Rnn make stepnet member (#3469)

* make stepnet member

* add pybind support

* fix Inputs Outputs

* remove unique_ptr

paddle/framework/pybind.cc

@@ -20,6 +20,7 @@ limitations under the License. */
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/tensor_py.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/string/to_string.h"
@@ -241,6 +242,11 @@ All parameter, weight, gradient are variables in Paddle.
               const std::shared_ptr<operators::NetOp> &net) -> void {
              self.AddOp(std::static_pointer_cast<OperatorBase>(net));
            })
+      .def("add_op",
+           [](operators::NetOp &self,
+              const std::shared_ptr<operators::RecurrentOp> &rnn) -> void {
+             self.AddOp(std::static_pointer_cast<OperatorBase>(rnn));
+           })
       .def("complete_add_op", &operators::NetOp::CompleteAddOp)
       .def("complete_add_op", [](std::shared_ptr<operators::NetOp> &self) {
         self->CompleteAddOp();
@@ -248,6 +254,29 @@ All parameter, weight, gradient are variables in Paddle.
 
   ExposeOperator(net);
 
+  // recurrent_op
+  py::class_<operators::RecurrentOp, std::shared_ptr<operators::RecurrentOp>>
+      rnn(m, "RecurrentOp");
+
+  rnn.def_static(
+         "create",
+         [](py::bytes protobin) -> std::shared_ptr<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 std::dynamic_pointer_cast<operators::RecurrentOp>(rnn_op);
+         })
+      .def("set_stepnet",
+           [](operators::RecurrentOp &self,
+              const std::shared_ptr<operators::NetOp> &net) -> void {
+             self.set_stepnet(net);
+           });
+  ExposeOperator(rnn);
+
   m.def("unique_integer", UniqueIntegerGenerator);
 
   m.def("is_compile_gpu", IsCompileGPU);

paddle/operators/CMakeLists.txt

@@ -66,6 +66,5 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
 
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
     DEPS framework_proto tensor op_registry operator net_op)
-cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
 op_library(uniform_random_op
         SRCS uniform_random_op.cc uniform_random_op.cu)

paddle/operators/recurrent_op.cc

@@ -36,15 +36,13 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      true /*infer_shape_mode*/);
   InitMemories(step_scopes[0], true /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(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 /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->InferShape(*step_scopes[i]);
+    (*stepnet_)->InferShape(*step_scopes[i]);
   }
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
                      true /*infer_shape_mode*/);
@@ -56,7 +54,6 @@ void RecurrentAlgorithm::Run(const Scope& scope,
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      false /*infer_shape_mode*/);
   InitMemories(step_scopes[0], false /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(arg_->step_net);
 
   for (size_t step_id = 0; step_id < seq_len_; step_id++) {
     // create output alias variables
@@ -64,7 +61,7 @@ void RecurrentAlgorithm::Run(const Scope& scope,
       rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1,
                         false /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
+    (*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
   }
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
                      false /*infer_shape_mode*/);
@@ -78,18 +75,16 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
   auto step_scopes = step_scopes_var->GetMutable<std::vector<Scope*>>();
 
   // Now all variables in scope must be created outside of op.
-  auto net_var = scope.FindVar(arg_->step_net);
+  PADDLE_ENFORCE_NOT_NULL(stepnet_);
-  PADDLE_ENFORCE(net_var != nullptr, "no stepnet called %s in scope",
+  PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs");
-                 arg_->step_net);
+  PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs");
-  auto net_op = net_var->GetMutable<NetOp>();
-  PADDLE_ENFORCE(!net_op->Outputs().empty(), "net_op has no outputs");
 
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
       auto& step_scope = scope.NewScope();
 
       // create step net's temp inputs
-      for (auto& input : net_op->Inputs()) {
+      for (auto& input : (*stepnet_)->Inputs()) {
         // the weight are located in parent scope
         for (auto& var_name : input.second) {
           if (!step_scope.FindVar(var_name)) {
@@ -98,7 +93,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
         }
       }
       // create stepnet's outputs
-      for (const auto& output : net_op->Outputs()) {
+      for (const auto& output : (*stepnet_)->Outputs()) {
         for (auto& var_name : output.second) {
           step_scope.NewVar(var_name);
         }
@@ -140,9 +135,8 @@ RecurrentOp::RecurrentOp(const std::string& type,
                          const framework::OperatorBase::VarNameMap& outputs,
                          const framework::AttributeMap& attrs)
     : OperatorBase(type, inputs, outputs, attrs) {
-  std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
+  rnn::InitArgument(kArgName, &arg_, *this);
-  rnn::InitArgument(kArgName, arg.get(), *this);
+  alg_.Init(&arg_, &stepnet_);
-  alg_.Init(std::move(arg));
 }
 
 class RecurrentAlgorithmProtoAndCheckerMaker
@@ -158,7 +152,6 @@ class RecurrentAlgorithmProtoAndCheckerMaker
         .AsDuplicable();
     AddInput(name.boot_memories, "variables to initialize memories.")
         .AsDuplicable();
-    AddInput(name.step_net, "network shared by all steps.");
 
     AddOutput(name.outlinks, "the outputs that need to concated for all steps.")
         .AsDuplicable();
@@ -180,14 +173,12 @@ void RecurrentGradientAlgorithm::Run(
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      false /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(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 /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
+    (*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
   }
   LinkBootMemoryGradients(step_scopes[0], false);
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
@@ -219,14 +210,12 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
   auto step_scopes = GetStepScopes(scope);
   rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
                      true /*infer_shape_mode*/);
-  Variable* net = scope.FindVar(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 /*infer_shape_mode*/);
     }
-    net->GetMutable<NetOp>()->InferShape(*step_scopes[step_id]);
+    (*stepnet_)->InferShape(*step_scopes[step_id]);
   }
   rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
                      true /*infer_shape_mode*/);
@@ -238,9 +227,8 @@ RecurrentGradientOp::RecurrentGradientOp(
     const framework::OperatorBase::VarNameMap& outputs,
     const framework::AttributeMap& attrs)
     : OperatorBase(type, inputs, outputs, attrs) {
-  std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
+  rnn::InitArgument(kArgName, &arg_, *this);
-  rnn::InitArgument(kArgName, arg.get(), *this);
+  alg_.Init(&arg_, &stepnet_);
-  alg_.Init(std::move(arg));
 }
 
 }  // namespace operators

paddle/operators/recurrent_op.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include "paddle/framework/operator.h"
+#include "paddle/operators/net_op.h"
 #include "paddle/operators/rnn/recurrent_op_utils.h"
 
 namespace paddle {
@@ -33,7 +34,11 @@ class RecurrentAlgorithm {
   void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const;
 
-  void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
+  void Init(rnn::Argument* arg, std::shared_ptr<NetOp>* stepnet) {
+    PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before.");
+    arg_ = arg;
+    stepnet_ = stepnet;
+  }
 
   /**
    * InferShape must be called before Run.
@@ -58,7 +63,8 @@ class RecurrentAlgorithm {
   void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const;
 
  private:
-  std::unique_ptr<rnn::Argument> arg_;
+  std::shared_ptr<NetOp>* stepnet_;
+  rnn::Argument* arg_;
   mutable size_t seq_len_;
 };
 
@@ -74,7 +80,11 @@ class RecurrentGradientAlgorithm {
    * operator.
    */
  public:
-  void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
+  void Init(rnn::Argument* arg, std::shared_ptr<NetOp>* 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;
@@ -95,8 +105,9 @@ class RecurrentGradientAlgorithm {
   }
 
  private:
-  std::unique_ptr<rnn::Argument> arg_;
+  rnn::Argument* arg_;
   mutable size_t seq_len_;
+  std::shared_ptr<NetOp>* stepnet_;
 };
 
 class RecurrentOp final : public framework::OperatorBase {
@@ -115,10 +126,15 @@ class RecurrentOp final : public framework::OperatorBase {
     alg_.Run(scope, dev_ctx);
   }
 
+  void set_stepnet(std::shared_ptr<NetOp> net) { stepnet_ = net; }
+  const NetOp* stepnet() const { return stepnet_.get(); }
+
   static const rnn::ArgumentName kArgName;
 
  private:
   RecurrentAlgorithm alg_;
+  rnn::Argument arg_;
+  std::shared_ptr<NetOp> stepnet_;
 };
 
 class RecurrentGradientOp final : public framework::OperatorBase {
@@ -141,8 +157,13 @@ class RecurrentGradientOp final : public framework::OperatorBase {
 
   static const rnn::ArgumentName kArgName;
 
+  void set_stepnet(const std::shared_ptr<NetOp>& net) { stepnet_ = net; }
+  const NetOp* stepnet() const { return stepnet_.get(); }
+
  private:
   RecurrentGradientAlgorithm alg_;
+  std::shared_ptr<NetOp> stepnet_;
+  rnn::Argument arg_;
 };
 
 }  // namespace operators

paddle/operators/rnn/recurrent_op_utils.cc

@@ -106,7 +106,6 @@ void LinkMemories(const std::vector<Scope*>& scopes,
 
 void InitArgument(const ArgumentName& name, Argument* arg,
                   const framework::OperatorBase& op) {
-  arg->step_net = op.Input(name.step_net);
   arg->step_scopes = op.Output(name.step_scopes);
 
   auto inlinks = op.Inputs(name.inlinks);

python/paddle/v2/framework/op.py

@@ -177,4 +177,26 @@ class OperatorFactory(object):
         return self.get_op_info(type).attrs
 
 
+class __RecurrentOp__(object):
+    __proto__ = None
+    type = 'recurrent_op'
+
+    def __init__(self):
+        # cache recurrent_op's proto
+        if self.__proto__ is None:
+            for op_proto in get_all_op_protos():
+                if op_proto.type == self.type:
+                    self.__proto__ = op_proto
+
+    def __call__(self, *args, **kwargs):
+        if self.type not in args and 'type' not in kwargs:
+            kwargs['type'] = self.type
+        # create proto
+        create_method = OpDescCreationMethod(self.__proto__)
+        proto = create_method(*args, **kwargs)
+        # create rnnop
+        return core.RecurrentOp.create(proto.SerializeToString())
+
+
 Operator = OperatorFactory()  # Default global factory
+RecurrentOp = __RecurrentOp__()

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

@@ -2,7 +2,7 @@ import logging
 import paddle.v2.framework.core as core
 import unittest
 import numpy as np
-from paddle.v2.framework.op import Operator
+from paddle.v2.framework.op import Operator, RecurrentOp
 
 
 def py_sigmoid(x):
@@ -98,11 +98,11 @@ class TestRecurrentOp(unittest.TestCase):
     def forward(self):
         self.scope = core.Scope()
         self.create_global_variables()
+        self.create_rnn_op()
         self.create_step_net()
-        rnn_op = self.create_rnn_op()
         ctx = core.DeviceContext.create(core.CPUPlace())
-        rnn_op.infer_shape(self.scope)
+        self.rnnop.infer_shape(self.scope)
-        rnn_op.run(self.scope, ctx)
+        self.rnnop.run(self.scope, ctx)
         return np.array(self.scope.find_var("h").get_tensor())
 
     def create_global_variables(self):
@@ -128,8 +128,7 @@ class TestRecurrentOp(unittest.TestCase):
 
     def create_rnn_op(self):
         # create RNNOp
-        rnnop = Operator(
+        self.rnnop = RecurrentOp(
-            "recurrent_op",
             # inputs
             inlinks=["x"],
             boot_memories=["h_boot"],
@@ -142,14 +141,9 @@ class TestRecurrentOp(unittest.TestCase):
             outlink_alias=["h@alias"],
             pre_memories=["h@pre"],
             memories=["h@alias"])
-        return rnnop
 
     def create_step_net(self):
-        var = self.scope.new_var("stepnet")
+        stepnet = core.Net.create()
-        stepnet = var.get_net()
-
-        # x_fc_op = Operator("fc", X="x@alias", W="W", Y="Wx")
-        # h_fc_op = Operator("fc", X="h@pre", W="U", Y="Uh")
         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("add_two", X="Wx", Y="Uh", Out="sum")
@@ -158,6 +152,7 @@ class TestRecurrentOp(unittest.TestCase):
         for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
             stepnet.add_op(op)
         stepnet.complete_add_op(True)
+        self.rnnop.set_stepnet(stepnet)
 
     def test_forward(self):
         print 'test recurrent op forward'