diff --git a/mlir/examples/toy/Ch5/mlir/LowerToAffineLoops.cpp b/mlir/examples/toy/Ch5/mlir/LowerToAffineLoops.cpp
--- a/mlir/examples/toy/Ch5/mlir/LowerToAffineLoops.cpp
+++ b/mlir/examples/toy/Ch5/mlir/LowerToAffineLoops.cpp
@@ -155,9 +155,15 @@
     // operations.
     auto valueShape = memRefType.getShape();
     SmallVector<Value, 8> constantIndices;
-    for (auto i : llvm::seq<int64_t>(
-             0, *std::max_element(valueShape.begin(), valueShape.end())))
-      constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
+
+    if (!valueShape.empty()) {
+      for (auto i : llvm::seq<int64_t>(
+              0, *std::max_element(valueShape.begin(), valueShape.end())))
+       constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
+    } else {
+      // This is the case of a tensor of rank 0.
+      constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, 0));
+    }
 
     // The constant operation represents a multi-dimensional constant, so we
     // will need to generate a store for each of the elements. The following
diff --git a/mlir/examples/toy/Ch6/mlir/LowerToAffineLoops.cpp b/mlir/examples/toy/Ch6/mlir/LowerToAffineLoops.cpp
--- a/mlir/examples/toy/Ch6/mlir/LowerToAffineLoops.cpp
+++ b/mlir/examples/toy/Ch6/mlir/LowerToAffineLoops.cpp
@@ -155,10 +155,15 @@
     // operations.
     auto valueShape = memRefType.getShape();
     SmallVector<Value, 8> constantIndices;
-    for (auto i : llvm::seq<int64_t>(
-             0, *std::max_element(valueShape.begin(), valueShape.end())))
-      constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
 
+    if (!valueShape.empty()) {
+      for (auto i : llvm::seq<int64_t>(
+              0, *std::max_element(valueShape.begin(), valueShape.end())))
+       constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
+    } else {
+      // This is the case of a tensor of rank 0.
+      constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, 0));
+    }
     // The constant operation represents a multi-dimensional constant, so we
     // will need to generate a store for each of the elements. The following
     // functor recursively walks the dimensions of the constant shape,
diff --git a/mlir/examples/toy/Ch7/mlir/LowerToAffineLoops.cpp b/mlir/examples/toy/Ch7/mlir/LowerToAffineLoops.cpp
--- a/mlir/examples/toy/Ch7/mlir/LowerToAffineLoops.cpp
+++ b/mlir/examples/toy/Ch7/mlir/LowerToAffineLoops.cpp
@@ -155,9 +155,15 @@
     // operations.
     auto valueShape = memRefType.getShape();
     SmallVector<Value, 8> constantIndices;
-    for (auto i : llvm::seq<int64_t>(
-             0, *std::max_element(valueShape.begin(), valueShape.end())))
-      constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
+
+    if (!valueShape.empty()) {
+      for (auto i : llvm::seq<int64_t>(
+              0, *std::max_element(valueShape.begin(), valueShape.end())))
+       constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
+    } else {
+      // This is the case of a tensor of rank 0.
+      constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, 0));
+    }
 
     // The constant operation represents a multi-dimensional constant, so we
     // will need to generate a store for each of the elements. The following