[mlir] [VectorOps] Rewriting of vector.extract/insert_slices to other vector ops

[aartbik]

Summary

Rewrites the extract/insert_slices operation in terms of
strided_slice/insert_strided_slice ops with intermediate
tuple uses (that should get optimimized away with typical
usage). This is done in a separate "pass" to enable testing
this particular rewriting in isolation.

[nicolasvasilache]

I really like how this has evolved from the original point, it is almost good to go on my end.

mlir/lib/Dialect/VectorOps/VectorTransforms.cpp

774

drop trivial braces plz, this is the MLIR style.

797

So I really like what you're doing re exposing and classifying patterns by intention, other places in the codebase should also do that and document it: "this set of patterns is useful for X"

Now, the selection of patterns you chose to add is a bit trickier IMO and I think we should:

1. name this populateVectorSlicesLoweringPatterns because it lowers out of these ops. Since it does not lower the type it is not a conversion so LoweringPatterns seems an appropriate name.
2. insert all the extra patterns (all the necessary tuple stuff) that ensure the Insert/ExtractSlices indeed go away, otherwise it will be surprising that the VectorSlicesLoweringPatterns are not enough to lower the vector slices.
3. explicitly list at the API doc level the patterns that are included (including the ) so people can easily look them up.

After we have enough of those, we will end up with pattern collections that implement behaviors.
This will have a granularity somewhere in between (1) individual patterns and (2) full transformations.
I expect this to be very powerful and independently testable like you do.

I am particularly sensitive to this in light of https://reviews.llvm.org/D73145 in which I could not break the phase ordering/dependence for now.

@rriddle what's your take on this?
Do you see a need / opportunity to have core infra support for collections of patterns and tests?

Side note:
So far I have shelved the debugging of why https://reviews.llvm.org/D73145 does not work with fused patterns but we need to resolve it.

mlir/test/lib/Transforms/TestVectorTransforms.cpp

41

Re pattern selection, it would be greate that populateVectorSlicesLoweringPatterns has everything it needs to make the test pass.
So concretely, this line should go away (and we should hunt other opportunities to improving other populateXXX methods by following your model).

[nicolasvasilache]

oh yes and trivial braces everywhere plz, I just annotated one.

[aartbik]

addressed ntv's comments

[aartbik]

mlir/lib/Dialect/VectorOps/VectorTransforms.cpp

774

you would think I knew that by now, but old habits die hard....

797

Done all (renamed and added doc), except that we don't need any specific tuple stuff anymore! Dead tuples are removed by DCE (in the greedy rewriter at least) while get-tuples-on-tuples are folded away automatically as well!

mlir/test/lib/Transforms/TestVectorTransforms.cpp

41

It has. I simply overlooked this line. It is not needed!

[merge_guards_bot]

Unit tests: fail. 62144 tests passed, 6 failed and 811 were skipped.

failed: MLIR.Dialect/VectorOps/vector-slices-transforms.mlir
failed: libc++.std/language_support/cmp/cmp_partialord/partialord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongeq/cmp.strongeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongord/strongord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakeq/cmp.weakeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakord/weakord.pass.cpp

clang-tidy: pass.

clang-format: pass.

Build artifacts: diff.json, clang-tidy.txt, clang-format.patch, CMakeCache.txt, console-log.txt, test-results.xml

Pre-merge checks is in beta. Report issue. Please join beta or enable it for your project.

[aartbik]

rebase

[merge_guards_bot]

Unit tests: fail. 62151 tests passed, 5 failed and 811 were skipped.

failed: libc++.std/language_support/cmp/cmp_partialord/partialord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongeq/cmp.strongeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongord/strongord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakeq/cmp.weakeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakord/weakord.pass.cpp

clang-tidy: pass.

clang-format: pass.

Build artifacts: diff.json, clang-tidy.txt, clang-format.patch, CMakeCache.txt, console-log.txt, test-results.xml

Pre-merge checks is in beta. Report issue. Please join beta or enable it for your project.

[nicolasvasilache]

mlir/lib/Dialect/VectorOps/VectorTransforms.cpp

797

Sorry I don't understand how the tuples get magically removed.
Both rewrites insert tuple/tuple_get but I do not see what patterns removes them concretely.
Maybe in these particular tests you wrote things degenerate into DCE (and that's great!), but I imagine it is possible to write tests where tuple ops will remain right?

799

Also, let's plz rename to XXXLowering given the naming argument above.

[aartbik]

renaming, added more comments

mlir/lib/Dialect/VectorOps/VectorTransforms.cpp

797

Well if of course does not guarantee that all tuple ops are eliminated (it only deals with slices ops rewriting), since tuple values may "leak" going in already. Take for example

func @extract_slices(%arg0: vector<3x3xf32>) -> tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>> {

%0 = vector.extract_slices %arg0, [2, 2], [1, 1]
  : vector<3x3xf32> into tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>>
return %0 : tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>>

}

this will be lowered to

func @extract_slices(%arg0: vector<3x3xf32>) -> tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>> {

  %0 = vector.strided_slice %arg0 {offsets = [0, 0], sizes = [2, 2], strides = [1, 1]} : vector<3x3xf32> to vector<2x2xf32>
  %1 = vector.strided_slice %arg0 {offsets = [0, 2], sizes = [2, 1], strides = [1, 1]} : vector<3x3xf32> to vector<2x1xf32>
  %2 = vector.strided_slice %arg0 {offsets = [2, 0], sizes = [1, 2], strides = [1, 1]} : vector<3x3xf32> to vector<1x2xf32>
  %3 = vector.strided_slice %arg0 {offsets = [2, 2], sizes = [1, 1], strides = [1, 1]} : vector<3x3xf32> to vector<1x1xf32>
  %4 = vector.tuple %0, %1, %2, %3 : vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>
  return %4 : tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>>
}

Here the tuple leaks, because there is no way to remove it. However, the lowering guarantees that any uses of slices where the tuple values are consumed, are lowered into something without tuples, even for the newly introduced operations.

I have added the comment on the lowering API a bit to reflect this.

[aartbik]

added "leaking" tuple to test

[aartbik]

PTAL

[merge_guards_bot]

Unit tests: fail. 62151 tests passed, 5 failed and 811 were skipped.

failed: libc++.std/language_support/cmp/cmp_partialord/partialord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongeq/cmp.strongeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongord/strongord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakeq/cmp.weakeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakord/weakord.pass.cpp

clang-tidy: pass.

clang-format: pass.

Build artifacts: diff.json, clang-tidy.txt, clang-format.patch, CMakeCache.txt, console-log.txt, test-results.xml

Pre-merge checks is in beta. Report issue. Please join beta or enable it for your project.

[merge_guards_bot]

Unit tests: fail. 62151 tests passed, 5 failed and 811 were skipped.

failed: libc++.std/language_support/cmp/cmp_partialord/partialord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongeq/cmp.strongeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_strongord/strongord.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakeq/cmp.weakeq.pass.cpp
failed: libc++.std/language_support/cmp/cmp_weakord/weakord.pass.cpp

clang-tidy: pass.

clang-format: pass.

Build artifacts: diff.json, clang-tidy.txt, clang-format.patch, CMakeCache.txt, console-log.txt, test-results.xml

Pre-merge checks is in beta. Report issue. Please join beta or enable it for your project.

[aartbik]

rebase to master

[merge_guards_bot]

Unit tests: pass. 62191 tests passed, 0 failed and 815 were skipped.

clang-tidy: pass.

clang-format: pass.

Build artifacts: diff.json, clang-tidy.txt, clang-format.patch, CMakeCache.txt, console-log.txt, test-results.xml

Pre-merge checks is in beta. Report issue. Please join beta or enable it for your project.

[nicolasvasilache]

Well my point here is that if you added the patterns to fold the tuples, you would do as good a job as you could in the absence of region/function boundaries.
Here you created an example that leaks in an unfixable way (because we have conciously decided to no lower the tuple<vector> type to LLVMIR and take the strong position that it must canonicalize/fold/DCE away).

Really the only thing I am trying to get is to either have:

1. the pattern collection be this:

void mlir::vector::populateVectorSlicesLoweringPatterns(
    OwningRewritePatternList &patterns, MLIRContext *context) {
  patterns.insert<ExtractSlicesOpLowering, InsertSlicesOpLowering, TupleGetFolderOp, OtherRelevantTuplePatterns>(context);
}

2. a good justification why that would be a bad idea.

The outcome I am looking for is an API that makes is easy and unsurprising to add patterns that guarantee Slices are lowered away (and it should be a "compiler bug" if they are remaining).
All this because we refuse to have a representation for tuple<vector<4x8x16x32xf32>.... > in LLVMIR (unless strong data comes to suggest this inuition is wrong).

Does this make sense?

[aartbik]

Hmm. I would argue that the tuple discussion makes less sense in the context of this particular rewriting (since it really deals with getting rid of extract_slices/insert_slices using other slice ops). Cleaning up tuple uses is a nice bonus, but not a requirement, since tuples are really a part of the vector dialect to start with, as the leaking example shows. If you want something that blocks any tuple uses, I would argue that we need another pattern population for that, and run that at places where we need to guarantee they are gone (such as the lowering to LLVM, although the "legality" part takes care of it there already).

Other than this comment, I am not sure what you would like to see added in this CL.

[nicolasvasilache]

ok, fair enough, thank you Aart!

[nicolasvasilache]

Note: I was probably overfitting on the issues I have with https://reviews.llvm.org/D73145 :)

[rriddle]

mlir/include/mlir/Dialect/VectorOps/VectorOps.h

51

I don't understand this comment. I don't see how this lowering removes tuples. The pattern driver performs some DCE, but I don't see how that is related to these patterns.

[aartbik]

mlir/include/mlir/Dialect/VectorOps/VectorOps.h

51

Yes, this assumes the patterns are run through the greedy driver (or a similar driver that calls DCE and folds as part of the rewriting). I was trying to convey that rewriting of a typical extract_slices or insert_slices where the individual parts are always consumed somehow leaves no tuple behind. I can send out a follow-up CL with some rephrased comments to continue the discussion there.

mlir/include/mlir/Dialect/VectorOps/VectorOps.h

 /// Collect a set of vector-to-vector canonicalization patterns.
 void populateVectorToVectorCanonicalizationPatterns(
     OwningRewritePatternList &patterns, MLIRContext *context);
 
 /// Collect a set of vector-to-vector transformation patterns.
 void populateVectorToVectorTransformationPatterns(
     OwningRewritePatternList &patterns, MLIRContext *context);
 
+/// Collect a set of vector slices transformation patterns:
+///    ExtractSlicesOpLowering, InsertSlicesOpLowering
+/// Useful for clients that want to express all vector "slices"
+/// ops in terms of more elementary vector "slice" ops. If all
+/// "produced" tuple values are "consumed" (the most common
+/// use for "slices" ops), this lowering removes all tuple related
+/// operations as well (through DCE and folding). If tuple values
+/// "leak" coming in, however, some tuple related ops will remain.
+void populateVectorSlicesLoweringPatterns(OwningRewritePatternList &patterns,
+                                          MLIRContext *context);
+
 /// Returns the integer type required for subscripts in the vector dialect.
 IntegerType getVectorSubscriptType(Builder &builder);
 
 /// Returns an integer array attribute containing the given values using
 /// the integer type required for subscripts in the vector dialect.
 ArrayAttr getVectorSubscriptAttr(Builder &b, ArrayRef<int64_t> values);
 
 #define GET_OP_CLASSES
 #include "mlir/Dialect/VectorOps/VectorOps.h.inc"
 
 } // end namespace vector
 } // end namespace mlir
 
 #endif // MLIR_DIALECT_VECTOROPS_VECTOROPS_H

mlir/lib/Dialect/VectorOps/VectorTransforms.cpp

 //===- VectorToLoops.cpp - Conversion within the Vector dialect -----------===//
 //
 // Part of the MLIR Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements target-independent rewrites as 1->N patterns.
 //
 //===----------------------------------------------------------------------===//
 
 #include <type_traits>
 
 #include "mlir/Dialect/AffineOps/AffineOps.h"
+#include "mlir/Dialect/StandardOps/Ops.h"
 #include "mlir/Dialect/VectorOps/VectorOps.h"
 #include "mlir/Dialect/VectorOps/VectorTransforms.h"
 #include "mlir/Dialect/VectorOps/VectorUtils.h"
 #include "mlir/IR/AffineExpr.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/Attributes.h"
 #include "mlir/IR/Builders.h"
 #include "mlir/IR/Function.h"
 #include "mlir/IR/Location.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/IR/Module.h"
 #include "mlir/IR/OperationSupport.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/IR/Types.h"
 #include "mlir/Support/Functional.h"
+#include "mlir/Support/MathExtras.h"
 #include "mlir/Support/STLExtras.h"
 
 #include "llvm/Support/CommandLine.h"
 #include "llvm/Support/Debug.h"
 #include "llvm/Support/raw_ostream.h"
 
 #define DEBUG_TYPE "vector-to-vector"
 
PatternMatchResult matchAndRewrite(vector::TupleGetOp tupleGetOp,
 
     // Forward Value from 'tupleOp' at 'tupleGetOp.index'.
     Value tupleValue = tupleOp.getOperand(tupleGetOp.getIndex());
     rewriter.replaceOp(tupleGetOp, tupleValue);
     return matchSuccess();
   }
 };
 
+/// Progressive lowering of ExtractSlicesOp to tuple of StridedSliceOp.
+/// One:
+///   %x = vector.extract_slices %0
+/// is replaced by:
+///   %a = vector.strided_slice %0
+///   %b = vector.strided_slice %0
+///   ..
+///   %x = vector.tuple %a, %b, ..
+class ExtractSlicesOpLowering
+    : public OpRewritePattern<vector::ExtractSlicesOp> {
+public:
+  using OpRewritePattern<vector::ExtractSlicesOp>::OpRewritePattern;
+
+  // TODO(ajcbik): refactor slice utilities out into VectorUtils.h
+  PatternMatchResult matchAndRewrite(vector::ExtractSlicesOp op,
+                                     PatternRewriter &rewriter) const override {
+    auto loc = op.getLoc();
+
+    VectorType vectorType = op.getSourceVectorType();
+    int64_t rank = vectorType.getRank();
+    auto shape = vectorType.getShape();
+
+    SmallVector<int64_t, 4> sizes;
+    op.getSizes(sizes);
+    SmallVector<int64_t, 4> strides;
+    op.getStrides(strides); // all-ones at the moment
+
+    // Compute the number of slices in each dimension.
+    SmallVector<int64_t, 4> sliceDimCounts(rank);
+    for (int64_t r = 0; r < rank; ++r)
+      sliceDimCounts[r] = ceilDiv(shape[r], sizes[r]);
+
+    // For each element in the tuple, generate the proper strided slice.
+    auto basis = computeStrides(sliceDimCounts);
+    TupleType tupleType = op.getResultTupleType();
+    int64_t tupleSize = tupleType.size();
+    SmallVector<Value, 4> tupleValues(tupleSize);
+    for (int64_t i = 0; i < tupleSize; ++i) {
+      // De-linearize w.r.t. 'basis'.
+      auto vectorOffsets = delinearize(i, basis);
+      // Convert from unrolled vector-space offsets to element-space offsets.
+      auto elementOffsets = mlir::functional::zipMap(
+          [](int64_t v1, int64_t v2) { return v1 * v2; }, vectorOffsets, sizes);
+      // Compute the size of each slice.
+      SmallVector<int64_t, 4> sliceSizes(rank);
+      for (int64_t r = 0; r < rank; ++r)
+        sliceSizes[r] = std::min(sizes[r], shape[r] - elementOffsets[r]);
+      // Insert in tuple.
+      tupleValues[i] = rewriter.create<vector::StridedSliceOp>(
+          loc, op.vector(), elementOffsets, sliceSizes, strides);
+    }
+
+    rewriter.replaceOpWithNewOp<vector::TupleOp>(op, tupleType, tupleValues);
+    return matchSuccess();
+  }
+};
+
+/// Progressive lowering of InsertSlicesOp to series of InsertStridedSliceOp.
+/// One:
+///   %x = vector.insert_slices %0
+/// is replaced by:
+///   %r0 = vector.splat 0
+//    %t1 = vector.tuple_get %0, 0
+///   %r1 = vector.insert_strided_slice %r0, %t1
+//    %t2 = vector.tuple_get %0, 1
+///   %r2 = vector.insert_strided_slice %r1, %t2
+///   ..
+///   %x  = ..
+class InsertSlicesOpLowering : public OpRewritePattern<vector::InsertSlicesOp> {
+public:
+  using OpRewritePattern<vector::InsertSlicesOp>::OpRewritePattern;
+
+  // TODO(ajcbik): refactor slice utilities out into VectorUtils.h
+  PatternMatchResult matchAndRewrite(vector::InsertSlicesOp op,
+                                     PatternRewriter &rewriter) const override {
+    auto loc = op.getLoc();
+
+    VectorType vectorType = op.getResultVectorType();
+    int64_t rank = vectorType.getRank();
+    auto shape = vectorType.getShape();
+
+    SmallVector<int64_t, 4> sizes;
+    op.getSizes(sizes);
+    SmallVector<int64_t, 4> strides;
+    op.getStrides(strides); // all-ones at the moment
+
+    // Compute the number of slices in each dimension.
+    SmallVector<int64_t, 4> sliceDimCounts(rank);
+    for (int64_t r = 0; r < rank; ++r)
+      sliceDimCounts[r] = ceilDiv(shape[r], sizes[r]);
+
+    // Prepare result.
+    auto elemType = vectorType.getElementType();
+    Value zero = rewriter.create<ConstantOp>(loc, elemType,
+                                             rewriter.getZeroAttr(elemType));
+    Value result = rewriter.create<SplatOp>(loc, vectorType, zero);
+
+    // For each element in the tuple, extract the proper strided slice.
+    auto basis = computeStrides(sliceDimCounts);
+    TupleType tupleType = op.getSourceTupleType();
+    int64_t tupleSize = tupleType.size();
+    SmallVector<Value, 4> tupleValues(tupleSize);
+    for (int64_t i = 0; i < tupleSize; ++i) {
+      // De-linearize w.r.t. 'basis'.
+      auto vectorOffsets = delinearize(i, basis);
+      // Convert from unrolled vector-space offsets to element-space offsets.
+      auto elementOffsets = mlir::functional::zipMap(
+          [](int64_t v1, int64_t v2) { return v1 * v2; }, vectorOffsets, sizes);
+      // Compute the size of each slice.
+      SmallVector<int64_t, 4> sliceSizes(rank);
+      for (int64_t r = 0; r < rank; ++r)
+        sliceSizes[r] = std::min(sizes[r], shape[r] - elementOffsets[r]);
+      // Extract from tuple into the result.
+      auto index = rewriter.getI64IntegerAttr(i);
+      auto tupleGet = rewriter.create<vector::TupleGetOp>(
+          loc, tupleType.getType(i), op.getOperand(), index);
+      result = rewriter.create<vector::InsertStridedSliceOp>(
+          loc, tupleGet, result, elementOffsets, strides);
+    }
+
+    rewriter.replaceOp(op, result);
+    return matchSuccess();
+  }
+};
+
 } // namespace
 
 // TODO(andydavis) Add pattern to rewrite ExtractSlices(ConstantMaskOp).
 // TODO(andydavis) Add this as DRR pattern.
 void mlir::vector::populateVectorToVectorTransformationPatterns(
     OwningRewritePatternList &patterns, MLIRContext *context) {
   patterns.insert<SplitTransferReadOp, SplitTransferWriteOp, TupleGetFolderOp>(
       context);
 }
+
+void mlir::vector::populateVectorSlicesLoweringPatterns(
+    OwningRewritePatternList &patterns, MLIRContext *context) {
+  patterns.insert<ExtractSlicesOpLowering, InsertSlicesOpLowering>(context);
+}

mlir/test/Dialect/VectorOps/vector-slices-transforms.mlir

+// RUN: mlir-opt %s -test-vector-slices-conversion | FileCheck %s
+
+// CHECK-LABEL: func @extract_slices(%arg0: vector<3x3xf32>)
+//       CHECK: %[[SS:.*]] = vector.strided_slice %arg0 {offsets = [0, 0], sizes = [2, 2], strides = [1, 1]}
+//       CHECK: return %[[SS]]
+
+func @extract_slices(%arg0: vector<3x3xf32>) -> vector<2x2xf32> {
+  %0 = vector.extract_slices %arg0, [2, 2], [1, 1]
+    : vector<3x3xf32> into tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>>
+  %1 = vector.tuple_get %0, 0 : tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>>
+  return %1 : vector<2x2xf32>
+}
+
+// CHECK-LABEL: func @insert_slices(%arg0: vector<2x2xf32>, %arg1: vector<2x1xf32>, %arg2: vector<1x2xf32>, %arg3: vector<1x1xf32>)
+//       CHECK: %[[C0:.*]] = constant dense<0.000000e+00> : vector<3x3xf32>
+//       CHECK: %[[I0:.*]] = vector.insert_strided_slice %arg0, %[[C0]] {offsets = [0, 0], strides = [1, 1]}
+//       CHECK: %[[I1:.*]] = vector.insert_strided_slice %arg1, %[[I0]] {offsets = [0, 2], strides = [1, 1]}
+//       CHECK: %[[I2:.*]] = vector.insert_strided_slice %arg2, %[[I1]] {offsets = [2, 0], strides = [1, 1]}
+//       CHECK: %[[I3:.*]] = vector.insert_strided_slice %arg3, %[[I2]] {offsets = [2, 2], strides = [1, 1]}
+//       CHECK: return %[[I3]]
+
+func @insert_slices(%arg0: vector<2x2xf32>,
+                    %arg1: vector<2x1xf32>,
+                    %arg2: vector<1x2xf32>,
+                    %arg3: vector<1x1xf32>) -> vector<3x3xf32> {
+  %0 = vector.tuple %arg0, %arg1, %arg2, %arg3
+    : vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>
+  %1 = vector.insert_slices %0, [2, 2], [1, 1]
+    : tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>> into vector<3x3xf32>
+  return %1 : vector<3x3xf32>
+}
+
+// CHECK-LABEL: func @extract_insert_slices(%arg0: vector<3x3xf32>)
+//       CHECK: %[[C:.*]] = constant dense<0.000000e+00> : vector<3x3xf32>
+//       CHECK: %[[X0:.*]] = vector.strided_slice %arg0 {offsets = [0, 0], sizes = [2, 2], strides = [1, 1]}
+//       CHECK: %[[X1:.*]] = vector.strided_slice %arg0 {offsets = [0, 2], sizes = [2, 1], strides = [1, 1]}
+//       CHECK: %[[X2:.*]] = vector.strided_slice %arg0 {offsets = [2, 0], sizes = [1, 2], strides = [1, 1]}
+//       CHECK: %[[X3:.*]] = vector.strided_slice %arg0 {offsets = [2, 2], sizes = [1, 1], strides = [1, 1]}
+//       CHECK: %[[X4:.*]] = vector.insert_strided_slice %[[X0]], %[[C0]] {offsets = [0, 0], strides = [1, 1]}
+//       CHECK: %[[X5:.*]] = vector.insert_strided_slice %[[X1]], %[[X4]] {offsets = [0, 2], strides = [1, 1]}
+//       CHECK: %[[X6:.*]] = vector.insert_strided_slice %[[X2]], %[[X5]] {offsets = [2, 0], strides = [1, 1]}
+//       CHECK: %[[X7:.*]] = vector.insert_strided_slice %[[X3]], %[[X6]] {offsets = [2, 2], strides = [1, 1]}
+//       CHECK:return %[[X7]]
+
+func @extract_insert_slices(%arg0: vector<3x3xf32>) -> vector<3x3xf32> {
+  %0 = vector.extract_slices %arg0, [2, 2], [1, 1]
+    : vector<3x3xf32> into tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>>
+  %1 = vector.insert_slices %0, [2, 2], [1, 1]
+    : tuple<vector<2x2xf32>, vector<2x1xf32>, vector<1x2xf32>, vector<1x1xf32>> into vector<3x3xf32>
+  return %1 : vector<3x3xf32>
+}
+
+// CHECK-LABEL: func @extract_slices_tuple_leaks(%arg0: vector<4xf32>)
+//       CHECK: %[[X0:.*]] = vector.strided_slice %arg0 {offsets = [0], sizes = [2], strides = [1]}
+//       CHECK: %[[X1:.*]] = vector.strided_slice %arg0 {offsets = [2], sizes = [2], strides = [1]}
+//       CHECK: %[[X2:.*]] = vector.tuple %[[X0]], %[[X1]]
+//       CHECK: return %[[X2]]
+
+func @extract_slices_tuple_leaks(%arg0: vector<4xf32>) -> tuple<vector<2xf32>, vector<2xf32>> {
+  %0 = vector.extract_slices %arg0, [2], [1] : vector<4xf32> into tuple<vector<2xf32>, vector<2xf32>>
+  return %0 : tuple<vector<2xf32>, vector<2xf32>>
+}
+

mlir/test/lib/Transforms/TestVectorTransforms.cpp

 #include "mlir/Dialect/VectorOps/VectorTransforms.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/Pass/Pass.h"
 
 using namespace mlir;
 using namespace mlir::vector;
 
 namespace {
+
 #include "TestVectorTransformPatterns.h.inc"
 
 struct TestVectorToVectorConversion
     : public FunctionPass<TestVectorToVectorConversion> {
   void runOnFunction() override {
     OwningRewritePatternList patterns;
     auto *context = &getContext();
     populateWithGenerated(context, &patterns);
     populateVectorToVectorCanonicalizationPatterns(patterns, context);
     populateVectorToVectorTransformationPatterns(patterns, context);
     applyPatternsGreedily(getFunction(), patterns);
   }
 };
+
+struct TestVectorSlicesConversion
+    : public FunctionPass<TestVectorSlicesConversion> {
+  void runOnFunction() override {
+    OwningRewritePatternList patterns;
+    populateVectorSlicesLoweringPatterns(patterns, &getContext());
+    applyPatternsGreedily(getFunction(), patterns);
+  }
+};
+
 } // end anonymous namespace
 
 static PassRegistration<TestVectorToVectorConversion>
     pass("test-vector-to-vector-conversion",
          "Test conversion patterns between ops in the vector dialect");
+
+static PassRegistration<TestVectorSlicesConversion> slices_pass(
+    "test-vector-slices-conversion",
+    "Test conversion patterns that lower slices ops in the vector dialect");