[Mlir-commits] [mlir] [MLIR] Add pattern to bubble up tensor.extract_slice (PR #126898)
ofri frishman
llvmlistbot at llvm.org
Sun Feb 16 05:01:25 PST 2025
https://github.com/ofri-frishman updated https://github.com/llvm/llvm-project/pull/126898
>From 004a3e065368ab9e2d77ba1c33d976310a950cdf Mon Sep 17 00:00:00 2001
From: Ofri Frishman <ofri.frishman at mobileye.com>
Date: Wed, 12 Feb 2025 13:12:48 +0200
Subject: [PATCH] [MLIR] Add pattern to bubble up tensor.extract_slice
Add a pattern that bubbles up tensor.extract_slice through
tensor.expand_shape, and add a transform op to tensor dialect
to directly use this pattern.
This pattern enables tiling and fusing op chains which contain
tensor.expand_shape if added as a cleanup pattern of tile and fuse
utility.
Without this pattern that would not be possible, as
tensor.expand_shape does not implement the tiling interface.
In addition, registering this pattern as a cleanup pattern for
transform.structured.fuse.
The pattren was first implement in IREE project by
Quinn Dawkins and is being upstreamed.
---
.../Tensor/TransformOps/TensorTransformOps.td | 10 +
.../Dialect/Tensor/Transforms/Transforms.h | 6 +
.../TransformOps/LinalgTransformOps.cpp | 1 +
.../TransformOps/TensorTransformOps.cpp | 5 +
.../Tensor/Transforms/ReshapePatterns.cpp | 180 ++++++++++++++++++
.../Dialect/Linalg/transform-op-fuse.mlir | 138 ++++++++++++++
.../Tensor/bubble-up-extract-slice-op.mlir | 74 +++++++
7 files changed, 414 insertions(+)
create mode 100644 mlir/test/Dialect/Tensor/bubble-up-extract-slice-op.mlir
diff --git a/mlir/include/mlir/Dialect/Tensor/TransformOps/TensorTransformOps.td b/mlir/include/mlir/Dialect/Tensor/TransformOps/TensorTransformOps.td
index 81bab1b0c82f7..a265e25a94aea 100644
--- a/mlir/include/mlir/Dialect/Tensor/TransformOps/TensorTransformOps.td
+++ b/mlir/include/mlir/Dialect/Tensor/TransformOps/TensorTransformOps.td
@@ -111,6 +111,16 @@ def ApplyReassociativeReshapeFoldingPatternsOp : Op<Transform_Dialect,
let assemblyFormat = "attr-dict";
}
+def ApplyBubbleUpExtractSlicePatternsOp : Op<Transform_Dialect,
+ "apply_patterns.tensor.bubble_up_extract_slice",
+ [DeclareOpInterfaceMethods<PatternDescriptorOpInterface>]> {
+ let description = [{
+ Indicates that tensor.extract_slice and its producer should swap location.
+ }];
+
+ let assemblyFormat = "attr-dict";
+}
+
def ApplyRewriteTensorOpsAsConstantPatternsOp : Op<Transform_Dialect,
"apply_patterns.tensor.rewrite_as_constant",
[DeclareOpInterfaceMethods<PatternDescriptorOpInterface>]> {
diff --git a/mlir/include/mlir/Dialect/Tensor/Transforms/Transforms.h b/mlir/include/mlir/Dialect/Tensor/Transforms/Transforms.h
index ae695e0326ca1..dc4558a605a59 100644
--- a/mlir/include/mlir/Dialect/Tensor/Transforms/Transforms.h
+++ b/mlir/include/mlir/Dialect/Tensor/Transforms/Transforms.h
@@ -58,6 +58,12 @@ void populateFoldTensorSubsetIntoVectorTransferPatterns(
void populateMergeConsecutiveInsertExtractSlicePatterns(
RewritePatternSet &patterns);
+/// Appends patterns that are used to bubble up tensor.extract slice op above
+/// its producer. When used as cleanup patterns of tile and fuse, enables fusing
+/// the producer with the consumer even if the producer does not implement the
+/// tiling interface.
+void populateBubbleUpExtractSliceOpPatterns(RewritePatternSet &patterns);
+
/// Populates `patterns` with patterns that drop redundant tensor.insert_slice
/// rank expansions.
void populateDropRedundantInsertSliceRankExpansionPatterns(
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index 51d1df52598c7..5146bebe0108e 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -582,6 +582,7 @@ transform::FuseOp::apply(transform::TransformRewriter &rewriter,
RewritePatternSet patterns(context);
tensor::ExtractSliceOp::getCanonicalizationPatterns(patterns, context);
tensor::populateMergeConsecutiveInsertExtractSlicePatterns(patterns);
+ tensor::populateBubbleUpExtractSliceOpPatterns(patterns);
tileAndFuseOptions.cleanupPatterns = std::move(patterns);
}
diff --git a/mlir/lib/Dialect/Tensor/TransformOps/TensorTransformOps.cpp b/mlir/lib/Dialect/Tensor/TransformOps/TensorTransformOps.cpp
index 99199252710f9..e4c60781ca270 100644
--- a/mlir/lib/Dialect/Tensor/TransformOps/TensorTransformOps.cpp
+++ b/mlir/lib/Dialect/Tensor/TransformOps/TensorTransformOps.cpp
@@ -125,6 +125,11 @@ void transform::ApplyReassociativeReshapeFoldingPatternsOp::populatePatterns(
tensor::populateReassociativeReshapeFoldingPatterns(patterns);
}
+void transform::ApplyBubbleUpExtractSlicePatternsOp::populatePatterns(
+ RewritePatternSet &patterns) {
+ tensor::populateBubbleUpExtractSliceOpPatterns(patterns);
+}
+
void transform::ApplyRewriteTensorOpsAsConstantPatternsOp::populatePatterns(
RewritePatternSet &patterns) {
ControlFoldFn defaultControlFn = [](OpOperand *fusedOperand) {
diff --git a/mlir/lib/Dialect/Tensor/Transforms/ReshapePatterns.cpp b/mlir/lib/Dialect/Tensor/Transforms/ReshapePatterns.cpp
index 5edd7a02bc42b..345653cda563b 100644
--- a/mlir/lib/Dialect/Tensor/Transforms/ReshapePatterns.cpp
+++ b/mlir/lib/Dialect/Tensor/Transforms/ReshapePatterns.cpp
@@ -6,9 +6,12 @@
//
//===----------------------------------------------------------------------===//
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tensor/Transforms/Transforms.h"
#include "mlir/IR/PatternMatch.h"
+#include "mlir/Interfaces/ValueBoundsOpInterface.h"
#include "llvm/Support/Debug.h"
using namespace mlir;
@@ -210,6 +213,178 @@ struct BubbleUpExpandThroughParallelCollapse
}
};
+/// Converts `tensor.extract_slice(tensor.expand_shape)` to
+/// `tensor.expand_shape(tensor.extract_slice)`.
+/// For this transformation to be possible, the slice must be fully contiguous
+/// within each reassociation group of the expand_shape. If the transformation
+/// is not possible, or if the slice is rank reducting, the function returns
+/// failure.
+///
+/// Example:
+/// ```
+/// %reshape = tensor.expand_shape %in [[0, 1], [2, 3], [4, 5, 6]]
+/// tensor<8x16x32xf32> to tensor<2x4x2x8x4x2x4xf32>
+/// %slice = tensor.extract_slice %reshape ...
+/// tensor<2x4x2x8x4x2x4xf32> to tensor<2x4x1x5x1x1x4xf32>
+///
+/// // The transformation is possible because each reassociation group has a
+/// // contiguous slice. (i.e., [2x4->2x4], [2x8->1x5], [4x2x4->1x1x4])
+/// // After the transformation:
+///
+/// %slice = tensor.extract_slice %in ...
+/// tensor<8x16x32xf32> to tensor<8x5x4xf32>
+/// %reshape = tensor.expand_shape %slice [[0, 1], [2, 3], [4, 5, 6]]
+/// tensor<8x5x4xf32> to tensor<2x4x1x5x1x1x4xf32>
+/// ```
+///
+/// Note - this pattern could be reworked to be a swap pattern between
+/// `tensor.expand_shape` and `tensor.extract_slice`, but is currently
+/// implemented only as a bubble up pattern for `tensor.extract_slice`.
+struct BubbleUpExpandShapeThroughExtractSlice
+ : public OpRewritePattern<tensor::ExtractSliceOp> {
+ using OpRewritePattern<tensor::ExtractSliceOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(tensor::ExtractSliceOp sliceOp,
+ PatternRewriter &rewriter) const override {
+ auto expandShapeOp =
+ sliceOp.getSource().getDefiningOp<tensor::ExpandShapeOp>();
+ if (!expandShapeOp) {
+ return rewriter.notifyMatchFailure(
+ sliceOp, "slice source not produced by expand_shape");
+ }
+
+ if (!sliceOp.hasUnitStride()) {
+ return rewriter.notifyMatchFailure(sliceOp,
+ "unsupported: non-unit stride");
+ }
+
+ SmallVector<OpFoldResult> offsets = sliceOp.getMixedOffsets();
+ SmallVector<OpFoldResult> sizes = sliceOp.getMixedSizes();
+
+ if (static_cast<size_t>(sliceOp.getResultType().getRank()) !=
+ sizes.size()) {
+ return rewriter.notifyMatchFailure(sliceOp,
+ "unimplemented: rank reducing slice");
+ }
+
+ // Helper variables and function for accumulating the new offset and length
+ // values.
+ Location loc = expandShapeOp->getLoc();
+ AffineExpr d0, d1, d2;
+ bindDims(rewriter.getContext(), d0, d1, d2);
+ // Multiply two integers.
+ auto mul = [&](OpFoldResult v1, OpFoldResult v2) {
+ auto mulMap = AffineMap::get(2, 0, {d0 * d1});
+ return affine::makeComposedFoldedAffineApply(rewriter, loc, mulMap,
+ {v1, v2});
+ };
+
+ SmallVector<OpFoldResult> outputShape =
+ getMixedValues(expandShapeOp.getStaticOutputShape(),
+ expandShapeOp.getOutputShape(), rewriter);
+
+ auto isZeroOffsetAndFullSize =
+ [](OpFoldResult offset, OpFoldResult sliceSize, OpFoldResult size) {
+ if (!isConstantIntValue(offset, 0))
+ return false;
+ FailureOr<bool> maybeEqual =
+ ValueBoundsConstraintSet::areEqual(sliceSize, size);
+ return llvm::succeeded(maybeEqual) && maybeEqual.value();
+ };
+
+ // First verify that this is a full slice of the expanded tensor.
+ for (const ReassociationIndices &indices :
+ expandShapeOp.getReassociationIndices()) {
+ int64_t i = 0;
+ int64_t e = indices.size();
+ // Find the first expanded dim after the first dim with non-unit extracted
+ // size.
+ for (; i < e; ++i) {
+ if (!isConstantIntValue(sizes[indices[i]], 1)) {
+ // +1 to skip the first non-unit size dim.
+ i++;
+ break;
+ }
+ }
+
+ // Verify that all subsequent dimensions extract the full size of the
+ // source tensor.
+ for (; i < e; ++i) {
+ int64_t expandedDim = indices[i];
+ if (!isZeroOffsetAndFullSize(offsets[expandedDim], sizes[expandedDim],
+ outputShape[expandedDim])) {
+ return rewriter.notifyMatchFailure(
+ sliceOp, "Not a contiguous slice of the expanded tensor.");
+ }
+ }
+ }
+
+ // Compute new offsets, lengths, and strides.
+ SmallVector<OpFoldResult> newOffsets, newLengths, newStrides;
+ for (const ReassociationIndices &indices :
+ expandShapeOp.getReassociationIndices()) {
+ OpFoldResult newSize = rewriter.getIndexAttr(1);
+ SmallVector<OpFoldResult> basis, delinOffsets;
+
+ int64_t i = 0;
+ int64_t e = indices.size();
+ // Offset = cumulative product of leading unit extracted dims.
+ for (; i < e; ++i) {
+ int64_t expandedDim = indices[i];
+ if (!isConstantIntValue(sizes[expandedDim], 1))
+ break;
+
+ basis.push_back(outputShape[expandedDim]);
+ delinOffsets.push_back(offsets[expandedDim]);
+ }
+
+ if (i != e) {
+ int64_t expandedDim = indices[i];
+ basis.push_back(outputShape[expandedDim]);
+ delinOffsets.push_back(offsets[expandedDim]);
+ newSize = sizes[expandedDim];
+ i++;
+ }
+
+ for (; i < e; ++i) {
+ OpFoldResult fullSize = outputShape[indices[i]];
+ basis.push_back(fullSize);
+ delinOffsets.push_back(rewriter.getIndexAttr(0));
+ newSize = mul(newSize, fullSize);
+ }
+ SmallVector<Value> offsetVals =
+ llvm::map_to_vector(delinOffsets, [&](OpFoldResult ofr) {
+ return getValueOrCreateConstantIndexOp(rewriter, loc, ofr);
+ });
+ OpFoldResult newOffset =
+ rewriter
+ .create<affine::AffineLinearizeIndexOp>(loc, offsetVals, basis,
+ /*disjoint=*/true)
+ .getResult();
+ newOffsets.push_back(newOffset);
+ newLengths.push_back(newSize);
+
+ // Only unit stride supported.
+ newStrides.push_back(rewriter.getIndexAttr(1));
+ }
+
+ // The shape of the result can be obtained from the sizes passed in.
+ SmallVector<Value> dynDims;
+ SmallVector<int64_t> shape;
+ dispatchIndexOpFoldResults(sizes, dynDims, shape);
+ RankedTensorType resultType = RankedTensorType::get(
+ shape, expandShapeOp.getResultType().getElementType());
+
+ // Create a new ExtractSliceOp and ExpandShapeOp.
+ Value newSliceOp = rewriter.create<tensor::ExtractSliceOp>(
+ loc, expandShapeOp.getSrc(), newOffsets, newLengths, newStrides);
+ rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
+ sliceOp, resultType, newSliceOp,
+ expandShapeOp.getReassociationIndices(), sizes);
+ return success();
+ }
+};
+
} // namespace
void mlir::tensor::populateReassociativeReshapeFoldingPatterns(
@@ -227,3 +402,8 @@ void mlir::tensor::populateBubbleUpExpandShapePatterns(
RewritePatternSet &patterns) {
patterns.add<BubbleUpExpandThroughParallelCollapse>(patterns.getContext());
}
+
+void mlir::tensor::populateBubbleUpExtractSliceOpPatterns(
+ RewritePatternSet &patterns) {
+ patterns.add<BubbleUpExpandShapeThroughExtractSlice>(patterns.getContext());
+}
diff --git a/mlir/test/Dialect/Linalg/transform-op-fuse.mlir b/mlir/test/Dialect/Linalg/transform-op-fuse.mlir
index ac1ca9319d335..c32a035cdc6e8 100644
--- a/mlir/test/Dialect/Linalg/transform-op-fuse.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-fuse.mlir
@@ -278,3 +278,141 @@ module attributes {transform.with_named_sequence} {
transform.yield
}
}
+
+// -----
+
+// CHECK-LABEL: func.func @swap_expand_shape_with_extract_slice
+// CHECK: scf.for %[[X:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: scf.for %[[Y:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: scf.for %[[Z:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: %[[LINEAR_IDX:.+]] = affine.linearize_index disjoint [%[[X]], %[[Y]], %[[Z]]] by (2, 3, 10)
+// CHECK: %[[SLICE:.+]] = tensor.extract_slice %{{.*}}[%[[LINEAR_IDX]]] [5] [1] : tensor<60xf32> to tensor<5xf32>
+// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[SLICE]] {{\[\[}}0, 1, 2]] output_shape [1, 1, 5]
+// CHECK: linalg.exp ins(%[[EXPAND]]
+func.func @swap_expand_shape_with_extract_slice(%0: tensor<60xf32>) -> tensor<2x3x10xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2]] output_shape [2, 3, 10] : tensor<60xf32> into tensor<2x3x10xf32>
+ %empty = tensor.empty() : tensor<2x3x10xf32>
+ %exp = linalg.exp ins(%expand : tensor<2x3x10xf32>) outs(%empty : tensor<2x3x10xf32>) -> tensor<2x3x10xf32>
+ return %exp : tensor<2x3x10xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.exp"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ %transformed, %loops:3 = transform.structured.fuse %0 [1, 1, 5] interchange [0, 1, 2] apply_cleanup = true :
+ (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">, !transform.any_op, !transform.any_op)
+ transform.yield
+ }
+}
+
+// -----
+
+// CHECK-LABEL: func.func @swap_expand_shape_with_extract_slice_full_inner_dim
+// CHECK: scf.for %[[X:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: scf.for %[[Y:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: %[[LINEAR_IDX:.+]] = affine.linearize_index disjoint [%[[X]], %[[Y]]{{.*}} by (3, 4, 10)
+// CHECK: %[[SLICE:.+]] = tensor.extract_slice %{{.*}}[%[[LINEAR_IDX]]] [20] [1] : tensor<120xf32> to tensor<20xf32>
+// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[SLICE]] {{\[\[}}0, 1, 2]] output_shape [1, 2, 10]
+// CHECK: linalg.exp ins(%[[EXPAND]]
+func.func @swap_expand_shape_with_extract_slice_full_inner_dim(%0: tensor<120xf32>) -> tensor<3x4x10xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2]] output_shape [3, 4, 10] : tensor<120xf32> into tensor<3x4x10xf32>
+ %empty = tensor.empty() : tensor<3x4x10xf32>
+ %exp = linalg.exp ins(%expand : tensor<3x4x10xf32>) outs(%empty : tensor<3x4x10xf32>) -> tensor<3x4x10xf32>
+ return %exp : tensor<3x4x10xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.exp"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ %transformed, %loops:2 = transform.structured.fuse %0 [1, 2, 0] interchange [0, 1, 2] apply_cleanup = true :
+ (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">, !transform.any_op)
+ transform.yield
+ }
+}
+
+// -----
+
+// CHECK-LABEL: func.func @no_swap_expand_shape_with_extract_slice_non_contiguous
+// CHECK: tensor.expand_shape
+// CHECK: scf.for
+// CHECK: scf.for
+// CHECK: scf.for
+// CHECK: linalg.exp
+func.func @no_swap_expand_shape_with_extract_slice_non_contiguous(%0: tensor<120xf32>) -> tensor<3x4x10xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2]] output_shape [3, 4, 10] : tensor<120xf32> into tensor<3x4x10xf32>
+ %empty = tensor.empty() : tensor<3x4x10xf32>
+ %exp = linalg.exp ins(%expand : tensor<3x4x10xf32>) outs(%empty : tensor<3x4x10xf32>) -> tensor<3x4x10xf32>
+ return %exp : tensor<3x4x10xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.exp"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ %transformed, %loops:3 = transform.structured.fuse %0 [1, 2, 5] interchange [0, 1, 2] apply_cleanup = true :
+ (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">, !transform.any_op, !transform.any_op)
+ transform.yield
+ }
+}
+
+// -----
+
+// CHECK-LABEL: func.func @swap_expand_shape_with_extract_slice_multiple_expanded_dims
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: scf.for %[[X:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: scf.for %[[Y:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: scf.for %[[Z:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: scf.for %[[W:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: %[[LINEAR_IDX0:.+]] = affine.linearize_index disjoint [%[[X]], %[[Y]], %[[C0]]] by (3, 4, 10)
+// CHECK: %[[LINEAR_IDX1:.+]] = affine.linearize_index disjoint [%[[Z]], %[[W]]] by (7, 8)
+// CHECK: %[[SLICE:.+]] = tensor.extract_slice %{{.*}}[%[[LINEAR_IDX0]], %[[LINEAR_IDX1]]] [20, 4] [1, 1] : tensor<120x56xf32> to tensor<20x4xf32>
+// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[SLICE]] {{\[\[}}0, 1, 2], [3, 4]] output_shape [1, 2, 10, 1, 4]
+// CHECK: linalg.exp ins(%[[EXPAND]]
+module {
+ func.func @swap_expand_shape_with_extract_slice_multiple_expanded_dims(%0: tensor<120x56xf32>) -> tensor<3x4x10x7x8xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2], [3, 4]] output_shape [3, 4, 10, 7, 8] : tensor<120x56xf32> into tensor<3x4x10x7x8xf32>
+ %empty = tensor.empty() : tensor<3x4x10x7x8xf32>
+ %exp = linalg.exp ins(%expand : tensor<3x4x10x7x8xf32>) outs(%empty : tensor<3x4x10x7x8xf32>) -> tensor<3x4x10x7x8xf32>
+ return %exp : tensor<3x4x10x7x8xf32>
+ }
+}
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.exp"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ %transformed, %loops:4 = transform.structured.fuse %0 [1, 2, 0, 1, 4] interchange [0, 1, 2, 3, 4] apply_cleanup = true :
+ (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">, !transform.any_op, !transform.any_op, !transform.any_op)
+ transform.yield
+ }
+}
+
+// -----
+
+// CHECK: scf.for %[[X:[A-Za-z0-9]+]] = {{.*}}
+// CHECK: %[[LINEAR_IDX:.+]] = affine.linearize_index disjoint [%[[X]], {{.*}} by (8, 32)
+// CHECK: %[[SLICE:.+]] = tensor.extract_slice %{{.*}}[0, 0, %[[LINEAR_IDX]]] [1, 1800, 32] [1, 1, 1] : tensor<1x1800x256xf32> to tensor<1x1800x32xf32>
+// CHECK: %[[ABS:.+]] = linalg.abs ins(%[[SLICE]]
+// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[ABS]] {{\[\[}}0], [1], [2, 3]] output_shape [1, 1800, 1, 32]
+// CHECK: linalg.exp ins(%[[EXPAND]]
+module {
+ func.func @swap_expand_shape_with_extract_slice_and_fuse_with_expand_producer(%0: tensor<1x1800x256xf32>) -> tensor<1x1800x8x32xf32> {
+ %empty1 = tensor.empty() : tensor<1x1800x256xf32>
+ %exp1 = linalg.abs ins(%0 : tensor<1x1800x256xf32>) outs(%empty1 : tensor<1x1800x256xf32>) -> tensor<1x1800x256xf32>
+ %expand = tensor.expand_shape %exp1 [[0], [1], [2, 3]] output_shape [1, 1800, 8, 32] : tensor<1x1800x256xf32> into tensor<1x1800x8x32xf32>
+ %empty2 = tensor.empty() : tensor<1x1800x8x32xf32>
+ %exp2 = linalg.exp ins(%expand : tensor<1x1800x8x32xf32>) outs(%empty2 : tensor<1x1800x8x32xf32>) -> tensor<1x1800x8x32xf32>
+ return %exp2 : tensor<1x1800x8x32xf32>
+ }
+}
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.exp"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ %transformed, %loops:1 = transform.structured.fuse %0 [0, 0, 1, 0] interchange [0, 1, 2, 3] apply_cleanup = true :
+ (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">)
+ transform.yield
+ }
+}
+
+
+
+
diff --git a/mlir/test/Dialect/Tensor/bubble-up-extract-slice-op.mlir b/mlir/test/Dialect/Tensor/bubble-up-extract-slice-op.mlir
new file mode 100644
index 0000000000000..ee92e4328d900
--- /dev/null
+++ b/mlir/test/Dialect/Tensor/bubble-up-extract-slice-op.mlir
@@ -0,0 +1,74 @@
+// RUN: mlir-opt -split-input-file -transform-interpreter %s | FileCheck %s
+
+// CHECK-LABEL: func.func @bubble_up_extract_slice_through_expand_shape(
+// CHECK-SAME: %[[ARG0:.*]]: tensor<60xf32>) -> tensor<1x1x5xf32> {
+// CHECK: %[[C1:.+]] = arith.constant 5 : index
+// CHECK: %[[EXTRACT:.*]] = tensor.extract_slice %[[ARG0]]{{\[}}%[[C1]]] [5] [1] : tensor<60xf32> to tensor<5xf32>
+// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %[[EXTRACT]] {{\[\[}}0, 1, 2]] output_shape [1, 1, 5] : tensor<5xf32> into tensor<1x1x5xf32>
+// CHECK: return %[[EXPAND]] : tensor<1x1x5xf32>
+
+func.func @bubble_up_extract_slice_through_expand_shape(%0: tensor<60xf32>) -> tensor<1x1x5xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2]] output_shape [2, 3, 10] : tensor<60xf32> into tensor<2x3x10xf32>
+ %extract = tensor.extract_slice %expand[0, 0, 5][1, 1, 5][1, 1, 1] : tensor<2x3x10xf32> to tensor<1x1x5xf32>
+ return %extract : tensor<1x1x5xf32>
+}
+
+
+// CHECK-LABEL: func.func @no_bubble_up_extract_slice_on_non_contiguous(
+// CHECK: %[[EXPAND:.*]] = tensor.expand_shape
+// CHECK: %[[EXTRACT:.*]] = tensor.extract_slice
+// CHECK: return %[[EXTRACT]]
+
+func.func @no_bubble_up_extract_slice_on_non_contiguous(%0: tensor<60xf32>) -> tensor<1x2x5xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2]] output_shape [2, 3, 10] : tensor<60xf32> into tensor<2x3x10xf32>
+ %extract = tensor.extract_slice %expand[0, 0, 5][1, 2, 5][1, 1, 1] : tensor<2x3x10xf32> to tensor<1x2x5xf32>
+ return %extract : tensor<1x2x5xf32>
+}
+
+
+// CHECK-LABEL: func.func @no_bubble_up_extract_slice_on_stride(
+// CHECK: %[[EXPAND:.*]] = tensor.expand_shape
+// CHECK: %[[EXTRACT:.*]] = tensor.extract_slice
+// CHECK: return %[[EXTRACT]]
+
+func.func @no_bubble_up_extract_slice_on_stride(%0: tensor<60xf32>) -> tensor<1x1x5xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2]] output_shape [2, 3, 10] : tensor<60xf32> into tensor<2x3x10xf32>
+ %extract = tensor.extract_slice %expand[0, 0, 5][1, 1, 5][1, 1, 2] : tensor<2x3x10xf32> to tensor<1x1x5xf32>
+ return %extract : tensor<1x1x5xf32>
+}
+
+
+// CHECK-LABEL: func.func @no_bubble_up_extract_slice_on_rank_reducing(
+// CHECK: %[[EXPAND:.*]] = tensor.expand_shape
+// CHECK: %[[EXTRACT:.*]] = tensor.extract_slice
+// CHECK: return %[[EXTRACT]]
+
+func.func @no_bubble_up_extract_slice_on_rank_reducing(%0: tensor<60xf32>) -> tensor<1x5xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2]] output_shape [2, 3, 10] : tensor<60xf32> into tensor<2x3x10xf32>
+ %extract = tensor.extract_slice %expand[0, 0, 5][1, 1, 5][1, 1, 1] : tensor<2x3x10xf32> to tensor<1x5xf32>
+ return %extract : tensor<1x5xf32>
+}
+
+
+// CHECK-LABEL: func.func @bubble_up_extract_slice_through_expand_shape_multiple_expanded_dims(
+// CHECK-SAME: %[[ARG0:.*]]: tensor<120x56xf32>) -> tensor<1x2x10x1x4xf32> {
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[EXTRACT:.*]] = tensor.extract_slice %[[ARG0]]{{\[}}%[[C0]], %[[C0]]] [20, 4] [1, 1] : tensor<120x56xf32> to tensor<20x4xf32>
+// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %[[EXTRACT]] {{\[\[}}0, 1, 2], [3, 4]] output_shape [1, 2, 10, 1, 4] : tensor<20x4xf32> into tensor<1x2x10x1x4xf32>
+// CHECK: return %[[EXPAND]] : tensor<1x2x10x1x4xf32>
+
+func.func @bubble_up_extract_slice_through_expand_shape_multiple_expanded_dims(%0: tensor<120x56xf32>) -> tensor<1x2x10x1x4xf32> {
+ %expand = tensor.expand_shape %0 [[0, 1, 2], [3, 4]] output_shape [3, 4, 10, 7, 8] : tensor<120x56xf32> into tensor<3x4x10x7x8xf32>
+ %extract = tensor.extract_slice %expand[0, 0, 0, 0, 0][1, 2, 10, 1, 4][1, 1, 1, 1, 1] : tensor<3x4x10x7x8xf32> to tensor<1x2x10x1x4xf32>
+ return %extract : tensor<1x2x10x1x4xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%root: !transform.any_op {transform.readonly}) {
+ %func_op = transform.structured.match ops{["func.func"]} in %root : (!transform.any_op) -> !transform.op<"func.func">
+ transform.apply_patterns to %func_op {
+ transform.apply_patterns.tensor.bubble_up_extract_slice
+ } : !transform.op<"func.func">
+ transform.yield
+ }
+}
\ No newline at end of file
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