diff --git a/paddle/fluid/operators/elementwise/elementwise_op_function.h b/paddle/fluid/operators/elementwise/elementwise_op_function.h
index 312978a010b30ceb394d25269d9f4daa804702e0..2df7dd06f2cc89ba58f278952fea2b324d747610 100644
--- a/paddle/fluid/operators/elementwise/elementwise_op_function.h
+++ b/paddle/fluid/operators/elementwise/elementwise_op_function.h
@@ -240,7 +240,7 @@ inline void GetBroadcastDimsArrays(const framework::DDim &x_dims,
                   x_dims, y_dims, x_dims_array[i], y_dims_array[i], i));
     if ((x_dims_array[i] > 1 || y_dims_array[i] > 1) ||
         (x_dims_array[i] == 1 && y_dims_array[i] == 1)) {
-      out_dims_array[i] = std::max(x_dims_array[i], y_dims_array[i]);
+      out_dims_array[i] = (std::max)(x_dims_array[i], y_dims_array[i]);
     } else {
       out_dims_array[i] = -1;
     }
@@ -1779,7 +1779,7 @@ void CommonElementwiseBroadcastForward(
     const framework::Tensor *y, framework::Tensor *z,
     const framework::DDim &x_dims, const framework::DDim &y_dims, Functor func,
     int axis, const bool is_xsize_larger = true) {
-  int max_dim = std::max(x_dims.size(), y_dims.size());
+  int max_dim = (std::max)(x_dims.size(), y_dims.size());
   axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
   PADDLE_ENFORCE_GE(
       axis, 0,
diff --git a/paddle/fluid/operators/viterbi_decode_op.cc b/paddle/fluid/operators/viterbi_decode_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..bf1cdeed65a8427c19410347209faa099673cb7c
--- /dev/null
+++ b/paddle/fluid/operators/viterbi_decode_op.cc
@@ -0,0 +1,109 @@
+/* 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/viterbi_decode_op.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class ViterbiDecodeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", "ViterbiDecode");
+    OP_INOUT_CHECK(ctx->HasInput("Transition"), "Input", "Transition",
+                   "ViterbiDecode");
+    OP_INOUT_CHECK(ctx->HasInput("Length"), "Input", "Length", "ViterbiDecode");
+    OP_INOUT_CHECK(ctx->HasOutput("Scores"), "Output", "Scores",
+                   "ViterbiDecode");
+    OP_INOUT_CHECK(ctx->HasOutput("Path"), "Output", "Path", "ViterbiDecode");
+    auto in_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE_EQ(in_dims.size(), 3,
+                      platform::errors::InvalidArgument(
+                          "The rank of Input in ViterbiDecode  must be 3. But "
+                          "received Input's rank is %d.",
+                          in_dims.size()));
+    auto length_dims = ctx->GetInputDim("Length");
+    PADDLE_ENFORCE_EQ(length_dims.size(), 1,
+                      platform::errors::InvalidArgument(
+                          "The rank of Length in ViterbiDecode must be 1. But "
+                          "received Length's rank is %d.",
+                          length_dims.size()));
+    auto transition_dims = ctx->GetInputDim("Transition");
+    PADDLE_ENFORCE_EQ(
+        transition_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "The rank of Transition in ViterbiDecode must be 2. But "
+            "received Transition's rank is %d.",
+            transition_dims.size()));
+    if (ctx->IsRuntime()) {
+      PADDLE_ENFORCE_EQ(
+          in_dims[0], length_dims[0],
+          platform::errors::InvalidArgument(
+              "The batch size of Input and Length should be equal."));
+      PADDLE_ENFORCE_EQ(in_dims[2], transition_dims[0],
+                        platform::errors::InvalidArgument(
+                            "The number of tags of Input (%d) and Transition "
+                            "(%d) should be equal.",
+                            transition_dims[0], in_dims[2]));
+    }
+    ctx->SetOutputDim("Scores", length_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "Input"),
+        ctx.device_context());
+  }
+};
+
+class ViterbiDecodeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput(
+        "Input",
+        "The unary emission tensor. The shape of Input must be (batch_size,"
+        "sequence_length, num_tags). ");
+    AddInput("Transition",
+             "The transition matrix. The shape of Transition must be ( "
+             "num_tags, num_tags). ");
+    AddInput("Length",
+             "The input length tensor storing real length of each sequence for "
+             "correctness. The shape of Length MUST be (batch_size).");
+    AddOutput("Scores",
+              "The scores tensor containing the score for the Viterbi "
+              "sequence. The shape of Scores MUST be (batch_size).");
+    AddOutput("Path",
+              "The paths tensor containing the highest scoring tag indices. "
+              "The shape of Scores MUST be (batch_size, sequence_length).");
+    AddAttr<bool>("include_bos_eos_tag",
+                  "If set to True, the last row and the last column of "
+                  "transitions will be considered as start tag.")
+        .SetDefault(true);
+    AddComment(R"DOC(
+      )DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace platform = paddle::platform;
+REGISTER_OP_WITHOUT_GRADIENT(viterbi_decode, ops::ViterbiDecodeOp,
+                             ops::ViterbiDecodeOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    viterbi_decode, ops::ViterbiDecodeKernel<platform::CPUDeviceContext, float>,
+    ops::ViterbiDecodeKernel<platform::CPUDeviceContext, double>);
diff --git a/paddle/fluid/operators/viterbi_decode_op.cu b/paddle/fluid/operators/viterbi_decode_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..086ff05b08461245fc4cb1592dd107027db1f86a
--- /dev/null
+++ b/paddle/fluid/operators/viterbi_decode_op.cu
@@ -0,0 +1,200 @@
+/* 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/elementwise/elementwise_functor.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_broadcast.cu.h"
+#include "paddle/fluid/operators/gather.cu.h"
+#include "paddle/fluid/operators/viterbi_decode_op.h"
+
+#ifdef __NVCC__
+#include "cub/cub.cuh"
+#endif
+#ifdef __HIPCC__
+#include <hipcub/hipcub.hpp>
+namespace cub = hipcub;
+#endif
+
+namespace paddle {
+namespace operators {
+
+#define FIXED_BLOCK_DIM_CASE_BASE(log2_block_dim, ...)  \
+  case (1 << (log2_block_dim)): {                       \
+    constexpr auto kBlockDim = (1 << (log2_block_dim)); \
+    __VA_ARGS__;                                        \
+  } break
+
+#define FIXED_BLOCK_DIM_CASE(...)               \
+  FIXED_BLOCK_DIM_CASE_BASE(10, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(9, ##__VA_ARGS__);  \
+  FIXED_BLOCK_DIM_CASE_BASE(8, ##__VA_ARGS__);  \
+  FIXED_BLOCK_DIM_CASE_BASE(7, ##__VA_ARGS__);  \
+  FIXED_BLOCK_DIM_CASE_BASE(6, ##__VA_ARGS__);  \
+  FIXED_BLOCK_DIM_CASE_BASE(5, ##__VA_ARGS__);  \
+  FIXED_BLOCK_DIM_CASE_BASE(4, ##__VA_ARGS__);  \
+  FIXED_BLOCK_DIM_CASE_BASE(3, ##__VA_ARGS__);
+
+int64_t ComputeBlockSize(int64_t col) {
+  if (col > 512)
+    return 1024;
+  else if (col > 256)
+    return 512;
+  else if (col > 128)
+    return 256;
+  else if (col > 64)
+    return 128;
+  else if (col > 32)
+    return 64;
+  else if (col > 16)
+    return 32;
+  else if (col > 8)
+    return 16;
+  else
+    return 8;
+}
+
+template <template <typename T> typename BinaryFunctor, typename T>
+struct BinaryOperation<platform::CUDADeviceContext, BinaryFunctor, T> {
+  void operator()(const platform::CUDADeviceContext& dev_ctx, const Tensor& lhs,
+                  const Tensor& rhs, Tensor* output) {
+    std::vector<const Tensor*> ins{&lhs, &rhs};
+    std::vector<Tensor*> outs{output};
+    LaunchElementwiseCudaKernel<ElementwiseType::kBinary, T, T>(
+        dev_ctx, ins, &outs, -1, BinaryFunctor<T>());
+  }
+};
+
+template <template <typename T> typename CompareFunctor, typename T>
+struct GetMask<platform::CUDADeviceContext, CompareFunctor, T> {
+  void operator()(const framework::ExecutionContext& ctx, const Tensor& lhs,
+                  const Tensor& rhs, Tensor* mask) {
+    std::vector<const Tensor*> ins = {&lhs, &rhs};
+    std::vector<Tensor*> outs = {mask};
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    LaunchSameDimsElementwiseCudaKernel<ElementwiseType::kBinary, int64_t, T>(
+        dev_ctx, ins, &outs, CompareFunctor<int64_t>());
+  }
+};
+
+template <typename T, typename IndType, size_t BlockDim>
+__global__ void ArgmaxCUDAKernel(const int64_t height,     // n * h
+                                 const int64_t width,      // c
+                                 const int64_t post_size,  // h
+                                 const T* in, IndType* out_idx, T* out) {
+  typedef cub::BlockReduce<cub::KeyValuePair<int, T>, BlockDim> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  cub::ArgMax reducer;
+  T init = (std::numeric_limits<T>::lowest)();  // for windows compile
+  for (int idx = blockIdx.x; idx < height; idx += gridDim.x) {
+    cub::KeyValuePair<int, T> kv_pair = {-1, init};
+    int h = idx / post_size;
+    int w = idx % post_size;
+    for (int k = threadIdx.x; k < width; k += blockDim.x) {
+      kv_pair =
+          reducer({k, in[h * width * post_size + k * post_size + w]}, kv_pair);
+    }
+    kv_pair = BlockReduce(temp_storage).Reduce(kv_pair, reducer);
+    if (threadIdx.x == 0) {
+      // return max, argmax
+      if (out_idx != nullptr) out_idx[idx] = static_cast<IndType>(kv_pair.key);
+      if (out != nullptr) out[idx] = kv_pair.value;
+    }
+    __syncthreads();
+  }
+}
+
+__global__ void ARangeKernel(int64_t* data, int num, int64_t scale) {
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  for (int start = idx; idx < num; idx += gridDim.x) {
+    data[idx] = idx * scale;
+  }
+}
+
+template <>
+struct ARange<platform::CUDADeviceContext> {
+  void operator()(const platform::CUDADeviceContext& dev_ctx, int64_t* data,
+                  int num, int64_t scale) {
+    int64_t kBlockDim = ComputeBlockSize(num);
+    // kBlockDim > num at most of time, so we can set grid = 1
+    ARangeKernel<<<1, kBlockDim, 0, dev_ctx.stream()>>>(data, num, scale);
+  }
+};
+
+template <typename T, typename IndType>
+struct Argmax<platform::CUDADeviceContext, T, IndType> {
+  void operator()(const framework::ExecutionContext& ctx, const Tensor& input,
+                  Tensor* out_idx, Tensor* out, int axis) {
+    framework::DDim input_dims = input.dims();
+    int64_t numel = input.numel();
+    int64_t groups = numel / input_dims[axis];
+    int64_t pre = 1;
+    int64_t post = 1;
+    int64_t n = input_dims[axis];
+    for (int i = 0; i < axis; i++) {
+      pre *= input_dims[i];
+    }
+    for (int i = axis + 1; i < input_dims.size(); i++) {
+      post *= input_dims[i];
+    }
+    const auto& dev_ctx = ctx.cuda_device_context();
+    auto cu_stream = dev_ctx.stream();
+    int64_t max_grid_dimx = dev_ctx.GetCUDAMaxGridDimSize().x;
+    int64_t height = pre * post;
+    int64_t width = n;
+    int64_t grid_size = height < max_grid_dimx ? height : max_grid_dimx;
+    const T* in_data = input.data<T>();
+    IndType* out_idx_data = out_idx->data<IndType>();
+    T* out_data = out->data<T>();
+    switch (ComputeBlockSize(width)) {
+      FIXED_BLOCK_DIM_CASE(
+          ArgmaxCUDAKernel<T, IndType,
+                           kBlockDim><<<grid_size, kBlockDim, 0, cu_stream>>>(
+              height, width, post, in_data, out_idx_data, out_data));
+    }
+  }
+};
+
+template <typename T>
+struct GetMaxValue<platform::CUDADeviceContext, T> {
+  void operator()(const platform::CUDADeviceContext& dev_ctx,
+                  const Tensor& input, T* max_value) {
+    Tensor out_data;
+    out_data.Resize(framework::make_ddim({1}));
+    out_data.mutable_data<T>(platform::CUDAPlace());
+    switch (ComputeBlockSize(input.numel())) {
+      FIXED_BLOCK_DIM_CASE(
+          ArgmaxCUDAKernel<T, T,
+                           kBlockDim><<<1, kBlockDim, 0, dev_ctx.stream()>>>(
+              1, input.numel(), 1, input.data<int64_t>(), nullptr,
+              out_data.data<int64_t>()));
+    }
+    Tensor max_value_tensor;
+    framework::TensorCopy(out_data, platform::CPUPlace(), &max_value_tensor);
+    *max_value = max_value_tensor.data<T>()[0];
+  }
+};
+
+template <typename T, typename IndexT>
+struct Gather<platform::CUDADeviceContext, T, IndexT> {
+  void operator()(const platform::CUDADeviceContext& ctx, const Tensor& src,
+                  const Tensor& index, Tensor* output) {
+    GPUGather<T, IndexT>(ctx, src, index, output);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace platform = paddle::platform;
+REGISTER_OP_CUDA_KERNEL(
+    viterbi_decode,
+    ops::ViterbiDecodeKernel<platform::CUDADeviceContext, float>,
+    ops::ViterbiDecodeKernel<platform::CUDADeviceContext, double>);
diff --git a/paddle/fluid/operators/viterbi_decode_op.h b/paddle/fluid/operators/viterbi_decode_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..4da137f77433d5dcf7aab5d98126556e8d7311b4
--- /dev/null
+++ b/paddle/fluid/operators/viterbi_decode_op.h
@@ -0,0 +1,415 @@
+/* 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 <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/operators/controlflow/compare_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_functor.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+#include "paddle/fluid/operators/gather.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
+#include "paddle/fluid/operators/transpose_op.h"
+#include "paddle/fluid/operators/unique_op.h"
+#ifdef PADDLE_WITH_MKLML
+#include <omp.h>
+#endif
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+
+template <typename DeviceContext, typename T, typename IndType>
+struct Argmax {
+  void operator()(const framework::ExecutionContext& ctx, const Tensor& input,
+                  Tensor* out_idx, Tensor* out, int axis) {
+    framework::DDim input_dims = input.dims();
+    int64_t pre = 1;
+    int64_t post = 1;
+    int64_t n = input_dims[axis];
+    for (int i = 0; i < axis; i++) {
+      pre *= input_dims[i];
+    }
+    for (int i = axis + 1; i < input_dims.size(); i++) {
+      post *= input_dims[i];
+    }
+    int64_t height = pre * post;
+    int64_t width = n;
+    const T* in_data = input.data<T>();
+    IndType* out_idx_data = out_idx->data<IndType>();
+    T* out_data = out->data<T>();
+// Reduce
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+    for (int64_t i = 0; i < height; ++i) {
+      int64_t h = i / post;
+      int64_t w = i % post;
+      IndType max_idx = -1;
+      T max_value = (std::numeric_limits<T>::lowest)();  // for windows compile
+      for (int64_t j = 0; j < width; ++j) {
+        if (in_data[h * width * post + j * post + w] > max_value) {
+          max_value = in_data[h * width * post + j * post + w];
+          max_idx = j;
+        }
+      }
+      out_data[i] = max_value;
+      out_idx_data[i] = max_idx;
+    }
+  }
+};
+
+template <typename DeviceContext>
+struct ARange {
+  void operator()(const DeviceContext& dev_ctx, int64_t* data, int end,
+                  int64_t scale) {
+    for (int i = 0; i < end; ++i) {
+      data[i] = i * scale;
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+struct GetMaxValue {
+  void operator()(const DeviceContext& dev_ctx, const Tensor& input,
+                  T* max_value) {
+    auto input_ptr = input.data<T>();
+    auto num = input.numel();
+    *max_value = *std::max_element(input_ptr, input_ptr + num);
+  }
+};
+
+template <typename DeviceContext, typename T, typename IndexT = int>
+struct Gather {
+  void operator()(const DeviceContext& ctx, const Tensor& src,
+                  const Tensor& index, Tensor* output) {
+    CPUGather<T, IndexT>(ctx, src, index, output);
+  }
+};
+
+template <typename T, typename Functor, typename OutT = T>
+void SameDimsBinaryOP(const Tensor& lhs, const Tensor& rhs, Tensor* out) {
+  const T* lhs_ptr = lhs.data<T>();
+  const T* rhs_ptr = rhs.data<T>();
+  OutT* out_ptr = out->data<OutT>();
+  Functor functor;
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+  for (int i = 0; i < out->numel(); ++i) {
+    out_ptr[i] = functor(lhs_ptr[i], rhs_ptr[i]);
+  }
+}
+
+template <typename DeviceContext, template <typename T> typename CompareFunctor,
+          typename T>
+struct GetMask {
+  void operator()(const framework::ExecutionContext& ctx, const Tensor& lhs,
+                  const Tensor& rhs, Tensor* mask) {
+    SameDimsBinaryOP<int64_t, CompareFunctor<int64_t>, T>(lhs, rhs, mask);
+  }
+};
+
+template <bool is_multi_threads>
+struct GetInputIndex {
+  void operator()(const std::vector<int>& lhs_dims,
+                  const std::vector<int>& rhs_dims,
+                  const std::vector<int>& output_dims,
+                  const std::vector<int>& lhs_strides,
+                  const std::vector<int>& rhs_strides,
+                  const std::vector<int>& output_strides, int output_idx,
+                  int* index_array, int* lhs_idx, int* rhs_idx) {
+    int out_dims_size = output_strides.size();
+    for (int j = 0; j < out_dims_size; ++j) {
+      int curr_idx = output_idx / output_strides[j];
+      output_idx %= output_strides[j];
+      *lhs_idx += (lhs_dims[j] > 1) ? curr_idx * lhs_strides[j] : 0;
+      *rhs_idx += (rhs_dims[j] > 1) ? curr_idx * rhs_strides[j] : 0;
+    }
+  }
+};
+
+template <>
+struct GetInputIndex<false> {
+  void operator()(const std::vector<int>& lhs_dims,
+                  const std::vector<int>& rhs_dims,
+                  const std::vector<int>& output_dims,
+                  const std::vector<int>& lhs_strides,
+                  const std::vector<int>& rhs_strides,
+                  const std::vector<int>& output_strides, int output_idx,
+                  int* index_array, int* lhs_idx, int* rhs_idx) {
+    int out_dims_size = output_strides.size();
+    *lhs_idx = GetElementwiseIndex(lhs_dims.data(), out_dims_size, index_array);
+    *rhs_idx = GetElementwiseIndex(rhs_dims.data(), out_dims_size, index_array);
+    UpdateElementwiseIndexArray(output_dims.data(), out_dims_size, index_array);
+  }
+};
+
+template <typename T, typename Functor, bool is_multi_threads = false>
+void SimpleBroadcastBinaryOP(const Tensor& lhs, const Tensor& rhs,
+                             Tensor* out) {
+  const T* lhs_ptr = lhs.data<T>();
+  const T* rhs_ptr = rhs.data<T>();
+  T* out_ptr = out->data<T>();
+  int out_size = static_cast<int>(out->dims().size());
+  std::vector<int> out_dims(out_size);
+  std::vector<int> lhs_dims(out_size);
+  std::vector<int> rhs_dims(out_size);
+  std::copy(lhs.dims().Get(), lhs.dims().Get() + out_size, lhs_dims.data());
+  std::copy(rhs.dims().Get(), rhs.dims().Get() + out_size, rhs_dims.data());
+  std::copy(out->dims().Get(), out->dims().Get() + out_size, out_dims.data());
+  std::vector<int> output_strides(out_size, 1);
+  std::vector<int> lhs_strides(out_size, 1);
+  std::vector<int> rhs_strides(out_size, 1);
+  std::vector<int> index_array(out_size, 0);
+  // calculate strides
+  for (int i = out_size - 2; i >= 0; --i) {
+    output_strides[i] = output_strides[i + 1] * out_dims[i + 1];
+    lhs_strides[i] = lhs_strides[i + 1] * lhs_dims[i + 1];
+    rhs_strides[i] = rhs_strides[i + 1] * rhs_dims[i + 1];
+  }
+  Functor functor;
+  GetInputIndex<is_multi_threads> get_input_index;
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+  for (int i = 0; i < out->numel(); ++i) {
+    int lhs_idx = 0;
+    int rhs_idx = 0;
+    get_input_index(lhs_dims, rhs_dims, out_dims, lhs_strides, rhs_strides,
+                    output_strides, i, index_array.data(), &lhs_idx, &rhs_idx);
+    out_ptr[i] = functor(lhs_ptr[lhs_idx], rhs_ptr[rhs_idx]);
+  }
+}
+
+template <typename DeviceContext, template <typename T> typename BinaryFunctor,
+          typename T>
+struct BinaryOperation {
+  void operator()(const DeviceContext& dev_ctx, const Tensor& lhs,
+                  const Tensor& rhs, Tensor* output) {
+    if (lhs.dims() == rhs.dims()) {
+      SameDimsBinaryOP<T, BinaryFunctor<T>>(lhs, rhs, output);
+    } else {
+      bool is_multi_threads = false;
+#ifdef PADDLE_WITH_MKLML
+      if (omp_get_max_threads() > 1) {
+        is_multi_threads = true;
+      }
+#endif
+      if (is_multi_threads) {
+        SimpleBroadcastBinaryOP<T, BinaryFunctor<T>, true>(lhs, rhs, output);
+      } else {
+        SimpleBroadcastBinaryOP<T, BinaryFunctor<T>, false>(lhs, rhs, output);
+      }
+    }
+  }
+};
+
+class TensorBuffer {
+ public:
+  explicit TensorBuffer(const LoDTensor& in) : buffer_(in), offset_(0) {
+    buffer_.Resize({buffer_.numel()});
+  }
+  Tensor GetBufferBlock(std::initializer_list<int64_t> shape) {
+    int64_t size = std::accumulate(shape.begin(), shape.end(), 1,
+                                   std::multiplies<int64_t>());
+    Tensor block = buffer_.Slice(offset_, offset_ + size);
+    offset_ += size;
+    block.Resize(shape);
+    return block;
+  }
+
+ private:
+  LoDTensor buffer_;  // need to resize 1-D Tensor
+  int offset_;
+};
+
+template <typename DeviceContext, typename T>
+class ViterbiDecodeKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    bool include_bos_eos_tag = ctx.Attr<bool>("include_bos_eos_tag");
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto curr_place = ctx.GetPlace();
+    auto* input = ctx.Input<Tensor>("Input");
+    auto batch_size = static_cast<int>(input->dims()[0]);
+    auto seq_len = static_cast<int>(input->dims()[1]);
+    auto n_labels = static_cast<int>(input->dims()[2]);
+    math::SetConstant<DeviceContext, T> float_functor;
+    math::SetConstant<DeviceContext, int64_t> int_functor;
+    std::vector<Tensor> historys;
+    // We create tensor buffer in order to avoid allocating memory frequently
+    // 10 means allocate 10*batch_size bytes memory, such as int_mask, zero...
+    int buffer_size = batch_size * (n_labels + 1) * seq_len + 10 * batch_size;
+    LoDTensor int_buffer;
+    int_buffer.Resize(framework::make_ddim({buffer_size}));
+    int_buffer.mutable_data<int64_t>(ctx.GetPlace());
+    TensorBuffer int_tensor_buffer(int_buffer);
+    // create float tensor buffer
+    // 10 means allocate 10*batch_size*n_labels bytes, such as alpha, alpha_max
+    buffer_size = batch_size * (seq_len + 10) * n_labels +
+                  (batch_size + 2) * n_labels * n_labels;
+    LoDTensor float_buffer;
+    float_buffer.Resize(framework::make_ddim({buffer_size}));
+    float_buffer.mutable_data<T>(ctx.GetPlace());
+    TensorBuffer float_tensor_buffer(float_buffer);
+    auto* length = ctx.Input<Tensor>("Length");
+    Tensor left_length = int_tensor_buffer.GetBufferBlock({batch_size, 1});
+    framework::TensorCopy(*length, curr_place, dev_ctx, &left_length);
+    int64_t max_seq_len = 0;
+    GetMaxValue<DeviceContext, int64_t> get_max_value;
+    get_max_value(dev_ctx, left_length, &max_seq_len);
+
+    auto* scores = ctx.Output<Tensor>("Scores");
+    scores->mutable_data<T>(curr_place);
+    auto* path = ctx.Output<Tensor>("Path");
+    path->Resize({batch_size, max_seq_len});
+    path->mutable_data<int64_t>(curr_place);
+    Tensor tpath = int_tensor_buffer.GetBufferBlock({max_seq_len, batch_size});
+    auto batch_path = Unbind(tpath);
+    for (auto it = batch_path.begin(); it != batch_path.end(); ++it) {
+      it->Resize({batch_size});
+    }
+    // create and init required tensor
+    Tensor input_exp =
+        float_tensor_buffer.GetBufferBlock({seq_len, batch_size, n_labels});
+    TransCompute<DeviceContext, T>(3, dev_ctx, *input, &input_exp, {1, 0, 2});
+    auto* transition = ctx.Input<Tensor>("Transition");
+    Tensor trans_exp = float_tensor_buffer.GetBufferBlock({n_labels, n_labels});
+    framework::TensorCopy(*transition, curr_place, dev_ctx, &trans_exp);
+    trans_exp.Resize({1, n_labels, n_labels});
+    Tensor alpha = float_tensor_buffer.GetBufferBlock({batch_size, n_labels});
+    Tensor zero = int_tensor_buffer.GetBufferBlock({batch_size, 1});
+    int_functor(dev_ctx, &zero, 0);
+    Tensor one = int_tensor_buffer.GetBufferBlock({batch_size, 1});
+    int_functor(dev_ctx, &one, 1);
+    Tensor float_one = float_tensor_buffer.GetBufferBlock({batch_size, 1});
+    float_functor(dev_ctx, &float_one, static_cast<T>(1.0));
+    Tensor alpha_trn_sum =
+        float_tensor_buffer.GetBufferBlock({batch_size, n_labels, n_labels});
+    Tensor alpha_max =
+        float_tensor_buffer.GetBufferBlock({batch_size, n_labels});
+    Tensor alpha_argmax =
+        int_tensor_buffer.GetBufferBlock({seq_len, batch_size, n_labels});
+    auto alpha_argmax_unbind = Unbind(alpha_argmax);
+    Tensor alpha_nxt =
+        float_tensor_buffer.GetBufferBlock({batch_size, n_labels});
+    Tensor int_mask = int_tensor_buffer.GetBufferBlock({batch_size});
+    Tensor zero_len_mask = int_tensor_buffer.GetBufferBlock({batch_size});
+    Tensor float_mask = float_tensor_buffer.GetBufferBlock({batch_size, 1});
+    Tensor stop_trans = float_tensor_buffer.GetBufferBlock({1, 1, n_labels});
+    Tensor start_trans = float_tensor_buffer.GetBufferBlock({1, 1, n_labels});
+    Tensor rest_trans =
+        float_tensor_buffer.GetBufferBlock({1, n_labels - 2, n_labels});
+    Tensor last_ids = int_tensor_buffer.GetBufferBlock({batch_size});
+    Tensor last_ids_tmp = int_tensor_buffer.GetBufferBlock({batch_size});
+    Tensor batch_offset = int_tensor_buffer.GetBufferBlock({batch_size});
+    Tensor gather_idx = int_tensor_buffer.GetBufferBlock({batch_size});
+    std::vector<const Tensor*> shape{&rest_trans, &stop_trans, &start_trans};
+    std::vector<Tensor*> outputs{&rest_trans, &stop_trans, &start_trans};
+    math::SplitFunctor<DeviceContext, T> split_functor;
+    split_functor(dev_ctx, trans_exp, shape, 1, &outputs);
+    stop_trans.Resize({1, n_labels});
+    start_trans.Resize({1, n_labels});
+    auto logit0 = input_exp.Slice(0, 1);
+    logit0.Resize({batch_size, n_labels});
+    BinaryOperation<DeviceContext, AddFunctor, T> AddFloat;
+    BinaryOperation<DeviceContext, AddFunctor, int64_t> AddInt;
+    BinaryOperation<DeviceContext, MulFunctor, T> MulFloat;
+    BinaryOperation<DeviceContext, MulFunctor, int64_t> MulInt;
+    BinaryOperation<DeviceContext, SubFunctor, T> SubFloat;
+    BinaryOperation<DeviceContext, SubFunctor, int64_t> SubInt;
+    if (include_bos_eos_tag) {
+      AddFloat(dev_ctx, logit0, start_trans, &alpha);
+      GetMask<DeviceContext, EqualFunctor, T>()(ctx, left_length, one,
+                                                &float_mask);
+      MulFloat(dev_ctx, stop_trans, float_mask, &alpha_nxt);
+      AddFloat(dev_ctx, alpha, alpha_nxt, &alpha);
+    } else {
+      alpha = logit0;
+    }
+    SubInt(dev_ctx, left_length, one, &left_length);
+    Argmax<DeviceContext, T, int64_t> argmax;
+    for (int64_t i = 1; i < max_seq_len; ++i) {
+      Tensor logit = input_exp.Slice(i, i + 1);
+      logit.Resize({batch_size, n_labels});
+      Tensor& alpha_exp = alpha.Resize({batch_size, n_labels, 1});
+      AddFloat(dev_ctx, alpha_exp, trans_exp, &alpha_trn_sum);
+      auto alpha_argmax_temp = alpha_argmax_unbind[i - 1];
+      alpha_argmax_temp.Resize({batch_size, n_labels});
+      argmax(ctx, alpha_trn_sum, &alpha_argmax_temp, &alpha_max, 1);
+      historys.emplace_back(alpha_argmax_temp);
+      AddFloat(dev_ctx, alpha_max, logit, &alpha_nxt);
+      alpha.Resize({batch_size, n_labels});
+      // mask = paddle.cast((left_length > 0), dtype='float32')
+      // alpha = mask * alpha_nxt + (1 - mask) * alpha
+      GetMask<DeviceContext, GreaterThanFunctor, T>()(ctx, left_length, zero,
+                                                      &float_mask);
+      // alpha_nxt = mask * alpha_nxt
+      MulFloat(dev_ctx, alpha_nxt, float_mask, &alpha_nxt);
+      // inv_mask = 1 - mask
+      SubFloat(dev_ctx, float_one, float_mask, &float_mask);
+      // alpha = (1 - mask) * alpha
+      MulFloat(dev_ctx, alpha, float_mask, &alpha);
+      // alpha += alpha_nxt
+      AddFloat(dev_ctx, alpha, alpha_nxt, &alpha);
+      if (include_bos_eos_tag) {
+        GetMask<DeviceContext, EqualFunctor, T>()(ctx, left_length, one,
+                                                  &float_mask);
+        // alpha += mask * trans_exp[:, self.stop_idx]
+        MulFloat(dev_ctx, stop_trans, float_mask, &alpha_nxt);
+        AddFloat(dev_ctx, alpha, alpha_nxt, &alpha);
+      }
+      SubInt(dev_ctx, left_length, one, &left_length);
+    }
+    argmax(ctx, alpha, &last_ids, scores, 1);
+    left_length.Resize({batch_size});
+    GetMask<DeviceContext, GreaterEqualFunctor, int64_t>()(ctx, left_length,
+                                                           zero, &int_mask);
+    // last_ids_update = last_ids * tag_mask
+    int last_ids_index = 1;
+    int actual_len = (std::min)(seq_len, static_cast<int>(max_seq_len));
+    MulInt(dev_ctx, last_ids, int_mask,
+           &batch_path[actual_len - last_ids_index]);
+    // The algorithm below can refer to
+    // https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/layers/crf.py#L438
+    ARange<DeviceContext> arange;
+    arange(dev_ctx, batch_offset.data<int64_t>(), batch_size, n_labels);
+    Gather<DeviceContext, int64_t, int64_t> gather;
+    for (auto hist = historys.rbegin(); hist != historys.rend(); ++hist) {
+      ++last_ids_index;
+      AddInt(dev_ctx, left_length, one, &left_length);
+      AddInt(dev_ctx, batch_offset, last_ids, &gather_idx);
+      Tensor& last_ids_update = batch_path[actual_len - last_ids_index];
+      hist->Resize({batch_size * n_labels});
+      gather(dev_ctx, *hist, gather_idx, &last_ids_update);
+      GetMask<DeviceContext, GreaterThanFunctor, int64_t>()(ctx, left_length,
+                                                            zero, &int_mask);
+      MulInt(dev_ctx, last_ids_update, int_mask, &last_ids_update);
+      GetMask<DeviceContext, EqualFunctor, int64_t>()(ctx, left_length, zero,
+                                                      &zero_len_mask);
+      MulInt(dev_ctx, last_ids, zero_len_mask, &last_ids_tmp);
+      SubInt(dev_ctx, one, zero_len_mask, &zero_len_mask);
+      MulInt(dev_ctx, last_ids_update, zero_len_mask, &last_ids_update);
+      AddInt(dev_ctx, last_ids_update, last_ids_tmp, &last_ids_update);
+      GetMask<DeviceContext, LessThanFunctor, int64_t>()(ctx, left_length, zero,
+                                                         &int_mask);
+      MulInt(dev_ctx, last_ids, int_mask, &last_ids);
+      AddInt(dev_ctx, last_ids_update, last_ids, &last_ids);
+    }
+    TransCompute<DeviceContext, int64_t>(2, dev_ctx, tpath, path, {1, 0});
+  }
+};
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/fluid/tests/unittests/test_viterbi_decode_op.py b/python/paddle/fluid/tests/unittests/test_viterbi_decode_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f64322e975454c3492b3ce0e4d83de66d02992f
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_viterbi_decode_op.py
@@ -0,0 +1,134 @@
+#   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.
+import numpy as np
+from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.fluid import core
+import unittest
+import paddle
+paddle.enable_static()
+
+
+class Decoder(object):
+    def __init__(self, transitions, use_tag=True):
+        self.transitions = transitions
+        self.use_tag = use_tag
+        self.start_idx, self.stop_idx = -1, -2
+
+    def __call__(self, inputs, length):
+        bs, seq_len, n_label = inputs.shape
+        inputs_t = np.transpose(inputs, (1, 0, 2))
+        trans_exp = np.expand_dims(self.transitions, axis=0)
+        historys = []
+        left_length = np.array(length)
+        max_seq_len = np.amax(left_length)
+        left_length = np.expand_dims(left_length, 1)
+        alpha = np.full((bs, n_label), -1e4, dtype='float32') if self.use_tag \
+            else np.zeros((bs, n_label), dtype='float32')
+        alpha[:, -1] = 0
+        for i, logit in enumerate(inputs_t[:max_seq_len]):
+            if i == 0 and not self.use_tag:
+                alpha = logit
+                left_length = left_length - 1
+                continue
+            alpha_exp = np.expand_dims(alpha, 2)
+            alpha_trn_sum = alpha_exp + trans_exp
+            max_res = np.amax(alpha_trn_sum, 1), np.argmax(alpha_trn_sum, 1)
+            historys = historys + [max_res[1]] if i >= 1 else []
+            alpha_nxt = max_res[0] + logit
+            mask = (left_length > 0)
+            alpha = mask * alpha_nxt + (1 - mask) * alpha
+            if self.use_tag:
+                alpha += (left_length == 1) * trans_exp[:, self.stop_idx]
+            left_length = left_length - 1
+        scores, last_ids = np.amax(alpha, 1), np.argmax(alpha, 1)
+        left_length = left_length[:, 0]
+        last_ids_update = last_ids * (left_length >= 0)
+        batch_path = [last_ids_update]
+        batch_offset = np.arange(bs) * n_label
+        for hist in reversed(historys):
+            left_length = left_length + 1
+            gather_idx = batch_offset + last_ids
+            last_ids_update = np.take(hist, gather_idx) * (left_length > 0)
+            mask = (left_length == 0)
+            last_ids_update = last_ids_update * (1 - mask) + last_ids * mask
+            batch_path.insert(0, last_ids_update)
+            last_ids = last_ids_update + (left_length < 0) * last_ids
+        batch_path = np.stack(batch_path, 1)
+        return scores, batch_path
+
+
+class TestViterbiOp(OpTest):
+    def set_attr(self):
+        self.dtype = "float32" if core.is_compiled_with_rocm() else "float64"
+        self.use_tag = True
+        self.bz, self.len, self.ntags = 4, 8, 10
+
+    def setUp(self):
+        self.op_type = "viterbi_decode"
+        self.set_attr()
+        bz, length, ntags = self.bz, self.len, self.ntags
+        self.input = np.random.randn(bz, length, ntags).astype(self.dtype)
+        self.trans = np.random.randn(ntags, ntags).astype(self.dtype)
+        self.length = np.random.randint(1, length + 1, [bz]).astype('int64')
+        decoder = Decoder(self.trans, self.use_tag)
+        scores, path = decoder(self.input, self.length)
+        self.inputs = {
+            'Input': self.input,
+            'Transition': self.trans,
+            'Length': self.length
+        }
+        self.attrs = {'include_bos_eos_tag': self.use_tag, }
+        self.outputs = {'Scores': scores, 'Path': path}
+
+    def test_output(self):
+        self.check_output()
+
+
+class TestViterbiAPI(unittest.TestCase):
+    def set_attr(self):
+        self.use_tag = True
+        self.bz, self.len, self.ntags = 4, 8, 10
+        self.places = [fluid.CPUPlace(), fluid.CUDAPlace(0)]    \
+            if core.is_compiled_with_cuda() else [fluid.CPUPlace()]
+
+    def setUp(self):
+        self.set_attr()
+        bz, length, ntags = self.bz, self.len, self.ntags
+        self.input = np.random.randn(bz, length, ntags).astype('float32')
+        self.transitions = np.random.randn(ntags, ntags).astype('float32')
+        self.length = np.random.randint(1, length + 1, [bz]).astype('int64')
+        decoder = Decoder(self.transitions, self.use_tag)
+        self.scores, self.path = decoder(self.input, self.length)
+
+    def check_static_result(self, place):
+        bz, length, ntags = self.bz, self.len, self.ntags
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            Input = fluid.data(
+                name="Input", shape=[bz, length, ntags], dtype="float32")
+            Transition = fluid.data(
+                name="Transition", shape=[ntags, ntags], dtype="float32")
+            Length = fluid.data(name="Length", shape=[bz], dtype="int64")
+            decoder = paddle.text.ViterbiDecoder(Transition, self.use_tag)
+            score, path = decoder(Input, Length)
+            exe = fluid.Executor(place)
+            feed_list = {
+                "Input": self.input,
+                "Transition": self.transitions,
+                "Length": self.length
+            }
+            fetches = exe.run(feed=feed_list, fetch_list=[score, path])
+            np.testing.assert_allclose(fetches[0], self.scores, rtol=1e-5)
+            np.testing.assert_allclose(fetches[1], self.path)
+
+    def test_static_net(self):
+        for place in self.places:
+            self.check_static_result(place)
diff --git a/python/paddle/text/__init__.py b/python/paddle/text/__init__.py
index 00eaae5b29e93f52efa2438273b555dcf35e313b..f6bfa1c73585511bb8077c1986e03b36065d603e 100644
--- a/python/paddle/text/__init__.py
+++ b/python/paddle/text/__init__.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .viterbi_decode import ViterbiDecoder, viterbi_decode
 from .datasets import Conll05st  # noqa: F401
 from .datasets import Imdb  # noqa: F401
 from .datasets import Imikolov  # noqa: F401
@@ -20,7 +21,6 @@ from .datasets import UCIHousing  # noqa: F401
 from .datasets import WMT14  # noqa: F401
 from .datasets import WMT16  # noqa: F401
 
-
 __all__ = [ #noqa
            'Conll05st',
            'Imdb',
@@ -28,5 +28,7 @@ __all__ = [ #noqa
            'Movielens',
            'UCIHousing',
            'WMT14',
-           'WMT16'
+           'WMT16',
+           'ViterbiDecoder',
+           'viterbi_decode'
 ]
diff --git a/python/paddle/text/viterbi_decode.py b/python/paddle/text/viterbi_decode.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eec29f26ada7f00e638f7dd763b42291e964cd0
--- /dev/null
+++ b/python/paddle/text/viterbi_decode.py
@@ -0,0 +1,132 @@
+#   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.
+
+from ..nn import Layer
+from ..fluid.framework import core, in_dygraph_mode
+from ..fluid.layer_helper import LayerHelper
+from ..fluid.data_feeder import check_variable_and_dtype, check_type
+
+__all__ = ['viterbi_decode', 'ViterbiDecoder']
+
+
+def viterbi_decode(potentials,
+                   transition_params,
+                   lengths,
+                   include_bos_eos_tag=True,
+                   name=None):
+    """
+    Decode the highest scoring sequence of tags computed by transitions and potentials and get the viterbi path.
+ 
+    Args:
+        potentials (Tensor): The input tensor of unary emission. This is a 3-D
+            tensor with shape of [batch_size, sequence_length, num_tags]. The data type is float32 or float64. 
+        transition_params (Tensor): The input tensor of transition matrix. This is a 2-D
+            tensor with shape of [num_tags, num_tags]. The data type is float32 or float64. 
+        lengths (Tensor):  The input tensor of length of each sequence. This is a 1-D tensor with shape of [batch_size]. The data type is int64. 
+        include_bos_eos_tag (`bool`, optional): If set to True, the last row and the last column of transitions will be considered
+            as start tag, the second to last row and the second to last column of transitions will be considered as stop tag. Defaults to ``True``.
+        name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please
+            refer to :ref:`api_guide_Name`.
+
+    Returns:
+        scores(Tensor): The output tensor containing the score for the Viterbi sequence. The shape is [batch_size]
+            and the data type is float32 or float64.
+        paths(Tensor): The output tensor containing the highest scoring tag indices.  The shape is [batch_size, sequence_length]
+            and  the data type is int64.
+
+    Example:
+        .. code-block:: python
+
+            import paddle
+            paddle.seed(102)
+            batch_size, seq_len, num_tags = 2, 4, 3
+            emission = paddle.rand((batch_size, seq_len, num_tags), dtype='float32')
+            length = paddle.randint(1, seq_len + 1, [batch_size])
+            tags = paddle.randint(0, num_tags, [batch_size, seq_len])
+            transition = paddle.rand((num_tags, num_tags), dtype='float32')
+            scores, path = paddle.text.viterbi_decode(emission, transition, length, False) # scores: [3.37089300, 1.56825531], path: [[1, 0, 0], [1, 1, 0]]
+    """
+    if in_dygraph_mode():
+        return core.ops.viterbi_decode(potentials, transition_params, lengths,
+                                       'include_bos_eos_tag',
+                                       include_bos_eos_tag)
+    check_variable_and_dtype(potentials, 'input', ['float32', 'float64'],
+                             'viterbi_decode')
+    check_variable_and_dtype(transition_params, 'transitions',
+                             ['float32', 'float64'], 'viterbi_decode')
+    check_variable_and_dtype(lengths, 'length', 'int64', 'viterbi_decode')
+    check_type(include_bos_eos_tag, 'include_tag', bool, 'viterbi_decode')
+    helper = LayerHelper('viterbi_decode', **locals())
+    attrs = {'include_bos_eos_tag': include_bos_eos_tag}
+    scores = helper.create_variable_for_type_inference(potentials.dtype)
+    path = helper.create_variable_for_type_inference('int64')
+    helper.append_op(
+        type='viterbi_decode',
+        inputs={
+            'Input': potentials,
+            'Transition': transition_params,
+            'Length': lengths
+        },
+        outputs={'Scores': scores,
+                 'Path': path},
+        attrs=attrs)
+    return scores, path
+
+
+class ViterbiDecoder(Layer):
+    """ 
+    Decode the highest scoring sequence of tags computed by transitions and potentials and get the viterbi path. 
+
+    Args:
+        transitions (`Tensor`): The transition matrix.  Its dtype is float32 and has a shape of `[num_tags, num_tags]`.
+        include_bos_eos_tag (`bool`, optional): If set to True, the last row and the last column of transitions will be considered
+            as start tag, the second to last row and the second to last column of transitions will be considered as stop tag. Defaults to ``True``.
+        name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please
+            refer to :ref:`api_guide_Name`.
+
+    Shape:
+        potentials (Tensor): The input tensor of unary emission. This is a 3-D tensor with shape of 
+            [batch_size, sequence_length, num_tags]. The data type is float32 or float64. 
+        lengths (Tensor):  The input tensor of length of each sequence. This is a 1-D tensor with shape of
+            [batch_size]. The data type is int64. 
+
+    Returns:
+        scores(Tensor): The output tensor containing the score for the Viterbi sequence. The shape is [batch_size]
+            and the data type is float32 or float64.
+        paths(Tensor): The output tensor containing the highest scoring tag indices.  The shape is [batch_size, sequence_length]
+            and the data type is int64.
+
+    Example:
+        .. code-block:: python
+
+            import paddle
+            paddle.seed(102)
+            batch_size, seq_len, num_tags = 2, 4, 3
+            emission = paddle.rand((batch_size, seq_len, num_tags), dtype='float32')
+            length = paddle.randint(1, seq_len + 1, [batch_size])
+            tags = paddle.randint(0, num_tags, [batch_size, seq_len])
+            transition = paddle.rand((num_tags, num_tags), dtype='float32')
+            decoder = paddle.text.ViterbiDecoder(transition, include_bos_eos_tag=False)
+            scores, path = decoder(emission, length) # scores: [3.37089300, 1.56825531], path: [[1, 0, 0], [1, 1, 0]]
+    """
+
+    def __init__(self, transitions, include_bos_eos_tag=True, name=None):
+        super(ViterbiDecoder, self).__init__()
+        self.transitions = transitions
+        self.include_bos_eos_tag = include_bos_eos_tag
+        self.name = name
+
+    def forward(self, potentials, lengths):
+        return viterbi_decode(potentials, self.transitions, lengths,
+                              self.include_bos_eos_tag, self.name)