return add;
}
+Value LoopEmitter::genSegmentHigh(OpBuilder &builder, Location loc, size_t tid,
+ size_t lvl, Value pos, Value pHi) {
+ Value prevCrd = genIndexLoad(builder, loc, crdBuffer[tid][lvl], pos);
+ // De-duplicates repeated elements.
+ //
+ // while (pos < pHi && coord[pos] == prev_coord)
+ // pos++;
+ // return pos;
+ auto whileOp = builder.create<scf::WhileOp>(
+ loc, builder.getIndexType(), pos,
+ /*beforeBuilder=*/
+ [this, tid, lvl, pHi, prevCrd](OpBuilder &builder, Location loc,
+ ValueRange ivs) {
+ Value inBound = builder.create<arith::CmpIOp>(
+ loc, arith::CmpIPredicate::ult, ivs[0], pHi);
+ auto ifOp =
+ builder.create<scf::IfOp>(loc, builder.getI1Type(), inBound, true);
+ {
+ OpBuilder::InsertionGuard guard(builder);
+ // Load the next coordinates only when inbound (to avoid OOB
+ // acccesses).
+ builder.setInsertionPointToStart(ifOp.thenBlock());
+ Value nxCrd = genIndexLoad(builder, loc, crdBuffer[tid][lvl], ivs[0]);
+ Value cont = builder.create<arith::CmpIOp>(
+ loc, arith::CmpIPredicate::eq, nxCrd, prevCrd);
+ builder.create<scf::YieldOp>(loc, cont);
+ // Else, the position is out of bound, yield false to terminate the
+ // loop.
+ builder.setInsertionPointToStart(ifOp.elseBlock());
+ builder.create<scf::YieldOp>(loc, constantI1(builder, loc, false));
+ }
+ builder.create<scf::ConditionOp>(loc, ifOp.getResults()[0], ivs);
+ },
+ /*afterBuilder=*/
+ [](OpBuilder &builder, Location loc, ValueRange ivs) {
+ // pos ++
+ Value nxPos = builder.create<arith::AddIOp>(
+ loc, ivs[0], constantIndex(builder, loc, 1));
+ builder.create<scf::YieldOp>(loc, nxPos);
+ });
+ // Return the segment high.
+ return whileOp.getResult(0);
+}
+
Value LoopEmitter::genSparseCrd(OpBuilder &builder, Location loc, size_t tid,
size_t dstLvl) {
Value crd = constantIndex(builder, loc, 0);
this->isSparseSlices.assign(tensors.size(), false);
this->dimTypes.assign(tensors.size(), std::vector<DimLevelType>());
this->pidxs.assign(tensors.size(), std::vector<Value>());
+ this->segHi.assign(tensors.size(), std::vector<Value>());
this->coord.assign(tensors.size(), std::vector<Value>());
this->highs.assign(tensors.size(), std::vector<Value>());
this->lvlSizes.assign(tensors.size(), std::vector<Value>());
// Initialize using empty value.
pidxs[tid].assign(rank, Value());
+ segHi[tid].assign(rank, Value());
coord[tid].assign(rank, Value());
highs[tid].assign(rank, Value());
lvlSizes[tid].assign(rank, Value());
void LoopEmitter::enterNewLoopSeq(OpBuilder &builder, Location loc,
ArrayRef<size_t> tids,
ArrayRef<size_t> dims) {
+ // TODO: sort
assert(loopSeqStack.size() == loopStack.size());
// Universal Index starts from 0.
loopSeqStack.emplace_back(constantIndex(builder, loc, 0));
loopTag);
assert(loopStack.size() == loopSeqStack.size());
+ for (auto [tid, dim] : llvm::zip(tids, dims)) {
+ if (!isUniqueDLT(dimTypes[tid][dim])) {
+ segHi[tid][dim] = genSegmentHigh(builder, loc, tid, dim, pidxs[tid][dim],
+ highs[tid][dim]);
+ }
+ }
+
// Emits extra locals
emitExtraLocalsForTensorsAtDenseDims(builder, loc, tids, dims);
}
if (isSingletonDLT(dimType)) {
Value pLo = lvl == 0 ? c0 : pidxs[tid][lvl - 1];
- Value pHi = builder.create<arith::AddIOp>(loc, pLo, c1);
-
+ Value pHi;
+ // If this is non-unique, the pHi is bound by the segment high of the
+ // previous level.
+ if (!isUniqueDLT(dimTypes[tid][lvl - 1]))
+ pHi = segHi[tid][lvl - 1];
+
+ // If pHi is still uninitialized, we set it to one as it is a singleton
+ // level.
+ // NOTE: Even if the level is non-unique, the pHi might not have been set
+ // in the previous statement, as we only compute segment high when we are
+ // coiterating non-unique levels.
+ if (!pHi)
+ pHi = builder.create<arith::AddIOp>(loc, pLo, c1);
pidxs[tid][lvl] = pLo;
highs[tid][lvl] = pHi;
return;
Value op3 = pidxs[tid][dim];
Value cmp =
builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq, op1, iv);
- Value add = builder.create<arith::AddIOp>(loc, op3, one);
+ // If the loop contains a coiteration with non-unique level, we fast
+ // forward all the duplicated coords by setting the position to the
+ // segment high.
+ Value add = !isUniqueDLT(dimTypes[tid][dim])
+ ? segHi[tid][dim]
+ : builder.create<arith::AddIOp>(loc, op3, one);
operands.push_back(builder.create<arith::SelectOp>(loc, cmp, add, op3));
// Following loops continue iteration from the break point of the
// current while loop.
pidxs[tid][dim] = whileOp->getResult(o++);
// The coordinates are invalid now.
coord[tid][dim] = nullptr;
+ // The segment high are invalid now
+ segHi[tid][dim] = nullptr;
// highs remains unchanged.
}
}
Value genAddress(OpBuilder &builder, Location loc, size_t tid, size_t dim,
Value iv);
+ /// Generates the segment high for a non-unique level (to fast forward
+ /// duplicated coordinates).
+ Value genSegmentHigh(OpBuilder &builder, Location loc, size_t tid, size_t lvl,
+ Value pos, Value pHi);
+
/// Generates instructions to compute the coordinate of tensors[tid][lvl]
/// under the current loop context. The final argument is the
/// collapsed-output level, whereas this function handles converting
/// are updated to remain current within the current loop.
// TODO: we may want to rename "pidx(s)" to `posCursor(s)` or similar.
std::vector<std::vector<Value>> pidxs;
+ // The segment upper bound for non-uniques level after de-duplication.
+ std::vector<std::vector<Value>> segHi;
std::vector<std::vector<Value>> coord;
std::vector<std::vector<Value>> highs;
std::vector<std::vector<Value>> lvlSizes;
unsigned ldx = at == 0 ? -1u : env.topSortAt(at - 1);
unsigned lts = env.merger().optimizeSet(env.merger().buildLattices(exp, idx));
- // TODO: sort
- // TODO: dedup
-
// Start a loop sequence.
bool needsUniv = startLoopSeq(env, rewriter, exp, at, idx, ldx, lts);
return %0 : tensor<32xf64>
}
-// CHECK-LABEL: func.func @mateltmul(
-// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
-// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
-// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x64xf64>) -> tensor<32x64xf64> {
-// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f64
-// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
-// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
-// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
-// CHECK-DAG: linalg.fill ins(%[[VAL_3]] : f64) outs(%[[VAL_14]] : memref<32x64xf64>)
-// CHECK-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
-// CHECK-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
-// CHECK-DAG: %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
-// CHECK-DAG: %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<?xindex>
-// CHECK: %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_15]], %[[VAL_21:.*]] = %[[VAL_17]]) : (index, index) -> (index, index) {
-// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_16]] : index
-// CHECK: %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_18]] : index
-// CHECK: %[[VAL_24:.*]] = arith.andi %[[VAL_22]], %[[VAL_23]] : i1
-// CHECK: scf.condition(%[[VAL_24]]) %[[VAL_20]], %[[VAL_21]] : index, index
-// CHECK: } do {
-// CHECK: ^bb0(%[[VAL_25:.*]]: index, %[[VAL_26:.*]]: index):
-// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_25]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_26]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK: %[[VAL_29:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_27]] : index
-// CHECK: %[[VAL_30:.*]] = arith.select %[[VAL_29]], %[[VAL_28]], %[[VAL_27]] : index
-// CHECK: %[[VAL_31:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
-// CHECK: %[[VAL_32:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
-// CHECK: %[[VAL_33:.*]] = arith.andi %[[VAL_31]], %[[VAL_32]] : i1
-// CHECK: scf.if %[[VAL_33]] {
-// CHECK: %[[VAL_34:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
-// CHECK: %[[VAL_35:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
-// CHECK: %[[VAL_36:.*]]:2 = scf.while (%[[VAL_37:.*]] = %[[VAL_25]], %[[VAL_38:.*]] = %[[VAL_26]]) : (index, index) -> (index, index) {
-// CHECK: %[[VAL_39:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_34]] : index
-// CHECK: %[[VAL_40:.*]] = arith.cmpi ult, %[[VAL_38]], %[[VAL_35]] : index
-// CHECK: %[[VAL_41:.*]] = arith.andi %[[VAL_39]], %[[VAL_40]] : i1
-// CHECK: scf.condition(%[[VAL_41]]) %[[VAL_37]], %[[VAL_38]] : index, index
+// CHECK-LABEL: func.func @mateltmul(
+// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
+// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
+// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x64xf64>) -> tensor<32x64xf64> {
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant false
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
+// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
+// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
+// CHECK: linalg.fill ins(%[[VAL_4]] : f64) outs(%[[VAL_15]] : memref<32x64xf64>)
+// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
+// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_6]]] : memref<?xindex>
+// CHECK: %[[VAL_20:.*]]:2 = scf.while (%[[VAL_21:.*]] = %[[VAL_16]], %[[VAL_22:.*]] = %[[VAL_18]]) : (index, index) -> (index, index) {
+// CHECK: %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_17]] : index
+// CHECK: %[[VAL_24:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_19]] : index
+// CHECK: %[[VAL_25:.*]] = arith.andi %[[VAL_23]], %[[VAL_24]] : i1
+// CHECK: scf.condition(%[[VAL_25]]) %[[VAL_21]], %[[VAL_22]] : index, index
+// CHECK: } do {
+// CHECK: ^bb0(%[[VAL_26:.*]]: index, %[[VAL_27:.*]]: index):
+// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_26]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_27]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_29]], %[[VAL_28]] : index
+// CHECK: %[[VAL_31:.*]] = arith.select %[[VAL_30]], %[[VAL_29]], %[[VAL_28]] : index
+// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_26]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_33:.*]] = scf.while (%[[VAL_34:.*]] = %[[VAL_26]]) : (index) -> index {
+// CHECK: %[[VAL_35:.*]] = arith.cmpi ult, %[[VAL_34]], %[[VAL_17]] : index
+// CHECK: %[[VAL_36:.*]] = scf.if %[[VAL_35]] -> (i1) {
+// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_34]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_38:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_32]] : index
+// CHECK: scf.yield %[[VAL_38]] : i1
+// CHECK: } else {
+// CHECK: scf.yield %[[VAL_3]] : i1
+// CHECK: }
+// CHECK: scf.condition(%[[VAL_39:.*]]) %[[VAL_34]] : index
// CHECK: } do {
-// CHECK: ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index):
-// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_42]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_43]]] : memref<?xindex, strided<[?], offset: ?>>
-// 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: scf.if %[[VAL_50]] {
-// CHECK: %[[VAL_51:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_42]]] : memref<?xf64>
-// CHECK: %[[VAL_52:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_43]]] : memref<?xf64>
-// CHECK: %[[VAL_53:.*]] = arith.mulf %[[VAL_51]], %[[VAL_52]] : f64
-// CHECK: memref.store %[[VAL_53]], %[[VAL_14]]{{\[}}%[[VAL_30]], %[[VAL_47]]] : memref<32x64xf64>
+// CHECK: ^bb0(%[[VAL_40:.*]]: index):
+// CHECK: %[[VAL_41:.*]] = arith.addi %[[VAL_40]], %[[VAL_6]] : index
+// CHECK: scf.yield %[[VAL_41]] : index
+// CHECK: }
+// CHECK: %[[VAL_42:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_27]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_43:.*]] = scf.while (%[[VAL_44:.*]] = %[[VAL_27]]) : (index) -> index {
+// CHECK: %[[VAL_45:.*]] = arith.cmpi ult, %[[VAL_44]], %[[VAL_19]] : index
+// CHECK: %[[VAL_46:.*]] = scf.if %[[VAL_45]] -> (i1) {
+// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_44]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_42]] : index
+// CHECK: scf.yield %[[VAL_48]] : i1
// CHECK: } else {
+// CHECK: scf.yield %[[VAL_3]] : i1
// CHECK: }
-// CHECK: %[[VAL_54:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
-// CHECK: %[[VAL_55:.*]] = arith.addi %[[VAL_42]], %[[VAL_5]] : index
-// CHECK: %[[VAL_56:.*]] = arith.select %[[VAL_54]], %[[VAL_55]], %[[VAL_42]] : index
-// CHECK: %[[VAL_57:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
-// CHECK: %[[VAL_58:.*]] = arith.addi %[[VAL_43]], %[[VAL_5]] : index
-// CHECK: %[[VAL_59:.*]] = arith.select %[[VAL_57]], %[[VAL_58]], %[[VAL_43]] : index
-// CHECK: scf.yield %[[VAL_56]], %[[VAL_59]] : index, index
+// CHECK: scf.condition(%[[VAL_49:.*]]) %[[VAL_44]] : index
+// CHECK: } do {
+// CHECK: ^bb0(%[[VAL_50:.*]]: index):
+// CHECK: %[[VAL_51:.*]] = arith.addi %[[VAL_50]], %[[VAL_6]] : index
+// CHECK: scf.yield %[[VAL_51]] : index
+// CHECK: }
+// CHECK: %[[VAL_52:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_31]] : index
+// CHECK: %[[VAL_53:.*]] = arith.cmpi eq, %[[VAL_29]], %[[VAL_31]] : index
+// CHECK: %[[VAL_54:.*]] = arith.andi %[[VAL_52]], %[[VAL_53]] : i1
+// CHECK: scf.if %[[VAL_54]] {
+// CHECK: %[[VAL_55:.*]]:2 = scf.while (%[[VAL_56:.*]] = %[[VAL_26]], %[[VAL_57:.*]] = %[[VAL_27]]) : (index, index) -> (index, index) {
+// CHECK: %[[VAL_58:.*]] = arith.cmpi ult, %[[VAL_56]], %[[VAL_59:.*]] : index
+// CHECK: %[[VAL_60:.*]] = arith.cmpi ult, %[[VAL_57]], %[[VAL_61:.*]] : index
+// CHECK: %[[VAL_62:.*]] = arith.andi %[[VAL_58]], %[[VAL_60]] : i1
+// CHECK: scf.condition(%[[VAL_62]]) %[[VAL_56]], %[[VAL_57]] : index, index
+// CHECK: } do {
+// CHECK: ^bb0(%[[VAL_63:.*]]: index, %[[VAL_64:.*]]: index):
+// CHECK: %[[VAL_65:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_63]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_66:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_64]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK: %[[VAL_67:.*]] = arith.cmpi ult, %[[VAL_66]], %[[VAL_65]] : index
+// CHECK: %[[VAL_68:.*]] = arith.select %[[VAL_67]], %[[VAL_66]], %[[VAL_65]] : index
+// CHECK: %[[VAL_69:.*]] = arith.cmpi eq, %[[VAL_65]], %[[VAL_68]] : index
+// CHECK: %[[VAL_70:.*]] = arith.cmpi eq, %[[VAL_66]], %[[VAL_68]] : index
+// CHECK: %[[VAL_71:.*]] = arith.andi %[[VAL_69]], %[[VAL_70]] : i1
+// CHECK: scf.if %[[VAL_71]] {
+// CHECK: %[[VAL_72:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_63]]] : memref<?xf64>
+// CHECK: %[[VAL_73:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_64]]] : memref<?xf64>
+// CHECK: %[[VAL_74:.*]] = arith.mulf %[[VAL_72]], %[[VAL_73]] : f64
+// CHECK: memref.store %[[VAL_74]], %[[VAL_15]]{{\[}}%[[VAL_31]], %[[VAL_68]]] : memref<32x64xf64>
+// CHECK: } else {
+// CHECK: }
+// CHECK: %[[VAL_75:.*]] = arith.cmpi eq, %[[VAL_65]], %[[VAL_68]] : index
+// CHECK: %[[VAL_76:.*]] = arith.addi %[[VAL_63]], %[[VAL_6]] : index
+// CHECK: %[[VAL_77:.*]] = arith.select %[[VAL_75]], %[[VAL_76]], %[[VAL_63]] : index
+// CHECK: %[[VAL_78:.*]] = arith.cmpi eq, %[[VAL_66]], %[[VAL_68]] : index
+// CHECK: %[[VAL_79:.*]] = arith.addi %[[VAL_64]], %[[VAL_6]] : index
+// CHECK: %[[VAL_80:.*]] = arith.select %[[VAL_78]], %[[VAL_79]], %[[VAL_64]] : index
+// CHECK: scf.yield %[[VAL_77]], %[[VAL_80]] : index, index
+// CHECK: } attributes {"Emitted from" = "linalg.generic"}
+// CHECK: } else {
// CHECK: }
-// CHECK: } else {
-// CHECK: }
-// CHECK: %[[VAL_60:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
-// CHECK: %[[VAL_61:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
-// CHECK: %[[VAL_62:.*]] = arith.select %[[VAL_60]], %[[VAL_61]], %[[VAL_25]] : index
-// CHECK: %[[VAL_63:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
-// CHECK: %[[VAL_64:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
-// CHECK: %[[VAL_65:.*]] = arith.select %[[VAL_63]], %[[VAL_64]], %[[VAL_26]] : index
-// CHECK: scf.yield %[[VAL_62]], %[[VAL_65]] : index, index
+// CHECK: %[[VAL_81:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_31]] : index
+// CHECK: %[[VAL_82:.*]] = arith.select %[[VAL_81]], %[[VAL_83:.*]], %[[VAL_26]] : index
+// CHECK: %[[VAL_84:.*]] = arith.cmpi eq, %[[VAL_29]], %[[VAL_31]] : index
+// CHECK: %[[VAL_85:.*]] = arith.select %[[VAL_84]], %[[VAL_86:.*]], %[[VAL_27]] : index
+// CHECK: scf.yield %[[VAL_82]], %[[VAL_85]] : index, index
+// CHECK: } attributes {"Emitted from" = "linalg.generic"}
+// CHECK: %[[VAL_87:.*]] = bufferization.to_tensor %[[VAL_15]] : memref<32x64xf64>
+// CHECK: return %[[VAL_87]] : tensor<32x64xf64>
// CHECK: }
-// CHECK: %[[VAL_66:.*]] = bufferization.to_tensor %[[VAL_14]] : memref<32x64xf64>
-// CHECK: return %[[VAL_66]] : tensor<32x64xf64>
-// CHECK: }
func.func @mateltmul(%argx: tensor<32x64xf64, #SortedCOO>,
%argy: tensor<32x64xf64, #SortedCOO>,
%argz: tensor<32x64xf64>) -> tensor<32x64xf64> {
--- /dev/null
+// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
+// DEFINE: %{run} = mlir-cpu-runner \
+// DEFINE: -e entry -entry-point-result=void \
+// DEFINE: -shared-libs=%mlir_c_runner_utils | \
+// DEFINE: FileCheck %s
+//
+// RUN: %{compile} | %{run}
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{option} = enable-runtime-library=false
+// RUN: %{compile} | %{run}
+//
+// Do the same run, but now with direct IR generation and vectorization.
+// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
+// RUN: %{compile} | %{run}
+//
+// Do the same run, but now with direct IR generation and, if available, VLA
+// vectorization.
+// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
+// REDEFINE: %{run} = %lli \
+// REDEFINE: --entry-function=entry_lli \
+// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
+// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
+// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
+// REDEFINE: FileCheck %s
+// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
+
+#SortedCOO = #sparse_tensor.encoding<{
+ dimLevelType = [ "compressed-nu", "singleton" ]
+}>
+
+#CSR = #sparse_tensor.encoding<{
+ dimLevelType = [ "dense", "compressed" ]
+}>
+
+#trait = {
+ indexing_maps = [
+ affine_map<(i,j) -> (i,j)>, // A
+ affine_map<(i,j) -> (i,j)>, // B
+ affine_map<(i,j) -> (i,j)> // X (out)
+ ],
+ iterator_types = ["parallel", "parallel"],
+ doc = "X(i,j) = A(i,j) + B(i,j)"
+}
+
+module {
+ func.func @add_coo_csr(%arga: tensor<8x8xf32, #CSR>,
+ %argb: tensor<8x8xf32, #SortedCOO>)
+ -> tensor<8x8xf32> {
+ %empty = tensor.empty() : tensor<8x8xf32>
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = linalg.fill
+ ins(%zero : f32)
+ outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
+ %0 = linalg.generic #trait
+ ins(%arga, %argb: tensor<8x8xf32, #CSR>,
+ tensor<8x8xf32, #SortedCOO>)
+ outs(%init: tensor<8x8xf32>) {
+ ^bb(%a: f32, %b: f32, %x: f32):
+ %0 = arith.addf %a, %b : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x8xf32>
+ return %0 : tensor<8x8xf32>
+ }
+
+ func.func @add_coo_coo(%arga: tensor<8x8xf32, #SortedCOO>,
+ %argb: tensor<8x8xf32, #SortedCOO>)
+ -> tensor<8x8xf32> {
+ %empty = tensor.empty() : tensor<8x8xf32>
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = linalg.fill
+ ins(%zero : f32)
+ outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
+ %0 = linalg.generic #trait
+ ins(%arga, %argb: tensor<8x8xf32, #SortedCOO>,
+ tensor<8x8xf32, #SortedCOO>)
+ outs(%init: tensor<8x8xf32>) {
+ ^bb(%a: f32, %b: f32, %x: f32):
+ %0 = arith.addf %a, %b : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x8xf32>
+ return %0 : tensor<8x8xf32>
+ }
+
+ func.func @add_coo_dense(%arga: tensor<8x8xf32>,
+ %argb: tensor<8x8xf32, #SortedCOO>)
+ -> tensor<8x8xf32> {
+ %empty = tensor.empty() : tensor<8x8xf32>
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = linalg.fill
+ ins(%zero : f32)
+ outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
+ %0 = linalg.generic #trait
+ ins(%arga, %argb: tensor<8x8xf32>,
+ tensor<8x8xf32, #SortedCOO>)
+ outs(%init: tensor<8x8xf32>) {
+ ^bb(%a: f32, %b: f32, %x: f32):
+ %0 = arith.addf %a, %b : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x8xf32>
+ return %0 : tensor<8x8xf32>
+ }
+
+ func.func @entry() {
+ %c0 = arith.constant 0 : index
+ %c1 = arith.constant 1 : index
+ %c8 = arith.constant 8 : index
+
+ %A = arith.constant dense<
+ [ [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ],
+ [ 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1 ],
+ [ 2.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2 ],
+ [ 3.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3 ],
+ [ 4.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4 ],
+ [ 5.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5 ],
+ [ 6.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6 ],
+ [ 7.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7 ] ]
+ > : tensor<8x8xf32>
+ %B = arith.constant dense<
+ [ [ 7.8, 2.8, 3.8, 0.8, 3.8, 0.1, 7.8, 8.8 ],
+ [ 3.3, 2.3, 1.3, 4.3, 3.3, 6.3, 9.3, 8.3 ],
+ [ 6.6, 2.6, 3.6, 4.6, 3.6, 6.6, 7.6, 7.6 ],
+ [ 1.0, 3.0, 3.0, 4.0, 3.0, 6.0, 7.0, 8.0 ],
+ [ 0.1, 2.1, 3.1, 4.1, 3.1, 6.1, 7.1, 8.1 ],
+ [ 4.4, 2.4, 3.4, 4.4, 3.4, 6.4, 8.4, 8.4 ],
+ [ 5.5, 3.5, 1.5, 4.5, 3.5, 6.5, 7.5, 8.5 ],
+ [ 7.7, 2.7, 3.7, 0.7, 5.7, 3.7, 3.7, 0.7 ] ]
+ > : tensor<8x8xf32>
+
+ // Stress test with a "sparse" version of A and B.
+ %CSR_A = sparse_tensor.convert %A
+ : tensor<8x8xf32> to tensor<8x8xf32, #CSR>
+ %COO_A = sparse_tensor.convert %A
+ : tensor<8x8xf32> to tensor<8x8xf32, #SortedCOO>
+ %COO_B = sparse_tensor.convert %B
+ : tensor<8x8xf32> to tensor<8x8xf32, #SortedCOO>
+
+ %C1 = call @add_coo_dense(%A, %COO_B) : (tensor<8x8xf32>,
+ tensor<8x8xf32, #SortedCOO>)
+ -> tensor<8x8xf32>
+ %C2 = call @add_coo_csr(%CSR_A, %COO_B) : (tensor<8x8xf32, #CSR>,
+ tensor<8x8xf32, #SortedCOO>)
+ -> tensor<8x8xf32>
+ %C3 = call @add_coo_coo(%COO_A, %COO_B) : (tensor<8x8xf32, #SortedCOO>,
+ tensor<8x8xf32, #SortedCOO>)
+ -> tensor<8x8xf32>
+ //
+ // Verify computed matrix C.
+ //
+ // CHECK-COUNT-3: ( 8.8, 4.8, 6.8, 4.8, 8.8, 6.1, 14.8, 16.8 )
+ // CHECK-NEXT-COUNT-3: ( 4.4, 4.4, 4.4, 8.4, 8.4, 12.4, 16.4, 16.4 )
+ // CHECK-NEXT-COUNT-3: ( 8.8, 4.8, 6.8, 8.8, 8.8, 12.8, 14.8, 15.8 )
+ // CHECK-NEXT-COUNT-3: ( 4.3, 5.3, 6.3, 8.3, 8.3, 12.3, 14.3, 16.3 )
+ // CHECK-NEXT-COUNT-3: ( 4.5, 4.5, 6.5, 8.5, 8.5, 12.5, 14.5, 16.5 )
+ // CHECK-NEXT-COUNT-3: ( 9.9, 4.9, 6.9, 8.9, 8.9, 12.9, 15.9, 16.9 )
+ // CHECK-NEXT-COUNT-3: ( 12.1, 6.1, 5.1, 9.1, 9.1, 13.1, 15.1, 17.1 )
+ // CHECK-NEXT-COUNT-3: ( 15.4, 5.4, 7.4, 5.4, 11.4, 10.4, 11.4, 9.4 )
+ //
+ %f0 = arith.constant 0.0 : f32
+ scf.for %i = %c0 to %c8 step %c1 {
+ %v1 = vector.transfer_read %C1[%i, %c0], %f0
+ : tensor<8x8xf32>, vector<8xf32>
+ %v2 = vector.transfer_read %C2[%i, %c0], %f0
+ : tensor<8x8xf32>, vector<8xf32>
+ %v3 = vector.transfer_read %C3[%i, %c0], %f0
+ : tensor<8x8xf32>, vector<8xf32>
+ vector.print %v1 : vector<8xf32>
+ vector.print %v2 : vector<8xf32>
+ vector.print %v3 : vector<8xf32>
+ }
+
+ // Release resources.
+ bufferization.dealloc_tensor %C1 : tensor<8x8xf32>
+ bufferization.dealloc_tensor %C2 : tensor<8x8xf32>
+ bufferization.dealloc_tensor %C3 : tensor<8x8xf32>
+ bufferization.dealloc_tensor %CSR_A : tensor<8x8xf32, #CSR>
+ bufferization.dealloc_tensor %COO_A : tensor<8x8xf32, #SortedCOO>
+ bufferization.dealloc_tensor %COO_B : tensor<8x8xf32, #SortedCOO>
+
+
+ return
+ }
+}
\ No newline at end of file
projects / platform / upstream / llvm.git / commitdiff