[-Wsign-compare] warning fixes batch 4

tensorflow/compiler/xla/service/name_uniquer.cc

@@ -51,7 +51,7 @@ NameUniquer::NameUniquer(const string& separator) {
   if (!absl::ascii_isalpha(c) && c != '_') {
     result[0] = '_';
   }
-  for (int i = 1; i < result.length(); i++) {
+  for (int i = 1, iter_limit = result.length(); i < iter_limit; i++) {
     if (!IsAllowed(result[i])) {
       result[i] = '_';
     }

tensorflow/core/framework/node_def_util.cc

@@ -509,7 +509,8 @@ Status InputTypeForNode(const NodeDef& node_def, const OpDef& op_def,
   DataTypeVector input_types;
   for (const auto& arg : op_def.input_arg()) {
     TF_RETURN_IF_ERROR(AddArgToSig(node_def, arg, &input_types));
-    if (input_types.size() > input_port) {
+    int input_types_size = input_types.size();
+    if (input_types_size > input_port) {
       const DataType dtype = input_types[input_port];
       *input_type = dtype;
       return Status::OK();
@@ -532,7 +533,8 @@ Status OutputTypeForNode(const NodeDef& node_def, const OpDef& op_def,
   DataTypeVector output_types;
   for (const auto& arg : op_def.output_arg()) {
     TF_RETURN_IF_ERROR(AddArgToSig(node_def, arg, &output_types));
-    if (output_types.size() > output_port) {
+    int output_types_size = output_types.size(); 
+    if (output_types_size > output_port) {
       const DataType dtype = output_types[output_port];
       *output_type = dtype;
       return Status::OK();

tensorflow/core/framework/op_def_util.cc

@@ -661,7 +661,7 @@ Status OpDefCompatible(const OpDef& old_op, const OpDef& new_op) {
            "' vs. '", new_in_sig, "'");
   VALIDATE(old_in_ref.size() == new_in_ref.size(),  // Should not happen
            "Unexpected change in input ref lists.");
-  for (int i = 0; i < old_in_ref.size(); ++i) {
+  for (int i = 0, iter_limit = old_in_ref.size(); i < iter_limit; ++i) {
     // Allowed to remove "ref" from an input (or leave it unchanged).
     VALIDATE(old_in_ref[i] || !new_in_ref[i], "Input ", i,
              " changed from non-ref to ref");
@@ -677,7 +677,7 @@ Status OpDefCompatible(const OpDef& old_op, const OpDef& new_op) {
            old_out_sig, "' vs. '", new_out_sig, "'");
   VALIDATE(old_out_ref.size() == new_out_ref.size(),  // Should not happen
            "Unexpected change in output ref lists");
-  for (int i = 0; i < old_out_ref.size(); ++i) {
+  for (int i = 0, iter_limit = old_in_ref.size(); i < iter_limit; ++i) {
     // Allowed to add "ref" to an output (or leave it unchanged).
     VALIDATE(!old_out_ref[i] || new_out_ref[i], "Output ", i,
              " changed from ref to non-ref");

tensorflow/core/kernels/data/captured_function.cc

@@ -115,7 +115,7 @@ Status RunShortCircuit(const ShortCircuitInfo& info,
                        const CapturedFunction* const func,
                        std::vector<Tensor>* rets) {
   VLOG(3) << "Running function " << func->func().name() << " short circuit";
-  size_t num_args = args.size();
+  const int num_args = args.size();
   rets->reserve(info.indices.size());
   for (size_t i = 0; i < info.indices.size(); ++i) {
     if (info.indices[i] < num_args) {
@@ -131,7 +131,7 @@ Status RunShortCircuit(const ShortCircuitInfo& info, std::vector<Tensor>&& args,
                        const CapturedFunction* const func,
                        std::vector<Tensor>* rets) {
   VLOG(3) << "Running function " << func->func().name() << " short circuit";
-  size_t num_args = args.size();
+  const int num_args = args.size();
   rets->reserve(info.indices.size());
   for (size_t i = 0; i < info.indices.size(); ++i) {
     if (info.indices[i] < num_args) {
@@ -198,7 +198,7 @@ Status CreateShortCircuitInfo(OpKernelConstruction* ctx,
       last_use[indices[i]] = i;
     }
     can_move.resize(indices.size());
-    for (size_t i = 0; i < indices.size(); ++i) {
+    for (int i = 0, iter_limit = indices.size(); i < iter_limit; ++i) {
       can_move[i] = last_use[indices[i]] == i;
     }
   }
@@ -278,11 +278,12 @@ class CallFrameBase : public CallFrameInterface {
 
   // Callee methods.
   Status SetRetval(int index, const Tensor& val) override {
-    if (index < retvals_.size() && val.dtype() == ret_types_[index] &&
+    const int retvals_size_ = retvals_.size();
+    if (index < retvals_size_ && val.dtype() == ret_types_[index] &&
         !retvals_[index]) {
       retvals_[index] = val;
       return Status::OK();
-    } else if (index >= retvals_.size()) {
+    } else if (index >= retvals_size_) {
       return errors::InvalidArgument("Return value ", index,
                                      " is out of range.");
     } else if (val.dtype() != ret_types_[index]) {
@@ -317,10 +318,12 @@ class OwnedArgsCallFrame : public CallFrameBase {
 
   // Callee methods.
   Status GetArg(int index, const Tensor** val) override {
-    if (index < args_.size()) {
+    const int args_size_ = args_.size();
+    const int captured_inputs_size_ = captured_inputs_->size();
+    if (index < args_size_) {
       *val = &args_[index];
       return Status::OK();
-    } else if (index < args_.size() + captured_inputs_->size()) {
+    } else if (index < args_size_ + captured_inputs_size_ ) {
       *val = &(*captured_inputs_)[index - args_.size()];
       return Status::OK();
     } else {
@@ -336,7 +339,7 @@ class OwnedArgsCallFrame : public CallFrameBase {
     *val = std::move(args_[index]);
   }
   bool CanConsumeArg(int index) const override {
-    return index >= 0 && index < args_.size();
+    return index >= 0 && index < static_cast<int>(args_.size());
   }
 
  private:
@@ -359,11 +362,13 @@ class BorrowedArgsCallFrame : public CallFrameBase {
 
   // Callee methods.
   Status GetArg(int index, const Tensor** val) override {
-    if (index < args_.size()) {
+    const int args_size_ = args_.size(); 
+    const int captured_inputs_size_ = captured_inputs_->size(); 
+    if (index < args_size_ ) {
       *val = &args_[index];
       return Status::OK();
-    } else if (index < args_.size() + captured_inputs_->size()) {
+    } else if (index < args_size_ + captured_inputs_size_) {
-      *val = &(*captured_inputs_)[index - args_.size()];
+      *val = &(*captured_inputs_)[index - args_size_];
       return Status::OK();
     } else {
       return errors::InvalidArgument("Argument ", index, " is out of range.");
@@ -613,7 +618,7 @@ Status CapturedFunction::Instantiate(
       }
     }
 
-    for (size_t i = 0; i < fdef->signature().output_arg_size(); ++i) {
+    for (int i = 0, iter_limit = fdef->signature().output_arg_size(); i < iter_limit; ++i) {
       inst_opts.output_devices.push_back(inst_opts.target);
     }
 

tensorflow/core/kernels/data/single_threaded_executor.cc

@@ -51,8 +51,8 @@ class SingleThreadedExecutorImpl : public Executor {
     std::vector<Node*> ordered_nodes;
     ordered_nodes.reserve(graph.num_nodes());
     GetReversePostOrder(graph, &ordered_nodes);
-
+    int ordered_nodes_size = ordered_nodes.size();
-    if (ordered_nodes.size() != graph.num_nodes()) {
+    if (ordered_nodes_size != graph.num_nodes()) {
       return errors::InvalidArgument("Graph had ", graph.num_nodes(),
                                      " but reverse post-order had ",
                                      ordered_nodes.size());

tensorflow/core/kernels/initializable_lookup_table.cc

@@ -74,7 +74,7 @@ Status InitializableLookupTable::Initialize(InitTableIterator& iter) {
 
 Status InitializableLookupTable::AreEntriesSame(const InitTableIterator& iter,
                                                 bool* result) {
-  *result = iter.total_size() == size();
+  *result = static_cast<size_t>(iter.total_size()) == size();
   return Status::OK();
 }
 

tensorflow/core/kernels/lookup_util.cc

@@ -132,7 +132,7 @@ class TextFileLineIterator
     std::vector<string> tokens;
     if (!ignore_split_) {
       tokens = str_util::Split(line, delimiter_);
-      if (std::max(key_index_, value_index_) >= tokens.size()) {
+      if ( static_cast<size_t>(std::max(key_index_, value_index_)) >= tokens.size()) {
         status_ = errors::InvalidArgument(
             "Invalid number of columns in ", filename_, " line ", next_id_,
             " (", line, ") : expected ", std::max(key_index_, value_index_),

tensorflow/core/profiler/convert/op_metrics_to_record.cc

@@ -37,8 +37,8 @@ std::vector<const OpMetrics*> SortedOpMetricsDb(const OpMetricsDb& metrics_db,
     return std::make_tuple(a->self_time_ps(), b->name()) >
            std::make_tuple(b->self_time_ps(), a->name());
   };
-
+  int result_size = result.size();
-  if (max_records != -1 && result.size() > max_records) {
+  if (max_records != -1 && result_size > max_records) {
     absl::c_partial_sort(result, result.begin() + max_records, comp);
     result.resize(max_records);
   } else {

tensorflow/core/profiler/convert/xplane_to_tf_functions.cc

@@ -206,7 +206,7 @@ class TfFunctionExecutions {
 
   std::string DebugString() const {
     std::string result = "\nActivations:\n";
-    for (auto i = 0; i < activations_.size(); i++) {
+    for (int i = 0, iter_limit = activations_.size(); i < iter_limit; i++) {
       absl::StrAppend(&result, "[", i, "] ", activations_[i].DebugString(),
                       "\n");
     }

tensorflow/core/profiler/utils/event_span.cc

@@ -128,7 +128,7 @@ std::vector<EventTypeSpan> ToNonOverlappedEvents(
   if (event_boundaries.empty()) return result;
   result.reserve(event_boundaries.size());
   PriorityTracker priority_tracker;
-  for (int64 i = 0; i < (event_boundaries.size() - 1); i++) {
+  for (int64 i = 0, iter_limit = (event_boundaries.size() - 1); i < iter_limit; i++) {
     EventType highest_priority = priority_tracker.Update(event_boundaries[i]);
     result.push_back({highest_priority, Timespan::FromEndPoints(
                                             event_boundaries[i].time_ps,
@@ -325,12 +325,12 @@ Timespan StepDetails::StepTime() const {
 
 std::string StepDetails::DebugString() const {
   std::string result = "([";
-  for (int i = 0; i < markers_.size(); i++) {
+  for (int i = 0, iter_limit = markers_.size(); i < iter_limit; i++) {
     if (i > 0) absl::StrAppend(&result, ", ");
     absl::StrAppend(&result, PrintStepMarker(markers_[i]));
   }
   absl::StrAppend(&result, "], [");
-  for (int i = 0; i < events_.size(); i++) {
+  for (int i = 0, iter_limit = events_.size(); i < iter_limit; i++) {
     if (i > 0) absl::StrAppend(&result, ", ");
     absl::StrAppend(&result, PrintEventTypeSpan(events_[i]));
   }

tensorflow/core/util/padding.cc

@@ -37,7 +37,8 @@ Status CheckValidPadding(Padding padding_type,
                          const std::vector<int64>& explicit_paddings,
                          int num_dims, TensorFormat data_format) {
   if (padding_type == Padding::EXPLICIT) {
-    if (explicit_paddings.size() != 2 * num_dims) {
+    int explicit_paddings_size = explicit_paddings.size(); 
+    if (explicit_paddings_size != 2 * num_dims) {
       return errors::InvalidArgument(
           "explicit_paddings attribute must contain ", 2 * num_dims,
           " values, but got: ", explicit_paddings.size());

tensorflow/python/client/session_ref.cc

@@ -146,7 +146,7 @@ class SessionLogger {
     // Build an index from fetch tensor name to first index in
     // output_tensor_names.
     std::unordered_map<string, int> output_name_to_offset;
-    for (int i = 0; i < output_tensor_names.size(); ++i) {
+    for (int i = 0, iter_limit = output_tensor_names.size(); i < iter_limit; ++i) {
       const string& name = output_tensor_names[i];
       if (output_name_to_offset.insert(std::make_pair(name, i)).second) {
         req->add_fetch(name);

tensorflow/python/framework/python_op_gen.cc

@@ -63,7 +63,7 @@ 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; i < l.size(); ++i) {
+  for (int i = 0, iter_limit = l.size(); i < iter_limit; ++i) {
     if (i > 0) {
       strings::StrAppend(&ret, ", ");
     }
@@ -75,11 +75,11 @@ 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; i < output_sizes.size(); ++i) {
+  for (int i = 0, iter_limit = output_sizes.size(); i < iter_limit; ++i) {
     if (!output_sizes[i].empty()) {
       strings::StrAppend(result, prefix, var, " = ");
       if (i > 0) strings::StrAppend(result, var, "[:", i, "] + ");
-      if (i + 1 < output_sizes.size()) {
+      if (i + 1 < iter_limit) {
         // Special case i == 0 to avoid "0 +" in the generated code.
         if (i == 0) {
           strings::StrAppend(result, "[", var, "[:", output_sizes[i], "]] + ",
@@ -295,7 +295,7 @@ 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(); i < params_no_default_.size(); ++i) {
+  for (int i = op_def_.input_arg_size(), iter_limit = params_no_default_.size(); i < iter_limit; ++i) {
     attrs_.push_back(params_no_default_[i].GetName());
   }
   for (const auto& p : params_with_default_) {
@@ -331,7 +331,7 @@ string GenEagerPythonOp::Code() {
                      parameters_with_defaults.empty() ? "" : ", ", "name=None");
 
   // Add attr_expressions_ for attrs that are params.
-  for (int i = 0; i < attrs_.size(); ++i) {
+  for (int i = 0, iter_limit = attrs_.size(); i < iter_limit; ++i) {
     const string& attr_name = attrs_[i];
     const string& attr_api_name =
         param_names_[i + op_def_.input_arg_size()].GetRenameTo();
@@ -522,7 +522,7 @@ bool GenEagerPythonOp::GetEagerFunctionSetup(const string& indentation,
     }
   }
 
-  for (int i = 0; i < attrs_.size(); ++i) {
+  for (int i = 0, iter_limit = attrs_.size(); i < iter_limit; ++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_);

tensorflow/python/framework/python_op_gen_internal.cc

@@ -561,10 +561,10 @@ 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(); i < params_no_default.size(); ++i) {
+  for (int i = op_def_.input_arg_size(), iter_limit = params_no_default.size(); i < iter_limit; ++i) {
     attrs_.push_back(params_no_default[i].GetName());
   }
-  for (int i = 0; i < params_with_default.size(); ++i) {
+  for (int i = 0, iter_limit = params_with_default.size(); i < iter_limit; ++i) {
     attrs_.push_back(params_with_default[i].GetName());
   }
 

tensorflow/stream_executor/device_description.cc

@@ -127,7 +127,7 @@ bool ThreadDimOk(const DeviceDescription &device_description,
                  const ThreadDim &thread_dim) {
   auto total_threads = thread_dim.x * thread_dim.y * thread_dim.z;
   auto threads_per_block_limit = device_description.threads_per_block_limit();
-  if (total_threads > threads_per_block_limit) {
+  if (total_threads > static_cast<long long unsigned int>(threads_per_block_limit)) {
     VLOG(2) << "exceeded total-thread-per-block limit: " << total_threads
             << " vs limit " << threads_per_block_limit;
     return false;