https://github.com/banach-space updated
https://github.com/llvm/llvm-project/pull/149293
From 159b519fbf48c962507191c4e3e5622e40595da6 Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzyn...@arm.com>
Date: Wed, 16 Jul 2025 17:08:55 +0000
Subject: [PATCH 1/5] [mlir][linalg] Enable scalable vectorization of
linalg.unpack (WIP)
This patch updates `vectorizeAsTensorUnpackOp` to support scalable
vectorization by requiring user-specified vector sizes for both the
_read_ and _write_ operations involved in `linalg.unpack`. Detailed
rationale and an example are provided below.
Conceptually, `linalg.unpack` consists of the following high-level steps:
1. _Read_ from the source tensor.
2. Transpose the value read in step (1).
3. _Write_ the value from step (2) into the destination tensor.
Currently, when vectorizing with user-provided vector sizes, only the
sizes for the _write_ operation (step 3) are required. Sizes for the
_read_ operation (step 1) are inferred from static shapes and inner tile
sizes.
This logic breaks when the input shapes or tile sizes are dynamic
(indeed, `vectorizeUnPackOpPrecondition` rejects such cases ATM and the
vectorization fails). This patch addresses the issue by requiring
explicit vector sizes for both the read and write sides, enabling
scalable vectorization in such cases.
Example:
```mlir
func.func @unpack(%in: tensor<1x1x8x?xf32>, %out: tensor<8x?xf32>) ->
tensor<8x?xf32> {
%vs = vector.vscale
%c8 = arith.constant 8 : index
%tile_size = arith.muli %vs, %c8 : index
%unpack = linalg.unpack %in
inner_dims_pos = [0, 1]
inner_tiles = [8, %tile_size]
into %out : tensor<1x1x8x?xf32> -> tensor<8x?xf32>
return %unpack : tensor<8x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
%0 = transform.structured.match ops{["linalg.unpack"]} in %arg0 :
(!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [1, 1, 8, [8], 8, [8]] :
!transform.any_op
// \ / \ /
// read-sizes write-sizes
transform.yield
}
}
```
Finally, this patch also extends `createReadOrMaskedRead` and
`createWriteOrMaskedWrite` to take scalable flags.
---
.../mlir/Dialect/Vector/Utils/VectorUtils.h | 2 +-
.../Linalg/Transforms/Vectorization.cpp | 130 ++++++++++++------
mlir/lib/Dialect/Vector/Utils/VectorUtils.cpp | 22 +--
.../Linalg/vectorization/linalg-ops.mlir | 98 +++++++++++--
4 files changed, 190 insertions(+), 62 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Vector/Utils/VectorUtils.h
b/mlir/include/mlir/Dialect/Vector/Utils/VectorUtils.h
index 7cd70e42d363c..8bd54cf31b893 100644
--- a/mlir/include/mlir/Dialect/Vector/Utils/VectorUtils.h
+++ b/mlir/include/mlir/Dialect/Vector/Utils/VectorUtils.h
@@ -228,7 +228,7 @@ bool isLinearizableVector(VectorType type);
Value createReadOrMaskedRead(OpBuilder &builder, Location loc, Value source,
ArrayRef<int64_t> inputVectorSizes, Value
padValue,
bool useInBoundsInsteadOfMasking = false,
- ArrayRef<bool> scalableDims = {});
+ ArrayRef<bool> inputScalableVecDims = {});
/// Returns success if `inputVectorSizes` is a valid masking configuraion for
/// given `shape`, i.e., it meets:
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index bbf21a535fbd0..a4328a35e75ce 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1812,7 +1812,8 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter,
linalg::PackOp packOp,
inputShape[innerDimsPos[idx]] *= size;
auto maskedRead = vector::createReadOrMaskedRead(
rewriter, loc, packOp.getSource(), inputShape, padValue,
- useInBoundsInsteadOfMasking);
+ useInBoundsInsteadOfMasking,
+ /*inputScalableVecSizes=*/{});
// Create ShapeCastOp.
SmallVector<int64_t> destShape(inputVectorSizes);
@@ -1838,18 +1839,23 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter,
linalg::PackOp packOp,
return success();
}
-/// Vectorize a `linalg::UnPackOp` to these 4 Ops:
-/// Vector::TransferReadOp - Reads a vector from the source tensor
-/// vector::TransposeOp - Transpose the Source tensor
-/// ShapeCastOp - Reshape the data based on the target.
-/// vector::TransferWriteOp. - Write the result vector back to the
destination
-/// tensor.
-/// If the vector sizes are not provided:
+/// Vectorize `linalg.unpack %src into %dest` as:
+/// // Reads a vector from the source tensor
+/// %read = vector.transfer_read %src
+/// // Transpose %read as specified in `outer_dims_perm` attribute
+/// %tr = vector.transpose %read
+/// // Reshape the data based on the target
+/// %sc = vector.shape_cast %tr
+/// // Write the result vector to the destination tensor.
+/// vector.transfer_write %sc into %dest
+///
+/// If the vector sizes are not provided:
/// * the vector sizes are determined by the input operand and attributes,
/// * update the inBounds attribute instead of masking.
static LogicalResult
vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
ArrayRef<int64_t> inputVectorSizes,
+ ArrayRef<bool> inputScalableVecDims,
SmallVectorImpl<Value> &newResults) {
// TODO: Introduce a parent class that will handle the insertion point
update.
@@ -1866,25 +1872,54 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
auto destSize = unpackOp.getDestRank();
- if (!inputVectorSizes.empty())
- assert(inputVectorSizes.size() == destSize &&
+ if (!inputVectorSizes.empty()) {
+ assert(inputVectorSizes.size() == destSize + sourceShape.size() &&
"Incorrect number of input vector sizes");
+ }
+
+ SmallVector<bool> readScalableVectorFlags;
+ SmallVector<bool> writeScalableVectorFlags;
+ SmallVector<int64_t> readVectorSizes;
+ SmallVector<int64_t> writeVectorSizes;
- // vectorSizes is the shape of the vector that will be used to do final
+ // Split input-vector-sizes into vector sizes for the read and write
+ // operations.
+ if (!inputVectorSizes.empty()) {
+ readVectorSizes.append(inputVectorSizes.begin(),
+ inputVectorSizes.begin() + sourceShape.size());
+ writeVectorSizes.append(inputVectorSizes.begin() + sourceShape.size(),
+ inputVectorSizes.end());
+ }
+ if (!inputScalableVecDims.empty()) {
+ readScalableVectorFlags.append(inputScalableVecDims.begin(),
+ inputScalableVecDims.begin() +
+ sourceShape.size());
+ writeScalableVectorFlags.append(inputScalableVecDims.begin() +
+ sourceShape.size(),
+ inputScalableVecDims.end());
+ } else {
+ readScalableVectorFlags = SmallVector<bool>(sourceShape.size(), false);
+ writeScalableVectorFlags = SmallVector<bool>(destSize, false);
+ }
+
+ // writeVectorSizes is the shape of the vector that will be used to do final
// write on the destination tensor. It is set like this: Let's say the
// source tensor is rank 'M' and the dest tensor rank 'N', where N <= M.
// Thus:
- // 1. vectorSizes = sourceShape.take_front(N)
- // 2. if outer_dims_perms is present: do that permutation on vectorSizes.
+ // 1. writeVectorSizes = sourceShape.take_front(N)
+ // 2. if outer_dims_perms is present: do that permutation on
writeVectorSizes.
// 3. multiply all the locations in vectorSize pointed by innerDimPos by the
// innerTiles attribute value.
- SmallVector<int64_t> vectorSizes(inputVectorSizes);
- if (vectorSizes.empty()) {
- llvm::append_range(vectorSizes, sourceShape.take_front(destSize));
+ // SmallVector<int64_t> writeVectorSizes(inputVectorSizes);
+ if (writeVectorSizes.empty()) {
+ if (ShapedType::isDynamicShape(sourceShape))
+ return failure();
+
+ llvm::append_range(writeVectorSizes, sourceShape.take_front(destSize));
if (!outerDimsPerm.empty())
- applyPermutationToVector(vectorSizes, outerDimsPerm);
+ applyPermutationToVector(writeVectorSizes, outerDimsPerm);
for (auto [i, pos] : llvm::enumerate(innerDimPos))
- vectorSizes[pos] *= innerTiles[i];
+ writeVectorSizes[pos] *= innerTiles[i];
useInBoundsInsteadOfMasking = true;
}
@@ -1908,17 +1943,20 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
// After applying outer_dims_perm: [8, 16]
// After appending the rest of the sourceShape: [8, 16, 32, 16]
- SmallVector<int64_t> readVectorSizes(vectorSizes.begin(), vectorSizes.end());
-
- for (auto [index, size] : enumerate(innerTiles)) {
- readVectorSizes[innerDimPos[index]] =
- llvm::divideCeil(readVectorSizes[innerDimPos[index]], size);
- }
- if (!outerDimsPerm.empty()) {
- applyPermutationToVector(readVectorSizes, outerDimsPerm);
+ if (readVectorSizes.empty()) {
+ // Compute read-vector-sizes based on the write-vector-sizes and inner tile
+ // sizes. Note, this will only work when all sizes are static.
+ readVectorSizes = writeVectorSizes;
+ for (auto [index, size] : enumerate(innerTiles)) {
+ readVectorSizes[innerDimPos[index]] =
+ llvm::divideCeil(readVectorSizes[innerDimPos[index]], size);
+ }
+ if (!outerDimsPerm.empty()) {
+ applyPermutationToVector(readVectorSizes, outerDimsPerm);
+ }
+ readVectorSizes.append(sourceShape.begin() + writeVectorSizes.size(),
+ sourceShape.end());
}
- readVectorSizes.append(sourceShape.begin() + vectorSizes.size(),
- sourceShape.end());
Location loc = unpackOp->getLoc();
@@ -1930,7 +1968,7 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
// to shape of source, then a mask is necessary.
Value readResult = vector::createReadOrMaskedRead(
rewriter, loc, unpackOp.getSource(), readVectorSizes, padValue,
- /*useInBoundsInsteadOfMasking=*/false);
+ /*useInBoundsInsteadOfMasking=*/false, readScalableVectorFlags);
PackingMetadata packMetadata;
SmallVector<int64_t> lastDimToInsertPosPerm =
@@ -1949,15 +1987,17 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
RankedTensorType collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
stripMineTensorType, packMetadata.reassociations);
mlir::VectorType vecCollapsedType =
- VectorType::get(collapsedType.getShape(),
collapsedType.getElementType());
+ VectorType::get(collapsedType.getShape(), collapsedType.getElementType(),
+ writeScalableVectorFlags);
vector::ShapeCastOp shapeCastOp = vector::ShapeCastOp::create(
rewriter, loc, vecCollapsedType, transposeOp->getResult(0));
- // writeVectorSizes had to match the shapecast shape for dynamic sizes,
+ // writeVectorSizesFinal had to match the shapecast shape for dynamic sizes,
// otherwise the validator complains that the mask size is invalid.
- SmallVector<int64_t> writeVectorSizes(
+ // FIXME: We should not override write-vector-sizes like this.
+ SmallVector<int64_t> writeVectorSizesFinal(
unpackOp.getDestType().hasStaticShape()
- ? vectorSizes
+ ? writeVectorSizes
: shapeCastOp.getResultVectorType().getShape());
Operation *write = createWriteOrMaskedWrite(
rewriter, loc, shapeCastOp.getResult(), unpackOp.getDest(),
@@ -1988,7 +2028,7 @@ vectorizeAsTensorPadOp(RewriterBase &rewriter,
tensor::PadOp padOp,
assert(succeeded(status) && "failed to reify result shapes");
auto maskedRead = vector::createReadOrMaskedRead(
rewriter, loc, padOp.getSource(), inputVectorSizes, padValue,
- /*useInBoundsInsteadOfMasking=*/false);
+ /*useInBoundsInsteadOfMasking=*/false, /*inputScalableVecSizes=*/{});
// Create Xfer write Op
Value dest = tensor::EmptyOp::create(rewriter, loc, reifiedReturnShapes[0],
@@ -2072,6 +2112,9 @@ static LogicalResult
vectorizeUnPackOpPrecondition(linalg::UnPackOp unpackOp,
ArrayRef<int64_t> inputVectorSizes) {
+ // FIXME!!!
+ return success();
+
if (llvm::any_of(unpackOp.getInnerTiles(), [](OpFoldResult res) {
return !getConstantIntValue(res).has_value();
})) {
@@ -2408,6 +2451,7 @@ vectorizePackOpPrecondition(linalg::PackOp packOp,
LDBG("pad value is not constant: " << packOp << "\n");
return failure();
}
+
ArrayRef<int64_t> resultTensorShape = packOp.getDestType().getShape();
bool satisfyEmptyCond = true;
if (inputVectorSizes.empty()) {
@@ -2486,12 +2530,14 @@ vectorizeScalableVectorPrecondition(Operation *op,
if (numOfScalableDims == 0)
return success();
+ // TODO: Check the following!
auto linalgOp = dyn_cast<LinalgOp>(op);
- // Cond 1: There's been no need for scalable vectorisation of
- // non-linalg Ops so far
- if (!linalgOp)
- return failure();
+ // Cond 1: Reject Ops that don't implement the LinalgOp interface, with the
+ // exception of UnpackOp for which there is a dedicated hook.
+ if (!linalgOp) {
+ return isa<linalg::UnPackOp>(op) ? success() : failure();
+ }
// Cond 2: There's been no need for more than 2 scalable dims so far
if (numOfScalableDims > 2)
@@ -2587,7 +2633,7 @@ vectorizeScalableVectorPrecondition(Operation *op,
isa<linalg::MatmulTransposeAOp>(op) ||
isa<linalg::DepthwiseConv1DNwcWcOp>(op) ||
isa<linalg::MatvecOp>(op) || isa<linalg::Mmt4DOp>(op) ||
- hasReductionIterator(linalgOp));
+ isa<linalg::UnPackOp>(op) || hasReductionIterator(linalgOp));
}
LogicalResult mlir::linalg::vectorizeOpPrecondition(
@@ -2722,7 +2768,8 @@ FailureOr<VectorizationResult> mlir::linalg::vectorize(
})
.Case<linalg::UnPackOp>([&](auto unpackOp) {
return vectorizeAsTensorUnpackOp(rewriter, unpackOp,
- inputVectorSizes, results);
+ inputVectorSizes,
+ inputScalableVecDims, results);
})
.Case<tensor::InsertSliceOp>([&](auto sliceOp) {
return vectorizeAsInsertSliceOp(rewriter, sliceOp,
inputVectorSizes,
@@ -3114,7 +3161,8 @@ vectorizeAsInsertSliceOp(RewriterBase &rewriter,
tensor::InsertSliceOp sliceOp,
vecType.getRank(), arith::ConstantIndexOp::create(rewriter, loc, 0));
Value read = mlir::vector::createReadOrMaskedRead(
rewriter, loc, source, vecType.getShape(), padValue,
- /*useInBoundsInsteadOfMasking=*/inputVectorSizes.empty());
+ /*useInBoundsInsteadOfMasking=*/inputVectorSizes.empty(),
+ /*inputScalableVecSizes=*/{});
// Create write
auto writeIndices =
diff --git a/mlir/lib/Dialect/Vector/Utils/VectorUtils.cpp
b/mlir/lib/Dialect/Vector/Utils/VectorUtils.cpp
index c045063e8194f..a379229d3d5a5 100644
--- a/mlir/lib/Dialect/Vector/Utils/VectorUtils.cpp
+++ b/mlir/lib/Dialect/Vector/Utils/VectorUtils.cpp
@@ -281,14 +281,16 @@ vector::createUnrollIterator(VectorType vType, int64_t
targetRank) {
// Attempt to unroll until targetRank or the first scalable dimension (which
// cannot be unrolled).
auto shapeToUnroll = vType.getShape().drop_back(targetRank);
- auto scalableDimsToUnroll = vType.getScalableDims().drop_back(targetRank);
- auto it = llvm::find(scalableDimsToUnroll, true);
- auto firstScalableDim = it - scalableDimsToUnroll.begin();
+ auto inputScalableVecDimsToUnroll =
+ vType.getScalableDims().drop_back(targetRank);
+ auto it = llvm::find(inputScalableVecDimsToUnroll, true);
+ auto firstScalableDim = it - inputScalableVecDimsToUnroll.begin();
if (firstScalableDim == 0)
return {};
// All scalable dimensions should be removed now.
- scalableDimsToUnroll = scalableDimsToUnroll.slice(0, firstScalableDim);
- assert(!llvm::is_contained(scalableDimsToUnroll, true) &&
+ inputScalableVecDimsToUnroll =
+ inputScalableVecDimsToUnroll.slice(0, firstScalableDim);
+ assert(!llvm::is_contained(inputScalableVecDimsToUnroll, true) &&
"unexpected leading scalable dimension");
// Create an unroll iterator for leading dimensions.
shapeToUnroll = shapeToUnroll.slice(0, firstScalableDim);
@@ -321,15 +323,15 @@ Value vector::createReadOrMaskedRead(OpBuilder &builder,
Location loc,
ArrayRef<int64_t> inputVectorSizes,
Value padValue,
bool useInBoundsInsteadOfMasking,
- ArrayRef<bool> scalableDims) {
+ ArrayRef<bool> inputScalableVecDims) {
assert(!llvm::is_contained(inputVectorSizes, ShapedType::kDynamic) &&
"invalid input vector sizes");
auto sourceShapedType = cast<ShapedType>(source.getType());
auto sourceShape = sourceShapedType.getShape();
assert(sourceShape.size() == inputVectorSizes.size() &&
"expected same ranks.");
- auto vectorType =
- VectorType::get(inputVectorSizes, padValue.getType(), scalableDims);
+ auto vectorType = VectorType::get(inputVectorSizes, padValue.getType(),
+ inputScalableVecDims);
assert(padValue.getType() == sourceShapedType.getElementType() &&
"expected same pad element type to match source element type");
int64_t readRank = inputVectorSizes.size();
@@ -358,8 +360,8 @@ Value vector::createReadOrMaskedRead(OpBuilder &builder,
Location loc,
? memref::getMixedSizes(builder, loc, source)
: tensor::getMixedSizes(builder, loc, source);
- auto maskType =
- VectorType::get(inputVectorSizes, builder.getI1Type(), scalableDims);
+ auto maskType = VectorType::get(inputVectorSizes, builder.getI1Type(),
+ inputScalableVecDims);
Value mask =
vector::CreateMaskOp::create(builder, loc, maskType, mixedSourceDims);
return mlir::vector::maskOperation(builder, transferReadOp, mask)
diff --git a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
index d41d86117793b..ec227b46b409e 100644
--- a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
@@ -940,9 +940,9 @@ module attributes {transform.with_named_sequence} {
///----------------------------------------------------------------------------------------
// CHECK-LABEL: func @test_vectorize_dynamic_shapes_unpack
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x?xf32>,
-// CHECK-SAME: %[[ARG_1:.*]]: tensor<?x?x16x2xf32>
-func.func @test_vectorize_dynamic_shapes_unpack(%arg0: tensor<?x?xf32>, %arg1:
tensor<?x?x16x2xf32>) -> tensor<?x?xf32> {
+// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
+// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x16x2xf32>
+func.func @test_vectorize_dynamic_shapes_unpack(%dest: tensor<?x?xf32>, %src:
tensor<?x?x16x2xf32>) -> tensor<?x?xf32> {
// CHECK: %[[C0:.*]] = arith.constant 0
// CHECK: %[[C01:.*]] = arith.constant 0
// CHECK: %[[C02:.*]] = arith.constant 0
@@ -956,15 +956,93 @@ func.func @test_vectorize_dynamic_shapes_unpack(%arg0:
tensor<?x?xf32>, %arg1: t
// CHECK: %[[trans0:.*]] = vector.transpose %[[read0]], [0, 3, 1, 2] :
vector<2x1x16x2xf32> to vector<2x2x1x16xf32>
// CHECK: %[[sc0:.*]] = vector.shape_cast %[[trans0]] : vector<2x2x1x16xf32>
to vector<4x16xf32>
// CHECK: %[[writeMsk0:.*]] = vector.create_mask {{.*}} : vector<4x16xi1>
-// CHECK: %[[write0:.*]] = vector.mask %[[writeMsk0:.*]] {{.*}}
vector.transfer_write %[[sc0]], %[[ARG_0]]
+// CHECK: %[[write0:.*]] = vector.mask %[[writeMsk0:.*]] {{.*}}
vector.transfer_write %[[sc0]], %[[SRC]]
// CHECK: return %[[write0]]
- %ret = linalg.unpack %arg1 inner_dims_pos = [1, 0] inner_tiles = [16, 2] into
%arg0 : tensor<?x?x16x2xf32> -> tensor<?x?xf32>
+ %ret = linalg.unpack %src inner_dims_pos = [1, 0] inner_tiles = [16, 2] into
%dest : tensor<?x?x16x2xf32> -> tensor<?x?xf32>
return %ret : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
%0 = transform.structured.match ops{["linalg.unpack"]} in %arg0 :
(!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4, 16] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 1, 16, 2, 4, 16] :
!transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_dynamic_shapes_unpack_scalable_vec
+// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
+// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x16x2xf32>
+func.func @test_vectorize_dynamic_shapes_unpack_scalable_vec(%dest:
tensor<?x?xf32>, %src: tensor<?x?x16x2xf32>) -> tensor<?x?xf32> {
+ // CHECK: %[[C0:.*]] = arith.constant 0
+ // CHECK: %[[DIM:.*]] = tensor.dim %[[DEST]], %[[C0]] : tensor<?x?xf32>
+ // CHECK: %[[C1:.*]] = arith.constant 1 : index
+ // CHECK: %[[DIM0:.*]] = tensor.dim %[[DEST]], %[[C1]] : tensor<?x?xf32>
+ // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00
+ // CHECK: %[[C01:.*]] = arith.constant 0
+ // CHECK: %[[C02:.*]] = arith.constant 0
+ // CHECK: %[[DIM4:.*]] = tensor.dim %[[SRC]], %[[C02]] : tensor<?x?x16x2xf32>
+ // CHECK: %[[CNST14:.*]] = arith.constant 1
+ // CHECK: %[[DIM6:.*]] = tensor.dim %[[SRC]], %[[CNST14]] :
tensor<?x?x16x2xf32>
+ // CHECK: %[[CNST16:.*]] = arith.constant 16 : index
+ // CHECK: %[[CNST2:.*]] = arith.constant 2 : index
+ // CHECK: %[[MASK_READ:.*]] = vector.create_mask %[[DIM4]], %[[DIM6]],
%[[CNST16]], %[[CNST2]] : vector<2x1x[16]x2xi1>
+ // CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {{.*}}
vector.transfer_read %{{.*}} : tensor<?x?x16x2xf32>, vector<2x1x[16]x2xf32> } :
vector<2x1x[16]x2xi1> -> vector<2x1x[16]x2xf32>
+ // CHECK: %[[TR:.*]] = vector.transpose %[[READ]], [0, 3, 1, 2] :
vector<2x1x[16]x2xf32> to vector<2x2x1x[16]xf32>
+ // CHECK: %[[SC:.*]] = vector.shape_cast %[[TR]] : vector<2x2x1x[16]xf32> to
vector<4x[16]xf32>
+ // CHECK: %[[MASK_WRITE:.*]] = vector.create_mask {{.*}} : vector<4x[16]xi1>
+ // CHECK: %[[WRITE:.*]] = vector.mask %[[MASK_WRITE:.*]] {{.*}}
vector.transfer_write %[[SC]], %[[DEST]]
+ // CHECK: return %[[WRITE]]
+ %ret = linalg.unpack %src inner_dims_pos = [1, 0] inner_tiles = [16, 2] into
%dest : tensor<?x?x16x2xf32> -> tensor<?x?xf32>
+ return %ret : tensor<?x?xf32>
+}
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.unpack"]} in %arg0 :
(!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 1, [16], 2, 4, [16]] :
!transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
+// CHECK-LABEL: func
@test_vectorize_dynamic_shapes_unpack_scalable_vec_and_tile_size
+// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
+// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x?x2xf32>
+func.func
@test_vectorize_dynamic_shapes_unpack_scalable_vec_and_tile_size(%dest:
tensor<?x?xf32>, %src: tensor<?x?x?x2xf32>) -> tensor<?x?xf32> {
+ // CHECK: %[[C0:.*]] = arith.constant 0
+ // CHECK: %[[DIM:.*]] = tensor.dim %[[DEST]], %[[C0]] : tensor<?x?xf32>
+ // CHECK: %[[C1:.*]] = arith.constant 1 : index
+ // CHECK: %[[DIM0:.*]] = tensor.dim %[[DEST]], %[[C1]] : tensor<?x?xf32>
+ // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00
+ // CHECK: %[[C01:.*]] = arith.constant 0
+ // CHECK: %[[C02:.*]] = arith.constant 0
+ // CHECK: %[[DIM4:.*]] = tensor.dim %[[SRC]], %[[C02]] : tensor<?x?x?x2xf32>
+ // CHECK: %[[C1_2:.*]] = arith.constant 1
+ // CHECK: %[[DIM6:.*]] = tensor.dim %[[SRC]], %[[C1_2]] : tensor<?x?x?x2xf32>
+ // CHECK: %[[C2:.*]] = arith.constant 2 : index
+ // CHECK: %[[DIM_2:.*]] = tensor.dim %[[SRC]], %[[C2]] : tensor<?x?x?x2xf32>
+ // CHECK: %[[C2_1:.*]] = arith.constant 2 : index
+ // CHECK: %[[MASK_READ:.*]] = vector.create_mask %[[DIM4]], %[[DIM6]],
%[[DIM_2]], %[[C2_1]] : vector<2x1x[16]x2xi1>
+ // CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {{.*}}
vector.transfer_read %{{.*}} : tensor<?x?x?x2xf32>, vector<2x1x[16]x2xf32> } :
vector<2x1x[16]x2xi1> -> vector<2x1x[16]x2xf32>
+ // CHECK: %[[TR:.*]] = vector.transpose %[[READ]], [0, 3, 1, 2] :
vector<2x1x[16]x2xf32> to vector<2x2x1x[16]xf32>
+ // CHECK: %[[SC:.*]] = vector.shape_cast %[[TR]] : vector<2x2x1x[16]xf32> to
vector<4x[16]xf32>
+ // CHECK: %[[MASK_WRITE:.*]] = vector.create_mask {{.*}} : vector<4x[16]xi1>
+ // CHECK: %[[WRITE:.*]] = vector.mask %[[MASK_WRITE:.*]] {{.*}}
vector.transfer_write %[[SC]], %[[DEST]]
+ // CHECK: return %[[WRITE]]
+
+ %vs = vector.vscale
+ %c16 = arith.constant 16 : index
+ %tile_size = arith.muli %vs, %c16 : index
+
+ %ret = linalg.unpack %src inner_dims_pos = [1, 0] inner_tiles = [%tile_size,
2] into %dest : tensor<?x?x?x2xf32> -> tensor<?x?xf32>
+ return %ret : tensor<?x?xf32>
+}
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.unpack"]} in %arg0 :
(!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 1, [16], 2, 4, [16]] :
!transform.any_op
transform.yield
}
}
@@ -997,7 +1075,7 @@ func.func @test_vectorize_unpack(%source:
tensor<8x8x32x16xf32>, %dest: tensor<2
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
%0 = transform.structured.match ops{["linalg.unpack"]} in %arg0 :
(!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [512, 128] :
!transform.any_op
+ transform.structured.vectorize %0 vector_sizes [16, 8, 32, 16, 512, 128] :
!transform.any_op
transform.yield
}
}
@@ -1022,7 +1100,7 @@ func.func @test_vectorize_unpack_no_masks(%source:
tensor<8x8x32x16xf32>, %dest:
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
%0 = transform.structured.match ops{["linalg.unpack"]} in %arg0 :
(!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [256, 128] :
!transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 8, 32, 16, 256, 128] :
!transform.any_op
transform.yield
}
}
@@ -1047,7 +1125,7 @@ func.func @test_vectorize_unpack_no_masks(%source:
tensor<8x8x32x16xf32>, %dest:
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
%0 = transform.structured.match ops{["linalg.unpack"]} in %arg0 :
(!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [256, 128] :
!transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 8, 32, 16, 256, 128] :
!transform.any_op
transform.yield
}
}
@@ -1170,7 +1248,7 @@ module attributes {transform.with_named_sequence} {
func.func @test_vectorize_padded_pack(%arg0: tensor<32x7x15xf32>, %arg1:
tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
%pad = arith.constant 0.000000e+00 : f32
- %pack = linalg.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1]
inner_tiles = [16, 2] into %arg1 : tensor<32x7x15xf32> ->
tensor<32x4x1x16x2xf32>
+ %pack = linalg.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1]
inner_tiles = [16, [2]] into %arg1 : tensor<32x7x15xf32> ->
tensor<32x4x1x16x2xf32>
return %pack : tensor<32x4x1x16x2xf32>
}
// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
From 1aff0b11aa0aaaec5ad685d0a859e794aaa4e1b6 Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzyn...@arm.com>
Date: Thu, 24 Jul 2025 20:52:12 +0000
Subject: [PATCH 2/5] fixup! [mlir][linalg] Enable scalable vectorization of
linalg.unpack (WIP)
Remove leftover code + comments
---
mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp | 4 ----
1 file changed, 4 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index a4328a35e75ce..7d3db766af933 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1910,7 +1910,6 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
// 2. if outer_dims_perms is present: do that permutation on
writeVectorSizes.
// 3. multiply all the locations in vectorSize pointed by innerDimPos by the
// innerTiles attribute value.
- // SmallVector<int64_t> writeVectorSizes(inputVectorSizes);
if (writeVectorSizes.empty()) {
if (ShapedType::isDynamicShape(sourceShape))
return failure();
@@ -2112,9 +2111,6 @@ static LogicalResult
vectorizeUnPackOpPrecondition(linalg::UnPackOp unpackOp,
ArrayRef<int64_t> inputVectorSizes) {
- // FIXME!!!
- return success();
-
if (llvm::any_of(unpackOp.getInnerTiles(), [](OpFoldResult res) {
return !getConstantIntValue(res).has_value();
})) {
From 3b482fcb0013d971aa5befb9b99da166a34bb1a5 Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzyn...@arm.com>
Date: Fri, 25 Jul 2025 09:24:12 +0000
Subject: [PATCH 3/5] fixup! fixup! [mlir][linalg] Enable scalable
vectorization of linalg.unpack (WIP)
Fix pre-condition calculation
---
.../Linalg/Transforms/Vectorization.cpp | 43 ++++++++++++++-----
1 file changed, 32 insertions(+), 11 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 7d3db766af933..4b76d9d9f5179 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -2106,24 +2106,45 @@ vectorizeDynamicLinalgOpPrecondition(linalg::LinalgOp
op,
return success();
}
-/// Need to check if the inner-tiles are static/constant.
+//// This hook considers two cases:
+/// (1) If the input-vector-sizes are empty, then the vector sizes will be
+/// infered. This is only possible when all shapes are static.
+/// (2) If the input-vector-sizes are non-empty (i.e. user provided), then
+/// carry out basic sanity-checking.
static LogicalResult
vectorizeUnPackOpPrecondition(linalg::UnPackOp unpackOp,
ArrayRef<int64_t> inputVectorSizes) {
+ // If there are no input vector sizes and all shapes are static, there is
+ // nothing left to check.
+ if (inputVectorSizes.empty() && unpackOp.getDestType().hasStaticShape() &&
+ unpackOp.getSourceType().hasStaticShape())
+ return success();
- if (llvm::any_of(unpackOp.getInnerTiles(), [](OpFoldResult res) {
- return !getConstantIntValue(res).has_value();
- })) {
- LDBG("Inner-tiles must be constant: " << unpackOp << "\n");
+ // The input vector sizes must be equal to:
+ // * read-vector-rank + write-vector-rank
+ if (!inputVectorSizes.empty()) {
+ if (inputVectorSizes.size() !=
+ unpackOp.getDestRank() + unpackOp.getSourceRank()) {
+ LDBG("Incorrect number of input vector sizes");
+ return failure();
+ }
+ }
+
+ // Check the vector sizes for the write operation.
+ if (failed(vector::isValidMaskedInputVector(
+ unpackOp.getDestType().getShape(),
+ inputVectorSizes.take_back(unpackOp.getDestRank())))) {
+ LDBG("Incorrect number of input vector sizes");
return failure();
}
- ArrayRef<int64_t> resultShape = unpackOp.getDestType().getShape();
- bool satisfyEmptyCond = inputVectorSizes.empty() &&
- unpackOp.getDestType().hasStaticShape() &&
- unpackOp.getSourceType().hasStaticShape();
- if (!satisfyEmptyCond &&
- failed(vector::isValidMaskedInputVector(resultShape, inputVectorSizes)))
+
+ // Check the vector sizes for the read operation.
+ if (failed(vector::isValidMaskedInputVector(
+ unpackOp.getSourceType().getShape(),
+ inputVectorSizes.take_front(unpackOp.getSourceRank())))) {
+ LDBG("Incorrect number of input vector sizes");
return failure();
+ }
return success();
}
From b3894996379080b4a24bebe5cc8c938d42ce4243 Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzyn...@arm.com>
Date: Fri, 25 Jul 2025 10:20:12 +0000
Subject: [PATCH 4/5] fixup! fixup! [mlir][linalg] Enable scalable
vectorization of linalg.unpack (WIP)
Improve documentation + fix test after rebasing on top of
* https://github.com/llvm/llvm-project/pull/150602
---
.../Linalg/Transforms/Vectorization.cpp | 79 +++++++++----------
.../Linalg/vectorization/linalg-ops.mlir | 41 ++++------
2 files changed, 52 insertions(+), 68 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 4b76d9d9f5179..a38bf04a2745a 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1857,6 +1857,13 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
ArrayRef<int64_t> inputVectorSizes,
ArrayRef<bool> inputScalableVecDims,
SmallVectorImpl<Value> &newResults) {
+ if (!inputVectorSizes.empty()) {
+ assert(inputVectorSizes.size() ==
+ unpackOp.getDestRank() + unpackOp.getSourceRank() &&
+ "Invalid number of input vector sizes!");
+ assert(inputVectorSizes.size() == inputScalableVecDims.size() &&
+ "Incompatible number of vector sizes and vector scalable flags!");
+ }
// TODO: Introduce a parent class that will handle the insertion point
update.
OpBuilder::InsertionGuard g(rewriter);
@@ -1872,44 +1879,41 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
auto destSize = unpackOp.getDestRank();
- if (!inputVectorSizes.empty()) {
- assert(inputVectorSizes.size() == destSize + sourceShape.size() &&
- "Incorrect number of input vector sizes");
- }
-
- SmallVector<bool> readScalableVectorFlags;
- SmallVector<bool> writeScalableVectorFlags;
+ // 1. Obtain vector sizes for the read and write operation.s
SmallVector<int64_t> readVectorSizes;
SmallVector<int64_t> writeVectorSizes;
+ SmallVector<bool> readScalableVectorFlags;
+ SmallVector<bool> writeScalableVectorFlags;
- // Split input-vector-sizes into vector sizes for the read and write
- // operations.
+ // CASE 1: Vector sizes are user-specified.
+ // 1.0 This is the trivial case, simply split the input vector sizes.
if (!inputVectorSizes.empty()) {
readVectorSizes.append(inputVectorSizes.begin(),
inputVectorSizes.begin() + sourceShape.size());
writeVectorSizes.append(inputVectorSizes.begin() + sourceShape.size(),
inputVectorSizes.end());
- }
- if (!inputScalableVecDims.empty()) {
readScalableVectorFlags.append(inputScalableVecDims.begin(),
inputScalableVecDims.begin() +
sourceShape.size());
writeScalableVectorFlags.append(inputScalableVecDims.begin() +
sourceShape.size(),
inputScalableVecDims.end());
- } else {
- readScalableVectorFlags = SmallVector<bool>(sourceShape.size(), false);
- writeScalableVectorFlags = SmallVector<bool>(destSize, false);
}
- // writeVectorSizes is the shape of the vector that will be used to do final
- // write on the destination tensor. It is set like this: Let's say the
- // source tensor is rank 'M' and the dest tensor rank 'N', where N <= M.
- // Thus:
- // 1. writeVectorSizes = sourceShape.take_front(N)
- // 2. if outer_dims_perms is present: do that permutation on
writeVectorSizes.
- // 3. multiply all the locations in vectorSize pointed by innerDimPos by the
- // innerTiles attribute value.
+ // CASE 2: Vector sizes have to be inferred.
+ //
+ // 1.1 Infer vector sizes for the write operation.
+ //
+ // Let:
+ // * rank(source tensor) = 'M'
+ // * rank(dest tensor) = 'N',
+ // and N <= M. The steps are:
+ // 1. writeVectorSizes = sourceShape.take_front(N)
+ // 2. Multiply all the locations in writeVectorSize pointed by
inner_dims_pos
+ // by the corresponding values from the `inner_tiles` attribute value.
+ // 3. If outer_dims_perms is present, permutate writeVectorSizes
accordingly.
+ //
+ // Note, this will only work when all sizes are static!
if (writeVectorSizes.empty()) {
if (ShapedType::isDynamicShape(sourceShape))
return failure();
@@ -1923,28 +1927,17 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter,
linalg::UnPackOp unpackOp,
useInBoundsInsteadOfMasking = true;
}
- // readVectorSizes is the size of tensor used to read and apply mask. It is
- // set like this: Let's say the vectorSize (VS) array is size 'N' and
- // the sourceShape(SS) is 'M' where M >= N and InnerTileSizes (IT) of
- // size M-N
- // Thus:
- // - initially: readVectorSizes = vectorInputSizes
- // - Divide all the readMaskShape locations pointed by innerDimPos
- // by the innerTileSize attribute value.
- // - if outer_dims_perms is present: do that permutation on readVectorSizes.
- // - Append the remaining shape from SS
- // E.g. let's say let's say unpackTensorType.getShape() = <8x8x32x16>
- // inner Dim Pos = [0, 1] and Inner Tiles = [32, 16], vector_sizes are [512,
- // 128] and outer_dims_perm is [1, 0] then read shape is:
- // ReadVectorSizes(initial): [512, 128]
- // Final Value(after innerDim Adjustment): [512/32, 128/16]
- // = [16, 8]
- // After applying outer_dims_perm: [8, 16]
- // After appending the rest of the sourceShape: [8, 16, 32, 16]
-
+ // 1.2 Infer vector sizes for the read operation.
+ //
+ // The steps are:
+ // 1. readVectorSizes = vectorInputSizes
+ // 2. Take readVectorSizes from 1. and divide all locations pointed by
+ // the inner_dims_pos attribyte by the `inner_tiles` attribute value.
+ // 3. If outer_dims_perms is present, permutate readVectorSizes accordingly.
+ // 4. Append the remaining sizes from the source tensor.
+ //
+ // Note, this will only work when all sizes are static!
if (readVectorSizes.empty()) {
- // Compute read-vector-sizes based on the write-vector-sizes and inner tile
- // sizes. Note, this will only work when all sizes are static.
readVectorSizes = writeVectorSizes;
for (auto [index, size] : enumerate(innerTiles)) {
readVectorSizes[innerDimPos[index]] =
diff --git a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
index ec227b46b409e..fcb8b02d3faa3 100644
--- a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
@@ -943,23 +943,22 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x16x2xf32>
func.func @test_vectorize_dynamic_shapes_unpack(%dest: tensor<?x?xf32>, %src:
tensor<?x?x16x2xf32>) -> tensor<?x?xf32> {
-// CHECK: %[[C0:.*]] = arith.constant 0
-// CHECK: %[[C01:.*]] = arith.constant 0
-// CHECK: %[[C02:.*]] = arith.constant 0
-// CHECK: %[[DIM_0:.*]] = tensor.dim %[[ARG_1]], %[[C02]] :
tensor<?x?x16x2xf32>
-// CHECK: %[[C1:.*]] = arith.constant 1
-// CHECK: %[[DIM6:.*]] = tensor.dim %[[ARG_1]], %[[C1]] : tensor<?x?x16x2xf32>
-// CHECK: %[[CNST16:.*]] = arith.constant 16 : index
-// CHECK: %[[CNST2:.*]] = arith.constant 2 : index
-// CHECK: %[[readMsk0:.*]] = vector.create_mask %[[DIM_0]], %[[DIM6]],
%[[CNST16]], %[[CNST2]] : vector<2x1x16x2xi1>
-// CHECK: %[[read0:.*]] = vector.mask %[[readMsk0]] {{.*}}
vector.transfer_read %{{.*}} : tensor<?x?x16x2xf32>, vector<2x1x16x2xf32> } :
vector<2x1x16x2xi1> -> vector<2x1x16x2xf32>
-// CHECK: %[[trans0:.*]] = vector.transpose %[[read0]], [0, 3, 1, 2] :
vector<2x1x16x2xf32> to vector<2x2x1x16xf32>
-// CHECK: %[[sc0:.*]] = vector.shape_cast %[[trans0]] : vector<2x2x1x16xf32>
to vector<4x16xf32>
-// CHECK: %[[writeMsk0:.*]] = vector.create_mask {{.*}} : vector<4x16xi1>
-// CHECK: %[[write0:.*]] = vector.mask %[[writeMsk0:.*]] {{.*}}
vector.transfer_write %[[sc0]], %[[SRC]]
-// CHECK: return %[[write0]]
- %ret = linalg.unpack %src inner_dims_pos = [1, 0] inner_tiles = [16, 2] into
%dest : tensor<?x?x16x2xf32> -> tensor<?x?xf32>
- return %ret : tensor<?x?xf32>
+ // CHECK: %[[C0:.*]] = arith.constant 0 : index
+ // CHECK: %[[C0_1:.*]] = arith.constant 0 : index
+ // CHECK: %[[DIM_0:.*]] = tensor.dim %[[SRC]], %[[C0_1]] :
tensor<?x?x16x2xf32>
+ // CHECK: %[[C1:.*]] = arith.constant 1
+ // CHECK: %[[DIM6:.*]] = tensor.dim %[[SRC]], %[[C1]] : tensor<?x?x16x2xf32>
+ // CHECK: %[[CNST16:.*]] = arith.constant 16 : index
+ // CHECK: %[[CNST2:.*]] = arith.constant 2 : index
+ // CHECK: %[[MASK_READ:.*]] = vector.create_mask %[[DIM_0]], %[[DIM6]],
%[[CNST16]], %[[CNST2]] : vector<2x1x16x2xi1>
+ // CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {{.*}}
vector.transfer_read %{{.*}} : tensor<?x?x16x2xf32>, vector<2x1x16x2xf32> } :
vector<2x1x16x2xi1> -> vector<2x1x16x2xf32>
+ // CHECK: %[[TR:.*]] = vector.transpose %[[READ]], [0, 3, 1, 2] :
vector<2x1x16x2xf32> to vector<2x2x1x16xf32>
+ // CHECK: %[[SC:.*]] = vector.shape_cast %[[TR]] : vector<2x2x1x16xf32> to
vector<4x16xf32>
+ // CHECK: %[[MASK_WRITE:.*]] = vector.create_mask {{.*}} : vector<4x16xi1>
+ // CHECK: %[[WRITE:.*]] = vector.mask %[[MASK_WRITE:.*]] {{.*}}
vector.transfer_write %[[SC]], %[[DEST]]
+ // CHECK: return %[[WRITE]]
+ %ret = linalg.unpack %src inner_dims_pos = [1, 0] inner_tiles = [16, 2] into
%dest : tensor<?x?x16x2xf32> -> tensor<?x?xf32>
+ return %ret : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op
{transform.readonly}) {
@@ -975,10 +974,6 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x16x2xf32>
func.func @test_vectorize_dynamic_shapes_unpack_scalable_vec(%dest:
tensor<?x?xf32>, %src: tensor<?x?x16x2xf32>) -> tensor<?x?xf32> {
- // CHECK: %[[C0:.*]] = arith.constant 0
- // CHECK: %[[DIM:.*]] = tensor.dim %[[DEST]], %[[C0]] : tensor<?x?xf32>
- // CHECK: %[[C1:.*]] = arith.constant 1 : index
- // CHECK: %[[DIM0:.*]] = tensor.dim %[[DEST]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00
// CHECK: %[[C01:.*]] = arith.constant 0
// CHECK: %[[C02:.*]] = arith.constant 0
@@ -1011,10 +1006,6 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x?x2xf32>
func.func
@test_vectorize_dynamic_shapes_unpack_scalable_vec_and_tile_size(%dest:
tensor<?x?xf32>, %src: tensor<?x?x?x2xf32>) -> tensor<?x?xf32> {
- // CHECK: %[[C0:.*]] = arith.constant 0
- // CHECK: %[[DIM:.*]] = tensor.dim %[[DEST]], %[[C0]] : tensor<?x?xf32>
- // CHECK: %[[C1:.*]] = arith.constant 1 : index
- // CHECK: %[[DIM0:.*]] = tensor.dim %[[DEST]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00
// CHECK: %[[C01:.*]] = arith.constant 0
// CHECK: %[[C02:.*]] = arith.constant 0
From 0a09007080ba009348794c2c5d3f4a6795995118 Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzyn...@arm.com>
Date: Fri, 25 Jul 2025 10:49:19 +0000
Subject: [PATCH 5/5] fixup! fixup! fixup! [mlir][linalg] Enable scalable
vectorization of linalg.unpack (WIP)
Remove unintended test change
---
mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
index fcb8b02d3faa3..9c9ddb54d1d5f 100644
--- a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
@@ -1239,7 +1239,7 @@ module attributes {transform.with_named_sequence} {
func.func @test_vectorize_padded_pack(%arg0: tensor<32x7x15xf32>, %arg1:
tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
%pad = arith.constant 0.000000e+00 : f32
- %pack = linalg.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1]
inner_tiles = [16, [2]] into %arg1 : tensor<32x7x15xf32> ->
tensor<32x4x1x16x2xf32>
+ %pack = linalg.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1]
inner_tiles = [16, 2] into %arg1 : tensor<32x7x15xf32> ->
tensor<32x4x1x16x2xf32>
return %pack : tensor<32x4x1x16x2xf32>
}
// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
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