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[mlir][Linalg] Fix linalg.generic iteration domain collapse for dynamic dims #118208

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merged 3 commits into from
Feb 19, 2025
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[mlir][Linalg] Fix linalg.generic iteration domain collapse for dynamic dims #118208

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593b5b8
[mlir][Linalg] Fix linalg.generic iteration domain collapse for dynam…
Groverkss Dec 1, 2024
533294d
Address comments
Groverkss Feb 9, 2025
5e764ec
Update mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
Groverkss Feb 19, 2025
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31 changes: 19 additions & 12 deletions mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1548,10 +1548,9 @@ static Value getCollapsedOpOperand(Location loc, LinalgOp op,

/// Modify the `linalg.index` operations in the original generic op, to its
/// value in the collapsed operation.
void generateCollapsedIndexingRegion(Location loc, Block *block,
const CollapsingInfo &collapsingInfo,
ValueRange loopRange,
RewriterBase &rewriter) {
static void generateCollapsedIndexingRegion(
Location loc, Block *block, const CollapsingInfo &collapsingInfo,
ArrayRef<OpFoldResult> loopRange, RewriterBase &rewriter) {
OpBuilder::InsertionGuard g(rewriter);
rewriter.setInsertionPointToStart(block);

Expand All @@ -1572,10 +1571,12 @@ void generateCollapsedIndexingRegion(Location loc, Block *block,
Value newIndexVal =
rewriter.create<linalg::IndexOp>(loc, foldedDims.index());
for (auto dim : llvm::reverse(foldedDimsRef.drop_front())) {
Value loopDim =
getValueOrCreateConstantIndexOp(rewriter, loc, loopRange[dim]);
indexReplacementVals[dim] =
rewriter.create<arith::RemSIOp>(loc, newIndexVal, loopRange[dim]);
rewriter.createOrFold<arith::RemSIOp>(loc, newIndexVal, loopDim);
newIndexVal =
rewriter.create<arith::DivSIOp>(loc, newIndexVal, loopRange[dim]);
rewriter.createOrFold<arith::DivSIOp>(loc, newIndexVal, loopDim);
}
indexReplacementVals[foldedDims.value().front()] = newIndexVal;
}
Expand Down Expand Up @@ -1722,14 +1723,13 @@ FailureOr<CollapseResult> mlir::linalg::collapseOpIterationDims(
LinalgOp collapsedOp = createCollapsedOp(op, collapsingInfo, rewriter);

Location loc = op->getLoc();
SmallVector<OpFoldResult> loopBound =
llvm::map_to_vector(loopRanges, [](Range range) { return range.size; });

if (collapsedOp.hasIndexSemantics()) {
// Collect the loop range of the generic op.
OpBuilder::InsertionGuard g(rewriter);
rewriter.setInsertionPoint(collapsedOp);
SmallVector<Value> loopBound =
llvm::map_to_vector(loopRanges, [&](Range range) {
return getValueOrCreateConstantIndexOp(rewriter, loc, range.size);
});
generateCollapsedIndexingRegion(loc, &collapsedOp->getRegion(0).front(),
collapsingInfo, loopBound, rewriter);
}
Expand All @@ -1747,15 +1747,22 @@ FailureOr<CollapseResult> mlir::linalg::collapseOpIterationDims(
op.getIndexingMapMatchingResult(originalResult.value());
SmallVector<ReassociationIndices> reassociation =
getOperandReassociation(indexingMap, collapsingInfo);
assert(
indexingMap.isProjectedPermutation() &&
"Expected indexing map to be a projected permutation for collapsing");
SmallVector<OpFoldResult> resultShape =
applyPermutationMap(indexingMap, ArrayRef(loopBound));
Value result;
if (isa<MemRefType>(collapsedOpResult.getType())) {
MemRefType expandShapeResultType = MemRefType::get(
originalResultType.getShape(), originalResultType.getElementType());
result = rewriter.create<memref::ExpandShapeOp>(
loc, expandShapeResultType, collapsedOpResult, reassociation);
loc, expandShapeResultType, collapsedOpResult, reassociation,
resultShape);
} else {
result = rewriter.create<tensor::ExpandShapeOp>(
loc, originalResultType, collapsedOpResult, reassociation);
loc, originalResultType, collapsedOpResult, reassociation,
resultShape);
}
results.push_back(result);
} else {
Expand Down
63 changes: 45 additions & 18 deletions mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir
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Expand Up @@ -225,6 +225,38 @@ func.func @fuse_by_collapsing_dynamic(%arg0 : tensor<?x?x?x?x?xi32>,

// -----

#map0 = affine_map<(d0, d1) -> (d0, d1)>
func.func @fuse_by_collapsing_dynamic_2(%arg0 : tensor<?xf32>, %sz0: index, %sz1: index) -> tensor<?x?xf32> {
%0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [%sz0, %sz1] : tensor<?xf32> into tensor<?x?xf32>
%init = tensor.empty(%sz1, %sz0) : tensor<?x?xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map0],
iterator_types = ["parallel", "parallel"]}
ins(%0 : tensor<?x?xf32>)
outs(%init : tensor<?x?xf32>) {
^bb0(%b0 : f32, %b1 : f32):
%out = arith.negf %b0 : f32
linalg.yield %out : f32
} -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}

// CHECK-LABEL: func @fuse_by_collapsing_dynamic_2
// CHECK-SAME: %[[ARG0:.+]]: tensor<?xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]]
// CHECK-DAG: %[[DIM0:.+]] = tensor.dim %[[EXPANDED]], %[[C0]]
// CHECK-DAG: %[[DIM1:.+]] = tensor.dim %[[EXPANDED]], %[[C1]]
// CHECK: %[[OUT:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0]] : tensor<?xf32>)
// CHECK-SAME: outs(%{{.*}} : tensor<?xf32>)
// CHECK: %[[EXPANDED_1:.+]] = tensor.expand_shape %[[OUT]]
// CHECK-SAME: output_shape [%[[DIM0]], %[[DIM1]]]
// CHECK: return %[[EXPANDED_1]]

// -----

#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d3)>
func.func @fuse_reductions(%arg0 : tensor<2x?x5xf32>, %arg1 : tensor<2x5xf32>, %sz0: index) -> tensor<2x5xf32> {
Expand Down Expand Up @@ -425,10 +457,11 @@ func.func @fuse_only_one_reassociation(%arg0 : tensor<?x?xf32>, %arg1 : tensor<4
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @fuse_only_one_reassociation
// CHECK-SAME: (%[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<4x?x?x8xf32>, %[[SZ0:.+]]: index, %[[SZ1:.+]]: index)
// CHECK-DAG: %[[C8:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]{{\]}} output_shape [%[[SZ0]], 4, %[[SZ1]], 8]
// CHECK-DAG: %[[DIM:.+]] = tensor.dim %[[EXPAND_ARG0]], %[[C0]] : tensor<?x4x?x8xf32>
// CHECK-DAG: %[[DIM_2:.+]] = tensor.dim %[[EXPAND_ARG0]], %[[C2]] : tensor<?x4x?x8xf32>
// CHECK-DAG: %[[COLLAPSE_ARG0:.+]] = tensor.collapse_shape %[[EXPAND_ARG0]] {{\[}}[0], [1], [2, 3]{{\]}}
// CHECK-DAG: %[[COLLAPSE_ARG1_0:.+]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0], [1], [2, 3]{{\]}}
// CHECK-DAG: %[[COLLAPSE_ARG1_1:.+]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0], [1], [2, 3]{{\]}}
Expand All @@ -437,10 +470,7 @@ func.func @fuse_only_one_reassociation(%arg0 : tensor<?x?xf32>, %arg1 : tensor<4
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[COLLAPSE_ARG0]], %[[COLLAPSE_ARG1_0]] :
// CHECK-SAME: outs(%[[COLLAPSE_ARG1_1]] :
// CHECK: %[[DIM:.+]] = tensor.dim %[[GENERIC]], %[[C1]] : tensor<4x?x?xf32>
// CHECK: %[[DIM_2:.+]] = tensor.dim %[[GENERIC]], %[[C2]] : tensor<4x?x?xf32>
// CHECK: %[[VAL_1:.+]] = arith.divsi %[[DIM_2]], %[[C8]] : index
// CHECK: %[[EXPANDED_3:.+]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0], [1], [2, 3]] output_shape [4, %[[DIM]], %[[VAL_1]], 8] : tensor<4x?x?xf32> into tensor<4x?x?x8xf32>
// CHECK: %[[EXPANDED_3:.+]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0], [1], [2, 3]] output_shape [4, %[[DIM]], %[[DIM_2]], 8] : tensor<4x?x?xf32> into tensor<4x?x?x8xf32>
// CHECK: return %[[EXPANDED_3]]

// -----
Expand Down Expand Up @@ -475,15 +505,16 @@ func.func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>, %sz0: index, %sz1:
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK: func @fold_non_consecutive_dims(
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xi32>, %[[SZ0:.+]]: index, %[[SZ1:.+]]: index)
// CHECK: %[[C1:.+]] = arith.constant 1 : index
// CHECK: %[[C4:.+]] = arith.constant 4 : index
// CHECK: %[[C8:.+]] = arith.constant 8 : index
// CHECK: %[[C0:.+]] = arith.constant 0 : index
// CHECK: %[[C2:.+]] = arith.constant 2 : index
// CHECK-DAG: %[[C4:.+]] = arith.constant 4 : index
// CHECK-DAG: %[[C8:.+]] = arith.constant 8 : index
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[SZ0]], 4, %[[SZ1]], 8] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
// CHECK: %[[DIM:.+]] = tensor.dim %[[EXPANDED]], %[[C0]]
// CHECK: %[[DIM_0:.+]] = tensor.dim %[[EXPANDED]], %[[C2]]
// CHECK-DAG: %[[DIM:.+]] = tensor.dim %[[EXPANDED]], %[[C0]]
// CHECK-DAG: %[[DIM_0:.+]] = tensor.dim %[[EXPANDED]], %[[C2]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM_0]], %[[DIM]])
// CHECK-DAG: %[[DIM_1:.+]] = tensor.dim %[[EXPANDED]], %[[C0]]
// CHECK-DAG: %[[DIM_2:.+]] = tensor.dim %[[EXPANDED]], %[[C2]]
// CHECK: %[[COLLAPSE_INIT:.+]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1], [2, 3]{{\]}}
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
Expand All @@ -502,11 +533,7 @@ func.func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>, %sz0: index, %sz1:
// CHECK-DAG: %[[T6:.+]] = arith.addi %[[T5]], %[[T3]]
// CHECK-DAG: %[[T7:.+]] = arith.index_cast %[[T6]]
// CHECK: linalg.yield %[[T7]]
// CHECK: %[[DIM_1:.+]] = tensor.dim %[[GENERIC]], %[[C0]] : tensor<?x?xi32>
// CHECK: %[[DIM_2:.+]] = tensor.dim %[[GENERIC]], %[[C1]] : tensor<?x?xi32>
// CHECK: %[[VAL_2:.+]] = arith.divsi %[[DIM_1]], %[[C8]] : index
// CHECK: %[[VAL_3:.+]] = arith.divsi %[[DIM_2]], %[[C4]] : index
// CHECK: %[[EXPANDED_3:.+]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_2]], 8, %[[VAL_3]], 4] : tensor<?x?xi32> into tensor<?x8x?x4xi32>
// CHECK: %[[EXPANDED_3:.+]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[DIM_2]], 8, %[[DIM_1]], 4] : tensor<?x?xi32> into tensor<?x8x?x4xi32>
// CHECK: return %[[EXPANDED_3]]

// -----
Expand Down
7 changes: 3 additions & 4 deletions mlir/test/Dialect/Linalg/fusion-push-reshape.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -5,15 +5,14 @@

// CHECK-LABEL: func @reshape
// CHECK-SAME: (%[[A:.*]]: tensor<?x16xf32>, %[[B:.*]]: tensor<16xf32>, %[[INIT:.*]]: tensor<?x112x16xf32>, %[[SZ0:.*]]: index)
// CHECK: %[[C112:.*]] = arith.constant 112 : index
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[A]]
// CHECK: %[[DIM:.*]] = tensor.dim %[[EXPANDED]], %[[C0]]
// CHECK: %[[RI:.*]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1], [2]] : tensor<?x112x16xf32> into tensor<?x16xf32>
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP3]], #[[$MAP2]]],
// CHECK-SAME: iterator_types = ["parallel", "parallel"]}
// CHECK-SAME: ins(%[[A]], %[[B]] : tensor<?x16xf32>, tensor<16xf32>) outs(%[[RI]] : tensor<?x16xf32>)
// CHECK: %[[DIM:.*]] = tensor.dim %[[R]], %[[C0]] : tensor<?x16xf32>
// CHECK: %[[VAL_1:.*]] = arith.divsi %[[DIM]], %[[C112]] : index
// CHECK: %[[RR:.*]] = tensor.expand_shape %[[R]] {{\[\[}}0, 1], [2]] output_shape [%[[VAL_1]], 112, 16] : tensor<?x16xf32> into tensor<?x112x16xf32>
// CHECK: %[[RR:.*]] = tensor.expand_shape %[[R]] {{\[\[}}0, 1], [2]] output_shape [%[[DIM]], 112, 16] : tensor<?x16xf32> into tensor<?x112x16xf32>
// CHECK: return %[[RR]] : tensor<?x112x16xf32>
func.func @reshape(%A: tensor<?x16xf32>, %B: tensor<16xf32>, %init: tensor<?x112x16xf32>, %sz0: index) -> tensor<?x112x16xf32> {
%0 = tensor.expand_shape %A [[0, 1], [2]] output_shape [%sz0, 112, 16]
Expand Down
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