[mlir][linalg] New tiling option: Scalarize dynamic dims

This tiling option scalarizes all dynamic dimensions, i.e., it tiles all dynamic dimensions by 1.

This option is useful for linalg ops with partly dynamic tensor dimensions. E.g., such ops can appear in the partial iteration after loop peeling. After scalarizing dynamic dims, those ops can be vectorized.

Differential Revision: https://reviews.llvm.org/D109268

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

@@ -479,6 +479,10 @@ struct LinalgTilingOptions {
   /// proper interaction with folding.
   LinalgTilingOptions &setTileSizes(ArrayRef<int64_t> ts);
 
+  /// Tile all dynamic dimensions by 1. I.e., scalarize those dimensions.
+  /// Note: `scalarizeDynamicDims` and `setTileSizes` cannot be used together.
+  LinalgTilingOptions &scalarizeDynamicDims();
+
   /// The interchange vector to reorder the tiled loops.
   SmallVector<unsigned, 4> interchangeVector = {};
 

mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp

@@ -107,6 +107,7 @@ void mlir::linalg::LinalgTransformationFilter::
 
 LinalgTilingOptions &
 mlir::linalg::LinalgTilingOptions::setTileSizes(ArrayRef<int64_t> ts) {
+  assert(!tileSizeComputationFunction && "tile sizes already set");
   SmallVector<int64_t, 4> tileSizes(ts.begin(), ts.end());
   tileSizeComputationFunction = [tileSizes](OpBuilder &b, Operation *op) {
     OpBuilder::InsertionGuard guard(b);
@@ -120,6 +121,30 @@ mlir::linalg::LinalgTilingOptions::setTileSizes(ArrayRef<int64_t> ts) {
   return *this;
 }
 
+LinalgTilingOptions &mlir::linalg::LinalgTilingOptions::scalarizeDynamicDims() {
+  assert(!tileSizeComputationFunction && "tile sizes already set");
+  tileSizeComputationFunction = [](OpBuilder &b, Operation *op) {
+    SmallVector<Value, 4> tileSizes;
+    auto linalgOp = dyn_cast<LinalgOp>(op);
+    if (!linalgOp)
+      return tileSizes;
+    Location loc = linalgOp.getLoc();
+    auto allShapeSizes = linalgOp.createFlatListOfOperandDims(b, loc);
+    AffineMap map = linalgOp.getShapesToLoopsMap();
+    if (!map)
+      return tileSizes;
+    auto shapeSizes = applyMapToValues(b, loc, map, allShapeSizes);
+    // If the shape size is dynamic, tile by 1. Otherwise, do not tile (tile
+    // size 0).
+    for (Value shapeSize : shapeSizes)
+      tileSizes.push_back(getConstantIntValue(shapeSize).hasValue()
+                              ? b.create<ConstantIndexOp>(loc, 0)
+                              : b.create<ConstantIndexOp>(loc, 1));
+    return tileSizes;
+  };
+  return *this;
+}
+
 /// Try to compute a static bounding box for `operand`
 /// Return success if either:
 ///   1. The operand is already statically shaped, `result` is left unchanged.

mlir/test/Dialect/Linalg/tile-scalarize-dynamic-dims.mlir

@@ -0,0 +1,27 @@
+// RUN: mlir-opt %s -test-linalg-transform-patterns="test-tile-scalarize-dynamic-dims" -for-loop-canonicalization -canonicalize -split-input-file | \
+// RUN:     FileCheck %s
+
+// CHECK-LABEL: func @matmul_partly_dynamic_tensor(
+//  CHECK-SAME:     %[[ARG0:.*]]: tensor<?x?xf32>, %[[ARG1:.*]]: tensor<?x2000xf32>
+//   CHECK-DAG:   %[[C0:.*]] = constant 0 : index
+//   CHECK-DAG:   %[[C1:.*]] = constant 1 : index
+//       CHECK:   tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
+//       CHECK:   %[[UB1:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
+//       CHECK:   %[[UB2:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
+//       CHECK:   scf.for %[[IV0:.*]] = %[[C0]] to %[[UB1]] step %[[C1]]
+//       CHECK:     scf.for %[[IV1:.*]] = %[[C0]] to %[[UB2]] step %[[C1]]
+//       CHECK:       %[[S1:.*]] = tensor.extract_slice %[[ARG0]][%[[IV0]], %[[IV1]]] [1, 1] [1, 1] : tensor<?x?xf32> to tensor<1x1xf32>
+//       CHECK:       %[[S2:.*]] = tensor.extract_slice %[[ARG1]][%[[IV1]], 0] [1, 2000] [1, 1] : tensor<?x2000xf32> to tensor<1x2000xf32>
+//       CHECK:       %[[S3:.*]] = tensor.extract_slice %{{.*}}[%[[IV0]], 0] [1, 2000] [1, 1] : tensor<?x2000xf32> to tensor<1x2000xf32>
+//       CHECK:       linalg.matmul ins(%[[S1]], %[[S2]] : tensor<1x1xf32>, tensor<1x2000xf32>) outs(%[[S3]] : tensor<1x2000xf32>) -> tensor<1x2000xf32>
+func @matmul_partly_dynamic_tensor(%arg0: tensor<?x?xf32>, %arg1: tensor<?x2000xf32>)
+    -> tensor<?x2000xf32> {
+  %c0 = constant 0 : index
+  %c1 = constant 1 : index
+  %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
+  %out = linalg.init_tensor [%d0, 2000] : tensor<?x2000xf32>
+  %r = linalg.matmul {__internal_linalg_transform__ = "tile"}
+      ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x2000xf32>)
+      outs(%out: tensor<?x2000xf32>) -> tensor<?x2000xf32>
+  return %r : tensor<?x2000xf32>
+}

mlir/test/lib/Dialect/Linalg/TestLinalgTransforms.cpp

@@ -87,6 +87,10 @@ struct TestLinalgTransforms
   Option<bool> testTilePattern{*this, "test-tile-pattern",
                                llvm::cl::desc("Test tile pattern"),
                                llvm::cl::init(false)};
+  Option<bool> testTileScalarizeDynamicDims{
+      *this, "test-tile-scalarize-dynamic-dims",
+      llvm::cl::desc("Test tiling of dynamic dims by 1"),
+      llvm::cl::init(false)};
   Option<int> testHoistPadding{*this, "test-hoist-padding",
                                llvm::cl::desc("Test hoist padding"),
                                llvm::cl::init(0)};
@@ -566,12 +570,19 @@ static Value getNeutralOfLinalgOp(OpBuilder &b, OpOperand &op) {
 }
 
 static void applyTilePattern(FuncOp funcOp, ArrayRef<int64_t> tileSizes,
-                             bool padTiles, ArrayRef<int64_t> peeledLoops) {
+                             bool padTiles, ArrayRef<int64_t> peeledLoops,
+                             bool scalarizeDynamicDims) {
   MLIRContext *context = funcOp.getContext();
   RewritePatternSet tilingPattern(context);
   auto linalgTilingOptions =
-      linalg::LinalgTilingOptions().setTileSizes(tileSizes).setPeeledLoops(
-          peeledLoops);
+      linalg::LinalgTilingOptions().setPeeledLoops(peeledLoops);
+  if (scalarizeDynamicDims) {
+    linalgTilingOptions.scalarizeDynamicDims();
+    assert(tileSizes.empty() &&
+           "tileSizes and scalarizeDynamicDims is mutually exclusive");
+  } else {
+    linalgTilingOptions.setTileSizes(tileSizes);
+  }
   if (padTiles)
     linalgTilingOptions.setPaddingValueComputationFunction(
         getNeutralOfLinalgOp);
@@ -709,7 +720,11 @@ void TestLinalgTransforms::runOnFunction() {
     return applyTiledLoopPeelingPattern(getFunction(), testTiledLoopPeeling,
                                         skipPartial);
   if (testTilePattern)
-    return applyTilePattern(getFunction(), tileSizes, padTiles, peeledLoops);
+    return applyTilePattern(getFunction(), tileSizes, padTiles, peeledLoops,
+                            /*scalarizeDynamicDims=*/false);
+  if (testTileScalarizeDynamicDims)
+    return applyTilePattern(getFunction(), tileSizes, padTiles,
+                            /*peeledLoops=*/{}, /*scalarizeDynamicDims=*/true);
   if (testHoistPadding) {
     getFunction().walk([&](linalg::PadTensorOp padTensorOp) {
       (void)linalg::hoistPaddingOnTensors(padTensorOp, testHoistPadding);