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[mlir][linalg] Add folder for broadcast(broadcast) -> broadcast #150825

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Merged
merged 2 commits into from
Jul 29, 2025
Merged

[mlir][linalg] Add folder for broadcast(broadcast) -> broadcast #150825

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[mlir][linalg] Add folder for broadcast(broadcast) -> broadcast
CoTinker Jul 27, 2025
ba7f3ea
rename pattern
CoTinker Jul 28, 2025
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32 changes: 31 additions & 1 deletion mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2292,9 +2292,39 @@ Speculation::Speculatability BroadcastOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}

/// Fold back-to-back broadcasts together.
struct FoldBroadcasts : OpRewritePattern<linalg::BroadcastOp> {
using OpRewritePattern<linalg::BroadcastOp>::OpRewritePattern;

LogicalResult matchAndRewrite(linalg::BroadcastOp broadcastOp,
PatternRewriter &rewriter) const override {
auto defBroadcastOp = broadcastOp.getInput().getDefiningOp<BroadcastOp>();
if (!defBroadcastOp)
return failure();
ArrayRef<int64_t> defDimensions = defBroadcastOp.getDimensions();
ArrayRef<int64_t> dimensions = broadcastOp.getDimensions();
SmallVector<int64_t> foldedDims(dimensions);
Value init = broadcastOp.getInit();
int64_t initRank = cast<ShapedType>(init.getType()).getRank();
// Mapping from input dims to init dims.
SmallVector<int64_t> dimMap;
for (auto dim : llvm::seq<int64_t>(0, initRank)) {
if (!llvm::is_contained(dimensions, dim))
dimMap.push_back(dim);
}
for (auto dim : defDimensions)
foldedDims.push_back(dimMap[dim]);

llvm::sort(foldedDims);
rewriter.replaceOpWithNewOp<BroadcastOp>(
broadcastOp, defBroadcastOp.getInput(), init, foldedDims);
return success();
}
};

void BroadcastOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<EraseIdentityLinalgOp<BroadcastOp>>(context);
results.add<EraseIdentityLinalgOp<BroadcastOp>, FoldBroadcasts>(context);
}

//===----------------------------------------------------------------------===//
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46 changes: 46 additions & 0 deletions mlir/test/Dialect/Linalg/canonicalize.mlir
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Expand Up @@ -1176,6 +1176,52 @@ func.func @broadcast_same_shape(%input: tensor<2x3xf32>, %init: tensor<2x3xf32>)

// -----

// CHECK-LABEL: @broadcast_broadcast_fold
// CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor<2xf32>
// CHECK-SAME: %[[INIT1:[a-zA-Z0-9]+]]: tensor<2x3xf32>
// CHECK-SAME: %[[INIT2:[a-zA-Z0-9]+]]: tensor<2x3x4xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.broadcast ins(%[[INPUT]] : tensor<2xf32>) outs(%[[INIT2]] : tensor<2x3x4xf32>) dimensions = [1, 2]
// CHECK-NOT: linalg.broadcast
// CHECK: return %[[BROADCAST]] : tensor<2x3x4xf32>
func.func @broadcast_broadcast_fold(%input: tensor<2xf32>,
%init1: tensor<2x3xf32>,
%init2: tensor<2x3x4xf32>) -> tensor<2x3x4xf32> {
%broadcast1 = linalg.broadcast
ins(%input: tensor<2xf32>)
outs(%init1: tensor<2x3xf32>)
dimensions = [1]
%broadcast2 = linalg.broadcast
ins(%broadcast1: tensor<2x3xf32>)
outs(%init2: tensor<2x3x4xf32>)
dimensions = [2]
func.return %broadcast2 : tensor<2x3x4xf32>
}

// -----

// CHECK-LABEL: @broadcast_broadcast_fold
// CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor<2xf32>
// CHECK-SAME: %[[INIT1:[a-zA-Z0-9]+]]: tensor<2x4xf32>
// CHECK-SAME: %[[INIT2:[a-zA-Z0-9]+]]: tensor<2x3x4xf32>
// CHECK: %[[BROADCAST:.+]] = linalg.broadcast ins(%[[INPUT]] : tensor<2xf32>) outs(%[[INIT2]] : tensor<2x3x4xf32>) dimensions = [1, 2]
// CHECK-NOT: linalg.broadcast
// CHECK: return %[[BROADCAST]] : tensor<2x3x4xf32>
func.func @broadcast_broadcast_fold(%input: tensor<2xf32>,
%init1: tensor<2x4xf32>,
%init2: tensor<2x3x4xf32>) -> tensor<2x3x4xf32> {
%broadcast1 = linalg.broadcast
ins(%input: tensor<2xf32>)
outs(%init1: tensor<2x4xf32>)
dimensions = [1]
%broadcast2 = linalg.broadcast
ins(%broadcast1: tensor<2x4xf32>)
outs(%init2: tensor<2x3x4xf32>)
dimensions = [1]
func.return %broadcast2 : tensor<2x3x4xf32>
}

// -----

func.func @transpose_1d(%input: tensor<16xf32>,
%init: tensor<16xf32>) -> tensor<16xf32> {
%transpose = linalg.transpose
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