diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt
index 4546d12a903084e7a746b967c39d67a0ade4c0cd..9fdb148864e1825186ddb578f7de7f8c76e83af5 100644
--- a/paddle/gserver/tests/CMakeLists.txt
+++ b/paddle/gserver/tests/CMakeLists.txt
@@ -30,6 +30,12 @@ add_unittest_without_exec(test_CRFLayerGrad
 add_test(NAME test_CRFLayerGrad
     COMMAND test_CRFLayerGrad)
 
+################ test_SeqSliceLayerGrad ####################
+add_unittest_without_exec(test_SeqSliceLayerGrad
+    test_SeqSliceLayerGrad.cpp
+    LayerGradUtil.cpp)
+add_test(NAME test_SeqSliceLayerGrad
+    COMMAND test_SeqSliceLayerGrad)
 
 add_unittest_without_exec(test_ActivationGrad
     test_ActivationGrad.cpp
diff --git a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..e456dd5db7f4576e8e5a79d29e9b58e65ba5e122
--- /dev/null
+++ b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp
@@ -0,0 +1,214 @@
+/* Copyright (c) 2016 Baidu, Inc. 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. */
+
+#include <gtest/gtest.h>
+#include "ModelConfig.pb.h"
+#include "paddle/gserver/layers/DataLayer.h"
+#include "paddle/trainer/Trainer.h"
+
+#include "LayerGradUtil.h"
+#include "paddle/testing/TestUtil.h"
+
+using namespace paddle;  // NOLINT
+using namespace std;     // NOLINT
+
+DECLARE_int32(gpu_id);
+DECLARE_bool(thread_local_rand_use_global_seed);
+
+const int MAX_SEQ_NUM = 5;
+const int MAX_SEQ_LEN = 5;
+const int MAX_BEAM_SIZE = 3;
+
+vector<real> randSampling(real range, int n) {
+  CHECK_GE(range, n);
+  vector<real> num(range);
+  iota(begin(num), end(num), 0.);
+  if (range == n) return num;
+
+  random_shuffle(begin(num), end(num));
+  num.resize(n);
+  sort(begin(num), end(num));
+  return num;
+}
+
+void genSeqInfo(vector<int>& seqStartPos, vector<int>& subSeqStartPos) {
+  seqStartPos.resize(1, 0);
+  subSeqStartPos.resize(1, 0);
+
+  // srand((size_t)(time(NULL)));
+  srand(1);
+  int seqNum = 1 + (rand() % MAX_SEQ_NUM);
+  for (int i = 0; i < seqNum; ++i) {
+    int subSeqNum = 1 + (rand() % MAX_SEQ_NUM);
+    for (int j = 0; j < subSeqNum; ++j)
+      subSeqStartPos.push_back(subSeqStartPos.back() +
+                               (1 + (rand() % MAX_SEQ_LEN)));
+    seqStartPos.push_back(subSeqStartPos.back());
+  }
+}
+
+/*
+  generate start indices according to sequence start positions.
+ */
+void genStarts(vector<int>& seqStartPos,
+               vector<vector<real>>& starts,
+               size_t beamSize) {
+  starts.clear();
+  starts.resize(seqStartPos.size() - 1, vector<real>(beamSize, -1.));
+
+  for (size_t i = 0; i < seqStartPos.size() - 1; ++i) {
+    int seqLen = seqStartPos[i + 1] - seqStartPos[i];
+    vector<real> randStarts =
+        randSampling(seqLen, min(seqLen, static_cast<int>(beamSize)));
+    copy(begin(randStarts), end(randStarts), begin(starts[i]));
+  }
+}
+
+/*
+  generate end indices according to sequence start positions and start indices.
+ */
+void genEnds(vector<int>& seqStartPos,
+             vector<vector<real>>& starts,
+             vector<vector<real>>& ends,
+             size_t beamSize) {
+  CHECK_EQ(seqStartPos.size() - 1, starts.size());
+  ends.clear();
+  ends.resize(seqStartPos.size() - 1, vector<real>(beamSize, -1.));
+
+  for (size_t i = 0; i < starts.size(); ++i) {
+    for (size_t j = 0; j < starts[i].size(); ++j) {
+      int seqLen = seqStartPos[i + 1] - seqStartPos[i];
+      CHECK_GE(seqLen - 1, starts[i][j]);
+      if (starts[i][j] == -1.) break;
+      if (starts[i][j] == (seqLen - 1)) {
+        ends[i][j] = starts[i][j];
+      } else {
+        ends[i][j] = starts[i][j] + randSampling(seqLen - starts[i][j], 1)[0];
+      }
+    }
+  }
+}
+
+void genTestData(vector<int>& seqStartPos,
+                 vector<int>& subSeqStartPos,
+                 vector<vector<real>>& starts,
+                 vector<vector<real>>& ends,
+                 bool hasSubseq) {
+  size_t beamSize = MAX_BEAM_SIZE;
+  genSeqInfo(seqStartPos, subSeqStartPos);
+
+  genStarts(hasSubseq ? subSeqStartPos : seqStartPos, starts, beamSize);
+  genEnds(hasSubseq ? subSeqStartPos : seqStartPos, starts, ends, beamSize);
+}
+
+template <typename T>
+void flatten2dVector(vector<vector<T>>& inVec, vector<T>& outVec) {
+  size_t totalSize{0};
+  for (auto const& items : inVec) totalSize += items.size();
+  outVec.reserve(totalSize);
+
+  for (auto& items : inVec)
+    move(items.begin(), items.end(), back_inserter(outVec));
+}
+
+void testSeqSliceLayer(bool hasSubseq,
+                       bool useGpu,
+                       vector<int>& seqStartPos,
+                       vector<int>& subSeqStartPos,
+                       vector<vector<real>>& starts,
+                       vector<vector<real>>& ends) {
+  // layer size is not crutial for this layer,
+  // so here use a small layer size in the unittest.
+  const size_t layerSize{4};
+  TestConfig config;
+  config.layerConfig.set_type("seq_slice");
+  config.layerConfig.set_size(layerSize);
+
+  // add the first input
+  MatrixPtr seqInputPtr =
+      Matrix::create(hasSubseq ? subSeqStartPos.back() : seqStartPos.back(),
+                     layerSize,
+                     false,
+                     false);
+  seqInputPtr->randomizeUniform();
+
+  if (hasSubseq) {
+    config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
+                                "seq_input",
+                                seqInputPtr,
+                                seqStartPos,
+                                subSeqStartPos});
+  } else {
+    config.inputDefs.push_back(
+        {INPUT_SELF_DEFINE_DATA, "seq_input", seqInputPtr, seqStartPos});
+  }
+  config.layerConfig.add_inputs();
+
+  // add start indices
+  if (starts.size()) {
+    vector<real> startsToVec;
+    flatten2dVector(starts, startsToVec);
+
+    MatrixPtr startMatrixPtr =
+        Matrix::create(starts.size(), starts[0].size(), false, false);
+    startMatrixPtr->copyFrom(startsToVec.data(), startsToVec.size());
+
+    config.inputDefs.push_back(
+        {INPUT_SELF_DEFINE_DATA, "starts", startMatrixPtr});
+    config.layerConfig.add_inputs();
+  }
+
+  // add end indices
+  if (ends.size()) {
+    vector<real> endsToVec;
+    flatten2dVector(ends, endsToVec);
+    MatrixPtr endMatrixPtr =
+        Matrix::create(ends.size(), ends[0].size(), false, false);
+    config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, "ends", endMatrixPtr});
+    config.layerConfig.add_inputs();
+  }
+
+  testLayerGrad(config, "seq_slice", /*batchSize*/ 100, false, useGpu, false);
+}
+
+TEST(Layer, SeqSliceLayer) {
+  vector<int> seqStartPos;
+  vector<int> subSeqStartPos;
+  vector<vector<real>> starts;
+  vector<vector<real>> ends;
+
+  genSeqInfo(seqStartPos, subSeqStartPos);
+  for (bool hasSubseq : {false, true}) {
+    genTestData(seqStartPos, subSeqStartPos, starts, ends, hasSubseq);
+    for (bool useGpu : {false, true}) {
+      vector<vector<real>> tmp;
+      testSeqSliceLayer(
+          hasSubseq, useGpu, seqStartPos, subSeqStartPos, tmp, ends);
+      testSeqSliceLayer(
+          hasSubseq, useGpu, seqStartPos, subSeqStartPos, starts, tmp);
+      testSeqSliceLayer(
+          hasSubseq, useGpu, seqStartPos, subSeqStartPos, starts, ends);
+    }
+  }
+}
+
+int main(int argc, char** argv) {
+  initMain(argc, argv);
+  hl_start();
+  hl_init(FLAGS_gpu_id);
+  FLAGS_thread_local_rand_use_global_seed = true;
+  srand(1);
+  testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}