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[MLIR][Vector] Add unroll pattern for vector.create_mask #169119

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Merged
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Dec 3, 2025
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[MLIR][Vector] Add unroll pattern for vector.create_mask #169119

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8642537
Add unroll pattern for vector.create_mask
nbpatel Nov 14, 2025
c31276a
Merge branch 'main' into vector-mask-unroll
nbpatel Nov 21, 2025
f2af423
Fix format
nbpatel Nov 21, 2025
42522d1
Address Feedback
nbpatel Nov 27, 2025
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4 changes: 3 additions & 1 deletion mlir/include/mlir/Dialect/Vector/IR/VectorOps.td
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Expand Up @@ -2605,7 +2605,9 @@ def Vector_ConstantMaskOp :
}

def Vector_CreateMaskOp :
Vector_Op<"create_mask", [Pure]>,
Vector_Op<"create_mask", [Pure,
DeclareOpInterfaceMethods<VectorUnrollOpInterface>
]>,
Arguments<(ins Variadic<Index>:$operands)>,
Results<(outs VectorOfAnyRankOf<[I1]>)> {
let summary = "creates a vector mask";
Expand Down
95 changes: 93 additions & 2 deletions mlir/lib/Dialect/Vector/Transforms/VectorUnroll.cpp
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Expand Up @@ -1003,6 +1003,96 @@ struct UnrollFromElements : OpRewritePattern<vector::FromElementsOp> {
vector::UnrollVectorOptions options;
};

/// This pattern unrolls `vector.create_mask` operations into smaller mask
/// operations based on the target unroll shape. Each unrolled slice computes
/// its local mask size in each dimension (d) as:
/// min(max(originalMaskSize[d] - offset[d], 0), unrolledDimSize[d]).
/// Example:
/// Given a create_mask operation:
/// %0 = vector.create_mask %c6, %c10 : vector<8x16xi1> // mask first 6x10
/// elements
///
/// and a target unroll shape of <4x8>, the pattern produces:
///
/// %false = arith.constant dense<false> : vector<8x16xi1>
///
/// Slice [0,0]:
/// mask size = min(max(6-0, 0), 4) x min(max(10-0, 0), 8) = 4x8
/// %mask00 = vector.create_mask %c4, %c8 : vector<4x8xi1>
/// %r0 = vector.insert_strided_slice %mask00, %false [0, 0], [1, 1]
/// : vector<4x8xi1> into vector<8x16xi1>
/// Slice [0,8]:
/// mask size = min(max(6-0, 0), 4) x min(max(10-8, 0), 8) = 4x2
/// %mask01 = vector.create_mask %c4, %c2 : vector<4x8xi1>
/// %r1 = vector.insert_strided_slice %mask01, %r0 [0, 8], [1, 1]
/// : vector<4x8xi1> into vector<8x16xi1>
/// Slice [4,0]:
/// mask size = min(max(6-4, 0), 4) x min(max(10-0, 0), 8) = 2x8
/// %mask10 = vector.create_mask %c2, %c8 : vector<4x8xi1>
/// %r2 = vector.insert_strided_slice %mask10, %r1 [4, 0], [1, 1]
/// : vector<4x8xi1> into vector<8x16xi1>
/// Slice [4,8]:
/// mask size = min(max(6-4, 0), 4) x min(max(10-8, 0), 8) = 2x2
/// %mask11 = vector.create_mask %c2, %c2 : vector<4x8xi1>
/// %result = vector.insert_strided_slice %mask11, %r2 [4, 8], [1, 1]
/// : vector<4x8xi1> into vector<8x16xi1>
struct UnrollCreateMaskPattern : public OpRewritePattern<vector::CreateMaskOp> {
UnrollCreateMaskPattern(MLIRContext *context,
const vector::UnrollVectorOptions &options,
PatternBenefit benefit = 1)
: OpRewritePattern<vector::CreateMaskOp>(context, benefit),
options(options) {}

LogicalResult matchAndRewrite(vector::CreateMaskOp createMaskOp,
PatternRewriter &rewriter) const override {
auto targetShape = getTargetShape(options, createMaskOp);
if (!targetShape)
return failure();

VectorType resultType = createMaskOp.getVectorType();
SmallVector<int64_t> originalSize = *createMaskOp.getShapeForUnroll();
Location loc = createMaskOp.getLoc();

Value result = arith::ConstantOp::create(rewriter, loc, resultType,
rewriter.getZeroAttr(resultType));
auto targetVectorType = VectorType::get(*targetShape, rewriter.getI1Type());
SmallVector<int64_t> strides(targetShape->size(), 1);

// In each dimension (d), each unrolled vector computes its mask size as:
// min(max(originalMaskOperands[d] - offset[d], 0), unrolledDimSize[d]).
for (SmallVector<int64_t> offsets :
StaticTileOffsetRange(originalSize, *targetShape)) {
SmallVector<Value> unrolledOperands;

for (auto [i, originalMaskOperand] :
llvm::enumerate(createMaskOp.getOperands())) {
Value offsetVal =
arith::ConstantIndexOp::create(rewriter, loc, offsets[i]);
Value adjustedMaskSize = arith::SubIOp::create(
rewriter, loc, originalMaskOperand, offsetVal);
Value zero = arith::ConstantIndexOp::create(rewriter, loc, 0);
Value unrolledDimSize =
arith::ConstantIndexOp::create(rewriter, loc, (*targetShape)[i]);
Value nonNegative =
arith::MaxSIOp::create(rewriter, loc, adjustedMaskSize, zero);
Value unrolledOperand =
arith::MinSIOp::create(rewriter, loc, nonNegative, unrolledDimSize);
unrolledOperands.push_back(unrolledOperand);
}

auto unrolledMask = vector::CreateMaskOp::create(
rewriter, loc, targetVectorType, unrolledOperands);
result = rewriter.createOrFold<vector::InsertStridedSliceOp>(
loc, unrolledMask, result, offsets, strides);
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@dcaballe dcaballe Nov 27, 2025

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We'd need to use createOrFold for many if not all of the previous operations. For the example that you provided in the documentation, I'd expect everything to fold into a single constant mask op. We should make sure that such a folding happens in the tests.

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@nbpatel nbpatel Dec 1, 2025

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Thanks for pointing that out..changed it

}
rewriter.replaceOp(createMaskOp, result);
return success();
}

private:
vector::UnrollVectorOptions options;
};

/// Checks whether extractShape is a contiguous slice of shape.
/// For extractShape to be contiguous in shape:
/// 1) All but the leading dimension of extractShape and shape must match
Expand Down Expand Up @@ -1202,8 +1292,9 @@ void mlir::vector::populateVectorUnrollPatterns(
UnrollReductionPattern, UnrollMultiReductionPattern,
UnrollTransposePattern, UnrollGatherPattern, UnrollLoadPattern,
UnrollStorePattern, UnrollBroadcastPattern, UnrollFromElements,
UnrollToElements, UnrollStepPattern, UnrollShapeCastPattern>(
patterns.getContext(), options, benefit);
UnrollToElements, UnrollStepPattern, UnrollShapeCastPattern,
UnrollCreateMaskPattern>(patterns.getContext(), options,
benefit);
}

void mlir::vector::populateVectorToElementsUnrollPatterns(
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39 changes: 39 additions & 0 deletions mlir/test/Dialect/Vector/vector-unroll-options.mlir
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Expand Up @@ -497,6 +497,45 @@ func.func @elementwise_4D_to_2D(%v1: vector<2x2x2x2xf32>, %v2: vector<2x2x2x2xf3
// CHECK-NOT: arith.addf
// CHECK: return

func.func @vector_create_mask(%size1: index, %size2: index) -> vector<16x16xi1> {
%0 = vector.create_mask %size1, %size2 : vector<16x16xi1>
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@dcaballe dcaballe Nov 27, 2025

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please, add a test with (full or partial?) constant indices to make sure that folding happens

return %0 : vector<16x16xi1>
}

// CHECK-LABEL: func @vector_create_mask
// CHECK-SAME: (%[[ARG0:.*]]: index, %[[ARG1:.*]]: index) -> vector<16x16xi1>
// CHECK: %[[CST:.*]] = arith.constant dense<false> : vector<16x16xi1>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[C8:.*]] = arith.constant 8 : index
// CHECK: %[[MAX0:.*]] = arith.maxsi %[[ARG0]], %[[C0]] : index
// CHECK: %[[MIN0:.*]] = arith.minsi %[[MAX0]], %[[C8]] : index
// CHECK: %[[MAX1:.*]] = arith.maxsi %[[ARG1]], %[[C0]] : index
// CHECK: %[[MIN1:.*]] = arith.minsi %[[MAX1]], %[[C8]] : index
// CHECK: %[[MASK00:.*]] = vector.create_mask %[[MIN0]], %[[MIN1]] : vector<8x8xi1>
// CHECK: %[[INS00:.*]] = vector.insert_strided_slice %[[MASK00]], %[[CST]] {offsets = [0, 0], strides = [1, 1]} : vector<8x8xi1> into vector<16x16xi1>
// CHECK: %[[MAX0_2:.*]] = arith.maxsi %[[ARG0]], %[[C0]] : index
// CHECK: %[[MIN0_2:.*]] = arith.minsi %[[MAX0_2]], %[[C8]] : index
// CHECK: %[[SUB1:.*]] = arith.subi %[[ARG1]], %[[C8]] : index
// CHECK: %[[MAX1_2:.*]] = arith.maxsi %[[SUB1]], %[[C0]] : index
// CHECK: %[[MIN1_2:.*]] = arith.minsi %[[MAX1_2]], %[[C8]] : index
// CHECK: %[[MASK01:.*]] = vector.create_mask %[[MIN0_2]], %[[MIN1_2]] : vector<8x8xi1>
// CHECK: %[[INS01:.*]] = vector.insert_strided_slice %[[MASK01]], %[[INS00]] {offsets = [0, 8], strides = [1, 1]} : vector<8x8xi1> into vector<16x16xi1>
// CHECK: %[[SUB0:.*]] = arith.subi %[[ARG0]], %[[C8]] : index
// CHECK: %[[MAX0_3:.*]] = arith.maxsi %[[SUB0]], %[[C0]] : index
// CHECK: %[[MIN0_3:.*]] = arith.minsi %[[MAX0_3]], %[[C8]] : index
// CHECK: %[[MAX1_3:.*]] = arith.maxsi %[[ARG1]], %[[C0]] : index
// CHECK: %[[MIN1_3:.*]] = arith.minsi %[[MAX1_3]], %[[C8]] : index
// CHECK: %[[MASK10:.*]] = vector.create_mask %[[MIN0_3]], %[[MIN1_3]] : vector<8x8xi1>
// CHECK: %[[INS10:.*]] = vector.insert_strided_slice %[[MASK10]], %[[INS01]] {offsets = [8, 0], strides = [1, 1]} : vector<8x8xi1> into vector<16x16xi1>
// CHECK: %[[SUB0_2:.*]] = arith.subi %[[ARG0]], %[[C8]] : index
// CHECK: %[[MAX0_4:.*]] = arith.maxsi %[[SUB0_2]], %[[C0]] : index
// CHECK: %[[MIN0_4:.*]] = arith.minsi %[[MAX0_4]], %[[C8]] : index
// CHECK: %[[SUB1_2:.*]] = arith.subi %[[ARG1]], %[[C8]] : index
// CHECK: %[[MAX1_4:.*]] = arith.maxsi %[[SUB1_2]], %[[C0]] : index
// CHECK: %[[MIN1_4:.*]] = arith.minsi %[[MAX1_4]], %[[C8]] : index
// CHECK: %[[MASK11:.*]] = vector.create_mask %[[MIN0_4]], %[[MIN1_4]] : vector<8x8xi1>
// CHECK: %[[INS11:.*]] = vector.insert_strided_slice %[[MASK11]], %[[INS10]] {offsets = [8, 8], strides = [1, 1]} : vector<8x8xi1> into vector<16x16xi1>
// CHECK: return %[[INS11]] : vector<16x16xi1>

func.func @shape_cast_1D(%v: vector<16xf32>) -> vector<2x2x4xf32> {
%0 = vector.shape_cast %v : vector<16xf32> to vector<2x2x4xf32>
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6 changes: 6 additions & 0 deletions mlir/test/lib/Dialect/Vector/TestVectorTransforms.cpp
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Expand Up @@ -178,6 +178,12 @@ struct TestVectorUnrollingPatterns
.setFilterConstraint([](Operation *op) {
return success(isa<vector::StepOp>(op));
}));
populateVectorUnrollPatterns(
patterns, UnrollVectorOptions()
.setNativeShape(ArrayRef<int64_t>{8, 8})
.setFilterConstraint([](Operation *op) {
return success(isa<vector::CreateMaskOp>(op));
}));
populateVectorUnrollPatterns(
patterns,
UnrollVectorOptions()
Expand Down