code style

Author: seemingwang

.github/ISSUE_TEMPLATE/---document-issue-.md

@@ -56,4 +56,4 @@ For example: no sample code; The sample code is not helpful; The sample code not
 For example:Chinese API in this doc is inconsistent with English API, including params, description, sample code, formula, etc.
 
 #### Other
-For example: The doc link is broken; The doc page is missing; Dead link in docs.
+For example: The doc link is broken; The doc page is missing; Dead link in docs.

paddle/fluid/distributed/service/graph_brpc_client.h

@@ -14,10 +14,10 @@
 
 #pragma once
 
+#include <ThreadPool.h>
 #include <memory>
 #include <string>
 #include <vector>
-#include <ThreadPool.h>
 
 #include <utility>
 #include "ThreadPool.h"

paddle/fluid/distributed/table/common_graph_table.cc

@@ -124,7 +124,7 @@ int32_t GraphTable::load_nodes(const std::string &path, std::string node_type) {
     std::ifstream file(path);
     std::string line;
     while (std::getline(file, line)) {
-      count ++;
+      count++;
       auto values = paddle::string::split_string<std::string>(line, "\t");
       if (values.size() < 2) continue;
       auto id = std::stoull(values[1]);
@@ -160,12 +160,12 @@ int32_t GraphTable::load_nodes(const std::string &path, std::string node_type) {
                   << " not in feature_map.";
         }
       }
-      valid_count ++;
+      valid_count++;
     }
   }
 
-  VLOG(0) << valid_count << "/" << count << " nodes in type " << 
-      node_type << " are loaded successfully in " << path;
+  VLOG(0) << valid_count << "/" << count << " nodes in type " << node_type
+          << " are loaded successfully in " << path;
   return 0;
 }
 
@@ -209,10 +209,11 @@ int32_t GraphTable::load_edges(const std::string &path, bool reverse_edge) {
       size_t index = src_shard_id - shard_start;
       shards[index].add_graph_node(src_id)->build_edges(is_weighted);
       shards[index].add_neighboor(src_id, dst_id, weight);
-      valid_count ++;
+      valid_count++;
     }
   }
-  VLOG(0) << valid_count << "/" << count << " edges are loaded successfully in " << path;
+  VLOG(0) << valid_count << "/" << count << " edges are loaded successfully in "
+          << path;
 
   // Build Sampler j
 

paddle/fluid/distributed/table/graph_edge.cc

@@ -17,14 +17,13 @@
 namespace paddle {
 namespace distributed {
 
-void GraphEdgeBlob::add_edge(uint64_t id, float weight=1){
+void GraphEdgeBlob::add_edge(uint64_t id, float weight = 1) {
   id_arr.push_back(id);
 }
 
-void WeightedGraphEdgeBlob::add_edge(uint64_t id, float weight=1){
+void WeightedGraphEdgeBlob::add_edge(uint64_t id, float weight = 1) {
   id_arr.push_back(id);
   weight_arr.push_back(weight);
 }
-
 }
 }

paddle/fluid/distributed/table/graph_node.cc

@@ -17,13 +17,12 @@
 namespace paddle {
 namespace distributed {
 
-
 GraphNode::~GraphNode() {
-  if (sampler != nullptr){
+  if (sampler != nullptr) {
     delete sampler;
     sampler = nullptr;
   }
-  if (edges != nullptr){
+  if (edges != nullptr) {
     delete edges;
     edges = nullptr;
   }
@@ -33,9 +32,7 @@ int Node::weight_size = sizeof(float);
 int Node::id_size = sizeof(uint64_t);
 int Node::int_size = sizeof(int);
 
-int Node::get_size(bool need_feature) {
-  return id_size + int_size; 
-}
+int Node::get_size(bool need_feature) { return id_size + int_size; }
 
 void Node::to_buffer(char* buffer, bool need_feature) {
   memcpy(buffer, &id, id_size);
@@ -45,36 +42,34 @@ void Node::to_buffer(char* buffer, bool need_feature) {
   memcpy(buffer, &feat_num, sizeof(int));
 }
 
-void Node::recover_from_buffer(char* buffer) {
-  memcpy(&id, buffer, id_size);
-}
+void Node::recover_from_buffer(char* buffer) { memcpy(&id, buffer, id_size); }
 
 int FeatureNode::get_size(bool need_feature) {
-  int size = id_size + int_size; // id, feat_num
-  if (need_feature){
+  int size = id_size + int_size;  // id, feat_num
+  if (need_feature) {
     size += feature.size() * int_size;
-    for (const std::string& fea: feature){
+    for (const std::string& fea : feature) {
       size += fea.size();
     }
   }
   return size;
 }
 
 void GraphNode::build_edges(bool is_weighted) {
-  if (edges == nullptr){
-    if (is_weighted == true){
+  if (edges == nullptr) {
+    if (is_weighted == true) {
       edges = new WeightedGraphEdgeBlob();
     } else {
       edges = new GraphEdgeBlob();
     }
   }
 }
 void GraphNode::build_sampler(std::string sample_type) {
-  if (sample_type == "random"){
+  if (sample_type == "random") {
     sampler = new RandomSampler();
-  } else if (sample_type == "weighted"){
+  } else if (sample_type == "weighted") {
     sampler = new WeightedSampler();
-  } 
+  }
   sampler->build(edges);
 }
 void FeatureNode::to_buffer(char* buffer, bool need_feature) {
@@ -87,7 +82,7 @@ void FeatureNode::to_buffer(char* buffer, bool need_feature) {
     feat_num += feature.size();
     memcpy(buffer, &feat_num, sizeof(int));
     buffer += sizeof(int);
-    for (int i = 0; i < feat_num; ++i){
+    for (int i = 0; i < feat_num; ++i) {
       feat_len = feature[i].size();
       memcpy(buffer, &feat_len, sizeof(int));
       buffer += sizeof(int);
@@ -99,14 +94,13 @@ void FeatureNode::to_buffer(char* buffer, bool need_feature) {
   }
 }
 void FeatureNode::recover_from_buffer(char* buffer) {
-
   int feat_num, feat_len;
   memcpy(&id, buffer, id_size);
   buffer += id_size;
-  
+
   memcpy(&feat_num, buffer, sizeof(int));
   buffer += sizeof(int);
-  
+
   feature.clear();
   for (int i = 0; i < feat_num; ++i) {
     memcpy(&feat_len, buffer, sizeof(int));
@@ -118,7 +112,6 @@ void FeatureNode::recover_from_buffer(char* buffer) {
     str[feat_len] = '\0';
     feature.push_back(std::string(str));
   }
-
 }
 }
 }

paddle/fluid/distributed/table/weighted_sampler.cc

@@ -14,74 +14,73 @@
 
 #include "paddle/fluid/distributed/table/weighted_sampler.h"
 #include <iostream>
-#include<unordered_map>
+#include <unordered_map>
 namespace paddle {
 namespace distributed {
 
-void RandomSampler::build(GraphEdgeBlob* edges) {
-  this->edges = edges;
-}
+void RandomSampler::build(GraphEdgeBlob *edges) { this->edges = edges; }
 
 std::vector<int> RandomSampler::sample_k(int k) {
   int n = edges->size();
-  if (k > n){
+  if (k > n) {
     k = n;
   }
   struct timespec tn;
   clock_gettime(CLOCK_REALTIME, &tn);
   srand(tn.tv_nsec);
   std::vector<int> sample_result;
   std::unordered_map<int, int> replace_map;
-  while(k--){
+  while (k--) {
     int rand_int = rand() % n;
     auto iter = replace_map.find(rand_int);
-    if(iter == replace_map.end()){
+    if (iter == replace_map.end()) {
       sample_result.push_back(rand_int);
-    }else{
+    } else {
       sample_result.push_back(iter->second);
     }
 
     iter = replace_map.find(n - 1);
-    if(iter == replace_map.end()){
+    if (iter == replace_map.end()) {
       replace_map[rand_int] = n - 1;
-    }else{
+    } else {
       replace_map[rand_int] = iter->second;
     }
     --n;
   }
   return sample_result;
 }
 
-WeightedSampler::WeightedSampler(){
+WeightedSampler::WeightedSampler() {
   left = nullptr;
   right = nullptr;
   edges = nullptr;
 }
 
 WeightedSampler::~WeightedSampler() {
-  if(left != nullptr){
+  if (left != nullptr) {
     delete left;
     left = nullptr;
   }
-  if(right != nullptr){
+  if (right != nullptr) {
     delete right;
     right = nullptr;
   }
 }
 
-void WeightedSampler::build(GraphEdgeBlob* edges) {
-  if(left != nullptr){
+void WeightedSampler::build(GraphEdgeBlob *edges) {
+  if (left != nullptr) {
     delete left;
     left = nullptr;
   }
-  if(right != nullptr){
+  if (right != nullptr) {
     delete right;
     right = nullptr;
   }
-  return build_one((WeightedGraphEdgeBlob*)edges, 0, edges->size());
+  return build_one((WeightedGraphEdgeBlob *)edges, 0, edges->size());
 }
 
-void WeightedSampler::build_one(WeightedGraphEdgeBlob *edges, int start, int end) {
+void WeightedSampler::build_one(WeightedGraphEdgeBlob *edges, int start,
+                                int end) {
   count = 0;
   this->edges = edges;
   if (start + 1 == end) {
@@ -137,7 +136,7 @@ int WeightedSampler::sample(
   if (right_count == 0 ||
       left_count > 0 && left->weight - left_subtract >= query_weight) {
     return_idx = left->sample(query_weight, subtract_weight_map,
-                             subtract_count_map, subtract);
+                              subtract_count_map, subtract);
   } else {
     return_idx =
         right->sample(query_weight - (left->weight - left_subtract),

paddle/fluid/distributed/table/weighted_sampler.h

@@ -21,39 +21,38 @@ namespace paddle {
 namespace distributed {
 
 class Sampler {
-public:
+ public:
   virtual ~Sampler() {}
-  virtual void build(GraphEdgeBlob* edges) = 0;
+  virtual void build(GraphEdgeBlob *edges) = 0;
   virtual std::vector<int> sample_k(int k) = 0;
 };
 
-class RandomSampler: public Sampler {
-public:
+class RandomSampler : public Sampler {
+ public:
   virtual ~RandomSampler() {}
-  virtual void build(GraphEdgeBlob* edges);
+  virtual void build(GraphEdgeBlob *edges);
   virtual std::vector<int> sample_k(int k);
-  GraphEdgeBlob* edges;
+  GraphEdgeBlob *edges;
 };
 
-class WeightedSampler: public Sampler {
+class WeightedSampler : public Sampler {
  public:
   WeightedSampler();
   virtual ~WeightedSampler();
   WeightedSampler *left, *right;
   float weight;
   int count;
   int idx;
-  GraphEdgeBlob * edges;
-  virtual void build(GraphEdgeBlob* edges);
+  GraphEdgeBlob *edges;
+  virtual void build(GraphEdgeBlob *edges);
   virtual void build_one(WeightedGraphEdgeBlob *edges, int start, int end);
   virtual std::vector<int> sample_k(int k);
 
  private:
-  int sample(
-      float query_weight,
-      std::unordered_map<WeightedSampler *, float> &subtract_weight_map,
-      std::unordered_map<WeightedSampler *, int> &subtract_count_map,
-      float &subtract);
+  int sample(float query_weight,
+             std::unordered_map<WeightedSampler *, float> &subtract_weight_map,
+             std::unordered_map<WeightedSampler *, int> &subtract_count_map,
+             float &subtract);
 };
 }
 }

paddle/fluid/inference/api/demo_ci/clean.sh

@@ -1,3 +1,17 @@
+# 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.
+
 set -x
 cd `dirname $0`
 rm -rf build/ data/

paddle/fluid/train/demo/run.sh

@@ -1,5 +1,19 @@
 #!/bin/bash
 
+# 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.
+
 set -x
 
 PADDLE_ROOT=$1

paddle/fluid/train/imdb_demo/run.sh

@@ -1,3 +1,16 @@
+# 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.
 
 set -exu
 build/demo_trainer --flagfile="train.cfg"