Merge pull request #3350 from reyoung/feature/use_constexpr_for_str_const

Make const variables in operator.h fit google style

paddle/framework/backward.cc

@@ -133,8 +133,9 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
     std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
     for (std::string& grad_input : grad_op->inputs_) {
       if (no_grad_names.count(grad_input)) {
-        std::string prefix =
-            grad_input.substr(0, grad_input.size() - kGradVarSuffix.size());
+        // +1 for \0
+        std::string prefix = grad_input.substr(
+            0, grad_input.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
         grad_input = prefix + kZeroVarSuffix;
 
         // If part of input gradient of that operator is not calculated, fill
@@ -167,7 +168,7 @@ std::shared_ptr<OperatorBase> Backward(
   std::unordered_set<std::string> no_grad_names;
   no_grad_names.reserve(no_grad_vars.size());
 
-  no_grad_names.insert(kEmptyVarName + kGradVarSuffix);
+  no_grad_names.insert(std::string(kEmptyVarName) + kGradVarSuffix);
 
   for (auto& name : no_grad_vars) {
     no_grad_names.insert(name + kGradVarSuffix);

paddle/framework/backward_test.cc

@@ -171,10 +171,10 @@ TEST(Backward, simple_op_grad) {
   ASSERT_EQ(4UL, gop->inputs_.size());
   ASSERT_EQ(f::kEmptyVarName, gop->inputs_[0]);
   ASSERT_EQ("rowwise_add_grad", gop->type_);
-  ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
-  ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
+  ASSERT_EQ(f::GradVarName("X"), gop->outputs_[0]);
+  ASSERT_EQ(f::GradVarName("b"), gop->outputs_[1]);
 
-  ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
+  ASSERT_EQ(f::GradVarName("X"), gop->Output(f::GradVarName("X")));
 }
 
 TEST(Backward, simple_op_not_need_grad) {
@@ -182,7 +182,7 @@ TEST(Backward, simple_op_not_need_grad) {
   ASSERT_NE(fwd, nullptr);
   auto gop = f::Backward(*fwd, {"X"});
   ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
-                      "X" + f::kGradVarSuffix),
+                      f::GradVarName("X")),
             gop->outputs_.end());
 
   auto no_input_gop = f::Backward(*fwd, {"X", "b"});
@@ -250,18 +250,18 @@ TEST(Backward, net_input_of_network_not_need_grad) {
   all_output.erase(f::kEmptyVarName);
 
   for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
-    ASSERT_NE(all_output.find(out + f::kGradVarSuffix), all_output.end());
+    ASSERT_NE(all_output.find(f::GradVarName(out)), all_output.end());
   }
 
   // Not Generated X
-  ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), all_output.end());
+  ASSERT_EQ(all_output.find(f::GradVarName("X")), all_output.end());
 
   ASSERT_EQ(2UL, bwd_net->ops_.size());
   ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
   auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
   ASSERT_EQ(3UL, first_fc_grad->ops_.size());
   ASSERT_EQ(f::kEmptyVarName,
-            first_fc_grad->ops_[2]->Output("A" + f::kGradVarSuffix));
+            first_fc_grad->ops_[2]->Output(f::GradVarName("A")));
 }
 
 TEST(Backward, net_shared_weight) {
@@ -313,15 +313,15 @@ TEST(Backward, op_part_of_output_are_not_need) {
   ASSERT_EQ(1UL, fill_zero.inputs_.size());
   ASSERT_EQ("Z", fill_zero.inputs_[0]);
   ASSERT_EQ(1UL, fill_zero.outputs_.size());
-  ASSERT_EQ("Z" + f::kZeroVarSuffix, fill_zero.outputs_[0]);
+  ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.outputs_[0]);
 
   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("Z" + f::kZeroVarSuffix, d_many_out.Input("z" + f::kGradVarSuffix));
-  ASSERT_EQ("Y" + f::kGradVarSuffix, d_many_out.Input("y" + f::kGradVarSuffix));
-  ASSERT_EQ("X" + f::kGradVarSuffix,
-            d_many_out.Output("x" + f::kGradVarSuffix));
+  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")));
+  ASSERT_EQ(f::GradVarName("X"), d_many_out.Output(f::GradVarName("x")));
 }
 
 TEST(Backward, op_part_of_input_are_not_need) {
@@ -331,10 +331,9 @@ TEST(Backward, op_part_of_input_are_not_need) {
   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("A" + f::kGradVarSuffix), f::kEmptyVarName);
-  ASSERT_EQ(grad_mul.Output("B" + f::kGradVarSuffix), "b" + f::kGradVarSuffix);
-  ASSERT_EQ(grad_mul.Input("Out" + f::kGradVarSuffix),
-            "out" + f::kGradVarSuffix);
+  ASSERT_EQ(grad_mul.Output(f::GradVarName("A")), f::kEmptyVarName);
+  ASSERT_EQ(grad_mul.Output(f::GradVarName("B")), f::GradVarName("b"));
+  ASSERT_EQ(grad_mul.Input(f::GradVarName("Out")), f::GradVarName("out"));
   ASSERT_EQ(grad_mul.Input("A"), "a");
   ASSERT_EQ(grad_mul.Input("B"), "b");
   ASSERT_EQ(grad_mul.Input("Out"), "out");

paddle/framework/grad_op_builder_test.cc

@@ -83,21 +83,19 @@ TEST(GradOpBuilder, MutiInOut) {
   EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
   EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
             std::vector<std::string>({"out2_1", "out2_2"}));
-  EXPECT_EQ(grad_test_op->Input("Out1" + f::kGradVarSuffix),
-            "out1" + f::kGradVarSuffix);
-  EXPECT_EQ(grad_test_op->Inputs("Out2_mult" + f::kGradVarSuffix),
+  EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")),
+            f::GradVarName("out1"));
+  EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")),
             std::vector<std::string>(
-                {"out2_1" + f::kGradVarSuffix, "out2_2" + f::kGradVarSuffix}));
+                {f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
 
   ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
-  EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
-            "in1" + f::kGradVarSuffix);
-  EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
-            std::vector<std::string>({"in2_1" + f::kGradVarSuffix,
-                                      "in2_2" + f::kGradVarSuffix,
-                                      "in2_3" + f::kGradVarSuffix}));
-  EXPECT_EQ(grad_test_op->Output("In3" + f::kGradVarSuffix),
-            "in3" + f::kGradVarSuffix);
+  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"),
+                                      f::GradVarName("in2_2"),
+                                      f::GradVarName("in2_3")}));
+  EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3"));
 }
 
 TEST(GradOpBuilder, IOIgnoredInGradient) {
@@ -119,19 +117,18 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
   EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
             std::vector<std::string>({"out1_1", "out1_2"}));
   EXPECT_EQ(grad_test_op->Input("Out2"), f::kEmptyVarName);
-  EXPECT_EQ(grad_test_op->Inputs("Out1_mult" + f::kGradVarSuffix),
+  EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
             std::vector<std::string>(
-                {"out1_1" + f::kGradVarSuffix, "out1_2" + f::kGradVarSuffix}));
-  EXPECT_EQ(grad_test_op->Input("Out2" + f::kGradVarSuffix),
-            "out2" + f::kGradVarSuffix);
+                {f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
+  EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
+            f::GradVarName("out2"));
 
   ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
-  EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
-            "in1" + f::kGradVarSuffix);
-  EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
+  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>(
-                {"in2_1" + f::kGradVarSuffix, "in2_2" + f::kGradVarSuffix}));
-  EXPECT_EQ(grad_test_op->Outputs("In3_mult" + f::kGradVarSuffix),
+                {f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
+  EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")),
             std::vector<std::string>(
-                {"in3_1" + f::kGradVarSuffix, "in3_2" + f::kGradVarSuffix}));
+                {f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
 }

paddle/framework/operator.h

@@ -33,19 +33,19 @@ namespace paddle {
 namespace framework {
 
 /// If a variable is a empty variable, that name will be used.
-const std::string kEmptyVarName = "@EMPTY@";
+constexpr char kEmptyVarName[] = "@EMPTY@";
 
 /// If a variable is a temporary variable, that name will be set in Python,
 /// but it will be convert to a unique name in scope after OpCreator.
-const std::string kTempVarName = "@TEMP@";
+constexpr char kTempVarName[] = "@TEMP@";
 
 /// If a variable's name has a certain suffix, it means that the
 /// variable is the gradient of another varibale.
 /// e.g. Variable "x@GRAD" is the gradient of varibale "x".
-const std::string kGradVarSuffix = "@GRAD";
+constexpr char kGradVarSuffix[] = "@GRAD";
 
 /// Variables with this suffix are supposed to be filled up with zeros.
-const std::string kZeroVarSuffix = "@ZERO";
+constexpr char kZeroVarSuffix[] = "@ZERO";
 
 inline std::string GradVarName(const std::string& var_name) {
   return var_name + kGradVarSuffix;

paddle/operators/mean_op.cc

@@ -41,7 +41,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
 class MeanGradOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<Tensor>("X" + framework::kGradVarSuffix)
+    ctx.Output<Tensor>(framework::GradVarName("X"))
         ->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };

paddle/operators/mean_op.h

@@ -48,10 +48,10 @@ template <typename Place, typename T>
 class MeanGradKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto OG = context.Input<Tensor>("Out" + framework::kGradVarSuffix);
+    auto OG = context.Input<Tensor>(framework::GradVarName("Out"));
     PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
                    "Mean Gradient should be scalar");
-    auto IG = context.Output<Tensor>("X" + framework::kGradVarSuffix);
+    auto IG = context.Output<Tensor>(framework::GradVarName("X"));
     IG->mutable_data<T>(context.GetPlace());
 
     T ig_size = (T)framework::product(IG->dims());