diff --git a/lib/Dialect/TritonGPU/Transforms/AMDReorderInstructions.cpp b/lib/Dialect/TritonGPU/Transforms/AMDReorderInstructions.cpp
index 8e672cba8861..e802e44c7679 100644
--- a/lib/Dialect/TritonGPU/Transforms/AMDReorderInstructions.cpp
+++ b/lib/Dialect/TritonGPU/Transforms/AMDReorderInstructions.cpp
@@ -1,3 +1,26 @@
+/*
+ * Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining
+ * a copy of this software and associated documentation files
+ * (the "Software"), to deal in the Software without restriction,
+ * including without limitation the rights to use, copy, modify, merge,
+ * publish, distribute, sublicense, and/or sell copies of the Software,
+ * and to permit persons to whom the Software is furnished to do so,
+ * subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be
+ * included in all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+ * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
 #include "mlir/Analysis/SliceAnalysis.h"
 #include "mlir/IR/Dominance.h"
 #include "mlir/IR/Verifier.h"
@@ -23,7 +46,11 @@ class TritonAMDGPUReorderInstructionsPass
     mlir::DominanceInfo dom(m);
 
     for (auto operand : operands) {
-      operandsSorted.push_back(operand);
+      // Sort only operands for which defining op can be fetched. This will
+      // exclude, for example, block arguments.
+      if (operand.getDefiningOp()) {
+        operandsSorted.push_back(operand);
+      }
     }
 
     if (operandsSorted.size() == 1) {
@@ -34,10 +61,8 @@ class TritonAMDGPUReorderInstructionsPass
               [&](const Value &a, const Value &b) {
                 Operation *operandA = a.getDefiningOp();
                 Operation *operandB = b.getDefiningOp();
-                if (operandA && operandB) {
-                  return dom.dominates(operandA, operandB);
-                }
-                return false;
+                assert(operandA && operandB);
+                return dom.dominates(operandA, operandB);
               });
   }
 
@@ -97,18 +122,22 @@ class TritonAMDGPUReorderInstructionsPass
     SmallVector<Value> operandsSorted;
     sortOperandsByDominance(operands, operandsSorted);
 
-    if (!operandsSorted.empty() &&
-        operandsSorted[operandsSorted.size() - 1].getDefiningOp()) {
-
-      moveAfter(op, operandsSorted[operandsSorted.size() - 1].getDefiningOp());
-      if (failed(mlir::verify(m))) {
-        assert(false);
+    if (!operandsSorted.empty()) {
+      auto dominantOperandOp =
+          operandsSorted[operandsSorted.size() - 1].getDefiningOp();
+      if (dominantOperandOp) {
+        moveAfter(op, dominantOperandOp);
+        assert(succeeded(mlir::verify(m)));
       }
     }
 
     movedOperations.push_back(op);
   }
 
+  // Moves Q tensor in Flash Attention algorithm out of the
+  // "main" flash attention loop. Since Q tensor is the loop invariant, this way
+  // we ensure that loading of Q tensor, Q tensor transformations and related
+  // layout conversions happen only once.
   void moveQTensorOutOfTheLoop(ModuleOp m) {
     m.walk([&](tt::DotOp dotOp) {
       if (isFAChainDot(dotOp)) {
diff --git a/python/perf-kernels/06-fused-attention-transV.py b/python/perf-kernels/06-fused-attention-transV.py
index cf27d874f687..a3bd99074de5 100644
--- a/python/perf-kernels/06-fused-attention-transV.py
+++ b/python/perf-kernels/06-fused-attention-transV.py
@@ -564,7 +564,6 @@ def forward(ctx, q, k, v, causal, sm_scale, split_kernel=False):
 
         ## restore the grid for bwd kernel
         best_config = _attn_fwd.get_best_config()
-        # print(best_config)
         block_m = int(best_config.__str__().split(",")[0].split("BLOCK_M:")[1])
         grid = (triton.cdiv(q.shape[2], block_m), q.shape[0] * q.shape[1], 1)