# Server

class `paddle::``ProtoServer`

  It implements the rpc framework, which launchs one thread for each connection. Here define one parameter server as single TCP server binding on single port. All connections share single tcp ProtoServer object, each connection handles all requests from specified trainer within single worker thread. to accelerate bandwidth efficiency and harness multicore for pserver optimization to reduce pserver latency, you could launch more port for single NIC hardward with port=N(N>1) for small cluster job.

  Inherits from paddle::SocketServer

  Subclassed by paddle::ParameterServer2

  Public Types

  typedef std::function<void(const google::protobuf::MessageLite &protoOut, const std::vector<iovec> &outputIovs)> `ProtoResponseCallbackEx`
  typedef std::function<void(const google::protobuf::MessageLite &protoOut)> `ProtoResponseCallback`

  Public Functions

  `ProtoServer`(const std::string &addr, int port, int rdmaCpu = -1)

    rdmaCpu controls the cpu affinity of RDMA server daemon, which could benifit performance. rdmaCpu = -1 means TCP is used instead of RDMA transport.

  template <class ProtoIn>
  void `registerServiceFunction`(const std::string &funcName, std::function<void(const ProtoIn &request, ProtoResponseCallback callback)> func)

    Register a service function for this server void(const ProtoIn& request, ProtoResponseCallback callback) The service function process the request and call the callback after it finishes the request.

    Use macro REGISTER_SERVICE_FUNCTION as a helper to simplify the use.

  template <class ProtoIn>
  void `registerServiceFunctionEx`(const std::string &funcName, std::function<void(const ProtoIn&, std::unique_ptr<MsgReader> msgReader, ProtoResponseCallbackEx callback)> func)

    Register a service function for this server The signature of the service function is void(const ProtoIn&, std::unique_ptr<MsgReader> msgReader, ProtoResponseCallbackEx callback) The service function process the request and call the callback after it finishes the request. The extended service function can take extra input blocks from the communication channel by reading msgReader. It can also send extra blocks to the communication channel by providing outputIovs as the argument for the callback function.

    Use macro REGISTER_SERVICE_FUNCTION_EX as a helper to simplify the use.

    create wrapper function for parameter server high level function and register the wrapper function into function mapping.

  template <class ProtoIn>
  void `registerServiceFunction`(const std::string &funcName, std::function<void(const ProtoIn&, ProtoResponseCallback callback)> func)

  Protected Types

  typedef std::function<void(std::unique_ptr<MsgReader> msgReader, ResponseCallback callback)> `ServiceFunction`

  Protected Functions

  void `handleRequest`(std::unique_ptr<MsgReader> msgReader, ResponseCallback callback)

    handle rpc request

    Note
      it lookups rpc function mapping table to find function pointer, then call this function with further reading data from connection
    Parameters
      - `msgReader`: Message reader for reading data from connection
      - `callback`: equal to channel->writeMessage
  void `registerServiceFunctionImp`(const std::string &funcName, ServiceFunction func)

    register one RPC function in function mapping

    Parameters
      - `funcName`: function name string
      - `func`: rpc function wrapped with reading and writing data

  Protected Attributes

  ThreadLocal<struct timeval> `handleRequestBegin_`

    Tuning bare network overhead: the beginning of receiving request.

  std::map<std::string, ServiceFunction> `nameToFuncMap_`

    mapping to find rpc function while handling request

class `paddle::``ParameterServer2`

  Client interface for the parameter server

  it implements several rpc API for remote parameter client usage. for sync-sgd, client needs one controller thread to build connections to all pservers, these controller connections do barriers synchronization with these connections used for transfering data. each data connection uses block based fine grained synchronization to gain better scalability. Merging gradients from different trainers are concurrently executed with block units, so that some network overhead will be hidden in merging gradient. for async-sgd, the difference is that pserver will do optimization immediately if the gradients are ready, so that pserver needs to prepare separate buffer to store value for sending back to trainer to prevent from being polluted.

  Inherits from paddle::ProtoServer

  Public Types

  typedef void (`ParameterServer2`::*`OperatorFunction`)(const Operation &operation, OperationResult *result)

  Public Functions

  `ParameterServer2`(const std::string &addr, int port, int rdmaCpu = -1)

    disable default parameter for overloading :the id of cpu core hosting RDMA server(0-N) -1 means using TCP transport instead of RDMA

  `~ParameterServer2`()
  template <typename Dtype>
  void `reduceAndSendData`(const SendDataRequest &request, std::unique_ptr<MsgReader> &msgReader, ProtoResponseCallbackEx &callback)

    service functions

  void `templateReduceSum`(const SendDataRequest &request, std::unique_ptr<MsgReader> &msgReader, ProtoResponseCallbackEx &callback)
  void `sendParameter`(const SendParameterRequest &request, std::unique_ptr<MsgReader> msgReader, ProtoResponseCallbackEx callback)

    framework for sending parameters

    Note
      different parameter data type can be sent to pserver. in most case, the api is used to send gradients from trainer to pserver. it also can be used to retrieve parameters from pserver
  void `sendData`(const SendDataRequest &request, std::unique_ptr<MsgReader> msgReader, ProtoResponseCallbackEx callback)
  void `setConfig`(const SetConfigRequest &request, ProtoResponseCallback callback)

    send config to pserver

    Note
      it can help pserver to understand the configuration for optimization, logging control, duplicated initialization, etc.
  void `getStatus`(const GetStatusRequest &request, ProtoResponseCallback callback)

    get status for pserver

    Note
      used to check if parameters are ready at pserver
  void `setStatus`(const SetStatusRequest &request, ProtoResponseCallback callback)

    set status for pserver

    Note
      used to check if parameters are ready at pserver, since parameters at pserver are initialized by trainer
  void `doOperation`(const DoOperationRequest &request, ProtoResponseCallback callback)

    framework for doing some operation at pserver end

    Note
      if sync-sgd is used, controller will calling op_SGD action for gradient optimization. check avaiable operations in opFuncs[]
  void `createVector`(const CreateVectorRequest &request, ProtoResponseCallback callback)

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

  void `releaseVector`(const ReleaseVectorRequest &request, ProtoResponseCallback callback)
  void `createMatrix`(const CreateMatrixRequest &request, ProtoResponseCallback callback)

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

  void `releaseMatrix`(const ReleaseMatrixRequest &request, ProtoResponseCallback callback)
  void `waitPassStart`(const WaitPassStartRequest &request, ProtoResponseCallback callback)

    stateful control for indicationg sync pass start

    Note
      it is valuable for logging and state control, especially for sync-sgd control
  void `waitPassFinish`(const WaitPassFinishRequest &request, ProtoResponseCallback callback)

    stateful control for indicationg sync pass end

    Note
      it is valuable for logging and state control, especially for sync-sgd control
  void `synchronize`(const SynchronizeRequest &request, ProtoResponseCallback callback)

    synchronize all distributed trainers

    Note
      it’s general api for synchronizing trainer and pserver
  void `asyncFinishPass`(const SynchronizeRequest &request, ProtoResponseCallback callback)

    stateful control for indicating async pass is finished

    Note
      it is valuable for logging control, state reset, etc.
  void `loadValueVector`(const LoadValueRequest &request, ProtoResponseCallback callback)
  void `saveValueVector`(const SaveValueRequest &request, ProtoResponseCallback callback)
  bool `init`()

    initialize parameter server

  void `setParameter`(const SendParameterRequest &request, std::vector<Buffer> &inputBuffers, SendParameterResponse *response, std::vector<Buffer> *outputBuffers)

    set parameters at pserver

    Note
      do parameter initialization if neccessy.
  void `asyncSGD`(const SendParameterRequest &request, std::vector<Buffer> &inputBuffers, SendParameterResponse *response, std::vector<Buffer> *outputBuffers)

    receive gradients and do optimization for async-sgd

    Note
      this api asynchronizately receives all data from all trainers, and immediately do optimization and return optimizated value for trainer. this above routine are block based atomic updating, which means different block could based different stale gradient. it will discard some lagged gradients by default for better convergence.
  void `addGradient`(const SendParameterRequest &request, std::vector<Buffer> &inputBuffers, SendParameterResponse *response, std::vector<Buffer> *outputBuffers)

    merge gradients from all trainer

    Note
      this api use block based parallelization as fine grained parallelization which benifits lock contention and latency hidden for communication, also can harness multi-core efficiently. it also implements the synchronization for sync-sgd
  void `getParameter`(const SendParameterRequest &request, std::vector<Buffer> &inputBuffers, SendParameterResponse *response, std::vector<Buffer> *outputBuffers)

    get dense parameters from pserver

    Note
      for some specified condition, trainer will get parameters from pservers. e.g. if all parameters are stored at perver end for big model training trainer can use it to retrieve all parameters if necessary.
  void `getParameterSparse`(const SendParameterRequest &request, std::vector<Buffer> &inputBuffers, SendParameterResponse *response, std::vector<Buffer> *outputBuffers)

    get sparse value from parameter server

    Note
      with sparse enabled, pservers own all latest value while trainer only retrieve value that only are needed. e.g. trainer will do prefetch action to retrieve necessary latest value from pserver for sparse calculation.
  void `op_SGD`(const Operation &operation, OperationResult *result)
  void `op_RESET`(const Operation &operation, OperationResult *result)
  void `op_utv`(const Operation &operation, OperationResult *result)
  void `op_au_bv`(const Operation &operation, OperationResult *result)
  void `op_COPY`(const Operation &operation, OperationResult *result)
  void `op_au`(const Operation &operation, OperationResult *result)
  void `op_au_bv_cw`(const Operation &operation, OperationResult *result)
  void `op_make_steepest_desc_dir`(const Operation &operation, OperationResult *result)
  void `op_fix_dir_signs`(const Operation &operation, OperationResult *result)
  void `op_dir_deriv`(const Operation &operation, OperationResult *result)
  void `op_fix_omega_signs`(const Operation &operation, OperationResult *result)
  void `op_cost`(const Operation &operation, OperationResult *result)
  void `op_start_pass`(const Operation &operation, OperationResult *result)
  void `op_finish_pass`(const Operation &operation, OperationResult *result)
  void `op_apply`(const Operation &operation, OperationResult *result)
  void `op_randomize`(const Operation &operation, OperationResult *result)
  void `op_load`(const Operation &operation, OperationResult *result)
  void `op_save`(const Operation &operation, OperationResult *result)
  void `tuningSgdMidOutput`()

    output log in at the middle stage of training

    Note
      flush log histroy and state at the end for sgd
  void `tuningSgdFinished`()

    output log in at the end stage of training

    Note
      flush log histroy and state at the end for sgd. it will also flush some stateful stat for next pass.
  void `tuningAsyncsgdMidOutput`()

    output log in at the middle stage of training

    Note
      flush log histroy and state at the end for async-sgd. it will log some performance log if some lagged node are found
  void `tuningAsyncsgdFinished`()

    output log in at the end stage of training

    Note
      flush log histroy and state at the end for async-sgd.

  Public Static Attributes

  const std::string `kRetMsgInvalidMatrixHandle`
  const std::string `kRetMsgInvalidVectorHandle`
  const std::string `kRetMsgUnknownOperation`
  ParameterServer2::OperatorFunction `opFuncs`

    doOperation will call following operations indirectly e.g. for sync-sgd control, the controller in remote updater will send op_SGD command to pserver, then send sendParameter request to pserver immediately. the two function at pserver end will do cooperation to achieve the sync-sgd gradient merge and optimization. the most following operations are specified for owlqn, all operations are under the context of doOperation function

  Protected Types

  typedef std::pair<size_t, int64_t> `BlockKey`
  typedef std::unordered_map<BlockKey, int64_t, BlockKeyHash> `BlockMap`

    all parameters are stored in CpuVector with a blockMap_ data structure to index block data required by requests.

  typedef std::vector<std::pair<int64_t, int64_t>> `BlockSegments`
  typedef std::function<void(int64_t blockId, const VectorPtr vecs[])> `ExecFunc`

    framework routine for block parallelization e.g. for optimization on all blocks at pserver end, this routine can facilitize the parallelize of do optimization on all blocks with multithreads.

  Protected Functions

  bool `asyncGrdientCommitCheckAndStat`(const SendParameterRequest &request)

    async gradient commit control

  void `printAsyncGradientCommitStatAndReset`()
  void `mergeSegments`(BlockSegments *segments)
  void `clearUnusedSegments`(CpuVector *vec)

    set the unused segments to zero

  void `readAllBlocks`(MsgReader *msgReader, std::vector<ParameterServer2::Buffer> *buffers)

    read all data from connection and store it in static pre-allocated buffer

  const ParameterConfig &`getParameterConfig`(const ParameterBlock &block)
  const ParameterConfig &`getParameterConfig`(int64_t blockId) const

    it implictly check blockOffsetMap_ while retrieving blockId

  template <class Response>
  bool `isValidVectorHandle`(int64_t handle, Response *response)
  template <class Response>
  bool `isValidMatrixHandle`(int64_t handle, Response *response)
  int64_t `getBlockOffset`(const ParameterBlock &block) const

    get block offset

    Note
      block.begin_dim is added to the block offset. return -1 if block cannot be found
  int64_t `getBlockId`(const ParameterBlock &block) const

    return -1 if block cannot be found

  void `sendBackParameter`(const ParameterBlock &block, int parameterType, SendParameterResponse *response, std::vector<Buffer> *outputBuffers)

    prepare data for sending back

    Note
      modify reponse and outputBuffers for sending parameter back to client. The buffer for socket sending uses vectors_[parameterType] directly for dense with sync-sgd
  void `sendBackParameter`(const ParameterBlock &block, int parameterType, SendParameterResponse *response, Buffer *buffer, std::vector<Buffer> *outputBuffers)

    prepare data for sending back

    Note
      modify response and outputBuffers for sending parameter back to client. The buffer for socket sending uses buffer->base The parameter values are copied from vectors_[parameterType] to buffer->base. for dense with async-sgd
  void `sendBackParameterSparse`(const ParameterBlock &block, int parameterType, SendParameterResponse *response, Buffer *buffer, size_t width, std::vector<Buffer> *outputBuffers)

    prepare data for sending back

    Note
      specified for sparse
  void `parallelExecForEachBlock`(ExecFunc func)
  void `blockTraverse`(BlockInfo &info, const ParameterConfig &config, int64_t offset, size_t size, const VectorPtr vecs[], const ParameterOptimizer::TraverseCallback &callback)

  Protected Attributes

  RWLock `parameterMutex_`

    parameter_ mutex.

  BlockMap `blockOffsetMap_`

    <(para, block), global offset(byte) in all parameters>

  BlockMap `blockIdMap_`

    <(para, block), global idx [0, nBlocksInAllParameters]>

  std::vector<CpuVectorPtr> `vectors_`
  std::vector<CpuMatrixPtr> `matrices_`
  std::vector<CpuMemHandlePtr> `dataMems_`
  std::unordered_map<size_t, ParameterConfig> `configMap_`

    mapping between parameter and config different parameter allows different config, such as decay_rate. for each request, it need to read config for adding gradient and optmization.

  std::vector<BlockInfo> `blockInfos_`
  BlockSegments `usedSegments_`

    Because some blocks might not be fully used. We keep a record of which segments are used.

  PServerStatus `status_`

    record pserver status, all status defined in ParameterService.pb

  std::atomic<int64_t> `numSamplesProcessed_`

    record all samples processed which could be used by optimizater

  double `cost_`
  int `mpiSize_`
  int `dataSize_`
  OptimizationConfig `config_`

    configuration for current parameter optimizer

  ThreadLocal<ReadWriteBuffer<real, ALIGN_HINT>> `readWriteBuffer_`

    to buffer the data from network for further processing to reduce redundant memory allocation.

  int64_t `size_`

    size of the parameter

  ThreadBarrier `gradientReadyBarrier_`

    for synchronized training, check details in addGradient() and doOperation()

  ThreadBarrier `parameterReadyBarrier_`
  ThreadBarrier `passBarrier_`
  ThreadLocal<std::vector<SendParameterRequest>> `requestVec_`
  ThreadLocal<std::vector<ProtoResponseCallbackEx>> `callbackVec_`
  std::atomic<int> `numPassFinishClients_`
  bool `allClientPassFinish_`
  std::vector<std::unique_ptr<ThreadBarrier>> `synchronizeBarriers_`
  std::atomic<int> `serverId_`
  int64_t `asyncLaggedThreshold_`

    for lagged async gradient gradient commit control in Async Sgd. discard lagged gradients from too slow nodes, whose gradients exhibits bad quality. Algorithm: pserver:

    1. initial asyncUpdaterSteps = 0, asyncTrainerSteps_[N] = 0. syncUpdaterSteps means the version of parameter value.
    2. when pull arrives, record asyncUpdateSteps_ into syncTrainerSteps_[trainer_id]
    3. when push arrives, compare asyncUpdateSteps_ with syncTrainerSteps_[trainer_id] if delta > threshold, discard current gradient, else commit gradient.
    4. reset asyncUpdaterSteps_ and asyncTrainerSteps_[N] when pass finished Note: it can not discard all lag-gradient strictly in some special condition. part of gradients could be discarded if ConcurrentRemoteParameterUpdater is sed. this algorithm is implemented in asynSGD()
  std::atomic<int64_t> `asyncUpdateSteps_`
  std::vector<int64_t> `asyncTrainerSteps_`
  size_t `asyncLaggedGradientsNum_`
  std::vector<size_t> `asyncUpdateStat_`

    stat all async update

  std::vector<size_t> `asyncTrainerDiscardStat_`

    stat per trainer_id

  std::vector<size_t> `asyncTrainerCommitStat_`

    stat per trainer_id

  std::unique_ptr<SyncThreadPool> `syncThreadPool_`

    only used by controller and other control cmd from trainer number 0

  bool `isSparseServer_`

    pserver for sparse remote update parameters

  std::atomic<int64_t> `batchId_`

    barrier performance tuning sync-sgd required

  ThreadLocal<struct timeval> `addGradBegin_`

    the beginning of addGradient without network overhead

  std::unique_ptr<StatSet> `statSet_`

    tuning barrier performance to better control log for sparse and dense parameter, we use different log entities for different parameterServer objects. it will output lots of performance stats to perceive the overhead of network, fluctuation of computation from forwardbackward and network, computation from optimization at pserver end, barrier overhead, etc. to understand tuning data, focus on the synchronization between addGradient and doOperation which indirectly call op_SGD operation controlled by remote updater controller

  struct `BlockInfo`

    to parallelize the multi-thread and multi-connnection computation at pserver, it use block unit to reduce the contention for computation, even further use block level optimizater control for each block for some special reason annotated below.

    Public Members

    const ParameterConfig *`config`
    std::unique_ptr<std::mutex> `lock`
    uint64_t `offset`

      global offset for all parameters

    std::unique_ptr<ParameterOptimizer> `optimizer`

      Async sgd in pserver is very different from sync sgd. Each trainer follows startBatch, update*, finishBatch as in sync sgd, but all these actions are almost executed by multi-core and multi-thread simutaneously, so that async sgd optimization is based on block level in reality, then per block optimization is necessary indeed. In addition, per block optimization is also perfered for performance with multithreads.

  struct `BlockKeyHash`

    Public Functions

    size_t `operator()`(const BlockKey &key) const
  struct `Buffer`

    Public Members

    real *`base`
    size_t `size`
  template <typename T, size_t AlignBytes>
  class `ReadWriteBuffer`

    The ReadWriteBuffer is based on std::vector, but aligned for avx/sse compute. And add some helper method to allocate memory aligned blocks.

    Parameters
      - `T`: type of element.
      - `AlignBytes`: the memory aligned bytes for allocated blocks.

    Inherits from std::vector< T, AlignedAllocator< T, AlignBytes > >

    Public Functions

    void `resizeWithAlignHints`(size_t size, size_t alignBlockCount = 1)

      Resize Buffer, with block count that will be allocated. Each block will be memory aligned in AlignBytes.

      Parameters
        - `size`: The element count in all blocks.
        - `alignBlockCount`: The block count that will be allocated.
    void `resetAlignAlloc`()

      reset aligned allocate blocks.

    T *`nextBlock`(size_t blockSize)

      get next aligned block address.

      Return
        Aligned block address.
      Parameters
        - `blockSize`: is the element count in each block.

    Public Static Attributes

    constexpr bool `IsTLargerThanAlign`

      IsTLargerThanAlign compiled time calculated constant for is type T larger than alignments.

    constexpr size_t `AlignElementCount`

      if AlignBytes > sizeof(T), then will calcuate how many elements can be stored in AlignBytes.

    Private Members

    size_t `curOffset_`