[MLIR][Linalg] pack, unpack to take memref inputs #129036

ita9naiwa · 2025-02-27T10:55:18Z

#129004

Change to use ShapedType, not RankedTensorType
- accordingly add branches to handle both MemrefType and TensorType
- Add Memref::CollapseShapeOp::inferCollapsedType to handle some MemrefType

github-actions · 2025-02-27T10:58:35Z

✅ With the latest revision this PR passed the C/C++ code formatter.

llvmbot · 2025-03-01T00:40:43Z

@llvm/pr-subscribers-mlir-memref

Author: Hyunsung Lee (ita9naiwa)

Changes

#129004

Change to use ShapedType, not RankedTensorType
- accordingly add branches to handle both MemrefType and TensorType
- Add Memref::CollapseShapeOp::inferCollapsedType to handle some MemrefType

Patch is 21.53 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/129036.diff

11 Files Affected:

(modified) mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td (+12-11)
(modified) mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td (+1)
(modified) mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td (+5)
(modified) mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h (+2-2)
(modified) mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp (+15-13)
(modified) mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp (+6-6)
(modified) mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp (+19-6)
(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+1-1)
(modified) mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp (+31-1)
(modified) mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp (+6-6)
(modified) mlir/test/Dialect/Linalg/loops.mlir (+16)

diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
index 1e48a5e3a20ee..785c7cc924159 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
@@ -43,10 +43,10 @@ class Linalg_RelayoutOp<string mnemonic, list<Trait> traits = []> :
   code commonExtraClassDeclaration = [{
     size_t getSourceRank() { return getSourceType().getRank(); };
     size_t getDestRank() { return getDestType().getRank(); };
-    RankedTensorType getSourceType() {
-      return ::llvm::cast<RankedTensorType>(getSource().getType()); };
-    RankedTensorType getDestType() {
-      return ::llvm::cast<RankedTensorType>(getDest().getType()); };
+    ShapedType getSourceType() {
+      return ::llvm::cast<ShapedType>(getSource().getType()); };
+    ShapedType getDestType() {
+      return ::llvm::cast<ShapedType>(getDest().getType()); };
 
     MutableOperandRange getDpsInitsMutable() { return getDestMutable(); }
 
@@ -152,14 +152,14 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     // Note: Only tiled dimensions can be padded.
     ```
   }];
-  let arguments = (ins AnyRankedTensor:$source,
-                       AnyRankedTensor:$dest,
+  let arguments = (ins AnyShaped:$source,
+                       AnyShaped:$dest,
                        Optional<AnyType>:$padding_value,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$outer_dims_perm,
                        DenseI64ArrayAttr:$inner_dims_pos,
                        Variadic<Index>:$inner_tiles,
                        DenseI64ArrayAttr:$static_inner_tiles);
-  let results = (outs AnyRankedTensor:$result);
+  let results = (outs AnyShaped:$result);
   let assemblyFormat = [{
     $source
     (`padding_value` `(` $padding_value^ `:` type($padding_value) `)`)?
@@ -190,7 +190,7 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     // Method to get the `RankedTensorType` of the result based on the inner
     // tiles, position of the inner tiles (innerDimsPos)  and interchange vector
     // of outer loops (outerDimsPerm).
-    static RankedTensorType inferPackedType(RankedTensorType sourceType,
+    static RankedTensorType inferPackedType(ShapedType sourceType,
         ArrayRef<int64_t> innerTileSizes, ArrayRef<int64_t> innerDimsPos,
         ArrayRef<int64_t> outerDimsPerm = {});
 
@@ -229,6 +229,7 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     /// 2. pads the other ones, and
     /// 3. doesn't shuffle the dimensions
     bool isLikePad();
+
   }];
 
   let hasCanonicalizeMethod = 1;
@@ -279,13 +280,13 @@ def Linalg_UnPackOp : Linalg_RelayoutOp<"unpack"> {
         : tensor<8x16x8x32xf32> -> tensor<128x256xf32>
     ```
   }];
-  let arguments = (ins AnyRankedTensor:$source,
-                       AnyRankedTensor:$dest,
+  let arguments = (ins AnyShaped:$source,
+                       AnyShaped:$dest,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$outer_dims_perm,
                        DenseI64ArrayAttr:$inner_dims_pos,
                        Variadic<Index>:$inner_tiles,
                        DenseI64ArrayAttr:$static_inner_tiles);
-  let results = (outs AnyRankedTensor:$result);
+  let results = (outs AnyShaped:$result);
   let assemblyFormat = [{
     $source
     (`outer_dims_perm` `=` $outer_dims_perm^)?
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td b/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
index 2dec2fc4396f4..467d862d277eb 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
@@ -10,6 +10,7 @@
 #define LINALG_IR_RELAYOUTOPINTERFACE
 
 include "mlir/Interfaces/DestinationStyleOpInterface.td"
+include "mlir/Dialect/Linalg/IR/LinalgInterfaces.td"
 include "mlir/IR/OpBase.td"
 
 def LinalgRelayoutOpInterface : OpInterface<"RelayoutOpInterface"> {
diff --git a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
index 4c8a214049ea9..8bcc1882b454d 100644
--- a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
+++ b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
@@ -1799,6 +1799,11 @@ def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape", [
 
     static MemRefType computeCollapsedType(
         MemRefType srcType, ArrayRef<ReassociationIndices> reassociation);
+    static MemRefType
+        inferCollapsedType(MemRefType type, ArrayRef<AffineMap> reassociation);
+    static MemRefType
+        inferCollapsedType(MemRefType type,
+                           SmallVector<ReassociationIndices> reassociation);
   }];
 
   let hasVerifier = 1;
diff --git a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
index 3af89a6ab3799..a86bf74a7b6a1 100644
--- a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
+++ b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
@@ -451,7 +451,7 @@ getLinearizedDimensions(ArrayRef<ReassociationIndices> reassociationIndices);
 ///    %4 = tensor.extract_slice %0 [%3#0, %3#1, %3#2, 0] [1, 1, 1, 10] [1, 1, 1, 1] :
 ///          tensor<3x7x11x10xf32> to tensor<1x1x1x10xf32>
 ///
-///    %5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] : 
+///    %5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] :
 ///          tensor<1x1x1x10xf32> into tensor<1x10xf32>
 ///    %6 = tensor.insert_slice %5 into %arg0 [%iv, 0] [1, 10] [1, 1] :
 ///          tensor<1x10xf32> into tensor<10x10xf32>
@@ -573,7 +573,7 @@ PackingMetadata computePackingMetadata(int64_t packedRank,
 /// Removes the op and replaces the constant with a new constant of the result
 /// shape. When an optional cst attribute is passed, it is reshaped only if the
 /// splat value matches the value in the attribute.
-OpFoldResult reshapeConstantSource(DenseElementsAttr source, TensorType result,
+OpFoldResult reshapeConstantSource(DenseElementsAttr source, ShapedType result,
                                    std::optional<Attribute> cst = std::nullopt);
 } // namespace mlir
 
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 07b19e5cb1a89..a19039fbca67d 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -803,7 +803,7 @@ struct FoldInsertPadIntoFill : public OpRewritePattern<tensor::InsertSliceOp> {
           rewriter, loc, addMap, {std::get<0>(p), std::get<1>(p)}));
     }
 
-    RankedTensorType srcPadType = srcPadOp.getSourceType();
+    ShapedType srcPadType = srcPadOp.getSourceType();
     SmallVector<OpFoldResult, 4> newSizes;
     for (int i = 0, e = srcPadType.getRank(); i < e; ++i) {
       if (srcPadType.isDynamicDim(i)) {
@@ -4433,9 +4433,9 @@ static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
     return op->emitError("invalid zero tile factor");
 
   // Verify inner_dims_pos and outer_dims_perm.
-  RankedTensorType unpackedType = (std::is_same<OpTy, PackOp>::value)
-                                      ? packOrUnPack.getSourceType()
-                                      : packOrUnPack.getDestType();
+  ShapedType unpackedType = (std::is_same<OpTy, PackOp>::value)
+                                ? packOrUnPack.getSourceType()
+                                : packOrUnPack.getDestType();
   size_t unpackedRank = unpackedType.getRank();
   ArrayRef<int64_t> innerDimsPos = packOrUnPack.getInnerDimsPos();
   ArrayRef<int64_t> outerDimPerm = packOrUnPack.getOuterDimsPerm();
@@ -4747,7 +4747,7 @@ SmallVector<OpFoldResult> PackOp::getResultShape(
 
 /// Get the expected packed type based on source type, tile factors, position of
 /// the inner tiles and permutation of the outer tiled loop.
-RankedTensorType PackOp::inferPackedType(RankedTensorType sourceType,
+RankedTensorType PackOp::inferPackedType(ShapedType sourceType,
                                          ArrayRef<int64_t> innerTileSizes,
                                          ArrayRef<int64_t> innerDimsPos,
                                          ArrayRef<int64_t> outerDimsPerm) {
@@ -4943,7 +4943,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
           rewriter.create<tensor::CastOp>(loc, newSrcType, packOp.getSource());
     }
     Value dest = packOp.getDest();
-    RankedTensorType originalResultType = packOp.getDestType();
+    ShapedType originalResultType = packOp.getDestType();
     bool needUpdateDestType = (destShape != originalResultType.getShape());
     if (needUpdateDestType) {
       auto newDestType = packOp.getDestType().clone(destShape);
@@ -4953,7 +4953,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
     rewriter.modifyOpInPlace(packOp, [&] {
       packOp.getSourceMutable().assign(source);
       packOp.getDestMutable().assign(dest);
-      packOp.getResult().setType(cast<RankedTensorType>(dest.getType()));
+      packOp.getResult().setType(cast<ShapedType>(dest.getType()));
     });
     // Insert a cast if needed
     if (needUpdateDestType) {
@@ -4969,8 +4969,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
 }
 
 template <typename PackOrUnpackOp>
-static bool isLikePadUnPad(PackOrUnpackOp packOp,
-                           RankedTensorType packedTensorType) {
+static bool isLikePadUnPad(PackOrUnpackOp packOp, ShapedType packedTensorType) {
   static_assert(std::is_same<PackOrUnpackOp, PackOp>::value ||
                     std::is_same<PackOrUnpackOp, UnPackOp>::value,
                 "Function meant for pack/unpack");
@@ -5002,9 +5001,12 @@ static bool isLikePadUnPad(PackOrUnpackOp packOp,
 }
 
 bool PackOp::isLikePad() {
-  auto packedTensorType =
-      llvm::cast<RankedTensorType>((*this)->getResultTypes().front());
-  return isLikePadUnPad(*this, packedTensorType);
+  if (auto packedTensorType =
+          llvm::dyn_cast<RankedTensorType>((*this)->getResultTypes().front()))
+    return isLikePadUnPad(*this, packedTensorType);
+  if (auto packedTensorType =
+          llvm::dyn_cast<MemRefType>((*this)->getResultTypes().front()))
+    return isLikePadUnPad(*this, packedTensorType);
 }
 
 OpFoldResult PackOp::fold(FoldAdaptor adaptor) {
@@ -5274,7 +5276,7 @@ LogicalResult UnPackOp::canonicalize(UnPackOp unPackOp,
 }
 
 bool UnPackOp::isLikeUnPad() {
-  RankedTensorType packedTensorType = getSourceType();
+  ShapedType packedTensorType = getSourceType();
   return isLikePadUnPad(*this, packedTensorType);
 }
 
diff --git a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
index 0984b6988b93b..599aa3b6668df 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
@@ -111,7 +111,7 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
     if (packOp.getPaddingValue())
       return rewriter.notifyMatchFailure(packOp, "expects no padding value");
 
-    RankedTensorType sourceType = packOp.getSourceType();
+    ShapedType sourceType = packOp.getSourceType();
     if (failed(isPackOnInnerMostDim(rewriter, packOp)) &&
         failed(isPackOn1D(rewriter, packOp, sourceType.getShape(),
                           packOp.getStaticTiles())) &&
@@ -119,7 +119,7 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
       return failure();
     }
 
-    RankedTensorType destType = packOp.getDestType();
+    ShapedType destType = packOp.getDestType();
     auto reassociation =
         getReassociationIndicesForReshape(sourceType, destType);
     if (!reassociation)
@@ -157,8 +157,8 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
           "expects outer_dims_perm is empty or an identity permutation");
     }
 
-    RankedTensorType sourceType = unpackOp.getSourceType();
-    RankedTensorType destType = unpackOp.getDestType();
+    ShapedType sourceType = unpackOp.getSourceType();
+    ShapedType destType = unpackOp.getDestType();
     if (!sourceType.hasStaticShape() || !destType.hasStaticShape())
       return rewriter.notifyMatchFailure(unpackOp, "expects static shapes");
 
@@ -173,7 +173,7 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
 
   LogicalResult matchAndRewrite(UnPackOp unpackOp,
                                 PatternRewriter &rewriter) const override {
-    RankedTensorType destType = unpackOp.getDestType();
+    ShapedType destType = unpackOp.getDestType();
     if (failed(isUnpackOnInnerMostDim(rewriter, unpackOp)) &&
         failed(isPackOn1D(rewriter, unpackOp, destType.getShape(),
                           unpackOp.getStaticTiles())) &&
@@ -181,7 +181,7 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
       return failure();
     }
 
-    RankedTensorType sourceType = unpackOp.getSourceType();
+    ShapedType sourceType = unpackOp.getSourceType();
     auto reassociation =
         getReassociationIndicesForReshape(sourceType, destType);
     if (!reassociation)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
index dcd50cc44f81b..98dab332b2f40 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
@@ -17,6 +17,7 @@
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/Transforms/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tensor/IR/TensorTilingInterfaceImpl.h"
@@ -359,7 +360,7 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPoint(unPackOp);
 
-  RankedTensorType packedTensorType = unPackOp.getSourceType();
+  ShapedType packedTensorType = unPackOp.getSourceType();
   int64_t packedRank = packedTensorType.getRank();
 
   OpFoldResult zero = rewriter.getIndexAttr(0), one = rewriter.getIndexAttr(1);
@@ -396,10 +397,22 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
   applyPermutationToVector(stripMinedShape, packedToStripMinedShapePerm);
 
   // 3. Transpose packedShape to stripMinedShape.
-  RankedTensorType stripMinedTensorType =
-      RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape);
-  RankedTensorType collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
-      stripMinedTensorType, packingMetadata.reassociations);
+  ShapedType stripMinedType;
+  if (auto tensorType = packedTensorType.dyn_cast<TensorType>()) {
+    stripMinedType =
+        RankedTensorType::get(stripMinedShape, tensorType.getElementType());
+  } else if (auto memrefType = packedTensorType.dyn_cast<MemRefType>()) {
+    stripMinedType =
+        MemRefType::get(stripMinedShape, memrefType.getElementType());
+  }
+  ShapedType collapsedType;
+  if (stripMinedType.isa<TensorType>()) {
+    collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
+        cast<RankedTensorType>(stripMinedType), packingMetadata.reassociations);
+  } else if (stripMinedType.isa<MemRefType>()) {
+    collapsedType = memref::CollapseShapeOp::inferCollapsedType(
+        cast<MemRefType>(stripMinedType), packingMetadata.reassociations);
+  }
 
   // Get dynamic dims from input tensor based on packedToStripMinedShapePerm
   // permutation.
@@ -407,7 +420,7 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
       tensor::getMixedSizes(rewriter, loc, unPackOp.getSource());
   applyPermutationToVector(dims, packedToStripMinedShapePerm);
   auto emptyOp = rewriter.create<tensor::EmptyOp>(
-      loc, dims, stripMinedTensorType.getElementType());
+      loc, dims, stripMinedType.getElementType());
   auto transposeOp = rewriter.create<linalg::TransposeOp>(
       loc, unPackOp.getSource(), emptyOp, packedToStripMinedShapePerm);
 
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index ae04c2b6b2a5b..25ad5e38addbe 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1669,7 +1669,7 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPoint(unpackOp);
 
-  RankedTensorType unpackTensorType = unpackOp.getSourceType();
+  ShapedType unpackTensorType = unpackOp.getSourceType();
 
   ArrayRef<int64_t> innerDimPos = unpackOp.getInnerDimsPos();
   ArrayRef<int64_t> innerTiles = unpackOp.getStaticInnerTiles();
diff --git a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
index 11597505e7888..ba12cc34d6457 100644
--- a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
+++ b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
@@ -9,6 +9,7 @@
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Arith/Utils/Utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
 #include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/Builders.h"
@@ -1124,7 +1125,7 @@ struct DimOfMemRefReshape : public OpRewritePattern<DimOp> {
         }
       } // else dim.getIndex is a block argument to reshape->getBlock and
         // dominates reshape
-    }   // Check condition 2
+    } // Check condition 2
     else if (dim->getBlock() != reshape->getBlock() &&
              !dim.getIndex().getParentRegion()->isProperAncestor(
                  reshape->getParentRegion())) {
@@ -2525,6 +2526,35 @@ MemRefType CollapseShapeOp::computeCollapsedType(
                          srcType.getMemorySpace());
 }
 
+MemRefType
+CollapseShapeOp::inferCollapsedType(MemRefType type,
+                                    ArrayRef<AffineMap> reassociation) {
+  auto shape = type.getShape();
+  SmallVector<int64_t, 4> newShape;
+  assert(isReassociationValid(reassociation) && "invalid reassociation");
+  unsigned currentDim = 0;
+  for (AffineMap m : reassociation) {
+    unsigned dim = m.getNumResults();
+    auto band = shape.slice(currentDim, dim);
+    int64_t size = 1;
+    if (llvm::is_contained(band, ShapedType::kDynamic))
+      size = ShapedType::kDynamic;
+    else
+      for (unsigned d = 0; d < dim; ++d)
+        size *= shape[currentDim + d];
+    newShape.push_back(size);
+    currentDim += dim;
+  }
+  return MemRefType::get(newShape, type.getElementType());
+}
+
+MemRefType CollapseShapeOp::inferCollapsedType(
+    MemRefType type, SmallVector<ReassociationIndices> reassociation) {
+  return inferCollapsedType(
+      type, getSymbolLessAffineMaps(convertReassociationIndicesToExprs(
+                type.getContext(), reassociation)));
+}
+
 void CollapseShapeOp::build(OpBuilder &b, OperationState &result, Value src,
                             ArrayRef<ReassociationIndices> reassociation,
                             ArrayRef<NamedAttribute> attrs) {
diff --git a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
index 0336423c57b1d..9a2bd3493f6af 100644
--- a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
+++ b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
@@ -315,11 +315,11 @@ SmallVector<Range> SliceFromCollapseHelper::getExtractSliceParams(
     // have proven that these are not sliced. In this case we just take
     // the full extent of each dimension in the reassociation list.
     if (linearizedDimensions[it.index()]) {
-      llvm::append_range(
-          offsetsSizesAndStrides,
-          llvm::map_range(it.value(), [&](int64_t idx) -> Range {
-            return {zeroAttr, collapseShapeInputShape[idx], oneAttr};
-          }));
+      llvm::append_range(offsetsSizesAndStrides,
+                         llvm::map_range(it.value(), [&](int64_t idx) -> Range {
+                           return {zeroAttr, collapseShapeInputShape[idx],
+                                   oneAttr};
+                         }));
       continue;
     }
 
@@ -485,7 +485,7 @@ PackingMetadata mlir::computePackingMetadata(int64_t packedRank,
 }
 
 OpFoldResult mlir::reshapeConstantSource(DenseElementsAttr source,
-                            ...
[truncated]

llvmbot · 2025-03-01T00:40:43Z

@llvm/pr-subscribers-mlir-linalg

Author: Hyunsung Lee (ita9naiwa)

Changes

#129004

Change to use ShapedType, not RankedTensorType
- accordingly add branches to handle both MemrefType and TensorType
- Add Memref::CollapseShapeOp::inferCollapsedType to handle some MemrefType

Patch is 21.53 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/129036.diff

11 Files Affected:

(modified) mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td (+12-11)
(modified) mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td (+1)
(modified) mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td (+5)
(modified) mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h (+2-2)
(modified) mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp (+15-13)
(modified) mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp (+6-6)
(modified) mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp (+19-6)
(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+1-1)
(modified) mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp (+31-1)
(modified) mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp (+6-6)
(modified) mlir/test/Dialect/Linalg/loops.mlir (+16)

diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
index 1e48a5e3a20ee..785c7cc924159 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
@@ -43,10 +43,10 @@ class Linalg_RelayoutOp<string mnemonic, list<Trait> traits = []> :
   code commonExtraClassDeclaration = [{
     size_t getSourceRank() { return getSourceType().getRank(); };
     size_t getDestRank() { return getDestType().getRank(); };
-    RankedTensorType getSourceType() {
-      return ::llvm::cast<RankedTensorType>(getSource().getType()); };
-    RankedTensorType getDestType() {
-      return ::llvm::cast<RankedTensorType>(getDest().getType()); };
+    ShapedType getSourceType() {
+      return ::llvm::cast<ShapedType>(getSource().getType()); };
+    ShapedType getDestType() {
+      return ::llvm::cast<ShapedType>(getDest().getType()); };
 
     MutableOperandRange getDpsInitsMutable() { return getDestMutable(); }
 
@@ -152,14 +152,14 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     // Note: Only tiled dimensions can be padded.
     ```
   }];
-  let arguments = (ins AnyRankedTensor:$source,
-                       AnyRankedTensor:$dest,
+  let arguments = (ins AnyShaped:$source,
+                       AnyShaped:$dest,
                        Optional<AnyType>:$padding_value,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$outer_dims_perm,
                        DenseI64ArrayAttr:$inner_dims_pos,
                        Variadic<Index>:$inner_tiles,
                        DenseI64ArrayAttr:$static_inner_tiles);
-  let results = (outs AnyRankedTensor:$result);
+  let results = (outs AnyShaped:$result);
   let assemblyFormat = [{
     $source
     (`padding_value` `(` $padding_value^ `:` type($padding_value) `)`)?
@@ -190,7 +190,7 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     // Method to get the `RankedTensorType` of the result based on the inner
     // tiles, position of the inner tiles (innerDimsPos)  and interchange vector
     // of outer loops (outerDimsPerm).
-    static RankedTensorType inferPackedType(RankedTensorType sourceType,
+    static RankedTensorType inferPackedType(ShapedType sourceType,
         ArrayRef<int64_t> innerTileSizes, ArrayRef<int64_t> innerDimsPos,
         ArrayRef<int64_t> outerDimsPerm = {});
 
@@ -229,6 +229,7 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     /// 2. pads the other ones, and
     /// 3. doesn't shuffle the dimensions
     bool isLikePad();
+
   }];
 
   let hasCanonicalizeMethod = 1;
@@ -279,13 +280,13 @@ def Linalg_UnPackOp : Linalg_RelayoutOp<"unpack"> {
         : tensor<8x16x8x32xf32> -> tensor<128x256xf32>
     ```
   }];
-  let arguments = (ins AnyRankedTensor:$source,
-                       AnyRankedTensor:$dest,
+  let arguments = (ins AnyShaped:$source,
+                       AnyShaped:$dest,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$outer_dims_perm,
                        DenseI64ArrayAttr:$inner_dims_pos,
                        Variadic<Index>:$inner_tiles,
                        DenseI64ArrayAttr:$static_inner_tiles);
-  let results = (outs AnyRankedTensor:$result);
+  let results = (outs AnyShaped:$result);
   let assemblyFormat = [{
     $source
     (`outer_dims_perm` `=` $outer_dims_perm^)?
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td b/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
index 2dec2fc4396f4..467d862d277eb 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
@@ -10,6 +10,7 @@
 #define LINALG_IR_RELAYOUTOPINTERFACE
 
 include "mlir/Interfaces/DestinationStyleOpInterface.td"
+include "mlir/Dialect/Linalg/IR/LinalgInterfaces.td"
 include "mlir/IR/OpBase.td"
 
 def LinalgRelayoutOpInterface : OpInterface<"RelayoutOpInterface"> {
diff --git a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
index 4c8a214049ea9..8bcc1882b454d 100644
--- a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
+++ b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
@@ -1799,6 +1799,11 @@ def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape", [
 
     static MemRefType computeCollapsedType(
         MemRefType srcType, ArrayRef<ReassociationIndices> reassociation);
+    static MemRefType
+        inferCollapsedType(MemRefType type, ArrayRef<AffineMap> reassociation);
+    static MemRefType
+        inferCollapsedType(MemRefType type,
+                           SmallVector<ReassociationIndices> reassociation);
   }];
 
   let hasVerifier = 1;
diff --git a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
index 3af89a6ab3799..a86bf74a7b6a1 100644
--- a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
+++ b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
@@ -451,7 +451,7 @@ getLinearizedDimensions(ArrayRef<ReassociationIndices> reassociationIndices);
 ///    %4 = tensor.extract_slice %0 [%3#0, %3#1, %3#2, 0] [1, 1, 1, 10] [1, 1, 1, 1] :
 ///          tensor<3x7x11x10xf32> to tensor<1x1x1x10xf32>
 ///
-///    %5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] : 
+///    %5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] :
 ///          tensor<1x1x1x10xf32> into tensor<1x10xf32>
 ///    %6 = tensor.insert_slice %5 into %arg0 [%iv, 0] [1, 10] [1, 1] :
 ///          tensor<1x10xf32> into tensor<10x10xf32>
@@ -573,7 +573,7 @@ PackingMetadata computePackingMetadata(int64_t packedRank,
 /// Removes the op and replaces the constant with a new constant of the result
 /// shape. When an optional cst attribute is passed, it is reshaped only if the
 /// splat value matches the value in the attribute.
-OpFoldResult reshapeConstantSource(DenseElementsAttr source, TensorType result,
+OpFoldResult reshapeConstantSource(DenseElementsAttr source, ShapedType result,
                                    std::optional<Attribute> cst = std::nullopt);
 } // namespace mlir
 
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 07b19e5cb1a89..a19039fbca67d 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -803,7 +803,7 @@ struct FoldInsertPadIntoFill : public OpRewritePattern<tensor::InsertSliceOp> {
           rewriter, loc, addMap, {std::get<0>(p), std::get<1>(p)}));
     }
 
-    RankedTensorType srcPadType = srcPadOp.getSourceType();
+    ShapedType srcPadType = srcPadOp.getSourceType();
     SmallVector<OpFoldResult, 4> newSizes;
     for (int i = 0, e = srcPadType.getRank(); i < e; ++i) {
       if (srcPadType.isDynamicDim(i)) {
@@ -4433,9 +4433,9 @@ static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
     return op->emitError("invalid zero tile factor");
 
   // Verify inner_dims_pos and outer_dims_perm.
-  RankedTensorType unpackedType = (std::is_same<OpTy, PackOp>::value)
-                                      ? packOrUnPack.getSourceType()
-                                      : packOrUnPack.getDestType();
+  ShapedType unpackedType = (std::is_same<OpTy, PackOp>::value)
+                                ? packOrUnPack.getSourceType()
+                                : packOrUnPack.getDestType();
   size_t unpackedRank = unpackedType.getRank();
   ArrayRef<int64_t> innerDimsPos = packOrUnPack.getInnerDimsPos();
   ArrayRef<int64_t> outerDimPerm = packOrUnPack.getOuterDimsPerm();
@@ -4747,7 +4747,7 @@ SmallVector<OpFoldResult> PackOp::getResultShape(
 
 /// Get the expected packed type based on source type, tile factors, position of
 /// the inner tiles and permutation of the outer tiled loop.
-RankedTensorType PackOp::inferPackedType(RankedTensorType sourceType,
+RankedTensorType PackOp::inferPackedType(ShapedType sourceType,
                                          ArrayRef<int64_t> innerTileSizes,
                                          ArrayRef<int64_t> innerDimsPos,
                                          ArrayRef<int64_t> outerDimsPerm) {
@@ -4943,7 +4943,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
           rewriter.create<tensor::CastOp>(loc, newSrcType, packOp.getSource());
     }
     Value dest = packOp.getDest();
-    RankedTensorType originalResultType = packOp.getDestType();
+    ShapedType originalResultType = packOp.getDestType();
     bool needUpdateDestType = (destShape != originalResultType.getShape());
     if (needUpdateDestType) {
       auto newDestType = packOp.getDestType().clone(destShape);
@@ -4953,7 +4953,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
     rewriter.modifyOpInPlace(packOp, [&] {
       packOp.getSourceMutable().assign(source);
       packOp.getDestMutable().assign(dest);
-      packOp.getResult().setType(cast<RankedTensorType>(dest.getType()));
+      packOp.getResult().setType(cast<ShapedType>(dest.getType()));
     });
     // Insert a cast if needed
     if (needUpdateDestType) {
@@ -4969,8 +4969,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
 }
 
 template <typename PackOrUnpackOp>
-static bool isLikePadUnPad(PackOrUnpackOp packOp,
-                           RankedTensorType packedTensorType) {
+static bool isLikePadUnPad(PackOrUnpackOp packOp, ShapedType packedTensorType) {
   static_assert(std::is_same<PackOrUnpackOp, PackOp>::value ||
                     std::is_same<PackOrUnpackOp, UnPackOp>::value,
                 "Function meant for pack/unpack");
@@ -5002,9 +5001,12 @@ static bool isLikePadUnPad(PackOrUnpackOp packOp,
 }
 
 bool PackOp::isLikePad() {
-  auto packedTensorType =
-      llvm::cast<RankedTensorType>((*this)->getResultTypes().front());
-  return isLikePadUnPad(*this, packedTensorType);
+  if (auto packedTensorType =
+          llvm::dyn_cast<RankedTensorType>((*this)->getResultTypes().front()))
+    return isLikePadUnPad(*this, packedTensorType);
+  if (auto packedTensorType =
+          llvm::dyn_cast<MemRefType>((*this)->getResultTypes().front()))
+    return isLikePadUnPad(*this, packedTensorType);
 }
 
 OpFoldResult PackOp::fold(FoldAdaptor adaptor) {
@@ -5274,7 +5276,7 @@ LogicalResult UnPackOp::canonicalize(UnPackOp unPackOp,
 }
 
 bool UnPackOp::isLikeUnPad() {
-  RankedTensorType packedTensorType = getSourceType();
+  ShapedType packedTensorType = getSourceType();
   return isLikePadUnPad(*this, packedTensorType);
 }
 
diff --git a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
index 0984b6988b93b..599aa3b6668df 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
@@ -111,7 +111,7 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
     if (packOp.getPaddingValue())
       return rewriter.notifyMatchFailure(packOp, "expects no padding value");
 
-    RankedTensorType sourceType = packOp.getSourceType();
+    ShapedType sourceType = packOp.getSourceType();
     if (failed(isPackOnInnerMostDim(rewriter, packOp)) &&
         failed(isPackOn1D(rewriter, packOp, sourceType.getShape(),
                           packOp.getStaticTiles())) &&
@@ -119,7 +119,7 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
       return failure();
     }
 
-    RankedTensorType destType = packOp.getDestType();
+    ShapedType destType = packOp.getDestType();
     auto reassociation =
         getReassociationIndicesForReshape(sourceType, destType);
     if (!reassociation)
@@ -157,8 +157,8 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
           "expects outer_dims_perm is empty or an identity permutation");
     }
 
-    RankedTensorType sourceType = unpackOp.getSourceType();
-    RankedTensorType destType = unpackOp.getDestType();
+    ShapedType sourceType = unpackOp.getSourceType();
+    ShapedType destType = unpackOp.getDestType();
     if (!sourceType.hasStaticShape() || !destType.hasStaticShape())
       return rewriter.notifyMatchFailure(unpackOp, "expects static shapes");
 
@@ -173,7 +173,7 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
 
   LogicalResult matchAndRewrite(UnPackOp unpackOp,
                                 PatternRewriter &rewriter) const override {
-    RankedTensorType destType = unpackOp.getDestType();
+    ShapedType destType = unpackOp.getDestType();
     if (failed(isUnpackOnInnerMostDim(rewriter, unpackOp)) &&
         failed(isPackOn1D(rewriter, unpackOp, destType.getShape(),
                           unpackOp.getStaticTiles())) &&
@@ -181,7 +181,7 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
       return failure();
     }
 
-    RankedTensorType sourceType = unpackOp.getSourceType();
+    ShapedType sourceType = unpackOp.getSourceType();
     auto reassociation =
         getReassociationIndicesForReshape(sourceType, destType);
     if (!reassociation)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
index dcd50cc44f81b..98dab332b2f40 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
@@ -17,6 +17,7 @@
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/Transforms/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tensor/IR/TensorTilingInterfaceImpl.h"
@@ -359,7 +360,7 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPoint(unPackOp);
 
-  RankedTensorType packedTensorType = unPackOp.getSourceType();
+  ShapedType packedTensorType = unPackOp.getSourceType();
   int64_t packedRank = packedTensorType.getRank();
 
   OpFoldResult zero = rewriter.getIndexAttr(0), one = rewriter.getIndexAttr(1);
@@ -396,10 +397,22 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
   applyPermutationToVector(stripMinedShape, packedToStripMinedShapePerm);
 
   // 3. Transpose packedShape to stripMinedShape.
-  RankedTensorType stripMinedTensorType =
-      RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape);
-  RankedTensorType collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
-      stripMinedTensorType, packingMetadata.reassociations);
+  ShapedType stripMinedType;
+  if (auto tensorType = packedTensorType.dyn_cast<TensorType>()) {
+    stripMinedType =
+        RankedTensorType::get(stripMinedShape, tensorType.getElementType());
+  } else if (auto memrefType = packedTensorType.dyn_cast<MemRefType>()) {
+    stripMinedType =
+        MemRefType::get(stripMinedShape, memrefType.getElementType());
+  }
+  ShapedType collapsedType;
+  if (stripMinedType.isa<TensorType>()) {
+    collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
+        cast<RankedTensorType>(stripMinedType), packingMetadata.reassociations);
+  } else if (stripMinedType.isa<MemRefType>()) {
+    collapsedType = memref::CollapseShapeOp::inferCollapsedType(
+        cast<MemRefType>(stripMinedType), packingMetadata.reassociations);
+  }
 
   // Get dynamic dims from input tensor based on packedToStripMinedShapePerm
   // permutation.
@@ -407,7 +420,7 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
       tensor::getMixedSizes(rewriter, loc, unPackOp.getSource());
   applyPermutationToVector(dims, packedToStripMinedShapePerm);
   auto emptyOp = rewriter.create<tensor::EmptyOp>(
-      loc, dims, stripMinedTensorType.getElementType());
+      loc, dims, stripMinedType.getElementType());
   auto transposeOp = rewriter.create<linalg::TransposeOp>(
       loc, unPackOp.getSource(), emptyOp, packedToStripMinedShapePerm);
 
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index ae04c2b6b2a5b..25ad5e38addbe 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1669,7 +1669,7 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPoint(unpackOp);
 
-  RankedTensorType unpackTensorType = unpackOp.getSourceType();
+  ShapedType unpackTensorType = unpackOp.getSourceType();
 
   ArrayRef<int64_t> innerDimPos = unpackOp.getInnerDimsPos();
   ArrayRef<int64_t> innerTiles = unpackOp.getStaticInnerTiles();
diff --git a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
index 11597505e7888..ba12cc34d6457 100644
--- a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
+++ b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
@@ -9,6 +9,7 @@
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Arith/Utils/Utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
 #include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/Builders.h"
@@ -1124,7 +1125,7 @@ struct DimOfMemRefReshape : public OpRewritePattern<DimOp> {
         }
       } // else dim.getIndex is a block argument to reshape->getBlock and
         // dominates reshape
-    }   // Check condition 2
+    } // Check condition 2
     else if (dim->getBlock() != reshape->getBlock() &&
              !dim.getIndex().getParentRegion()->isProperAncestor(
                  reshape->getParentRegion())) {
@@ -2525,6 +2526,35 @@ MemRefType CollapseShapeOp::computeCollapsedType(
                          srcType.getMemorySpace());
 }
 
+MemRefType
+CollapseShapeOp::inferCollapsedType(MemRefType type,
+                                    ArrayRef<AffineMap> reassociation) {
+  auto shape = type.getShape();
+  SmallVector<int64_t, 4> newShape;
+  assert(isReassociationValid(reassociation) && "invalid reassociation");
+  unsigned currentDim = 0;
+  for (AffineMap m : reassociation) {
+    unsigned dim = m.getNumResults();
+    auto band = shape.slice(currentDim, dim);
+    int64_t size = 1;
+    if (llvm::is_contained(band, ShapedType::kDynamic))
+      size = ShapedType::kDynamic;
+    else
+      for (unsigned d = 0; d < dim; ++d)
+        size *= shape[currentDim + d];
+    newShape.push_back(size);
+    currentDim += dim;
+  }
+  return MemRefType::get(newShape, type.getElementType());
+}
+
+MemRefType CollapseShapeOp::inferCollapsedType(
+    MemRefType type, SmallVector<ReassociationIndices> reassociation) {
+  return inferCollapsedType(
+      type, getSymbolLessAffineMaps(convertReassociationIndicesToExprs(
+                type.getContext(), reassociation)));
+}
+
 void CollapseShapeOp::build(OpBuilder &b, OperationState &result, Value src,
                             ArrayRef<ReassociationIndices> reassociation,
                             ArrayRef<NamedAttribute> attrs) {
diff --git a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
index 0336423c57b1d..9a2bd3493f6af 100644
--- a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
+++ b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
@@ -315,11 +315,11 @@ SmallVector<Range> SliceFromCollapseHelper::getExtractSliceParams(
     // have proven that these are not sliced. In this case we just take
     // the full extent of each dimension in the reassociation list.
     if (linearizedDimensions[it.index()]) {
-      llvm::append_range(
-          offsetsSizesAndStrides,
-          llvm::map_range(it.value(), [&](int64_t idx) -> Range {
-            return {zeroAttr, collapseShapeInputShape[idx], oneAttr};
-          }));
+      llvm::append_range(offsetsSizesAndStrides,
+                         llvm::map_range(it.value(), [&](int64_t idx) -> Range {
+                           return {zeroAttr, collapseShapeInputShape[idx],
+                                   oneAttr};
+                         }));
       continue;
     }
 
@@ -485,7 +485,7 @@ PackingMetadata mlir::computePackingMetadata(int64_t packedRank,
 }
 
 OpFoldResult mlir::reshapeConstantSource(DenseElementsAttr source,
-                            ...
[truncated]

llvmbot · 2025-03-01T00:40:44Z

@llvm/pr-subscribers-mlir

Author: Hyunsung Lee (ita9naiwa)

Changes

#129004

Change to use ShapedType, not RankedTensorType
- accordingly add branches to handle both MemrefType and TensorType
- Add Memref::CollapseShapeOp::inferCollapsedType to handle some MemrefType

Patch is 21.53 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/129036.diff

11 Files Affected:

(modified) mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td (+12-11)
(modified) mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td (+1)
(modified) mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td (+5)
(modified) mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h (+2-2)
(modified) mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp (+15-13)
(modified) mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp (+6-6)
(modified) mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp (+19-6)
(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+1-1)
(modified) mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp (+31-1)
(modified) mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp (+6-6)
(modified) mlir/test/Dialect/Linalg/loops.mlir (+16)

diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
index 1e48a5e3a20ee..785c7cc924159 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td
@@ -43,10 +43,10 @@ class Linalg_RelayoutOp<string mnemonic, list<Trait> traits = []> :
   code commonExtraClassDeclaration = [{
     size_t getSourceRank() { return getSourceType().getRank(); };
     size_t getDestRank() { return getDestType().getRank(); };
-    RankedTensorType getSourceType() {
-      return ::llvm::cast<RankedTensorType>(getSource().getType()); };
-    RankedTensorType getDestType() {
-      return ::llvm::cast<RankedTensorType>(getDest().getType()); };
+    ShapedType getSourceType() {
+      return ::llvm::cast<ShapedType>(getSource().getType()); };
+    ShapedType getDestType() {
+      return ::llvm::cast<ShapedType>(getDest().getType()); };
 
     MutableOperandRange getDpsInitsMutable() { return getDestMutable(); }
 
@@ -152,14 +152,14 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     // Note: Only tiled dimensions can be padded.
     ```
   }];
-  let arguments = (ins AnyRankedTensor:$source,
-                       AnyRankedTensor:$dest,
+  let arguments = (ins AnyShaped:$source,
+                       AnyShaped:$dest,
                        Optional<AnyType>:$padding_value,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$outer_dims_perm,
                        DenseI64ArrayAttr:$inner_dims_pos,
                        Variadic<Index>:$inner_tiles,
                        DenseI64ArrayAttr:$static_inner_tiles);
-  let results = (outs AnyRankedTensor:$result);
+  let results = (outs AnyShaped:$result);
   let assemblyFormat = [{
     $source
     (`padding_value` `(` $padding_value^ `:` type($padding_value) `)`)?
@@ -190,7 +190,7 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     // Method to get the `RankedTensorType` of the result based on the inner
     // tiles, position of the inner tiles (innerDimsPos)  and interchange vector
     // of outer loops (outerDimsPerm).
-    static RankedTensorType inferPackedType(RankedTensorType sourceType,
+    static RankedTensorType inferPackedType(ShapedType sourceType,
         ArrayRef<int64_t> innerTileSizes, ArrayRef<int64_t> innerDimsPos,
         ArrayRef<int64_t> outerDimsPerm = {});
 
@@ -229,6 +229,7 @@ def Linalg_PackOp : Linalg_RelayoutOp<"pack", [
     /// 2. pads the other ones, and
     /// 3. doesn't shuffle the dimensions
     bool isLikePad();
+
   }];
 
   let hasCanonicalizeMethod = 1;
@@ -279,13 +280,13 @@ def Linalg_UnPackOp : Linalg_RelayoutOp<"unpack"> {
         : tensor<8x16x8x32xf32> -> tensor<128x256xf32>
     ```
   }];
-  let arguments = (ins AnyRankedTensor:$source,
-                       AnyRankedTensor:$dest,
+  let arguments = (ins AnyShaped:$source,
+                       AnyShaped:$dest,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$outer_dims_perm,
                        DenseI64ArrayAttr:$inner_dims_pos,
                        Variadic<Index>:$inner_tiles,
                        DenseI64ArrayAttr:$static_inner_tiles);
-  let results = (outs AnyRankedTensor:$result);
+  let results = (outs AnyShaped:$result);
   let assemblyFormat = [{
     $source
     (`outer_dims_perm` `=` $outer_dims_perm^)?
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td b/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
index 2dec2fc4396f4..467d862d277eb 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td
@@ -10,6 +10,7 @@
 #define LINALG_IR_RELAYOUTOPINTERFACE
 
 include "mlir/Interfaces/DestinationStyleOpInterface.td"
+include "mlir/Dialect/Linalg/IR/LinalgInterfaces.td"
 include "mlir/IR/OpBase.td"
 
 def LinalgRelayoutOpInterface : OpInterface<"RelayoutOpInterface"> {
diff --git a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
index 4c8a214049ea9..8bcc1882b454d 100644
--- a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
+++ b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
@@ -1799,6 +1799,11 @@ def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape", [
 
     static MemRefType computeCollapsedType(
         MemRefType srcType, ArrayRef<ReassociationIndices> reassociation);
+    static MemRefType
+        inferCollapsedType(MemRefType type, ArrayRef<AffineMap> reassociation);
+    static MemRefType
+        inferCollapsedType(MemRefType type,
+                           SmallVector<ReassociationIndices> reassociation);
   }];
 
   let hasVerifier = 1;
diff --git a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
index 3af89a6ab3799..a86bf74a7b6a1 100644
--- a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
+++ b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
@@ -451,7 +451,7 @@ getLinearizedDimensions(ArrayRef<ReassociationIndices> reassociationIndices);
 ///    %4 = tensor.extract_slice %0 [%3#0, %3#1, %3#2, 0] [1, 1, 1, 10] [1, 1, 1, 1] :
 ///          tensor<3x7x11x10xf32> to tensor<1x1x1x10xf32>
 ///
-///    %5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] : 
+///    %5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] :
 ///          tensor<1x1x1x10xf32> into tensor<1x10xf32>
 ///    %6 = tensor.insert_slice %5 into %arg0 [%iv, 0] [1, 10] [1, 1] :
 ///          tensor<1x10xf32> into tensor<10x10xf32>
@@ -573,7 +573,7 @@ PackingMetadata computePackingMetadata(int64_t packedRank,
 /// Removes the op and replaces the constant with a new constant of the result
 /// shape. When an optional cst attribute is passed, it is reshaped only if the
 /// splat value matches the value in the attribute.
-OpFoldResult reshapeConstantSource(DenseElementsAttr source, TensorType result,
+OpFoldResult reshapeConstantSource(DenseElementsAttr source, ShapedType result,
                                    std::optional<Attribute> cst = std::nullopt);
 } // namespace mlir
 
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 07b19e5cb1a89..a19039fbca67d 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -803,7 +803,7 @@ struct FoldInsertPadIntoFill : public OpRewritePattern<tensor::InsertSliceOp> {
           rewriter, loc, addMap, {std::get<0>(p), std::get<1>(p)}));
     }
 
-    RankedTensorType srcPadType = srcPadOp.getSourceType();
+    ShapedType srcPadType = srcPadOp.getSourceType();
     SmallVector<OpFoldResult, 4> newSizes;
     for (int i = 0, e = srcPadType.getRank(); i < e; ++i) {
       if (srcPadType.isDynamicDim(i)) {
@@ -4433,9 +4433,9 @@ static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
     return op->emitError("invalid zero tile factor");
 
   // Verify inner_dims_pos and outer_dims_perm.
-  RankedTensorType unpackedType = (std::is_same<OpTy, PackOp>::value)
-                                      ? packOrUnPack.getSourceType()
-                                      : packOrUnPack.getDestType();
+  ShapedType unpackedType = (std::is_same<OpTy, PackOp>::value)
+                                ? packOrUnPack.getSourceType()
+                                : packOrUnPack.getDestType();
   size_t unpackedRank = unpackedType.getRank();
   ArrayRef<int64_t> innerDimsPos = packOrUnPack.getInnerDimsPos();
   ArrayRef<int64_t> outerDimPerm = packOrUnPack.getOuterDimsPerm();
@@ -4747,7 +4747,7 @@ SmallVector<OpFoldResult> PackOp::getResultShape(
 
 /// Get the expected packed type based on source type, tile factors, position of
 /// the inner tiles and permutation of the outer tiled loop.
-RankedTensorType PackOp::inferPackedType(RankedTensorType sourceType,
+RankedTensorType PackOp::inferPackedType(ShapedType sourceType,
                                          ArrayRef<int64_t> innerTileSizes,
                                          ArrayRef<int64_t> innerDimsPos,
                                          ArrayRef<int64_t> outerDimsPerm) {
@@ -4943,7 +4943,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
           rewriter.create<tensor::CastOp>(loc, newSrcType, packOp.getSource());
     }
     Value dest = packOp.getDest();
-    RankedTensorType originalResultType = packOp.getDestType();
+    ShapedType originalResultType = packOp.getDestType();
     bool needUpdateDestType = (destShape != originalResultType.getShape());
     if (needUpdateDestType) {
       auto newDestType = packOp.getDestType().clone(destShape);
@@ -4953,7 +4953,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
     rewriter.modifyOpInPlace(packOp, [&] {
       packOp.getSourceMutable().assign(source);
       packOp.getDestMutable().assign(dest);
-      packOp.getResult().setType(cast<RankedTensorType>(dest.getType()));
+      packOp.getResult().setType(cast<ShapedType>(dest.getType()));
     });
     // Insert a cast if needed
     if (needUpdateDestType) {
@@ -4969,8 +4969,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
 }
 
 template <typename PackOrUnpackOp>
-static bool isLikePadUnPad(PackOrUnpackOp packOp,
-                           RankedTensorType packedTensorType) {
+static bool isLikePadUnPad(PackOrUnpackOp packOp, ShapedType packedTensorType) {
   static_assert(std::is_same<PackOrUnpackOp, PackOp>::value ||
                     std::is_same<PackOrUnpackOp, UnPackOp>::value,
                 "Function meant for pack/unpack");
@@ -5002,9 +5001,12 @@ static bool isLikePadUnPad(PackOrUnpackOp packOp,
 }
 
 bool PackOp::isLikePad() {
-  auto packedTensorType =
-      llvm::cast<RankedTensorType>((*this)->getResultTypes().front());
-  return isLikePadUnPad(*this, packedTensorType);
+  if (auto packedTensorType =
+          llvm::dyn_cast<RankedTensorType>((*this)->getResultTypes().front()))
+    return isLikePadUnPad(*this, packedTensorType);
+  if (auto packedTensorType =
+          llvm::dyn_cast<MemRefType>((*this)->getResultTypes().front()))
+    return isLikePadUnPad(*this, packedTensorType);
 }
 
 OpFoldResult PackOp::fold(FoldAdaptor adaptor) {
@@ -5274,7 +5276,7 @@ LogicalResult UnPackOp::canonicalize(UnPackOp unPackOp,
 }
 
 bool UnPackOp::isLikeUnPad() {
-  RankedTensorType packedTensorType = getSourceType();
+  ShapedType packedTensorType = getSourceType();
   return isLikePadUnPad(*this, packedTensorType);
 }
 
diff --git a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
index 0984b6988b93b..599aa3b6668df 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
@@ -111,7 +111,7 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
     if (packOp.getPaddingValue())
       return rewriter.notifyMatchFailure(packOp, "expects no padding value");
 
-    RankedTensorType sourceType = packOp.getSourceType();
+    ShapedType sourceType = packOp.getSourceType();
     if (failed(isPackOnInnerMostDim(rewriter, packOp)) &&
         failed(isPackOn1D(rewriter, packOp, sourceType.getShape(),
                           packOp.getStaticTiles())) &&
@@ -119,7 +119,7 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
       return failure();
     }
 
-    RankedTensorType destType = packOp.getDestType();
+    ShapedType destType = packOp.getDestType();
     auto reassociation =
         getReassociationIndicesForReshape(sourceType, destType);
     if (!reassociation)
@@ -157,8 +157,8 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
           "expects outer_dims_perm is empty or an identity permutation");
     }
 
-    RankedTensorType sourceType = unpackOp.getSourceType();
-    RankedTensorType destType = unpackOp.getDestType();
+    ShapedType sourceType = unpackOp.getSourceType();
+    ShapedType destType = unpackOp.getDestType();
     if (!sourceType.hasStaticShape() || !destType.hasStaticShape())
       return rewriter.notifyMatchFailure(unpackOp, "expects static shapes");
 
@@ -173,7 +173,7 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
 
   LogicalResult matchAndRewrite(UnPackOp unpackOp,
                                 PatternRewriter &rewriter) const override {
-    RankedTensorType destType = unpackOp.getDestType();
+    ShapedType destType = unpackOp.getDestType();
     if (failed(isUnpackOnInnerMostDim(rewriter, unpackOp)) &&
         failed(isPackOn1D(rewriter, unpackOp, destType.getShape(),
                           unpackOp.getStaticTiles())) &&
@@ -181,7 +181,7 @@ struct SimplifyUnPackToCollapseShape : public OpRewritePattern<UnPackOp> {
       return failure();
     }
 
-    RankedTensorType sourceType = unpackOp.getSourceType();
+    ShapedType sourceType = unpackOp.getSourceType();
     auto reassociation =
         getReassociationIndicesForReshape(sourceType, destType);
     if (!reassociation)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
index dcd50cc44f81b..98dab332b2f40 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
@@ -17,6 +17,7 @@
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/Transforms/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tensor/IR/TensorTilingInterfaceImpl.h"
@@ -359,7 +360,7 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPoint(unPackOp);
 
-  RankedTensorType packedTensorType = unPackOp.getSourceType();
+  ShapedType packedTensorType = unPackOp.getSourceType();
   int64_t packedRank = packedTensorType.getRank();
 
   OpFoldResult zero = rewriter.getIndexAttr(0), one = rewriter.getIndexAttr(1);
@@ -396,10 +397,22 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
   applyPermutationToVector(stripMinedShape, packedToStripMinedShapePerm);
 
   // 3. Transpose packedShape to stripMinedShape.
-  RankedTensorType stripMinedTensorType =
-      RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape);
-  RankedTensorType collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
-      stripMinedTensorType, packingMetadata.reassociations);
+  ShapedType stripMinedType;
+  if (auto tensorType = packedTensorType.dyn_cast<TensorType>()) {
+    stripMinedType =
+        RankedTensorType::get(stripMinedShape, tensorType.getElementType());
+  } else if (auto memrefType = packedTensorType.dyn_cast<MemRefType>()) {
+    stripMinedType =
+        MemRefType::get(stripMinedShape, memrefType.getElementType());
+  }
+  ShapedType collapsedType;
+  if (stripMinedType.isa<TensorType>()) {
+    collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
+        cast<RankedTensorType>(stripMinedType), packingMetadata.reassociations);
+  } else if (stripMinedType.isa<MemRefType>()) {
+    collapsedType = memref::CollapseShapeOp::inferCollapsedType(
+        cast<MemRefType>(stripMinedType), packingMetadata.reassociations);
+  }
 
   // Get dynamic dims from input tensor based on packedToStripMinedShapePerm
   // permutation.
@@ -407,7 +420,7 @@ linalg::lowerUnPack(RewriterBase &rewriter, linalg::UnPackOp unPackOp,
       tensor::getMixedSizes(rewriter, loc, unPackOp.getSource());
   applyPermutationToVector(dims, packedToStripMinedShapePerm);
   auto emptyOp = rewriter.create<tensor::EmptyOp>(
-      loc, dims, stripMinedTensorType.getElementType());
+      loc, dims, stripMinedType.getElementType());
   auto transposeOp = rewriter.create<linalg::TransposeOp>(
       loc, unPackOp.getSource(), emptyOp, packedToStripMinedShapePerm);
 
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index ae04c2b6b2a5b..25ad5e38addbe 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1669,7 +1669,7 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPoint(unpackOp);
 
-  RankedTensorType unpackTensorType = unpackOp.getSourceType();
+  ShapedType unpackTensorType = unpackOp.getSourceType();
 
   ArrayRef<int64_t> innerDimPos = unpackOp.getInnerDimsPos();
   ArrayRef<int64_t> innerTiles = unpackOp.getStaticInnerTiles();
diff --git a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
index 11597505e7888..ba12cc34d6457 100644
--- a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
+++ b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
@@ -9,6 +9,7 @@
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Arith/Utils/Utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
 #include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/Builders.h"
@@ -1124,7 +1125,7 @@ struct DimOfMemRefReshape : public OpRewritePattern<DimOp> {
         }
       } // else dim.getIndex is a block argument to reshape->getBlock and
         // dominates reshape
-    }   // Check condition 2
+    } // Check condition 2
     else if (dim->getBlock() != reshape->getBlock() &&
              !dim.getIndex().getParentRegion()->isProperAncestor(
                  reshape->getParentRegion())) {
@@ -2525,6 +2526,35 @@ MemRefType CollapseShapeOp::computeCollapsedType(
                          srcType.getMemorySpace());
 }
 
+MemRefType
+CollapseShapeOp::inferCollapsedType(MemRefType type,
+                                    ArrayRef<AffineMap> reassociation) {
+  auto shape = type.getShape();
+  SmallVector<int64_t, 4> newShape;
+  assert(isReassociationValid(reassociation) && "invalid reassociation");
+  unsigned currentDim = 0;
+  for (AffineMap m : reassociation) {
+    unsigned dim = m.getNumResults();
+    auto band = shape.slice(currentDim, dim);
+    int64_t size = 1;
+    if (llvm::is_contained(band, ShapedType::kDynamic))
+      size = ShapedType::kDynamic;
+    else
+      for (unsigned d = 0; d < dim; ++d)
+        size *= shape[currentDim + d];
+    newShape.push_back(size);
+    currentDim += dim;
+  }
+  return MemRefType::get(newShape, type.getElementType());
+}
+
+MemRefType CollapseShapeOp::inferCollapsedType(
+    MemRefType type, SmallVector<ReassociationIndices> reassociation) {
+  return inferCollapsedType(
+      type, getSymbolLessAffineMaps(convertReassociationIndicesToExprs(
+                type.getContext(), reassociation)));
+}
+
 void CollapseShapeOp::build(OpBuilder &b, OperationState &result, Value src,
                             ArrayRef<ReassociationIndices> reassociation,
                             ArrayRef<NamedAttribute> attrs) {
diff --git a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
index 0336423c57b1d..9a2bd3493f6af 100644
--- a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
+++ b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
@@ -315,11 +315,11 @@ SmallVector<Range> SliceFromCollapseHelper::getExtractSliceParams(
     // have proven that these are not sliced. In this case we just take
     // the full extent of each dimension in the reassociation list.
     if (linearizedDimensions[it.index()]) {
-      llvm::append_range(
-          offsetsSizesAndStrides,
-          llvm::map_range(it.value(), [&](int64_t idx) -> Range {
-            return {zeroAttr, collapseShapeInputShape[idx], oneAttr};
-          }));
+      llvm::append_range(offsetsSizesAndStrides,
+                         llvm::map_range(it.value(), [&](int64_t idx) -> Range {
+                           return {zeroAttr, collapseShapeInputShape[idx],
+                                   oneAttr};
+                         }));
       continue;
     }
 
@@ -485,7 +485,7 @@ PackingMetadata mlir::computePackingMetadata(int64_t packedRank,
 }
 
 OpFoldResult mlir::reshapeConstantSource(DenseElementsAttr source,
-                            ...
[truncated]

mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td

mlir/test/Dialect/Linalg/loops.mlir

mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

mlir/include/mlir/Dialect/Linalg/IR/RelayoutOpInterface.td

adam-smnk · 2025-03-03T09:28:32Z

I expect most of the existing linalg.pack/unpack transforms can't handle memrefs. It'd be great to restrict them to ops with pure tensor semantics.

ita9naiwa · 2025-03-30T09:03:27Z

I want to know how to track down all the patterns related to Tensor Pack/UnPack Ops

I tracked down with grep matching linalg::PackOp and linalg::UnPackOp.

I bailed out transformations and rewrite patterns using e.g.,

// TODO(issues/129004): Support MemRef PackOp. Temporarily return failure.
  if (!packOp.hasPureTensorSemantics()) {
    return failure();
  }

hanhanW

Looks better to me, thanks! RE TODO comments: I only expect that they are available for PackAndUnpackPatterns, Vectorization, and canonicalization. Other transform might not able to handle the memref case atm. I suggest removing the TODO from other files, and people can start their projects later if they need it.

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

hanhanW · 2025-04-01T21:57:34Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

-  // Insert tensor.cast ops if static shape inference is available..
+  // Insert either tensor.cast or memref.cast ops
+  // if static shape inference is available..
+  bool hasTensorSemantics = packOp.hasPureTensorSemantics();


nit: it is only used in the below closure, let's move it into the if-body. Also, we are missing tests if we add such support in the PR. E.g.,

llvm-project/mlir/test/Dialect/Linalg/canonicalize.mlir

Lines 1343 to 1380 in e55164a

// -----

func.func @infer_src_shape_pack(%src: tensor<?x?x?x?xf32>, %dest: tensor<10x20x30x40x16xf32>) -> tensor<10x20x30x40x16xf32> {

%cst = arith.constant 0.000000e+00 : f32

%pack = linalg.pack %src

padding_value(%cst : f32)

outer_dims_perm = [2, 1, 3, 0]

inner_dims_pos = [2]

inner_tiles = [16]

into %dest : tensor<?x?x?x?xf32> -> tensor<10x20x30x40x16xf32>

return %pack : tensor<10x20x30x40x16xf32>

}

// CHECK-LABEL: func.func @infer_src_shape_pack

// CHECK-SAME: %[[SRC:[0-9a-zA-Z]+]]

// CHECK-SAME: %[[DEST:[0-9a-zA-Z]+]]

// CHECK: %[[CAST_SRC:.+]] = tensor.cast %[[SRC]] : tensor<?x?x?x?xf32> to tensor<40x20x?x30xf32>

// CHECK: %[[PACK:.+]] = linalg.pack %[[CAST_SRC]] {{.+}} into %[[DEST]]

// CHECK: return %[[PACK]]

// -----

func.func @infer_dest_shape_pack(%src: tensor<30x20x?x10xf32>, %dest: tensor<?x?x?x?x16xf32>) -> tensor<?x?x?x?x16xf32> {

%cst = arith.constant 0.000000e+00 : f32

%pack = linalg.pack %src

padding_value(%cst : f32)

outer_dims_perm = [2, 1, 3, 0]

inner_dims_pos = [2]

inner_tiles = [16]

into %dest : tensor<30x20x?x10xf32> -> tensor<?x?x?x?x16xf32>

return %pack : tensor<?x?x?x?x16xf32>

}

// CHECK-LABEL: func.func @infer_dest_shape_pack

// CHECK-SAME: %[[SRC:[0-9a-zA-Z]+]]

// CHECK-SAME: %[[DEST:[0-9a-zA-Z]+]]

// CHECK: %[[CAST_DEST:.+]] = tensor.cast %[[DEST]] : tensor<?x?x?x?x16xf32> to tensor<?x20x10x30x16xf32>

// CHECK: %[[PACK:.+]] = linalg.pack %[[SRC]] {{.+}} into %[[CAST_DEST]]

// CHECK: %[[CAST_PACK:.+]] = tensor.cast %[[PACK]] : tensor<?x20x10x30x16xf32> to tensor<?x?x?x?x16xf32>

// CHECK: return %[[CAST_PACK]]

hanhanW · 2025-04-01T22:00:14Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

+      if (hasTensorSemantics)
+        dest =
+            rewriter.create<tensor::CastOp>(loc, newDestType, packOp.getDest());


There are three types in the pack ops on tensors. (1) source type (2) dest type (3) result type.

In the shape inference, we need casting for (1) and (2), so here you also need to take memref into account. (A new test will capture the failure). For (3), where is updated in the modifyOpInPlace{...}, we update the result type if and only if it is on tensors.

The (3) only happens on tensors because memref variant only has (1) and (2) types.

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

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

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

mlir/test/Dialect/Linalg/roundtrip.mlir

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

hanhanW

Thank you for addressing most of the comments. I think we are close to land the PR. I left some comments about TODO and comments, and I think we are missing some tests for canonicalization patterns. Please add tests to reflect the changes.

hanhanW · 2025-04-03T17:37:48Z

mlir/lib/Conversion/OpenMPToLLVM/OpenMPToLLVM.cpp

@@ -78,7 +78,7 @@ struct OpenMPOpConversion : public ConvertOpToLLVMPattern<T> {
                                    omp::FlushOp, omp::MapBoundsOp,
                                    omp::ThreadprivateOp>::value) {
        if (isa<MemRefType>(originalOperand.getType())) {
-          // TODO: Support memref type in variable operands
+          // TODO: Support Memref PackOp. Temporarily return failure.


Please remove the change. I think you are not intended to update this. :)

hanhanW · 2025-04-03T17:39:41Z

mlir/test/Dialect/Linalg/invalid.mlir

+
+

nit: delete one blank line

hanhanW · 2025-04-03T17:41:59Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

+      if (hasTensorSemantics) {
+        auto castOp =
+            rewriter.create<tensor::CastOp>(loc, originalResultType, packOp);
+        rewriter.replaceAllUsesExcept(packOp, castOp, castOp);
+      } else {
+        auto castOp =
+            rewriter.create<memref::CastOp>(loc, originalResultType, packOp);
+        rewriter.replaceAllUsesExcept(packOp, castOp, castOp);
+      }


Yes, I think so.

hanhanW · 2025-04-03T17:47:27Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

-/// a way that ensures that they agree on which dimensions are dynamic.
+/// Helper for PackOp::{getResultShape,inferPackedTensorType}. Returns the shape
+/// of the packed type. Having a shared helper helps implement these two methods
+/// in a way that ensures that they agree on which dimensions are dynamic.


We should replace the function with the new inferPackedShape method. I don't see the value of having an indirect call. I.e.,

static SmallVector<int64_t> getPackOpResultTypeShape( ArrayRef<int64_t> sourceShape, ArrayRef<int64_t> innerTileSizes, ArrayRef<int64_t> innerDimsPos, ArrayRef<int64_t> outerDimsPerm) {

can become

SmallVector<int64_t> PackOp::inferPackedShape(ArrayRef<int64_t> inputShape, ArrayRef<int64_t> innerTileSizes, ArrayRef<int64_t> innerDimsPos, ArrayRef<int64_t> outerDimsPerm) {

Please remember to add a similar comment to td, like it helps ensure all the shape inference methods agree on which dimensions are dynamic.

hanhanW · 2025-04-03T17:54:36Z

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

+  if (!packOp.hasPureTensorSemantics())
+    return failure();
+


I think it is a redundant check, as all the precondition checks happen in matchAndRewrite methods. Can you remove it?

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

hanhanW · 2025-04-03T18:13:41Z

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

Please remove all the TODOs from this file. I think they are not trivial, because we don't have memref.pad op. No one will clear the TODO in this case.

hanhanW · 2025-04-03T18:16:58Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

 #include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include <iostream>


IWYU, please delete the include.

hanhanW · 2025-04-03T18:19:14Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

+    if (!op.hasPureTensorSemantics())
+      return failure();


Let's add a TODO for consistency. It is a reasonable folder to me and we should support it (in follow-ups).

This is unpack, not pack.

hanhanW · 2025-04-03T18:22:11Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

@@ -4951,7 +4993,7 @@ LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
    rewriter.modifyOpInPlace(packOp, [&] {
      packOp.getSourceMutable().assign(source);
      packOp.getDestMutable().assign(dest);
-      packOp.getResult().setType(cast<RankedTensorType>(dest.getType()));
+      packOp.getResult().setType(cast<ShapedType>(dest.getType()));


SGTM, please add the tests to https://github.com/llvm/llvm-project/blob/main/mlir/test/Dialect/Linalg/canonicalize.mlir

EDIT: You should have inner_dims_pos in the fold_pack_unpack_memref test. (We can have canonicalization patterns to fold them away if the configuration is empty and the types statically match.)

ita9naiwa · 2025-04-13T04:35:38Z

I accidently clang-formatted invalid.mlir, I will fix very soon.

Signed-off-by: Hyunsung Lee <[email protected]>

hanhanW

The canonicalization pattern is off, which leads to an invalid IR. Other parts look good, just some nits about comments.

hanhanW · 2025-04-18T19:23:13Z

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

+  if (!unpackOp.hasPureTensorSemantics()) {
+    return failure();
+  }


mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp

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

hanhanW · 2025-04-18T22:12:15Z

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

+    if (!op.hasPureTensorSemantics())
+      return failure();


This is unpack, not pack.

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td

Co-authored-by: Han-Chung Wang <[email protected]> Signed-off-by: Hyunsung Lee <[email protected]>

hanhanW

Sorry that I made a mistake in the review about the folding. After taking a look at the real IR tests, I think there are two issues.

The memref version should not return a memref. By definition, the op performs packing from the source buffer and store the result to the destination buffer. Like other linalg operations, it should not return any value when it has buffer semantics. In other linalg ops, the Variadic<AnyRankedTensor> adds the check. It was pointed out in a comment from @adam-smnk, and I think it is not resolved.

Invalid case:

%packed = linalg.pack %unpacked inner_dims_pos = [0, 1] inner_tiles = [4, 4] into %buf_packed : memref<40x80xf32> -> memref<10x20x4x4xf32>

It should be like this valid case:

linalg.pack %unpacked
  inner_dims_pos = [0, 1] inner_tiles = [4, 4]
  into %buf_packed
  : memref<40x80xf32> -> memref<10x20x4x4xf32>

The second issue is about the folding on memrefs. TLDR is that we should disable it. Because the current logic is not correct. How I'd implement the folding is that we need to look at the source memref and other uses of the source memref. It could be expensive and the logic is more complicated on memrefs. The below is not foldable if there are other ops using %buf_unpacked in the middle. Things become way more complicated when control flow is involved. I think this kind of folding should be implemented in a pass manner. (Please remember to remove the test cases in canonicalzation.mlir if they are disabled. I also did not see the reason why you added some folding tests to tensor, while the PR is scoped to add the support for memref. Maybe we can drop the tests for tensors as well?)

linalg.unpack %t
  inner_dims_pos = [0, 1] inner_tiles = [4, 4]
  into %buf_unpacked
  : memref<10x20x4x4xf32> -> memref<40x80xf32>
linalg.pack %unpacked
  inner_dims_pos = [0, 1] inner_tiles = [4, 4]
  into %buf_packed
  : memref<40x80xf32> -> memref<10x20x4x4xf32>

(The rest issue is about reverting non-related code changes, I think you mentioned that you will remove them before landing the PR.)

ita9naiwa · 2025-07-12T06:07:56Z

Apologies for the delay — I’ve been recovering from a medical issue. I’ll resume this soon.

- Revert OpenMP conversion changes - Revert DataLayoutPropagation tensor semantic checks removal - Revert Vectorization tensor semantic checks removal - Revert Transforms tensor semantic checks removal - Revert ReshapeOpsUtils formatting changes - Revert MemRefOps.td whitespace changes Keep only essential changes for memref support in pack/unpack

…ack/unpack - Add hasPureTensorSemantics() check at the beginning of PackOp::canonicalize() - Add hasPureTensorSemantics() check at the beginning of UnPackOp::canonicalize() - Remove memref folding tests from canonicalize.mlir - Add tests to verify memref pack/unpack canonicalization is disabled This prevents complex canonicalization patterns from running on memref versions of pack/unpack operations, following buffer semantics.

ita9naiwa · 2025-07-15T03:03:11Z

@hanhanW Only one remaining issue:

The memref version should not return a memref

memref version still returning. I tried to fix according to the other similar op (linalg.softmax) but failing.

draft

4d523ad

ita9naiwa changed the title ~~[draft][mlir][~~ [draft][mlir][linalg] pack, unpack to take memref inputs Feb 27, 2025

ita9naiwa added 8 commits February 28, 2025 08:00

draft

4f2dbf4

draft

226230c

init

0c184df

lint

19201c6

lint

b99b920

add

be6a119

remove tensor casting

eee8805

add test

c5b3c39

ita9naiwa marked this pull request as ready for review March 1, 2025 00:40

ita9naiwa requested review from banach-space, dcaballe, hanhanW, nicolasvasilache, Groverkss and rengolin as code owners March 1, 2025 00:40

llvmbot added mlir:linalg mlir mlir:memref labels Mar 1, 2025

ita9naiwa changed the title ~~[draft][mlir][linalg] pack, unpack to take memref inputs~~ [MLIR][Linalg] pack, unpack to take memref inputs Mar 1, 2025

rengolin requested review from adam-smnk and chelini March 1, 2025 05:59

banach-space mentioned this pull request Mar 1, 2025

[mlir][linalg] Linalg::PackOp Linalg::UnPackOp to support memref input #129004

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fix build error

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Merge branch 'main' into ita9naiwa/pack-memref

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add invalid pack/unpack cases

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fix roundtrip test

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fix upon review

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fix upon review

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ita9naiwa requested a review from hanhanW April 2, 2025 22:13

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Merge branch 'main' into ita9naiwa/pack-memref

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ita9naiwa requested a review from hanhanW April 14, 2025 10:25

fix upon review

17ad838

Signed-off-by: Hyunsung Lee <[email protected]>

ita9naiwa force-pushed the ita9naiwa/pack-memref branch from e660c40 to 17ad838 Compare April 17, 2025 07:53

hanhanW reviewed Apr 18, 2025

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Update mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td

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Co-authored-by: Han-Chung Wang <[email protected]> Signed-off-by: Hyunsung Lee <[email protected]>

ita9naiwa force-pushed the ita9naiwa/pack-memref branch from 7b86f9b to 2aca3fd Compare April 20, 2025 04:55

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Merge branch 'main' into ita9naiwa/pack-memref

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ita9naiwa requested a review from hanhanW August 11, 2025 04:53

	// -----

	func.func @infer_src_shape_pack(%src: tensor<?x?x?x?xf32>, %dest: tensor<10x20x30x40x16xf32>) -> tensor<10x20x30x40x16xf32> {
	%cst = arith.constant 0.000000e+00 : f32
	%pack = linalg.pack %src
	padding_value(%cst : f32)
	outer_dims_perm = [2, 1, 3, 0]
	inner_dims_pos = [2]
	inner_tiles = [16]
	into %dest : tensor<?x?x?x?xf32> -> tensor<10x20x30x40x16xf32>
	return %pack : tensor<10x20x30x40x16xf32>
	}
	// CHECK-LABEL: func.func @infer_src_shape_pack
	// CHECK-SAME: %[[SRC:[0-9a-zA-Z]+]]
	// CHECK-SAME: %[[DEST:[0-9a-zA-Z]+]]
	// CHECK: %[[CAST_SRC:.+]] = tensor.cast %[[SRC]] : tensor<?x?x?x?xf32> to tensor<40x20x?x30xf32>
	// CHECK: %[[PACK:.+]] = linalg.pack %[[CAST_SRC]] {{.+}} into %[[DEST]]
	// CHECK: return %[[PACK]]

	// -----

	func.func @infer_dest_shape_pack(%src: tensor<30x20x?x10xf32>, %dest: tensor<?x?x?x?x16xf32>) -> tensor<?x?x?x?x16xf32> {
	%cst = arith.constant 0.000000e+00 : f32
	%pack = linalg.pack %src
	padding_value(%cst : f32)
	outer_dims_perm = [2, 1, 3, 0]
	inner_dims_pos = [2]
	inner_tiles = [16]
	into %dest : tensor<30x20x?x10xf32> -> tensor<?x?x?x?x16xf32>
	return %pack : tensor<?x?x?x?x16xf32>
	}
	// CHECK-LABEL: func.func @infer_dest_shape_pack
	// CHECK-SAME: %[[SRC:[0-9a-zA-Z]+]]
	// CHECK-SAME: %[[DEST:[0-9a-zA-Z]+]]
	// CHECK: %[[CAST_DEST:.+]] = tensor.cast %[[DEST]] : tensor<?x?x?x?x16xf32> to tensor<?x20x10x30x16xf32>
	// CHECK: %[[PACK:.+]] = linalg.pack %[[SRC]] {{.+}} into %[[CAST_DEST]]
	// CHECK: %[[CAST_PACK:.+]] = tensor.cast %[[PACK]] : tensor<?x20x10x30x16xf32> to tensor<?x?x?x?x16xf32>
	// CHECK: return %[[CAST_PACK]]

		#include "mlir/Dialect/Linalg/IR/Linalg.h"
		#include <iostream>

[MLIR][Linalg] pack, unpack to take memref inputs #129036

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[MLIR][Linalg] pack, unpack to take memref inputs #129036

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