index
-  // expected-error@+1 {{unexpected mismatch with static dimension size 10}}
-  %0 = sparse_tensor.init [%c10, %c20] : tensor<?x10xf32, #SparseMatrix>
-  return %0 : tensor<?x10xf32, #SparseMatrix>
-}
-
-// -----
-
 func.func @invalid_pointers_dense(%arg0: tensor<128xf64>) -> memref<?xindex> {
   %c = arith.constant 0 : index
   // expected-error@+1 {{expected a sparse tensor to get pointers}}
   %0 = sparse_tensor.pointers %arg0, %c : tensor<128xf64> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----

mlir/test/Dialect/SparseTensor/roundtrip.mlir

 // RUN: mlir-opt %s -split-input-file | mlir-opt | FileCheck %s
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_new(
 // CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[T:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<128xf64, #{{.*}}>
 //       CHECK: return %[[T]] : tensor<128xf64, #{{.*}}>
 func.func @sparse_new(%arg0: !llvm.ptr<i8>) -> tensor<128xf64, #SparseVector> {
   %0 = sparse_tensor.new %arg0 : !llvm.ptr<i8> to tensor<128xf64, #SparseVector>
   return %0 : tensor<128xf64, #SparseVector>
 }
 
 // -----
 
-#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
-
-// CHECK-LABEL: func @sparse_init()
-//   CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index
-//   CHECK-DAG: %[[C32:.*]] = arith.constant 32 : index
-//       CHECK: %[[T:.*]] = sparse_tensor.init[%[[C16]], %[[C32]]] : tensor<?x32xf64, #{{.*}}>
-//       CHECK: return %[[T]] : tensor<?x32xf64, #{{.*}}>
-func.func @sparse_init() -> tensor<?x32xf64, #SparseMatrix> {
-  %d1 = arith.constant 16 : index
-  %d2 = arith.constant 32 : index
-  %0 = sparse_tensor.init [%d1, %d2] : tensor<?x32xf64, #SparseMatrix>
-  return %0 : tensor<?x32xf64, #SparseMatrix>
-}
-
-// -----
-
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_release(
 // CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>
 //       CHECK: sparse_tensor.release %[[A]] : tensor<128xf64, #{{.*}}>
 //       CHECK: return
 func.func @sparse_release(%arg0: tensor<128xf64, #SparseVector>) {
   sparse_tensor.release %arg0 : tensor<128xf64, #SparseVector>

mlir/test/Dialect/SparseTensor/sparse_expand.mlir

 // CHECK-CONVERT: memref.dealloc %[[A]] : memref<?xf64>
 // CHECK-CONVERT: memref.dealloc %[[B]] : memref<?xi1>
 // CHECK-CONVERT: memref.dealloc %[[C]] : memref<?xindex>
 // CHECK-CONVERT: call @endInsert
 //
 func.func @kernel(%arga: tensor<?x?xf64, #DCSC>) -> tensor<?xf64, #SV> {
   %c0 = arith.constant 0 : index
   %n = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSC>
-  %v = sparse_tensor.init [%n] : tensor<?xf64, #SV>
+  %v = bufferization.alloc_tensor(%n) : tensor<?xf64, #SV>
   %0 = linalg.generic #rowsum
     ins(%arga: tensor<?x?xf64, #DCSC>)
     outs(%v: tensor<?xf64, #SV>) {
     ^bb(%a: f64, %x: f64):
       %1 = arith.addf %x, %a : f64
       linalg.yield %1 : f64
   } -> tensor<?xf64, #SV>
   return %0 : tensor<?xf64, #SV>
 }

mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir

func.func @divbyc(%arga: tensor<32xf64, #SV>,
   } -> tensor<32xf64>
   return %0 : tensor<32xf64>
 }
 
 // CHECK-LABEL: func @zero_preserving_math(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> {
 // CHECK-DAG:     %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 1 : index
-// CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 32 : index
-// CHECK:         %[[VAL_4:.*]] = sparse_tensor.init{{\[}}%[[VAL_3]]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:         %[[VAL_4:.*]] = bufferization.alloc_tensor() : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_1]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_1]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:         %[[VAL_8:.*]] = memref.alloca(%[[VAL_2]]) : memref<?xindex>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:         %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_2]] {
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>

 // CHECK:           %[[VAL_21:.*]] = math.tanh %[[VAL_20]] : f64
 // CHECK:           sparse_tensor.lex_insert %[[VAL_4]], %[[VAL_8]], %[[VAL_21]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>, memref<?xindex>, 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 = sparse_tensor.init [%c32] : tensor<32xf64, #SV>
+  %xinp = bufferization.alloc_tensor() : tensor<32xf64, #SV>
   %0 = linalg.generic #trait1
      ins(%arga: tensor<32xf64, #SV>)
     outs(%xinp: tensor<32xf64, #SV>) {
       ^bb(%a: f64, %x: f64):
   %0 = math.abs %a : f64
         %1 = math.ceil %0 : f64
         %2 = math.floor %1 : f64
         %3 = math.sqrt %2 : f64
         %4 = math.expm1 %3 : f64
         %5 = math.log1p %4 : f64
         %6 = math.sin %5 : f64
         %7 = math.tanh %6 : f64
         linalg.yield %7 : f64
   } -> tensor<32xf64, #SV>
   return %0 : tensor<32xf64, #SV>
 }

mlir/test/Dialect/SparseTensor/sparse_index.mlir

 }
 
 // CHECK-LABEL:   func @dense_index(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.init{{\[}}%[[VAL_3]], %[[VAL_4]]] : tensor<?x?xi64, #sparse_tensor.encoding
+// CHECK-DAG:       %[[VAL_5:.*]] = bufferization.alloc_tensor(%[[VAL_3]], %[[VAL_4]]) : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_7:.*]] = tensor.dim %[[VAL_5]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_8:.*]] = tensor.dim %[[VAL_5]], %[[VAL_2]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_5]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK:           scf.for %[[VAL_10:.*]] = %[[VAL_1]] to %[[VAL_7]] step %[[VAL_2]] {
 // CHECK:             scf.for %[[VAL_11:.*]] = %[[VAL_1]] to %[[VAL_8]] step %[[VAL_2]] {
 // CHECK:               %[[VAL_12:.*]] = arith.muli %[[VAL_8]], %[[VAL_10]] : index
 // CHECK:               %[[VAL_13:.*]] = arith.addi %[[VAL_12]], %[[VAL_11]] : index

 // CHECK:           return %[[VAL_21]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK:         }
 func.func @dense_index(%arga: tensor<?x?xi64, #DenseMatrix>)
                       -> tensor<?x?xi64, #DenseMatrix> {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 0 : index
   %0 = tensor.dim %arga, %c0 : tensor<?x?xi64, #DenseMatrix>
   %1 = tensor.dim %arga, %c1 : tensor<?x?xi64, #DenseMatrix>
-  %init = sparse_tensor.init [%0, %1] : tensor<?x?xi64, #DenseMatrix>
+  %init = bufferization.alloc_tensor(%0, %1) : tensor<?x?xi64, #DenseMatrix>
   %r = linalg.generic #trait
       ins(%arga: tensor<?x?xi64, #DenseMatrix>)
      outs(%init: tensor<?x?xi64, #DenseMatrix>) {
       ^bb(%a: i64, %x: i64):
         %i = linalg.index 0 : index
         %j = linalg.index 1 : index
         %ii = arith.index_cast %i : index to i64
         %jj = arith.index_cast %j : index to i64
         %m1 = arith.muli %ii, %a : i64
         %m2 = arith.muli %jj, %m1 : i64
         linalg.yield %m2 : i64
   } -> tensor<?x?xi64, #DenseMatrix>
   return %r : tensor<?x?xi64, #DenseMatrix>
 }
 
 // CHECK-LABEL:   func @sparse_index(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.init{{\[}}%[[VAL_4]], %[[VAL_5]]] : tensor<?x?xi64, #sparse_tensor.encoding
+// CHECK-DAG:       %[[VAL_6:.*]] = bufferization.alloc_tensor(%[[VAL_4]], %[[VAL_5]]) : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK:           %[[VAL_12:.*]] = memref.alloca(%[[VAL_3]]) : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>

 // 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
   %c1 = arith.constant 0 : index
   %0 = tensor.dim %arga, %c0 : tensor<?x?xi64, #SparseMatrix>
   %1 = tensor.dim %arga, %c1 : tensor<?x?xi64, #SparseMatrix>
-  %init = sparse_tensor.init [%0, %1] : tensor<?x?xi64, #SparseMatrix>
+  %init = bufferization.alloc_tensor(%0, %1) : tensor<?x?xi64, #SparseMatrix>
   %r = linalg.generic #trait
       ins(%arga: tensor<?x?xi64, #SparseMatrix>)
      outs(%init: tensor<?x?xi64, #SparseMatrix>) {
       ^bb(%a: i64, %x: i64):
         %i = linalg.index 0 : index
         %j = linalg.index 1 : index
         %ii = arith.index_cast %i : index to i64
         %jj = arith.index_cast %j : index to i64
         %m1 = arith.muli %ii, %a : i64
         %m2 = arith.muli %jj, %m1 : i64
         linalg.yield %m2 : i64
   } -> tensor<?x?xi64, #SparseMatrix>
   return %r : tensor<?x?xi64, #SparseMatrix>
 }
 

mlir/test/Dialect/SparseTensor/sparse_kernels.mlir

 }
 
 //
 // Computes C = A x B with all matrices sparse (SpMSpM) in DCSR.
 //
 // CHECK-LABEL:   func @matmul2(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> {
-// CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 4 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant false
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant true
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.init{{\[}}%[[VAL_2]], %[[VAL_2]]] : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:           %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>

 // CHECK:             sparse_tensor.compress %[[VAL_8]], %[[VAL_19]], %[[VAL_24]], %[[VAL_25]], %[[VAL_26]], %[[VAL_76:.*]]#2 : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>, memref<?xindex>, memref<?xf64>, memref<?xi1>, memref<?xindex>, index
 // CHECK:           }
 // CHECK:           %[[VAL_77:.*]] = sparse_tensor.load %[[VAL_8]] hasInserts : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           return %[[VAL_77]] : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         }
 func.func @matmul2(%A: tensor<4x8xf64, #DCSR>,
               %B: tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {
   %c4 = arith.constant 4 : index
-  %C = sparse_tensor.init [%c4, %c4] : tensor<4x4xf64, #DCSR>
+  %C = bufferization.alloc_tensor() : tensor<4x4xf64, #DCSR>
   %D = linalg.matmul
     ins(%A, %B: tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>)
        outs(%C: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>
   return %D: tensor<4x4xf64, #DCSR>
 }
 
 // CHECK-LABEL:   func @conv2d(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<8x8xi32>,

mlir/test/Dialect/SparseTensor/sparse_out.mlir

#trait_scale = {
   iterator_types = ["parallel", "parallel"],
   doc = "X(i,j) = A(i,j) * 2.0"
 }
 
 // CHECK-LABEL:   func @sparse_truly_dynamic(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 2.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 10 : index
-// CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 20 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0 : index
-// CHECK:           %[[VAL_7:.*]] = sparse_tensor.init{{\[}}%[[VAL_2]], %[[VAL_3]]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
+// CHECK:           %[[VAL_7:.*]] = bufferization.alloc_tensor() : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{.*}}>>
 // CHECK:           %[[VAL_11:.*]] = memref.alloca(%[[VAL_5]]) : memref<?xindex>
 // CHECK:           scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_2]] step %[[VAL_4]] {
 // CHECK:             memref.store %[[VAL_12]], %[[VAL_11]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // 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:               sparse_tensor.lex_insert %[[VAL_7]], %[[VAL_11]], %[[VAL_19]] : 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
-  %d10 = arith.constant 10 : index
-  %d20 = arith.constant 20 : index
-  %xm = sparse_tensor.init [%d10, %d20] : tensor<10x20xf32, #DCSR>
+  %xm = bufferization.alloc_tensor() : tensor<10x20xf32, #DCSR>
   %0 = linalg.generic #trait_scale
      ins(%arga: tensor<10x20xf32, #CSR>)
       outs(%xm: tensor<10x20xf32, #DCSR>) {
       ^bb(%a: f32, %x: f32):
         %1 = arith.mulf %a, %s : f32
         linalg.yield %1 : f32
   } -> tensor<10x20xf32, #DCSR>
   return %0 : tensor<10x20xf32, #DCSR>

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>)
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : i32
 // CHECK:           %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xi32, #{{.*}}>>
 // CHECK:           %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xi32, #{{.*}}>>
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.init{{\[}}%[[VAL_6]], %[[VAL_7]]] : tensor<?x?xi32, #{{.*}}>>
+// CHECK:           %[[VAL_8:.*]] = bufferization.alloc_tensor(%[[VAL_6]], %[[VAL_7]]) : tensor<?x?xi32, #{{.*}}>>
 // CHECK:           %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xindex>
 // CHECK:           %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xi32>
 // CHECK:           %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_2]] : tensor<?x?x?xi32, #{{.*}}>> to memref<?xindex>

 // CHECK:           return %[[VAL_112]] : tensor<?x?xi32, #{{.*}}>
 // CHECK:         }
 func.func @sumred(%arga: tensor<?x?x?xi32, #SparseTensor>,
              %argb: tensor<?x?x?xi32, #SparseTensor>) -> tensor<?x?xi32, #DCSR> {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %d0 = tensor.dim %arga, %c0 : tensor<?x?x?xi32, #SparseTensor>
   %d1 = tensor.dim %arga, %c1 : tensor<?x?x?xi32, #SparseTensor>
-  %xinit = sparse_tensor.init [%d0, %d1] : tensor<?x?xi32, #DCSR>
+  %xinit = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xi32, #DCSR>
   %0 = linalg.generic #trait_sumred
     ins(%arga, %argb: tensor<?x?x?xi32, #SparseTensor>,
                       tensor<?x?x?xi32, #SparseTensor>)
     outs(%xinit: tensor<?x?xi32, #DCSR>) {
       ^bb(%a: i32, %b: i32, %x: i32):
         %0 = arith.muli %a, %b : i32
         %1 = arith.addi %x, %0 : i32
         linalg.yield %1 : i32

 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant false
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK:           %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.init{{\[}}%[[VAL_7]], %[[VAL_8]]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_9:.*]] = bufferization.alloc_tensor(%[[VAL_7]], %[[VAL_8]]) : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
 // CHECK:           %[[VAL_15:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_17:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>

 // CHECK:           return %[[VAL_78]] : tensor<?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         }
 func.func @matmat(%arga: tensor<?x?xf32, #DCSR>,
              %argb: tensor<?x?xf32, #DCSR>) -> tensor<?x?xf32, #DCSR> {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %d0 = tensor.dim %arga, %c0 : tensor<?x?xf32, #DCSR>
   %d1 = tensor.dim %argb, %c1 : tensor<?x?xf32, #DCSR>
-  %cinit = sparse_tensor.init [%d0, %d1] : tensor<?x?xf32, #DCSR>
+  %cinit = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf32, #DCSR>
   %0 = linalg.generic #trait_matmat
        ins(%arga, %argb: tensor<?x?xf32, #DCSR>,
                          tensor<?x?xf32, #DCSR>)
       outs(%cinit: tensor<?x?xf32, #DCSR>) {
     ^bb(%a: f32, %b: f32, %c: f32):
       %1 = arith.mulf %a, %b : f32
       %2 = arith.addf %c, %1 : f32
       linalg.yield %2 : f32
   } -> tensor<?x?xf32, #DCSR>
   return %0 : tensor<?x?xf32, #DCSR>
 }

mlir/test/Integration/Dialect/SparseTensor/CPU/dense_output.mlir

module {
   //
   // A kernel that assigns elements from A to X.
   //
   func.func @dense_output(%arga: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64, #DenseMatrix> {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #SparseMatrix>
     %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #SparseMatrix>
-    %init = sparse_tensor.init [%d0, %d1] : tensor<?x?xf64, #DenseMatrix>
+    %init = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DenseMatrix>
     %0 = linalg.generic #trait_assign
        ins(%arga: tensor<?x?xf64, #SparseMatrix>)
       outs(%init: tensor<?x?xf64, #DenseMatrix>) {
       ^bb(%a: f64, %x: f64):
         linalg.yield %a : f64
     } -> tensor<?x?xf64, #DenseMatrix>
     return %0 : tensor<?x?xf64, #DenseMatrix>
   }

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_binary.mlir

 
 module {
   // Creates a new sparse vector using the minimum values from two input sparse vectors.
   // When there is no overlap, include the present value in the output.
   func.func @vector_min(%arga: tensor<?xf64, #SparseVector>,
                         %argb: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_vec_op
        ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xf64, #SparseVector>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = sparse_tensor.binary %a, %b : f64, f64 to f64
             overlap={
               ^bb0(%a0: f64, %b0: f64):
                 %cmp = arith.cmpf "olt", %a0, %b0 : f64
                 %2 = arith.select %cmp, %a0, %b0: f64
                 sparse_tensor.yield %2 : f64
             }
             left=identity
             right=identity
           linalg.yield %1 : f64
     } -> tensor<?xf64, #SparseVector>
     return %0 : tensor<?xf64, #SparseVector>
   }
 
   // Creates a new sparse vector by multiplying a sparse vector with a dense vector.
   // When there is no overlap, leave the result empty.
   func.func @vector_mul(%arga: tensor<?xf64, #SparseVector>,
                         %argb: tensor<?xf64>) -> tensor<?xf64, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_vec_op
        ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64>)
         outs(%xv: tensor<?xf64, #SparseVector>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = sparse_tensor.binary %a, %b : f64, f64 to f64
             overlap={
               ^bb0(%a0: f64, %b0: f64):
                 %ret = arith.mulf %a0, %b0 : f64
                 sparse_tensor.yield %ret : f64
             }
             left={}
             right={}
           linalg.yield %1 : f64
     } -> tensor<?xf64, #SparseVector>
     return %0 : tensor<?xf64, #SparseVector>
   }
 
   // Take a set difference of two sparse vectors. The result will include only those
   // sparse elements present in the first, but not the second vector.
   func.func @vector_setdiff(%arga: tensor<?xf64, #SparseVector>,
                             %argb: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_vec_op
        ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xf64, #SparseVector>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = sparse_tensor.binary %a, %b : f64, f64 to f64
             overlap={}
             left=identity
             right={}
           linalg.yield %1 : f64
     } -> tensor<?xf64, #SparseVector>
     return %0 : tensor<?xf64, #SparseVector>
   }
 
   // Return the index of each entry
   func.func @vector_index(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xi32, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xi32, #SparseVector>
     %0 = linalg.generic #trait_vec_scale
        ins(%arga: tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xi32, #SparseVector>) {
         ^bb(%a: f64, %x: i32):
           %idx = linalg.index 0 : index
           %1 = sparse_tensor.binary %a, %idx : f64, index to i32
             overlap={
               ^bb0(%x0: f64, %i: index):
module {
   // Adds two sparse matrices when they intersect. Where they don't intersect,
   // negate the 2nd argument's values; ignore 1st argument-only values.
   func.func @matrix_intersect(%arga: tensor<?x?xf64, #DCSR>,
                               %argb: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>
     %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>
-    %xv = sparse_tensor.init [%d0, %d1] : tensor<?x?xf64, #DCSR>
+    %xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
     %0 = linalg.generic #trait_mat_op
        ins(%arga, %argb: tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>)
         outs(%xv: tensor<?x?xf64, #DCSR>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = sparse_tensor.binary %a, %b: f64, f64 to f64
             overlap={
               ^bb0(%x0: f64, %y0: f64):
                 %ret = arith.addf %x0, %y0 : f64
func.func @entry() {
     sparse_tensor.release %sm2 : tensor<?x?xf64, #DCSR>
     sparse_tensor.release %0 : tensor<?xf64, #SparseVector>
     sparse_tensor.release %1 : tensor<?xf64, #SparseVector>
     sparse_tensor.release %2 : tensor<?xf64, #SparseVector>
     sparse_tensor.release %3 : tensor<?xi32, #SparseVector>
     sparse_tensor.release %5 : tensor<?x?xf64, #DCSR>
     return
   }
-}
+}
 

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex32.mlir

 }
 
 module {
   func.func @cadd(%arga: tensor<?xcomplex<f32>, #SparseVector>,
                   %argb: tensor<?xcomplex<f32>, #SparseVector>)
                       -> tensor<?xcomplex<f32>, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xcomplex<f32>, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f32>, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
                          tensor<?xcomplex<f32>, #SparseVector>)
         outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
         ^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
           %1 = complex.add %a, %b : complex<f32>
           linalg.yield %1 : complex<f32>
     } -> tensor<?xcomplex<f32>, #SparseVector>
     return %0 : tensor<?xcomplex<f32>, #SparseVector>
   }
 
   func.func @cmul(%arga: tensor<?xcomplex<f32>, #SparseVector>,
                   %argb: tensor<?xcomplex<f32>, #SparseVector>)
                       -> tensor<?xcomplex<f32>, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xcomplex<f32>, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f32>, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
                          tensor<?xcomplex<f32>, #SparseVector>)
         outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
         ^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
           %1 = complex.mul %a, %b : complex<f32>
           linalg.yield %1 : complex<f32>
     } -> tensor<?xcomplex<f32>, #SparseVector>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex64.mlir

 }
 
 module {
   func.func @cadd(%arga: tensor<?xcomplex<f64>, #SparseVector>,
                   %argb: tensor<?xcomplex<f64>, #SparseVector>)
                       -> tensor<?xcomplex<f64>, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xcomplex<f64>, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f64>, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
                          tensor<?xcomplex<f64>, #SparseVector>)
         outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
         ^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
           %1 = complex.add %a, %b : complex<f64>
           linalg.yield %1 : complex<f64>
     } -> tensor<?xcomplex<f64>, #SparseVector>
     return %0 : tensor<?xcomplex<f64>, #SparseVector>
   }
 
   func.func @cmul(%arga: tensor<?xcomplex<f64>, #SparseVector>,
                   %argb: tensor<?xcomplex<f64>, #SparseVector>)
                       -> tensor<?xcomplex<f64>, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xcomplex<f64>, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f64>, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
                          tensor<?xcomplex<f64>, #SparseVector>)
         outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
         ^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
           %1 = complex.mul %a, %b : complex<f64>
           linalg.yield %1 : complex<f64>
     } -> tensor<?xcomplex<f64>, #SparseVector>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir

 //
 module {
 
   //
   // Kernel that uses index in the index notation (conjunction).
   //
   func.func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>)
                                  -> tensor<8xi64, #SparseVector> {
-    %d0 = arith.constant 8 : index
-    %init = sparse_tensor.init [%d0] : tensor<8xi64, #SparseVector>
+    %init = bufferization.alloc_tensor() : tensor<8xi64, #SparseVector>
     %r = linalg.generic #trait_1d
         ins(%arga: tensor<8xi64, #SparseVector>)
        outs(%init: tensor<8xi64, #SparseVector>) {
         ^bb(%a: i64, %x: i64):
           %i = linalg.index 0 : index
           %ii = arith.index_cast %i : index to i64
           %m1 = arith.muli %a, %ii : i64
           linalg.yield %m1 : i64
     } -> tensor<8xi64, #SparseVector>
     return %r : tensor<8xi64, #SparseVector>
   }
 
   //
   // Kernel that uses index in the index notation (disjunction).
   //
   func.func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>)
                                  -> tensor<8xi64, #SparseVector> {
-    %d0 = arith.constant 8 : index
-    %init = sparse_tensor.init [%d0] : tensor<8xi64, #SparseVector>
+    %init = bufferization.alloc_tensor() : tensor<8xi64, #SparseVector>
     %r = linalg.generic #trait_1d
         ins(%arga: tensor<8xi64, #SparseVector>)
        outs(%init: tensor<8xi64, #SparseVector>) {
         ^bb(%a: i64, %x: i64):
           %i = linalg.index 0 : index
           %ii = arith.index_cast %i : index to i64
           %m1 = arith.addi %a, %ii : i64
           linalg.yield %m1 : i64
     } -> tensor<8xi64, #SparseVector>
     return %r : tensor<8xi64, #SparseVector>
   }
 
   //
   // Kernel that uses indices in the index notation (conjunction).
   //
   func.func @sparse_index_2d_conj(%arga: tensor<3x4xi64, #SparseMatrix>)
                                  -> tensor<3x4xi64, #SparseMatrix> {
-    %d0 = arith.constant 3 : index
-    %d1 = arith.constant 4 : index
-    %init = sparse_tensor.init [%d0, %d1] : tensor<3x4xi64, #SparseMatrix>
+    %init = bufferization.alloc_tensor() : tensor<3x4xi64, #SparseMatrix>
     %r = linalg.generic #trait_2d
         ins(%arga: tensor<3x4xi64, #SparseMatrix>)
        outs(%init: tensor<3x4xi64, #SparseMatrix>) {
         ^bb(%a: i64, %x: i64):
           %i = linalg.index 0 : index
           %j = linalg.index 1 : index
           %ii = arith.index_cast %i : index to i64
           %jj = arith.index_cast %j : index to i64
           %m1 = arith.muli %ii, %a : i64
           %m2 = arith.muli %jj, %m1 : i64
           linalg.yield %m2 : i64
     } -> tensor<3x4xi64, #SparseMatrix>
     return %r : tensor<3x4xi64, #SparseMatrix>
   }
 
   //
   // Kernel that uses indices in the index notation (disjunction).
   //
   func.func @sparse_index_2d_disj(%arga: tensor<3x4xi64, #SparseMatrix>)
                                  -> tensor<3x4xi64, #SparseMatrix> {
-    %d0 = arith.constant 3 : index
-    %d1 = arith.constant 4 : index
-    %init = sparse_tensor.init [%d0, %d1] : tensor<3x4xi64, #SparseMatrix>
+    %init = bufferization.alloc_tensor() : tensor<3x4xi64, #SparseMatrix>
     %r = linalg.generic #trait_2d
         ins(%arga: tensor<3x4xi64, #SparseMatrix>)
        outs(%init: tensor<3x4xi64, #SparseMatrix>) {
         ^bb(%a: i64, %x: i64):
           %i = linalg.index 0 : index
           %j = linalg.index 1 : index
           %ii = arith.index_cast %i : index to i64
           %jj = arith.index_cast %j : index to i64
           %m1 = arith.addi %ii, %a : i64
           %m2 = arith.addi %jj, %m1 : i64
           linalg.yield %m2 : i64
     } -> tensor<3x4xi64, #SparseMatrix>
     return %r : tensor<3x4xi64, #SparseMatrix>
   }
 
   func.func @add_outer_2d(%arg0: tensor<2x3xf32, #SparseMatrix>)
                          -> tensor<2x3xf32, #SparseMatrix> {
-    %c2 = arith.constant 2 : index
-    %c3 = arith.constant 3 : index
-    %0 = sparse_tensor.init[%c2, %c3] : tensor<2x3xf32, #SparseMatrix>
+    %0 = bufferization.alloc_tensor() : tensor<2x3xf32, #SparseMatrix>
     %1 = linalg.generic #trait_2d
       ins(%arg0 : tensor<2x3xf32, #SparseMatrix>)
       outs(%0 : tensor<2x3xf32, #SparseMatrix>) {
     ^bb0(%arg1: f32, %arg2: f32):
       %2 = linalg.index 0 : index
       %3 = arith.index_cast %2 : index to i64
       %4 = arith.uitofp %3 : i64 to f32
       %5 = arith.addf %arg1, %4 : f32

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul.mlir

func.func @matmul1(%A: tensor<4x8xf64>,
     return %D: tensor<4x4xf64>
   }
 
   //
   // Computes C = A x B with all matrices sparse (SpMSpM) in CSR.
   //
   func.func @matmul2(%A: tensor<4x8xf64, #CSR>,
                 %B: tensor<8x4xf64, #CSR>) -> tensor<4x4xf64, #CSR> {
-    %c4 = arith.constant 4 : index
-    %C = sparse_tensor.init [%c4, %c4] : tensor<4x4xf64, #CSR>
+    %C = bufferization.alloc_tensor() : tensor<4x4xf64, #CSR>
     %D = linalg.matmul
       ins(%A, %B: tensor<4x8xf64, #CSR>, tensor<8x4xf64, #CSR>)
          outs(%C: tensor<4x4xf64, #CSR>) -> tensor<4x4xf64, #CSR>
     return %D: tensor<4x4xf64, #CSR>
   }
 
   //
   // Computes C = A x B with all matrices sparse (SpMSpM) in DCSR.
   //
   func.func @matmul3(%A: tensor<4x8xf64, #DCSR>,
                 %B: tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {
-    %c4 = arith.constant 4 : index
-    %C = sparse_tensor.init [%c4, %c4] : tensor<4x4xf64, #DCSR>
+    %C = bufferization.alloc_tensor() : tensor<4x4xf64, #DCSR>
     %D = linalg.matmul
       ins(%A, %B: tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>)
          outs(%C: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>
     return %D: tensor<4x4xf64, #DCSR>
   }
 
   //
   // Main driver.

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matrix_ops.mlir

 module {
   // Scales a sparse matrix into a new sparse matrix.
   func.func @matrix_scale(%arga: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
     %s = arith.constant 2.0 : f64
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>
     %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>
-    %xm = sparse_tensor.init [%d0, %d1] : tensor<?x?xf64, #DCSR>
+    %xm = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
     %0 = linalg.generic #trait_scale
        ins(%arga: tensor<?x?xf64, #DCSR>)
         outs(%xm: tensor<?x?xf64, #DCSR>) {
         ^bb(%a: f64, %x: f64):
           %1 = arith.mulf %a, %s : f64
           linalg.yield %1 : f64
     } -> tensor<?x?xf64, #DCSR>
     return %0 : tensor<?x?xf64, #DCSR>
module {
 
   // Adds two sparse matrices element-wise into a new sparse matrix.
   func.func @matrix_add(%arga: tensor<?x?xf64, #DCSR>,
                    %argb: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>
     %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>
-    %xv = sparse_tensor.init [%d0, %d1] : tensor<?x?xf64, #DCSR>
+    %xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>)
         outs(%xv: tensor<?x?xf64, #DCSR>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = arith.addf %a, %b : f64
           linalg.yield %1 : f64
     } -> tensor<?x?xf64, #DCSR>
     return %0 : tensor<?x?xf64, #DCSR>
   }
 
   // Multiplies two sparse matrices element-wise into a new sparse matrix.
   func.func @matrix_mul(%arga: tensor<?x?xf64, #DCSR>,
                    %argb: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>
     %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>
-    %xv = sparse_tensor.init [%d0, %d1] : tensor<?x?xf64, #DCSR>
+    %xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>)
         outs(%xv: tensor<?x?xf64, #DCSR>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = arith.mulf %a, %b : f64
           linalg.yield %1 : f64
     } -> tensor<?x?xf64, #DCSR>
     return %0 : tensor<?x?xf64, #DCSR>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_out_mult_elt.mlir

#trait_mult_elt = {
   iterator_types = ["parallel", "parallel"],
   doc = "X(i,j) = A(i,j) * B(i,j)"
 }
 
 module {
   // Sparse kernel.
   func.func @sparse_mult_elt(
       %arga: tensor<32x16xf32, #DCSR>, %argb: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> {
-    %c16 = arith.constant 16 : index
-    %c32 = arith.constant 32 : index
-    %argx = sparse_tensor.init [%c32, %c16] : tensor<32x16xf32, #DCSR>
+    %argx = bufferization.alloc_tensor() : tensor<32x16xf32, #DCSR>
     %0 = linalg.generic #trait_mult_elt
       ins(%arga, %argb: tensor<32x16xf32, #DCSR>, tensor<32x16xf32, #DCSR>)
       outs(%argx: tensor<32x16xf32, #DCSR>) {
         ^bb(%a: f32, %b: f32, %x: f32):
           %1 = arith.mulf %a, %b : f32
           linalg.yield %1 : f32
     } -> tensor<32x16xf32, #DCSR>
     return %0 : tensor<32x16xf32, #DCSR>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_out_reduction.mlir

 module {
   func.func @redsum(%arga: tensor<?x?x?xi32, #SparseTensor>,
                %argb: tensor<?x?x?xi32, #SparseTensor>)
              -> tensor<?x?xi32, #SparseMatrix> {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %d0 = tensor.dim %arga, %c0 : tensor<?x?x?xi32, #SparseTensor>
     %d1 = tensor.dim %arga, %c1 : tensor<?x?x?xi32, #SparseTensor>
-    %xinit = sparse_tensor.init [%d0, %d1] : tensor<?x?xi32, #SparseMatrix>
+    %xinit = bufferization.alloc_tensor(%d0, %d1): tensor<?x?xi32, #SparseMatrix>
     %0 = linalg.generic #redsum
       ins(%arga, %argb: tensor<?x?x?xi32, #SparseTensor>,
                         tensor<?x?x?xi32, #SparseTensor>)
       outs(%xinit: tensor<?x?xi32, #SparseMatrix>) {
         ^bb(%a: i32, %b: i32, %x: i32):
           %0 = arith.muli %a, %b : i32
           %1 = arith.addi %x, %0 : i32
           linalg.yield %1 : i32

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_re_im.mlir

#trait_op = {
   doc = "x(i) = OP a(i)"
 }
 
 module {
   func.func @cre(%arga: tensor<?xcomplex<f32>, #SparseVector>)
                 -> tensor<?xf32, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf32, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf32, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga: tensor<?xcomplex<f32>, #SparseVector>)
         outs(%xv: tensor<?xf32, #SparseVector>) {
         ^bb(%a: complex<f32>, %x: f32):
           %1 = complex.re %a : complex<f32>
           linalg.yield %1 : f32
     } -> tensor<?xf32, #SparseVector>
     return %0 : tensor<?xf32, #SparseVector>
   }
 
   func.func @cim(%arga: tensor<?xcomplex<f32>, #SparseVector>)
                 -> tensor<?xf32, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf32, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf32, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga: tensor<?xcomplex<f32>, #SparseVector>)
         outs(%xv: tensor<?xf32, #SparseVector>) {
         ^bb(%a: complex<f32>, %x: f32):
           %1 = complex.im %a : complex<f32>
           linalg.yield %1 : f32
     } -> tensor<?xf32, #SparseVector>
     return %0 : tensor<?xf32, #SparseVector>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_mm_fusion.mlir

module {
 
   //
   // A kernel that computes a direct sampled matrix matrix multiplication
   // (with sparse result).
   //
   func.func @sparse_sampled_dd(%args: tensor<8x8xf64, #SM>,
                           %arga: tensor<8x8xf64>,
                           %argb: tensor<8x8xf64>) -> tensor<8x8xf64, #SM> {
-    %c8 = arith.constant 8 : index
-    %1 = sparse_tensor.init [%c8, %c8] : tensor<8x8xf64, #SM>
+    %1 = bufferization.alloc_tensor() : tensor<8x8xf64, #SM>
     %2 = linalg.generic #trait_sampled_dense_dense
       ins(%args, %arga, %argb: tensor<8x8xf64, #SM>,
                                tensor<8x8xf64>, tensor<8x8xf64>)
       outs(%1: tensor<8x8xf64, #SM>) {
         ^bb(%s: f64, %a: f64, %b: f64, %x: f64):
           %p = arith.mulf %a, %b : f64
           %q = arith.mulf %s, %p : f64
           %r = arith.addf %x, %q : f64
%2 = linalg.generic #trait_matmul
       ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>)
       outs(%1 : tensor<8x8xf64>) {
         ^bb0(%a: f64, %b: f64, %x: f64):
           %p = arith.mulf %a, %b : f64
           %q = arith.addf %x, %p : f64
           linalg.yield %q : f64
     } -> tensor<8x8xf64>
     // Sample the result with elements-wise multiplication with sparse matrix.
-    %c8 = arith.constant 8 : index
-    %3 = sparse_tensor.init [%c8, %c8] : tensor<8x8xf64, #SM>
+    %3 = bufferization.alloc_tensor() : tensor<8x8xf64, #SM>
     %4 = linalg.generic #trait_scale
       ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>)
       outs(%3 : tensor<8x8xf64, #SM>) {
         ^bb0(%t: f64, %s: f64, %x: f64):
           %r = arith.mulf %t, %s : f64
           linalg.yield %r : f64
     } -> tensor<8x8xf64, #SM>
     return %4 : tensor<8x8xf64, #SM>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sign.mlir

module {
   // +NaN: +NaN
   // -NaN: -NaN
   // +0.0: +0.0
   // -0.0: -0.0
   func.func @sparse_sign(%arg0: tensor<?xf64, #SparseVector>)
                              -> tensor<?xf64, #SparseVector> {
     %c0 = arith.constant 0 : index
     %d = tensor.dim %arg0, %c0 : tensor<?xf64, #SparseVector>
-    %xin = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xin = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_op
       ins(%arg0: tensor<?xf64, #SparseVector>)
       outs(%xin: tensor<?xf64, #SparseVector>) {
       ^bb0(%a: f64, %x: f64) :
         %result = sparse_tensor.unary %a : f64 to f64
           present={
             ^bb1(%s: f64):
               %z = arith.constant 0.0 : f64

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tensor_ops.mlir

module {
   func.func @tensor_scale(%arga: tensor<?x?x?xf64, #ST1>) -> tensor<?x?x?xf64, #ST2> {
     %s = arith.constant 2.0 : f64
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %c2 = arith.constant 2 : index
     %d0 = tensor.dim %arga, %c0 : tensor<?x?x?xf64, #ST1>
     %d1 = tensor.dim %arga, %c1 : tensor<?x?x?xf64, #ST1>
     %d2 = tensor.dim %arga, %c2 : tensor<?x?x?xf64, #ST1>
-    %xm = sparse_tensor.init [%d0, %d1, %d2] : tensor<?x?x?xf64, #ST2>
+    %xm = bufferization.alloc_tensor(%d0, %d1, %d2) : tensor<?x?x?xf64, #ST2>
     %0 = linalg.generic #trait_scale
        ins(%arga: tensor<?x?x?xf64, #ST1>)
         outs(%xm: tensor<?x?x?xf64, #ST2>) {
         ^bb(%a: f64, %x: f64):
           %1 = arith.mulf %a, %s : f64
           linalg.yield %1 : f64
     } -> tensor<?x?x?xf64, #ST2>
     return %0 : tensor<?x?x?xf64, #ST2>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_transpose.mlir

module {
   // Transposing a sparse row-wise matrix into another sparse row-wise
   // matrix would fail direct codegen, since it introduces a cycle in
   // the iteration graph. This can be avoided by converting the incoming
   // matrix into a sparse column-wise matrix first.
   //
   func.func @sparse_transpose(%arga: tensor<3x4xf64, #DCSR>) -> tensor<4x3xf64, #DCSR> {
     %t = sparse_tensor.convert %arga : tensor<3x4xf64, #DCSR> to tensor<3x4xf64, #DCSC>
 
-    %c3 = arith.constant 3 : index
-    %c4 = arith.constant 4 : index
-    %i = sparse_tensor.init [%c4, %c3] : tensor<4x3xf64, #DCSR>
+    %i = bufferization.alloc_tensor() : tensor<4x3xf64, #DCSR>
 
     %0 = linalg.generic #transpose_trait
        ins(%t: tensor<3x4xf64, #DCSC>)
        outs(%i: tensor<4x3xf64, #DCSR>) {
        ^bb(%a: f64, %x: f64):
          linalg.yield %a : f64
      } -> tensor<4x3xf64, #DCSR>
 

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_unary.mlir

 
 module {
   // Invert the structure of a sparse vector. Present values become missing.
   // Missing values are filled with 1 (i32).
   func.func @vector_complement(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> {
     %c = arith.constant 0 : index
     %ci1 = arith.constant 1 : i32
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xi32, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xi32, #SparseVector>
     %0 = linalg.generic #trait_vec_scale
        ins(%arga: tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xi32, #SparseVector>) {
         ^bb(%a: f64, %x: i32):
           %1 = sparse_tensor.unary %a : f64 to i32
             present={}
             absent={
               sparse_tensor.yield %ci1 : i32
             }
           linalg.yield %1 : i32
     } -> tensor<?xi32, #SparseVector>
     return %0 : tensor<?xi32, #SparseVector>
   }
 
   // Negate existing values. Fill missing ones with +1.
   func.func @vector_negation(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
     %c = arith.constant 0 : index
     %cf1 = arith.constant 1.0 : f64
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_vec_scale
        ins(%arga: tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xf64, #SparseVector>) {
         ^bb(%a: f64, %x: f64):
           %1 = sparse_tensor.unary %a : f64 to f64
             present={
               ^bb0(%x0: f64):
                 %ret = arith.negf %x0 : f64
module {
   // Clips values to the range [3, 7].
   func.func @matrix_clip(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %cfmin = arith.constant 3.0 : f64
     %cfmax = arith.constant 7.0 : f64
     %d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
     %d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
-    %xv = sparse_tensor.init [%d0, %d1] : tensor<?x?xf64, #DCSR>
+    %xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
     %0 = linalg.generic #trait_mat_scale
        ins(%argx: tensor<?x?xf64, #DCSR>)
         outs(%xv: tensor<?x?xf64, #DCSR>) {
         ^bb(%a: f64, %x: f64):
           %1 = sparse_tensor.unary %a: f64 to f64
             present={
               ^bb0(%x0: f64):
                 %mincmp = arith.cmpf "ogt", %x0, %cfmin : f64
func.func @entry() {
     // Release the resources.
     sparse_tensor.release %sv1 : tensor<?xf64, #SparseVector>
     sparse_tensor.release %sm1 : tensor<?x?xf64, #DCSR>
     sparse_tensor.release %0 : tensor<?xi32, #SparseVector>
     sparse_tensor.release %1 : tensor<?xf64, #SparseVector>
     sparse_tensor.release %2 : tensor<?x?xf64, #DCSR>
     return
   }
-}
+}
 

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_vector_ops.mlir

 }
 
 module {
   // Scales a sparse vector into a new sparse vector.
   func.func @vector_scale(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
     %s = arith.constant 2.0 : f64
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_scale
        ins(%arga: tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xf64, #SparseVector>) {
         ^bb(%a: f64, %x: f64):
           %1 = arith.mulf %a, %s : f64
           linalg.yield %1 : f64
     } -> tensor<?xf64, #SparseVector>
     return %0 : tensor<?xf64, #SparseVector>
func.func @vector_scale_inplace(%argx: tensor<?xf64, #SparseVector>
     return %0 : tensor<?xf64, #SparseVector>
   }
 
   // Adds two sparse vectors into a new sparse vector.
   func.func @vector_add(%arga: tensor<?xf64, #SparseVector>,
                    %argb: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xf64, #SparseVector>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = arith.addf %a, %b : f64
           linalg.yield %1 : f64
     } -> tensor<?xf64, #SparseVector>
     return %0 : tensor<?xf64, #SparseVector>
   }
 
   // Multiplies two sparse vectors into a new sparse vector.
   func.func @vector_mul(%arga: tensor<?xf64, #SparseVector>,
                    %argb: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xf64, #SparseVector>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = arith.mulf %a, %b : f64
           linalg.yield %1 : f64
     } -> tensor<?xf64, #SparseVector>
     return %0 : tensor<?xf64, #SparseVector>
   }
 
   // Multiplies two sparse vectors into a new "annotated" dense vector.
   func.func @vector_mul_d(%arga: tensor<?xf64, #SparseVector>,
                      %argb: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #DenseVector> {
     %c = arith.constant 0 : index
     %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
-    %xv = sparse_tensor.init [%d] : tensor<?xf64, #DenseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #DenseVector>
     %0 = linalg.generic #trait_op
        ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64, #SparseVector>)
         outs(%xv: tensor<?xf64, #DenseVector>) {
         ^bb(%a: f64, %b: f64, %x: f64):
           %1 = arith.mulf %a, %b : f64
           linalg.yield %1 : f64
     } -> tensor<?xf64, #DenseVector>
     return %0 : tensor<?xf64, #DenseVector>

mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py

indexing_maps = [
     affine_map<(i,j) -> (i,j)>   // X (out)
   ],
   iterator_types = ["parallel", "parallel"],
   doc = "X(i,j) = A(i,j) + B(i,j)"
 }
 
 func.func @sparse_add_elt(
     %arga: tensor<3x4xf64, #DCSR>, %argb: tensor<3x4xf64, #DCSR>) -> tensor<3x4xf64, #DCSR> {
-  %c3 = arith.constant 3 : index
-  %c4 = arith.constant 4 : index
-  %argx = sparse_tensor.init [%c3, %c4] : tensor<3x4xf64, #DCSR>
+  %argx = bufferization.alloc_tensor() : tensor<3x4xf64, #DCSR>
   %0 = linalg.generic #trait_add_elt
     ins(%arga, %argb: tensor<3x4xf64, #DCSR>, tensor<3x4xf64, #DCSR>)
     outs(%argx: tensor<3x4xf64, #DCSR>) {
       ^bb(%a: f64, %b: f64, %x: f64):
         %1 = arith.addf %a, %b : f64
         linalg.yield %1 : f64
   } -> tensor<3x4xf64, #DCSR>
   return %0 : tensor<3x4xf64, #DCSR>

mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco.py

 import os
 import threading
 
 # Import MLIR related modules.
 from mlir import execution_engine
 from mlir import ir
 from mlir import runtime
 from mlir.dialects import arith
+from mlir.dialects import bufferization
 from mlir.dialects import builtin
 from mlir.dialects import func
 from mlir.dialects import linalg
 from mlir.dialects import sparse_tensor
 from mlir.dialects.linalg.opdsl import lang
 
 from . import mlir_pytaco_utils as utils
 
if self.dst_format is None or self.dst_format.rank() == 0:
       tensor = linalg.InitTensorOp(self.dst_dims, ir_type).result
       zero = arith.ConstantOp(ir_type, 0.0)
       return linalg.fill(zero, outs=[tensor])
 
     # Initialize the sparse tensor.
     mlir_type = _mlir_tensor_type(self.dst_dtype, self.dst_dims,
                                   self.dst_format.mlir_tensor_attr())
     index_type = ir.IndexType.get()
-    dims = [arith.ConstantOp(index_type, d).result for d in mlir_type.shape]
-    return sparse_tensor.InitOp(mlir_type, dims)
+    return bufferization.AllocTensorOp(mlir_type, [])
 
 
 class _Stats:
   """Information to describe how a tensor expression is implemented.
 
   Currently, we only record the temporary tensors introduced for splitting the
   original expression.
   """