[Mlir-commits] [llvm] [mlir] [MLIR] Generalize expand_shape to take shape as explicit input (PR #69267)
Gaurav Shukla
llvmlistbot at llvm.org
Thu Apr 18 13:11:24 PDT 2024
https://github.com/Shukla-Gaurav updated https://github.com/llvm/llvm-project/pull/69267
>From b06207c0dce2eea019c1795bfab98388100c69b8 Mon Sep 17 00:00:00 2001
From: Ramiro Leal-Cavazos <ramiroleal050 at gmail.com>
Date: Mon, 16 Oct 2023 17:02:23 -0700
Subject: [PATCH] [MLIR] Generalize expand_shape to take shape as explicit
input
This patch generalizes tensor.expand_shape and memref.expand_shape to consume
the output shape as a list of SSA values. This enables us to implement generic
reshape operations with dynamic shapes using collapse_shape/expand_shape pairs.
The output_shape input to expand_shape follows the static/dynamic representation
that's also used in `tensor.extract_slice`.
Differential Revision: https://reviews.llvm.org/D140821
---
.../mlir/Dialect/MemRef/IR/MemRefOps.td | 80 +++--
.../mlir/Dialect/Tensor/IR/TensorOps.td | 79 +++--
.../include/mlir/Dialect/Tensor/Utils/Utils.h | 4 +
.../mlir/Dialect/Utils/ReshapeOpsUtils.h | 58 +++-
.../mlir/Dialect/Utils/StaticValueUtils.h | 5 +-
.../Conversion/TosaToLinalg/TosaToLinalg.cpp | 1 -
mlir/lib/Dialect/Linalg/IR/CMakeLists.txt | 1 +
mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp | 8 +-
.../Transforms/ConvertConv2DToImg2Col.cpp | 2 +-
.../Transforms/DataLayoutPropagation.cpp | 10 +-
.../Linalg/Transforms/DropUnitDims.cpp | 13 +-
.../Linalg/Transforms/ElementwiseOpFusion.cpp | 67 ++--
.../Linalg/Transforms/SplitReduction.cpp | 1 +
.../Dialect/Linalg/Transforms/Transforms.cpp | 8 +-
mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp | 78 ++++-
.../SparseTensor/Transforms/CMakeLists.txt | 1 +
.../Transforms/SparseTensorRewriting.cpp | 7 +-
mlir/lib/Dialect/Tensor/IR/TensorOps.cpp | 82 ++++-
.../BufferizableOpInterfaceImpl.cpp | 3 +
.../Transforms/PackAndUnpackPatterns.cpp | 24 +-
mlir/lib/Dialect/Tensor/Utils/Utils.cpp | 14 +
mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp | 81 ++++-
mlir/lib/Dialect/Utils/StaticValueUtils.cpp | 7 +-
.../expand-then-convert-to-llvm.mlir | 16 +-
.../MemRefToLLVM/memref-to-llvm.mlir | 4 +-
.../TosaToLinalg/tosa-to-linalg.mlir | 29 +-
.../TosaToTensor/tosa-to-tensor.mlir | 114 +++++--
...ot-bufferize-empty-tensor-elimination.mlir | 2 +-
.../Linalg/bubble-up-extract-slice-op.mlir | 4 +-
mlir/test/Dialect/Linalg/collapse-dim.mlir | 6 +-
.../Linalg/convert-conv2d-to-img2col.mlir | 20 +-
.../Linalg/data-layout-propagation.mlir | 30 +-
.../Dialect/Linalg/drop-unit-extent-dims.mlir | 108 ++++---
.../Dialect/Linalg/flatten-elementwise.mlir | 2 +-
.../fuse-with-reshape-by-collapsing.mlir | 101 +++---
.../Dialect/Linalg/fusion-push-reshape.mlir | 24 +-
.../Linalg/reshape_control_fusion.mlir | 2 +-
mlir/test/Dialect/Linalg/reshape_fusion.mlir | 292 ++++++++++++------
.../resolve-shaped-type-result-dims.mlir | 5 +-
.../Linalg/transform-op-split-reduction.mlir | 28 +-
.../Linalg/vectorization-with-patterns.mlir | 4 +-
mlir/test/Dialect/MemRef/canonicalize.mlir | 35 +--
.../MemRef/expand-strided-metadata.mlir | 16 +-
.../Dialect/MemRef/fold-memref-alias-ops.mlir | 22 +-
mlir/test/Dialect/MemRef/invalid.mlir | 38 +--
mlir/test/Dialect/MemRef/ops.mlir | 72 +++--
.../Dialect/MemRef/runtime-verification.mlir | 5 +-
.../Dialect/SparseTensor/sparse_reshape.mlir | 12 +-
mlir/test/Dialect/Tensor/bufferize.mlir | 24 +-
mlir/test/Dialect/Tensor/canonicalize.mlir | 112 ++++---
mlir/test/Dialect/Tensor/fold-empty-op.mlir | 5 +-
.../Tensor/fold-reassociative-reshapes.mlir | 6 +-
mlir/test/Dialect/Tensor/invalid.mlir | 21 +-
mlir/test/Dialect/Tensor/ops.mlir | 18 +-
.../Dialect/Tensor/simplify-pack-unpack.mlir | 14 +-
.../llvm-project-overlay/mlir/BUILD.bazel | 1 +
56 files changed, 1192 insertions(+), 634 deletions(-)
diff --git a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
index 39e66cd9e6e5ab..14b8d95ea15b41 100644
--- a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
+++ b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
@@ -1548,7 +1548,6 @@ def MemRef_ReshapeOp: MemRef_Op<"reshape", [
class MemRef_ReassociativeReshapeOp<string mnemonic, list<Trait> traits = []> :
MemRef_Op<mnemonic, !listconcat(traits,
[Pure, ViewLikeOpInterface])>,
- Arguments<(ins AnyStridedMemRef:$src, IndexListArrayAttr:$reassociation)>,
Results<(outs AnyStridedMemRef:$result)>{
code commonExtraClassDeclaration = [{
@@ -1573,10 +1572,6 @@ class MemRef_ReassociativeReshapeOp<string mnemonic, list<Trait> traits = []> :
Value getViewSource() { return getSrc(); }
}];
- let assemblyFormat = [{
- $src $reassociation attr-dict `:` type($src) `into` type($result)
- }];
-
let hasFolder = 1;
let hasCanonicalizer = 1;
let hasVerifier = 1;
@@ -1598,14 +1593,10 @@ def MemRef_ExpandShapeOp : MemRef_ReassociativeReshapeOp<"expand_shape", [
Example:
```mlir
- %r = memref.expand_shape %0 [[0, 1], [2]]
- : memref<?x?xf32> into memref<?x5x?xf32>
+ %r = memref.expand_shape %0 [[0, 1], [2]] output_shape [%sz0, %sz1, 32]
+ : memref<?x32xf32> into memref<?x?x32xf32>
```
- At most one dimension of a reassociation group (e.g., [0, 1] above) may be
- dynamic in the result type. Otherwise, the op would be ambiguous, as it
- would not be clear how the source dimension is extended.
-
If an op can be statically proven to be invalid (e.g, an expansion from
`memref<10xf32>` to `memref<2x6xf32>`), it is rejected by the verifier. If
it cannot statically be proven invalid (e.g., the full example above; it is
@@ -1622,41 +1613,80 @@ def MemRef_ExpandShapeOp : MemRef_ReassociativeReshapeOp<"expand_shape", [
there must be a dynamic result dimension in the corresponding reassociation
group. Same for strides.
+ The representation for the output shape supports a partially-static
+ specification via attributes specified through the `static_output_shape`
+ argument. A special sentinel value `ShapedType::kDynamic` encodes that the
+ corresponding entry has a dynamic value. There must be exactly as many SSA
+ inputs in `output_shape` as there are `ShapedType::kDynamic` entries in
+ `static_output_shape`.
+
Note: This op currently assumes that the inner strides are of the
source/result layout map are the faster-varying ones.
}];
+ let arguments = (ins AnyStridedMemRef:$src, IndexListArrayAttr:$reassociation,
+ Variadic<Index>:$output_shape,
+ DenseI64ArrayAttr:$static_output_shape);
+
+ let assemblyFormat = [{
+ $src $reassociation `output_shape`
+ custom<DynamicIndexList>($output_shape, $static_output_shape) attr-dict `:`
+ type($src) `into` type($result)
+ }];
+
let builders = [
// Builders using ReassociationIndices.
OpBuilder<(ins "Type":$resultType, "Value":$src,
"ArrayRef<ReassociationIndices>":$reassociation,
- CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
+ "ArrayRef<OpFoldResult>":$outputShape)>,
+
+ // It will infer output shape using inferOutputShape() method.
+ OpBuilder<(ins "Type":$resultType, "Value":$src,
+ "ArrayRef<ReassociationIndices>":$reassociation)>,
+
+ // Builder using ReassociationExprs.
+ OpBuilder<(ins "Type":$resultType, "Value":$src,
+ "ArrayRef<ReassociationExprs>":$reassociation),
[{
- build($_builder, $_state, resultType, src, attrs);
- $_state.addAttribute("reassociation",
- getReassociationIndicesAttribute($_builder, reassociation));
+ auto reassociationIndices =
+ convertReassociationMapsToIndices(reassociation);
+ build($_builder, $_state, resultType, src, reassociationIndices);
}]>,
- // Builder using ReassociationExprs.
OpBuilder<(ins "Type":$resultType, "Value":$src,
"ArrayRef<ReassociationExprs>":$reassociation,
- CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
+ "ArrayRef<OpFoldResult>":$outputShape),
[{
auto reassociationMaps =
- convertReassociationMapsToIndices($_builder, reassociation);
- build($_builder, $_state, resultType, src, reassociationMaps, attrs);
+ convertReassociationMapsToIndices(reassociation);
+ build($_builder, $_state, resultType, src, reassociationMaps,
+ outputShape);
}]>,
+ // Builder that infers the result layout map. The result shape must be
+ // specified. Otherwise, the op may be ambiguous. The output shape for
+ // the op will be inferred using the inferOutputShape() method.
+ OpBuilder<(ins "ArrayRef<int64_t>":$resultShape, "Value":$src,
+ "ArrayRef<ReassociationIndices>":$reassociation)>,
+
// Builder that infers the result layout map. The result shape must be
// specified. Otherwise, the op may be ambiguous.
OpBuilder<(ins "ArrayRef<int64_t>":$resultShape, "Value":$src,
- "ArrayRef<ReassociationIndices>":$reassociation)>
+ "ArrayRef<ReassociationIndices>":$reassociation,
+ "ArrayRef<OpFoldResult>":$outputShape)>
];
let extraClassDeclaration = commonExtraClassDeclaration # [{
static FailureOr<MemRefType> computeExpandedType(
MemRefType srcType, ArrayRef<int64_t> resultShape,
ArrayRef<ReassociationIndices> reassociation);
+
+ // Infer the output shape for a memref.expand_shape when it is possible
+ // to do so.
+ static FailureOr<SmallVector<OpFoldResult>> inferOutputShape(
+ OpBuilder &b, Location loc, MemRefType expandedType,
+ ArrayRef<ReassociationIndices> reassociation,
+ ArrayRef<OpFoldResult> inputShape);
}];
let hasVerifier = 1;
@@ -1707,6 +1737,12 @@ def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape", [
source/result layout map are the faster-varying ones.
}];
+ let arguments = (ins AnyStridedMemRef:$src, IndexListArrayAttr:$reassociation);
+
+ let assemblyFormat = [{
+ $src $reassociation attr-dict `:` type($src) `into` type($result)
+ }];
+
let builders = [
// Builders for a contracting reshape whose result type is computed from
// `src` and `reassociation`.
@@ -1718,7 +1754,7 @@ def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape", [
CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
[{
auto reassociationMaps =
- convertReassociationMapsToIndices($_builder, reassociation);
+ convertReassociationMapsToIndices(reassociation);
build($_builder, $_state, src, reassociationMaps, attrs);
}]>,
@@ -1736,7 +1772,7 @@ def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape", [
CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
[{
auto reassociationMaps =
- convertReassociationMapsToIndices($_builder, reassociation);
+ convertReassociationMapsToIndices(reassociation);
build($_builder, $_state, resultType, src, reassociationMaps, attrs);
}]>
];
diff --git a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
index cf7f3e89079c1c..a403e89a39f98c 100644
--- a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
+++ b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
@@ -1062,8 +1062,7 @@ class Tensor_ReassociativeReshapeOp<string mnemonic, list<Trait> traits = []> :
Tensor_Op<mnemonic, !listconcat(traits, [
DeclareOpInterfaceMethods<OpAsmOpInterface, ["getAsmResultNames"]>,
Pure])>,
- Arguments<(ins AnyRankedTensor:$src, IndexListArrayAttr:$reassociation)>,
- Results<(outs AnyRankedTensor:$result)> {
+ Results<(outs AnyTensor:$result)> {
code commonExtraClassDeclaration = [{
static StringRef getReassociationAttrStrName() { return "reassociation"; }
@@ -1086,10 +1085,6 @@ class Tensor_ReassociativeReshapeOp<string mnemonic, list<Trait> traits = []> :
}
}];
- let assemblyFormat = [{
- $src $reassociation attr-dict `:` type($src) `into` type($result)
- }];
-
let hasFolder = 1;
let hasCanonicalizer = 1;
let hasVerifier = 1;
@@ -1102,43 +1097,75 @@ def Tensor_ExpandShapeOp : Tensor_ReassociativeReshapeOp<"expand_shape"> {
rank than the operand `src` whose dimension sizes are a reassociation of
`src`.
- A reassociation is defined as a continuous grouping of dimensions. It is
- represented with an array of DenseI64ArrayAttr attribute. Entries in the
- array are referred to as reassociation maps.
+ A reassociation is defined as a continuous grouping of dimensions and is
+ represented with an array of DenseI64ArrayAttr attribute. The reassociation
+ maps applied to the result tensor with the higher rank must result in the
+ operand tensor with the smaller rank.
- The reassociation maps are applied to the result shape to obtain the operand
- shape.
+ The representation for the output shape supports a partially-static
+ specification via attributes specified through the `static_output_shape`
+ argument. A special sentinel value `ShapedType::kDynamic` encodes that the
+ corresponding entry has a dynamic value. There must be exactly as many SSA
+ inputs in `output_shape` as there are `ShapedType::kDynamic` entries in
+ `static_output_shape`.
Example:
```mlir
// Dimension expansion i -> (i', j') and (k) -> (k')
- %b = tensor.expand_shape %a [[0, 1], [2]]
- : tensor<?x?xf32> into tensor<?x?x?xf32>
+ %b = tensor.expand_shape %a [[0, 1], [2]] output_shape [%sz0, %sz1, 32]
+ : tensor<?x32xf32> into tensor<?x?x32xf32>
```
}];
+
+ let arguments = (ins AnyTensor:$src, IndexListArrayAttr:$reassociation,
+ Variadic<Index>:$output_shape,
+ DenseI64ArrayAttr:$static_output_shape);
+
+ let assemblyFormat = [{
+ $src $reassociation `output_shape`
+ custom<DynamicIndexList>($output_shape, $static_output_shape) attr-dict `:`
+ type($src) `into` type($result)
+ }];
+
let builders = [
// Builders using ReassociationIndices.
OpBuilder<(ins "Type":$resultType, "Value":$src,
"ArrayRef<ReassociationIndices>":$reassociation,
- CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
+ "ArrayRef<OpFoldResult>":$outputShape)>,
+
+ // It will infer output shape using inferOutputShape() method.
+ OpBuilder<(ins "Type":$resultType, "Value":$src,
+ "ArrayRef<ReassociationIndices>":$reassociation)>,
+
+ // Builder using ReassociationExprs.
+ OpBuilder<(ins "Type":$resultType, "Value":$src,
+ "ArrayRef<ReassociationExprs>":$reassociation),
[{
- build($_builder, $_state, resultType, src, attrs);
- $_state.addAttribute("reassociation",
- getReassociationIndicesAttribute($_builder, reassociation));
+ auto reassociationIndices =
+ convertReassociationMapsToIndices(reassociation);
+ build($_builder, $_state, resultType, src, reassociationIndices);
}]>,
OpBuilder<(ins "Type":$resultType, "Value":$src,
"ArrayRef<ReassociationExprs>":$reassociation,
- CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
+ "ArrayRef<OpFoldResult>":$outputShape),
[{
- auto reassociationMaps =
- convertReassociationMapsToIndices($_builder, reassociation);
- build($_builder, $_state, resultType, src, reassociationMaps, attrs);
+ auto reassociationIndices =
+ convertReassociationMapsToIndices(reassociation);
+ build($_builder, $_state, resultType, src, reassociationIndices,
+ outputShape);
}]>
];
let extraClassDeclaration = commonExtraClassDeclaration # [{
int64_t getCorrespondingSourceDim(int64_t resultDim);
+
+ // Infer the output shape for a tensor.expand_shape when it is possible
+ // to do so.
+ static FailureOr<SmallVector<OpFoldResult>> inferOutputShape(
+ OpBuilder &b, Location loc, RankedTensorType expandedType,
+ ArrayRef<ReassociationIndices> reassociation,
+ ArrayRef<OpFoldResult> inputShape);
}];
let hasVerifier = 1;
@@ -1146,6 +1173,7 @@ def Tensor_ExpandShapeOp : Tensor_ReassociativeReshapeOp<"expand_shape"> {
def Tensor_CollapseShapeOp : Tensor_ReassociativeReshapeOp<"collapse_shape"> {
let summary = "operation to produce a tensor with a smaller rank";
+ let arguments = (ins AnyTensor:$src, IndexListArrayAttr:$reassociation);
let description = [{
The `tensor.collapse_shape` op produces a new tensor of lower (or equal)
rank whose dimension sizes are a reassociation of the original `src` dimensions.
@@ -1163,6 +1191,11 @@ def Tensor_CollapseShapeOp : Tensor_ReassociativeReshapeOp<"collapse_shape"> {
: tensor<?x?x?xf32> into tensor<?x?xf32>
```
}];
+
+ let assemblyFormat = [{
+ $src $reassociation attr-dict `:` type($src) `into` type($result)
+ }];
+
let builders = [
// Builders for a contracting reshape whose result type is computed from
// `src` and `reassociation`.
@@ -1174,7 +1207,7 @@ def Tensor_CollapseShapeOp : Tensor_ReassociativeReshapeOp<"collapse_shape"> {
CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
[{
auto reassociationMaps =
- convertReassociationMapsToIndices($_builder, reassociation);
+ convertReassociationMapsToIndices(reassociation);
build($_builder, $_state, src, reassociationMaps, attrs);
}]>,
@@ -1192,7 +1225,7 @@ def Tensor_CollapseShapeOp : Tensor_ReassociativeReshapeOp<"collapse_shape"> {
CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
[{
auto reassociationMaps =
- convertReassociationMapsToIndices($_builder, reassociation);
+ convertReassociationMapsToIndices(reassociation);
build($_builder, $_state, resultType, src, reassociationMaps, attrs);
}]>
];
diff --git a/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h b/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h
index d09c9e36f6ff88..cd092016987e9b 100644
--- a/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h
+++ b/mlir/include/mlir/Dialect/Tensor/Utils/Utils.h
@@ -26,6 +26,10 @@ PadOp createPadHighOp(RankedTensorType type, Value source, Value pad,
SmallVector<Value> createDynamicDimValues(OpBuilder &b, Location loc,
Value rankedTensor);
+/// Creates Reshape op.
+template <typename ReshapeOp>
+Value createReshapeOp(ReshapeOp oldReshapeOp, OpBuilder &b, Location loc, RankedTensorType resultTy, Value src);
+
/// Returns the transposed `rankedTensorType` if `transposeVector` is non-empty.
/// Fail if `transposeVector` is not a permutation matching the tensor rank.
FailureOr<RankedTensorType>
diff --git a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
index ae9824f728da4d..dae23006cd97e6 100644
--- a/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
+++ b/mlir/include/mlir/Dialect/Utils/ReshapeOpsUtils.h
@@ -30,6 +30,27 @@ using ReassociationExprs = SmallVector<AffineExpr, 2>;
/// Attribute name for the ArrayAttr which encodes reassociation indices.
constexpr StringRef getReassociationAttrName() { return "reassociation"; }
+// Infer the output shape for a {memref|tensor}.expand_shape when it is possible
+// to do so.
+//
+// Note: This should *only* be used to implement
+// `ExpandShapeOp::inferOutputShape` in both the memref and tensor namespaces.
+// If you need to infer the output shape you should use the static method of
+// `ExpandShapeOp` instead of calling this.
+//
+// `inputShape` is the shape of the tensor or memref being expanded as a
+// sequence of SSA values or constants. `expandedType` is the output shape of
+// the expand_shape operation. `reassociation` is the reassociation denoting
+// the output dims each input dim is mapped to.
+//
+// Returns the output shape in `outputShape` and `staticOutputShape`, following
+// the conventions for the output_shape and static_output_shape inputs to the
+// expand_shape ops.
+std::optional<SmallVector<OpFoldResult>> inferExpandShapeOutputShape(
+ OpBuilder &b, Location loc, ShapedType expandedType,
+ ArrayRef<ReassociationIndices> reassociation,
+ ArrayRef<OpFoldResult> inputShape);
+
/// Compose reassociation maps that are used in pair of reshape ops where one
/// is a producer and other is the consumer. Only valid to use this method when
/// both the producer and consumer are collapsing dimensions or both are
@@ -62,7 +83,7 @@ getReassociationIndicesAttribute(OpBuilder &b,
/// Convert Array<Array<AffineExpr>> to Array<Array<int64_t>>.
SmallVector<ReassociationIndices, 2> convertReassociationMapsToIndices(
- OpBuilder &b, ArrayRef<ReassociationExprs> reassociationExprs);
+ ArrayRef<ReassociationExprs> reassociationExprs);
/// Return the reassociations maps to use to reshape given the source type and
/// the target type when possible. Return std::nullopt when this computation
@@ -140,14 +161,11 @@ static LogicalResult verifyReshapeLikeTypes(Op op, T expandedType,
op.getReassociationIndices(), isExpansion);
}
-/// Verify that shapes of the reshaped types using following rules
-/// 1) if a dimension in the collapsed type is static, then the corresponding
-/// dimensions in the expanded shape should be
+/// Verify that shapes of the reshaped types using following rule:
+/// if a dimension in the collapsed type is static, then the corresponding
+/// dimensions in the expanded shape should be
/// a) static
/// b) the product should be same as the collaped shape.
-/// 2) if a dimension in the collaped type is dynamic, one and only one of the
-/// corresponding dimensions in the expanded type should be dynamic. This
-/// rule is only needed with reshape operations that are expanding.
LogicalResult reshapeLikeShapesAreCompatible(
function_ref<LogicalResult(const Twine &)> emitError,
ArrayRef<int64_t> collapsedShape, ArrayRef<int64_t> expandedShape,
@@ -156,9 +174,11 @@ LogicalResult reshapeLikeShapesAreCompatible(
/// Returns true iff the type is a MemRefType and has a non-identity layout.
bool hasNonIdentityLayout(Type type);
+enum class ReshapeOpKind { kExpand, kCollapse };
+
/// Pattern to collapse producer/consumer reshape ops that are both collapsing
/// dimensions or are both expanding dimensions.
-template <typename ReshapeOpTy>
+template <typename ReshapeOpTy, ReshapeOpKind opKind>
struct ComposeReassociativeReshapeOps : public OpRewritePattern<ReshapeOpTy> {
using OpRewritePattern<ReshapeOpTy>::OpRewritePattern;
LogicalResult matchAndRewrite(ReshapeOpTy reshapeOp,
@@ -181,8 +201,18 @@ struct ComposeReassociativeReshapeOps : public OpRewritePattern<ReshapeOpTy> {
rewriter.getContext());
if (!reassociationIndices)
return failure();
- rewriter.replaceOpWithNewOp<ReshapeOpTy>(
- reshapeOp, resultType, srcReshapeOp.getSrc(), *reassociationIndices);
+
+ if constexpr (opKind == ReshapeOpKind::kExpand) {
+ SmallVector<OpFoldResult> outputShape(
+ getMixedValues(reshapeOp.getStaticOutputShape(),
+ reshapeOp.getOutputShape(), rewriter));
+ rewriter.replaceOpWithNewOp<ReshapeOpTy>(
+ reshapeOp, resultType, srcReshapeOp.getSrc(), *reassociationIndices,
+ outputShape);
+ } else {
+ rewriter.replaceOpWithNewOp<ReshapeOpTy>(
+ reshapeOp, resultType, srcReshapeOp.getSrc(), *reassociationIndices);
+ }
return success();
}
};
@@ -215,7 +245,8 @@ struct ComposeReassociativeReshapeOps : public OpRewritePattern<ReshapeOpTy> {
//
/// When `rank(srcType) < rank(resultType)`, then we just swap `reassociation_1`
/// `reassociation_2` and produce `expand_shape`.
-template <typename CollapseOpTy, typename ExpandOpTy, typename CastOpTy>
+template <typename CollapseOpTy, typename ExpandOpTy, typename CastOpTy,
+ typename DimOpTy, typename TensorTy>
struct ComposeCollapseOfExpandOp : public OpRewritePattern<CollapseOpTy> {
using OpRewritePattern<CollapseOpTy>::OpRewritePattern;
LogicalResult matchAndRewrite(CollapseOpTy collapseOp,
@@ -322,8 +353,11 @@ struct ComposeExpandOfCollapseOp : public OpRewritePattern<ExpandOpTy> {
if (!composedReassociation)
return failure();
+ SmallVector<OpFoldResult> outputShape(getMixedValues(
+ expandOp.getStaticOutputShape(), expandOp.getOutputShape(), rewriter));
rewriter.replaceOpWithNewOp<ExpandOpTy>(
- expandOp, resultType, collapseOp.getSrc(), *composedReassociation);
+ expandOp, resultType, collapseOp.getSrc(), *composedReassociation,
+ outputShape);
return success();
}
diff --git a/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h b/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
index 20f019666a2e6a..594bcf5dbb399a 100644
--- a/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
+++ b/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
@@ -125,9 +125,8 @@ SmallVector<OpFoldResult> getMixedValues(ArrayRef<int64_t> staticValues,
/// Decompose a vector of mixed static or dynamic values into the
/// corresponding pair of arrays. This is the inverse function of
/// `getMixedValues`.
-std::pair<ArrayAttr, SmallVector<Value>>
-decomposeMixedValues(Builder &b,
- const SmallVectorImpl<OpFoldResult> &mixedValues);
+std::pair<SmallVector<int64_t>, SmallVector<Value>>
+decomposeMixedValues(const SmallVectorImpl<OpFoldResult> &mixedValues);
/// Helper to sort `values` according to matching `keys`.
SmallVector<Value>
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index af19ebaea937d0..4b29449c0302f3 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -17,7 +17,6 @@
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
-#include "mlir/Dialect/Tensor/Utils/Utils.h"
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
diff --git a/mlir/lib/Dialect/Linalg/IR/CMakeLists.txt b/mlir/lib/Dialect/Linalg/IR/CMakeLists.txt
index c187563b8f0c4e..0e60dd0cfbcee2 100644
--- a/mlir/lib/Dialect/Linalg/IR/CMakeLists.txt
+++ b/mlir/lib/Dialect/Linalg/IR/CMakeLists.txt
@@ -32,6 +32,7 @@ add_mlir_dialect_library(MLIRLinalgDialect
MLIRMathDialect
MLIRMemRefDialect
MLIRTensorDialect
+ MLIRTensorUtils
MLIRTilingInterface
MLIRValueBoundsOpInterface
MLIRViewLikeInterface
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 9c5c58fa1fabfb..725bc330e4d564 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -22,6 +22,7 @@
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Dialect/Tensor/Utils/Utils.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
@@ -586,12 +587,11 @@ struct FoldFillWithTensorReshape : OpRewritePattern<TensorReshapeOp> {
return failure();
Location loc = oldFill.getLoc();
- auto newInit = rewriter.create<TensorReshapeOp>(
- loc, reshapeOp.getResultType(), oldFill.output(),
- reshapeOp.getReassociation());
+ Value newInit = tensor::createReshapeOp(reshapeOp, rewriter, loc, reshapeOp.getResultType(), oldFill.output());
+ if(!newInit)
+ return failure();
rewriter.replaceOpWithNewOp<FillOp>(reshapeOp, ValueRange{oldFill.value()},
ValueRange{newInit});
-
return success();
}
};
diff --git a/mlir/lib/Dialect/Linalg/Transforms/ConvertConv2DToImg2Col.cpp b/mlir/lib/Dialect/Linalg/Transforms/ConvertConv2DToImg2Col.cpp
index 420b04b3ee28cf..81d44ba04fa1d8 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/ConvertConv2DToImg2Col.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ConvertConv2DToImg2Col.cpp
@@ -349,7 +349,7 @@ rewriteInIm2Col(RewriterBase &rewriter,
SmallVector<ReassociationIndices> batchMatVecReassociationIndice = {{0, 1},
{2, 3}};
- Value batchMatVecResultReshaped = rewriter.create<tensor::ExpandShapeOp>(
+ auto batchMatVecResultReshaped = rewriter.create<tensor::ExpandShapeOp>(
loc, transposedOutputTensor.getType(), batchMatVecResult.getResult(0),
batchMatVecReassociationIndice);
diff --git a/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp b/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp
index 7fd88dec71d491..9a2493a59e019e 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp
@@ -757,7 +757,10 @@ pushDownUnPackOpThroughExpandShape(tensor::UnPackOp unPackOp,
ArrayRef<int64_t> innerDimsPos = unPackOp.getInnerDimsPos();
ArrayRef<int64_t> outerDimsPerm = unPackOp.getOuterDimsPerm();
- ArrayRef<int64_t> dstShape = expandOp.getType().getShape();
+ auto expandTy = expandOp.getType().dyn_cast<RankedTensorType>();
+ if (!expandTy)
+ return failure();
+ ArrayRef<int64_t> dstShape = expandTy.getShape();
SmallVector<ReassociationIndices> reassocIndices =
expandOp.getReassociationIndices();
// Project inner tile pos to the dim pos after expanding. For example, if dims
@@ -796,9 +799,8 @@ pushDownUnPackOpThroughExpandShape(tensor::UnPackOp unPackOp,
nextPos += 1;
}
- RankedTensorType newExpandType =
- tensor::PackOp::inferPackedType(expandOp.getType(), innerTileSizes,
- projectedInnerDimsPos, newOuterDimsPerm);
+ RankedTensorType newExpandType = tensor::PackOp::inferPackedType(
+ expandTy, innerTileSizes, projectedInnerDimsPos, newOuterDimsPerm);
auto newExpandOp = rewriter.create<tensor::ExpandShapeOp>(
expandOp.getLoc(), newExpandType, unPackOp.getSource(),
newReassocIndices);
diff --git a/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp b/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp
index 023ea277bcf499..a2bb0c49d1252a 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp
@@ -23,6 +23,7 @@
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tensor/Transforms/Transforms.h"
#include "mlir/Dialect/Tensor/Utils/Utils.h"
+#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/BuiltinTypes.h"
@@ -272,8 +273,9 @@ expandValue(RewriterBase &rewriter, Location loc, Value result, Value origDest,
assert(rankReductionStrategy ==
ControlDropUnitDims::RankReductionStrategy::ReassociativeReshape &&
"unknown rank reduction strategy");
- return rewriter.create<tensor::ExpandShapeOp>(loc, origResultType, result,
- reassociation);
+ return rewriter
+ .create<tensor::ExpandShapeOp>(loc, origResultType, result, reassociation)
+ .getResult();
}
/// Collapse the given `value` so that the type matches the type of
@@ -536,9 +538,10 @@ LogicalResult linalg::dropUnitDims(RewriterBase &rewriter, GenericOp genericOp,
resultReplacements.push_back(result);
continue;
}
- resultReplacements.push_back(expandValue(rewriter, loc, result, origDest,
- reassociations[opOperandIndex],
- options.rankReductionStrategy));
+ Value expandedValue = expandValue(
+ rewriter, loc, result, origDest, reassociations[opOperandIndex],
+ options.rankReductionStrategy);
+ resultReplacements.push_back(expandedValue);
}
rewriter.replaceOp(genericOp, resultReplacements);
diff --git a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
index 373e9cfc3ce719..bf3a737f1c2bf3 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
@@ -625,14 +625,14 @@ LogicalResult ExpansionInfo::compute(LinalgOp linalgOp,
return success();
}
-/// Epanding the body of a linalg operation requires adaptations of the accessed
-/// loop indices. Specifically, access of indices in the original operation need
-/// to be replaced with linearizations of indices in the expanded op. That
-/// requires the shape of the expanded dimensions to be static (at least all but
-/// the most significant). For now check that these are all statically sized.
-/// Note that this could be extended to handle dynamic case, but the
-/// implementation below uses `affine.apply` which seems to have issues when the
-/// shapes are not static.
+/// Expanding the body of a linalg operation requires adaptations of the
+/// accessed loop indices. Specifically, access of indices in the original
+/// operation need to be replaced with linearizations of indices in the expanded
+/// op. That requires the shape of the expanded dimensions to be static (at
+/// least all but the most significant). For now check that these are all
+/// statically sized. Note that this could be extended to handle dynamic case,
+/// but the implementation below uses `affine.apply` which seems to have issues
+/// when the shapes are not static.
static LogicalResult isLinalgOpExpandable(LinalgOp linalgOp,
const ExpansionInfo &expansionInfo,
PatternRewriter &rewriter) {
@@ -750,6 +750,31 @@ static void updateExpandedGenericOpRegion(PatternRewriter &rewriter,
}
}
+/// Checks if a single dynamic dimension expanded into multiple dynamic
+/// dimensions.
+static LogicalResult
+validateDynamicDimExpansion(LinalgOp linalgOp,
+ const ExpansionInfo &expansionInfo,
+ PatternRewriter &rewriter) {
+ for (unsigned i : llvm::seq<unsigned>(0, expansionInfo.getOrigOpNumDims())) {
+ ArrayRef<int64_t> expandedShape = expansionInfo.getExpandedShapeOfDim(i);
+ if (expandedShape.size() == 1)
+ continue;
+ bool foundDynamic = false;
+ for (int64_t shape : expandedShape) {
+ if (ShapedType::isDynamic(shape)) {
+ if (foundDynamic) {
+ return rewriter.notifyMatchFailure(
+ linalgOp, "cannot infer expanded shape with multiple dynamic "
+ "dims in the same reassociation group");
+ }
+ foundDynamic = true;
+ }
+ }
+ }
+ return success();
+}
+
/// Implements the fusion of a tensor.collapse_shape or a tensor.expand_shape op
/// and a generic op as explained in `isFusableWithReshapeByExpansion`. Assumes
/// that those conditions have been satisfied.
@@ -759,6 +784,8 @@ fuseWithReshapeByExpansion(LinalgOp linalgOp, Operation *reshapeOp,
PatternRewriter &rewriter) {
assert(isFusableWithReshapeByDimExpansion(linalgOp, fusableOpOperand) &&
"preconditions for fuse operation failed");
+
+ Location loc = linalgOp.getLoc();
// Check if reshape is expanding or collapsing.
auto expandingReshapeOp = dyn_cast<tensor::ExpandShapeOp>(*reshapeOp);
auto collapsingReshapeOp = dyn_cast<tensor::CollapseShapeOp>(*reshapeOp);
@@ -778,6 +805,11 @@ fuseWithReshapeByExpansion(LinalgOp linalgOp, Operation *reshapeOp,
expandedType.getShape(), collapsedType.getShape(), rewriter)))
return std::nullopt;
+ // TODO: With the support of multiple dynamic dims expansion in
+ // tensor.expand_shape op, this case can be handled.
+ if (failed(validateDynamicDimExpansion(linalgOp, expansionInfo, rewriter)))
+ return std::nullopt;
+
if (failed(isLinalgOpExpandable(linalgOp, expansionInfo, rewriter)))
return std::nullopt;
@@ -816,15 +848,13 @@ fuseWithReshapeByExpansion(LinalgOp linalgOp, Operation *reshapeOp,
/*isExpandingReshape=*/true)))
return std::nullopt;
expandedOpOperands.push_back(rewriter.create<tensor::ExpandShapeOp>(
- linalgOp.getLoc(), expandedOperandType, opOperand->get(),
- reassociation));
+ loc, expandedOperandType, opOperand->get(), reassociation));
continue;
}
}
expandedOpOperands.push_back(opOperand->get());
}
- Location loc = linalgOp.getLoc();
SmallVector<Value> outputs;
for (OpOperand &opOperand : linalgOp.getDpsInitsMutable()) {
AffineMap indexingMap = linalgOp.getMatchingIndexingMap(&opOperand);
@@ -843,8 +873,7 @@ fuseWithReshapeByExpansion(LinalgOp linalgOp, Operation *reshapeOp,
/*isExpandingReshape=*/true)))
return std::nullopt;
outputs.push_back(rewriter.create<tensor::ExpandShapeOp>(
- linalgOp.getLoc(), expandedOutputType, opOperand.get(),
- reassociation));
+ loc, expandedOutputType, opOperand.get(), reassociation));
} else {
outputs.push_back(opOperand.get());
}
@@ -1615,15 +1644,17 @@ FailureOr<CollapseResult> mlir::linalg::collapseOpIterationDims(
op.getIndexingMapMatchingResult(originalResult.value());
SmallVector<ReassociationIndices> reassociation =
getOperandReassociation(indexingMap, collapsingInfo);
+ Value result;
if (isa<MemRefType>(collapsedOpResult.getType())) {
- Value result = rewriter.create<memref::ExpandShapeOp>(
- loc, originalResultType, collapsedOpResult, reassociation);
- results.push_back(result);
+ MemRefType expandShapeResultType = MemRefType::get(
+ originalResultType.getShape(), originalResultType.getElementType());
+ result = rewriter.create<memref::ExpandShapeOp>(
+ loc, expandShapeResultType, collapsedOpResult, reassociation);
} else {
- Value result = rewriter.create<tensor::ExpandShapeOp>(
+ result = rewriter.create<tensor::ExpandShapeOp>(
loc, originalResultType, collapsedOpResult, reassociation);
- results.push_back(result);
}
+ results.push_back(result);
} else {
results.push_back(collapsedOpResult);
}
diff --git a/mlir/lib/Dialect/Linalg/Transforms/SplitReduction.cpp b/mlir/lib/Dialect/Linalg/Transforms/SplitReduction.cpp
index 6559c86c9e0ff5..5bfdbc6d0bb59c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/SplitReduction.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/SplitReduction.cpp
@@ -114,6 +114,7 @@ FailureOr<SplitReductionResult> mlir::linalg::splitReduction(
Type newType = RankedTensorType::get(
newShape,
cast<RankedTensorType>(operand->get().getType()).getElementType());
+
Value newInput = b.create<tensor::ExpandShapeOp>(
loc, newType, operand->get(), reassociation);
newInputs.push_back(newInput);
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
index a17bc8e4cd318f..c41a899b2e6f5c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
@@ -329,11 +329,13 @@ FailureOr<LowerPackResult> linalg::lowerPack(RewriterBase &rewriter,
/*transposeOp=*/nullptr};
}
}
+
// 5. Expand from the padded result to the stripMinedShape.
+ auto expandShapeResultType =
+ RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape);
auto reshapeOp = rewriter.create<tensor::ExpandShapeOp>(
- loc,
- RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape),
- padOp.getResult(), packingMetadata.reassociations);
+ loc, expandShapeResultType, padOp.getResult(),
+ packingMetadata.reassociations);
// 6. Transpose stripMinedShape to packedShape.
SmallVector<int64_t> transpPerm =
diff --git a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
index 836dcb8f329e70..86e8191c04b3aa 100644
--- a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
+++ b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
@@ -2237,6 +2237,41 @@ FailureOr<MemRefType> ExpandShapeOp::computeExpandedType(
srcType.getMemorySpace());
}
+FailureOr<SmallVector<OpFoldResult>> ExpandShapeOp::inferOutputShape(
+ OpBuilder &b, Location loc, MemRefType expandedType,
+ ArrayRef<ReassociationIndices> reassociation,
+ ArrayRef<OpFoldResult> inputShape) {
+ std::optional<SmallVector<OpFoldResult>> outputShape =
+ inferExpandShapeOutputShape(b, loc, expandedType, reassociation, inputShape);
+ if(!outputShape)
+ return failure();
+ return *outputShape;
+}
+
+void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
+ Type resultType, Value src,
+ ArrayRef<ReassociationIndices> reassociation,
+ ArrayRef<OpFoldResult> outputShape) {
+ auto [staticOutputShape, dynamicOutputShape] =
+ decomposeMixedValues(SmallVector<OpFoldResult>(outputShape));
+ build(builder, result, resultType.cast<MemRefType>(), src,
+ getReassociationIndicesAttribute(builder, reassociation),
+ dynamicOutputShape, staticOutputShape);
+ }
+
+void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
+ Type resultType, Value src,
+ ArrayRef<ReassociationIndices> reassociation) {
+ SmallVector<OpFoldResult> inputShape =
+ getMixedSizes(builder, result.location, src);
+ MemRefType memrefResultTy = resultType.cast<MemRefType>();
+ FailureOr<SmallVector<OpFoldResult>> outputShape =
+ inferOutputShape(builder, result.location, memrefResultTy, reassociation, inputShape);
+ // Failure of this assertion usually indicates presence of multiple
+ // dynamic dimensions in the same reassociation group.
+ assert(succeeded(outputShape) && "unable to infer output shape");
+ build(builder, result, memrefResultTy, src, reassociation, *outputShape);}
+
void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
ArrayRef<int64_t> resultShape, Value src,
ArrayRef<ReassociationIndices> reassociation) {
@@ -2250,6 +2285,19 @@ void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
build(builder, result, *resultType, src, reassociation);
}
+void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
+ ArrayRef<int64_t> resultShape, Value src,
+ ArrayRef<ReassociationIndices> reassociation, ArrayRef<OpFoldResult> outputShape) {
+ // Only ranked memref source values are supported.
+ auto srcType = llvm::cast<MemRefType>(src.getType());
+ FailureOr<MemRefType> resultType =
+ ExpandShapeOp::computeExpandedType(srcType, resultShape, reassociation);
+ // Failure of this assertion usually indicates a problem with the source
+ // type, e.g., could not get strides/offset.
+ assert(succeeded(resultType) && "could not compute layout");
+ build(builder, result, *resultType, src, reassociation, outputShape);
+}
+
LogicalResult ExpandShapeOp::verify() {
MemRefType srcType = getSrcType();
MemRefType resultType = getResultType();
@@ -2266,7 +2314,7 @@ LogicalResult ExpandShapeOp::verify() {
if (failed(verifyCollapsedShape(getOperation(), srcType.getShape(),
resultType.getShape(),
getReassociationIndices(),
- /*allowMultipleDynamicDimsPerGroup=*/false)))
+ /*allowMultipleDynamicDimsPerGroup=*/true)))
return failure();
// Compute expected result type (including layout map).
@@ -2280,14 +2328,28 @@ LogicalResult ExpandShapeOp::verify() {
return emitOpError("expected expanded type to be ")
<< *expectedResultType << " but found " << resultType;
+ if ((int64_t)getStaticOutputShape().size() != resultType.getRank())
+ return emitOpError("expected number of static shape bounds to be equal to "
+ "the output rank (")
+ << resultType.getRank() << ") but found "
+ << getStaticOutputShape().size() << " inputs instead";
+
+ if ((int64_t)getOutputShape().size() !=
+ llvm::count(getStaticOutputShape(), ShapedType::kDynamic))
+ return emitOpError("mismatch in dynamic dims in output_shape and "
+ "static_output_shape: static_output_shape has ")
+ << llvm::count(getStaticOutputShape(), ShapedType::kDynamic)
+ << " dynamic dims while output_shape has " << getOutputShape().size()
+ << " values";
+
return success();
}
void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
- results.add<ComposeReassociativeReshapeOps<ExpandShapeOp>,
- ComposeExpandOfCollapseOp<ExpandShapeOp, CollapseShapeOp>>(
- context);
+ results.add<
+ ComposeReassociativeReshapeOps<ExpandShapeOp, ReshapeOpKind::kExpand>,
+ ComposeExpandOfCollapseOp<ExpandShapeOp, CollapseShapeOp>>(context);
}
/// Compute the layout map after collapsing a given source MemRef type with the
@@ -2488,9 +2550,11 @@ struct CollapseShapeOpMemRefCastFolder
void CollapseShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
- results.add<ComposeReassociativeReshapeOps<CollapseShapeOp>,
- ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp, CastOp>,
- CollapseShapeOpMemRefCastFolder>(context);
+ results.add<
+ ComposeReassociativeReshapeOps<CollapseShapeOp, ReshapeOpKind::kCollapse>,
+ ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp, CastOp,
+ memref::DimOp, MemRefType>,
+ CollapseShapeOpMemRefCastFolder>(context);
}
OpFoldResult ExpandShapeOp::fold(FoldAdaptor adaptor) {
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/CMakeLists.txt b/mlir/lib/Dialect/SparseTensor/Transforms/CMakeLists.txt
index af3a1b48f45af9..1a3ac2276b2228 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/CMakeLists.txt
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/CMakeLists.txt
@@ -47,6 +47,7 @@ add_mlir_dialect_library(MLIRSparseTensorTransforms
MLIRSparseTensorEnums
MLIRSparseTensorUtils
MLIRTensorDialect
+ MLIRTensorUtils
MLIRTransforms
MLIRVectorDialect
)
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
index b117c1694e45b8..8f03c55860c892 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
@@ -25,6 +25,7 @@
#include "mlir/Dialect/SparseTensor/IR/SparseTensorType.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Dialect/Tensor/Utils/Utils.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Matchers.h"
@@ -952,8 +953,10 @@ struct ReshapeRewriter : public OpRewritePattern<ReshapeOp> {
auto rtp = getRankedTensorType(op.getResult());
auto denseTp =
RankedTensorType::get(rtp.getShape(), rtp.getElementType());
- auto reshape = rewriter.create<ReshapeOp>(loc, denseTp, op.getSrc(),
- op.getReassociation());
+ Value reshape = tensor::createReshapeOp(op, rewriter, loc, denseTp, op.getSrc());
+ if(!reshape)
+ return failure();
+
Value convert = rewriter.create<ConvertOp>(loc, rtp, reshape);
rewriter.replaceOp(op, convert);
return success();
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
index 0ce40e81371209..f3be9c3c9713fc 100644
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -1606,6 +1606,41 @@ int64_t ExpandShapeOp::getCorrespondingSourceDim(int64_t resultDim) {
llvm_unreachable("could not find reassociation group");
}
+FailureOr<SmallVector<OpFoldResult>> ExpandShapeOp::inferOutputShape(
+ OpBuilder &b, Location loc, RankedTensorType expandedType,
+ ArrayRef<ReassociationIndices> reassociation,
+ ArrayRef<OpFoldResult> inputShape) {
+ std::optional<SmallVector<OpFoldResult>> outputShape =
+ inferExpandShapeOutputShape(b, loc, expandedType, reassociation, inputShape);
+ if(!outputShape)
+ return failure();
+ return *outputShape;
+}
+
+void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
+ Type resultType, Value src,
+ ArrayRef<ReassociationIndices> reassociation,
+ ArrayRef<OpFoldResult> outputShape) {
+ auto [staticOutputShape, dynamicOutputShape] =
+ decomposeMixedValues(SmallVector<OpFoldResult>(outputShape));
+ build(builder, result, resultType.cast<RankedTensorType>(), src,
+ getReassociationIndicesAttribute(builder, reassociation),
+ dynamicOutputShape, staticOutputShape);
+}
+
+void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
+ Type resultType, Value src,
+ ArrayRef<ReassociationIndices> reassociation) {
+ SmallVector<OpFoldResult> inputShape =
+ getMixedSizes(builder, result.location, src);
+ auto tensorResultTy = resultType.cast<RankedTensorType>();
+ FailureOr<SmallVector<OpFoldResult>> outputShape =
+ inferOutputShape(builder, result.location, tensorResultTy, reassociation, inputShape);
+ // Failure of this assertion usually indicates presence of multiple
+ // dynamic dimensions in the same reassociation group.
+ assert(succeeded(outputShape) && "unable to infer output shape");
+ build(builder, result, tensorResultTy, src, reassociation, *outputShape);}
+
SmallVector<AffineMap, 4> CollapseShapeOp::getReassociationMaps() {
return getSymbolLessAffineMaps(getReassociationExprs());
}
@@ -1689,7 +1724,24 @@ static LogicalResult verifyTensorReshapeOp(TensorReshapeOp op,
}
LogicalResult ExpandShapeOp::verify() {
- return verifyTensorReshapeOp(*this, getResultType(), getSrcType());
+ auto srcType = getSrcType();
+ auto resultType = getResultType();
+
+ if ((int64_t)getStaticOutputShape().size() != resultType.getRank())
+ return emitOpError("expected number of static shape dims to be equal to "
+ "the output rank (")
+ << resultType.getRank() << ") but found "
+ << getStaticOutputShape().size() << " inputs instead";
+
+ if ((int64_t)getOutputShape().size() !=
+ llvm::count(getStaticOutputShape(), ShapedType::kDynamic))
+ return emitOpError("mismatch in dynamic dims in output_shape and "
+ "static_output_shape: static_output_shape has ")
+ << llvm::count(getStaticOutputShape(), ShapedType::kDynamic)
+ << " dynamic dims while output_shape has " << getOutputShape().size()
+ << " values";
+
+ return verifyTensorReshapeOp(*this, resultType, srcType);
}
LogicalResult CollapseShapeOp::verify() {
@@ -1873,23 +1925,25 @@ struct FoldDimOfCollapseShape : public OpRewritePattern<DimOp> {
void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
- results.add<ComposeReassociativeReshapeOps<ExpandShapeOp>,
- ComposeExpandOfCollapseOp<ExpandShapeOp, CollapseShapeOp>,
- FoldReshapeWithConstant<ExpandShapeOp>,
- FoldReshapeWithSplat<ExpandShapeOp>,
- FoldReshapeWithFromElements<ExpandShapeOp>, FoldDimOfExpandShape,
- FoldDimOfCollapseShape>(context);
+ results.add<
+ ComposeReassociativeReshapeOps<ExpandShapeOp, ReshapeOpKind::kExpand>,
+ ComposeExpandOfCollapseOp<ExpandShapeOp, CollapseShapeOp>,
+ FoldReshapeWithConstant<ExpandShapeOp>,
+ FoldReshapeWithSplat<ExpandShapeOp>,
+ FoldReshapeWithFromElements<ExpandShapeOp>, FoldDimOfExpandShape,
+ FoldDimOfCollapseShape>(context);
}
void CollapseShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
- results
- .add<ComposeReassociativeReshapeOps<CollapseShapeOp>,
- ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp, CastOp>,
- FoldReshapeWithConstant<CollapseShapeOp>,
- FoldReshapeWithSplat<CollapseShapeOp>,
- FoldReshapeWithFromElements<CollapseShapeOp>, FoldCollapseOfCastOp>(
- context);
+ results.add<
+ ComposeReassociativeReshapeOps<CollapseShapeOp, ReshapeOpKind::kCollapse>,
+ ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp, CastOp,
+ tensor::DimOp, RankedTensorType>,
+ FoldReshapeWithConstant<CollapseShapeOp>,
+ FoldReshapeWithSplat<CollapseShapeOp>,
+ FoldReshapeWithFromElements<CollapseShapeOp>, FoldCollapseOfCastOp>(
+ context);
}
OpFoldResult ExpandShapeOp::fold(FoldAdaptor adaptor) {
diff --git a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
index 58ea4cc4da3c36..d078a575f40dda 100644
--- a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
@@ -338,6 +338,9 @@ struct ExpandShapeOpInterface
// Memref result type is inferred by the builder based on reassociation
// indices and result shape.
+ // TODO: Instead of inferring the output shape argument of
+ // memref.expand_shape op, use output_shape argument of tensor.expand_shape
+ // op.
replaceOpWithNewBufferizedOp<memref::ExpandShapeOp>(
rewriter, op, tensorResultType.getShape(), *buffer,
expandShapeOp.getReassociationIndices());
diff --git a/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp b/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
index 666ac56c6cd5cd..7011ce23b55a6b 100644
--- a/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
+++ b/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
@@ -52,12 +52,16 @@ static LogicalResult isPackOn1D(RewriterBase &rewriter, Operation *op,
struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
using OpRewritePattern<PackOp>::OpRewritePattern;
- Value insertExpand(RewriterBase &rewriter, Location loc, Value operand,
- Type newOperandType, ArrayAttr reassociation) const {
+ FailureOr<Value>
+ insertExpand(RewriterBase &rewriter, Location loc, Value operand,
+ Type newOperandType,
+ ArrayRef<ReassociationIndices> reassociation) const {
if (operand.getType() == newOperandType)
return operand;
- return rewriter.create<tensor::ExpandShapeOp>(loc, newOperandType, operand,
- reassociation);
+ return rewriter
+ .create<tensor::ExpandShapeOp>(loc, newOperandType, operand,
+ reassociation)
+ .getResult();
}
/// Returns success() if it is only packing on the innermost dimension.
@@ -96,10 +100,14 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
getReassociationIndicesForReshape(sourceType, destType);
if (!reassociation)
return failure();
- Value expanded = insertExpand(
- rewriter, packOp.getLoc(), packOp.getSource(), destType,
- getReassociationIndicesAttribute(rewriter, *reassociation));
- rewriter.replaceOp(packOp, expanded);
+ FailureOr<Value> expanded =
+ insertExpand(rewriter, packOp.getLoc(), packOp.getSource(), destType,
+ *reassociation);
+ if (failed(expanded)) {
+ return rewriter.notifyMatchFailure(
+ packOp, "unable to expand source of tensor.pack");
+ }
+ rewriter.replaceOp(packOp, *expanded);
return success();
}
};
diff --git a/mlir/lib/Dialect/Tensor/Utils/Utils.cpp b/mlir/lib/Dialect/Tensor/Utils/Utils.cpp
index 15381ec520e211..efda7988721395 100644
--- a/mlir/lib/Dialect/Tensor/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Tensor/Utils/Utils.cpp
@@ -53,6 +53,20 @@ SmallVector<Value> mlir::tensor::createDynamicDimValues(OpBuilder &b,
return dynamicDims;
}
+template <typename ReshapeOp>
+Value mlir::tensor::createReshapeOp(ReshapeOp oldReshapeOp, OpBuilder &b, Location loc, RankedTensorType resultTy, Value src){
+ if constexpr (std::is_same<ReshapeOp, mlir::tensor::ExpandShapeOp>::value){
+ return b.create<ReshapeOp>(loc, resultTy, src,
+ oldReshapeOp.getReassociation(), oldReshapeOp.getOutputShape(),
+ oldReshapeOp.getStaticOutputShape()).getResult();
+ }
+ if constexpr (std::is_same<ReshapeOp, mlir::tensor::CollapseShapeOp>::value){
+ return b.create<ReshapeOp>(loc, resultTy, src,
+ oldReshapeOp.getReassociation()).getResult();
+ }
+ return {};
+}
+
FailureOr<RankedTensorType>
mlir::tensor::computeTransposedType(RankedTensorType rankedTensorType,
ArrayRef<int64_t> transposeVector) {
diff --git a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
index 41c7af4593c77c..2f289838a1cafc 100644
--- a/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
+++ b/mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
@@ -8,6 +8,7 @@
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
+#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Builders.h"
@@ -16,6 +17,65 @@
using namespace mlir;
+std::optional<SmallVector<OpFoldResult>> mlir::inferExpandShapeOutputShape(
+ OpBuilder &b, Location loc, ShapedType expandedType,
+ ArrayRef<ReassociationIndices> reassociation, ArrayRef<OpFoldResult> inputShape) {
+
+ SmallVector<Value> outputShapeValues;
+ SmallVector<int64_t> outputShapeInts;
+ // For zero-rank inputs, all dims in result shape are unit extent.
+ if (inputShape.empty()) {
+ outputShapeInts.resize(expandedType.getRank(), 1);
+ return getMixedValues(outputShapeInts, outputShapeValues, b);
+ }
+
+ // Check for all static shapes.
+ if (expandedType.hasStaticShape()) {
+ ArrayRef<int64_t> staticShape = expandedType.getShape();
+ outputShapeInts.assign(staticShape.begin(), staticShape.end());
+ return getMixedValues(outputShapeInts, outputShapeValues, b);
+ }
+
+ outputShapeInts.resize(expandedType.getRank(), ShapedType::kDynamic);
+ for (const auto &it : llvm::enumerate(reassociation)) {
+ ReassociationIndices indexGroup = it.value();
+
+ int64_t indexGroupStaticSizesProductInt = 1;
+ bool foundDynamicShape = false;
+ for (int64_t index : indexGroup) {
+ int64_t outputDimSize = expandedType.getDimSize(index);
+ // Cannot infer expanded shape with multiple dynamic dims in the
+ // same reassociation group!
+ if (ShapedType::isDynamic(outputDimSize)) {
+ if (foundDynamicShape)
+ return std::nullopt;
+ foundDynamicShape = true;
+ } else {
+ outputShapeInts[index] = outputDimSize;
+ indexGroupStaticSizesProductInt *= outputDimSize;
+ }
+ }
+ if (!foundDynamicShape)
+ continue;
+
+ int64_t inputIndex = it.index();
+ // Call get<Value>() under the assumption that we're not casting
+ // dynamism.
+ Value indexGroupSize = inputShape[inputIndex].get<Value>();
+ Value indexGroupStaticSizesProduct =
+ b.create<arith::ConstantIndexOp>(loc, indexGroupStaticSizesProductInt);
+ Value dynamicDimSize = b.createOrFold<arith::DivUIOp>(
+ loc, indexGroupSize, indexGroupStaticSizesProduct);
+ outputShapeValues.push_back(dynamicDimSize);
+ }
+
+ if ((int64_t)outputShapeValues.size() !=
+ llvm::count(outputShapeInts, ShapedType::kDynamic))
+ return std::nullopt;
+
+ return getMixedValues(outputShapeInts, outputShapeValues, b);
+}
+
std::optional<SmallVector<ReassociationIndices>>
mlir::getReassociationIndicesForReshape(ShapedType sourceType,
ShapedType targetType) {
@@ -168,7 +228,7 @@ ArrayAttr mlir::getReassociationIndicesAttribute(
}
SmallVector<ReassociationIndices, 2> mlir::convertReassociationMapsToIndices(
- OpBuilder &b, ArrayRef<ReassociationExprs> reassociationExprs) {
+ ArrayRef<ReassociationExprs> reassociationExprs) {
SmallVector<ReassociationIndices, 2> reassociationIndices;
for (const auto &exprs : reassociationExprs) {
ReassociationIndices indices;
@@ -230,24 +290,17 @@ LogicalResult mlir::reshapeLikeShapesAreCompatible(
ArrayRef<ReassociationIndices> reassociationMaps, bool isExpandingReshape) {
unsigned expandedDimStart = 0;
for (const auto &map : llvm::enumerate(reassociationMaps)) {
- std::optional<int64_t> dynamicShape;
+ bool foundDynamicShape = false;
int64_t linearizedStaticShape = 1;
+
for (const auto &dim : llvm::enumerate(
expandedShape.slice(expandedDimStart, map.value().size()))) {
- if (ShapedType::isDynamic(dim.value())) {
- if (isExpandingReshape && dynamicShape) {
- return emitError("invalid to have a single dimension (" +
- Twine(map.index()) +
- ") expanded into multiple dynamic dims (" +
- Twine(expandedDimStart + dynamicShape.value()) +
- "," + Twine(expandedDimStart + dim.index()) + ")");
- }
- dynamicShape = dim.index();
- } else {
+ if (ShapedType::isDynamic(dim.value()))
+ foundDynamicShape = true;
+ else
linearizedStaticShape *= dim.value();
- }
}
- if (dynamicShape) {
+ if (foundDynamicShape) {
if (!ShapedType::isDynamic(collapsedShape[map.index()])) {
return emitError(
"expected dimension " + Twine(map.index()) +
diff --git a/mlir/lib/Dialect/Utils/StaticValueUtils.cpp b/mlir/lib/Dialect/Utils/StaticValueUtils.cpp
index 1e8197e1094424..74a53709592dd2 100644
--- a/mlir/lib/Dialect/Utils/StaticValueUtils.cpp
+++ b/mlir/lib/Dialect/Utils/StaticValueUtils.cpp
@@ -180,9 +180,8 @@ SmallVector<OpFoldResult> getMixedValues(ArrayRef<int64_t> staticValues,
/// Decompose a vector of mixed static or dynamic values into the corresponding
/// pair of arrays. This is the inverse function of `getMixedValues`.
-std::pair<ArrayAttr, SmallVector<Value>>
-decomposeMixedValues(Builder &b,
- const SmallVectorImpl<OpFoldResult> &mixedValues) {
+std::pair<SmallVector<int64_t>, SmallVector<Value>>
+decomposeMixedValues(const SmallVectorImpl<OpFoldResult> &mixedValues) {
SmallVector<int64_t> staticValues;
SmallVector<Value> dynamicValues;
for (const auto &it : mixedValues) {
@@ -193,7 +192,7 @@ decomposeMixedValues(Builder &b,
dynamicValues.push_back(it.get<Value>());
}
}
- return {b.getI64ArrayAttr(staticValues), dynamicValues};
+ return {staticValues, dynamicValues};
}
/// Helper to sort `values` according to matching `keys`.
diff --git a/mlir/test/Conversion/MemRefToLLVM/expand-then-convert-to-llvm.mlir b/mlir/test/Conversion/MemRefToLLVM/expand-then-convert-to-llvm.mlir
index 87d613986c7c3f..b86103422b0745 100644
--- a/mlir/test/Conversion/MemRefToLLVM/expand-then-convert-to-llvm.mlir
+++ b/mlir/test/Conversion/MemRefToLLVM/expand-then-convert-to-llvm.mlir
@@ -453,7 +453,7 @@ func.func @collapse_shape_dynamic_with_non_identity_layout(
func.func @expand_shape_static(%arg0: memref<3x4x5xf32>) -> memref<1x3x4x1x5xf32> {
// Reshapes that expand a contiguous tensor with some 1's.
- %0 = memref.expand_shape %arg0 [[0, 1], [2], [3, 4]]
+ %0 = memref.expand_shape %arg0 [[0, 1], [2], [3, 4]] output_shape [1, 3, 4, 1, 5]
: memref<3x4x5xf32> into memref<1x3x4x1x5xf32>
return %0 : memref<1x3x4x1x5xf32>
}
@@ -510,7 +510,7 @@ func.func @collapse_shape_fold_zero_dim(%arg0 : memref<1x1xf32>) -> memref<f32>
// -----
func.func @expand_shape_zero_dim(%arg0 : memref<f32>) -> memref<1x1xf32> {
- %0 = memref.expand_shape %arg0 [] : memref<f32> into memref<1x1xf32>
+ %0 = memref.expand_shape %arg0 [] output_shape [1, 1] : memref<f32> into memref<1x1xf32>
return %0 : memref<1x1xf32>
}
@@ -571,13 +571,13 @@ func.func @collapse_shape_dynamic(%arg0 : memref<1x2x?xf32>) -> memref<1x?xf32>
// -----
-func.func @expand_shape_dynamic(%arg0 : memref<1x?xf32>) -> memref<1x2x?xf32> {
- %0 = memref.expand_shape %arg0 [[0], [1, 2]]: memref<1x?xf32> into memref<1x2x?xf32>
+func.func @expand_shape_dynamic(%arg0 : memref<1x?xf32>, %sz0: index) -> memref<1x2x?xf32> {
+ %0 = memref.expand_shape %arg0 [[0], [1, 2]] output_shape [1, 2, %sz0]: memref<1x?xf32> into memref<1x2x?xf32>
return %0 : memref<1x2x?xf32>
}
// CHECK-LABEL: func.func @expand_shape_dynamic(
-// CHECK-SAME: %[[ARG:.*]]: memref<1x?xf32>) -> memref<1x2x?xf32> {
+// CHECK-SAME: %[[ARG:.*]]: memref<1x?xf32>, %[[SZ0:.*]]: index) -> memref<1x2x?xf32> {
// CHECK: %[[MEM:.*]] = builtin.unrealized_conversion_cast %[[ARG]] : memref<1x?xf32> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
// CHECK: %[[BASE_BUFFER:.*]] = llvm.extractvalue %[[MEM]][0] : !llvm.struct<(ptr, ptr, i64,
// CHECK: %[[ALIGNED_BUFFER:.*]] = llvm.extractvalue %[[MEM]][1] : !llvm.struct<(ptr, ptr, i64,
@@ -614,15 +614,15 @@ func.func @expand_shape_dynamic(%arg0 : memref<1x?xf32>) -> memref<1x2x?xf32> {
// -----
func.func @expand_shape_dynamic_with_non_identity_layout(
- %arg0 : memref<1x?xf32, strided<[?, ?], offset: ?>>) ->
+ %arg0 : memref<1x?xf32, strided<[?, ?], offset: ?>>, %sz0: index) ->
memref<1x2x?xf32, strided<[?, ?, ?], offset: ?>> {
- %0 = memref.expand_shape %arg0 [[0], [1, 2]]:
+ %0 = memref.expand_shape %arg0 [[0], [1, 2]] output_shape [1, 2, %sz0] :
memref<1x?xf32, strided<[?, ?], offset: ?>> into
memref<1x2x?xf32, strided<[?, ?, ?], offset: ?>>
return %0 : memref<1x2x?xf32, strided<[?, ?, ?], offset: ?>>
}
// CHECK-LABEL: func.func @expand_shape_dynamic_with_non_identity_layout(
-// CHECK-SAME: %[[ARG:.*]]: memref<1x?xf32, strided<[?, ?], offset: ?>>) -> memref<1x2x?xf32, strided<[?, ?, ?], offset: ?>> {
+// CHECK-SAME: %[[ARG:.*]]: memref<1x?xf32, strided<[?, ?], offset: ?>>, %[[SZ0:.*]]: index) -> memref<1x2x?xf32, strided<[?, ?, ?], offset: ?>> {
// CHECK: %[[MEM:.*]] = builtin.unrealized_conversion_cast %[[ARG]] : memref<1x?xf32, strided<[?, ?], offset: ?>> to !llvm.struct<(ptr, ptr, i64, array<2 x i64>, array<2 x i64>)>
// CHECK: %[[BASE_BUFFER:.*]] = llvm.extractvalue %[[MEM]][0] : !llvm.struct<(ptr, ptr, i64,
// CHECK: %[[ALIGNED_BUFFER:.*]] = llvm.extractvalue %[[MEM]][1] : !llvm.struct<(ptr, ptr, i64,
diff --git a/mlir/test/Conversion/MemRefToLLVM/memref-to-llvm.mlir b/mlir/test/Conversion/MemRefToLLVM/memref-to-llvm.mlir
index 37999d6fc14ad1..baf9cfe610a5a0 100644
--- a/mlir/test/Conversion/MemRefToLLVM/memref-to-llvm.mlir
+++ b/mlir/test/Conversion/MemRefToLLVM/memref-to-llvm.mlir
@@ -334,9 +334,9 @@ memref.global "private" @gv4 : memref<f32> = dense<1.0> {alignment = 64}
// CHECK-LABEL: func @expand_shape_static(
// CHECK-SAME: %[[ARG:.*]]: memref<{{.*}}>)
func.func @expand_shape_static(%arg0: memref<3x4x5xf32>) -> memref<1x3x4x1x5xf32> {
- // CHECK: memref.expand_shape %[[ARG]] {{\[}}[0, 1], [2], [3, 4]]
+ // CHECK: memref.expand_shape %[[ARG]] {{\[}}[0, 1], [2], [3, 4]] output_shape [1, 3, 4, 1, 5]
// Reshapes that expand a contiguous tensor with some 1's.
- %0 = memref.expand_shape %arg0 [[0, 1], [2], [3, 4]]
+ %0 = memref.expand_shape %arg0 [[0, 1], [2], [3, 4]] output_shape [1, 3, 4, 1, 5]
: memref<3x4x5xf32> into memref<1x3x4x1x5xf32>
return %0 : memref<1x3x4x1x5xf32>
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index 445e8be47678d5..4eaecfd117ece7 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -348,7 +348,7 @@ func.func @test_add_2d_all_dynamic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]:
func.func @test_add_2d_different_ranks(%arg0: tensor<3x4xf32>, %arg1: tensor<2x3x4xf32>) -> tensor<2x3x4xf32> {
- // CHECK: %[[ARG0_EXPANDED:.*]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2]] : tensor<3x4xf32> into tensor<1x3x4xf32>
+ // CHECK: %[[ARG0_EXPANDED:.*]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2]] output_shape [1, 3, 4] : tensor<3x4xf32> into tensor<1x3x4xf32>
// CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<2x3x4xf32>
// CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[ARG0_EXPANDED]], %[[ARG1]] : tensor<1x3x4xf32>, tensor<2x3x4xf32>) outs(%[[VAL_0]] : tensor<2x3x4xf32>) {
// CHECK: ^bb0(%[[VAL_1:.*]]: f32, %[[VAL_2:.*]]: f32, %[[VAL_3:.*]]: f32):
@@ -871,7 +871,7 @@ func.func @reduce_float(%arg0: tensor<5x4xf32>) -> () {
// CHECK: [[RES:%.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32
// CHECK: linalg.yield [[RES]] : f32
// CHECK: }
- // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] : tensor<4xf32> into tensor<1x4xf32>
+ // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xf32> into tensor<1x4xf32>
%0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32>
// CHECK: [[INIT:%.+]] = tensor.empty() : tensor<5xf32>
@@ -882,7 +882,7 @@ func.func @reduce_float(%arg0: tensor<5x4xf32>) -> () {
// CHECK: [[RES:%.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32
// CHECK: linalg.yield [[RES]] : f32
// CHECK: }
- // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] : tensor<5xf32> into tensor<5x1xf32>
+ // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [5, 1] : tensor<5xf32> into tensor<5x1xf32>
%1 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<5x4xf32>) -> tensor<5x1xf32>
// CHECK: arith.constant 1.0
@@ -920,7 +920,10 @@ func.func @reduce_float_dyn(%arg0: tensor<?x5x4xf32>) -> () {
// CHECK: %[[RES:.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[RES]] : f32
// CHECK: }
- // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] : tensor<?x4xf32> into tensor<?x1x4xf32>
+ // CHECK: %[[C0_0:.+]] = arith.constant 0 : index
+ // CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C0_0]] : tensor<?x4xf32>
+ // CHECK: %[[C1:.+]] = arith.constant 1 : index
+ // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] output_shape [%[[DIM_1]], 1, 4] : tensor<?x4xf32> into tensor<?x1x4xf32>
%0 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<?x5x4xf32>) -> tensor<?x1x4xf32>
return
}
@@ -938,7 +941,7 @@ func.func @reduce_float_dyn_rank_1(%arg0: tensor<?xf32>) -> () {
// CHECK: %[[RES:.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[RES]] : f32
// CHECK: }
- // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}] : tensor<f32> into tensor<1xf32>
+ // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}] output_shape [1] : tensor<f32> into tensor<1xf32>
%0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<?xf32>) -> tensor<1xf32>
return
}
@@ -958,7 +961,10 @@ func.func @reduce_float_dyn_nonzero_batch(%arg0: tensor<5x?x4xf32>) -> () {
// CHECK: %[[RES:.+]] = arith.mulf %[[ARG1]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[RES]] : f32
// CHECK: }
- // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] : tensor<5x?xf32> into tensor<5x?x1xf32>
+ // CHECK: %[[C1_0:.+]] = arith.constant 1 : index
+ // CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C1_0]] : tensor<5x?xf32>
+ // CHECK: %[[C1_2:.+]] = arith.constant 1 : index
+ // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] output_shape [5, %[[DIM_1]], 1] : tensor<5x?xf32> into tensor<5x?x1xf32>
%0 = tosa.reduce_prod %arg0 {axis = 2 : i32} : (tensor<5x?x4xf32>) -> tensor<5x?x1xf32>
return
}
@@ -978,7 +984,10 @@ func.func @reduce_float_dyn_multiple(%arg0: tensor<?x?xf32>) -> () {
// CHECK: %[[MAX:.+]] = arith.maximumf %[[ARG1]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[MAX]] : f32
// CHECK: }
- // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0, 1]] : tensor<?xf32> into tensor<?x1xf32>
+ // CHECK: %[[C0_0:.+]] = arith.constant 0 : index
+ // CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C0_0]] : tensor<?xf32>
+ // CHECK: %[[C1_2:.+]] = arith.constant 1 : index
+ // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0, 1]] output_shape [%[[DIM_1]], 1] : tensor<?xf32> into tensor<?x1xf32>
%0 = tosa.reduce_max %arg0 {axis = 1 : i32} : (tensor<?x?xf32>) -> tensor<?x1xf32>
return
}
@@ -996,7 +1005,7 @@ func.func @reduce_int(%arg0: tensor<5x4xi32>) -> () {
// CHECK: [[RES:%.+]] = arith.addi %[[ARG1]], %[[ARG2]] : i32
// CHECK: linalg.yield [[RES]] : i32
// CHECK: }
- // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] : tensor<4xi32> into tensor<1x4xi32>
+ // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xi32> into tensor<1x4xi32>
%0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32>
// CHECK: [[INIT:%.+]] = tensor.empty()
@@ -1007,7 +1016,7 @@ func.func @reduce_int(%arg0: tensor<5x4xi32>) -> () {
// CHECK: [[RES:%.+]] = arith.addi %[[ARG1]], %[[ARG2]] : i32
// CHECK: linalg.yield [[RES]] : i32
// CHECK: }
- // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] : tensor<5xi32> into tensor<5x1xi32>
+ // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [5, 1] : tensor<5xi32> into tensor<5x1xi32>
%1 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<5x4xi32>) -> tensor<5x1xi32>
// CHECK: arith.constant 1
@@ -1043,7 +1052,7 @@ func.func @reduce_bool(%arg0: tensor<5x4xi1>) -> () {
// CHECK: [[RES:%.+]] = arith.andi %[[ARG1]], %[[ARG2]] : i1
// CHECK: linalg.yield [[RES]] : i1
// CHECK: }
- // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] : tensor<4xi1> into tensor<1x4xi1>
+ // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xi1> into tensor<1x4xi1>
%0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<5x4xi1>) -> tensor<1x4xi1>
// CHECK: arith.constant false
diff --git a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
index a8a3c42e168422..b8c3d56f21f10c 100644
--- a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
+++ b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
@@ -14,7 +14,7 @@ func.func @test_reshape_0d_same_s2s_explicit(%arg0: tensor<f32>) -> tensor<f32>
// CHECK-LABEL: test_reshape_0d_up_s2d_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<f32>
-// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] : tensor<f32> into tensor<1xf32>
+// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: %[[VAL_1:.*]] = tensor.cast %[[VAL_0]] : tensor<1xf32> to tensor<?xf32>
// CHECK: return %[[VAL_1]] : tensor<?xf32>
func.func @test_reshape_0d_up_s2d_auto(%arg0: tensor<f32>) -> tensor<?xf32> {
@@ -26,7 +26,7 @@ func.func @test_reshape_0d_up_s2d_auto(%arg0: tensor<f32>) -> tensor<?xf32> {
// CHECK-LABEL: test_reshape_0d_up_s2d_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<f32>
-// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] : tensor<f32> into tensor<1xf32>
+// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: %[[VAL_1:.*]] = tensor.cast %[[VAL_0]] : tensor<1xf32> to tensor<?xf32>
// CHECK: return %[[VAL_1]] : tensor<?xf32>
func.func @test_reshape_0d_up_s2d_explicit(%arg0: tensor<f32>) -> tensor<?xf32> {
@@ -38,7 +38,7 @@ func.func @test_reshape_0d_up_s2d_explicit(%arg0: tensor<f32>) -> tensor<?xf32>
// CHECK-LABEL: test_reshape_0d_up_s2s_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<f32>
-// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] : tensor<f32> into tensor<1xf32>
+// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: return %[[VAL_0]] : tensor<1xf32>
func.func @test_reshape_0d_up_s2s_auto(%arg0: tensor<f32>) -> tensor<1xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: -1>} : (tensor<f32>) -> tensor<1xf32>
@@ -49,7 +49,7 @@ func.func @test_reshape_0d_up_s2s_auto(%arg0: tensor<f32>) -> tensor<1xf32> {
// CHECK-LABEL: test_reshape_0d_up_s2s_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<f32>
-// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] : tensor<f32> into tensor<1xf32>
+// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: return %[[VAL_0]] : tensor<1xf32>
func.func @test_reshape_0d_up_s2s_explicit(%arg0: tensor<f32>) -> tensor<1xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 1>} : (tensor<f32>) -> tensor<1xf32>
@@ -83,8 +83,12 @@ func.func @test_reshape_1d_down_s2s_explicit(%arg0: tensor<1xf32>) -> tensor<f32
// CHECK-LABEL: test_reshape_1d_up_d2d_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?xf32>
-// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<?xf32> into tensor<2x?xf32>
-// CHECK: return %[[VAL_0]] : tensor<2x?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %arg0, %[[C0]] : tensor<?xf32>
+// CHECK: %[[C2:.*]] = arith.constant 2 : index
+// CHECK: %[[VAL_0:.*]] = arith.divui %[[DIM]], %[[C2]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[ARG_0]] {{\[\[}}0, 1]] output_shape [2, %[[VAL_0]]] : tensor<?xf32> into tensor<2x?xf32>
+// CHECK: return %[[EXPANDED]] : tensor<2x?xf32>
func.func @test_reshape_1d_up_d2d_auto(%arg0: tensor<?xf32>) -> tensor<2x?xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, -1>} : (tensor<?xf32>) -> tensor<2x?xf32>
return %0 : tensor<2x?xf32>
@@ -94,7 +98,7 @@ func.func @test_reshape_1d_up_d2d_auto(%arg0: tensor<?xf32>) -> tensor<2x?xf32>
// CHECK-LABEL: test_reshape_1d_up_s2s_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<6xf32>
-// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<6xf32> into tensor<2x3xf32>
+// CHECK: %[[VAL_0:.*]] = tensor.expand_shape %[[ARG_0]] {{\[\[}}0, 1]] output_shape [2, 3] : tensor<6xf32> into tensor<2x3xf32>
// CHECK: return %[[VAL_0]] : tensor<2x3xf32>
func.func @test_reshape_1d_up_s2s_explicit(%arg0: tensor<6xf32>) -> tensor<2x3xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 3>} : (tensor<6xf32>) -> tensor<2x3xf32>
@@ -128,8 +132,12 @@ func.func @test_reshape_2d_down_s2s_explicit(%arg0: tensor<2x3xf32>) -> tensor<6
// CHECK-LABEL: test_reshape_2d_same_d2d_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?x2xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<?x2xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] : tensor<?xf32> into tensor<2x?xf32>
-// CHECK: return %[[VAL_1]] : tensor<2x?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C2:.*]] = arith.constant 2 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C2]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] output_shape [2, %[[DIV]]] : tensor<?xf32> into tensor<2x?xf32>
+// CHECK: return %[[EXPANDED]] : tensor<2x?xf32>
func.func @test_reshape_2d_same_d2d_auto(%arg0: tensor<?x2xf32>) -> tensor<2x?xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, -1>} : (tensor<?x2xf32>) -> tensor<2x?xf32>
return %0 : tensor<2x?xf32>
@@ -140,7 +148,7 @@ func.func @test_reshape_2d_same_d2d_auto(%arg0: tensor<?x2xf32>) -> tensor<2x?xf
// CHECK-LABEL: test_reshape_2d_same_s2d_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<2x4xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<2x4xf32> into tensor<8xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] : tensor<8xf32> into tensor<4x2xf32>
+// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] output_shape [4, 2] : tensor<8xf32> into tensor<4x2xf32>
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<4x2xf32> to tensor<?x2xf32>
// CHECK: return %[[VAL_2]] : tensor<?x2xf32>
func.func @test_reshape_2d_same_s2d_auto(%arg0: tensor<2x4xf32>) -> tensor<?x2xf32> {
@@ -153,7 +161,7 @@ func.func @test_reshape_2d_same_s2d_auto(%arg0: tensor<2x4xf32>) -> tensor<?x2xf
// CHECK-LABEL: test_reshape_2d_same_s2d_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<2x4xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<2x4xf32> into tensor<8xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] : tensor<8xf32> into tensor<4x2xf32>
+// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] output_shape [4, 2] : tensor<8xf32> into tensor<4x2xf32>
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<4x2xf32> to tensor<?x2xf32>
// CHECK: return %[[VAL_2]] : tensor<?x2xf32>
func.func @test_reshape_2d_same_s2d_explicit(%arg0: tensor<2x4xf32>) -> tensor<?x2xf32> {
@@ -166,7 +174,7 @@ func.func @test_reshape_2d_same_s2d_explicit(%arg0: tensor<2x4xf32>) -> tensor<?
// CHECK-LABEL: test_reshape_2d_same_s2s_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<3x2xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1]] : tensor<3x2xf32> into tensor<6xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] : tensor<6xf32> into tensor<2x3xf32>
+// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1]] output_shape [2, 3] : tensor<6xf32> into tensor<2x3xf32>
// CHECK: return %[[VAL_1]] : tensor<2x3xf32>
func.func @test_reshape_2d_same_s2s_explicit(%arg0: tensor<3x2xf32>) -> tensor<2x3xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 3>} : (tensor<3x2xf32>) -> tensor<2x3xf32>
@@ -178,7 +186,11 @@ func.func @test_reshape_2d_same_s2s_explicit(%arg0: tensor<3x2xf32>) -> tensor<2
// CHECK-LABEL: test_reshape_3d_same_d2d_auto_empty
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<3x2x?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<3x2x?xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<0x3x?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C0_0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C0_0]] : index
+// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [0, 3, %[[DIV]]] : tensor<?xf32> into tensor<0x3x?xf32>
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<0x3x?xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_auto_empty(%arg0: tensor<3x2x?xf32>) -> tensor<?x?x?xf32> {
@@ -191,7 +203,11 @@ func.func @test_reshape_3d_same_d2d_auto_empty(%arg0: tensor<3x2x?xf32>) -> tens
// CHECK-LABEL: test_reshape_3d_same_d2d_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<2x?x?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<2x?x?xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<2x?x4xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C8:.*]] = arith.constant 8 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C8]] : index
+// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [2, %[[DIV]], 4] : tensor<?xf32> into tensor<2x?x4xf32>
// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<2x?x4xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_auto(%arg0: tensor<2x?x?xf32>) -> tensor<?x?x?xf32> {
@@ -204,7 +220,11 @@ func.func @test_reshape_3d_same_d2d_auto(%arg0: tensor<2x?x?xf32>) -> tensor<?x?
// CHECK-LABEL: test_reshape_3d_same_d2d_auto_identity
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?x3x4xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<?x3x4xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<2x3x?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C6:.*]] = arith.constant 6 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C6]] : index
+// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [2, 3, %[[DIV]]] : tensor<?xf32> into tensor<2x3x?xf32>
// CHECK: return %[[VAL_1]] : tensor<2x3x?xf32>
func.func @test_reshape_3d_same_d2d_auto_identity(%arg0: tensor<?x3x4xf32>) -> tensor<2x3x?xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 3, -1>} : (tensor<?x3x4xf32>) -> tensor<2x3x?xf32>
@@ -216,8 +236,12 @@ func.func @test_reshape_3d_same_d2d_auto_identity(%arg0: tensor<?x3x4xf32>) -> t
// CHECK-LABEL: test_reshape_3d_same_d2d_explicit_empty
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<3x2x?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<3x2x?xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<?x3x2xf32>
-// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<?x3x2xf32> to tensor<?x?x?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C6:.*]] = arith.constant 6 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C6]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [%[[DIV]], 3, 2] : tensor<?xf32> into tensor<?x3x2xf32>
+// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x2xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_explicit_empty(%arg0: tensor<3x2x?xf32>) -> tensor<?x?x?xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 0, 3, 2>} : (tensor<3x2x?xf32>) -> tensor<?x?x?xf32>
@@ -229,8 +253,12 @@ func.func @test_reshape_3d_same_d2d_explicit_empty(%arg0: tensor<3x2x?xf32>) ->
// CHECK-LABEL: test_reshape_3d_same_d2d_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<?x?x?xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<?x3x4xf32>
-// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<?x3x4xf32> to tensor<?x?x?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C12:.*]] = arith.constant 12 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C12]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [%[[DIV]], 3, 4] : tensor<?xf32> into tensor<?x3x4xf32>
+// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x4xf32> to tensor<?x?x?xf32>
// CHECK: return %[[VAL_2]] : tensor<?x?x?xf32>
func.func @test_reshape_3d_same_d2d_explicit(%arg0: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 3, 4>} : (tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
@@ -253,8 +281,12 @@ func.func @test_reshape_3d_same_d2d_explicit_identity(%arg0: tensor<?x3x4xf32>)
// CHECK-LABEL: test_reshape_3d_same_d2s_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<?x?x?xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<2x?x4xf32>
-// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<2x?x4xf32> to tensor<2x3x4xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C8:.*]] = arith.constant 8 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C8]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [2, %[[DIV]], 4] : tensor<?xf32> into tensor<2x?x4xf32>
+// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<2x?x4xf32> to tensor<2x3x4xf32>
// CHECK: return %[[VAL_2]] : tensor<2x3x4xf32>
func.func @test_reshape_3d_same_d2s_auto(%arg0: tensor<?x?x?xf32>) -> tensor<2x3x4xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, -1, 4>} : (tensor<?x?x?xf32>) -> tensor<2x3x4xf32>
@@ -266,8 +298,12 @@ func.func @test_reshape_3d_same_d2s_auto(%arg0: tensor<?x?x?xf32>) -> tensor<2x3
// CHECK-LABEL: test_reshape_3d_same_d2s_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<?x?x?xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<?x3x4xf32>
-// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<?x3x4xf32> to tensor<2x3x4xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C12:.*]] = arith.constant 12 : index
+// CHECK: %[[DIV:.*]] = arith.divui %[[DIM]], %[[C12]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] output_shape [%[[DIV]], 3, 4] : tensor<?xf32> into tensor<?x3x4xf32>
+// CHECK: %[[VAL_2:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x4xf32> to tensor<2x3x4xf32>
// CHECK: return %[[VAL_2]] : tensor<2x3x4xf32>
func.func @test_reshape_3d_same_d2s_explicit(%arg0: tensor<?x?x?xf32>) -> tensor<2x3x4xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 3, 4>} : (tensor<?x?x?xf32>) -> tensor<2x3x4xf32>
@@ -288,10 +324,14 @@ func.func @test_reshape_3d_same_s2s_explicit_identity(%arg0: tensor<2x3x4xf32>)
// CHECK-LABEL: test_reshape_3d_up_d2s_explicit
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
-// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<?x?x?xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2, 3]] : tensor<?xf32> into tensor<?x3x2x1xf32>
-// CHECK: %[[VAL_2:.*]] = tensor.cast %[[VAL_1]] : tensor<?x3x2x1xf32> to tensor<1x3x2x1xf32>
-// CHECK: return %[[VAL_2]] : tensor<1x3x2x1xf32>
+// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2]] : tensor<?x?x?xf32> into tensor<?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[COLLAPSED]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C6:.*]] = arith.constant 6 : index
+// CHECK: %[[VAL_0:.*]] = arith.divui %[[DIM]], %[[C6]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2, 3]] output_shape [%[[VAL_0]], 3, 2, 1] : tensor<?xf32> into tensor<?x3x2x1xf32>
+// CHECK: %[[CAST:.*]] = tensor.cast %[[EXPANDED]] : tensor<?x3x2x1xf32> to tensor<1x3x2x1xf32>
+// CHECK: return %[[CAST]] : tensor<1x3x2x1xf32>
func.func @test_reshape_3d_up_d2s_explicit(%input: tensor<?x?x?xf32>) -> tensor<1x3x2x1xf32> {
%0 = tosa.reshape %input {new_shape = array<i64: 1, 3, 2, 1>} : (tensor<?x?x?xf32>) -> tensor<1x3x2x1xf32>
return %0 : tensor<1x3x2x1xf32>
@@ -313,9 +353,13 @@ func.func @test_reshape_4d_down_d2s_explicit(%arg0: tensor<?x?x?x?xf32>) -> tens
// CHECK-LABEL: test_reshape_5d_down_d2d_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<?x?x?x2x3xf32>
-// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2, 3, 4]] : tensor<?x?x?x2x3xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<?x2x3xf32>
-// CHECK: return %[[VAL_1]] : tensor<?x2x3xf32>
+// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2, 3, 4]] : tensor<?x?x?x2x3xf32> into tensor<?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[COLLAPSED]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C6:.*]] = arith.constant 6 : index
+// CHECK: %[[VAL_0:.*]] = arith.divui %[[DIM]], %[[C6]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2]] output_shape [%[[VAL_0]], 2, 3] : tensor<?xf32> into tensor<?x2x3xf32>
+// CHECK: return %[[EXPANDED]] : tensor<?x2x3xf32>
func.func @test_reshape_5d_down_d2d_auto(%arg0: tensor<?x?x?x2x3xf32>) -> tensor<?x2x3xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: -1, 2, 3>} : (tensor<?x?x?x2x3xf32>) -> tensor<?x2x3xf32>
return %0 : tensor<?x2x3xf32>
@@ -325,9 +369,13 @@ func.func @test_reshape_5d_down_d2d_auto(%arg0: tensor<?x?x?x2x3xf32>) -> tensor
// CHECK-LABEL: test_reshape_6d_down_d2d_auto
// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: tensor<1x2x?x5x7x11xf32>
-// CHECK: %[[VAL_0:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2, 3, 4, 5]] : tensor<1x2x?x5x7x11xf32> into tensor<?xf32>
-// CHECK: %[[VAL_1:.*]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1, 2]] : tensor<?xf32> into tensor<?x5x77xf32>
-// CHECK: return %[[VAL_1]] : tensor<?x5x77xf32>
+// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[ARG_0]] {{\[\[}}0, 1, 2, 3, 4, 5]] : tensor<1x2x?x5x7x11xf32> into tensor<?xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %[[COLLAPSED]], %[[C0]] : tensor<?xf32>
+// CHECK: %[[C385:.*]] = arith.constant 385 : index
+// CHECK: %[[VAL_0:.*]] = arith.divui %[[DIM]], %[[C385]] : index
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2]] output_shape [%[[VAL_0]], 5, 77] : tensor<?xf32> into tensor<?x5x77xf32>
+// CHECK: return %[[EXPANDED]] : tensor<?x5x77xf32>
func.func @test_reshape_6d_down_d2d_auto(%arg0: tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32> {
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: -1, 5, 77>} : (tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32>
return %0 : tensor<?x5x77xf32>
diff --git a/mlir/test/Dialect/Bufferization/Transforms/one-shot-bufferize-empty-tensor-elimination.mlir b/mlir/test/Dialect/Bufferization/Transforms/one-shot-bufferize-empty-tensor-elimination.mlir
index 9a3e14b6d39178..efe59af97d9649 100644
--- a/mlir/test/Dialect/Bufferization/Transforms/one-shot-bufferize-empty-tensor-elimination.mlir
+++ b/mlir/test/Dialect/Bufferization/Transforms/one-shot-bufferize-empty-tensor-elimination.mlir
@@ -132,7 +132,7 @@ func.func @shape_mismatch(%t: tensor<5x6x128xf32>) -> tensor<5x6x128xf32> {
%cst = arith.constant 8.0 : f32
%0 = tensor.empty() : tensor<128xf32>
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<128xf32>) -> tensor<128xf32>
- %2 = tensor.expand_shape %1 [[0, 1, 2]]
+ %2 = tensor.expand_shape %1 [[0, 1, 2]] output_shape [1, 1, 128]
: tensor<128xf32> into tensor<1x1x128xf32>
%3 = tensor.insert_slice %2 into %t[2, 3, 0][1, 1, 128][1, 1, 1]
: tensor<1x1x128xf32> into tensor<5x6x128xf32>
diff --git a/mlir/test/Dialect/Linalg/bubble-up-extract-slice-op.mlir b/mlir/test/Dialect/Linalg/bubble-up-extract-slice-op.mlir
index 0e353a1fa43fcb..4bf81820f0e805 100644
--- a/mlir/test/Dialect/Linalg/bubble-up-extract-slice-op.mlir
+++ b/mlir/test/Dialect/Linalg/bubble-up-extract-slice-op.mlir
@@ -165,7 +165,9 @@ func.func @rank_reducing_slice(%width : index) -> tensor<1x1x1x?xf32> {
%init = tensor.empty(%width) : tensor<1x?xf32>
%fill = linalg.fill ins(%cst : f32) outs(%init : tensor<1x?xf32>) -> tensor<1x?xf32>
%slice = tensor.extract_slice %fill[0, 0] [1, %width] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
- %expand = tensor.expand_shape %slice [[0, 1, 2, 3]] : tensor<?xf32> into tensor<1x1x1x?xf32>
+ %c0 = arith.constant 0 : index
+ %sz0 = tensor.dim %slice, %c0 : tensor<?xf32>
+ %expand = tensor.expand_shape %slice [[0, 1, 2, 3]] output_shape [1, 1, 1, %sz0] : tensor<?xf32> into tensor<1x1x1x?xf32>
return %expand : tensor<1x1x1x?xf32>
}
diff --git a/mlir/test/Dialect/Linalg/collapse-dim.mlir b/mlir/test/Dialect/Linalg/collapse-dim.mlir
index 547320f5338747..61bedecbdca5a4 100644
--- a/mlir/test/Dialect/Linalg/collapse-dim.mlir
+++ b/mlir/test/Dialect/Linalg/collapse-dim.mlir
@@ -52,7 +52,7 @@ func.func @collapse_parallel(
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[S]] : tensor<32x2x40960xf32>) outs(%[[D]] : tensor<2x32x40960xf32>) {
// CHECK: } -> tensor<2x32x40960xf32>
-// CHECK: tensor.expand_shape %[[R]] {{\[}}[0], [1], [2, 3]] : tensor<2x32x40960xf32> into tensor<2x32x10x4096xf32>
+// CHECK: tensor.expand_shape %[[R]] {{\[}}[0], [1], [2, 3]] output_shape [2, 32, 10, 4096] : tensor<2x32x40960xf32> into tensor<2x32x10x4096xf32>
// -----
@@ -127,8 +127,8 @@ func.func @uncollapsable_strided_memref(%arg0: memref<2x6x24x48xi32>, %arg1: mem
// CHECK: %[[VAL_4:.*]] = tensor.collapse_shape %[[VAL_2]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32> into tensor<1x2x60xf32>
// CHECK: %[[VAL_5:.*]] = tensor.collapse_shape %[[VAL_3]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32> into tensor<1x2x60xf32>
// CHECK: %[[VAL_6:.*]] = linalg.copy ins(%[[VAL_4]] : tensor<1x2x60xf32>) outs(%[[VAL_5]] : tensor<1x2x60xf32>) -> tensor<1x2x60xf32>
-// CHECK: %[[VAL_7:.*]] = tensor.expand_shape %[[VAL_6]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x60xf32> into tensor<1x2x12x5xf32>
-// CHECK: %[[VAL_8:.*]] = tensor.expand_shape %[[VAL_7]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x12x5xf32> into tensor<1x2x3x4x5xf32, 3 : i64>
+// CHECK: %[[VAL_7:.*]] = tensor.expand_shape %[[VAL_6]] {{\[\[}}0], [1], [2, 3]] output_shape [1, 2, 12, 5] : tensor<1x2x60xf32> into tensor<1x2x12x5xf32>
+// CHECK: %[[VAL_8:.*]] = tensor.expand_shape %[[VAL_7]] {{\[\[}}0], [1], [2, 3], [4]] output_shape [1, 2, 3, 4, 5] : tensor<1x2x12x5xf32> into tensor<1x2x3x4x5xf32, 3 : i64>
// CHECK: return %[[VAL_8]] : tensor<1x2x3x4x5xf32, 3 : i64>
// CHECK: }
diff --git a/mlir/test/Dialect/Linalg/convert-conv2d-to-img2col.mlir b/mlir/test/Dialect/Linalg/convert-conv2d-to-img2col.mlir
index a6431996353121..c7c846d7ecc9c5 100644
--- a/mlir/test/Dialect/Linalg/convert-conv2d-to-img2col.mlir
+++ b/mlir/test/Dialect/Linalg/convert-conv2d-to-img2col.mlir
@@ -50,7 +50,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: linalg.yield %[[EXTRACTED_INPUT]] : f32
// CHECK: IR printer: transformed
-// CHECK: tensor.expand_shape %{{[^ ]*}} {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
+// CHECK: tensor.expand_shape %{{[^ ]*}} {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
@@ -78,7 +78,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = arith.addf %[[MUL]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
// CHECK: } -> tensor<1x196x16xf32>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
// CHECK: return %[[RESULT]]
func.func @conv_16433136(%arg0: tensor<1x16x16x4xf32>, %arg1: tensor<3x3x4x16xf32>, %arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32> {
@@ -204,7 +204,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = arith.addf %[[MUL]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
// CHECK: } -> tensor<8x196x16xf32>
-// CHECK: %[[CS_FINAL:.+]] = tensor.expand_shape %[[MATMUL]] {{\[}}[0], [1, 2], [3]] : tensor<8x196x16xf32> into tensor<8x14x14x16xf32>
+// CHECK: %[[CS_FINAL:.+]] = tensor.expand_shape %[[MATMUL]] {{\[}}[0], [1, 2], [3]] output_shape [8, 14, 14, 16] : tensor<8x196x16xf32> into tensor<8x14x14x16xf32>
// CHECK: return %[[CS_FINAL]]
func.func @batch_nhwc_conv(%arg0: tensor<8x16x16x4xf32>, %arg1: tensor<3x3x4x16xf32>, %arg2: tensor<8x14x14x16xf32>) -> tensor<8x14x14x16xf32> {
%0 = linalg.conv_2d_nhwc_hwcf
@@ -269,7 +269,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = arith.addf %[[MUL]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
// CHECK: } -> tensor<8x16x196xf32>
-// CHECK: %[[CS_FINAL:.+]] = tensor.expand_shape %[[MATMUL]] {{\[}}[0], [1], [2, 3]] : tensor<8x16x196xf32> into tensor<8x16x14x14xf32>
+// CHECK: %[[CS_FINAL:.+]] = tensor.expand_shape %[[MATMUL]] {{\[}}[0], [1], [2, 3]] output_shape [8, 16, 14, 14] : tensor<8x16x196xf32> into tensor<8x16x14x14xf32>
// CHECK: return %[[CS_FINAL]]
func.func @batch_nchw_conv(%arg0: tensor<8x4x16x16xf32>, %arg1: tensor<16x4x3x3xf32>, %arg2: tensor<8x16x14x14xf32>) -> tensor<8x16x14x14xf32> {
%0 = linalg.conv_2d_nchw_fchw
@@ -310,7 +310,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: linalg.yield %[[EXTRACTED_INPUT]] : f32
// CHECK: IR printer: transformed
-// CHECK: tensor.expand_shape %{{[^ ]*}} {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
+// CHECK: tensor.expand_shape %{{[^ ]*}} {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
@@ -338,7 +338,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = arith.addf %[[MUL]], %[[ARG2]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
// CHECK: } -> tensor<1x196x16xf32>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xf32> into tensor<1x14x14x16xf32>
// CHECK: return %[[RESULT]]
func.func @conv_2d_nhwc_fhwc(%arg0: tensor<1x16x16x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<1x14x14x16xf32>) -> tensor<1x14x14x16xf32> {
@@ -378,7 +378,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = arith.addi %[[MUL]], %[[ARG2]] : i32
// CHECK: linalg.yield %[[ADD]] : i32
// CHECK: } -> tensor<1x196x16xi32>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xi32> into tensor<1x14x14x16xi32>
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xi32> into tensor<1x14x14x16xi32>
// CHECK: return %[[RESULT]]
func.func @conv_integer_extend(%arg0: tensor<1x16x16x4xi8>, %arg1: tensor<3x3x4x16xi8>, %arg2: tensor<1x14x14x16xi32>) -> tensor<1x14x14x16xi32> {
@@ -416,7 +416,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = complex.add %[[MUL]], %[[ARG2]] : complex<f32>
// CHECK: linalg.yield %[[ADD]] : complex<f32>
// CHECK: } -> tensor<1x196x16xcomplex<f32>>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xcomplex<f32>> into tensor<1x14x14x16xcomplex<f32>>
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xcomplex<f32>> into tensor<1x14x14x16xcomplex<f32>>
// CHECK: return %[[RESULT]]
func.func @conv_complex(%arg0: tensor<1x16x16x4xcomplex<f32>>, %arg1: tensor<3x3x4x16xcomplex<f32>>, %arg2: tensor<1x14x14x16xcomplex<f32>>) -> tensor<1x14x14x16xcomplex<f32>> {
@@ -459,7 +459,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = complex.add %[[MUL]], %[[ARG2]] : complex<f32>
// CHECK: linalg.yield %[[ADD]] : complex<f32>
// CHECK: } -> tensor<1x196x16xcomplex<f32>>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xcomplex<f32>> into tensor<1x14x14x16xcomplex<f32>>
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xcomplex<f32>> into tensor<1x14x14x16xcomplex<f32>>
// CHECK: return %[[RESULT]]
func.func @conv_complex_extended(%arg0: tensor<1x16x16x4xcomplex<f32>>, %arg1: tensor<3x3x4x16xcomplex<f16>>, %arg2: tensor<1x14x14x16xcomplex<f32>>) -> tensor<1x14x14x16xcomplex<f32>> {
@@ -500,7 +500,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[ADD:.+]] = complex.add %[[MUL]], %[[ARG2]] : complex<f32>
// CHECK: linalg.yield %[[ADD]] : complex<f32>
// CHECK: } -> tensor<1x196x16xcomplex<f32>>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] : tensor<1x196x16xcomplex<f32>> into tensor<1x14x14x16xcomplex<f32>>
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[MATMUL_RESULT]] {{\[}}[0], [1, 2], [3]] output_shape [1, 14, 14, 16] : tensor<1x196x16xcomplex<f32>> into tensor<1x14x14x16xcomplex<f32>>
// CHECK: return %[[RESULT]]
func.func @conv_complex_f16_extended(%arg0: tensor<1x16x16x4xcomplex<f32>>, %arg1: tensor<3x3x4x16xf16>, %arg2: tensor<1x14x14x16xcomplex<f32>>) -> tensor<1x14x14x16xcomplex<f32>> {
diff --git a/mlir/test/Dialect/Linalg/data-layout-propagation.mlir b/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
index 79d61ab757e327..bee08503298fd4 100644
--- a/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
+++ b/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
@@ -988,17 +988,20 @@ func.func @no_bubble_up_pack_through_non_divisible_collapse(%1: tensor<3072x64x4
// -----
-func.func @push_down_unpack_through_expand(%5: tensor<?x32x8x8xf32>, %dim: index) -> tensor<?x256x256xf32> {
+func.func @push_down_unpack_through_expand(%5: tensor<?x32x8x8xf32>, %dim: index, %sz0: index) -> tensor<?x256x256xf32> {
%6 = tensor.empty(%dim) : tensor<?x256xf32>
%unpack = tensor.unpack %5 outer_dims_perm = [0, 1] inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %6 : tensor<?x32x8x8xf32> -> tensor<?x256xf32>
- %expanded = tensor.expand_shape %unpack [[0, 1], [2]] : tensor<?x256xf32> into tensor<?x256x256xf32>
+ %expanded = tensor.expand_shape %unpack [[0, 1], [2]] output_shape [%sz0, 256, 256] : tensor<?x256xf32> into tensor<?x256x256xf32>
func.return %expanded : tensor<?x256x256xf32>
}
// CHECK-LABEL: func.func @push_down_unpack_through_expand
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]
+// CHECK: %[[C32:.+]] = arith.constant 32 : index
// CHECK: %[[C0:.+]] = arith.constant 0 : index
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2], [3], [4]] : tensor<?x32x8x8xf32> into tensor<?x32x32x8x8xf32>
+// CHECK: %[[DIM0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x32x8x8xf32>
+// CHECK: %[[SZ0:.+]] = arith.divui %[[DIM0]], %[[C32]] : index
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2], [3], [4]] output_shape [%[[SZ0]], 32, 32, 8, 8] : tensor<?x32x8x8xf32> into tensor<?x32x32x8x8xf32>
// CHECK: %[[DIM:.+]] = tensor.dim %[[EXPANDED]], %[[C0]] : tensor<?x32x32x8x8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty(%[[DIM]]) : tensor<?x256x256xf32>
// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[EXPANDED:.+]] outer_dims_perm = [0, 1, 2] inner_dims_pos = [1, 2] inner_tiles = [8, 8] into %[[EMPTY]] : tensor<?x32x32x8x8xf32> -> tensor<?x256x256xf32>
@@ -1009,12 +1012,12 @@ func.func @push_down_unpack_through_expand(%5: tensor<?x32x8x8xf32>, %dim: index
func.func @push_down_permuted_unpack_through_expand(%5: tensor<4x32x384x8x8xf32>) -> tensor<4x12x256x256xf32> {
%6 = tensor.empty() : tensor<4x3072x256xf32>
%unpack = tensor.unpack %5 outer_dims_perm = [0, 2, 1] inner_dims_pos = [2, 1] inner_tiles = [8, 8] into %6 : tensor<4x32x384x8x8xf32> -> tensor<4x3072x256xf32>
- %expanded = tensor.expand_shape %unpack [[0], [1, 2], [3]] : tensor<4x3072x256xf32> into tensor<4x12x256x256xf32>
+ %expanded = tensor.expand_shape %unpack [[0], [1, 2], [3]] output_shape [4, 12, 256, 256] : tensor<4x3072x256xf32> into tensor<4x12x256x256xf32>
func.return %expanded : tensor<4x12x256x256xf32>
}
// CHECK-LABEL: @push_down_permuted_unpack_through_expand
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1], [2, 3], [4], [5]] : tensor<4x32x384x8x8xf32> into tensor<4x32x12x32x8x8xf32>
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1], [2, 3], [4], [5]] output_shape [4, 32, 12, 32, 8, 8] : tensor<4x32x384x8x8xf32> into tensor<4x32x12x32x8x8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<4x12x256x256xf32>
// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[EXPANDED]] outer_dims_perm = [0, 3, 1, 2] inner_dims_pos = [3, 2] inner_tiles = [8, 8] into %[[EMPTY]] : tensor<4x32x12x32x8x8xf32> -> tensor<4x12x256x256xf32>
// CHECK: return %[[UNPACK]] : tensor<4x12x256x256xf32>
@@ -1024,29 +1027,32 @@ func.func @push_down_permuted_unpack_through_expand(%5: tensor<4x32x384x8x8xf32>
func.func @push_down_unpack_through_unit_expand(%5: tensor<6x32x8x8xf32>) -> tensor<3x16x1x256xf32> {
%6 = tensor.empty() : tensor<48x256xf32>
%unpack = tensor.unpack %5 outer_dims_perm = [0, 1] inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %6 : tensor<6x32x8x8xf32> -> tensor<48x256xf32>
- %expanded = tensor.expand_shape %unpack [[0, 1, 2], [3]] : tensor<48x256xf32> into tensor<3x16x1x256xf32>
+ %expanded = tensor.expand_shape %unpack [[0, 1, 2], [3]] output_shape [3, 16, 1, 256] : tensor<48x256xf32> into tensor<3x16x1x256xf32>
func.return %expanded : tensor<3x16x1x256xf32>
}
// CHECK-LABEL: func.func @push_down_unpack_through_unit_expand
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1, 2], [3], [4], [5]] : tensor<6x32x8x8xf32> into tensor<3x2x1x32x8x8xf32>
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1, 2], [3], [4], [5]] output_shape [3, 2, 1, 32, 8, 8] : tensor<6x32x8x8xf32> into tensor<3x2x1x32x8x8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<3x16x1x256xf32>
// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[EXPANDED]] outer_dims_perm = [0, 1, 2, 3] inner_dims_pos = [1, 3] inner_tiles = [8, 8] into %[[EMPTY]] : tensor<3x2x1x32x8x8xf32> -> tensor<3x16x1x256xf32>
// CHECK: return %[[UNPACK]] : tensor<3x16x1x256xf32>
// -----
-func.func @push_down_unpack_through_expand_on_outer_dims(%5: tensor<?x32x8xf32>, %dim: index) -> tensor<?x256x256xf32> {
+func.func @push_down_unpack_through_expand_on_outer_dims(%5: tensor<?x32x8xf32>, %dim: index, %sz0: index) -> tensor<?x256x256xf32> {
%6 = tensor.empty(%dim) : tensor<?x256xf32>
%unpack = tensor.unpack %5 outer_dims_perm = [0, 1] inner_dims_pos = [1] inner_tiles = [8] into %6 : tensor<?x32x8xf32> -> tensor<?x256xf32>
- %expanded = tensor.expand_shape %unpack [[0, 1], [2]] : tensor<?x256xf32> into tensor<?x256x256xf32>
+ %expanded = tensor.expand_shape %unpack [[0, 1], [2]] output_shape [%sz0, 256, 256] : tensor<?x256xf32> into tensor<?x256x256xf32>
func.return %expanded : tensor<?x256x256xf32>
}
// CHECK-LABEL: func.func @push_down_unpack_through_expand_on_outer_dims
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]
+// CHECK: %[[C256:.+]] = arith.constant 256 : index
// CHECK: %[[C0:.+]] = arith.constant 0 : index
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2], [3]] : tensor<?x32x8xf32> into tensor<?x256x32x8xf32>
+// CHECK: %[[DIM0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x32x8xf32>
+// CHECK: %[[SZ0:.+]] = arith.divui %[[DIM0]], %[[C256]] : index
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2], [3]] output_shape [%[[SZ0]], 256, 32, 8] : tensor<?x32x8xf32> into tensor<?x256x32x8xf32>
// CHECK: %[[DIM:.+]] = tensor.dim %[[EXPANDED]], %[[C0]] : tensor<?x256x32x8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty(%[[DIM]]) : tensor<?x256x256xf32>
// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[EXPANDED:.+]] outer_dims_perm = [0, 1, 2] inner_dims_pos = [2] inner_tiles = [8] into %[[EMPTY]] : tensor<?x256x32x8xf32> -> tensor<?x256x256xf32>
@@ -1057,11 +1063,11 @@ func.func @push_down_unpack_through_expand_on_outer_dims(%5: tensor<?x32x8xf32>,
func.func @no_push_down_unpack_through_non_divisible_expand(%5: tensor<384x32x8x8xf32>) -> tensor<256x12x256xf32> {
%6 = tensor.empty() : tensor<3072x256xf32>
%unpack = tensor.unpack %5 outer_dims_perm = [0, 1] inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %6 : tensor<384x32x8x8xf32> -> tensor<3072x256xf32>
- %expanded = tensor.expand_shape %unpack [[0, 1], [2]] : tensor<3072x256xf32> into tensor<256x12x256xf32>
+ %expanded = tensor.expand_shape %unpack [[0, 1], [2]] output_shape [256, 12, 256] : tensor<3072x256xf32> into tensor<256x12x256xf32>
func.return %expanded : tensor<256x12x256xf32>
}
// CHECK-LABEL: func.func @no_push_down_unpack_through_non_divisible_expand
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[ARG0]]
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[UNPACK]] {{\[}}[0, 1], [2]] : tensor<3072x256xf32> into tensor<256x12x256xf32>
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[UNPACK]] {{\[}}[0, 1], [2]] output_shape [256, 12, 256] : tensor<3072x256xf32> into tensor<256x12x256xf32>
// CHECK: return %[[EXPANDED]] : tensor<256x12x256xf32>
diff --git a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
index c140b6abcc37a2..a9cbaaf7fdc485 100644
--- a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
+++ b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
@@ -25,13 +25,22 @@ func.func @drop_one_trip_loops(%arg0 : tensor<?x1x?xf32>, %arg1 : f32, %shape: t
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2) -> ()>
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<()[s0, s1] -> (s0 * s1)>
// CHECK-LABEL: func @drop_one_trip_loops
-// CHECK: tensor.collapse_shape %{{.*}} {{\[}}[0, 1], [2]]
-// CHECK: tensor.collapse_shape %{{.*}} {{\[}}[0, 1], [2, 3], [4]]
+// CHECK: %[[C2:.*]] = arith.constant 2 : index
+// CHECK: %[[C1:.*]] = arith.constant 1 : index
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: tensor.collapse_shape %{{.*}} {{\[\[}}0, 1], [2]]
+// CHECK: tensor.collapse_shape %{{.*}} {{\[\[}}0, 1], [2, 3], [4]]
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP3]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2, 3], [4]]
+// CHECK: %[[DIM:.*]] = tensor.dim %{{.*}}, %[[C0]]
+// CHECK: %[[VAL_1:.*]] = affine.apply #[[$MAP4]]()[%[[DIM]], %[[C1]]]
+// CHECK: %[[DIM_1:.*]] = tensor.dim %{{.*}}, %[[C2]]
+// CHECK: %[[VAL_2:.*]] = affine.apply #[[$MAP4]]()[%[[DIM_1]], %[[C1]]]
+// CHECK: %[[DIM_2:.*]] = tensor.dim %{{.*}}, %[[C2]]
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %{{.*}} {{\[\[}}0, 1], [2, 3], [4]] output_shape [%[[VAL_1]], 1, %[[VAL_2]], 1, %[[DIM_2]]] : tensor<?x?x?xf32> into tensor<?x1x?x1x?xf32>
// CHECK-SLICES-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
// CHECK-SLICES-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2) -> ()>
@@ -70,13 +79,18 @@ func.func @drop_one_trip_loops_all_ones(%arg0 : tensor<1x1x1xf32>, %arg1 : f32,
}
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0) -> ()>
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0) -> (d0)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<()[s0, s1, s2, s3, s4] -> ((((s0 * s1) * s2) * s3) * s4)>
// CHECK-LABEL: func @drop_one_trip_loops_all_ones
+// CHECK: %[[C2:.*]] = arith.constant 2 : index
+// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: tensor.collapse_shape %{{.*}} []
// CHECK: tensor.collapse_shape %{{.*}} {{\[}}[0, 1, 2, 3, 4]]
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[$MAP1]], #[[$MAP1]], #[[$MAP2]]]
// CHECK-SAME: iterator_types = ["parallel"]
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1, 2, 3, 4]]
+// CHECK: %[[DIM:.*]] = tensor.dim %{{.*}}, %[[C2]] : tensor<1x1x?x1x1xf32>
+// CHECK: %[[SZ:.*]] = affine.apply #[[$MAP3]]()[%[[C1]], %[[C1]], %[[DIM]], %[[C1]], %[[C1]]]
+// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %{{.*}} {{\[\[}}0, 1, 2, 3, 4]] output_shape [1, 1, %[[SZ]], 1, 1] : tensor<?xf32> into tensor<1x1x?x1x1xf32>
// -----
@@ -232,8 +246,8 @@ func.func @leading_dim_1_canonicalization(%arg0: tensor<1x5xf32>, %shape: tensor
func.func @broadcast_test(%arg0 : tensor<5xf32>, %arg1 : tensor<5xf32>, %shape : tensor<5x5xf32>) -> tensor<5x5xf32>
{
- %0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<5xf32> into tensor<1x5xf32>
- %1 = tensor.expand_shape %arg1 [[0, 1]] : tensor<5xf32> into tensor<5x1xf32>
+ %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [1, 5] : tensor<5xf32> into tensor<1x5xf32>
+ %1 = tensor.expand_shape %arg1 [[0, 1]] output_shape [5, 1] : tensor<5xf32> into tensor<5x1xf32>
%2 = linalg.generic #trait
ins(%0, %1 : tensor<1x5xf32>, tensor<5x1xf32>)
outs(%shape : tensor<5x5xf32>) {
@@ -331,7 +345,6 @@ func.func @fold_unit_dim_for_empty_tensor(%input: tensor<1x1000xf32>) -> tensor<
// CHECK: func @fold_unit_dim_for_empty_tensor
-
// CHECK: %[[INPUT_RESHAPE:.+]] = tensor.collapse_shape %{{.+}} {{\[}}[0, 1]] : tensor<1x1000xf32> into tensor<1000xf32>
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<f32>
// CHECK: %[[FILL:.+]] = linalg.fill ins(%cst : f32) outs(%[[INIT]] : tensor<f32>) -> tensor<f32>
@@ -340,7 +353,7 @@ func.func @fold_unit_dim_for_empty_tensor(%input: tensor<1x1000xf32>) -> tensor<
// CHECK-SAME: iterator_types = ["reduction"]
// CHECK-SAME: ins(%[[INPUT_RESHAPE]] : tensor<1000xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<f32>)
-// CHECK: %[[GENERIC_RESHAPE:.+]] = tensor.expand_shape %[[GENERIC]] [] : tensor<f32> into tensor<1xf32>
+// CHECK: %[[GENERIC_RESHAPE:.+]] = tensor.expand_shape %[[GENERIC]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: return %[[GENERIC_RESHAPE:.+]] : tensor<1xf32>
@@ -364,11 +377,11 @@ func.func @fold_slice(
// CHECK: %[[SLICE1:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK-SAME: to tensor<?x?x?xf32>
// CHECK: %[[RESULT1:.+]] = tensor.expand_shape %[[SLICE1]]
-// CHECK-SAME: [0, 1], [2], [3, 4, 5, 6]
+// CHECK-SAME: {{\[\[}}0, 1], [2], [3, 4, 5, 6]] output_shape [1, %arg5, %arg6, 1, %arg7, 1, 1] : tensor<?x?x?xf32> into tensor<1x?x?x1x?x1x1xf32>
// CHECK: %[[SLICE2:.+]] = tensor.extract_slice %[[ARG1]]
// CHECK-SAME: to tensor<?x?x?xf32>
// CHECK: %[[RESULT2:.+]] = tensor.expand_shape %[[SLICE2]]
-// CHECK-SAME: [0, 1], [2], [3, 4, 5, 6]
+// CHECK-SAME: {{\[\[}}0, 1], [2], [3, 4, 5, 6]] output_shape [1, %arg5, %arg6, 1, %arg7, 1, 1] : tensor<?x?x?xf32> into tensor<1x?x?x1x?x1x1xf32>
// CHECK: return %[[RESULT1]], %[[RESULT2]]
// -----
@@ -391,20 +404,27 @@ func.func @unit_dim_for_reduction(%arg0: tensor<1x?x1x?xf32>) -> tensor<1x?xf32>
} -> tensor<1x?xf32>
return %3 : tensor<1x?xf32>
}
-// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)>
-// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1) -> (d0)>
+// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0)>
+// CHECK-DAG: #[[MAP3:.+]] = affine_map<()[s0, s1, s2] -> ((s0 * s1) * s2)>
// CHECK: func @unit_dim_for_reduction
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x?x1x?xf32>
-// CHECK-DAG: %[[RESHAPE:.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1, 2], [3]]
-// CHECK: %[[INIT:.+]] = tensor.empty(%{{.+}}) : tensor<?xf32>
-// CHECK: %[[FILL:.+]] = linalg.fill ins(%{{.+}}{{.*}}outs(%[[INIT]]
-// CHECK: %[[RESULT:.+]] = linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP3]]]
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[CST:.+]] = arith.constant 1.000000e+00 : f32
+// CHECK: %[[C3:.+]] = arith.constant 3 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %arg0, %[[C3]] : tensor<1x?x1x?xf32>
+// CHECK: %[[RESHAPE:.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1, 2], [3]]
+// CHECK: %[[INIT:.+]] = tensor.empty(%{{.+}}) : tensor<?xf32>
+// CHECK: %[[FILL:.+]] = linalg.fill ins(%{{.+}}{{.*}}outs(%[[INIT]]
+// CHECK: %[[RESULT:.+]] = linalg.generic
+// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP2]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%[[RESHAPE]] : tensor<?x?xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?xf32>)
-// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]]
-// CHECK: return %[[RESULT_RESHAPE]]
+// CHECK: %[[DIM_0:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<1x?x1x?xf32>
+// CHECK: %[[VAL_3:.*]] = affine.apply #[[$MAP3]]()[%[[C1]], %[[DIM_0]], %[[C1]]]
+// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0, 1]] output_shape [1, %[[VAL_3]]] : tensor<?xf32> into tensor<1x?xf32>
+// CHECK: return %[[EXPANDED]] : tensor<1x?xf32>
// -----
@@ -437,7 +457,7 @@ func.func @unit_dim_for_both_reduction(%arg0: tensor<1x?x1x1xf32>) -> tensor<1x1
// CHECK-SAME: iterator_types = ["parallel"]
// CHECK-SAME: ins(%[[RESHAPE]], %[[FILL]] : tensor<?xf32>, tensor<1xf32>)
// CHECK-SAME: outs(%[[INIT2]] : tensor<1xf32>)
-// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]]
+// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]] output_shape [1, 1]
// CHECK: return %[[RESULT_RESHAPE]]
// -----
@@ -460,20 +480,28 @@ func.func @unit_dim_for_reduction_inner(%arg0: tensor<?x1x?x1xf32>) -> tensor<?x
} -> tensor<?x1xf32>
return %3 : tensor<?x1xf32>
}
-// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)>
-// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1) -> (d0)>
+// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0)>
+// CHECK-DAG: #[[MAP3:.+]] = affine_map<()[s0, s1] -> (s0 * s1)>
// CHECK: func @unit_dim_for_reduction_inner
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x1x?x1xf32>
-// CHECK-DAG: %[[RESHAPE:.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]]
-// CHECK: %[[INIT:.+]] = tensor.empty(%{{.+}}) : tensor<?xf32>
-// CHECK: %[[FILL:.+]] = linalg.fill ins(%{{.+}}{{.*}}outs(%[[INIT]]
-// CHECK: %[[RESULT:.+]] = linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP3]]]
+// CHECK: %[[C1:.*]] = arith.constant 1 : index
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[CST:.*]] = arith.constant 1.000000e+00 : f32
+// CHECK: %[[C2:.*]] = arith.constant 2 : index
+// CHECK: %[[DIM:.*]] = tensor.dim %arg0, %[[C2]] : tensor<?x1x?x1xf32>
+// CHECK: %[[RESHAPE:.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]]
+// CHECK: %[[INIT:.+]] = tensor.empty(%{{.+}}) : tensor<?xf32>
+// CHECK: %[[FILL:.+]] = linalg.fill ins(%{{.+}}{{.*}}outs(%[[INIT]]
+// CHECK: %[[RESULT:.+]] = linalg.generic
+// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP2]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%[[RESHAPE]] : tensor<?x?xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?xf32>)
-// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]]
-// CHECK: return %[[RESULT_RESHAPE]]
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x1x?x1xf32>
+// CHECK: %[[VAL_3:.+]] = affine.apply #[[$MAP3]]()[%[[DIM_0]], %[[C1]]]
+// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]] output_shape [%[[VAL_3]], 1] : tensor<?xf32> into tensor<?x1xf32>
+// CHECK: return %[[RESULT_RESHAPE]]
// -----
@@ -484,7 +512,7 @@ func.func @slice_unit_dims(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {
// CHECK-LABEL: func @slice_unit_dims
// CHECK: %[[SLICE:.+]] = tensor.extract_slice
// CHECK-SAME: tensor<1x3xf32> to tensor<f32>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[SLICE]] []
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[SLICE]] [] output_shape [1, 1]
// CHECK: return %[[RESULT]]
// -----
@@ -496,7 +524,7 @@ func.func @rank_reduced_extract_slice(%arg0: tensor<1x1x3x1x3xf32>) -> tensor<1x
// CHECK-LABEL: func @rank_reduced_extract_slice
// CHECK: %[[SLICE:.+]] = tensor.extract_slice
// CHECK-SAME: tensor<1x1x3x1x3xf32> to tensor<3x3xf32>
-// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[SLICE]] {{\[}}[0, 1], [2]]
+// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[SLICE]] {{\[}}[0, 1], [2]] output_shape [1, 3, 3]
// CHECK: return %[[RESULT]]
// -----
@@ -709,8 +737,8 @@ func.func @leading_dim_1_canonicalization(%arg0: memref<1x5xf32>, %shape: memref
func.func @broadcast_test(%arg0 : memref<5xf32>, %arg1 : memref<5xf32>, %shape : memref<5x5xf32>) -> memref<5x5xf32>
{
- %0 = memref.expand_shape %arg0 [[0, 1]] : memref<5xf32> into memref<1x5xf32>
- %1 = memref.expand_shape %arg1 [[0, 1]] : memref<5xf32> into memref<5x1xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1]] output_shape [1, 5] : memref<5xf32> into memref<1x5xf32>
+ %1 = memref.expand_shape %arg1 [[0, 1]] output_shape [5, 1] : memref<5xf32> into memref<5x1xf32>
linalg.generic #trait
ins(%0, %1 : memref<1x5xf32>, memref<5x1xf32>)
outs(%shape : memref<5x5xf32>) {
@@ -966,7 +994,7 @@ func.func @drop_unit_pad_dims(%arg0: tensor<1x1x3x1x1xf32>) -> tensor<1x2x3x1x3x
// CHECK: %[[PADDED:.+]] = tensor.pad %[[COLLAPSE]] low[1, 0, 0] high[0, 0, 2]
// CHECK: } : tensor<1x3x1xf32> to tensor<2x3x3xf32>
// CHECK: tensor.expand_shape %[[PADDED]]
-// CHECK-SAME: {{\[}}[0, 1], [2, 3], [4]{{\]}} : tensor<2x3x3xf32> into tensor<1x2x3x1x3xf32>
+// CHECK-SAME: {{\[}}[0, 1], [2, 3], [4]{{\]}} output_shape [1, 2, 3, 1, 3] : tensor<2x3x3xf32> into tensor<1x2x3x1x3xf32>
// CHECK-SLICES-LABEL: func @drop_unit_pad_dims
// CHECK-SLICES: %[[EXTRACT:.+]] = tensor.extract_slice
@@ -989,13 +1017,19 @@ func.func @drop_unit_pad_dynamic_dims(%arg0: tensor<1x?xf32>) -> tensor<1x?xf32>
return %0 : tensor<1x?xf32>
}
+// CHECK-DAG: #[[$MAP:.+]] = affine_map<()[s0, s1] -> (s0 * s1)>
+// CHECK-DAG: #[[$MAP1:.+]] = affine_map<()[s0] -> (s0 + 11)>
// CHECK-LABEL: func @drop_unit_pad_dynamic_dims
+// CHECK: %[[C1:.*]] = arith.constant 1 : index
+// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[COLLAPSE:.+]] = tensor.collapse_shape
// CHECK-SAME: {{\[}}[0, 1]{{\]}} : tensor<1x?xf32> into tensor<?xf32>
// CHECK: %[[PADDED:.+]] = tensor.pad %[[COLLAPSE]] low[5] high[6]
// CHECK: } : tensor<?xf32> to tensor<?xf32>
-// CHECK: tensor.expand_shape %[[PADDED]]
-// CHECK-SAME: {{\[}}[0, 1]{{\]}} : tensor<?xf32> into tensor<1x?xf32>
+// CHECK: %[[DIM:.+]] = tensor.dim %{{.*}}, %[[C1]] : tensor<1x?xf32>
+// CHECK: %[[VAL_0:.+]] = affine.apply #[[$MAP]]()[%[[C1]], %[[DIM]]]
+// CHECK: %[[VAL_1:.+]] = affine.apply #[[$MAP1]]()[%[[VAL_0]]]
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[PADDED]] {{\[\[}}0, 1]] output_shape [1, %[[VAL_1]]] : tensor<?xf32> into tensor<1x?xf32>
// CHECK-SLICES: #[[$MAP:.+]] = affine_map<()[s0] -> (s0 + 11)>
@@ -1052,4 +1086,4 @@ func.func @drop_known_unit_constant_low_high(%arg0: tensor<1x383x128xf32>) -> te
// CHECK: %[[PADDED:.+]] = tensor.pad %[[COLLAPSE]] low[1, 0] high[0, 0]
// CHECK: } : tensor<383x128xf32> to tensor<384x128xf32>
// CHECK: tensor.expand_shape %[[PADDED]]
-// CHECK-SAME: {{\[}}[0, 1], [2]] : tensor<384x128xf32> into tensor<1x384x128xf32>
+// CHECK-SAME: {{\[}}[0, 1], [2]] output_shape [1, 384, 128] : tensor<384x128xf32> into tensor<1x384x128xf32>
diff --git a/mlir/test/Dialect/Linalg/flatten-elementwise.mlir b/mlir/test/Dialect/Linalg/flatten-elementwise.mlir
index 5a27fe76b13411..9fe50a521d2d81 100644
--- a/mlir/test/Dialect/Linalg/flatten-elementwise.mlir
+++ b/mlir/test/Dialect/Linalg/flatten-elementwise.mlir
@@ -26,7 +26,7 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: %[[ARG1:.*]]: tensor<32x7xf32>
// CHECK-NEXT: %[[FLATTENED:.*]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0, 1]]
// CHECK-NEXT: %[[FLATTENED_RESULT:.*]] = linalg.fill ins(%[[ARG0]] : f32) outs(%[[FLATTENED]] : tensor<224xf32>)
-// CHECK-NEXT: %[[RESULT:.*]] = tensor.expand_shape %[[FLATTENED_RESULT]] {{\[}}[0, 1]]
+// CHECK-NEXT: %[[RESULT:.*]] = tensor.expand_shape %[[FLATTENED_RESULT]] {{\[}}[0, 1]] output_shape [32, 7] : tensor<224xf32> into tensor<32x7xf32>
func.func @fill_tensor(%cst: f32, %arg: tensor<32x7xf32>) -> tensor<32x7xf32> {
%0 = linalg.fill ins(%cst: f32) outs(%arg: tensor<32x7xf32>) -> tensor<32x7xf32>
return %0 : tensor<32x7xf32>
diff --git a/mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir b/mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir
index 50d308b6a9fee1..0d40df534a3bb7 100644
--- a/mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir
+++ b/mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir
@@ -9,8 +9,7 @@
#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4, d5, d6, d7)>
func.func @fuse_by_collapsing(%arg0 : tensor<2x12x5x336x9xi32>,
%arg1 : tensor<2x3x4xi32>, %arg2 : tensor<5x6x7x8xi32>) -> tensor<2x3x4x5x6x7x8x9xi32> {
- %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]]
- : tensor<2x12x5x336x9xi32> into tensor<2x3x4x5x6x7x8x9xi32>
+ %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] output_shape [2, 3, 4, 5, 6, 7, 8, 9] : tensor<2x12x5x336x9xi32> into tensor<2x3x4x5x6x7x8x9xi32>
%init = tensor.empty() : tensor<2x3x4x5x6x7x8x9xi32>
%generic = linalg.generic {
indexing_maps = [#map0, #map1, #map2, #map3],
@@ -40,7 +39,7 @@ func.func @fuse_by_collapsing(%arg0 : tensor<2x12x5x336x9xi32>,
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1_RESHAPE]], %[[ARG2_RESHAPE]] :
// CHECK-SAME: outs(%[[INIT_RESHAPE]] :
-// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[COLLAPSED_OP]] {{\[}}[0], [1, 2], [3], [4, 5, 6], [7]{{\]}}
+// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[COLLAPSED_OP]] {{\[}}[0], [1, 2], [3], [4, 5, 6], [7]{{\]}} output_shape [2, 3, 4, 5, 6, 7, 8, 9]
// CHECK: return %[[RESULT_RESHAPE]]
// CONTROL: func @fuse_by_collapsing(
@@ -60,8 +59,7 @@ func.func @fuse_by_collapsing(%arg0 : tensor<2x12x5x336x9xi32>,
#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4, d5, d6, d7)>
func.func @fuse_by_collapsing_indexing_op(%arg0 : tensor<2x12x5x336x9xi32>,
%arg1 : tensor<2x3x4xi32>, %arg2 : tensor<5x6x7x8xi32>) -> tensor<2x3x4x5x6x7x8x9xi32> {
- %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]]
- : tensor<2x12x5x336x9xi32> into tensor<2x3x4x5x6x7x8x9xi32>
+ %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] output_shape [2, 3, 4, 5, 6, 7, 8, 9] : tensor<2x12x5x336x9xi32> into tensor<2x3x4x5x6x7x8x9xi32>
%init = tensor.empty() : tensor<2x3x4x5x6x7x8x9xi32>
%generic = linalg.generic {
indexing_maps = [#map0, #map1, #map2, #map3],
@@ -122,8 +120,7 @@ func.func @fuse_by_collapsing_indexing_op(%arg0 : tensor<2x12x5x336x9xi32>,
#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4, d5, d6, d7)>
func.func @fuse_by_collapsing_change_reshape_order(%arg0 : tensor<9x56x2x60x6xi32>,
%arg1 : tensor<7x8x2xi32>, %arg2 : tensor<6x3x4x5xi32>) -> tensor<2x3x4x5x6x7x8x9xi32> {
- %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]]
- : tensor<9x56x2x60x6xi32> into tensor<9x7x8x2x3x4x5x6xi32>
+ %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] output_shape [9, 7, 8, 2, 3, 4, 5, 6] : tensor<9x56x2x60x6xi32> into tensor<9x7x8x2x3x4x5x6xi32>
%init = tensor.empty() : tensor<2x3x4x5x6x7x8x9xi32>
%generic = linalg.generic {
indexing_maps = [#map0, #map1, #map2, #map3],
@@ -154,7 +151,7 @@ func.func @fuse_by_collapsing_change_reshape_order(%arg0 : tensor<9x56x2x60x6xi3
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1_RESHAPE]], %[[ARG2_RESHAPE]] :
// CHECK-SAME: outs(%[[INIT_RESHAPE]] :
-// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[COLLAPSED_OP]] {{\[}}[0], [1, 2, 3], [4], [5, 6], [7]{{\]}}
+// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[COLLAPSED_OP]] {{\[}}[0], [1, 2, 3], [4], [5, 6], [7]{{\]}} output_shape [2, 3, 4, 5, 6, 7, 8, 9]
// CHECK: return %[[RESULT_RESHAPE]]
// -----
@@ -165,11 +162,11 @@ func.func @fuse_by_collapsing_change_reshape_order(%arg0 : tensor<9x56x2x60x6xi3
#map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d4, d1, d2, d3)>
#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4, d5, d6, d7)>
func.func @fuse_by_collapsing_dynamic(%arg0 : tensor<?x?x?x?x?xi32>,
- %arg1 : tensor<?x?x?xi32>, %arg2 : tensor<?x?x?x?xi32>) -> tensor<?x3x?x5x?x7x?x?xi32> {
+ %arg1 : tensor<?x?x?xi32>, %arg2 : tensor<?x?x?x?xi32>, %sz0: index, %sz1: index, %sz2: index, %sz3: index, %sz4: index) -> tensor<?x3x?x5x?x7x?x?xi32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
- %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]]
+ %expand = tensor.expand_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] output_shape [%sz0, 7, %sz1, %sz2, 3, %sz3, 5, %sz4]
: tensor<?x?x?x?x?xi32> into tensor<?x7x?x?x3x?x5x?xi32>
%d0 = tensor.dim %arg1, %c2 : tensor<?x?x?xi32>
%d2 = tensor.dim %arg2, %c2 : tensor<?x?x?x?xi32>
@@ -203,8 +200,8 @@ func.func @fuse_by_collapsing_dynamic(%arg0 : tensor<?x?x?x?x?xi32>,
} -> tensor<?x3x?x5x?x7x?x?xi32>
return %generic : tensor<?x3x?x5x?x7x?x?xi32>
}
-// CHECK: func @fuse_by_collapsing_dynamic(
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?x?xi32>
+// CHECK: func @fuse_by_collapsing_dynamic
+// CHECK-SAME: (%[[ARG0:.+]]: tensor<?x?x?x?x?xi32>, %[[SZ0:.+]]: index, %[[SZ1:.+]]: index, %[[SZ2:.+]]: index, %[[SZ3:.+]]: index, %[[SZ4:.+]]: index)
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
// CHECK-DAG: %[[C5:.+]] = arith.constant 5 : index
// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[ARG0]]
@@ -224,8 +221,8 @@ func.func @fuse_by_collapsing_dynamic(%arg0 : tensor<?x?x?x?x?xi32>,
#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d3)>
-func.func @fuse_reductions(%arg0 : tensor<2x?x5xf32>, %arg1 : tensor<2x5xf32>) -> tensor<2x5xf32> {
- %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] : tensor<2x?x5xf32> into tensor<2x6x?x5xf32>
+func.func @fuse_reductions(%arg0 : tensor<2x?x5xf32>, %arg1 : tensor<2x5xf32>, %sz0: index) -> tensor<2x5xf32> {
+ %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] output_shape [2, 6, %sz0, 5] : tensor<2x?x5xf32> into tensor<2x6x?x5xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1],
iterator_types = ["parallel", "reduction", "reduction", "parallel"]}
@@ -240,7 +237,8 @@ func.func @fuse_reductions(%arg0 : tensor<2x?x5xf32>, %arg1 : tensor<2x5xf32>) -
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
// CHECK: func @fuse_reductions(
// CHECK-SAME: %[[ARG0:.+]]: tensor<2x?x5xf32>
-// CHECK-SAME: %[[ARG1:.+]]: tensor<2x5xf32>) -> tensor<2x5xf32>
+// CHECK-SAME: %[[ARG1:.+]]: tensor<2x5xf32>
+// CHECK-SAME: %[[SZ0:.+]]: index) -> tensor<2x5xf32>
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction", "parallel"]
@@ -253,7 +251,7 @@ func.func @fuse_reductions(%arg0 : tensor<2x?x5xf32>, %arg1 : tensor<2x5xf32>) -
#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1)>
func.func @no_fuse_unpreserved_folding(%arg0 : tensor<2x12x5xf32>, %arg1 : tensor<2x3xf32>) -> tensor<2x3x4x5xf32> {
- %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] : tensor<2x12x5xf32> into tensor<2x3x4x5xf32>
+ %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] output_shape [2, 3, 4, 5] : tensor<2x12x5xf32> into tensor<2x3x4x5xf32>
%init = tensor.empty(): tensor<2x3x4x5xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1, #map0],
@@ -280,7 +278,7 @@ func.func @no_fuse_unpreserved_folding(%arg0 : tensor<2x12x5xf32>, %arg1 : tenso
#map1 = affine_map<(d0, d1, d2, d3) -> (d0)>
#map2 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d1, d3)>
func.func @no_fuse_unpreserved_folding_transpose(%arg0 : tensor<2x12x5xf32>, %arg1 : tensor<2xf32>) -> tensor<2x4x3x5xf32> {
- %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] : tensor<2x12x5xf32> into tensor<2x3x4x5xf32>
+ %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] output_shape [2, 3, 4, 5] : tensor<2x12x5xf32> into tensor<2x3x4x5xf32>
%init = tensor.empty() : tensor<2x4x3x5xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1, #map2],
@@ -307,7 +305,7 @@ func.func @no_fuse_unpreserved_folding_transpose(%arg0 : tensor<2x12x5xf32>, %ar
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1)>
#map2 = affine_map<(d0, d1, d2, d3) -> (d0, d3)>
func.func @no_fuse_mismatched_iterator_types(%arg0 : tensor<2x12x5xf32>, %arg1 : tensor<2x3xf32>) -> tensor<2x5xf32> {
- %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] : tensor<2x12x5xf32> into tensor<2x3x4x5xf32>
+ %0 = tensor.expand_shape %arg0 [[0], [1, 2], [3]] output_shape [2, 3, 4, 5] : tensor<2x12x5xf32> into tensor<2x3x4x5xf32>
%init = tensor.empty() : tensor<2x5xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1, #map2],
@@ -335,8 +333,8 @@ func.func @no_fuse_mismatched_iterator_types(%arg0 : tensor<2x12x5xf32>, %arg1 :
#map1 = affine_map<(d0, d1, d2, d3) -> (d2, d3)>
#map2 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
func.func @control_fusion(%arg0 : tensor<6xf32>, %arg1 : tensor<20xf32>) -> tensor<2x3x4x5xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<6xf32> into tensor<2x3xf32>
- %1 = tensor.expand_shape %arg1 [[0, 1]] : tensor<20xf32> into tensor<4x5xf32>
+ %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [2, 3] : tensor<6xf32> into tensor<2x3xf32>
+ %1 = tensor.expand_shape %arg1 [[0, 1]] output_shape [4, 5] : tensor<20xf32> into tensor<4x5xf32>
%init = tensor.empty() : tensor<2x3x4x5xf32>
%2 = linalg.generic {
indexing_maps = [#map0, #map1, #map2],
@@ -359,8 +357,8 @@ func.func @control_fusion(%arg0 : tensor<6xf32>, %arg1 : tensor<20xf32>) -> tens
// CHECK-SAME: iterator_types = ["parallel", "parallel"]
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] :
// CHECK-SAME: outs(%{{.+}}: tensor<6x20xf32>)
-// CHECK: %[[RESHAPE1:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0], [1, 2]{{\]}}
-// CHECK: %[[RESHAPE2:.+]] = tensor.expand_shape %[[RESHAPE1]] {{\[}}[0, 1], [2], [3]{{\]}}
+// CHECK: %[[RESHAPE1:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0], [1, 2]{{\]}} output_shape [6, 4, 5]
+// CHECK: %[[RESHAPE2:.+]] = tensor.expand_shape %[[RESHAPE1]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, 3, 4, 5]
// CHECK: return %[[RESHAPE2]]
// CONTROL-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
@@ -375,14 +373,14 @@ func.func @control_fusion(%arg0 : tensor<6xf32>, %arg1 : tensor<20xf32>) -> tens
// CONTROL: %[[GENERIC:.+]] = linalg.generic
// CONTROL-SAME: ins(%[[EXPAND]], %[[ARG1]] :
// CONTROL-SAME: outs(%[[INIT_RESHAPE]] :
-// CONTROL: %[[RESULT:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0], [1], [2, 3]{{\]}}
+// CONTROL: %[[RESULT:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0], [1], [2, 3]{{\]}} output_shape [2, 3, 4, 5]
// -----
// Corner case that isnt handled currently.
#map = affine_map<(d0) -> (d0)>
func.func @zero_D_test(%arg0: tensor<f32>) -> tensor<1xf32> {
- %0 = tensor.expand_shape %arg0 [] : tensor<f32> into tensor<1xf32>
+ %0 = tensor.expand_shape %arg0 [] output_shape [1] : tensor<f32> into tensor<1xf32>
%init = tensor.empty() : tensor<1xf32>
%1 = linalg.generic {
indexing_maps = [#map, #map],
@@ -404,8 +402,8 @@ func.func @zero_D_test(%arg0: tensor<f32>) -> tensor<1xf32> {
#map0 = affine_map<(d0, d1, d2, d3) -> (d1, d0, d2, d3)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
-func.func @fuse_only_one_reassociation(%arg0 : tensor<?x?xf32>, %arg1 : tensor<4x?x?x8xf32>) -> tensor<4x?x?x8xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?xf32> into tensor<?x4x?x8xf32>
+func.func @fuse_only_one_reassociation(%arg0 : tensor<?x?xf32>, %arg1 : tensor<4x?x?x8xf32>, %sz0: index, %sz1: index) -> tensor<4x?x?x8xf32> {
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [%sz0, 4, %sz1, 8] : tensor<?x?xf32> into tensor<?x4x?x8xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1, #map1],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
@@ -419,10 +417,12 @@ func.func @fuse_only_one_reassociation(%arg0 : tensor<?x?xf32>, %arg1 : tensor<4
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-// CHECK: func @fuse_only_one_reassociation(
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>
-// CHECK-SAME: %[[ARG1:.+]]: tensor<4x?x?x8xf32>
-// CHECK-DAG: %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]{{\]}}
+// CHECK: func @fuse_only_one_reassociation
+// CHECK-SAME: (%[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<4x?x?x8xf32>, %[[SZ0:.+]]: index, %[[SZ1:.+]]: index)
+// CHECK-DAG: %[[C8:.*]] = arith.constant 8 : index
+// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]{{\]}} output_shape [%[[SZ0]], 4, %[[SZ1]], 8]
// CHECK-DAG: %[[COLLAPSE_ARG0:.+]] = tensor.collapse_shape %[[EXPAND_ARG0]] {{\[}}[0], [1], [2, 3]{{\]}}
// CHECK-DAG: %[[COLLAPSE_ARG1_0:.+]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0], [1], [2, 3]{{\]}}
// CHECK-DAG: %[[COLLAPSE_ARG1_1:.+]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0], [1], [2, 3]{{\]}}
@@ -431,17 +431,20 @@ func.func @fuse_only_one_reassociation(%arg0 : tensor<?x?xf32>, %arg1 : tensor<4
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[COLLAPSE_ARG0]], %[[COLLAPSE_ARG1_0]] :
// CHECK-SAME: outs(%[[COLLAPSE_ARG1_1]] :
-// CHECK: %[[EXPAND_GENERIC:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0], [1], [2, 3]{{\]}}
-// CHECK: return %[[EXPAND_GENERIC]]
+// CHECK: %[[DIM:.+]] = tensor.dim %[[GENERIC]], %[[C1]] : tensor<4x?x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[GENERIC]], %[[C2]] : tensor<4x?x?xf32>
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_2]], %[[C8]] : index
+// CHECK: %[[EXPANDED_3:.+]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0], [1], [2, 3]] output_shape [4, %[[DIM]], %[[VAL_1]], 8] : tensor<4x?x?xf32> into tensor<4x?x?x8xf32>
+// CHECK: return %[[EXPANDED_3]]
// -----
#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3, d1)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d3, d1, d0, d2)>
-func.func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>) -> tensor<?x8x?x4xi32> {
+func.func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>, %sz0: index, %sz1: index) -> tensor<?x8x?x4xi32> {
%c0 = arith.constant 0 : index
%c2 = arith.constant 2 : index
- %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [%sz0, 4, %sz1, 8] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
%d0 = tensor.dim %0, %c0 : tensor<?x4x?x8xi32>
%d1 = tensor.dim %0, %c2 : tensor<?x4x?x8xi32>
%init = tensor.empty(%d1, %d0) : tensor<?x8x?x4xi32>
@@ -465,10 +468,16 @@ func.func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>) -> tensor<?x8x?x4x
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK: func @fold_non_consecutive_dims(
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xi32>)
-// CHECK-DAG: %[[C4:.+]] = arith.constant 4 : index
-// CHECK-DAG: %[[C8:.+]] = arith.constant 8 : index
-// CHECK: %[[INIT:.+]] = tensor.empty
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xi32>, %[[SZ0:.+]]: index, %[[SZ1:.+]]: index)
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C4:.+]] = arith.constant 4 : index
+// CHECK: %[[C8:.+]] = arith.constant 8 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[C2:.+]] = arith.constant 2 : index
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[SZ0]], 4, %[[SZ1]], 8] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
+// CHECK: %[[DIM:.+]] = tensor.dim %[[EXPANDED]], %[[C0]]
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[EXPANDED]], %[[C2]]
+// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM_0]], %[[DIM]])
// CHECK: %[[COLLAPSE_INIT:.+]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1], [2, 3]{{\]}}
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
@@ -487,8 +496,12 @@ func.func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>) -> tensor<?x8x?x4x
// CHECK-DAG: %[[T6:.+]] = arith.addi %[[T5]], %[[T3]]
// CHECK-DAG: %[[T7:.+]] = arith.index_cast %[[T6]]
// CHECK: linalg.yield %[[T7]]
-// CHECK: %[[EXPAND_GENERIC:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0, 1], [2, 3]{{\]}}
-// CHECK: return %[[EXPAND_GENERIC]]
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[GENERIC]], %[[C0]] : tensor<?x?xi32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[GENERIC]], %[[C1]] : tensor<?x?xi32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_1]], %[[C8]] : index
+// CHECK: %[[VAL_3:.+]] = arith.divui %[[DIM_2]], %[[C4]] : index
+// CHECK: %[[EXPANDED_3:.+]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_2]], 8, %[[VAL_3]], 4] : tensor<?x?xi32> into tensor<?x8x?x4xi32>
+// CHECK: return %[[EXPANDED_3]]
// -----
@@ -496,10 +509,10 @@ func.func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>) -> tensor<?x8x?x4x
// So no change in the code.
#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3, d1)>
#map1 = affine_map<(d0, d1, d2, d3) -> ()>
-func.func @no_fold_non_consecutive_reduction_dims(%arg0 : tensor<?x?xi32>) -> tensor<i32> {
+func.func @no_fold_non_consecutive_reduction_dims(%arg0 : tensor<?x?xi32>, %sz0: index, %sz1: index) -> tensor<i32> {
%c0 = arith.constant 0 : index
%c2 = arith.constant 2 : index
- %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [%sz0, 4, %sz1, 8] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
%init = tensor.empty() : tensor<i32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1],
@@ -519,8 +532,8 @@ func.func @no_fold_non_consecutive_reduction_dims(%arg0 : tensor<?x?xi32>) -> te
return %1 : tensor<i32>
}
// CHECK: func @no_fold_non_consecutive_reduction_dims(
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xi32>)
-// CHECK: %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]{{\]}}
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xi32>, %[[SZ0:.+]]: index, %[[SZ1:.+]]: index)
+// CHECK: %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]{{\]}} output_shape [%[[SZ0]], 4, %[[SZ1]], 8]
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: ins(%[[EXPAND_ARG0]] :
// CHECK: return %[[GENERIC]]
diff --git a/mlir/test/Dialect/Linalg/fusion-push-reshape.mlir b/mlir/test/Dialect/Linalg/fusion-push-reshape.mlir
index f1c729ef963ba8..751ece37bc094f 100644
--- a/mlir/test/Dialect/Linalg/fusion-push-reshape.mlir
+++ b/mlir/test/Dialect/Linalg/fusion-push-reshape.mlir
@@ -4,15 +4,19 @@
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1) -> (d1)>
// CHECK-LABEL: func @reshape
-// CHECK-SAME: (%[[A:.*]]: tensor<?x16xf32>, %[[B:.*]]: tensor<16xf32>, %[[INIT:.*]]: tensor<?x112x16xf32>)
+// CHECK-SAME: (%[[A:.*]]: tensor<?x16xf32>, %[[B:.*]]: tensor<16xf32>, %[[INIT:.*]]: tensor<?x112x16xf32>, %[[SZ0:.*]]: index)
+// CHECK: %[[C112:.*]] = arith.constant 112 : index
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[RI:.*]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1], [2]] : tensor<?x112x16xf32> into tensor<?x16xf32>
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP3]], #[[$MAP2]]],
// CHECK-SAME: iterator_types = ["parallel", "parallel"]}
// CHECK-SAME: ins(%[[A]], %[[B]] : tensor<?x16xf32>, tensor<16xf32>) outs(%[[RI]] : tensor<?x16xf32>)
-// CHECK: %[[RR:.*]] = tensor.expand_shape %[[R]] {{\[}}[0, 1], [2]] : tensor<?x16xf32> into tensor<?x112x16xf32>
+// CHECK: %[[DIM:.*]] = tensor.dim %[[R]], %[[C0]] : tensor<?x16xf32>
+// CHECK: %[[VAL_1:.*]] = arith.divui %[[DIM]], %[[C112]] : index
+// CHECK: %[[RR:.*]] = tensor.expand_shape %[[R]] {{\[\[}}0, 1], [2]] output_shape [%[[VAL_1]], 112, 16] : tensor<?x16xf32> into tensor<?x112x16xf32>
// CHECK: return %[[RR]] : tensor<?x112x16xf32>
-func.func @reshape(%A: tensor<?x16xf32>, %B: tensor<16xf32>, %init: tensor<?x112x16xf32>) -> tensor<?x112x16xf32> {
- %0 = tensor.expand_shape %A [[0, 1], [2]]
+func.func @reshape(%A: tensor<?x16xf32>, %B: tensor<16xf32>, %init: tensor<?x112x16xf32>, %sz0: index) -> tensor<?x112x16xf32> {
+ %0 = tensor.expand_shape %A [[0, 1], [2]] output_shape [%sz0, 112, 16]
: tensor<?x16xf32> into tensor<?x112x16xf32>
%2 = linalg.generic {indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d2)>,
@@ -39,13 +43,13 @@ func.func @reshape(%A: tensor<?x16xf32>, %B: tensor<16xf32>, %init: tensor<?x112
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP2]], #[[$MAP3]], #[[$MAP2]]],
// CHECK-SAME: iterator_types = ["parallel", "parallel"]}
// CHECK-SAME: ins(%[[A]], %[[B]], %[[C]] : tensor<12544x16xf32>, tensor<12544x16xf32>, tensor<16xf32>) outs(%[[RI]] : tensor<12544x16xf32>)
-// CHECK: %[[RR:.*]] = tensor.expand_shape %[[R]] {{\[}}[0, 1], [2]] : tensor<12544x16xf32> into tensor<112x112x16xf32>
+// CHECK: %[[RR:.*]] = tensor.expand_shape %[[R]] {{\[}}[0, 1], [2]] output_shape [112, 112, 16] : tensor<12544x16xf32> into tensor<112x112x16xf32>
// CHECK: return %[[RR]] : tensor<112x112x16xf32>
func.func @reshape_multiple(%A: tensor<12544x16xf32>, %B: tensor<12544x16xf32>,
%C: tensor<16xf32>) -> tensor<112x112x16xf32> {
- %0 = tensor.expand_shape %A [[0, 1], [2]]
+ %0 = tensor.expand_shape %A [[0, 1], [2]] output_shape [112, 112, 16]
: tensor<12544x16xf32> into tensor<112x112x16xf32>
- %1 = tensor.expand_shape %B [[0, 1], [2]]
+ %1 = tensor.expand_shape %B [[0, 1], [2]] output_shape [112, 112, 16]
: tensor<12544x16xf32> into tensor<112x112x16xf32>
%2 = tensor.empty() : tensor<112x112x16xf32>
%3 = linalg.generic {indexing_maps = [
@@ -69,11 +73,11 @@ func.func @reshape_multiple(%A: tensor<12544x16xf32>, %B: tensor<12544x16xf32>,
// Negative test, since the second source is broadcasted from d1 we cannot merge
// d0 and d1 dimensions
// CHECK-LABEL: func @reshape_negative
-// CHECK: tensor.expand_shape {{.*}} : tensor<12544x16xf32> into tensor<112x112x16xf32>
+// CHECK: tensor.expand_shape {{.*}} {{\[\[}}0, 1], [2]] output_shape [112, 112, 16] : tensor<12544x16xf32> into tensor<112x112x16xf32>
// CHECK: linalg.generic
// CHECK: } -> tensor<112x112x16xf32>
func.func @reshape_negative(%A: tensor<12544x16xf32>, %B: tensor<112xf32>) -> tensor<112x112x16xf32> {
- %20 = tensor.expand_shape %A [[0, 1], [2]]
+ %20 = tensor.expand_shape %A [[0, 1], [2]] output_shape [112, 112, 16]
: tensor<12544x16xf32> into tensor<112x112x16xf32>
%21 = tensor.empty() : tensor<112x112x16xf32>
%22 = linalg.generic {indexing_maps = [
@@ -96,7 +100,7 @@ func.func @type_correctness(%arg0 : tensor<6x5xi32>, %arg1 : tensor<5xf32>,
%cst_6 = arith.constant 1.000000e+00 : f32
%cst_7 = arith.constant 7.000000e+00 : f32
%cst_8 = arith.constant 1.1920929E-7 : f32
- %25 = tensor.expand_shape %arg0 [[0, 1], [2]]
+ %25 = tensor.expand_shape %arg0 [[0, 1], [2]] output_shape [2, 3, 5]
: tensor<6x5xi32> into tensor<2x3x5xi32>
%26 = tensor.empty() : tensor<2x3x5xf32>
%28 = linalg.generic {
diff --git a/mlir/test/Dialect/Linalg/reshape_control_fusion.mlir b/mlir/test/Dialect/Linalg/reshape_control_fusion.mlir
index ab948988b7b6e7..0f0337a3604e00 100644
--- a/mlir/test/Dialect/Linalg/reshape_control_fusion.mlir
+++ b/mlir/test/Dialect/Linalg/reshape_control_fusion.mlir
@@ -48,7 +48,7 @@ func.func @control_consumer_reshape_fusion(%arg0 : tensor<1x?x?xf32>, %arg1 : te
^bb0(%arg2: f32):
linalg.yield %cst : f32
} -> tensor<?x?xf32>
- %0 = tensor.expand_shape %fill [[0, 1], [2]] : tensor<?x?xf32> into tensor<1x?x?xf32>
+ %0 = tensor.expand_shape %fill [[0, 1], [2]] output_shape [1, %d0, %d1] : tensor<?x?xf32> into tensor<1x?x?xf32>
%1 = linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x?x?xf32>, tensor<1x?x?xf32>)
outs(%0 : tensor<1x?x?xf32>) -> tensor<1x?x?xf32>
return %1 : tensor<1x?x?xf32>
diff --git a/mlir/test/Dialect/Linalg/reshape_fusion.mlir b/mlir/test/Dialect/Linalg/reshape_fusion.mlir
index 342c067b5c4ba4..f42666f81bbadd 100644
--- a/mlir/test/Dialect/Linalg/reshape_fusion.mlir
+++ b/mlir/test/Dialect/Linalg/reshape_fusion.mlir
@@ -30,10 +30,20 @@ func.func @generic_op_reshape_producer_fusion(%arg0 : tensor<?x?x4x?xf32>,
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x4x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: f32
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0], [1], [2, 3]
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0], [1], [2, 3]
+// CHECK: %[[C4:.+]] = arith.constant 4 : index
+// CHECK: %[[C2:.+]] = arith.constant 2 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG1]], %[[C2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM_1]], %[[C4]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0], [1], [2, 3]] output_shape [%[[DIM]], %[[DIM_0]], %[[VAL_0]], 4] : tensor<?x?x?xf32> into tensor<?x?x?x4xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_3:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_4:.+]] = tensor.dim %[[ARG1]], %[[C2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_4]], %[[C4]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0], [1], [2, 3]] output_shape [%[[DIM_2]], %[[DIM_3]], %[[VAL_1]], 4] : tensor<?x?x?xf32> into tensor<?x?x?x4xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP5]], #[[MAP6]], #[[MAP7]], #[[MAP6]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
@@ -50,7 +60,9 @@ func.func @generic_op_reshape_producer_fusion(%arg0 : tensor<?x?x4x?xf32>,
#map1 = affine_map<(d0, d1) -> ()>
func.func @generic_op_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
%arg1 : tensor<?x?xf32>,
- %arg2 : f32) ->
+ %arg2 : f32,
+ %sz0: index,
+ %sz1: index) ->
tensor<?x4x?x5xf32>
{
%0 = linalg.generic {
@@ -63,7 +75,7 @@ func.func @generic_op_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
%2 = arith.addf %1, %arg5 : f32
linalg.yield %2 : f32
} -> tensor<?x?xf32>
- %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] :
+ %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] output_shape [%sz0, 4, %sz1, 5] :
tensor<?x?xf32> into tensor<?x4x?x5xf32>
return %1 : tensor<?x4x?x5xf32>
}
@@ -75,14 +87,22 @@ func.func @generic_op_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: f32
-// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x?x5xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x?x5xf32>
+// CHECK-SAME: %[[SZ0:.+]]: index, %[[SZ1:.+]]: index
+// CHECK: %[[C20:.+]] = arith.constant 20 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM_0]], %[[C20]] : index
+// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM]], 4, %[[VAL_0]], 5] : tensor<?x?xf32> into tensor<?x4x?x5xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_2]], %[[C20]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM_1]], 4, %[[VAL_1]], 5] : tensor<?x?xf32> into tensor<?x4x?x5xf32>
+// CHECK: %[[DIM_4:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_5:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_5]], %[[C20]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM_4]], 4, %[[VAL_2]], 5] : tensor<?x?xf32> into tensor<?x4x?x5xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP2]], #[[MAP3]], #[[MAP2]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
@@ -94,7 +114,7 @@ func.func @generic_op_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
// -----
func.func @reshape_as_consumer_permutation
- (%a : tensor<?x?x?xf32>, %b : tensor<?x?xf32>)
+ (%a : tensor<?x?x?xf32>, %b : tensor<?x?xf32>, %sz0: index, %sz1: index, %sz2: index)
-> tensor<?x2x?x3x4x?xf32> {
%c = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d0, d2)>,
@@ -107,8 +127,7 @@ func.func @reshape_as_consumer_permutation
%1 = arith.addf %arg0, %arg1 : f32
linalg.yield %1 : f32
} -> tensor<?x?x?xf32>
- %d = tensor.expand_shape %c [[0, 1], [2], [3, 4, 5]]
- : tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
+ %d = tensor.expand_shape %c [[0, 1], [2], [3, 4, 5]] output_shape [%sz0, 2, %sz1, 3, 4, %sz2] : tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
return %d : tensor<?x2x?x3x4x?xf32>
}
// CHECK-DAG: #[[MAP8:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d2, d3, d4, d0, d1, d5)>
@@ -117,15 +136,27 @@ func.func @reshape_as_consumer_permutation
// CHECK: func @reshape_as_consumer_permutation
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
-// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0, 1, 2], [3, 4], [5]
-// CHECK-SAME: tensor<?x?x?xf32> into tensor<3x4x?x?x2x?xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0, 1, 2], [3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<3x4x?x?xf32>
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0, 1], [2], [3, 4, 5]]
-// CHECK-SAME: tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
+// CHECK-SAME: %[[SZ0:.+]]: index, %[[SZ1:.+]]: index, %[[SZ2:.+]]: index
+// CHECK: %[[C12:.+]] = arith.constant 12 : index
+// CHECK: %[[C2:.+]] = arith.constant 2 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM]], %[[C12]] : index
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_0]], %[[C2]] : index
+// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1, 2], [3, 4], [5]] output_shape [3, 4, %[[VAL_0]], %[[VAL_1]], 2, %[[DIM_1]]] : tensor<?x?x?xf32> into tensor<3x4x?x?x2x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_3:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_2]], %[[C12]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1, 2], [3]] output_shape [3, 4, %[[VAL_2]], %[[DIM_3]]] : tensor<?x?xf32> into tensor<3x4x?x?xf32>
+// CHECK: %[[DIM_5:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_6:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_7:.+]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_3:.+]] = arith.divui %[[DIM_5]], %[[C2]] : index
+// CHECK: %[[VAL_4:.+]] = arith.divui %[[DIM_7]], %[[C12]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2], [3, 4, 5]] output_shape [%[[VAL_3]], 2, %[[DIM_6]], 3, 4, %[[VAL_4]]] : tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP8]], #[[MAP9]], #[[MAP10]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel", "parallel", "parallel"]
@@ -152,7 +183,7 @@ func.func @generic_op_reshape_consumer_static(%arg0: tensor<264x4xf32>)
%2 = arith.mulf %arg1, %arg2 : f32
linalg.yield %2 : f32
} -> tensor<264x4xf32>
- %2 = tensor.expand_shape %1 [[0, 1], [2]] :
+ %2 = tensor.expand_shape %1 [[0, 1], [2]] output_shape [8, 33, 4] :
tensor<264x4xf32> into tensor<8x33x4xf32>
return %2 : tensor<8x33x4xf32>
}
@@ -163,12 +194,8 @@ func.func @generic_op_reshape_consumer_static(%arg0: tensor<264x4xf32>)
// CHECK-DAG: %[[CST:.+]] = arith.constant
// CHECK-SAME: : tensor<8x33x4xf32>
// CHECK-DAG: %[[INIT:.+]] = tensor.empty()
-// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0, 1], [2]
-// CHECK-SAME: tensor<264x4xf32> into tensor<8x33x4xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[INIT]]
-// CHECK-SAME: [0, 1], [2]
-// CHECK-SAME: : tensor<264x4xf32> into tensor<8x33x4xf32>
+// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2]] output_shape [8, 33, 4] : tensor<264x4xf32> into tensor<8x33x4xf32>
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1], [2]] output_shape [8, 33, 4] : tensor<264x4xf32> into tensor<8x33x4xf32>
// CHECK: %[[T2:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP2]], #[[MAP2]]]
// CHECK-SAME: ["parallel", "parallel", "parallel"]
@@ -232,7 +259,8 @@ func.func @indexed_consumer_reshape_producer_fusion(%arg0 : tensor<?x?x4x?xi32>,
#map0 = affine_map<(d0, d1) -> (d0, d1)>
func.func @indexed_producer_reshape_consumer_fusion(%arg0 : tensor<?x?xi32>,
- %arg1 : tensor<?x?xi32>) ->
+ %arg1 : tensor<?x?xi32>,
+ %sz0: index, %sz1: index) ->
tensor<?x?x4x5xi32>
{
%0 = linalg.generic {
@@ -250,7 +278,7 @@ func.func @indexed_producer_reshape_consumer_fusion(%arg0 : tensor<?x?xi32>,
%5 = arith.addi %3, %4 : i32
linalg.yield %5 : i32
} -> tensor<?x?xi32>
- %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] :
+ %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] output_shape [%sz0, %sz1, 4, 5] :
tensor<?x?xi32> into tensor<?x?x4x5xi32>
return %1 : tensor<?x?x4x5xi32>
}
@@ -302,8 +330,7 @@ func.func @reshape_as_consumer_permutation
%7 = arith.addi %5, %6 : i32
linalg.yield %7 : i32
} -> tensor<6x4x210xi32>
- %d = tensor.expand_shape %c [[0, 1], [2], [3, 4, 5]]
- : tensor<6x4x210xi32> into tensor<2x3x4x5x6x7xi32>
+ %d = tensor.expand_shape %c [[0, 1], [2], [3, 4, 5]] output_shape [2, 3, 4, 5, 6, 7] : tensor<6x4x210xi32> into tensor<2x3x4x5x6x7xi32>
return %d : tensor<2x3x4x5x6x7xi32>
}
@@ -319,13 +346,9 @@ func.func @reshape_as_consumer_permutation
// CHECK-SAME: %[[ARG0:.+]]: tensor<210x6x4xi32>
// CHECK-SAME: %[[ARG1:.+]]: tensor<210x4xi32>
// CHECK-DAG: %[[INIT:.+]] = tensor.empty()
-// CHECK-DAG: %[[T1:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0, 1, 2], [3, 4], [5]
-// CHECK-DAG: %[[T2:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0, 1, 2], [3]
-// CHECK-DAG: %[[T3:.+]] = tensor.expand_shape %[[INIT]]
-// CHECK-SAME: [0, 1], [2], [3, 4, 5]
-// CHECK-SAME: : tensor<6x4x210xi32> into tensor<2x3x4x5x6x7xi32>
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1, 2], [3, 4], [5]] output_shape [5, 6, 7, 2, 3, 4] : tensor<210x6x4xi32> into tensor<5x6x7x2x3x4xi32>
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1, 2], [3]] output_shape [5, 6, 7, 4] : tensor<210x4xi32> into tensor<5x6x7x4xi32>
+// CHECK: %[[T3:.+]] = tensor.expand_shape %[[VAL_0]] {{\[\[}}0, 1], [2], [3, 4, 5]] output_shape [2, 3, 4, 5, 6, 7] : tensor<6x4x210xi32> into tensor<2x3x4x5x6x7xi32>
// CHECK: %[[T4:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
// CHECK-SAME: ins(%[[T1]], %[[T2]] : tensor<5x6x7x2x3x4xi32>, tensor<5x6x7x4xi32>)
@@ -411,7 +434,8 @@ func.func @reshape_as_producer_projected_permutation(
#map0 = affine_map<(d0, d1) -> (d0, d1)>
#map1 = affine_map<(d0, d1) -> (d1, d0)>
func.func @generic_op_reshape_consumer_fusion_projected(%arg0 : tensor<?x?xf32>,
- %arg1 : tensor<?x?xf32>) ->
+ %arg1 : tensor<?x?xf32>,
+ %sz0: index, %sz1: index) ->
tensor<?x?x4x5xf32>
{
%0 = linalg.generic {
@@ -423,7 +447,7 @@ func.func @generic_op_reshape_consumer_fusion_projected(%arg0 : tensor<?x?xf32>,
%1 = arith.mulf %arg3, %arg4 : f32
linalg.yield %1 : f32
} -> tensor<?x?xf32>
- %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] :
+ %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] output_shape [%sz0, %sz1, 4, 5] :
tensor<?x?xf32> into tensor<?x?x4x5xf32>
return %1 : tensor<?x?x4x5xf32>
}
@@ -433,15 +457,22 @@ func.func @generic_op_reshape_consumer_fusion_projected(%arg0 : tensor<?x?xf32>,
// CHECK: func @generic_op_reshape_consumer_fusion_projected
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
-// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0, 1, 2], [3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x5x?xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0, 1, 2], [3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x5x?xf32>
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x?x4x5xf32>
+// CHECK-SAME: %[[SZ0:.+]]: index, %[[SZ1:.+]]: index
+// CHECK: %[[C20:.+]] = arith.constant 20 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM]], %[[C20]] : index
+// CHECK: %[[T0:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1, 2], [3]] output_shape [%[[VAL_0]], 4, 5, %[[DIM_0]]] : tensor<?x?xf32> into tensor<?x4x5x?xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_1]], %[[C20]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1, 2], [3]] output_shape [%[[VAL_1]], 4, 5, %[[DIM_2]]] : tensor<?x?xf32> into tensor<?x4x5x?xf32>
+// CHECK: %[[DIM_4:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_5:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_5]], %[[C20]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM_4]], %[[VAL_2]], 4, 5] : tensor<?x?xf32> into tensor<?x?x4x5xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP4]], #[[MAP4]], #[[MAP5]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
@@ -466,6 +497,7 @@ func.func @no_fuse_dynamic_dims(%arg0: tensor<?x?xf32>) -> tensor<?xf32> {
} -> tensor<?xf32>
return %3 : tensor<?xf32>
}
+
// CHECK: func @no_fuse_dynamic_dims
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>
// CHECK: %[[RESHAPE:.+]] = tensor.collapse_shape %[[ARG0]]
@@ -503,7 +535,8 @@ func.func @no_fuse_mismatched_dynamism(%arg0: tensor<2x1xi64>, %arg1: tensor<?xi
// -----
func.func @reshape_as_consumer_permutation_with_multiple_results
- (%a : tensor<?x?x?xf32>, %b : tensor<?x?xf32>)
+ (%a : tensor<?x?x?xf32>, %b : tensor<?x?xf32>, %sz0: index,
+ %sz1: index, %sz2: index, %sz3: index, %sz4: index)
-> (tensor<?x2x?x3x4x?xf32>, tensor<?x?x2x3x4x?xf32>) {
%c:2 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d0, d2)>,
@@ -517,10 +550,8 @@ func.func @reshape_as_consumer_permutation_with_multiple_results
%1 = arith.addf %arg0, %arg1 : f32
linalg.yield %1, %1 : f32, f32
} -> (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
- %d = tensor.expand_shape %c#0 [[0, 1], [2], [3, 4, 5]]
- : tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
- %e = tensor.expand_shape %c#1 [[0], [1, 2], [3, 4, 5]]
- : tensor<?x?x?xf32> into tensor<?x?x2x3x4x?xf32>
+ %d = tensor.expand_shape %c#0 [[0, 1], [2], [3, 4, 5]] output_shape [%sz0, 2, %sz1, 3, 4, %sz2] : tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
+ %e = tensor.expand_shape %c#1 [[0], [1, 2], [3, 4, 5]] output_shape [%sz3, %sz4, 2, 3, 4, %sz2] : tensor<?x?x?xf32> into tensor<?x?x2x3x4x?xf32>
return %d, %e : tensor<?x2x?x3x4x?xf32>, tensor<?x?x2x3x4x?xf32>
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d2, d3, d4, d0, d1, d5)>
@@ -528,17 +559,40 @@ func.func @reshape_as_consumer_permutation_with_multiple_results
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d5, d2, d3, d4)>
// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d5, d0, d1, d2, d3, d4)>
// CHECK: func @reshape_as_consumer_permutation_with_multiple_results
-// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?x?xf32>
-// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>
-// CHECK-DAG: %[[RESHAPE0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1, 2], [3, 4], [5]{{\]}}
-// CHECK-DAG: %[[RESHAPE1:.+]] = tensor.expand_shape %[[ARG1]] {{\[}}[0, 1, 2], [3]{{\]}}
-// CHECK-DAG: %[[RESHAPE2:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2], [3, 4, 5]{{\]}}
-// CHECK-DAG: %[[RESHAPE3:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2], [3, 4, 5]{{\]}}
-// CHECK: %[[GENERIC:.+]]:2 = linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]], #[[MAP3]]]
-// CHECK-SAME: ins(%[[RESHAPE0]], %[[RESHAPE1]] :
-// CHECK-SAME: outs(%[[RESHAPE2]], %[[RESHAPE3]] :
-// CHECK: return %[[GENERIC]]#0, %[[GENERIC]]#1
+// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?x?xf32>
+// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>
+// CHECK-SAME: %[[SZ0:.+]]: index, %[[SZ1:.+]]: index, %[[SZ2:.+]]: index, %[[SZ3:.+]]: index, %[[SZ4:.+]]: index
+// CHECK: %[[C12:.+]] = arith.constant 12 : index
+// CHECK: %[[C2:.+]] = arith.constant 2 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM]], %[[C12]] : index
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_0]], %[[C2]] : index
+// CHECK: %[[RESHAPE0:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1, 2], [3, 4], [5]] output_shape [3, 4, %[[VAL_0]], %[[VAL_1]], 2, %[[DIM_1]]] : tensor<?x?x?xf32> into tensor<3x4x?x?x2x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_3:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_2]], %[[C12]] : index
+// CHECK: %[[RESHAPE1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1, 2], [3]] output_shape [3, 4, %[[VAL_2]], %[[DIM_3]]] : tensor<?x?xf32> into tensor<3x4x?x?xf32>
+// CHECK: %[[DIM_5:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_6:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_7:.+]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_3:.+]] = arith.divui %[[DIM_5]], %[[C2]] : index
+// CHECK: %[[VAL_4:.+]] = arith.divui %[[DIM_7]], %[[C12]] : index
+// CHECK: %[[RESHAPE2:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2], [3, 4, 5]] output_shape [%[[VAL_3]], 2, %[[DIM_6]], 3, 4, %[[VAL_4]]] : tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
+// CHECK: %[[DIM_9:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_10:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?x?xf32>
+// CHECK: %[[DIM_11:.+]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_5:.+]] = arith.divui %[[DIM_10]], %[[C2]] : index
+// CHECK: %[[VAL_6:.+]] = arith.divui %[[DIM_11]], %[[C12]] : index
+// CHECK: %[[RESHAPE3:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0], [1, 2], [3, 4, 5]] output_shape [%[[DIM_9]], %[[VAL_5]], 2, 3, 4, %[[VAL_6]]] : tensor<?x?x?xf32> into tensor<?x?x2x3x4x?xf32>
+// CHECK: %[[GENERIC:.+]]:2 = linalg.generic
+// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]], #[[MAP3]]]
+// CHECK-SAME: ins(%[[RESHAPE0]], %[[RESHAPE1]] :
+// CHECK-SAME: outs(%[[RESHAPE2]], %[[RESHAPE3]] :
+// CHECK: return %[[GENERIC]]#0, %[[GENERIC]]#1
// -----
@@ -556,7 +610,7 @@ module {
%2 = arith.addf %arg4, %arg5 : f32
linalg.yield %2, %2 : f32, f32
} -> (tensor<512xf32>, tensor<200x512xf32>)
- %1 = tensor.expand_shape %0#1 [[0, 1, 2], [3]] : tensor<200x512xf32> into tensor<25x8x1x512xf32>
+ %1 = tensor.expand_shape %0#1 [[0, 1, 2], [3]] output_shape [25, 8, 1, 512] : tensor<200x512xf32> into tensor<25x8x1x512xf32>
return %1 : tensor<25x8x1x512xf32>
}
}
@@ -567,7 +621,7 @@ module {
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<512xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<512xf32>
// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: tensor<200x512xf32>
-// CHECK: %[[OUTS:.+]] = tensor.expand_shape %[[ARG3]] {{\[}}[0, 1, 2], [3]{{\]}}
+// CHECK: %[[OUTS:.+]] = tensor.expand_shape %[[ARG3]] {{\[\[}}0, 1, 2], [3]] output_shape [25, 8, 1, 512] : tensor<200x512xf32> into tensor<25x8x1x512xf32>
// CHECK: %[[GENERIC:.+]]:2 = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP0]], #[[MAP0]], #[[MAP1]]]
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] :
@@ -581,7 +635,9 @@ module {
#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
func.func @generic_op_reshape_consumer_fusion_reduction(%arg0 : tensor<?x?xf32>,
%arg1 : tensor<?x?xf32>,
- %arg2 : tensor<?x?xf32>) ->
+ %arg2 : tensor<?x?xf32>,
+ %sz0: index,
+ %sz1: index) ->
tensor<?x?x4x5xf32>
{
%0 = linalg.generic {
@@ -593,7 +649,7 @@ func.func @generic_op_reshape_consumer_fusion_reduction(%arg0 : tensor<?x?xf32>,
%1 = arith.mulf %arg3, %arg4 : f32
linalg.yield %1 : f32
} -> tensor<?x?xf32>
- %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] :
+ %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] output_shape [%sz0, %sz1, 4, 5] :
tensor<?x?xf32> into tensor<?x?x4x5xf32>
return %1 : tensor<?x?x4x5xf32>
}
@@ -605,12 +661,18 @@ func.func @generic_op_reshape_consumer_fusion_reduction(%arg0 : tensor<?x?xf32>,
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0, 1, 2], [3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x5x?xf32>
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG2]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x?x4x5xf32>
+// CHECK-SAME: %[[SZ0:.+]]: index, %[[SZ1:.+]]: index
+// CHECK: %[[C20:.+]] = arith.constant 20 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM]], %[[C20]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1, 2], [3]] output_shape [%[[VAL_0]], 4, 5, %[[DIM_0]]] : tensor<?x?xf32> into tensor<?x4x5x?xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG2]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG2]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_2]], %[[C20]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG2]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM_1]], %[[VAL_1]], 4, 5] : tensor<?x?xf32> into tensor<?x?x4x5xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel", "reduction"]
@@ -650,10 +712,21 @@ func.func @generic_op_reshape_producer_fusion_with_reduction(%arg0 : tensor<?x7x
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x7x?x8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x4x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0, 1], [2], [3, 4]
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG2]]
-// CHECK-SAME: [0, 1], [2, 3]
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C7:.+]] = arith.constant 7 : index
+// CHECK: %[[C8:.+]] = arith.constant 8 : index
+// CHECK: %[[C2:.+]] = arith.constant 2 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x4x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG1]], %[[C2]] : tensor<?x4x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM]], %[[C8]] : index
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_0]], %[[C7]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1], [2], [3, 4]] output_shape [%[[VAL_0]], 8, 4, %[[VAL_1]], 7] : tensor<?x4x?xf32> into tensor<?x8x4x?x7xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG2]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG2]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_1]], %[[C8]] : index
+// CHECK: %[[VAL_3:.+]] = arith.divui %[[DIM_2]], %[[C7]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG2]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_2]], 8, %[[VAL_3]], 7] : tensor<?x?xf32> into tensor<?x8x?x7xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]]
// CHECK-SAME: ["parallel", "parallel", "reduction", "parallel", "parallel"]
@@ -668,12 +741,14 @@ func.func @generic_op_reshape_producer_fusion_with_reduction(%arg0 : tensor<?x7x
func.func @linalg_add_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
%arg1 : tensor<?x?xf32>,
- %arg2 : tensor<?x?xf32>) ->
+ %arg2 : tensor<?x?xf32>,
+ %sz0: index,
+ %sz1: index) ->
tensor<?x?x4x5xf32>
{
%0 = linalg.add ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
- %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] :
+ %1 = tensor.expand_shape %0 [[0], [1, 2, 3]] output_shape [%sz0, %sz1, 4, 5] :
tensor<?x?xf32> into tensor<?x?x4x5xf32>
return %1 : tensor<?x?x4x5xf32>
}
@@ -683,15 +758,22 @@ func.func @linalg_add_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x?x4x5xf32>
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x?x4x5xf32>
-// CHECK: %[[T3:.+]] = tensor.expand_shape %[[ARG2]]
-// CHECK-SAME: [0], [1, 2, 3]
-// CHECK-SAME: tensor<?x?xf32> into tensor<?x?x4x5xf32>
+// CHECK-SAME: %[[SZ0:.+]]: index, %[[SZ1:.+]]: index
+// CHECK: %[[C20:.+]] = arith.constant 20 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM_0]], %[[C20]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG0]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM]], %[[VAL_0]], 4, 5] : tensor<?x?xf32> into tensor<?x?x4x5xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_2]], %[[C20]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM_1]], %[[VAL_1]], 4, 5] : tensor<?x?xf32> into tensor<?x?x4x5xf32>
+// CHECK: %[[DIM_4:.+]] = tensor.dim %[[ARG2]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_5:.+]] = tensor.dim %[[ARG2]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_5]], %[[C20]] : index
+// CHECK: %[[T3:.+]] = tensor.expand_shape %[[ARG2]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM_4]], %[[VAL_2]], 4, 5] : tensor<?x?xf32> into tensor<?x?x4x5xf32>
// CHECK: %[[T4:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
@@ -721,10 +803,20 @@ func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x7x?x8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
-// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]]
-// CHECK-SAME: [0, 1], [2, 3]
-// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG2]]
-// CHECK-SAME: [0, 1], [2, 3]
+// CHECK: %[[C8:.+]] = arith.constant 8 : index
+// CHECK: %[[C7:.+]] = arith.constant 7 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_0:.+]] = arith.divui %[[DIM]], %[[C7]] : index
+// CHECK: %[[VAL_1:.+]] = arith.divui %[[DIM_0]], %[[C8]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_0]], 7, %[[VAL_1]], 8] : tensor<?x?xf32> into tensor<?x7x?x8xf32>
+// CHECK: %[[DIM_1:.+]] = tensor.dim %[[ARG2]], %[[C0]] : tensor<?x?xf32>
+// CHECK: %[[DIM_2:.+]] = tensor.dim %[[ARG2]], %[[C1]] : tensor<?x?xf32>
+// CHECK: %[[VAL_2:.+]] = arith.divui %[[DIM_1]], %[[C7]] : index
+// CHECK: %[[VAL_3:.+]] = arith.divui %[[DIM_2]], %[[C8]] : index
+// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG2]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_2]], 7, %[[VAL_3]], 8] : tensor<?x?xf32> into tensor<?x7x?x8xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[$MAP]], #[[$MAP]], #[[$MAP]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
diff --git a/mlir/test/Dialect/Linalg/resolve-shaped-type-result-dims.mlir b/mlir/test/Dialect/Linalg/resolve-shaped-type-result-dims.mlir
index 4262cd23e7469d..8fb84248c9613b 100644
--- a/mlir/test/Dialect/Linalg/resolve-shaped-type-result-dims.mlir
+++ b/mlir/test/Dialect/Linalg/resolve-shaped-type-result-dims.mlir
@@ -199,13 +199,12 @@ func.func @empty_tensor_dim_of_linalg_result(%arg_0 : tensor<?xf32>,
// -----
-func.func @dim_reshape_expansion(%arg0 : tensor<6x5x?xf32>) -> (index, index, index)
+func.func @dim_reshape_expansion(%arg0 : tensor<6x5x?xf32>, %sz0: index) -> (index, index, index)
{
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c4 = arith.constant 4 : index
- %0 = tensor.expand_shape %arg0 [[0, 1], [2], [3, 4, 5]]
- : tensor<6x5x?xf32> into tensor<2x3x5x4x?x7xf32>
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2], [3, 4, 5]] output_shape [2, 3, 5, 4, %sz0, 7] : tensor<6x5x?xf32> into tensor<2x3x5x4x?x7xf32>
%1 = tensor.dim %0, %c1 : tensor<2x3x5x4x?x7xf32>
%2 = tensor.dim %0, %c3 : tensor<2x3x5x4x?x7xf32>
%3 = tensor.dim %0, %c4 : tensor<2x3x5x4x?x7xf32>
diff --git a/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir b/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
index 006d6105677e97..31e9fd00cffa04 100644
--- a/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
@@ -13,8 +13,8 @@ func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: ten
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: @matmul_split
// CHECK-DAG: %[[ID:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<16x256xf32> into tensor<16x4x64xf32>
-// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<256x32xf32> into tensor<4x64x32xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] output_shape [16, 4, 64] : tensor<16x256xf32> into tensor<16x4x64xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] output_shape [4, 64, 32] : tensor<256x32xf32> into tensor<4x64x32xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<16x32x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<16x32x4xf32>) -> tensor<16x32x4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]]
@@ -65,7 +65,7 @@ func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: ten
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0) -> ()>
//CHECK-LABEL: @generic_split_1d
// CHECK-DAG: %[[ID:.*]] = arith.constant 1.000000e+00 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] : tensor<32xf32> into tensor<4x8xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] output_shape [4, 8] : tensor<32xf32> into tensor<4x8xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
// CHECK: %[[G:.*]] = linalg.generic
@@ -119,8 +119,8 @@ func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: func @generic_split_3d
// CHECK-DAG: %[[ID:.*]] = arith.constant 0xFF800000 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<4x8x2xf32>
-// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x4x8xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] output_shape [4, 8, 2] : tensor<32x2xf32> into tensor<4x8x2xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] output_shape [5, 4, 8] : tensor<5x32xf32> into tensor<5x4x8xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
@@ -177,8 +177,8 @@ func.func @generic_split_3d_ninf(%input: tensor<32x2xf32>, %input_2: tensor<5x32
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: func @generic_split_3d_ninf
// CHECK-DAG: %[[ID:.*]] = arith.constant -3.40282347E+38 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<4x8x2xf32>
-// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x4x8xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] output_shape [4, 8, 2] : tensor<32x2xf32> into tensor<4x8x2xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] output_shape [5, 4, 8] : tensor<5x32xf32> into tensor<5x4x8xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
@@ -218,8 +218,8 @@ func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: ten
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: @matmul_split
// CHECK-DAG: %[[ID:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<16x256xf32> into tensor<16x64x4xf32>
-// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<256x32xf32> into tensor<64x4x32xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] output_shape [16, 64, 4] : tensor<16x256xf32> into tensor<16x64x4xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] output_shape [64, 4, 32] : tensor<256x32xf32> into tensor<64x4x32xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<16x32x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<16x32x4xf32>) -> tensor<16x32x4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]]
@@ -270,7 +270,7 @@ func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: ten
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0) -> ()>
//CHECK-LABEL: @generic_split_1d
// CHECK-DAG: %[[ID:.*]] = arith.constant 1.000000e+00 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] : tensor<32xf32> into tensor<8x4xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] output_shape [8, 4] : tensor<32xf32> into tensor<8x4xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
// CHECK: %[[G:.*]] = linalg.generic
@@ -324,8 +324,8 @@ func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: func @generic_split_3d
// CHECK-DAG: %[[ID:.*]] = arith.constant 0x7F800000 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<8x4x2xf32>
-// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x8x4xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] output_shape [8, 4, 2] : tensor<32x2xf32> into tensor<8x4x2xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] output_shape [5, 8, 4] : tensor<5x32xf32> into tensor<5x8x4xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
@@ -382,8 +382,8 @@ func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: func @generic_split_3d
// CHECK-DAG: %[[ID:.*]] = arith.constant 3.40282347E+38 : f32
-// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<8x4x2xf32>
-// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x8x4xf32>
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] output_shape [8, 4, 2] : tensor<32x2xf32> into tensor<8x4x2xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] output_shape [5, 8, 4] : tensor<5x32xf32> into tensor<5x8x4xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
diff --git a/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
index 58d4b21ea2dd90..d7ff1ded9d9332 100644
--- a/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
@@ -1710,10 +1710,12 @@ module attributes {transform.with_named_sequence} {
#map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: @not_vectorizable
func.func @not_vectorizable(%arg0: tensor<1x?xf32>, %arg1: index, %arg2: index, %arg3: index) -> tensor<1x128xf32> {
+ %c0 = arith.constant 0 : index
%0 = tensor.empty() : tensor<1x128xf32>
%1 = scf.for %arg5 = %arg2 to %arg1 step %arg3 iter_args(%arg6 = %0) -> (tensor<1x128xf32>) {
%extracted_slice = tensor.extract_slice %arg6[0, 0] [1, %arg1] [1, 1] : tensor<1x128xf32> to tensor<?xf32>
- %expanded = tensor.expand_shape %extracted_slice [[0, 1]] : tensor<?xf32> into tensor<1x?xf32>
+ %sz0 = tensor.dim %extracted_slice, %c0 : tensor<?xf32>
+ %expanded = tensor.expand_shape %extracted_slice [[0, 1]] output_shape [1, %sz0] : tensor<?xf32> into tensor<1x?xf32>
%extracted_slice_0 = tensor.extract_slice %arg0[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
%extracted_slice_1 = tensor.extract_slice %expanded[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
%2 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%extracted_slice_0 : tensor<?xf32>) outs(%extracted_slice_1 : tensor<?xf32>) {
diff --git a/mlir/test/Dialect/MemRef/canonicalize.mlir b/mlir/test/Dialect/MemRef/canonicalize.mlir
index 506ed1f1c10b10..f442a61dc31ed1 100644
--- a/mlir/test/Dialect/MemRef/canonicalize.mlir
+++ b/mlir/test/Dialect/MemRef/canonicalize.mlir
@@ -13,7 +13,7 @@ func.func @collapse_shape_identity_fold(%arg0 : memref<5xi8>) -> memref<5xi8> {
// CHECK-LABEL: expand_shape_identity_fold
// CHECK-NEXT: return
func.func @expand_shape_identity_fold(%arg0 : memref<5x4xi8>) -> memref<5x4xi8> {
- %0 = memref.expand_shape %arg0 [[0], [1]] : memref<5x4xi8> into memref<5x4xi8>
+ %0 = memref.expand_shape %arg0 [[0], [1]] output_shape [5, 4] : memref<5x4xi8> into memref<5x4xi8>
return %0 : memref<5x4xi8>
}
@@ -23,7 +23,7 @@ func.func @expand_shape_identity_fold(%arg0 : memref<5x4xi8>) -> memref<5x4xi8>
// CHECK-NEXT: return
func.func @collapse_expand_rank0_cancel(%arg0 : memref<1x1xi8>) -> memref<1x1xi8> {
%0 = memref.collapse_shape %arg0 [] : memref<1x1xi8> into memref<i8>
- %1 = memref.expand_shape %0 [] : memref<i8> into memref<1x1xi8>
+ %1 = memref.expand_shape %0 [] output_shape [1, 1] : memref<i8> into memref<1x1xi8>
return %1 : memref<1x1xi8>
}
@@ -455,9 +455,9 @@ func.func @compose_collapse_of_collapse(%arg0 : memref<?x?x?x?x?xf32>)
// -----
func.func @do_not_compose_collapse_of_expand_non_identity_layout(
- %arg0: memref<?x?xf32, strided<[?, 1], offset: 0>>)
+ %arg0: memref<?x?xf32, strided<[?, 1], offset: 0>>, %sz0: index, %sz1: index)
-> memref<?xf32, strided<[?], offset: 0>> {
- %1 = memref.expand_shape %arg0 [[0, 1], [2]] :
+ %1 = memref.expand_shape %arg0 [[0, 1], [2]] output_shape [%sz0, 4, %sz1] :
memref<?x?xf32, strided<[?, 1], offset: 0>> into
memref<?x4x?xf32, strided<[?, ?, 1], offset: 0>>
%2 = memref.collapse_shape %1 [[0, 1, 2]] :
@@ -471,35 +471,34 @@ func.func @do_not_compose_collapse_of_expand_non_identity_layout(
// -----
-func.func @compose_expand_of_expand(%arg0 : memref<?x?xf32>)
+func.func @compose_expand_of_expand(%arg0 : memref<?x?xf32>, %sz0: index, %sz1: index, %sz2: index, %sz3: index)
-> memref<?x6x4x5x?xf32> {
- %0 = memref.expand_shape %arg0 [[0, 1], [2]]
+ %0 = memref.expand_shape %arg0 [[0, 1], [2]] output_shape [%sz0, 4, %sz1]
: memref<?x?xf32> into memref<?x4x?xf32>
- %1 = memref.expand_shape %0 [[0, 1], [2], [3, 4]]
- : memref<?x4x?xf32> into memref<?x6x4x5x?xf32>
+ %1 = memref.expand_shape %0 [[0, 1], [2], [3, 4]] output_shape [%sz2, 6, 4, 5, %sz3] : memref<?x4x?xf32> into memref<?x6x4x5x?xf32>
return %1 : memref<?x6x4x5x?xf32>
}
// CHECK-LABEL: func @compose_expand_of_expand
-// CHECK: memref.expand_shape %{{.*}} {{\[}}[0, 1, 2], [3, 4]]
+// CHECK: memref.expand_shape %{{.*}} {{\[}}[0, 1, 2], [3, 4]] output_shape [%{{.*}}, 6, 4, 5, %{{.*}}]
// CHECK-NOT: memref.expand_shape
// -----
func.func @compose_expand_of_expand_of_zero_dim(%arg0 : memref<f32>)
-> memref<1x1x1xf32> {
- %0 = memref.expand_shape %arg0 [] : memref<f32> into memref<1xf32>
- %1 = memref.expand_shape %0 [[0, 1, 2]]
+ %0 = memref.expand_shape %arg0 [] output_shape [1] : memref<f32> into memref<1xf32>
+ %1 = memref.expand_shape %0 [[0, 1, 2]] output_shape [1, 1, 1]
: memref<1xf32> into memref<1x1x1xf32>
return %1 : memref<1x1x1xf32>
}
// CHECK-LABEL: func @compose_expand_of_expand_of_zero_dim
-// CHECK: memref.expand_shape %{{.*}} []
+// CHECK: memref.expand_shape %{{.*}} [] output_shape [1, 1, 1]
// CHECK-SAME: memref<f32> into memref<1x1x1xf32>
// -----
func.func @fold_collapse_of_expand(%arg0 : memref<12x4xf32>) -> memref<12x4xf32> {
- %0 = memref.expand_shape %arg0 [[0, 1], [2]]
+ %0 = memref.expand_shape %arg0 [[0, 1], [2]] output_shape [3, 4, 4]
: memref<12x4xf32> into memref<3x4x4xf32>
%1 = memref.collapse_shape %0 [[0, 1], [2]]
: memref<3x4x4xf32> into memref<12x4xf32>
@@ -510,9 +509,9 @@ func.func @fold_collapse_of_expand(%arg0 : memref<12x4xf32>) -> memref<12x4xf32>
// -----
-func.func @fold_collapse_collapse_of_expand(%arg0 : memref<?x?xf32>)
+func.func @fold_collapse_collapse_of_expand(%arg0 : memref<?x?xf32>, %sz0: index, %sz1: index)
-> memref<?x?xf32> {
- %0 = memref.expand_shape %arg0 [[0, 1], [2]]
+ %0 = memref.expand_shape %arg0 [[0, 1], [2]] output_shape [%sz0, 4, %sz1]
: memref<?x?xf32> into memref<?x4x?xf32>
%1 = memref.collapse_shape %0 [[0, 1], [2]]
: memref<?x4x?xf32> into memref<?x?xf32>
@@ -525,7 +524,7 @@ func.func @fold_collapse_collapse_of_expand(%arg0 : memref<?x?xf32>)
func.func @fold_memref_expand_cast(%arg0 : memref<?x?xf32>) -> memref<2x4x4xf32> {
%0 = memref.cast %arg0 : memref<?x?xf32> to memref<8x4xf32>
- %1 = memref.expand_shape %0 [[0, 1], [2]]
+ %1 = memref.expand_shape %0 [[0, 1], [2]] output_shape [2, 4, 4]
: memref<8x4xf32> into memref<2x4x4xf32>
return %1 : memref<2x4x4xf32>
}
@@ -981,10 +980,10 @@ func.func @memref_realloc_dead(%src : memref<2xf32>, %v : f32) -> memref<2xf32>{
// CHECK-SAME: %[[m:.*]]: memref<?xf32, strided<[1]>, 3>
// CHECK: %[[casted:.*]] = memref.cast %[[m]] : memref<?xf32, strided<[1]>, 3> to memref<?xf32, 3
// CHECK: return %[[casted]]
-func.func @collapse_expand_fold_to_cast(%m: memref<?xf32, strided<[1]>, 3>)
+func.func @collapse_expand_fold_to_cast(%m: memref<?xf32, strided<[1]>, 3>, %sz0: index)
-> (memref<?xf32, 3>)
{
- %0 = memref.expand_shape %m [[0, 1]]
+ %0 = memref.expand_shape %m [[0, 1]] output_shape [1, %sz0]
: memref<?xf32, strided<[1]>, 3> into memref<1x?xf32, 3>
%1 = memref.collapse_shape %0 [[0, 1]]
: memref<1x?xf32, 3> into memref<?xf32, 3>
diff --git a/mlir/test/Dialect/MemRef/expand-strided-metadata.mlir b/mlir/test/Dialect/MemRef/expand-strided-metadata.mlir
index 28b70043005940..fdfaa72168d188 100644
--- a/mlir/test/Dialect/MemRef/expand-strided-metadata.mlir
+++ b/mlir/test/Dialect/MemRef/expand-strided-metadata.mlir
@@ -421,10 +421,11 @@ func.func @simplify_expand_shape(
%base: memref<?x?xf32, strided<[?,?], offset:?>>,
%offset0: index, %offset1: index, %offset2: index,
%size0: index, %size1: index, %size2: index,
- %stride0: index, %stride1: index, %stride2: index)
+ %stride0: index, %stride1: index, %stride2: index,
+ %sz0: index, %sz1: index)
-> memref<?x7x8x9x10x2x?x3xf32, strided<[?, ?, ?, ?, ?, ?, ?, ?], offset: ?>> {
- %subview = memref.expand_shape %base[[0, 1, 2, 3],[4, 5, 6, 7]] :
+ %subview = memref.expand_shape %base [[0, 1, 2, 3],[4, 5, 6, 7]] output_shape [%sz0, 7, 8, 9, 10, 2, %sz1, 3] :
memref<?x?xf32, strided<[?,?], offset: ?>> into
memref<?x7x8x9x10x2x?x3xf32, strided<[?, ?, ?, ?, ?, ?, ?, ?], offset: ?>>
@@ -491,7 +492,7 @@ func.func @extract_strided_metadata_of_expand_shape_all_static(
index, index, index, index, index,
index, index, index, index, index) {
- %expand_shape = memref.expand_shape %arg[[0, 1, 2], [3, 4]] :
+ %expand_shape = memref.expand_shape %arg[[0, 1, 2], [3, 4]] output_shape [3, 5, 2, 2, 2] :
memref<30x4xi16> into memref<3x5x2x2x2xi16>
%base, %offset, %sizes:5, %strides:5 = memref.extract_strided_metadata %expand_shape :
@@ -595,12 +596,13 @@ func.func @extract_strided_metadata_of_expand_shape_all_dynamic(
%base: memref<?x?xf32, strided<[?,?], offset:?>>,
%offset0: index, %offset1: index, %offset2: index,
%size0: index, %size1: index, %size2: index,
- %stride0: index, %stride1: index, %stride2: index)
+ %stride0: index, %stride1: index, %stride2: index,
+ %sz0: index, %sz1: index)
-> (memref<f32>, index,
index, index, index, index, index, index, index, index,
index, index, index, index, index, index, index, index) {
- %subview = memref.expand_shape %base[[0, 1, 2, 3],[4, 5, 6, 7]] :
+ %subview = memref.expand_shape %base[[0, 1, 2, 3],[4, 5, 6, 7]] output_shape [%sz0, 7, 8, 9, 10, 2, %sz1, 3] :
memref<?x?xf32, strided<[?,?], offset: ?>> into
memref<?x7x8x9x10x2x?x3xf32, strided<[?, ?, ?, ?, ?, ?, ?, ?], offset: ?>>
@@ -643,7 +645,7 @@ func.func @extract_strided_metadata_of_expand_shape_all_static_0_rank(
index, index, index, index, index,
index, index, index, index, index) {
- %expand_shape = memref.expand_shape %arg[] :
+ %expand_shape = memref.expand_shape %arg[] output_shape [1, 1, 1, 1, 1] :
memref<i16, strided<[], offset: ?>> into memref<1x1x1x1x1xi16, strided<[1,1,1,1,1], offset: ?>>
%base, %offset, %sizes:5, %strides:5 = memref.extract_strided_metadata %expand_shape :
@@ -1513,4 +1515,4 @@ func.func @zero_sized_memred(%arg0: f32) -> (memref<f16, 3>, index,index,index)
%sizes, %strides :
memref<f16,3>, index,
index, index
-}
\ No newline at end of file
+}
diff --git a/mlir/test/Dialect/MemRef/fold-memref-alias-ops.mlir b/mlir/test/Dialect/MemRef/fold-memref-alias-ops.mlir
index 5b853a6cc5a37a..254cd4015eed94 100644
--- a/mlir/test/Dialect/MemRef/fold-memref-alias-ops.mlir
+++ b/mlir/test/Dialect/MemRef/fold-memref-alias-ops.mlir
@@ -412,7 +412,7 @@ func.func @fold_static_stride_subview_with_affine_load_store(%arg0 : memref<12x3
// CHECK-LABEL: fold_static_stride_subview_with_affine_load_store_expand_shape
// CHECK-SAME: (%[[ARG0:.*]]: memref<12x32xf32>, %[[ARG1:.*]]: index, %[[ARG2:.*]]: index, %[[ARG3:.*]]: index) -> f32 {
func.func @fold_static_stride_subview_with_affine_load_store_expand_shape(%arg0 : memref<12x32xf32>, %arg1 : index, %arg2 : index, %arg3 : index) -> f32 {
- %0 = memref.expand_shape %arg0 [[0, 1], [2]] : memref<12x32xf32> into memref<2x6x32xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1], [2]] output_shape [2, 6, 32] : memref<12x32xf32> into memref<2x6x32xf32>
%1 = affine.load %0[%arg1, %arg2, %arg3] : memref<2x6x32xf32>
return %1 : f32
}
@@ -458,7 +458,7 @@ func.func @fold_dynamic_size_collapse_shape_with_affine_load(%arg0 : memref<?x6x
// CHECK-LABEL: fold_static_stride_subview_with_affine_load_store_expand_shape_3d
// CHECK-SAME: (%[[ARG0:.*]]: memref<12x32xf32>, %[[ARG1:.*]]: index, %[[ARG2:.*]]: index, %[[ARG3:.*]]: index, %[[ARG4:.*]]: index) -> f32 {
func.func @fold_static_stride_subview_with_affine_load_store_expand_shape_3d(%arg0 : memref<12x32xf32>, %arg1 : index, %arg2 : index, %arg3 : index, %arg4: index) -> f32 {
- %0 = memref.expand_shape %arg0 [[0, 1, 2], [3]] : memref<12x32xf32> into memref<2x2x3x32xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1, 2], [3]] output_shape [2, 2, 3, 32] : memref<12x32xf32> into memref<2x2x3x32xf32>
%1 = affine.load %0[%arg1, %arg2, %arg3, %arg4] : memref<2x2x3x32xf32>
return %1 : f32
}
@@ -469,15 +469,17 @@ func.func @fold_static_stride_subview_with_affine_load_store_expand_shape_3d(%ar
// -----
// CHECK-LABEL: fold_dynamic_subview_with_memref_load_store_expand_shape
-func.func @fold_dynamic_subview_with_memref_load_store_expand_shape(%arg0 : memref<16x?xf32, strided<[16, 1]>>, %arg1 : index, %arg2 : index) -> f32 {
+// CHECK-SAME: (%[[ARG0:.*]]: memref<16x?xf32, strided<[16, 1]>>, %[[ARG1:.*]]: index, %[[ARG2:.*]]: index, %[[SZ0:.*]]: index)
+func.func @fold_dynamic_subview_with_memref_load_store_expand_shape(%arg0 : memref<16x?xf32, strided<[16, 1]>>, %arg1 : index, %arg2 : index, %sz0: index) -> f32 {
%c0 = arith.constant 0 : index
- %expand_shape = memref.expand_shape %arg0 [[0, 1], [2, 3]] : memref<16x?xf32, strided<[16, 1]>> into memref<1x16x?x1xf32, strided<[256, 16, 1, 1]>>
+ %expand_shape = memref.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [1, 16, %sz0, 1] : memref<16x?xf32, strided<[16, 1]>> into memref<1x16x?x1xf32, strided<[256, 16, 1, 1]>>
%0 = memref.load %expand_shape[%c0, %arg1, %arg2, %c0] : memref<1x16x?x1xf32, strided<[256, 16, 1, 1]>>
return %0 : f32
}
-// CHECK: %[[EXPAND_SHAPE:.+]] = memref.expand_shape {{.+}} : memref<16x?xf32, strided<[16, 1]>> into memref<1x16x?x1xf32, strided<[256, 16, 1, 1]>>
-// CHECK: %[[LOAD:.+]] = memref.load %[[EXPAND_SHAPE]]
-// CHECK: return %[[LOAD]]
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[EXPAND_SHAPE:.*]] = memref.expand_shape %[[ARG0]] {{\[\[}}0, 1], [2, 3]] output_shape [1, 16, %[[SZ0]], 1] : memref<16x?xf32, strided<[16, 1]>> into memref<1x16x?x1xf32, strided<[256, 16, 1, 1]>>
+// CHECK: %[[VAL_0:.*]] = memref.load %[[EXPAND_SHAPE]][%[[C0]], %[[ARG1]], %[[ARG2]], %[[C0]]] : memref<1x16x?x1xf32, strided<[256, 16, 1, 1]>>
+// CHECK: return %[[VAL_0]] : f32
// -----
@@ -486,7 +488,7 @@ func.func @fold_dynamic_subview_with_memref_load_store_expand_shape(%arg0 : memr
// CHECK-LABEL: fold_static_stride_subview_with_affine_load_store_expand_shape
// CHECK-SAME: (%[[ARG0:.*]]: memref<1024x1024xf32>, %[[ARG1:.*]]: memref<1xf32>, %[[ARG2:.*]]: index)
func.func @fold_static_stride_subview_with_affine_load_store_expand_shape(%arg0: memref<1024x1024xf32>, %arg1: memref<1xf32>, %arg2: index) -> f32 {
- %0 = memref.expand_shape %arg0 [[0, 1], [2, 3]] : memref<1024x1024xf32> into memref<1x1024x1024x1xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [1, 1024, 1024, 1] : memref<1024x1024xf32> into memref<1x1024x1024x1xf32>
affine.for %arg3 = 0 to 1 {
affine.for %arg4 = 0 to 1024 {
affine.for %arg5 = 0 to 1020 {
@@ -515,7 +517,7 @@ func.func @fold_static_stride_subview_with_affine_load_store_expand_shape(%arg0:
// CHECK-LABEL: fold_static_stride_subview_with_affine_load_store_expand_shape_when_access_index_is_an_expression
// CHECK-SAME: (%[[ARG0:.*]]: memref<1024x1024xf32>, %[[ARG1:.*]]: memref<1xf32>, %[[ARG2:.*]]: index)
func.func @fold_static_stride_subview_with_affine_load_store_expand_shape_when_access_index_is_an_expression(%arg0: memref<1024x1024xf32>, %arg1: memref<1xf32>, %arg2: index) -> f32 {
- %0 = memref.expand_shape %arg0 [[0, 1], [2, 3]] : memref<1024x1024xf32> into memref<1x1024x1024x1xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [1, 1024, 1024, 1] : memref<1024x1024xf32> into memref<1x1024x1024x1xf32>
affine.for %arg3 = 0 to 1 {
affine.for %arg4 = 0 to 1024 {
affine.for %arg5 = 0 to 1020 {
@@ -544,7 +546,7 @@ func.func @fold_static_stride_subview_with_affine_load_store_expand_shape_when_a
// CHECK-LABEL: fold_static_stride_subview_with_affine_load_store_expand_shape_with_constant_access_index
// CHECK-SAME: (%[[ARG0:.*]]: memref<1024x1024xf32>, %[[ARG1:.*]]: memref<1xf32>, %[[ARG2:.*]]: index)
func.func @fold_static_stride_subview_with_affine_load_store_expand_shape_with_constant_access_index(%arg0: memref<1024x1024xf32>, %arg1: memref<1xf32>, %arg2: index) -> f32 {
- %0 = memref.expand_shape %arg0 [[0, 1], [2, 3]] : memref<1024x1024xf32> into memref<1x1024x1024x1xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [1, 1024, 1024, 1] : memref<1024x1024xf32> into memref<1x1024x1024x1xf32>
%cst = arith.constant 0 : index
affine.for %arg3 = 0 to 1 {
affine.for %arg4 = 0 to 1024 {
diff --git a/mlir/test/Dialect/MemRef/invalid.mlir b/mlir/test/Dialect/MemRef/invalid.mlir
index 1aef417549d9a1..21bbffc5b5a9c5 100644
--- a/mlir/test/Dialect/MemRef/invalid.mlir
+++ b/mlir/test/Dialect/MemRef/invalid.mlir
@@ -392,9 +392,9 @@ func.func @copy_different_eltype(%arg0: memref<2xf32>, %arg1: memref<2xf16>) {
// -----
-func.func @expand_shape(%arg0: memref<?x?xf32>) {
+func.func @expand_shape(%arg0: memref<?x?xf32>, %sz0: index, %sz1: index) {
// expected-error @+1 {{invalid number of reassociation groups: found 1, expected 2}}
- %0 = memref.expand_shape %arg0 [[0, 1]] : memref<?x?xf32> into memref<?x5x?xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1]] output_shape [%sz0, 5, %sz1] : memref<?x?xf32> into memref<?x5x?xf32>
return
}
@@ -402,7 +402,7 @@ func.func @expand_shape(%arg0: memref<?x?xf32>) {
func.func @expand_shape(%arg0: memref<f32>) {
// expected-error @+1 {{rank 0 memrefs can only be extended/collapsed with/from ones}}
- %0 = memref.expand_shape %arg0 [] : memref<f32> into memref<1x2xf32>
+ %0 = memref.expand_shape %arg0 [] output_shape [1, 2] : memref<f32> into memref<1x2xf32>
return
}
@@ -415,9 +415,9 @@ func.func @collapse_shape_out_of_bounds(%arg0: memref<?x?xf32>) {
// -----
-func.func @expand_shape_out_of_bounds(%arg0: memref<?xf32>) {
+func.func @expand_shape_out_of_bounds(%arg0: memref<?xf32>, %sz0: index) {
// expected-error @+1 {{op reassociation index 2 is out of bounds}}
- %0 = memref.expand_shape %arg0 [[0, 1, 2]] : memref<?xf32> into memref<4x?xf32>
+ %0 = memref.expand_shape %arg0 [[0, 1, 2]] output_shape [4, %sz0] : memref<?xf32> into memref<4x?xf32>
}
// -----
@@ -425,7 +425,7 @@ func.func @expand_shape_out_of_bounds(%arg0: memref<?xf32>) {
func.func @expand_shape_invalid_result_layout(
%arg0: memref<30x20xf32, strided<[4000, 2], offset: 100>>) {
// expected-error @+1 {{expected expanded type to be 'memref<2x15x20xf32, strided<[60000, 4000, 2], offset: 100>>' but found 'memref<2x15x20xf32, strided<[5000, 4000, 2], offset: 100>>'}}
- %0 = memref.expand_shape %arg0 [[0, 1], [2]] :
+ %0 = memref.expand_shape %arg0 [[0, 1], [2]] output_shape [2, 15, 20] :
memref<30x20xf32, strided<[4000, 2], offset: 100>>
into memref<2x15x20xf32, strided<[5000, 4000, 2], offset: 100>>
}
@@ -462,7 +462,7 @@ func.func @collapse_shape_invalid_reassociation_expansion(%arg0: memref<?xf32>)
// like this. Verify that a sensible error is emitted in this case.
func.func @expand_shape_invalid_reassociation(%arg0: memref<2x3x1xf32>) {
// expected-error @+1 {{'memref.expand_shape' op has source rank 3 and result rank 2. This is not an expansion (3 > 2)}}
- %0 = memref.expand_shape %arg0 [[0], [1], [1]] :
+ %0 = memref.expand_shape %arg0 [[0], [1], [1]] output_shape [2, 3] :
memref<2x3x1xf32> into memref<2x3xf32>
}
@@ -495,20 +495,10 @@ func.func @collapse_shape_wrong_collapsed_type(%arg0: memref<?x?x?xf32>) {
// -----
-func.func @expand_shape_illegal_dynamic_memref
- (%arg0: memref<?x?x?xf32>) -> memref<?x?x?x4x?xf32> {
- // expected-error @+1 {{at most one dimension in a reassociation group may be dynamic}}
- %0 = memref.expand_shape %arg0 [[0], [1], [2, 3, 4]]
- : memref<?x?x?xf32> into memref<?x?x?x4x?xf32>
- return %0 : memref<?x?x?x4x?xf32>
-}
-
-// -----
-
func.func @expand_shape_illegal_static_memref
(%arg0: memref<2x3x20xf32>) -> memref<2x3x2x4x5xf32> {
// expected-error @+1 {{collapsed dim size (20) must equal reassociation group size (40)}}
- %0 = memref.expand_shape %arg0 [[0], [1], [2, 3, 4]]
+ %0 = memref.expand_shape %arg0 [[0], [1], [2, 3, 4]] output_shape [2, 3, 2, 4, 5]
: memref<2x3x20xf32> into memref<2x3x2x4x5xf32>
return %0 : memref<2x3x2x4x5xf32>
}
@@ -525,30 +515,30 @@ func.func @collapse_shape_illegal_static_memref
// -----
-func.func @expand_shape_illegal_mixed_memref(%arg0 : memref<?x?xf32>)
+func.func @expand_shape_illegal_mixed_memref(%arg0 : memref<?x?xf32>, %sz0: index)
-> memref<?x4x5xf32> {
// expected-error @+1 {{collapsed dim (1) must be dynamic if and only if reassociation group is dynamic}}
- %0 = memref.expand_shape %arg0 [[0, 1], [2]]
+ %0 = memref.expand_shape %arg0 [[0, 1], [2]] output_shape [%sz0, 4, 5]
: memref<?x?xf32> into memref<?x4x5xf32>
return %0 : memref<?x4x5xf32>
}
// -----
-func.func @expand_shape_illegal_mixed_memref_2(%arg0 : memref<?x?xf32>)
+func.func @expand_shape_illegal_mixed_memref_2(%arg0 : memref<?x?xf32>, %sz0: index)
-> memref<?x4x5xf32> {
// expected-error @+1 {{collapsed dim (1) must be dynamic if and only if reassociation group is dynamic}}
- %0 = memref.expand_shape %arg0 [[0], [1, 2]]
+ %0 = memref.expand_shape %arg0 [[0], [1, 2]] output_shape [%sz0, 4, 5]
: memref<?x?xf32> into memref<?x4x5xf32>
return %0 : memref<?x4x5xf32>
}
// -----
-func.func @expand_shape_invalid_static_dim_size(%arg0 : memref<?x21xf32>)
+func.func @expand_shape_invalid_static_dim_size(%arg0 : memref<?x21xf32>, %sz0: index)
-> memref<?x4x5xf32> {
// expected-error @+1 {{collapsed dim size (21) must equal reassociation group size (20)}}
- %0 = memref.expand_shape %arg0 [[0], [1, 2]]
+ %0 = memref.expand_shape %arg0 [[0], [1, 2]] output_shape [%sz0, 4, 5]
: memref<?x21xf32> into memref<?x4x5xf32>
return %0 : memref<?x4x5xf32>
}
diff --git a/mlir/test/Dialect/MemRef/ops.mlir b/mlir/test/Dialect/MemRef/ops.mlir
index 2d69904f27db5e..60fb0ffeee2403 100644
--- a/mlir/test/Dialect/MemRef/ops.mlir
+++ b/mlir/test/Dialect/MemRef/ops.mlir
@@ -106,9 +106,9 @@ func.func @expand_collapse_shape_static(
%0 = memref.collapse_shape %arg0 [[0, 1], [2]] :
memref<3x4x5xf32> into memref<12x5xf32>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]] output_shape [3, 4, 5]
// CHECK-SAME: memref<12x5xf32> into memref<3x4x5xf32>
- %r0 = memref.expand_shape %0 [[0, 1], [2]] :
+ %r0 = memref.expand_shape %0 [[0, 1], [2]] output_shape [3, 4, 5] :
memref<12x5xf32> into memref<3x4x5xf32>
// CHECK: memref.collapse_shape {{.*}} {{\[}}[0], [1, 2]]
@@ -116,9 +116,9 @@ func.func @expand_collapse_shape_static(
%1 = memref.collapse_shape %arg0 [[0], [1, 2]] :
memref<3x4x5xf32> into memref<3x20xf32>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0], [1, 2]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0], [1, 2]] output_shape [3, 4, 5]
// CHECK-SAME: memref<3x20xf32> into memref<3x4x5xf32>
- %r1 = memref.expand_shape %1 [[0], [1, 2]] :
+ %r1 = memref.expand_shape %1 [[0], [1, 2]] output_shape [3, 4, 5] :
memref<3x20xf32> into memref<3x4x5xf32>
// CHECK: memref.collapse_shape {{.*}} {{\[}}[0, 1, 2]]
@@ -126,29 +126,29 @@ func.func @expand_collapse_shape_static(
%2 = memref.collapse_shape %arg0 [[0, 1, 2]] :
memref<3x4x5xf32> into memref<60xf32>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1, 2]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1, 2]] output_shape [3, 4, 5]
// CHECK-SAME: memref<60xf32> into memref<3x4x5xf32>
- %r2 = memref.expand_shape %2 [[0, 1, 2]] :
+ %r2 = memref.expand_shape %2 [[0, 1, 2]] output_shape [3, 4, 5] :
memref<60xf32> into memref<3x4x5xf32>
-// CHECK: memref.expand_shape {{.*}} []
+// CHECK: memref.expand_shape {{.*}} [] output_shape [1, 1]
// CHECK-SAME: memref<f32> into memref<1x1xf32>
- %r5 = memref.expand_shape %arg5 [] :
+ %r5 = memref.expand_shape %arg5 [] output_shape [1, 1] :
memref<f32> into memref<1x1xf32>
// Reshapes with a custom layout map.
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0], [1, 2]]
- %l0 = memref.expand_shape %arg3 [[0], [1, 2]] :
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0], [1, 2]] output_shape [30, 4, 5]
+ %l0 = memref.expand_shape %arg3 [[0], [1, 2]] output_shape [30, 4, 5] :
memref<30x20xf32, strided<[4000, 2], offset: 100>>
into memref<30x4x5xf32, strided<[4000, 10, 2], offset: 100>>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]]
- %l1 = memref.expand_shape %arg3 [[0, 1], [2]] :
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]] output_shape [2, 15, 20]
+ %l1 = memref.expand_shape %arg3 [[0, 1], [2]] output_shape [2, 15, 20] :
memref<30x20xf32, strided<[4000, 2], offset: 100>>
into memref<2x15x20xf32, strided<[60000, 4000, 2], offset: 100>>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0], [1, 2]]
- %r4 = memref.expand_shape %arg4 [[0], [1, 2]] :
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0], [1, 2]] output_shape [1, 1, 5]
+ %r4 = memref.expand_shape %arg4 [[0], [1, 2]] output_shape [1, 1, 5] :
memref<1x5xf32, strided<[5, 1], offset: ?>> into
memref<1x1x5xf32, strided<[5, 5, 1], offset: ?>>
@@ -164,9 +164,9 @@ func.func @expand_collapse_shape_static(
memref<2049xi64, strided<[?], offset: ?>>
// Reshapes that expand and collapse back a contiguous buffer with some 1's.
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2], [3, 4]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2], [3, 4]] output_shape [1, 3, 4, 1, 5]
// CHECK-SAME: memref<3x4x5xf32> into memref<1x3x4x1x5xf32>
- %3 = memref.expand_shape %arg0 [[0, 1], [2], [3, 4]] :
+ %3 = memref.expand_shape %arg0 [[0, 1], [2], [3, 4]] output_shape [1, 3, 4, 1, 5]:
memref<3x4x5xf32> into memref<1x3x4x1x5xf32>
// CHECK: memref.collapse_shape {{.*}} {{\[}}[0, 1], [2], [3, 4]]
@@ -176,15 +176,18 @@ func.func @expand_collapse_shape_static(
// Reshapes on tensors.
// CHECK: tensor.expand_shape {{.*}}: tensor<3x4x5xf32> into tensor<1x3x4x1x5xf32>
- %t0 = tensor.expand_shape %arg1 [[0, 1], [2], [3, 4]] :
+ %t0 = tensor.expand_shape %arg1 [[0, 1], [2], [3, 4]] output_shape [1, 3, 4, 1, 5] :
tensor<3x4x5xf32> into tensor<1x3x4x1x5xf32>
// CHECK: tensor.collapse_shape {{.*}}: tensor<1x3x4x1x5xf32> into tensor<3x4x5xf32>
%rt0 = tensor.collapse_shape %t0 [[0, 1], [2], [3, 4]] :
tensor<1x3x4x1x5xf32> into tensor<3x4x5xf32>
+// CHECK: tensor.dim %arg2, {{.*}} : tensor<3x?x5xf32>
// CHECK: tensor.expand_shape {{.*}}: tensor<3x?x5xf32> into tensor<1x3x?x1x5xf32>
- %t1 = tensor.expand_shape %arg2 [[0, 1], [2], [3, 4]] :
+ %c1 = arith.constant 1 : index
+ %sz1 = tensor.dim %arg2, %c1 : tensor<3x?x5xf32>
+ %t1 = tensor.expand_shape %arg2 [[0, 1], [2], [3, 4]] output_shape [1, 3, %sz1, 1, 5] :
tensor<3x?x5xf32> into tensor<1x3x?x1x5xf32>
// CHECK: tensor.collapse_shape {{.*}}: tensor<1x3x?x1x5xf32> into tensor<1x?x5xf32>
@@ -197,15 +200,18 @@ func.func @expand_collapse_shape_static(
func.func @expand_collapse_shape_dynamic(%arg0: memref<?x?x?xf32>,
%arg1: memref<?x?x?xf32, strided<[?, ?, 1], offset: 0>>,
%arg2: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>,
- %arg3: memref<?x42xf32, strided<[42, 1], offset: 0>>) {
+ %arg3: memref<?x42xf32, strided<[42, 1], offset: 0>>,
+ %arg4: index,
+ %arg5: index,
+ %arg6: index) {
// CHECK: memref.collapse_shape {{.*}} {{\[}}[0, 1], [2]]
// CHECK-SAME: memref<?x?x?xf32> into memref<?x?xf32>
%0 = memref.collapse_shape %arg0 [[0, 1], [2]] :
memref<?x?x?xf32> into memref<?x?xf32>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]] output_shape [%arg4, 4, %arg5]
// CHECK-SAME: memref<?x?xf32> into memref<?x4x?xf32>
- %r0 = memref.expand_shape %0 [[0, 1], [2]] :
+ %r0 = memref.expand_shape %0 [[0, 1], [2]] output_shape [%arg4, 4, %arg5] :
memref<?x?xf32> into memref<?x4x?xf32>
// CHECK: memref.collapse_shape {{.*}} {{\[}}[0, 1], [2]]
@@ -214,9 +220,9 @@ func.func @expand_collapse_shape_dynamic(%arg0: memref<?x?x?xf32>,
memref<?x?x?xf32, strided<[?, ?, 1], offset: 0>> into
memref<?x?xf32, strided<[?, 1], offset: 0>>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]] output_shape [%arg4, 4, %arg5]
// CHECK-SAME: memref<?x?xf32, strided<[?, 1]>> into memref<?x4x?xf32, strided<[?, ?, 1]>>
- %r1 = memref.expand_shape %1 [[0, 1], [2]] :
+ %r1 = memref.expand_shape %1 [[0, 1], [2]] output_shape [%arg4, 4, %arg5] :
memref<?x?xf32, strided<[?, 1], offset: 0>> into
memref<?x4x?xf32, strided<[?, ?, 1], offset: 0>>
@@ -226,9 +232,9 @@ func.func @expand_collapse_shape_dynamic(%arg0: memref<?x?x?xf32>,
memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>> into
memref<?x?xf32, strided<[?, 1], offset: ?>>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1], [2]] output_shape [%arg4, 4, %arg5]
// CHECK-SAME: memref<?x?xf32, strided<[?, 1], offset: ?>> into memref<?x4x?xf32, strided<[?, ?, 1], offset: ?>>
- %r2 = memref.expand_shape %2 [[0, 1], [2]] :
+ %r2 = memref.expand_shape %2 [[0, 1], [2]] output_shape [%arg4, 4, %arg5] :
memref<?x?xf32, strided<[?, 1], offset: ?>> into
memref<?x4x?xf32, strided<[?, ?, 1], offset: ?>>
@@ -238,9 +244,9 @@ func.func @expand_collapse_shape_dynamic(%arg0: memref<?x?x?xf32>,
memref<?x42xf32, strided<[42, 1], offset: 0>> into
memref<?xf32, strided<[1]>>
-// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1]]
+// CHECK: memref.expand_shape {{.*}} {{\[}}[0, 1]] output_shape [%arg6, 42]
// CHECK-SAME: memref<?xf32, strided<[1]>> into memref<?x42xf32>
- %r3 = memref.expand_shape %3 [[0, 1]] :
+ %r3 = memref.expand_shape %3 [[0, 1]] output_shape [%arg6, 42] :
memref<?xf32, strided<[1]>> into memref<?x42xf32>
return
}
@@ -248,12 +254,12 @@ func.func @expand_collapse_shape_dynamic(%arg0: memref<?x?x?xf32>,
func.func @expand_collapse_shape_zero_dim(%arg0 : memref<1x1xf32>, %arg1 : memref<f32>)
-> (memref<f32>, memref<1x1xf32>) {
%0 = memref.collapse_shape %arg0 [] : memref<1x1xf32> into memref<f32>
- %1 = memref.expand_shape %0 [] : memref<f32> into memref<1x1xf32>
+ %1 = memref.expand_shape %0 [] output_shape [1, 1] : memref<f32> into memref<1x1xf32>
return %0, %1 : memref<f32>, memref<1x1xf32>
}
// CHECK-LABEL: func @expand_collapse_shape_zero_dim
// CHECK: memref.collapse_shape %{{.*}} [] : memref<1x1xf32> into memref<f32>
-// CHECK: memref.expand_shape %{{.*}} [] : memref<f32> into memref<1x1xf32>
+// CHECK: memref.expand_shape %{{.*}} [] output_shape [1, 1] : memref<f32> into memref<1x1xf32>
func.func @collapse_shape_to_dynamic
(%arg0: memref<?x?x?x4x?xf32>) -> memref<?x?x?xf32> {
@@ -270,16 +276,18 @@ func.func @collapse_shape_to_dynamic
// CHECK-LABEL: func @expand_collapse_shape_transposed_layout
func.func @expand_collapse_shape_transposed_layout(
%m0: memref<?x?xf32, strided<[1, 10], offset: 0>>,
- %m1: memref<4x5x6xf32, strided<[1, ?, 1000], offset: 0>>) {
+ %m1: memref<4x5x6xf32, strided<[1, ?, 1000], offset: 0>>,
+ %sz0: index,
+ %sz1: index) {
- %r0 = memref.expand_shape %m0 [[0], [1, 2]] :
+ %r0 = memref.expand_shape %m0 [[0], [1, 2]] output_shape [%sz0, %sz1, 5] :
memref<?x?xf32, strided<[1, 10], offset: 0>> into
memref<?x?x5xf32, strided<[1, 50, 10], offset: 0>>
%rr0 = memref.collapse_shape %r0 [[0], [1, 2]] :
memref<?x?x5xf32, strided<[1, 50, 10], offset: 0>> into
memref<?x?xf32, strided<[1, 10], offset: 0>>
- %r1 = memref.expand_shape %m1 [[0, 1], [2], [3, 4]] :
+ %r1 = memref.expand_shape %m1 [[0, 1], [2], [3, 4]] output_shape [2, 2, 5, 2, 3] :
memref<4x5x6xf32, strided<[1, ?, 1000], offset: 0>> into
memref<2x2x5x2x3xf32, strided<[2, 1, ?, 3000, 1000], offset: 0>>
%rr1 = memref.collapse_shape %r1 [[0, 1], [2], [3, 4]] :
diff --git a/mlir/test/Dialect/MemRef/runtime-verification.mlir b/mlir/test/Dialect/MemRef/runtime-verification.mlir
index 4d7fcf6ac7cbbc..28777a3e886722 100644
--- a/mlir/test/Dialect/MemRef/runtime-verification.mlir
+++ b/mlir/test/Dialect/MemRef/runtime-verification.mlir
@@ -2,13 +2,14 @@
// CHECK-LABEL: func @expand_shape(
// CHECK-SAME: %[[m:.*]]: memref<?xf32>
+// CHECK-SAME: %[[sz0:.*]]: index
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c5:.*]] = arith.constant 5 : index
// CHECK-DAG: %[[dim:.*]] = memref.dim %[[m]], %[[c0]]
// CHECK: %[[mod:.*]] = arith.remsi %[[dim]], %[[c5]]
// CHECK: %[[cmpi:.*]] = arith.cmpi eq, %[[mod]], %[[c0]]
// CHECK: cf.assert %[[cmpi]], "ERROR: Runtime op verification failed
-func.func @expand_shape(%m: memref<?xf32>) -> memref<?x5xf32> {
- %0 = memref.expand_shape %m [[0, 1]] : memref<?xf32> into memref<?x5xf32>
+func.func @expand_shape(%m: memref<?xf32>, %sz0: index) -> memref<?x5xf32> {
+ %0 = memref.expand_shape %m [[0, 1]] output_shape [%sz0, 5] : memref<?xf32> into memref<?x5xf32>
return %0 : memref<?x5xf32>
}
diff --git a/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir b/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
index edb53fa024c26b..c96f9c31443db3 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
@@ -12,7 +12,7 @@
//
// CHECK-ROUND-LABEL: func.func @sparse_expand(
// CHECK-ROUND-SAME: %[[A:.*]]: tensor<100xf64, #sparse{{[0-9]*}}>) -> tensor<10x10xf64, #sparse{{[0-9]*}}>
-// CHECK-ROUND: %[[E:.*]] = tensor.expand_shape %[[A]] {{\[\[}}0, 1]] : tensor<100xf64, #sparse{{[0-9]*}}> into tensor<10x10xf64, #sparse{{[0-9]*}}>
+// CHECK-ROUND: %[[E:.*]] = tensor.expand_shape %[[A]] {{\[\[}}0, 1]] output_shape [10, 10] : tensor<100xf64, #sparse{{[0-9]*}}> into tensor<10x10xf64, #sparse{{[0-9]*}}>
// CHECK-ROUND: return %[[E]] : tensor<10x10xf64, #sparse{{[0-9]*}}>
//
// CHECK-LABEL: func.func @sparse_expand(
@@ -39,7 +39,7 @@
// CHECK: return %[[NT1]] : tensor<10x10xf64, #sparse{{[0-9]*}}>
//
func.func @sparse_expand(%arg0: tensor<100xf64, #SparseVector>) -> tensor<10x10xf64, #SparseMatrix> {
- %0 = tensor.expand_shape %arg0 [[0, 1]] :
+ %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [10, 10] :
tensor<100xf64, #SparseVector> into tensor<10x10xf64, #SparseMatrix>
return %0 : tensor<10x10xf64, #SparseMatrix>
}
@@ -94,8 +94,8 @@ func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<10
// roundtrip:
//
// CHECK-ROUND-LABEL: func.func @dynamic_sparse_expand(
-// CHECK-ROUND-SAME: %[[A:.*]]: tensor<?xf64, #sparse{{[0-9]*}}>) -> tensor<?x10xf64, #sparse{{[0-9]*}}>
-// CHECK-ROUND: %[[E:.*]] = tensor.expand_shape %[[A]] {{\[\[}}0, 1]] : tensor<?xf64, #sparse{{[0-9]*}}> into tensor<?x10xf64, #sparse{{[0-9]*}}>
+// CHECK-ROUND-SAME: %[[A:.*]]: tensor<?xf64, #sparse{{[0-9]*}}>, %[[SZ0:.*]]: index) -> tensor<?x10xf64, #sparse{{[0-9]*}}>
+// CHECK-ROUND: %[[E:.*]] = tensor.expand_shape %[[A]] {{\[\[}}0, 1]] output_shape [%[[SZ0]], 10] : tensor<?xf64, #sparse{{[0-9]*}}> into tensor<?x10xf64, #sparse{{[0-9]*}}>
// CHECK-ROUND: return %[[E]] : tensor<?x10xf64, #sparse{{[0-9]*}}>
//
// CHECK-LABEL: func.func @dynamic_sparse_expand(
@@ -127,8 +127,8 @@ func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<10
// CHECK-NOT: sparse_tensor.convert
// CHECK: return %[[NT1]] : tensor<?x10xf64, #sparse{{[0-9]*}}>
//
-func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?x10xf64, #SparseMatrix> {
- %0 = tensor.expand_shape %arg0 [[0, 1]] :
+func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>, %sz0: index) -> tensor<?x10xf64, #SparseMatrix> {
+ %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [%sz0, 10] :
tensor<?xf64, #SparseVector> into tensor<?x10xf64, #SparseMatrix>
return %0 : tensor<?x10xf64, #SparseMatrix>
}
diff --git a/mlir/test/Dialect/Tensor/bufferize.mlir b/mlir/test/Dialect/Tensor/bufferize.mlir
index 815bc383af95a6..4f553adcc500fb 100644
--- a/mlir/test/Dialect/Tensor/bufferize.mlir
+++ b/mlir/test/Dialect/Tensor/bufferize.mlir
@@ -367,11 +367,14 @@ func.func @tensor.insert(%t1: tensor<5xf32>, %idx1: index, %f: f32) -> tensor<5x
// CHECK-LABEL: func @tensor.expand_shape(
// CHECK-SAME: %[[t1:.*]]: tensor<?x10xf32>
-func.func @tensor.expand_shape(%t1: tensor<?x10xf32>) -> tensor<2x?x10xf32> {
+func.func @tensor.expand_shape(%t1: tensor<?x10xf32>, %sz0: index) -> tensor<2x?x10xf32> {
// CHECK: %[[m1:.*]] = bufferization.to_memref %[[t1]] : memref<?x10xf32>
- // CHECK: %[[expanded:.*]] = memref.expand_shape %[[m1]] [
- // CHECK-SAME: [0, 1], [2]] : memref<?x10xf32> into memref<2x?x10xf32>
- %0 = tensor.expand_shape %t1 [[0, 1], [2]]
+ // CHECK: %[[C0:.*]] = arith.constant 0 : index
+ // CHECK: %[[DIM:.*]] = memref.dim %[[m1]], %[[C0]] : memref<?x10xf32>
+ // CHECK: %[[C2:.*]] = arith.constant 2 : index
+ // CHECK: %[[VAL_1:.*]] = arith.divui %[[DIM]], %[[C2]] : index
+ // CHECK: %[[expanded:.*]] = memref.expand_shape %[[m1]] {{\[\[}}0, 1], [2]] output_shape [2, %[[VAL_1]], 10] : memref<?x10xf32> into memref<2x?x10xf32>
+ %0 = tensor.expand_shape %t1 [[0, 1], [2]] output_shape [2, %sz0, 10]
: tensor<?x10xf32> into tensor<2x?x10xf32>
// CHECK: %[[r:.*]] = bufferization.to_tensor %[[expanded]]
@@ -384,14 +387,15 @@ func.func @tensor.expand_shape(%t1: tensor<?x10xf32>) -> tensor<2x?x10xf32> {
// CHECK-LABEL: func @tensor.expand_shape_of_slice(
// CHECK-SAME: %[[t1:.*]]: tensor<?x20xf32>
func.func @tensor.expand_shape_of_slice(
- %t1: tensor<?x20xf32>, %o1: index, %s1: index) -> tensor<?x7x2x5xf32> {
+ %t1: tensor<?x20xf32>, %o1: index, %s1: index, %sz0: index) -> tensor<?x7x2x5xf32> {
// CHECK: %[[m1:.*]] = bufferization.to_memref %[[t1]] : memref<?x20xf32>
// CHECK: %[[subview:.*]] = memref.subview %[[m1]][%{{.*}}, 5] [%{{.*}}, 10] [1, 1] : memref<?x20xf32> to memref<?x10xf32, strided<[20, 1], offset: ?>>
%0 = tensor.extract_slice %t1[%o1, 5][%s1, 10][1, 1] :
tensor<?x20xf32> to tensor<?x10xf32>
- // CHECK: %[[expanded:.*]] = memref.expand_shape %[[subview]] [
- // CHECK-SAME: [0, 1], [2, 3]] : memref<?x10xf32, strided<[20, 1], offset: ?>> into memref<?x7x2x5xf32, strided<[140, 20, 5, 1], offset: ?>>
- %1 = tensor.expand_shape %0 [[0, 1], [2, 3]] :
+ // CHECK: %[[C7:.*]] = arith.constant 7 : index
+ // CHECK: %[[VAL_1:.*]] = arith.divui %{{.*}}, %[[C7]] : index
+ // CHECK: %[[expanded:.*]] = memref.expand_shape %[[subview]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_1]], 7, 2, 5] : memref<?x10xf32, strided<[20, 1], offset: ?>> into memref<?x7x2x5xf32, strided<[140, 20, 5, 1], offset: ?>>
+ %1 = tensor.expand_shape %0 [[0, 1], [2, 3]] output_shape [%sz0, 7, 2, 5] :
tensor<?x10xf32> into tensor<?x7x2x5xf32>
// CHECK: %[[r:.*]] = bufferization.to_tensor %[[expanded]]
// CHECK: return %[[r]]
@@ -407,8 +411,8 @@ func.func @tensor.expand_shape_of_scalar_slice(
// CHECK: %[[m1:.*]] = bufferization.to_memref %[[t1]] : memref<?xf32>
// CHECK: %[[subview:.*]] = memref.subview %[[m1]][%{{.*}}] [1] [1] : memref<?xf32> to memref<f32, strided<[], offset: ?>>
%0 = tensor.extract_slice %t1[%o1][1][1] : tensor<?xf32> to tensor<f32>
- // CHECK: %[[expanded:.*]] = memref.expand_shape %[[subview]] [] : memref<f32, strided{{.*}}> into memref<1xf32, strided<[1], offset: ?>>
- %1 = tensor.expand_shape %0 [] : tensor<f32> into tensor<1xf32>
+ // CHECK: %[[expanded:.*]] = memref.expand_shape %[[subview]] [] output_shape [1] : memref<f32, strided{{.*}}> into memref<1xf32, strided<[1], offset: ?>>
+ %1 = tensor.expand_shape %0 [] output_shape [1] : tensor<f32> into tensor<1xf32>
// CHECK: %[[r:.*]] = bufferization.to_tensor %[[expanded]]
// CHECK: return %[[r]]
return %1 : tensor<1xf32>
diff --git a/mlir/test/Dialect/Tensor/canonicalize.mlir b/mlir/test/Dialect/Tensor/canonicalize.mlir
index ac365c9d297e88..23921a824f2136 100644
--- a/mlir/test/Dialect/Tensor/canonicalize.mlir
+++ b/mlir/test/Dialect/Tensor/canonicalize.mlir
@@ -4,7 +4,7 @@
// CHECK-LABEL: expand_shape_identity_fold
// CHECK-NEXT: return
func.func @expand_shape_identity_fold(%arg0 : tensor<5xf32>) -> tensor<5xf32> {
- %0 = tensor.expand_shape %arg0 [[0]] : tensor<5xf32> into tensor<5xf32>
+ %0 = tensor.expand_shape %arg0 [[0]] output_shape [5] : tensor<5xf32> into tensor<5xf32>
return %0 : tensor<5xf32>
}
@@ -13,7 +13,7 @@ func.func @expand_shape_identity_fold(%arg0 : tensor<5xf32>) -> tensor<5xf32> {
// CHECK-LABEL: expand_shape_rank0_identity_fold
// CHECK-NEXT: return
func.func @expand_shape_rank0_identity_fold(%arg0 : tensor<f32>) -> tensor<f32> {
- %0 = tensor.expand_shape %arg0 [] : tensor<f32> into tensor<f32>
+ %0 = tensor.expand_shape %arg0 [] output_shape [] : tensor<f32> into tensor<f32>
return %0 : tensor<f32>
}
@@ -1051,29 +1051,28 @@ func.func @fold_overlapping_insert(%input : tensor<?x?x?xf32>, %slice1: tensor<4
// -----
-func.func @compose_expand_of_expand(%arg0 : tensor<?x?xf32>)
+func.func @compose_expand_of_expand(%arg0 : tensor<?x?xf32>, %arg1: index, %arg2: index, %arg3: index, %arg4: index)
-> tensor<?x6x4x?x5xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1], [2]]
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2]] output_shape [%arg1, 4, %arg2]
: tensor<?x?xf32> into tensor<?x4x?xf32>
- %1 = tensor.expand_shape %0 [[0, 1], [2], [3, 4]]
- : tensor<?x4x?xf32> into tensor<?x6x4x?x5xf32>
+ %1 = tensor.expand_shape %0 [[0, 1], [2], [3, 4]] output_shape [%arg3, 6, 4, %arg4, 5] : tensor<?x4x?xf32> into tensor<?x6x4x?x5xf32>
return %1 : tensor<?x6x4x?x5xf32>
}
// CHECK-LABEL: compose_expand_of_expand
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1, 2], [3, 4]]
+// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1, 2], [3, 4]] output_shape [%arg3, 6, 4, %arg4, 5]
// CHECK-NOT: tensor.expand_shape
// -----
func.func @compose_expand_of_expand_of_zero_dim(%arg0 : tensor<f32>)
-> tensor<1x1x1xf32> {
- %0 = tensor.expand_shape %arg0 [] : tensor<f32> into tensor<1xf32>
- %1 = tensor.expand_shape %0 [[0, 1, 2]]
+ %0 = tensor.expand_shape %arg0 [] output_shape [1] : tensor<f32> into tensor<1xf32>
+ %1 = tensor.expand_shape %0 [[0, 1, 2]] output_shape [1, 1, 1]
: tensor<1xf32> into tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
// CHECK-LABEL: compose_expand_of_expand_of_zero_dim
-// CHECK: tensor.expand_shape %{{.*}} []
+// CHECK: tensor.expand_shape %{{.*}} [] output_shape [1, 1, 1]
// CHECK-SAME: tensor<f32> into tensor<1x1x1xf32>
// -----
@@ -1093,7 +1092,7 @@ func.func @collapse_of_cast(%t: tensor<8x12x32xf32>) -> tensor<?x32xf32> {
// -----
func.func @fold_collapse_of_expand(%arg0 : tensor<12x4xf32>) -> tensor<12x4xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1], [2]]
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2]] output_shape [3, 4, 4]
: tensor<12x4xf32> into tensor<3x4x4xf32>
%1 = tensor.collapse_shape %0 [[0, 1], [2]]
: tensor<3x4x4xf32> into tensor<12x4xf32>
@@ -1104,9 +1103,9 @@ func.func @fold_collapse_of_expand(%arg0 : tensor<12x4xf32>) -> tensor<12x4xf32>
// -----
-func.func @fold_collapse_of_expand_dynamic(%arg0 : tensor<?x?xf32>)
+func.func @fold_collapse_of_expand_dynamic(%arg0 : tensor<?x?xf32>, %arg1: index, %arg2: index)
-> tensor<?x?xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1], [2]]
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2]] output_shape [%arg1, 4, %arg2]
: tensor<?x?xf32> into tensor<?x4x?xf32>
%1 = tensor.collapse_shape %0 [[0, 1], [2]]
: tensor<?x4x?xf32> into tensor<?x?xf32>
@@ -1121,7 +1120,7 @@ func.func @compose_expand_of_collapse(%arg0 : tensor<2x3x4x5x6x7x8xf32>)
-> tensor<24x5x42x8xf32> {
%0 = tensor.collapse_shape %arg0 [[0, 1, 2, 3, 4, 5, 6]]
: tensor<2x3x4x5x6x7x8xf32> into tensor<40320xf32>
- %1 = tensor.expand_shape %0 [[0, 1, 2, 3]]
+ %1 = tensor.expand_shape %0 [[0, 1, 2, 3]] output_shape [24, 5, 42, 8]
: tensor<40320xf32> into tensor<24x5x42x8xf32>
return %1 : tensor<24x5x42x8xf32>
}
@@ -1137,7 +1136,7 @@ func.func @compose_expand_of_collapse_7D(%arg0 : tensor<24x5x42x8xf32>)
-> tensor<2x3x4x5x6x7x8xf32> {
%0 = tensor.collapse_shape %arg0 [[0, 1, 2, 3]]
: tensor<24x5x42x8xf32> into tensor<40320xf32>
- %1 = tensor.expand_shape %0 [[0, 1, 2, 3, 4, 5, 6]]
+ %1 = tensor.expand_shape %0 [[0, 1, 2, 3, 4, 5, 6]] output_shape [2, 3, 4, 5, 6, 7, 8]
: tensor<40320xf32> into tensor<2x3x4x5x6x7x8xf32>
return %1 : tensor<2x3x4x5x6x7x8xf32>
}
@@ -1149,16 +1148,16 @@ func.func @compose_expand_of_collapse_7D(%arg0 : tensor<24x5x42x8xf32>)
// -----
-func.func @compose_collapse_of_expand(%arg : tensor<?x?x?xi64>)
+func.func @compose_collapse_of_expand(%arg : tensor<?x?x?xi64>, %arg1: index, %arg2: index, %arg3: index)
-> tensor<?x?xi64> {
- %0 = tensor.expand_shape %arg [[0], [1], [2, 3]]
+ %0 = tensor.expand_shape %arg [[0], [1], [2, 3]] output_shape [%arg1, %arg2, %arg3, 1]
: tensor<?x?x?xi64> into tensor<?x?x?x1xi64>
%1 = tensor.collapse_shape %0 [[0, 1], [2, 3]]
: tensor<?x?x?x1xi64> into tensor<?x?xi64>
return %1 : tensor<?x?xi64>
}
// CHECK-LABEL: func @compose_collapse_of_expand
-// CHECK: (%[[ARG:.*]]: tensor<?x?x?xi64>)
+// CHECK: (%[[ARG:.*]]: tensor<?x?x?xi64>, %[[ARG1:.*]]: index, %[[ARG2:.*]]: index, %[[ARG3:.*]]: index)
// CHECK-NEXT: tensor.collapse_shape %[[ARG]]
// CHECK-SAME: [0, 1], [2]
// CHECK-SAME: : tensor<?x?x?xi64> into tensor<?x?xi64>
@@ -1167,14 +1166,14 @@ func.func @compose_collapse_of_expand(%arg : tensor<?x?x?xi64>)
func.func @compose_collapse_of_expand_1D(%arg0 : tensor<2048xf32>)
-> tensor<4x512xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1, 2, 3]]
+ %0 = tensor.expand_shape %arg0 [[0, 1, 2, 3]] output_shape [1, 4, 1, 512]
: tensor<2048xf32> into tensor<1x4x1x512xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3]]
: tensor<1x4x1x512xf32> into tensor<4x512xf32>
return %1 : tensor<4x512xf32>
}
// CHECK: func @compose_collapse_of_expand_1D
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1]]
+// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1]] output_shape [4, 512]
// CHECK-SAME: tensor<2048xf32> into tensor<4x512xf32>
// -----
@@ -1183,14 +1182,14 @@ func.func @compose_expand_of_collapse_0_rank_to_expand(%arg0 : tensor<1x1x1xf32>
-> tensor<1x1x1x1xf32> {
%0 = tensor.collapse_shape %arg0 []
: tensor<1x1x1xf32> into tensor<f32>
- %1 = tensor.expand_shape %0 []
+ %1 = tensor.expand_shape %0 [] output_shape [1, 1, 1, 1]
: tensor<f32> into tensor<1x1x1x1xf32>
return %1 : tensor<1x1x1x1xf32>
}
// CHECK: func @compose_expand_of_collapse_0_rank_to_expand
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x1x1xf32>
// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0], [1], [2, 3]
+// CHECK-SAME: {{\[}}[0], [1], [2, 3]] output_shape [1, 1, 1, 1]
// CHECK: return %[[RESULT]]
// -----
@@ -1199,7 +1198,7 @@ func.func @compose_expand_of_collapse_0_rank_to_collapse(%arg0 : tensor<1x1x1x1x
-> tensor<1x1x1xf32> {
%0 = tensor.collapse_shape %arg0 []
: tensor<1x1x1x1xf32> into tensor<f32>
- %1 = tensor.expand_shape %0 []
+ %1 = tensor.expand_shape %0 [] output_shape [1, 1, 1]
: tensor<f32> into tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
@@ -1214,8 +1213,8 @@ func.func @compose_expand_of_collapse_0_rank_to_collapse(%arg0 : tensor<1x1x1x1x
// CHECK-LABEL: func @zero_rank_reshape_multi
func.func @zero_rank_reshape_multi(%arg0: tensor<f32>) -> tensor<f32> {
// CHECK: return %arg0
- %0 = tensor.expand_shape %arg0 [] : tensor<f32> into tensor<1xf32>
- %1 = tensor.expand_shape %0 [[0, 1]] : tensor<1xf32> into tensor<1x1xf32>
+ %0 = tensor.expand_shape %arg0 [] output_shape [1] : tensor<f32> into tensor<1xf32>
+ %1 = tensor.expand_shape %0 [[0, 1]] output_shape [1, 1] : tensor<1xf32> into tensor<1x1xf32>
%2 = tensor.collapse_shape %1 [] : tensor<1x1xf32> into tensor<f32>
return %2 : tensor<f32>
}
@@ -1250,7 +1249,7 @@ func.func @compose_collapse_of_collapse_zero_dim(%arg0 : tensor<1x1x1xf32>)
// -----
func.func @fold_collapse_of_expand_1D(%arg0 : tensor<4x512xf32>) -> tensor<2048xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1, 2], [3]]
+ %0 = tensor.expand_shape %arg0 [[0, 1, 2], [3]] output_shape [1, 4, 1, 512]
: tensor<4x512xf32> into tensor<1x4x1x512xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2, 3]]
: tensor<1x4x1x512xf32> into tensor<2048xf32>
@@ -1264,42 +1263,40 @@ func.func @fold_collapse_of_expand_1D(%arg0 : tensor<4x512xf32>) -> tensor<2048x
func.func @fold_collapse_of_expand_unit_dims(%arg0 : tensor<2048x1x1xf32>)
-> tensor<4x512x1x1xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1, 2, 3], [4], [5]]
- : tensor<2048x1x1xf32> into tensor<1x4x1x512x1x1xf32>
+ %0 = tensor.expand_shape %arg0 [[0, 1, 2, 3], [4], [5]] output_shape [1, 4, 1, 512, 1, 1] : tensor<2048x1x1xf32> into tensor<1x4x1x512x1x1xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3], [4], [5]]
: tensor<1x4x1x512x1x1xf32> into tensor<4x512x1x1xf32>
return %1 : tensor<4x512x1x1xf32>
}
// CHECK: func @fold_collapse_of_expand_unit_dims
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2], [3]]
+// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2], [3]] output_shape [4, 512, 1, 1]
// CHECK-SAME: tensor<2048x1x1xf32> into tensor<4x512x1x1xf32>
// -----
func.func @compose_collapse_of_expand_unit_dims(%arg0 : tensor<2048x1x2048xf32>)
-> tensor<4x512x1x512x4xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1, 2, 3, 4], [5], [6, 7, 8]]
- : tensor<2048x1x2048xf32> into tensor<1x4x1x512x1x1x512x1x4xf32>
+ %0 = tensor.expand_shape %arg0 [[0, 1, 2, 3, 4], [5], [6, 7, 8]] output_shape [1, 4, 1, 512, 1, 1, 512, 1, 4] : tensor<2048x1x2048xf32> into tensor<1x4x1x512x1x1x512x1x4xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3, 4], [5], [6, 7], [8]]
: tensor<1x4x1x512x1x1x512x1x4xf32> into tensor<4x512x1x512x4xf32>
return %1 : tensor<4x512x1x512x4xf32>
}
// CHECK: func @compose_collapse_of_expand_unit_dims
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2], [3, 4]]
+// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2], [3, 4]] output_shape [4, 512, 1, 512, 4]
// CHECK-SAME: tensor<2048x1x2048xf32> into tensor<4x512x1x512x4xf32>
// -----
func.func @compose_collapse_of_expand_trailing_unit_dims(%arg0: tensor<2xf32>)
-> tensor<2x1xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1, 2]]
+ %0 = tensor.expand_shape %arg0 [[0, 1, 2]] output_shape [2, 1, 1]
: tensor<2xf32> into tensor<2x1x1xf32>
%1 = tensor.collapse_shape %0 [[0], [1, 2]]
: tensor<2x1x1xf32> into tensor<2x1xf32>
return %1 : tensor<2x1xf32>
}
// CHECK: func @compose_collapse_of_expand_trailing_unit_dims
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1]]
+// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1]] output_shape [2, 1]
// CHECK-SAME: tensor<2xf32> into tensor<2x1xf32>
// -----
@@ -1321,14 +1318,13 @@ func.func @compose_collapse_of_collapse_unit_dims_dynamic(
func.func @fold_collapse_of_expand_trailing_unit_dims(%arg0: tensor<2xf32>)
-> tensor<2x1xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1, 2]]
- : tensor<2xf32> into tensor<2x1x1xf32>
+ %0 = tensor.expand_shape %arg0 [[0, 1, 2]] output_shape [2, 1, 1] : tensor<2xf32> into tensor<2x1x1xf32>
%1 = tensor.collapse_shape %0 [[0], [1, 2]]
: tensor<2x1x1xf32> into tensor<2x1xf32>
return %1 : tensor<2x1xf32>
}
// CHECK: func @fold_collapse_of_expand_trailing_unit_dims
-// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1]]
+// CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1]] output_shape [2, 1]
// CHECK-SAME: tensor<2xf32> into tensor<2x1xf32>
// -----
@@ -1349,8 +1345,7 @@ func.func @fold_collapse_of_collapse_trailing_unit_dims_dynamic(
func.func @fold_collapse_of_expand_trailing_unit_dims(%arg0: tensor<12x42x1x1xf32>)
-> tensor<12x42xf32> {
- %0 = tensor.expand_shape %arg0 [[0], [1], [2], [3, 4]]
- : tensor<12x42x1x1xf32> into tensor<12x42x1x1x1xf32>
+ %0 = tensor.expand_shape %arg0 [[0], [1], [2], [3, 4]] output_shape [12, 42, 1, 1, 1] : tensor<12x42x1x1xf32> into tensor<12x42x1x1x1xf32>
%1 = tensor.collapse_shape %0 [[0], [1, 2, 3, 4]]
: tensor<12x42x1x1x1xf32> into tensor<12x42xf32>
return %1 : tensor<12x42xf32>
@@ -1361,9 +1356,9 @@ func.func @fold_collapse_of_expand_trailing_unit_dims(%arg0: tensor<12x42x1x1xf3
// -----
-func.func @fold_collapse_of_expand_unit_dims_in_middle(%arg0 : tensor<?x?x?xf32>)
+func.func @fold_collapse_of_expand_unit_dims_in_middle(%arg0 : tensor<?x?x?xf32>, %sz0: index, %sz1: index, %sz2: index)
-> tensor<?x?xf32> {
- %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3]]
+ %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3]] output_shape [%sz0, %sz1, 1, %sz2]
: tensor<?x?x?xf32> into tensor<?x?x1x?xf32>
%1 = tensor.collapse_shape %0 [[0], [1, 2, 3]]
: tensor<?x?x1x?xf32> into tensor<?x?xf32>
@@ -1378,7 +1373,7 @@ func.func @fold_collapse_of_expand_unit_dims_in_middle(%arg0 : tensor<?x?x?xf32>
func.func @no_fold_collapse_of_expand_incompatible(%arg0 : tensor<4x6x8xf32>)
-> tensor<2x6x16xf32> {
- %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3], [4]]
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3], [4]] output_shape [2, 2, 3, 2, 8]
: tensor<4x6x8xf32> into tensor<2x2x3x2x8xf32>
%1 = tensor.collapse_shape %0 [[0], [1, 2], [3, 4]]
: tensor<2x2x3x2x8xf32> into tensor<2x6x16xf32>
@@ -1392,7 +1387,7 @@ func.func @no_fold_collapse_of_expand_incompatible(%arg0 : tensor<4x6x8xf32>)
func.func @no_fold_collapse_of_expand_empty_expr(%arg0: tensor<3x2x2xf32>)
-> tensor<12x1xf32> {
- %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3]]
+ %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3]] output_shape [3, 2, 2, 1]
: tensor<3x2x2xf32> into tensor<3x2x2x1xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3]]
: tensor<3x2x2x1xf32> into tensor<12x1xf32>
@@ -1401,7 +1396,7 @@ func.func @no_fold_collapse_of_expand_empty_expr(%arg0: tensor<3x2x2xf32>)
// CHECK: func @no_fold_collapse_of_expand_empty_expr
// CHECK-SAME: %[[ARG0:.+]]: tensor<3x2x2xf32>
// CHECK: %[[RARG0:.+]] = tensor.expand_shape %[[ARG0]]
-// CHECK-SAME: [0], [1], [2, 3]
+// CHECK-SAME: {{\[}}[0], [1], [2, 3]] output_shape [3, 2, 2, 1]
// CHECK: %[[RES:.+]] = tensor.collapse_shape %[[RARG0]]
// CHECK-SAME: [0, 1, 2], [3]
// CHECK: return %[[RES:.+]] : tensor<12x1xf32>
@@ -1410,7 +1405,7 @@ func.func @no_fold_collapse_of_expand_empty_expr(%arg0: tensor<3x2x2xf32>)
func.func @reshape_splat_constant_int32() -> tensor<2x4x2xi32> {
%c0 = arith.constant dense<42> : tensor<2x8xi32>
- %0 = tensor.expand_shape %c0 [[0], [1, 2]]
+ %0 = tensor.expand_shape %c0 [[0], [1, 2]] output_shape [2, 4, 2]
: tensor<2x8xi32> into tensor<2x4x2xi32>
return %0 : tensor<2x4x2xi32>
}
@@ -1421,7 +1416,7 @@ func.func @reshape_splat_constant_int32() -> tensor<2x4x2xi32> {
// -----
func.func @expand_shape_splat(%arg : f32) -> tensor<2x2x2xf32> {
%c0 = tensor.splat %arg : tensor<2x4xf32>
- %0 = tensor.expand_shape %c0 [[0], [1, 2]]
+ %0 = tensor.expand_shape %c0 [[0], [1, 2]] output_shape [2, 2, 2]
: tensor<2x4xf32> into tensor<2x2x2xf32>
return %0 : tensor<2x2x2xf32>
}
@@ -1434,13 +1429,12 @@ func.func @expand_shape_splat(%arg : f32) -> tensor<2x2x2xf32> {
// -----
// CHECK-LABEL: @expand_shape_splat_dynamic_no_fold
-// CHECK-SAME: %[[F:.+]]: f32
-// CHECK-SAME: %[[M:.+]]: index
-func.func @expand_shape_splat_dynamic_no_fold(%arg: f32, %m: index) -> tensor<2x2x?xf32> {
- // CHECK: %[[SPLAT:.+]] = tensor.splat %[[F]][%[[M]]]
+// CHECK-SAME: (%[[F:.+]]: f32, %[[M:.+]]: index, %[[SZ0:.+]]: index)
+func.func @expand_shape_splat_dynamic_no_fold(%arg: f32, %m: index, %sz0: index) -> tensor<2x2x?xf32> {
+ // CHECK: %[[SPLAT:.+]] = tensor.splat %[[F]][%[[M]]] : tensor<2x?xf32>
// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[SPLAT]]
%c0 = tensor.splat %arg[%m] : tensor<2x?xf32>
- %0 = tensor.expand_shape %c0 [[0], [1, 2]] : tensor<2x?xf32> into tensor<2x2x?xf32>
+ %0 = tensor.expand_shape %c0 [[0], [1, 2]] output_shape [2, 2, %sz0] : tensor<2x?xf32> into tensor<2x2x?xf32>
return %0 : tensor<2x2x?xf32>
}
@@ -1475,7 +1469,7 @@ func.func @collapse_shape_splat_dynamic_no_fold(%f: f32, %m: index) -> tensor<2x
func.func @reshape_splat_constant_int16() -> tensor<2x4x2xi16> {
%c0 = arith.constant dense<42> : tensor<2x8xi16>
- %0 = tensor.expand_shape %c0 [[0], [1, 2]]
+ %0 = tensor.expand_shape %c0 [[0], [1, 2]] output_shape [2, 4, 2]
: tensor<2x8xi16> into tensor<2x4x2xi16>
return %0 : tensor<2x4x2xi16>
}
@@ -1488,7 +1482,7 @@ func.func @reshape_splat_constant_int16() -> tensor<2x4x2xi16> {
func.func @reshape_splat_constant_float32() -> tensor<2x4x2xf32> {
%c0 = arith.constant dense<42.0> : tensor<2x8xf32>
- %0 = tensor.expand_shape %c0 [[0], [1, 2]]
+ %0 = tensor.expand_shape %c0 [[0], [1, 2]] output_shape [2, 4, 2]
: tensor<2x8xf32> into tensor<2x4x2xf32>
return %0 : tensor<2x4x2xf32>
}
@@ -1501,7 +1495,7 @@ func.func @reshape_splat_constant_float32() -> tensor<2x4x2xf32> {
func.func @reshape_splat_constant_float64() -> tensor<2x4x2xf64> {
%c0 = arith.constant dense<42.0> : tensor<2x8xf64>
- %0 = tensor.expand_shape %c0 [[0], [1, 2]]
+ %0 = tensor.expand_shape %c0 [[0], [1, 2]] output_shape [2, 4, 2]
: tensor<2x8xf64> into tensor<2x4x2xf64>
return %0 : tensor<2x4x2xf64>
}
@@ -1851,7 +1845,7 @@ func.func @fold_expand_shape_from_elements(%arg0: i32) -> tensor<1xi32> {
// CHECK: %[[FROM:.+]] = tensor.from_elements %arg0 : tensor<1xi32>
// CHECK: return %[[FROM]] : tensor<1xi32>
%0 = tensor.from_elements %arg0 : tensor<i32>
- %1 = tensor.expand_shape %0 [] : tensor<i32> into tensor<1xi32>
+ %1 = tensor.expand_shape %0 [] output_shape [1] : tensor<i32> into tensor<1xi32>
return %1 : tensor<1xi32>
}
@@ -2073,9 +2067,9 @@ func.func @empty_tensor_canonicalize(%i : index) {
// CHECK: %[[dim:.*]] = tensor.dim %[[t]], %[[c1]] : tensor<?x?xf32>
// CHECK: %[[apply:.*]] = affine.apply #[[$map]]()[%[[dim]]]
// CHECK: return %[[apply]]
-func.func @dim_of_expand_shape(%t: tensor<?x?xf32>) -> index {
+func.func @dim_of_expand_shape(%t: tensor<?x?xf32>, %sz0: index, %sz1: index) -> index {
%c2 = arith.constant 2 : index
- %0 = tensor.expand_shape %t [[0], [1, 2, 3, 4, 5]]
+ %0 = tensor.expand_shape %t [[0], [1, 2, 3, 4, 5]] output_shape [%sz0, 1, %sz1, 5, 1, 8]
: tensor<?x?xf32> into tensor<?x1x?x5x1x8xf32>
%1 = tensor.dim %0, %c2 : tensor<?x1x?x5x1x8xf32>
return %1 : index
@@ -2107,9 +2101,9 @@ func.func @dim_of_collapse_shape(%t: tensor<?x?x?x7x?xf32>) -> index {
// CHECK-LABEL: func @collapse_expand_fold_to_cast(
// CHECK-SAME: %[[t:.*]]: tensor<?xf32>
// CHECK: return %[[t]]
-func.func @collapse_expand_fold_to_cast(%t: tensor<?xf32>) -> (tensor<?xf32>)
+func.func @collapse_expand_fold_to_cast(%t: tensor<?xf32>, %sz0: index) -> (tensor<?xf32>)
{
- %0 = tensor.expand_shape %t [[0, 1]] : tensor<?xf32> into tensor<1x?xf32>
+ %0 = tensor.expand_shape %t [[0, 1]] output_shape [1, %sz0] : tensor<?xf32> into tensor<1x?xf32>
%1 = tensor.collapse_shape %0 [[0, 1]] : tensor<1x?xf32> into tensor<?xf32>
return %1 : tensor<?xf32>
}
diff --git a/mlir/test/Dialect/Tensor/fold-empty-op.mlir b/mlir/test/Dialect/Tensor/fold-empty-op.mlir
index 15f841f2128edb..e200a4f8926130 100644
--- a/mlir/test/Dialect/Tensor/fold-empty-op.mlir
+++ b/mlir/test/Dialect/Tensor/fold-empty-op.mlir
@@ -13,10 +13,9 @@ module attributes {transform.with_named_sequence} {
// CHECK: #[[$MAP:.+]] = affine_map<()[s0] -> (s0 floordiv 28)>
// CHECK: #[[$MAP2:.+]] = affine_map<()[s0] -> (s0 * 28)>
-func.func @empty_reshape_expansion(%arg0 : index) -> tensor<2x3x5x4x?x7xf32> {
+func.func @empty_reshape_expansion(%arg0 : index, %sz0: index) -> tensor<2x3x5x4x?x7xf32> {
%0 = tensor.empty(%arg0) : tensor<6x5x?xf32>
- %1 = tensor.expand_shape %0 [[0, 1], [2], [3, 4, 5]]
- : tensor<6x5x?xf32> into tensor<2x3x5x4x?x7xf32>
+ %1 = tensor.expand_shape %0 [[0, 1], [2], [3, 4, 5]] output_shape [2, 3, 5, 4, %sz0, 7] : tensor<6x5x?xf32> into tensor<2x3x5x4x?x7xf32>
return %1 : tensor<2x3x5x4x?x7xf32>
}
// CHECK-LABEL: func @empty_reshape_expansion
diff --git a/mlir/test/Dialect/Tensor/fold-reassociative-reshapes.mlir b/mlir/test/Dialect/Tensor/fold-reassociative-reshapes.mlir
index 625408dfefe216..d3ac6ce792f365 100644
--- a/mlir/test/Dialect/Tensor/fold-reassociative-reshapes.mlir
+++ b/mlir/test/Dialect/Tensor/fold-reassociative-reshapes.mlir
@@ -11,9 +11,11 @@ func.func @expand_shape_of_rank_reducing_extract(
{
%0 = tensor.extract_slice %t[0, 0, 0, 0][%idx, 1, 1, 5][1, 1, 1, 1]
: tensor<?x?x?x?xf32> to tensor<?x1x5xf32>
- %1 = tensor.expand_shape %0 [[0], [1, 2], [3]]
+ %c0 = arith.constant 0 : index
+ %sz0 = tensor.dim %0, %c0 : tensor<?x1x5xf32>
+ %1 = tensor.expand_shape %0 [[0], [1, 2], [3]] output_shape [%sz0, 1, 1, 5]
: tensor<?x1x5xf32> into tensor<?x1x1x5xf32>
- %2 = tensor.expand_shape %0 [[0, 1], [2], [3]]
+ %2 = tensor.expand_shape %0 [[0, 1], [2], [3]] output_shape [%sz0, 1, 1, 5]
: tensor<?x1x5xf32> into tensor<?x1x1x5xf32>
return %1, %2 : tensor<?x1x1x5xf32>, tensor<?x1x1x5xf32>
}
diff --git a/mlir/test/Dialect/Tensor/invalid.mlir b/mlir/test/Dialect/Tensor/invalid.mlir
index 79ca0de68a1e9b..3617ed5d61afee 100644
--- a/mlir/test/Dialect/Tensor/invalid.mlir
+++ b/mlir/test/Dialect/Tensor/invalid.mlir
@@ -273,21 +273,10 @@ func.func @insert_slice_wrong_dynamic_type(%t1: tensor<?x4x4xf32>, %t2: tensor<8
// -----
-func.func @illegal_expanding_reshape_dynamic_tensor
- (%arg0: tensor<?x?x?xf32>) -> tensor<?x?x?x4x?xf32> {
- // expected-error @+1 {{invalid to have a single dimension (2) expanded into multiple dynamic dims (2,4)}}
- %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3, 4]]
- : tensor<?x?x?xf32> into tensor<?x?x?x4x?xf32>
- return %0 : tensor<?x?x?x4x?xf32>
-}
-
-// -----
-
-
func.func @illegal_expanding_reshape_static_tensor
(%arg0: tensor<2x3x20xf32>) -> tensor<2x3x2x4x5xf32> {
// expected-error @+1 {{expected dimension 2 of collapsed type to be static value of 40}}
- %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3, 4]]
+ %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3, 4]] output_shape [2, 3, 2, 4, 5]
: tensor<2x3x20xf32> into tensor<2x3x2x4x5xf32>
return %0 : tensor<2x3x2x4x5xf32>
}
@@ -304,20 +293,20 @@ func.func @illegal_collapsing_reshape_static_tensor
// -----
-func.func @illegal_expanding_reshape_mixed_tensor(%arg0 : tensor<?x?xf32>)
+func.func @illegal_expanding_reshape_mixed_tensor(%arg0 : tensor<?x?xf32>, %sz0: index)
-> tensor<?x4x5xf32> {
// expected-error @+1 {{expected dimension 1 of collapsed type to be static value of 5}}
- %0 = tensor.expand_shape %arg0 [[0, 1], [2]]
+ %0 = tensor.expand_shape %arg0 [[0, 1], [2]] output_shape [%sz0, 4, 5]
: tensor<?x?xf32> into tensor<?x4x5xf32>
return %0 : tensor<?x4x5xf32>
}
// -----
-func.func @illegal_expanding_reshape_mixed_tensor_2(%arg0 : tensor<?x?xf32>)
+func.func @illegal_expanding_reshape_mixed_tensor_2(%arg0 : tensor<?x?xf32>, %sz0: index)
-> tensor<?x4x5xf32> {
// expected-error @+1 {{expected dimension 1 of collapsed type to be static value of 20}}
- %0 = tensor.expand_shape %arg0 [[0], [1, 2]]
+ %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [%sz0, 4, 5]
: tensor<?x?xf32> into tensor<?x4x5xf32>
return %0 : tensor<?x4x5xf32>
}
diff --git a/mlir/test/Dialect/Tensor/ops.mlir b/mlir/test/Dialect/Tensor/ops.mlir
index 2b0a74acce0826..378137a14b59ff 100644
--- a/mlir/test/Dialect/Tensor/ops.mlir
+++ b/mlir/test/Dialect/Tensor/ops.mlir
@@ -194,12 +194,26 @@ func.func @insert_slice(
func.func @tensor_reshape_zero_dim(%arg0 : tensor<1x1xf32>, %arg1 : tensor<f32>)
-> (tensor<f32>, tensor<1x1xf32>) {
%0 = tensor.collapse_shape %arg0 [] : tensor<1x1xf32> into tensor<f32>
- %1 = tensor.expand_shape %0 [] : tensor<f32> into tensor<1x1xf32>
+ %1 = tensor.expand_shape %0 [] output_shape [1, 1] : tensor<f32> into tensor<1x1xf32>
return %0, %1 : tensor<f32>, tensor<1x1xf32>
}
// CHECK-LABEL: func @tensor_reshape_zero_dim
// CHECK: tensor.collapse_shape %{{.*}} [] : tensor<1x1xf32> into tensor<f32>
-// CHECK: tensor.expand_shape %{{.*}} [] : tensor<f32> into tensor<1x1xf32>
+// CHECK: tensor.expand_shape %{{.*}} [] output_shape [1, 1] : tensor<f32> into tensor<1x1xf32>
+
+// -----
+
+func.func @tensor_expand_shape_dynamic_dim(%arg0 : tensor<?x?xf32>, %sz0 : index, %sz1 : index, %sz2 : index)
+ -> (tensor<5x?x?x?xf32>) {
+ %1 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] output_shape [5, %sz0, %sz1, %sz2] : tensor<?x?xf32> into tensor<5x?x?x?xf32>
+ return %1 : tensor<5x?x?x?xf32>
+}
+
+// CHECK-LABEL: func.func @tensor_expand_shape_dynamic_dim(%arg0: tensor<?x?xf32>, %arg1: index, %arg2: index, %arg3: index) -> tensor<5x?x?x?xf32> {
+// CHECK: %expanded = tensor.expand_shape %arg0 {{\[\[}}0, 1], [2, 3{{\]\]}} output_shape [5, %arg1, %arg2, %arg3] : tensor<?x?xf32> into tensor<5x?x?x?xf32>
+// CHECK: return %expanded : tensor<5x?x?x?xf32>
+// CHECK: }
+
// -----
diff --git a/mlir/test/Dialect/Tensor/simplify-pack-unpack.mlir b/mlir/test/Dialect/Tensor/simplify-pack-unpack.mlir
index 9948c0246e6ed6..5a2eade0ecccf1 100644
--- a/mlir/test/Dialect/Tensor/simplify-pack-unpack.mlir
+++ b/mlir/test/Dialect/Tensor/simplify-pack-unpack.mlir
@@ -2,7 +2,7 @@
// CHECK-LABEL: func.func @single_dim_packing(
// CHECK-SAME: %[[ARG0:.+]]: tensor<256xf32>)
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1]] : tensor<256xf32> into tensor<8x32xf32>
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1]] output_shape [8, 32] : tensor<256xf32> into tensor<8x32xf32>
// CHECK: return %[[EXPANDED]] : tensor<8x32xf32>
func.func @single_dim_packing(%arg0: tensor<256xf32>) -> tensor<8x32xf32> {
%empty = tensor.empty() : tensor<8x32xf32>
@@ -27,7 +27,7 @@ func.func @single_dim_packing_with_padding(%arg0: tensor<255xf32>) -> tensor<8x3
// CHECK-LABEL: func.func @single_last_inner_dim_packing(
// CHECK-SAME: %[[ARG0:.+]]: tensor<5x256xf32>)
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2]] : tensor<5x256xf32> into tensor<5x8x32xf32>
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2]] output_shape [5, 8, 32] : tensor<5x256xf32> into tensor<5x8x32xf32>
// CHECK: return %[[EXPANDED]] : tensor<5x8x32xf32>
func.func @single_last_inner_dim_packing(%arg0: tensor<5x256xf32>) -> tensor<5x8x32xf32> {
%empty = tensor.empty() : tensor<5x8x32xf32>
@@ -39,7 +39,7 @@ func.func @single_last_inner_dim_packing(%arg0: tensor<5x256xf32>) -> tensor<5x8
// CHECK-LABEL: func.func @pack_1d_with_outer_dims_perm(
// CHECK-SAME: %[[ARG0:.+]]: tensor<64xf32>)
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1]] : tensor<64xf32> into tensor<2x32xf32>
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1]] output_shape [2, 32] : tensor<64xf32> into tensor<2x32xf32>
// CHECK: return %[[EXPANDED]] : tensor<2x32xf32>
func.func @pack_1d_with_outer_dims_perm(%arg0: tensor<64xf32>) -> tensor<2x32xf32> {
%empty = tensor.empty() : tensor<2x32xf32>
@@ -51,7 +51,7 @@ func.func @pack_1d_with_outer_dims_perm(%arg0: tensor<64xf32>) -> tensor<2x32xf3
// CHECK-LABEL: func.func @single_last_inner_dim_packing_with_identity_outer_dims_perm(
// CHECK-SAME: %[[ARG0:.+]]: tensor<5x256xf32>)
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2]] : tensor<5x256xf32> into tensor<5x8x32xf32>
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2]] output_shape [5, 8, 32] : tensor<5x256xf32> into tensor<5x8x32xf32>
// CHECK: return %[[EXPANDED]] : tensor<5x8x32xf32>
func.func @single_last_inner_dim_packing_with_identity_outer_dims_perm(%arg0: tensor<5x256xf32>) -> tensor<5x8x32xf32> {
%empty = tensor.empty() : tensor<5x8x32xf32>
@@ -85,7 +85,7 @@ func.func @single_first_inner_dim_packing(%arg0: tensor<256x5xf32>) -> tensor<8x
// CHECK-LABEL: func.func @pack_1x32_to_1x32x1x1
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2, 3]]
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2, 3]] output_shape [1, 32, 1, 1]
// CHECK: return %[[EXPANDED]]
func.func @pack_1x32_to_1x32x1x1(%arg0 : tensor<1x32xf32>) -> tensor<1x32x1x1xf32> {
%empty = tensor.empty() : tensor<1x32x1x1xf32>
@@ -98,7 +98,7 @@ func.func @pack_1x32_to_1x32x1x1(%arg0 : tensor<1x32xf32>) -> tensor<1x32x1x1xf3
// CHECK-LABEL: func.func @pack_1x32_to_1x16x1x2
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2, 3]]
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2, 3]] output_shape [1, 16, 1, 2]
// CHECK: return %[[EXPANDED]]
func.func @pack_1x32_to_1x16x1x2(%arg0 : tensor<1x32xf32>) -> tensor<1x16x1x2xf32> {
%empty = tensor.empty() : tensor<1x16x1x2xf32>
@@ -111,7 +111,7 @@ func.func @pack_1x32_to_1x16x1x2(%arg0 : tensor<1x32xf32>) -> tensor<1x16x1x2xf3
// CHECK-LABEL: func.func @pack_32x1_to_16x1x2x1
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]
-// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1, 2], [3]]
+// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1, 2], [3]] output_shape [1, 16, 2, 1]
// CHECK: return %[[EXPANDED]]
func.func @pack_32x1_to_16x1x2x1(%arg0 : tensor<32x1xf32>) -> tensor<1x16x2x1xf32> {
%empty = tensor.empty() : tensor<1x16x2x1xf32>
diff --git a/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel b/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
index 58538b66c5e0c7..2589044c38ad8c 100644
--- a/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
+++ b/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
@@ -3791,6 +3791,7 @@ cc_library(
includes = ["include"],
deps = [
":DialectUtilsIncGen",
+ ":ArithDialect",
":IR",
":Support",
"//llvm:Support",
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