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Differential D105607

[mlir][linalg] Fix typo in ExtractSliceOfPadTensorSwapPattern
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Authored by springerm on Jul 7 2021, 11:24 PM.

Details

Reviewers
nicolasvasilache
ThomasRaoux
Commits
rGb70dde522d50: [mlir][linalg] Fix typo in ExtractSliceOfPadTensorSwapPattern

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springerm created this revision.Jul 7 2021, 11:24 PM
Herald added subscribers: dcaballe, cota, mravishankar and 17 others. · View Herald TranscriptJul 7 2021, 11:24 PM
springerm requested review of this revision.Jul 7 2021, 11:24 PM
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springerm updated this revision to Diff 357140.Jul 7 2021, 11:27 PM

change name of unit test

Harbormaster completed remote builds in B112922: Diff 357140.Jul 8 2021, 12:40 AM
nicolasvasilache accepted this revision.Jul 14 2021, 12:10 AM
This revision is now accepted and ready to land.Jul 14 2021, 12:10 AM
This revision was landed with ongoing or failed builds.Jul 14 2021, 6:30 AM
Closed by commit rGb70dde522d50: [mlir][linalg] Fix typo in ExtractSliceOfPadTensorSwapPattern (authored by springerm). · Explain Why
This revision was automatically updated to reflect the committed changes.
springerm added a commit: rGb70dde522d50: [mlir][linalg] Fix typo in ExtractSliceOfPadTensorSwapPattern.

Revision Contents

PathSize
mlir/
lib/
Dialect/
Linalg/
Transforms/
2 lines
test/
Dialect/
Linalg/
24 lines

Diff 357139

mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp

Show First 20 Lines • Show All 948 Lines • ▼ Show 20 LinesLogicalResult ExtractSliceOfPadTensorSwapPattern::matchAndRewrite(
// do not generate a SliceOp. (The result shape of the SliceOp would // do not generate a SliceOp. (The result shape of the SliceOp would
// have a dimension of size 0, the semantics of which is unclear.) // have a dimension of size 0, the semantics of which is unclear.)
auto createGenerateOp = [&]() { auto createGenerateOp = [&]() {
// The shape of the GenerateOp is the same as the existing SliceOp. // The shape of the GenerateOp is the same as the existing SliceOp.
RankedTensorType type = sliceOp.getType(); RankedTensorType type = sliceOp.getType();
SmallVector<Value> dynDims; SmallVector<Value> dynDims;
for (unsigned i = 0; i < type.getRank(); ++i) { for (unsigned i = 0; i < type.getRank(); ++i) {
if (type.isDynamicDim(i)) if (type.isDynamicDim(i))
dynDims.push_back(asValue(rewriter, loc, sliceOp.getMixedOffsets()[i])); dynDims.push_back(asValue(rewriter, loc, sliceOp.getMixedSizes()[i]));
} }
// Create GenerateOp. // Create GenerateOp.
auto generateOp = rewriter.create<tensor::GenerateOp>(loc, type, dynDims); auto generateOp = rewriter.create<tensor::GenerateOp>(loc, type, dynDims);
// Copy region to new op. // Copy region to new op.
BlockAndValueMapping bvm; BlockAndValueMapping bvm;
padOp.region().cloneInto(&generateOp.getRegion(), bvm); padOp.region().cloneInto(&generateOp.getRegion(), bvm);
▲ Show 20 Lines • Show All 61 Lines • Show Last 20 Lines

mlir/test/Dialect/Linalg/subtensor-of-padtensor.mlir

Show First 20 Lines • Show All 147 Lines • ▼ Show 20 Linesfunc @dynamic_high_pad(%arg0 : tensor<?x5xf32>, %h1: index, %pad : f32) -> tensor<3x4xf32> {
%0 = linalg.pad_tensor %arg0 low[0, 0] high[%h1, 8] { %0 = linalg.pad_tensor %arg0 low[0, 0] high[%h1, 8] {
^bb0(%arg1: index, %arg2: index): ^bb0(%arg1: index, %arg2: index):
linalg.yield %pad : f32 linalg.yield %pad : f32
} : tensor<?x5xf32> to tensor<?x13xf32> } : tensor<?x5xf32> to tensor<?x13xf32>
%1 = tensor.extract_slice %0[2, 4] [3, 4] [1, 1] : tensor<?x13xf32> to tensor<3x4xf32> %1 = tensor.extract_slice %0[2, 4] [3, 4] [1, 1] : tensor<?x13xf32> to tensor<3x4xf32>
return %1 : tensor<3x4xf32> return %1 : tensor<3x4xf32>
} }
// -----
// CHECK-LABEL: @dynamic_extract_offset
// CHECK-SAME: %[[ARG0:.*]]: tensor<?x5xf32>, %[[ARG1:.*]]: index
// CHECK-NOT: linalg.pad_tensor
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[RESULT:.*]] = scf.if %{{.*}} -> (tensor<?x4xf32>) {
// CHECK: %[[GEN:.*]] = tensor.generate %[[ARG1]]
// CHECK: scf.yield %[[GEN]]
// CHECK: } else {
// CHECK: %[[SUBTENSOR:.*]] = tensor.extract_slice %[[ARG0]][%{{.*}}, 4] [%{{.*}}, 1] [1, 1] : tensor<?x5xf32> to tensor<?x1xf32>
// CHECK: %[[PADTENSOR:.*]] = linalg.pad_tensor %[[SUBTENSOR]] low[0, 0] high[%{{.*}}, 3]
// CHECK: scf.yield %[[PADTENSOR]]
// CHECK: }
// CHECK: return %[[RESULT]]
func @dynamic_extract_offset(%arg0 : tensor<?x5xf32>, %s1: index, %pad : f32) -> tensor<?x4xf32> {
%0 = linalg.pad_tensor %arg0 low[0, 0] high[7, 8] {
^bb0(%arg1: index, %arg2: index):
linalg.yield %pad : f32
} : tensor<?x5xf32> to tensor<?x13xf32>
%1 = tensor.extract_slice %0[2, 4] [%s1, 4] [1, 1] : tensor<?x13xf32> to tensor<?x4xf32>
return %1 : tensor<?x4xf32>
}