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[mlir][linalg] Fix padding shape computation in PadTilingInterface for convs #149576

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a0b980a
[mlir] Fix padding shape computation in PadTilingInterface
yzhang93 Jul 18, 2025
01f2f3a
Add cases for non-unit strides and dilations
yzhang93 Jul 22, 2025
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41 changes: 37 additions & 4 deletions mlir/lib/Dialect/Linalg/Transforms/PadTilingInterface.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,28 @@ getFullRankPaddingSizes(Builder &b, ArrayRef<OpFoldResult> indexingSizes,
return paddingSizes;
}

/// Extracts the constant multiplier from an affine expression of the form
/// `d * c` or `c * d`, where `d` is an AffineDimExpr and `c` is an
/// AffineConstantExpr. Returns 1 if the expression is not a simple
/// multiplication of a dimension and a constant.
static int64_t extractConstantMultiplier(AffineExpr expr) {
if (auto binOp = dyn_cast<AffineBinaryOpExpr>(expr)) {
if (binOp.getKind() == AffineExprKind::Mul) {
auto lhsD = dyn_cast<AffineDimExpr>(binOp.getLHS());
auto rhsC = dyn_cast<AffineConstantExpr>(binOp.getRHS());
if (lhsD && rhsC) {
return rhsC.getValue();
}
auto lhsC = dyn_cast<AffineConstantExpr>(binOp.getLHS());
auto rhsD = dyn_cast<AffineDimExpr>(binOp.getRHS());
if (lhsC && rhsD) {
return lhsC.getValue();
}
}
}
return 1;
}

/// Compute the padded shape of the given value `v` of `RankedTensorType` given
/// - `indexingSizes` a list of OpFoldResult.
/// - an `indexingMap` that encodes how the shape of varies with increases
Expand Down Expand Up @@ -114,24 +136,33 @@ SmallVector<OpFoldResult> linalg::computePaddedShape(
/*compressDims=*/true);

// If we are padding to the next multiple of, compose with ceil(sz) * sz.
OpFoldResult paddingDimOfr;
if (options.padToMultipleOf) {
AffineExpr d0, s0;
bindDims(rewriter.getContext(), d0);
bindSymbols(rewriter.getContext(), s0);
AffineMap ceilMap = AffineMap::get(1, 1, d0.ceilDiv(s0) * s0);
AffineMap composedMap = projectedMap.compose(ceilMap);
OpFoldResult paddingDimOfr = affine::makeComposedFoldedAffineApply(
paddingDimOfr = affine::makeComposedFoldedAffineApply(
rewriter, loc, composedMap,
{indexingSizes[paddingDim], paddingSize},
/*composeAffineMin=*/true);
terms.push_back(paddingDimOfr);
} else {
// Otherwise just set to paddingSize.
OpFoldResult paddingDimOfr = affine::makeComposedFoldedAffineApply(
paddingDimOfr = affine::makeComposedFoldedAffineApply(
rewriter, loc, projectedMap, paddingSize);
terms.push_back(paddingDimOfr);
}

// Adjust for the maximum accessed index, which is (paddingSize - 1) *
// multiplier.
AffineExpr d0;
bindDims(rewriter.getContext(), d0);
int64_t multiplier = extractConstantMultiplier(projectedMap.getResult(0));
AffineMap subtractMap = AffineMap::get(1, 0, d0 - multiplier);
OpFoldResult maxAccessIdx = affine::makeComposedFoldedAffineApply(
rewriter, loc, subtractMap, {paddingDimOfr});
terms.push_back(maxAccessIdx);

LLVM_DEBUG(DBGS() << "------new term: " << terms.back() << "\n");
}

Expand All @@ -148,6 +179,8 @@ SmallVector<OpFoldResult> linalg::computePaddedShape(
AffineExpr sumExpr = dims.front();
for (unsigned i = 1; i < dims.size(); ++i)
sumExpr = sumExpr + dims[i];
// Add 1 to the maximum accessed index and get the final padded size.
sumExpr = sumExpr + rewriter.getAffineConstantExpr(1);
OpFoldResult paddedDimOfr =
affine::makeComposedFoldedAffineApply(rewriter, loc, sumExpr, terms);
paddedShape[resultIndex] = paddedDimOfr;
Expand Down
149 changes: 141 additions & 8 deletions mlir/test/Dialect/Linalg/transform-op-pad-tiling-interface-multiple-of.mlir
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Expand Up @@ -52,22 +52,22 @@ module {

// CHECK-LABEL: @generic
// CHECK-SAME: %[[T0:.*]]: tensor<7x5xf32>,
// CHECK-SAME: %[[T1:.*]]: tensor<7x11x12xf32>)
func.func @generic(%arg0: tensor<7x5xf32>, %arg1: tensor<7x11x12xf32>) -> tensor<7x11x12xf32> {
// CHECK-SAME: %[[T1:.*]]: tensor<7x11x11xf32>)
func.func @generic(%arg0: tensor<7x5xf32>, %arg1: tensor<7x11x11xf32>) -> tensor<7x11x11xf32> {

// CHECK-DAG: %[[CST:.*]] = arith.constant 0.

// CHECK: %[[PAD0:.*]] = tensor.pad %[[T0]] low[0, 0] high[2, 0]
// CHECK: : tensor<7x5xf32> to tensor<9x5xf32>
// CHECK: %[[PAD1:.*]] = tensor.pad %[[T1]] low[0, 0, 0] high[2, 4, 2] {
// CHECK: : tensor<7x11x12xf32> to tensor<9x15x14xf32>
// CHECK: : tensor<7x11x11xf32> to tensor<9x15x13xf32>
// CHECK-NEXT: linalg.generic
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0] [7, 11, 12] [1, 1, 1] : tensor<9x15x14xf32> to tensor<7x11x12xf32>
%0 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0 : tensor<7x5xf32>) outs(%arg1 : tensor<7x11x12xf32>) {
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0] [7, 11, 11] [1, 1, 1] : tensor<9x15x13xf32> to tensor<7x11x11xf32>
%0 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0 : tensor<7x5xf32>) outs(%arg1 : tensor<7x11x11xf32>) {
^bb0(%in: f32, %out: f32):
linalg.yield %in : f32
} -> tensor<7x11x12xf32>
return %0 : tensor<7x11x12xf32>
Comment on lines -55 to -70
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Why was this changed?

} -> tensor<7x11x11xf32>
return %0 : tensor<7x11x11xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
Expand All @@ -83,7 +83,7 @@ module {
// -----

// CHECK-DAG: #[[$MAP0:.*]] = affine_map<()[s0, s1] -> (-s1 + (s0 ceildiv 3) * 3)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<()[s0, s1] -> (-s1 + (s0 ceildiv 3) * 3 + 5)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<()[s0, s1] -> (-s1 + (s0 ceildiv 3) * 3 + 4)>
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<()[s0] -> (s0 + 5)>

#map = affine_map<(d0, d1, d2) -> (d0, d1)>
Expand Down Expand Up @@ -272,3 +272,136 @@ module attributes {transform.with_named_sequence} {
}
}

// -----

// CHECK-LABEL: pad_conv
func.func @pad_conv(%arg0: tensor<1x16x16x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32> {

// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 2, 12]
// CHECK: : tensor<1x16x16x4xf32> to tensor<1x16x18x16xf32>
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 0, 12]
// CHECK: : tensor<16x3x3x4xf32> to tensor<16x3x3x16xf32>
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 2, 0]
// CHECK: : tensor<1x14x14x16xf32> to tensor<1x14x16x16xf32>
// CHECK-NEXT: linalg.conv_2d_nhwc_fhwc
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0, 0] [1, 14, 14, 16] [1, 1, 1, 1] : tensor<1x14x16x16xf32> to tensor<1x14x14x16xf32>

%0 = linalg.conv_2d_nhwc_fhwc
{dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%arg0, %arg1: tensor<1x16x16x4xf32>, tensor<16x3x3x4xf32>)
outs(%arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32>
return %0 : tensor<1x14x14x16xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [0, 0, 16, 0, 0, 0, 16] pad_to_multiple_of {
padding_values = [0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}

// -----

// CHECK-DAG: #[[$MAP0:.*]] = affine_map<()[s0, s1] -> (-s1 + (s0 ceildiv 16) * 16 + 2)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<()[s0, s1] -> (-s1 + (s0 ceildiv 16) * 16)>

// CHECK-LABEL: pad_conv_dynamic
func.func @pad_conv_dynamic(%arg0: tensor<1x16x?x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<1x14x?x16xf32>) -> tensor<1x14x?x16xf32> {

// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK: %[[D0_0:.*]] = tensor.dim %{{.*}}, %[[C2]] : tensor<1x14x?x16xf32>
// CHECK: %[[D0_1:.*]] = tensor.dim %{{.*}}, %[[C2]] : tensor<1x16x?x4xf32>
// CHECK: %[[H0:.*]] = affine.apply #[[$MAP0]]()[%[[D0_0]], %[[D0_1]]]
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, %[[H0]], 12]
// CHECK: : tensor<1x16x?x4xf32> to tensor<1x16x?x16xf32>
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 0, 12]
// CHECK: : tensor<16x3x3x4xf32> to tensor<16x3x3x16xf32>
// CHECK: %[[D1_0:.*]] = tensor.dim %{{.*}}, %[[C2]] : tensor<1x14x?x16xf32>
// CHECK: %[[H1:.*]] = affine.apply #[[$MAP1]]()[%[[D0_0]], %[[D1_0]]]
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, %[[H1]], 0]
// CHECK: : tensor<1x14x?x16xf32> to tensor<1x14x?x16xf32>
// CHECK: %[[D2_0:.*]] = tensor.dim %{{.*}}, %[[C2]] : tensor<1x14x?x16xf32>
// CHECK-NEXT: linalg.conv_2d_nhwc_fhwc
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0, 0] [1, 14, %[[D2_0]], 16] [1, 1, 1, 1] : tensor<1x14x?x16xf32> to tensor<1x14x?x16xf32>

%0 = linalg.conv_2d_nhwc_fhwc
{dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%arg0, %arg1: tensor<1x16x?x4xf32>, tensor<16x3x3x4xf32>)
outs(%arg2: tensor<1x14x?x16xf32>) -> tensor<1x14x?x16xf32>
return %0 : tensor<1x14x?x16xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [0, 0, 16, 0, 0, 0, 16] pad_to_multiple_of {
padding_values = [0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}

// -----

// CHECK-LABEL: pad_conv_strided
func.func @pad_conv_strided(%arg0: tensor<1x42x42x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32> {

// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 6, 12]
// CHECK: : tensor<1x42x42x4xf32> to tensor<1x42x48x16xf32>
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 0, 12]
// CHECK: : tensor<16x3x3x4xf32> to tensor<16x3x3x16xf32>
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 2, 0]
// CHECK: : tensor<1x14x14x16xf32> to tensor<1x14x16x16xf32>
// CHECK-NEXT: linalg.conv_2d_nhwc_fhwc
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0, 0] [1, 14, 14, 16] [1, 1, 1, 1] : tensor<1x14x16x16xf32> to tensor<1x14x14x16xf32>

%0 = linalg.conv_2d_nhwc_fhwc
{dilations = dense<1> : tensor<2xi64>, strides = dense<3> : tensor<2xi64> }
ins(%arg0, %arg1: tensor<1x42x42x4xf32>, tensor<16x3x3x4xf32>)
outs(%arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32>
return %0 : tensor<1x14x14x16xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [0, 0, 16, 0, 0, 0, 16] pad_to_multiple_of {
padding_values = [0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}

// -----

// CHECK-LABEL: pad_conv_dilated
func.func @pad_conv_dilated(%arg0: tensor<1x18x18x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32> {

// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 2, 12]
// CHECK: : tensor<1x18x18x4xf32> to tensor<1x18x20x16xf32>
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 0, 12]
// CHECK: : tensor<16x3x3x4xf32> to tensor<16x3x3x16xf32>
// CHECK: tensor.pad %{{.*}} low[0, 0, 0, 0] high[0, 0, 2, 0]
// CHECK: : tensor<1x14x14x16xf32> to tensor<1x14x16x16xf32>
// CHECK-NEXT: linalg.conv_2d_nhwc_fhwc
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0, 0] [1, 14, 14, 16] [1, 1, 1, 1] : tensor<1x14x16x16xf32> to tensor<1x14x14x16xf32>

%0 = linalg.conv_2d_nhwc_fhwc
{dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
ins(%arg0, %arg1: tensor<1x18x18x4xf32>, tensor<16x3x3x4xf32>)
outs(%arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32>
return %0 : tensor<1x14x14x16xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [0, 0, 16, 0, 0, 0, 16] pad_to_multiple_of {
padding_values = [0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32, 0.0 : f32]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
24 changes: 12 additions & 12 deletions mlir/test/Dialect/Linalg/transform-op-pad-tiling-interface.mlir
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Expand Up @@ -69,22 +69,22 @@ module {

// CHECK-LABEL: @generic
// CHECK-SAME: %[[T0:.*]]: tensor<7x5xf32>,
// CHECK-SAME: %[[T1:.*]]: tensor<7x11x12xf32>)
func.func @generic(%arg0: tensor<7x5xf32>, %arg1: tensor<7x11x12xf32>) -> tensor<7x11x12xf32> {
// CHECK-SAME: %[[T1:.*]]: tensor<7x11x11xf32>)
func.func @generic(%arg0: tensor<7x5xf32>, %arg1: tensor<7x11x11xf32>) -> tensor<7x11x11xf32> {

// CHECK-DAG: %[[CST:.*]] = arith.constant 0.

// CHECK: %[[PAD0:.*]] = tensor.pad %[[T0]] low[0, 0] high[1, 0]
// CHECK: : tensor<7x5xf32> to tensor<8x5xf32>
// CHECK: %[[PAD1:.*]] = tensor.pad %[[T1]] low[0, 0, 0] high[1, 3, 1] {
// CHECK: : tensor<7x11x12xf32> to tensor<8x14x13xf32>
// CHECK: : tensor<7x11x11xf32> to tensor<8x14x12xf32>
// CHECK-NEXT: linalg.generic
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0] [7, 11, 12] [1, 1, 1] : tensor<8x14x13xf32> to tensor<7x11x12xf32>
%0 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0 : tensor<7x5xf32>) outs(%arg1 : tensor<7x11x12xf32>) {
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0] [7, 11, 11] [1, 1, 1] : tensor<8x14x12xf32> to tensor<7x11x11xf32>
%0 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0 : tensor<7x5xf32>) outs(%arg1 : tensor<7x11x11xf32>) {
^bb0(%in: f32, %out: f32):
linalg.yield %in : f32
} -> tensor<7x11x12xf32>
return %0 : tensor<7x11x12xf32>
Comment on lines -72 to -87
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why were these test inputs changed?

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Please refer to the explanation below. I think this test is similar to the convolution case that the final shape after (d0 + d1) mapping might be wrong.

} -> tensor<7x11x11xf32>
return %0 : tensor<7x11x11xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
Expand All @@ -102,7 +102,7 @@ module {


// CHECK-DAG: #[[$MAP0:.*]] = affine_map<()[s0] -> (-s0 + 8)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<()[s0] -> (-s0 + 13)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<()[s0] -> (-s0 + 12)>
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<()[s0] -> (s0 + 5)>

#map = affine_map<(d0, d1, d2) -> (d0, d1)>
Expand All @@ -127,13 +127,13 @@ module {
// CHECK: %[[D2_0:.*]] = tensor.dim %{{.*}}, %[[C2]] : tensor<?x11x?xf32>
// CHECK: %[[H2:.*]] = affine.apply #[[$MAP1]]()[%[[D2_0]]]
// CHECK: tensor.pad %{{.*}} low[0, 0, 0] high[%[[H1]], 3, %[[H2]]] {
// CHECK: : tensor<?x11x?xf32> to tensor<8x14x13xf32>
// CHECK: : tensor<?x11x?xf32> to tensor<8x14x12xf32>
//
// CHECK: %[[D0_2:.*]] = tensor.dim %{{.*}}, %[[C0]] : tensor<?x5xf32>
// CHECK: %[[D2_1:.*]] = affine.apply #[[$MAP2]]()[%[[D0_2]]]
// CHECK: linalg.generic {{.*}} ins(%{{.*}} : tensor<8x5xf32>) outs(%{{.*}} : tensor<8x14x13xf32>) {
// CHECK: } -> tensor<8x14x13xf32>
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0] [%[[D0_2]], 11, %[[D2_1]]] [1, 1, 1] : tensor<8x14x13xf32> to tensor<?x11x?xf32>
// CHECK: linalg.generic {{.*}} ins(%{{.*}} : tensor<8x5xf32>) outs(%{{.*}} : tensor<8x14x12xf32>) {
// CHECK: } -> tensor<8x14x12xf32>
// CHECK: tensor.extract_slice %{{.*}}[0, 0, 0] [%[[D0_2]], 11, %[[D2_1]]] [1, 1, 1] : tensor<8x14x12xf32> to tensor<?x11x?xf32>
//
%0 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0 : tensor<?x5xf32>) outs(%arg1 : tensor<?x11x?xf32>) {
^bb0(%in: f32, %out: f32):
Expand Down