[mlir][tensor][linalg] Introduce DataLayoutPropagation pass.

[hanchung]

Summary

It introduces a pattern that swaps linalg.generic + tensor.pack to
tensor.pack + linalg.generic. It requires all the iteration types
being parallel; the indexing map of output operand is identiy. They can
all be relaxed in the future.

The user can decide whether the propagation should be applied or not by
passing a control function.

[hanchung]

remove unused variable

update indexing_map

[chelini]

mlir/include/mlir/Dialect/Utils/IndexingUtils.h

83 ↗

(On Diff #478427)

I think we should update the comment here. You get [c, a].

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

151

is llvm::None padding? Can we also propagate if we have padding?

171

We can use:

rewriter.inlineRegionBefore(genericOp.getRegion(), newGenericOp.getRegion(), newGenericOp.getRegion().begin());

[nicolasvasilache]

mlir/include/mlir/Dialect/Utils/IndexingUtils.h

85 ↗

(On Diff #478427)

Why do you need the , N part ?

mlir/test/Dialect/Linalg/data-layout-propagation.mlir

12

please use linalg.map for all the generics in this test, they are simple maps

[nicolasvasilache]

mlir/include/mlir/Dialect/Linalg/Passes.td

49 ↗

(On Diff #478427)

Should this be a test pass ?
We don't want to start adding (and especially maintaining) heuristics at this point, right?

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

84

this is lacking docs

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

34

Please add this to PackOp

static PackOp::createDestinationTensor(...)

This is where one would expect to find this helper.

59

more docs plz, input IR, output IR etc.

68

The functionality should be implemented in a function that takes relevant pieces of IR (i.e. the packop) and returns the relevant pieces of IR (the new linalg op and the new unpack op).
The pattern should just call those.

This way we can connect a transform op and even replace that test pass.

87

this is lacking docs, and more especially why you need some non-serializable C++ lambda injected here

104

Seems like a very complex piece of code to leave undocumented and inlined here.

What can be hoisted, what becomes a util on some op etc?

[hanchung]

mlir/include/mlir/Dialect/Utils/IndexingUtils.h

83 ↗

(On Diff #478427)

I misunderstood the semantics of outer_dims_perm. It should be either a permutation or empty. The method does not make sense to me now.. We won't need this method anymore. I enhance the verifier in https://reviews.llvm.org/D138936

[hanchung]

address comments

[hanchung]

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

34

The trade-off is that we can't use tensor::createDimValues if we add it to PackOp. Otherwise, there will be cyclic deps. TensorOps -> TensorUtils -> TensorOps

68

Do we want it being able to connect to a transform op? My understanding is that transform ops aims to apply the pattern once, while propagation patterns would be applied until IR gets converged. The pass is at the position similar to element-wise fusion, IMO.

87

It was intended to provide a callback function for users. So the users can decide what to propagate. Since the pass is in early development phase, we can drop the support; and add it back when needed.

151

It could introduce undefined behavior if we unconditionally propagate pack op through all the ops. E.g., if the padding value is zero and there are division ops in a generic op. Some values of padding area could be NaN (0/0).

I disable the propagation in the pattern by default, and thinking to add an option for enabling aggressive propagation in the future.

mlir/test/Dialect/Linalg/data-layout-propagation.mlir

12

IIUC, linalg.map op is named op. I can't simply update the test file because the pattern works on linalg::GenericOp. Are you suggesting me to update the pattern taking linalg::LinalgOp instead? I think that could work for linalg.map, linalg.transpose, etc; I can take a stab at it, just wanna clarify before updating it.

[hanchung]

move the pass to test-only

[mravishankar]

mlir/include/mlir/Dialect/Linalg/Passes.td

49 ↗

(On Diff #478427)

+1 to this point. Might be better to expose the core utility method or patterns as populate methods, and move the pass itself to a test pass.

mlir/include/mlir/Dialect/Utils/IndexingUtils.h

85 ↗

(On Diff #478427)

If you want to update the SmallVector in place, you could use MutableArrayRef<..>.

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

68

It is still worth separating the core implementation into a stand-alone function (even if it is static and not exposed right now). Using fixed point is one way of applying the functionality. Even the elementwise op fusion patterns are just a wrapper around the core functions that implement the transformation.

87

This is the control that is based on call site constraints. Instead of using heuristics in the core method that decides when to apply this, we defer the control to the caller, and make a go/no-go decision....

[nicolasvasilache]

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

87

Injecting lambda form above allows inversion of control that is convenient very short term and has almost always proven to very quickly turn to technical debt.

Given recent offline discussions I had and other parts of the codebase I have seen, I am going to more seriously push back against this anti-pattern globally.
Injecting C++ callback control from above is a sign of missing abstractions and should almost always be disallowed.
The alternative is often to refactor multiple times until the right abstractions emerge.

In other words, the transformations we add must be functional-style, statically return the pieces of IR (existing created or updated) that make sense for that transform.
This is not something customizable, if you need more information then statically return more information: no backchannels through lambdas.

If you need different behaviors, instead of injecting a dynamic mechanism through a lambda, write another transformation that takes more/different inputs and return more/different outputs.
Refactor the reusable utility helpers to avoid copy-pasta.

These transformations can then be plugged into patterns and transform dialect ops who can be responsible for the switch between different static behaviors.

[hanchung]

remove unneeded changes

move implementations to functions

[hanchung]

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

87

thanks for the details! So we provide methods upstream, and let upstream patterns and downstream projects use the methods. They can always wrap the methods into a custom patterns w/o a callback function. I moved them to functions and the pattern becomes a wrapper of the method.

[nicolasvasilache]

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

87

We discussed offline with Mahesh and this may be one of the cases that I could see as an exception to the rule.

Some loose characteristics of potential green/red flags that seem to emerge are:

1. the information conveyed by the callback cannot be serialized in IR in a reasonable form
2. the callback use remains superficial and does not permeate the transform implementation but is called once at the high level (e.g. in the match part of a pattern or in a transform dialect op)
3. the callback does not capture or bypass the return mechanism
4. maybe others ?

I'd love a more principled discussion on the topic and that we come up with good guidelines we can follow.

Removing the blocker, thanks for your patience.

Edit: I don't know how to do this without accepting or resigning but consider my concern addressed :)

[hanchung]

mlir/test/Dialect/Linalg/data-layout-propagation.mlir

12

It's quite tricky because the builder and clone method can't be reused. The most tricky part is about updating indexing_maps. The builder and clone method does not take indexing_maps into account. Also, there is not a clean way to use setting methods for them.

[chelini]

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

181

We also need to make sure that the result of the current linalg generic operation has no users. Otherwise, the linalg generic remains alive without a body.

231

struct instead of class?

[chelini]

Overall looks good to me. Just left two minor comments.

[hanchung]

inlineRegionBefore -> cloneRegionBefore

class -> struct

[hanchung]

rebase

[mravishankar]

Thanks! Overall looks good to me. Have a few nits.

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

82

Nit: Move the comments before the statement to make it read better.

85

Nice! TIL.

[nicolasvasilache]

mlir/test/Dialect/Linalg/data-layout-propagation.mlir

12

@pifon2a ?

[hanchung]

move comments above the variable declaration

[hanchung]

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

181

I see the point. I think we should use cloneRegionBefore instead of inline*.

mlir/test/Dialect/Linalg/data-layout-propagation.mlir

12

I'm going to land the PR to unblock some work on @chelini side tomorrow. If it's an easy fix, I can give it a shot. If not, I'd prefer fixing it afterwards. Thanks!

[hanchung]

All the comments have been addressed except testing with linalg.map. I did some study and think that's not feasible to address. The pass works on the generic ops similar to ElementWiseFusion. If it can be done in elementwise fusion, it can also be done here. Since the other pass is testing with this form, I think it's not a big concern. I'm going to land the revision, and prefer addressing the issue afterwards if we know a way to address it.

[nicolasvasilache]

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

64

std::pair

81

nit: typo

84

This seems like it will crash if not a projected permutation.
Plz add a clear assert at the top-level of the func if this can never happen or consider failing.

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

 /// Patterns for fusing linalg operation on tensors.
 
 /// Pattern to fuse `linalg.generic` -> `linalg.generic` operations
 /// when both operations are fusable elementwise operations.
 void populateElementwiseOpsFusionPatterns(
     RewritePatternSet &patterns,
     const ControlFusionFn &controlElementwiseOpFusion);
 
+/// Patterns to bubble up or down data layout ops across other operations.
+void populateDataLayoutPropagationPatterns(RewritePatternSet &patterns);
+
 /// Pattern to remove dead operands and results of `linalg.generic` operations.
 /// This is effectively DCE for a linalg op.
 void populateEraseUnusedOperandsAndResultsPatterns(RewritePatternSet &patterns);
 
 /// Function type to control generic op dimension collapsing. It is expected
 /// to return an array of `ReassociationIndices` representing dimensions that
 /// should be merged.
 using GetCollapsableDimensionsFn =

mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td

def Tensor_PackOp : Tensor_RelayoutOp<"pack", [
 
   let extraClassDeclaration = commonExtraClassDeclaration # [{
     // Method to get the `ShapedType` of the result based on the inner tiles,
     // position of the inner tiles (innerDimsPos)  and interchange vector of
     // outer loops (outerDimsPerm).
     static ShapedType inferPackedType(ShapedType sourceType,
         ArrayRef<int64_t> innerTileSizes, ArrayRef<int64_t> innerDimsPos,
         ArrayRef<int64_t> outerDimsPerm = {});
+
+    static Value createDestinationTensor(OpBuilder &b, Location loc,
+        Value source, ArrayRef<OpFoldResult> innerTileSizes,
+        ArrayRef<int64_t> innerDimsPos, ArrayRef<int64_t> outerDimsPerm);
   }];
 }
 
 //===----------------------------------------------------------------------===//
 // UnPackOp
 //===----------------------------------------------------------------------===//
 
 def Tensor_UnPackOp : Tensor_RelayoutOp<"unpack"> {

mlir/lib/Dialect/Linalg/Transforms/CMakeLists.txt

 add_mlir_dialect_library(MLIRLinalgTransforms
   BubbleUpExtractSlice.cpp
   BufferizableOpInterfaceImpl.cpp
   Bufferize.cpp
   ConstantFold.cpp
+  DataLayoutPropagation.cpp
   DecomposeLinalgOps.cpp
   Detensorize.cpp
   DropUnitDims.cpp
   ElementwiseOpFusion.cpp
   ElementwiseToLinalg.cpp
   EraseUnusedOperandsAndResults.cpp
   FusePadOpWithLinalgProducer.cpp
   Fusion.cpp

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

+//===- DataLayoutPropagation.cpp -----------------------------------------===///
+//
+// Part of the LLVM 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
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Linalg/Passes.h"
+
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
+#include "mlir/Dialect/Linalg/Utils/Utils.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Dialect/Tensor/Utils/Utils.h"
+#include "mlir/Dialect/Utils/IndexingUtils.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+
+namespace mlir {
+#define GEN_PASS_DEF_LINALGDATALAYOUTPROPAGATION
+#include "mlir/Dialect/Linalg/Passes.h.inc"
+} // namespace mlir
+
+using namespace mlir;
+using namespace mlir::linalg;
+
+#define DEBUG_TYPE "linalg-data-layout-propagation"
+
+namespace {
+
+/// Returns a tuple for packed operand and indexing_map with the assumptions:
+///   1) The generic op is the producer of the pack op.
+///   2) The generic op has only one result.
+///   3) The indexing map of the output operand is identity.
+/// If the operand is a scalar or packing dimensions are all irrelevant to the
+/// operand, the opreand and the updated indexing map will be returned.
+/// Otherwise, it returns the packed operand and the updated indexing map. E.g.,
+///
+///   #map0 = affine_map<(d0, d1) -> (d0, d1)>
+///   #map1 = affine_map<(d0, d1) -> (d0)>
+///   #map2 = affine_map<(d0, d1) -> (d1)>
+///   %0 = linalg.generic {indexing_maps = [#map1, #map2, #map0],
+///                        iterator_types = ["parallel", "parallel"]}
+///      ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
+///      outs(%init : tensor<?x?xf32>) {
+///    ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
+///      %4 = arith.addf %arg3, %arg4 : f32
+///      linalg.yield %4 : f32
+///  } -> tensor<?x?xf32>
+///  %1 = tensor.pack %0
+///    inner_dims_pos = [0, 1]
+///    inner_tiles = [8, 2]
+///    into %dest : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
+///
+///  Taking the first input operand as an example, the inner tile size of d1 is
+///  8. Thus, the below operation and `affine_map<(d0, d1, d2, d3)> ->
+///  affine_map<(d1, d3)>` will be returned.
+///
+///  %pack = tensor.pack %arg0
+///    inner_dims_pos = [0]
+///    inner_tiles = [8]
+///    into %init : tensor<?xf32> -> tensor<?x8xf32>
+static std::tuple<Value, AffineMap>
+getOrCreatePackedViewOfOperand(OpBuilder &b, Location loc,
+                               tensor::PackOp packOp, GenericOp genericOp,
+                               OpOperand *opOperand) {
+  int numOrigLoops = genericOp.getNumLoops();
+  int64_t numInnerLoops = packOp.getInnerDimsPos().size();
+  int64_t numLoops = numOrigLoops + numInnerLoops;
+  AffineMap origIndexingMap = genericOp.getMatchingIndexingMap(opOperand);
+  SmallVector<AffineExpr> exprs(origIndexingMap.getResults());
+
+  if (genericOp.isScalar(opOperand))
+    return std::make_tuple(
+        opOperand->get(),
+        AffineMap::get(numLoops, 0, exprs, packOp.getContext()));
+
+  llvm::SetVector<int64_t> innerDimsPosSet(packOp.getInnerDimsPos().begin(),
+                                           packOp.getInnerDimsPos().end());
+  // Mapping from AffinDimExpr of indexing maps to the operand shape dimension.
+  DenseMap<int64_t, int64_t> iterMapToDim;
+  for (auto [index, expr] : llvm::enumerate(origIndexingMap.getResults())) {
+    int64_t dimPos = expr.cast<AffineDimExpr>().getPosition();
+    if (!innerDimsPosSet.contains(dimPos))
+      continue;
+    iterMapToDim[dimPos] = index;
+  }
+
+  // Construct the information of packing data dimensions and new indexing maps
+  // for the operand.
+  SmallVector<int64_t> innerDimsPos;
+  SmallVector<OpFoldResult> innerTileSizes;
+  for (auto [index, value] : llvm::enumerate(
+           llvm::zip(packOp.getInnerDimsPos(), packOp.getMixedTiles()))) {
+    int64_t dimPos = std::get<0>(value);
+    if (!iterMapToDim.count(dimPos))
+      continue;
+    innerDimsPos.push_back(iterMapToDim[dimPos]);
+    innerTileSizes.push_back(std::get<1>(value));
+    exprs.push_back(b.getAffineDimExpr(numOrigLoops + index));
+  }
+  auto indexingMap = AffineMap::get(numLoops, 0, exprs, packOp.getContext());
+
+  SmallVector<int64_t> outerDimsPerm;
+  for (auto outDim : packOp.getOuterDimsPerm()) {
+    if (!iterMapToDim.count(outDim))
+      continue;
+    outerDimsPerm.push_back(iterMapToDim[outDim]);
+  }
+
+  // The operand does not have dimensions that relates to pack op.
+  if (innerDimsPos.empty() && outerDimsPerm.empty())
+    return std::make_tuple(opOperand->get(), indexingMap);
+
+  auto empty = tensor::PackOp::createDestinationTensor(
+      b, loc, opOperand->get(), innerTileSizes, innerDimsPos, outerDimsPerm);
+  auto packedOperand = b.create<tensor::PackOp>(
+      loc, opOperand->get(), empty, innerDimsPos, innerTileSizes,
+      packOp.getPaddingValue(), outerDimsPerm);
+  return std::make_tuple(packedOperand, indexingMap);
+}
+
+/// Bubbles up tensor.pack op through elementwise generic op. This
+/// swap pack(generic) to generic(pack). The new generic op works on packed
+/// domain; pack ops are created for input and output operands. E.g.,
+///
+///     #map0 = affine_map<(d0, d1) -> (d0, d1)>
+///     %0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
+///     %1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
+///     %2 = tensor.empty(%0, %1) : tensor<?x?xf32>
+///     %3 = linalg.generic {indexing_maps = [#map0, #map0],
+///                          iterator_types = ["parallel", "parallel"]}
+///         ins(%arg0 : tensor<?x?xf32>)
+///         outs(%2 : tensor<?x?xf32>) {
+///       ^bb0(%arg3: f32, %arg4: f32):
+///         %4 = arith.addf %arg3, %arg3 : f32
+///         linalg.yield %4 : f32
+///     } -> tensor<?x?xf32>
+///     %4 = tensor.pack %3
+///       inner_dims_pos = [0, 1]
+///       inner_tiles = [8, 2]
+///       into %dest : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
+///
+/// will be converted to
+///
+///     #map = affine_map<()[s0] -> (s0 ceildiv 8)>
+///     #map1 = affine_map<()[s0] -> (s0 ceildiv 2)>
+///     #map2 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+///     %dim = tensor.dim %arg0, %c0 : tensor<?x?xf32>
+///     %dim_0 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
+///     %0 = affine.apply #map()[%dim]
+///     %1 = affine.apply #map1()[%dim_0]
+///     %2 = tensor.empty(%0, %1) : tensor<?x?x8x2xf32>
+///     %pack = tensor.pack %arg0
+///       inner_dims_pos = [0, 1]
+///       inner_tiles = [8, 2]
+///       into %2 : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
+///     %3 = linalg.generic {indexing_maps = [#map2, #map2],
+///       iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+///       ins(%pack : tensor<?x?x8x2xf32>)
+///       outs(%arg1 : tensor<?x?x8x2xf32>) {
+///     ^bb0(%in: f32, %out: f32):
+///       %4 = arith.addf %in, %in : f32
+///       linalg.yield %4 : f32
+///     } -> tensor<?x?x8x2xf32>
+static FailureOr<GenericOp>
+bubbleUpPackOpThroughElemGenericOp(RewriterBase &rewriter,
+                                   tensor::PackOp packOp) {
+  auto genericOp = packOp.getSource().getDefiningOp<GenericOp>();
+  if (!genericOp)
+    return failure();
+
+  if (!isElementwise(genericOp))
+    return failure();
+
+  // TODO: Relax the restriction. We are able to bubble up the pack op through
+  // multi-result generic op. It just needs more work.
+  if (genericOp.getNumResults() != 1)
+    return failure();
+
+  // TODO: Add an option for allowing padding values. It could introduce
+  // undefined behavior if we unconditionally propagate pack op through all
+  // the ops. E.g., if the padding value is zero and there are division ops in
+  // a generic op. Some values of padding area could be NaN (0/0).
+  if (packOp.getPaddingValue())
+    return failure();
+
+  OpOperand *opOperand = genericOp.getDpsInitOperand(0);
+  // TODO: Add support for all permutation indexing maps.
+  if (!genericOp.getMatchingIndexingMap(opOperand).isIdentity())
+    return rewriter.notifyMatchFailure(
+        packOp, "the result of generic op does not have identity indexing_map");
+
+  Location loc = packOp.getLoc();
+  SmallVector<Value> inputOperands;
+  SmallVector<AffineMap> indexingMaps;
+  for (OpOperand *inputOperand : genericOp.getDpsInputOperands()) {
+    auto [packedOperand, packedIndexingMap] = getOrCreatePackedViewOfOperand(
+        rewriter, loc, packOp, genericOp, inputOperand);
+    inputOperands.push_back(packedOperand);
+    indexingMaps.push_back(packedIndexingMap);
+  }
+
+  int64_t numLoops = genericOp.getNumLoops();
+  int64_t numInnerLoops = packOp.getInnerDimsPos().size();
+  int64_t newNumLoops = numLoops + numInnerLoops;
+  SmallVector<utils::IteratorType> iterTypes =
+      genericOp.getIteratorTypesArray();
+  iterTypes.append(numInnerLoops, utils::IteratorType::parallel);
+
+  SmallVector<AffineExpr> outExprs(
+      genericOp.getMatchingIndexingMap(opOperand).getResults());
+  for (int i = 0; i < numInnerLoops; ++i)
+    outExprs.push_back(rewriter.getAffineDimExpr(numLoops + i));
+  indexingMaps.push_back(
+      AffineMap::get(newNumLoops, 0, outExprs, rewriter.getContext()));
+
+  auto newGenericOp = rewriter.create<linalg::GenericOp>(
+      loc, packOp.getDestType(), inputOperands, packOp.getDest(), indexingMaps,
+      iterTypes, /*bodyBuild=*/nullptr,
+      linalg::getPrunedAttributeList(genericOp));
+  rewriter.cloneRegionBefore(genericOp.getRegion(), newGenericOp.getRegion(),
+                             newGenericOp.getRegion().begin());
+  return newGenericOp;
+}
+
+// Wrapper pattern that applies bubbleUpPackOpThroughElemGenericOp method.
+struct BubbleUpPackOpThroughElemGenericOpPattern
+    : public OpRewritePattern<tensor::PackOp> {
+  using OpRewritePattern<tensor::PackOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tensor::PackOp packOp,
+                                PatternRewriter &rewriter) const override {
+    auto genericOp = bubbleUpPackOpThroughElemGenericOp(rewriter, packOp);
+    if (failed(genericOp))
+      return failure();
+    rewriter.replaceOp(packOp, genericOp.value().getResults());
+    return success();
+  }
+};
+} // namespace
+
+void mlir::linalg::populateDataLayoutPropagationPatterns(
+    RewritePatternSet &patterns) {
+  patterns.insert<BubbleUpPackOpThroughElemGenericOpPattern>(
+      patterns.getContext());
+}

mlir/lib/Dialect/Tensor/IR/TensorOps.cpp

ShapedType PackOp::inferPackedType(ShapedType sourceType,
   if (!outerDimsPerm.empty())
     applyPermutationToVector(resultShape, outerDimsPerm);
 
   // Append the inner tile dimensions.
   resultShape.append(innerTileSizes.begin(), innerTileSizes.end());
   return RankedTensorType::get(resultShape, sourceType.getElementType());
 }
 
+Value PackOp::createDestinationTensor(OpBuilder &b, Location loc, Value source,
+                                      ArrayRef<OpFoldResult> innerTileSizes,
+                                      ArrayRef<int64_t> innerDimsPos,
+                                      ArrayRef<int64_t> outerDimsPerm) {
+  AffineExpr dim0, dim1;
+  bindDims(b.getContext(), dim0, dim1);
+  auto ceilDiv = [&](OpFoldResult v1, OpFoldResult v2) -> OpFoldResult {
+    return makeComposedFoldedAffineApply(b, loc, dim0.ceilDiv(dim1), {v1, v2});
+  };
+
+  SmallVector<OpFoldResult> mixedSizes;
+  for (auto [index, value] :
+       llvm::enumerate(source.getType().cast<RankedTensorType>().getShape())) {
+    if (ShapedType::isDynamic(value))
+      mixedSizes.push_back(b.create<DimOp>(loc, source, index).getResult());
+    else
+      mixedSizes.push_back(b.getIndexAttr(value));
+  }
+  for (auto it : llvm::zip(innerDimsPos, innerTileSizes)) {
+    int64_t dimPos = std::get<0>(it);
+    OpFoldResult tileSize = std::get<1>(it);
+    mixedSizes[dimPos] = ceilDiv(mixedSizes[dimPos], tileSize);
+  }
+  if (!outerDimsPerm.empty())
+    applyPermutationToVector<OpFoldResult>(mixedSizes, outerDimsPerm);
+
+  mixedSizes.append(innerTileSizes.begin(), innerTileSizes.end());
+  auto elemType = source.getType().cast<ShapedType>().getElementType();
+  return b.create<tensor::EmptyOp>(loc, mixedSizes, elemType);
+}
+
 /// Returns true if the tiles and the tiled dims are constant.
 template <typename OpTy>
 bool areTilesAndTiledDimsAllConstant(OpTy op) {
   static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
                 "applies to only pack or unpack operations");
   ShapedType packedType = (std::is_same<OpTy, PackOp>::value)
                               ? op.getDestType()
                               : op.getSourceType();

mlir/test/Dialect/Linalg/data-layout-propagation.mlir

+// RUN: mlir-opt %s -test-linalg-data-layout-propagation -split-input-file | FileCheck %s
+
+#map0 = affine_map<(d0, d1) -> (d0, d1)>
+func.func @dynamic_elem_pack(%arg0: tensor<?x?xf32>, %dest: tensor<?x?x8x2xf32>) -> tensor<?x?x8x2xf32>
+{
+  %c0 = arith.constant 0 : index
+  %c1 = arith.constant 1 : index
+  %0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
+  %1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
+  %2 = tensor.empty(%0, %1) : tensor<?x?xf32>
+  %3 = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel"]}
+      ins(%arg0 : tensor<?x?xf32>)
+      outs(%2 : tensor<?x?xf32>) {
+    ^bb0(%arg3: f32, %arg4: f32):
+      %4 = arith.addf %arg3, %arg3 : f32
+      linalg.yield %4 : f32
+  } -> tensor<?x?xf32>
+  %4 = tensor.pack %3
+    inner_dims_pos = [0, 1]
+    inner_tiles = [8, 2]
+    into %dest : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
+  return %4 : tensor<?x?x8x2xf32>
+}
+// CHECK-DAG:  #[[MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
+// CHECK-DAG:  #[[MAP1:.+]] = affine_map<()[s0] -> (s0 ceildiv 2)>
+// CHECK-DAG:  #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK:      func.func @dynamic_elem_pack
+// CHECK-SAME:   %[[ARG0:[a-zA-Z0-9]+]]
+// CHECK-SAME:   %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-DAG:    %[[C0:.+]] = arith.constant 0 : index
+// CHECK-DAG:    %[[C1:.+]] = arith.constant 1 : index
+// CHECK-DAG:    %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
+// CHECK-DAG:    %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
+// CHECK-DAG:    %[[OUTER_D0:.+]] = affine.apply #[[MAP0]]()[%[[D0]]]
+// CHECK-DAG:    %[[OUTER_D1:.+]] = affine.apply #[[MAP1]]()[%[[D1]]]
+// CHECK:        %[[ARG0_EMPTY:.+]] = tensor.empty(%[[OUTER_D0]], %[[OUTER_D1]]) : tensor<?x?x8x2xf32>
+// CHECK:        %[[PACK_ARG0:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME:     inner_dims_pos = [0, 1] inner_tiles = [8, 2]
+// CHECK-SAME:     into %[[ARG0_EMPTY]]
+// CHECK:        %[[ELEM:.+]] = linalg.generic
+// CHECK-SAME:     indexing_maps = [#[[MAP2]], #[[MAP2]]]
+// CHECK-SAME:     iterator_types = ["parallel", "parallel", "parallel", "parallel"]
+// CHECK-SAME:     ins(%[[PACK_ARG0]]
+// CHECK-SAME:     outs(%[[DEST]]
+// CHECK:        return %[[ELEM]] : tensor<?x?x8x2xf32>
+
+// -----
+
+#map0 = affine_map<(d0, d1) -> (d0, d1)>
+func.func @elem_pack_transpose_inner_dims(%arg0: tensor<128x256xi32>, %dest: tensor<4x16x16x32xi32>) -> tensor<4x16x16x32xi32>{
+  %init = tensor.empty() : tensor<128x256xi32>
+  %elem = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel"]}
+      ins(%arg0 : tensor<128x256xi32>)
+      outs(%init : tensor<128x256xi32>) {
+    ^bb0(%arg3: i32, %arg4: i32):
+      %4 = arith.addi %arg3, %arg3 : i32
+      linalg.yield %4 : i32
+  } -> tensor<128x256xi32>
+  %pack = tensor.pack %elem
+    inner_dims_pos = [1, 0]
+    inner_tiles = [16, 32]
+    into %dest : tensor<128x256xi32> -> tensor<4x16x16x32xi32>
+  return %pack : tensor<4x16x16x32xi32>
+}
+// CHECK-DAG:  #[[MAP:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK:      func.func @elem_pack_transpose_inner_dims
+// CHECK-SAME:   %[[ARG0:[a-zA-Z0-9]+]]
+// CHECK-SAME:   %[[DEST:[a-zA-Z0-9]+]]
+// CHECK:        %[[ARG0_EMPTY:.+]] = tensor.empty() : tensor<4x16x16x32xi32>
+// CHECK:        %[[PACK_ARG0:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME:     inner_dims_pos = [1, 0] inner_tiles = [16, 32]
+// CHECK-SAME:     into %[[ARG0_EMPTY]]
+// CHECK:        %[[ELEM:.+]] = linalg.generic
+// CHECK-SAME:     indexing_maps = [#[[MAP]], #[[MAP]]]
+// CHECK-SAME:     iterator_types = ["parallel", "parallel", "parallel", "parallel"]
+// CHECK-SAME:     ins(%[[PACK_ARG0]]
+// CHECK-SAME:     outs(%[[DEST]]
+// CHECK:        return %[[ELEM]] : tensor<4x16x16x32xi32>
+
+// -----
+
+#map0 = affine_map<(d0, d1) -> (d0, d1)>
+func.func @elem_pack_transpose_outer_dims(%arg0: tensor<128x256xi32>, %dest: tensor<16x4x32x16xi32>) -> tensor<16x4x32x16xi32>{
+  %init = tensor.empty() : tensor<128x256xi32>
+  %elem = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel"]}
+      ins(%arg0 : tensor<128x256xi32>)
+      outs(%init : tensor<128x256xi32>) {
+    ^bb0(%arg3: i32, %arg4: i32):
+      %4 = arith.addi %arg3, %arg3 : i32
+      linalg.yield %4 : i32
+  } -> tensor<128x256xi32>
+  %pack = tensor.pack %elem
+    outer_dims_perm = [1, 0]
+    inner_dims_pos = [0, 1]
+    inner_tiles = [32, 16]
+    into %dest : tensor<128x256xi32> -> tensor<16x4x32x16xi32>
+  return %pack : tensor<16x4x32x16xi32>
+}
+// CHECK-DAG:  #[[MAP:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK:      func.func @elem_pack_transpose_outer_dims
+// CHECK-SAME:   %[[ARG0:[a-zA-Z0-9]+]]
+// CHECK-SAME:   %[[DEST:[a-zA-Z0-9]+]]
+// CHECK:        %[[ARG0_EMPTY:.+]] = tensor.empty() : tensor<16x4x32x16xi32>
+// CHECK:        %[[PACK_ARG0:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME:     outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 16]
+// CHECK-SAME:     into %[[ARG0_EMPTY]]
+// CHECK:        %[[ELEM:.+]] = linalg.generic
+// CHECK-SAME:     indexing_maps = [#[[MAP]], #[[MAP]]]
+// CHECK-SAME:     iterator_types = ["parallel", "parallel", "parallel", "parallel"]
+// CHECK-SAME:     ins(%[[PACK_ARG0]]
+// CHECK-SAME:     outs(%[[DEST]]
+// CHECK:        return %[[ELEM]] : tensor<16x4x32x16xi32>
+
+// -----
+
+#map0 = affine_map<(d0, d1) -> (d0, d1)>
+func.func @elem_pack_transpose_inner_and_outer_dims(%arg0: tensor<128x256xi32>, %dest: tensor<16x4x16x32xi32>) -> tensor<16x4x16x32xi32>{
+  %init = tensor.empty() : tensor<128x256xi32>
+  %elem = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel"]}
+      ins(%arg0 : tensor<128x256xi32>)
+      outs(%init : tensor<128x256xi32>) {
+    ^bb0(%arg3: i32, %arg4: i32):
+      %4 = arith.addi %arg3, %arg3 : i32
+      linalg.yield %4 : i32
+  } -> tensor<128x256xi32>
+  %pack = tensor.pack %elem
+    outer_dims_perm = [1, 0]
+    inner_dims_pos = [1, 0]
+    inner_tiles = [16, 32]
+    into %dest : tensor<128x256xi32> -> tensor<16x4x16x32xi32>
+  return %pack : tensor<16x4x16x32xi32>
+}
+// CHECK-DAG:  #[[MAP:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK:      func.func @elem_pack_transpose_inner_and_outer_dims
+// CHECK-SAME:   %[[ARG0:[a-zA-Z0-9]+]]
+// CHECK-SAME:   %[[DEST:[a-zA-Z0-9]+]]
+// CHECK:        %[[ARG0_EMPTY:.+]] = tensor.empty() : tensor<16x4x16x32xi32>
+// CHECK:        %[[PACK_ARG0:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME:     outer_dims_perm = [1, 0] inner_dims_pos = [1, 0] inner_tiles = [16, 32]
+// CHECK-SAME:     into %[[ARG0_EMPTY]]
+// CHECK:        %[[ELEM:.+]] = linalg.generic
+// CHECK-SAME:     indexing_maps = [#[[MAP]], #[[MAP]]]
+// CHECK-SAME:     iterator_types = ["parallel", "parallel", "parallel", "parallel"]
+// CHECK-SAME:     ins(%[[PACK_ARG0]]
+// CHECK-SAME:     outs(%[[DEST]]
+// CHECK:        return %[[ELEM]] : tensor<16x4x16x32xi32>
+
+// -----
+
+#map0 = affine_map<(d0, d1) -> (d0, d1)>
+#map1 = affine_map<(d0, d1) -> (d0)>
+#map2 = affine_map<(d0, d1) -> (d1)>
+func.func @dynamic_broadcast_pack(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>, %dest: tensor<?x?x8x2xf32>) -> tensor<?x?x8x2xf32>
+{
+  %c0 = arith.constant 0 : index
+  %0 = tensor.dim %arg0, %c0 : tensor<?xf32>
+  %1 = tensor.dim %arg1, %c0 : tensor<?xf32>
+  %2 = tensor.empty(%0, %1) : tensor<?x?xf32>
+  %3 = linalg.generic {indexing_maps = [#map1, #map2, #map0], iterator_types = ["parallel", "parallel"]}
+      ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
+      outs(%2 : tensor<?x?xf32>) {
+    ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
+      %4 = arith.addf %arg3, %arg4 : f32
+      linalg.yield %4 : f32
+  } -> tensor<?x?xf32>
+  %4 = tensor.pack %3
+    inner_dims_pos = [0, 1]
+    inner_tiles = [8, 2]
+    into %dest : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
+  return %4 : tensor<?x?x8x2xf32>
+}
+// CHECK-DAG:  #[[MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
+// CHECK-DAG:  #[[MAP1:.+]] = affine_map<()[s0] -> (s0 ceildiv 2)>
+// CHECK-DAG:  #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d2)>
+// CHECK-DAG:  #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3) -> (d1, d3)>
+// CHECK-DAG:  #[[MAP4:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK:      func.func @dynamic_broadcast_pack
+// CHECK-SAME:   %[[ARG0:[a-zA-Z0-9]+]]
+// CHECK-SAME:   %[[ARG1:[a-zA-Z0-9]+]]
+// CHECK-SAME:   %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-DAG:    %[[C0:.+]] = arith.constant 0 : index
+// CHECK-DAG:    %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
+// CHECK-DAG:    %[[OUTER_D0:.+]] = affine.apply #[[MAP0]]()[%[[D0]]]
+// CHECK:        %[[ARG0_EMPTY:.+]] = tensor.empty(%[[OUTER_D0]]) : tensor<?x8xf32>
+// CHECK:        %[[PACK_ARG0:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME:     inner_dims_pos = [0] inner_tiles = [8]
+// CHECK-SAME:     into %[[ARG0_EMPTY]]
+// CHECK-DAG:    %[[D1:.+]] = tensor.dim %[[ARG1]], %[[C0]]
+// CHECK-DAG:    %[[OUTER_D1:.+]] = affine.apply #[[MAP1]]()[%[[D1]]]
+// CHECK:        %[[ARG1_EMPTY:.+]] = tensor.empty(%[[OUTER_D1]]) : tensor<?x2xf32>
+// CHECK:        %[[PACK_ARG1:.+]] = tensor.pack %[[ARG1]]
+// CHECK-SAME:     inner_dims_pos = [0] inner_tiles = [2]
+// CHECK-SAME:     into %[[ARG1_EMPTY]]
+// CHECK:        %[[ELEM:.+]] = linalg.generic
+// CHECK-SAME:     indexing_maps = [#[[MAP2]], #[[MAP3]], #[[MAP4]]]
+// CHECK-SAME:     iterator_types = ["parallel", "parallel", "parallel", "parallel"]
+// CHECK-SAME:     ins(%[[PACK_ARG0]], %[[PACK_ARG0]]
+// CHECK-SAME:     outs(%[[DEST]]
+// CHECK:        return %[[ELEM]] : tensor<?x?x8x2xf32>
+
+// -----
+
+#map0 = affine_map<(d0, d1) -> (d0, d1)>
+#map1 = affine_map<(d0, d1) -> (d0)>
+#map2 = affine_map<(d0, d1) -> (d1)>
+func.func @transpose_pack(%arg0: tensor<100x128x200x256xi32>, %arg1: tensor<100xi32>, %arg2: tensor<128xi32>, %dest: tensor<100x200x4x16x16x32xi32>) -> tensor<100x200x4x16x16x32xi32>
+{
+  %init_transpose = tensor.empty() : tensor<100x200x128x256xi32>
+  %transpose = linalg.generic {
+      indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>,
+                       affine_map<(d0, d1, d2, d3) -> (d0)>,
+                       affine_map<(d0, d1, d2, d3) -> (d1)>,
+                       affine_map<(d0, d1, d2, d3) -> (d0, d2, d1, d3)>],
+      iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+      ins(%arg0, %arg1, %arg2 : tensor<100x128x200x256xi32>, tensor<100xi32>, tensor<128xi32>)
+      outs(%init_transpose : tensor<100x200x128x256xi32>) {
+    ^bb0(%b0 : i32, %b1 : i32, %b2 : i32, %b3 : i32):
+      %0 = arith.addi %b0, %b1 : i32
+      %1 = arith.addi %0, %b2 : i32
+      linalg.yield %1 : i32
+    } -> tensor<100x200x128x256xi32>
+  %4 = tensor.pack %transpose
+    inner_dims_pos = [3, 2]
+    inner_tiles = [16, 32]
+    into %dest : tensor<100x200x128x256xi32> -> tensor<100x200x4x16x16x32xi32>
+  return %4 : tensor<100x200x4x16x16x32xi32>
+}
+// CHECK: func.func @transpose_pack
+// CHECK:   linalg.generic
+// CHECK:   tensor.pack

mlir/test/lib/Dialect/Linalg/CMakeLists.txt

 # Exclude tests from libMLIR.so
 add_mlir_library(MLIRLinalgTestPasses
+  TestDataLayoutPropagation.cpp
   TestLinalgDecomposeOps.cpp
   TestLinalgElementwiseFusion.cpp
   TestLinalgFusionTransforms.cpp
   TestLinalgHoisting.cpp
   TestLinalgTransforms.cpp
   TestPadFusion.cpp
 
   EXCLUDE_FROM_LIBMLIR

mlir/test/lib/Dialect/Linalg/TestDataLayoutPropagation.cpp

+//===- TestDataLayoutPropagation.cpp --------------------------------------===//
+//
+// Part of the LLVM 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
+
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Pass/PassManager.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+
+using namespace mlir;
+
+namespace {
+struct TestDataLayoutPropagationPass
+    : public PassWrapper<TestDataLayoutPropagationPass, OperationPass<>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestDataLayoutPropagationPass)
+
+  void getDependentDialects(DialectRegistry &registry) const override {
+    registry
+        .insert<AffineDialect, linalg::LinalgDialect, tensor::TensorDialect>();
+  }
+
+  StringRef getArgument() const final {
+    return "test-linalg-data-layout-propagation";
+  }
+  StringRef getDescription() const final {
+    return "Test data layout propagation";
+  }
+
+  void runOnOperation() override {
+    MLIRContext *context = &getContext();
+    RewritePatternSet patterns(context);
+    linalg::populateDataLayoutPropagationPatterns(patterns);
+    if (failed(
+            applyPatternsAndFoldGreedily(getOperation(), std::move(patterns))))
+      return signalPassFailure();
+  }
+};
+} // namespace
+
+namespace mlir {
+namespace test {
+void registerTestDataLayoutPropagation() {
+  PassRegistration<TestDataLayoutPropagationPass>();
+}
+} // namespace test
+} // namespace mlir

mlir/tools/mlir-opt/mlir-opt.cpp

 void registerTestArithEmulateWideIntPass();
 void registerTestAliasAnalysisPass();
 void registerTestBuiltinAttributeInterfaces();
 void registerTestCallGraphPass();
 void registerTestConstantFold();
 void registerTestControlFlowSink();
 void registerTestGpuSerializeToCubinPass();
 void registerTestGpuSerializeToHsacoPass();
+void registerTestDataLayoutPropagation();
 void registerTestDataLayoutQuery();
 void registerTestDeadCodeAnalysisPass();
 void registerTestDecomposeCallGraphTypes();
 void registerTestDiagnosticsPass();
 void registerTestDialectConversionPasses();
 void registerTestDominancePass();
 void registerTestDynamicPipelinePass();
 void registerTestExpandMathPass();
void registerTestPasses() {
   mlir::test::registerTestDialectConversionPasses();
 #if MLIR_CUDA_CONVERSIONS_ENABLED
   mlir::test::registerTestGpuSerializeToCubinPass();
 #endif
 #if MLIR_ROCM_CONVERSIONS_ENABLED
   mlir::test::registerTestGpuSerializeToHsacoPass();
 #endif
   mlir::test::registerTestDecomposeCallGraphTypes();
+  mlir::test::registerTestDataLayoutPropagation();
   mlir::test::registerTestDataLayoutQuery();
   mlir::test::registerTestDeadCodeAnalysisPass();
   mlir::test::registerTestDominancePass();
   mlir::test::registerTestDynamicPipelinePass();
   mlir::test::registerTestExpandMathPass();
   mlir::test::registerTestFooAnalysisPass();
   mlir::test::registerTestComposeSubView();
   mlir::test::registerTestMultiBuffering();