Merge pull request #3460 from jacquesqiao/public_to_protected

change operator public to protected

paddle/framework/backward.cc

@@ -22,7 +22,7 @@ namespace paddle {
 namespace framework {
 
 template <typename Map, typename T>
-static void ForEachVarName(Map& names, T callback) {
+static void ForEachVarName(const Map& names, T callback) {
   for (auto& name : names) {
     for (auto& n : name.second) {
       if (callback(n)) return;
@@ -44,7 +44,7 @@ static bool AllInSet(
 
 static std::shared_ptr<OperatorBase> NOP() {
   auto net_op = std::make_shared<operators::NetOp>();
-  net_op->type_ = "@NOP@";
+  net_op->SetType("@NOP@");
   net_op->CompleteAddOp();
   return net_op;
 }
@@ -70,18 +70,18 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
     std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
   //  If all input gradients of forwarding operator do not need to calculate,
   //  just return an NOP. Not return null ptr because NOP does not take
-  //  much time for calculation, but it is useful for simplifying logic.
-  if (AllInSet(forwardOp.inputs_ /*names*/, kGradVarSuffix /*suffix*/,
+  //  too much time for calculation, but it is useful for simplifying logic.
+  if (AllInSet(forwardOp.Inputs() /*names*/, kGradVarSuffix /*suffix*/,
                no_grad_names /*set*/)) {
     return NOP();
   }
 
   //  All output gradients of forwarding operator do not need to calculate.
   //  Then all input gradients cannot be computed at all, and we put them into
   //  `no_grad_names` set. Return an NOP.
-  if (AllInSet(forwardOp.outputs_ /*names*/, kGradVarSuffix /*suffix*/,
+  if (AllInSet(forwardOp.Outputs() /*names*/, kGradVarSuffix /*suffix*/,
                no_grad_names /*set*/)) {
-    ForEachVarName(forwardOp.inputs_,
+    ForEachVarName(forwardOp.Inputs(),
                    [&no_grad_names](const std::string& name) -> bool {
                      no_grad_names.insert(GradVarName(name));
                      return false;
@@ -107,7 +107,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
       auto fwd = *it;
       auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id);
       net->AddOp(bwd);
-      ForEachVarName(bwd->outputs_,
+      ForEachVarName(bwd->Outputs(),
                      [&dup_output_ops, local_op_id](const std::string& out) {
                        dup_output_ops[out].emplace_back(local_op_id);
                        return false;
@@ -154,13 +154,13 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
   } else {
     std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
 
-    ForEachVarName(grad_op->inputs_, [&no_grad_names,
-                                      &net](std::string& grad_input) {
+    ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net,
+                                       grad_op](const std::string& grad_input) {
       if (no_grad_names.count(grad_input)) {
         // +1 for \0
         std::string prefix = grad_input.substr(
             0, grad_input.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
-        grad_input = prefix + kZeroVarSuffix;
+        grad_op->Rename(grad_input, prefix + kZeroVarSuffix);
 
         // If part of input gradient of that operator is not calculated, fill
         // zero variables to that input gradient.
@@ -170,10 +170,10 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
       return false;
     });
 
-    ForEachVarName(grad_op->outputs_,
-                   [&no_grad_names](std::string& grad_output) {
+    ForEachVarName(grad_op->Outputs(),
+                   [&no_grad_names, &grad_op](const std::string& grad_output) {
                      if (no_grad_names.count(grad_output)) {
-                       grad_output = kEmptyVarName;
+                       grad_op->Rename(grad_output, kEmptyVarName);
                      }
                      return false;
                    });
@@ -183,7 +183,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
     }
     net->AddOp(grad_op);
   }
-  net->type_ = "@GENERATED_BACKWARD@";
+  net->SetType("@GENERATED_BACKWARD@");
   net->CompleteAddOp();
   return net;
 }  // namespace framework

paddle/framework/backward_test.cc

@@ -164,8 +164,8 @@ TEST(Backward, simple_op_grad) {
       "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
   ASSERT_NE(fwd, nullptr);
   auto gop = f::OpRegistry::CreateGradOp(*fwd);
-  ASSERT_EQ(1UL, gop->inputs_.size());
-  ASSERT_EQ("rowwise_add_grad", gop->type_);
+  ASSERT_EQ(1UL, gop->Inputs().size());
+  ASSERT_EQ("rowwise_add_grad", gop->Type());
   ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
   ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
 }
@@ -201,13 +201,13 @@ TEST(Backward, net_fc_backward_normal) {
   ASSERT_EQ(3UL, net->ops_.size());
 
   f::OperatorBase &d_sigmoid = *net->ops_[0];
-  ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
+  ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
 
   f::OperatorBase &d_add = *net->ops_[1];
-  ASSERT_EQ("rowwise_add_grad", d_add.type_);
+  ASSERT_EQ("rowwise_add_grad", d_add.Type());
 
   f::OperatorBase &d_mul = *net->ops_[2];
-  ASSERT_EQ("mul_grad", d_mul.type_);
+  ASSERT_EQ("mul_grad", d_mul.Type());
 }
 
 TEST(Backward, net_fc_backward_not_have_b) {
@@ -227,10 +227,10 @@ TEST(Backward, net_fc_backward_not_have_b) {
   ASSERT_EQ(2UL, net->ops_.size());
 
   f::OperatorBase &d_sigmoid = *net->ops_[0];
-  ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
+  ASSERT_EQ("sigmoid_grad", d_sigmoid.Type());
 
   f::OperatorBase &d_mul = *net->ops_[1];
-  ASSERT_EQ("mul_grad", d_mul.type_);
+  ASSERT_EQ("mul_grad", d_mul.Type());
 }
 
 TEST(Backward, net_input_of_network_not_need_grad) {
@@ -284,7 +284,7 @@ TEST(Backward, net_shared_weight) {
   ASSERT_TRUE(bwd->IsNetOp());
   auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
   ASSERT_EQ(3UL, bwd_net->ops_.size());
-  ASSERT_EQ("add", bwd_net->ops_[2]->type_);
+  ASSERT_EQ("add", bwd_net->ops_[2]->Type());
 }
 
 TEST(Backward, op_register_grad_not_for_network) {
@@ -325,15 +325,15 @@ TEST(Backward, op_part_of_output_are_not_need) {
   ASSERT_EQ(net->ops_.size(), 2UL);
 
   auto &fill_zero = *net->ops_[0];
-  ASSERT_EQ("fill_zeros_like", fill_zero.type_);
+  ASSERT_EQ("fill_zeros_like", fill_zero.Type());
   ASSERT_EQ(1UL, fill_zero.Inputs("Src").size());
   ASSERT_EQ("Z", fill_zero.Input("Src"));
   ASSERT_EQ(1UL, fill_zero.Outputs("Dst").size());
   ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.Output("Dst"));
 
   auto &d_many_out = *net->ops_[1];
-  ASSERT_EQ("many_output_op_grad", d_many_out.type_);
-  ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size());  // I/O/OG
+  ASSERT_EQ("many_output_op_grad", d_many_out.Type());
+  ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.Inputs().size());  // I/O/OG
   ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix,
             d_many_out.Input(f::GradVarName("z")));
   ASSERT_EQ(f::GradVarName("Y"), d_many_out.Input(f::GradVarName("y")));
@@ -345,9 +345,9 @@ TEST(Backward, op_part_of_input_are_not_need) {
                                      {{"Out", {"out"}}}, {});
   auto backward = f::Backward(*fwd, {"a"});
   auto &grad_mul = *backward;
-  ASSERT_EQ(grad_mul.type_, "mul_grad");
-  ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
-  ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
+  ASSERT_EQ(grad_mul.Type(), "mul_grad");
+  ASSERT_EQ(grad_mul.Inputs().size(), 2UL + 1UL + 1UL);
+  ASSERT_EQ(grad_mul.Outputs().size(), 2UL);
   ASSERT_EQ(grad_mul.Output(f::GradVarName("X")), f::kEmptyVarName);
   ASSERT_EQ(grad_mul.Output(f::GradVarName("Y")), f::GradVarName("b"));
   ASSERT_EQ(grad_mul.Input(f::GradVarName("Out")), f::GradVarName("out"));
@@ -385,18 +385,18 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
   auto &grad_fc = *bwd_net->ops_[0];
 
   const char *all = paddle::operators::NetOp::kAll;
-  EXPECT_EQ(grad_fc.inputs_[all].size(),
+  EXPECT_EQ(grad_fc.Inputs(all).size(),
             2UL       /* external input number */
                 + 1UL /* external output number*/
                 + 1UL /* number of gradient of external output*/
                 + 2U /* internal variable number*/);
-  EXPECT_EQ(grad_fc.outputs_[all].size(),
+  EXPECT_EQ(grad_fc.Outputs(all).size(),
             2UL       /* input number of mul*/
                 + 2UL /* input number of rowwise_add
                        */
                 + 1UL /* input number of sigmod */);
-  EXPECT_EQ(bwd_net->ops_[1]->inputs_[all].size(), 0UL);
-  EXPECT_EQ(bwd_net->ops_[1]->outputs_[all].size(), 0UL);
-  EXPECT_EQ(bwd_net->ops_[2]->inputs_[all].size(), 0UL);
-  EXPECT_EQ(bwd_net->ops_[2]->outputs_[all].size(), 0UL);
+  EXPECT_EQ(bwd_net->ops_[1]->Inputs(all).size(), 0UL);
+  EXPECT_EQ(bwd_net->ops_[1]->Outputs(all).size(), 0UL);
+  EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
+  EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL);
 }

paddle/framework/grad_op_builder.cc

@@ -13,7 +13,6 @@ express or implied. See the License for the specific language governing
 permissions and limitations under the License. */
 
 #include "paddle/framework/grad_op_builder.h"
-#include "paddle/framework/framework.pb.h"
 #include "paddle/framework/op_registry.h"
 
 namespace paddle {
@@ -23,9 +22,9 @@ enum class OpArgType { IN, OUT };
 static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
                        bool is_grad, OperatorBase::VarNameMap* vars) {
   const auto& src_inout =
-      src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
+      src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs();
   auto& dst_inout = *vars;
-  const OpProto* proto = OpRegistry::op_info_map().at(src_op->type_).proto_;
+  const OpProto* proto = OpRegistry::op_info_map().at(src_op->Type()).proto_;
   const auto& src_arg_list =
       src_type == OpArgType::IN ? proto->inputs() : proto->outputs();
   for (const auto& arg : src_arg_list) {
@@ -41,14 +40,14 @@ static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
 }
 
 OperatorBase* BuildGradOp(const OperatorBase* op) {
-  auto it = OpRegistry::op_info_map().find(op->type_);
+  auto it = OpRegistry::op_info_map().find(op->Type());
   PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(),
-                 "'%s' has not been registered.", op->type_);
+                 "'%s' has not been registered.", op->Type());
   PADDLE_ENFORCE(it->second.proto_ != nullptr, "'%s' has no OpProto.",
-                 op->type_);
+                 op->Type());
   std::string grad_op_type = it->second.grad_op_type_;
   PADDLE_ENFORCE(!grad_op_type.empty(), "'%s' has no gradient operator.",
-                 op->type_);
+                 op->Type());
 
   OperatorBase::VarNameMap inputs;
   OperatorBase::VarNameMap outputs;
@@ -60,7 +59,7 @@ OperatorBase* BuildGradOp(const OperatorBase* op) {
   it = OpRegistry::op_info_map().find(grad_op_type);
   PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(),
                  "'%s' has not been registered.", grad_op_type);
-  return it->second.creator_(grad_op_type, inputs, outputs, op->attrs_);
+  return it->second.creator_(grad_op_type, inputs, outputs, op->Attrs());
 }
 
 }  // namespace framework

paddle/framework/grad_op_builder_test.cc

@@ -44,8 +44,8 @@ TEST(GradOpBuilder, AddTwo) {
       "add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
   std::shared_ptr<f::OperatorBase> grad_add_op =
       f::OpRegistry::CreateGradOp(*add_op);
-  EXPECT_EQ(grad_add_op->inputs_.size(), 4UL);
-  EXPECT_EQ(grad_add_op->outputs_.size(), 2UL);
+  EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
+  EXPECT_EQ(grad_add_op->Outputs().size(), 2UL);
   EXPECT_EQ(grad_add_op->Input("X"), "x");
   EXPECT_EQ(grad_add_op->Input("Y"), "y");
   EXPECT_EQ(grad_add_op->Input("Out"), "out");
@@ -66,7 +66,7 @@ TEST(GradOpBuilder, MutiInOut) {
   std::shared_ptr<f::OperatorBase> grad_test_op =
       f::OpRegistry::CreateGradOp(*test_op);
 
-  ASSERT_EQ(grad_test_op->inputs_.size(), 3UL + 2UL + 2UL);
+  ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL);
   EXPECT_EQ(grad_test_op->Input("In1"), "in1");
   EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
             std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
@@ -80,7 +80,7 @@ TEST(GradOpBuilder, MutiInOut) {
             std::vector<std::string>(
                 {f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
 
-  ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
+  ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
   EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
   EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
             std::vector<std::string>({f::GradVarName("in2_1"),
@@ -99,7 +99,7 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
       f::OpRegistry::CreateGradOp(*test_op);
 
   // 'In2' and 'Out2' are ignored in gradient calculating
-  ASSERT_EQ(grad_test_op->inputs_.size(), 2UL + 1UL + 2UL);
+  ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL);
   EXPECT_EQ(grad_test_op->Input("In1"), "in1");
   EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
             std::vector<std::string>({"in3_1", "in3_2"}));
@@ -111,7 +111,7 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
   EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
             f::GradVarName("out2"));
 
-  ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
+  ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
   EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
   EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
             std::vector<std::string>(

paddle/framework/operator.h

@@ -97,6 +97,8 @@ class OperatorBase {
   /// rename inputs outputs name
   void Rename(const std::string& old_name, const std::string& new_name);
 
+  const VarNameMap& Inputs() const { return inputs_; }
+  const VarNameMap& Outputs() const { return outputs_; }
   //! Get a input with argument's name described in `op_proto`
   const std::string& Input(const std::string& name) const;
   //! Get a input which has multiple variables.
@@ -110,10 +112,11 @@ class OperatorBase {
 
   virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
 
-  std::string Type() const { return type_; }
+  const std::string& Type() const { return type_; }
+  void SetType(const std::string& type) { type_ = type; }
   const AttributeMap& Attrs() const { return attrs_; }
 
- public:
+ protected:
   std::string type_;
   // NOTE: in case of OpGrad, inputs_ contains:
   // I (Inputs)

paddle/framework/pybind.cc

@@ -53,15 +53,15 @@ void ExposeOperator(ClassType &m) {
       .def("run", &ClassType::type::Run)
       .def("type",
            [](const typename ClassType::type &op) -> std::string {
-             return op.type_;
+             return op.Type();
            })
       .def("outputs",
            [](const typename ClassType::type &op)
                -> std::map<std::string, std::vector<std::string>> {
-                 return op.outputs_;
+                 return op.Outputs();
                })
       .def("inputs",
-           [](const typename ClassType::type &op) { return op.inputs_; })
+           [](const typename ClassType::type &op) { return op.Inputs(); })
       .def("__str__", &ClassType::type::DebugString)
       .def("no_intermediate_outputs",
            [](const typename ClassType::type &op) {
@@ -232,7 +232,7 @@ All parameter, weight, gradient are variables in Paddle.
   net.def_static("create",
                  []() -> std::shared_ptr<operators::NetOp> {
                    auto retv = std::make_shared<operators::NetOp>();
-                   retv->type_ = "plain_net";
+                   retv->SetType("plain_net");
                    return retv;
                  })
       .def("add_op", &operators::NetOp::AddOp)

paddle/operators/net_op.cc

@@ -29,7 +29,7 @@ void NetOp::CompleteAddOp(bool calc) {
   std::set<std::string> input_set;
   std::set<std::string> output_set;
   for (auto& op : ops_) {
-    for (auto& ipt : op->inputs_) {
+    for (auto& ipt : op->Inputs()) {
       for (auto& var_name : ipt.second) {
         if (!Contains(output_set, var_name)) {  // Not other op's output
           input_set.insert(var_name);
@@ -39,7 +39,7 @@ void NetOp::CompleteAddOp(bool calc) {
       }
     }
 
-    for (auto& opt : op->outputs_) {
+    for (auto& opt : op->Outputs()) {
       for (auto& var_name : opt.second) {
         output_set.insert(var_name);
       }

paddle/operators/net_op_test.cc

@@ -49,8 +49,8 @@ TEST(OpKernel, all) {
 
   net->CompleteAddOp();
   AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"},
-                               net->inputs_.at(NetOp::kAll));
-  AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_.at(NetOp::kAll));
+                               net->Inputs(NetOp::kAll));
+  AssertSameVectorWithoutOrder({"y", "z"}, net->Outputs(NetOp::kAll));
 
   auto final_outs = net->OutputVars(false);
 

paddle/operators/recurrent_op.cc

@@ -82,14 +82,14 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
   PADDLE_ENFORCE(net_var != nullptr, "no stepnet called %s in scope",
                  arg_->step_net);
   auto net_op = net_var->GetMutable<NetOp>();
-  PADDLE_ENFORCE(!net_op->outputs_.empty(), "net_op has no outputs");
+  PADDLE_ENFORCE(!net_op->Outputs().empty(), "net_op has no outputs");
 
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
       auto& step_scope = scope.NewScope();
 
       // create step net's temp inputs
-      for (auto& input : net_op->inputs_) {
+      for (auto& input : net_op->Inputs()) {
         // the weight are located in parent scope
         for (auto& var_name : input.second) {
           if (!step_scope.FindVar(var_name)) {
@@ -98,7 +98,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
         }
       }
       // create stepnet's outputs
-      for (const auto& output : net_op->outputs_) {
+      for (const auto& output : net_op->Outputs()) {
         for (auto& var_name : output.second) {
           step_scope.NewVar(var_name);
         }