[mlir][linalg] Simplify `createWriteOrMaskedWrite` (NFC) #141567

banach-space · 2025-05-27T09:11:42Z

This patch removes inputVecSizesForLeadingDims from the parameter list
of createWriteOrMaskedWrite. That argument is unnecessary — vector sizes
can be obtained from the vecToStore parameter. Since this doesn't change
behavior or test results, it's marked as NFC.

Additional cleanups:

Renamed vectorToStore to vecToStore for consistency and brevity.
Rewrote a conditional at the end of the function to use early exit,
improving readability:

  // BEFORE:
  if (maskingRequried) {
    Value maskForWrite = ...;
    write = maskOperation(write, maskForWrite);
  }
  return write;

  // AFTER
  if (!maskingRequried)
    return write;

  Value maskFroWrite = ...;
  return vector::maskOperation(builder, write, maskForWrite);

llvmbot · 2025-05-27T09:12:21Z

@llvm/pr-subscribers-mlir-llvm

Author: Andrzej Warzyński (banach-space)

Changes

[[mlir][linalg] Refactor vectorization hooks to improve code reuse
[mlir][linalg] Simplify createWriteOrMaskedWrite (NFC)

Full diff: https://github.com/llvm/llvm-project/pull/141567.diff

1 Files Affected:

(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+40-78)

diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 0113ba86a5ae3..2abb2f0ea467c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1590,61 +1590,46 @@ static bool isMaskTriviallyFoldable(SmallVector<OpFoldResult> &maskSizes,
 /// Creates an optionally masked TransferWriteOp
 ///
 /// Generates the following operation:
-///   %res = vector.transfer_write %vectorToStore into %dest
+///   %res = vector.transfer_write %vecToStore into %dest
 ///
-/// If the leading N dimensions of the vector to store do not match
-/// `inputVecSizesForLeadingDims` (N = rank(inputVecSizesForLeadingDims)),
-/// masking is applied to ensure correctness:
+/// If shape(vecToStore) != shape(dest), masking is used to ensure correctness:
 ///
-///   %mask = vector.create_mask(%destShape) : %vectorToStoreShape
+///   %mask = vector.create_mask(%destShape) : %vecToStoreShape
 ///   %res = vector.mask %mask {
-///     vector.transfer_write %vectorToStore into %dest
+///     vector.transfer_write %vecToStore into %dest
 ///   }
 ///
-/// The mask shape is identical to `vectorToStore` (with the element type ==
+/// The mask shape is identical to `vecToStore` (with the element type ==
 /// i1), and the mask values are based on the shape of the `dest` tensor.
 ///
 /// If `useInBoundsInsteadOfMasking` is set to `true`, the `in_bounds` attribute
 /// is used instead of masking:
 ///
-///   %write = vector.transfer_write %vectorToStore into %dest
+///   %write = vector.transfer_write %vecToStore into %dest
 ///   in_bounds_flags = (...)
 ///   %res = vector.transfer_write %input into %dest
 ///       {in_bounds = in_bounds_flags}
 ///
-/// `writeIndices` specifies the offsets to use. If empty, all indices are set
-/// to 0.
-///
-/// NOTE: When N < rank(vectorToStore), the missing vector sizes are taken from
-/// `valueToStore`.
-/// TODO: `inputVecSizesForLeadingDims` should not be required - these sizes are
-/// already provided in `vectorToStore`.
+/// Finally, `writeIndices` specifies the offsets to use. If empty, all indices
+/// are set to 0.
 static Operation *
-createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
-                         Value dest,
-                         ArrayRef<int64_t> inputVecSizesForLeadingDims,
-                         SmallVector<Value> writeIndices = {},
+createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vecToStore,
+                         Value dest, SmallVector<Value> writeIndices = {},
                          bool useInBoundsInsteadOfMasking = false) {
 
   ShapedType destType = cast<ShapedType>(dest.getType());
   int64_t destRank = destType.getRank();
   auto destShape = destType.getShape();
 
-  VectorType vecToStoreType = cast<VectorType>(vectorToStore.getType());
+  VectorType vecToStoreType = cast<VectorType>(vecToStore.getType());
   int64_t vecToStoreRank = vecToStoreType.getRank();
   auto vecToStoreShape = vecToStoreType.getShape();
 
   // Compute the in_bounds attribute
   SmallVector<bool> inBoundsVal(vecToStoreRank, true);
   if (useInBoundsInsteadOfMasking) {
-    // In this case, assume that all the required vector sizes have been
-    // provided.
-    assert(inputVecSizesForLeadingDims.size() ==
-               static_cast<size_t>(vecToStoreType.getRank()) &&
-           "Insufficient number of input vector sizes!");
-    // Update the inBounds attribute.
     for (unsigned i = 0; i < destRank; i++)
-      inBoundsVal[i] = (destShape[i] == inputVecSizesForLeadingDims[i]) &&
+      inBoundsVal[i] = (destShape[i] == vecToStoreShape[i]) &&
                        !ShapedType::isDynamic(destShape[i]);
   }
 
@@ -1660,7 +1645,7 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   // Generate the xfer_write Op
   Operation *write =
       builder.create<vector::TransferWriteOp>(loc,
-                                              /*vector=*/vectorToStore,
+                                              /*vector=*/vecToStore,
                                               /*source=*/dest,
                                               /*indices=*/writeIndices,
                                               /*inBounds=*/inBoundsVal);
@@ -1669,46 +1654,25 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   if (useInBoundsInsteadOfMasking)
     return write;
 
-  assert(llvm::none_of(
-             destShape.drop_front(inputVecSizesForLeadingDims.size()),
-             [](int64_t size) { return size == ShapedType::kDynamic; }) &&
-         "Only dims aligned with inputVecSizesForLeadingDims may be dynamic");
-
-  // Check if masking is needed.
-  bool needMaskForWrite =
-      !llvm::equal(inputVecSizesForLeadingDims,
-                   destShape.take_front(destRank - vecToStoreRank +
-                                        inputVecSizesForLeadingDims.size()));
-
-  // If masking is needed, generate the mask and mask the operation.
-  if (needMaskForWrite) {
-    // Get the mask shape + type. Missing mask dimensions are taken from
-    // `vectorToStore`.
-    SmallVector<int64_t> writeMaskShape;
-    writeMaskShape.append(inputVecSizesForLeadingDims.begin(),
-                          inputVecSizesForLeadingDims.end());
-    if (vecToStoreRank >
-        static_cast<int64_t>(inputVecSizesForLeadingDims.size()))
-      writeMaskShape.append(vecToStoreShape.begin() +
-                                inputVecSizesForLeadingDims.size(),
-                            vecToStoreShape.end());
-    auto writeMaskType = VectorType::get(writeMaskShape, builder.getI1Type());
-
-    SmallVector<OpFoldResult> destSizes =
-        tensor::getMixedSizes(builder, loc, dest);
-    SmallVector<OpFoldResult> maskSizes(destSizes.end() - writeMaskShape.size(),
-                                        destSizes.end());
-
-    if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
-                                writeMaskShape))
-      return write;
-
-    Value maskForWrite = builder.createOrFold<vector::CreateMaskOp>(
-        loc, writeMaskType, maskSizes);
-    write = mlir::vector::maskOperation(builder, write, maskForWrite);
-  }
+  // Check if masking is needed. If not, exit.
+  if (llvm::equal(vecToStoreShape, destShape.take_back(vecToStoreRank)))
+    return write;
+
+  // Compute the mask and mask the write Op.
+  auto writeMaskType = VectorType::get(vecToStoreShape, builder.getI1Type());
+
+  SmallVector<OpFoldResult> destSizes =
+      tensor::getMixedSizes(builder, loc, dest);
+  SmallVector<OpFoldResult> maskSizes(destSizes.end() - vecToStoreRank,
+                                      destSizes.end());
+
+  if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
+                              vecToStoreShape))
+    return write;
 
-  return write;
+  Value maskForWrite =
+      builder.createOrFold<vector::CreateMaskOp>(loc, writeMaskType, maskSizes);
+  return mlir::vector::maskOperation(builder, write, maskForWrite);
 }
 
 /// Vectorize linalg::PackOp with (1) static inner_tiles (2) constant
@@ -1808,10 +1772,10 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, linalg::PackOp packOp,
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0],
       transposeOp.getResult().getType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, transposeOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, /*writeIndices=*/{},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, transposeOp.getResult(), dest,
+                               /*writeIndices=*/{},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -1949,7 +1913,6 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
       shapeCastOp.getResult().getType().getElementType());
   Operation *write = createWriteOrMaskedWrite(
       rewriter, loc, shapeCastOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/writeVectorSizes,
       /*writeIndices=*/{}, useInBoundsInsteadOfMasking);
   newResults.push_back(write->getResult(0));
   return success();
@@ -1982,10 +1945,9 @@ vectorizeAsTensorPadOp(RewriterBase &rewriter, tensor::PadOp padOp,
   // Create Xfer write Op
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0], padOp.getResultType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, maskedRead, dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, {},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, maskedRead, dest, {},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -3041,8 +3003,8 @@ vectorizeAsInsertSliceOp(RewriterBase &rewriter, tensor::InsertSliceOp sliceOp,
   // Create write
   auto writeIndices =
       getValueOrCreateConstantIndexOp(rewriter, loc, sliceOp.getMixedOffsets());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, read, sliceOp.getDest(), vecType.getShape(), writeIndices);
+  Operation *write = createWriteOrMaskedWrite(rewriter, loc, read,
+                                              sliceOp.getDest(), writeIndices);
 
   // 4. Finalize
   newResults.push_back(write->getResult(0));

llvmbot · 2025-05-27T09:12:21Z

@llvm/pr-subscribers-mlir-linalg

Author: Andrzej Warzyński (banach-space)

Changes

[[mlir][linalg] Refactor vectorization hooks to improve code reuse
[mlir][linalg] Simplify createWriteOrMaskedWrite (NFC)

Full diff: https://github.com/llvm/llvm-project/pull/141567.diff

1 Files Affected:

(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+40-78)

diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 0113ba86a5ae3..2abb2f0ea467c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1590,61 +1590,46 @@ static bool isMaskTriviallyFoldable(SmallVector<OpFoldResult> &maskSizes,
 /// Creates an optionally masked TransferWriteOp
 ///
 /// Generates the following operation:
-///   %res = vector.transfer_write %vectorToStore into %dest
+///   %res = vector.transfer_write %vecToStore into %dest
 ///
-/// If the leading N dimensions of the vector to store do not match
-/// `inputVecSizesForLeadingDims` (N = rank(inputVecSizesForLeadingDims)),
-/// masking is applied to ensure correctness:
+/// If shape(vecToStore) != shape(dest), masking is used to ensure correctness:
 ///
-///   %mask = vector.create_mask(%destShape) : %vectorToStoreShape
+///   %mask = vector.create_mask(%destShape) : %vecToStoreShape
 ///   %res = vector.mask %mask {
-///     vector.transfer_write %vectorToStore into %dest
+///     vector.transfer_write %vecToStore into %dest
 ///   }
 ///
-/// The mask shape is identical to `vectorToStore` (with the element type ==
+/// The mask shape is identical to `vecToStore` (with the element type ==
 /// i1), and the mask values are based on the shape of the `dest` tensor.
 ///
 /// If `useInBoundsInsteadOfMasking` is set to `true`, the `in_bounds` attribute
 /// is used instead of masking:
 ///
-///   %write = vector.transfer_write %vectorToStore into %dest
+///   %write = vector.transfer_write %vecToStore into %dest
 ///   in_bounds_flags = (...)
 ///   %res = vector.transfer_write %input into %dest
 ///       {in_bounds = in_bounds_flags}
 ///
-/// `writeIndices` specifies the offsets to use. If empty, all indices are set
-/// to 0.
-///
-/// NOTE: When N < rank(vectorToStore), the missing vector sizes are taken from
-/// `valueToStore`.
-/// TODO: `inputVecSizesForLeadingDims` should not be required - these sizes are
-/// already provided in `vectorToStore`.
+/// Finally, `writeIndices` specifies the offsets to use. If empty, all indices
+/// are set to 0.
 static Operation *
-createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
-                         Value dest,
-                         ArrayRef<int64_t> inputVecSizesForLeadingDims,
-                         SmallVector<Value> writeIndices = {},
+createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vecToStore,
+                         Value dest, SmallVector<Value> writeIndices = {},
                          bool useInBoundsInsteadOfMasking = false) {
 
   ShapedType destType = cast<ShapedType>(dest.getType());
   int64_t destRank = destType.getRank();
   auto destShape = destType.getShape();
 
-  VectorType vecToStoreType = cast<VectorType>(vectorToStore.getType());
+  VectorType vecToStoreType = cast<VectorType>(vecToStore.getType());
   int64_t vecToStoreRank = vecToStoreType.getRank();
   auto vecToStoreShape = vecToStoreType.getShape();
 
   // Compute the in_bounds attribute
   SmallVector<bool> inBoundsVal(vecToStoreRank, true);
   if (useInBoundsInsteadOfMasking) {
-    // In this case, assume that all the required vector sizes have been
-    // provided.
-    assert(inputVecSizesForLeadingDims.size() ==
-               static_cast<size_t>(vecToStoreType.getRank()) &&
-           "Insufficient number of input vector sizes!");
-    // Update the inBounds attribute.
     for (unsigned i = 0; i < destRank; i++)
-      inBoundsVal[i] = (destShape[i] == inputVecSizesForLeadingDims[i]) &&
+      inBoundsVal[i] = (destShape[i] == vecToStoreShape[i]) &&
                        !ShapedType::isDynamic(destShape[i]);
   }
 
@@ -1660,7 +1645,7 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   // Generate the xfer_write Op
   Operation *write =
       builder.create<vector::TransferWriteOp>(loc,
-                                              /*vector=*/vectorToStore,
+                                              /*vector=*/vecToStore,
                                               /*source=*/dest,
                                               /*indices=*/writeIndices,
                                               /*inBounds=*/inBoundsVal);
@@ -1669,46 +1654,25 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   if (useInBoundsInsteadOfMasking)
     return write;
 
-  assert(llvm::none_of(
-             destShape.drop_front(inputVecSizesForLeadingDims.size()),
-             [](int64_t size) { return size == ShapedType::kDynamic; }) &&
-         "Only dims aligned with inputVecSizesForLeadingDims may be dynamic");
-
-  // Check if masking is needed.
-  bool needMaskForWrite =
-      !llvm::equal(inputVecSizesForLeadingDims,
-                   destShape.take_front(destRank - vecToStoreRank +
-                                        inputVecSizesForLeadingDims.size()));
-
-  // If masking is needed, generate the mask and mask the operation.
-  if (needMaskForWrite) {
-    // Get the mask shape + type. Missing mask dimensions are taken from
-    // `vectorToStore`.
-    SmallVector<int64_t> writeMaskShape;
-    writeMaskShape.append(inputVecSizesForLeadingDims.begin(),
-                          inputVecSizesForLeadingDims.end());
-    if (vecToStoreRank >
-        static_cast<int64_t>(inputVecSizesForLeadingDims.size()))
-      writeMaskShape.append(vecToStoreShape.begin() +
-                                inputVecSizesForLeadingDims.size(),
-                            vecToStoreShape.end());
-    auto writeMaskType = VectorType::get(writeMaskShape, builder.getI1Type());
-
-    SmallVector<OpFoldResult> destSizes =
-        tensor::getMixedSizes(builder, loc, dest);
-    SmallVector<OpFoldResult> maskSizes(destSizes.end() - writeMaskShape.size(),
-                                        destSizes.end());
-
-    if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
-                                writeMaskShape))
-      return write;
-
-    Value maskForWrite = builder.createOrFold<vector::CreateMaskOp>(
-        loc, writeMaskType, maskSizes);
-    write = mlir::vector::maskOperation(builder, write, maskForWrite);
-  }
+  // Check if masking is needed. If not, exit.
+  if (llvm::equal(vecToStoreShape, destShape.take_back(vecToStoreRank)))
+    return write;
+
+  // Compute the mask and mask the write Op.
+  auto writeMaskType = VectorType::get(vecToStoreShape, builder.getI1Type());
+
+  SmallVector<OpFoldResult> destSizes =
+      tensor::getMixedSizes(builder, loc, dest);
+  SmallVector<OpFoldResult> maskSizes(destSizes.end() - vecToStoreRank,
+                                      destSizes.end());
+
+  if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
+                              vecToStoreShape))
+    return write;
 
-  return write;
+  Value maskForWrite =
+      builder.createOrFold<vector::CreateMaskOp>(loc, writeMaskType, maskSizes);
+  return mlir::vector::maskOperation(builder, write, maskForWrite);
 }
 
 /// Vectorize linalg::PackOp with (1) static inner_tiles (2) constant
@@ -1808,10 +1772,10 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, linalg::PackOp packOp,
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0],
       transposeOp.getResult().getType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, transposeOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, /*writeIndices=*/{},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, transposeOp.getResult(), dest,
+                               /*writeIndices=*/{},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -1949,7 +1913,6 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
       shapeCastOp.getResult().getType().getElementType());
   Operation *write = createWriteOrMaskedWrite(
       rewriter, loc, shapeCastOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/writeVectorSizes,
       /*writeIndices=*/{}, useInBoundsInsteadOfMasking);
   newResults.push_back(write->getResult(0));
   return success();
@@ -1982,10 +1945,9 @@ vectorizeAsTensorPadOp(RewriterBase &rewriter, tensor::PadOp padOp,
   // Create Xfer write Op
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0], padOp.getResultType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, maskedRead, dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, {},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, maskedRead, dest, {},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -3041,8 +3003,8 @@ vectorizeAsInsertSliceOp(RewriterBase &rewriter, tensor::InsertSliceOp sliceOp,
   // Create write
   auto writeIndices =
       getValueOrCreateConstantIndexOp(rewriter, loc, sliceOp.getMixedOffsets());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, read, sliceOp.getDest(), vecType.getShape(), writeIndices);
+  Operation *write = createWriteOrMaskedWrite(rewriter, loc, read,
+                                              sliceOp.getDest(), writeIndices);
 
   // 4. Finalize
   newResults.push_back(write->getResult(0));

llvmbot · 2025-05-27T09:12:21Z

@llvm/pr-subscribers-mlir

Author: Andrzej Warzyński (banach-space)

Changes

[[mlir][linalg] Refactor vectorization hooks to improve code reuse
[mlir][linalg] Simplify createWriteOrMaskedWrite (NFC)

Full diff: https://github.com/llvm/llvm-project/pull/141567.diff

1 Files Affected:

(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+40-78)

diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 0113ba86a5ae3..2abb2f0ea467c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1590,61 +1590,46 @@ static bool isMaskTriviallyFoldable(SmallVector<OpFoldResult> &maskSizes,
 /// Creates an optionally masked TransferWriteOp
 ///
 /// Generates the following operation:
-///   %res = vector.transfer_write %vectorToStore into %dest
+///   %res = vector.transfer_write %vecToStore into %dest
 ///
-/// If the leading N dimensions of the vector to store do not match
-/// `inputVecSizesForLeadingDims` (N = rank(inputVecSizesForLeadingDims)),
-/// masking is applied to ensure correctness:
+/// If shape(vecToStore) != shape(dest), masking is used to ensure correctness:
 ///
-///   %mask = vector.create_mask(%destShape) : %vectorToStoreShape
+///   %mask = vector.create_mask(%destShape) : %vecToStoreShape
 ///   %res = vector.mask %mask {
-///     vector.transfer_write %vectorToStore into %dest
+///     vector.transfer_write %vecToStore into %dest
 ///   }
 ///
-/// The mask shape is identical to `vectorToStore` (with the element type ==
+/// The mask shape is identical to `vecToStore` (with the element type ==
 /// i1), and the mask values are based on the shape of the `dest` tensor.
 ///
 /// If `useInBoundsInsteadOfMasking` is set to `true`, the `in_bounds` attribute
 /// is used instead of masking:
 ///
-///   %write = vector.transfer_write %vectorToStore into %dest
+///   %write = vector.transfer_write %vecToStore into %dest
 ///   in_bounds_flags = (...)
 ///   %res = vector.transfer_write %input into %dest
 ///       {in_bounds = in_bounds_flags}
 ///
-/// `writeIndices` specifies the offsets to use. If empty, all indices are set
-/// to 0.
-///
-/// NOTE: When N < rank(vectorToStore), the missing vector sizes are taken from
-/// `valueToStore`.
-/// TODO: `inputVecSizesForLeadingDims` should not be required - these sizes are
-/// already provided in `vectorToStore`.
+/// Finally, `writeIndices` specifies the offsets to use. If empty, all indices
+/// are set to 0.
 static Operation *
-createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
-                         Value dest,
-                         ArrayRef<int64_t> inputVecSizesForLeadingDims,
-                         SmallVector<Value> writeIndices = {},
+createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vecToStore,
+                         Value dest, SmallVector<Value> writeIndices = {},
                          bool useInBoundsInsteadOfMasking = false) {
 
   ShapedType destType = cast<ShapedType>(dest.getType());
   int64_t destRank = destType.getRank();
   auto destShape = destType.getShape();
 
-  VectorType vecToStoreType = cast<VectorType>(vectorToStore.getType());
+  VectorType vecToStoreType = cast<VectorType>(vecToStore.getType());
   int64_t vecToStoreRank = vecToStoreType.getRank();
   auto vecToStoreShape = vecToStoreType.getShape();
 
   // Compute the in_bounds attribute
   SmallVector<bool> inBoundsVal(vecToStoreRank, true);
   if (useInBoundsInsteadOfMasking) {
-    // In this case, assume that all the required vector sizes have been
-    // provided.
-    assert(inputVecSizesForLeadingDims.size() ==
-               static_cast<size_t>(vecToStoreType.getRank()) &&
-           "Insufficient number of input vector sizes!");
-    // Update the inBounds attribute.
     for (unsigned i = 0; i < destRank; i++)
-      inBoundsVal[i] = (destShape[i] == inputVecSizesForLeadingDims[i]) &&
+      inBoundsVal[i] = (destShape[i] == vecToStoreShape[i]) &&
                        !ShapedType::isDynamic(destShape[i]);
   }
 
@@ -1660,7 +1645,7 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   // Generate the xfer_write Op
   Operation *write =
       builder.create<vector::TransferWriteOp>(loc,
-                                              /*vector=*/vectorToStore,
+                                              /*vector=*/vecToStore,
                                               /*source=*/dest,
                                               /*indices=*/writeIndices,
                                               /*inBounds=*/inBoundsVal);
@@ -1669,46 +1654,25 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   if (useInBoundsInsteadOfMasking)
     return write;
 
-  assert(llvm::none_of(
-             destShape.drop_front(inputVecSizesForLeadingDims.size()),
-             [](int64_t size) { return size == ShapedType::kDynamic; }) &&
-         "Only dims aligned with inputVecSizesForLeadingDims may be dynamic");
-
-  // Check if masking is needed.
-  bool needMaskForWrite =
-      !llvm::equal(inputVecSizesForLeadingDims,
-                   destShape.take_front(destRank - vecToStoreRank +
-                                        inputVecSizesForLeadingDims.size()));
-
-  // If masking is needed, generate the mask and mask the operation.
-  if (needMaskForWrite) {
-    // Get the mask shape + type. Missing mask dimensions are taken from
-    // `vectorToStore`.
-    SmallVector<int64_t> writeMaskShape;
-    writeMaskShape.append(inputVecSizesForLeadingDims.begin(),
-                          inputVecSizesForLeadingDims.end());
-    if (vecToStoreRank >
-        static_cast<int64_t>(inputVecSizesForLeadingDims.size()))
-      writeMaskShape.append(vecToStoreShape.begin() +
-                                inputVecSizesForLeadingDims.size(),
-                            vecToStoreShape.end());
-    auto writeMaskType = VectorType::get(writeMaskShape, builder.getI1Type());
-
-    SmallVector<OpFoldResult> destSizes =
-        tensor::getMixedSizes(builder, loc, dest);
-    SmallVector<OpFoldResult> maskSizes(destSizes.end() - writeMaskShape.size(),
-                                        destSizes.end());
-
-    if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
-                                writeMaskShape))
-      return write;
-
-    Value maskForWrite = builder.createOrFold<vector::CreateMaskOp>(
-        loc, writeMaskType, maskSizes);
-    write = mlir::vector::maskOperation(builder, write, maskForWrite);
-  }
+  // Check if masking is needed. If not, exit.
+  if (llvm::equal(vecToStoreShape, destShape.take_back(vecToStoreRank)))
+    return write;
+
+  // Compute the mask and mask the write Op.
+  auto writeMaskType = VectorType::get(vecToStoreShape, builder.getI1Type());
+
+  SmallVector<OpFoldResult> destSizes =
+      tensor::getMixedSizes(builder, loc, dest);
+  SmallVector<OpFoldResult> maskSizes(destSizes.end() - vecToStoreRank,
+                                      destSizes.end());
+
+  if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
+                              vecToStoreShape))
+    return write;
 
-  return write;
+  Value maskForWrite =
+      builder.createOrFold<vector::CreateMaskOp>(loc, writeMaskType, maskSizes);
+  return mlir::vector::maskOperation(builder, write, maskForWrite);
 }
 
 /// Vectorize linalg::PackOp with (1) static inner_tiles (2) constant
@@ -1808,10 +1772,10 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, linalg::PackOp packOp,
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0],
       transposeOp.getResult().getType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, transposeOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, /*writeIndices=*/{},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, transposeOp.getResult(), dest,
+                               /*writeIndices=*/{},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -1949,7 +1913,6 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
       shapeCastOp.getResult().getType().getElementType());
   Operation *write = createWriteOrMaskedWrite(
       rewriter, loc, shapeCastOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/writeVectorSizes,
       /*writeIndices=*/{}, useInBoundsInsteadOfMasking);
   newResults.push_back(write->getResult(0));
   return success();
@@ -1982,10 +1945,9 @@ vectorizeAsTensorPadOp(RewriterBase &rewriter, tensor::PadOp padOp,
   // Create Xfer write Op
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0], padOp.getResultType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, maskedRead, dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, {},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, maskedRead, dest, {},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -3041,8 +3003,8 @@ vectorizeAsInsertSliceOp(RewriterBase &rewriter, tensor::InsertSliceOp sliceOp,
   // Create write
   auto writeIndices =
       getValueOrCreateConstantIndexOp(rewriter, loc, sliceOp.getMixedOffsets());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, read, sliceOp.getDest(), vecType.getShape(), writeIndices);
+  Operation *write = createWriteOrMaskedWrite(rewriter, loc, read,
+                                              sliceOp.getDest(), writeIndices);
 
   // 4. Finalize
   newResults.push_back(write->getResult(0));

llvmbot · 2025-05-27T09:12:22Z

@llvm/pr-subscribers-mlir-vector

Author: Andrzej Warzyński (banach-space)

Changes

[[mlir][linalg] Refactor vectorization hooks to improve code reuse
[mlir][linalg] Simplify createWriteOrMaskedWrite (NFC)

Full diff: https://github.com/llvm/llvm-project/pull/141567.diff

1 Files Affected:

(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+40-78)

diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 0113ba86a5ae3..2abb2f0ea467c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1590,61 +1590,46 @@ static bool isMaskTriviallyFoldable(SmallVector<OpFoldResult> &maskSizes,
 /// Creates an optionally masked TransferWriteOp
 ///
 /// Generates the following operation:
-///   %res = vector.transfer_write %vectorToStore into %dest
+///   %res = vector.transfer_write %vecToStore into %dest
 ///
-/// If the leading N dimensions of the vector to store do not match
-/// `inputVecSizesForLeadingDims` (N = rank(inputVecSizesForLeadingDims)),
-/// masking is applied to ensure correctness:
+/// If shape(vecToStore) != shape(dest), masking is used to ensure correctness:
 ///
-///   %mask = vector.create_mask(%destShape) : %vectorToStoreShape
+///   %mask = vector.create_mask(%destShape) : %vecToStoreShape
 ///   %res = vector.mask %mask {
-///     vector.transfer_write %vectorToStore into %dest
+///     vector.transfer_write %vecToStore into %dest
 ///   }
 ///
-/// The mask shape is identical to `vectorToStore` (with the element type ==
+/// The mask shape is identical to `vecToStore` (with the element type ==
 /// i1), and the mask values are based on the shape of the `dest` tensor.
 ///
 /// If `useInBoundsInsteadOfMasking` is set to `true`, the `in_bounds` attribute
 /// is used instead of masking:
 ///
-///   %write = vector.transfer_write %vectorToStore into %dest
+///   %write = vector.transfer_write %vecToStore into %dest
 ///   in_bounds_flags = (...)
 ///   %res = vector.transfer_write %input into %dest
 ///       {in_bounds = in_bounds_flags}
 ///
-/// `writeIndices` specifies the offsets to use. If empty, all indices are set
-/// to 0.
-///
-/// NOTE: When N < rank(vectorToStore), the missing vector sizes are taken from
-/// `valueToStore`.
-/// TODO: `inputVecSizesForLeadingDims` should not be required - these sizes are
-/// already provided in `vectorToStore`.
+/// Finally, `writeIndices` specifies the offsets to use. If empty, all indices
+/// are set to 0.
 static Operation *
-createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
-                         Value dest,
-                         ArrayRef<int64_t> inputVecSizesForLeadingDims,
-                         SmallVector<Value> writeIndices = {},
+createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vecToStore,
+                         Value dest, SmallVector<Value> writeIndices = {},
                          bool useInBoundsInsteadOfMasking = false) {
 
   ShapedType destType = cast<ShapedType>(dest.getType());
   int64_t destRank = destType.getRank();
   auto destShape = destType.getShape();
 
-  VectorType vecToStoreType = cast<VectorType>(vectorToStore.getType());
+  VectorType vecToStoreType = cast<VectorType>(vecToStore.getType());
   int64_t vecToStoreRank = vecToStoreType.getRank();
   auto vecToStoreShape = vecToStoreType.getShape();
 
   // Compute the in_bounds attribute
   SmallVector<bool> inBoundsVal(vecToStoreRank, true);
   if (useInBoundsInsteadOfMasking) {
-    // In this case, assume that all the required vector sizes have been
-    // provided.
-    assert(inputVecSizesForLeadingDims.size() ==
-               static_cast<size_t>(vecToStoreType.getRank()) &&
-           "Insufficient number of input vector sizes!");
-    // Update the inBounds attribute.
     for (unsigned i = 0; i < destRank; i++)
-      inBoundsVal[i] = (destShape[i] == inputVecSizesForLeadingDims[i]) &&
+      inBoundsVal[i] = (destShape[i] == vecToStoreShape[i]) &&
                        !ShapedType::isDynamic(destShape[i]);
   }
 
@@ -1660,7 +1645,7 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   // Generate the xfer_write Op
   Operation *write =
       builder.create<vector::TransferWriteOp>(loc,
-                                              /*vector=*/vectorToStore,
+                                              /*vector=*/vecToStore,
                                               /*source=*/dest,
                                               /*indices=*/writeIndices,
                                               /*inBounds=*/inBoundsVal);
@@ -1669,46 +1654,25 @@ createWriteOrMaskedWrite(OpBuilder &builder, Location loc, Value vectorToStore,
   if (useInBoundsInsteadOfMasking)
     return write;
 
-  assert(llvm::none_of(
-             destShape.drop_front(inputVecSizesForLeadingDims.size()),
-             [](int64_t size) { return size == ShapedType::kDynamic; }) &&
-         "Only dims aligned with inputVecSizesForLeadingDims may be dynamic");
-
-  // Check if masking is needed.
-  bool needMaskForWrite =
-      !llvm::equal(inputVecSizesForLeadingDims,
-                   destShape.take_front(destRank - vecToStoreRank +
-                                        inputVecSizesForLeadingDims.size()));
-
-  // If masking is needed, generate the mask and mask the operation.
-  if (needMaskForWrite) {
-    // Get the mask shape + type. Missing mask dimensions are taken from
-    // `vectorToStore`.
-    SmallVector<int64_t> writeMaskShape;
-    writeMaskShape.append(inputVecSizesForLeadingDims.begin(),
-                          inputVecSizesForLeadingDims.end());
-    if (vecToStoreRank >
-        static_cast<int64_t>(inputVecSizesForLeadingDims.size()))
-      writeMaskShape.append(vecToStoreShape.begin() +
-                                inputVecSizesForLeadingDims.size(),
-                            vecToStoreShape.end());
-    auto writeMaskType = VectorType::get(writeMaskShape, builder.getI1Type());
-
-    SmallVector<OpFoldResult> destSizes =
-        tensor::getMixedSizes(builder, loc, dest);
-    SmallVector<OpFoldResult> maskSizes(destSizes.end() - writeMaskShape.size(),
-                                        destSizes.end());
-
-    if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
-                                writeMaskShape))
-      return write;
-
-    Value maskForWrite = builder.createOrFold<vector::CreateMaskOp>(
-        loc, writeMaskType, maskSizes);
-    write = mlir::vector::maskOperation(builder, write, maskForWrite);
-  }
+  // Check if masking is needed. If not, exit.
+  if (llvm::equal(vecToStoreShape, destShape.take_back(vecToStoreRank)))
+    return write;
+
+  // Compute the mask and mask the write Op.
+  auto writeMaskType = VectorType::get(vecToStoreShape, builder.getI1Type());
+
+  SmallVector<OpFoldResult> destSizes =
+      tensor::getMixedSizes(builder, loc, dest);
+  SmallVector<OpFoldResult> maskSizes(destSizes.end() - vecToStoreRank,
+                                      destSizes.end());
+
+  if (isMaskTriviallyFoldable(maskSizes, writeIndices, destShape,
+                              vecToStoreShape))
+    return write;
 
-  return write;
+  Value maskForWrite =
+      builder.createOrFold<vector::CreateMaskOp>(loc, writeMaskType, maskSizes);
+  return mlir::vector::maskOperation(builder, write, maskForWrite);
 }
 
 /// Vectorize linalg::PackOp with (1) static inner_tiles (2) constant
@@ -1808,10 +1772,10 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, linalg::PackOp packOp,
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0],
       transposeOp.getResult().getType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, transposeOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, /*writeIndices=*/{},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, transposeOp.getResult(), dest,
+                               /*writeIndices=*/{},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -1949,7 +1913,6 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
       shapeCastOp.getResult().getType().getElementType());
   Operation *write = createWriteOrMaskedWrite(
       rewriter, loc, shapeCastOp.getResult(), dest,
-      /*inputVecSizesForLeadingDims=*/writeVectorSizes,
       /*writeIndices=*/{}, useInBoundsInsteadOfMasking);
   newResults.push_back(write->getResult(0));
   return success();
@@ -1982,10 +1945,9 @@ vectorizeAsTensorPadOp(RewriterBase &rewriter, tensor::PadOp padOp,
   // Create Xfer write Op
   Value dest = rewriter.create<tensor::EmptyOp>(
       loc, reifiedReturnShapes[0], padOp.getResultType().getElementType());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, maskedRead, dest,
-      /*inputVecSizesForLeadingDims=*/inputVectorSizes, {},
-      /*useInBoundsInsteadOfMasking=*/false);
+  Operation *write =
+      createWriteOrMaskedWrite(rewriter, loc, maskedRead, dest, {},
+                               /*useInBoundsInsteadOfMasking=*/false);
   newResults.push_back(write->getResult(0));
   return success();
 }
@@ -3041,8 +3003,8 @@ vectorizeAsInsertSliceOp(RewriterBase &rewriter, tensor::InsertSliceOp sliceOp,
   // Create write
   auto writeIndices =
       getValueOrCreateConstantIndexOp(rewriter, loc, sliceOp.getMixedOffsets());
-  Operation *write = createWriteOrMaskedWrite(
-      rewriter, loc, read, sliceOp.getDest(), vecType.getShape(), writeIndices);
+  Operation *write = createWriteOrMaskedWrite(rewriter, loc, read,
+                                              sliceOp.getDest(), writeIndices);
 
   // 4. Finalize
   newResults.push_back(write->getResult(0));

This patch refactors two vectorization hooks in Vectorization.cpp: * `createWriteOrMaskedWrite` gains a new parameter for write indices, aligning it with its counterpart `createReadOrMaskedRead`. * `vectorizeAsInsertSliceOp` is updated to reuse both of the above hooks, rather than re-implementing similar logic. CONTEXT ------- This is effectively a refactoring of the logic for vectorizing `tensor.insert_slice`. Recent updates added masking support: * #122927 * #123031 At the time, reuse of the shared `create*` hooks wasn't feasible due to missing parameters and overly rigid assumptions. This patch resolves that and moves us closer to a more maintainable structure. CHANGES IN `vectorizeAsInsertSliceOp` ------------------------------------- * Introduces a clear distinction between the destination tensor and the vector to store, via named variables like `destType`/`vecToStoreType`, `destShape`/`vecToStoreShape`, etc. * Ensures the correct rank and shape are used for attributes like in_bounds. For example, the size of the in_bounds array now matches the source vector rank, not the tensor rank. * Drops the assumption that `vecToStoreRank == destRank` — this doesn't hold in many real examples. * Deduces mask dimensions from `vecToStoreShape` (vector) instead of `destShape` (tensor). (Eventually we should not require `inputVecSizesForLeadingDims` at all — mask shape should be inferred.) NEW HELPER: `isMaskTriviallyFoldable` ------------------------------------- Adds a utility to detect when masking is unnecessary. This avoids inserting redundant masks and reduces the burden on canonicalization to clean them up later. Example where masking is provably unnecessary: ```mlir %2 = vector.mask %1 { vector.transfer_write %0, %arg1[%c0, %c0, %c0, %c0, %c0, %c0] {in_bounds = [true, true, true]} : vector<1x2x3xf32>, tensor<9x8x7x1x2x3xf32> } : vector<1x2x3xi1> -> tensor<9x8x7x1x2x3xf32> ``` Also, without this hook, tests are more complicated and require more matching. TEST CHANGES ----------- This patch primarily affects vectorization of: * `tensor.insert_slice`, now refactored to use shared hooks. `tensor.pad` vectorization patterns, which internally use `tensor.insert_slice`, are also _effectively_ updated. Note, only pad-with-patterns.mlir is affected. Most test updates involve the insertion of masks that were previously missing — this reflects a correctness fix, not a regression. In all cases, the added masks are indeed required. You’ll also notice more repeated constants (`arith.constant 0 : index`), due to increased use of helper hooks. This will be cleaned up separately via a constant cache (see #138265 for discussion). NOTE FOR REVIEWERS ------------------ This is a fairly substantial rewrite. You may find it easier to review `createWriteOrMaskedWrite` as a new method rather than diffing line-by-line. TODOs (future PRs) ------------------ Further alignment of `createWriteOrMaskedWrite` and `createReadOrMaskedRead`: * Move `createWriteOrMaskedWrite` next to `createReadOrMaskedRead` (in VectorUtils.cpp) * Make `createReadOrMaskedRead` leverage `isMaskTriviallyFoldable`. * Extend `isMaskTriviallyFoldable` with value-bounds-analysis. See the updated test in transform-vector.mlir for an example that would benefit from this. (* This method will eventually be moved out of Vectorization.cpp, which isn't the right long-term home for it.)

This patch removes `inputVecSizesForLeadingDims` from the parameter list of `createWriteOrMaskedWrite`. That argument is unnecessary — vector sizes can be obtained from the `vecToStore` parameter. Since this doesn't change behavior or test results, it's marked as NFC. Additional cleanups: * Renamed `vectorToStore` to `vecToStore` for consistency and brevity. * Rewrote a conditional at the end of the function to use early exit, improving readability: ```cpp // BEFORE: if (maskingRequried) { Value maskForWrite = ...; write = maskOperation(write, maskForWrite); } return write; // AFTER if (!maskingRequried) return write; Value maskFroWrite = ...; return vector::maskOperation(builder, write, maskForWrite); ``` This change addresses a TODO from #141244.

rengolin

Nice clean up, thanks!

banach-space requested review from dcaballe, nicolasvasilache, Groverkss and hanhanW as code owners May 27, 2025 09:11

llvmbot added mlir:llvm mlir:linalg mlir:vectorops mlir mlir:vector labels May 27, 2025

banach-space changed the base branch from main to users/banach-space/vector/update_vectorize_insert_slice May 27, 2025 09:12

banach-space changed the title ~~users/banach space/vector/update create write~~ [mlir][linalg] Simplify createWriteOrMaskedWrite (NFC) May 27, 2025

banach-space requested a review from Max191 May 27, 2025 09:13

Max191 approved these changes May 29, 2025

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banach-space added 2 commits May 29, 2025 17:30

banach-space force-pushed the users/banach-space/vector/update_create_write branch from 53d435d to ca24a26 Compare May 29, 2025 16:31

banach-space requested review from JDevlieghere, cyndyishida, rupprecht, keith, aaronmondal, aaupov, maksfb, rafaelauler, ayermolo and yota9 as code owners May 29, 2025 16:31

ldionne removed request for a team, maksfb, rafaelauler, Superty, nicolasvasilache, lanza, yota9, dcaballe, cyndyishida, aaronmondal, PeimingLiu, aartbik, ayermolo, Groverkss and yinying-lisa-li May 29, 2025 17:07

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