Refine InferShape for recurrent_network_op.

* the tensor only contains shape and does not hold memory when inferring shape.

paddle/operators/recurrent_network_op.h

@@ -72,19 +72,22 @@ struct ArgumentName {
  */
 void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
                    const std::vector<Link>& inlinks,
-                   const size_t seq_len);
+                   const size_t seq_len,
+                   bool infer_shape);
 
 /**
  * Process outputs of step nets and merge to variables.
  */
 void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
                    const std::vector<Link>& outlinks,
-                   const size_t seq_len);
+                   const size_t seq_len,
+                   bool infer_shape);
 
 void LinkMemories(std::vector<std::shared_ptr<Scope>>& step_scopes,
                   const std::vector<MemoryAttr>& memories,
-                  size_t step_id,
-                  int offset);
+                  const size_t step_id,
+                  const int offset,
+                  bool infer_shape);
 
 void InitArgument(const ArgumentName& name, Argument* arg);
 
@@ -125,7 +128,7 @@ protected:
                 ->GetMutable<std::vector<std::shared_ptr<Scope>>>();
   }
 
-  void InitMemories(std::shared_ptr<Scope> step_scopes) const;
+  void InitMemories(std::shared_ptr<Scope> step_scopes, bool infer_shape) const;
 
 private:
   std::unique_ptr<rnn::Argument> arg_;
@@ -149,7 +152,8 @@ public:
   void Run(const std::shared_ptr<Scope>& scope,
            const platform::DeviceContext& dev_ctx) const;
 
-  void LinkBootMemoryGradients(std::shared_ptr<Scope> step_scopes) const;
+  void LinkBootMemoryGradients(std::shared_ptr<Scope> step_scopes,
+                               bool infer_shape) const;
 
   /**
    * InferShape must be called before Run.

paddle/operators/recurrent_network_op_test.cc

@@ -56,7 +56,7 @@ protected:
     w->GetMutable<Tensor>()->mutable_data<float>(
         make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
 
-    for (auto boot : std::vector<std::string>{"x_boot", "h_boot"}) {
+    for (auto boot : std::vector<std::string>{"h_boot"}) {
       LOG(INFO) << "create global variable " << boot;
       Variable* h_boot = scope_->CreateVariable(boot);
       h_boot->GetMutable<Tensor>()->mutable_data<float>(
@@ -80,7 +80,6 @@ protected:
     op_desc.add_inputs("x0");
     op_desc.add_inputs("x1");
     // boot_memories 3
-    op_desc.add_inputs("x_boot");
     op_desc.add_inputs("h_boot");
     // step net 5
     op_desc.add_inputs("step_net");
@@ -92,7 +91,7 @@ protected:
     auto _input_format = std::vector<int>{
         0,  // in_link
         3,  // memories
-        5   // step_net
+        4   // step_net
     };
     auto input_format = op_desc.add_attrs();
     input_format->set_name("input_format");
@@ -130,12 +129,11 @@ protected:
       inlink_alias->add_strings(item);
     }
     // pre memories
-    for (const auto& item :
-         std::vector<std::string>{"rnn/x@pre", "rnn/h@pre"}) {
+    for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
       pre_memories->add_strings(item);
     }
     // memories
-    for (const auto& item : std::vector<std::string>{"rnn/x", "rnn/h"}) {
+    for (const auto& item : std::vector<std::string>{"rnn/h"}) {
       memories->add_strings(item);
     }
     // output alias
@@ -152,14 +150,11 @@ protected:
     LOG(INFO) << "create variable step_net";
     Variable* var = scope_->CreateVariable("step_net");
     auto net = var->GetMutable<NetOp>();
-    // rnn/s is net's input or output?
-    net->inputs_ = {"rnn/h@pre", "rnn/w", "rnn/x"};
-    net->inputs_ = {"rnn/s", "rnn/h"};
     net->AddOp(
         OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
 
     net->AddOp(
-        OpRegistry::CreateOp("add_two", {"rnn/x", "rnn/s"}, {"rnn/h"}, {}));
+        OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
     net->CompleteAddOp();
   }
 
@@ -303,7 +298,7 @@ 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);
+    rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10, true);
   }
 
   void LinkeMemories() {
@@ -318,7 +313,7 @@ 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);
+      rnn::LinkMemories(*step_scopes, memories, i, -1, true);
     }
   }
 
@@ -347,7 +342,7 @@ TEST(RecurrentOp, LinkMemories) {
     scope->CreateVariable("pre_h");
     auto tensor = scope->CreateVariable("h")->GetMutable<Tensor>();
     float* data = tensor->mutable_data<float>(make_ddim({15, 20}), CPUPlace());
-    for (int i = 0; i < 15 * 20; ++i) {
+    for (int j = 0; j < 15 * 20; ++j) {
       data[i] = rand() * (1. / (double)RAND_MAX);
     }
     step_scopes.push_back(scope);
@@ -362,7 +357,7 @@ TEST(RecurrentOp, LinkMemories) {
   memories.push_back(mem_attr);
 
   for (int i = 1; i < len; ++i) {
-    rnn::LinkMemories(step_scopes, memories, i, -1);
+    rnn::LinkMemories(step_scopes, memories, i, -1, false);
   }
   // check
   for (int i = 0; i < len - 1; ++i) {
@@ -372,13 +367,13 @@ TEST(RecurrentOp, LinkMemories) {
                          ->GetVariable("pre_h")
                          ->GetMutable<Tensor>()
                          ->data<float>();
-    for (size_t i = 0; i < 15 * 20; ++i) {
-      ASSERT_FLOAT_EQ(a[i], b[i]);
+    for (size_t j = 0; j < 15 * 20; ++j) {
+      ASSERT_FLOAT_EQ(a[j], b[j]);
     }
   }
 
   for (int i = len - 2; i >= 0; --i) {
-    rnn::LinkMemories(step_scopes, memories, i, 1);
+    rnn::LinkMemories(step_scopes, memories, i, 1, false);
   }
   // check
   for (int i = len - 2; i >= 0; --i) {
@@ -390,8 +385,8 @@ TEST(RecurrentOp, LinkMemories) {
                          ->GetVariable("h")
                          ->GetMutable<Tensor>()
                          ->data<float>();
-    for (size_t i = 0; i < 15 * 20; ++i) {
-      ASSERT_FLOAT_EQ(a[i], b[i]);
+    for (size_t j = 0; j < 15 * 20; ++j) {
+      ASSERT_FLOAT_EQ(a[j], b[j]);
     }
   }
 }