Rewrite StaticRNN with Executor (#5224)

* Init commit

* Make executor use ProgramDescBind

* Change Attribute from BlockDesc to BlockDescBind

* Since we will get the program desc in RNN, just BlockDesc is not
  enough.

* Add DeviceContext to Executor API

* Rewrite RNN

* Pass Python

* AddBiasOp does not care num_flatten_dims

* Stash

* Fix MacOS Compile

* Pass RNN forward

* add python test

* refactor test

* Make compile pass

* add gradopmaker

* First draft done

* Polish code

* add grad op maker and grad infershape

* Polish code

* Fix backward.cc bug

* Fix infershape

* Rename function

* add backward test

* simplify recurrent test

* Update

* Pass unittest

* Add comments & refine test

* Add comments

* refactor test

* Complete Unittest

* fix StepScopes enforce

* Remove unused unittest

* no type error

* Update

* Make RNN Pass unittest

paddle/framework/backward.cc

@@ -24,7 +24,6 @@
 #include "paddle/framework/op_registry.h"
 #include "paddle/operators/dynamic_recurrent_op.h"
 #include "paddle/operators/net_op.h"
-#include "paddle/operators/recurrent_op.h"
 
 namespace paddle {
 namespace framework {
@@ -38,7 +37,7 @@ static inline std::unique_ptr<OperatorBase> CreateGradOp(
   op_desc.SetType(op.Type());
   op_desc.SetAttrMap(op.Attrs());
   auto& info = OpInfoMap::Instance().Get(op.Type());
-  auto grad_descs = info.GradOpMaker()(op_desc, no_grad_set, grad_to_var);
+  auto grad_descs = info.GradOpMaker()(op_desc, no_grad_set, grad_to_var, {});
   std::vector<std::unique_ptr<OperatorBase>> grad_ops;
   grad_ops.reserve(grad_descs.size());
   std::transform(grad_descs.begin(), grad_descs.end(),
@@ -220,19 +219,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
                    });
 
     // process recurrent gradient op as a special operator.
-    if (forwardOp.Type() == "recurrent") {
-      // NOTE clean up cycle call somewhere (RNN's stepnet constains itself),
-      // or this will result in infinite loop.
-      const auto& rnnop =
-          *static_cast<const operators::RecurrentOp*>(&forwardOp);
-      auto rnn_grad_op =
-          static_cast<operators::RecurrentGradientOp*>(grad_op.get());
-      const auto& stepnet_op =
-          *static_cast<const OperatorBase*>(&rnnop.stepnet());
-      // create stepnet's gradient op
-      rnn_grad_op->set_stepnet(
-          BackwardRecursive(stepnet_op, no_grad_names, grad_to_var, uniq_id));
-    } else if (forwardOp.Type() == "dynamic_recurrent") {
+    if (forwardOp.Type() == "dynamic_recurrent") {
       // NOTE clean up cycle call somewhere (RNN's stepnet constains itself),
       // or this will result in infinite loop.
       const auto& rnnop =
@@ -331,7 +318,7 @@ static void CreateGradVarInBlock(
           continue;
         }
         auto pname = FwdName(arg);
-        auto* param = block_desc->FindVar(pname);
+        auto* param = block_desc->FindVarRecursive(pname);
         auto* grad = block_desc->FindVar(arg);
         if (param == nullptr) {
           LOG(WARNING) << "Cannot find forward variable of " << arg
@@ -348,7 +335,9 @@ static void CreateGradVarInBlock(
 
 std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
     const OpDescBind* op_desc, std::unordered_set<std::string>* no_grad_vars,
-    std::unordered_map<std::string, std::string>* grad_to_var) {
+    std::unordered_map<std::string, std::string>* grad_to_var,
+    const std::vector<BlockDescBind*>& grad_block =
+        std::vector<BlockDescBind*>()) {
   std::vector<std::unique_ptr<OpDescBind>> grad_op_descs;
   // All input gradients of forwarding operator do not need to calculate.
   const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
@@ -364,9 +353,10 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
     return grad_op_descs;  // empty vector
   }
 
-  grad_op_descs = OpInfoMap::Instance()
-                      .Get(op_desc->Type())
-                      .GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var);
+  grad_op_descs =
+      OpInfoMap::Instance()
+          .Get(op_desc->Type())
+          .GradOpMaker()(*op_desc, *no_grad_vars, grad_to_var, grad_block);
 
   std::list<std::unique_ptr<OpDescBind>> pending_fill_zeros_ops;
   for (auto& desc : grad_op_descs) {
@@ -400,21 +390,20 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
   std::vector<std::unique_ptr<OpDescBind>> backward_descs;
 
   for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
-    std::vector<std::unique_ptr<OpDescBind>> op_grads =
-        MakeOpGrad(*it, no_grad_vars, grad_to_var);
+    std::vector<std::unique_ptr<OpDescBind>> op_grads;
 
     if ((*it)->Type() == "recurrent") {
-      PADDLE_ENFORCE_EQ(
-          op_grads.size(), static_cast<size_t>(1),
-          "rnn_op's gradient process should contain only one op.");
       int step_block_idx = (*it)->GetBlockAttr("step_block");
       auto backward_block_op_descs = MakeBlockBackward(
           program_desc, step_block_idx, no_grad_vars, grad_to_var);
-      BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block);
+      BlockDescBind* backward_block =
+          program_desc.AppendBlock(*program_desc.MutableBlock(step_block_idx));
       for (auto& ptr : backward_block_op_descs) {
         backward_block->AppendAllocatedOp(std::move(ptr));
       }
-      op_grads[0]->SetBlockAttr("step_block", *backward_block);
+      op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
+    } else {
+      op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
     }
 
     for (const auto& desc : op_grads) {

paddle/framework/block_desc.h

@@ -88,6 +88,8 @@ class BlockDescBind {
 
   BlockDesc *Proto();
 
+  ProgramDescBind *Program() { return this->prog_; }
+
  private:
   void ClearPBOps();
   void ClearPBVars();

paddle/framework/details/op_registry.h

@@ -108,8 +108,9 @@ struct OpInfoFiller<T, kGradOpDescMaker> {
     info->grad_op_maker_ = [](
         const OpDescBind& fwd_op,
         const std::unordered_set<std::string>& no_grad_set,
-        std::unordered_map<std::string, std::string>* grad_to_var) {
-      T maker(fwd_op, no_grad_set, grad_to_var);
+        std::unordered_map<std::string, std::string>* grad_to_var,
+        const std::vector<BlockDescBind*>& grad_block) {
+      T maker(fwd_op, no_grad_set, grad_to_var, grad_block);
       return maker();
     };
   }

paddle/framework/executor.cc

@@ -31,7 +31,7 @@ namespace framework {
 const std::string kFeedOpType = "feed";
 const std::string kFetchOpType = "fetch";
 
-Executor::Executor(const std::vector<platform::Place>& places) {
+Executor::Executor(const std::vector<platform::Place>& places) : own_(true) {
   PADDLE_ENFORCE_GT(places.size(), 0);
   device_contexts_.resize(places.size());
   for (size_t i = 0; i < places.size(); i++) {
@@ -52,8 +52,10 @@ Executor::Executor(const std::vector<platform::Place>& places) {
 }
 
 Executor::~Executor() {
-  for (auto& device_context : device_contexts_) {
-    delete device_context;
+  if (own_) {
+    for (auto& device_context : device_contexts_) {
+      delete device_context;
+    }
   }
 }
 
@@ -66,14 +68,18 @@ static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
     var->GetMutable<FeedFetchList>();
   } else if (var_type == VarDesc::FETCH_LIST) {
     var->GetMutable<FeedFetchList>();
+  } else if (var_type == VarDesc::STEP_SCOPES) {
+    var->GetMutable<std::vector<framework::Scope>>();
   } else {
     PADDLE_THROW(
-        "Variable type must be "
-        "LoDTensor/SelectedRows/FEED_MINIBATCH/FETCH_LIST.");
+        "Variable type %d is not in "
+        "[LoDTensor, SelectedRows, FEED_MINIBATCH, FETCH_LIST]",
+        var_type);
   }
 }
 
-void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id) {
+void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
+                   bool create_local_scope) {
   // TODO(tonyyang-svail):
   //    - only runs on the first device (i.e. no interdevice communication)
   //    - will change to use multiple blocks for RNN op and Cond Op
@@ -81,29 +87,42 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id) {
   auto& block = pdesc.Block(block_id);
   auto& device = device_contexts_[0];
 
-  Scope& local_scope = scope->NewScope();
-
-  for (auto& var : block.AllVars()) {
-    if (var->Persistable()) {
-      auto* ptr = scope->Var(var->Name());
-      CreateTensor(ptr, var->GetType());
-      VLOG(3) << "Create Variable " << var->Name()
-              << " global, which pointer is " << ptr;
-    } else {
-      auto* ptr = local_scope.Var(var->Name());
+  Scope* local_scope = scope;
+  if (create_local_scope) {
+    local_scope = &scope->NewScope();
+    for (auto& var : block.AllVars()) {
+      if (var->Persistable()) {
+        auto* ptr = scope->Var(var->Name());
+        CreateTensor(ptr, var->GetType());
+        VLOG(3) << "Create Variable " << var->Name()
+                << " global, which pointer is " << ptr;
+      } else {
+        auto* ptr = local_scope->Var(var->Name());
+        CreateTensor(ptr, var->GetType());
+        VLOG(3) << "Create Variable " << var->Name()
+                << " locally, which pointer is " << ptr;
+      }
+    }
+  } else {
+    for (auto& var : block.AllVars()) {
+      auto* ptr = local_scope->Var(var->Name());
       CreateTensor(ptr, var->GetType());
-      VLOG(3) << "Create Variable " << var->Name()
-              << " locally, which pointer is " << ptr;
+      VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
+              << ptr;
     }
   }
 
   for (auto& op_desc : block.AllOps()) {
     auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
-    op->Run(local_scope, *device);
+    op->Run(*local_scope, *device);
+  }
+  if (create_local_scope) {
+    scope->DeleteScope(local_scope);
   }
-
-  scope->DeleteScope(&local_scope);
 }
 
+Executor::Executor(const platform::DeviceContext& device)
+    : device_contexts_({&device}), own_(false) {}
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/executor.h

@@ -25,6 +25,7 @@ namespace framework {
 class Executor {
  public:
   explicit Executor(const std::vector<platform::Place>& places);
+  explicit Executor(const platform::DeviceContext& devices);
   ~Executor();
 
   /* @Brief
@@ -34,10 +35,11 @@ class Executor {
    *  ProgramDesc
    *  Scope
    */
-  void Run(const ProgramDescBind&, Scope*, int);
+  void Run(const ProgramDescBind&, Scope*, int, bool create_local_scope = true);
 
  private:
-  std::vector<platform::DeviceContext*> device_contexts_;
+  std::vector<const platform::DeviceContext*> device_contexts_;
+  bool own_;
 };
 
 }  // namespace framework

paddle/framework/grad_op_desc_maker.h

@@ -15,6 +15,7 @@
 #pragma once
 #include <string>
 #include <unordered_set>
+#include <vector>
 #include "paddle/framework/op_desc.h"
 #include "paddle/framework/operator.h"
 
@@ -26,8 +27,13 @@ class GradOpDescMakerBase {
   explicit GradOpDescMakerBase(
       const OpDescBind& fwd_op,
       const std::unordered_set<std::string>& no_grad_set,
-      std::unordered_map<std::string, std::string>* grad_to_var)
-      : fwd_op_(fwd_op), no_grad_set_(no_grad_set), grad_to_var_(grad_to_var) {}
+      std::unordered_map<std::string, std::string>* grad_to_var,
+      const std::vector<BlockDescBind*>& grad_block =
+          std::vector<BlockDescBind*>())
+      : fwd_op_(fwd_op),
+        no_grad_set_(no_grad_set),
+        grad_to_var_(grad_to_var),
+        grad_block_(grad_block) {}
 
   virtual ~GradOpDescMakerBase() = default;
   virtual std::vector<std::unique_ptr<OpDescBind>> operator()() const = 0;
@@ -102,6 +108,9 @@ class GradOpDescMakerBase {
   const OpDescBind& fwd_op_;
   const std::unordered_set<std::string>& no_grad_set_;
   std::unordered_map<std::string, std::string>* grad_to_var_;
+
+ protected:
+  std::vector<BlockDescBind*> grad_block_;
 };
 
 class SingleGradOpDescMaker : public GradOpDescMakerBase {

paddle/framework/op_desc.cc

@@ -327,6 +327,19 @@ void OpDescBind::InferShape(const BlockDescBind &block) const {
   PADDLE_ENFORCE(static_cast<bool>(infer_shape),
                  "%s's infer_shape has not been registered", this->Type());
   CompileTimeInferShapeContext ctx(*this, block);
+  if (VLOG_IS_ON(10)) {
+    std::ostringstream sout;
+    auto inames = this->InputArgumentNames();
+    sout << " From [";
+    std::copy(inames.begin(), inames.end(),
+              std::ostream_iterator<std::string>(sout, ", "));
+    sout << "] to [";
+    auto onames = this->OutputArgumentNames();
+    std::copy(onames.begin(), onames.end(),
+              std::ostream_iterator<std::string>(sout, ", "));
+    sout << "]";
+    VLOG(10) << sout.str();
+  }
   infer_shape(&ctx);
 }
 

paddle/framework/operator.cc

@@ -126,7 +126,7 @@ OperatorBase::OperatorBase(const std::string& type,
 
 std::vector<std::string> OperatorBase::InputVars() const {
   std::vector<std::string> ret_val;
-  for (auto& o : outputs_) {
+  for (auto& o : inputs_) {
     ret_val.reserve(ret_val.size() + o.second.size());
     ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
   }
@@ -394,7 +394,19 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
 void OperatorWithKernel::Run(const Scope& scope,
                              const platform::DeviceContext& dev_ctx) const {
-  VLOG(3) << "Running operator " << this->Type();
+  if (VLOG_IS_ON(1)) {
+    auto inputs = this->InputVars();
+    auto outputs = this->OutputVars(true);
+    std::ostringstream sout;
+    sout << "Run operator " << this->Type() << " From [";
+    std::ostream_iterator<std::string> out_it(sout, ",");
+    std::copy(inputs.begin(), inputs.end(), out_it);
+    sout << "] to [";
+    std::copy(outputs.begin(), outputs.end(), out_it);
+    sout << "]";
+    VLOG(1) << sout.str();
+  }
+
   RuntimeInferShapeContext infer_shape_ctx(*this, scope);
   this->InferShape(&infer_shape_ctx);
 

paddle/framework/scope.cc

@@ -47,8 +47,12 @@ Variable* Scope::Var(const std::string& name) {
   return v;
 }
 
-Variable* Scope::Var() {
-  return Var(string::Sprintf("%p.%d", this, vars_.size()));
+Variable* Scope::Var(std::string* name) {
+  auto var_name = string::Sprintf("%p.%d", this, vars_.size());
+  if (name != nullptr) {
+    *name = var_name;
+  }
+  return Var(var_name);
 }
 
 Variable* Scope::FindVar(const std::string& name) const {

paddle/framework/scope.h

@@ -49,7 +49,7 @@ class Scope {
   Variable* Var(const std::string& name);
 
   /// Create a variable with a scope-unique name.
-  Variable* Var();
+  Variable* Var(std::string* name = nullptr);
 
   /// Find a variable in the scope or any of its ancestors.  Returns
   /// nullptr if cannot find.

paddle/framework/tensor.h

@@ -125,7 +125,7 @@ class Tensor {
    * @param[in] end_idx     The index of the end row(exclusive) to slice.
    *                        The index number begins from 0.
    */
-  inline Tensor Slice(const int& begin_idx, const int& end_idx) const;
+  inline Tensor Slice(int begin_idx, int end_idx) const;
 
   platform::Place place() const {
     PADDLE_ENFORCE_NOT_NULL(

paddle/framework/tensor_impl.h

@@ -228,7 +228,7 @@ inline void Tensor::CopyFromVector(const std::vector<T>& src,
 #endif
 }
 
-inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
+inline Tensor Tensor::Slice(int begin_idx, int end_idx) const {
   check_memory_size();
   PADDLE_ENFORCE_GE(begin_idx, 0,
                     "The start row index must be greater than 0.");

paddle/framework/type_defs.h

@@ -29,6 +29,7 @@ class OpDescBind;
 class BlockDescBind;
 class BlockDesc;
 class InferShapeContext;
+class BlockDescBind;
 
 using VariableNameMap = std::map<std::string, std::vector<std::string>>;
 
@@ -46,7 +47,8 @@ using OpCreator = std::function<OperatorBase*(
 
 using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDescBind>>(
     const OpDescBind&, const std::unordered_set<std::string>& /*no_grad_set*/,
-    std::unordered_map<std::string, std::string>* /*grad_to_var*/)>;
+    std::unordered_map<std::string, std::string>* /*grad_to_var*/,
+    const std::vector<BlockDescBind*>& grad_block)>;
 
 using InferVarTypeFN = std::function<void(const OpDescBind& /*op_desc*/,
                                           BlockDescBind* /*block*/)>;

paddle/operators/CMakeLists.txt

@@ -131,9 +131,10 @@ add_subdirectory(math)
 add_subdirectory(nccl)
 
 set(DEPS_OPS
-    recurrent_op
     cond_op
     cross_entropy_op
+    recurrent_op
+    dynamic_recurrent_op
     softmax_with_cross_entropy_op
     sum_op
     pool_op
@@ -142,9 +143,6 @@ set(DEPS_OPS
     sequence_conv_op
     lstm_op)
 
-
-op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
-  DEPS framework_proto tensor net_op)
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
 op_library(cross_entropy_op DEPS cross_entropy)
 op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
@@ -156,7 +154,9 @@ op_library(nccl_op DEPS nccl_common)
 endif()
 op_library(sequence_conv_op DEPS context_project)
 op_library(lstm_op DEPS sequence2batch lstm_compute)
-
+op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
+        DEPS net_op tensor_array)
+op_library(recurrent_op SRCS recurrent_op.cc DEPS executor)
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})
     op_library(${src})
@@ -168,8 +168,9 @@ cc_test(gather_test SRCS gather_test.cc DEPS tensor)
 cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
 cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
 cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory)
-cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc DEPS dynamic_recurrent_op recurrent_op tensor_array)
-
+cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc
+        rnn/recurrent_op_utils.cc
+        DEPS dynamic_recurrent_op)
 if(WITH_GPU)
   nv_test(nccl_op_test SRCS nccl_op_test.cu DEPS nccl_op gpu_info device_context)
 endif()

paddle/operators/mul_op.cc

@@ -29,9 +29,14 @@ class MulOpShapeInference : public framework::InferShapeBase {
 
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
+
     int x_num_col_dims = ctx->Attrs().Get<int>("x_num_col_dims");
     int y_num_col_dims = ctx->Attrs().Get<int>("y_num_col_dims");
 
+    VLOG(3) << "mul operator x.shape=" << x_dims << " y.shape=" << y_dims
+            << " x_num_col_dims=" << x_num_col_dims
+            << " y_num_col_dims=" << y_num_col_dims;
+
     PADDLE_ENFORCE_GT(
         x_dims.size(), x_num_col_dims,
         "The input tensor X's rank of MulOp should be larger than "

paddle/operators/recurrent_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License. */
-
-#pragma once
-
-#include "paddle/framework/operator.h"
-#include "paddle/operators/net_op.h"
-#include "paddle/operators/rnn/recurrent_op_utils.h"
-
-namespace paddle {
-namespace operators {
-
-// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
-// TODO(Superjom)
-// 1. No-padding computing for sequences with indifinite length in one batch.
-// 2. Hierarchical RNN for sequence with sub-sequence.
-// 3. Internal Memory.
-// 4. More Complex RNN architecture, such as Gated Feedback RNN.
-//    Refer to: https://arxiv.org/pdf/1502.02367.pdf
-
-class RecurrentAlgorithm {
- public:
-  void Run(const framework::Scope& scope,
-           const platform::DeviceContext& dev_ctx) const;
-
-  void Init(rnn::Argument* arg,
-            std::unique_ptr<framework::OperatorBase>* stepnet) {
-    PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before.");
-    arg_ = arg;
-    stepnet_ = stepnet;
-  }
-
- protected:
-  /*
-   * The step scopes will be stored in the father scope as a variable.
-   *
-   * NOTE the scopes are reused in both the forward and backward, so just
-   * create once and expand its size if more steps need.
-   */
-  void CreateScopes(const framework::Scope& scope, size_t seq_len) const;
-
-  const std::vector<framework::Scope*>& GetStepScopes(
-      const framework::Scope& scope) const {
-    return *scope.FindVar(arg_->step_scopes)
-                ->GetMutable<std::vector<framework::Scope*>>();
-  }
-
-  void InitMemories(framework::Scope* step_scopes) const;
-
- private:
-  std::unique_ptr<framework::OperatorBase>* stepnet_;
-  rnn::Argument* arg_;
-};
-
-class RecurrentGradientAlgorithm {
-  /**
-   * RNN's backward alogorithm.
-   *
-   * To accelerate the development of RecurrentGradientOp, we decouple RNN's
-   * algorithm and `OperatorBase`'s implementation, the former contains the core
-   * implementation of a RNN, and will keep stable even if the framework changes
-   * a
-   * lot, and the latter is a wrapper acts like an dapter for it to make RNN an
-   * operator.
-   */
- public:
-  void Init(rnn::Argument* arg,
-            std::unique_ptr<framework::OperatorBase>* stepnet) {
-    PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before.");
-    arg_ = std::move(arg);
-    stepnet_ = stepnet;
-  }
-
-  void Run(const framework::Scope& scope,
-           const platform::DeviceContext& dev_ctx) const;
-
-  void LinkBootMemoryGradients(framework::Scope* step_scopes) const;
-
- protected:
-  inline const std::vector<framework::Scope*>& GetStepScopes(
-      const framework::Scope& scope) const {
-    return *scope.FindVar(arg_->step_scopes)
-                ->GetMutable<std::vector<framework::Scope*>>();
-  }
-
- private:
-  rnn::Argument* arg_;
-  std::unique_ptr<framework::OperatorBase>* stepnet_;
-};
-
-class RecurrentOp : public framework::OperatorBase {
- public:
-  RecurrentOp(const std::string& type, const framework::VariableNameMap& inputs,
-              const framework::VariableNameMap& outputs,
-              const framework::AttributeMap& attrs);
-
-  RecurrentOp(const RecurrentOp& o)
-      : framework::OperatorBase(
-            static_cast<const framework::OperatorBase&>(o)) {
-    // TODO(yuyang18): Implement copy ctor well.
-    PADDLE_THROW("Not implemented");
-  }
-
-  void Run(const framework::Scope& scope,
-           const platform::DeviceContext& dev_ctx) const override {
-    alg_.Run(scope, dev_ctx);
-  }
-
-  void set_stepnet(std::unique_ptr<OperatorBase> net) {
-    stepnet_ = std::move(net);
-  }
-
-  const OperatorBase& stepnet() const { return *stepnet_; }
-
-  static const rnn::ArgumentName kArgName;
-
- private:
-  RecurrentAlgorithm alg_;
-  rnn::Argument arg_;
-  std::unique_ptr<OperatorBase> stepnet_;
-};
-
-class RecurrentGradientOp : public framework::OperatorBase {
- public:
-  RecurrentGradientOp(const std::string& type,
-                      const framework::VariableNameMap& inputs,
-                      const framework::VariableNameMap& outputs,
-                      const framework::AttributeMap& attrs);
-
-  RecurrentGradientOp(const RecurrentGradientOp& o)
-      : framework::OperatorBase(
-            static_cast<const framework::OperatorBase&>(o)) {
-    // TODO(yuyang18): Implement Copy ctor.
-    PADDLE_THROW("Not Implemented");
-  }
-
-  void Run(const framework::Scope& scope,
-           const platform::DeviceContext& dev_ctx) const override {
-    alg_.Run(scope, dev_ctx);
-  }
-
-  static const rnn::ArgumentName kArgName;
-
-  /*
-   * set a stepnet that is created according to a RecurrentOp's stepnet.
-   */
-  void set_stepnet(std::unique_ptr<OperatorBase> net) {
-    stepnet_ = std::move(net);
-  }
-  const OperatorBase& stepnet() const { return *stepnet_; }
-
- private:
-  RecurrentGradientAlgorithm alg_;
-  std::unique_ptr<OperatorBase> stepnet_;
-  rnn::Argument arg_;
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/operators/rnn_memory_helper_op.cc

@@ -133,11 +133,10 @@ class RNNMemoryHelperGradOpShapeInference : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *ctx) const override {
     auto x_grad_name = framework::GradVarName("X");
-    auto out_grad_name = framework::GradVarName("Out");
-    PADDLE_ENFORCE(ctx->HasInput(out_grad_name), "");
     PADDLE_ENFORCE(ctx->HasOutput(x_grad_name), "");
-    ctx->SetOutputDim(x_grad_name, ctx->GetInputDim(out_grad_name));
-    ctx->ShareLoD(out_grad_name, /*->*/ x_grad_name);
+    PADDLE_ENFORCE(ctx->HasInput("X"), "");
+    ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", /*->*/ x_grad_name);
   }
 };
 

paddle/operators/sum_op.h

@@ -29,22 +29,27 @@ template <typename Place, typename T>
 class SumKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto& in_vars = context.MultiInputVar("X");
+    auto in_vars = context.MultiInputVar("X");
     int N = in_vars.size();
     auto out_var = context.OutputVar("Out");
 
+    bool in_place = out_var == in_vars[0];
+
     if (out_var->IsType<framework::LoDTensor>()) {
       auto* out = context.Output<Tensor>("Out");
       out->mutable_data<T>(context.GetPlace());
 
       auto result = EigenVector<T>::Flatten(*out);
 
-      math::SetConstant<Place, T> constant_functor;
-      constant_functor(context.device_context(), out, 0.0);
+      if (!in_place) {
+        math::SetConstant<Place, T> constant_functor;
+        constant_functor(context.device_context(), out, 0.0);
+      }
 
       math::SelectedRowsAddToTensor<Place, T> functor;
       auto place = context.GetEigenDevice<Place>();
-      for (int i = 0; i < N; i++) {
+      // If in_place, just skip the first tensor
+      for (int i = in_place ? 1 : 0; i < N; i++) {
         if (in_vars[i]->IsType<framework::LoDTensor>()) {
           auto& in_t = in_vars[i]->Get<framework::LoDTensor>();
           auto in = EigenVector<T>::Flatten(in_t);
@@ -57,6 +62,7 @@ class SumKernel : public framework::OpKernel<T> {
         }
       }
     } else if (out_var->IsType<framework::SelectedRows>()) {
+      PADDLE_ENFORCE(!in_place, "SelectedRows not support inplace sum now");
       auto* out = context.Output<SelectedRows>("Out");
       auto* out_value = out->mutable_value();
 

paddle/pybind/pybind.cc

@@ -28,7 +28,6 @@ limitations under the License. */
 #include "paddle/operators/cond_op.h"
 #include "paddle/operators/dynamic_recurrent_op.h"
 #include "paddle/operators/net_op.h"
-#include "paddle/operators/recurrent_op.h"
 #include "paddle/platform/enforce.h"
 #include "paddle/platform/place.h"
 #include "paddle/pybind/exception.h"
@@ -428,25 +427,6 @@ All parameter, weight, gradient are variables in Paddle.
         return self.UnstackShared(source);
       });
 
-  // recurrent_op
-  py::class_<operators::RecurrentOp, OperatorBase>(m, "RecurrentOp")
-      .def_static(
-          "create",
-          [](py::bytes protobin) -> operators::RecurrentOp * {
-            OpDesc desc;
-            PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
-                           "Cannot parse user input to OpDesc");
-            PADDLE_ENFORCE(desc.IsInitialized(),
-                           "User OpDesc is not initialized, reason %s",
-                           desc.InitializationErrorString());
-            auto rnn_op = OpRegistry::CreateOp(desc);
-            return static_cast<operators::RecurrentOp *>(rnn_op.release());
-          })
-      .def("set_stepnet", [](operators::RecurrentOp &self,
-                             const operators::NetOp &net) -> void {
-        self.set_stepnet(net.Clone());
-      });
-
   py::class_<operators::DynamicRecurrentOp, OperatorBase>(m,
                                                           "DynamicRecurrentOp")
       .def_static("create",

python/paddle/v2/framework/executor.py

@@ -62,7 +62,7 @@ class Executor(object):
                 outputs={'Out': [fetch_var]},
                 attrs={'col': i})
 
-        self.executor.run(program.desc, scope, 0)
+        self.executor.run(program.desc, scope, 0, True)
         return [
             core.get_fetch_variable(scope, fetch_var_name, i)
             for i in xrange(len(fetch_list))

python/paddle/v2/framework/framework.py

@@ -270,7 +270,8 @@ class Operator(object):
 
         self.desc.check_attrs()
         no_kernel_op_set = {
-            'feed', 'fetch', 'save', 'load', 'rnn_memory_helper_grad'
+            'feed', 'fetch', 'save', 'load', 'recurrent',
+            'rnn_memory_helper_grad'
         }
         if type not in no_kernel_op_set:
             self.desc.infer_var_type(self.block.desc)

python/paddle/v2/framework/layers.py

 from paddle.v2.framework.layer_helper import LayerHelper, unique_name
 import paddle.v2.framework.core as core
-from paddle.v2.framework.framework import OpProtoHolder, Variable, Program
+from paddle.v2.framework.framework import OpProtoHolder, Variable, Program, \
+    Operator
 from paddle.v2.framework.initializer import ConstantInitializer
 import re
 
@@ -32,7 +33,6 @@ def fc(input,
         param_shape = [
             reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
         ] + [size]
-
         w = helper.create_parameter(
             attr=param_attr, shape=param_shape, dtype=dtype)
         tmp = helper.create_tmp_variable(dtype)
@@ -88,8 +88,17 @@ def data(name,
          program=None,
          init_program=None):
     helper = LayerHelper('data', **locals())
+    shape = list(shape)
+    for i in xrange(len(shape)):
+        if shape[i] is None:
+            shape[i] = -1
+            append_batch_size = False
+        elif shape[i] < 0:
+            append_batch_size = False
+
     if append_batch_size:
         shape = [-1] + shape  # append batch size as -1
+
     return helper.create_global_variable(
         name=name, shape=shape, dtype=data_type, type=type)
 
@@ -165,6 +174,9 @@ _create_op_func_('mul')
 _create_op_func_('elementwise_add')
 _create_op_func_('dropout')
 _create_op_func_('reshape')
+_create_op_func_('elementwise_add')
+_create_op_func_('sigmoid')
+_create_op_func_('scale')
 
 
 def cast(x, data_type, program=None):
@@ -193,7 +205,7 @@ def concat(input, axis, program=None, init_program=None):
 def sums(input, program=None, init_program=None):
     helper = LayerHelper('sum', **locals())
     out = helper.create_tmp_variable(dtype=helper.input_dtype())
-    helper.append_op(type='sum', inputs={'X': [input]}, outputs={'Out': out})
+    helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
     return out
 
 
@@ -346,7 +358,7 @@ def conv2d(input,
                'paddings': padding,
                'groups': groups})
 
-    pre_act = helper.append_bias_op(pre_bias)
+    pre_act = helper.append_bias_op(pre_bias, 1)
 
     return helper.append_activation(pre_act)
 
@@ -518,6 +530,8 @@ class StaticRNNGuard(BlockGuard):
         return super(StaticRNNGuard, self).__enter__()
 
     def __exit__(self, exc_type, exc_val, exc_tb):
+        if exc_type is not None:
+            return False
         self.rnn.status = StaticRNN.AFTER_RNN_BLOCK
         self.rnn.complete_rnn_op()
         return super(StaticRNNGuard, self).__exit__(exc_type, exc_val, exc_tb)
@@ -577,7 +591,7 @@ class StaticRNN(object):
                 outputs={'Out': [boot_var]},
                 attrs={
                     'value': init_value,
-                    'shape': boot_var.shape,
+                    'shape': [40] + list(boot_var.shape[1:]),
                     'data_type': boot_var.data_type
                 })
 
@@ -596,14 +610,14 @@ class StaticRNN(object):
         if not isinstance(x, Variable):
             raise TypeError("step input takes a Variable")
         if self.seq_len is None:
-            self.seq_len = x.shape[1]
-        elif self.seq_len != x.shape[1]:
+            self.seq_len = x.shape[0]
+        elif self.seq_len != x.shape[0]:
             raise ValueError("Static RNN only take fix seq_len input")
 
         ipt = self.helper.create_variable(
             name=x.name,
             dtype=x.data_type,
-            shape=[-1] + list(x.shape[2:]),
+            shape=list(x.shape[1:]),
             type=x.type)
         self.inputs.append(ipt)
         return ipt
@@ -613,10 +627,17 @@ class StaticRNN(object):
         if not isinstance(o, Variable):
             raise TypeError("step output takes a Variable")
 
+        tmp_o = self.helper.create_tmp_variable(dtype=o.data_type)
+        self.helper.append_op(
+            type='rnn_memory_helper',
+            inputs={'X': [o]},
+            outputs={'Out': tmp_o},
+            attrs={'data_type': o.data_type})
+
         out_var = self.parent_block().create_var(
-            name=o.name,
-            shape=[-1, self.seq_len] + list(o.shape[1:]),
-            dtype=o.data_type)
+            name=tmp_o.name,
+            shape=[self.seq_len] + list(tmp_o.shape),
+            dtype=tmp_o.data_type)
 
         self.outputs.append(out_var)
 
@@ -647,6 +668,68 @@ class StaticRNN(object):
             return self.outputs
 
     def complete_rnn_op(self):
-        # TODO(yuyang18): Create RNN Op here.
-        # Implement this method after RNN op complete.
-        pass
+        program = self.helper.program
+        rnn_block = program.current_block()
+        parent_block = self.parent_block()
+
+        local_inputs = set()
+
+        for op in rnn_block.ops:
+            assert isinstance(op, Operator)
+            for oname in op.output_names:
+                for out_var_name in op.output(oname):
+                    local_inputs.add(out_var_name)
+
+        for var in self.inputs:
+            local_inputs.add(var.name)
+        for m in self.memories:
+            local_inputs.add(m)
+
+        params = list()
+        for op in rnn_block.ops:
+            assert isinstance(op, Operator)
+            for iname in op.input_names:
+                for in_var_name in op.input(iname):
+                    if in_var_name not in local_inputs:
+                        params.append(in_var_name)
+
+        parameters = [parent_block.var(name) for name in params]
+
+        step_scope = parent_block.create_var(
+            type=core.VarDesc.VarType.STEP_SCOPES)
+
+        inlinks = [parent_block.var(i.name) for i in self.inputs]
+        outlinks = self.outputs
+
+        boot_memories = []
+        pre_memories = []
+        memories = []
+        for _, mem in self.memories.iteritems():
+            boot_memories.append(mem.init)
+            pre_memories.append(mem.pre_mem.name)
+            mem_var = rnn_block.var(mem.mem.name)
+            assert isinstance(mem_var, Variable)
+            new_mem = self.helper.create_tmp_variable(dtype=mem_var.data_type)
+
+            rnn_block.append_op(
+                type='rnn_memory_helper',
+                inputs={'X': [mem_var]},
+                outputs={'Out': [new_mem]},
+                attrs={'data_type': mem_var.data_type})
+
+            memories.append(new_mem.name)
+
+        parent_block.append_op(
+            type='recurrent',
+            inputs={
+                'inputs': inlinks,
+                'initial_states': boot_memories,
+                'parameters': parameters
+            },
+            outputs={'outputs': outlinks,
+                     'step_scopes': [step_scope]},
+            attrs={
+                'ex_states': pre_memories,
+                'states': memories,
+                'step_block': rnn_block
+            })

python/paddle/v2/framework/tests/test_recurrent_op.py

-import logging
-import paddle.v2.framework.core as core
 import unittest
-import numpy as np
-from paddle.v2.framework.op import Operator, RecurrentOp
-from op_test import get_numeric_gradient
-
 
-def py_sigmoid(x):
-    return 1. / (1. + np.exp(-x))
+import logging
 
+from op_test import get_numeric_gradient
+from paddle.v2.framework.layers import *
+from paddle.v2.framework.framework import Program
+from paddle.v2.framework.executor import Executor
+from paddle.v2.framework.backward import append_backward_ops
+import numpy as np
+import paddle.v2.framework.core as core
 
-class PySimpleRNN(object):
-    '''
-    A simple implementation of RNN based on numpy, to futhur test RecurrentOp's alogorithm
-    '''
 
-    def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11):
-        self.x = np.random.normal(size=(sent_len, batch_size,
-                                        input_dim)).astype("float32")
-        self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32")
-        self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32")
-        self.h_boot = np.random.normal(size=(batch_size,
-                                             input_dim)).astype("float32")
+class PyRNNBase(object):
+    def __init__(self, input_shape, output_shape):
+        self.x = np.ones(shape=input_shape).astype("float32")
+        self.y = np.zeros(shape=output_shape).astype("float32")
 
-        # memories
-        self.mems = [
-            np.zeros(shape=(batch_size, input_dim)).astype("float32")
-            for i in range(sent_len)
-        ]
+    def step(self):
+        pass
 
     def forward(self):
-        xs = self.segment_inputs()
         for step_id in range(self.x.shape[0]):
-            self.step(step_id, xs[step_id])
-        return self.concat_outputs()
+            self.step(step_id, self.x[step_id])
+        return np.array([np.mean(self.y)])
 
     def segment_inputs(self):
         return [self.x[i] for i in range(self.x.shape[0])]
 
-    def concat_outputs(self):
-        return np.array(self.mems).astype("float32")
+
+class PySimpleRNN1(PyRNNBase):
+    def __init__(self, input_shape, output_shape):
+        super(PySimpleRNN1, self).__init__(input_shape, output_shape)
+
+        seq_len, batch_size, input_dim = input_shape
+        self.h_boot = np.random.normal(size=(batch_size,
+                                             input_dim)).astype("float32")
+
+        self.scale = 1.0 / 2.0
+        men_dim = (seq_len, batch_size, input_dim)
+        self.mems = np.zeros(shape=men_dim).astype("float32")
+
+    def step(self, step_id, x):
+        if step_id == 0:
+            pre_mem = self.h_boot
+        else:
+            pre_mem = self.mems[step_id - 1]
+        self.mems[step_id] = (pre_mem + x) * self.scale
+        self.y[step_id] = self.mems[step_id]
+
+
+class PySimpleRNN2(PyRNNBase):
+    def __init__(self, input_shape, output_shape):
+        super(PySimpleRNN2, self).__init__(input_shape, output_shape)
+
+        seq_len, batch_size, input_dim = input_shape
+        self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32")
+        self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32")
+        self.h_boot = np.ones(shape=(batch_size, input_dim)).astype("float32")
+
+        men_dim = (seq_len, batch_size, input_dim)
+        self.mems = np.zeros(shape=men_dim).astype("float32")
 
     def step(self, step_id, x):
-        '''
-        run a step
-        '''
-        mem = self.mems[step_id]
         if step_id > 0:
             pre_mem = self.mems[step_id - 1]
         else:
@@ -53,108 +69,124 @@ class PySimpleRNN(object):
         xW = np.matmul(x, self.W).astype("float32")
         hU = np.matmul(pre_mem, self.U).astype("float32")
 
-        sum = xW + hU
-        self.mems[step_id] = py_sigmoid(sum)
-
+        def py_sigmoid(x):
+            return 1. / (1. + np.exp(-x))
 
-class PySimpleRNNTest(unittest.TestCase):
-    def setUp(self):
-        self.rnn = PySimpleRNN()
-
-    def test_forward(self):
-        output = self.rnn.forward()
+        self.mems[step_id] = py_sigmoid(xW + hU)
+        self.y[step_id] = self.mems[step_id]
 
 
-def create_tensor(scope, name, shape, np_data):
-    tensor = scope.var(name).get_tensor()
-    tensor.set_dims(shape)
-    tensor.set(np_data, core.CPUPlace())
+def create_tensor(np_data, place):
+    tensor = core.LoDTensor()
+    tensor.set(np_data, place)
     return tensor
 
 
-class RecurrentOpTest(unittest.TestCase):
+class RecurrentOpTest1(unittest.TestCase):
     '''
     Test RNNOp
-
     equation:
-        h_t = \sigma (W x_t + U h_{t-1})
-    weights:
-        - W
-        - U
+        h_t = ( x_t + h_{t-1} ) / scale
     vars:
         - x
     memories:
         - h
     outputs:
-       - h
+        - h
     '''
 
-    input_dim = 30
-    batch_size = 50
-    weight_dim = 15
-    sent_len = 11
+    input_dim = 2
+    batch_size = 1
+    sent_len = 1
+
+    def init_program(self):
+        self.program = Program()
+        self.init_program = Program()
+        self.p_info = {
+            "program": self.program,
+            "init_program": self.init_program
+        }
+        self.place = core.CPUPlace()
 
     def setUp(self):
-        self.py_rnn = PySimpleRNN(self.input_dim, self.batch_size,
-                                  self.weight_dim, self.sent_len)
+        self.init_program()
+        self.data_field = {"x", "h_boot"}
 
-    def forward(self):
-        self.scope = core.Scope()
-        self.create_global_variables()
-        self.create_rnn_op()
-        self.create_step_net()
-        ctx = core.DeviceContext.create(core.CPUPlace())
-        self.rnnop.run(self.scope, ctx)
-        return np.array(self.scope.find_var("h@mem").get_tensor()).astype(
-            "float32")
-
-    def create_global_variables(self):
-        # create inlink
-        x_np_data = self.py_rnn.x
-        create_tensor(self.scope, "x",
-                      [self.sent_len, self.batch_size, self.input_dim],
-                      x_np_data)
-        W_np_data = self.py_rnn.W
-        create_tensor(self.scope, "W", [self.input_dim, self.input_dim],
-                      W_np_data)
-
-        U_np_data = self.py_rnn.U
-        create_tensor(self.scope, "U", [self.input_dim, self.input_dim],
-                      U_np_data)
-
-        h_boot_np_data = self.py_rnn.h_boot
-        create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim],
-                      h_boot_np_data)
-        self.scope.var("step_scopes")
-        self.scope.var("h@mem")
+        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape)
+
+        self.output = mean(x=self.create_rnn_op(), **self.p_info)
 
     def create_rnn_op(self):
-        # create RNNOp
-        self.rnnop = RecurrentOp(
-            # inputs
-            inputs=["x"],
-            initial_states=["h_boot"],
-            step_net="stepnet",
-            # outputs
-            outputs=["h@mem"],
-            step_scopes="step_scopes",
-            # attributes
-            ex_states=["h@pre"],
-            states=["h@mem"])
-
-    def create_step_net(self):
-        stepnet = core.Net.create()
-        x_fc_op = Operator("mul", X="x", Y="W", Out="Wx")
-        h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
-        sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
-        sig_op = Operator("sigmoid", X="sum", Y="h@mem")
-
-        for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
-            stepnet.append_op(op)
-        stepnet.complete_add_op(True)
-        self.rnnop.set_stepnet(stepnet)
-
-    def test_forward(self):
+        x = data(
+            shape=[self.sent_len, self.batch_size, self.input_dim],
+            data_type='float32',
+            name='x',
+            append_batch_size=False,
+            **self.p_info)
+        h_boot = data(
+            shape=[self.input_dim],
+            data_type='float32',
+            name='h_boot',
+            **self.p_info)
+
+        rnn = StaticRNN(program=self.program)
+        with rnn.step():
+            h_pre = rnn.memory(init=h_boot)
+            x_t = rnn.step_input(x)
+
+            h = scale(
+                x=elementwise_add(
+                    x=h_pre, y=x_t, **self.p_info),
+                scale=self.py_rnn.scale,
+                **self.p_info)
+
+            rnn.update_memory(h_pre, h)
+            rnn.output(h)
+
+        return rnn()
+
+    def forward(self):
+        self.feed_map = {
+            x: create_tensor(getattr(self.py_rnn, x), self.place)
+            for x in self.data_field
+        }
+        exe = Executor(self.place)
+        out = exe.run(self.program,
+                      feed=self.feed_map,
+                      fetch_list=[self.output])
+
+        return np.array(out[0])
+
+    def backward(self):
+        self.feed_map = {
+            x: create_tensor(getattr(self.py_rnn, x), self.place)
+            for x in self.data_field
+        }
+        fetch_list = [
+            self.program.global_block().var(x + "@GRAD")
+            for x in self.data_field
+        ]
+
+        exe = Executor(self.place)
+        return exe.run(self.program, feed=self.feed_map, fetch_list=fetch_list)
+
+    def test_backward(self):
+        self.check_forward()
+
+        append_backward_ops(self.output)
+
+        ana_grad = [np.array(x) for x in self.backward()]
+
+        num_grad = self.get_numerical_gradient()
+        for idx, name in enumerate(self.data_field):
+            self.assertEqual(num_grad[idx].shape, ana_grad[idx].shape)
+            self.assertTrue(
+                np.isclose(
+                    num_grad[idx], ana_grad[idx], rtol=0.1).all())
+
+    def check_forward(self):
         print 'test recurrent op forward'
         pd_output = self.forward()
         py_output = self.py_rnn.forward()
@@ -164,44 +196,190 @@ class RecurrentOpTest(unittest.TestCase):
         self.assertEqual(pd_output.shape, py_output.shape)
         self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
 
+    def get_numerical_gradient(self, delta=0.005):
+        dloss_dout = 1.0
+        feed_list = [getattr(self.py_rnn, x) for x in self.data_field]
+        grad_list = [np.zeros_like(x) for x in feed_list]
+        for feed, grad in zip(feed_list, grad_list):
+            for f, g in np.nditer([feed, grad], op_flags=['readwrite']):
+                o = float(f)
+                f[...] = o + delta
+                y_pos = self.forward()
 
-class RecurrentGradientOpTest(unittest.TestCase):
-    def create_forward_op(self):
-        self.forward_op = RecurrentOp(
-            # inputs
-            inputs=["x"],
-            initial_states=["h_boot"],
-            step_net="stepnet",
-            # outputs
-            outputs=["h"],
-            step_scopes="step_scopes",
-            # attributes
-            ex_states=["h@pre"],
-            states=["h@alias"])
-
-        # create a stepnet for RNN
-        stepnet = core.Net.create()
-        x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
-        h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
-        sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
-        sig_op = Operator("sigmoid", X="sum", Y="h@alias")
-
-        for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
-            stepnet.append_op(op)
-        stepnet.complete_add_op(True)
-        self.forward_op.set_stepnet(stepnet)
-
-    def create_gradient_op(self):
-        a = set()
-        backward_op = core.RecurrentOp.backward(self.forward_op, a)
-
-    def test_grad(self):
-        self.create_forward_op()
-        self.create_gradient_op()
+                f[...] = o - delta
+                y_neg = self.forward()
+
+                f[...] = o
+                dout_dfeed = (y_pos - y_neg) / (delta * 2)
+                g[...] = dout_dfeed[0]
+
+        return grad_list
+
+
+class RecurrentOpTest2(RecurrentOpTest1):
+    '''
+    Test RNNOp
+    equation:
+        h_t = \sigma (W x_t + U h_{t-1})
+    weights:
+        - W
+        - U
+    vars:
+        - x
+    memories:
+        - h
+    outputs:
+       - h
+    '''
+
+    input_dim = 2
+    batch_size = 10
+    sent_len = 2
+
+    def setUp(self):
+        self.init_program()
+
+        self.data_field = {"x", "h_boot", "W", "U"}
+
+        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)
+
+        self.output = mean(x=self.create_rnn_op(), **self.p_info)
+
+    def create_rnn_op(self):
+        x = data(
+            shape=[self.sent_len, self.batch_size, self.input_dim],
+            data_type='float32',
+            name='x',
+            append_batch_size=False,
+            **self.p_info)
+        h_boot = data(
+            shape=[self.input_dim],
+            data_type='float32',
+            name='h_boot',
+            **self.p_info)
+
+        rnn = StaticRNN(program=self.program)
+        with rnn.step():
+            h_pre = rnn.memory(init=h_boot)
+            x_t = rnn.step_input(x)
+
+            temp_l = fc(input=x_t,
+                        size=self.input_dim,
+                        param_attr={'name': 'W'},
+                        bias_attr=False,
+                        **self.p_info)
+            temp_r = fc(input=h_pre,
+                        size=self.input_dim,
+                        param_attr={'name': 'U'},
+                        bias_attr=False,
+                        **self.p_info)
+
+            h = sigmoid(
+                x=elementwise_add(
+                    x=temp_l, y=temp_r, **self.p_info),
+                **self.p_info)
+
+            rnn.update_memory(h_pre, h)
+            rnn.output(h)
+
+        return rnn()
+
+
+class RecurrentOpTest3(RecurrentOpTest1):
+    '''
+    Test RNNOp with two memories
+    equation:
+        h_1 = h_pre_1
+        h_2 = h_pre_2
+        y = h_1 + h_2
+    vars:
+        - x
+    memories:
+        - h_1, h_2
+    outputs:
+       - y
+    '''
+
+    class PySimpleRNN3(PyRNNBase):
+        def __init__(self, input_shape, output_shape):
+            super(RecurrentOpTest3.PySimpleRNN3, self).__init__(input_shape,
+                                                                output_shape)
+
+            seq_len, batch_size, input_dim = input_shape
+            self.h_boot1 = np.random.normal(size=(batch_size,
+                                                  input_dim)).astype("float32")
+            self.h_boot2 = np.random.normal(size=(batch_size,
+                                                  input_dim)).astype("float32")
+
+            men_dim = (seq_len, batch_size, input_dim)
+            self.mems1 = np.zeros(shape=men_dim).astype("float32")
+            self.mems2 = np.zeros(shape=men_dim).astype("float32")
+
+        def step(self, step_id, x):
+            if step_id == 0:
+                pre_mem1 = self.h_boot1
+                pre_mem2 = self.h_boot2
+            else:
+                pre_mem1 = self.mems1[step_id - 1]
+                pre_mem2 = self.mems2[step_id - 1]
+            self.mems1[step_id] = pre_mem1
+            self.mems2[step_id] = pre_mem2
+            self.y[step_id] = self.mems1[step_id] + self.mems2[step_id] + x
+
+    input_dim = 1
+    batch_size = 1
+    sent_len = 2
+
+    def setUp(self):
+        self.init_program()
+
+        self.data_field = {"x", "h_boot1", "h_boot2"}
+
+        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
+        self.py_rnn = RecurrentOpTest3.PySimpleRNN3(self.input_shape,
+                                                    self.output_shape)
+
+        self.output = mean(x=self.create_rnn_op(), **self.p_info)
+
+    def create_rnn_op(self):
+        x = data(
+            shape=[self.sent_len, self.batch_size, self.input_dim],
+            data_type='float32',
+            name='x',
+            append_batch_size=False,
+            **self.p_info)
+        h_boot1 = data(
+            shape=[self.batch_size, self.input_dim],
+            data_type='float32',
+            name='h_boot1',
+            append_batch_size=False,
+            **self.p_info)
+        h_boot2 = data(
+            shape=[self.batch_size, self.input_dim],
+            data_type='float32',
+            name='h_boot2',
+            append_batch_size=False,
+            **self.p_info)
+
+        rnn = StaticRNN(program=self.program)
+        with rnn.step():
+            h_pre1 = rnn.memory(init=h_boot1)
+            h_pre2 = rnn.memory(init=h_boot2)
+            x_t = rnn.step_input(x)
+
+            mem1 = scale(x=h_pre1, scale=1.0, **self.p_info)
+            mem2 = scale(x=h_pre2, scale=1.0, **self.p_info)
+            out = sums(input=[mem1, x_t, mem2], **self.p_info)
+
+            rnn.update_memory(h_pre1, mem1)
+            rnn.update_memory(h_pre2, mem2)
+            rnn.output(out)
+
+        return rnn()
 
 
 if __name__ == '__main__':
-    exit(
-        0
-    )  # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
     unittest.main()

python/paddle/v2/framework/tests/test_rnn_helpers.py

-import unittest
-from paddle.v2.framework.layers import *
-from paddle.v2.framework.framework import g_program
-
-
-class TestRNN(unittest.TestCase):
-    def test_rnn(self):
-        img = data(
-            shape=[
-                80,  # sequence length
-                22,  # image height
-                22
-            ],  # image width
-            data_type='float32',
-            name='image')
-        hidden = fc(input=img, size=100, act='sigmoid', num_flatten_dims=2)
-        self.assertEqual((-1, 80, 100), hidden.shape)
-        hidden = fc(input=hidden, size=100, act='sigmoid', num_flatten_dims=2)
-        self.assertEqual((-1, 80, 100), hidden.shape)
-
-        rnn = StaticRNN()
-        with rnn.step():
-            hidden = rnn.step_input(hidden)
-            self.assertEqual((-1, 100), hidden.shape)
-            memory = rnn.memory(shape=(-1, 32), dtype='float32', init_value=0.0)
-
-            rnn_out = fc(input=[hidden, memory], size=32, act='sigmoid')
-            self.assertEqual((-1, 32), rnn_out.shape)
-            rnn.update_memory(memory, rnn_out)
-            rnn.output(rnn_out)
-
-        out = rnn()
-        self.assertEqual((-1, 80, 32), out.shape)
-        print g_program
-
-
-if __name__ == '__main__':
-    unittest.main()