reduce sample threads (#37098)

* 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
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/table/common_graph_table.cc

@@ -125,7 +125,7 @@ FeatureNode *GraphShard::add_feature_node(uint64_t id) {
   return (FeatureNode *)bucket[node_location[id]];
 }
 
-void GraphShard::add_neighboor(uint64_t id, uint64_t dst_id, float weight) {
+void GraphShard::add_neighbor(uint64_t id, uint64_t dst_id, float weight) {
   find_node(id)->add_edge(dst_id, weight);
 }
 
@@ -277,7 +277,7 @@ 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);
+      shards[index].add_neighbor(src_id, dst_id, weight);
       valid_count++;
     }
   }
@@ -399,7 +399,6 @@ int32_t GraphTable::random_sample_neighbors(
   size_t node_num = buffers.size();
   std::function<void(char *)> char_del = [](char *c) { delete[] c; };
   std::vector<std::future<int>> tasks;
-  if (use_cache) {
   std::vector<std::vector<uint32_t>> seq_id(shard_end - shard_start);
   std::vector<std::vector<SampleKey>> id_list(shard_end - shard_start);
   size_t index;
@@ -413,8 +412,11 @@ int32_t GraphTable::random_sample_neighbors(
     tasks.push_back(_shards_task_pool[i]->enqueue([&, i, this]() -> int {
       uint64_t node_id;
       std::vector<std::pair<SampleKey, SampleResult>> r;
-        auto response =
+      LRUResponse response = LRUResponse::blocked;
+      if (use_cache) {
+        response =
             scaled_lru->query(i, id_list[i].data(), id_list[i].size(), r);
+      }
       int index = 0;
       uint32_t idx;
       std::vector<SampleResult> sample_res;
@@ -471,44 +473,6 @@ int32_t GraphTable::random_sample_neighbors(
     t.get();
   }
   return 0;
-  }
-  for (size_t idx = 0; idx < node_num; ++idx) {
-    uint64_t &node_id = node_ids[idx];
-    std::shared_ptr<char> &buffer = buffers[idx];
-    int &actual_size = actual_sizes[idx];
-
-    int thread_pool_index = get_thread_pool_index(node_id);
-    auto rng = _shards_task_rng_pool[thread_pool_index];
-
-    tasks.push_back(_shards_task_pool[thread_pool_index]->enqueue([&]() -> int {
-      Node *node = find_node(node_id);
-
-      if (node == nullptr) {
-        actual_size = 0;
-        return 0;
-      }
-      std::vector<int> res = node->sample_k(sample_size, rng);
-      actual_size = res.size() * (Node::id_size + Node::weight_size);
-      int offset = 0;
-      uint64_t id;
-      float weight;
-      char *buffer_addr = new char[actual_size];
-      buffer.reset(buffer_addr, char_del);
-      for (int &x : res) {
-        id = node->get_neighbor_id(x);
-        weight = node->get_neighbor_weight(x);
-        memcpy(buffer_addr + offset, &id, Node::id_size);
-        offset += Node::id_size;
-        memcpy(buffer_addr + offset, &weight, Node::weight_size);
-        offset += Node::weight_size;
-      }
-      return 0;
-    }));
-  }
-  for (size_t idx = 0; idx < node_num; ++idx) {
-    tasks[idx].get();
-  }
-  return 0;
 }
 
 int32_t GraphTable::get_node_feat(const std::vector<uint64_t> &node_ids,

paddle/fluid/distributed/table/common_graph_table.h

@@ -64,7 +64,7 @@ class GraphShard {
   Node *find_node(uint64_t id);
   void delete_node(uint64_t id);
   void clear();
-  void add_neighboor(uint64_t id, uint64_t dst_id, float weight);
+  void add_neighbor(uint64_t id, uint64_t dst_id, float weight);
   std::unordered_map<uint64_t, int> get_node_location() {
     return node_location;
   }
@@ -81,9 +81,7 @@ struct SampleKey {
   uint64_t node_key;
   size_t sample_size;
   SampleKey(uint64_t _node_key, size_t _sample_size)
-      : node_key(_node_key), sample_size(_sample_size) {
-    // std::cerr<<"in constructor of samplekey\n";
-  }
+      : node_key(_node_key), sample_size(_sample_size) {}
   bool operator==(const SampleKey &s) const {
     return node_key == s.node_key && sample_size == s.sample_size;
   }
@@ -143,7 +141,7 @@ class RandomSampleLRU {
       for (size_t i = 0; i < length; i++) {
         auto iter = key_map.find(keys[i]);
         if (iter != key_map.end()) {
-          res.push_back({keys[i], iter->second->data});
+          res.emplace_back(keys[i], iter->second->data);
           iter->second->ttl--;
           if (iter->second->ttl == 0) {
             remove(iter->second, true);
@@ -252,8 +250,6 @@ class ScaledLRU {
           }
         }
 
-        // shrink();
-        // std::cerr<<"shrink job in queue\n";
         auto status =
             thread_pool->enqueue([this]() -> int { return shrink(); });
         status.wait();
@@ -263,10 +259,8 @@ class ScaledLRU {
   }
   ~ScaledLRU() {
     std::unique_lock<std::mutex> lock(mutex_);
-    // std::cerr<<"cancel shrink job\n";
     stop = true;
     cv_.notify_one();
-    // pthread_cancel(shrink_job.native_handle());
   }
   LRUResponse query(size_t index, K *keys, size_t length,
                     std::vector<std::pair<K, V>> &res) {
@@ -280,10 +274,7 @@ class ScaledLRU {
     std::string t = "";
     for (size_t i = 0; i < lru_pool.size(); i++) {
       node_size += lru_pool[i].node_size;
-      // t += std::to_string(i) + "->" + std::to_string(lru_pool[i].node_size) +
-      // " ";
     }
-    // std::cout<<t<<std::endl;
 
     if (node_size <= size_limit) return 0;
     if (pthread_rwlock_wrlock(&rwlock) == 0) {
@@ -299,7 +290,7 @@ class ScaledLRU {
           }
         }
         if (global_count > size_limit) {
-          // std::cout<<"before shrinking cache, cached nodes count =
+          // VLOG(0)<<"before shrinking cache, cached nodes count =
           // "<<global_count<<std::endl;
           size_t remove = global_count - size_limit;
           while (remove--) {
@@ -313,11 +304,11 @@ class ScaledLRU {
             remove_node.lru_pointer->key_map.erase(remove_node.node->key);
             remove_node.lru_pointer->remove(remove_node.node, true);
           }
-          // std::cout<<"after shrinking cache, cached nodes count =
+          // VLOG(0)<<"after shrinking cache, cached nodes count =
           // "<<global_count<<std::endl;
         }
       } catch (...) {
-        // std::cout << "shrink cache failed"<<std::endl;
+        // VLOG(0) << "shrink cache failed"<<std::endl;
         pthread_rwlock_unlock(&rwlock);
         return -1;
       }
@@ -330,7 +321,7 @@ class ScaledLRU {
     if (diff != 0) {
       __sync_fetch_and_add(&global_count, diff);
       if (global_count > int(1.5 * size_limit)) {
-        // std::cout<<"global_count too large "<<global_count<<" enter start
+        // VLOG(0)<<"global_count too large "<<global_count<<" enter start
         // shrink task\n";
         thread_pool->enqueue([this]() -> int { return shrink(); });
       }

paddle/fluid/distributed/test/graph_node_test.cc

@@ -681,6 +681,28 @@ void testCache() {
   }
   st.query(0, &skey, 1, r);
   ASSERT_EQ((int)r.size(), 0);
+  ::paddle::distributed::ScaledLRU<::paddle::distributed::SampleKey,
+                                   ::paddle::distributed::SampleResult>
+      cache1(2, 1, 4);
+  str = new char[18];
+  strcpy(str, "3433776521");
+  result = new ::paddle::distributed::SampleResult(strlen(str), str);
+  cache1.insert(1, &skey, result, 1);
+  ::paddle::distributed::SampleKey skey1 = {8, 1};
+  char* str1 = new char[18];
+  strcpy(str1, "3xcf2eersfd");
+  usleep(3000);  // sleep 3ms to guaruntee that skey1's time stamp is larger
+                 // than skey;
+  auto result1 = new ::paddle::distributed::SampleResult(strlen(str1), str1);
+  cache1.insert(0, &skey1, result1, 1);
+  sleep(1);  // sleep 1 s to guarantee that shrinking work is done
+  cache1.query(1, &skey, 1, r);
+  ASSERT_EQ((int)r.size(), 0);
+  cache1.query(0, &skey1, 1, r);
+  ASSERT_EQ((int)r.size(), 1);
+  char* p1 = (char*)r[0].second.buffer.get();
+  for (int j = 0; j < r[0].second.actual_size; j++) ASSERT_EQ(p1[j], str1[j]);
+  r.clear();
 }
 void testGraphToBuffer() {
   ::paddle::distributed::GraphNode s, s1;