follow comments.

paddle/operators/recurrent_network_op.cc

@@ -30,11 +30,14 @@ namespace rnn {
 void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
                    const std::vector<Link>& inlinks,
                    const size_t seq_len,
-                   bool infer_shape) {
+                   bool infer_shape_mode) {
   PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
   for (size_t i = 0; i < inlinks.size(); ++i) {
-    Tensor* input =
-        step_scopes[0]->GetVariable(inlinks[i].external)->GetMutable<Tensor>();
+    auto input_var = step_scopes[0]->GetVariable(inlinks[i].external);
+    PADDLE_ENFORCE(input_var != nullptr,
+                   "input link [%s] is not in scope.",
+                   inlinks[i].external);
+    Tensor* input = input_var->GetMutable<Tensor>();
     DDim dims = input->dims();
     PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
                    "all the inlinks must have same length");
@@ -43,7 +46,7 @@ void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
       Tensor* step_input = step_scopes[j]
                                ->CreateVariable(inlinks[i].internal)
                                ->GetMutable<Tensor>();
-      if (!infer_shape) {
+      if (!infer_shape_mode) {
         *step_input = input->Slice<float>(j, j + 1);
       }
       step_input->Resize(step_dims);
@@ -54,12 +57,14 @@ void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
 void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
                    const std::vector<Link>& outlinks,
                    const size_t seq_len,
-                   bool infer_shape) {
+                   bool infer_shape_mode) {
   for (size_t i = 0; i < outlinks.size(); i++) {
+    PADDLE_ENFORCE(step_scopes[0]->HasVariable(outlinks[i].external),
+                   "output link [%s] is not in scope.",
+                   outlinks[i].external);
     Tensor* output =
         step_scopes[0]->GetVariable(outlinks[i].external)->GetMutable<Tensor>();
-
-    if (infer_shape) {
+    if (infer_shape_mode) {
       DDim step_dims = step_scopes[0]
                            ->GetVariable(outlinks[i].internal)
                            ->GetMutable<Tensor>()
@@ -69,16 +74,15 @@ void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
       output->Resize(make_ddim(dims_vec));
     } else {
       output->mutable_data<float>(platform::CPUPlace());
-    }
-
-    for (size_t j = 0; j < seq_len; j++) {
-      Tensor* step_output = step_scopes[j]
-                                ->GetVariable(outlinks[i].internal)
-                                ->GetMutable<Tensor>();
-      // TODO(luotao02) data type and platform::DeviceContext() should set
-      // correctly
-      (output->Slice<float>(j, j + 1))
-          .CopyFrom<float>(*step_output, platform::CPUPlace());
+      for (size_t j = 0; j < seq_len; j++) {
+        Tensor* step_output = step_scopes[j]
+                                  ->GetVariable(outlinks[i].internal)
+                                  ->GetMutable<Tensor>();
+        // TODO(luotao02) data type and platform::DeviceContext() should set
+        // correctly
+        (output->Slice<float>(j, j + 1))
+            .CopyFrom<float>(*step_output, platform::CPUPlace());
+      }
     }
   }
 }
@@ -87,7 +91,7 @@ void LinkMemories(std::vector<std::shared_ptr<Scope>>& scopes,
                   const std::vector<rnn::MemoryAttr>& memories,
                   const size_t step_id,
                   const int offset,
-                  bool infer_shape) {
+                  bool infer_shape_mode) {
   PADDLE_ENFORCE(step_id < scopes.size(),
                  "step [%d] is out of range of step scopes' size [%d]",
                  step_id,
@@ -107,7 +111,7 @@ void LinkMemories(std::vector<std::shared_ptr<Scope>>& scopes,
     auto mem = scope->GetVariable(attr.pre_var)->GetMutable<Tensor>();
     // maybe share variable is better?
     auto linked_mem = linked_scope->GetVariable(attr.var)->GetMutable<Tensor>();
-    if (infer_shape) {
+    if (infer_shape_mode) {
       mem->Resize(linked_mem->dims());
     } else {
       mem->ShareDataWith<float>(*linked_mem);
@@ -179,43 +183,39 @@ void RecurrentAlgorithm::InferShape(const std::shared_ptr<Scope>& scope) const {
                  ->GetMutable<Tensor>()
                  ->dims()[0];
   CreateScopes(scope);
-
   auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, true);
-
-  InitMemories(step_scopes[0], true);
-
-  PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
-                 "stepnet [%s] is not in scope.",
-                 arg_->step_net);
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
+  InitMemories(step_scopes[0], true /*infer_shape_mode*/);
   Variable* net = scope->GetVariable(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
   for (size_t i = 0; i < seq_len_; i++) {
     if (i > 0) {
-      rnn::LinkMemories(step_scopes, arg_->memories, i, -1, true);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, i, -1, true /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->InferShape(step_scopes[i]);
   }
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, true);
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
 }
 
 void RecurrentAlgorithm::Run(const std::shared_ptr<Scope>& scope,
                              const platform::DeviceContext& dev_ctx) const {
   auto step_scopes = GetStepScopes(scope);
-
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, false);
-
-  InitMemories(step_scopes[0], false);
-
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
+  InitMemories(step_scopes[0], false /*infer_shape_mode*/);
   Variable* net = scope->GetVariable(arg_->step_net);
   for (size_t step_id = 0; step_id < seq_len_; step_id++) {
     if (step_id > 0) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1, false);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, step_id, -1, false /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->Run(step_scopes[step_id], dev_ctx);
   }
-
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, false);
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
 }
 
 void RecurrentAlgorithm::CreateScopes(std::shared_ptr<Scope> scope) const {
@@ -227,7 +227,6 @@ void RecurrentAlgorithm::CreateScopes(std::shared_ptr<Scope> scope) const {
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
       std::shared_ptr<Scope> step_scope = std::make_shared<Scope>(scope);
-
       // Now all variables in scope must be created outside of op.
       auto net_op = scope->GetVariable(arg_->step_net)->GetMutable<NetOp>();
       for (auto& input : net_op->inputs_) {
@@ -237,14 +236,13 @@ void RecurrentAlgorithm::CreateScopes(std::shared_ptr<Scope> scope) const {
       for (auto& output : net_op->outputs_) {
         step_scope->CreateVariable(output);
       }
-
       step_scopes->push_back(std::make_shared<Scope>(step_scope));
     }
   }
 }
 
 void RecurrentAlgorithm::InitMemories(std::shared_ptr<Scope> step_scope,
-                                      bool infer_shape) const {
+                                      bool infer_shape_mode) const {
   for (auto& attr : arg_->memories) {
     Tensor* pre_mem =
         step_scope->CreateVariable(attr.pre_var)->GetMutable<Tensor>();
@@ -254,7 +252,7 @@ void RecurrentAlgorithm::InitMemories(std::shared_ptr<Scope> step_scope,
                    attr.boot_var);
     Tensor* boot_mem =
         step_scope->GetVariable(attr.boot_var)->GetMutable<Tensor>();
-    if (infer_shape) {
+    if (infer_shape_mode) {
       pre_mem->Resize(boot_mem->dims());
     } else {
       pre_mem->ShareDataWith<float>(*boot_mem);
@@ -320,23 +318,23 @@ void RecurrentGradientAlgorithm::Run(
     const std::shared_ptr<Scope>& scope,
     const platform::DeviceContext& dev_ctx) const {
   auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, false);
-  PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
-                 "step net is not in scope.");
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
   Variable* net = scope->GetVariable(arg_->step_net);
-  PADDLE_ENFORCE(net != nullptr, "failed to get step net");
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, false);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, step_id, 1, false /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->Run(step_scopes[step_id], dev_ctx);
   }
   LinkBootMemoryGradients(step_scopes[0], false);
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, false);
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
 }
 
 void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
-    std::shared_ptr<Scope> step_scope, bool infer_shape) const {
+    std::shared_ptr<Scope> step_scope, bool infer_shape_mode) const {
   for (auto& attr : arg_->memories) {
     Tensor* mem_grad =
         step_scope->CreateVariable(attr.var)->GetMutable<Tensor>();
@@ -346,7 +344,7 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
                    attr.boot_var);
     Tensor* boot_mem_grad =
         step_scope->CreateVariable(attr.boot_var)->GetMutable<Tensor>();
-    if (infer_shape) {
+    if (infer_shape_mode) {
       boot_mem_grad->Resize(mem_grad->dims());
     } else {
       boot_mem_grad->ShareDataWith<float>(*mem_grad);
@@ -360,21 +358,20 @@ void RecurrentGradientAlgorithm::InferShape(
                  ->GetMutable<Tensor>()
                  ->dims()[0];
   auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, true);
-
-  PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
-                 "step net is not in scope.");
+  rnn::SegmentInputs(
+      step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
   Variable* net = scope->GetVariable(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
-
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, true);
+      rnn::LinkMemories(
+          step_scopes, arg_->memories, step_id, 1, true /*infer_shape_mode*/);
     }
     net->GetMutable<NetOp>()->InferShape(step_scopes[step_id]);
   }
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, true);
-  LinkBootMemoryGradients(step_scopes[0], true);
+  rnn::ConcatOutputs(
+      step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
+  LinkBootMemoryGradients(step_scopes[0], true /*infer_shape_mode*/);
 }
 
 void RecurrentGradientOp::Init() {

paddle/operators/recurrent_network_op.h

@@ -73,7 +73,7 @@ struct ArgumentName {
 void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
                    const std::vector<Link>& inlinks,
                    const size_t seq_len,
-                   bool infer_shape);
+                   bool infer_shape_mode);
 
 /**
  * Process outputs of step nets and merge to variables.
@@ -81,13 +81,13 @@ void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
 void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
                    const std::vector<Link>& outlinks,
                    const size_t seq_len,
-                   bool infer_shape);
+                   bool infer_shape_mode);
 
 void LinkMemories(std::vector<std::shared_ptr<Scope>>& step_scopes,
                   const std::vector<MemoryAttr>& memories,
                   const size_t step_id,
                   const int offset,
-                  bool infer_shape);
+                  bool infer_shape_mode);
 
 void InitArgument(const ArgumentName& name, Argument* arg);
 
@@ -128,7 +128,8 @@ protected:
                 ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
   }
 
-  void InitMemories(std::shared_ptr<Scope> step_scopes, bool infer_shape) const;
+  void InitMemories(std::shared_ptr<Scope> step_scopes,
+                    bool infer_shape_mode) const;
 
 private:
   std::unique_ptr<rnn::Argument> arg_;
@@ -153,7 +154,7 @@ public:
            const platform::DeviceContext& dev_ctx) const;
 
   void LinkBootMemoryGradients(std::shared_ptr<Scope> step_scopes,
-                               bool infer_shape) const;
+                               bool infer_shape_mode) const;
 
   /**
    * InferShape must be called before Run.

paddle/operators/recurrent_network_op_test.cc

@@ -298,7 +298,10 @@ protected:
     std::vector<std::shared_ptr<Scope>>* step_scopes =
         scope_->GetVariable("step_scopes")
             ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
-    rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10, true);
+    rnn::SegmentInputs(*step_scopes,
+                       std::vector<rnn::Link>{inlink},
+                       10,
+                       true /*infer_shape_mode*/);
   }
 
   void LinkeMemories() {
@@ -313,7 +316,8 @@ protected:
         scope_->GetVariable("step_scopes")
             ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
     for (int i = 1; i < 10; ++i) {
-      rnn::LinkMemories(*step_scopes, memories, i, -1, true);
+      rnn::LinkMemories(
+          *step_scopes, memories, i, -1, true /*infer_shape_mode*/);
     }
   }
 
@@ -343,7 +347,7 @@ TEST(RecurrentOp, LinkMemories) {
     auto tensor = scope->CreateVariable("h")->GetMutable<Tensor>();
     float* data = tensor->mutable_data<float>(make_ddim({15, 20}), CPUPlace());
     for (int j = 0; j < 15 * 20; ++j) {
-      data[i] = rand() * (1. / (double)RAND_MAX);
+      data[j] = rand() * (1. / (double)RAND_MAX);
     }
     step_scopes.push_back(scope);
   }
@@ -357,7 +361,7 @@ TEST(RecurrentOp, LinkMemories) {
   memories.push_back(mem_attr);
 
   for (int i = 1; i < len; ++i) {
-    rnn::LinkMemories(step_scopes, memories, i, -1, false);
+    rnn::LinkMemories(step_scopes, memories, i, -1, false /*infer_shape_mode*/);
   }
   // check
   for (int i = 0; i < len - 1; ++i) {
@@ -373,7 +377,7 @@ TEST(RecurrentOp, LinkMemories) {
   }
 
   for (int i = len - 2; i >= 0; --i) {
-    rnn::LinkMemories(step_scopes, memories, i, 1, false);
+    rnn::LinkMemories(step_scopes, memories, i, 1, false /*infer_shape_mode*/);
   }
   // check
   for (int i = len - 2; i >= 0; --i) {