Merge branch 'develop' into develop

paddle/fluid/distributed/fleet.cc

@@ -146,41 +146,6 @@ void FleetWrapper::CreateClient2ClientConnection() {
       client2client_max_retry_);
 }
 
-std::future<int32_t> FleetWrapper::PullSparseVarsAsync(
-    const Scope& scope, const uint64_t table_id,
-    const std::vector<std::string>& var_names, std::vector<uint64_t>* fea_keys,
-    std::vector<std::vector<float>>* fea_values, int fea_value_dim) {
-  fea_keys->clear();
-  fea_keys->resize(0);
-  fea_keys->reserve(MAX_FEASIGN_NUM);
-  for (auto name : var_names) {
-    Variable* var = scope.FindVar(name);
-    if (var == nullptr) {
-      continue;
-    }
-    LoDTensor* tensor = var->GetMutable<LoDTensor>();
-    CHECK(tensor != nullptr) << "tensor of var " << name << " is null";
-    int64_t* ids = tensor->data<int64_t>();
-    size_t len = tensor->numel();
-    for (auto i = 0u; i < len; ++i) {
-      if (ids[i] == 0u) {
-        continue;
-      }
-      fea_keys->push_back(static_cast<uint64_t>(ids[i]));
-    }
-  }
-  fea_values->resize(fea_keys->size() + 1);
-  for (auto& t : *fea_values) {
-    t.resize(fea_value_dim);
-  }
-  std::vector<float*> pull_result_ptr;
-  for (auto& t : *fea_values) {
-    pull_result_ptr.push_back(t.data());
-  }
-  return pserver_ptr_->_worker_ptr->pull_sparse(
-      pull_result_ptr.data(), table_id, fea_keys->data(), fea_keys->size());
-}
-
 void FleetWrapper::PullSparseVarsSync(
     const Scope& scope, const uint64_t table_id,
     const std::vector<std::string>& var_names, std::vector<uint64_t>* fea_keys,
@@ -224,8 +189,10 @@ void FleetWrapper::PullSparseVarsSync(
   for (auto& t : *fea_values) {
     pull_result_ptr.push_back(t.data());
   }
+  bool training = true;
   auto status = pserver_ptr_->_worker_ptr->pull_sparse(
-      pull_result_ptr.data(), table_id, fea_keys->data(), fea_keys->size());
+      pull_result_ptr.data(), table_id, fea_keys->data(), fea_keys->size(),
+      training);
   pull_sparse_status.push_back(std::move(status));
   for (auto& t : pull_sparse_status) {
     t.wait();
@@ -238,9 +205,13 @@ void FleetWrapper::PullSparseVarsSync(
   }
 }
 
+// is_training is true means training, false means inference, the behavior is
+// different on pserver
+
 void FleetWrapper::PullSparseToTensorSync(const uint64_t table_id, int fea_dim,
                                           uint64_t padding_id,
                                           platform::Place place,
+                                          bool is_training,
                                           std::vector<const LoDTensor*>* inputs,
                                           std::vector<LoDTensor*>* outputs) {
   std::vector<uint64_t> fea_keys;
@@ -279,7 +250,8 @@ void FleetWrapper::PullSparseToTensorSync(const uint64_t table_id, int fea_dim,
   }
   auto* communicator = Communicator::GetInstance();
   auto status = communicator->_worker_ptr->pull_sparse(
-      pull_result_ptr.data(), table_id, fea_keys.data(), fea_keys.size());
+      pull_result_ptr.data(), table_id, fea_keys.data(), fea_keys.size(),
+      is_training);
   status.wait();
   auto ret = status.get();
   if (ret != 0) {

paddle/fluid/distributed/fleet.h

@@ -84,19 +84,14 @@ class FleetWrapper {
                           int fea_dim,
                           const std::vector<std::string>& var_emb_names);
 
-  // Pull sparse variables from server in async mode
-  // Param<in>: scope, table_id, var_names, fea_keys, fea_dim
-  // Param<out>: fea_values std::future
-  std::future<int32_t> PullSparseVarsAsync(
-      const Scope& scope, const uint64_t table_id,
-      const std::vector<std::string>& var_names,
-      std::vector<uint64_t>* fea_keys,
-      std::vector<std::vector<float>>* fea_values, int fea_dim);
-
   // Pull sparse variables from server in sync mode
   // pull immediately to tensors
+  // is_training is true means training, false means inference, the behavior is
+  // different on pserver
+
   void PullSparseToTensorSync(const uint64_t table_id, int fea_dim,
                               uint64_t padding_id, platform::Place place,
+                              bool is_training,
                               std::vector<const LoDTensor*>* inputs,  // NOLINT
                               std::vector<LoDTensor*>* outputs);      // NOLINT
 

paddle/fluid/distributed/service/brpc_ps_client.cc

@@ -768,8 +768,8 @@ std::future<int32_t> BrpcPsClient::push_global_step(int table_id,
 
 std::future<int32_t> BrpcPsClient::pull_sparse(float **select_values,
                                                size_t table_id,
-                                               const uint64_t *keys,
-                                               size_t num) {
+                                               const uint64_t *keys, size_t num,
+                                               bool is_training) {
   size_t request_call_num = _server_channels.size();
 
   auto shard_sorted_kvs = std::make_shared<
@@ -837,16 +837,27 @@ std::future<int32_t> BrpcPsClient::pull_sparse(float **select_values,
     uint32_t kv_request_count = 0;
     size_t sorted_kv_size = sorted_kvs.size();
     auto &request_buffer = closure->cntl(i)->request_attachment();
+
+    request_buffer.append((void *)&is_training, sizeof(bool));
+    std::vector<uint32_t> keys_counter;
+    keys_counter.reserve(sorted_kv_size);
+
     for (size_t kv_idx = 0; kv_idx < sorted_kv_size; ++kv_idx) {
       ++kv_request_count;
+      uint32_t keys = 1;
       last_key = sorted_kvs[kv_idx].first;
       request_buffer.append((void *)&last_key, sizeof(uint64_t));
       while (kv_idx < sorted_kv_size - 1 &&
              last_key == sorted_kvs[kv_idx + 1].first) {
         ++kv_idx;
+        ++keys;
       }
+      keys_counter.push_back(keys);
     }
 
+    request_buffer.append((void *)keys_counter.data(),
+                          sizeof(uint32_t) * keys_counter.size());
+
     if (kv_request_count == 0) {
       closure->Run();
     } else {
@@ -956,7 +967,7 @@ int32_t BrpcPsClient::recv_and_save_table(const uint64_t table_id,
   }
 
   auto status = pull_sparse((float **)save_vec.data(), table_id,
-                            save_key.data(), save_key.size());
+                            save_key.data(), save_key.size(), true);
   status.wait();
 
   // create lod tensor

paddle/fluid/distributed/service/brpc_ps_client.h

@@ -148,7 +148,8 @@ class BrpcPsClient : public PSClient {
 
   virtual std::future<int32_t> pull_sparse(float **select_values,
                                            size_t table_id,
-                                           const uint64_t *keys, size_t num);
+                                           const uint64_t *keys, size_t num,
+                                           bool is_training);
 
   virtual std::future<int32_t> print_table_stat(uint32_t table_id);
 

paddle/fluid/distributed/service/brpc_ps_server.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/distributed/service/brpc_ps_server.h"
 #include <thread>  // NOLINT
+#include "paddle/fluid/distributed/table/depends/sparse_utils.h"
 #include "paddle/fluid/distributed/table/table.h"
 #include "paddle/fluid/framework/archive.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -337,33 +338,39 @@ int32_t BrpcPsService::pull_sparse(Table *table,
                                    brpc::Controller *cntl) {
   platform::RecordEvent record_event("PsService->pull_sparse");
   CHECK_TABLE_EXIST(table, request, response)
-  thread_local std::string push_sparse_request_buffer;
+
   auto &req_io_buffer = cntl->request_attachment();
   auto req_buffer_size = req_io_buffer.size();
+
   if (req_buffer_size < 1) {
     set_response_code(response, -1, "req attachment is empty");
     return 0;
   }
+
   if (request.params_size() < 1) {
     set_response_code(response, -1,
                       "PsRequestMessage.params is requeired at "
                       "least 1 for num of sparse_key");
     return 0;
   }
+
   uint32_t num = *(uint32_t *)(request.params(0).c_str());
-  push_sparse_request_buffer.resize(0);
-  push_sparse_request_buffer.reserve(req_buffer_size);
-  const char *data = (const char *)cntl->request_attachment().fetch(
-      const_cast<char *>(push_sparse_request_buffer.data()), req_buffer_size);
-  /*
-  Attachment Content:
-  |---keysData---|
-  |---8*{num}B---|
-  */
-  const uint64_t *keys = (const uint64_t *)data;
+  auto dim = table->value_accesor()->select_dim();
+
+  thread_local std::string req_buffer;
+  req_buffer.reserve(req_buffer_size);
+
+  const void *data = cntl->request_attachment().fetch(
+      const_cast<char *>(req_buffer.data()), req_buffer_size);
+
+  auto value = PullSparseValue(num, dim);
+
+  value.DeserializeFromBytes(const_cast<void *>(data));
+
   std::vector<float> res_data;
-  res_data.resize(num * table->value_accesor()->select_size() / sizeof(float));
-  table->pull_sparse(res_data.data(), keys, num);
+  res_data.resize(num * dim);
+  table->pull_sparse(res_data.data(), value);
+
   cntl->response_attachment().append((char *)res_data.data(),
                                      res_data.size() * sizeof(float));
   return 0;

paddle/fluid/distributed/service/communicator.cc

@@ -320,9 +320,11 @@ void Communicator::RpcRecvSparse(const std::string &varname, int table_id,
     push_g_vec.push_back(tensor->data<float>() + i * dim);
   }
 
+  bool training = true;
+
   auto status = _worker_ptr->pull_sparse(
       (float **)push_g_vec.data(), table_id,  // NOLINT
-      sparse_push_keys.data(), sparse_push_keys.size());
+      sparse_push_keys.data(), sparse_push_keys.size(), training);
   status.wait();
   return;
 }

paddle/fluid/distributed/service/ps_client.h

@@ -112,10 +112,11 @@ class PSClient {
   // future结束前keys和values缓冲区不能再次使用
   // 整合多个线程请求的keys，聚集并分散发送到server
   // 返回结果后，遍历buffer并对values赋值
+  // is_training 用于区分请求是训练/预测，server端对于特征和准入会有不同的处理.
   virtual std::future<int32_t> pull_sparse(float **select_values,
                                            size_t table_id,
-                                           const uint64_t *keys,
-                                           size_t num) = 0;
+                                           const uint64_t *keys, size_t num,
+                                           bool is_training) = 0;
 
   virtual std::future<int32_t> print_table_stat(uint32_t table_id) = 0;
 

paddle/fluid/distributed/table/common_graph_table.h

@@ -103,13 +103,16 @@ class GraphTable : public SparseTable {
 
   Node *find_node(uint64_t id);
 
-  virtual int32_t pull_sparse(float *values, const uint64_t *keys, size_t num) {
+  virtual int32_t pull_sparse(float *values,
+                              const PullSparseValue &pull_value) {
     return 0;
   }
+
   virtual int32_t push_sparse(const uint64_t *keys, const float *values,
                               size_t num) {
     return 0;
   }
+
   virtual void clear() {}
   virtual int32_t flush() { return 0; }
   virtual int32_t shrink(const std::string &param) { return 0; }
@@ -140,5 +143,5 @@ class GraphTable : public SparseTable {
 
   std::vector<std::shared_ptr<::ThreadPool>> _shards_task_pool;
 };
-}
-};
+}  // namespace distributed
+};  // namespace paddle

paddle/fluid/distributed/table/common_sparse_table.cc

@@ -254,7 +254,6 @@ int32_t CommonSparseTable::initialize_value() {
   }
 
   auto accessor = _config.accessor();
-
   std::vector<uint64_t> feasigns;
 
   for (size_t x = 0; x < accessor.fea_dim(); ++x) {
@@ -271,9 +270,14 @@ int32_t CommonSparseTable::initialize_value() {
     std::vector<uint64_t> ids(bucket_feasigns);
     std::copy(feasigns.begin() + buckets[x], feasigns.begin() + buckets[x + 1],
               ids.begin());
+
+    std::vector<uint32_t> fres;
+    fres.resize(ids.size(), 1);
+
+    auto pull_value = PullSparseValue(ids, fres, param_dim_);
     std::vector<float> pulls;
     pulls.resize(bucket_feasigns * param_dim_);
-    pull_sparse(pulls.data(), ids.data(), bucket_feasigns);
+    pull_sparse(pulls.data(), pull_value);
   }
 
   return 0;
@@ -399,32 +403,36 @@ int32_t CommonSparseTable::pour() {
   return 0;
 }
 
-int32_t CommonSparseTable::pull_sparse(float* pull_values, const uint64_t* keys,
-                                       size_t num) {
+int32_t CommonSparseTable::pull_sparse(float* pull_values,
+                                       const PullSparseValue& pull_value) {
   rwlock_->RDLock();
 
-  std::vector<std::vector<uint64_t>> offset_bucket;
-  offset_bucket.resize(task_pool_size_);
-
-  for (int x = 0; x < num; ++x) {
-    auto y = keys[x] % task_pool_size_;
-    offset_bucket[y].push_back(x);
-  }
-
-  std::vector<std::future<int>> tasks(task_pool_size_);
+  auto shard_num = task_pool_size_;
+  std::vector<std::future<int>> tasks(shard_num);
 
-  for (int shard_id = 0; shard_id < task_pool_size_; ++shard_id) {
+  for (int shard_id = 0; shard_id < shard_num; ++shard_id) {
     tasks[shard_id] = _shards_task_pool[shard_id]->enqueue(
-        [this, shard_id, &keys, &offset_bucket, &pull_values]() -> int {
+        [this, shard_id, shard_num, &pull_value, &pull_values]() -> int {
           auto& block = shard_values_[shard_id];
-          auto& offsets = offset_bucket[shard_id];
 
-          for (int i = 0; i < offsets.size(); ++i) {
-            auto offset = offsets[i];
-            auto id = keys[offset];
-            auto* value = block->Init(id);
-            std::copy_n(value + param_offset_, param_dim_,
-                        pull_values + param_dim_ * offset);
+          std::vector<int> offsets;
+          pull_value.Fission(shard_id, shard_num, &offsets);
+
+          if (pull_value.is_training_) {
+            for (auto& offset : offsets) {
+              auto feasign = pull_value.feasigns_[offset];
+              auto frequencie = pull_value.frequencies_[offset];
+              auto* value = block->Init(feasign, true, frequencie);
+              std::copy_n(value + param_offset_, param_dim_,
+                          pull_values + param_dim_ * offset);
+            }
+          } else {
+            for (auto& offset : offsets) {
+              auto feasign = pull_value.feasigns_[offset];
+              auto* value = block->Init(feasign, false);
+              std::copy_n(value + param_offset_, param_dim_,
+                          pull_values + param_dim_ * offset);
+            }
           }
 
           return 0;

paddle/fluid/distributed/table/common_sparse_table.h

@@ -61,8 +61,7 @@ class CommonSparseTable : public SparseTable {
   int32_t save(const std::string& path, const std::string& param);
 
   virtual std::pair<int64_t, int64_t> print_table_stat();
-  virtual int32_t pull_sparse(float* pull_values, const uint64_t* keys,
-                              size_t num);
+  virtual int32_t pull_sparse(float* values, const PullSparseValue& pull_value);
 
   virtual int32_t push_sparse(const uint64_t* keys, const float* values,
                               size_t num);

paddle/fluid/distributed/table/common_table.h

@@ -98,8 +98,8 @@ class DenseTable : public Table {
   virtual ~DenseTable() {}
 
   virtual void *get_shard(size_t shard_idx) { return 0; }
-  int32_t pull_sparse(float *values, const uint64_t *keys,
-                      size_t num) override {
+  int32_t pull_sparse(float *values,
+                      const PullSparseValue &pull_value) override {
     return 0;
   }
   int32_t push_sparse(const uint64_t *keys, const float *values,
@@ -123,8 +123,8 @@ class BarrierTable : public Table {
 
   int32_t push_dense(const float *values, size_t num) override { return 0; }
 
-  int32_t pull_sparse(float *values, const uint64_t *keys,
-                      size_t num) override {
+  int32_t pull_sparse(float *values,
+                      const PullSparseValue &pull_value) override {
     return 0;
   }
   int32_t push_sparse(const uint64_t *keys, const float *values,