tidy

Signed-off-by: James Newling <[email protected]>

Author: newling

mlir/lib/Dialect/Vector/IR/VectorOps.cpp

@@ -2609,7 +2609,6 @@ struct BroadcastFolder : public OpRewritePattern<BroadcastOp> {
     return success();
   }
 };
-
 } // namespace
 
 void BroadcastOp::getCanonicalizationPatterns(RewritePatternSet &results,
@@ -6156,9 +6155,9 @@ class FoldTransposeCreateMask final : public OpRewritePattern<TransposeOp> {
   }
 };
 
-/// Folds transpose(broadcast(x)) into broadcast(x) if the transpose is
-/// 'order preserving', where 'order preserving' here means the flattened
-/// inputs and outputs of the transpose have identical values.
+/// Folds transpose(broadcast(x)) to broadcast(x) if the transpose is
+/// 'order preserving', where 'order preserving' means the flattened
+/// inputs and outputs of the transpose have identical (numerical) values.
 ///
 /// Example:
 /// ```
@@ -6188,10 +6187,10 @@ class FoldTransposeBroadcast : public OpRewritePattern<vector::TransposeOp> {
 
     auto inputType = dyn_cast<VectorType>(broadcast.getSourceType());
     bool inputIsScalar = !inputType;
-    auto inputShape = inputType.getShape();
-    auto inputRank = inputType.getRank();
-    auto outputRank = transpose.getType().getRank();
-    auto deltaRank = outputRank - inputRank;
+    ArrayRef<int64_t> inputShape = inputType.getShape();
+    int64_t inputRank = inputType.getRank();
+    int64_t outputRank = transpose.getType().getRank();
+    int64_t deltaRank = outputRank - inputRank;
 
     // transpose(broadcast(scalar)) -> broadcast(scalar) is always valid
     if (inputIsScalar)
@@ -6200,9 +6199,9 @@ class FoldTransposeBroadcast : public OpRewritePattern<vector::TransposeOp> {
     // Return true if all permutation destinations for indices in [low, high)
     // are in [low, high), so the permutation is local to the group.
     auto isGroupBound = [&](int low, int high) {
-      auto perm = transpose.getPermutation();
+      ArrayRef<int64_t> permutation = transpose.getPermutation();
       for (int j = low; j < high; ++j) {
-        if (perm[j] < low || perm[j] >= high) {
+        if (permutation[j] < low || permutation[j] >= high) {
           return false;
         }
       }
@@ -6233,10 +6232,15 @@ class FoldTransposeBroadcast : public OpRewritePattern<vector::TransposeOp> {
       return false;
     }
 
+    // The preceding logic ensures that by this point, the ouutput of the
+    // transpose is definitely broadcastable from the input shape. So we don't
+    // need to call 'vector::isBroadcastableTo', but asserting here just as a
+    // sanity check:
     bool isBroadcastable =
         vector::isBroadcastableTo(inputType, transpose.getResultVectorType()) ==
         vector::BroadcastableToResult::Success;
-    assert(isBroadcastable && "it should be broadcastable at this point");
+    assert(isBroadcastable &&
+           "(I think) it must be broadcastable at this point.");
 
     return true;
   }

mlir/test/Dialect/Vector/canonicalize.mlir

@@ -1,8 +1,5 @@
 // RUN: mlir-opt %s -canonicalize="test-convergence" -split-input-file -allow-unregistered-dialect | FileCheck %s
 
-
-
-
 // CHECK-LABEL: create_vector_mask_to_constant_mask
 func.func @create_vector_mask_to_constant_mask() -> (vector<4x3xi1>) {
   %c2 = arith.constant 2 : index