diff --git a/lite/tests/kernels/CMakeLists.txt b/lite/tests/kernels/CMakeLists.txt
index 3d6eb9eb8ace0dde1fba92af88ab3af20a87a2ed..c55f62c02977cec54b1ef679a038e06cb576b6b8 100644
--- a/lite/tests/kernels/CMakeLists.txt
+++ b/lite/tests/kernels/CMakeLists.txt
@@ -44,6 +44,7 @@ if(LITE_BUILD_EXTRA)
     lite_cc_test(test_kernel_assign_value_compute SRCS assign_value_compute_test.cc DEPS arena_framework ${xpu_kernels} ${npu_kernels} ${x86_kernels} ${bm_kernels} ${cuda_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
     lite_cc_test(test_kernel_box_clip_compute SRCS box_clip_compute_test.cc DEPS arena_framework ${xpu_kernels} ${npu_kernels} ${x86_kernels} ${cuda_kernels} ${bm_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
     lite_cc_test(test_kernel_reduce_mean_compute SRCS reduce_mean_compute_test.cc DEPS arena_framework ${xpu_kernels} ${npu_kernels} ${x86_kernels} ${cuda_kernels} ${bm_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
+    lite_cc_test(test_kernel_reduce_sum_compute SRCS reduce_sum_compute_test.cc DEPS arena_framework ${xpu_kernels} ${npu_kernels} ${x86_kernels} ${cuda_kernels} ${bm_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
     lite_cc_test(test_kernel_reduce_prod_compute SRCS reduce_prod_compute_test.cc DEPS arena_framework ${xpu_kernels} ${npu_kernels} ${x86_kernels} ${cuda_kernels} ${bm_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
     lite_cc_test(test_kernel_stack_compute SRCS stack_compute_test.cc DEPS arena_framework ${xpu_kernels} ${npu_kernels} ${x86_kernels} ${cuda_kernels} ${bm_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
     lite_cc_test(test_kernel_range_compute SRCS range_compute_test.cc DEPS arena_framework ${xpu_kernels} ${npu_kernels} ${x86_kernels} ${cuda_kernels} ${bm_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
diff --git a/lite/tests/kernels/reduce_sum_compute_test.cc b/lite/tests/kernels/reduce_sum_compute_test.cc
new file mode 100644
index 0000000000000000000000000000000000000000..9cfe213750b1191c1ef8fe7fba1b1c1035c2ae42
--- /dev/null
+++ b/lite/tests/kernels/reduce_sum_compute_test.cc
@@ -0,0 +1,350 @@
+// Copyright (c) 2019 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.
+
+#include <gtest/gtest.h>
+#include "lite/api/paddle_use_kernels.h"
+#include "lite/api/paddle_use_ops.h"
+#include "lite/core/arena/framework.h"
+
+namespace paddle {
+namespace lite {
+
+void reduce_sum_n(const float* src,
+                  float* dst,
+                  int num_in,
+                  int channel_in,
+                  int height_in,
+                  int width_in) {
+  int hw_size = height_in * width_in;
+  int chw_size = channel_in * hw_size;
+  int data_index, src_index;
+  for (int c = 0; c < channel_in; ++c) {
+    for (int h = 0; h < height_in; ++h) {
+      for (int w = 0; w < width_in; ++w) {
+        data_index = c * hw_size + h * width_in + w;
+        dst[data_index] = 0.0;
+        for (int n = 0; n < num_in; ++n) {
+          src_index = n * chw_size + data_index;
+          dst[data_index] += static_cast<float>(src[src_index]);
+        }
+      }
+    }
+  }
+}
+
+void reduce_sum_c(const float* src,
+                  float* dst,
+                  int num_in,
+                  int channel_in,
+                  int height_in,
+                  int width_in) {
+  int hw_size = height_in * width_in;
+  int chw_size = hw_size * channel_in;
+  int data_index, src_index0, src_index;
+  for (int n = 0; n < num_in; ++n) {
+    for (int h = 0; h < height_in; ++h) {
+      for (int w = 0; w < width_in; ++w) {
+        data_index = n * hw_size + h * width_in + w;
+        src_index0 = n * chw_size + h * width_in + w;
+        dst[data_index] = 0.0;
+        for (int c = 0; c < channel_in; ++c) {
+          src_index = src_index0 + c * hw_size;
+          dst[data_index] += static_cast<float>(src[src_index]);
+        }
+      }
+    }
+  }
+}
+
+void reduce_sum_h(const float* src,
+                  float* dst,
+                  int num_in,
+                  int channel_in,
+                  int height_in,
+                  int width_in) {
+  int cw_size = channel_in * width_in;
+  int chw_size = cw_size * height_in;
+  int hw_size = height_in * width_in;
+  int data_index, src_index, src_index0;
+  for (int n = 0; n < num_in; ++n) {
+    for (int c = 0; c < channel_in; ++c) {
+      for (int w = 0; w < width_in; ++w) {
+        data_index = n * cw_size + c * width_in + w;
+        src_index0 = n * chw_size + c * hw_size + w;
+        dst[data_index] = 0.0;
+        for (int h = 0; h < height_in; ++h) {
+          src_index = src_index0 + h * width_in;
+          dst[data_index] += static_cast<float>(src[src_index]);
+        }
+      }
+    }
+  }
+}
+
+void reduce_sum_w(const float* src,
+                  float* dst,
+                  int num_in,
+                  int channel_in,
+                  int height_in,
+                  int width_in) {
+  int ch_size = channel_in * height_in;
+  int hw_size = height_in * width_in;
+  int chw_size = ch_size * width_in;
+  int data_index = 0;
+  int src_index0 = 0;
+  int src_index = 0;
+  for (int n = 0; n < num_in; ++n) {
+    for (int c = 0; c < channel_in; ++c) {
+      for (int h = 0; h < height_in; ++h) {
+        data_index = n * ch_size + c * height_in + h;
+        src_index0 = n * chw_size + c * hw_size + h * width_in;
+        dst[data_index] = 0.0;
+        for (int w = 0; w < width_in; ++w) {
+          src_index = src_index0 + w;
+          dst[data_index] += static_cast<float>(src[src_index]);
+        }
+      }
+    }
+  }
+}
+
+void reduce_sum_all(const float* src,
+                    float* dst,
+                    int num_in,
+                    int channel_in,
+                    int height_in,
+                    int width_in) {
+  float sum = 0.0;
+  int src_index;
+  int n_id, c_id;
+  for (int n = 0; n < num_in; ++n) {
+    n_id = n * channel_in * height_in * width_in;
+    for (int c = 0; c < channel_in; ++c) {
+      c_id = c * height_in * width_in;
+      for (int h = 0; h < height_in; ++h) {
+        for (int w = 0; w < width_in; ++w) {
+          src_index = n_id + c_id + h * width_in + w;
+          sum = sum + src[src_index];
+        }
+      }
+    }
+  }
+  dst[0] = sum;
+}
+
+void reduce_sum_nc(const float* src,
+                   float* dst,
+                   int num_in,
+                   int channel_in,
+                   int height_in,
+                   int width_in) {
+  // reduce n first.
+  DDimLite ddimA({1, channel_in, height_in, width_in});
+  lite::Tensor tensor_tmp;
+  tensor_tmp.Resize(ddimA);
+  float* tmp_out = tensor_tmp.mutable_data<float>();
+  reduce_sum_n(src, tmp_out, num_in, channel_in, height_in, width_in);
+  reduce_sum_c(tmp_out, dst, 1, channel_in, height_in, width_in);
+}
+
+void reduce_sum_ch(const float* src,
+                   float* dst,
+                   int num_in,
+                   int channel_in,
+                   int height_in,
+                   int width_in) {
+  // reduce c first
+  DDimLite ddimA({num_in, 1, height_in, width_in});
+  lite::Tensor tensor_tmp;
+  tensor_tmp.Resize(ddimA);
+  float* tmp_out = tensor_tmp.mutable_data<float>();
+  reduce_sum_c(src, tmp_out, num_in, channel_in, height_in, width_in);
+  reduce_sum_h(tmp_out, dst, num_in, 1, height_in, width_in);
+}
+
+void reduce_sum_hw(const float* src,
+                   float* dst,
+                   int num_in,
+                   int channel_in,
+                   int height_in,
+                   int width_in) {
+  // reduce h first
+  DDimLite ddimA({num_in, channel_in, 1, width_in});
+  lite::Tensor tensor_tmp;
+  tensor_tmp.Resize(ddimA);
+  float* tmp_out = tensor_tmp.mutable_data<float>();
+  reduce_sum_h(src, tmp_out, num_in, channel_in, height_in, width_in);
+  reduce_sum_w(tmp_out, dst, num_in, channel_in, 1, width_in);
+}
+
+class ReduceSumComputeTester : public arena::TestCase {
+ protected:
+  // common attributes for this op.
+  std::string input_ = "x";
+  std::string output_ = "out";
+  std::vector<int> dim_{0};
+  bool keep_dim_ = false;
+  bool reduce_all_ = false;
+  DDim x_dims_{{3, 2, 3, 4}};
+
+ public:
+  ReduceSumComputeTester(const Place& place,
+                         const std::string& alias,
+                         std::vector<int> dim,
+                         bool keep_dim,
+                         bool reduce_all,
+                         DDim x_dims)
+      : TestCase(place, alias),
+        dim_(dim),
+        keep_dim_(keep_dim),
+        reduce_all_(reduce_all),
+        x_dims_(x_dims) {}
+
+  void RunBaseline(Scope* scope) override {
+    auto* x = scope->FindMutableTensor(input_);
+    const auto* x_data = x->data<float>();
+    auto* out = scope->NewTensor(output_);
+    auto x_rank = x_dims_.size();
+    if (!dim_.empty()) {
+      for (int i = 0; i < dim_.size(); i++) {
+        if (dim_[i] < 0) {
+          dim_[i] += x_rank;
+        }
+      }
+    }
+
+    sort(dim_.begin(), dim_.end());
+    std::vector<int64_t> out_dims;
+    if (reduce_all_) {
+      if (keep_dim_) {
+        out_dims.resize(x_rank);
+        for (int i = 0; i < x_rank; ++i) {
+          out_dims[i] = 1;
+        }
+      } else {
+        out_dims.push_back(1);
+      }
+    } else {
+      for (int i = 0; i < x_dims_.size(); i++) {
+        out_dims.push_back(x_dims_[i]);
+      }
+      if (keep_dim_) {
+        for (size_t i = 0; i < dim_.size(); ++i) {
+          out_dims[dim_[i]] = 1L;
+        }
+      } else {
+        int64_t kDelFlag = -2;
+        for (size_t i = 0; i < dim_.size(); ++i) {
+          out_dims[dim_[i]] = kDelFlag;
+        }
+        out_dims.erase(remove(out_dims.begin(), out_dims.end(), kDelFlag),
+                       out_dims.end());
+      }
+    }
+    out->Resize(DDim(out_dims));
+
+    auto* out_data = out->mutable_data<float>();
+    int in_n = x_dims_[0];
+    int in_c = x_dims_[1];
+    int in_h = x_dims_[2];
+    int in_w = x_dims_[3];
+
+    if (reduce_all_) {
+      reduce_sum_all(x_data, out_data, in_n, in_c, in_h, in_w);
+    } else if (dim_.size() == 1) {
+      switch (dim_[0]) {
+        case 0:
+          reduce_sum_n(x_data, out_data, in_n, in_c, in_h, in_w);
+          break;
+        case 1:
+          reduce_sum_c(x_data, out_data, in_n, in_c, in_h, in_w);
+          break;
+        case 2:
+          reduce_sum_h(x_data, out_data, in_n, in_c, in_h, in_w);
+          break;
+        case 3:
+          reduce_sum_w(x_data, out_data, in_n, in_c, in_h, in_w);
+          break;
+        default:
+          LOG(FATAL) << "error!!!";
+      }
+    } else if (dim_.size() == 2) {
+      if (dim_[0] == 0 && dim_[1] == 1) {
+        reduce_sum_nc(x_data, out_data, in_n, in_c, in_h, in_w);
+      } else if (dim_[0] == 1 && dim_[1] == 2) {
+        reduce_sum_ch(x_data, out_data, in_n, in_c, in_h, in_w);
+      } else if (dim_[0] == 2 && dim_[1] == 3) {
+        reduce_sum_hw(x_data, out_data, in_n, in_c, in_h, in_w);
+      } else {
+        LOG(FATAL) << "invalid dims_!!";
+      }
+    }
+  }
+
+  void PrepareOpDesc(cpp::OpDesc* op_desc) {
+    op_desc->SetType("reduce_sum");
+    op_desc->SetInput("X", {input_});
+    op_desc->SetOutput("Out", {output_});
+    op_desc->SetAttr("dim", dim_);
+    op_desc->SetAttr("keep_dim", keep_dim_);
+    op_desc->SetAttr("reduce_all", reduce_all_);
+  }
+
+  void PrepareData() override {
+    std::vector<float> data(x_dims_.production());
+    for (int i = 0; i < x_dims_.production(); i++) {
+      data[i] = i * 1.0;
+    }
+    SetCommonTensor(input_, x_dims_, data.data());
+  }
+};
+
+void test_reduce_sum(Place place) {
+  std::vector<std::vector<int>> reduce_dim{
+      {0}, {1}, {2}, {3}, {0, 1}, {1, 2}, {2, 3}, {-2, -1}};
+  for (auto n : {1, 3}) {
+    for (auto c : {1, 2}) {
+      for (auto h : {1, 3}) {
+        for (auto w : {1, 3}) {
+          for (bool keep_dim : {false, true}) {
+            for (bool reduce_all : {false, true}) {
+              for (auto dim : reduce_dim) {
+                auto x_dims = DDim(std::vector<int64_t>({n, c, h, w}));
+                std::unique_ptr<arena::TestCase> tester(
+                    new ReduceSumComputeTester(
+                        place, "def", dim, keep_dim, reduce_all, x_dims));
+                arena::Arena arena(std::move(tester), place, 2e-5);
+                arena.TestPrecision();
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+TEST(ReduceSum, precision) {
+#ifdef LITE_WITH_X86
+  Place place(TARGET(kX86));
+  test_reduce_sum(place);
+#endif
+  // #ifdef LITE_WITH_ARM
+  //  Place place(TARGET(kARM));
+  //  test_reduce_sum(place);
+  // #endif
+}
+
+}  // namespace lite
+}  // namespace paddle