diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec
index c4c336424e332ddaf91dfcadbdc0a9343633ed18..8c4a4b6f45f86b05ef0df500c345ca2fb0ecc6d8 100644
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -61,11 +61,11 @@ paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'en
 paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None))
-paddle.fluid.InferenceTranspiler.__init__
+paddle.fluid.InferenceTranspiler.__init__ 
 paddle.fluid.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
 paddle.fluid.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.DistributeTranspilerConfig.__init__
+paddle.fluid.DistributeTranspilerConfig.__init__ 
 paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords='kwargs', defaults=(None, None, None, None, None, 1, 0, None))
 paddle.fluid.ParallelExecutor.run ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True))
 paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ExecutionStrategy) -> None
@@ -174,6 +174,7 @@ paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=Non
 paddle.fluid.layers.pad2d ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None))
 paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
+paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
@@ -348,7 +349,7 @@ paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=[
 paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None))
-paddle.fluid.transpiler.InferenceTranspiler.__init__
+paddle.fluid.transpiler.InferenceTranspiler.__init__ 
 paddle.fluid.transpiler.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
 paddle.fluid.transpiler.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
@@ -358,7 +359,7 @@ paddle.fluid.transpiler.HashName.reset ArgSpec(args=['self'], varargs=None, keyw
 paddle.fluid.transpiler.RoundRobin.__init__ ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.RoundRobin.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.RoundRobin.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.DistributeTranspilerConfig.__init__
+paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ 
 paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True, False))
 paddle.fluid.nets.sequence_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max'))
 paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,))
@@ -425,4 +426,4 @@ paddle.fluid.Scope.__init__ __init__(self: paddle.fluid.core.Scope) -> None
 paddle.fluid.Scope.drop_kids drop_kids(self: paddle.fluid.core.Scope) -> None
 paddle.fluid.Scope.find_var find_var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
 paddle.fluid.Scope.new_scope new_scope(self: paddle.fluid.core.Scope) -> paddle.fluid.core.Scope
-paddle.fluid.Scope.var var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
\ No newline at end of file
+paddle.fluid.Scope.var var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
diff --git a/paddle/fluid/operators/sequence_concat_op.cc b/paddle/fluid/operators/sequence_concat_op.cc
index c989c1cb38ed58ea5232b886f3ea18d7cc35971a..397a3182953e3f1afaeadeff6d53a4f22fb95d26 100644
--- a/paddle/fluid/operators/sequence_concat_op.cc
+++ b/paddle/fluid/operators/sequence_concat_op.cc
@@ -37,42 +37,38 @@ class SeqConcatOpMaker : public framework::OpProtoAndCheckerMaker {
 class SeqConcatShapeInferer : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
-    try {
-      PADDLE_ENFORCE(context->HasInputs("X"),
-                     "Input(X) of Sequence Concat Op should not be null.");
-      PADDLE_ENFORCE(context->HasOutput("Out"),
-                     "Output(Out) of Sequence Concat Op should not be null.");
+    PADDLE_ENFORCE(context->HasInputs("X"),
+                   "Input(X) of Sequence Concat Op should not be null.");
+    PADDLE_ENFORCE(context->HasOutput("Out"),
+                   "Output(Out) of Sequence Concat Op should not be null.");
 
-      PADDLE_ENFORCE_GT(context->HasInputs("X"), 1,
-                        "The number of input sequences is at least two.");
-      auto x_dims = context->GetInputsDim("X");
-      int64_t batch_size = 0;
-      int64_t feature_size = 0;
-      std::vector<int64_t> out_dims;
-      for (auto &x_dim : x_dims) {
-        if (out_dims.empty()) {
-          out_dims = framework::vectorize(x_dim);
-        }
-        batch_size += x_dim[0];
-        if (feature_size == 0) {
-          feature_size = framework::product(x_dim) / x_dim[0];
-        } else {
-          PADDLE_ENFORCE_EQ(
-              feature_size, framework::product(x_dim) / x_dim[0],
-              "Inputs of sequence concat must have same feature size");
-        }
+    PADDLE_ENFORCE_GT(context->Inputs("X").size(), 1,
+                      "The number of input sequences is at least two.");
+    auto x_dims = context->GetInputsDim("X");
+    int64_t batch_size = 0;
+    int64_t feature_size = 0;
+    std::vector<int64_t> out_dims;
+    for (auto &x_dim : x_dims) {
+      if (out_dims.empty()) {
+        out_dims = framework::vectorize(x_dim);
       }
-      if (batch_size < 0) {
-        batch_size = -1;  // Normalize batch size for compile time.
+      batch_size += x_dim[0];
+      if (feature_size == 0) {
+        feature_size = framework::product(x_dim) / x_dim[0];
+      } else {
+        PADDLE_ENFORCE_EQ(
+            feature_size, framework::product(x_dim) / x_dim[0],
+            "Inputs of sequence concat must have same feature size");
       }
-      out_dims[0] = batch_size;
-      context->SetOutputDim("Out", framework::make_ddim(out_dims));
-      if (!context->IsRuntime()) {  // Runtime LoD infershape will be computed
-                                    // in Kernel.
-        context->ShareLoD("X", "Out");
-      }
-    } catch (...) {
-      PADDLE_THROW("Unknown error");
+    }
+    if (batch_size < 0) {
+      batch_size = -1;  // Normalize batch size for compile time.
+    }
+    out_dims[0] = batch_size;
+    context->SetOutputDim("Out", framework::make_ddim(out_dims));
+    if (!context->IsRuntime()) {  // Runtime LoD infershape will be computed
+      // in Kernel.
+      context->ShareLoD("X", "Out");
     }
   }
 };
diff --git a/python/paddle/fluid/tests/unittests/test_seq_concat_op.py b/python/paddle/fluid/tests/unittests/test_seq_concat_op.py
deleted file mode 100644
index 9d1d139721ad7ee3e29d44c9b3e7c666b78a4556..0000000000000000000000000000000000000000
--- a/python/paddle/fluid/tests/unittests/test_seq_concat_op.py
+++ /dev/null
@@ -1,124 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# 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.
-
-from __future__ import print_function
-
-import unittest
-import numpy as np
-import sys
-from op_test import OpTest
-
-
-def to_abs_offset_lod(lod):
-    offset_lod = [[0] for i in lod]
-    for i, level in enumerate(lod):
-        for seq_len in level:
-            offset_lod[i].append(offset_lod[i][-1] + seq_len)
-
-    if len(offset_lod) == 0 or len(offset_lod) == 1:
-        return offset_lod
-    import copy
-    new_offset_lod = copy.deepcopy(offset_lod)
-    for idx, val in enumerate(offset_lod[0]):
-        new_offset_lod[0][idx] = offset_lod[1][val]
-    return new_offset_lod
-
-
-def seq_concat(inputs, level):
-    lod0 = inputs['X'][0][1][1]
-    lod1 = inputs['X'][1][1][1]
-    x0 = inputs['X'][0][1][0]
-    x1 = inputs['X'][1][1][0]
-    level_idx = len(lod0) - level - 1
-    outs = []
-    for i in range(len(lod0[level_idx])):
-        sub_x0 = x0[to_abs_offset_lod(lod0)[level_idx][i]:to_abs_offset_lod(
-            lod0)[level_idx][i + 1], :]
-        sub_x1 = x1[to_abs_offset_lod(lod1)[level_idx][i]:to_abs_offset_lod(
-            lod1)[level_idx][i + 1], :]
-        outs.append(np.concatenate((sub_x0, sub_x1), axis=0))
-    return np.concatenate(outs, axis=0)
-
-
-class TestSeqConcatOp(OpTest):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((4, 8, 3)).astype('float32')
-        lod1 = [[2, 2], [1, 1, 1, 1]]
-        axis = 1
-        level = 1
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        self.outputs = {'Out': (np.concatenate([x0, x1], axis=1), lod0)}
-
-    def setUp(self):
-        self.op_type = "sequence_concat"
-        self.set_data()
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['x0'], 'Out')
-
-
-class TestSeqConcatOpLevelZeroNestedSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((7, 6, 3)).astype('float32')
-        lod1 = [[2, 2], [1, 2, 2, 2]]
-        axis = 0
-        level = 0
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[2, 2], [2, 3, 3, 3]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-class TestSeqConcatOplevelOneNestedSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 6, 3)).astype('float32')
-        lod0 = [[2, 2], [1, 1, 1, 1]]
-        x1 = np.random.random((7, 6, 3)).astype('float32')
-        lod1 = [[3, 1], [1, 2, 2, 2]]
-        axis = 0
-        level = 1
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[5, 3], [1, 1, 1, 2, 2, 1, 1, 2]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-class TestSeqConcatOpLevelZeroSequence(TestSeqConcatOp):
-    def set_data(self):
-        # two level, batch size is 3
-        x0 = np.random.random((4, 3, 4)).astype('float32')
-        lod0 = [[1, 1, 1, 1]]
-        x1 = np.random.random((7, 3, 4)).astype('float32')
-        lod1 = [[1, 2, 2, 2]]
-        axis = 0
-        level = 0
-        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
-        self.attrs = {'axis': axis, 'level': level}
-        out_lod = [[2, 3, 3, 3]]
-        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
-
-
-if __name__ == '__main__':
-    unittest.main()