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[MLIR] Specify new padOp's output type in DropPadUnitDims (#150706)

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Previously when dropping unit dim from a pad with mixed dynamic/static
input/output shapes, the resulting shape would take on the Type of the
input, resulting in invalid IR.

Also did some minor cleanup to the formatting of the
`drop_unit_dim_corresponding_to_dynamic_dim` test to make it match the
rest of the file.

---------

Signed-off-by: dan <danimal197@gmail.com>
This commit is contained in:
Daniel Garvey 2025-07-31 05:51:38 -05:00 committed by GitHub
parent cdf75df183
commit 1e504bef20
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GPG Key ID: B5690EEEBB952194
2 changed files with 48 additions and 22 deletions
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15
mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp
View File

@ -637,6 +637,7 @@ struct DropPadUnitDims : public OpRewritePattern<tensor::PadOp> {
}
ArrayRef<int64_t> sourceShape = padOp.getSourceType().getShape();
ArrayRef<int64_t> resultShape = padOp.getResultType().getShape();
int64_t padRank = sourceShape.size();
auto isStaticZero = [](OpFoldResult f) {
@ -647,16 +648,18 @@ struct DropPadUnitDims : public OpRewritePattern<tensor::PadOp> {
allowedUnitDims.end());
llvm::SmallDenseSet<unsigned> unitDims;
SmallVector<int64_t> newShape;
SmallVector<int64_t> newResultShape;
SmallVector<OpFoldResult> newLowPad;
SmallVector<OpFoldResult> newHighPad;
for (const auto [dim, size, low, high] :
zip_equal(llvm::seq(static_cast<int64_t>(0), padRank), sourceShape,
padOp.getMixedLowPad(), padOp.getMixedHighPad())) {
for (const auto [dim, size, outSize, low, high] : zip_equal(
llvm::seq(static_cast<int64_t>(0), padRank), sourceShape,
resultShape, padOp.getMixedLowPad(), padOp.getMixedHighPad())) {
if (unitDimsFilter.contains(dim) && size == 1 && isStaticZero(low) &&
isStaticZero(high)) {
unitDims.insert(dim);
} else {
newShape.push_back(size);
newResultShape.push_back(outSize);
newLowPad.push_back(low);
newHighPad.push_back(high);
}
@ -686,8 +689,10 @@ struct DropPadUnitDims : public OpRewritePattern<tensor::PadOp> {
collapseValue(rewriter, padOp.getLoc(), padOp.getSource(), newShape,
reassociationMap, options.rankReductionStrategy);
auto newPadOp = tensor::PadOp::create(
rewriter, padOp.getLoc(), /*result=*/Type(), collapsedSource, newLowPad,
auto newResultType = RankedTensorType::get(
newResultShape, padOp.getResultType().getElementType());
auto newPadOp = rewriter.create<tensor::PadOp>(
padOp.getLoc(), /*result=*/newResultType, collapsedSource, newLowPad,
newHighPad, paddingVal, padOp.getNofold());
Value dest = padOp.getResult();

55
mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
View File

@ -1076,6 +1076,44 @@ func.func @drop_known_unit_constant_low_high(%arg0: tensor<1x383x128xf32>) -> te
// -----
func.func @drop_unit_dim_mixed_static_dynamic(%arg0: tensor<1x?xf32>) -> tensor<1x16xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cst = arith.constant 0.000000e+00 : f32
%padded = tensor.pad %arg0 low[%c0, %c1] high[%c0, %c0] {
^bb0(%arg1: index, %arg2: index):
tensor.yield %cst : f32
} : tensor<1x?xf32> to tensor<1x16xf32>
return %padded : tensor<1x16xf32>
}
// CHECK-LABEL: func @drop_unit_dim_mixed_static_dynamic
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[COLLAPSE:.+]] = tensor.collapse_shape %[[ARGS:.*]] : tensor<1x?xf32> into tensor<?xf32>
// CHECK: %[[PADDED:.*]] = tensor.pad %[[COLLAPSE]] low[1] high[0] {
// CHECK: ^bb0(%[[IDX:.*]]: index):
// CHECK: tensor.yield %[[CST]] : f32
// CHECK: } : tensor<?xf32> to tensor<16xf32>
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[PADDED]] {{\[\[}}0, 1]] output_shape [1, 16] : tensor<16xf32> into tensor<1x16xf32>
// CHECK: return %[[EXPANDED]] : tensor<1x16xf32>
// -----
#map = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 + d4, d2 + d5, d6)>
#map1 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3)>
#map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)>
module {
func.func @drop_unit_dim_corresponding_to_dynamic_dim(%arg0: tensor<1x?x?x1xf32>, %arg1: index) -> tensor<?x1x61x1xf32> {
%cst = arith.constant dense<1.000000e+00> : tensor<1x1x1x1xf32>
%0 = tensor.empty(%arg1) : tensor<?x1x61x1xf32>
%1 = linalg.generic {indexing_maps = [#map, #map1, #map2], iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"]} ins(%arg0, %cst : tensor<1x?x?x1xf32>, tensor<1x1x1x1xf32>) outs(%0 : tensor<?x1x61x1xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%2 = arith.mulf %in, %in_0 : f32
%3 = arith.addf %out, %2 : f32
linalg.yield %3 : f32
} -> tensor<?x1x61x1xf32>
return %1 : tensor<?x1x61x1xf32>
}
}
// CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (0, d0)>
// CHECK: #[[$MAP2:.+]] = affine_map<(d0) -> ()>
@ -1097,23 +1135,6 @@ func.func @drop_known_unit_constant_low_high(%arg0: tensor<1x383x128xf32>) -> te
// CHECK: return %[[VAL_14]] : tensor<?x1x61x1xf32>
// CHECK: }
#map = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 + d4, d2 + d5, d6)>
#map1 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3)>
#map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)>
module {
func.func @drop_unit_dim_corresponding_to_dynamic_dim(%arg0: tensor<1x?x?x1xf32>, %arg1: index) -> tensor<?x1x61x1xf32> {
%cst = arith.constant dense<1.000000e+00> : tensor<1x1x1x1xf32>
%0 = tensor.empty(%arg1) : tensor<?x1x61x1xf32>
%1 = linalg.generic {indexing_maps = [#map, #map1, #map2], iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"]} ins(%arg0, %cst : tensor<1x?x?x1xf32>, tensor<1x1x1x1xf32>) outs(%0 : tensor<?x1x61x1xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%2 = arith.mulf %in, %in_0 : f32
%3 = arith.addf %out, %2 : f32
linalg.yield %3 : f32
} -> tensor<?x1x61x1xf32>
return %1 : tensor<?x1x61x1xf32>
}
}
// -----
func.func @no_fold_empty_tensor_dim_out_of_bounds(%arg0: tensor<1x?x10xf32>) -> tensor<1x?xf32> {