Remove all 64/32 bit warnings in tensorflow/cc

Change: 153637886

5 files changed, 30 insertions, 30 deletions

diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc
index 26f15975c1..b7e9948e9d 100644
--- a/tensorflow/cc/framework/cc_op_gen.cc
+++ b/tensorflow/cc/framework/cc_op_gen.cc
@@ -730,7 +730,7 @@ void OpInfo::GetOutput(string* out) const {
     // One output, no need for NameRangeMap
     if (is_list_output[0]) {
       strings::StrAppend(out,
-                         "  for (int64 i = 0; i < ret->num_outputs(); ++i)\n");
+                         "  for (int32 i = 0; i < ret->num_outputs(); ++i)\n");
       strings::StrAppend(out, "    this->", output_names[0],
                          ".push_back(Output(ret, i));\n");
     } else {
@@ -753,7 +753,7 @@ void OpInfo::GetOutput(string* out) const {
     const string arg_range = strings::StrCat(
         "_outputs_range[\"", graph_op_def.output_arg(i).name(), "\"]");
     if (is_list_output[i]) {
-      strings::StrAppend(out, "  for (int64 i = ", arg_range, ".first; i < ",
+      strings::StrAppend(out, "  for (int32 i = ", arg_range, ".first; i < ",
                          arg_range, ".second; ++i)\n");
       strings::StrAppend(out, "    this->", output_names[i],
                          ".push_back(Output(ret, i));\n");
diff --git a/tensorflow/cc/framework/gradient_checker.cc b/tensorflow/cc/framework/gradient_checker.cc
index 849a8eed6f..8f20ff1457 100644
--- a/tensorflow/cc/framework/gradient_checker.cc
+++ b/tensorflow/cc/framework/gradient_checker.cc
@@ -40,8 +40,8 @@ Status ComputeTheoreticalJacobianTranspose(
     const std::vector<Tensor>& x_datas, const OutputList& ys,
     const std::vector<TensorShape>& y_shapes,
     std::vector<Tensor>& jacobian_ts) {
-  int y_num = y_shapes.size();
-  int x_num = x_shapes.size();
+  size_t y_num = y_shapes.size();
+  size_t x_num = x_shapes.size();
   // Call AddSymbolicGradients to get 'dxs' (we will feed 'dys').
   OutputList dys;
   for (const auto& y_shape : y_shapes) {
@@ -130,8 +130,8 @@ Status ComputeNumericJacobianTranspose(const Scope& scope, const OutputList& xs,
                                        const T delta,
                                        std::vector<Tensor>& x_datas,
                                        std::vector<Tensor>& jacobian_ts) {
-  int y_num = y_shapes.size();
-  int x_num = x_shapes.size();
+  size_t y_num = y_shapes.size();
+  size_t x_num = x_shapes.size();
 
   ClientSession session(scope);
   for (int x_idx = 0; x_idx < x_num; x_idx++) {
@@ -176,8 +176,8 @@ void InitJacobians(const OutputList& xs,
                    const std::vector<TensorShape>& x_shapes,
                    const std::vector<TensorShape>& y_shapes,
                    std::vector<Tensor>& jacobians) {
-  int y_num = y_shapes.size();
-  int x_num = x_shapes.size();
+  size_t y_num = y_shapes.size();
+  size_t x_num = x_shapes.size();
 
   jacobians.resize(y_num * x_num);
   for (int x_idx = 0; x_idx < x_num; x_idx++) {
diff --git a/tensorflow/cc/framework/gradients.cc b/tensorflow/cc/framework/gradients.cc
index 4ada9351ca..8c00a6f704 100644
--- a/tensorflow/cc/framework/gradients.cc
+++ b/tensorflow/cc/framework/gradients.cc
@@ -210,8 +210,8 @@ Status SymbolicGradientBuilder::Initialize() {
 
   {
     // Initialize backprop with `grad_inputs_`.
-    const int num_dy = grad_inputs_.size();
-    for (int i = 0; i < num_dy; ++i) {
+    const size_t num_dy = grad_inputs_.size();
+    for (size_t i = 0; i < num_dy; ++i) {
       TF_RETURN_IF_ERROR(BackpropAlongEdge(grad_inputs_[i], outputs_[i]));
     }
   }
@@ -308,7 +308,7 @@ Status SymbolicGradientBuilder::AddGradients() {
       continue;
     }
 
-    const int num_no_grad = no_grad_dy_indices.size();
+    const size_t num_no_grad = no_grad_dy_indices.size();
     if (IsPrimitiveOpWithNoGrad(n->type_string()) || num_no_grad == num_y) {
       // No grad defined for this op, or all outputs returned 'NoGradient':
       // Backprop 'NoGradient' along the in edges.
diff --git a/tensorflow/cc/framework/ops.cc b/tensorflow/cc/framework/ops.cc
index 50df891a4c..920a8e7955 100644
--- a/tensorflow/cc/framework/ops.cc
+++ b/tensorflow/cc/framework/ops.cc
@@ -20,7 +20,7 @@ namespace tensorflow {
 
 Operation::Operation(Node* n) : inputs_(GetInputs(n)), node_(n) {}
 
-Output Operation::input(int i) const {
+Output Operation::input(int32 i) const {
   CHECK_NOTNULL(node_);
   CHECK_GE(i, 0);
   CHECK_LT(i, node_->num_inputs());
@@ -37,14 +37,14 @@ Output Operation::input(int i) const {
   return Output(inputs_[i].first, inputs_[i].second);
 }
 
-Output Operation::output(int i) const {
+Output Operation::output(int32 i) const {
   CHECK_NOTNULL(node_);
   CHECK_GE(i, 0);
   CHECK_LT(i, node_->num_outputs());
   return Output(node_, i);
 }
 
-uint64 Operation::hash(int64 index) const {
+uint64 Operation::hash(int32 index) const {
   return ::tensorflow::Hash64(reinterpret_cast<const char*>(&node_),
                               sizeof(Node*), index);
 }
diff --git a/tensorflow/cc/framework/ops.h b/tensorflow/cc/framework/ops.h
index 889d5db31d..8d4154220c 100644
--- a/tensorflow/cc/framework/ops.h
+++ b/tensorflow/cc/framework/ops.h
@@ -39,22 +39,22 @@ class Operation {
   Operation() : node_(nullptr) {}
   explicit Operation(Node* n);
 
-  int num_inputs() const { return node_->num_inputs(); }
-  DataType input_type(int o) const { return node_->input_type(o); }
-  Output input(int i) const;
+  int32 num_inputs() const { return node_->num_inputs(); }
+  DataType input_type(int32 o) const { return node_->input_type(o); }
+  Output input(int32 i) const;
 
-  int num_outputs() const { return node_->num_outputs(); }
-  DataType output_type(int o) const { return node_->output_type(o); }
-  Output output(int i) const;
+  int32 num_outputs() const { return node_->num_outputs(); }
+  DataType output_type(int32 o) const { return node_->output_type(o); }
+  Output output(int32 i) const;
 
   Node* node() const { return node_; }
 
-  uint64 hash(int64 index) const;
+  uint64 hash(int32 index) const;
 
   bool operator==(const Operation& other) const { return node_ == other.node_; }
 
  private:
-  typedef std::vector<std::pair<Node*, int64>> Inputs;
+  typedef std::vector<std::pair<Node*, int32>> Inputs;
   static Inputs GetInputs(Node* node);
 
   Inputs inputs_;
@@ -66,12 +66,12 @@ class Output {
  public:
   Output() = default;
   explicit Output(Node* n) : op_(n) {}
-  Output(Node* n, int64 index) : op_(n), index_(index) {}
-  Output(const Operation& op, int64 index) : op_(op), index_(index) {}
+  Output(Node* n, int32 index) : op_(n), index_(index) {}
+  Output(const Operation& op, int32 index) : op_(op), index_(index) {}
 
   Operation op() const { return op_; }
   Node* node() const { return op().node(); }
-  int64 index() const { return index_; }
+  int32 index() const { return index_; }
   DataType type() const { return op_.output_type(index_); }
   string name() const { return strings::StrCat(node()->name(), ":", index()); }
   bool operator==(const Output& other) const {
@@ -82,14 +82,14 @@ class Output {
 
  private:
   Operation op_ = Operation(nullptr);
-  int64 index_ = 0;
+  int32 index_ = 0;
 };
 
 /// Hash class that can be used for e.g. storing Outputs in an unordered_map
 struct OutputHash {
   std::size_t operator()(const Output& output) const {
     return Hash64Combine(std::hash<Node*>()(output.node()),
-                         std::hash<int64>()(output.index()));
+                         std::hash<int32>()(output.index()));
   }
 };
 
@@ -230,12 +230,12 @@ class Input {
 
   /// Constructor specifying a node name, index and datatype. This should only
   /// be used for specifying a backward edge, needed by control flow.
-  Input(const string& name, int i, DataType dt)
+  Input(const string& name, int32 i, DataType dt)
       : node_name_(name), index_(i), data_type_(dt) {}
 
   Node* node() const { return output_.node(); }
   string node_name() const { return node_name_; }
-  int index() const { return node_name_.empty() ? output_.index() : index_; }
+  int32 index() const { return node_name_.empty() ? output_.index() : index_; }
   DataType data_type() const { return data_type_; }
   Status status() const { return status_; }
   const Tensor& tensor() const { return tensor_; }
@@ -245,7 +245,7 @@ class Input {
   Output output_ = Output(Operation(nullptr), 0);
   Tensor tensor_;
   const string node_name_ = "";
-  int index_ = 0;
+  int32 index_ = 0;
   DataType data_type_ = DT_INVALID;
 };