diff --git a/tensorflow/compiler/tf2xla/ops/xla_ops.cc b/tensorflow/compiler/tf2xla/ops/xla_ops.cc
index 862da1f3f95..f4b9e9654d2 100644
--- a/tensorflow/compiler/tf2xla/ops/xla_ops.cc
+++ b/tensorflow/compiler/tf2xla/ops/xla_ops.cc
@@ -441,7 +441,8 @@ REGISTER_OP("XlaReduce")
         auto dim_in_range = [rank](int64 dim) {
           return dim >= 0 && dim < rank;
         };
-        if (rank < dimensions_to_reduce.size() ||
+        const int dimensions_to_reduce_size = dimensions_to_reduce.size();
+        if (rank < dimensions_to_reduce_size ||
             dims_set.size() != dimensions_to_reduce.size() ||
             !absl::c_all_of(dimensions_to_reduce, dim_in_range)) {
           return errors::InvalidArgument(
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 92e98b3fed4..456c1826572 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -62,14 +62,14 @@ InferenceContext::InferenceContext(
   }
   std::vector<std::unique_ptr<std::vector<ShapeAndType>>> handle_data(
       input_shapes.size());
-  for (int i = 0; i < input_handle_shapes_and_types.size(); ++i) {
+  for (int i = 0, end = input_handle_shapes_and_types.size(); i < end; ++i) {
     const auto& v = input_handle_shapes_and_types[i];
     if (v == nullptr) {
       continue;
     }
     handle_data[i].reset(new std::vector<ShapeAndType>(v->size()));
     auto& new_v = *handle_data[i];
-    for (int j = 0; j < v->size(); ++j) {
+    for (int j = 0, end = v->size(); j < end; ++j) {
       const auto& p = (*v)[j];
       construction_status_.Update(
           MakeShapeFromPartialTensorShape(p.first, &new_v[j].shape));
@@ -123,11 +123,12 @@ Status InferenceContext::set_output(StringPiece output_name,
   } else {
     const int start = result->second.first;
     const int size = result->second.second - start;
-    if (size != shapes.size()) {
+    const int shapes_size = shapes.size();
+    if (size != shapes_size) {
       return errors::InvalidArgument("Must have exactly ", shapes.size(),
                                      " shapes.");
     }
-    for (int i = 0; i < size; ++i) {
+    for (int i = 0; i < shapes_size; ++i) {
       outputs_[i + start] = shapes[i];
     }
   }
@@ -181,7 +182,8 @@ void InferenceContext::PreInputInit(
 }
 
 Status InferenceContext::ExpandOutputs(int new_output_size) {
-  if (new_output_size < outputs_.size()) {
+  const int outputs_size = outputs_.size();
+  if (new_output_size < outputs_size) {
     return errors::InvalidArgument("Trying to reduce number of outputs of op.");
   }
   outputs_.resize(new_output_size, nullptr);
@@ -209,8 +211,8 @@ void InferenceContext::PostInputInit(
     }
     input_handle_shapes_and_types_ = std::move(input_handle_data);
   }
-
-  if (inputs_.size() != num_inputs_from_node_def) {
+  const int inputs_size = inputs_.size();
+  if (inputs_size != num_inputs_from_node_def) {
     construction_status_ = errors::InvalidArgument(
         "Wrong number of inputs passed: ", inputs_.size(), " while ",
         num_inputs_from_node_def, " expected based on NodeDef");
@@ -718,7 +720,8 @@ Status InferenceContext::MakeShapeFromShapeTensorTreatScalarAsUnknownShape(
   TF_RETURN_IF_ERROR(WithRankAtMost(input(input_idx), 1, &input_shape));
 
   requested_input_tensor_as_partial_shape_[input_idx] = true;
-  if (input_idx < input_tensors_as_shapes_.size() &&
+  const int input_tensors_as_shapes_size = input_tensors_as_shapes_.size();
+  if (input_idx < input_tensors_as_shapes_size &&
       input_tensors_as_shapes_[input_idx].IsSet() &&
       RankKnown(input_tensors_as_shapes_[input_idx])) {
     *out = input_tensors_as_shapes_[input_idx];
@@ -736,7 +739,8 @@ Status InferenceContext::MakeShapeFromShapeTensor(int input_idx,
   TF_RETURN_IF_ERROR(WithRank(input(input_idx), 1, &input_shape));
 
   requested_input_tensor_as_partial_shape_[input_idx] = true;
-  if (input_idx < input_tensors_as_shapes_.size() &&
+  const int input_tensors_as_shapes_size = input_tensors_as_shapes_.size();
+  if (input_idx < input_tensors_as_shapes_size &&
       input_tensors_as_shapes_[input_idx].IsSet() &&
       RankKnown(input_tensors_as_shapes_[input_idx])) {
     *out = input_tensors_as_shapes_[input_idx];
@@ -1099,14 +1103,16 @@ Status InferenceContext::AttachContext(const Status& status) {
   std::vector<string> input_from_tensors_str;
   std::vector<string> input_from_tensors_as_shape_str;
   input_from_tensors_as_shape_str.reserve(inputs_.size());
-  for (int i = 0; i < inputs_.size(); ++i) {
+  for (int i = 0, end = inputs_.size(); i < end; ++i) {
+    const int input_tensors_as_shapes_size = input_tensors_as_shapes_.size();
+    const int input_tensors_size = input_tensors_.size();
     if (requested_input_tensor_as_partial_shape_[i] &&
-        i < input_tensors_as_shapes_.size() &&
+        i < input_tensors_as_shapes_size &&
         input_tensors_as_shapes_[i].IsSet() &&
         RankKnown(input_tensors_as_shapes_[i])) {
       input_from_tensors_as_shape_str.push_back(strings::StrCat(
           "input[", i, "] = ", DebugString(input_tensors_as_shapes_[i])));
-    } else if (requested_input_tensor_[i] && i < input_tensors_.size() &&
+    } else if (requested_input_tensor_[i] && i < input_tensors_size &&
                input_tensors_[i] != nullptr) {
       input_from_tensors_str.push_back(strings::StrCat(
           "input[", i, "] = <",
@@ -1140,7 +1146,7 @@ bool InferenceContext::MergeHandleShapesAndTypes(
   }
   std::vector<ShapeAndType> new_values(shapes_and_types.size());
   bool refined = false;
-  for (int i = 0; i < shapes_and_types.size(); ++i) {
+  for (int i = 0, end = shapes_and_types.size(); i < end; ++i) {
     const ShapeAndType& existing = (*to_update)[i];
     if (shapes_and_types[i].dtype == existing.dtype) {
       new_values[i].dtype = existing.dtype;
@@ -1164,7 +1170,7 @@ bool InferenceContext::MergeHandleShapesAndTypes(
   if (!refined) {
     return false;
   }
-  for (int i = 0; i < new_values.size(); ++i) {
+  for (int i = 0, end = new_values.size(); i < end; ++i) {
     (*to_update)[i] = new_values[i];
   }
   return true;
@@ -1199,7 +1205,7 @@ bool InferenceContext::RelaxHandleShapesAndMergeTypes(
     return false;
   }
   std::vector<ShapeAndType> new_values(shapes_and_types.size());
-  for (int i = 0; i < shapes_and_types.size(); ++i) {
+  for (int i = 0, end = shapes_and_types.size(); i < end; ++i) {
     const ShapeAndType& existing = (*to_update)[i];
     if (shapes_and_types[i].dtype == existing.dtype) {
       new_values[i].dtype = existing.dtype;
diff --git a/tensorflow/core/grappler/optimizers/common_subgraph_elimination.cc b/tensorflow/core/grappler/optimizers/common_subgraph_elimination.cc
index 2489cf93e78..57f7e7c664b 100644
--- a/tensorflow/core/grappler/optimizers/common_subgraph_elimination.cc
+++ b/tensorflow/core/grappler/optimizers/common_subgraph_elimination.cc
@@ -73,8 +73,7 @@ class UniqueNodes {
     if (it == memoized_signatures_.end()) return;
 
     std::vector<NodeDef*>& candidates = rep_[it->second];
-    for (int i = 0, candidates_size = candidates.size(); i < candidates_size;
-         ++i) {
+    for (int i = 0, end = candidates.size(); i < end; ++i) {
       if (candidates[i] == node) {
         std::swap(candidates[i], candidates[candidates.size() - 1]);
         candidates.resize(candidates.size() - 1);
diff --git a/tensorflow/core/grappler/optimizers/debug_stripper.cc b/tensorflow/core/grappler/optimizers/debug_stripper.cc
index d0aa79a24d7..dfc58ab7ae9 100644
--- a/tensorflow/core/grappler/optimizers/debug_stripper.cc
+++ b/tensorflow/core/grappler/optimizers/debug_stripper.cc
@@ -63,8 +63,7 @@ Status DebugStripper::Optimize(Cluster* cluster, const GrapplerItem& item,
       node.mutable_attr()->swap(new_attr);
       // As Identity op only takes one input, mark redundant inputs as control
       // input.
-      for (int i = 1, node_input_size = node.input_size(); i < node_input_size;
-           ++i) {
+      for (int i = 1, end = node.input_size(); i < end; ++i) {
         if (!IsControlInput(node.input(i))) {
           *node.mutable_input(i) = AsControlDependency(NodeName(node.input(i)));
         }
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index 3f5e3a8ea3a..5956fea4695 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -401,7 +401,7 @@ Status SplitIdentityNInputs(GraphDef* graph,
     }
 
     const int num_non_control_inputs = NumNonControlInputs(*node);
-    int terminal_second_size = terminal.second.size();
+    const int terminal_second_size = terminal.second.size();
     if (node->attr().count("T") == 0 ||
         node->attr().at("T").list().type_size() != num_non_control_inputs ||
         terminal_second_size >= num_non_control_inputs) {
diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc
index af2bd16328b..151bb9d5d86 100644
--- a/tensorflow/core/grappler/utils.cc
+++ b/tensorflow/core/grappler/utils.cc
@@ -357,8 +357,7 @@ void PermuteNodesInPlace(GraphDef* graph, std::vector<int>* permutation,
     }
     permutation->swap(inv_perm);
   }
-  for (int n = 0, permutation_size = permutation->size();
-       n + 1 < permutation_size; ++n) {
+  for (int n = 0, end = permutation->size(); n + 1 < end; ++n) {
     while (n != (*permutation)[n]) {
       std::size_t r = (*permutation)[n];
       graph->mutable_node()->SwapElements(n, r);
diff --git a/tensorflow/core/grappler/utils/topological_sort.cc b/tensorflow/core/grappler/utils/topological_sort.cc
index a7bef1c7014..9f108b0f396 100644
--- a/tensorflow/core/grappler/utils/topological_sort.cc
+++ b/tensorflow/core/grappler/utils/topological_sort.cc
@@ -81,8 +81,8 @@ Status ComputeTopologicalOrder(
     int ready_node = (*ready_nodes)[front];
     for (int fanout : graph_view.GetFanout(ready_node)) {
       ++num_ready_inputs[fanout];
-      if (num_ready_inputs[fanout] ==
-          static_cast<int>(graph_view.GetFanin(fanout).size())) {
+      const int max_size = graph_view.GetFanin(fanout).size();
+      if (num_ready_inputs[fanout] == max_size) {
         ready_nodes->push_back(fanout);
         ++back;
       }
@@ -96,8 +96,8 @@ Status ComputeTopologicalOrder(
                  "at node = "
               << graph.node(back).DebugString();
       for (int i = 0; i < graph_view.num_nodes(); ++i) {
-        if (num_ready_inputs[i] !=
-            static_cast<int>(graph_view.GetFanin(i).size())) {
+        const int max_size = graph_view.GetFanin(i).size();
+        if (num_ready_inputs[i] != max_size) {
           VLOG(1) << "Node not ready: " << graph.node(i).DebugString();
         }
       }
diff --git a/tensorflow/core/profiler/utils/derived_timeline.cc b/tensorflow/core/profiler/utils/derived_timeline.cc
index 43d8305f93c..364ce270439 100644
--- a/tensorflow/core/profiler/utils/derived_timeline.cc
+++ b/tensorflow/core/profiler/utils/derived_timeline.cc
@@ -130,8 +130,7 @@ void DerivedXLineBuilder::ExpandOrAddLevelEvent(const XEvent& event,
 }
 
 void DerivedXLineBuilder::ResetLastEvents(int level) {
-  for (int i = level, iter_limit = last_event_by_level_.size(); i < iter_limit;
-       ++i) {
+  for (int i = level, end = last_event_by_level_.size(); i < end; ++i) {
     last_event_by_level_[i] = absl::nullopt;
   }
   if (level == 0) ResetDependentLines();
diff --git a/tensorflow/core/profiler/utils/xplane_utils.cc b/tensorflow/core/profiler/utils/xplane_utils.cc
index 1fef12580dd..867d1315053 100644
--- a/tensorflow/core/profiler/utils/xplane_utils.cc
+++ b/tensorflow/core/profiler/utils/xplane_utils.cc
@@ -164,7 +164,7 @@ void SortXSpace(XSpace* space) {
 // smaller than these value.
 void NormalizeTimestamps(XPlane* plane, uint64 start_time_ns) {
   for (XLine& line : *plane->mutable_lines()) {
-    if (line.timestamp_ns() >= static_cast<long int>(start_time_ns)) {
+    if (line.timestamp_ns() >= static_cast<int64>(start_time_ns)) {
       line.set_timestamp_ns(line.timestamp_ns() - start_time_ns);
     }
   }
diff --git a/tensorflow/core/util/bcast.h b/tensorflow/core/util/bcast.h
index 075de84964e..0a2c68d3f82 100644
--- a/tensorflow/core/util/bcast.h
+++ b/tensorflow/core/util/bcast.h
@@ -133,13 +133,13 @@ BCastList<N>::BCastList(const BCastList::Vec (&x)[N],
                         const bool return_flattened_batch_indices) {
   typedef BCastList::Vec Vec;
   bool all_equal = true;
-  int largest_rank = 0;
+  size_t largest_rank = 0;
   output_batch_size_ = 1;
   for (int i = 0; i < N; ++i) {
     if (x[i] != x[0]) {
       all_equal = false;
     }
-    if (static_cast<int>(x[i].size()) > largest_rank) {
+    if (x[i].size() > largest_rank) {
       largest_rank = x[i].size();
     }
   }
@@ -176,7 +176,7 @@ BCastList<N>::BCastList(const BCastList::Vec (&x)[N],
 
   // 1-extend and align all vectors.
   for (int i = 0; i < N; ++i) {
-    if (static_cast<int>(copy[i].size()) < largest_rank) {
+    if (copy[i].size() < largest_rank) {
       copy[i].resize(largest_rank, 1);
     }
   }
diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc
index 8a3c940a566..c6c5dfb7b37 100644
--- a/tensorflow/python/framework/python_op_gen.cc
+++ b/tensorflow/python/framework/python_op_gen.cc
@@ -90,7 +90,7 @@ void AddInferredAttr(const string& indentation, const string& attr_name,
 string VectorToTuple(const std::vector<string>& l) {
   if (l.size() == 1) return strings::StrCat("(", l.front(), ",)");
   string ret = "(";
-  for (int i = 0, iter_limit = l.size(); i < iter_limit; ++i) {
+  for (int i = 0, end = l.size(); i < end; ++i) {
     if (i > 0) {
       strings::StrAppend(&ret, ", ");
     }
@@ -102,11 +102,11 @@ string VectorToTuple(const std::vector<string>& l) {
 
 void Unflatten(const string& prefix, const std::vector<string>& output_sizes,
                const string& var, string* result) {
-  for (int i = 0, iter_limit = output_sizes.size(); i < iter_limit; ++i) {
+  for (int i = 0, end = output_sizes.size(); i < end; ++i) {
     if (!output_sizes[i].empty()) {
       strings::StrAppend(result, prefix, var, " = ");
       if (i > 0) strings::StrAppend(result, var, "[:", i, "] + ");
-      if (i + 1 < iter_limit) {
+      if (i + 1 < end) {
         // Special case i == 0 to avoid "0 +" in the generated code.
         if (i == 0) {
           strings::StrAppend(result, "[", var, "[:", output_sizes[i], "]] + ",
@@ -334,8 +334,8 @@ string GenEagerPythonOp::Code() {
   // from the end of params_no_default_, and adding params_no_default_.
   attrs_.reserve(params_no_default_.size() - op_def_.input_arg_size() +
                  params_with_default_.size());
-  for (int i = op_def_.input_arg_size(), iter_limit = params_no_default_.size();
-       i < iter_limit; ++i) {
+  for (int i = op_def_.input_arg_size(), end = params_no_default_.size();
+       i < end; ++i) {
     attrs_.push_back(params_no_default_[i].GetName());
   }
   for (const auto& p : params_with_default_) {
@@ -397,7 +397,7 @@ string GenEagerPythonOp::Code() {
                      parameters_with_defaults.empty() ? "" : ", ", "name=None");
 
   // Add attr_expressions_ for attrs that are params.
-  for (int i = 0, iter_limit = attrs_.size(); i < iter_limit; ++i) {
+  for (int i = 0, end = attrs_.size(); i < end; ++i) {
     const string& attr_name = attrs_[i];
     const string& attr_api_name =
         param_names_[i + op_def_.input_arg_size()].GetRenameTo();
@@ -678,7 +678,7 @@ bool GenEagerPythonOp::GetEagerFunctionSetup(const string& indentation,
     }
   }
 
-  for (int i = 0, iter_limit = attrs_.size(); i < iter_limit; ++i) {
+  for (int i = 0, end = attrs_.size(); i < end; ++i) {
     const string& attr_name = attrs_[i];
     const auto& param = param_names_[i + op_def_.input_arg_size()];
     const auto& attr = *FindAttr(attr_name, op_def_);
diff --git a/tensorflow/python/framework/python_op_gen_internal.cc b/tensorflow/python/framework/python_op_gen_internal.cc
index b345a8da68f..adcde1052fd 100644
--- a/tensorflow/python/framework/python_op_gen_internal.cc
+++ b/tensorflow/python/framework/python_op_gen_internal.cc
@@ -562,12 +562,12 @@ string GenPythonOp::Code() {
   // from the end of args_no_default, and adding args_no_default.
   attrs_.reserve(params_no_default.size() - op_def_.input_arg_size() +
                  params_with_default.size());
-  for (int i = op_def_.input_arg_size(), iter_limit = params_no_default.size();
-       i < iter_limit; ++i) {
+  for (int i = op_def_.input_arg_size(), end = params_no_default.size();
+       i < end; ++i) {
     attrs_.push_back(params_no_default[i].GetName());
   }
-  for (const auto& param : params_with_default) {
-    attrs_.push_back(param.GetName());
+  for (int i = 0, end = params_with_default.size(); i < end; ++i) {
+    attrs_.push_back(params_with_default[i].GetName());
   }
 
   param_names_.reserve(params_no_default.size() + params_with_default.size());