# Client

class `paddle::``BaseClient`

  it manages all connections to pservers. it exists two modes to manage connections to all pservers. Firstly, one connection owns two threads that separately manage to send and receive data. Secondly, each thread uses one connection for all activation in it. the first solution arms with sendThreads_/recvThreads_ and sendJobQueue_/ recvJobQueue_. the second solution use some shared thread pool to manage connections. In addition to pserver, metric learning also uses network to exchange features within multi-machines, so this class just abstracts some basic threads and queue buffer creation for them

  Subclassed by paddle::ParameterClient2

  Public Types

  typedef std::shared_ptr<SendJob> `SendJobPtr`
  typedef Queue<SendJobPtr> `SendQueue`

  Public Functions

  `BaseClient`(bool separate = false, int numPorts = FLAGS_ports_num)
  `~BaseClient`()
  template <class DataType>
  void `putData`(int clientId, SendDataType type, DataType *datas, size_t size, DataUpdateMode mode)

    send data to server, support only synchronize

  template <class DataType>
  void `putOwnData`(int clientId, SendDataType type, DataType *datas, size_t size)
  template <class DataType>
  void `getAllData`(int clientId, SendDataType type, DataType *datas, size_t size)
  template <class DataType>
  void `reduce`(DataType *sendBuf, DataType *recvBuf, size_t size, int clientId, int rootId)

    Reduces values on all clients. This reduce just support SUM. The results are saved in recvBuf of rootId client

  virtual TransDataType `getTransDtype`(const std::type_info &info)

    return trans data type according to the input type

  Protected Types

  typedef std::unique_ptr<std::thread> `ThreadPtr`
  typedef std::vector<std::vector<iovec>> `InputIovs`
  typedef std::vector<SendParameterRequest> `SendRequest`
  typedef std::vector<SendDataRequest> `SendDataRequestVec`

  Protected Functions

  int `calcClientId`(int i, int serviceNum)
  void `startThreads`()

    start threads in sendThreads_ and recvThreads_

  void `finishThreads`()

    finish threads in sendThreads_ and recvThreads_

  template <class DataType>
  void `prepareData`(int clientId, SendDataType type, DataUpdateMode updateMode, DataType *datas, size_t size, SendJob *sendJob)
  template <class DataType>
  void `sendData`(int clientId, SendDataType type, DataUpdateMode updateMode, DataType *datas, size_t size)

    send data to all data servers

    Note
      each trainer sends all its data to all data servers it’s for broadcast data synchronization, such as features synchronization in metric learning.
  void `recvData`()

    recv data from all data servers

    Note
      synchronize all recv threads
  template <typename ProtoIn, typename ProtoOut>
  void `multiCall`(const char *funcName, const ProtoIn &request, std::vector<ProtoOut> *responses)

    send request, and recv responses

  void `synchronize`(SyncObject syncObjectId = SYNC_DEFAULT)

    synchronize all trainers and pservers

    Note
      used to ensure that data of all trainers have been received
  virtual void `send`(int threadId) = 0

    use multithread to separately send data

    Note
      each thread should read its own JobQueue to handle requests each thread should calcClientId() to retrieve connections managed by himself. send and recv are implemented in child class.
  virtual void `recv`(int threadId) = 0

    use multithread to separately receive data

    Note
      almost same as send()

  Protected Attributes

  bool `stopping_`
  int `serviceNum_`

    nodes * ports that means the number of real pservers

  int `threadNum_`

    threads num for managing all services. Normally the number of pservers are relatively less than several hundreds so that using thread-based parallelization can benifit traffic performance and pserver’s sgd optimization performance.

  std::vector<ProtoClient> `clients_`

    the connection manager at client end

  std::vector<ThreadPtr> `sendThreads_`

    send threads for parallelization

  std::vector<ThreadPtr> `recvThreads_`

    recv threads for parallelization

  std::unique_ptr<ThreadBarrier> `recvSyncBarrier_`
  std::vector<std::unique_ptr<SendQueue>> `sendJobQueue_`

    send/recv queue cooperates with each other to accomplish overlapping communication with forwardBackward action.

  std::vector<std::unique_ptr<SendQueue>> `recvJobQueue_`

    queue for buffering recv request

  SendJob `sendJob_`

    specific for dserver

  int `numPorts_`

    port num for each node

  bool `separateSendAndRecv_`

    if set, overlapped optimization is disabled

  std::vector<CpuMemHandlePtr> `recvDataMems_`

  Protected Static Functions

  static int `divup`(int a, int b)

    for a > 0, b > 0: return the smallest x s.t. b*x >= a

  struct `SendJob`

    Public Members

    InputIovs `parallelInputIovs`

      store parameters related blocks data

    SendRequest `parallelRequests`

      store protobuf request

    SendDataRequestVec `parallelDataRequests`

      store data, such as features for metric learning

class `paddle::``ParameterClient2`

  The client interface for parameter server. ParameterClient2 supports 2 modes for managing connections to parameter servers, in the 1st mode one connection is shared by 2 threads that are separately responsible for sending and recieving activities, in the 2nd mode one connection is owned by only one thread, and all the sending and recieving activities run in that single thread. TODO(yanfei): Additional core idea to further optimizate pserver performance is to do sync-sgd based parameter level instead of pserver level. full-parallelization based parameter level for sync-sgd also can sense forwardbackward computation layer-by-layer for more deeper layer model. Firstly, pserver can do full-parallelization on all computation based parameter level instead of waiting for all gradients are finished and start to send back parameters value immediately if parameter is ready instead of waiting for all parameters value are ready Secondly, parameter client can write back parameters to GPU instead of waiting until all parameters are received to CPU host end.

  Inherits from paddle::BaseClient

  Public Functions

  `ParameterClient2`(bool separate = false, int port = FLAGS_port, int numPorts = FLAGS_ports_num)

    Constructor.

    Parameters
      - `separate`: True if sending and recieving activities are separated into 2 threads, otherwise false.
      - `port`: Port number that parameter client runs on.
      - `numPorts`: Number of ports parameter clients occupies, numPorts * pserver number is the total number of connections the parameter client maintains.
  `~ParameterClient2`()
  bool `init`(const std::vector<ParameterPtr> &parameters)
  void `sendAndReceiveParameter`(ParameterUpdateMode updateMode, ParameterType parameterType, const std::vector<ParameterSegments> &segments, int64_t numSamples, real cost, bool sendBackParameter, ParameterType sendBackParameterType, ParameterType recvParameterType)

    service functions

    Sends the segments in parameter to parameter servers, then receives the response from the servers.

    Note
      Only parameterType will be sent.
    Parameters
      - `updateMode`: Indicates how parameters should be updated on the server side.
      - `parameterType`: Type of parameter that will be sent.
      - `segments`: Segments in the parameter that will be sent.
      - `numSamples`: Number of samples this update is based on.
      - `cost`: Cost of the batch, will be used to calculate global object value.
      - `sendBackParameter`: True if the updated parameters should be sent back, otherwise false.
      - `sendBackParameterType`: Send back parameter type on pserver, PARAMETER_VALUE by default
      - `recvParameterType`: pserver[sendBackParameterType] will be copy to client[recvParameterType]
  void `sendAndReceiveParameter`(ParameterUpdateMode updateMode, ParameterType parameterType, int64_t numSamples, real cost, bool sendBackParameter, ParameterType sendBackParameterType = PARAMETER_VALUE, ParameterType recvParameterType = PARAMETER_VALUE)

    Sends all parameters to parameter servers, and receives the response from the servers.

  void `sendParameter`(ParameterUpdateMode updateMode, ParameterType parameterType, const std::vector<ParameterSegments> &segments, int64_t numSamples, real cost, bool sendBackParameter, BatchStatus batchStatus)

    Sends the segments in parameter to parameter servers. Each sendParameter() must be paired with a recvParameter() in the future. Only parameterType will be sent.

    Note
      This function is non-blocking. This means that parameter should not change between this call and recvParameter()
    Parameters
      - `updateMode`: Indicates how parameters should be updated on the server side.
      - `parameterType`: Type of parameter that will be sent.
      - `segments`: Segments in the parameter that will be sent.
      - `numSamples`: Number of samples this update is based on.
      - `cost`: Cost of the batch, will be used to calculate global object value.
      - `sendBackParameter`: True if the updated parameters should be sent back, otherwise false.
      - `batchStatus`: Status of the batch.
  void `recvParameter`()
  void `sendParameter`(ParameterUpdateMode updateMode, ParameterType parameterType, int64_t numSamples, real cost, bool sendBackParameter, BatchStatus batchStatus)

    Sends all parameters to parameter servers, recvParameter() have to be invoked afterwards.

    Note
      This function is non-blocking. This means that if parameter should not changes between this call and recvParameter()
  void `getParameter`(ParameterType recvParameterType = PARAMETER_VALUE, ParameterType sendBackParameterType = PARAMETER_VALUE)

    Get all parameters from parameter servers.

  void `getParameterSparse`(ParameterType recvParameterType = PARAMETER_VALUE, ParameterType sendBackParameterType = PARAMETER_VALUE)

    Get parameters by sparse row ids from parameter servers.

  void `setParameter`()

    Set all parameters on parameter servers using the local parameters.

  void `setParameterZero`()

    Set all parameters on parameter servers, values will be zero means do not sending local parameters

  void `waitPassStart`()

    Wait until all gradient servers start one pass.

    Note
      This is now only used by the gradient servers for “sgd” algorithm. Calling this function means that the calling gradient server is ready to start a new pass.
  void `waitPassFinish`()

    Wait until all gradient servers finish one pass.

    Note
      This is now only used by the gradient servers for “sgd” algorithm. Calling this function means that the calling gradient server finishes one pass.
  void `synchronize`(SyncObject syncObjectId = SYNC_DEFAULT)

    Wait until all gradient servers call this function.

  void `asyncFinishPass`(SyncObject syncObjectId = SYNC_DEFAULT)

    Called when async-sgd finish pass.

  void `asyncStartPass`(SyncObject syncObjectId = SYNC_DEFAULT)
  void `doOperation`(PreparedOperations &ops, bool waitForGradient, bool sendBackParameter, bool releasePass = true)

    Execute the prepared operations on pservers, fetch the results and aggregate results from different pservers.

    Parameters
      - `ops`: Prepared operations that will be executed on pservers.
      - `waitForGradient`: If true, wait for gradient to be ready before starting the operations.
      - `sendBackParameter`: If true, send back the parameter to clients after the operations are finished.
      - `If`: true, and if all clients call waitPassFinish, signal all clients finish the pass.
  void `setConfig`(const OptimizationConfig &optConfig, const std::string &saveDir = "", bool isSparseServer = false)

    Set the configuration of pserver, including parameter config and optimization config

  bool `inStatus`(PServerStatus status)

    Return true if all pservers are in the given status.

  bool `isPassFinish`()
  void `setStatus`(PServerStatus status)

    Set pserver status.

  void `waitForStatus`(PServerStatus status)

    Wait until all pservers are at status.

    Note
      This function is not suitable for frequent use, because it sleeps 1 second each time when condition is satisfied.
  PServerVector `createVector`()

    Create a column vector. The size is the dimension of parameter.

  void `releaseVector`(PServerVector handle)

    Release the PServerVector given handle.

  PServerMatrix `createMatrix`(int32_t numCols)

    Create a column major matrix. The number of rows is the dimension of parameter. The number of columns is specifed by numCols.

  void `releaseMatrix`(PServerMatrix handle)

    Release the PServerMatrix given handle.

  real `vectorDotProduct`(PServerVector u, PServerVector v)

    Calculate the dot product of u and v.

  void `vectorScale`(PServerVector u, real a)

    Scale u by a.

  void `vectorCopy`(PServerVector src, PServerVector dst)

    Copy from src to dest.

  void `vectorAddMult`(PServerVector u, PServerVector v, real a)

    u += v * a

  void `vectorAddMultInto`(PServerVector u, PServerVector v, PServerVector w, real a)

    u = v + w * a

  void `vectorScaleInto`(PServerVector u, PServerVector v, real a)

    u = v * a

  PServerVector `getPServerParameterValue`()

    Return pserver parameter value.

  PServerVector `getPServerParameterGradient`()

    Return pserver parameter gradient.

  void `loadValueVector`(const std::string &dirName)

    Tell pservers to load value vector from file.

    Parameters
      - `dirName`: The directory that contains the value vector file.
  void `saveValueVector`(const std::string &dirName)

    Tell pservers to save value vector to file.

  void `setTrainerId`(int trainerId)
  void `setForwardbackwardTime`(uint64_t delta)

  Public Static Functions

  int `calcParameterBlockSize`(const std::vector<ParameterPtr> &parameters, size_t serviceNum)

  Protected Functions

  template <typename ProtoIn, typename ProtoOut>
  void `multiCall`(const char *funcName, const ProtoIn &request, std::vector<ProtoOut> *responses)

  Protected Attributes

  int `port_`

    start port number of pserver it deduce all ports for dense and sparse with some rules

  int `trainerId_`

    identify the trainer id using this client

  uint64_t `forwardbackwordTime_`
  std::unordered_map<size_t, ParameterPtr> `parameterMap_`

    map id to parameter used for decoding protobuf data

  std::vector<ParameterSegments> `allSegments_`

    segments for all parameters that needed to sync

  std::unique_ptr<SparseParameterDistribution> `sparseDistribution_`

    module for sensing sparse parameters distribution on all pservers

  std::unique_ptr<SyncThreadPool> `syncThreadPool_`

    thread pool for parallelizing all connections to pservers

  bool `passFinish_`

  Private Functions

  void `destroy`()
  void `sendParallel`(int tid, size_t numThreads, ParameterType recvParameterType)

    management function for parallelizing send/recv all connections to all pservers. it is called under one SyncThreadPool. it supports to use N thread to control M connections. the receiving actions can be started until all sending action to all connections owned by current thread are finished. Different connections controlled by different threads can transfer data asynchronously.

  void `send`(int threadId)

    sending thread routine for asynchronously send data

  void `recv`(int threadId)

    receiving thread routing for asynchronously receive data

  void `prepareSendData`(ParameterUpdateMode updateMode, ParameterType parameterType, const std::vector<ParameterSegments> &parameterSegments, int64_t numSamples, real cost, bool sendBackParameter, ParameterType sendBackParameterType, BatchStatus batchStatus, SendJob *sendJob)

    main routine to build data for pserver

    Note
      it can prepare different kinds of parameter type data. it can be regarded as layer for bridging real parameters data and protobuf data for communication. TODO(yanfei): can abstract additional layer to encode and decode data to/from protobuf data.
  void `initThreads`()

    start necessary threads for threadPool