[Mlir-commits] [mlir] [mlir] [linalg] Add pattern to swap transpose with broadcast (PR #97063)
donald chen
llvmlistbot at llvm.org
Sat Jul 20 02:55:28 PDT 2024
https://github.com/cxy-1993 updated https://github.com/llvm/llvm-project/pull/97063
>From aa2fd98fc97ee1f931288d82a7fa5911dd1d6a96 Mon Sep 17 00:00:00 2001
From: cxy <chenxunyu1993 at gmail.com>
Date: Thu, 27 Jun 2024 00:00:03 +0800
Subject: [PATCH 1/4] [mlir] [linalg] Add canonicalize pattern to swap
transpose with broadcast
Add canonicalize pattern that implement canonicalize:
transpose(broadcast(input)) -> broadcast(transpose(input))
Reduce the cost of transpose.
---
.../mlir/Dialect/Utils/IndexingUtils.h | 8 ++
mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp | 60 ++++++++++++++-
mlir/lib/Dialect/Utils/IndexingUtils.cpp | 25 +++++++
mlir/test/Dialect/Linalg/canonicalize.mlir | 75 ++++++++++++++++++-
4 files changed, 166 insertions(+), 2 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Utils/IndexingUtils.h b/mlir/include/mlir/Dialect/Utils/IndexingUtils.h
index b774359552aa5..7849782e5442b 100644
--- a/mlir/include/mlir/Dialect/Utils/IndexingUtils.h
+++ b/mlir/include/mlir/Dialect/Utils/IndexingUtils.h
@@ -243,6 +243,14 @@ SmallVector<int64_t>
computePermutationVector(int64_t permSize, ArrayRef<int64_t> positions,
ArrayRef<int64_t> desiredPositions);
+/// Returns a permutation vector that drop the input dims in
+/// dropPositions from inputPerm.
+///
+/// For example, inputPerm = {2, 4, 0, 1, 3} and dropPositions= {1, 2} would
+/// result in a {2, 0, 1} permutation vector.
+SmallVector<int64_t> dropDims(ArrayRef<int64_t> inputPerm,
+ ArrayRef<int64_t> dropPositions);
+
/// Helper to return a subset of `arrayAttr` as a vector of int64_t.
// TODO: Port everything relevant to DenseArrayAttr and drop this util.
SmallVector<int64_t> getI64SubArray(ArrayAttr arrayAttr, unsigned dropFront = 0,
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 57d126603ebd7..2ccb6e98a34a7 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -1890,9 +1890,67 @@ struct FoldTransposeWithTranspose : OpRewritePattern<linalg::TransposeOp> {
}
};
+/// This pattern canonicalize transpose by swapping the order of
+/// broadcast and transpose:
+/// transpose(broadcast(input)) -> broadcast(transpose(input))
+struct SwapTransposeWithBroadcast : OpRewritePattern<linalg::TransposeOp> {
+ using OpRewritePattern<linalg::TransposeOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(linalg::TransposeOp transposeOp,
+ PatternRewriter &rewriter) const override {
+ Value input = transposeOp.getInput();
+ BroadcastOp broadcastOp = input.getDefiningOp<BroadcastOp>();
+ if (!input.hasOneUse() || !broadcastOp)
+ return failure();
+
+ ArrayRef<int64_t> dimensions = broadcastOp.getDimensions();
+ ArrayRef<int64_t> perms = transposeOp.getPermutation();
+
+ // Get new perms and new dimensions.
+ SmallVector<int64_t> resultPerms = dropDims(perms, dimensions);
+ SmallVector<int64_t> invertPerm = invertPermutationVector(perms);
+ SmallVector<int64_t> resultDimensions;
+ for (unsigned i = 0; i < dimensions.size(); i++) {
+ resultDimensions.push_back(invertPerm[dimensions[i]]);
+ }
+
+ // Create transpose result.
+ Value broadcastInput = broadcastOp.getInput();
+ Location loc = transposeOp.getLoc();
+ MLIRContext *ctx = transposeOp.getContext();
+ SmallVector<OpFoldResult> dims;
+ auto broadcastInputTy =
+ mlir::cast<RankedTensorType>(broadcastInput.getType());
+ for (unsigned i = 0; i < broadcastInputTy.getRank(); i++) {
+ if (broadcastInputTy.isDynamicDim(i)) {
+ dims.push_back(rewriter.create<tensor::DimOp>(loc, broadcastInput, i)
+ ->getResult(0));
+ } else {
+ dims.push_back(IntegerAttr::get(IndexType::get(ctx),
+ broadcastInputTy.getDimSize(i)));
+ }
+ }
+ SmallVector<OpFoldResult> transposeResultShapes =
+ applyPermutation(dims, resultPerms);
+ Value transposeInit = rewriter.create<tensor::EmptyOp>(
+ transposeOp.getLoc(), transposeResultShapes,
+ broadcastInputTy.getElementType());
+
+ // Create broadcast(transpose(input)).
+ Value transposeResult =
+ rewriter
+ .create<TransposeOp>(loc, broadcastOp.getInput(), transposeInit,
+ resultPerms)
+ ->getResult(0);
+ rewriter.replaceOpWithNewOp<BroadcastOp>(
+ transposeOp, transposeResult, transposeOp.getInit(), resultDimensions);
+ return success();
+ }
+};
+
void TransposeOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
- results.add<FoldTransposeWithTranspose>(context);
+ results.add<FoldTransposeWithTranspose, SwapTransposeWithBroadcast>(context);
}
//===----------------------------------------------------------------------===//
diff --git a/mlir/lib/Dialect/Utils/IndexingUtils.cpp b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
index aba225be720c3..ddc1129a5a75f 100644
--- a/mlir/lib/Dialect/Utils/IndexingUtils.cpp
+++ b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
@@ -252,6 +252,31 @@ mlir::computePermutationVector(int64_t permSize, ArrayRef<int64_t> positions,
return res;
}
+SmallVector<int64_t> mlir::dropDims(ArrayRef<int64_t> inputPerm,
+ ArrayRef<int64_t> dropPositions) {
+ assert(inputPerm.size() >= dropPositions.size() &&
+ "expect inputPerm size large than position to drop");
+ SmallVector<int64_t> res;
+ for (unsigned inputIndex = 0; inputIndex < inputPerm.size(); ++inputIndex) {
+ int64_t targetIndex = inputPerm[inputIndex];
+ bool shouldDrop = false;
+ for (unsigned dropIndex = 0; dropIndex < dropPositions.size();
+ dropIndex++) {
+ if (dropPositions[dropIndex] == inputPerm[inputIndex]) {
+ shouldDrop = true;
+ break;
+ }
+ if (dropPositions[dropIndex] < inputPerm[inputIndex]) {
+ targetIndex--;
+ }
+ }
+ if (!shouldDrop) {
+ res.push_back(targetIndex);
+ }
+ }
+ return res;
+}
+
SmallVector<int64_t> mlir::getI64SubArray(ArrayAttr arrayAttr,
unsigned dropFront,
unsigned dropBack) {
diff --git a/mlir/test/Dialect/Linalg/canonicalize.mlir b/mlir/test/Dialect/Linalg/canonicalize.mlir
index 928030a81dc02..d34bc8c1c54f6 100644
--- a/mlir/test/Dialect/Linalg/canonicalize.mlir
+++ b/mlir/test/Dialect/Linalg/canonicalize.mlir
@@ -1017,7 +1017,7 @@ func.func @broadcast_same_shape(%input: tensor<2x3xf32>, %init: tensor<2x3xf32>)
return %0 : tensor<2x3xf32>
}
-// ----
+// -----
func.func @transpose_1d(%input: tensor<16xf32>,
%init: tensor<16xf32>) -> tensor<16xf32> {
@@ -1096,3 +1096,76 @@ func.func @transpose_transpose_fold(%input: tensor<5x4x3xf32>,
func.return %transpose2 : tensor<3x4x5xf32>
}
+// -----
+
+func.func @broadcast_transpose_fold(%input: tensor<2x4x5xf32>,
+ %init1: tensor<1x2x3x4x5x6xf32>,
+ %init2: tensor<1x6x2x3x5x4xf32>) -> tensor<1x6x2x3x5x4xf32> {
+ // CHECK-LABEL: @broadcast_transpose_fold
+ // CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor<2x4x5xf32>
+ // CHECK-SAME: %[[INIT1:[a-zA-Z0-9]+]]: tensor<1x2x3x4x5x6xf32>
+ // CHECK-SAME: %[[INIT2:[a-zA-Z0-9]+]]: tensor<1x6x2x3x5x4xf32>
+ // CHECK: %[[TMP_INIT:.+]] = tensor.empty() : tensor<2x5x4xf32>
+ // CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[INPUT]] : tensor<2x4x5xf32>) outs(%[[TMP_INIT]] : tensor<2x5x4xf32>) permutation = [0, 2, 1]
+ // CHECK: %[[BROADCAST:.+]] = linalg.broadcast ins(%[[TRANSPOSE]] : tensor<2x5x4xf32>) outs(%[[INIT2]] : tensor<1x6x2x3x5x4xf32>) dimensions = [0, 3, 1]
+ // CHECK: return %[[BROADCAST]] : tensor<1x6x2x3x5x4xf32>
+ %broadcast = linalg.broadcast
+ ins(%input : tensor<2x4x5xf32>)
+ outs(%init1 : tensor<1x2x3x4x5x6xf32>)
+ dimensions = [0, 2, 5]
+ %transpose = linalg.transpose
+ ins(%broadcast : tensor<1x2x3x4x5x6xf32>)
+ outs(%init2 : tensor<1x6x2x3x5x4xf32>)
+ permutation = [0, 5, 1, 2, 4, 3]
+ func.return %transpose : tensor<1x6x2x3x5x4xf32>
+}
+
+// -----
+
+func.func @broadcast_transpose_fold_dynamic(%input: tensor<?x?x5xf32>,
+ %init1: tensor<1x?x3x?x5x6xf32>,
+ %init2: tensor<1x3x?x6x5x?xf32>) -> tensor<1x3x?x6x5x?xf32> {
+ // CHECK-LABEL: @broadcast_transpose_fold_dynamic
+ // CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor<?x?x5xf32>
+ // CHECK-SAME: %[[INIT1:[a-zA-Z0-9]+]]: tensor<1x?x3x?x5x6xf32>
+ // CHECK-SAME: %[[INIT2:[a-zA-Z0-9]+]]: tensor<1x3x?x6x5x?xf32>
+ // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
+ // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
+ // CHECK: %[[DIM0:.+]] = tensor.dim %[[INPUT]], %[[C0]] : tensor<?x?x5xf32>
+ // CHECK: %[[DIM1:.+]] = tensor.dim %[[INPUT]], %[[C1]] : tensor<?x?x5xf32>
+ // CHECK: %[[TMP_INIT:.+]] = tensor.empty(%[[DIM1]], %[[DIM0]]) : tensor<?x5x?xf32>
+ // CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[INPUT]] : tensor<?x?x5xf32>) outs(%[[TMP_INIT]] : tensor<?x5x?xf32>) permutation = [1, 2, 0]
+ // CHECK: %[[BROADCAST:.+]] = linalg.broadcast ins(%[[TRANSPOSE]] : tensor<?x5x?xf32>) outs(%[[INIT2]] : tensor<1x3x?x6x5x?xf32>) dimensions = [0, 1, 3]
+ // CHECK: return %[[BROADCAST]] : tensor<1x3x?x6x5x?xf32>
+ %broadcast = linalg.broadcast
+ ins(%input : tensor<?x?x5xf32>)
+ outs(%init1 : tensor<1x?x3x?x5x6xf32>)
+ dimensions = [0, 2, 5]
+ %transpose = linalg.transpose
+ ins(%broadcast : tensor<1x?x3x?x5x6xf32>)
+ outs(%init2 : tensor<1x3x?x6x5x?xf32>)
+ permutation = [0, 2, 3, 5, 4, 1]
+ func.return %transpose : tensor<1x3x?x6x5x?xf32>
+}
+
+// -----
+
+func.func @broadcast_transpose_fold_2dim(%input: tensor<2xf32>,
+ %init1: tensor<2x4xf32>,
+ %init2: tensor<4x2xf32>) -> tensor<4x2xf32> {
+ // CHECK-LABEL: @broadcast_transpose_fold_2dim
+ // CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor<2xf32>
+ // CHECK-SAME: %[[INIT1:[a-zA-Z0-9]+]]: tensor<2x4xf32>
+ // CHECK-SAME: %[[INIT2:[a-zA-Z0-9]+]]: tensor<4x2xf32>
+ // CHECK: %[[BROADCAST:.+]] = linalg.broadcast ins(%[[INPUT]] : tensor<2xf32>) outs(%[[INIT2]] : tensor<4x2xf32>) dimensions = [0]
+ // CHECK: return %[[BROADCAST]] : tensor<4x2xf32>
+ %broadcast = linalg.broadcast
+ ins(%input : tensor<2xf32>)
+ outs(%init1 : tensor<2x4xf32>)
+ dimensions = [1]
+ %transpose = linalg.transpose
+ ins(%broadcast : tensor<2x4xf32>)
+ outs(%init2 : tensor<4x2xf32>)
+ permutation = [1, 0]
+ func.return %transpose : tensor<4x2xf32>
+}
>From 79a7960d9e87311e609455e75a39a65f9cd20875 Mon Sep 17 00:00:00 2001
From: cxy <chenxunyu1993 at gmail.com>
Date: Wed, 17 Jul 2024 11:51:00 +0800
Subject: [PATCH 2/4] update docs
---
mlir/docs/Canonicalization.md | 61 ++++++++++++++++++++++++++++++++++-
1 file changed, 60 insertions(+), 1 deletion(-)
diff --git a/mlir/docs/Canonicalization.md b/mlir/docs/Canonicalization.md
index d1cba572af212..dba58ad22c9e9 100644
--- a/mlir/docs/Canonicalization.md
+++ b/mlir/docs/Canonicalization.md
@@ -33,6 +33,11 @@ together.
Some important things to think about w.r.t. canonicalization patterns:
+* The goal of canonicalization is to make subsequent optimizations more
+ effective. Therefore, performance improvements are not necessary for
+ canonicalization. But it is generally better to define a canonicalize
+ pattern that do not harm the performance.
+
* Pass pipelines should not rely on the canonicalizer pass for correctness.
They should work correctly with all instances of the canonicalization pass
removed.
@@ -51,6 +56,60 @@ Some important things to think about w.r.t. canonicalization patterns:
* It is always good to eliminate operations entirely when possible, e.g. by
folding known identities (like "x + 0 = x").
+* Canonicalize isn't a great place to put pattens with expensive compile time
+ (i.e. have O(n) complexity) or complicated cost models.
+
+* Canonicalize shouldn't drop the semantic of original operation.
+
+For example, a pattern that transform
+
+```
+ %res = vector.transpose %0, [1, 0] : vector<nx1x<eltty>> to vector<1xnx<elty>>
+```
+
+to
+
+```
+ %res = vector.shape_cast %0 : vector<nx1x<eltty>> to vector<1xnx<elty>>
+```
+
+is not a good canonicalize pattern because it drops the transpose semantic.
+
+
+A pattern that transform (linalg.transpose is only use of %broadcast)
+
+```
+ %broadcast = linalg.broadcast
+ ins(%input : tensor<2x4x5xf32>)
+ outs(%init1 : tensor<1x2x3x4x5x6xf32>)
+ dimensions = [0, 2, 5]
+ %transpose = linalg.transpose
+ ins(%broadcast : tensor<1x2x3x4x5x6xf32>)
+ outs(%init2 : tensor<1x6x2x3x5x4xf32>)
+ permutation = [0, 5, 1, 2, 4, 3]
+```
+
+to
+
+```
+ %tranpose = linalg.transpose
+ ins(%input : tensor<2x4x5xf32>)
+ outs(%tmp_init : tensor<2x5x4xf32>)
+ permutation = [0, 2, 1]
+ %broadcast = linalg.broadcast
+ ins(%transpose : tensor<2x5x4xf32>)
+ outs(%init2 : tensor<1x6x2x3x5x4xf32>)
+ dimensions = [0, 3, 1]
+```
+
+is a good canonicalize pattern because:
+
+1. This pattern is converge.
+2. This pattern always transforms the program towards reducing the amount of
+ computational data, which is a clear lattice.
+3. This is not a one-off pattern, new matches may be generated during the
+ application process.
+
## Globally Applied Rules
These transformations are applied to all levels of IR:
@@ -189,7 +248,7 @@ each of the operands, returning the corresponding constant attribute. These
operands are those that implement the `ConstantLike` trait. If any of the
operands are non-constant, a null `Attribute` value is provided instead. For
example, if MyOp provides three operands [`a`, `b`, `c`], but only `b` is
-constant then `adaptor` will return Attribute() for `getA()` and `getC()`,
+constant then `adaptor` will return Attribute() for `getA()` and `getC()`,
and b-value for `getB()`.
Also above, is the use of `OpFoldResult`. This class represents the possible
>From 31f35c2b945574c31e966bf61308a7a27ec05207 Mon Sep 17 00:00:00 2001
From: cxy <chenxunyu1993 at gmail.com>
Date: Sat, 20 Jul 2024 17:46:37 +0800
Subject: [PATCH 3/4] Revert "update docs"
This reverts commit 79a7960d9e87311e609455e75a39a65f9cd20875.
---
mlir/docs/Canonicalization.md | 61 +----------------------------------
1 file changed, 1 insertion(+), 60 deletions(-)
diff --git a/mlir/docs/Canonicalization.md b/mlir/docs/Canonicalization.md
index dba58ad22c9e9..d1cba572af212 100644
--- a/mlir/docs/Canonicalization.md
+++ b/mlir/docs/Canonicalization.md
@@ -33,11 +33,6 @@ together.
Some important things to think about w.r.t. canonicalization patterns:
-* The goal of canonicalization is to make subsequent optimizations more
- effective. Therefore, performance improvements are not necessary for
- canonicalization. But it is generally better to define a canonicalize
- pattern that do not harm the performance.
-
* Pass pipelines should not rely on the canonicalizer pass for correctness.
They should work correctly with all instances of the canonicalization pass
removed.
@@ -56,60 +51,6 @@ Some important things to think about w.r.t. canonicalization patterns:
* It is always good to eliminate operations entirely when possible, e.g. by
folding known identities (like "x + 0 = x").
-* Canonicalize isn't a great place to put pattens with expensive compile time
- (i.e. have O(n) complexity) or complicated cost models.
-
-* Canonicalize shouldn't drop the semantic of original operation.
-
-For example, a pattern that transform
-
-```
- %res = vector.transpose %0, [1, 0] : vector<nx1x<eltty>> to vector<1xnx<elty>>
-```
-
-to
-
-```
- %res = vector.shape_cast %0 : vector<nx1x<eltty>> to vector<1xnx<elty>>
-```
-
-is not a good canonicalize pattern because it drops the transpose semantic.
-
-
-A pattern that transform (linalg.transpose is only use of %broadcast)
-
-```
- %broadcast = linalg.broadcast
- ins(%input : tensor<2x4x5xf32>)
- outs(%init1 : tensor<1x2x3x4x5x6xf32>)
- dimensions = [0, 2, 5]
- %transpose = linalg.transpose
- ins(%broadcast : tensor<1x2x3x4x5x6xf32>)
- outs(%init2 : tensor<1x6x2x3x5x4xf32>)
- permutation = [0, 5, 1, 2, 4, 3]
-```
-
-to
-
-```
- %tranpose = linalg.transpose
- ins(%input : tensor<2x4x5xf32>)
- outs(%tmp_init : tensor<2x5x4xf32>)
- permutation = [0, 2, 1]
- %broadcast = linalg.broadcast
- ins(%transpose : tensor<2x5x4xf32>)
- outs(%init2 : tensor<1x6x2x3x5x4xf32>)
- dimensions = [0, 3, 1]
-```
-
-is a good canonicalize pattern because:
-
-1. This pattern is converge.
-2. This pattern always transforms the program towards reducing the amount of
- computational data, which is a clear lattice.
-3. This is not a one-off pattern, new matches may be generated during the
- application process.
-
## Globally Applied Rules
These transformations are applied to all levels of IR:
@@ -248,7 +189,7 @@ each of the operands, returning the corresponding constant attribute. These
operands are those that implement the `ConstantLike` trait. If any of the
operands are non-constant, a null `Attribute` value is provided instead. For
example, if MyOp provides three operands [`a`, `b`, `c`], but only `b` is
-constant then `adaptor` will return Attribute() for `getA()` and `getC()`,
+constant then `adaptor` will return Attribute() for `getA()` and `getC()`,
and b-value for `getB()`.
Also above, is the use of `OpFoldResult`. This class represents the possible
>From 3598ef6df838dd3f2f3713742628f390fe031441 Mon Sep 17 00:00:00 2001
From: cxy <chenxunyu1993 at gmail.com>
Date: Sat, 20 Jul 2024 17:52:22 +0800
Subject: [PATCH 4/4] fix coding style according to comment
---
mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp | 7 ++++---
mlir/lib/Dialect/Utils/IndexingUtils.cpp | 7 ++++---
2 files changed, 8 insertions(+), 6 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 2ccb6e98a34a7..684c9913ad33a 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -1910,9 +1910,9 @@ struct SwapTransposeWithBroadcast : OpRewritePattern<linalg::TransposeOp> {
SmallVector<int64_t> resultPerms = dropDims(perms, dimensions);
SmallVector<int64_t> invertPerm = invertPermutationVector(perms);
SmallVector<int64_t> resultDimensions;
- for (unsigned i = 0; i < dimensions.size(); i++) {
+ unsigned dimensionSize = dimensions.size();
+ for (unsigned i = 0; i < dimensionsSize; ++i)
resultDimensions.push_back(invertPerm[dimensions[i]]);
- }
// Create transpose result.
Value broadcastInput = broadcastOp.getInput();
@@ -1921,7 +1921,8 @@ struct SwapTransposeWithBroadcast : OpRewritePattern<linalg::TransposeOp> {
SmallVector<OpFoldResult> dims;
auto broadcastInputTy =
mlir::cast<RankedTensorType>(broadcastInput.getType());
- for (unsigned i = 0; i < broadcastInputTy.getRank(); i++) {
+ unsigned inputRank = broadcastInputTy.getRank();
+ for (unsigned i = 0; i < inputRank; ++i) {
if (broadcastInputTy.isDynamicDim(i)) {
dims.push_back(rewriter.create<tensor::DimOp>(loc, broadcastInput, i)
->getResult(0));
diff --git a/mlir/lib/Dialect/Utils/IndexingUtils.cpp b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
index ddc1129a5a75f..8badda484fd99 100644
--- a/mlir/lib/Dialect/Utils/IndexingUtils.cpp
+++ b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
@@ -257,11 +257,12 @@ SmallVector<int64_t> mlir::dropDims(ArrayRef<int64_t> inputPerm,
assert(inputPerm.size() >= dropPositions.size() &&
"expect inputPerm size large than position to drop");
SmallVector<int64_t> res;
- for (unsigned inputIndex = 0; inputIndex < inputPerm.size(); ++inputIndex) {
+ permSize = inputPerm.size();
+ for (unsigned inputIndex = 0; inputIndex < permSize; ++inputIndex) {
int64_t targetIndex = inputPerm[inputIndex];
bool shouldDrop = false;
- for (unsigned dropIndex = 0; dropIndex < dropPositions.size();
- dropIndex++) {
+ unsigned dropSize = dropPositions.size();
+ for (unsigned dropIndex = 0; dropIndex < dropSize; dropIndex++) {
if (dropPositions[dropIndex] == inputPerm[inputIndex]) {
shouldDrop = true;
break;
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