[mlir][sparse] code cleanup (using inferred type to construct to_[buf…

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

@@ -496,11 +496,11 @@ static Value genFirstPosOrCrds(OpBuilder &builder, Location loc, Value a,
   if (format == CuSparseFormat::kCOO) {
     // Library uses SoA COO, direct IR uses AoS COO.
     if (enableRT)
-      return genToCoordinates(builder, loc, a, 0);
-    return genToCoordinatesBuffer(builder, loc, a);
+      return builder.create<ToCoordinatesOp>(loc, a, 0);
+    return builder.create<ToCoordinatesBufferOp>(loc, a);
   }
   // Formats CSR/CSC and BSR use positions at 1.
-  return genToPositions(builder, loc, a, 1);
+  return builder.create<ToPositionsOp>(loc, a, 1);
 }
 
 /// Generates the second coordinates of a sparse matrix.
@@ -510,7 +510,7 @@ static Value genSecondCrds(OpBuilder &builder, Location loc, Value a,
   if (isCOO && !enableRT)
     return Value(); // nothing needed
   // Formats CSR/CSC and BSR use coordinates at 1.
-  return genToCoordinates(builder, loc, a, 1);
+  return builder.create<ToCoordinatesOp>(loc, a, 1);
 }
 
 /// Generates the sparse matrix handle.
@@ -584,7 +584,7 @@ static LogicalResult rewriteSpMV(PatternRewriter &rewriter,
   Value szX = linalg::createOrFoldDimOp(rewriter, loc, a, 1);
   Value memR = genFirstPosOrCrds(rewriter, loc, a, format, enableRT);
   Value memC = genSecondCrds(rewriter, loc, a, format, enableRT); // or empty
-  Value memV = genToValues(rewriter, loc, a);
+  Value memV = rewriter.create<ToValuesOp>(loc, a);
   Value rowA = genAllocCopy(rewriter, loc, memR, tokens);
   Value colA = memC ? genAllocCopy(rewriter, loc, memC, tokens) : Value();
   Value valA = genAllocCopy(rewriter, loc, memV, tokens);
@@ -682,7 +682,7 @@ static LogicalResult rewriteSpMM(PatternRewriter &rewriter,
   Value szn = linalg::createOrFoldDimOp(rewriter, loc, b, 1);
   Value memR = genFirstPosOrCrds(rewriter, loc, a, format, enableRT);
   Value memC = genSecondCrds(rewriter, loc, a, format, enableRT); // or empty
-  Value memV = genToValues(rewriter, loc, a);
+  Value memV = rewriter.create<ToValuesOp>(loc, a);
   Value rowA = genAllocCopy(rewriter, loc, memR, tokens);
   Value colA = memC ? genAllocCopy(rewriter, loc, memC, tokens) : Value();
   Value valA = genAllocCopy(rewriter, loc, memV, tokens);
@@ -785,10 +785,10 @@ static LogicalResult rewriteSpGEMM(PatternRewriter &rewriter,
   Value szn = linalg::createOrFoldDimOp(rewriter, loc, b, 1);
   Value amemR = genFirstPosOrCrds(rewriter, loc, a, format, enableRT);
   Value amemC = genSecondCrds(rewriter, loc, a, format, enableRT); // not empty
-  Value amemV = genToValues(rewriter, loc, a);
+  Value amemV = rewriter.create<ToValuesOp>(loc, a);
   Value bmemR = genFirstPosOrCrds(rewriter, loc, b, format, enableRT);
   Value bmemC = genSecondCrds(rewriter, loc, b, format, enableRT); // not empty
-  Value bmemV = genToValues(rewriter, loc, b);
+  Value bmemV = rewriter.create<ToValuesOp>(loc, b);
   Value rowA = genAllocCopy(rewriter, loc, amemR, tokens);
   Value colA = genAllocCopy(rewriter, loc, amemC, tokens);
   Value valA = genAllocCopy(rewriter, loc, amemV, tokens);
@@ -1081,7 +1081,7 @@ static LogicalResult rewriteSDDMM(PatternRewriter &rewriter,
   Value matB = genAllocCopy(rewriter, loc, bufB, tokens);
   Value memR = genFirstPosOrCrds(rewriter, loc, c, format, enableRT);
   Value memC = genSecondCrds(rewriter, loc, c, format, enableRT); // or empty
-  Value memV = genToValues(rewriter, loc, c);
+  Value memV = rewriter.create<ToValuesOp>(loc, c);
   Value rowC = genAllocCopy(rewriter, loc, memR, tokens);
   Value colC = memC ? genAllocCopy(rewriter, loc, memC, tokens) : Value();
   Value valC = genAllocCopy(rewriter, loc, memV, tokens);

mlir/lib/Dialect/SparseTensor/Transforms/Utils/CodegenUtils.cpp

@@ -554,41 +554,6 @@ sparse_tensor::genToMemref(OpBuilder &builder, Location loc, Value tensor) {
       .getResult();
 }
 
-Value sparse_tensor::genToPositions(OpBuilder &builder, Location loc,
-                                    Value tensor, Level lvl) {
-  const auto srcTp = getSparseTensorType(tensor);
-  const Type posTp = srcTp.getPosType();
-  const Type memTp = get1DMemRefType(posTp, /*withLayout=*/false);
-  return builder.create<ToPositionsOp>(loc, memTp, tensor,
-                                       builder.getIndexAttr(lvl));
-}
-
-Value sparse_tensor::genToCoordinates(OpBuilder &builder, Location loc,
-                                      Value tensor, Level lvl) {
-  const auto srcTp = getSparseTensorType(tensor);
-  const Type crdTp = srcTp.getCrdType();
-  const Type memTp =
-      get1DMemRefType(crdTp, /*withLayout=*/lvl >= srcTp.getAoSCOOStart());
-  return builder.create<ToCoordinatesOp>(loc, memTp, tensor,
-                                         builder.getIndexAttr(lvl));
-}
-
-Value sparse_tensor::genToCoordinatesBuffer(OpBuilder &builder, Location loc,
-                                            Value tensor) {
-  const auto srcTp = getSparseTensorType(tensor);
-  const Type crdTp = srcTp.getCrdType();
-  const Type memTp = get1DMemRefType(crdTp, /*withLayout=*/false);
-  return builder.create<ToCoordinatesBufferOp>(loc, memTp, tensor);
-}
-
-Value sparse_tensor::genToValues(OpBuilder &builder, Location loc,
-                                 Value tensor) {
-  RankedTensorType srcTp = getRankedTensorType(tensor);
-  Type valTp = get1DMemRefType(srcTp.getElementType(),
-                               /*withLayout=*/false);
-  return builder.create<ToValuesOp>(loc, valTp, tensor);
-}
-
 Value sparse_tensor::genValMemSize(OpBuilder &builder, Location loc,
                                    Value tensor) {
   return getDescriptorFromTensorTuple(tensor).getValMemSize(builder, loc);

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

@@ -228,17 +228,6 @@ void deallocDenseTensor(OpBuilder &builder, Location loc, Value buffer);
 void sizesFromSrc(OpBuilder &builder, SmallVectorImpl<Value> &sizes,
                   Location loc, Value src);
 
-/// Generates a 1D MemRefType with a dynamic size. When withLayout is set, the
-/// returned memref has a layout has unknown strides and offsets. Otherwise,
-/// a memref with a standard unit stride zero offset layout is returned.
-inline MemRefType get1DMemRefType(Type etp, bool withLayout) {
-  auto layout = withLayout ? StridedLayoutAttr::StridedLayoutAttr::get(
-                                 etp.getContext(), ShapedType::kDynamic,
-                                 {ShapedType::kDynamic})
-                           : StridedLayoutAttr();
-  return MemRefType::get(ShapedType::kDynamic, etp, layout);
-}
-
 /// Scans to top of generated loop.
 Operation *getTop(Operation *op);
 
@@ -281,22 +270,6 @@ void storeAll(OpBuilder &builder, Location loc, Value mem, ValueRange vs,
 TypedValue<BaseMemRefType> genToMemref(OpBuilder &builder, Location loc,
                                        Value tensor);
 
-/// Infers the result type and generates `ToPositionsOp`.
-Value genToPositions(OpBuilder &builder, Location loc, Value tensor, Level lvl);
-
-/// Infers the result type and generates `ToCoordinatesOp`.  If the
-/// level is within a COO region, the result type is a memref with unknown
-/// stride and offset.  Otherwise, the result type is a memref without
-/// any specified layout.
-Value genToCoordinates(OpBuilder &builder, Location loc, Value tensor,
-                       Level lvl);
-
-/// Infers the result type and generates `ToCoordinatesBufferOp`.
-Value genToCoordinatesBuffer(OpBuilder &builder, Location loc, Value tensor);
-
-/// Infers the result type and generates `ToValuesOp`.
-Value genToValues(OpBuilder &builder, Location loc, Value tensor);
-
 /// Generates code to retrieve the values size for the sparse tensor.
 Value genValMemSize(OpBuilder &builder, Location loc, Value tensor);
 

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

@@ -259,7 +259,7 @@ void LoopEmitter::initializeLoopEmit(
       // Annotated sparse tensors.
       // We also need the value buffer for all-dense annotated "sparse"
       // tensors.
-      valBuffer[t] = genToValues(builder, loc, tensor);
+      valBuffer[t] = builder.create<ToValuesOp>(loc, tensor);
     }
     // NOTE: we can also prepare for 0 lvl here in advance, this will hoist
     // some loop preparation from tensor iteration, but will also (undesirably)

mlir/lib/Dialect/SparseTensor/Transforms/Utils/SparseTensorLevel.cpp

@@ -1281,21 +1281,21 @@ sparse_tensor::makeSparseTensorLevel(OpBuilder &b, Location l, Value t,
   case LevelFormat::Batch:
     llvm_unreachable("not implemented");
   case LevelFormat::Compressed: {
-    Value pos = genToPositions(b, l, t, lvl);
-    Value crd = genToCoordinates(b, l, t, lvl);
+    Value pos = b.create<ToPositionsOp>(l, t, lvl);
+    Value crd = b.create<ToCoordinatesOp>(l, t, lvl);
     return std::make_unique<CompressedLevel>(tid, lvl, lt, sz, pos, crd);
   }
   case LevelFormat::LooseCompressed: {
-    Value pos = genToPositions(b, l, t, lvl);
-    Value crd = genToCoordinates(b, l, t, lvl);
+    Value pos = b.create<ToPositionsOp>(l, t, lvl);
+    Value crd = b.create<ToCoordinatesOp>(l, t, lvl);
     return std::make_unique<LooseCompressedLevel>(tid, lvl, lt, sz, pos, crd);
   }
   case LevelFormat::Singleton: {
-    Value crd = genToCoordinates(b, l, t, lvl);
+    Value crd = b.create<ToCoordinatesOp>(l, t, lvl);
     return std::make_unique<SingletonLevel>(tid, lvl, lt, sz, crd);
   }
   case LevelFormat::NOutOfM: {
-    Value crd = genToCoordinates(b, l, t, lvl);
+    Value crd = b.create<ToCoordinatesOp>(l, t, lvl);
     return std::make_unique<NOutOfMLevel>(tid, lvl, lt, sz, crd);
   }
   case LevelFormat::Undef: