renorm op (#38130)

* graph engine demo

* upload unsaved changes

* fix dependency error

* fix shard_num problem

* py client

* remove lock and graph-type

* add load direct graph

* add load direct graph

* add load direct graph

* batch random_sample

* batch_sample_k

* fix num_nodes size

* batch brpc

* batch brpc

* add test

* add test

* add load_nodes; change add_node function

* change sample return type to pair

* resolve conflict

* resolved conflict

* resolved conflict

* separate server and client

* merge pair type

* fix

* resolved conflict

* fixed segment fault; high-level VLOG for load edges and load nodes

* random_sample return 0

* rm useless loop

* test:load edge

* fix ret -1

* test: rm sample

* rm sample

* random_sample return future

* random_sample return int

* test fake node

* fixed here

* memory leak

* remove test code

* fix return problem

* add common_graph_table

* random sample node &test & change data-structure from linkedList to vector

* add common_graph_table

* sample with srand

* add node_types

* optimize nodes sample

* recover test

* random sample

* destruct weighted sampler

* GraphEdgeBlob

* WeightedGraphEdgeBlob to GraphEdgeBlob

* WeightedGraphEdgeBlob to GraphEdgeBlob

* pybind sample nodes api

* pull nodes with step

* fixed pull_graph_list bug; add test for pull_graph_list by step

* add graph table;name

* add graph table;name

* add pybind

* add pybind

* add FeatureNode

* add FeatureNode

* add FeatureNode Serialize

* add FeatureNode Serialize

* get_feat_node

* avoid local rpc

* fix get_node_feat

* fix get_node_feat

* remove log

* get_node_feat return  py:bytes

* merge develop with graph_engine

* fix threadpool.h head

* fix

* fix typo

* resolve conflict

* fix conflict

* recover lost content

* fix pybind of FeatureNode

* recover cmake

* recover tools

* resolve conflict

* resolve linking problem

* code style

* change test_server port

* fix code problems

* remove shard_num config

* remove redundent threads

* optimize start server

* remove logs

* fix code problems by reviewers' suggestions

* move graph files into a folder

* code style change

* remove graph operations from base table

* optimize get_feat function of graph engine

* fix long long count problem

* remove redandunt graph files

* remove unused shell

* recover dropout_op_pass.h

* fix potential stack overflow when request number is too large & node add & node clear & node remove

* when sample k is larger than neigbor num, return directly

* using random seed generator of paddle to speed up

* fix bug of random sample k

* fix code style

* fix code style

* add remove graph to fleet_py.cc

* fix blocking_queue problem

* fix style

* fix

* recover capacity check

* add remove graph node; add set_feature

* add remove graph node; add set_feature

* add remove graph node; add set_feature

* add remove graph node; add set_feature

* fix distributed op combining problems

* optimize

* remove logs

* fix MultiSlotDataGenerator error

* cache for graph engine

* fix type compare error

* more test&fix thread terminating problem

* remove header

* change time interval of shrink

* use cache when sample nodes

* remove unused function

* change unique_ptr to shared_ptr

* simplify cache template

* cache api on client

* fix

* reduce sample threads when cache is not used

* reduce cache memory

* cache optimization

* remove test function

* remove extra fetch function

* graph-engine data transfer optimization

* support graph_split load&query

* remove logs

* change shards to pointer vector

* use inference

* remove test code

* renorm op

* simplify renorm op

* recover local changes

* recover renorm op kernel

* fix init

* add blanklines in renorm doc

* fix import

* fix import
Co-authored-by: NHuang Zhengjie <270018958@qq.com>
Co-authored-by: NWeiyue Su <weiyue.su@gmail.com>
Co-authored-by: Nsuweiyue <suweiyue@baidu.com>
Co-authored-by: Nluobin06 <luobin06@baidu.com>
Co-authored-by: Nliweibin02 <liweibin02@baidu.com>
Co-authored-by: Ntangwei12 <tangwei12@baidu.com>

paddle/fluid/distributed/test/graph_node_split_test.cc

@@ -272,4 +272,4 @@ void RunGraphSplit() {
   worker_ptr_->finalize_worker();
 }
 
-TEST(RunGraphSplit, Run) { RunGraphSplit(); }
+TEST(RunGraphSplit, Run) { RunGraphSplit(); }
\ No newline at end of file

paddle/fluid/operators/renorm_op.cc

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/operators/renorm_op.h"
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
+
+namespace paddle {
+namespace operators {
+
+class RenormOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  using DDim = paddle::framework::DDim;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "abs");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "abs");
+
+    auto in_dims = ctx->GetInputDim("X");
+
+    ctx->SetOutputDim("Out", in_dims);
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class RenormOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor), The input tensor of renorm op.");
+    AddOutput("Out", "(Tensor), The output tensor of renorm op.");
+    AddAttr<float>("p", "(float, norm's power");
+    AddAttr<int>("axis",
+                 "int,the dimension to slice over to get the sub-tensors");
+    AddAttr<float>("max_norm", "(float, the norm upper-bound");
+    AddAttr<bool>("use_cudnn",
+                  "(bool, default false) Only used in cudnn kernel, need "
+                  "install cudnn")
+        .SetDefault(false);
+    AddAttr<bool>("use_mkldnn",
+                  "(bool, default false) Only used in mkldnn kernel")
+        .SetDefault(false);
+    AddComment(R"DOC(
+Renorm Operator.
+
+This operator is used to scale tensor sliced by axis if its p-norm execeeds maxnorm
+
+)DOC");
+  }
+};
+
+class RenormGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   "Out@Grad", "AbsGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   "X@Grad", "AbsGrad");
+
+    auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+    ctx->SetOutputDim(framework::GradVarName("X"), dout_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto dtype = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+    return framework::OpKernelType(dtype, ctx.GetPlace());
+  }
+};
+
+template <typename T>
+class RenormGradMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+  void Apply(GradOpPtr<T> retv) const override {
+    retv->SetType("renorm_grad");
+    retv->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    retv->SetInput("X", this->Input("X"));
+    retv->SetAttrMap(this->Attrs());
+    retv->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(renorm, ops::RenormOp, ops::RenormOpMaker,
+                  ops::RenormGradMaker<paddle::framework::OpDesc>,
+                  ops::RenormGradMaker<paddle::imperative::OpBase>);
+
+REGISTER_OPERATOR(renorm_grad, ops::RenormGradOp);
+
+REGISTER_OP_CPU_KERNEL(renorm, ops::CPURenormKernel<float>,
+                       ops::CPURenormKernel<double>);
+
+REGISTER_OP_CPU_KERNEL(renorm_grad, ops::CPURenormGradKernel<float>,
+                       ops::CPURenormGradKernel<double>);
\ No newline at end of file

paddle/fluid/operators/renorm_op.cu

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <algorithm>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_impl.cu.h"
+#include "paddle/fluid/operators/reduce_ops/reduce_functor_op.h"
+#include "paddle/fluid/operators/reduce_ops/reduce_op.cu.h"
+#include "paddle/fluid/operators/reduce_ops/reduce_op.h"
+#include "paddle/fluid/operators/renorm_op.h"
+#include "paddle/fluid/operators/utils.h"
+#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
+#include "stdio.h"
+
+namespace paddle {
+namespace operators {
+
+__device__ __forceinline__ float inline_pow(float base, float exponent) {
+  return pow(base, exponent);
+}
+
+__device__ __forceinline__ double inline_pow(double base, double exponent) {
+  return pow(base, exponent);
+}
+
+__device__ __forceinline__ float inline_abs(float x) { return abs(x); }
+__device__ __forceinline__ double inline_abs(double x) { return abs(x); }
+
+template <typename Tx, typename Ty = Tx>
+struct UnsignedPowFunctor {
+  HOSTDEVICE explicit inline UnsignedPowFunctor(float porder) {
+    this->porder = porder;
+  }
+  HOSTDEVICE inline Ty operator()(const Tx& x) const {
+    return static_cast<Ty>(inline_pow(inline_abs(x), static_cast<Tx>(porder)));
+  }
+  float porder;
+};
+
+template <typename T>
+__global__ void RenormKernelFunc3(int64_t size, T* dim_value, float p,
+                                  float max_norm) {
+  int64_t i = ((int64_t)blockIdx.x) * blockDim.x + threadIdx.x;
+  if (i < size) {
+    T temp = pow(dim_value[i], (T)(1.0 / p));
+    dim_value[i] = 1.0;
+    if (temp > max_norm) dim_value[i] = max_norm / temp;
+  }
+}
+
+template <typename T>
+__global__ void RenormKernelFunc4(T* x_data, T* out_data, int64_t size,
+                                  T* dim_value, int64_t dimension_each,
+                                  int64_t dim_divisor) {
+  int64_t i = ((int64_t)blockIdx.x) * blockDim.x + threadIdx.x;
+  auto dim_index = i / dim_divisor % dimension_each;
+  if (i < size) {
+    if (dim_value[dim_index] < 1.0)
+      out_data[i] = dim_value[dim_index] * x_data[i];
+    else
+      out_data[i] = x_data[i];
+  }
+}
+
+template <typename T>
+__global__ void RenormGradKernelFunc1(T* x_data, T* dout_data, T* pow_value,
+                                      T* mul_value, int64_t size,
+                                      int64_t dimension_each, float p,
+                                      int64_t dim_divisor) {
+  int64_t i = ((int64_t)blockIdx.x) * blockDim.x + threadIdx.x;
+  auto dim_index = i / dim_divisor % dimension_each;
+  if (i < size) {
+    pow_value[i] = pow(abs(x_data[i]), (T)p);
+    mul_value[i] = x_data[i] * dout_data[i];
+  }
+}
+
+template <typename T>
+__global__ void RenormGradKernelFunc2(T* x_data, T* dout_data, T* dx_data,
+                                      int64_t size, T* dim_value,
+                                      T* dim_power_sum, T* weight_derivative,
+                                      int64_t dimension_each, float p,
+                                      float max_norm, int64_t dim_divisor) {
+  int64_t i = ((int64_t)blockIdx.x) * blockDim.x + threadIdx.x;
+  auto dim_index = i / dim_divisor % dimension_each;
+  if (i < dimension_each) {
+    dim_power_sum[i] = 0;
+    auto temp = pow(dim_value[i], (T)(1.0 / p));
+    if (temp > max_norm) {
+      dim_power_sum[i] = pow(dim_value[i], (T)(-1.0 - 1.0 / p)) * -1 * max_norm;
+      dim_value[i] = max_norm / temp;
+    } else
+      dim_value[i] = 1.0;
+  }
+  __syncthreads();
+  if (i < size) {
+    dx_data[i] = dim_value[dim_index] * dout_data[i];
+    dx_data[i] = dx_data[i] +
+                 weight_derivative[dim_index] * dim_power_sum[dim_index] *
+                     pow(abs(x_data[i]), T(p - 1.0)) *
+                     (x_data[i] >= 0 ? 1 : -1);
+  }
+}
+
+template <typename T>
+class CUDARenormKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* x = context.Input<Tensor>("X");
+    Tensor* out = context.Output<Tensor>("Out");
+    auto numel = x->numel();
+    T* x_data = (T*)x->data<T>();
+    auto input_dims = x->dims();
+    float max_norm = context.Attr<float>("max_norm");
+    float p = context.Attr<float>("p");
+    int dim = context.Attr<int>("axis");
+    auto dimension_each = input_dims[dim];
+    auto dim_size = input_dims.size();
+    framework::Tensor pow_value, dim_value;
+    int64_t dim_divisor = 1, pre_mul = 1;
+    for (int i = dim + 1; i < dim_size; i++) dim_divisor *= input_dims[i];
+    for (int i = 0; i < dim; i++) pre_mul *= input_dims[i];
+    pow_value.Resize(
+        framework::make_ddim({pre_mul, dimension_each, dim_divisor}));
+    dim_value.Resize(framework::make_ddim({dimension_each}));
+    pow_value.mutable_data<T>(context.GetPlace());
+    out->Resize(framework::make_ddim(framework::vectorize(input_dims)));
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+    auto stream = context.cuda_device_context().stream();
+    int block = std::min(numel, static_cast<int64_t>(256));
+    using MT = typename details::MPTypeTrait<T>::Type;
+    int grid = (numel + block - 1) / block;
+
+    int block2 = std::min(dimension_each, static_cast<int64_t>(256));
+    int grid2 = (dimension_each + block2 - 1) / block2;
+    std::vector<const framework::Tensor*> ins = {x};
+    std::vector<framework::Tensor*> outs = {&pow_value};
+    auto func = UnsignedPowFunctor<MT, T>(p);
+    const auto& cuda_ctx =
+        context.template device_context<platform::CUDADeviceContext>();
+
+    LaunchSameDimsElementwiseCudaKernel<ElementwiseType::kUnary, MT, T,
+                                        UnsignedPowFunctor<MT, T>>(
+        cuda_ctx, ins, &outs, func);
+    std::vector<int> reduce_axis = {0, 2};
+    TensorReduceFunctorImpl<T, T, kps::AddFunctor, kps::IdentityFunctor<T>>(
+        pow_value, &dim_value, kps::IdentityFunctor<T>(), reduce_axis, stream);
+    RenormKernelFunc3<T><<<grid2, block2, 0, stream>>>(
+        numel, dim_value.mutable_data<T>(context.GetPlace()), p, max_norm);
+    RenormKernelFunc4<T><<<grid, block, 0, stream>>>(
+        x_data, out_data, numel, dim_value.mutable_data<T>(context.GetPlace()),
+        dimension_each, dim_divisor);
+    // platform::GpuStreamSync(stream);
+  }
+};
+
+template <typename T>
+class CUDAGradRenormKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const framework::Tensor* d_out =
+        ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    const framework::Tensor* x = ctx.Input<framework::Tensor>("X");
+    framework::Tensor* d_x =
+        ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    auto numel = d_out->numel();
+    T* dout_data = (T*)d_out->data<T>();
+    T* x_data = (T*)x->data<T>();
+    auto input_dims = x->dims();
+    float max_norm = ctx.Attr<float>("max_norm");
+    float p = ctx.Attr<float>("p");
+    int dim = ctx.Attr<int>("axis");
+    auto dimension_each = input_dims[dim];
+    auto dim_size = input_dims.size();
+    int64_t dim_divisor = 1, pre_mul = 1;
+    for (int i = dim + 1; i < dim_size; i++) dim_divisor *= input_dims[i];
+    for (int i = 0; i < dim; i++) pre_mul *= input_dims[i];
+    d_x->Resize(framework::make_ddim(framework::vectorize(input_dims)));
+    T* dx_data = d_x->mutable_data<T>(ctx.GetPlace());
+    framework::Tensor pow_value, mul_value, dim_value, dim_power_sum,
+        weight_derivative;
+    pow_value.Resize(
+        framework::make_ddim({pre_mul, dimension_each, dim_divisor}));
+    mul_value.Resize(
+        framework::make_ddim({pre_mul, dimension_each, dim_divisor}));
+    dim_value.Resize(framework::make_ddim({dimension_each}));
+    dim_power_sum.Resize(framework::make_ddim({dimension_each}));
+    weight_derivative.Resize(framework::make_ddim({dimension_each}));
+    auto stream = ctx.cuda_device_context().stream();
+    int block = std::min(numel, static_cast<int64_t>(256));
+    int grid = (numel + block - 1) / block;
+    pow_value.mutable_data<T>(ctx.GetPlace());
+    mul_value.mutable_data<T>(ctx.GetPlace());
+    dim_value.mutable_data<T>(ctx.GetPlace());
+    dim_power_sum.mutable_data<T>(ctx.GetPlace());
+    weight_derivative.mutable_data<T>(ctx.GetPlace());
+    RenormGradKernelFunc1<T><<<grid, block, 0, stream>>>(
+        x_data, dout_data, pow_value.mutable_data<T>(ctx.GetPlace()),
+        mul_value.mutable_data<T>(ctx.GetPlace()), numel, dimension_each, p,
+        dim_divisor);
+    std::vector<int> reduce_axis = {0, 2};
+    TensorReduceFunctorImpl<T, T, kps::AddFunctor, kps::IdentityFunctor<T>>(
+        pow_value, &dim_value, kps::IdentityFunctor<T>(), reduce_axis, stream);
+    TensorReduceFunctorImpl<T, T, kps::AddFunctor, kps::IdentityFunctor<T>>(
+        mul_value, &weight_derivative, kps::IdentityFunctor<T>(), reduce_axis,
+        stream);
+    RenormGradKernelFunc2<T><<<grid, block, 0, stream>>>(
+        x_data, dout_data, dx_data, numel,
+        dim_value.mutable_data<T>(ctx.GetPlace()),
+        dim_power_sum.mutable_data<T>(ctx.GetPlace()),
+        weight_derivative.mutable_data<T>(ctx.GetPlace()), dimension_each, p,
+        max_norm, dim_divisor);
+    // platform::GpuStreamSync(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(renorm, ops::CUDARenormKernel<float>,
+                        ops::CUDARenormKernel<double>);
+
+REGISTER_OP_CUDA_KERNEL(renorm_grad, ops::CUDAGradRenormKernel<float>,
+                        ops::CUDAGradRenormKernel<double>);
\ No newline at end of file

paddle/fluid/operators/renorm_op.h

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "math.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/math/complex_functors.h"
+#include "paddle/fluid/platform/for_range.h"
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+// template <typename T>
+// struct NormDimValueFunctor<T> {
+//   NormDimValueFunctor(T* input, T* output, int64_t dim_divisor, int64_t
+//   dimension_each, float p)
+//       : input_(input), output_(output),dim_divisor_(dim_divisor),
+//       dimension_each_(dimension_each),p_(p) {}
+
+//   HOSTDEVICE void operator()(int64_t i) const {
+//       auto dim_index = i / dim_divsor % dimension_each;
+//       dim_value[dim_index] += std::pow(std::abs(input[i]), p);
+//   }
+
+//   T* input_;
+//   T* output_;
+//   int64_t dimension_each_, dim_divisor_;
+//   float p_,max_norm_;
+
+// };
+// template <typename DeviceContext, typename T>
+template <typename T>
+class CPURenormKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* x = context.Input<Tensor>("X");
+    Tensor* out = context.Output<Tensor>("Out");
+    auto numel = x->numel();
+    auto* x_data = x->data<T>();
+    auto input_dims = x->dims();
+    float max_norm = context.Attr<float>("max_norm");
+    float p = context.Attr<float>("p");
+    int dim = context.Attr<int>("axis");
+    auto dimension_each = input_dims[dim];
+    auto dim_size = input_dims.size();
+    int64_t dim_divisor = 1;
+    for (int i = dim + 1; i < dim_size; i++) dim_divisor *= input_dims[i];
+
+    // auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    // std::vector<int64_t> dim_index(dim_size, 0);
+    std::vector<T> dim_value(dimension_each,
+                             0);  // dim_value = (x1^p + x2^p + x3^p....)^(1/p)
+
+    auto* out_data =
+        out->mutable_data<T>(context.GetPlace(), size_t(numel * sizeof(T)));
+
+    int64_t index = 0, dim_index = 0;
+    for (int64_t i = 0; i < numel; i++) {
+      // auto dim_index = i / dim_divsor % dimension_each;
+      dim_value[dim_index] += std::pow(std::abs(x_data[i]), p);
+      index++;
+      if (index == dim_divisor) {
+        dim_index++;
+        if (dim_index == dimension_each) {
+          dim_index = 0;
+        }
+        index = 0;
+      }
+    }
+    for (int64_t i = 0; i < dimension_each; i++) {
+      dim_value[i] = std::pow(dim_value[i], 1.0 / p);
+      if (dim_value[i] > max_norm)
+        dim_value[i] = max_norm / dim_value[i];
+      else
+        dim_value[i] = 1.0;
+      // dim_index[i] = 0;
+    }
+    index = dim_index = 0;
+    for (int64_t i = 0; i < numel; i++) {
+      // auto dim_index = i / dim_divsor % dimension_each;
+      out_data[i] = dim_value[dim_index] < 1.0
+                        ? dim_value[dim_index] * x_data[i]
+                        : x_data[i];
+      index++;
+      if (index == dim_divisor) {
+        dim_index++;
+        if (dim_index == dimension_each) {
+          dim_index = 0;
+        }
+        index = 0;
+      }
+    }
+  }
+};
+
+// template <typename DeviceContext, typename T>
+template <typename T>
+class CPURenormGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    const framework::Tensor* d_out =
+        ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    const framework::Tensor* x = ctx.Input<framework::Tensor>("X");
+    framework::Tensor* d_x =
+        ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    auto numel = d_out->numel();
+    auto* dout_data = d_out->data<T>();
+    auto* x_data = x->data<T>();
+    auto input_dims = x->dims();
+    float max_norm = ctx.Attr<float>("max_norm");
+    float p = ctx.Attr<float>("p");
+    int dim = ctx.Attr<int>("axis");
+    auto dimension_each = input_dims[dim];
+    auto dim_size = input_dims.size();
+    int64_t dim_divisor = 1;
+    for (int i = dim + 1; i < dim_size; i++) dim_divisor *= input_dims[i];
+    auto* dx_data = d_x->mutable_data<T>(
+        ctx.GetPlace(), static_cast<size_t>(numel * sizeof(T)));
+    std::vector<T> dim_value(dimension_each, 0),
+        dim_power_sum(dimension_each, 0),
+        weight_derivative(dimension_each, 0.0);
+    int64_t index = 0, dim_index = 0;
+    for (int64_t i = 0; i < numel; i++) {
+      // auto dim_index = i / dim_divsor % dimension_each;
+      dim_value[dim_index] += std::pow(std::abs(x_data[i]), p);
+      index++;
+      if (index == dim_divisor) {
+        dim_index++;
+        if (dim_index == dimension_each) {
+          dim_index = 0;
+        }
+        index = 0;
+      }
+    }
+    for (int64_t i = 0; i < dimension_each; i++) {
+      auto temp = std::pow(dim_value[i], 1.0 / p);
+      if (temp > max_norm) {
+        dim_power_sum[i] =
+            std::pow(dim_value[i], (T)(-1.0 - 1.0 / p)) * -1 * max_norm;
+        dim_value[i] = max_norm / temp;
+      } else
+        dim_value[i] = 1.0;
+    }
+    index = dim_index = 0;
+    for (int64_t i = 0; i < numel; i++) {
+      // auto dim_index = i / dim_divsor % dimension_each;
+      dx_data[i] = dim_value[dim_index] * dout_data[i];
+      weight_derivative[dim_index] += x_data[i] * dout_data[i];
+      index++;
+      if (index == dim_divisor) {
+        dim_index++;
+        if (dim_index == dimension_each) {
+          dim_index = 0;
+        }
+        index = 0;
+      }
+    }
+    index = dim_index = 0;
+    for (int64_t i = 0; i < numel; i++) {
+      // auto dim_index = i / dim_divsor % dimension_each;
+      dx_data[i] += weight_derivative[dim_index] * dim_power_sum[dim_index] *
+                    std::pow(std::abs(x_data[i]), p - 1.0) *
+                    (x_data[i] >= 0 ? 1 : -1);
+      index++;
+      if (index == dim_divisor) {
+        dim_index++;
+        if (dim_index == dimension_each) {
+          dim_index = 0;
+        }
+        index = 0;
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/__init__.py

@@ -211,6 +211,7 @@ from .tensor.math import remainder  # noqa: F401
 from .tensor.math import mod  # noqa: F401
 from .tensor.math import floor_mod  # noqa: F401
 from .tensor.math import multiply  # noqa: F401
+from .tensor.math import renorm  # noqa: F401
 from .tensor.math import add  # noqa: F401
 from .tensor.math import subtract  # noqa: F401
 from .tensor.math import logsumexp  # noqa: F401

python/paddle/fluid/tests/unittests/test_renorm_op.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import paddle
+import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+
+paddle.set_device('cpu')
+
+
+class TestRenormAPI(unittest.TestCase):
+    def input_data(self):
+        self.data_x = np.array(
+            [[[2.0, 2, -2], [3, 0.3, 3]], [[2, -8, 2], [3.1, 3.7, 3]]])
+        self.p = 1.0
+        self.dim = 2
+        self.max_norm = 2.05
+
+    def test_renorm_api(self):
+        paddle.enable_static()
+        self.input_data()
+
+        # case 1:
+        with program_guard(Program(), Program()):
+            #x = fluid.layers.data(name = 'x',shape=[-1, 2, 3])
+            x = paddle.static.data(name="x", shape=[-1, 2, 3], dtype='float64')
+            z = paddle.renorm(x, self.p, self.dim, self.max_norm)
+            exe = fluid.Executor(fluid.CPUPlace())
+            res, = exe.run(feed={"x": self.data_x},
+                           fetch_list=[z],
+                           return_numpy=False)
+        expected = np.array([[[0.40594056, 0.29285714, -0.41000000],
+                              [0.60891086, 0.04392857, 0.61500001]],
+                             [[0.40594056, -1.17142856, 0.41000000],
+                              [0.62920785, 0.54178572, 0.61500001]]])
+        self.assertTrue(np.allclose(expected, np.array(res)))
+
+    def test_dygraph_api(self):
+        self.input_data()
+        # case axis none
+        with fluid.dygraph.guard():
+            input = [[[2.0, 2, -2], [3, 0.3, 3]], [[2, -8, 2], [3.1, 3.7, 3]]]
+            x = paddle.to_tensor(input, stop_gradient=False)
+            y = paddle.renorm(x, 1.0, 2, 2.05)
+            expected = np.array([[[0.40594056, 0.29285714, -0.41000000],
+                                  [0.60891086, 0.04392857, 0.61500001]],
+                                 [[0.40594056, -1.17142856, 0.41000000],
+                                  [0.62920785, 0.54178572, 0.61500001]]])
+            self.assertTrue(np.allclose(expected, np.array(y)))
+            z = paddle.mean(y)
+            z.backward(retain_graph=True)
+            expected_grad = np.array(
+                [[[0, 0.01394558, 0.02733333], [0, 0.01394558, 0.00683333]],
+                 [[0, 0.01045918, 0.00683333], [0, 0.01394558, 0.00683333]]])
+            self.assertTrue(np.allclose(expected_grad, np.array(x.grad)))
+        #test exception:
+        with fluid.dygraph.guard():
+            input = [[[2.0, 2, -2], [3, 0.3, 3]], [[2, -8, 2], [3.1, 3.7, 3]]]
+            x = paddle.to_tensor(input, stop_gradient=False)
+            exp = False
+            try:
+                paddle.renorm(x, 1.0, 8, 2.05)
+            except:
+                exp = True
+            self.assertTrue(exp)
+            exp = False
+            try:
+                paddle.renorm(x, 1.0, -4, 2.05)
+            except:
+                exp = True
+            self.assertTrue(exp)
+            y = paddle.renorm(x, 1.0, -1, 2.05)
+            expected = np.array([[[0.40594056, 0.29285714, -0.41000000],
+                                  [0.60891086, 0.04392857, 0.61500001]],
+                                 [[0.40594056, -1.17142856, 0.41000000],
+                                  [0.62920785, 0.54178572, 0.61500001]]])
+            self.assertTrue(np.allclose(expected, np.array(y)))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/tensor/math.py

@@ -1194,6 +1194,62 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
         type="addmm", inputs=inputs, attrs=attrs, outputs={"Out": out})
     return out
 
+def renorm(x, p, axis, max_norm):
+    """
+    **renorm**
+
+    This operator is used to calculate the p-norm along the axis,
+    suppose the input-shape on axis dimension has the value of T, then
+    the tensor is split into T parts, the p-norm should be calculated for each
+    part, if the p-norm for part i is larger than max-norm, then each element 
+    in part i should be re-normalized at the same scale so that part-i' p-norm equals
+    max-norm exactly, otherwise part-i stays unchanged.
+
+    Args:
+        x (Tensor): The input Tensor
+        p (float): The power of the norm operation.
+        axis (int): the dimension to slice the tensor.
+        max-norm (float): the maximal norm limit.
+
+    Returns:
+        Tensor: the renorm Tensor.
+
+    Examples:
+        ..  code-block:: python
+            
+            import paddle
+            input = [[[2.0,2,-2],[3,0.3,3]],[[2,-8,2],[3.1,3.7,3]]]
+            x = paddle.to_tensor(input,dtype='float32')
+            y = paddle.renorm(x, 1.0, 2, 2.05)
+            print(y)        
+    #        [[[ 0.40594056,  0.29285714, -0.41000000],
+    #          [ 0.60891086,  0.04392857,  0.61500001]],
+    #         [[ 0.40594056, -1.17142856,  0.41000000],
+    #          [ 0.62920785,  0.54178572,  0.61500001]]])
+    
+    """
+    input_shape = x.shape
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'renorm')
+    if not axis < len(input_shape):
+        raise ValueError("the axis:{} should be less then the shape's size {}:{}".format(axis,len(input_shape),input_shape))
+    if not axis >=0:
+        if not axis >= -1 * len(input_shape):
+            raise ValueError("the axis:{} should not be less than -1 * length of input_shape:{}".format(axis,-1 * len(input_shape)))
+        axis = axis + len(input_shape)
+    if in_dygraph_mode():
+        out = core.ops.renorm(x, 'p',p, 'axis',axis, 'max_norm', max_norm)
+        return out
+
+    inputs = {'X': x}
+    attrs = {'p': p, 'axis': axis, 'max_norm':max_norm}
+
+    helper = LayerHelper("renorm", **locals())
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
+
+    helper.append_op(
+        type="renorm", inputs=inputs, attrs=attrs, outputs={"Out": out})
+    return out
+
 
 
 def inner(x, y, name=None):