diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.h b/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.h --- a/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.h +++ b/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.h @@ -177,6 +177,9 @@ void sizesFromSrc(OpBuilder &builder, SmallVector &sizes, Location loc, Value src); +/// Scans to top of generated loop. +Operation *getTop(Operation *op); + //===----------------------------------------------------------------------===// // Inlined constant generators. // diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp --- a/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp @@ -929,3 +929,13 @@ for (unsigned i = 0; i < rank; i++) sizes.push_back(linalg::createOrFoldDimOp(builder, loc, src, i)); } + +Operation *mlir::sparse_tensor::getTop(Operation *op) { + for (; isa(op->getParentOp()) || + isa(op->getParentOp()) || + isa(op->getParentOp()) || + isa(op->getParentOp()); + op = op->getParentOp()) + ; + return op; +} diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp --- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp @@ -31,6 +31,9 @@ namespace { +// TODO: start using these when insertions are implemented +// static constexpr uint64_t DimSizesIdx = 0; +// static constexpr uint64_t DimCursorIdx = 1; static constexpr uint64_t MemSizesIdx = 2; static constexpr uint64_t FieldsIdx = 3; @@ -632,13 +635,7 @@ filled, index); rewriter.create(loc, fields); // Deallocate the buffers on exit of the full loop nest. - Operation *parent = op; - for (; isa(parent->getParentOp()) || - isa(parent->getParentOp()) || - isa(parent->getParentOp()) || - isa(parent->getParentOp()); - parent = parent->getParentOp()) - ; + Operation *parent = getTop(op); rewriter.setInsertionPointAfter(parent); rewriter.create(loc, values); rewriter.create(loc, filled); diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp --- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp @@ -1060,13 +1060,7 @@ EmitCInterface::On); rewriter.replaceOp(op, adaptor.getTensor()); // Deallocate the buffers on exit of the loop nest. - Operation *parent = op; - for (; isa(parent->getParentOp()) || - isa(parent->getParentOp()) || - isa(parent->getParentOp()) || - isa(parent->getParentOp()); - parent = parent->getParentOp()) - ; + Operation *parent = getTop(op); rewriter.setInsertionPointAfter(parent); rewriter.create(loc, values); rewriter.create(loc, filled); 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 @@ -58,7 +58,10 @@ std::vector &ts) : options(o), loopEmitter(tensors, /*isLastOutput=*/true, /*isSparseOut=*/op != nullptr), - sparseOut(op), outerParNest(nest), topSort(ts) {} + sparseOut(op), outerParNest(nest), topSort(ts) { + if (op) + insChain = op->get(); + } /// Sparsification options. SparsificationOptions options; /// Loop emitter helper class. @@ -74,6 +77,7 @@ // in the innermost loop nest (`expValues` through `expCount`). OpOperand *sparseOut; unsigned outerParNest; + Value insChain; // bookkeeping for insertion chain Value expValues; Value expFilled; Value expAdded; @@ -560,7 +564,8 @@ assert(codegen.loopEmitter.getLoopIV(i)); indices.push_back(codegen.loopEmitter.getLoopIV(i)); } - builder.create(loc, rhs, t->get(), indices); + codegen.insChain = + builder.create(loc, rhs, codegen.insChain, indices); return; } // Generates insertion code along expanded access pattern. @@ -633,13 +638,26 @@ // to indicate missing output. assert(merger.exp(exp).kind == kUnary || merger.exp(exp).kind == kBinary); } else if (merger.exp(exp).kind == kSelect) { - scf::IfOp ifOp = builder.create(loc, rhs); + // Select operation insertion. + Value insChain = codegen.insChain; + assert(insChain); + SmallVector types; + types.push_back(codegen.insChain.getType()); + scf::IfOp ifOp = + builder.create(loc, types, rhs, /*else=*/true); builder.setInsertionPointToStart(&ifOp.getThenRegion().front()); // Existing value was preserved to be used here. assert(merger.exp(exp).val); Value v0 = merger.exp(exp).val; genInsertionStore(codegen, builder, op, t, v0); merger.exp(exp).val = Value(); + // Yield modified insertion chain along true branch. + builder.create(op.getLoc(), codegen.insChain); + // Yield original insertion chain along false branch. + builder.setInsertionPointToStart(&ifOp.getElseRegion().front()); + builder.create(loc, insChain); + // Done with if statement. + codegen.insChain = ifOp->getResult(0); builder.setInsertionPointAfter(ifOp); } else { genInsertionStore(codegen, builder, op, t, rhs); @@ -811,7 +829,11 @@ at != codegen.outerParNest) return; // not needed at this level assert(codegen.redVal == nullptr); - // Generate start or end of an expanded access pattern. + // Generate start or end of an expanded access pattern. Note that because + // an expension does not rely on the ongoing contents of the sparse storage + // scheme, we can use the original tensor as incoming SSA value (which + // simplifies codegen a bit). If expansion on the actual contents is ever + // needed, we will need to use the SSA value in the insertion chain instead. Value tensor = lhs->get(); Location loc = op.getLoc(); if (atStart) { @@ -836,9 +858,9 @@ assert(codegen.loopEmitter.getLoopIV(i)); indices.push_back(codegen.loopEmitter.getLoopIV(i)); } - builder.create(loc, codegen.expValues, codegen.expFilled, - codegen.expAdded, codegen.expCount, tensor, - indices); + codegen.insChain = builder.create( + loc, codegen.expValues, codegen.expFilled, codegen.expAdded, + codegen.expCount, codegen.insChain, indices); codegen.expValues = codegen.expFilled = codegen.expAdded = codegen.expCount = Value(); } @@ -882,21 +904,26 @@ bool isParallel = isParallelFor(codegen, isOuter, isReduction, isSparse); assert(!isParallel); - // Emit a sequential or vector loop. + // Emit a sequential for loop. SmallVector operands; if (codegen.redVal) operands.push_back(codegen.redVal); if (codegen.expValues) operands.push_back(codegen.expCount); + if (codegen.insChain) + operands.push_back(codegen.insChain); Operation *loop = codegen.loopEmitter.enterLoopOverTensorAtDim( builder, loc, tid, dim, operands, isParallel, extraTids, extraDims); - // The operands should be updated by loop emitter already. + unsigned o = 0; if (codegen.redVal) - updateReduc(merger, codegen, operands.front()); + updateReduc(merger, codegen, operands[o++]); if (codegen.expValues) - codegen.expCount = operands.back(); + codegen.expCount = operands[o++]; + if (codegen.insChain) + codegen.insChain = operands[o++]; + assert(o == operands.size()); return loop; } @@ -907,7 +934,6 @@ ArrayRef condTids, ArrayRef condDims, ArrayRef extraTids, ArrayRef extraDims) { - SmallVector operands; // Construct the while-loop with a parameter for each index. @@ -915,15 +941,21 @@ operands.push_back(codegen.redVal); if (codegen.expValues) operands.push_back(codegen.expCount); + if (codegen.insChain) + operands.push_back(codegen.insChain); Operation *loop = codegen.loopEmitter.enterCoIterationOverTensorsAtDims( builder, op.getLoc(), condTids, condDims, needsUniv, operands, extraTids, extraDims); + unsigned o = 0; if (codegen.redVal) - updateReduc(merger, codegen, operands.front()); + updateReduc(merger, codegen, operands[o++]); if (codegen.expValues) - codegen.expCount = operands.back(); + codegen.expCount = operands[o++]; + if (codegen.insChain) + codegen.insChain = operands[o++]; + assert(o == operands.size()); return loop; } @@ -955,7 +987,7 @@ scf::WhileOp whileOp) { Location loc = op.getLoc(); // Finalize each else branch of all if statements. - if (codegen.redVal || codegen.expValues) { + if (codegen.redVal || codegen.expValues || codegen.insChain) { while (auto ifOp = dyn_cast_or_null( builder.getInsertionBlock()->getParentOp())) { unsigned y = 0; @@ -968,6 +1000,10 @@ yields.push_back(codegen.expCount); codegen.expCount = ifOp->getResult(y++); } + if (codegen.insChain) { + yields.push_back(codegen.insChain); + codegen.insChain = ifOp->getResult(y++); + } assert(y == yields.size()); builder.create(loc, yields); builder.setInsertionPointAfter(ifOp); @@ -1007,6 +1043,8 @@ types.push_back(codegen.redVal.getType()); if (codegen.expValues) types.push_back(builder.getIndexType()); + if (codegen.insChain) + types.push_back(codegen.insChain.getType()); scf::IfOp ifOp = builder.create(loc, types, cond, /*else=*/true); builder.setInsertionPointToStart(&ifOp.getThenRegion().front()); return ifOp; @@ -1015,7 +1053,7 @@ /// Generates end of true branch of if-statement within a while-loop. static void endIf(Merger &merger, CodeGen &codegen, OpBuilder &builder, linalg::GenericOp op, scf::IfOp ifOp, Operation *loop, - Value redInput, Value cntInput) { + Value redInput, Value cntInput, Value insInput) { SmallVector operands; if (codegen.redVal) { operands.push_back(codegen.redVal); @@ -1025,6 +1063,10 @@ operands.push_back(codegen.expCount); codegen.expCount = cntInput; } + if (codegen.insChain) { + operands.push_back(codegen.insChain); + codegen.insChain = insInput; + } if (!operands.empty()) builder.create(op.getLoc(), operands); builder.setInsertionPointToStart(&ifOp.getElseRegion().front()); @@ -1160,15 +1202,21 @@ reduc.push_back(codegen.redVal); if (codegen.expValues) reduc.push_back(codegen.expCount); + if (codegen.insChain) + reduc.push_back(codegen.insChain); auto loopRet = codegen.loopEmitter.exitCurrentLoop(builder, op.getLoc(), reduc); assert(reduc.size() == loopRet.size()); + unsigned o = 0; if (codegen.redVal) - updateReduc(merger, codegen, loopRet.front()); + updateReduc(merger, codegen, loopRet[o++]); if (codegen.expValues) - codegen.expCount = loopRet.back(); + codegen.expCount = loopRet[o++]; + if (codegen.insChain) + codegen.insChain = loopRet[o++]; + assert(o == loopRet.size()); return needsUniv; } @@ -1203,6 +1251,9 @@ unsigned ldx = at == 0 ? -1u : codegen.topSort[at - 1]; unsigned lts = merger.optimizeSet(merger.buildLattices(exp, idx)); + // TODO: sort + // TODO: dedup + // Start a loop sequence. bool needsUniv = startLoopSeq(merger, codegen, rewriter, op, exp, at, idx, ldx, lts); @@ -1219,6 +1270,7 @@ // loop-body, possibly with if statements for coiteration. Value redInput = codegen.redVal; Value cntInput = codegen.expCount; + Value insInput = codegen.insChain; bool isWhile = dyn_cast(loop) != nullptr; for (unsigned j = 0; j < lsize; j++) { unsigned lj = merger.set(lts)[j]; @@ -1229,7 +1281,8 @@ scf::IfOp ifOp = genIf(merger, codegen, rewriter, op, idx, merger.lat(lj).simple); genStmt(merger, codegen, rewriter, op, ej, at + 1); - endIf(merger, codegen, rewriter, op, ifOp, loop, redInput, cntInput); + endIf(merger, codegen, rewriter, op, ifOp, loop, redInput, cntInput, + insInput); } else { genStmt(merger, codegen, rewriter, op, ej, at + 1); } @@ -1249,12 +1302,16 @@ static void genResult(Merger &merger, CodeGen &codegen, RewriterBase &rewriter, linalg::GenericOp op) { OpOperand *lhs = op.getOutputOperand(0); - Type resType = lhs->get().getType(); + Value tensor = lhs->get(); + Type resType = tensor.getType(); if (getSparseTensorEncoding(resType)) { // The sparse tensor rematerializes from the original sparse tensor's - // underlying sparse storage format. - rewriter.replaceOpWithNewOp(op, resType, lhs->get(), - codegen.sparseOut == lhs); + // underlying sparse storage format. For an insertion chain, the + // tensor materializes from the chain with 'hasInserts' enabled. + bool hasInserts = codegen.sparseOut == lhs; + if (hasInserts) + tensor = codegen.insChain; + rewriter.replaceOpWithNewOp(op, resType, tensor, hasInserts); } else { // To rematerialize an non-annotated tensor, simply load it // from the bufferized value. diff --git a/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir b/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir @@ -12,7 +12,7 @@ } // CHECK-LABEL: @main( -// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<4x5xi32, +// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<4x5xi32, // CHECK-DAG: %[[TMP_c3:.*]] = arith.constant 3 : index // CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index // CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index @@ -24,21 +24,24 @@ // CHECK: %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] // CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref // CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref -// CHECK: scf.for %[[TMP_arg1:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] { +// CHECK: %[[T:.*]] = scf.for %[[TMP_arg1:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] {{.*}} { // CHECK: %[[TMP_9:.*]] = memref.load %[[TMP_2]][%[[TMP_arg1]]] : memref -// CHECK: scf.for %[[TMP_arg2:.*]] = %[[TMP_c0]] to %[[TMP_c3]] step %[[TMP_c1]] { +// CHECK: %[[L1:.*]] = scf.for %[[TMP_arg2:.*]] = %[[TMP_c0]] to %[[TMP_c3]] step %[[TMP_c1]] {{.*}} { // CHECK: %[[TMP_10:.*]] = memref.load %[[TMP_3]][%[[TMP_arg1]]] : memref // CHECK: %[[TMP_11:.*]] = arith.addi %[[TMP_arg1]], %[[TMP_c1]] : index // CHECK: %[[TMP_12:.*]] = memref.load %[[TMP_3]][%[[TMP_11]]] : memref -// CHECK: scf.for %[[TMP_arg3:.*]] = %[[TMP_10]] to %[[TMP_12]] step %[[TMP_c1]] { +// CHECK: %[[L2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_10]] to %[[TMP_12]] step %[[TMP_c1]] {{.*}} { // CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_4]][%[[TMP_arg3]]] : memref // CHECK: %[[TMP_14:.*]] = memref.load %[[TMP_5]][%[[TMP_arg3]]] : memref -// CHECK: %[[TMP_15:.*]] = sparse_tensor.insert %[[TMP_14]] into %[[TMP_0]][%[[TMP_9]], %[[TMP_arg2]], %[[TMP_13]]] +// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[TMP_14]] into %{{.*}}[%[[TMP_9]], %[[TMP_arg2]], %[[TMP_13]]] +// CHECK: scf.yield %[[Y]] // CHECK: } +// CHECK: scf.yield %[[L2]] // CHECK: } +// CHECK: scf.yield %[[L1]] // CHECK: } -// CHECK: %[[TMP_8:.*]] = sparse_tensor.load %[[TMP_0]] hasInserts -// CHECK: return %[[TMP_8]] +// CHECK: %[[TMP_8:.*]] = sparse_tensor.load %[[T]] hasInserts +// CHECK: return %[[TMP_8]] module @func_sparse { func.func public @main(%arg0: tensor<4x5xi32, #DCSR>) -> tensor<4x3x5xi32, #SparseTensor> { %0 = bufferization.alloc_tensor() : tensor<4x3x5xi32, #SparseTensor> diff --git a/mlir/test/Dialect/SparseTensor/sparse_expand.mlir b/mlir/test/Dialect/SparseTensor/sparse_expand.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_expand.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_expand.mlir @@ -84,7 +84,7 @@ // CHECK-SPARSE-DAG: %[[C0:.*]] = arith.constant 0 : index // CHECK-SPARSE-DAG: %[[C1:.*]] = arith.constant 1 : index // CHECK-SPARSE-DAG: %[[C8:.*]] = arith.constant 8 : index -// CHECK-SPARSE: scf.for %{{.*}} = %[[C0]] to %[[C8]] step %[[C1]] { +// CHECK-SPARSE: %[[T:.*]] = scf.for %{{.*}} = %[[C0]] to %[[C8]] step %[[C1]] {{.*}} { // CHECK-SPARSE: %[[A:.*]], %[[B:.*]], %[[C:.*]], %{{.*}} = sparse_tensor.expand // CHECK-SPARSE: %[[COUNT:.*]] = scf.for {{.*}} { // CHECK-SPARSE: scf.for {{.*}} { @@ -92,7 +92,7 @@ // CHECK-SPARSE: } // CHECK-SPARSE: sparse_tensor.compress %[[A]], %[[B]], %[[C]], %[[COUNT]] into // CHECK-SPARSE: } -// CHECK-SPARSE: %[[RET:.*]] = sparse_tensor.load %{{.*}} hasInserts +// CHECK-SPARSE: %[[RET:.*]] = sparse_tensor.load %[[T]] hasInserts // CHECK-SPARSE: return %[[RET]] // // CHECK-CONVERT-LABEL: func @matmul1( @@ -106,7 +106,7 @@ // CHECK-CONVERT: %[[C:.*]] = memref.alloc(%[[C4]]) : memref // CHECK-CONVERT: linalg.fill ins(%{{.*}} : f64) outs(%[[A]] : memref) // CHECK-CONVERT: linalg.fill ins(%{{.*}} : i1) outs(%[[B]] : memref) -// CHECK-CONVERT: scf.for %{{.*}} = %[[C0]] to %[[C8]] step %[[C1]] { +// CHECK-CONVERT: scf.for %{{.*}} = %[[C0]] to %[[C8]] step %[[C1]] {{.*}} { // CHECK-CONVERT: scf.for {{.*}} { // CHECK-CONVERT: scf.for {{.*}} { // CHECK-CONVERT: } @@ -132,7 +132,7 @@ // CHECK-SPARSE-DAG: %[[C0:.*]] = arith.constant 0 : index // CHECK-SPARSE-DAG: %[[C1:.*]] = arith.constant 1 : index // CHECK-SPARSE-DAG: %[[C4:.*]] = arith.constant 4 : index -// CHECK-SPARSE: scf.for %{{.*}} = %[[C0]] to %[[C4]] step %[[C1]] { +// CHECK-SPARSE: %[[T:.*]] = scf.for %{{.*}} = %[[C0]] to %[[C4]] step %[[C1]] {{.*}} { // CHECK-SPARSE: %[[A:.*]], %[[B:.*]], %[[C:.*]], %{{.*}} = sparse_tensor.expand // CHECK-SPARSE: %[[COUNT:.*]] = scf.for {{.*}} { // CHECK-SPARSE: scf.for {{.*}} { @@ -140,7 +140,7 @@ // CHECK-SPARSE: } // CHECK-SPARSE: sparse_tensor.compress %[[A]], %[[B]], %[[C]], %[[COUNT]] // CHECK-SPARSE: } -// CHECK-SPARSE: %[[RET:.*]] = sparse_tensor.load %{{.*}} hasInserts +// CHECK-SPARSE: %[[RET:.*]] = sparse_tensor.load %[[T]] hasInserts // CHECK-SPARSE: return %[[RET]] // // CHECK-CONVERT-LABEL: func @matmul2( @@ -154,7 +154,7 @@ // CHECK-CONVERT: %[[C:.*]] = memref.alloc(%[[C8]]) : memref // CHECK-CONVERT: linalg.fill ins(%{{.*}} : f64) outs(%[[A]] : memref) // CHECK-CONVERT: linalg.fill ins(%{{.*}} : i1) outs(%[[B]] : memref) -// CHECK-CONVERT: scf.for %{{.*}} = %[[C0]] to %[[C4]] step %[[C1]] { +// CHECK-CONVERT: scf.for %{{.*}} = %[[C0]] to %[[C4]] step %[[C1]] {{.*}} { // CHECK-CONVERT: scf.for {{.*}} { // CHECK-CONVERT: scf.for {{.*}} { // CHECK-CONVERT: } diff --git a/mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir b/mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir @@ -360,7 +360,7 @@ // CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref // CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref // CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref -// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] { +// CHECK: %[[T:.*]] = scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] {{.*}} { // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_9]]] : memref // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref // CHECK: %[[VAL_12:.*]] = math.absf %[[VAL_11]] : f64 @@ -371,9 +371,10 @@ // CHECK: %[[VAL_17:.*]] = math.log1p %[[VAL_16]] : f64 // CHECK: %[[VAL_18:.*]] = math.sin %[[VAL_17]] : f64 // CHECK: %[[VAL_19:.*]] = math.tanh %[[VAL_18]] : f64 -// CHECK: sparse_tensor.insert %[[VAL_19]] into %[[VAL_3]]{{\[}}%[[VAL_10]]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> +// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[VAL_19]] into %{{.*}}[%[[VAL_10]]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> +// CHECK: scf.yield %[[Y]] // CHECK: } -// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[VAL_3]] hasInserts : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> +// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> // CHECK: return %[[VAL_20]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> // CHECK: } func.func @zero_preserving_math(%arga: tensor<32xf64, #SV>) -> tensor<32xf64, #SV> { @@ -407,13 +408,14 @@ // CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xcomplex, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref> // CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref -// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_2]] { +// CHECK: %[[T:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_2]] {{.*}} { // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_10]]] : memref> // CHECK: %[[VAL_13:.*]] = complex.div %[[VAL_12]], %[[VAL_3]] : complex -// CHECK: sparse_tensor.insert %[[VAL_13]] into %[[VAL_4]]{{\[}}%[[VAL_11]]] : tensor<32xcomplex, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> +// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[VAL_13]] into %{{.*}}[%[[VAL_11]]] : tensor<32xcomplex, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> +// CHECK: scf.yield %[[Y]] // CHECK: } -// CHECK: %[[VAL_14:.*]] = sparse_tensor.load %[[VAL_4]] hasInserts : tensor<32xcomplex, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> +// CHECK: %[[VAL_14:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<32xcomplex, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> // CHECK: return %[[VAL_14]] : tensor<32xcomplex, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> // CHECK: } func.func @complex_divbyc(%arg0: tensor<32xcomplex, #SV>) -> tensor<32xcomplex, #SV> { diff --git a/mlir/test/Dialect/SparseTensor/sparse_index.mlir b/mlir/test/Dialect/SparseTensor/sparse_index.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_index.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_index.mlir @@ -83,22 +83,24 @@ // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref -// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_2]] { +// CHECK: %[[T:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_2]] {{.*}} { // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref // CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref -// CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_2]] { +// CHECK: %[[L:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_2]] {{.*}} { // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref // CHECK: %[[VAL_20:.*]] = arith.index_cast %[[VAL_19]] : index to i64 // CHECK: %[[VAL_21:.*]] = arith.index_cast %[[VAL_14]] : index to i64 // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref // CHECK: %[[VAL_23:.*]] = arith.muli %[[VAL_21]], %[[VAL_22]] : i64 // CHECK: %[[VAL_24:.*]] = arith.muli %[[VAL_20]], %[[VAL_23]] : i64 -// CHECK: sparse_tensor.insert %[[VAL_24]] into %[[VAL_5]]{{\[}}%[[VAL_14]], %[[VAL_19]]] : tensor) diff --git a/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir b/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir @@ -13,11 +13,11 @@ // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref // CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32> // CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref @@ -61,81 +61,82 @@ // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant false // CHECK-DAG: %[[VAL_5:.*]] = arith.constant true -// CHECK: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref -// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_3]] { -// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref -// CHECK: %[[VAL_21:.*]], %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]] = sparse_tensor.expand %[[VAL_6]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref, memref, memref -// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref -// CHECK: %[[VAL_26:.*]] = arith.addi %[[VAL_19]], %[[VAL_3]] : index -// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_26]]] : memref -// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_2]]] : memref -// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_3]]] : memref -// CHECK: %[[VAL_30:.*]]:3 = scf.while (%[[VAL_31:.*]] = %[[VAL_25]], %[[VAL_32:.*]] = %[[VAL_28]], %[[VAL_33:.*]] = %[[VAL_24]]) : (index, index, index) -> (index, index, index) { -// CHECK: %[[VAL_34:.*]] = arith.cmpi ult, %[[VAL_31]], %[[VAL_27]] : index -// CHECK: %[[VAL_35:.*]] = arith.cmpi ult, %[[VAL_32]], %[[VAL_29]] : index -// CHECK: %[[VAL_36:.*]] = arith.andi %[[VAL_34]], %[[VAL_35]] : i1 -// CHECK: scf.condition(%[[VAL_36]]) %[[VAL_31]], %[[VAL_32]], %[[VAL_33]] : index, index, index +// CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_3]] iter_args(%[[VAL_21:.*]] = %[[VAL_6]]) -> (tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref +// CHECK: %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]] = sparse_tensor.expand %[[VAL_6]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref, memref, memref +// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref +// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_20]], %[[VAL_3]] : index +// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_28]]] : memref +// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_2]]] : memref +// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_3]]] : memref +// CHECK: %[[VAL_32:.*]]:4 = scf.while (%[[VAL_33:.*]] = %[[VAL_27]], %[[VAL_34:.*]] = %[[VAL_30]], %[[VAL_35:.*]] = %[[VAL_26]], %[[VAL_36:.*]] = %[[VAL_21]]) : (index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> (index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_37:.*]] = arith.cmpi ult, %[[VAL_33]], %[[VAL_29]] : index +// CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_34]], %[[VAL_31]] : index +// CHECK: %[[VAL_39:.*]] = arith.andi %[[VAL_37]], %[[VAL_38]] : i1 +// CHECK: scf.condition(%[[VAL_39]]) %[[VAL_33]], %[[VAL_34]], %[[VAL_35]], %[[VAL_36]] : index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } do { -// CHECK: ^bb0(%[[VAL_37:.*]]: index, %[[VAL_38:.*]]: index, %[[VAL_39:.*]]: index): -// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_37]]] : memref -// CHECK: %[[VAL_41:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_38]]] : memref -// CHECK: %[[VAL_42:.*]] = arith.cmpi ult, %[[VAL_41]], %[[VAL_40]] : index -// CHECK: %[[VAL_43:.*]] = arith.select %[[VAL_42]], %[[VAL_41]], %[[VAL_40]] : index -// CHECK: %[[VAL_44:.*]] = arith.cmpi eq, %[[VAL_40]], %[[VAL_43]] : index -// CHECK: %[[VAL_45:.*]] = arith.cmpi eq, %[[VAL_41]], %[[VAL_43]] : index -// CHECK: %[[VAL_46:.*]] = arith.andi %[[VAL_44]], %[[VAL_45]] : i1 -// CHECK: %[[VAL_47:.*]] = scf.if %[[VAL_46]] -> (index) { -// CHECK: %[[VAL_48:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_37]]] : memref -// CHECK: %[[VAL_49:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_38]]] : memref -// CHECK: %[[VAL_50:.*]] = arith.addi %[[VAL_38]], %[[VAL_3]] : index -// CHECK: %[[VAL_51:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_50]]] : memref -// CHECK: %[[VAL_52:.*]] = scf.for %[[VAL_53:.*]] = %[[VAL_49]] to %[[VAL_51]] step %[[VAL_3]] iter_args(%[[VAL_54:.*]] = %[[VAL_39]]) -> (index) { -// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_53]]] : memref -// CHECK: %[[VAL_56:.*]] = memref.load %[[VAL_21]]{{\[}}%[[VAL_55]]] : memref -// CHECK: %[[VAL_57:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_53]]] : memref -// CHECK: %[[VAL_58:.*]] = arith.mulf %[[VAL_48]], %[[VAL_57]] : f64 -// CHECK: %[[VAL_59:.*]] = arith.addf %[[VAL_56]], %[[VAL_58]] : f64 -// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_22]]{{\[}}%[[VAL_55]]] : memref -// CHECK: %[[VAL_61:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_4]] : i1 -// CHECK: %[[VAL_62:.*]] = scf.if %[[VAL_61]] -> (index) { -// CHECK: memref.store %[[VAL_5]], %[[VAL_22]]{{\[}}%[[VAL_55]]] : memref -// CHECK: memref.store %[[VAL_55]], %[[VAL_23]]{{\[}}%[[VAL_54]]] : memref -// CHECK: %[[VAL_63:.*]] = arith.addi %[[VAL_54]], %[[VAL_3]] : index -// CHECK: scf.yield %[[VAL_63]] : index +// CHECK: ^bb0(%[[VAL_40:.*]]: index, %[[VAL_41:.*]]: index, %[[VAL_42:.*]]: index, %[[VAL_43:.*]]: tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>): +// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_40]]] : memref +// CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_41]]] : memref +// CHECK: %[[VAL_46:.*]] = arith.cmpi ult, %[[VAL_45]], %[[VAL_44]] : index +// CHECK: %[[VAL_47:.*]] = arith.select %[[VAL_46]], %[[VAL_45]], %[[VAL_44]] : index +// CHECK: %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index +// CHECK: %[[VAL_49:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index +// CHECK: %[[VAL_50:.*]] = arith.andi %[[VAL_48]], %[[VAL_49]] : i1 +// CHECK: %[[VAL_51:.*]]:2 = scf.if %[[VAL_50]] -> (index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_52:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_40]]] : memref +// CHECK: %[[VAL_53:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_41]]] : memref +// CHECK: %[[VAL_54:.*]] = arith.addi %[[VAL_41]], %[[VAL_3]] : index +// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_54]]] : memref +// CHECK: %[[VAL_56:.*]] = scf.for %[[VAL_57:.*]] = %[[VAL_53]] to %[[VAL_55]] step %[[VAL_3]] iter_args(%[[VAL_58:.*]] = %[[VAL_42]]) -> (index) { +// CHECK: %[[VAL_59:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_57]]] : memref +// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_23]]{{\[}}%[[VAL_59]]] : memref +// CHECK: %[[VAL_61:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_57]]] : memref +// CHECK: %[[VAL_62:.*]] = arith.mulf %[[VAL_52]], %[[VAL_61]] : f64 +// CHECK: %[[VAL_63:.*]] = arith.addf %[[VAL_60]], %[[VAL_62]] : f64 +// CHECK: %[[VAL_64:.*]] = memref.load %[[VAL_24]]{{\[}}%[[VAL_59]]] : memref +// CHECK: %[[VAL_65:.*]] = arith.cmpi eq, %[[VAL_64]], %[[VAL_4]] : i1 +// CHECK: %[[VAL_66:.*]] = scf.if %[[VAL_65]] -> (index) { +// CHECK: memref.store %[[VAL_5]], %[[VAL_24]]{{\[}}%[[VAL_59]]] : memref +// CHECK: memref.store %[[VAL_59]], %[[VAL_25]]{{\[}}%[[VAL_58]]] : memref +// CHECK: %[[VAL_67:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index +// CHECK: scf.yield %[[VAL_67]] : index // CHECK: } else { -// CHECK: scf.yield %[[VAL_54]] : index +// CHECK: scf.yield %[[VAL_58]] : index // CHECK: } -// CHECK: memref.store %[[VAL_59]], %[[VAL_21]]{{\[}}%[[VAL_55]]] : memref -// CHECK: scf.yield %[[VAL_64:.*]] : index +// CHECK: memref.store %[[VAL_63]], %[[VAL_23]]{{\[}}%[[VAL_59]]] : memref +// CHECK: scf.yield %[[VAL_68:.*]] : index // CHECK: } -// CHECK: scf.yield %[[VAL_65:.*]] : index +// CHECK: scf.yield %[[VAL_69:.*]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } else { -// CHECK: scf.yield %[[VAL_39]] : index +// CHECK: scf.yield %[[VAL_42]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } -// CHECK: %[[VAL_66:.*]] = arith.cmpi eq, %[[VAL_40]], %[[VAL_43]] : index -// CHECK: %[[VAL_67:.*]] = arith.addi %[[VAL_37]], %[[VAL_3]] : index -// CHECK: %[[VAL_68:.*]] = arith.select %[[VAL_66]], %[[VAL_67]], %[[VAL_37]] : index -// CHECK: %[[VAL_69:.*]] = arith.cmpi eq, %[[VAL_41]], %[[VAL_43]] : index -// CHECK: %[[VAL_70:.*]] = arith.addi %[[VAL_38]], %[[VAL_3]] : index -// CHECK: %[[VAL_71:.*]] = arith.select %[[VAL_69]], %[[VAL_70]], %[[VAL_38]] : index -// CHECK: scf.yield %[[VAL_68]], %[[VAL_71]], %[[VAL_72:.*]] : index, index, index +// CHECK: %[[VAL_70:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index +// CHECK: %[[VAL_71:.*]] = arith.addi %[[VAL_40]], %[[VAL_3]] : index +// CHECK: %[[VAL_72:.*]] = arith.select %[[VAL_70]], %[[VAL_71]], %[[VAL_40]] : index +// CHECK: %[[VAL_73:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index +// CHECK: %[[VAL_74:.*]] = arith.addi %[[VAL_41]], %[[VAL_3]] : index +// CHECK: %[[VAL_75:.*]] = arith.select %[[VAL_73]], %[[VAL_74]], %[[VAL_41]] : index +// CHECK: scf.yield %[[VAL_72]], %[[VAL_75]], %[[VAL_76:.*]]#0, %[[VAL_76]]#1 : index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } -// CHECK: sparse_tensor.compress %[[VAL_21]], %[[VAL_22]], %[[VAL_23]], %[[VAL_73:.*]]#2 into %[[VAL_6]]{{\[}}%[[VAL_20]]] : memref, memref, memref, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_77:.*]] = sparse_tensor.compress %[[VAL_23]], %[[VAL_24]], %[[VAL_25]], %[[VAL_78:.*]]#2 into %[[VAL_78]]#3{{\[}}%[[VAL_22]]] : memref, memref, memref, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: scf.yield %[[VAL_77]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } -// CHECK: %[[VAL_74:.*]] = sparse_tensor.load %[[VAL_6]] hasInserts : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> -// CHECK: return %[[VAL_74]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_79:.*]] = sparse_tensor.load %[[VAL_80:.*]] hasInserts : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: return %[[VAL_79]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } func.func @matmul2(%A: tensor<4x8xf64, #DCSR>, %B: tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> { @@ -154,12 +155,12 @@ // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 6 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32> -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32> +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref // CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref @@ -204,12 +205,12 @@ // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : i64 -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref // CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref @@ -253,12 +254,12 @@ // CHECK-SAME: %[[VAL_2:.*2]]: tensor) -> tensor { // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref // CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_11]][] : memref // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref diff --git a/mlir/test/Dialect/SparseTensor/sparse_out.mlir b/mlir/test/Dialect/SparseTensor/sparse_out.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_out.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_out.mlir @@ -23,27 +23,27 @@ } // CHECK-LABEL: func.func @sparse_simply_dynamic1( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> { -// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 2.000000e+00 : f32 -// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref -// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref -// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref -// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] { -// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref -// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_3]] : index -// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref -// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] { -// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref -// CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_16]], %[[VAL_1]] : f32 -// CHECK: memref.store %[[VAL_17]], %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> { +// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2.000000e+00 : f32 +// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref +// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref +// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] { +// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_9]]] : memref +// CHECK: %[[VAL_11:.*]] = arith.addi %[[VAL_9]], %[[VAL_2]] : index +// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref +// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_2]] { +// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref +// CHECK: %[[VAL_15:.*]] = arith.mulf %[[VAL_14]], %[[VAL_3]] : f32 +// CHECK: memref.store %[[VAL_15]], %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref // CHECK: } // CHECK: } -// CHECK: %[[VAL_18:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> -// CHECK: return %[[VAL_18]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> +// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } func.func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> { %c = arith.constant 2.0 : f32 @@ -57,12 +57,12 @@ } // CHECK-LABEL: func.func @sparse_simply_dynamic2( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> { // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_3:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> -// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> -// CHECK: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> +// CHECK-DAG: %[[VAL_3:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref // CHECK: %[[VAL_6:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref // CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_2]]] : memref // CHECK: scf.for %[[VAL_8:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_2]] { @@ -76,8 +76,8 @@ // CHECK: memref.store %[[VAL_15]], %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref // CHECK: } // CHECK: } -// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> -// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> +// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } func.func @sparse_simply_dynamic2(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> { %0 = linalg.generic #trait_scale_inpl @@ -104,23 +104,25 @@ // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2.000000e+00 : f32 -// CHECK: %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref -// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] { -// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref -// CHECK: %[[VAL_11:.*]] = arith.addi %[[VAL_9]], %[[VAL_3]] : index -// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref -// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_3]] { -// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref -// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref -// CHECK: %[[VAL_16:.*]] = arith.mulf %[[VAL_15]], %[[VAL_4]] : f32 -// CHECK: sparse_tensor.insert %[[VAL_16]] into %[[VAL_5]]{{\[}}%[[VAL_9]], %[[VAL_14]]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK-DAG: %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref +// CHECK: %[[VAL_9:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] iter_args(%[[VAL_11:.*]] = %[[VAL_5]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref +// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_10]], %[[VAL_3]] : index +// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref +// CHECK: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_11]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_20:.*]] = arith.mulf %[[VAL_19]], %[[VAL_4]] : f32 +// CHECK: %[[VAL_21:.*]] = sparse_tensor.insert %[[VAL_20]] into %[[VAL_17]]{{\[}}%[[VAL_10]], %[[VAL_18]]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: scf.yield %[[VAL_21]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } +// CHECK: scf.yield %[[VAL_22:.*]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } -// CHECK: %[[VAL_17:.*]] = sparse_tensor.load %[[VAL_5]] hasInserts : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> -// CHECK: return %[[VAL_17]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_23:.*]] = sparse_tensor.load %[[VAL_24:.*]] hasInserts : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: return %[[VAL_23]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20xf32, #DCSR> { %s = arith.constant 2.0 : f32 @@ -172,102 +174,106 @@ // CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_2]]] : memref // CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_3]]] : memref -// CHECK: %[[VAL_26:.*]]:2 = scf.while (%[[VAL_27:.*]] = %[[VAL_22]], %[[VAL_28:.*]] = %[[VAL_24]]) : (index, index) -> (index, index) { -// CHECK: %[[VAL_29:.*]] = arith.cmpi ult, %[[VAL_27]], %[[VAL_23]] : index -// CHECK: %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_25]] : index -// CHECK: %[[VAL_31:.*]] = arith.andi %[[VAL_29]], %[[VAL_30]] : i1 -// CHECK: scf.condition(%[[VAL_31]]) %[[VAL_27]], %[[VAL_28]] : index, index +// CHECK: %[[VAL_26:.*]]:3 = scf.while (%[[VAL_27:.*]] = %[[VAL_22]], %[[VAL_28:.*]] = %[[VAL_24]], %[[VAL_29:.*]] = %[[VAL_7]]) : (index, index, tensor>) -> (index, index, tensor>) { +// CHECK: %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_27]], %[[VAL_23]] : index +// CHECK: %[[VAL_31:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_25]] : index +// CHECK: %[[VAL_32:.*]] = arith.andi %[[VAL_30]], %[[VAL_31]] : i1 +// CHECK: scf.condition(%[[VAL_32]]) %[[VAL_27]], %[[VAL_28]], %[[VAL_29]] : index, index, tensor> // CHECK: } do { -// CHECK: ^bb0(%[[VAL_32:.*]]: index, %[[VAL_33:.*]]: index): -// CHECK: %[[VAL_34:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_32]]] : memref -// CHECK: %[[VAL_35:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_33]]] : memref -// CHECK: %[[VAL_36:.*]] = arith.cmpi ult, %[[VAL_35]], %[[VAL_34]] : index -// CHECK: %[[VAL_37:.*]] = arith.select %[[VAL_36]], %[[VAL_35]], %[[VAL_34]] : index -// CHECK: %[[VAL_38:.*]] = arith.cmpi eq, %[[VAL_34]], %[[VAL_37]] : index -// CHECK: %[[VAL_39:.*]] = arith.cmpi eq, %[[VAL_35]], %[[VAL_37]] : index -// CHECK: %[[VAL_40:.*]] = arith.andi %[[VAL_38]], %[[VAL_39]] : i1 -// CHECK: scf.if %[[VAL_40]] { -// CHECK: %[[VAL_41:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_32]]] : memref -// CHECK: %[[VAL_42:.*]] = arith.addi %[[VAL_32]], %[[VAL_3]] : index -// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_42]]] : memref -// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_33]]] : memref +// CHECK: ^bb0(%[[VAL_33:.*]]: index, %[[VAL_34:.*]]: index, %[[VAL_35:.*]]: tensor>): +// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_33]]] : memref +// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_34]]] : memref +// CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_36]] : index +// CHECK: %[[VAL_39:.*]] = arith.select %[[VAL_38]], %[[VAL_37]], %[[VAL_36]] : index +// CHECK: %[[VAL_40:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index +// CHECK: %[[VAL_41:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index +// CHECK: %[[VAL_42:.*]] = arith.andi %[[VAL_40]], %[[VAL_41]] : i1 +// CHECK: %[[VAL_43:.*]] = scf.if %[[VAL_42]] -> (tensor>) { +// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_33]]] : memref // CHECK: %[[VAL_45:.*]] = arith.addi %[[VAL_33]], %[[VAL_3]] : index -// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_45]]] : memref -// CHECK: %[[VAL_47:.*]]:2 = scf.while (%[[VAL_48:.*]] = %[[VAL_41]], %[[VAL_49:.*]] = %[[VAL_44]]) : (index, index) -> (index, index) { -// CHECK: %[[VAL_50:.*]] = arith.cmpi ult, %[[VAL_48]], %[[VAL_43]] : index -// CHECK: %[[VAL_51:.*]] = arith.cmpi ult, %[[VAL_49]], %[[VAL_46]] : index -// CHECK: %[[VAL_52:.*]] = arith.andi %[[VAL_50]], %[[VAL_51]] : i1 -// CHECK: scf.condition(%[[VAL_52]]) %[[VAL_48]], %[[VAL_49]] : index, index +// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_45]]] : memref +// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_34]]] : memref +// CHECK: %[[VAL_48:.*]] = arith.addi %[[VAL_34]], %[[VAL_3]] : index +// CHECK: %[[VAL_49:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_48]]] : memref +// CHECK: %[[VAL_50:.*]]:3 = scf.while (%[[VAL_51:.*]] = %[[VAL_44]], %[[VAL_52:.*]] = %[[VAL_47]], %[[VAL_53:.*]] = %[[VAL_35]]) : (index, index, tensor>) -> (index, index, tensor>) { +// CHECK: %[[VAL_54:.*]] = arith.cmpi ult, %[[VAL_51]], %[[VAL_46]] : index +// CHECK: %[[VAL_55:.*]] = arith.cmpi ult, %[[VAL_52]], %[[VAL_49]] : index +// CHECK: %[[VAL_56:.*]] = arith.andi %[[VAL_54]], %[[VAL_55]] : i1 +// CHECK: scf.condition(%[[VAL_56]]) %[[VAL_51]], %[[VAL_52]], %[[VAL_53]] : index, index, tensor> // CHECK: } do { -// CHECK: ^bb0(%[[VAL_53:.*]]: index, %[[VAL_54:.*]]: index): -// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_53]]] : memref -// CHECK: %[[VAL_56:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_54]]] : memref -// CHECK: %[[VAL_57:.*]] = arith.cmpi ult, %[[VAL_56]], %[[VAL_55]] : index -// CHECK: %[[VAL_58:.*]] = arith.select %[[VAL_57]], %[[VAL_56]], %[[VAL_55]] : index -// CHECK: %[[VAL_59:.*]] = arith.cmpi eq, %[[VAL_55]], %[[VAL_58]] : index -// CHECK: %[[VAL_60:.*]] = arith.cmpi eq, %[[VAL_56]], %[[VAL_58]] : index -// CHECK: %[[VAL_61:.*]] = arith.andi %[[VAL_59]], %[[VAL_60]] : i1 -// CHECK: scf.if %[[VAL_61]] { -// CHECK: %[[VAL_62:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_53]]] : memref -// CHECK: %[[VAL_63:.*]] = arith.addi %[[VAL_53]], %[[VAL_3]] : index -// CHECK: %[[VAL_64:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_63]]] : memref -// CHECK: %[[VAL_65:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_54]]] : memref -// CHECK: %[[VAL_66:.*]] = arith.addi %[[VAL_54]], %[[VAL_3]] : index -// CHECK: %[[VAL_67:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_66]]] : memref -// CHECK: %[[VAL_68:.*]]:3 = scf.while (%[[VAL_69:.*]] = %[[VAL_62]], %[[VAL_70:.*]] = %[[VAL_65]], %[[VAL_71:.*]] = %[[VAL_4]]) : (index, index, i32) -> (index, index, i32) { -// CHECK: %[[VAL_72:.*]] = arith.cmpi ult, %[[VAL_69]], %[[VAL_64]] : index -// CHECK: %[[VAL_73:.*]] = arith.cmpi ult, %[[VAL_70]], %[[VAL_67]] : index -// CHECK: %[[VAL_74:.*]] = arith.andi %[[VAL_72]], %[[VAL_73]] : i1 -// CHECK: scf.condition(%[[VAL_74]]) %[[VAL_69]], %[[VAL_70]], %[[VAL_71]] : index, index, i32 +// CHECK: ^bb0(%[[VAL_57:.*]]: index, %[[VAL_58:.*]]: index, %[[VAL_59:.*]]: tensor>): +// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_57]]] : memref +// CHECK: %[[VAL_61:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_58]]] : memref +// CHECK: %[[VAL_62:.*]] = arith.cmpi ult, %[[VAL_61]], %[[VAL_60]] : index +// CHECK: %[[VAL_63:.*]] = arith.select %[[VAL_62]], %[[VAL_61]], %[[VAL_60]] : index +// CHECK: %[[VAL_64:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_63]] : index +// CHECK: %[[VAL_65:.*]] = arith.cmpi eq, %[[VAL_61]], %[[VAL_63]] : index +// CHECK: %[[VAL_66:.*]] = arith.andi %[[VAL_64]], %[[VAL_65]] : i1 +// CHECK: %[[VAL_67:.*]] = scf.if %[[VAL_66]] -> (tensor>) { +// CHECK: %[[VAL_68:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_57]]] : memref +// CHECK: %[[VAL_69:.*]] = arith.addi %[[VAL_57]], %[[VAL_3]] : index +// CHECK: %[[VAL_70:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_69]]] : memref +// CHECK: %[[VAL_71:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_58]]] : memref +// CHECK: %[[VAL_72:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index +// CHECK: %[[VAL_73:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_72]]] : memref +// CHECK: %[[VAL_74:.*]]:4 = scf.while (%[[VAL_75:.*]] = %[[VAL_68]], %[[VAL_76:.*]] = %[[VAL_71]], %[[VAL_77:.*]] = %[[VAL_4]], %[[VAL_78:.*]] = %[[VAL_59]]) : (index, index, i32, tensor>) -> (index, index, i32, tensor>) { +// CHECK: %[[VAL_79:.*]] = arith.cmpi ult, %[[VAL_75]], %[[VAL_70]] : index +// CHECK: %[[VAL_80:.*]] = arith.cmpi ult, %[[VAL_76]], %[[VAL_73]] : index +// CHECK: %[[VAL_81:.*]] = arith.andi %[[VAL_79]], %[[VAL_80]] : i1 +// CHECK: scf.condition(%[[VAL_81]]) %[[VAL_75]], %[[VAL_76]], %[[VAL_77]], %[[VAL_78]] : index, index, i32, tensor> // CHECK: } do { -// CHECK: ^bb0(%[[VAL_75:.*]]: index, %[[VAL_76:.*]]: index, %[[VAL_77:.*]]: i32): -// CHECK: %[[VAL_78:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_75]]] : memref -// CHECK: %[[VAL_79:.*]] = memref.load %[[VAL_20]]{{\[}}%[[VAL_76]]] : memref -// CHECK: %[[VAL_80:.*]] = arith.cmpi ult, %[[VAL_79]], %[[VAL_78]] : index -// CHECK: %[[VAL_81:.*]] = arith.select %[[VAL_80]], %[[VAL_79]], %[[VAL_78]] : index -// CHECK: %[[VAL_82:.*]] = arith.cmpi eq, %[[VAL_78]], %[[VAL_81]] : index -// CHECK: %[[VAL_83:.*]] = arith.cmpi eq, %[[VAL_79]], %[[VAL_81]] : index -// CHECK: %[[VAL_84:.*]] = arith.andi %[[VAL_82]], %[[VAL_83]] : i1 -// CHECK: %[[VAL_85:.*]] = scf.if %[[VAL_84]] -> (i32) { -// CHECK: %[[VAL_86:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_75]]] : memref -// CHECK: %[[VAL_87:.*]] = memref.load %[[VAL_21]]{{\[}}%[[VAL_76]]] : memref -// CHECK: %[[VAL_88:.*]] = arith.muli %[[VAL_86]], %[[VAL_87]] : i32 -// CHECK: %[[VAL_89:.*]] = arith.addi %[[VAL_77]], %[[VAL_88]] : i32 -// CHECK: scf.yield %[[VAL_89]] : i32 +// CHECK: ^bb0(%[[VAL_82:.*]]: index, %[[VAL_83:.*]]: index, %[[VAL_84:.*]]: i32, %[[VAL_85:.*]]: tensor>): +// CHECK: %[[VAL_86:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_82]]] : memref +// CHECK: %[[VAL_87:.*]] = memref.load %[[VAL_20]]{{\[}}%[[VAL_83]]] : memref +// CHECK: %[[VAL_88:.*]] = arith.cmpi ult, %[[VAL_87]], %[[VAL_86]] : index +// CHECK: %[[VAL_89:.*]] = arith.select %[[VAL_88]], %[[VAL_87]], %[[VAL_86]] : index +// CHECK: %[[VAL_90:.*]] = arith.cmpi eq, %[[VAL_86]], %[[VAL_89]] : index +// CHECK: %[[VAL_91:.*]] = arith.cmpi eq, %[[VAL_87]], %[[VAL_89]] : index +// CHECK: %[[VAL_92:.*]] = arith.andi %[[VAL_90]], %[[VAL_91]] : i1 +// CHECK: %[[VAL_93:.*]]:2 = scf.if %[[VAL_92]] -> (i32, tensor>) { +// CHECK: %[[VAL_94:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_82]]] : memref +// CHECK: %[[VAL_95:.*]] = memref.load %[[VAL_21]]{{\[}}%[[VAL_83]]] : memref +// CHECK: %[[VAL_96:.*]] = arith.muli %[[VAL_94]], %[[VAL_95]] : i32 +// CHECK: %[[VAL_97:.*]] = arith.addi %[[VAL_84]], %[[VAL_96]] : i32 +// CHECK: scf.yield %[[VAL_97]], %[[VAL_85]] : i32, tensor> // CHECK: } else { -// CHECK: scf.yield %[[VAL_77]] : i32 +// CHECK: scf.yield %[[VAL_84]], %[[VAL_85]] : i32, tensor> // CHECK: } -// CHECK: %[[VAL_90:.*]] = arith.cmpi eq, %[[VAL_78]], %[[VAL_81]] : index -// CHECK: %[[VAL_91:.*]] = arith.addi %[[VAL_75]], %[[VAL_3]] : index -// CHECK: %[[VAL_92:.*]] = arith.select %[[VAL_90]], %[[VAL_91]], %[[VAL_75]] : index -// CHECK: %[[VAL_93:.*]] = arith.cmpi eq, %[[VAL_79]], %[[VAL_81]] : index -// CHECK: %[[VAL_94:.*]] = arith.addi %[[VAL_76]], %[[VAL_3]] : index -// CHECK: %[[VAL_95:.*]] = arith.select %[[VAL_93]], %[[VAL_94]], %[[VAL_76]] : index -// CHECK: scf.yield %[[VAL_92]], %[[VAL_95]], %[[VAL_96:.*]] : index, index, i32 +// CHECK: %[[VAL_98:.*]] = arith.cmpi eq, %[[VAL_86]], %[[VAL_89]] : index +// CHECK: %[[VAL_99:.*]] = arith.addi %[[VAL_82]], %[[VAL_3]] : index +// CHECK: %[[VAL_100:.*]] = arith.select %[[VAL_98]], %[[VAL_99]], %[[VAL_82]] : index +// CHECK: %[[VAL_101:.*]] = arith.cmpi eq, %[[VAL_87]], %[[VAL_89]] : index +// CHECK: %[[VAL_102:.*]] = arith.addi %[[VAL_83]], %[[VAL_3]] : index +// CHECK: %[[VAL_103:.*]] = arith.select %[[VAL_101]], %[[VAL_102]], %[[VAL_83]] : index +// CHECK: scf.yield %[[VAL_100]], %[[VAL_103]], %[[VAL_104:.*]]#0, %[[VAL_104]]#1 : index, index, i32, tensor> // CHECK: } -// CHECK: sparse_tensor.insert %[[VAL_97:.*]]#2 into %[[VAL_7]]{{\[}}%[[VAL_37]], %[[VAL_58]]] : tensor> +// CHECK: %[[VAL_105:.*]] = sparse_tensor.insert %[[VAL_106:.*]]#2 into %[[VAL_106]]#3{{\[}}%[[VAL_39]], %[[VAL_63]]] : tensor> +// CHECK: scf.yield %[[VAL_105]] : tensor> // CHECK: } else { +// CHECK: scf.yield %[[VAL_59]] : tensor> // CHECK: } -// CHECK: %[[VAL_98:.*]] = arith.cmpi eq, %[[VAL_55]], %[[VAL_58]] : index -// CHECK: %[[VAL_99:.*]] = arith.addi %[[VAL_53]], %[[VAL_3]] : index -// CHECK: %[[VAL_100:.*]] = arith.select %[[VAL_98]], %[[VAL_99]], %[[VAL_53]] : index -// CHECK: %[[VAL_101:.*]] = arith.cmpi eq, %[[VAL_56]], %[[VAL_58]] : index -// CHECK: %[[VAL_102:.*]] = arith.addi %[[VAL_54]], %[[VAL_3]] : index -// CHECK: %[[VAL_103:.*]] = arith.select %[[VAL_101]], %[[VAL_102]], %[[VAL_54]] : index -// CHECK: scf.yield %[[VAL_100]], %[[VAL_103]] : index, index +// CHECK: %[[VAL_107:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_63]] : index +// CHECK: %[[VAL_108:.*]] = arith.addi %[[VAL_57]], %[[VAL_3]] : index +// CHECK: %[[VAL_109:.*]] = arith.select %[[VAL_107]], %[[VAL_108]], %[[VAL_57]] : index +// CHECK: %[[VAL_110:.*]] = arith.cmpi eq, %[[VAL_61]], %[[VAL_63]] : index +// CHECK: %[[VAL_111:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index +// CHECK: %[[VAL_112:.*]] = arith.select %[[VAL_110]], %[[VAL_111]], %[[VAL_58]] : index +// CHECK: scf.yield %[[VAL_109]], %[[VAL_112]], %[[VAL_113:.*]] : index, index, tensor> // CHECK: } +// CHECK: scf.yield %[[VAL_114:.*]]#2 : tensor> // CHECK: } else { +// CHECK: scf.yield %[[VAL_35]] : tensor> // CHECK: } -// CHECK: %[[VAL_104:.*]] = arith.cmpi eq, %[[VAL_34]], %[[VAL_37]] : index -// CHECK: %[[VAL_105:.*]] = arith.addi %[[VAL_32]], %[[VAL_3]] : index -// CHECK: %[[VAL_106:.*]] = arith.select %[[VAL_104]], %[[VAL_105]], %[[VAL_32]] : index -// CHECK: %[[VAL_107:.*]] = arith.cmpi eq, %[[VAL_35]], %[[VAL_37]] : index -// CHECK: %[[VAL_108:.*]] = arith.addi %[[VAL_33]], %[[VAL_3]] : index -// CHECK: %[[VAL_109:.*]] = arith.select %[[VAL_107]], %[[VAL_108]], %[[VAL_33]] : index -// CHECK: scf.yield %[[VAL_106]], %[[VAL_109]] : index, index +// CHECK: %[[VAL_115:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index +// CHECK: %[[VAL_116:.*]] = arith.addi %[[VAL_33]], %[[VAL_3]] : index +// CHECK: %[[VAL_117:.*]] = arith.select %[[VAL_115]], %[[VAL_116]], %[[VAL_33]] : index +// CHECK: %[[VAL_118:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index +// CHECK: %[[VAL_119:.*]] = arith.addi %[[VAL_34]], %[[VAL_3]] : index +// CHECK: %[[VAL_120:.*]] = arith.select %[[VAL_118]], %[[VAL_119]], %[[VAL_34]] : index +// CHECK: scf.yield %[[VAL_117]], %[[VAL_120]], %[[VAL_121:.*]] : index, index, tensor> // CHECK: } -// CHECK: %[[VAL_110:.*]] = sparse_tensor.load %[[VAL_7]] hasInserts : tensor> -// CHECK: return %[[VAL_110]] : tensor> +// CHECK: %[[VAL_122:.*]] = sparse_tensor.load %[[VAL_123:.*]]#2 hasInserts : tensor> +// CHECK: return %[[VAL_122]] : tensor> // CHECK: } func.func @sumred(%arga: tensor, %argb: tensor) -> tensor { @@ -299,12 +305,12 @@ } // CHECK-LABEL: func.func @matmat( -// CHECK-SAME: %[[VAL_0:.*]]: tensor>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor>) -> tensor> { -// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_4:.*]] = arith.constant false -// CHECK: %[[VAL_5:.*]] = arith.constant true +// CHECK-SAME: %[[VAL_0:.*]]: tensor>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor>) -> tensor> { +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant false +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true // CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor> // CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor> // CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor(%[[VAL_6]], %[[VAL_7]]) : tensor> @@ -320,68 +326,69 @@ // CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref -// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] { -// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref -// CHECK: %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor> to memref, memref, memref -// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]]] : memref -// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_21]], %[[VAL_3]] : index -// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_28]]] : memref -// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_2]]] : memref -// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_3]]] : memref -// CHECK: %[[VAL_32:.*]]:3 = scf.while (%[[VAL_33:.*]] = %[[VAL_27]], %[[VAL_34:.*]] = %[[VAL_30]], %[[VAL_35:.*]] = %[[VAL_26]]) : (index, index, index) -> (index, index, index) { -// CHECK: %[[VAL_36:.*]] = arith.cmpi ult, %[[VAL_33]], %[[VAL_29]] : index -// CHECK: %[[VAL_37:.*]] = arith.cmpi ult, %[[VAL_34]], %[[VAL_31]] : index -// CHECK: %[[VAL_38:.*]] = arith.andi %[[VAL_36]], %[[VAL_37]] : i1 -// CHECK: scf.condition(%[[VAL_38]]) %[[VAL_33]], %[[VAL_34]], %[[VAL_35]] : index, index, index +// CHECK: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_8]]) -> (tensor>) { +// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref +// CHECK: %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]], %[[VAL_28:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor> to memref, memref, memref +// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref +// CHECK: %[[VAL_30:.*]] = arith.addi %[[VAL_22]], %[[VAL_3]] : index +// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_30]]] : memref +// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_2]]] : memref +// CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_3]]] : memref +// CHECK: %[[VAL_34:.*]]:4 = scf.while (%[[VAL_35:.*]] = %[[VAL_29]], %[[VAL_36:.*]] = %[[VAL_32]], %[[VAL_37:.*]] = %[[VAL_28]], %[[VAL_38:.*]] = %[[VAL_23]]) : (index, index, index, tensor>) -> (index, index, index, tensor>) { +// CHECK: %[[VAL_39:.*]] = arith.cmpi ult, %[[VAL_35]], %[[VAL_31]] : index +// CHECK: %[[VAL_40:.*]] = arith.cmpi ult, %[[VAL_36]], %[[VAL_33]] : index +// CHECK: %[[VAL_41:.*]] = arith.andi %[[VAL_39]], %[[VAL_40]] : i1 +// CHECK: scf.condition(%[[VAL_41]]) %[[VAL_35]], %[[VAL_36]], %[[VAL_37]], %[[VAL_38]] : index, index, index, tensor> // CHECK: } do { -// CHECK: ^bb0(%[[VAL_39:.*]]: index, %[[VAL_40:.*]]: index, %[[VAL_41:.*]]: index): -// CHECK: %[[VAL_42:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_39]]] : memref -// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_40]]] : memref -// CHECK: %[[VAL_44:.*]] = arith.cmpi ult, %[[VAL_43]], %[[VAL_42]] : index -// CHECK: %[[VAL_45:.*]] = arith.select %[[VAL_44]], %[[VAL_43]], %[[VAL_42]] : index -// CHECK: %[[VAL_46:.*]] = arith.cmpi eq, %[[VAL_42]], %[[VAL_45]] : index -// CHECK: %[[VAL_47:.*]] = arith.cmpi eq, %[[VAL_43]], %[[VAL_45]] : index -// CHECK: %[[VAL_48:.*]] = arith.andi %[[VAL_46]], %[[VAL_47]] : i1 -// CHECK: %[[VAL_49:.*]] = scf.if %[[VAL_48]] -> (index) { -// CHECK: %[[VAL_50:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_39]]] : memref -// CHECK: %[[VAL_51:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_40]]] : memref -// CHECK: %[[VAL_52:.*]] = arith.addi %[[VAL_40]], %[[VAL_3]] : index -// CHECK: %[[VAL_53:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_52]]] : memref -// CHECK: %[[VAL_54:.*]] = scf.for %[[VAL_55:.*]] = %[[VAL_51]] to %[[VAL_53]] step %[[VAL_3]] iter_args(%[[VAL_56:.*]] = %[[VAL_41]]) -> (index) { -// CHECK: %[[VAL_57:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_55]]] : memref -// CHECK: %[[VAL_58:.*]] = memref.load %[[VAL_23]]{{\[}}%[[VAL_57]]] : memref -// CHECK: %[[VAL_59:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_55]]] : memref -// CHECK: %[[VAL_60:.*]] = arith.mulf %[[VAL_50]], %[[VAL_59]] : f32 -// CHECK: %[[VAL_61:.*]] = arith.addf %[[VAL_58]], %[[VAL_60]] : f32 -// CHECK: %[[VAL_62:.*]] = memref.load %[[VAL_24]]{{\[}}%[[VAL_57]]] : memref -// CHECK: %[[VAL_63:.*]] = arith.cmpi eq, %[[VAL_62]], %[[VAL_4]] : i1 -// CHECK: %[[VAL_64:.*]] = scf.if %[[VAL_63]] -> (index) { -// CHECK: memref.store %[[VAL_5]], %[[VAL_24]]{{\[}}%[[VAL_57]]] : memref -// CHECK: memref.store %[[VAL_57]], %[[VAL_25]]{{\[}}%[[VAL_56]]] : memref -// CHECK: %[[VAL_65:.*]] = arith.addi %[[VAL_56]], %[[VAL_3]] : index -// CHECK: scf.yield %[[VAL_65]] : index +// CHECK: ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index, %[[VAL_44:.*]]: index, %[[VAL_45:.*]]: tensor>): +// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_42]]] : memref +// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_43]]] : memref +// CHECK: %[[VAL_48:.*]] = arith.cmpi ult, %[[VAL_47]], %[[VAL_46]] : index +// CHECK: %[[VAL_49:.*]] = arith.select %[[VAL_48]], %[[VAL_47]], %[[VAL_46]] : index +// CHECK: %[[VAL_50:.*]] = arith.cmpi eq, %[[VAL_46]], %[[VAL_49]] : index +// CHECK: %[[VAL_51:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_49]] : index +// CHECK: %[[VAL_52:.*]] = arith.andi %[[VAL_50]], %[[VAL_51]] : i1 +// CHECK: %[[VAL_53:.*]]:2 = scf.if %[[VAL_52]] -> (index, tensor>) { +// CHECK: %[[VAL_54:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_42]]] : memref +// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_43]]] : memref +// CHECK: %[[VAL_56:.*]] = arith.addi %[[VAL_43]], %[[VAL_3]] : index +// CHECK: %[[VAL_57:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_56]]] : memref +// CHECK: %[[VAL_58:.*]] = scf.for %[[VAL_59:.*]] = %[[VAL_55]] to %[[VAL_57]] step %[[VAL_3]] iter_args(%[[VAL_60:.*]] = %[[VAL_44]]) -> (index) { +// CHECK: %[[VAL_61:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_59]]] : memref +// CHECK: %[[VAL_62:.*]] = memref.load %[[VAL_25]]{{\[}}%[[VAL_61]]] : memref +// CHECK: %[[VAL_63:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_59]]] : memref +// CHECK: %[[VAL_64:.*]] = arith.mulf %[[VAL_54]], %[[VAL_63]] : f32 +// CHECK: %[[VAL_65:.*]] = arith.addf %[[VAL_62]], %[[VAL_64]] : f32 +// CHECK: %[[VAL_66:.*]] = memref.load %[[VAL_26]]{{\[}}%[[VAL_61]]] : memref +// CHECK: %[[VAL_67:.*]] = arith.cmpi eq, %[[VAL_66]], %[[VAL_4]] : i1 +// CHECK: %[[VAL_68:.*]] = scf.if %[[VAL_67]] -> (index) { +// CHECK: memref.store %[[VAL_5]], %[[VAL_26]]{{\[}}%[[VAL_61]]] : memref +// CHECK: memref.store %[[VAL_61]], %[[VAL_27]]{{\[}}%[[VAL_60]]] : memref +// CHECK: %[[VAL_69:.*]] = arith.addi %[[VAL_60]], %[[VAL_3]] : index +// CHECK: scf.yield %[[VAL_69]] : index // CHECK: } else { -// CHECK: scf.yield %[[VAL_56]] : index +// CHECK: scf.yield %[[VAL_60]] : index // CHECK: } -// CHECK: memref.store %[[VAL_61]], %[[VAL_23]]{{\[}}%[[VAL_57]]] : memref -// CHECK: scf.yield %[[VAL_66:.*]] : index +// CHECK: memref.store %[[VAL_65]], %[[VAL_25]]{{\[}}%[[VAL_61]]] : memref +// CHECK: scf.yield %[[VAL_70:.*]] : index // CHECK: } -// CHECK: scf.yield %[[VAL_67:.*]] : index +// CHECK: scf.yield %[[VAL_71:.*]], %[[VAL_45]] : index, tensor> // CHECK: } else { -// CHECK: scf.yield %[[VAL_41]] : index +// CHECK: scf.yield %[[VAL_44]], %[[VAL_45]] : index, tensor> // CHECK: } -// CHECK: %[[VAL_68:.*]] = arith.cmpi eq, %[[VAL_42]], %[[VAL_45]] : index -// CHECK: %[[VAL_69:.*]] = arith.addi %[[VAL_39]], %[[VAL_3]] : index -// CHECK: %[[VAL_70:.*]] = arith.select %[[VAL_68]], %[[VAL_69]], %[[VAL_39]] : index -// CHECK: %[[VAL_71:.*]] = arith.cmpi eq, %[[VAL_43]], %[[VAL_45]] : index -// CHECK: %[[VAL_72:.*]] = arith.addi %[[VAL_40]], %[[VAL_3]] : index -// CHECK: %[[VAL_73:.*]] = arith.select %[[VAL_71]], %[[VAL_72]], %[[VAL_40]] : index -// CHECK: scf.yield %[[VAL_70]], %[[VAL_73]], %[[VAL_74:.*]] : index, index, index +// CHECK: %[[VAL_72:.*]] = arith.cmpi eq, %[[VAL_46]], %[[VAL_49]] : index +// CHECK: %[[VAL_73:.*]] = arith.addi %[[VAL_42]], %[[VAL_3]] : index +// CHECK: %[[VAL_74:.*]] = arith.select %[[VAL_72]], %[[VAL_73]], %[[VAL_42]] : index +// CHECK: %[[VAL_75:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_49]] : index +// CHECK: %[[VAL_76:.*]] = arith.addi %[[VAL_43]], %[[VAL_3]] : index +// CHECK: %[[VAL_77:.*]] = arith.select %[[VAL_75]], %[[VAL_76]], %[[VAL_43]] : index +// CHECK: scf.yield %[[VAL_74]], %[[VAL_77]], %[[VAL_78:.*]]#0, %[[VAL_78]]#1 : index, index, index, tensor> // CHECK: } -// CHECK: sparse_tensor.compress %[[VAL_23]], %[[VAL_24]], %[[VAL_25]], %[[VAL_75:.*]]#2 into %[[VAL_8]]{{\[}}%[[VAL_22]]] : memref, memref, memref, tensor> +// CHECK: %[[VAL_79:.*]] = sparse_tensor.compress %[[VAL_25]], %[[VAL_26]], %[[VAL_27]], %[[VAL_80:.*]]#2 into %[[VAL_80]]#3{{\[}}%[[VAL_24]]] : memref, memref, memref, tensor> +// CHECK: scf.yield %[[VAL_79]] : tensor> // CHECK: } -// CHECK: %[[VAL_76:.*]] = sparse_tensor.load %[[VAL_8]] hasInserts : tensor> -// CHECK: return %[[VAL_76]] : tensor> +// CHECK: %[[VAL_81:.*]] = sparse_tensor.load %[[VAL_82:.*]] hasInserts : tensor> +// CHECK: return %[[VAL_81]] : tensor> // CHECK: } func.func @matmat(%arga: tensor, %argb: tensor) -> tensor { diff --git a/mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir b/mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir @@ -133,52 +133,53 @@ // CHECK-DAG: %[[VAL_6:.*]] = arith.constant false // CHECK-DAG: %[[VAL_7:.*]] = arith.constant true // CHECK-DAG: %[[VAL_8:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64> -// CHECK: %[[VAL_9:.*]] = bufferization.alloc_tensor() copy(%[[VAL_8]]) {bufferization.escape = [false]} : tensor<8x8xf64> -// CHECK: %[[VAL_10:.*]] = bufferization.alloc_tensor() {bufferization.escape = [false]} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64> -// CHECK: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_15:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_16:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref -// CHECK: %[[VAL_17:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.alloc_tensor() copy(%[[VAL_8]]) {bufferization.escape = [false]} : tensor<8x8xf64> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.alloc_tensor() {bufferization.escape = [false]} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64> +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref -// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] { -// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_20]]] : memref -// CHECK: %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]] = sparse_tensor.expand %[[VAL_10]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref, memref, memref -// CHECK: %[[VAL_26:.*]] = scf.for %[[VAL_27:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_28:.*]] = %[[VAL_25]]) -> (index) { -// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]], %[[VAL_27]]] : memref<8x8xf64> -// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_20]]] : memref -// CHECK: %[[VAL_31:.*]] = arith.addi %[[VAL_20]], %[[VAL_5]] : index -// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_31]]] : memref -// CHECK: %[[VAL_33:.*]] = scf.for %[[VAL_34:.*]] = %[[VAL_30]] to %[[VAL_32]] step %[[VAL_5]] iter_args(%[[VAL_35:.*]] = %[[VAL_28]]) -> (index) { -// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_34]]] : memref -// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_22]]{{\[}}%[[VAL_36]]] : memref -// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_27]], %[[VAL_36]]] : memref<8x8xf64> -// CHECK: %[[VAL_39:.*]] = arith.mulf %[[VAL_29]], %[[VAL_38]] : f64 -// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_34]]] : memref -// CHECK: %[[VAL_41:.*]] = arith.mulf %[[VAL_39]], %[[VAL_40]] : f64 -// CHECK: %[[VAL_42:.*]] = arith.addf %[[VAL_37]], %[[VAL_41]] : f64 -// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_23]]{{\[}}%[[VAL_36]]] : memref -// CHECK: %[[VAL_44:.*]] = arith.cmpi eq, %[[VAL_43]], %[[VAL_6]] : i1 -// CHECK: %[[VAL_45:.*]] = scf.if %[[VAL_44]] -> (index) { -// CHECK: memref.store %[[VAL_7]], %[[VAL_23]]{{\[}}%[[VAL_36]]] : memref -// CHECK: memref.store %[[VAL_36]], %[[VAL_24]]{{\[}}%[[VAL_35]]] : memref -// CHECK: %[[VAL_46:.*]] = arith.addi %[[VAL_35]], %[[VAL_5]] : index -// CHECK: scf.yield %[[VAL_46]] : index +// CHECK: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] iter_args(%[[VAL_22:.*]] = %[[VAL_10]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_21]]] : memref +// CHECK: %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]] = sparse_tensor.expand %[[VAL_10]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref, memref, memref +// CHECK: %[[VAL_28:.*]] = scf.for %[[VAL_29:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_30:.*]] = %[[VAL_27]]) -> (index) { +// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]], %[[VAL_29]]] : memref<8x8xf64> +// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_21]]] : memref +// CHECK: %[[VAL_33:.*]] = arith.addi %[[VAL_21]], %[[VAL_5]] : index +// CHECK: %[[VAL_34:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_33]]] : memref +// CHECK: %[[VAL_35:.*]] = scf.for %[[VAL_36:.*]] = %[[VAL_32]] to %[[VAL_34]] step %[[VAL_5]] iter_args(%[[VAL_37:.*]] = %[[VAL_30]]) -> (index) { +// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_36]]] : memref +// CHECK: %[[VAL_39:.*]] = memref.load %[[VAL_24]]{{\[}}%[[VAL_38]]] : memref +// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_29]], %[[VAL_38]]] : memref<8x8xf64> +// CHECK: %[[VAL_41:.*]] = arith.mulf %[[VAL_31]], %[[VAL_40]] : f64 +// CHECK: %[[VAL_42:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_36]]] : memref +// CHECK: %[[VAL_43:.*]] = arith.mulf %[[VAL_41]], %[[VAL_42]] : f64 +// CHECK: %[[VAL_44:.*]] = arith.addf %[[VAL_39]], %[[VAL_43]] : f64 +// CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_25]]{{\[}}%[[VAL_38]]] : memref +// CHECK: %[[VAL_46:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_6]] : i1 +// CHECK: %[[VAL_47:.*]] = scf.if %[[VAL_46]] -> (index) { +// CHECK: memref.store %[[VAL_7]], %[[VAL_25]]{{\[}}%[[VAL_38]]] : memref +// CHECK: memref.store %[[VAL_38]], %[[VAL_26]]{{\[}}%[[VAL_37]]] : memref +// CHECK: %[[VAL_48:.*]] = arith.addi %[[VAL_37]], %[[VAL_5]] : index +// CHECK: scf.yield %[[VAL_48]] : index // CHECK: } else { -// CHECK: scf.yield %[[VAL_35]] : index +// CHECK: scf.yield %[[VAL_37]] : index // CHECK: } -// CHECK: memref.store %[[VAL_42]], %[[VAL_22]]{{\[}}%[[VAL_36]]] : memref -// CHECK: scf.yield %[[VAL_47:.*]] : index +// CHECK: memref.store %[[VAL_44]], %[[VAL_24]]{{\[}}%[[VAL_38]]] : memref +// CHECK: scf.yield %[[VAL_49:.*]] : index // CHECK: } -// CHECK: scf.yield %[[VAL_48:.*]] : index +// CHECK: scf.yield %[[VAL_50:.*]] : index // CHECK: } -// CHECK: sparse_tensor.compress %[[VAL_22]], %[[VAL_23]], %[[VAL_24]], %[[VAL_49:.*]] into %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref, memref, memref, tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_51:.*]] = sparse_tensor.compress %[[VAL_24]], %[[VAL_25]], %[[VAL_26]], %[[VAL_52:.*]] into %[[VAL_22]]{{\[}}%[[VAL_23]]] : memref, memref, memref, tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: scf.yield %[[VAL_51]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } -// CHECK: %[[VAL_50:.*]] = sparse_tensor.load %[[VAL_10]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> -// CHECK: return %[[VAL_50]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_53:.*]] = sparse_tensor.load %[[VAL_54:.*]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: return %[[VAL_53]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } func.func @sparse_sampled_dd_unfused(%args: tensor<8x8xf64, #SM>, %arga: tensor<8x8xf64>, diff --git a/mlir/test/Dialect/SparseTensor/sparse_transpose.mlir b/mlir/test/Dialect/SparseTensor/sparse_transpose.mlir --- a/mlir/test/Dialect/SparseTensor/sparse_transpose.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_transpose.mlir @@ -19,29 +19,31 @@ // CHECK-SAME: %[[VAL_0:.*]]: tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> { // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> -// CHECK: %[[VAL_4:.*]] = sparse_tensor.convert %[[VAL_0]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref +// CHECK-DAG: %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.convert %[[VAL_0]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref -// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] { -// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref -// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref -// CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_12]], %[[VAL_2]] : index -// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref -// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_14]] to %[[VAL_16]] step %[[VAL_2]] { -// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref -// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref -// CHECK: sparse_tensor.insert %[[VAL_19]] into %[[VAL_3]]{{\[}}%[[VAL_13]], %[[VAL_18]]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_3]]) -> (tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref +// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref +// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index +// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref +// CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_16]] to %[[VAL_18]] step %[[VAL_2]] iter_args(%[[VAL_21:.*]] = %[[VAL_14]]) -> (tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref +// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref +// CHECK: %[[VAL_24:.*]] = sparse_tensor.insert %[[VAL_23]] into %[[VAL_21]]{{\[}}%[[VAL_15]], %[[VAL_22]]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: scf.yield %[[VAL_24]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } +// CHECK: scf.yield %[[VAL_25:.*]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } -// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[VAL_3]] hasInserts : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_26:.*]] = sparse_tensor.load %[[VAL_27:.*]] hasInserts : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: bufferization.dealloc_tensor %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> -// CHECK: return %[[VAL_20]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: return %[[VAL_26]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> // CHECK: } func.func @sparse_transpose_auto(%arga: tensor<3x4xf64, #DCSR>) -> tensor<4x3xf64, #DCSR> {