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[mlir][vector] Support scalable vectors when unrolling vector.bitcast #94197

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Merged
merged 3 commits into from
Jun 21, 2024
Merged

[mlir][vector] Support scalable vectors when unrolling vector.bitcast #94197

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6f3b57f
[mlir][vector] Support scalable vectors when unrolling vector.bitcast
MacDue Jun 11, 2024
d6162ea
Add leading scalable dim test
MacDue Jun 12, 2024
25f27ed
Fixup
MacDue Jun 21, 2024
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5 changes: 5 additions & 0 deletions mlir/include/mlir/Dialect/Utils/IndexingUtils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -287,6 +287,8 @@ class TileOffsetRangeImpl {
return getDynamicTileOffsets(linearIndex);
}

size_t getRank() const { return tileShape.size(); }

private:
/// The sub-shape that divides the larger outer shape (which is provided to
/// the constructor).
Expand Down Expand Up @@ -388,6 +390,9 @@ class StaticTileOffsetRange {
/// Returns the total number of tiles that fit in the larger shape.
size_t size() const { return params.getMaxLinearIndex(); }

/// Returns rank of the iterator's shape.
size_t getRank() const { return params.getRank(); }

private:
const ParamsTy params;
IteratorTy beginValue;
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17 changes: 6 additions & 11 deletions mlir/lib/Dialect/Vector/Transforms/LowerVectorBitCast.cpp
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Expand Up @@ -56,17 +56,12 @@ class UnrollBitCastOp final : public OpRewritePattern<vector::BitCastOp> {
if (!unrollIterator)
return failure();

// TODO: Support the scalable vector cases. It is not supported because
// the final rank could be values other than `targetRank`. It makes creating
// the result type of new vector.bitcast ops much harder.
if (resultType.isScalable()) {
return rewriter.notifyMatchFailure(op,
"unrolling vector.bitcast on scalable "
"vectors is not yet implemented");
}

ArrayRef<int64_t> shape = resultType.getShape().take_back(targetRank);
auto bitcastResType = VectorType::get(shape, resultType.getElementType());
auto unrollRank = unrollIterator->getRank();
ArrayRef<int64_t> shape = resultType.getShape().drop_front(unrollRank);
ArrayRef<bool> scalableDims =
resultType.getScalableDims().drop_front(unrollRank);
auto bitcastResType =
VectorType::get(shape, resultType.getElementType(), scalableDims);

Location loc = op.getLoc();
Value result = rewriter.create<arith::ConstantOp>(
Expand Down
34 changes: 33 additions & 1 deletion mlir/test/Dialect/Vector/vector-bitcast-lowering-transforms.mlir
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Expand Up @@ -38,7 +38,39 @@ func.func @vector_bitcast_4d_with_scalable_dim(%arg0: vector<1x2x[3]x4xi64>) ->
return %0 : vector<1x2x[3]x8xi32>
}
// CHECK-LABEL: func.func @vector_bitcast_4d_with_scalable_dim
// CHECK: vector.bitcast {{.+}} : vector<1x2x[3]x4xi64> to vector<1x2x[3]x8xi32>
// CHECK-SAME: %[[IN:[a-zA-Z0-9]+]]
// CHECK: %[[INIT:.+]] = arith.constant dense<0> : vector<1x2x[3]x8xi32>
// CHECK: %[[V1:.+]] = vector.extract %[[IN]][0, 0] : vector<[3]x4xi64> from vector<1x2x[3]x4xi64>
// CHECK: %[[B1:.+]] = vector.bitcast %[[V1]] : vector<[3]x4xi64> to vector<[3]x8xi32>
// CHECK: %[[R1:.+]] = vector.insert %[[B1]], %[[INIT]] [0, 0] : vector<[3]x8xi32> into vector<1x2x[3]x8xi32>
// CHECK: %[[V2:.+]] = vector.extract %[[IN]][0, 1] : vector<[3]x4xi64> from vector<1x2x[3]x4xi64>
// CHECK: %[[B2:.+]] = vector.bitcast %[[V2]] : vector<[3]x4xi64> to vector<[3]x8xi32>
// CHECK: %[[R2:.+]] = vector.insert %[[B2]], %[[R1]] [0, 1] : vector<[3]x8xi32> into vector<1x2x[3]x8xi32>
// CHECK: return %[[R2]] : vector<1x2x[3]x8xi32>

func.func @vector_bitcast_2d_trailing_scalable_dim(%arg0: vector<2x[2]xi64>) -> vector<2x[4]xi32> {
%0 = vector.bitcast %arg0 : vector<2x[2]xi64> to vector<2x[4]xi32>
return %0 : vector<2x[4]xi32>
}
// CHECK-LABEL: func.func @vector_bitcast_2d_trailing_scalable_dim
// CHECK-SAME: %[[IN:[a-zA-Z0-9]+]]
// CHECK: %[[INIT:.+]] = arith.constant dense<0> : vector<2x[4]xi32>
// CHECK: %[[V1:.+]] = vector.extract %[[IN]][0] : vector<[2]xi64> from vector<2x[2]xi64>
// CHECK: %[[B1:.+]] = vector.bitcast %[[V1]] : vector<[2]xi64> to vector<[4]xi32>
// CHECK: %[[R1:.+]] = vector.insert %[[B1]], %[[INIT]] [0] : vector<[4]xi32> into vector<2x[4]xi32>
// CHECK: %[[V2:.+]] = vector.extract %[[IN]][1] : vector<[2]xi64> from vector<2x[2]xi64>
// CHECK: %[[B2:.+]] = vector.bitcast %[[V2]] : vector<[2]xi64> to vector<[4]xi32>
// CHECK: %[[R2:.+]] = vector.insert %[[B2]], %[[R1]] [1] : vector<[4]xi32> into vector<2x[4]xi32>
// CHECK: return %[[R2]] : vector<2x[4]xi32>

func.func @negative_vector_bitcast_2d_leading_scalable_dim(%arg0: vector<[2]x2xi64>) -> vector<[2]x4xi32>
{
%0 = vector.bitcast %arg0 : vector<[2]x2xi64> to vector<[2]x4xi32>
return %0 : vector<[2]x4xi32>
}
// CHECK-LABEL: func.func @negative_vector_bitcast_2d_leading_scalable_dim
// CHECK-NOT: vector.extract
// CHECK-NOT: vector.insert

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {
Expand Down
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