diff --git a/paddle/fluid/operators/lu_op.cc b/paddle/fluid/operators/lu_op.cc
index 1f569950dad52..67bc9ba4fe774 100644
--- a/paddle/fluid/operators/lu_op.cc
+++ b/paddle/fluid/operators/lu_op.cc
@@ -13,6 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/lu_op.h"
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+#include "paddle/phi/infermeta/backward.h"
+#include "paddle/phi/infermeta/unary.h"
 
 namespace paddle {
 namespace operators {
@@ -39,39 +44,6 @@ class LUOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *context) const override {
-    OP_INOUT_CHECK(context->HasInput("X"), "Input", "X", "LU");
-    OP_INOUT_CHECK(context->HasOutput("Out"), "Output", "Out", "LU");
-    bool pivots = context->Attrs().Get<bool>("pivots");
-    auto x_dims = context->GetInputDim("X");
-    int x_rank = x_dims.size();
-    PADDLE_ENFORCE_GE(x_rank,
-                      2,
-                      platform::errors::InvalidArgument(
-                          "the rank of input must greater than 2"));
-    context->SetOutputDim("Out", x_dims);
-    int m = x_dims[x_rank - 1];
-    int n = x_dims[x_rank - 2];
-    int min_mn = std::min(m, n);
-    auto dims_vec = phi::vectorize(x_dims);
-    OP_INOUT_CHECK(context->HasOutput("Infos"), "Output", "Infos", "LU");
-    if (x_rank == 2) {
-      auto Infos_dim = std::vector<int>(1);
-      context->SetOutputDim("Infos", phi::make_ddim(Infos_dim));
-    } else {
-      auto Infos_dim =
-          std::vector<int>(dims_vec.begin(), dims_vec.begin() + x_rank - 2);
-      context->SetOutputDim("Infos", phi::make_ddim(Infos_dim));
-    }
-    if (pivots) {
-      OP_INOUT_CHECK(context->HasOutput("Pivots"), "Output", "Pivots", "LU");
-      auto Pivots_dim =
-          std::vector<int>(dims_vec.begin(), dims_vec.begin() + x_rank - 1);
-      Pivots_dim[x_rank - 2] = min_mn;
-      context->SetOutputDim("Pivots", phi::make_ddim(Pivots_dim));
-    }
-  }
-
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
@@ -99,57 +71,6 @@ class LUOpVarTypeInference : public framework::VarTypeInference {
   }
 };
 
-template <typename T>
-class LUKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const paddle::framework::ExecutionContext &ctx) const override {
-    auto pivots = ctx.Attr<bool>("pivots");
-    auto *xin = ctx.Input<framework::Tensor>("X");
-    auto *out = ctx.Output<framework::Tensor>("Out");
-    auto *IpivT = ctx.Output<framework::Tensor>("Pivots");
-    auto *InfoT = ctx.Output<framework::Tensor>("Infos");
-    PADDLE_ENFORCE_EQ(pivots,
-                      true,
-                      platform::errors::InvalidArgument(
-                          "lu without pivoting is not implemented on the CPU, "
-                          "but got pivots=False"));
-
-    math::DeviceIndependenceTensorOperations<phi::CPUContext, T> helper(ctx);
-    *out = helper.Transpose(*xin);
-
-    auto outdims = out->dims();
-    auto outrank = outdims.size();
-
-    int m = static_cast<int>(outdims[outrank - 1]);
-    int n = static_cast<int>(outdims[outrank - 2]);
-    int lda = std::max(1, m);
-
-    auto ipiv_dims = phi::slice_ddim(outdims, 0, outrank - 1);
-    ipiv_dims[outrank - 2] = std::min(m, n);
-    IpivT->Resize(ipiv_dims);
-    auto ipiv_data = IpivT->mutable_data<int>(ctx.GetPlace());
-
-    auto info_dims = phi::slice_ddim(outdims, 0, outrank - 2);
-    if (info_dims.size() == 0) {
-      info_dims = phi::make_ddim({1});
-    }
-    InfoT->Resize(info_dims);
-    auto info_data = InfoT->mutable_data<int>(ctx.GetPlace());
-
-    auto batchsize = product(info_dims);
-    batchsize = std::max(static_cast<int>(batchsize), 1);
-    auto out_data = out->mutable_data<T>(ctx.GetPlace());
-    for (int b = 0; b < batchsize; b++) {
-      auto out_data_item = &out_data[b * m * n];
-      int *info_data_item = &info_data[b];
-      int *ipiv_data_item = &ipiv_data[b * std::min(m, n)];
-      phi::funcs::lapackLu<T>(
-          m, n, out_data_item, lda, ipiv_data_item, info_data_item);
-    }
-    *out = helper.Transpose(*out);
-  }
-};
-
 template <typename T>
 class LUOpGradMaker : public framework::SingleGradOpMaker<T> {
  public:
@@ -184,23 +105,6 @@ class LUGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "lu");
-    OP_INOUT_CHECK(ctx->HasInput("Out"), "Input", "Out", "lu");
-    OP_INOUT_CHECK(ctx->HasInput("Pivots"), "Input", "Pivots", "lu");
-    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input",
-                   "Out@GRAD",
-                   "lu");
-
-    auto x_dims = ctx->GetInputDim("X");
-    auto x_grad_name = framework::GradVarName("X");
-
-    if (ctx->HasOutput(x_grad_name)) {
-      ctx->SetOutputDim(x_grad_name, x_dims);
-    }
-  }
-
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
@@ -219,19 +123,21 @@ DECLARE_INPLACE_OP_INFERER(LUGradOpInplaceInferer,
 namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 
+DECLARE_INFER_SHAPE_FUNCTOR(lu,
+                            LUInferMetaFunctor,
+                            PD_INFER_META(phi::LUInferMeta));
+DECLARE_INFER_SHAPE_FUNCTOR(lu_grad,
+                            LUGradInferMetaFunctor,
+                            PD_INFER_META(phi::LUGradInferMeta));
+
 REGISTER_OPERATOR(lu,
                   ops::LUOp,
                   ops::LUOpMaker,
                   ops::LUOpVarTypeInference,
                   ops::LUOpGradMaker<paddle::framework::OpDesc>,
                   ops::LUOpGradMaker<paddle::imperative::OpBase>,
-                  ops::LUOpInplaceInferer);
+                  LUInferMetaFunctor);
 REGISTER_OPERATOR(lu_grad,
                   ops::LUGradOp,
                   ops::LUGradOpVarTypeInference,
-                  ops::LUGradOpInplaceInferer);
-
-REGISTER_OP_CPU_KERNEL(lu, ops::LUKernel<float>, ops::LUKernel<double>);
-REGISTER_OP_CPU_KERNEL(lu_grad,
-                       ops::LUGradKernel<phi::CPUContext, float>,
-                       ops::LUGradKernel<phi::CPUContext, double>);
+                  LUGradInferMetaFunctor);
diff --git a/paddle/fluid/operators/lu_op.cu b/paddle/fluid/operators/lu_op.cu
deleted file mode 100644
index 35194b125abed..0000000000000
--- a/paddle/fluid/operators/lu_op.cu
+++ /dev/null
@@ -1,194 +0,0 @@
-/* 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. */
-
-#ifndef PADDLE_WITH_HIP
-// HIP not support cusolver
-
-#include "paddle/fluid/operators/lu_op.h"
-#include "paddle/fluid/memory/memory.h"
-#include "paddle/fluid/platform/dynload/cusolver.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-using CUDADeviceContext = paddle::platform::CUDADeviceContext;
-
-template <typename T>
-void cusolver_bufferSize(const cusolverDnHandle_t& cusolverH,
-                         int m,
-                         int n,
-                         T* d_A,
-                         int lda,
-                         int* lwork);
-template <typename T>
-void cusolver_getrf(const cusolverDnHandle_t& cusolverH,
-                    int m,
-                    int n,
-                    T* d_A,
-                    int lda,
-                    T* d_work,
-                    int* d_Ipiv,
-                    int* d_info);
-
-template <>
-void cusolver_bufferSize<float>(const cusolverDnHandle_t& cusolverH,
-                                int m,
-                                int n,
-                                float* d_A,
-                                int lda,
-                                int* lwork) {
-  PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusolverDnSgetrf_bufferSize(
-      cusolverH, m, n, d_A, lda, lwork));
-}
-
-template <>
-void cusolver_bufferSize<double>(const cusolverDnHandle_t& cusolverH,
-                                 int m,
-                                 int n,
-                                 double* d_A,
-                                 int lda,
-                                 int* lwork) {
-  PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusolverDnDgetrf_bufferSize(
-      cusolverH, m, n, d_A, lda, lwork));
-}
-
-template <>
-void cusolver_getrf<float>(const cusolverDnHandle_t& cusolverH,
-                           int m,
-                           int n,
-                           float* d_A,
-                           int lda,
-                           float* d_work,
-                           int* d_Ipiv,
-                           int* d_info) {
-  PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusolverDnSgetrf(
-      cusolverH, m, n, d_A, lda, d_work, d_Ipiv, d_info));
-}
-
-template <>
-void cusolver_getrf<double>(const cusolverDnHandle_t& cusolverH,
-                            int m,
-                            int n,
-                            double* d_A,
-                            int lda,
-                            double* d_work,
-                            int* d_Ipiv,
-                            int* d_info) {
-  PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusolverDnDgetrf(
-      cusolverH, m, n, d_A, lda, d_work, d_Ipiv, d_info));
-}
-
-template <typename T>
-void lu_decomposed_kernel(int m,
-                          int n,
-                          T* d_A,
-                          int lda,
-                          int* d_Ipiv,
-                          int* d_info,
-                          const framework::ExecutionContext& ctx) {
-  /* step 1: get cusolver handle*/
-  auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
-  auto cusolverH = dev_ctx.cusolver_dn_handle();
-
-  /* step 2: query working space of getrf */
-  int lwork;
-  cusolver_bufferSize(cusolverH, m, n, d_A, lda, &lwork);
-
-  auto work_buff = memory::Alloc(dev_ctx, lwork * sizeof(T));
-  T* d_work = reinterpret_cast<T*>(work_buff->ptr());
-
-  /* step 3: LU factorization */
-  if (d_Ipiv) {
-    cusolver_getrf(cusolverH, m, n, d_A, lda, d_work, d_Ipiv, d_info);
-  } else {
-    cusolver_getrf(cusolverH, m, n, d_A, lda, d_work, NULL, d_info);
-  }
-  PADDLE_ENFORCE_GPU_SUCCESS(cudaDeviceSynchronize());
-}
-
-template <typename T>
-class LUCUDAKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-#ifdef __HIPCC__
-    const int64_t kMaxBlockDim = 256;
-#else
-    const int64_t kMaxBlockDim = 512;
-#endif
-    auto* xin = ctx.Input<framework::Tensor>("X");
-    auto* out = ctx.Output<framework::Tensor>("Out");
-    auto* IpivT = ctx.Output<framework::Tensor>("Pivots");
-    auto* InfoT = ctx.Output<framework::Tensor>("Infos");
-    auto pivots = ctx.Attr<bool>("pivots");
-
-    math::DeviceIndependenceTensorOperations<
-        paddle::platform::CUDADeviceContext,
-        T>
-        helper(ctx);
-    *out = helper.Transpose(*xin);
-
-    auto outdims = out->dims();
-    auto outrank = outdims.size();
-
-    int m = static_cast<int>(outdims[outrank - 1]);
-    int n = static_cast<int>(outdims[outrank - 2]);
-    int lda = std::max(1, m);
-    if (pivots) {
-      auto ipiv_dims = phi::slice_ddim(outdims, 0, outrank - 1);
-      ipiv_dims[outrank - 2] = std::min(m, n);
-      IpivT->Resize(ipiv_dims);
-    }
-    auto ipiv_data = IpivT->mutable_data<int>(ctx.GetPlace());
-
-    auto info_dims = phi::slice_ddim(outdims, 0, outrank - 2);
-    if (info_dims.size() == 0) {
-      info_dims = phi::make_ddim({1});
-    }
-    InfoT->Resize(info_dims);
-    auto info_data = InfoT->mutable_data<int>(ctx.GetPlace());
-
-    auto batchsize = product(info_dims);
-    batchsize = std::max(static_cast<int>(batchsize), 1);
-    auto out_data = out->mutable_data<T>(ctx.GetPlace());
-    for (int b = 0; b < batchsize; b++) {
-      auto out_data_item = &out_data[b * m * n];
-      int* info_data_item = &info_data[b];
-      if (pivots) {
-        auto ipiv_data_item = &ipiv_data[b * std::min(m, n)];
-        lu_decomposed_kernel(
-            m, n, out_data_item, lda, ipiv_data_item, info_data_item, ctx);
-      } else {
-        lu_decomposed_kernel(
-            m, n, out_data_item, lda, NULL, info_data_item, ctx);
-      }
-    }
-    *out = helper.Transpose(*out);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-namespace plat = paddle::platform;
-
-REGISTER_OP_CUDA_KERNEL(lu,
-                        ops::LUCUDAKernel<float>,
-                        ops::LUCUDAKernel<double>);
-REGISTER_OP_CUDA_KERNEL(lu_grad,
-                        ops::LUGradKernel<plat::CUDADeviceContext, float>,
-                        ops::LUGradKernel<plat::CUDADeviceContext, double>);
-
-#endif  // not PADDLE_WITH_HIP
diff --git a/paddle/fluid/operators/lu_op.h b/paddle/fluid/operators/lu_op.h
index 4215074aeff01..1122937b6efe3 100644
--- a/paddle/fluid/operators/lu_op.h
+++ b/paddle/fluid/operators/lu_op.h
@@ -524,305 +524,5 @@ void Unpack_Pivot(const DeviceContext& dev_ctx,
   }
 }
 
-template <typename DeviceContext, typename T>
-class LUGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto xin = ctx.Input<framework::Tensor>("X");
-    auto out = ctx.Input<framework::Tensor>("Out");
-    auto P = ctx.Input<framework::Tensor>("Pivots");
-    auto dout = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
-    auto dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
-    dx->mutable_data<T>(ctx.GetPlace());
-
-    const auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    math::DeviceIndependenceTensorOperations<DeviceContext, T> helper(ctx);
-    auto blas = phi::funcs::GetBlas<DeviceContext, T>(ctx);
-
-    auto xdims = xin->dims();
-    int xrank = xdims.size();
-    int64_t m = xdims[xrank - 2];
-    int64_t n = xdims[xrank - 1];
-    int64_t k = std::min(m, n);
-
-    framework::Tensor L, U, L_narrow, U_narrow, L_narrow_mH, U_narrow_mH,
-        grad_narrow;
-    LU_Unpack<DeviceContext, T>(dev_ctx, out, &L, &U);
-
-    Tensor_narrow<DeviceContext, T>(ctx, &L, &L_narrow, 0, k, 0, k);
-    Tensor_narrow<DeviceContext, T>(ctx, &U, &U_narrow, 0, k, 0, k);
-    Tensor_narrow<DeviceContext, T>(ctx, dout, &grad_narrow, 0, k, 0, k);
-    auto graddims = grad_narrow.dims();
-
-    Tensor_Conj<DeviceContext, T>(dev_ctx, L_narrow, &L_narrow_mH);
-    Tensor_Conj<DeviceContext, T>(dev_ctx, U_narrow, &U_narrow_mH);
-    L_narrow_mH = helper.Transpose(L_narrow_mH);
-    U_narrow_mH = helper.Transpose(U_narrow_mH);
-
-    auto LmHdims = L_narrow_mH.dims();
-    auto UmHdims = U_narrow_mH.dims();
-
-    framework::Tensor phi_L, phi_U, phi, psi;
-    phi_L.Resize(LmHdims);
-    phi_L.mutable_data<T>(ctx.GetPlace());
-    phi_U.Resize(UmHdims);
-    phi_U.mutable_data<T>(ctx.GetPlace());
-    auto mat_dim_l = phi::funcs::CreateMatrixDescriptor(LmHdims, 0, false);
-    auto mat_dim_u = phi::funcs::CreateMatrixDescriptor(UmHdims, 0, false);
-    auto mat_dim_g = phi::funcs::CreateMatrixDescriptor(graddims, 0, false);
-    blas.MatMul(L_narrow_mH,
-                mat_dim_l,
-                grad_narrow,
-                mat_dim_g,
-                static_cast<T>(1),
-                &phi_L,
-                static_cast<T>(0));
-
-    blas.MatMul(grad_narrow,
-                mat_dim_g,
-                U_narrow_mH,
-                mat_dim_u,
-                static_cast<T>(1),
-                &phi_U,
-                static_cast<T>(0));
-
-    auto phil_rank = LmHdims.size();
-    auto phiu_rank = UmHdims.size();
-    platform::ForRange<DeviceContext> l_for_range(dev_ctx, phi_L.numel());
-    phi::funcs::TrilTriuCompute<T> tril_computer(phi_L.data<T>(),
-                                                 -1,
-                                                 true,
-                                                 LmHdims[phil_rank - 2],
-                                                 LmHdims[phil_rank - 1],
-                                                 phi_L.data<T>());
-    l_for_range(tril_computer);
-
-    platform::ForRange<DeviceContext> u_for_range(dev_ctx, phi_U.numel());
-    phi::funcs::TrilTriuCompute<T> triu_computer(phi_U.data<T>(),
-                                                 0,
-                                                 false,
-                                                 UmHdims[phiu_rank - 2],
-                                                 UmHdims[phiu_rank - 1],
-                                                 phi_U.data<T>());
-    u_for_range(triu_computer);
-
-    Tensor_Add<DeviceContext, T>(dev_ctx, phi_L, phi_U, &phi);
-    psi.Resize(xdims);
-    psi.mutable_data<T>(ctx.GetPlace());
-    phi::funcs::SetConstant<DeviceContext, T> setter;
-    setter(dev_ctx, &psi, static_cast<T>(0));
-
-    std::vector<int64_t> axes = {xrank - 2, xrank - 1};
-    std::vector<int64_t> slice_starts(2, 0);
-    std::vector<int64_t> slice_ends(2, 0);
-    auto valuedims = vectorize(xdims);
-
-    framework::Tensor Pmat;
-    Unpack_Pivot<DeviceContext, T>(dev_ctx, *P, &Pmat, m, k);
-
-    using Context =
-        typename framework::ConvertToPhiContext<DeviceContext>::TYPE;
-    auto& phi_dev_ctx = static_cast<const Context&>(dev_ctx);
-
-    if (m <= n) {
-      if (k < n) {
-        framework::Tensor U_complement, U_grad_complement, phi_complement,
-            phi_complement_l;
-        Tensor_narrow<DeviceContext, T>(ctx, &U, &U_complement, 0, k, k, n);
-        Tensor_narrow<DeviceContext, T>(
-            ctx, dout, &U_grad_complement, 0, k, k, n);
-        framework::Tensor U_complement_mH = helper.Transpose(U_complement);
-
-        Tensor_Conj<DeviceContext, T>(
-            dev_ctx, U_complement_mH, &U_complement_mH);
-
-        auto mat_dim_g = phi::funcs::CreateMatrixDescriptor(
-            U_grad_complement.dims(), 0, false);
-        auto mat_dim_u = phi::funcs::CreateMatrixDescriptor(
-            U_complement_mH.dims(), 0, false);
-        auto phidims = UmHdims;
-        phidims[UmHdims.size() - 2] = k;
-        phidims[UmHdims.size() - 1] = k;
-        phi_complement.Resize(phidims);
-        phi_complement.mutable_data<T>(ctx.GetPlace());
-        blas.MatMul(U_grad_complement,
-                    mat_dim_g,
-                    U_complement_mH,
-                    mat_dim_u,
-                    static_cast<T>(1),
-                    &phi_complement,
-                    static_cast<T>(0));
-
-        phi_complement_l.Resize(phidims);
-        phi_complement_l.mutable_data<T>(ctx.GetPlace());
-        const auto H = phidims[phidims.size() - 2];
-        const auto W = phidims[phidims.size() - 1];
-        platform::ForRange<DeviceContext> x_for_range(dev_ctx,
-                                                      phi_complement.numel());
-        phi::funcs::TrilTriuCompute<T> tril_computer(
-            phi_complement.data<T>(),
-            -1,
-            true,
-            H,
-            W,
-            phi_complement_l.data<T>());
-        x_for_range(tril_computer);
-
-        Tensor_Sub<DeviceContext, T>(dev_ctx, phi, phi_complement_l, &phi);
-
-        slice_starts[0] = 0;
-        slice_starts[1] = k;
-        slice_ends[0] = k;
-        slice_ends[1] = n;
-        valuedims[xrank - 2] = k;
-        valuedims[xrank - 1] = n - k;
-        SetValueCompute_dispatch<DeviceContext, T>(ctx,
-                                                   &psi,
-                                                   &U_grad_complement,
-                                                   &psi,
-                                                   axes,
-                                                   &slice_starts,
-                                                   &slice_ends,
-                                                   valuedims,
-                                                   xrank);
-      }
-
-      framework::Tensor psi_principal, phi_mH, psi_tmp;
-      Tensor_Conj<DeviceContext, T>(dev_ctx, phi, &phi_mH);
-      phi_mH = helper.Transpose(phi_mH);
-
-      phi::TriangularSolveKernel<T, Context>(
-          phi_dev_ctx, U_narrow, phi_mH, true, false, false, &psi_principal);
-
-      Tensor_Conj<DeviceContext, T>(dev_ctx, psi_principal, &psi_principal);
-      psi_principal = helper.Transpose(psi_principal);
-      slice_starts[0] = 0;
-      slice_starts[1] = 0;
-      slice_ends[0] = k;
-      slice_ends[1] = k;
-      valuedims[xrank - 2] = k;
-      valuedims[xrank - 1] = k;
-
-      SetValueCompute_dispatch<DeviceContext, T>(ctx,
-                                                 &psi,
-                                                 &psi_principal,
-                                                 &psi,
-                                                 axes,
-                                                 &slice_starts,
-                                                 &slice_ends,
-                                                 valuedims,
-                                                 xrank);
-
-      phi::TriangularSolveKernel<T, Context>(
-          phi_dev_ctx, L_narrow_mH, psi, true, false, true, &psi_tmp);
-
-      auto mat_dim_p =
-          phi::funcs::CreateMatrixDescriptor(Pmat.dims(), 0, false);
-      auto mat_dim_b =
-          phi::funcs::CreateMatrixDescriptor(psi_tmp.dims(), 0, false);
-      blas.MatMul(Pmat,
-                  mat_dim_p,
-                  psi_tmp,
-                  mat_dim_b,
-                  static_cast<T>(1),
-                  dx,
-                  static_cast<T>(0));
-    } else {
-      framework::Tensor L_complement, L_grad_complement, phi_complement,
-          phi_complement_u;
-      Tensor_narrow<DeviceContext, T>(ctx, &L, &L_complement, k, m, 0, k);
-      Tensor_narrow<DeviceContext, T>(
-          ctx, dout, &L_grad_complement, k, m, 0, k);
-      framework::Tensor L_complement_mH = helper.Transpose(L_complement);
-      Tensor_Conj<DeviceContext, T>(dev_ctx, L_complement_mH, &L_complement_mH);
-
-      auto mat_dim_g = phi::funcs::CreateMatrixDescriptor(
-          L_grad_complement.dims(), 0, false);
-      auto mat_dim_u =
-          phi::funcs::CreateMatrixDescriptor(L_complement_mH.dims(), 0, false);
-      auto phidims = LmHdims;
-      phidims[LmHdims.size() - 2] = k;
-      phidims[LmHdims.size() - 1] = k;
-      phi_complement.Resize(phidims);
-      phi_complement.mutable_data<T>(ctx.GetPlace());
-      blas.MatMul(L_complement_mH,
-                  mat_dim_u,
-                  L_grad_complement,
-                  mat_dim_g,
-                  static_cast<T>(1),
-                  &phi_complement,
-                  static_cast<T>(0));
-
-      phi_complement_u.Resize(phidims);
-      phi_complement_u.mutable_data<T>(ctx.GetPlace());
-      const auto H = phidims[phidims.size() - 2];
-      const auto W = phidims[phidims.size() - 1];
-      platform::ForRange<DeviceContext> x_for_range(dev_ctx,
-                                                    phi_complement.numel());
-      phi::funcs::TrilTriuCompute<T> triu_computer(
-          phi_complement.data<T>(), 0, false, H, W, phi_complement_u.data<T>());
-      x_for_range(triu_computer);
-
-      Tensor_Sub<DeviceContext, T>(dev_ctx, phi, phi_complement_u, &phi);
-
-      slice_starts[0] = k;
-      slice_starts[1] = 0;
-      slice_ends[0] = m;
-      slice_ends[1] = k;
-      valuedims[xrank - 2] = m - k;
-      valuedims[xrank - 1] = k;
-      SetValueCompute_dispatch<DeviceContext, T>(ctx,
-                                                 &psi,
-                                                 &L_grad_complement,
-                                                 &psi,
-                                                 axes,
-                                                 &slice_starts,
-                                                 &slice_ends,
-                                                 valuedims,
-                                                 xrank);
-      framework::Tensor psi_principal, phi_mH, psi_tmp, U_narrow_mH;
-
-      phi::TriangularSolveKernel<T, Context>(
-          phi_dev_ctx, L_narrow_mH, phi, true, false, true, &psi_principal);
-
-      slice_starts[0] = 0;
-      slice_starts[1] = 0;
-      slice_ends[0] = k;
-      slice_ends[1] = k;
-      valuedims[xrank - 2] = k;
-      valuedims[xrank - 1] = k;
-
-      SetValueCompute_dispatch<DeviceContext, T>(ctx,
-                                                 &psi,
-                                                 &psi_principal,
-                                                 &psi,
-                                                 axes,
-                                                 &slice_starts,
-                                                 &slice_ends,
-                                                 valuedims,
-                                                 xrank);
-
-      psi_tmp.Resize(psi.dims());
-      psi_tmp.mutable_data<T>(ctx.GetPlace());
-      auto mat_dim_p =
-          phi::funcs::CreateMatrixDescriptor(Pmat.dims(), 0, false);
-      auto mat_dim_b = phi::funcs::CreateMatrixDescriptor(psi.dims(), 0, false);
-      blas.MatMul(Pmat,
-                  mat_dim_p,
-                  psi,
-                  mat_dim_b,
-                  static_cast<T>(1),
-                  &psi_tmp,
-                  static_cast<T>(0));
-      psi_tmp = helper.Transpose(psi_tmp);
-
-      Tensor_Conj<DeviceContext, T>(dev_ctx, U_narrow, &U_narrow_mH);
-      phi::TriangularSolveKernel<T, Context>(
-          phi_dev_ctx, U_narrow_mH, psi_tmp, true, false, false, &psi);
-      *dx = helper.Transpose(psi);
-    }
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/phi/api/yaml/legacy_api.yaml b/paddle/phi/api/yaml/legacy_api.yaml
index 00a68bb0c44f6..d2d1bb0ecc698 100644
--- a/paddle/phi/api/yaml/legacy_api.yaml
+++ b/paddle/phi/api/yaml/legacy_api.yaml
@@ -1357,6 +1357,15 @@
     func : logsumexp
   backward : logsumexp_grad
 
+- api : lu
+  args : (Tensor x, bool pivot)
+  output : Tensor(out), Tensor(pivots), Tensor(infos)
+  infer_meta :
+    func : LUInferMeta
+  kernel :
+    func : lu
+  backward : lu_grad
+
 # masked_select
 - api : masked_select
   args : (Tensor x, Tensor mask)
diff --git a/paddle/phi/api/yaml/legacy_backward.yaml b/paddle/phi/api/yaml/legacy_backward.yaml
index abbb23cc253fa..7baa2b962a0c9 100644
--- a/paddle/phi/api/yaml/legacy_backward.yaml
+++ b/paddle/phi/api/yaml/legacy_backward.yaml
@@ -1203,6 +1203,15 @@
   kernel :
     func : logsumexp_grad
 
+- backward_api : lu_grad
+  forward : lu (Tensor x, bool pivot) -> Tensor(out), Tensor(pivots), Tensor(infos)
+  args : (Tensor x, Tensor out, Tensor pivots, Tensor out_grad, bool pivot)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : LUGradInferMeta
+  kernel :
+    func : lu_grad
+
 - backward_api : masked_select_grad
   forward : masked_select (Tensor x, Tensor mask) -> Tensor(out)
   args : (Tensor x, Tensor mask, Tensor out_grad)
diff --git a/paddle/phi/infermeta/backward.cc b/paddle/phi/infermeta/backward.cc
index 1eca092a5f22f..21e165b0c0b9e 100644
--- a/paddle/phi/infermeta/backward.cc
+++ b/paddle/phi/infermeta/backward.cc
@@ -429,6 +429,20 @@ void KernelWithXShapeInferMeta(const MetaTensor& xshape, MetaTensor* dx) {
   dx->share_lod(xshape);
 }
 
+void LUGradInferMeta(const MetaTensor& x,
+                     const MetaTensor& out,
+                     const MetaTensor& pivots,
+                     const MetaTensor& out_grad,
+                     bool pivot,
+                     MetaTensor* x_grad) {
+  auto x_dims = x.dims();
+
+  if (x_grad) {
+    x_grad->set_dims(x_dims);
+    x_grad->set_dtype(x.dtype());
+  }
+}
+
 void MaxPoolWithIndexGradInferMeta(const MetaTensor& x,
                                    const MetaTensor& mask,
                                    const MetaTensor& dout,
diff --git a/paddle/phi/infermeta/backward.h b/paddle/phi/infermeta/backward.h
index 5551b6bcbf183..1b1c27cc46dd3 100644
--- a/paddle/phi/infermeta/backward.h
+++ b/paddle/phi/infermeta/backward.h
@@ -195,6 +195,13 @@ void InverseGradInferMeta(const MetaTensor& out,
 
 void KernelWithXShapeInferMeta(const MetaTensor& xshape, MetaTensor* dx);
 
+void LUGradInferMeta(const MetaTensor& x,
+                     const MetaTensor& out,
+                     const MetaTensor& pivots,
+                     const MetaTensor& out_grad,
+                     bool pivot,
+                     MetaTensor* x_grad);
+
 void MaxPoolWithIndexGradInferMeta(const MetaTensor& x,
                                    const MetaTensor& mask,
                                    const MetaTensor& dout,
diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc
index edc455225e4dc..2f2be2fb39594 100644
--- a/paddle/phi/infermeta/unary.cc
+++ b/paddle/phi/infermeta/unary.cc
@@ -1240,15 +1240,59 @@ void MatrixPowerInferMeta(const MetaTensor& x, int n, MetaTensor* out) {
   out->set_dtype(x.dtype());
 }
 
+void LUInferMeta(const MetaTensor& x,
+                 bool pivot,
+                 MetaTensor* out,
+                 MetaTensor* pivots,
+                 MetaTensor* infos) {
+  auto x_dims = x.dims();
+  int x_rank = x_dims.size();
+
+  PADDLE_ENFORCE_NOT_NULL(
+      out, phi::errors::InvalidArgument("Output(Out) should not be nullptr."));
+  PADDLE_ENFORCE_GE(
+      x_rank,
+      2,
+      phi::errors::InvalidArgument("The rank of input must greater than 2."));
+  out->set_dims(x_dims);
+  out->set_dtype(x.dtype());
+  int m = x_dims[x_rank - 1];
+  int n = x_dims[x_rank - 2];
+  int min_mn = std::min(m, n);
+  auto dims_vec = phi::vectorize(x_dims);
+  PADDLE_ENFORCE_NOT_NULL(
+      infos,
+      phi::errors::InvalidArgument("Output(Infos) should not be nullptr."));
+  if (x_rank == 2) {
+    auto Infos_dim = std::vector<int>(1);
+    infos->set_dims(phi::make_ddim(Infos_dim));
+  } else {
+    auto Infos_dim =
+        std::vector<int>(dims_vec.begin(), dims_vec.begin() + x_rank - 2);
+    infos->set_dims(phi::make_ddim(Infos_dim));
+  }
+  infos->set_dtype(DataType::INT32);
+  if (pivot) {
+    PADDLE_ENFORCE_NOT_NULL(
+        pivots,
+        phi::errors::InvalidArgument("Output(Pivots) should not be nullptr."));
+    auto Pivots_dim =
+        std::vector<int>(dims_vec.begin(), dims_vec.begin() + x_rank - 1);
+    Pivots_dim[x_rank - 2] = min_mn;
+    pivots->set_dims(phi::make_ddim(Pivots_dim));
+    pivots->set_dtype(DataType::INT32);
+  }
+}
+
 void MatrixRankInferMeta(const MetaTensor& x,
                          bool use_default_tol,
                          bool hermitian,
                          MetaTensor* out) {
   auto dim_x = x.dims();
-  PADDLE_ENFORCE_GE(
-      dim_x.size(),
-      2,
-      phi::errors::InvalidArgument("The dims of input must be greater than 2"));
+  PADDLE_ENFORCE_GE(dim_x.size(),
+                    2,
+                    phi::errors::InvalidArgument(
+                        "The dims of input must be greater than 2."));
 
   if (hermitian) {
     int rows = dim_x[dim_x.size() - 2];
@@ -1279,11 +1323,11 @@ void MaxOutInferMeta(const MetaTensor& x,
       axis == 1 || axis == -1 || axis == 3,
       true,
       phi::errors::InvalidArgument(
-          "axis only supported 1, -1 or 3, but recevied axis is: %d", axis));
+          "axis only supported 1, -1 or 3, but recevied axis is: %d.", axis));
   PADDLE_ENFORCE_EQ(in_x_dims.size(),
                     4,
                     phi::errors::InvalidArgument(
-                        "x's dims should be 4, but received x's dims is: %d",
+                        "x's dims should be 4, but received x's dims is: %d.",
                         in_x_dims.size()));
 
   if (axis < 0) {
diff --git a/paddle/phi/infermeta/unary.h b/paddle/phi/infermeta/unary.h
index 1449e8cfe197d..48be4d8e25298 100644
--- a/paddle/phi/infermeta/unary.h
+++ b/paddle/phi/infermeta/unary.h
@@ -166,6 +166,12 @@ void LogsumexpInferMeta(const MetaTensor& input,
                         bool reduce_all,
                         MetaTensor* out);
 
+void LUInferMeta(const MetaTensor& x,
+                 bool pivot,
+                 MetaTensor* out,
+                 MetaTensor* pivots,
+                 MetaTensor* infos);
+
 void MatrixPowerInferMeta(const MetaTensor& x, int n, MetaTensor* out);
 
 void MatrixRankInferMeta(const MetaTensor& x,
diff --git a/paddle/phi/kernels/cpu/lu_grad_kernel.cc b/paddle/phi/kernels/cpu/lu_grad_kernel.cc
new file mode 100644
index 0000000000000..6443fb66428d0
--- /dev/null
+++ b/paddle/phi/kernels/cpu/lu_grad_kernel.cc
@@ -0,0 +1,22 @@
+// Copyright (c) 2022 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/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+#include "paddle/phi/kernels/impl/lu_grad_kernel_impl.h"
+#include "paddle/phi/kernels/lu_grad_kernel.h"
+
+PD_REGISTER_KERNEL(lu_grad, CPU, ALL_LAYOUT, phi::LUGradKernel, float, double) {
+}
diff --git a/paddle/phi/kernels/cpu/lu_kernel.cc b/paddle/phi/kernels/cpu/lu_kernel.cc
new file mode 100644
index 0000000000000..14cbab53663a2
--- /dev/null
+++ b/paddle/phi/kernels/cpu/lu_kernel.cc
@@ -0,0 +1,76 @@
+// Copyright (c) 2022 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/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
+
+#include "paddle/phi/kernels/impl/lu_kernel_impl.h"
+#include "paddle/phi/kernels/lu_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LUKernel(const Context& dev_ctx,
+              const DenseTensor& x,
+              bool pivot,
+              DenseTensor* out,
+              DenseTensor* pivots,
+              DenseTensor* infos) {
+  PADDLE_ENFORCE_EQ(pivot,
+                    true,
+                    errors::InvalidArgument(
+                        "lu without pivoting is not implemented on the CPU, "
+                        "but got pivots=False"));
+
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, x);
+
+  auto outdims = out->dims();
+  auto outrank = outdims.size();
+
+  int m = static_cast<int>(outdims[outrank - 1]);
+  int n = static_cast<int>(outdims[outrank - 2]);
+  int lda = std::max(1, m);
+
+  auto ipiv_dims = phi::slice_ddim(outdims, 0, outrank - 1);
+  ipiv_dims[outrank - 2] = std::min(m, n);
+  pivots->Resize(ipiv_dims);
+  dev_ctx.template Alloc<int>(pivots);
+  auto ipiv_data = pivots->data<int>();
+
+  auto info_dims = phi::slice_ddim(outdims, 0, outrank - 2);
+  if (info_dims.size() == 0) {
+    info_dims = phi::make_ddim({1});
+  }
+  infos->Resize(info_dims);
+  dev_ctx.template Alloc<int>(infos);
+  auto info_data = infos->data<int>();
+
+  auto batchsize = product(info_dims);
+  batchsize = std::max(static_cast<int>(batchsize), 1);
+  dev_ctx.template Alloc<T>(out);
+  auto out_data = out->data<T>();
+  for (int b = 0; b < batchsize; b++) {
+    auto out_data_item = &out_data[b * m * n];
+    int* info_data_item = &info_data[b];
+    int* ipiv_data_item = &ipiv_data[b * std::min(m, n)];
+    phi::funcs::lapackLu<T>(
+        m, n, out_data_item, lda, ipiv_data_item, info_data_item);
+  }
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, *out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(lu, CPU, ALL_LAYOUT, phi::LUKernel, float, double) {}
diff --git a/paddle/phi/kernels/gpu/lu_grad_kernel.cu b/paddle/phi/kernels/gpu/lu_grad_kernel.cu
new file mode 100644
index 0000000000000..88a15606d2261
--- /dev/null
+++ b/paddle/phi/kernels/gpu/lu_grad_kernel.cu
@@ -0,0 +1,22 @@
+// Copyright (c) 2022 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/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+#include "paddle/phi/kernels/impl/lu_grad_kernel_impl.h"
+#include "paddle/phi/kernels/lu_grad_kernel.h"
+
+PD_REGISTER_KERNEL(lu_grad, GPU, ALL_LAYOUT, phi::LUGradKernel, float, double) {
+}
diff --git a/paddle/phi/kernels/gpu/lu_kernel.cu b/paddle/phi/kernels/gpu/lu_kernel.cu
new file mode 100644
index 0000000000000..7f6070a805c8a
--- /dev/null
+++ b/paddle/phi/kernels/gpu/lu_kernel.cu
@@ -0,0 +1,185 @@
+// Copyright (c) 2022 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.
+
+#ifndef PADDLE_WITH_HIP
+// HIP not support cusolver
+
+#include "paddle/fluid/memory/malloc.h"
+#include "paddle/phi/backends/dynload/cusolver.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+#include "paddle/phi/kernels/impl/lu_kernel_impl.h"
+#include "paddle/phi/kernels/lu_kernel.h"
+
+namespace phi {
+
+template <typename T>
+void cusolver_bufferSize(const cusolverDnHandle_t& cusolverH,
+                         int m,
+                         int n,
+                         T* d_A,
+                         int lda,
+                         int* lwork);
+template <typename T>
+void cusolver_getrf(const cusolverDnHandle_t& cusolverH,
+                    int m,
+                    int n,
+                    T* d_A,
+                    int lda,
+                    T* d_work,
+                    int* d_Ipiv,
+                    int* d_info);
+
+template <>
+void cusolver_bufferSize<float>(const cusolverDnHandle_t& cusolverH,
+                                int m,
+                                int n,
+                                float* d_A,
+                                int lda,
+                                int* lwork) {
+  PADDLE_ENFORCE_GPU_SUCCESS(
+      dynload::cusolverDnSgetrf_bufferSize(cusolverH, m, n, d_A, lda, lwork));
+}
+
+template <>
+void cusolver_bufferSize<double>(const cusolverDnHandle_t& cusolverH,
+                                 int m,
+                                 int n,
+                                 double* d_A,
+                                 int lda,
+                                 int* lwork) {
+  PADDLE_ENFORCE_GPU_SUCCESS(
+      dynload::cusolverDnDgetrf_bufferSize(cusolverH, m, n, d_A, lda, lwork));
+}
+
+template <>
+void cusolver_getrf<float>(const cusolverDnHandle_t& cusolverH,
+                           int m,
+                           int n,
+                           float* d_A,
+                           int lda,
+                           float* d_work,
+                           int* d_Ipiv,
+                           int* d_info) {
+  PADDLE_ENFORCE_GPU_SUCCESS(dynload::cusolverDnSgetrf(
+      cusolverH, m, n, d_A, lda, d_work, d_Ipiv, d_info));
+}
+
+template <>
+void cusolver_getrf<double>(const cusolverDnHandle_t& cusolverH,
+                            int m,
+                            int n,
+                            double* d_A,
+                            int lda,
+                            double* d_work,
+                            int* d_Ipiv,
+                            int* d_info) {
+  PADDLE_ENFORCE_GPU_SUCCESS(dynload::cusolverDnDgetrf(
+      cusolverH, m, n, d_A, lda, d_work, d_Ipiv, d_info));
+}
+
+template <typename T, typename Context>
+void lu_decomposed_kernel(const Context& dev_ctx,
+                          int m,
+                          int n,
+                          T* d_A,
+                          int lda,
+                          int* d_Ipiv,
+                          int* d_info) {
+  /* step 1: get cusolver handle*/
+  auto cusolverH = dev_ctx.cusolver_dn_handle();
+
+  /* step 2: query working space of getrf */
+  int lwork;
+  cusolver_bufferSize(cusolverH, m, n, d_A, lda, &lwork);
+
+  auto work_buff = paddle::memory::Alloc(dev_ctx, lwork * sizeof(T));
+  T* d_work = reinterpret_cast<T*>(work_buff->ptr());
+
+  /* step 3: LU factorization */
+  if (d_Ipiv) {
+    cusolver_getrf(cusolverH, m, n, d_A, lda, d_work, d_Ipiv, d_info);
+  } else {
+    cusolver_getrf(cusolverH, m, n, d_A, lda, d_work, NULL, d_info);
+  }
+  PADDLE_ENFORCE_GPU_SUCCESS(cudaDeviceSynchronize());
+}
+
+template <typename T, typename Context>
+void LUKernel(const Context& dev_ctx,
+              const DenseTensor& x,
+              bool pivot,
+              DenseTensor* out,
+              DenseTensor* pivots,
+              DenseTensor* infos) {
+#ifdef __HIPCC__
+  const int64_t kMaxBlockDim = 256;
+#else
+  const int64_t kMaxBlockDim = 512;
+#endif
+
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, x);
+
+  auto outdims = out->dims();
+  auto outrank = outdims.size();
+
+  int m = static_cast<int>(outdims[outrank - 1]);
+  int n = static_cast<int>(outdims[outrank - 2]);
+  int lda = std::max(1, m);
+  if (pivot) {
+    auto ipiv_dims = phi::slice_ddim(outdims, 0, outrank - 1);
+    ipiv_dims[outrank - 2] = std::min(m, n);
+    pivots->Resize(ipiv_dims);
+  }
+  dev_ctx.template Alloc<int>(pivots);
+  auto ipiv_data = pivots->data<int>();
+
+  auto info_dims = phi::slice_ddim(outdims, 0, outrank - 2);
+  if (info_dims.size() == 0) {
+    info_dims = phi::make_ddim({1});
+  }
+  infos->Resize(info_dims);
+  dev_ctx.template Alloc<int>(infos);
+  auto info_data = infos->data<int>();
+
+  auto batchsize = product(info_dims);
+  batchsize = std::max(static_cast<int>(batchsize), 1);
+  dev_ctx.template Alloc<T>(out);
+  auto out_data = out->data<T>();
+  for (int b = 0; b < batchsize; b++) {
+    auto out_data_item = &out_data[b * m * n];
+    int* info_data_item = &info_data[b];
+    if (pivot) {
+      auto ipiv_data_item = &ipiv_data[b * std::min(m, n)];
+      lu_decomposed_kernel(
+          dev_ctx, m, n, out_data_item, lda, ipiv_data_item, info_data_item);
+    } else {
+      lu_decomposed_kernel(
+          dev_ctx, m, n, out_data_item, lda, NULL, info_data_item);
+    }
+  }
+  *out = Transpose2DTo6D<Context, T>(dev_ctx, *out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(lu,  // cuda_only
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::LUKernel,
+                   float,
+                   double) {}
+
+#endif  // not PADDLE_WITH_HIP
diff --git a/paddle/phi/kernels/impl/lu_grad_kernel_impl.h b/paddle/phi/kernels/impl/lu_grad_kernel_impl.h
new file mode 100644
index 0000000000000..523e33f29f8c1
--- /dev/null
+++ b/paddle/phi/kernels/impl/lu_grad_kernel_impl.h
@@ -0,0 +1,309 @@
+// Copyright (c) 2022 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 "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/triangular_solve_kernel.h"
+
+#include "paddle/phi/kernels/impl/lu_kernel_impl.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LUGradKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const DenseTensor& out,
+                  const DenseTensor& pivots,
+                  const DenseTensor& out_grad,
+                  bool pivot,
+                  DenseTensor* x_grad) {
+  dev_ctx.template Alloc<T>(x_grad);
+
+  auto blas = phi::funcs::GetBlas<Context, T>(dev_ctx);
+
+  auto xdims = x.dims();
+  int xrank = xdims.size();
+  int64_t m = xdims[xrank - 2];
+  int64_t n = xdims[xrank - 1];
+  int64_t k = std::min(m, n);
+
+  DenseTensor L, U, L_narrow, U_narrow, L_narrow_mH, U_narrow_mH, grad_narrow;
+  LU_Unpack<Context, T>(dev_ctx, &out, &L, &U);
+
+  Tensor_narrow<Context, T>(dev_ctx, &L, &L_narrow, 0, k, 0, k);
+  Tensor_narrow<Context, T>(dev_ctx, &U, &U_narrow, 0, k, 0, k);
+  Tensor_narrow<Context, T>(dev_ctx, &out_grad, &grad_narrow, 0, k, 0, k);
+  auto graddims = grad_narrow.dims();
+
+  Tensor_Conj<Context, T>(dev_ctx, L_narrow, &L_narrow_mH);
+  Tensor_Conj<Context, T>(dev_ctx, U_narrow, &U_narrow_mH);
+  L_narrow_mH = Transpose2DTo6D<Context, T>(dev_ctx, L_narrow_mH);
+  U_narrow_mH = Transpose2DTo6D<Context, T>(dev_ctx, U_narrow_mH);
+
+  auto LmHdims = L_narrow_mH.dims();
+  auto UmHdims = U_narrow_mH.dims();
+
+  DenseTensor phi_L, phi_U, phi, psi;
+  phi_L.Resize(LmHdims);
+  dev_ctx.template Alloc<T>(&phi_L);
+  phi_U.Resize(UmHdims);
+  dev_ctx.template Alloc<T>(&phi_U);
+  auto mat_dim_l = phi::funcs::CreateMatrixDescriptor(LmHdims, 0, false);
+  auto mat_dim_u = phi::funcs::CreateMatrixDescriptor(UmHdims, 0, false);
+  auto mat_dim_g = phi::funcs::CreateMatrixDescriptor(graddims, 0, false);
+  blas.MatMul(L_narrow_mH,
+              mat_dim_l,
+              grad_narrow,
+              mat_dim_g,
+              static_cast<T>(1),
+              &phi_L,
+              static_cast<T>(0));
+
+  blas.MatMul(grad_narrow,
+              mat_dim_g,
+              U_narrow_mH,
+              mat_dim_u,
+              static_cast<T>(1),
+              &phi_U,
+              static_cast<T>(0));
+
+  auto phil_rank = LmHdims.size();
+  auto phiu_rank = UmHdims.size();
+  phi::funcs::ForRange<Context> l_for_range(dev_ctx, phi_L.numel());
+  phi::funcs::TrilTriuCompute<T> tril_computer(phi_L.data<T>(),
+                                               -1,
+                                               true,
+                                               LmHdims[phil_rank - 2],
+                                               LmHdims[phil_rank - 1],
+                                               phi_L.data<T>());
+  l_for_range(tril_computer);
+
+  phi::funcs::ForRange<Context> u_for_range(dev_ctx, phi_U.numel());
+  phi::funcs::TrilTriuCompute<T> triu_computer(phi_U.data<T>(),
+                                               0,
+                                               false,
+                                               UmHdims[phiu_rank - 2],
+                                               UmHdims[phiu_rank - 1],
+                                               phi_U.data<T>());
+  u_for_range(triu_computer);
+
+  Tensor_Add<Context, T>(dev_ctx, phi_L, phi_U, &phi);
+  psi.Resize(xdims);
+  dev_ctx.template Alloc<T>(&psi);
+  phi::funcs::SetConstant<Context, T> setter;
+  setter(dev_ctx, &psi, static_cast<T>(0));
+
+  std::vector<int64_t> axes = {xrank - 2, xrank - 1};
+  std::vector<int64_t> slice_starts(2, 0);
+  std::vector<int64_t> slice_ends(2, 0);
+  auto valuedims = vectorize(xdims);
+
+  DenseTensor Pmat;
+  Unpack_Pivot<Context, T>(dev_ctx, pivots, &Pmat, m, k);
+
+  if (m <= n) {
+    if (k < n) {
+      DenseTensor U_complement, U_grad_complement, phi_complement,
+          phi_complement_l;
+      Tensor_narrow<Context, T>(dev_ctx, &U, &U_complement, 0, k, k, n);
+      Tensor_narrow<Context, T>(
+          dev_ctx, &out_grad, &U_grad_complement, 0, k, k, n);
+      DenseTensor U_complement_mH =
+          Transpose2DTo6D<Context, T>(dev_ctx, U_complement);
+
+      Tensor_Conj<Context, T>(dev_ctx, U_complement_mH, &U_complement_mH);
+
+      auto mat_dim_g = phi::funcs::CreateMatrixDescriptor(
+          U_grad_complement.dims(), 0, false);
+      auto mat_dim_u =
+          phi::funcs::CreateMatrixDescriptor(U_complement_mH.dims(), 0, false);
+      auto phidims = UmHdims;
+      phidims[UmHdims.size() - 2] = k;
+      phidims[UmHdims.size() - 1] = k;
+      phi_complement.Resize(phidims);
+      dev_ctx.template Alloc<T>(&phi_complement);
+      blas.MatMul(U_grad_complement,
+                  mat_dim_g,
+                  U_complement_mH,
+                  mat_dim_u,
+                  static_cast<T>(1),
+                  &phi_complement,
+                  static_cast<T>(0));
+
+      phi_complement_l.Resize(phidims);
+      dev_ctx.template Alloc<T>(&phi_complement_l);
+      const auto H = phidims[phidims.size() - 2];
+      const auto W = phidims[phidims.size() - 1];
+      phi::funcs::ForRange<Context> x_for_range(dev_ctx,
+                                                phi_complement.numel());
+      phi::funcs::TrilTriuCompute<T> tril_computer(
+          phi_complement.data<T>(), -1, true, H, W, phi_complement_l.data<T>());
+      x_for_range(tril_computer);
+
+      Tensor_Sub<Context, T>(dev_ctx, phi, phi_complement_l, &phi);
+
+      slice_starts[0] = 0;
+      slice_starts[1] = k;
+      slice_ends[0] = k;
+      slice_ends[1] = n;
+      valuedims[xrank - 2] = k;
+      valuedims[xrank - 1] = n - k;
+      SetValueCompute_dispatch<Context, T>(dev_ctx,
+                                           &psi,
+                                           &U_grad_complement,
+                                           &psi,
+                                           axes,
+                                           &slice_starts,
+                                           &slice_ends,
+                                           valuedims,
+                                           xrank);
+    }
+
+    DenseTensor psi_principal, phi_mH, psi_tmp;
+    Tensor_Conj<Context, T>(dev_ctx, phi, &phi_mH);
+    phi_mH = Transpose2DTo6D<Context, T>(dev_ctx, phi_mH);
+
+    phi::TriangularSolveKernel<T, Context>(
+        dev_ctx, U_narrow, phi_mH, true, false, false, &psi_principal);
+
+    Tensor_Conj<Context, T>(dev_ctx, psi_principal, &psi_principal);
+    psi_principal = Transpose2DTo6D<Context, T>(dev_ctx, psi_principal);
+    slice_starts[0] = 0;
+    slice_starts[1] = 0;
+    slice_ends[0] = k;
+    slice_ends[1] = k;
+    valuedims[xrank - 2] = k;
+    valuedims[xrank - 1] = k;
+
+    SetValueCompute_dispatch<Context, T>(dev_ctx,
+                                         &psi,
+                                         &psi_principal,
+                                         &psi,
+                                         axes,
+                                         &slice_starts,
+                                         &slice_ends,
+                                         valuedims,
+                                         xrank);
+
+    phi::TriangularSolveKernel<T, Context>(
+        dev_ctx, L_narrow_mH, psi, true, false, true, &psi_tmp);
+
+    auto mat_dim_p = phi::funcs::CreateMatrixDescriptor(Pmat.dims(), 0, false);
+    auto mat_dim_b =
+        phi::funcs::CreateMatrixDescriptor(psi_tmp.dims(), 0, false);
+    blas.MatMul(Pmat,
+                mat_dim_p,
+                psi_tmp,
+                mat_dim_b,
+                static_cast<T>(1),
+                x_grad,
+                static_cast<T>(0));
+  } else {
+    DenseTensor L_complement, L_grad_complement, phi_complement,
+        phi_complement_u;
+    Tensor_narrow<Context, T>(dev_ctx, &L, &L_complement, k, m, 0, k);
+    Tensor_narrow<Context, T>(
+        dev_ctx, &out_grad, &L_grad_complement, k, m, 0, k);
+    DenseTensor L_complement_mH =
+        Transpose2DTo6D<Context, T>(dev_ctx, L_complement);
+    Tensor_Conj<Context, T>(dev_ctx, L_complement_mH, &L_complement_mH);
+
+    auto mat_dim_g =
+        phi::funcs::CreateMatrixDescriptor(L_grad_complement.dims(), 0, false);
+    auto mat_dim_u =
+        phi::funcs::CreateMatrixDescriptor(L_complement_mH.dims(), 0, false);
+    auto phidims = LmHdims;
+    phidims[LmHdims.size() - 2] = k;
+    phidims[LmHdims.size() - 1] = k;
+    phi_complement.Resize(phidims);
+    dev_ctx.template Alloc<T>(&phi_complement);
+    blas.MatMul(L_complement_mH,
+                mat_dim_u,
+                L_grad_complement,
+                mat_dim_g,
+                static_cast<T>(1),
+                &phi_complement,
+                static_cast<T>(0));
+
+    phi_complement_u.Resize(phidims);
+    dev_ctx.template Alloc<T>(&phi_complement_u);
+    const auto H = phidims[phidims.size() - 2];
+    const auto W = phidims[phidims.size() - 1];
+    phi::funcs::ForRange<Context> x_for_range(dev_ctx, phi_complement.numel());
+    phi::funcs::TrilTriuCompute<T> triu_computer(
+        phi_complement.data<T>(), 0, false, H, W, phi_complement_u.data<T>());
+    x_for_range(triu_computer);
+
+    Tensor_Sub<Context, T>(dev_ctx, phi, phi_complement_u, &phi);
+
+    slice_starts[0] = k;
+    slice_starts[1] = 0;
+    slice_ends[0] = m;
+    slice_ends[1] = k;
+    valuedims[xrank - 2] = m - k;
+    valuedims[xrank - 1] = k;
+    SetValueCompute_dispatch<Context, T>(dev_ctx,
+                                         &psi,
+                                         &L_grad_complement,
+                                         &psi,
+                                         axes,
+                                         &slice_starts,
+                                         &slice_ends,
+                                         valuedims,
+                                         xrank);
+    DenseTensor psi_principal, phi_mH, psi_tmp, U_narrow_mH;
+
+    phi::TriangularSolveKernel<T, Context>(
+        dev_ctx, L_narrow_mH, phi, true, false, true, &psi_principal);
+
+    slice_starts[0] = 0;
+    slice_starts[1] = 0;
+    slice_ends[0] = k;
+    slice_ends[1] = k;
+    valuedims[xrank - 2] = k;
+    valuedims[xrank - 1] = k;
+
+    SetValueCompute_dispatch<Context, T>(dev_ctx,
+                                         &psi,
+                                         &psi_principal,
+                                         &psi,
+                                         axes,
+                                         &slice_starts,
+                                         &slice_ends,
+                                         valuedims,
+                                         xrank);
+
+    psi_tmp.Resize(psi.dims());
+    dev_ctx.template Alloc<T>(&psi_tmp);
+    auto mat_dim_p = phi::funcs::CreateMatrixDescriptor(Pmat.dims(), 0, false);
+    auto mat_dim_b = phi::funcs::CreateMatrixDescriptor(psi.dims(), 0, false);
+    blas.MatMul(Pmat,
+                mat_dim_p,
+                psi,
+                mat_dim_b,
+                static_cast<T>(1),
+                &psi_tmp,
+                static_cast<T>(0));
+    psi_tmp = Transpose2DTo6D<Context, T>(dev_ctx, psi_tmp);
+
+    Tensor_Conj<Context, T>(dev_ctx, U_narrow, &U_narrow_mH);
+    phi::TriangularSolveKernel<T, Context>(
+        dev_ctx, U_narrow_mH, psi_tmp, true, false, false, &psi);
+    *x_grad = Transpose2DTo6D<Context, T>(dev_ctx, psi);
+  }
+}
+
+}  // namespace phi
diff --git a/paddle/phi/kernels/impl/lu_kernel_impl.h b/paddle/phi/kernels/impl/lu_kernel_impl.h
new file mode 100644
index 0000000000000..ed3cc0801d9af
--- /dev/null
+++ b/paddle/phi/kernels/impl/lu_kernel_impl.h
@@ -0,0 +1,563 @@
+// Copyright (c) 2022 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 "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/elementwise_add_kernel.h"
+#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
+#include "paddle/phi/kernels/funcs/complex_functors.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/funcs/elementwise_base.h"
+#include "paddle/phi/kernels/funcs/elementwise_functor.h"
+#include "paddle/phi/kernels/funcs/for_range.h"
+#include "paddle/phi/kernels/funcs/slice_utils.h"
+#include "paddle/phi/kernels/funcs/tril_triu_compute.h"
+#include "paddle/phi/kernels/impl/set_value_kernel_impl.h"
+
+namespace phi {
+
+template <typename T>
+using SubFunctor = phi::funcs::SubtractFunctor<T>;
+
+template <typename Context, typename T, size_t D>
+void SetValueCompute(const Context& dev_ctx,
+                     DenseTensor* in,
+                     DenseTensor* value_tensor,
+                     DenseTensor* out,
+                     const std::vector<int64_t>& axes,
+                     std::vector<int64_t>* starts,
+                     std::vector<int64_t>* ends,
+                     const std::vector<int64_t>& shape) {
+  std::vector<int64_t> steps = {1, 1};
+  std::vector<int64_t> decrease_axes = {};
+  std::vector<int64_t> none_axes = {};
+
+  auto dtype = in->dtype();
+
+  auto in_dims = in->dims();
+  phi::funcs::CheckAndUpdateSliceAttrs<int64_t>(
+      in_dims, axes, starts, ends, &steps);
+  auto slice_dims =
+      phi::funcs::GetSliceDims(in_dims, axes, *starts, *ends, &steps);
+  auto decrease_slice_dims =
+      phi::funcs::GetDecreasedDims(slice_dims, decrease_axes);
+
+  auto slice_dims_for_assign = decrease_slice_dims;
+  if (!none_axes.empty()) {
+    std::vector<int64_t> slice_dims_with_none;
+
+    size_t none_axes_cur = 0, decrease_axes_cur = 0;
+    for (int i = 0; i < slice_dims.size(); ++i) {
+      while (none_axes_cur < none_axes.size() &&
+             none_axes[none_axes_cur] <= i) {
+        slice_dims_with_none.push_back(1);
+        none_axes_cur++;
+      }
+      if (decrease_axes_cur < decrease_axes.size() &&
+          decrease_axes[decrease_axes_cur] == i) {
+        decrease_axes_cur++;
+      } else {
+        slice_dims_with_none.push_back(slice_dims[i]);
+      }
+    }
+    while (none_axes_cur < none_axes.size()) {
+      slice_dims_with_none.push_back(1);
+      none_axes_cur++;
+    }
+
+    slice_dims_for_assign = phi::make_ddim(slice_dims_with_none);
+  }
+
+  auto place = dev_ctx.GetPlace();
+  auto& eigen_place = *dev_ctx.eigen_device();
+
+  // Here copy data from input to avoid data loss at PE and Graph level.
+  // TODO(liym27): Speed up in the future version.
+  // - Q: Why don't call ShareDataWith to speed up?
+  // - A: Because it's not supported to ShareDataWith on OP's input and output
+  // https://github.com/PaddlePaddle/Paddle/wiki/ShareDataWith-and-ShareBufferWith-are-prohibited-in-OP
+  // - Q: Why don't delete Input, after all, the input and output are the same
+  // Tensor at program level?
+  // - A: If deleting Input, the graph will be complex, such as there will
+  // be two ops points to the output in graph: op1 -> output <- set_value.
+  // In this case, we have to find a way to handle the running order of
+  // set_value is what we want.
+  phi::Copy(dev_ctx, *in, place, false, out);
+
+  DenseTensor slice_tensor(dtype), pad_tensor(dtype);
+  slice_tensor.Resize(slice_dims);
+  dev_ctx.template Alloc<T>(&slice_tensor);
+  pad_tensor.Resize(in_dims);
+  dev_ctx.template Alloc<T>(&pad_tensor);
+
+  auto pad_e = EigenTensor<T, D>::From(pad_tensor, in_dims);
+  auto out_e = EigenTensor<T, D>::From(*out);
+  auto slice_e = EigenTensor<T, D>::From(slice_tensor, slice_dims);
+
+  // Step 1: Set the value of out at `_index` to zero
+  slice_e.device(eigen_place) = slice_e.constant(T(0));
+
+  auto starts_indices = Eigen::DSizes<Eigen::DenseIndex, D>();
+  auto ends_indices = Eigen::DSizes<Eigen::DenseIndex, D>();
+  auto strides_indices = Eigen::DSizes<Eigen::DenseIndex, D>();
+
+  for (size_t i = 0; i < D; ++i) {
+    starts_indices[i] = 0;
+    ends_indices[i] = slice_dims[i];
+    strides_indices[i] = 1;
+  }
+  for (size_t i = 0; i < axes.size(); i++) {
+    int axis_index = axes[i];
+    starts_indices[axis_index] = (*starts)[i];
+    ends_indices[axis_index] = (*ends)[i];
+    strides_indices[axis_index] = steps[i];
+    if ((*starts)[i] ==
+        (*ends)[i]) {  // slice is empty, data will not be changed
+      return;
+    }
+  }
+
+  out_e.stridedSlice(starts_indices, ends_indices, strides_indices)
+      .device(eigen_place) = slice_e;
+
+  // Step 2: Set a tensor with the same shape as out tensor. And its data at
+  // '_index' is the same as value_tensor, and data out of '_index' to zero
+
+  // - Step 2.1 Set slice tensor with value
+
+  // NOTE(liym27): [ Why resize slice_tensor here? ]
+  // A: When do broadcasting on slice_tensor and value_tensor, the shape of
+  // slice_tensor should be decreased dims.
+  // e.g.
+  //  x[:,0] = value_tensor
+  // x's shape = [3, 4], value_tensor's shape = [3]
+  // We get slice_dims = [3, 1],  decrease_slice_dims = [3]
+  // If do broadcasting on Tensor with shape [3, 1] and [3], the result's
+  // shape is [3, 3], which cross the border;
+  // If do broadcasting on Tensor with shape [3] and [3], the result's shape
+  // is [3], which is right.
+
+  slice_tensor.Resize(slice_dims_for_assign);
+  if (value_tensor != nullptr) {
+    CheckIsDimsMatch(slice_dims_for_assign, value_tensor->dims());
+    phi::funcs::ElementwiseCompute<SubFunctor<T>, T, T>(dev_ctx,
+                                                        slice_tensor,
+                                                        *value_tensor,
+                                                        -1,
+                                                        SubFunctor<T>(),
+                                                        &slice_tensor);
+  } else {
+    DenseTensor value_t(dtype);
+    auto value_dims = phi::make_ddim(shape);
+    CheckIsDimsMatch(slice_dims_for_assign, value_dims);
+
+    value_t.Resize(value_dims);
+    dev_ctx.template Alloc<T>(&value_t);
+    phi::funcs::ElementwiseCompute<SubFunctor<T>, T, T>(
+        dev_ctx, slice_tensor, value_t, -1, SubFunctor<T>(), &slice_tensor);
+  }
+  slice_tensor.Resize(slice_dims);
+
+  // - Step 2.2 Pad slice tensor with 0
+  pad_e.device(eigen_place) = pad_e.constant(T(0));
+  pad_e.stridedSlice(starts_indices, ends_indices, strides_indices)
+      .device(eigen_place) = slice_e;
+
+  // Step 3: Set out tensor with value_tensor
+  out_e.device(eigen_place) = out_e - pad_e;
+}
+
+template <typename Context, typename T>
+void SetValueCompute_dispatch(const Context& dev_ctx,
+                              DenseTensor* in,
+                              DenseTensor* value_tensor,
+                              DenseTensor* out,
+                              const std::vector<int64_t>& axes,
+                              std::vector<int64_t>* starts,
+                              std::vector<int64_t>* ends,
+                              const std::vector<int64_t>& shape,
+                              int rank) {
+  switch (rank) {
+    case 1:
+      SetValueCompute<Context, T, 1>(
+          dev_ctx, in, value_tensor, out, axes, starts, ends, shape);
+      break;
+    case 2:
+      SetValueCompute<Context, T, 2>(
+          dev_ctx, in, value_tensor, out, axes, starts, ends, shape);
+      break;
+    case 3:
+      SetValueCompute<Context, T, 3>(
+          dev_ctx, in, value_tensor, out, axes, starts, ends, shape);
+      break;
+    case 4:
+      SetValueCompute<Context, T, 4>(
+          dev_ctx, in, value_tensor, out, axes, starts, ends, shape);
+      break;
+    case 5:
+      SetValueCompute<Context, T, 5>(
+          dev_ctx, in, value_tensor, out, axes, starts, ends, shape);
+      break;
+    case 6:
+      SetValueCompute<Context, T, 6>(
+          dev_ctx, in, value_tensor, out, axes, starts, ends, shape);
+      break;
+    default:
+      PADDLE_THROW(phi::errors::InvalidArgument(
+          "The rank of input should be less than 7, but received %d.", rank));
+  }
+}
+
+template <typename Context, typename T>
+void Tensor_Conj(const Context& dev_ctx,
+                 const DenseTensor& tensor,
+                 DenseTensor* out) {
+  out->Resize(tensor.dims());
+  phi::funcs::ForRange<Context> out_for_range(dev_ctx, tensor.numel());
+  dev_ctx.template Alloc<T>(out);
+  phi::funcs::ConjFunctor<T> out_functor(
+      tensor.data<T>(), tensor.numel(), out->data<T>());
+  out_for_range(out_functor);
+}
+
+template <typename Context, typename T>
+void Tensor_Add(const Context& dev_ctx,
+                const DenseTensor& src1,
+                const DenseTensor& src2,
+                DenseTensor* out) {
+  out->Resize(src1.dims());
+  dev_ctx.template Alloc<T>(out);
+
+  phi::AddRawKernel<T, Context>(dev_ctx, src1, src2, -1, out);
+}
+
+template <typename Context, typename T>
+void Tensor_Sub(const Context& dev_ctx,
+                const DenseTensor& src1,
+                const DenseTensor& src2,
+                DenseTensor* out) {
+  out->Resize(src1.dims());
+  dev_ctx.template Alloc<T>(out);
+
+  phi::SubtractRawKernel<T, Context>(dev_ctx, src1, src2, -1, out);
+}
+
+template <typename Context, typename T, size_t D>
+void SliceCompute(const Context& dev_ctx,
+                  const DenseTensor* in,
+                  DenseTensor* out,
+                  const std::vector<int>& axes_int,
+                  const std::vector<int>& starts_int,
+                  const std::vector<int>& ends_int) {
+  std::vector<int64_t> axes(axes_int.begin(), axes_int.end());
+  std::vector<int64_t> starts(starts_int.begin(), starts_int.end());
+  std::vector<int64_t> ends(ends_int.begin(), ends_int.end());
+
+  std::vector<int> decrease_axis = {};
+  std::vector<int> infer_flags = {};
+
+  PADDLE_ENFORCE_EQ(
+      starts.size(),
+      axes.size(),
+      phi::errors::InvalidArgument(
+          "The size of starts must be equal to the size of axes."));
+  PADDLE_ENFORCE_EQ(ends.size(),
+                    axes.size(),
+                    phi::errors::InvalidArgument(
+                        "The size of ends must be equal to the size of axes."));
+
+  // Step 2: Compute output
+
+  auto in_dims = in->dims();
+  auto out_dims = out->dims();
+  auto slice_dims = out_dims;
+
+  // 2.1 Infer output dims
+  for (size_t i = 0; i < axes.size(); ++i) {
+    // when start == -1 && end == start+1
+    if (starts[i] == -1 && ends[i] == 0 && infer_flags[i] == -1) {
+      auto ret = std::find(decrease_axis.begin(), decrease_axis.end(), axes[i]);
+      if (ret != decrease_axis.end()) {
+        ends[i] = in_dims[axes[i]];
+      }
+    }
+  }
+
+  phi::funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends);
+  slice_dims = phi::funcs::GetSliceDims<int64_t>(
+      in_dims, axes, starts, ends, nullptr, nullptr);
+  out_dims = phi::funcs::GetDecreasedDims(slice_dims, decrease_axis);
+
+  // 2.2 Get output
+  auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
+  auto extents = Eigen::DSizes<Eigen::DenseIndex, D>();
+
+  for (size_t i = 0; i < D; ++i) {
+    offsets[i] = 0;
+    extents[i] = slice_dims[i];
+  }
+  for (size_t i = 0; i < axes.size(); ++i) {
+    offsets[axes[i]] = starts[i];
+  }
+
+  out->Resize(slice_dims);
+  dev_ctx.template Alloc<T>(out);
+
+  auto in_t = EigenTensor<T, D>::From(*in, in_dims);
+  auto out_t = EigenTensor<T, D>::From(*out, slice_dims);
+  auto& eigen_place = *dev_ctx.eigen_device();
+
+  if (in->numel() <= Eigen::NumTraits<int>::highest()) {
+    // similar to tf.slice:
+    // if element number less than INT_MAX, change the type of index to int
+    Eigen::DSizes<int, D> offsets_32bit, extents_32bit;
+    for (size_t i = 0; i < D; i++) {
+      offsets_32bit[i] = offsets[i];
+      extents_32bit[i] = extents[i];
+    }
+    funcs::EigenSlice<std::decay_t<decltype(eigen_place)>, T, D>::Eval(
+        eigen_place,
+        To32BitIndex(out_t),
+        To32BitIndex(in_t),
+        offsets_32bit,
+        extents_32bit);
+  } else {
+    funcs::EigenSlice<std::decay_t<decltype(eigen_place)>, T, D>::Eval(
+        eigen_place, out_t, in_t, offsets, extents);
+  }
+
+  out->Resize(out_dims);
+  dev_ctx.template Alloc<T>(out);
+}
+
+template <typename Context, typename T>
+void Tensor_narrow(const Context& dev_ctx,
+                   const DenseTensor* src,
+                   DenseTensor* out,
+                   int row_s,
+                   int row_e,
+                   int col_s,
+                   int col_e) {
+  auto rank = src->dims().size();
+  std::vector<int> axes_int = {rank - 2, rank - 1};
+  std::vector<int> starts_int = {row_s, col_s};
+  std::vector<int> ends_int = {row_e, col_e};
+  switch (rank) {
+    case 1:
+      SliceCompute<Context, T, 1>(
+          dev_ctx, src, out, axes_int, starts_int, ends_int);
+      break;
+    case 2:
+      SliceCompute<Context, T, 2>(
+          dev_ctx, src, out, axes_int, starts_int, ends_int);
+      break;
+    case 3:
+      SliceCompute<Context, T, 3>(
+          dev_ctx, src, out, axes_int, starts_int, ends_int);
+      break;
+    case 4:
+      SliceCompute<Context, T, 4>(
+          dev_ctx, src, out, axes_int, starts_int, ends_int);
+      break;
+    case 5:
+      SliceCompute<Context, T, 5>(
+          dev_ctx, src, out, axes_int, starts_int, ends_int);
+      break;
+    case 6:
+      SliceCompute<Context, T, 6>(
+          dev_ctx, src, out, axes_int, starts_int, ends_int);
+      break;
+    default:
+      PADDLE_THROW(phi::errors::InvalidArgument(
+          "The rank of input should be less than 7, but received %d.", rank));
+  }
+}
+
+template <typename Context>
+void arange(const Context& dev_ctx,
+            DenseTensor* tmp,
+            int w,
+            int batchsize = 1,
+            int h = 1) {
+  tmp->Resize(phi::make_ddim({batchsize * w}));
+  dev_ctx.template HostAlloc<int32_t>(tmp);
+  auto tmpdata = tmp->data<int32_t>();
+  for (int b = 0; b < batchsize; b++) {
+    for (int i = 0; i < w; i++) {
+      tmpdata[b * w + i] = static_cast<int32_t>(b * h + i);
+    }
+  }
+}
+
+template <typename T>
+struct OneFunctor {
+  OneFunctor(T* output, int* idtptr, int w, int dim)
+      : output_(output), idtptr_(idtptr), w_(w), dim_(dim) {}
+
+  HOSTDEVICE void operator()(size_t idx) const {
+    output_[w_ * idtptr_[idx] + idx % dim_] = static_cast<T>(1);
+  }
+
+  T* output_;
+  int* idtptr_;
+  int w_;
+  int dim_;
+};
+
+template <typename Context, typename T>
+void LU_Unpack(const Context& dev_ctx,
+               const DenseTensor* LU,
+               DenseTensor* L,
+               DenseTensor* U) {
+  const auto udims = LU->dims();
+  L->Resize(udims);
+  U->Resize(udims);
+  const auto H = udims[udims.size() - 2];
+  const auto W = udims[udims.size() - 1];
+  dev_ctx.template Alloc<T>(L);
+  auto L_dataptr = L->data<T>();
+  phi::funcs::ForRange<Context> x_for_range(dev_ctx, LU->numel());
+  phi::funcs::TrilTriuCompute<T> tril_computer(
+      LU->data<T>(), -1, true, H, W, L_dataptr);
+  x_for_range(tril_computer);
+
+  dev_ctx.template Alloc<T>(U);
+  phi::funcs::TrilTriuCompute<T> triu_computer(
+      LU->data<T>(), 0, false, H, W, U->data<T>());
+  x_for_range(triu_computer);
+
+  // set L's diagonal 1
+  auto dim = std::min(H, W);
+  DenseTensor rowtensor, rt_dev;
+  auto batchsize = product(phi::slice_ddim(udims, 0, udims.size() - 2));
+  batchsize = std::max(static_cast<int>(batchsize), 1);
+  arange<Context>(dev_ctx, &rowtensor, dim, batchsize, H);
+  auto idtptr = rowtensor.data<int32_t>();
+  if (phi::AllocationType::GPU == dev_ctx.GetPlace().GetType()) {
+    phi::Copy(dev_ctx, rowtensor, dev_ctx.GetPlace(), false, &rt_dev);
+    idtptr = rt_dev.data<int32_t>();
+  }
+
+  phi::funcs::ForRange<Context> for_range(dev_ctx, rowtensor.numel());
+  OneFunctor<T> functor(L_dataptr, idtptr, W, dim);
+  for_range(functor);
+}
+
+template <typename Context, typename T>
+void scatterpivot(
+    const Context& dev_ctx, T* out_data, DenseTensor* idlst, int w, int dim) {
+  DenseTensor idlst_tmp;
+  idlst_tmp.Resize(idlst->dims());
+  dev_ctx.template Alloc<int32_t>(&idlst_tmp);
+  phi::Copy(dev_ctx, *idlst, dev_ctx.GetPlace(), false, &idlst_tmp);
+  auto idtptr = idlst_tmp.data<int32_t>();
+
+  phi::funcs::ForRange<Context> for_range(dev_ctx, idlst_tmp.numel());
+  OneFunctor<T> functor(out_data, idtptr, w, dim);
+  for_range(functor);
+}
+
+template <typename Context, typename T>
+void Unpack_Pivot(const Context& dev_ctx,
+                  const DenseTensor& Pivot,
+                  DenseTensor* P,
+                  int h,
+                  int w) {
+  auto dims = Pivot.dims();
+  auto Pdimvec = vectorize(dims);
+  auto prank = Pdimvec.size();
+  auto Pnum = dims[prank - 1];
+  DenseTensor Pivot_cpu;
+  phi::CPUPlace cpu;
+  phi::Copy(dev_ctx, Pivot, cpu, false, &Pivot_cpu);
+  auto pdataptr = Pivot_cpu.data<int32_t>();
+  Pdimvec[prank - 1] = h;
+  Pdimvec.emplace_back(h);
+  auto Pdim = phi::make_ddim(Pdimvec);
+  P->Resize(Pdim);
+  dev_ctx.template Alloc<T>(P);
+  auto pdata = P->data<T>();
+  phi::funcs::SetConstant<Context, T> setter;
+  setter(dev_ctx, P, static_cast<T>(0));
+
+  auto batchsize = product(phi::slice_ddim(dims, 0, prank - 1));
+  batchsize = std::max(static_cast<int>(batchsize), 1);
+  DenseTensor idt;
+  for (int i = 0; i < batchsize; i++) {
+    arange<Context>(dev_ctx, &idt, h);
+    auto idlst = idt.data<int32_t>();
+    for (int j = 0; j < Pnum; j++) {
+      if (idlst[pdataptr[i * Pnum + j] - 1] == idlst[j]) continue;
+      auto temp = idlst[j];
+      idlst[j] = idlst[pdataptr[i * Pnum + j] - 1];
+      idlst[pdataptr[i * Pnum + j] - 1] = temp;
+    }
+    scatterpivot(dev_ctx, &(pdata[i * h * h]), &idt, h, h);
+  }
+}
+
+template <typename Context, typename T>
+DenseTensor Transpose2DTo6D(const Context& dev_ctx, const DenseTensor& x) {
+  // transpose the last two dimision
+  DenseTensor ret;
+  auto x_dim = x.dims();
+  auto x_vec = phi::vectorize<int>(x_dim);
+  int rank = x_vec.size();
+  std::swap(x_vec[rank - 1], x_vec[rank - 2]);
+  std::vector<int> out_shape = x_vec;
+  std::vector<int> axis(rank);
+  for (int i = 0; i < rank; ++i) {
+    axis[i] = i;
+  }
+  std::swap(axis[rank - 1], axis[rank - 2]);
+  ret.Resize(phi::make_ddim(x_vec));
+  dev_ctx.template Alloc<T>(&ret);
+  switch (rank) {
+    case 2: {
+      phi::funcs::Transpose<Context, T, 2> trans;
+      trans(dev_ctx, x, &ret, axis);
+      break;
+    }
+    case 3: {
+      phi::funcs::Transpose<Context, T, 3> trans;
+      trans(dev_ctx, x, &ret, axis);
+      break;
+    }
+    case 4: {
+      phi::funcs::Transpose<Context, T, 4> trans;
+      trans(dev_ctx, x, &ret, axis);
+      break;
+    }
+    case 5: {
+      phi::funcs::Transpose<Context, T, 5> trans;
+      trans(dev_ctx, x, &ret, axis);
+      break;
+    }
+    case 6: {
+      phi::funcs::Transpose<Context, T, 6> trans;
+      trans(dev_ctx, x, &ret, axis);
+      break;
+    }
+    default: {
+      PADDLE_THROW(phi::errors::InvalidArgument(
+          "Invalid Rank number, "
+          "currently only support rank between 2~6"));
+    }
+  }
+  return ret;
+}
+
+}  // namespace phi
diff --git a/paddle/phi/kernels/lu_grad_kernel.h b/paddle/phi/kernels/lu_grad_kernel.h
new file mode 100644
index 0000000000000..23d4ac26840fa
--- /dev/null
+++ b/paddle/phi/kernels/lu_grad_kernel.h
@@ -0,0 +1,30 @@
+// Copyright (c) 2022 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 "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LUGradKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const DenseTensor& out,
+                  const DenseTensor& pivots,
+                  const DenseTensor& out_grad,
+                  bool pivot,
+                  DenseTensor* x_grad);
+
+}  // namespace phi
diff --git a/paddle/phi/kernels/lu_kernel.h b/paddle/phi/kernels/lu_kernel.h
new file mode 100644
index 0000000000000..636bfd0683f7f
--- /dev/null
+++ b/paddle/phi/kernels/lu_kernel.h
@@ -0,0 +1,29 @@
+// Copyright (c) 2022 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 "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void LUKernel(const Context& dev_ctx,
+              const DenseTensor& x,
+              bool pivot,
+              DenseTensor* out,
+              DenseTensor* pivots,
+              DenseTensor* infos);
+
+}  // namespace phi
diff --git a/paddle/phi/ops/compat/lu_sig.cc b/paddle/phi/ops/compat/lu_sig.cc
new file mode 100644
index 0000000000000..84ae337cae642
--- /dev/null
+++ b/paddle/phi/ops/compat/lu_sig.cc
@@ -0,0 +1,31 @@
+// Copyright (c) 2022 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/phi/core/compat/op_utils.h"
+
+namespace phi {
+
+KernelSignature LUOpArgumentMapping(const ArgumentMappingContext& ctx) {
+  return KernelSignature("lu", {"X"}, {"pivots"}, {"Out", "Pivots", "Infos"});
+}
+
+KernelSignature LUGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
+  return KernelSignature(
+      "lu_grad", {"X", "Out", "Pivots", "Out@GRAD"}, {"pivots"}, {"X@GRAD"});
+}
+
+}  // namespace phi
+
+PD_REGISTER_ARG_MAPPING_FN(lu, phi::LUOpArgumentMapping);
+PD_REGISTER_ARG_MAPPING_FN(lu_grad, phi::LUGradOpArgumentMapping);
diff --git a/python/paddle/fluid/tests/unittests/test_lu_op.py b/python/paddle/fluid/tests/unittests/test_lu_op.py
index 2989a0307400a..414dc66f84176 100644
--- a/python/paddle/fluid/tests/unittests/test_lu_op.py
+++ b/python/paddle/fluid/tests/unittests/test_lu_op.py
@@ -128,6 +128,8 @@ def set_output(self):
 
     def setUp(self):
         self.op_type = "lu"
+        self.python_api = paddle.tensor.linalg.lu
+        self.python_out_sig = ["Out", "Pivots"]
         self.config()
 
         self.inputs = {'X': np.random.random(self.x_shape).astype(self.dtype)}
@@ -140,10 +142,10 @@ def setUp(self):
         }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
-        self.check_grad(['X'], ['Out'])
+        self.check_grad(['X'], ['Out'], check_eager=True)
 
 
 # m = n 2D
diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py
index a77d6b5a2ad92..01c36a22c3757 100644
--- a/python/paddle/tensor/linalg.py
+++ b/python/paddle/tensor/linalg.py
@@ -2099,27 +2099,27 @@ def lu(x, pivot=True, get_infos=False, name=None):
 
             # one can verify : X = P @ L @ U ;     
     """
-    if paddle.in_dynamic_mode():
-        LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)
-        if get_infos:
-            return LU, Piv, Info
-        else:
-            return LU, Piv
-    check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')
-    helper = LayerHelper('lu', **locals())
-    lu = helper.create_variable_for_type_inference(dtype=x.dtype)
-    p = helper.create_variable_for_type_inference(dtype='int')
-    info = helper.create_variable_for_type_inference(dtype='int')
-    attrs = dict()
-    attrs['pivots'] = pivot
-    helper.append_op(type='lu',
-                     inputs={'X': x},
-                     outputs={
-                         'Out': lu,
-                         'Pivots': p,
-                         'Infos': info
-                     },
-                     attrs=attrs)
+
+    if in_dygraph_mode():
+        lu, p, info = _C_ops.final_state_lu(x, pivot)
+    elif paddle.in_dynamic_mode():
+        lu, p, info = _C_ops.lu(x, 'pivot', pivot)
+    else:
+        check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')
+        helper = LayerHelper('lu', **locals())
+        lu = helper.create_variable_for_type_inference(dtype=x.dtype)
+        p = helper.create_variable_for_type_inference(dtype='int')
+        info = helper.create_variable_for_type_inference(dtype='int')
+        attrs = dict()
+        attrs['pivot'] = pivot
+        helper.append_op(type='lu',
+                         inputs={'X': x},
+                         outputs={
+                             'Out': lu,
+                             'Pivots': p,
+                             'Infos': info
+                         },
+                         attrs=attrs)
     if get_infos:
         return lu, p, info
     else: