diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp --- a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp @@ -873,15 +873,35 @@ } /// Generates an index value. -static Value genIndexValue(Merger &merger, CodeGen &codegen, unsigned exp) { - assert(codegen.curVecLength == 1); // TODO: implement vectorization! +static Value genIndexValue(Merger &merger, CodeGen &codegen, + PatternRewriter &rewriter, unsigned exp, + unsigned ldx) { unsigned idx = merger.exp(exp).index; - return codegen.loops[idx]; + Value ival = codegen.loops[idx]; + Type itype = ival.getType(); + // During vectorization, we either encounter: + // (1) indices already in vector form, as in ... = ind[lo:hi], good to go, or + // (2) single index, as in ... = i, must convert to [i, i+1, ...] for inner i. + unsigned vl = codegen.curVecLength; + if (vl > 1 && !itype.isa()) { + Location loc = ival.getLoc(); + VectorType vtp = vectorType(codegen, itype); + ival = rewriter.create(loc, vtp, ival); + if (idx == ldx) { + SmallVector integers; + for (unsigned i = 0; i < vl; i++) + integers.push_back(APInt(/*width=*/64, i)); + auto values = DenseElementsAttr::get(vtp, integers); + Value incr = rewriter.create(loc, vtp, values); + ival = rewriter.create(loc, ival, incr); + } + } + return ival; } /// Recursively generates tensor expression. static Value genExp(Merger &merger, CodeGen &codegen, PatternRewriter &rewriter, - linalg::GenericOp op, unsigned exp) { + linalg::GenericOp op, unsigned exp, unsigned ldx) { Location loc = op.getLoc(); if (exp == -1u) return Value(); @@ -890,9 +910,11 @@ if (merger.exp(exp).kind == Kind::kInvariant) return genInvariantValue(merger, codegen, rewriter, exp); if (merger.exp(exp).kind == Kind::kIndex) - return genIndexValue(merger, codegen, exp); - Value v0 = genExp(merger, codegen, rewriter, op, merger.exp(exp).children.e0); - Value v1 = genExp(merger, codegen, rewriter, op, merger.exp(exp).children.e1); + return genIndexValue(merger, codegen, rewriter, exp, ldx); + Value v0 = + genExp(merger, codegen, rewriter, op, merger.exp(exp).children.e0, ldx); + Value v1 = + genExp(merger, codegen, rewriter, op, merger.exp(exp).children.e1, ldx); return merger.buildExp(rewriter, loc, exp, v0, v1); } @@ -1561,7 +1583,8 @@ unsigned exp, unsigned at) { // At each leaf, assign remaining tensor (sub)expression to output tensor. if (at == topSort.size()) { - Value rhs = genExp(merger, codegen, rewriter, op, exp); + unsigned ldx = topSort[at - 1]; + Value rhs = genExp(merger, codegen, rewriter, op, exp, ldx); genTensorStore(merger, codegen, rewriter, op, rhs); return; } @@ -1645,7 +1668,6 @@ LogicalResult matchAndRewrite(linalg::GenericOp op, PatternRewriter &rewriter) const override { - // Detects sparse annotations and translate the per-dimension sparsity // information for all tensors to loop indices in the kernel. assert(op.getNumOutputs() == 1); diff --git a/mlir/test/Dialect/SparseTensor/sparse_vector_index.mlir b/mlir/test/Dialect/SparseTensor/sparse_vector_index.mlir new file mode 100644 --- /dev/null +++ b/mlir/test/Dialect/SparseTensor/sparse_vector_index.mlir @@ -0,0 +1,124 @@ +// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py + +// The script is designed to make adding checks to +// a test case fast, it is *not* designed to be authoritative +// about what constitutes a good test! The CHECK should be +// minimized and named to reflect the test intent. + +// RUN: mlir-opt %s -sparsification="vectorization-strategy=2 vl=8" -canonicalize | \ +// RUN: FileCheck %s + +#SparseVector = #sparse_tensor.encoding<{ + dimLevelType = ["compressed"] +}> + +#trait_1d = { + indexing_maps = [ + affine_map<(i) -> (i)>, // a + affine_map<(i) -> (i)> // x (out) + ], + iterator_types = ["parallel"], + doc = "X(i) = a(i) op i" +} + +// CHECK-LABEL: func @sparse_index_1d_conj( +// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<8xi64> { +// CHECK-DAG: %[[VAL_1:.*]] = arith.constant dense<0> : vector<8xi64> +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant dense<0> : vector<8xindex> +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : i64 +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<8xi64> +// CHECK-DAG: linalg.fill ins(%[[VAL_5]] : i64) outs(%[[VAL_10]] : memref<8xi64>) +// CHECK-DAG: %[[VAL_11:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref +// CHECK-DAG: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref +// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_3]] { +// CHECK: %[[VAL_14:.*]] = affine.min #map0(%[[VAL_13]]){{\[}}%[[VAL_12]]] +// CHECK: %[[VAL_15:.*]] = vector.create_mask %[[VAL_14]] : vector<8xi1> +// CHECK: %[[VAL_16:.*]] = vector.maskedload %[[VAL_8]]{{\[}}%[[VAL_13]]], %[[VAL_15]], %[[VAL_2]] : memref, vector<8xi1>, vector<8xindex> into vector<8xindex> +// CHECK: %[[VAL_17:.*]] = vector.maskedload %[[VAL_9]]{{\[}}%[[VAL_13]]], %[[VAL_15]], %[[VAL_1]] : memref, vector<8xi1>, vector<8xi64> into vector<8xi64> +// CHECK: %[[VAL_18:.*]] = arith.index_cast %[[VAL_16]] : vector<8xindex> to vector<8xi64> +// CHECK: %[[VAL_19:.*]] = arith.muli %[[VAL_17]], %[[VAL_18]] : vector<8xi64> +// CHECK: vector.scatter %[[VAL_10]]{{\[}}%[[VAL_6]]] {{\[}}%[[VAL_16]]], %[[VAL_15]], %[[VAL_19]] : memref<8xi64>, vector<8xindex>, vector<8xi1>, vector<8xi64> +// CHECK: } +// CHECK: %[[VAL_20:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<8xi64> +// CHECK: return %[[VAL_20]] : tensor<8xi64> +// CHECK: } +func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8xi64> { + %init = linalg.init_tensor [8] : tensor<8xi64> + %r = linalg.generic #trait_1d + ins(%arga: tensor<8xi64, #SparseVector>) + outs(%init: tensor<8xi64>) { + ^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> + return %r : tensor<8xi64> +} + +// CHECK-LABEL: func @sparse_index_1d_disj( +// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<8xi64> { +// CHECK-DAG: %[[VAL_1:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : vector<8xindex> +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : i64 +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<8xi64> +// CHECK-DAG: linalg.fill ins(%[[VAL_3]] : i64) outs(%[[VAL_9]] : memref<8xi64>) +// CHECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref +// CHECK-DAG: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref +// CHECK: %[[VAL_12:.*]]:2 = scf.while (%[[VAL_13:.*]] = %[[VAL_10]], %[[VAL_14:.*]] = %[[VAL_5]]) : (index, index) -> (index, index) { +// CHECK: %[[VAL_15:.*]] = arith.cmpi ult, %[[VAL_13]], %[[VAL_11]] : index +// CHECK: scf.condition(%[[VAL_15]]) %[[VAL_13]], %[[VAL_14]] : index, index +// CHECK: } do { +// CHECK: ^bb0(%[[VAL_16:.*]]: index, %[[VAL_17:.*]]: index): +// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_19:.*]] = arith.cmpi eq, %[[VAL_18]], %[[VAL_17]] : index +// CHECK: scf.if %[[VAL_19]] { +// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_21:.*]] = arith.index_cast %[[VAL_17]] : index to i64 +// CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_20]], %[[VAL_21]] : i64 +// CHECK: memref.store %[[VAL_22]], %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<8xi64> +// CHECK: } else { +// CHECK: %[[VAL_23:.*]] = arith.index_cast %[[VAL_17]] : index to i64 +// CHECK: memref.store %[[VAL_23]], %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<8xi64> +// CHECK: } +// CHECK: %[[VAL_24:.*]] = arith.cmpi eq, %[[VAL_18]], %[[VAL_17]] : index +// CHECK: %[[VAL_25:.*]] = arith.addi %[[VAL_16]], %[[VAL_2]] : index +// CHECK: %[[VAL_26:.*]] = arith.select %[[VAL_24]], %[[VAL_25]], %[[VAL_16]] : index +// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_17]], %[[VAL_2]] : index +// CHECK: scf.yield %[[VAL_26]], %[[VAL_27]] : index, index +// CHECK: } +// CHECK: scf.for %[[VAL_28:.*]] = %[[VAL_29:.*]]#1 to %[[VAL_4]] step %[[VAL_4]] { +// CHECK: %[[VAL_30:.*]] = affine.min #map1(%[[VAL_28]]) +// CHECK: %[[VAL_31:.*]] = vector.create_mask %[[VAL_30]] : vector<8xi1> +// CHECK: %[[VAL_32:.*]] = vector.broadcast %[[VAL_28]] : index to vector<8xindex> +// CHECK: %[[VAL_33:.*]] = arith.addi %[[VAL_32]], %[[VAL_1]] : vector<8xindex> +// CHECK: %[[VAL_34:.*]] = arith.index_cast %[[VAL_33]] : vector<8xindex> to vector<8xi64> +// CHECK: vector.maskedstore %[[VAL_9]]{{\[}}%[[VAL_28]]], %[[VAL_31]], %[[VAL_34]] : memref<8xi64>, vector<8xi1>, vector<8xi64> +// CHECK: } +// CHECK: %[[VAL_35:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<8xi64> +// CHECK: return %[[VAL_35]] : tensor<8xi64> +// CHECK: } +func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8xi64> { + %init = linalg.init_tensor [8] : tensor<8xi64> + %r = linalg.generic #trait_1d + ins(%arga: tensor<8xi64, #SparseVector>) + outs(%init: tensor<8xi64>) { + ^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> + return %r : tensor<8xi64> +} diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index_dense.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index_dense.mlir new file mode 100644 --- /dev/null +++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index_dense.mlir @@ -0,0 +1,208 @@ +// RUN: mlir-opt %s --sparse-compiler | \ +// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ +// RUN: FileCheck %s +// +// Do the same run, but now with SIMDization as well. This should not change the outcome. +// +// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=2 vl=4" | \ +// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ +// RUN: FileCheck %s + +#SparseVector = #sparse_tensor.encoding<{ + dimLevelType = ["compressed"] +}> + +#SparseMatrix = #sparse_tensor.encoding<{ + dimLevelType = ["compressed", "compressed"] +}> + +#trait_1d = { + indexing_maps = [ + affine_map<(i) -> (i)>, // a + affine_map<(i) -> (i)> // x (out) + ], + iterator_types = ["parallel"], + doc = "X(i) = a(i) op i" +} + +#trait_2d = { + indexing_maps = [ + affine_map<(i,j) -> (i,j)>, // A + affine_map<(i,j) -> (i,j)> // X (out) + ], + iterator_types = ["parallel", "parallel"], + doc = "X(i,j) = A(i,j) op i op j" +} + +// +// Test with indices and sparse inputs. All outputs are dense. +// +module { + + // + // Kernel that uses index in the index notation (conjunction). + // + func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8xi64> { + %init = linalg.init_tensor [8] : tensor<8xi64> + %r = linalg.generic #trait_1d + ins(%arga: tensor<8xi64, #SparseVector>) + outs(%init: tensor<8xi64>) { + ^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> + return %r : tensor<8xi64> + } + + // + // Kernel that uses index in the index notation (disjunction). + // + func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8xi64> { + %init = linalg.init_tensor [8] : tensor<8xi64> + %r = linalg.generic #trait_1d + ins(%arga: tensor<8xi64, #SparseVector>) + outs(%init: tensor<8xi64>) { + ^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> + return %r : tensor<8xi64> + } + + // + // Kernel that uses indices in the index notation (conjunction). + // + func @sparse_index_2d_conj(%arga: tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64> { + %init = linalg.init_tensor [3,4] : tensor<3x4xi64> + %r = linalg.generic #trait_2d + ins(%arga: tensor<3x4xi64, #SparseMatrix>) + outs(%init: tensor<3x4xi64>) { + ^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> + return %r : tensor<3x4xi64> + } + + // + // Kernel that uses indices in the index notation (disjunction). + // + func @sparse_index_2d_disj(%arga: tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64> { + %init = linalg.init_tensor [3,4] : tensor<3x4xi64> + %r = linalg.generic #trait_2d + ins(%arga: tensor<3x4xi64, #SparseMatrix>) + outs(%init: tensor<3x4xi64>) { + ^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> + return %r : tensor<3x4xi64> + } + + // + // Main driver. + // + func @entry() { + %c0 = arith.constant 0 : index + %du = arith.constant -1 : i64 + + // Setup input sparse vector. + %v1 = arith.constant sparse<[[2], [4]], [ 10, 20]> : tensor<8xi64> + %sv = sparse_tensor.convert %v1 : tensor<8xi64> to tensor<8xi64, #SparseVector> + + // Setup input "sparse" vector. + %v2 = arith.constant dense<[ 1, 2, 4, 8, 16, 32, 64, 128 ]> : tensor<8xi64> + %dv = sparse_tensor.convert %v2 : tensor<8xi64> to tensor<8xi64, #SparseVector> + + // Setup input sparse matrix. + %m1 = arith.constant sparse<[[1,1], [2,3]], [10, 20]> : tensor<3x4xi64> + %sm = sparse_tensor.convert %m1 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix> + + // Setup input "sparse" matrix. + %m2 = arith.constant dense <[ [ 1, 1, 1, 1 ], + [ 1, 2, 1, 1 ], + [ 1, 1, 3, 4 ] ]> : tensor<3x4xi64> + %dm = sparse_tensor.convert %m2 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix> + + // Call the kernels. + %0 = call @sparse_index_1d_conj(%sv) : (tensor<8xi64, #SparseVector>) -> tensor<8xi64> + %1 = call @sparse_index_1d_disj(%sv) : (tensor<8xi64, #SparseVector>) -> tensor<8xi64> + %2 = call @sparse_index_1d_conj(%dv) : (tensor<8xi64, #SparseVector>) -> tensor<8xi64> + %3 = call @sparse_index_1d_disj(%dv) : (tensor<8xi64, #SparseVector>) -> tensor<8xi64> + %4 = call @sparse_index_2d_conj(%sm) : (tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64> + %5 = call @sparse_index_2d_disj(%sm) : (tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64> + %6 = call @sparse_index_2d_conj(%dm) : (tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64> + %7 = call @sparse_index_2d_disj(%dm) : (tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64> + + // Get the backing buffers. + %mem0 = bufferization.to_memref %0 : memref<8xi64> + %mem1 = bufferization.to_memref %1 : memref<8xi64> + %mem2 = bufferization.to_memref %2 : memref<8xi64> + %mem3 = bufferization.to_memref %3 : memref<8xi64> + %mem4 = bufferization.to_memref %4 : memref<3x4xi64> + %mem5 = bufferization.to_memref %5 : memref<3x4xi64> + %mem6 = bufferization.to_memref %6 : memref<3x4xi64> + %mem7 = bufferization.to_memref %7 : memref<3x4xi64> + + // + // Verify result. + // + // CHECK: ( 0, 0, 20, 0, 80, 0, 0, 0 ) + // CHECK-NEXT: ( 0, 1, 12, 3, 24, 5, 6, 7 ) + // CHECK-NEXT: ( 0, 2, 8, 24, 64, 160, 384, 896 ) + // CHECK-NEXT: ( 1, 3, 6, 11, 20, 37, 70, 135 ) + // CHECK-NEXT: ( ( 0, 0, 0, 0 ), ( 0, 10, 0, 0 ), ( 0, 0, 0, 120 ) ) + // CHECK-NEXT: ( ( 0, 1, 2, 3 ), ( 1, 12, 3, 4 ), ( 2, 3, 4, 25 ) ) + // CHECK-NEXT: ( ( 0, 0, 0, 0 ), ( 0, 2, 2, 3 ), ( 0, 2, 12, 24 ) ) + // CHECK-NEXT: ( ( 1, 2, 3, 4 ), ( 2, 4, 4, 5 ), ( 3, 4, 7, 9 ) ) + // + %vv0 = vector.transfer_read %mem0[%c0], %du: memref<8xi64>, vector<8xi64> + %vv1 = vector.transfer_read %mem1[%c0], %du: memref<8xi64>, vector<8xi64> + %vv2 = vector.transfer_read %mem2[%c0], %du: memref<8xi64>, vector<8xi64> + %vv3 = vector.transfer_read %mem3[%c0], %du: memref<8xi64>, vector<8xi64> + %vv4 = vector.transfer_read %mem4[%c0,%c0], %du: memref<3x4xi64>, vector<3x4xi64> + %vv5 = vector.transfer_read %mem5[%c0,%c0], %du: memref<3x4xi64>, vector<3x4xi64> + %vv6 = vector.transfer_read %mem6[%c0,%c0], %du: memref<3x4xi64>, vector<3x4xi64> + %vv7 = vector.transfer_read %mem7[%c0,%c0], %du: memref<3x4xi64>, vector<3x4xi64> + vector.print %vv0 : vector<8xi64> + vector.print %vv1 : vector<8xi64> + vector.print %vv2 : vector<8xi64> + vector.print %vv3 : vector<8xi64> + vector.print %vv4 : vector<3x4xi64> + vector.print %vv5 : vector<3x4xi64> + vector.print %vv6 : vector<3x4xi64> + vector.print %vv7 : vector<3x4xi64> + + // Release resources. + sparse_tensor.release %sv : tensor<8xi64, #SparseVector> + sparse_tensor.release %dv : tensor<8xi64, #SparseVector> + sparse_tensor.release %sm : tensor<3x4xi64, #SparseMatrix> + sparse_tensor.release %dm : tensor<3x4xi64, #SparseMatrix> + memref.dealloc %mem0 : memref<8xi64> + memref.dealloc %mem1 : memref<8xi64> + memref.dealloc %mem2 : memref<8xi64> + memref.dealloc %mem3 : memref<8xi64> + memref.dealloc %mem4 : memref<3x4xi64> + memref.dealloc %mem5 : memref<3x4xi64> + memref.dealloc %mem6 : memref<3x4xi64> + memref.dealloc %mem7 : memref<3x4xi64> + + return + } +}