diff --git a/paddle/fluid/operators/mode_op.cc b/paddle/fluid/operators/mode_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..90e513cb1cd07f907cad106632e0071325ae3f04
--- /dev/null
+++ b/paddle/fluid/operators/mode_op.cc
@@ -0,0 +1,155 @@
+/* Copyright (c) 2021 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 "paddle/fluid/operators/mode_op.h"
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class ModeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "mode");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "mode");
+    OP_INOUT_CHECK(ctx->HasOutput("Indices"), "Output", "Indices", "mode");
+
+    auto input_dims = ctx->GetInputDim("X");
+    const int& dim_size = input_dims.size();
+    int axis = static_cast<int>(ctx->Attrs().Get<int>("axis"));
+    PADDLE_ENFORCE_EQ(
+        (axis < dim_size) && (axis >= (-1 * dim_size)), true,
+        paddle::platform::errors::InvalidArgument(
+            "the axis of ModeOp must be [-%d, %d), but you set axis is %d",
+            dim_size, dim_size, axis));
+    PADDLE_ENFORCE_GE(input_dims.size(), 1,
+                      paddle::platform::errors::InvalidArgument(
+                          "input of ModeOp must have >= 1d shape"));
+    if (axis < 0) axis += dim_size;
+    bool keepdim = ctx->Attrs().Get<bool>("keepdim");
+    std::vector<int64_t> dimvec;
+    for (int64_t i = 0; i < axis; i++) {
+      dimvec.emplace_back(input_dims[i]);
+    }
+    if (keepdim) {
+      dimvec.emplace_back(static_cast<int64_t>(1));
+    }
+    for (int64_t i = axis + 1; i < dim_size; i++) {
+      dimvec.emplace_back(input_dims[i]);
+    }
+    framework::DDim dims = framework::make_ddim(dimvec);
+    PADDLE_ENFORCE_GE(input_dims.size(), 1, platform::errors::InvalidArgument(
+                                                "input shape should >= 1d"));
+    ctx->SetOutputDim("Out", dims);
+    ctx->SetOutputDim("Indices", dims);
+    ctx->ShareLoD("X", "Out");
+    ctx->ShareLoD("X", "Indices");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+    framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.device_context(),
+        layout_, library_);
+  }
+};
+
+class ModeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) The input of Mode op");
+    AddOutput("Out", "(Tensor) The output tensor of Topk op");
+    AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
+    AddAttr<int>("axis",
+                 "the axis to calculate mode values."
+                 "if not set, will calculate on last axis.")
+        .SetDefault(-1);
+    AddAttr<bool>("keepdim", "Keep the dim that to reduce.").SetDefault(false);
+    AddComment(R"DOC(
+This operator finds the mode of input Tensor. And outputs their values and indices as vectors. 
+)DOC");
+  }
+};
+
+class ModeOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("X"), true,
+        platform::errors::InvalidArgument("Input(X) should be not null"));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("Indices"), true,
+        platform::errors::InvalidArgument("Input(Indices) should be not null"));
+    PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
+                      platform::errors::InvalidArgument(
+                          "Grad Input(Out) should be not null"));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasOutput(framework::GradVarName("X")), true,
+        platform::errors::InvalidArgument("Grad Output(X) should be not null"));
+
+    auto x_dims = ctx->GetInputDim("X");
+    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto data_type = OperatorWithKernel::IndicateVarDataType(
+        ctx, framework::GradVarName("Out"));
+    return framework::OpKernelType(data_type, ctx.device_context());
+  }
+};
+
+template <typename T>
+class ModeGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("mode_grad");
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("Indices", this->Output("Indices"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(mode, ops::ModeOp, ops::ModeOpMaker,
+                  ops::ModeGradOpMaker<paddle::framework::OpDesc>,
+                  ops::ModeGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OP_CPU_KERNEL(mode,
+                       ops::ModeCPUKernel<paddle::platform::CPUPlace, float>,
+                       ops::ModeCPUKernel<paddle::platform::CPUPlace, double>,
+                       ops::ModeCPUKernel<paddle::platform::CPUPlace, int32_t>,
+                       ops::ModeCPUKernel<paddle::platform::CPUPlace, int64_t>);
+
+REGISTER_OPERATOR(mode_grad, ops::ModeOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    mode_grad, ops::ModeGradCPUKernel<paddle::platform::CPUPlace, float>,
+    ops::ModeGradCPUKernel<paddle::platform::CPUPlace, double>,
+    ops::ModeGradCPUKernel<paddle::platform::CPUPlace, int32_t>,
+    ops::ModeGradCPUKernel<paddle::platform::CPUPlace, int64_t>);
diff --git a/paddle/fluid/operators/mode_op.cu b/paddle/fluid/operators/mode_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..b42bdb548216e797e806e0bc1e0bacc8e442d320
--- /dev/null
+++ b/paddle/fluid/operators/mode_op.cu
@@ -0,0 +1,233 @@
+// Copyright (c) 2021 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 <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/inner_product.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/mode_op.h"
+#include "paddle/fluid/operators/top_k_function_cuda.h"
+#include "paddle/fluid/operators/top_k_v2_op.h"
+
+namespace paddle {
+namespace operators {
+
+int ComputeBlockSize(int col) {
+  if (col > 512)
+    return 1024;
+  else if (col > 256 && col <= 512)
+    return 512;
+  else if (col > 128 && col <= 256)
+    return 256;
+  else if (col > 64 && col <= 128)
+    return 128;
+  else
+    return 64;
+}
+
+template <typename T>
+void getModebySort(const platform::CUDADeviceContext& ctx,
+                   const framework::Tensor* input_tensor,
+                   const int64_t num_cols, const int64_t num_rows,
+                   T* out_tensor, int64_t* indices_tensor) {
+  framework::Tensor input_tmp;
+  framework::TensorCopy(*input_tensor, ctx.GetPlace(), &input_tmp);
+  T* input_tmp_data = input_tmp.mutable_data<T>(ctx.GetPlace());
+  input_tmp.Resize(framework::make_ddim({num_rows, num_cols}));
+  thrust::device_ptr<T> out_tensor_ptr(out_tensor);
+  thrust::device_ptr<int64_t> indices_tensor_ptr(indices_tensor);
+
+  for (int64_t i = 0; i < num_rows; ++i) {
+    T* begin = input_tmp_data + num_cols * i;
+    T* end = input_tmp_data + num_cols * (i + 1);
+    thrust::device_vector<int64_t> indices_data(num_cols);
+    thrust::sequence(thrust::device, indices_data.begin(),
+                     indices_data.begin() + num_cols);
+    thrust::sort_by_key(thrust::device, begin, end, indices_data.begin());
+    int unique = 1 + thrust::inner_product(thrust::device, begin, end - 1,
+                                           begin + 1, 0, thrust::plus<int>(),
+                                           thrust::not_equal_to<T>());
+    thrust::device_vector<T> keys_data(unique);
+    thrust::device_vector<int64_t> cnts_data(unique);
+    thrust::reduce_by_key(thrust::device, begin, end,
+                          thrust::constant_iterator<int>(1), keys_data.begin(),
+                          cnts_data.begin());
+    auto it = thrust::max_element(thrust::device, cnts_data.begin(),
+                                  cnts_data.begin() + unique);
+    T mode = keys_data[it - cnts_data.begin()];
+    int64_t counts = cnts_data[it - cnts_data.begin()];
+    auto pos = thrust::find(thrust::device, begin, end, mode);
+    int64_t index = indices_data[pos - begin + counts - 1];
+    out_tensor_ptr[i] = static_cast<T>(mode);
+    indices_tensor_ptr[i] = static_cast<int64_t>(index);
+  }
+}
+
+template <typename DeviceContext, typename T>
+class ModeOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_gpu_place(ctx.GetPlace()), true,
+        platform::errors::InvalidArgument(
+            "It must use CUDAPlace, you must check your device set."));
+    auto* input = ctx.Input<framework::Tensor>("X");
+    auto* output = ctx.Output<framework::Tensor>("Out");
+    auto* indices = ctx.Output<framework::Tensor>("Indices");
+    int axis = static_cast<int>(ctx.Attr<int>("axis"));
+    bool keepdim = static_cast<bool>(ctx.Attr<bool>("keepdim"));
+
+    // get the input dims
+    const auto& in_dims = input->dims();
+    // calcluate the real axis
+    if (axis < 0) axis += in_dims.size();
+
+    auto out_dims = output->dims();
+
+    const T* input_data = input->data<T>();
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+    int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
+
+    if (axis == in_dims.size() - 1) {
+      const int64_t& input_height = framework::product(
+          framework::slice_ddim(in_dims, 0, in_dims.size() - 1));
+      const int64_t& input_width = in_dims[in_dims.size() - 1];
+      const auto& dev_ctx = ctx.cuda_device_context();
+      getModebySort<T>(dev_ctx, input, input_width, input_height, output_data,
+                       indices_data);
+    } else {
+      std::vector<int> trans_axis;
+      for (int i = 0; i < axis; i++) {
+        trans_axis.emplace_back(i);
+      }
+      trans_axis.emplace_back(in_dims.size() - 1);
+      for (int i = axis + 1; i < in_dims.size() - 1; i++) {
+        trans_axis.emplace_back(i);
+      }
+      trans_axis.emplace_back(axis);
+
+      if (!keepdim) {
+        std::vector<int> tmp_out_shape;
+        for (int i = 0; i < axis; i++) {
+          tmp_out_shape.emplace_back(in_dims[i]);
+        }
+        tmp_out_shape.emplace_back(1);
+        for (int i = axis + 1; i < in_dims.size(); i++) {
+          tmp_out_shape.emplace_back(in_dims[i]);
+        }
+        framework::DDim tmp_out_dim = framework::make_ddim(tmp_out_shape);
+        output->Resize(tmp_out_dim);
+        indices->Resize(tmp_out_dim);
+      }
+
+      framework::DDim trans_shape(in_dims);
+      framework::DDim trans_out_shape(in_dims);
+      for (int i = 0; i < trans_axis.size(); i++) {
+        trans_shape[i] = in_dims[trans_axis[i]];
+        trans_out_shape[i] = in_dims[trans_axis[i]];
+      }
+      trans_out_shape[in_dims.size() - 1] = 1;
+
+      // second step, tranpose the input
+      framework::Tensor trans_input;
+      trans_input.mutable_data<T>(trans_shape, ctx.GetPlace());
+      int ndims = trans_axis.size();
+      const auto& dev_ctx = ctx.cuda_device_context();
+      TransCompute<platform::CUDADeviceContext, T>(ndims, dev_ctx, *input,
+                                                   &trans_input, trans_axis);
+      framework::Tensor trans_ind;
+      int64_t* trans_ind_data =
+          trans_ind.mutable_data<int64_t>(trans_out_shape, ctx.GetPlace());
+      framework::Tensor trans_out;
+      T* trans_out_data =
+          trans_out.mutable_data<T>(trans_out_shape, ctx.GetPlace());
+
+      const int64_t input_height = framework::product(
+          framework::slice_ddim(trans_shape, 0, trans_shape.size() - 1));
+      const int64_t input_width = trans_shape[trans_shape.size() - 1];
+      getModebySort<T>(dev_ctx, &trans_input, input_width, input_height,
+                       trans_out_data, trans_ind_data);
+      // last step, tranpose back the indices and output
+      TransCompute<platform::CUDADeviceContext, int64_t>(
+          ndims, dev_ctx, trans_ind, indices, trans_axis);
+      TransCompute<platform::CUDADeviceContext, T>(ndims, dev_ctx, trans_out,
+                                                   output, trans_axis);
+      if (!keepdim) {
+        output->Resize(out_dims);
+        indices->Resize(out_dims);
+      }
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class ModeOpGradCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_gpu_place(context.GetPlace()), true,
+        platform::errors::InvalidArgument(
+            "It must use CUDAPlace, you must check your device set."));
+    auto* x = context.Input<framework::Tensor>("X");
+    auto* out_grad =
+        context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* indices = context.Input<framework::Tensor>("Indices");
+    auto* x_grad =
+        context.Output<framework::Tensor>(framework::GradVarName("X"));
+    int axis = context.Attr<int>("axis");
+
+    const auto& in_dims = x->dims();
+    auto out_dims = indices->dims();
+
+    if (axis < 0) axis += in_dims.size();
+    // allocate the cuda memory for the x_grad
+    T* x_grad_data = x_grad->mutable_data<T>(context.GetPlace());
+    const T* out_grad_data = out_grad->data<T>();
+    const int64_t* indices_data = indices->data<int64_t>();
+
+    int pre, n, post;
+    GetDims(in_dims, axis, &pre, &n, &post);
+
+    // calcluate the block and grid num
+    auto& dev_ctx = context.cuda_device_context();
+    int block_size = ComputeBlockSize(post);
+    int max_threads = dev_ctx.GetMaxPhysicalThreadCount();
+    const int max_blocks = std::max(((max_threads - 1) / block_size + 1), 1);
+    int grid_size = std::min(max_blocks, pre);
+    AssignGradWithAxis<T><<<grid_size, block_size, 64 * 4, dev_ctx.stream()>>>(
+        out_grad_data, indices_data, x_grad_data, pre, post, n, 1);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    mode, ops::ModeOpCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ModeOpCUDAKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ModeOpCUDAKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ModeOpCUDAKernel<paddle::platform::CUDADeviceContext, int64_t>);
+REGISTER_OP_CUDA_KERNEL(
+    mode_grad,
+    ops::ModeOpGradCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ModeOpGradCUDAKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ModeOpGradCUDAKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ModeOpGradCUDAKernel<paddle::platform::CUDADeviceContext, int64_t>);
diff --git a/paddle/fluid/operators/mode_op.h b/paddle/fluid/operators/mode_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..dac0ff9279c09b5b1e623ae8969155ab3ceb2b8b
--- /dev/null
+++ b/paddle/fluid/operators/mode_op.h
@@ -0,0 +1,317 @@
+/* Copyright (c) 2021 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. */
+
+#pragma once
+#include <algorithm>
+#include <iostream>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/transpose_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T, typename Type>
+static void getMode(Type input_height, Type input_width, int input_dim,
+                    const framework::Tensor* input, T* t_out, Type* t_indices) {
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+  for (Type i = 0; i < input_height; ++i) {
+    std::vector<std::pair<T, Type>> col_vec;
+    col_vec.reserve(input_width);
+    if (input_dim == 1) {
+      auto e_input = framework::EigenVector<T>::Flatten(*input);
+      for (Type j = 0; j < input_width; ++j) {
+        col_vec.emplace_back(std::pair<T, Type>(e_input(j), j));
+      }
+    } else {
+      auto e_input = framework::EigenMatrix<T>::Reshape(*input, input_dim - 1);
+      for (Type j = 0; j < input_width; ++j) {
+        col_vec.emplace_back(std::pair<T, Type>(e_input(i, j), j));
+      }
+    }
+    std::sort(col_vec.begin(), col_vec.end(),
+              [](const std::pair<T, Type>& l, const std::pair<T, Type>& r) {
+                return (!std::isnan(static_cast<double>(l.first)) &&
+                        std::isnan(static_cast<double>(r.first))) ||
+                       (l.first < r.first);
+              });
+    T mode = 0;
+    int64_t indice = 0;
+    int64_t cur_freq = 0;
+    int64_t max_freq = 0;
+    for (int64_t i = 0; i < input_width; ++i) {
+      ++cur_freq;
+      if (i == input_width - 1 || (col_vec[i + 1].first != col_vec[i].first)) {
+        if (cur_freq > max_freq) {
+          max_freq = cur_freq;
+          mode = col_vec[i].first;
+          indice = col_vec[i].second;
+        }
+        cur_freq = 0;
+      }
+    }
+    t_out[i] = mode;
+    t_indices[i] = indice;
+  }
+}
+
+template <typename T, typename Type>
+static void ModeAssign(const Type& input_height, const Type& input_width,
+                       const int& input_dim, const framework::Tensor* input,
+                       const framework::Tensor* indices, T* output_data) {
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+  for (Type i = 0; i < input_height; ++i) {
+    if (input_dim == 1) {
+      auto e_input = framework::EigenVector<T>::Flatten(*input);
+      auto e_indices = framework::EigenVector<Type>::Flatten(*indices);
+      output_data[i * input_width + e_indices(0)] = e_input(0);
+    } else {
+      auto e_input = framework::EigenMatrix<T>::Reshape(*input, input_dim - 1);
+      auto e_indices =
+          framework::EigenMatrix<Type>::Reshape(*indices, input_dim - 1);
+      output_data[i * input_width + e_indices(i, 0)] = e_input(i, 0);
+    }
+  }
+}
+
+template <typename DeviceContext, typename T>
+class ModeCPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* input = context.Input<framework::Tensor>("X");
+    auto* output = context.Output<framework::Tensor>("Out");
+    auto* indices = context.Output<framework::Tensor>("Indices");
+    const auto& in_dims = input->dims();
+    bool keepdim = static_cast<bool>(context.Attr<bool>("keepdim"));
+
+    // axis < 0, cacluate the real axis
+    int axis = static_cast<int>(context.Attr<int>("axis"));
+    if (axis < 0) axis += in_dims.size();
+
+    T* output_data = output->mutable_data<T>(context.GetPlace());
+    int64_t* indices_data = indices->mutable_data<int64_t>(context.GetPlace());
+    auto out_dims = output->dims();
+    // if axis is not the last dim, transpose it to the last dim, do the
+    // calculation,
+    // then tranpose it back to orginal axis.
+    if (axis == in_dims.size() - 1) {
+      const int64_t& input_height = framework::product(
+          framework::slice_ddim(in_dims, 0, in_dims.size() - 1));
+      const int64_t& input_width = in_dims[in_dims.size() - 1];
+      getMode<T, int64_t>(input_height, input_width, in_dims.size(), input,
+                          output_data, indices_data);
+    } else {
+      std::vector<int> trans_axis;
+      for (int i = 0; i < axis; i++) {
+        trans_axis.emplace_back(i);
+      }
+      trans_axis.push_back(in_dims.size() - 1);
+      for (int i = axis + 1; i < in_dims.size() - 1; i++) {
+        trans_axis.emplace_back(i);
+      }
+      trans_axis.emplace_back(axis);
+
+      if (!keepdim) {
+        std::vector<int> tmp_out_shape;
+        for (int i = 0; i < axis; i++) {
+          tmp_out_shape.emplace_back(in_dims[i]);
+        }
+        tmp_out_shape.emplace_back(1);
+        for (int i = axis + 1; i < in_dims.size(); i++) {
+          tmp_out_shape.emplace_back(in_dims[i]);
+        }
+        framework::DDim tmp_out_dim = framework::make_ddim(tmp_out_shape);
+        output->Resize(tmp_out_dim);
+        indices->Resize(tmp_out_dim);
+      }
+
+      // get the trans input_dims, out_dims
+      framework::DDim trans_shape(in_dims);
+      framework::DDim trans_out_shape(in_dims);
+
+      for (size_t i = 0; i < trans_axis.size(); i++) {
+        trans_shape[i] = in_dims[trans_axis[i]];
+        trans_out_shape[i] = in_dims[trans_axis[i]];
+      }
+      trans_out_shape[in_dims.size() - 1] = 1;
+
+      framework::Tensor trans_input;
+      trans_input.mutable_data<T>(trans_shape, context.GetPlace());
+      int ndims = trans_axis.size();
+      auto& dev_context =
+          context.template device_context<platform::CPUDeviceContext>();
+
+      // transpose the input value
+      TransCompute<platform::CPUDeviceContext, T>(ndims, dev_context, *input,
+                                                  &trans_input, trans_axis);
+
+      const int64_t input_height = framework::product(
+          framework::slice_ddim(trans_shape, 0, trans_shape.size() - 1));
+      const int64_t input_width = trans_shape[trans_shape.size() - 1];
+      framework::Tensor tmp_out;
+      T* t_out = tmp_out.mutable_data<T>(trans_out_shape, context.GetPlace());
+      framework::Tensor tmp_indices;
+      auto* t_ind = tmp_indices.mutable_data<int64_t>(trans_out_shape,
+                                                      context.GetPlace());
+
+      getMode<T, int64_t>(input_height, input_width, in_dims.size(),
+                          &trans_input, t_out, t_ind);
+      // transpose back
+      TransCompute<platform::CPUDeviceContext, int64_t>(
+          ndims, dev_context, tmp_indices, indices, trans_axis);
+      TransCompute<platform::CPUDeviceContext, T>(ndims, dev_context, tmp_out,
+                                                  output, trans_axis);
+      if (!keepdim) {
+        output->Resize(out_dims);
+        indices->Resize(out_dims);
+      }
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class ModeGradCPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<framework::Tensor>("X");
+    auto* out_grad =
+        context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* indices = context.Input<framework::Tensor>("Indices");
+    auto* x_grad =
+        context.Output<framework::Tensor>(framework::GradVarName("X"));
+    int axis = static_cast<int>(context.Attr<int>("axis"));
+    bool keepdim = static_cast<bool>(context.Attr<bool>("keepdim"));
+
+    auto in_dims = x->dims();
+    auto out_dims = indices->dims();
+
+    // axis < 0, get the real axis
+    axis = (axis < 0) ? (in_dims.size() + axis) : axis;
+
+    if (!keepdim) {
+      std::vector<int> tmp_out_shape;
+      for (int i = 0; i < axis; i++) {
+        tmp_out_shape.emplace_back(out_dims[i]);
+      }
+      tmp_out_shape.emplace_back(1);
+      for (int i = axis + 1; i < in_dims.size(); i++) {
+        tmp_out_shape.emplace_back(out_dims[i - 1]);
+      }
+      out_dims = framework::make_ddim(tmp_out_shape);
+    }
+    T* x_grad_data = x_grad->mutable_data<T>(context.GetPlace());
+    if (axis == in_dims.size() - 1) {
+      // allocate the memory for the input_grad
+      // assign the out_grad to input_grad directly
+      const int64_t input_height = framework::product(
+          framework::slice_ddim(in_dims, 0, in_dims.size() - 1));
+      const int64_t input_width = in_dims[in_dims.size() - 1];
+
+      // init the output grad with 0, because some input elements has no grad
+      memset(x_grad_data, 0, x_grad->numel() * sizeof(T));
+      // Assign the output_grad to input_grad
+      if (keepdim) {
+        ModeAssign(input_height, input_width, in_dims.size(), out_grad, indices,
+                   x_grad_data);
+      } else {
+        auto& dev_context =
+            context.template device_context<platform::CPUDeviceContext>();
+        framework::Tensor out_grad_tmp;
+        framework::Tensor indices_tmp;
+        out_grad_tmp.mutable_data<T>(out_grad->dims(), dev_context.GetPlace());
+        indices_tmp.mutable_data<int64_t>(indices->dims(),
+                                          dev_context.GetPlace());
+        framework::TensorCopy(*out_grad, dev_context.GetPlace(), dev_context,
+                              &out_grad_tmp);
+        framework::TensorCopy(*indices, dev_context.GetPlace(), dev_context,
+                              &indices_tmp);
+        out_grad_tmp.Resize(out_dims);
+        indices_tmp.Resize(out_dims);
+        ModeAssign(input_height, input_width, in_dims.size(), &out_grad_tmp,
+                   &indices_tmp, x_grad_data);
+      }
+    } else {
+      // can not assign grad to input_grad, must do the transpose
+      std::vector<int> trans_axis;
+      for (int i = 0; i < axis; i++) {
+        trans_axis.emplace_back(i);
+      }
+      trans_axis.emplace_back(out_dims.size() - 1);
+      for (int i = axis + 1; i < out_dims.size() - 1; i++) {
+        trans_axis.emplace_back(i);
+      }
+      trans_axis.emplace_back(axis);
+      framework::DDim trans_shape(out_dims);
+      framework::DDim trans_in_shape(in_dims);
+      for (size_t i = 0; i < trans_axis.size(); i++) {
+        trans_shape[i] = out_dims[trans_axis[i]];
+        trans_in_shape[i] = in_dims[trans_axis[i]];
+      }
+      // transpose the out_grad, indices
+      framework::Tensor trans_dO;
+      trans_dO.mutable_data<T>(trans_shape, context.GetPlace());
+      framework::Tensor trans_ind;
+      trans_ind.mutable_data<int64_t>(trans_shape, context.GetPlace());
+      int ndims = trans_axis.size();
+      auto& dev_context =
+          context.template device_context<platform::CPUDeviceContext>();
+
+      if (keepdim) {
+        // Do transpose
+        TransCompute<platform::CPUDeviceContext, T>(
+            ndims, dev_context, *out_grad, &trans_dO, trans_axis);
+        TransCompute<platform::CPUDeviceContext, int64_t>(
+            ndims, dev_context, *indices, &trans_ind, trans_axis);
+      } else {
+        framework::Tensor out_grad_tmp;
+        framework::Tensor indices_tmp;
+        out_grad_tmp.mutable_data<T>(out_grad->dims(), dev_context.GetPlace());
+        indices_tmp.mutable_data<int64_t>(indices->dims(),
+                                          dev_context.GetPlace());
+        framework::TensorCopy(*out_grad, dev_context.GetPlace(), dev_context,
+                              &out_grad_tmp);
+        framework::TensorCopy(*indices, dev_context.GetPlace(), dev_context,
+                              &indices_tmp);
+        out_grad_tmp.Resize(out_dims);
+        indices_tmp.Resize(out_dims);
+        // Do transpose
+        TransCompute<platform::CPUDeviceContext, T>(
+            ndims, dev_context, out_grad_tmp, &trans_dO, trans_axis);
+        TransCompute<platform::CPUDeviceContext, int64_t>(
+            ndims, dev_context, indices_tmp, &trans_ind, trans_axis);
+      }
+      const int64_t input_height = framework::product(
+          framework::slice_ddim(trans_in_shape, 0, trans_in_shape.size() - 1));
+      const int64_t input_width = trans_in_shape[trans_in_shape.size() - 1];
+
+      // Assign the out_grad to tranpose input_grad
+      framework::Tensor tmp_out;
+      T* t_out = tmp_out.mutable_data<T>(trans_in_shape, context.GetPlace());
+      memset(t_out, 0, x_grad->numel() * sizeof(T));
+
+      ModeAssign<T, int64_t>(input_height, input_width, in_dims.size(),
+                             &trans_dO, &trans_ind, t_out);
+
+      // Transpose back
+      TransCompute<platform::CPUDeviceContext, T>(ndims, dev_context, tmp_out,
+                                                  x_grad, trans_axis);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py
index b2effed3c9c11731549a9ffbe8772a18112b64d9..67c514b56b901dc67e3ac2ada321b335b6e9255b 100755
--- a/python/paddle/__init__.py
+++ b/python/paddle/__init__.py
@@ -274,6 +274,7 @@ from .tensor.search import where  # noqa: F401
 from .tensor.search import index_select  # noqa: F401
 from .tensor.search import nonzero  # noqa: F401
 from .tensor.search import sort  # noqa: F401
+from .tensor.search import mode  # noqa: F401
 
 from .tensor.to_string import set_printoptions  # noqa: F401
 
@@ -400,6 +401,7 @@ __all__ = [  # noqa
            'cos',
            'tan',
            'mean',
+           'mode',
            'mv',
            'in_dynamic_mode',
            'min',
diff --git a/python/paddle/fluid/tests/unittests/test_mode_op.py b/python/paddle/fluid/tests/unittests/test_mode_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b0458f2e255fd981aae81b7da88eecd8f4598d3
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_mode_op.py
@@ -0,0 +1,178 @@
+#   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
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+
+
+def _mode1D(a):
+    sorted_inds = np.argsort(a, kind='stable')
+    sorted_array = a[sorted_inds]
+    max_freq = 0
+    cur_freq = 0
+    mode = -1
+    for i in range(len(sorted_array)):
+        cur_freq += 1
+        if i == len(sorted_array) - 1 or sorted_array[i] != sorted_array[i + 1]:
+            if cur_freq > max_freq:
+                mode = sorted_array[i]
+                index = sorted_inds[i]
+                max_freq = cur_freq
+        cur_freq = 0
+    return mode, index
+
+
+def cal_mode(a, axis, keepdim=False):
+    if axis < 0:
+        axis = len(a.shape) + axis
+    in_dims = list(range(a.ndim))
+    a_view = np.transpose(a, in_dims[:axis] + in_dims[axis + 1:] + [axis])
+    inds = np.ndindex(a_view.shape[:-1])
+    modes = np.empty(a_view.shape[:-1], dtype=a.dtype)
+    indexes = np.empty(a_view.shape[:-1], dtype=np.int64)
+    for ind in inds:
+        modes[ind], indexes[ind] = _mode1D(a_view[ind])
+    if keepdim:
+        newshape = list(a.shape)
+        newshape[axis] = 1
+        modes = modes.reshape(newshape)
+        indexes = indexes.reshape(newshape)
+    return modes, indexes
+
+
+class TestModeOp(OpTest):
+    def init_args(self):
+        self.axis = 1
+
+    def setUp(self):
+        self.op_type = "mode"
+        self.dtype = np.float64
+        np.random.seed(666)
+        self.input_data = np.random.rand(2, 64, 1)
+        self.init_args()
+        self.inputs = {'X': self.input_data}
+        self.attrs = {'axis': self.axis}
+        output, indices = cal_mode(self.input_data, axis=self.axis)
+        self.outputs = {'Out': output, 'Indices': indices}
+
+    def test_check_output(self):
+        paddle.enable_static()
+        self.check_output()
+
+    def test_check_grad(self):
+        paddle.enable_static()
+        self.check_grad(set(['X']), 'Out')
+
+
+class TestModeOpLastdim(OpTest):
+    def init_args(self):
+        self.axis = -1
+
+    def setUp(self):
+        self.op_type = "mode"
+        self.dtype = np.float64
+        np.random.seed(666)
+        self.input_data = np.random.rand(2, 1, 1, 2, 30)
+        self.init_args()
+        self.inputs = {'X': self.input_data}
+        self.attrs = {'axis': self.axis}
+        output, indices = cal_mode(self.input_data, axis=self.axis)
+        self.outputs = {'Out': output, 'Indices': indices}
+
+    def test_check_output(self):
+        paddle.enable_static()
+        self.check_output()
+
+    def test_check_grad(self):
+        paddle.enable_static()
+        self.check_grad(set(['X']), 'Out')
+
+
+class TestModeOpKernels(unittest.TestCase):
+    def setUp(self):
+        self.axises = [-1, 1]
+        np.random.seed(666)
+        self.inputs = np.ceil(np.random.rand(2, 10, 10) * 1000)
+
+    def test_mode_op(self):
+        def test_cpu_kernel():
+            paddle.set_device('cpu')
+            tensor = paddle.to_tensor(self.inputs)
+            for axis in self.axises:
+                value_expect, indice_expect = cal_mode(self.inputs, axis)
+                v, inds = paddle.mode(tensor, axis)
+                self.assertTrue(np.allclose(v.numpy(), value_expect))
+
+                value_expect, indice_expect = cal_mode(
+                    self.inputs, axis, keepdim=True)
+                v, inds = paddle.mode(tensor, axis, keepdim=True)
+                self.assertTrue(np.allclose(v.numpy(), value_expect))
+
+        def test_gpu_kernel():
+            paddle.set_device('gpu')
+            tensor = paddle.to_tensor(self.inputs)
+            for axis in self.axises:
+                value_expect, indice_expect = cal_mode(self.inputs, axis)
+                v, inds = paddle.mode(tensor, axis)
+                self.assertTrue(np.allclose(v.numpy(), value_expect))
+
+                value_expect, indice_expect = cal_mode(
+                    self.inputs, axis, keepdim=True)
+                v, inds = paddle.mode(tensor, axis, keepdim=True)
+                self.assertTrue(np.allclose(v.numpy(), value_expect))
+
+        paddle.disable_static()
+        test_cpu_kernel()
+        if fluid.core.is_compiled_with_cuda():
+            test_gpu_kernel()
+
+
+class TestModeOpErrors(unittest.TestCase):
+    def setUp(self):
+        self.x = paddle.uniform([2, 10, 20, 25], dtype='float32')
+
+        def test_dim_range_error():
+            self.x.mode(axis=5)
+
+        self.assertRaises(ValueError, test_dim_range_error)
+
+
+class TestModeOpInStatic(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(666)
+        self.input_data = np.ceil(
+            np.random.random((2, 10, 10)) * 1000, dtype=np.float64)
+
+    def test_run_static(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            input_tensor = paddle.static.data(
+                name="x", shape=[2, 10, 10], dtype="float64")
+
+            result = paddle.mode(input_tensor, axis=1)
+            expect_value = cal_mode(self.input_data, axis=1)[0]
+            exe = paddle.static.Executor(paddle.CPUPlace())
+            paddle_result = exe.run(feed={"x": self.input_data},
+                                    fetch_list=[result])[0]
+            self.assertTrue(np.allclose(paddle_result, expect_value))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py
index 11a29eadf9011fae6499f607ab0a738d38f1d94a..ce70181096c7eaf09ab4557651ed3c997ce0b802 100755
--- a/python/paddle/tensor/__init__.py
+++ b/python/paddle/tensor/__init__.py
@@ -247,6 +247,8 @@ from .search import nonzero  # noqa: F401
 from .search import sort  # noqa: F401
 from .search import index_sample  # noqa: F401
 from .search import masked_select  # noqa: F401
+from .search import mode  # noqa: F401
+
 from .stat import mean  # noqa: F401
 from .stat import std  # noqa: F401
 from .stat import var  # noqa: F401
@@ -462,6 +464,7 @@ tensor_method_func  = [ #noqa
            'gcd',
            'lcm',
            'diff',
+           "mode",
            'lerp',
            'lerp_',
            'erfinv',
diff --git a/python/paddle/tensor/search.py b/python/paddle/tensor/search.py
index f3587aa48ddcba4492d6cca310f6ada2b722da2b..afb8a08665263352af2645b6d21356e62a9b3ccb 100644
--- a/python/paddle/tensor/search.py
+++ b/python/paddle/tensor/search.py
@@ -470,6 +470,59 @@ def sort(x, axis=-1, descending=False, name=None):
     return out
 
 
+def mode(x, axis=-1, keepdim=False, name=None):
+    """
+    This OP is used to find values and indices of the modes at the optional axis.
+
+    Args:
+        x(Tensor): Tensor, an input N-D Tensor with type float32, float64, int32, int64.
+        axis(int, optional): Axis to compute indices along. The effective range
+            is [-R, R), where R is x.ndim. when axis < 0, it works the same way
+            as axis + R. Default is -1.
+        keepdim(bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimentions is one fewer than x since the axis is squeezed. Default is False.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64.
+
+    Examples:
+
+        .. code-block:: python
+
+           import paddle
+           
+           tensor = paddle.to_tensor([[[1,2,2],[2,3,3]],[[0,5,5],[9,9,0]]], dtype=paddle.float32)
+           res = paddle.mode(tensor, 2)
+           print(res)
+           # (Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+           #   [[2., 3.],
+           #    [5., 9.]]), Tensor(shape=[2, 2], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
+           #   [[1, 1],
+           #    [1, 0]]))
+           
+    """
+    if in_dygraph_mode():
+        return _C_ops.mode(x, "axis", axis, "keepdim", keepdim)
+
+    helper = LayerHelper("mode", **locals())
+    inputs = {"X": [x]}
+    attrs = {}
+    attrs['axis'] = axis
+    attrs['keepdim'] = keepdim
+
+    values = helper.create_variable_for_type_inference(dtype=x.dtype)
+    indices = helper.create_variable_for_type_inference(dtype="int64")
+
+    helper.append_op(
+        type="mode",
+        inputs=inputs,
+        outputs={"Out": [values],
+                 "Indices": [indices]},
+        attrs=attrs)
+    indices.stop_gradient = True
+    return values, indices
+
+
 def where(condition, x, y, name=None):
     r"""
     Return a tensor of elements selected from either $x$ or $y$, depending on $condition$.