diff --git a/lib/Dialect/TTNN/IR/TTNNOpsAttrs.cpp b/lib/Dialect/TTNN/IR/TTNNOpsAttrs.cpp
index 8e748dce1..c7bf769dd 100644
--- a/lib/Dialect/TTNN/IR/TTNNOpsAttrs.cpp
+++ b/lib/Dialect/TTNN/IR/TTNNOpsAttrs.cpp
@@ -66,26 +66,42 @@ bool TTNNLayoutAttr::hasInterleavedDRAMTensorMemoryLayout() const {
 }
 
 // Calculate the logical shape of the shard.
+//
 // Shard is defined as a piece of the tensor that is mapped to a single grid
-// core. It is assumed that the TensorMemoryLayout is sharded.
+// core. This function returns the shard shape for tensors with BLOCK SHARDED
+// tensor memory layout.
+//
+// All examples assume that the tensor is mapped to a 8x8 grid.
+// Example: tensor<32x32xbf16> -> {4, 4}
+// Example: tensor<65x65xbf16> -> {9, 9}
+//
+// return The logical shard shape in case of block sharded tensor memory layout.
 llvm::SmallVector<int64_t>
 TTNNLayoutAttr::calculateLogicalShardShapeForSharding(
     ArrayRef<int64_t> tensorShape, mlir::AffineMap linear, GridAttr grid) {
   assert(linear.getNumResults() == grid.getShape().size());
-  mlir::SmallVector<std::int64_t> logicalShape =
+  mlir::SmallVector<std::int64_t> physicalShape =
       ttmlir::utils::evalShape(linear, tensorShape);
   mlir::SmallVector<std::int64_t> shardShape(linear.getNumResults());
-  for (unsigned i = 0; i < linear.getNumResults(); ++i) {
+  for (size_t i = 0; i < linear.getNumResults(); ++i) {
     shardShape[i] =
-        (logicalShape[i] + grid.getShape()[i] - 1) / grid.getShape()[i];
+        (physicalShape[i] + grid.getShape()[i] - 1) / grid.getShape()[i];
   }
   return shardShape;
 }
 
 // Calculate the logical shape of the shard.
+//
 // Shard is defined as a piece of the tensor that is mapped to a single grid
-// core. It is assumed that the TensorMemoryLayout is interleaved and buffer
-// type is L1.
+// core. This function returns the shard shape for tensors with INTERLEAVED
+// tensor memory layout.
+//
+// All examples assume that the tensor is mapped to a 8x8 grid.
+// Example: tensor<1x1024xbf16> ( -> 32 tiles ) -> {1, 1}
+// Example: tensor<512x512xbf16> ( -> 256 tiles ) -> {1, 4}
+// Example: tensor<32x2049xbf16> ( -> 65 tiles ) -> {1, 2}
+//
+// return The logical shard shape in case of interleaved tensor memory layout.
 llvm::SmallVector<int64_t>
 TTNNLayoutAttr::calculateLogicalShardShapeForL1Interleaved(
     ArrayRef<int64_t> tensorShape, mlir::Type elementType,
@@ -93,12 +109,12 @@ TTNNLayoutAttr::calculateLogicalShardShapeForL1Interleaved(
   assert(linear.getNumResults() == grid.getShape().size());
   assert(mlir::isa<mlir::tt::TileType>(elementType));
 
-  mlir::SmallVector<std::int64_t> logicalShape =
+  mlir::SmallVector<std::int64_t> physicalShape =
       ttmlir::utils::evalShape(linear, tensorShape);
-  mlir::SmallVector<std::int64_t> logicalTiledShape =
-      mlir::cast<mlir::tt::TileType>(elementType).getTiledShape(logicalShape);
+  mlir::SmallVector<std::int64_t> physicalTiledShape =
+      mlir::cast<mlir::tt::TileType>(elementType).getTiledShape(physicalShape);
   uint64_t numOfTiles =
-      std::accumulate(logicalTiledShape.begin(), logicalTiledShape.end(), 1,
+      std::accumulate(physicalTiledShape.begin(), physicalTiledShape.end(), 1,
                       std::multiplies<std::int64_t>());
   uint64_t numOfGridUnits =
       std::accumulate(grid.getShape().begin(), grid.getShape().end(), 1,
@@ -325,13 +341,13 @@ mlir::AffineMap TTNNLayoutAttr::replaceMemoryMapSymbolsWithShardShape(
          "shard rank");
 
   SmallVector<AffineExpr> symReplacements;
-  for (unsigned i = 0; i < physicalMemoryMap.getNumSymbols(); ++i) {
+  for (size_t i = 0; i < physicalMemoryMap.getNumSymbols(); ++i) {
     symReplacements.push_back(
         getAffineConstantExpr(shardShape[i], getContext()));
   }
 
   SmallVector<AffineExpr> dimReplacements;
-  for (unsigned i = 0; i < physicalMemoryMap.getNumDims(); ++i) {
+  for (size_t i = 0; i < physicalMemoryMap.getNumDims(); ++i) {
     dimReplacements.push_back(getAffineDimExpr(i, getContext()));
   }
 
@@ -501,8 +517,7 @@ TTNNLayoutAttr TTNNLayoutAttr::get(
   AffineMap linear = collapsedLinearAffineMap(
       context, tensorShape, grid.getShape(), collapseIntervals);
 
-  // Calculate shard shape by evaluating the linear map.
-  //
+  // Calculate shard shape
   mlir::SmallVector<int64_t> shardShape;
   if (bufferType == BufferType::L1 &&
       memLayoutAttr.getValue() == TensorMemoryLayout::Interleaved) {