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[mlir] Add reshape propagation patterns for tensor.pad #94489

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55e452e
[mlir] Add reshape propagation patterns for tensor.pad
Max191 May 30, 2024
79ff60b
add tests, support dynamic expand
Max191 Jun 5, 2024
ecf0347
use control function
Max191 Jun 6, 2024
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146 changes: 146 additions & 0 deletions mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -956,6 +956,69 @@ class FoldWithProducerReshapeOpByExpansion
ControlFusionFn controlFoldingReshapes;
};

class FoldPadWithProducerReshapeOpByExpansion
: public OpRewritePattern<tensor::PadOp> {
public:
FoldPadWithProducerReshapeOpByExpansion(MLIRContext *context,
ControlFusionFn foldReshapes,
PatternBenefit benefit = 1)
: OpRewritePattern<tensor::PadOp>(context, benefit),
controlFoldingReshapes(std::move(foldReshapes)) {}

LogicalResult matchAndRewrite(tensor::PadOp padOp,
PatternRewriter &rewriter) const override {
tensor::CollapseShapeOp reshapeOp =
padOp.getSource().getDefiningOp<tensor::CollapseShapeOp>();
if (!reshapeOp)
return failure();
if (!reshapeOp->hasOneUse())
return failure();

if (!controlFoldingReshapes(&padOp.getSourceMutable())) {
return rewriter.notifyMatchFailure(padOp,
"fusion blocked by control function");
}

ArrayRef<int64_t> low = padOp.getStaticLow();
ArrayRef<int64_t> high = padOp.getStaticHigh();
SmallVector<ReassociationIndices> reassociations =
reshapeOp.getReassociationIndices();

for (auto [reInd, l, h] : llvm::zip_equal(reassociations, low, high)) {
if (reInd.size() != 1 && (l != 0 || h != 0))
return failure();
}

SmallVector<OpFoldResult> newLow, newHigh;
RankedTensorType expandedType = reshapeOp.getSrcType();
RankedTensorType paddedType = padOp.getResultType();
SmallVector<int64_t> expandedPaddedShape(expandedType.getShape());
for (auto [idx, reInd] : llvm::enumerate(reassociations)) {
if (reInd.size() == 1) {
expandedPaddedShape[reInd[0]] = paddedType.getShape()[idx];
}
for (size_t i = 0; i < reInd.size(); ++i) {
newLow.push_back(padOp.getMixedLowPad()[idx]);
newHigh.push_back(padOp.getMixedHighPad()[idx]);
}
}

Location loc = padOp->getLoc();
RankedTensorType expandedPaddedType = paddedType.clone(expandedPaddedShape);
auto newPadOp = rewriter.create<tensor::PadOp>(
loc, expandedPaddedType, reshapeOp.getSrc(), newLow, newHigh,
padOp.getConstantPaddingValue(), padOp.getNofold());

rewriter.replaceOpWithNewOp<tensor::CollapseShapeOp>(
padOp, padOp.getResultType(), newPadOp.getResult(), reassociations);

return success();
}

private:
ControlFusionFn controlFoldingReshapes;
};

/// Pattern to fold a tensor.expand_shape op with its producer generic op
/// by expanding the dimensionality of the loop in the producer op.
struct FoldReshapeWithGenericOpByExpansion
Expand Down Expand Up @@ -1702,6 +1765,85 @@ class FoldWithProducerReshapeOpByCollapsing
ControlFusionFn controlFoldingReshapes;
};

class FoldPadWithProducerReshapeOpByCollapsing
: public OpRewritePattern<tensor::PadOp> {
public:
FoldPadWithProducerReshapeOpByCollapsing(MLIRContext *context,
ControlFusionFn foldReshapes,
PatternBenefit benefit = 1)
: OpRewritePattern<tensor::PadOp>(context, benefit),
controlFoldingReshapes(std::move(foldReshapes)) {}

LogicalResult matchAndRewrite(tensor::PadOp padOp,
PatternRewriter &rewriter) const override {
tensor::ExpandShapeOp reshapeOp =
padOp.getSource().getDefiningOp<tensor::ExpandShapeOp>();
if (!reshapeOp)
return failure();
if (!reshapeOp->hasOneUse())
return failure();

if (!controlFoldingReshapes(&padOp.getSourceMutable())) {
return rewriter.notifyMatchFailure(padOp,
"fusion blocked by control function");
}

ArrayRef<int64_t> low = padOp.getStaticLow();
ArrayRef<int64_t> high = padOp.getStaticHigh();
SmallVector<ReassociationIndices> reassociations =
reshapeOp.getReassociationIndices();

for (auto reInd : reassociations) {
if (reInd.size() == 1)
continue;
if (llvm::any_of(reInd, [&](int64_t ind) {
return low[ind] != 0 || high[ind] != 0;
})) {
return failure();
}
}

SmallVector<OpFoldResult> newLow, newHigh;
RankedTensorType collapsedType = reshapeOp.getSrcType();
RankedTensorType paddedType = padOp.getResultType();
SmallVector<int64_t> collapsedPaddedShape(collapsedType.getShape());
SmallVector<OpFoldResult> expandedPaddedSizes(
getMixedValues(reshapeOp.getStaticOutputShape(),
reshapeOp.getOutputShape(), rewriter));
AffineExpr d0, d1, d2;
bindDims(rewriter.getContext(), d0, d1, d2);
auto addMap = AffineMap::get(3, 0, {d0 + d1 + d2});
Location loc = reshapeOp->getLoc();
for (auto [idx, reInd] : llvm::enumerate(reassociations)) {
OpFoldResult l = padOp.getMixedLowPad()[reInd[0]];
OpFoldResult h = padOp.getMixedHighPad()[reInd[0]];
if (reInd.size() == 1) {
collapsedPaddedShape[idx] = paddedType.getShape()[reInd[0]];
OpFoldResult paddedSize = affine::makeComposedFoldedAffineApply(
rewriter, loc, addMap, {l, h, expandedPaddedSizes[reInd[0]]});
expandedPaddedSizes[reInd[0]] = paddedSize;
}
newLow.push_back(l);
newHigh.push_back(h);
}

RankedTensorType collapsedPaddedType =
paddedType.clone(collapsedPaddedShape);
auto newPadOp = rewriter.create<tensor::PadOp>(
loc, collapsedPaddedType, reshapeOp.getSrc(), newLow, newHigh,
padOp.getConstantPaddingValue(), padOp.getNofold());

rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
padOp, padOp.getResultType(), newPadOp.getResult(), reassociations,
expandedPaddedSizes);

return success();
}

private:
ControlFusionFn controlFoldingReshapes;
};

/// Pattern to collapse dimensions.
template <typename LinalgType>
class CollapseLinalgDimensions : public OpRewritePattern<LinalgType> {
Expand Down Expand Up @@ -1937,6 +2079,8 @@ void mlir::linalg::populateFoldReshapeOpsByExpansionPatterns(
const ControlFusionFn &controlFoldingReshapes) {
patterns.add<FoldReshapeWithGenericOpByExpansion>(patterns.getContext(),
controlFoldingReshapes);
patterns.add<FoldPadWithProducerReshapeOpByExpansion>(patterns.getContext(),
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AFAICS both the patterns added here are "propagating by collapsing". You are moving the expand_shape down and collapse_shape up. In both cases the pad is happening on collapsed dimensions. So you should add both to the populateFoldReshapeOpsByCollapsingPatterns.

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FoldPadWithProducerReshapeOpByExpansion pushes the producer collapse_shape down, expanding the pad. FoldPadWithProducerReshapeOpByCollapsing pushes the producer expand_shape down, collapsing the pad. I think these are in the right places here.

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Ok, I twisted myself in a knot when looking at tests. You are right.

controlFoldingReshapes);
patterns.add<FoldWithProducerReshapeOpByExpansion>(patterns.getContext(),
controlFoldingReshapes);
}
Expand All @@ -1946,6 +2090,8 @@ void mlir::linalg::populateFoldReshapeOpsByCollapsingPatterns(
const ControlFusionFn &controlFoldingReshapes) {
patterns.add<FoldWithProducerReshapeOpByCollapsing>(patterns.getContext(),
controlFoldingReshapes);
patterns.add<FoldPadWithProducerReshapeOpByCollapsing>(
patterns.getContext(), controlFoldingReshapes);
}

void mlir::linalg::populateElementwiseOpsFusionPatterns(
Expand Down
68 changes: 68 additions & 0 deletions mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir
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Expand Up @@ -537,3 +537,71 @@ func.func @no_fold_non_consecutive_reduction_dims(%arg0 : tensor<?x?xi32>, %sz0:
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: ins(%[[EXPAND_ARG0]] :
// CHECK: return %[[GENERIC]]

// -----

func.func @fuse_by_collapsing_pad(%arg0 : tensor<2x12x5x336x9xi32>) -> tensor<8x3x4x17x6x7x8x14xi32> {
%expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] output_shape [2, 3, 4, 5, 6, 7, 8, 9] : tensor<2x12x5x336x9xi32> into tensor<2x3x4x5x6x7x8x9xi32>
%cst = arith.constant 0 : i32
%padded_0 = tensor.pad %expand low[1, 0, 0, 8, 0, 0, 0, 3] high[5, 0, 0, 4, 0, 0, 0, 2] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index,
%arg5: index, %arg6: index, %arg7: index, %arg8: index):
tensor.yield %cst : i32
} : tensor<2x3x4x5x6x7x8x9xi32> to tensor<8x3x4x17x6x7x8x14xi32>
return %padded_0 : tensor<8x3x4x17x6x7x8x14xi32>
}
// CHECK: func @fuse_by_collapsing_pad(
// CHECK-SAME: %[[ARG0:.+]]: tensor<2x12x5x336x9xi32>)
// CHECK: %[[PAD:.+]] = tensor.pad %[[ARG0]]
// CHECK-SAME: low[1, 0, 8, 0, 3] high[5, 0, 4, 0, 2]
// CHECK: tensor<2x12x5x336x9xi32> to tensor<8x12x17x336x14xi32>
// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[PAD]] {{\[}}[0], [1, 2], [3], [4, 5, 6], [7]]
// CHECK-SAME: output_shape [8, 3, 4, 17, 6, 7, 8, 14] : tensor<8x12x17x336x14xi32> into tensor<8x3x4x17x6x7x8x14xi32>
// CHECK: return %[[EXPAND]]

// -----

func.func @no_fuse_by_collapsing_pad(%arg0 : tensor<2x12x5x336x9xi32>) -> tensor<8x5x4x17x6x7x8x14xi32> {
%expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] output_shape [2, 3, 4, 5, 6, 7, 8, 9] : tensor<2x12x5x336x9xi32> into tensor<2x3x4x5x6x7x8x9xi32>
%cst = arith.constant 0 : i32
%padded_0 = tensor.pad %expand low[1, 2, 0, 8, 0, 0, 0, 3] high[5, 0, 0, 4, 0, 0, 0, 2] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index,
%arg5: index, %arg6: index, %arg7: index, %arg8: index):
tensor.yield %cst : i32
} : tensor<2x3x4x5x6x7x8x9xi32> to tensor<8x5x4x17x6x7x8x14xi32>
return %padded_0 : tensor<8x5x4x17x6x7x8x14xi32>
}
// CHECK: func @no_fuse_by_collapsing_pad(
// CHECK-SAME: %[[ARG0:.+]]: tensor<2x12x5x336x9xi32>)
// CHECK: %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2], [3], [4, 5, 6], [7]]
// CHECK-SAME: output_shape [2, 3, 4, 5, 6, 7, 8, 9] : tensor<2x12x5x336x9xi32> into tensor<2x3x4x5x6x7x8x9xi32>
// CHECK: %[[PAD:.+]] = tensor.pad %[[EXPAND_ARG0]]
// CHECK-SAME: low[1, 2, 0, 8, 0, 0, 0, 3] high[5, 0, 0, 4, 0, 0, 0, 2]
// CHECK: tensor<2x3x4x5x6x7x8x9xi32> to tensor<8x5x4x17x6x7x8x14xi32>
// CHECK: return %[[PAD]]

// -----

func.func @fuse_by_collapsing_dynamic_pad(%arg0 : tensor<?x?x?x?xf32>,
%s0 : index, %s1 : index, %s2 : index, %s3 : index, %s4 : index, %s5 : index,
%l0 : index, %l1 : index, %h0 : index, %h1 : index) -> tensor<?x?x?x?x?x?xf32> {
%expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5]] output_shape [%s0, %s1, %s2, %s3, %s4, %s5] : tensor<?x?x?x?xf32> into tensor<?x?x?x?x?x?xf32>
%cst = arith.constant 0.0 : f32
%padded_0 = tensor.pad %expand low[%l0, 0, 0, %l1, 0, 0] high[%h0, 0, 0, %h1, 0, 0] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index, %arg5: index, %arg6: index):
tensor.yield %cst : f32
} : tensor<?x?x?x?x?x?xf32> to tensor<?x?x?x?x?x?xf32>
return %padded_0 : tensor<?x?x?x?x?x?xf32>
}
// CHECK-DAG: #[[MAP:.+]] = affine_map<()[s0, s1, s2] -> (s0 + s1 + s2)>
// CHECK: func @fuse_by_collapsing_dynamic_pad(
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32>
// CHECK-SAME: %[[S0:.+]]: index, %[[S1:.+]]: index, %[[S2:.+]]: index, %[[S3:.+]]: index, %[[S4:.+]]: index, %[[S5:.+]]: index, %[[L0:.+]]: index, %[[L1:.+]]: index, %[[H0:.+]]: index, %[[H1:.+]]: index
// CHECK: %[[PAD_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[L0]], %[[H0]], %[[S0]]]
// CHECK: %[[PAD_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[L1]], %[[H1]], %[[S3]]]
// CHECK: %[[PAD:.+]] = tensor.pad %[[ARG0]]
// CHECK-SAME: low[%[[L0]], 0, %[[L1]], 0] high[%[[H0]], 0, %[[H1]], 0]
// CHECK: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32>
// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[PAD]] {{\[}}[0], [1, 2], [3], [4, 5]]
// CHECK-SAME: output_shape [%[[PAD_SIZE0]], %[[S1]], %[[S2]], %[[PAD_SIZE1]], %[[S4]], %[[S5]]] : tensor<?x?x?x?xf32> into tensor<?x?x?x?x?x?xf32>
// CHECK: return %[[EXPAND]]
61 changes: 61 additions & 0 deletions mlir/test/Dialect/Linalg/reshape_fusion.mlir
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Expand Up @@ -826,3 +826,64 @@ func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
// CHECK-SAME: [0, 1], [2, 3]
// CHECK-SAME: tensor<?x7x?x8xf32> into tensor<?x?xf32>
// CHECK: return %[[T4]]

// -----

func.func @fuse_by_expanding_pad(%arg0 : tensor<2x3x4x5x6x7x8x9xi32>) -> tensor<8x12x17x336x14xi32> {
%collapse = tensor.collapse_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] : tensor<2x3x4x5x6x7x8x9xi32> into tensor<2x12x5x336x9xi32>
%cst = arith.constant 0 : i32
%padded_0 = tensor.pad %collapse low[1, 0, 8, 0, 3] high[5, 0, 4, 0, 2] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : i32
} : tensor<2x12x5x336x9xi32> to tensor<8x12x17x336x14xi32>
return %padded_0 : tensor<8x12x17x336x14xi32>
}
// CHECK: func @fuse_by_expanding_pad(
// CHECK-SAME: %[[ARG0:.+]]: tensor<2x3x4x5x6x7x8x9xi32>)
// CHECK: %[[PAD:.+]] = tensor.pad %[[ARG0]]
// CHECK-SAME: low[1, 0, 0, 8, 0, 0, 0, 3] high[5, 0, 0, 4, 0, 0, 0, 2]
// CHECK: tensor<2x3x4x5x6x7x8x9xi32> to tensor<8x3x4x17x6x7x8x14xi32>
// CHECK: %[[COLLAPSE:.+]] = tensor.collapse_shape %[[PAD]] {{\[}}[0], [1, 2], [3], [4, 5, 6], [7]]
// CHECK-SAME: : tensor<8x3x4x17x6x7x8x14xi32> into tensor<8x12x17x336x14xi32>
// CHECK: return %[[COLLAPSE]]

// -----

func.func @no_fuse_by_expanding_pad(%arg0 : tensor<2x3x4x5x6x7x8x9xi32>) -> tensor<8x12x17x339x14xi32> {
%collapse = tensor.collapse_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] : tensor<2x3x4x5x6x7x8x9xi32> into tensor<2x12x5x336x9xi32>
%cst = arith.constant 0 : i32
%padded_0 = tensor.pad %collapse low[1, 0, 8, 0, 3] high[5, 0, 4, 3, 2] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : i32
} : tensor<2x12x5x336x9xi32> to tensor<8x12x17x339x14xi32>
return %padded_0 : tensor<8x12x17x339x14xi32>
}
// CHECK: func @no_fuse_by_expanding_pad(
// CHECK-SAME: %[[ARG0:.+]]: tensor<2x3x4x5x6x7x8x9xi32>)
// CHECK: %[[COLLAPSE:.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0], [1, 2], [3], [4, 5, 6], [7]]
// CHECK-SAME: : tensor<2x3x4x5x6x7x8x9xi32> into tensor<2x12x5x336x9xi32>
// CHECK: %[[PAD:.+]] = tensor.pad %[[COLLAPSE]]
// CHECK-SAME: low[1, 0, 8, 0, 3] high[5, 0, 4, 3, 2]
// CHECK: tensor<2x12x5x336x9xi32> to tensor<8x12x17x339x14xi32>
// CHECK: return %[[PAD]]

// -----

func.func @fuse_by_expanding_dynamic_pad(%arg0 : tensor<?x?x?x?x?x?xi32>, %l0: index, %l1: index, %h0: index, %h1: index) -> tensor<?x?x?x?xi32> {
%collapse = tensor.collapse_shape %arg0 [[0], [1, 2], [3], [4, 5]] : tensor<?x?x?x?x?x?xi32> into tensor<?x?x?x?xi32>
%cst = arith.constant 0 : i32
%padded_0 = tensor.pad %collapse low[%l0, 0, %l1, 0] high[%h0, 0, %h1, 0] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
tensor.yield %cst : i32
} : tensor<?x?x?x?xi32> to tensor<?x?x?x?xi32>
return %padded_0 : tensor<?x?x?x?xi32>
}
// CHECK: func @fuse_by_expanding_dynamic_pad(
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?x?x?xi32>
// CHECK-SAME: %[[L0:.+]]: index, %[[L1:.+]]: index, %[[H0:.+]]: index, %[[H1:.+]]: index
// CHECK: %[[PAD:.+]] = tensor.pad %[[ARG0]]
// CHECK-SAME: low[%[[L0]], 0, 0, %[[L1]], 0, 0] high[%[[H0]], 0, 0, %[[H1]], 0, 0]
// CHECK: tensor<?x?x?x?x?x?xi32> to tensor<?x?x?x?x?x?xi32>
// CHECK: %[[COLLAPSE:.+]] = tensor.collapse_shape %[[PAD]] {{\[}}[0], [1, 2], [3], [4, 5]]
// CHECK-SAME: : tensor<?x?x?x?x?x?xi32> into tensor<?x?x?x?xi32>
// CHECK: return %[[COLLAPSE]]
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