diff --git a/lite/kernels/x86/CMakeLists.txt b/lite/kernels/x86/CMakeLists.txt
index d40d4ac575a2284b252bcae138d78dfa4769bffc..ed2ac6637e1496a7e0aa968638d9bd0631f7f33d 100644
--- a/lite/kernels/x86/CMakeLists.txt
+++ b/lite/kernels/x86/CMakeLists.txt
@@ -19,6 +19,7 @@ add_kernel(pool_compute_x86 X86 basic SRCS pool_compute.cc DEPS ${lite_kernel_de
 add_kernel(stack_compute_x86 X86 basic SRCS stack_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(dropout_compute_x86 X86 basic SRCS dropout_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(transpose_compute_x86 X86 basic SRCS transpose_compute.cc DEPS ${lite_kernel_deps} math_function)
+add_kernel(layer_norm_compute_x86 X86 basic SRCS layer_norm_compute.cc DEPS ${lite_kernel_deps} jit_kernel_helper)
 # add_kernel(fc_compute_x86 X86 basic SRCS fc_compute.cc DEPS ${lite_kernel_deps})
 # lite_cc_library(batch_norm_compute_x86 SRCS batch_norm_compute.cc DEPS ${lite_kernel_deps})
 # lite_cc_library(uniform_random_compute_x86 SRCS uniform_random_compute.cc DEPS ${lite_kernel_deps} )
@@ -83,6 +84,7 @@ lite_cc_test(test_gru_compute_x86 SRCS gru_compute_test.cc DEPS gru_compute_x86)
 lite_cc_test(test_matmul_compute_x86 SRCS matmul_compute_test.cc DEPS matmul_compute_x86)
 lite_cc_test(test_cast_compute_x86 SRCS cast_compute_test.cc DEPS cast_compute_x86)
 lite_cc_test(test_pool2d_compute_x86 SRCS pool_compute_test.cc DEPS pool_compute_x86)
+lite_cc_test(test_layer_norm_compute_x86 SRCS layer_norm_compute_test.cc DEPS layer_norm_compute_x86)
 lite_cc_test(test_dropout_compute_x86 SRCS dropout_compute_test.cc DEPS dropout_compute_x86)
 lite_cc_test(test_transpose_compute_x86 SRCS transpose_compute_test.cc DEPS transpose_compute_x86)
 lite_cc_test(test_search_fc_compute_x86 SRCS search_fc_compute_test.cc DEPS search_fc_compute_x86)
diff --git a/lite/kernels/x86/layer_norm_compute.cc b/lite/kernels/x86/layer_norm_compute.cc
new file mode 100644
index 0000000000000000000000000000000000000000..4854a69a1d5f38bff102d984f990aea4ad723439
--- /dev/null
+++ b/lite/kernels/x86/layer_norm_compute.cc
@@ -0,0 +1,29 @@
+// 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 "lite/kernels/x86/layer_norm_compute.h"
+
+REGISTER_LITE_KERNEL(layer_norm,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::LayerNormCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindInput("Scale", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Y", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Mean", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Variance", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();
diff --git a/lite/kernels/x86/layer_norm_compute.h b/lite/kernels/x86/layer_norm_compute.h
new file mode 100644
index 0000000000000000000000000000000000000000..bbbdb91debfd7d7b046a3eb18a535462c69e358c
--- /dev/null
+++ b/lite/kernels/x86/layer_norm_compute.h
@@ -0,0 +1,91 @@
+// 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.
+
+#pragma once
+
+#include "lite/backends/x86/jit/helper.h"
+#include "lite/backends/x86/jit/kernel_base.h"
+#include "lite/backends/x86/jit/kernels.h"
+#include "lite/core/kernel.h"
+#include "lite/core/op_lite.h"
+#include "lite/core/op_registry.h"
+#include "lite/core/type_system.h"
+#include "lite/operators/layer_norm_op.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+template <typename T>
+class LayerNormCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::LayerNormParam;
+
+  void Run() override {
+    auto &param = *param_.get_mutable<param_t>();
+    float epsilon = param.epsilon;
+    auto Scale = param.Scale;
+    auto Bias = param.Bias;
+    auto x = param.X;
+
+    auto y = param.Y;
+    auto Mean = param.Mean;
+    auto Var = param.Variance;
+    auto begin_norm_axis = param.begin_norm_axis;
+
+    auto x_dims = x->dims();
+
+    y->mutable_data<T>();
+    Mean->mutable_data<T>();
+    Var->mutable_data<T>();
+
+    auto matrix_dim = x_dims.Flatten2D(begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
+    lite::DDim matrix_shape({left, right});
+
+    lite::Tensor in;
+    in.ShareDataWith(*x);
+    in.Resize(matrix_shape);
+    lite::Tensor out;
+    out.ShareDataWith(*y);
+    out.Resize(matrix_shape);
+
+    PADDLE_ENFORCE_EQ(Mean->numel(), left);
+    PADDLE_ENFORCE_EQ(Var->numel(), left);
+    PADDLE_ENFORCE_EQ(Scale->numel(), right);
+    PADDLE_ENFORCE_EQ(Bias->numel(), right);
+
+    auto ker = paddle::lite::jit::KernelFuncs<jit::LayerNormTuple<T>,
+                                              lite::fluid::CPUPlace>::Cache()
+                   .At(right);
+    ker(in.mutable_data<T>(),
+        out.mutable_data<T>(),
+        Mean->mutable_data<T>(),
+        Var->mutable_data<T>(),
+        Scale->data<T>(),
+        Bias->data<T>(),
+        static_cast<int>(left),
+        static_cast<const float>(epsilon),
+        right);
+  }
+
+  virtual ~LayerNormCompute() = default;
+};
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
diff --git a/lite/kernels/x86/layer_norm_compute_test.cc b/lite/kernels/x86/layer_norm_compute_test.cc
new file mode 100644
index 0000000000000000000000000000000000000000..fbac39505204b3799f6c5274f80690196e83a725
--- /dev/null
+++ b/lite/kernels/x86/layer_norm_compute_test.cc
@@ -0,0 +1,169 @@
+// 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 "lite/kernels/x86/layer_norm_compute.h"
+#include <gtest/gtest.h>
+#include <memory>
+#include <utility>
+#include <vector>
+#include "lite/backends/x86/jit/helper.h"
+#include "lite/backends/x86/jit/kernel_base.h"
+#include "lite/backends/x86/jit/kernels.h"
+#include "lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace x86 {
+
+std::vector<float> ref(lite::Tensor* x,
+                       lite::Tensor* Scale,
+                       lite::Tensor* Bias,
+                       lite::Tensor* y,
+                       lite::Tensor* Mean,
+                       lite::Tensor* Var,
+                       int begin_norm_axis,
+                       float epsilon) {
+  auto x_dims = x->dims();
+
+  y->mutable_data<float>();
+  Mean->mutable_data<float>();
+  Var->mutable_data<float>();
+
+  auto matrix_dim = x_dims.Flatten2D(begin_norm_axis);
+  int left = static_cast<int>(matrix_dim[0]);
+  int right = static_cast<int>(matrix_dim[1]);
+  lite::DDim matrix_shape({left, right});
+
+  x->Resize(matrix_shape);
+  Tensor out;
+  out.ShareDataWith(*y);
+  out.Resize(matrix_shape);
+
+  auto ker = paddle::lite::jit::KernelFuncs<jit::LayerNormTuple<float>,
+                                            lite::fluid::CPUPlace>::Cache()
+                 .At(right);
+  ker(x->mutable_data<float>(),
+      out.mutable_data<float>(),
+      Mean->mutable_data<float>(),
+      Var->mutable_data<float>(),
+      Scale->data<float>(),
+      Bias->data<float>(),
+      static_cast<int>(left),
+      static_cast<const float>(epsilon),
+      right);
+
+  std::vector<float> ref_data;
+  auto result = out.mutable_data<float>();
+  for (int i = 0; i < y->dims().production(); ++i) {
+    ref_data.emplace_back(result[i]);
+  }
+  return ref_data;
+}
+
+// layer_norm
+TEST(layer_norm_x86, retrive_op) {
+  auto layer_norm =
+      KernelRegistry::Global().Create<TARGET(kX86), PRECISION(kFloat)>(
+          "layer_norm");
+  ASSERT_FALSE(layer_norm.empty());
+  ASSERT_TRUE(layer_norm.front());
+}
+
+TEST(layer_norm_x86, init) {
+  lite::kernels::x86::LayerNormCompute<float> layer_norm;
+  ASSERT_EQ(layer_norm.precision(), PRECISION(kFloat));
+  ASSERT_EQ(layer_norm.target(), TARGET(kX86));
+}
+
+TEST(layer_norm_x86, run_test) {
+  lite::Tensor x;
+  lite::Tensor Scale;
+  lite::Tensor Bias;
+
+  lite::Tensor out;
+  lite::Tensor Mean;
+  lite::Tensor Var;
+
+  std::vector<int64_t> x_shape({1, 2, 3, 1});
+  x.Resize(lite::DDim(x_shape));
+  std::vector<int64_t> out_shape({1, 2, 3, 1});
+  out.Resize(lite::DDim(out_shape));
+
+  int begin_norm_axis = 0;
+  float epsilon = 1e-5;
+  int pre = 1;
+  int post = 1;
+  for (int i = 0; i < begin_norm_axis; ++i) {
+    pre *= x_shape[i];
+  }
+  for (int i = begin_norm_axis; i < x_shape.size(); ++i) {
+    post *= x_shape[i];
+  }
+  std::vector<int64_t> scale_shape({post});
+  Scale.Resize(scale_shape);
+  std::vector<int64_t> bias_shape({post});
+  Bias.Resize(bias_shape);
+
+  auto x_data = x.mutable_data<float>();
+  auto scale_data = Scale.mutable_data<float>();
+  auto bias_data = Bias.mutable_data<float>();
+  auto out_data = out.mutable_data<float>();
+  auto mean_data = Mean.mutable_data<float>();
+  auto var_data = Var.mutable_data<float>();
+
+  for (int64_t i = 0; i < x.dims().production(); ++i) {
+    x_data[i] = static_cast<float>(i);
+  }
+  for (int64_t i = 0; i < Scale.dims().production(); ++i) {
+    scale_data[i] = 1.5;
+  }
+  for (int64_t i = 0; i < Bias.dims().production(); ++i) {
+    bias_data[i] = 0.25;
+  }
+
+  LayerNormCompute<float> layer_norm;
+  operators::LayerNormParam param;
+
+  param.X = &x;
+  param.Y = &out;
+  param.Scale = &Scale;
+  param.Bias = &Bias;
+  param.Mean = &Mean;
+  param.Variance = &Var;
+  param.begin_norm_axis = begin_norm_axis;
+  param.epsilon = epsilon;
+
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<X86Context>();
+  layer_norm.SetContext(std::move(ctx));
+  layer_norm.SetParam(param);
+  layer_norm.Run();
+
+  std::vector<float> ref_data =
+      ref(&x, &Scale, &Bias, &out, &Mean, &Var, begin_norm_axis, epsilon);
+  for (int j = 0; j < out.dims().production(); ++j) {
+    EXPECT_NEAR(out_data[j], ref_data[j], 1e-5);
+    // LOG(INFO) << out_data[j];
+  }
+  LOG(INFO) << *mean_data;
+  LOG(INFO) << *var_data;
+}
+
+}  // namespace x86
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(layer_norm, kX86, kFloat, kNCHW, def);