Reforamt

paddle/framework/backward.cc

@@ -59,16 +59,14 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
   //  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
   //  too much time for calculation, but it is useful for simplifying logic.
-  if (AllInSet(forwardOp.inputs_, kGradVarSuffix,
-               no_grad_names)) {
+  if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
     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_, kGradVarSuffix,
-               no_grad_names)) {
+  if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
     for (auto& name : forwardOp.inputs_) {
       // Mark all input is not need
       no_grad_names.insert(name + kGradVarSuffix);
@@ -134,8 +132,8 @@ 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());
+        std::string prefix =
+            grad_input.substr(0, grad_input.size() - kGradVarSuffix.size());
         grad_input = prefix + kZeroVarSuffix;
 
         // If part of input gradient of that operator is not calculated, fill
@@ -168,8 +166,7 @@ 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(kEmptyVarName + kGradVarSuffix);
 
   for (auto& name : no_grad_vars) {
     no_grad_names.insert(name + kGradVarSuffix);
@@ -177,5 +174,6 @@ std::shared_ptr<OperatorBase> Backward(
   size_t uid = 0;
   return BackwardRecursive(forwardOp, no_grad_names, uid);
 }
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/backward_test.cc

@@ -168,8 +168,7 @@ TEST(Backward, simple_op_grad) {
   ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
   ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
 
-  ASSERT_EQ("X" + f::kGradVarSuffix,
-            gop->Output("X" + f::kGradVarSuffix));
+  ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
 }
 
 TEST(Backward, simple_op_not_need_grad) {
@@ -210,9 +209,9 @@ TEST(Backward, net_fc_backward_normal) {
 }
 
 TEST(Backward, net_fc_backward_not_have_b) {
-  std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
-      "fc", {"X", "w", f::kEmptyVarName},
-      {"mul_result", "add_result", "tmp"}, {});
+  std::shared_ptr<f::OperatorBase> fwd =
+      f::OpRegistry::CreateOp("fc", {"X", "w", f::kEmptyVarName},
+                              {"mul_result", "add_result", "tmp"}, {});
   ASSERT_NE(fwd, nullptr);
   std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
   ASSERT_TRUE(gop->IsNetOp());
@@ -245,21 +244,18 @@ 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(out + f::kGradVarSuffix), all_output.end());
   }
 
   // Not Generated X
-  ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix),
-            all_output.end());
+  ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), 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));
+  ASSERT_EQ(f::kEmptyVarName,
+            first_fc_grad->ops_[2]->Output("A" + f::kGradVarSuffix));
 }
 
 TEST(Backward, net_shared_weight) {
@@ -316,10 +312,8 @@ TEST(Backward, op_part_of_output_are_not_need) {
   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("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));
 }
@@ -331,10 +325,8 @@ 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.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.Input("A"), "a");
@@ -368,23 +360,4 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
   EXPECT_EQ(bwd_net->ops_[1]->outputs_.size(), 0UL);
   EXPECT_EQ(bwd_net->ops_[2]->inputs_.size(), 0UL);
   EXPECT_EQ(bwd_net->ops_[2]->outputs_.size(), 0UL);
-
-  /*
-    EXPECT_EQ(grad_fc.Output("X" + f::kGradVarSuffix),
-              f::kEmptyVarName);
-  EXPECT_EQ(grad_fc.Output("W" + f::kGradVarSuffix),
-    "w3" + f::kGradVarSuffix);
-  EXPECT_EQ(grad_fc.Output("b" + f::kGradVarSuffix),
-    "b3" + f::kGradVarSuffix);
-  EXPECT_EQ(grad_fc.Output("mul_result" + f::kGradVarSuffix),
-  "mul_out3" + f::kGradVarSuffix);
-
-  EXPECT_EQ(grad_fc.Input("Out" + f::kGradVarSuffix),
-  "out3" + f::kGradVarSuffix);
-  EXPECT_EQ(grad_fc.Input("X"), "out2");
-  EXPECT_EQ(grad_fc.Input("W"), "w3");
-  EXPECT_EQ(grad_fc.Input("mul_result"), "mul_out3");
-  EXPECT_EQ(grad_fc.Input("add_result"), "tmp_out3");
-  EXPECT_EQ(grad_fc.Input("Out"), "out3");
-  */
 }

paddle/framework/grad_op_builder.cc

@@ -56,8 +56,7 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
 
   for (const auto& arg : src_arg_list) {
     std::string src_name = arg.name();
-    std::string dst_name =
-        is_grad ? src_name + kGradVarSuffix : src_name;
+    std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
     (*dst_op->in_out_idxs_)[dst_name] = idx++;
     int src_arg_idx = src_op->in_out_idxs_->at(src_name);
     int src_begin =
@@ -65,10 +64,9 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
     int src_end = src_format == nullptr ? src_arg_idx + 1
                                         : src_format->at(src_arg_idx + 1);
     for (int i = src_begin; i < src_end; ++i) {
-      std::string s = is_grad ? src_inout[i] + kGradVarSuffix
-                              : arg.ignore_gradient()
-                                    ? kEmptyVarName
-                                    : src_inout[i];
+      std::string s =
+          is_grad ? src_inout[i] + kGradVarSuffix
+                  : (arg.ignore_gradient() ? kEmptyVarName : src_inout[i]);
       dst_inout.emplace_back(s);
     }
     if (dst_format != nullptr) {

paddle/framework/operator.h

@@ -45,13 +45,12 @@ const std::string kTempVarName = "@TEMP@";
 const std::string kGradVarSuffix = "@GRAD";
 
 /// Variables with this suffix are supposed to be filled up with zeros.
-const std::string kZeroVarSuffix =  "@ZERO";
+const std::string kZeroVarSuffix = "@ZERO";
 
 inline std::string GradVarName(const std::string& var_name) {
   return var_name + kGradVarSuffix;
 }
 
-
 class OperatorBase;
 class InferShapeContext;
 class ExecutionContext;

paddle/operators/softmax_op.cc

@@ -51,7 +51,7 @@ protected:
     PADDLE_ENFORCE(ctx.InputVar(framework::GradVarName("Y")) != nullptr,
                    "Input(Y@GRAD) should not be null");
     PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
-                   ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
+                       ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
                    "the shape of Input(0) and Input(1) should be the same");
     ctx.Output<Tensor>(framework::GradVarName("X"))
         ->Resize(ctx.Input<Tensor>("Y")->dims());