[Mlir-commits] [mlir] [mlir][vector] Canonicalize/fold 'order preserving' transposes (PR #135841)
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llvmlistbot at llvm.org
Tue Apr 15 18:01:17 PDT 2025
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir-vector
Author: James Newling (newling)
<details>
<summary>Changes</summary>
Handles special case where transpose doesn't permute any non-1 dimensions (and so is effectively a shape_cast) and is adjacent to a shape_cast that it can fold into. For example
```
%1 = vector.transpose %0, [1, 0, 3, 2] : vector<4x1x1x6xf32> to vector<1x4x6x1xf32>
```
can be folded into an adjacent shape_cast. An alternative to this PR would be to canonicalize such transposes to shape_casts directly, but I think it'll be difficult getting consensus that shape_cast is 'more canonical' than transpose, so this PR compromises with the less opinionated claim that
1) shape_cast is more canonical than shape_cast(transpose)
2) shape_cast is more canonical than transpose(shape_cast)
The pattern `ConvertIllegalShapeCastOpsToTransposes` that is specific to transposes with scalable dimensions reverses the canonicalization added here, so I've I've disabled this canonicalization for scalable vectors
---
Full diff: https://github.com/llvm/llvm-project/pull/135841.diff
2 Files Affected:
- (modified) mlir/lib/Dialect/Vector/IR/VectorOps.cpp (+79-9)
- (modified) mlir/test/Dialect/Vector/canonicalize.mlir (+64)
``````````diff
diff --git a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
index bee5c1fd6ed58..5da0ef0af032f 100644
--- a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
+++ b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
@@ -5621,6 +5621,29 @@ LogicalResult ShapeCastOp::verify() {
return success();
}
+namespace {
+
+/// Return true if `transpose` does not permute a pair of dimensions that are
+/// both not of size 1. By `order preserving` we mean that the flattened
+/// versions of the input and output vectors are (numerically) identical.
+/// In other words `transpose` is effectively a shape cast.
+bool isOrderPreserving(TransposeOp transpose) {
+ ArrayRef<int64_t> permutation = transpose.getPermutation();
+ ArrayRef<int64_t> inShape = transpose.getSourceVectorType().getShape();
+ int64_t current = 0;
+ for (auto p : permutation) {
+ if (inShape[p] != 1) {
+ if (p < current) {
+ return false;
+ }
+ current = p;
+ }
+ }
+ return true;
+}
+
+} // namespace
+
OpFoldResult ShapeCastOp::fold(FoldAdaptor adaptor) {
// No-op shape cast.
@@ -5629,13 +5652,15 @@ OpFoldResult ShapeCastOp::fold(FoldAdaptor adaptor) {
VectorType resultType = getType();
- // Canceling shape casts.
- if (auto otherOp = getSource().getDefiningOp<ShapeCastOp>()) {
-
- // Only allows valid transitive folding (expand/collapse dimensions).
- VectorType srcType = otherOp.getSource().getType();
+ // shape_cast(something(x)) -> x, or
+ // -> shape_cast(x).
+ //
+ // Confirms that a new shape_cast will have valid semantics (expands OR
+ // collapses dimensions).
+ auto maybeFold = [&](TypedValue<VectorType> source) -> OpFoldResult {
+ VectorType srcType = source.getType();
if (resultType == srcType)
- return otherOp.getSource();
+ return source;
if (srcType.getRank() < resultType.getRank()) {
if (!isValidShapeCast(srcType.getShape(), resultType.getShape()))
return {};
@@ -5645,8 +5670,25 @@ OpFoldResult ShapeCastOp::fold(FoldAdaptor adaptor) {
} else {
return {};
}
- setOperand(otherOp.getSource());
+ setOperand(source);
return getResult();
+ };
+
+ // Canceling shape casts.
+ if (auto otherOp = getSource().getDefiningOp<ShapeCastOp>()) {
+ TypedValue<VectorType> source = otherOp.getSource();
+ return maybeFold(source);
+ }
+
+ // shape_cast(transpose(x)) -> shape_cast(x)
+ if (auto transpose = getSource().getDefiningOp<TransposeOp>()) {
+ if (transpose.getType().isScalable())
+ return {};
+ if (isOrderPreserving(transpose)) {
+ TypedValue<VectorType> source = transpose.getVector();
+ return maybeFold(source);
+ }
+ return {};
}
// Cancelling broadcast and shape cast ops.
@@ -5675,7 +5717,7 @@ namespace {
/// Helper function that computes a new vector type based on the input vector
/// type by removing the trailing one dims:
///
-/// vector<4x1x1xi1> --> vector<4x1>
+/// vector<4x1x1xi1> --> vector<4x1xi1>
///
static VectorType trimTrailingOneDims(VectorType oldType) {
ArrayRef<int64_t> oldShape = oldType.getShape();
@@ -6161,12 +6203,40 @@ class FoldTransposeCreateMask final : public OpRewritePattern<TransposeOp> {
}
};
+/// Folds transpose(shape_cast) into a new shape_cast.
+class FoldTransposeShapeCast final : public OpRewritePattern<TransposeOp> {
+public:
+ using OpRewritePattern::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(TransposeOp transposeOp,
+ PatternRewriter &rewriter) const override {
+ auto shapeCastOp =
+ transposeOp.getVector().getDefiningOp<vector::ShapeCastOp>();
+ if (!shapeCastOp)
+ return failure();
+ if (!isOrderPreserving(transposeOp))
+ return failure();
+ if (transposeOp.getType().isScalable())
+ return failure();
+
+ VectorType resultType = transposeOp.getType();
+
+ // We don't need to check isValidShapeCast at this point, because it is
+ // guaranteed that merging the transpose into the the shape_cast is a valid
+ // shape_cast, because the transpose just inserts/removes ones.
+
+ rewriter.replaceOpWithNewOp<vector::ShapeCastOp>(transposeOp, resultType,
+ shapeCastOp.getSource());
+ return success();
+ }
+};
} // namespace
void vector::TransposeOp::getCanonicalizationPatterns(
RewritePatternSet &results, MLIRContext *context) {
results.add<FoldTransposeCreateMask, FoldTransposedScalarBroadcast,
- TransposeFolder, FoldTransposeSplat>(context);
+ FoldTransposeShapeCast, TransposeFolder, FoldTransposeSplat>(
+ context);
}
//===----------------------------------------------------------------------===//
diff --git a/mlir/test/Dialect/Vector/canonicalize.mlir b/mlir/test/Dialect/Vector/canonicalize.mlir
index 78b0ea78849e8..10144cb9034e4 100644
--- a/mlir/test/Dialect/Vector/canonicalize.mlir
+++ b/mlir/test/Dialect/Vector/canonicalize.mlir
@@ -3295,3 +3295,67 @@ func.func @fold_insert_constant_indices(%arg : vector<4x1xi32>) -> vector<4x1xi3
%res = vector.insert %1, %arg[%0, %0] : i32 into vector<4x1xi32>
return %res : vector<4x1xi32>
}
+
+// -----
+
+// In this test, the permutation maps the non-one dimensions (1 and 2) as follows:
+// 1 -> 0
+// 2 -> 4
+// Because 0 < 4, this permutation is order preserving and effectively a shape_cast.
+// CHECK-LABEL: @transpose_shape_cast
+// CHECK-SAME: %[[ARG:.*]]: vector<1x4x4x1x1xi8>) -> vector<4x4xi8> {
+// CHECK: %[[SHAPE_CAST:.*]] = vector.shape_cast %[[ARG]] :
+// CHECK-SAME: vector<1x4x4x1x1xi8> to vector<4x4xi8>
+// CHECK: return %[[SHAPE_CAST]] : vector<4x4xi8>
+func.func @transpose_shape_cast(%arg : vector<1x4x4x1x1xi8>) -> vector<4x4xi8> {
+ %0 = vector.transpose %arg, [1, 0, 3, 4, 2]
+ : vector<1x4x4x1x1xi8> to vector<4x1x1x1x4xi8>
+ %1 = vector.shape_cast %0 : vector<4x1x1x1x4xi8> to vector<4x4xi8>
+ return %1 : vector<4x4xi8>
+}
+
+// -----
+
+// In this test, the mapping of non-one indices (1 and 2) is as follows:
+// 1 -> 2
+// 2 -> 1
+// As this is not increasing (2 > 1), this transpose is not order
+// preserving and cannot be treated as a shape_cast.
+// CHECK-LABEL: @negative_transpose_shape_cast
+// CHECK-SAME: %[[ARG:.*]]: vector<1x4x4x1xi8>) -> vector<4x4xi8> {
+// CHECK: %[[TRANSPOSE:.*]] = vector.transpose %[[ARG]]
+// CHECK: %[[SHAPE_CAST:.*]] = vector.shape_cast %[[TRANSPOSE]]
+// CHECK: return %[[SHAPE_CAST]] : vector<4x4xi8>
+func.func @negative_transpose_shape_cast(%arg : vector<1x4x4x1xi8>) -> vector<4x4xi8> {
+ %0 = vector.transpose %arg, [0, 2, 1, 3]
+ : vector<1x4x4x1xi8> to vector<1x4x4x1xi8>
+ %1 = vector.shape_cast %0 : vector<1x4x4x1xi8> to vector<4x4xi8>
+ return %1 : vector<4x4xi8>
+}
+
+// -----
+
+// CHECK-LABEL: @shape_cast_transpose
+// CHECK-SAME: %[[ARG:.*]]: vector<2x3x1x1xi8>) -> vector<6x1x1xi8> {
+// CHECK: %[[SHAPE_CAST:.*]] = vector.shape_cast %[[ARG]] :
+// CHECK-SAME: vector<2x3x1x1xi8> to vector<6x1x1xi8>
+// CHECK: return %[[SHAPE_CAST]] : vector<6x1x1xi8>
+func.func @shape_cast_transpose(%arg : vector<2x3x1x1xi8>) -> vector<6x1x1xi8> {
+ %0 = vector.shape_cast %arg : vector<2x3x1x1xi8> to vector<6x1x1xi8>
+ %1 = vector.transpose %0, [0, 2, 1]
+ : vector<6x1x1xi8> to vector<6x1x1xi8>
+ return %1 : vector<6x1x1xi8>
+}
+
+// -----
+
+// CHECK-LABEL: @negative_shape_cast_transpose
+// CHECK-SAME: %[[ARG:.*]]: vector<6xi8>) -> vector<2x3xi8> {
+// CHECK: %[[SHAPE_CAST:.*]] = vector.shape_cast %[[ARG]] :
+// CHECK: %[[TRANSPOSE:.*]] = vector.transpose %[[SHAPE_CAST]]
+// CHECK: return %[[TRANSPOSE]] : vector<2x3xi8>
+func.func @negative_shape_cast_transpose(%arg : vector<6xi8>) -> vector<2x3xi8> {
+ %0 = vector.shape_cast %arg : vector<6xi8> to vector<3x2xi8>
+ %1 = vector.transpose %0, [1, 0] : vector<3x2xi8> to vector<2x3xi8>
+ return %1 : vector<2x3xi8>
+}
``````````
</details>
https://github.com/llvm/llvm-project/pull/135841
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