[mlir][sparse] Improve handling of dynamic-sizes for sparse=>dense conversion

[wrengr]

Summary

Allows the result to be more dynamically-sized than the source.

[wrengr]

Rebasing

[aartbik]

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

629

since the srcTensorTp is always more specific than the dest (but they are always congruent), passing in srcTensorTp too will actually make the dense alloc more static than it is now. Of course, you will have to cast the srcTensorTp to dstTensorTp eventually if they are not the same

perhaps not worth it, since the big savings (avoiding runtime calls) comes from your change in L627 already, and this will only make the alloc take less arguments...

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_sparse2dense.mlir

133–134

you had remove this in a previous revision, but somehow it came back

[wrengr]

Fixing rebase errors

[wrengr]

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

361

This change was from either D110790 or D112674, but got lost in the bad rebase

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_sparse2dense.mlir

133–134

Hrm, it's not on the perforce side; fixing the git rebase

[wrengr]

Removing comment

[aartbik]

Please remove the two lines as requested, and make sure we build green before submitting but no need for other review. LGTM

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

611–612

This seems a bit redundant, since this applies to all branches (and all ops we generate anyway).
So let's delete 611/612

629

per our offline discussion, not worth it, big savings is already done, and constant prop will do the rest

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

static void genAddEltCall(ConversionPatternRewriter &rewriter, Operation *op,
   Type pTp = LLVM::LLVMPointerType::get(rewriter.getI8Type());
   auto fn = getFunc(op, name, pTp, params, /*emitCInterface=*/true);
   rewriter.create<CallOp>(loc, pTp, fn, params);
 }
 
 /// Generates a call to `iter->getNext()`.  If there is a next element,
 /// then it is copied into the out-parameters `ind` and `elemPtr`,
 /// and the return value is true.  If there isn't a next element, then
-/// the return value is false.
+/// the memory for `iter` is freed and the return value is false.
 static Value genGetNextCall(ConversionPatternRewriter &rewriter, Operation *op,
                             Value iter, Value ind, Value elemPtr) {
   Location loc = op->getLoc();
   Type elemTp = elemPtr.getType().cast<ShapedType>().getElementType();
   StringRef name;
   if (elemTp.isF64())
     name = "getNextF64";
   else if (elemTp.isF32())
if (encDst && encSrc) {
       params[6] = constantI32(rewriter, loc, kFromCOO);
       params[7] = coo;
       rewriter.replaceOp(op, genNewCall(rewriter, op, params));
       return success();
     }
     if (!encDst && encSrc) {
       // This is sparse => dense conversion, which is handled as follows:
       //   dst = new Tensor(0);
-      //   iter = src->toCOO()->getIterator();
+      //   iter = src->toCOO();
+      //   iter->startIterator();
       //   while (elem = iter->getNext()) {
       //     dst[elem.indices] = elem.value;
       //   }
-      Location loc = op->getLoc();
-      RankedTensorType tensorTp = resType.dyn_cast<RankedTensorType>();
-      if (!tensorTp)
-        return failure();
-      unsigned rank = tensorTp.getRank();
+      RankedTensorType dstTensorTp = resType.cast<RankedTensorType>();
+      RankedTensorType srcTensorTp = srcType.cast<RankedTensorType>();
+      unsigned rank = dstTensorTp.getRank();
+      Type elemTp = dstTensorTp.getElementType();
+      // Fabricate a no-permutation encoding for newParams().
+      // The pointer/index types must be those of `src`.
+      // The dimLevelTypes aren't actually used by kToIter.
       encDst = SparseTensorEncodingAttr::get(
           op->getContext(),
           SmallVector<SparseTensorEncodingAttr::DimLevelType>(
               rank, SparseTensorEncodingAttr::DimLevelType::Dense),
           AffineMap(), encSrc.getPointerBitWidth(), encSrc.getIndexBitWidth());
       SmallVector<Value, 4> sizes;
       SmallVector<Value, 8> params;
-      sizesFromPtr(rewriter, sizes, op, encSrc, tensorTp, src);
+      sizesFromPtr(rewriter, sizes, op, encSrc, srcTensorTp, src);
       newParams(rewriter, params, op, encDst, kToIter, sizes, src);
       Value iter = genNewCall(rewriter, op, params);
       Value ind = genAlloca(rewriter, loc, rank, rewriter.getIndexType());
-      Value elemPtr = genAllocaScalar(rewriter, loc, tensorTp.getElementType());
-      Value dst = allocDenseTensor(rewriter, loc, tensorTp, sizes);
+      Value elemPtr = genAllocaScalar(rewriter, loc, elemTp);
+      Value dst = allocDenseTensor(rewriter, loc, dstTensorTp, sizes);
       SmallVector<Value> noArgs;
       SmallVector<Type> noTypes;
       auto whileOp = rewriter.create<scf::WhileOp>(loc, noTypes, noArgs);
       Block *before = rewriter.createBlock(&whileOp.before(), {}, noTypes);
       rewriter.setInsertionPointToEnd(before);
       Value cond = genGetNextCall(rewriter, op, iter, ind, elemPtr);
       rewriter.create<scf::ConditionOp>(loc, cond, before->getArguments());
       Block *after = rewriter.createBlock(&whileOp.after(), {}, noTypes);

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_sparse2dense.mlir

#Tensor6  = #sparse_tensor.encoding<{
   dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
 }>
 
 //
 // Integration test that tests conversions from sparse to dense tensors.
 //
 module {
   //
-  // Output utilities.
+  // Utilities for output and releasing memory.
   //
-  func @dumpf64(%arg0: tensor<2x3x4xf64>) {
+  func @dump(%arg0: tensor<2x3x4xf64>) {
     %c0 = arith.constant 0 : index
     %d0 = arith.constant -1.0 : f64
     %0 = vector.transfer_read %arg0[%c0, %c0, %c0], %d0: tensor<2x3x4xf64>, vector<2x3x4xf64>
     vector.print %0 : vector<2x3x4xf64>
     return
   }
+  func @dumpAndRelease_234(%arg0: tensor<2x3x4xf64>) {
+    call @dump(%arg0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<2x3x4xf64>
+    memref.dealloc %1 : memref<2x3x4xf64>
+    return
+  }
+  func @dumpAndRelease_p34(%arg0: tensor<?x3x4xf64>) {
+    %0 = tensor.cast %arg0 : tensor<?x3x4xf64> to tensor<2x3x4xf64>
+    call @dump(%0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<?x3x4xf64>
+    memref.dealloc %1 : memref<?x3x4xf64>
+    return
+  }
+  func @dumpAndRelease_2p4(%arg0: tensor<2x?x4xf64>) {
+    %0 = tensor.cast %arg0 : tensor<2x?x4xf64> to tensor<2x3x4xf64>
+    call @dump(%0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<2x?x4xf64>
+    memref.dealloc %1 : memref<2x?x4xf64>
+    return
+  }
+  func @dumpAndRelease_23p(%arg0: tensor<2x3x?xf64>) {
+    %0 = tensor.cast %arg0 : tensor<2x3x?xf64> to tensor<2x3x4xf64>
+    call @dump(%0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<2x3x?xf64>
+    memref.dealloc %1 : memref<2x3x?xf64>
+    return
+  }
+  func @dumpAndRelease_2pp(%arg0: tensor<2x?x?xf64>) {
+    %0 = tensor.cast %arg0 : tensor<2x?x?xf64> to tensor<2x3x4xf64>
+    call @dump(%0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<2x?x?xf64>
+    memref.dealloc %1 : memref<2x?x?xf64>
+    return
+  }
+  func @dumpAndRelease_p3p(%arg0: tensor<?x3x?xf64>) {
+    %0 = tensor.cast %arg0 : tensor<?x3x?xf64> to tensor<2x3x4xf64>
+    call @dump(%0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<?x3x?xf64>
+    memref.dealloc %1 : memref<?x3x?xf64>
+    return
+  }
+  func @dumpAndRelease_pp4(%arg0: tensor<?x?x4xf64>) {
+    %0 = tensor.cast %arg0 : tensor<?x?x4xf64> to tensor<2x3x4xf64>
+    call @dump(%0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<?x?x4xf64>
+    memref.dealloc %1 : memref<?x?x4xf64>
+    return
+  }
+  func @dumpAndRelease_ppp(%arg0: tensor<?x?x?xf64>) {
+    %0 = tensor.cast %arg0 : tensor<?x?x?xf64> to tensor<2x3x4xf64>
+    call @dump(%0) : (tensor<2x3x4xf64>) -> ()
+    %1 = memref.buffer_cast %arg0 : memref<?x?x?xf64>
+    memref.dealloc %1 : memref<?x?x?xf64>
+    return
+  }
 
   //
   // Main driver.
   //
   func @entry() {
     //
     // Initialize a 3-dim dense tensor.
     //
-    %t = arith.constant dense<[
+    %src = arith.constant dense<[
        [  [  1.0,  2.0,  3.0,  4.0 ],
           [  5.0,  6.0,  7.0,  8.0 ],
           [  9.0, 10.0, 11.0, 12.0 ] ],
        [  [ 13.0, 14.0, 15.0, 16.0 ],
           [ 17.0, 18.0, 19.0, 20.0 ],
           [ 21.0, 22.0, 23.0, 24.0 ] ]
     ]> : tensor<2x3x4xf64>
 
     //
     // Convert dense tensor directly to various sparse tensors.
-    //    tensor1: stored as 2x3x4
-    //    tensor2: stored as 3x4x2
-    //    tensor3: stored as 4x2x3
-    //
-    %1 = sparse_tensor.convert %t : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor1>
-    %2 = sparse_tensor.convert %t : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor2>
-    %3 = sparse_tensor.convert %t : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor3>
-    %4 = sparse_tensor.convert %t : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor4>
-    %5 = sparse_tensor.convert %t : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor5>
-    %6 = sparse_tensor.convert %t : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor6>
+    //
+    %s2341 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor1>
+    %s2342 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor2>
+    %s2343 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor3>
+    %s2344 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor4>
+    %s2345 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor5>
+    %s2346 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor6>
+
+    %sp344 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<?x3x4xf64, #Tensor4>
+    %sp345 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<?x3x4xf64, #Tensor5>
+    %sp346 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<?x3x4xf64, #Tensor6>
+    %s2p44 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x?x4xf64, #Tensor4>
+    %s2p45 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x?x4xf64, #Tensor5>
+    %s2p46 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x?x4xf64, #Tensor6>
+    %s23p4 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x?xf64, #Tensor4>
+    %s23p5 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x?xf64, #Tensor5>
+    %s23p6 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x?xf64, #Tensor6>
+    %s2pp4 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x?x?xf64, #Tensor4>
+    %s2pp5 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x?x?xf64, #Tensor5>
+    %s2pp6 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x?x?xf64, #Tensor6>
 
     //
     // Convert sparse tensor back to dense.
     //
-    %a = sparse_tensor.convert %1 : tensor<2x3x4xf64, #Tensor1> to tensor<2x3x4xf64>
-    %b = sparse_tensor.convert %2 : tensor<2x3x4xf64, #Tensor2> to tensor<2x3x4xf64>
-    %c = sparse_tensor.convert %3 : tensor<2x3x4xf64, #Tensor3> to tensor<2x3x4xf64>
-    %d = sparse_tensor.convert %4 : tensor<2x3x4xf64, #Tensor4> to tensor<2x3x4xf64>
-    %e = sparse_tensor.convert %5 : tensor<2x3x4xf64, #Tensor5> to tensor<2x3x4xf64>
-    %f = sparse_tensor.convert %6 : tensor<2x3x4xf64, #Tensor6> to tensor<2x3x4xf64>
-
-    //
-    // Check round-trip equality.
-    //
-    // CHECK:      ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
-    // CHECK-NEXT: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
-    // CHECK-NEXT: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
-    // CHECK-NEXT: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
-    // CHECK-NEXT: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
-    // CHECK-NEXT: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
-    // CHECK-NEXT: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
-    call @dumpf64(%t) : (tensor<2x3x4xf64>) -> ()
-    call @dumpf64(%a) : (tensor<2x3x4xf64>) -> ()
-    call @dumpf64(%b) : (tensor<2x3x4xf64>) -> ()
-    call @dumpf64(%c) : (tensor<2x3x4xf64>) -> ()
-    call @dumpf64(%d) : (tensor<2x3x4xf64>) -> ()
-    call @dumpf64(%e) : (tensor<2x3x4xf64>) -> ()
-    call @dumpf64(%f) : (tensor<2x3x4xf64>) -> ()
-
-    //
-    // Release the resources.
-    //
-    sparse_tensor.release %1 : tensor<2x3x4xf64, #Tensor1>
-    sparse_tensor.release %2 : tensor<2x3x4xf64, #Tensor2>
-    sparse_tensor.release %3 : tensor<2x3x4xf64, #Tensor3>
-    sparse_tensor.release %4 : tensor<2x3x4xf64, #Tensor4>
-    sparse_tensor.release %5 : tensor<2x3x4xf64, #Tensor5>
-    sparse_tensor.release %6 : tensor<2x3x4xf64, #Tensor6>
-
-    %ma = memref.buffer_cast %a : memref<2x3x4xf64>
-    %mb = memref.buffer_cast %b : memref<2x3x4xf64>
-    %mc = memref.buffer_cast %c : memref<2x3x4xf64>
-    %md = memref.buffer_cast %d : memref<2x3x4xf64>
-    %me = memref.buffer_cast %e : memref<2x3x4xf64>
-    %mf = memref.buffer_cast %f : memref<2x3x4xf64>
-
-    memref.dealloc %ma : memref<2x3x4xf64>
-    memref.dealloc %mb : memref<2x3x4xf64>
-    memref.dealloc %mc : memref<2x3x4xf64>
-    memref.dealloc %md : memref<2x3x4xf64>
-    memref.dealloc %me : memref<2x3x4xf64>
-    memref.dealloc %mf : memref<2x3x4xf64>
+    %d2341 = sparse_tensor.convert %s2341 : tensor<2x3x4xf64, #Tensor1> to tensor<2x3x4xf64>
+    %d2342 = sparse_tensor.convert %s2342 : tensor<2x3x4xf64, #Tensor2> to tensor<2x3x4xf64>
+    %d2343 = sparse_tensor.convert %s2343 : tensor<2x3x4xf64, #Tensor3> to tensor<2x3x4xf64>
+    %d2344 = sparse_tensor.convert %s2344 : tensor<2x3x4xf64, #Tensor4> to tensor<2x3x4xf64>
+    %d2345 = sparse_tensor.convert %s2345 : tensor<2x3x4xf64, #Tensor5> to tensor<2x3x4xf64>
+    %d2346 = sparse_tensor.convert %s2346 : tensor<2x3x4xf64, #Tensor6> to tensor<2x3x4xf64>
+
+    %dp344 = sparse_tensor.convert %sp344 : tensor<?x3x4xf64, #Tensor4> to tensor<?x3x4xf64>
+    %dp345 = sparse_tensor.convert %sp345 : tensor<?x3x4xf64, #Tensor5> to tensor<?x3x4xf64>
+    %dp346 = sparse_tensor.convert %sp346 : tensor<?x3x4xf64, #Tensor6> to tensor<?x3x4xf64>
+    %d2p44 = sparse_tensor.convert %s2p44 : tensor<2x?x4xf64, #Tensor4> to tensor<2x?x4xf64>
+    %d2p45 = sparse_tensor.convert %s2p45 : tensor<2x?x4xf64, #Tensor5> to tensor<2x?x4xf64>
+    %d2p46 = sparse_tensor.convert %s2p46 : tensor<2x?x4xf64, #Tensor6> to tensor<2x?x4xf64>
+    %d23p4 = sparse_tensor.convert %s23p4 : tensor<2x3x?xf64, #Tensor4> to tensor<2x3x?xf64>
+    %d23p5 = sparse_tensor.convert %s23p5 : tensor<2x3x?xf64, #Tensor5> to tensor<2x3x?xf64>
+    %d23p6 = sparse_tensor.convert %s23p6 : tensor<2x3x?xf64, #Tensor6> to tensor<2x3x?xf64>
+    %d2pp4 = sparse_tensor.convert %s2pp4 : tensor<2x?x?xf64, #Tensor4> to tensor<2x?x?xf64>
+    %d2pp5 = sparse_tensor.convert %s2pp5 : tensor<2x?x?xf64, #Tensor5> to tensor<2x?x?xf64>
+    %d2pp6 = sparse_tensor.convert %s2pp6 : tensor<2x?x?xf64, #Tensor6> to tensor<2x?x?xf64>
+
+    %dp3p4 = sparse_tensor.convert %sp344 : tensor<?x3x4xf64, #Tensor4> to tensor<?x3x?xf64>
+    %dp3p5 = sparse_tensor.convert %sp345 : tensor<?x3x4xf64, #Tensor5> to tensor<?x3x?xf64>
+    %dp3p6 = sparse_tensor.convert %sp346 : tensor<?x3x4xf64, #Tensor6> to tensor<?x3x?xf64>
+    %dpp44 = sparse_tensor.convert %s2p44 : tensor<2x?x4xf64, #Tensor4> to tensor<?x?x4xf64>
+    %dpp45 = sparse_tensor.convert %s2p45 : tensor<2x?x4xf64, #Tensor5> to tensor<?x?x4xf64>
+    %dpp46 = sparse_tensor.convert %s2p46 : tensor<2x?x4xf64, #Tensor6> to tensor<?x?x4xf64>
+    %dppp4 = sparse_tensor.convert %s2pp4 : tensor<2x?x?xf64, #Tensor4> to tensor<?x?x?xf64>
+    %dppp5 = sparse_tensor.convert %s2pp5 : tensor<2x?x?xf64, #Tensor5> to tensor<?x?x?xf64>
+    %dppp6 = sparse_tensor.convert %s2pp6 : tensor<2x?x?xf64, #Tensor6> to tensor<?x?x?xf64>
+
+    //
+    // Check round-trip equality.  And release dense tensors.
+    //
+    // CHECK-COUNT-28: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
+    call @dump(%src) : (tensor<2x3x4xf64>) -> ()
+    call @dumpAndRelease_234(%d2341) : (tensor<2x3x4xf64>) -> ()
+    call @dumpAndRelease_234(%d2342) : (tensor<2x3x4xf64>) -> ()
+    call @dumpAndRelease_234(%d2343) : (tensor<2x3x4xf64>) -> ()
+    call @dumpAndRelease_234(%d2344) : (tensor<2x3x4xf64>) -> ()
+    call @dumpAndRelease_234(%d2345) : (tensor<2x3x4xf64>) -> ()
+    call @dumpAndRelease_234(%d2346) : (tensor<2x3x4xf64>) -> ()
+    call @dumpAndRelease_p34(%dp344) : (tensor<?x3x4xf64>) -> ()
+    call @dumpAndRelease_p34(%dp345) : (tensor<?x3x4xf64>) -> ()
+    call @dumpAndRelease_p34(%dp346) : (tensor<?x3x4xf64>) -> ()
+    call @dumpAndRelease_2p4(%d2p44) : (tensor<2x?x4xf64>) -> ()
+    call @dumpAndRelease_2p4(%d2p45) : (tensor<2x?x4xf64>) -> ()
+    call @dumpAndRelease_2p4(%d2p46) : (tensor<2x?x4xf64>) -> ()
+    call @dumpAndRelease_23p(%d23p4) : (tensor<2x3x?xf64>) -> ()
+    call @dumpAndRelease_23p(%d23p5) : (tensor<2x3x?xf64>) -> ()
+    call @dumpAndRelease_23p(%d23p6) : (tensor<2x3x?xf64>) -> ()
+    call @dumpAndRelease_2pp(%d2pp4) : (tensor<2x?x?xf64>) -> ()
+    call @dumpAndRelease_2pp(%d2pp5) : (tensor<2x?x?xf64>) -> ()
+    call @dumpAndRelease_2pp(%d2pp6) : (tensor<2x?x?xf64>) -> ()
+    call @dumpAndRelease_p3p(%dp3p4) : (tensor<?x3x?xf64>) -> ()
+    call @dumpAndRelease_p3p(%dp3p5) : (tensor<?x3x?xf64>) -> ()
+    call @dumpAndRelease_p3p(%dp3p6) : (tensor<?x3x?xf64>) -> ()
+    call @dumpAndRelease_pp4(%dpp44) : (tensor<?x?x4xf64>) -> ()
+    call @dumpAndRelease_pp4(%dpp45) : (tensor<?x?x4xf64>) -> ()
+    call @dumpAndRelease_pp4(%dpp46) : (tensor<?x?x4xf64>) -> ()
+    call @dumpAndRelease_ppp(%dppp4) : (tensor<?x?x?xf64>) -> ()
+    call @dumpAndRelease_ppp(%dppp5) : (tensor<?x?x?xf64>) -> ()
+    call @dumpAndRelease_ppp(%dppp6) : (tensor<?x?x?xf64>) -> ()
+
+    //
+    // Release sparse tensors.
+    //
+    sparse_tensor.release %s2341 : tensor<2x3x4xf64, #Tensor1>
+    sparse_tensor.release %s2342 : tensor<2x3x4xf64, #Tensor2>
+    sparse_tensor.release %s2343 : tensor<2x3x4xf64, #Tensor3>
+    sparse_tensor.release %s2344 : tensor<2x3x4xf64, #Tensor4>
+    sparse_tensor.release %s2345 : tensor<2x3x4xf64, #Tensor5>
+    sparse_tensor.release %s2346 : tensor<2x3x4xf64, #Tensor6>
+    sparse_tensor.release %sp344 : tensor<?x3x4xf64, #Tensor4>
+    sparse_tensor.release %sp345 : tensor<?x3x4xf64, #Tensor5>
+    sparse_tensor.release %sp346 : tensor<?x3x4xf64, #Tensor6>
+    sparse_tensor.release %s2p44 : tensor<2x?x4xf64, #Tensor4>
+    sparse_tensor.release %s2p45 : tensor<2x?x4xf64, #Tensor5>
+    sparse_tensor.release %s2p46 : tensor<2x?x4xf64, #Tensor6>
+    sparse_tensor.release %s23p4 : tensor<2x3x?xf64, #Tensor4>
+    sparse_tensor.release %s23p5 : tensor<2x3x?xf64, #Tensor5>
+    sparse_tensor.release %s23p6 : tensor<2x3x?xf64, #Tensor6>
+    sparse_tensor.release %s2pp4 : tensor<2x?x?xf64, #Tensor4>
+    sparse_tensor.release %s2pp5 : tensor<2x?x?xf64, #Tensor5>
+    sparse_tensor.release %s2pp6 : tensor<2x?x?xf64, #Tensor6>
 
     return
   }
 }