[mlir][sparse] implement sparse_tensor.extract_value operation.

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

@@ -2,6 +2,7 @@
 #include "Utils/CodegenUtils.h"
 #include "Utils/SparseTensorIterator.h"
 
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
 #include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
@@ -10,8 +11,8 @@
 using namespace mlir;
 using namespace mlir::sparse_tensor;
 
-void convertLevelType(SparseTensorEncodingAttr enc, Level lvl,
-                      SmallVectorImpl<Type> &fields) {
+static void convertLevelType(SparseTensorEncodingAttr enc, Level lvl,
+                             SmallVectorImpl<Type> &fields) {
   // Position and coordinate buffer in the sparse structure.
   if (enc.getLvlType(lvl).isWithPosLT())
     fields.push_back(enc.getPosMemRefType());
@@ -71,6 +72,21 @@ class ExtractIterSpaceConverter
   }
 };
 
+/// Sparse codegen rule for number of entries operator.
+class ExtractValOpConverter : public OneToNOpConversionPattern<ExtractValOp> {
+public:
+  using OneToNOpConversionPattern::OneToNOpConversionPattern;
+  LogicalResult
+  matchAndRewrite(ExtractValOp op, OpAdaptor adaptor,
+                  OneToNPatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value pos = adaptor.getIterator().back();
+    Value valBuf = rewriter.create<ToValuesOp>(loc, op.getTensor());
+    rewriter.replaceOpWithNewOp<memref::LoadOp>(op, valBuf, pos);
+    return success();
+  }
+};
+
 class SparseIterateOpConverter : public OneToNOpConversionPattern<IterateOp> {
 public:
   using OneToNOpConversionPattern::OneToNOpConversionPattern;
@@ -193,6 +209,6 @@ void mlir::populateLowerSparseIterationToSCFPatterns(
     TypeConverter &converter, RewritePatternSet &patterns) {
 
   IterateOp::getCanonicalizationPatterns(patterns, patterns.getContext());
-  patterns.add<ExtractIterSpaceConverter, SparseIterateOpConverter>(
-      converter, patterns.getContext());
+  patterns.add<ExtractIterSpaceConverter, ExtractValOpConverter,
+               SparseIterateOpConverter>(converter, patterns.getContext());
 }

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

@@ -357,6 +357,9 @@ static Value genSubscript(CodegenEnv &env, OpBuilder &builder, OpOperand *t,
     const auto pos = env.emitter().getValPosits(tid);
     assert(!pos.empty());
     args.append(pos);
+    // Simply returns the tensor to extract value using iterators.
+    if (env.options().sparseEmitStrategy == SparseEmitStrategy::kSparseIterator)
+      return t->get();
   } else {
     // For dense tensors we push all level's coordinates onto `args`.
     const Level lvlRank = stt.getLvlRank();
@@ -512,9 +515,16 @@ static Value genTensorLoad(CodegenEnv &env, OpBuilder &builder, ExprId exp) {
       return genInsertionLoadReduce(env, builder, t);
     return genInsertionLoad(env, builder, t);
   }
+
   // Actual load.
   SmallVector<Value> args;
   Value ptr = genSubscript(env, builder, t, args);
+  if (llvm::isa<TensorType>(ptr.getType())) {
+    assert(env.options().sparseEmitStrategy ==
+               SparseEmitStrategy::kSparseIterator &&
+           args.size() == 1);
+    return builder.create<ExtractValOp>(loc, ptr, args.front());
+  }
   return builder.create<memref::LoadOp>(loc, ptr, args);
 }
 

mlir/lib/Dialect/SparseTensor/Transforms/Utils/LoopEmitter.h

@@ -221,6 +221,11 @@ class LoopEmitter {
   /// Getters.
   ///
   SmallVector<Value> getValPosits(TensorId tid) const {
+    // Returns the iterator if we are generating sparse (co)iterate-based loops.
+    if (emitStrategy == SparseEmitStrategy::kSparseIterator)
+      return {spIterVals[tid].back()};
+
+    // Returns {[batch coords], last-level position}.
     SmallVector<Value> batchCrds = iters[tid].back().back()->getBatchCrds();
     Value lastLvlPos = iters[tid].back().back()->getCurPosition().front();
     batchCrds.push_back(lastLvlPos);

mlir/test/Dialect/SparseTensor/sparse_kernels_to_iterator.mlir

@@ -1,4 +1,4 @@
-// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification="sparse-emit-strategy=sparse-iterator" --sparse-space-collapse --lower-sparse-iteration-to-scf | FileCheck %s
+// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification="sparse-emit-strategy=sparse-iterator" --cse --sparse-space-collapse --lower-sparse-iteration-to-scf --loop-invariant-code-motion | FileCheck %s
 
 
 #COO = #sparse_tensor.encoding<{
@@ -7,8 +7,7 @@
     d1 : singleton(nonunique, soa),
     d2 : singleton(nonunique, soa),
     d3 : singleton(soa)
-  ),
-  explicitVal = 1 : i32
+  )
 }>
 
 // CHECK-LABEL:   func.func @sqsum(
@@ -17,7 +16,10 @@
 // CHECK-DAG:       %[[POS_BUF:.*]] = sparse_tensor.positions %{{.*}} {level = 0 : index} : tensor<?x?x?x?xi32, #sparse> to memref<?xindex>
 // CHECK:           %[[POS_LO:.*]] = memref.load %[[POS_BUF]]{{\[}}%[[C0]]] : memref<?xindex>
 // CHECK:           %[[POS_HI:.*]] = memref.load %[[POS_BUF]]{{\[}}%[[C1]]] : memref<?xindex>
+// CHECK:           %[[VAL_BUF:.*]] = sparse_tensor.values %{{.*}} : tensor<?x?x?x?xi32, #sparse> to memref<?xi32>
 // CHECK:           %[[SQ_SUM:.*]] = scf.for %[[POS:.*]] = %[[POS_LO]] to %[[POS_HI]] step %[[C1]] {{.*}} {
+// CHECK:             %[[VAL:.*]] = memref.load %[[VAL_BUF]]{{\[}}%[[POS]]] : memref<?xi32>
+// CHECK:             %[[MUL:.*]] = arith.muli %[[VAL]], %[[VAL]] : i32
 // CHECK:             %[[SUM:.*]] = arith.addi
 // CHECK:             scf.yield %[[SUM]] : i32
 // CHECK:           }