[mlir][sparse] generalize sparse_tensor.convert on static/dynamic dimension sizes

[aartbik]

Summary

This revison lifts the artificial restriction on having exact matches between
source and destination type shapes. A static size may become dynamic. We still
reject changing a dynamic size into a static size to avoid the need for a
runtime "assert" on the conversion. This revision also refactors some of the
conversion code to share same-content buffers.

[aartbik]

fixed typo in example

[bixia]

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

244

Shall we add "or 10 vs. ?" ?

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

197

Can we add "for unknown dimension values in type"?

233

No need for this /?

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

27

s/change/shape/?

[aartbik]

addressed comments

[aartbik]

rebased with main

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

 
 def SparseTensor_ConvertOp : SparseTensor_Op<"convert",
   [NoSideEffect, SameOperandsAndResultType]>,
     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
-    and dimensions of the source and destination types must match exactly,
-    only the sparse encoding of these types may be different. The name `convert`
-    was preferred over `cast`, since the operation may incur a non-trivial cost.
+    and dimensions of the source and destination types must match, but the sparse
+    encoding of these types can obviously be different. The name `convert` was
+    preferred over `cast`, since the operation may incur a non-trivial cost.
 
     When converting between two different sparse tensor types, only explicitly
     stored values are moved from one underlying sparse storage format to
     the other. When converting from an unannotated dense tensor type to a
     sparse tensor type, an explicit test for nonzero values is used. When
     converting to an unannotated dense tensor type, implicit zeroes in the
     sparse storage format are made explicit. Note that the conversions can have
     non-trivial costs associated with them, since they may involve elaborate
     data structure transformations. Also, conversions from sparse tensor types
     into dense tensor types may be infeasible in terms of storage requirements.
 
     Examples:
 
     ```mlir
-    %0 = sparse_tensor.convert %1 : tensor<32x32xf32> to tensor<32x32xf32, #CSR>
-
-    %2 = sparse_tensor.convert %3 : tensor<8x8xi32, #CSC> to tensor<8x8xi32, #CSR>
+    %0 = sparse_tensor.convert %a : tensor<32x32xf32> to tensor<32x32xf32, #CSR>
+    %1 = sparse_tensor.convert %a : tensor<32x32xf32> to tensor<?x?xf32, #CSR>
+    %2 = sparse_tensor.convert %b : tensor<8x8xi32, #CSC> to tensor<8x8xi32, #CSR>
+    %3 = sparse_tensor.convert %c : tensor<4x8xf64, #CSR> to tensor<4x?xf64, #CSC>
+
+    // The following conversion is not allowed (since it would require a
+    // runtime assertion that the source's dimension size is actually 100).
+    %4 = sparse_tensor.convert %d : tensor<?xf64> to tensor<100xf64, #SV>
     ```
 
   }];
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($dest)";
   let hasFolder = 1;
 }
 
 def SparseTensor_ReleaseOp : SparseTensor_Op<"release", []>,

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

 }
 
 static LogicalResult verify(ConvertOp op) {
   if (auto tp1 = op.source().getType().dyn_cast<RankedTensorType>()) {
     if (auto tp2 = op.dest().getType().dyn_cast<RankedTensorType>()) {
       assert(tp1.getRank() == tp2.getRank());
       auto shape1 = tp1.getShape();
       auto shape2 = tp2.getShape();
+      // Accept size matches between the source and the destination type
+      // (e.g. 10 vs. 10, 10 vs. ?, or ? vs. ?), but reject direct mismatches or
+      // matches that would need a runtime assert (e.g. 10 vs. 20 or ? vs. 10).
       for (unsigned d = 0, rank = tp1.getRank(); d < rank; d++) {
-        if (shape1[d] != shape2[d])
+        if (shape1[d] != shape2[d] && shape2[d] != ShapedType::kDynamicSize)
           return op.emitError("unexpected conversion mismatch in dimension ")
                  << d;
       }
       return success();
     }
   }
   return op.emitError("unexpected type in convert");
 }

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

 /// Generates a constant zero of the given type.
 inline static Value constantZero(ConversionPatternRewriter &rewriter,
                                  Location loc, Type t) {
   return rewriter.create<arith::ConstantOp>(loc, t, rewriter.getZeroAttr(t));
 }
 
 /// Generates a constant of `index` type.
 inline static Value constantIndex(ConversionPatternRewriter &rewriter,
-                                  Location loc, unsigned i) {
+                                  Location loc, int64_t i) {
   return rewriter.create<arith::ConstantIndexOp>(loc, i);
 }
 
 /// Generates a constant of `i64` type.
 inline static Value constantI64(ConversionPatternRewriter &rewriter,
                                 Location loc, int64_t i) {
   return rewriter.create<arith::ConstantIntOp>(loc, i, 64);
 }
func = moduleBuilder.create<FuncOp>(
         FunctionType::get(context, operands.getTypes(), resultType));
     func.setPrivate();
     if (emitCInterface)
       func->setAttr("llvm.emit_c_interface", UnitAttr::get(context));
   }
   return result;
 }
 
+/// Generates dimension size call.
+static Value genDimSizeCall(ConversionPatternRewriter &rewriter, Operation *op,
+                            SparseTensorEncodingAttr &enc, Value src,
+                            int64_t idx) {
+  // Permute the index according to an optional dimension ordering.
+  if (AffineMap p = enc.getDimOrdering())
+    idx = p.getPermutedPosition(idx);
+  // Generate the call.
+  Location loc = op->getLoc();
+  StringRef name = "sparseDimSize";
+  SmallVector<Value, 2> params;
+  params.push_back(src);
+  params.push_back(constantIndex(rewriter, loc, idx));
+  Type iTp = rewriter.getIndexType();
+  auto fn = getFunc(op, name, iTp, params);
+  return rewriter.create<CallOp>(loc, iTp, fn, params).getResult(0);
+}
+
+/// Generates a call into the "swiss army knife" method of the sparse runtime
+/// support library for materializing sparse tensors into the computation.
+static Value genNewCall(ConversionPatternRewriter &rewriter, Operation *op,
+                        ArrayRef<Value> params) {
+  Location loc = op->getLoc();
+  StringRef name = "newSparseTensor";
+  Type pTp = LLVM::LLVMPointerType::get(rewriter.getI8Type());
+  auto fn = getFunc(op, name, pTp, params, /*emitCInterface=*/true);
+  auto call = rewriter.create<CallOp>(loc, pTp, fn, params);
+  return call.getResult(0);
+}
+
+/// Populates given sizes array from type.
+static void sizesFromType(ConversionPatternRewriter &rewriter,
+                          SmallVector<Value, 4> &sizes, Location loc,
+                          ShapedType stp) {
+  auto shape = stp.getShape();
+  for (unsigned i = 0, rank = stp.getRank(); i < rank; i++) {
+    uint64_t s = shape[i] == ShapedType::kDynamicSize ? 0 : shape[i];
+    sizes.push_back(constantIndex(rewriter, loc, s));
+  }
+}
+
+/// Populates given sizes array from source.
+static void sizesFromSrc(ConversionPatternRewriter &rewriter,
+                         SmallVector<Value, 4> &sizes, Location loc,
+                         Value src) {
+  ShapedType stp = src.getType().cast<ShapedType>();
+  for (unsigned i = 0, rank = stp.getRank(); i < rank; i++)
+    sizes.push_back(linalg::createOrFoldDimOp(rewriter, loc, src, i));
+}
+
+/// Populates given sizes array from type (for static sizes) and from
+/// an already converted into opague pointer source (for dynamic sizes).
+static void sizesFromPtr(ConversionPatternRewriter &rewriter,
+                         SmallVector<Value, 4> &sizes, Operation *op,
+                         SparseTensorEncodingAttr &enc, ShapedType stp,
+                         Value src) {
+  auto shape = stp.getShape();
+  for (unsigned i = 0, rank = stp.getRank(); i < rank; i++)
+    if (shape[i] == ShapedType::kDynamicSize)
+      sizes.push_back(genDimSizeCall(rewriter, op, enc, src, i));
+    else
+      sizes.push_back(constantIndex(rewriter, op->getLoc(), shape[i]));
+}
+
 /// Generates a temporary buffer of the given size and type.
 static Value genAlloca(ConversionPatternRewriter &rewriter, Location loc,
                        unsigned sz, Type tp) {
   auto memTp = MemRefType::get({ShapedType::kDynamicSize}, tp);
   Value a = constantIndex(rewriter, loc, sz);
   return rewriter.create<memref::AllocaOp>(loc, memTp, ValueRange{a});
 }
 
-/// Fills a temporary buffer of the given type with arguments.
+/// Generates a temporary buffer of the given type and given contents.
 static Value genBuffer(ConversionPatternRewriter &rewriter, Location loc,
                        ArrayRef<Value> values) {
   unsigned sz = values.size();
   assert(sz >= 1);
   Value buffer = genAlloca(rewriter, loc, sz, values[0].getType());
   for (unsigned i = 0; i < sz; i++) {
     Value idx = constantIndex(rewriter, loc, i);
     rewriter.create<memref::StoreOp>(loc, values[i], buffer, idx);
   }
   return buffer;
 }
 
-/// Generates a call into the "swiss army knife" method of the sparse runtime
-/// support library for materializing sparse tensors into the computation. The
-/// method returns the call value and assigns the permutation to 'perm'.
-static Value genNewCall(ConversionPatternRewriter &rewriter, Operation *op,
-                        SparseTensorEncodingAttr &enc, uint32_t action,
-                        Value &perm, ValueRange szs, Value ptr = Value()) {
+/// Populates parameters required to call the "swiss army knife" method of the
+/// sparse runtime support library for materializing sparse tensors into the
+/// computation.
+static void newParams(ConversionPatternRewriter &rewriter,
+                      SmallVector<Value, 8> &params, Operation *op,
+                      ValueRange szs, Value ptr = Value()) {
   Location loc = op->getLoc();
-  ShapedType resType = op->getResult(0).getType().cast<ShapedType>();
-  SmallVector<Value, 8> params;
-  // Sparsity annotations in tensor constant form.
-  SmallVector<Value, 4> attrs;
   ArrayRef<SparseTensorEncodingAttr::DimLevelType> dlt = enc.getDimLevelType();
   unsigned sz = dlt.size();
+  // Sparsity annotations.
+  SmallVector<Value, 4> attrs;
   for (unsigned i = 0; i < sz; i++)
     attrs.push_back(constantI8(rewriter, loc, getDimLevelTypeEncoding(dlt[i])));
   params.push_back(genBuffer(rewriter, loc, attrs));
-  // Dimension sizes array of the enveloping *dense* tensor. Useful for either
+  // Dimension sizes array of the enveloping tensor. Useful for either
   // verification of external data, or for construction of internal data.
-  auto shape = resType.getShape();
+  // The index type is casted to I64 for API consistency.
+  Type iTp = rewriter.getI64Type();
   SmallVector<Value, 4> sizes;
-  if (szs.size() > 0) {
-    for (Value s : szs)
-      sizes.push_back(
-          rewriter.create<arith::IndexCastOp>(loc, s, rewriter.getI64Type()));
-  } else {
-    for (unsigned i = 0; i < sz; i++) {
-      uint64_t s = shape[i] == ShapedType::kDynamicSize ? 0 : shape[i];
-      sizes.push_back(constantI64(rewriter, loc, s));
-    }
-  }
+    sizes.push_back(rewriter.create<arith::IndexCastOp>(loc, s, iTp));
   params.push_back(genBuffer(rewriter, loc, sizes));
   // Dimension order permutation array. This is the "identity" permutation by
   // default, or otherwise the "reverse" permutation of a given ordering, so
   // that indices can be mapped quickly to the right position.
   SmallVector<Value, 4> rev(sz);
   if (AffineMap p = enc.getDimOrdering()) {
     for (unsigned i = 0; i < sz; i++)
       rev[p.getDimPosition(i)] = constantI64(rewriter, loc, i);
   } else {
     for (unsigned i = 0; i < sz; i++)
       rev[i] = constantI64(rewriter, loc, i);
   }
-  perm = genBuffer(rewriter, loc, rev);
-  params.push_back(perm);
+  params.push_back(genBuffer(rewriter, loc, rev));
   // Secondary and primary types encoding.
+  ShapedType resType = op->getResult(0).getType().cast<ShapedType>();
   unsigned secPtr = getOverheadTypeEncoding(enc.getPointerBitWidth());
   unsigned secInd = getOverheadTypeEncoding(enc.getIndexBitWidth());
   unsigned primary = getPrimaryTypeEncoding(resType.getElementType());
   assert(primary);
   params.push_back(constantI64(rewriter, loc, secPtr));
   params.push_back(constantI64(rewriter, loc, secInd));
   params.push_back(constantI64(rewriter, loc, primary));
   // User action and pointer.
   Type pTp = LLVM::LLVMPointerType::get(rewriter.getI8Type());
   if (!ptr)
     ptr = rewriter.create<LLVM::NullOp>(loc, pTp);
   params.push_back(constantI32(rewriter, loc, action));
   params.push_back(ptr);
-  // Generate the call to create new tensor.
-  StringRef name = "newSparseTensor";
-  auto call = rewriter.create<CallOp>(
-      loc, pTp, getFunc(op, name, pTp, params, /*emitCInterface=*/true),
-      params);
-  return call.getResult(0);
 }
 
 /// Generates the comparison `v != 0` where `v` is of numeric type `t`.
 /// For floating types, we use the "unordered" comparator (i.e., returns
 /// true if `v` is NaN).
 static Value genIsNonzero(ConversionPatternRewriter &rewriter, Location loc,
                           Value v) {
   Type t = v.getType();
static void genAddEltCall(ConversionPatternRewriter &rewriter, Operation *op,
   else
     llvm_unreachable("Unknown element type");
   SmallVector<Value, 8> params;
   params.push_back(ptr);
   params.push_back(val);
   params.push_back(ind);
   params.push_back(perm);
   Type pTp = LLVM::LLVMPointerType::get(rewriter.getI8Type());
-  rewriter.create<CallOp>(
-      loc, pTp, getFunc(op, name, pTp, params, /*emitCInterface=*/true),
-      params);
+  auto fn = getFunc(op, name, pTp, params, /*emitCInterface=*/true);
+  rewriter.create<CallOp>(loc, pTp, fn, params);
 }
 
 /// If the tensor is a sparse constant, generates and returns the pair of
 /// the constants for the indices and the values.
 static Optional<std::pair<Value, Value>>
 genSplitSparseConstant(ConversionPatternRewriter &rewriter, Location loc,
                        Value tensor) {
   if (auto constOp = tensor.getDefiningOp<arith::ConstantOp>()) {

 /// Sparse conversion rule for dimension accesses.
 class SparseTensorToDimSizeConverter
     : public OpConversionPattern<tensor::DimOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(tensor::DimOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    Type resType = op.getType();
+    // Only rewrite annotated DimOp with constant index.
     auto enc = getSparseTensorEncoding(op.source().getType());
     if (!enc)
       return failure();
-    // Permute the dim index.
     Optional<int64_t> index = op.getConstantIndex();
     if (!index.hasValue())
       return failure();
-    int64_t idx = index.getValue();
-    if (AffineMap p = enc.getDimOrdering())
-      idx = p.getPermutedPosition(idx);
     // Generate the call.
-    StringRef name = "sparseDimSize";
-    SmallVector<Value, 2> params;
-    params.push_back(adaptor.getOperands()[0]);
-    params.push_back(constantIndex(rewriter, op.getLoc(), idx));
-    rewriter.replaceOpWithNewOp<CallOp>(
-        op, resType, getFunc(op, name, resType, params), params);
+    Value src = adaptor.getOperands()[0];
+    int64_t idx = index.getValue();
+    rewriter.replaceOp(op, genDimSizeCall(rewriter, op, enc, src, idx));
     return success();
   }
 };
 
 /// Sparse conversion rule for the new operator.
 class SparseTensorNewConverter : public OpConversionPattern<NewOp> {
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(NewOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Type resType = op.getType();
     auto enc = getSparseTensorEncoding(resType);
     if (!enc)
       return failure();
-    Value perm;
-    rewriter.replaceOp(op, genNewCall(rewriter, op, enc, kFromFile, perm, {},
-                                      adaptor.getOperands()[0]));
+    // Generate the call to construct tensor from ptr. The sizes are
+    // inferred from the result type of the new operator.
+    SmallVector<Value, 4> sizes;
+    SmallVector<Value, 8> params;
+    sizesFromType(rewriter, sizes, op.getLoc(), resType.cast<ShapedType>());
+    Value ptr = adaptor.getOperands()[0];
+    newParams(rewriter, params, op, enc, kFromFile, sizes, ptr);
+    rewriter.replaceOp(op, genNewCall(rewriter, op, params));
     return success();
   }
 };
 
 /// Sparse conversion rule for the init operator.
 class SparseTensorInitConverter : public OpConversionPattern<InitOp> {
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(InitOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Type resType = op.getType();
     auto enc = getSparseTensorEncoding(resType);
     if (!enc)
       return failure();
-    Value perm;
-    rewriter.replaceOp(
-        op, genNewCall(rewriter, op, enc, kEmpty, perm, adaptor.getOperands()));
+    // Generate the call to construct empty tensor. The sizes are
+    // explicitly defined by the arguments to the init operator.
+    SmallVector<Value, 8> params;
+    newParams(rewriter, params, op, enc, kEmpty, adaptor.getOperands());
+    rewriter.replaceOp(op, genNewCall(rewriter, op, params));
     return success();
   }
 };
 
 /// Sparse conversion rule for the convert operator.
 class SparseTensorConvertConverter : public OpConversionPattern<ConvertOp> {
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(ConvertOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
+    Location loc = op->getLoc();
     Type resType = op.getType();
+    Type srcType = op.source().getType();
     auto encDst = getSparseTensorEncoding(resType);
-    auto encSrc = getSparseTensorEncoding(op.source().getType());
-    auto src = adaptor.getOperands()[0];
+    auto encSrc = getSparseTensorEncoding(srcType);
+    Value src = adaptor.getOperands()[0];
     if (encDst && encSrc) {
       // This is a sparse => sparse conversion, which is handled as follows:
       //   t = src->toCOO();         ; src to COO in dst order
       //   dst = newSparseTensor(t)
       // Using the coordinate scheme as an intermediate does not always
       // yield the fastest conversion but avoids the need for a full
       // O(N^2) conversion matrix.
-      Value perm;
-      Value coo = genNewCall(rewriter, op, encDst, kToCOO, perm, {}, src);
-      rewriter.replaceOp(
-          op, genNewCall(rewriter, op, encDst, kFromCOO, perm, {}, coo));
+      SmallVector<Value, 4> sizes;
+      SmallVector<Value, 8> params;
+      sizesFromPtr(rewriter, sizes, op, encSrc, srcType.cast<ShapedType>(),
+                   src);
+      newParams(rewriter, params, op, encDst, kToCOO, sizes, src);
+      Value coo = genNewCall(rewriter, op, params);
+      params[6] = constantI32(rewriter, loc, kFromCOO);
+      params[7] = coo;
+      rewriter.replaceOp(op, genNewCall(rewriter, op, params));
       return success();
     }
     if (!encDst || encSrc) {
       // TODO: sparse => dense
       return failure();
     }
     // This is a dense => sparse conversion or a sparse constant in COO =>
     // sparse conversion, which is handled as follows:
matchAndRewrite(ConvertOp op, OpAdaptor adaptor,
     //     t->add(val, [i1,..,ik], [p1,..,pk])
     //
     // Note that the dense tensor traversal code is actually implemented
     // using MLIR IR to avoid having to expose too much low-level
     // memref traversal details to the runtime support library.
     // Also note that the code below only generates the "new" ops and
     // the loop-nest per se; whereas the entire body of the innermost
     // loop is generated by genAddElt().
-    Location loc = op->getLoc();
-    ShapedType shape = resType.cast<ShapedType>();
-    Value perm;
-    Value ptr = genNewCall(rewriter, op, encDst, kEmptyCOO, perm, {});
-    Value ind =
-        genAlloca(rewriter, loc, shape.getRank(), rewriter.getIndexType());
+    ShapedType stp = resType.cast<ShapedType>();
+    unsigned rank = stp.getRank();
+    SmallVector<Value, 4> sizes;
+    SmallVector<Value, 8> params;
+    sizesFromSrc(rewriter, sizes, loc, src);
+    newParams(rewriter, params, op, encDst, kEmptyCOO, sizes);
+    Value ptr = genNewCall(rewriter, op, params);
+    Value ind = genAlloca(rewriter, loc, rank, rewriter.getIndexType());
+    Value perm = params[2];
     SmallVector<Value> lo;
     SmallVector<Value> hi;
     SmallVector<Value> st;
     Value zero = constantIndex(rewriter, loc, 0);
     Value one = constantIndex(rewriter, loc, 1);
     auto indicesValues = genSplitSparseConstant(rewriter, loc, src);
     bool isCOOConstant = indicesValues.hasValue();
     Value indices;
     Value values;
     if (isCOOConstant) {
       indices = indicesValues->first;
       values = indicesValues->second;
       lo.push_back(zero);
       hi.push_back(linalg::createOrFoldDimOp(rewriter, loc, values, 0));
       st.push_back(one);
     } else {
-      for (unsigned i = 0, rank = shape.getRank(); i < rank; i++) {
+      for (unsigned i = 0; i < rank; i++) {
         lo.push_back(zero);
         hi.push_back(linalg::createOrFoldDimOp(rewriter, loc, src, i));
         st.push_back(one);
       }
     }
-    Type eltType = shape.getElementType();
-    unsigned rank = shape.getRank();
+    Type eltType = stp.getElementType();
     scf::buildLoopNest(
         rewriter, op.getLoc(), lo, hi, st, {},
         [&](OpBuilder &builder, Location loc, ValueRange ivs,
             ValueRange args) -> scf::ValueVector {
           Value val;
           if (isCOOConstant)
             val = genIndexAndValueForSparse(rewriter, loc, indices, values, ind,
                                             ivs, rank);
           else
             val = genIndexAndValueForDense(rewriter, loc, src, ind, ivs);
           genAddEltCall(rewriter, op, eltType, ptr, val, ind, perm);
           return {};
         });
-    rewriter.replaceOp(
-        op, genNewCall(rewriter, op, encDst, kFromCOO, perm, {}, ptr));
+    // Final call to construct sparse tensor storage.
+    params[6] = constantI32(rewriter, loc, kFromCOO);
+    params[7] = ptr;
+    rewriter.replaceOp(op, genNewCall(rewriter, op, params));
     return success();
   }
 };
 
 /// Sparse conversion rule for the release operator.
 class SparseTensorReleaseConverter : public OpConversionPattern<ReleaseOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(ReleaseOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     StringRef name = "delSparseTensor";
     TypeRange none;
-    rewriter.create<CallOp>(op.getLoc(), none,
-                            getFunc(op, name, none, adaptor.getOperands()),
-                            adaptor.getOperands());
+    auto fn = getFunc(op, name, none, adaptor.getOperands());
+    rewriter.create<CallOp>(op.getLoc(), none, fn, adaptor.getOperands());
     rewriter.eraseOp(op);
     return success();
   }
 };
 
 /// Sparse conversion rule for pointer accesses.
 class SparseTensorToPointersConverter
     : public OpConversionPattern<ToPointersOp> {
matchAndRewrite(ToPointersOp op, OpAdaptor adaptor,
     else if (eltType.isInteger(32))
       name = "sparsePointers32";
     else if (eltType.isInteger(16))
       name = "sparsePointers16";
     else if (eltType.isInteger(8))
       name = "sparsePointers8";
     else
       return failure();
-    rewriter.replaceOpWithNewOp<CallOp>(op, resType,
-                                        getFunc(op, name, resType,
-                                                adaptor.getOperands(),
-                                                /*emitCInterface=*/true),
-                                        adaptor.getOperands());
+    auto fn = getFunc(op, name, resType, adaptor.getOperands(),
+                      /*emitCInterface=*/true);
+    rewriter.replaceOpWithNewOp<CallOp>(op, resType, fn, adaptor.getOperands());
     return success();
   }
 };
 
 /// Sparse conversion rule for index accesses.
 class SparseTensorToIndicesConverter : public OpConversionPattern<ToIndicesOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
matchAndRewrite(ToIndicesOp op, OpAdaptor adaptor,
     else if (eltType.isInteger(32))
       name = "sparseIndices32";
     else if (eltType.isInteger(16))
       name = "sparseIndices16";
     else if (eltType.isInteger(8))
       name = "sparseIndices8";
     else
       return failure();
-    rewriter.replaceOpWithNewOp<CallOp>(op, resType,
-                                        getFunc(op, name, resType,
-                                                adaptor.getOperands(),
-                                                /*emitCInterface=*/true),
-                                        adaptor.getOperands());
+    auto fn = getFunc(op, name, resType, adaptor.getOperands(),
+                      /*emitCInterface=*/true);
+    rewriter.replaceOpWithNewOp<CallOp>(op, resType, fn, adaptor.getOperands());
     return success();
   }
 };
 
 /// Sparse conversion rule for value accesses.
 class SparseTensorToValuesConverter : public OpConversionPattern<ToValuesOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
matchAndRewrite(ToValuesOp op, OpAdaptor adaptor,
     else if (eltType.isInteger(32))
       name = "sparseValuesI32";
     else if (eltType.isInteger(16))
       name = "sparseValuesI16";
     else if (eltType.isInteger(8))
       name = "sparseValuesI8";
     else
       return failure();
-    rewriter.replaceOpWithNewOp<CallOp>(op, resType,
-                                        getFunc(op, name, resType,
-                                                adaptor.getOperands(),
-                                                /*emitCInterface=*/true),
-                                        adaptor.getOperands());
+    auto fn = getFunc(op, name, resType, adaptor.getOperands(),
+                      /*emitCInterface=*/true);
+    rewriter.replaceOpWithNewOp<CallOp>(op, resType, fn, adaptor.getOperands());
     return success();
   }
 };
 
 /// Sparse conversion rule for tensor reconstruction.
 class SparseTensorToTensorConverter : public OpConversionPattern<ToTensorOp> {
 public:
   using OpConversionPattern::OpConversionPattern;

mlir/test/Dialect/SparseTensor/conversion.mlir

 //   CHECK-DAG: %[[R:.*]] = memref.alloca() : memref<2xi64>
 //   CHECK-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<2xi8> to memref<?xi8>
 //   CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<2xi64> to memref<?xi64>
 //   CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<2xi64> to memref<?xi64>
 //   CHECK-DAG: %[[II:.*]] = arith.index_cast %[[I]] : index to i64
 //   CHECK-DAG: %[[JJ:.*]] = arith.index_cast %[[J]] : index to i64
 //   CHECK-DAG: memref.store %[[II]], %[[Q]][%[[C0]]] : memref<2xi64>
 //   CHECK-DAG: memref.store %[[JJ]], %[[Q]][%[[C1]]] : memref<2xi64>
-//       CHECK: %[[A:.*]] = llvm.mlir.null : !llvm.ptr<i8>
-//       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[A]])
+//       CHECK: %[[NP:.*]] = llvm.mlir.null : !llvm.ptr<i8>
+//       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[NP]])
 //       CHECK: return %[[T]] : !llvm.ptr<i8>
 func @sparse_init(%arg0: index, %arg1: index) -> tensor<?x?xf64, #SparseMatrix> {
   %0 = sparse_tensor.init [%arg0, %arg1] : tensor<?x?xf64, #SparseMatrix>
   return %0 : tensor<?x?xf64, #SparseMatrix>
 }
 
 // CHECK-LABEL: func @sparse_release(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
func @sparse_nop_convert(%arg0: tensor<64xf32, #SparseVector>) -> tensor<64xf32, #SparseVector> {
   %0 = sparse_tensor.convert %arg0 : tensor<64xf32, #SparseVector> to tensor<64xf32, #SparseVector>
   return %0 : tensor<64xf32, #SparseVector>
 }
 
 // CHECK-LABEL: func @sparse_convert_1d(
 //  CHECK-SAME: %[[A:.*]]: tensor<?xi32>) -> !llvm.ptr<i8>
 //   CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
 //   CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
+//   CHECK-DAG: %[[U:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?xi32>
 //   CHECK-DAG: %[[P:.*]] = memref.alloca() : memref<1xi8>
 //   CHECK-DAG: %[[Q:.*]] = memref.alloca() : memref<1xi64>
 //   CHECK-DAG: %[[R:.*]] = memref.alloca() : memref<1xi64>
 //   CHECK-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<1xi8> to memref<?xi8>
 //   CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<1xi64> to memref<?xi64>
 //   CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<1xi64> to memref<?xi64>
-//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.}})
+//       CHECK: %[[NP:.*]] = llvm.mlir.null : !llvm.ptr<i8>
+//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[NP]])
 //       CHECK: %[[M:.*]] = memref.alloca() : memref<1xindex>
 //       CHECK: %[[T:.*]] = memref.cast %[[M]] : memref<1xindex> to memref<?xindex>
-//       CHECK: %[[U:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?xi32>
 //       CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[U]] step %[[C1]] {
 //       CHECK:   %[[E:.*]] = tensor.extract %[[A]][%[[I]]] : tensor<?xi32>
 //       CHECK:   memref.store %[[I]], %[[M]][%[[C0]]] : memref<1xindex>
 //       CHECK:   call @addEltI32(%[[C]], %[[E]], %[[T]], %[[Z]])
 //       CHECK: }
-//       CHECK: %[[T:.*]] = call @newSparseTensor(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
+//       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
 //       CHECK: return %[[T]] : !llvm.ptr<i8>
 func @sparse_convert_1d(%arg0: tensor<?xi32>) -> tensor<?xi32, #SparseVector> {
   %0 = sparse_tensor.convert %arg0 : tensor<?xi32> to tensor<?xi32, #SparseVector>
   return %0 : tensor<?xi32, #SparseVector>
 }
 
 // CHECK-LABEL: func @sparse_convert_1d_ss(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
-//       CHECK: %[[C:.*]] = call @newSparseTensor(%{{.}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[A]])
-//       CHECK: %[[T:.*]] = call @newSparseTensor(%{{.}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
+//   CHECK-DAG: %[[P:.*]] = memref.alloca() : memref<1xi8>
+//   CHECK-DAG: %[[Q:.*]] = memref.alloca() : memref<1xi64>
+//   CHECK-DAG: %[[R:.*]] = memref.alloca() : memref<1xi64>
+//   CHECK-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<1xi8> to memref<?xi8>
+//   CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<1xi64> to memref<?xi64>
+//   CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<1xi64> to memref<?xi64>
+//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[A]])
+//       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
 //       CHECK: return %[[T]] : !llvm.ptr<i8>
 func @sparse_convert_1d_ss(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
   %0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
   return %0 : tensor<?xf32, #SparseVector32>
 }
 
 // CHECK-LABEL: func @sparse_convert_2d(
 //  CHECK-SAME: %[[A:.*]]: tensor<2x4xf64>) -> !llvm.ptr<i8>
 //   CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
 //   CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
 //   CHECK-DAG: %[[P:.*]] = memref.alloca() : memref<2xi8>
 //   CHECK-DAG: %[[Q:.*]] = memref.alloca() : memref<2xi64>
 //   CHECK-DAG: %[[R:.*]] = memref.alloca() : memref<2xi64>
 //   CHECK-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<2xi8> to memref<?xi8>
 //   CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<2xi64> to memref<?xi64>
 //   CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<2xi64> to memref<?xi64>
-//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.}})
+//       CHECK: %[[NP:.*]] = llvm.mlir.null : !llvm.ptr<i8>
+//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[NP]])
 //       CHECK: %[[M:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK: %[[T:.*]] = memref.cast %[[M]] : memref<2xindex> to memref<?xindex>
 //       CHECK: scf.for %[[I:.*]] = %[[C0]] to %{{.*}} step %[[C1]] {
 //       CHECK:   scf.for %[[J:.*]] = %[[C0]] to %{{.*}} step %[[C1]] {
 //       CHECK:     %[[E:.*]] = tensor.extract %[[A]][%[[I]], %[[J]]] : tensor<2x4xf64>
 //       CHECK:     memref.store %[[I]], %[[M]][%[[C0]]] : memref<2xindex>
 //       CHECK:     memref.store %[[J]], %[[M]][%[[C1]]] : memref<2xindex>
 //       CHECK:     call @addEltF64(%[[C]], %[[E]], %[[T]], %[[Z]])
 //       CHECK:   }
 //       CHECK: }
-//       CHECK: %[[T:.*]] = call @newSparseTensor(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
+//       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
 //       CHECK: return %[[T]] : !llvm.ptr<i8>
 func @sparse_convert_2d(%arg0: tensor<2x4xf64>) -> tensor<2x4xf64, #SparseMatrix> {
   %0 = sparse_tensor.convert %arg0 : tensor<2x4xf64> to tensor<2x4xf64, #SparseMatrix>
   return %0 : tensor<2x4xf64, #SparseMatrix>
 }
 
 // CHECK-LABEL: func @sparse_constant() -> !llvm.ptr<i8> {
 //   CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
 //   CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
 //   CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
 //   CHECK-DAG: %[[P:.*]] = memref.alloca() : memref<2xi8>
 //   CHECK-DAG: %[[Q:.*]] = memref.alloca() : memref<2xi64>
 //   CHECK-DAG: %[[R:.*]] = memref.alloca() : memref<2xi64>
 //   CHECK-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<2xi8> to memref<?xi8>
 //   CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<2xi64> to memref<?xi64>
 //   CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<2xi64> to memref<?xi64>
-//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.}})
+//       CHECK: %[[NP:.*]] = llvm.mlir.null : !llvm.ptr<i8>
+//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[NP]])
 //       CHECK: %[[M:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK: %[[N:.*]] = memref.cast %[[M]] : memref<2xindex> to memref<?xindex>
 //       CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[C2]] step %[[C1]] {
 //       CHECK:   memref.store %{{.*}}, %[[M]][%[[C0]]] : memref<2xindex>
 //       CHECK:   memref.store %{{.*}}, %[[M]][%[[C1]]] : memref<2xindex>
 //       CHECK:   %[[V:.*]] = tensor.extract %{{.*}}[%[[I]]] : tensor<2xf32>
 //       CHECK:   call @addEltF32(%{{.*}}, %[[V]], %[[N]], %{{.*}})
 //       CHECK: }
-//       CHECK: %[[T:.*]] = call @newSparseTensor(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
+//       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
 //       CHECK: return %[[T]] : !llvm.ptr<i8>
 func @sparse_constant() -> tensor<8x7xf32, #SparseMatrix>{
   // Initialize a tensor.
   %0 = arith.constant sparse<[[0, 0], [1, 6]], [1.0, 5.0]> : tensor<8x7xf32>
   // Convert the tensor to a sparse tensor.
   %1 = sparse_tensor.convert %0 : tensor<8x7xf32> to tensor<8x7xf32, #SparseMatrix>
   return %1 : tensor<8x7xf32, #SparseMatrix>
 }
 
 // CHECK-LABEL: func @sparse_convert_3d(
 //  CHECK-SAME: %[[A:.*]]: tensor<?x?x?xf64>) -> !llvm.ptr<i8>
 //   CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
 //   CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
 //   CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
+//   CHECK-DAG: %[[U1:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?x?x?xf64>
+//   CHECK-DAG: %[[U2:.*]] = tensor.dim %[[A]], %[[C1]] : tensor<?x?x?xf64>
+//   CHECK-DAG: %[[U3:.*]] = tensor.dim %[[A]], %[[C2]] : tensor<?x?x?xf64>
 //   CHECK-DAG: %[[P:.*]] = memref.alloca() : memref<3xi8>
 //   CHECK-DAG: %[[Q:.*]] = memref.alloca() : memref<3xi64>
 //   CHECK-DAG: %[[R:.*]] = memref.alloca() : memref<3xi64>
 //   CHECK-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<3xi8> to memref<?xi8>
 //   CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<3xi64> to memref<?xi64>
 //   CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<3xi64> to memref<?xi64>
-//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.}})
+//       CHECK: %[[NP:.*]] = llvm.mlir.null : !llvm.ptr<i8>
+//       CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[NP]])
 //       CHECK: %[[M:.*]] = memref.alloca() : memref<3xindex>
 //       CHECK: %[[N:.*]] = memref.cast %[[M]] : memref<3xindex> to memref<?xindex>
-//       CHECK: %[[U1:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?x?x?xf64>
-//       CHECK: %[[U2:.*]] = tensor.dim %[[A]], %[[C1]] : tensor<?x?x?xf64>
-//       CHECK: %[[U3:.*]] = tensor.dim %[[A]], %[[C2]] : tensor<?x?x?xf64>
 //       CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[U1]] step %[[C1]] {
 //       CHECK:   scf.for %[[J:.*]] = %[[C0]] to %[[U2]] step %[[C1]] {
 //       CHECK:     scf.for %[[K:.*]] = %[[C0]] to %[[U3]] step %[[C1]] {
 //       CHECK:       %[[E:.*]] = tensor.extract %[[A]][%[[I]], %[[J]], %[[K]]] : tensor<?x?x?xf64>
 //       CHECK:       memref.store %[[I]], %[[M]][%[[C0]]] : memref<3xindex>
 //       CHECK:       memref.store %[[J]], %[[M]][%[[C1]]] : memref<3xindex>
 //       CHECK:       memref.store %[[K]], %[[M]][%[[C2]]] : memref<3xindex>
 //       CHECK:       call @addEltF64(%[[C]], %[[E]], %[[N]], %[[Z]])
 //       CHECK:     }
 //       CHECK:   }
 //       CHECK: }
-//       CHECK: %[[T:.*]] = call @newSparseTensor(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
+//       CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[C]])
 //       CHECK: return %[[T]] : !llvm.ptr<i8>
 func @sparse_convert_3d(%arg0: tensor<?x?x?xf64>) -> tensor<?x?x?xf64, #SparseTensor> {
   %0 = sparse_tensor.convert %arg0 : tensor<?x?x?xf64> to tensor<?x?x?xf64, #SparseTensor>
   return %0 : tensor<?x?x?xf64, #SparseTensor>
 }
 
 // CHECK-LABEL: func @sparse_pointers(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)

mlir/test/Dialect/SparseTensor/invalid.mlir

func @sparse_convert_unranked(%arg0: tensor<*xf32>) -> tensor<10xf32> {
   %0 = sparse_tensor.convert %arg0 : tensor<*xf32> to tensor<10xf32>
   return %0 : tensor<10xf32>
 }
 
 // -----
 
 #CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
 
-func @sparse_convert_mismatch(%arg0: tensor<10x10xf32>) -> tensor<10x?xf32, #CSR> {
+func @sparse_convert_mismatch(%arg0: tensor<10x?xf32>) -> tensor<10x10xf32, #CSR> {
   // expected-error@+1 {{unexpected conversion mismatch in dimension 1}}
-  %0 = sparse_tensor.convert %arg0 : tensor<10x10xf32> to tensor<10x?xf32, #CSR>
-  return %0 : tensor<10x?xf32, #CSR>
+  %0 = sparse_tensor.convert %arg0 : tensor<10x?xf32> to tensor<10x10xf32, #CSR>
+  return %0 : tensor<10x10xf32, #CSR>
 }

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

+// RUN: mlir-opt %s \
+// RUN:   --sparsification --sparse-tensor-conversion \
+// RUN:   --linalg-bufferize --convert-linalg-to-loops \
+// RUN:   --convert-vector-to-scf --convert-scf-to-std \
+// RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
+// RUN:   --std-bufferize --finalizing-bufferize --lower-affine \
+// RUN:   --convert-vector-to-llvm --convert-memref-to-llvm --convert-math-to-llvm \
+// RUN:   --convert-std-to-llvm --reconcile-unrealized-casts | \
+// RUN: mlir-cpu-runner \
+// RUN:  -e entry -entry-point-result=void  \
+// RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
+// RUN: FileCheck %s
+
+#DCSR  = #sparse_tensor.encoding<{
+  dimLevelType = [ "compressed", "compressed" ]
+}>
+
+#DCSC  = #sparse_tensor.encoding<{
+  dimLevelType = [ "compressed", "compressed" ],
+  dimOrdering = affine_map<(i,j) -> (j,i)>
+}>
+
+//
+// Integration test that tests conversions between sparse tensors,
+// where the dynamic sizes of the shape of the enveloping tensor
+// may change (the actual underlying sizes obviously never change).
+//
+module {
+
+  //
+  // Helper method to print values array. The transfer actually
+  // reads more than required to verify size of buffer as well.
+  //
+  func @dump(%arg0: memref<?xf64>) {
+    %c = arith.constant 0 : index
+    %d = arith.constant -1.0 : f64
+    %0 = vector.transfer_read %arg0[%c], %d: memref<?xf64>, vector<8xf64>
+    vector.print %0 : vector<8xf64>
+    return
+  }
+
+  func @entry() {
+    %t1 = arith.constant sparse<
+      [ [0,0], [0,1], [0,63], [1,0], [1,1], [31,0], [31,63] ],
+        [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]> : tensor<32x64xf64>
+    %t2 = tensor.cast %t1 : tensor<32x64xf64> to tensor<?x?xf64>
+
+    // Four dense to sparse conversions.
+    %1 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSR>
+    %2 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSC>
+    %3 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSR>
+    %4 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSC>
+
+    // Two cross conversions.
+    %5 = sparse_tensor.convert %3 : tensor<?x?xf64, #DCSR> to tensor<?x?xf64, #DCSC>
+    %6 = sparse_tensor.convert %4 : tensor<?x?xf64, #DCSC> to tensor<?x?xf64, #DCSR>
+
+    //
+    // All proper row-/column-wise?
+    //
+    // CHECK: ( 1, 2, 3, 4, 5, 6, 7, -1 )
+    // CHECK: ( 1, 4, 6, 2, 5, 3, 7, -1 )
+    // CHECK: ( 1, 2, 3, 4, 5, 6, 7, -1 )
+    // CHECK: ( 1, 4, 6, 2, 5, 3, 7, -1 )
+    // CHECK: ( 1, 4, 6, 2, 5, 3, 7, -1 )
+    // CHECK: ( 1, 2, 3, 4, 5, 6, 7, -1 )
+    //
+    %m1 = sparse_tensor.values %1 : tensor<?x?xf64, #DCSR> to memref<?xf64>
+    %m2 = sparse_tensor.values %2 : tensor<?x?xf64, #DCSC> to memref<?xf64>
+    %m3 = sparse_tensor.values %3 : tensor<?x?xf64, #DCSR> to memref<?xf64>
+    %m4 = sparse_tensor.values %4 : tensor<?x?xf64, #DCSC> to memref<?xf64>
+    %m5 = sparse_tensor.values %5 : tensor<?x?xf64, #DCSC> to memref<?xf64>
+    %m6 = sparse_tensor.values %6 : tensor<?x?xf64, #DCSR> to memref<?xf64>
+    call @dump(%m1) : (memref<?xf64>) -> ()
+    call @dump(%m2) : (memref<?xf64>) -> ()
+    call @dump(%m3) : (memref<?xf64>) -> ()
+    call @dump(%m4) : (memref<?xf64>) -> ()
+    call @dump(%m5) : (memref<?xf64>) -> ()
+    call @dump(%m6) : (memref<?xf64>) -> ()
+
+    // Release the resources.
+    sparse_tensor.release %1 : tensor<?x?xf64, #DCSR>
+    sparse_tensor.release %2 : tensor<?x?xf64, #DCSC>
+    sparse_tensor.release %3 : tensor<?x?xf64, #DCSR>
+    sparse_tensor.release %4 : tensor<?x?xf64, #DCSC>
+    sparse_tensor.release %5 : tensor<?x?xf64, #DCSC>
+    sparse_tensor.release %6 : tensor<?x?xf64, #DCSR>
+
+    return
+  }
+}