Check and correct the output's lod_level in DynamicRNN related operators (#19144)

* Refine the InferShape of ReadFrom and WriteTo op, and add comment to explain why not call ShareLoD for runtime.
test=develop

* Add comment for ReorderLoDTensorByRank op.

* Add comment for lod_tensor_to_tensor_array op to explain why only call DecreaseLoDLevel for compile time.
test=develop

* ShrinkRNNMemory op should call ShareLoD for compile time.
test=develop

* Add the implementation of IncreaseLoDLevel and add the compile-time check of lod_level in InferShape of sequence_pool.
test=develop

* Refine the unittest of DynamicRNN.
test=develop

* Change PADDLE_ENFORCE to PADDLE_ENFORCE_NE.
test=develop

paddle/fluid/framework/op_desc.cc

@@ -86,7 +86,7 @@ class CompileTimeInferShapeContext : public InferShapeContext {
     auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
     if (in_var->GetType() != proto::VarType::LOD_TENSOR &&
         in_var->GetType() != proto::VarType::LOD_TENSOR_ARRAY) {
-      VLOG(3) << "input " << in << " is not LodTensor or LodTensorArray.";
+      VLOG(3) << "input " << in << " is not LoDTensor or LoDTensorArray.";
       return;
     }
     out_var->SetLoDLevel(in_var->GetLoDLevel());
@@ -94,6 +94,8 @@ class CompileTimeInferShapeContext : public InferShapeContext {
 
   void DecreaseLoDLevel(const std::string &in, const std::string &out,
                         size_t i = 0, size_t j = 0) const override {
+    // When in is a LoDTensor and out is a LoDTensorArray, there may need to
+    // decrease the lod_level.
     PADDLE_ENFORCE_LT(i, Inputs(in).size());
     PADDLE_ENFORCE_LT(j, Outputs(out).size());
     PADDLE_ENFORCE(Inputs(in)[i] != framework::kEmptyVarName,
@@ -102,17 +104,35 @@ class CompileTimeInferShapeContext : public InferShapeContext {
                    "The %s[%d] is @EMPTY@", out, j);
     auto *in_var = block_.FindVarRecursive(Inputs(in)[i]);
     auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
-    PADDLE_ENFORCE(out_var->GetType() == proto::VarType::LOD_TENSOR_ARRAY ||
-                       out_var->GetType() == proto::VarType::LOD_TENSOR,
-                   "The input %s should be LodTensorArray or LodTensor.",
-                   out_var->Name());
-    PADDLE_ENFORCE(in_var->GetType() == proto::VarType::LOD_TENSOR,
-                   "The input %s should be LodTensor.", in_var->Name());
+    PADDLE_ENFORCE_EQ(in_var->GetType(), proto::VarType::LOD_TENSOR,
+                      "The input %s should be LoDTensor.", in_var->Name());
+    PADDLE_ENFORCE_EQ(out_var->GetType(), proto::VarType::LOD_TENSOR_ARRAY,
+                      "The output %s should be LoDTensorArray.",
+                      out_var->Name());
     if (in_var->GetLoDLevel() > 0) {
       out_var->SetLoDLevel(in_var->GetLoDLevel() - 1);
     }
   }
 
+  void IncreaseLoDLevel(const std::string &in, const std::string &out,
+                        size_t i = 0, size_t j = 0) const override {
+    // When in is a LoDTensorArray and out is a LoDTensor, there may need to
+    // increase the lod_level.
+    PADDLE_ENFORCE_LT(i, Inputs(in).size());
+    PADDLE_ENFORCE_LT(j, Outputs(out).size());
+    PADDLE_ENFORCE_NE(Inputs(in)[i], framework::kEmptyVarName,
+                      "The %s[%d] is @EMPTY@", in, i);
+    PADDLE_ENFORCE_NE(Outputs(out)[j], framework::kEmptyVarName,
+                      "The %s[%d] is @EMPTY@", out, j);
+    auto *in_var = block_.FindVarRecursive(Inputs(in)[i]);
+    auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
+    PADDLE_ENFORCE_EQ(in_var->GetType(), proto::VarType::LOD_TENSOR_ARRAY,
+                      "The input %s should be LoDTensorArray.", in_var->Name());
+    PADDLE_ENFORCE_EQ(out_var->GetType(), proto::VarType::LOD_TENSOR,
+                      "The output %s should be LoDTensor.", out_var->Name());
+    out_var->SetLoDLevel(in_var->GetLoDLevel() + 1);
+  }
+
   std::vector<InferShapeVarPtr> GetInputVarPtrs(
       const std::string &name) override {
     const std::vector<std::string> arg_names = Inputs(name);

paddle/fluid/framework/operator.cc

@@ -657,7 +657,18 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
   void DecreaseLoDLevel(const std::string& in, const std::string& out,
                         size_t i = 0, size_t j = 0) const override {
-    PADDLE_THROW("DecreaseLoDLevel is only used in compile time.");
+    PADDLE_THROW(
+        "DecreaseLoDLevel is only used in compile time. The calculation of "
+        "output's actual lod is different among operators so that should be "
+        "set in the runtime kernel.");
+  }
+
+  void IncreaseLoDLevel(const std::string& in, const std::string& out,
+                        size_t i = 0, size_t j = 0) const override {
+    PADDLE_THROW(
+        "IncreaseLoDLevel is only used in compile time. The calculation of "
+        "output's actual lod is different among operators so that should be "
+        "set in the runtime kernel.");
   }
 
   bool IsRuntime() const override { return true; }

paddle/fluid/framework/shape_inference.h

@@ -68,6 +68,9 @@ class InferShapeContext {
   virtual void DecreaseLoDLevel(const std::string &in, const std::string &out,
                                 size_t i = 0, size_t j = 0) const = 0;
 
+  virtual void IncreaseLoDLevel(const std::string &in, const std::string &out,
+                                size_t i = 0, size_t j = 0) const = 0;
+
   virtual bool IsRuntime() const = 0;
 
   virtual std::vector<InferShapeVarPtr> GetInputVarPtrs(

paddle/fluid/operators/array_to_lod_tensor_op.cc

@@ -192,7 +192,21 @@ class ArrayToLoDTensorInferShape : public framework::InferShapeBase {
                    "ArrayToLoDTensorOp must has input X.");
     PADDLE_ENFORCE(context->HasInput("RankTable"),
                    "ArrayToLoDTensorOp must has input RankTable.");
+    // For compile-time, the first dim of input X and output Out should be -1.
+    // For runtime, the first dim of output Out should be the sum of all
+    // elements's first dim in input X. The output's dims will be re-computed in
+    // detail kernel implementation.
     context->SetOutputDim("Out", context->GetInputDim("X"));
+
+    // The output LoDTensor's lod_level should be input X's lod_level + 1.
+    // For compile-time, we call IncreaseLoDLevel to set output's lod_level.
+    // For runtime, output LoDTensor's lod is determined by input X's lod and
+    // the level specified by input RandTable.
+    // We cannot get X's detail lod and RankTable's level in this function, so
+    // leave this work to the detail kernel implementation.
+    if (!context->IsRuntime()) {
+      context->IncreaseLoDLevel("X", /*->*/ "Out");
+    }
   }
 };
 

paddle/fluid/operators/controlflow/tensor_array_read_write_op.cc

@@ -88,8 +88,21 @@ class WriteToArrayInferShape : public framework::InferShapeBase {
     if (!context->HasInput("X")) {
       return;
     }
+
     PADDLE_ENFORCE(context->HasOutput("Out"), NotHasOutError());
     context->SetOutputDim("Out", context->GetInputDim("X"));
+
+    // When compile time, we need to:
+    // - for ReadFromArray, share tensor_array X's lod_level to Out
+    // - for WriteToArray, share X's lod_level to tensor_array Out
+    // When runtime, we need to:
+    // - for ReadFromArray, share X[I]'s lod to Out
+    // - for WriteToArray, share X's lod to Out[I]
+    // but we cannot get I's value here, so leave this work to detail
+    // kernel implementation.
+    if (!context->IsRuntime()) {
+      context->ShareLoD("X", /*->*/ "Out");
+    }
   }
 
  protected:
@@ -166,19 +179,6 @@ $$T = A[i]$$
 };
 
 class ReadFromArrayInferShape : public WriteToArrayInferShape {
- public:
-  void operator()(framework::InferShapeContext *context) const override {
-    WriteToArrayInferShape::operator()(context);
-    if (!context->HasInput("X")) {
-      return;
-    }
-
-    // FIXME: just for compile time.
-    if (!context->IsRuntime()) {
-      context->ShareLoD("X", /*->*/ "Out");
-    }
-  }
-
  protected:
   const char *NotHasXError() const override {
     return "The input array X must be set";

paddle/fluid/operators/lod_tensor_to_array_op.cc

@@ -106,9 +106,10 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
     auto max_seq_len = items[0].length;
     auto rank_level = rank_table.level();
 
-    PADDLE_ENFORCE_LT(rank_level, x.lod().size(),
-                      "Input should be a LOD tensor, and size is at least %d",
-                      rank_level + 1);
+    PADDLE_ENFORCE_LT(
+        rank_level, x.lod().size(),
+        "Input should be a LoDTensor, and its lod_level should be at least %d",
+        rank_level + 1);
     out.resize(max_seq_len);
     std::vector<std::vector<CopyRange>> copy_ranges(max_seq_len);
 
@@ -167,10 +168,21 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
 class LoDTensorToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("X", "");
-    AddInput("RankTable", "");
-    AddOutput("Out", "");
-    AddComment("");
+    AddInput("X",
+             "(LoDTensor), the input lod tensor is a minibatch of sequences, "
+             "and will be split to a tensor_array according to "
+             "Input(RankTable).");
+    AddInput("RankTable", "(LoDRankTable), the rank table.");
+    AddOutput("Out",
+              "(LoDTensorArray), the result tensor_array, which is actually a "
+              "std::vector<LoDTensor>.");
+    AddComment(R"DOC(LoDTensorToArray operator.
+Input(X) is a minibatch of sequences. Input(RankTable) stores the order of the input sequences.
+The lod_tensor_to_array operator will spilt the input sequences to a tensor_array, with each
+element stores one sequence, according to the input rank_table.
+
+NOTE: this operator is an internal component of DynamicRNN, and cannot be called by users.
+)DOC");
   }
 };
 
@@ -187,10 +199,18 @@ class LoDTensorToArrayInferShape : public framework::InferShapeBase {
                    "Output(Out) of LoDTensorToArrayOp should not be null.");
 
     auto x_dim = context->GetInputDim("X");
-    // The first dim of each LoDTensor in Output can only be set at run-time.;
-    // We still have to Resize each LoDTensor in Output.
+    // For compile-time, the first dim of input X and output Out should be -1.
+    // For runtime, the first dim of input X should be the sum of all elements's
+    // first dim in output Out. The output's dims will be re-computed in detail
+    // kernel implementation.
     context->SetOutputDim("Out", x_dim);
-    // The lod level should be passed to out in compile time.
+
+    // The output LoDTensor's lod_level should be input X's lod_level - 1.
+    // For compile time, we call DecreaseLoDLevel to set output's lod_level.
+    // For runtime, output LoDTensor's lod is determined by input X's lod and
+    // the level specified by input RandTable.
+    // We cannot get X's detail lod and RankTable's level in this function, so
+    // leave this work to the detail kernel implementation.
     if (!context->IsRuntime()) {
       context->DecreaseLoDLevel("X", /*->*/ "Out");
     }

paddle/fluid/operators/reorder_lod_tensor_by_rank_op.cc

@@ -202,6 +202,9 @@ class IdentityInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
     context->SetOutputDim("Out", context->GetInputDim("X"));
+    // X'lod and Out'lod is different on runtime, so there is no need to call
+    // ShareLoD for runtime. While the setting of Out's lod is done in detail
+    // kernel implementation.
     if (!context->IsRuntime()) {
       context->ShareLoD("X", /*->*/ "Out");
     }

paddle/fluid/operators/sequence_ops/sequence_pool_op.cc

@@ -28,6 +28,16 @@ class SequencePoolOp : public framework::OperatorWithKernel {
                       "Input(X) of SequencePoolOp should not be null.");
     PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
                       "Output(Out) of SequencePoolOp should not be null.");
+
+    if (!ctx->IsRuntime()) {
+      // Check the lod_level for compile-time.
+      framework::VarDesc* x_desc =
+          boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("X")[0]);
+      PADDLE_ENFORCE_GT(
+          x_desc->GetLoDLevel(), 0,
+          "The LoD level Input(X) of sequence_pool should be larger than 0");
+    }
+
     ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
     if (ctx->Attrs().Get<std::string>("pooltype") == "MAX") {
       PADDLE_ENFORCE_EQ(

paddle/fluid/operators/shrink_rnn_memory_op.cc

@@ -100,8 +100,10 @@ class ShrinkRNNMemoryInferShape : public framework::InferShapeBase {
     PADDLE_ENFORCE(context->HasInput("I"));
     PADDLE_ENFORCE(context->HasInput("RankTable"));
     context->SetOutputDim("Out", context->GetInputDim("X"));
+    // For runtime, output's lod is computed according to input's lod, but
+    // remove the finished sequence. It is set in detail kernel implementation.
     if (!context->IsRuntime()) {
-      context->DecreaseLoDLevel("X", /*->*/ "Out");
+      context->ShareLoD("X", /*->*/ "Out");
     }
   }
 };

python/paddle/fluid/tests/unittests/test_dyn_rnn.py

@@ -27,13 +27,53 @@ from paddle.fluid.layers.control_flow import shrink_memory
 from fake_reader import fake_imdb_reader
 
 
-class TestDynRNN(unittest.TestCase):
+class TestDynamicRNN(unittest.TestCase):
     def setUp(self):
         self.word_dict_len = 5147
         self.BATCH_SIZE = 2
         reader = fake_imdb_reader(self.word_dict_len, self.BATCH_SIZE * 100)
         self.train_data = paddle.batch(reader, batch_size=self.BATCH_SIZE)
 
+    def _train(self,
+               main_program,
+               startup_program,
+               feed_list,
+               fetch_list,
+               is_nested=False,
+               max_iters=1):
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_program)
+        feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
+        data = next(self.train_data())
+
+        for iter_id in range(max_iters):
+            fetch_outs = exe.run(main_program,
+                                 feed=feeder.feed(data),
+                                 fetch_list=fetch_list,
+                                 return_numpy=False)
+            if len(fetch_list) == 3:
+                rnn_in_seq = fetch_outs[0]
+                rnn_out_seq = fetch_outs[1]
+                if not is_nested:
+                    # Check for lod set in runtime. When lod_level is 1,
+                    # the lod of DynamicRNN's output should be the same as input.
+                    self.assertEqual(rnn_in_seq.lod(), rnn_out_seq.lod())
+
+                loss_i = numpy.array(fetch_outs[2])
+            elif len(fetch_list) == 1:
+                loss_i = numpy.array(fetch_outs[0])
+            #print(loss_i)
+
+            self.assertEqual((1, ), loss_i.shape)
+            self.assertFalse(numpy.isnan(loss_i))
+            if iter_id == 0:
+                loss_0 = loss_i
+
+        if max_iters > 10:
+            # loss should be small after 10 mini-batch
+            self.assertLess(loss_i[0], loss_0[0])
+
     def test_plain_while_op(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()
@@ -44,10 +84,7 @@ class TestDynRNN(unittest.TestCase):
             sent_emb = fluid.layers.embedding(
                 input=sentence, size=[self.word_dict_len, 32], dtype='float32')
 
-            label = fluid.layers.data(name='label', shape=[1], dtype='float32')
-
             rank_table = lod_rank_table(x=sent_emb)
-
             sent_emb_array = lod_tensor_to_array(x=sent_emb, table=rank_table)
 
             seq_len = max_sequence_len(rank_table=rank_table)
@@ -61,7 +98,6 @@ class TestDynRNN(unittest.TestCase):
                 shape=[-1, 100],
                 dtype='float32')
             boot_mem.stop_gradient = False
-
             mem_array = fluid.layers.array_write(x=boot_mem, i=i)
 
             cond = fluid.layers.less_than(x=i, y=seq_len)
@@ -82,27 +118,29 @@ class TestDynRNN(unittest.TestCase):
                 fluid.layers.array_write(x=hidden, i=i, array=mem_array)
                 fluid.layers.less_than(x=i, y=seq_len, cond=cond)
 
-            all_timesteps = array_to_lod_tensor(x=out, table=rank_table)
-            last = fluid.layers.sequence_last_step(input=all_timesteps)
+            result_all_timesteps = array_to_lod_tensor(x=out, table=rank_table)
+            last = fluid.layers.sequence_last_step(input=result_all_timesteps)
+
             logits = fluid.layers.fc(input=last, size=1, act=None)
+            label = fluid.layers.data(name='label', shape=[1], dtype='float32')
             loss = fluid.layers.sigmoid_cross_entropy_with_logits(
                 x=logits, label=label)
             loss = fluid.layers.mean(loss)
             sgd = fluid.optimizer.SGD(1e-4)
             sgd.minimize(loss=loss)
-        cpu = fluid.CPUPlace()
-        exe = fluid.Executor(cpu)
-        exe.run(startup_program)
-        feeder = fluid.DataFeeder(feed_list=[sentence, label], place=cpu)
 
-        data = next(self.train_data())
-        val = exe.run(main_program, feed=feeder.feed(data),
-                      fetch_list=[loss])[0]
-        self.assertEqual((1, ), val.shape)
-        print(val)
-        self.assertFalse(numpy.isnan(val))
+        # Check for lod_level set in compile-time.
+        self.assertEqual(sent_emb.lod_level, result_all_timesteps.lod_level)
+
+        self._train(
+            main_program=main_program,
+            startup_program=startup_program,
+            feed_list=[sentence, label],
+            fetch_list=[sent_emb, result_all_timesteps, loss],
+            is_nested=False,
+            max_iters=1)
 
-    def test_train_dyn_rnn(self):
+    def test_train_dynamic_rnn(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()
         with fluid.program_guard(main_program, startup_program):
@@ -111,17 +149,18 @@ class TestDynRNN(unittest.TestCase):
             sent_emb = fluid.layers.embedding(
                 input=sentence, size=[self.word_dict_len, 32], dtype='float32')
 
-            rnn = fluid.layers.DynamicRNN()
-
-            with rnn.block():
-                in_ = rnn.step_input(sent_emb)
-                mem = rnn.memory(shape=[100], dtype='float32')
+            drnn = fluid.layers.DynamicRNN()
+            with drnn.block():
+                in_ = drnn.step_input(sent_emb)
+                mem = drnn.memory(shape=[100], dtype='float32')
                 out_ = fluid.layers.fc(input=[in_, mem], size=100, act='tanh')
-                rnn.update_memory(mem, out_)
-                rnn.output(out_)
+                drnn.update_memory(mem, out_)
+                drnn.output(out_)
 
-            last = fluid.layers.sequence_last_step(input=rnn())
+            drnn_result = drnn()
+            last = fluid.layers.sequence_last_step(input=drnn_result)
             logits = fluid.layers.fc(input=last, size=1, act=None)
+
             label = fluid.layers.data(name='label', shape=[1], dtype='float32')
             loss = fluid.layers.sigmoid_cross_entropy_with_logits(
                 x=logits, label=label)
@@ -129,37 +168,30 @@ class TestDynRNN(unittest.TestCase):
             sgd = fluid.optimizer.Adam(1e-3)
             sgd.minimize(loss=loss)
 
-        cpu = fluid.CPUPlace()
-        exe = fluid.Executor(cpu)
-        exe.run(startup_program)
-        feeder = fluid.DataFeeder(feed_list=[sentence, label], place=cpu)
-        data = next(self.train_data())
-        loss_0 = exe.run(main_program,
-                         feed=feeder.feed(data),
-                         fetch_list=[loss])[0]
-        for _ in range(100):
-            val = exe.run(main_program,
-                          feed=feeder.feed(data),
-                          fetch_list=[loss])[0]
-        # loss should be small after 100 mini-batch
-        self.assertLess(val[0], loss_0[0])
+        # Check for lod_level set in compile-time.
+        self.assertEqual(sent_emb.lod_level, drnn_result.lod_level)
 
-    # this unit test is just used to the two layer nested dyn_rnn.
-    def test_train_nested_dyn_rnn(self):
-        word_dict = [i for i in range(30)]
+        self._train(
+            main_program=main_program,
+            startup_program=startup_program,
+            feed_list=[sentence, label],
+            fetch_list=[sent_emb, drnn_result, loss],
+            is_nested=False,
+            max_iters=100)
 
-        def fake_reader():
-            seq_len, label = [[2, 2]], [0, 1]
-            data = []
-            for ele in seq_len:
-                for j in ele:
-                    data.append([numpy.random.randint(30) \
-                                 for _ in range(j)])
+    def _fake_reader(self):
+        seq_len, label = [[2, 2]], [0, 1]
+        data = []
+        for ele in seq_len:
+            for j in ele:
+                data.append([numpy.random.randint(30) for _ in range(j)])
 
-            while True:
-                yield data, label
+        while True:
+            yield data, label
 
-        train_data = paddle.batch(fake_reader, batch_size=2)
+    # this unit test is just used to the two layer nested dyn_rnn.
+    def test_train_nested_dynamic_rnn(self):
+        word_dict = [i for i in range(30)]
 
         main_program = fluid.Program()
         startup_program = fluid.Program()
@@ -169,63 +201,50 @@ class TestDynRNN(unittest.TestCase):
             label = fluid.layers.data(
                 name='label', shape=[1], dtype='float32', lod_level=1)
 
-            rnn = fluid.layers.DynamicRNN()
-            with rnn.block():
-                in_ = rnn.step_input(sentence)
-                assert in_.lod_level == 1, "the lod level of in_ should be 1"
-                sent_emb = fluid.layers.embedding(
-                    input=in_, size=[len(word_dict), 32], dtype='float32')
-                out_ = fluid.layers.fc(input=sent_emb, size=100, act='tanh')
-
-                rnn1 = fluid.layers.DynamicRNN()
-                with rnn1.block():
-                    in_1 = rnn1.step_input(out_)
+            drnn0 = fluid.layers.DynamicRNN()
+            with drnn0.block():
+                in_0 = drnn0.step_input(sentence)
+                assert in_0.lod_level == 1, "the lod level of in_ should be 1"
+                sentence_emb = fluid.layers.embedding(
+                    input=in_0, size=[len(word_dict), 32], dtype='float32')
+                out_0 = fluid.layers.fc(input=sentence_emb,
+                                        size=100,
+                                        act='tanh')
+
+                drnn1 = fluid.layers.DynamicRNN()
+                with drnn1.block():
+                    in_1 = drnn1.step_input(out_0)
                     assert in_1.lod_level == 0, "the lod level of in_1 should be 0"
                     out_1 = fluid.layers.fc(input=[in_1], size=100, act='tanh')
-                    rnn1.output(out_1)
+                    drnn1.output(out_1)
 
-                last = fluid.layers.sequence_last_step(input=rnn1())
-                rnn.output(last)
+                drnn1_result = drnn1()
+                last_1 = fluid.layers.sequence_last_step(input=drnn1_result)
+                drnn0.output(last_1)
 
-            last = rnn()
+            last = drnn0()
             logits = fluid.layers.fc(input=last, size=1, act=None)
             loss = fluid.layers.sigmoid_cross_entropy_with_logits(
                 x=logits, label=label)
             loss = fluid.layers.mean(loss)
             sgd = fluid.optimizer.SGD(1e-3)
-            #sgd = fluid.optimizer.Adam(1e-3)
             sgd.minimize(loss=loss)
 
-        cpu = fluid.CPUPlace()
-        exe = fluid.Executor(cpu)
-        exe.run(startup_program)
-        feeder = fluid.DataFeeder(feed_list=[sentence, label], place=cpu)
-        data = next(train_data())
-        val = exe.run(main_program, feed=feeder.feed(data),
-                      fetch_list=[loss])[0]
-
-        for _ in range(100):
-            val = exe.run(main_program,
-                          feed=feeder.feed(data),
-                          fetch_list=[loss])[0]
-            print(val)
+        train_data_orig = self.train_data
+        self.train_data = paddle.batch(self._fake_reader, batch_size=2)
+        self._train(
+            main_program=main_program,
+            startup_program=startup_program,
+            feed_list=[sentence, label],
+            fetch_list=[loss],
+            is_nested=True,
+            max_iters=100)
+        self.train_data = train_data_orig
 
     # this unit test is just used to the two layer nested dyn_rnn.
-    def test_train_nested_dyn_rnn2(self):
+    def test_train_nested_dynamic_rnn2(self):
         word_dict = [i for i in range(30)]
 
-        def fake_reader():
-            seq_len, label = [[2, 2]], [0, 1]
-            data = []
-            for ele in seq_len:
-                for j in ele:
-                    data.append([numpy.random.randint(30) \
-                                 for _ in range(j)])
-
-            while True:
-                yield data, label
-
-        train_data = paddle.batch(fake_reader, batch_size=2)
         hidden_size = 32
         main_program = fluid.Program()
         startup_program = fluid.Program()
@@ -235,14 +254,14 @@ class TestDynRNN(unittest.TestCase):
             label = fluid.layers.data(
                 name='label', shape=[1], dtype='float32', lod_level=1)
 
-            rnn = fluid.layers.DynamicRNN()
-            with rnn.block():
-                in_ = rnn.step_input(sentence)
-                sent_emb = fluid.layers.embedding(
-                    input=in_,
+            drnn0 = fluid.layers.DynamicRNN()
+            with drnn0.block():
+                in_0 = drnn0.step_input(sentence)
+                sentence_emb = fluid.layers.embedding(
+                    input=in_0,
                     size=[len(word_dict), hidden_size],
                     dtype='float32')
-                input_forward_proj = fluid.layers.fc(input=sent_emb,
+                input_forward_proj = fluid.layers.fc(input=sentence_emb,
                                                      size=hidden_size * 4,
                                                      act=None,
                                                      bias_attr=False)
@@ -251,36 +270,33 @@ class TestDynRNN(unittest.TestCase):
                     size=hidden_size * 4,
                     use_peepholes=False)
 
-                rnn1 = fluid.layers.DynamicRNN()
-                with rnn1.block():
-                    in_1 = rnn1.step_input(forward)
+                drnn1 = fluid.layers.DynamicRNN()
+                with drnn1.block():
+                    in_1 = drnn1.step_input(forward)
                     out_1 = fluid.layers.fc(input=[in_1], size=100, act='tanh')
-                    rnn1.output(out_1)
+                    drnn1.output(out_1)
 
-                last = fluid.layers.sequence_last_step(input=rnn1())
-                rnn.output(last)
+                last = fluid.layers.sequence_last_step(input=drnn1())
+                drnn0.output(last)
 
-            last = rnn()
+            last = drnn0()
             logits = fluid.layers.fc(input=last, size=1, act=None)
             loss = fluid.layers.sigmoid_cross_entropy_with_logits(
                 x=logits, label=label)
             loss = fluid.layers.mean(loss)
             sgd = fluid.optimizer.SGD(1e-3)
-            #sgd = fluid.optimizer.Adam(1e-3)
             sgd.minimize(loss=loss)
 
-        cpu = fluid.CPUPlace()
-        exe = fluid.Executor(cpu)
-        exe.run(startup_program)
-        feeder = fluid.DataFeeder(feed_list=[sentence, label], place=cpu)
-        data = next(train_data())
-        val = exe.run(main_program, feed=feeder.feed(data),
-                      fetch_list=[loss])[0]
-
-        for _ in range(100):
-            val = exe.run(main_program,
-                          feed=feeder.feed(data),
-                          fetch_list=[loss])[0]
+        train_data_orig = self.train_data
+        self.train_data = paddle.batch(self._fake_reader, batch_size=2)
+        self._train(
+            main_program=main_program,
+            startup_program=startup_program,
+            feed_list=[sentence, label],
+            fetch_list=[loss],
+            is_nested=True,
+            max_iters=100)
+        self.train_data = train_data_orig
 
 
 if __name__ == '__main__':