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A Param object in SINGA represents a set of parameters, e.g., a weight matrix or a bias vector. Basic user guide describes how to configure for a Param object, and Advanced user guide provides details on implementing users’ parameter initialization methods.

Basic user guide

The configuration of a Param object is inside a layer configuration, as the Param are associated with layers. An example configuration is like

layer {
  param {
    name : "p1"
    init {
      type : kConstant
      value: 1

The SGD algorithm starts with initializing all parameters according to user specified initialization method (the init field). For the above example, all parameters in Param “p1” will be initialized to constant value 1. The configuration fields of a Param object is defined in ParamProto:

  • name, an identifier string. It is an optional field. If not provided, SINGA will generate one based on layer name and its order in the layer.
  • init, field for setting initialization methods.
  • share_from, name of another Param object, from which this Param will share configurations and values.
  • lr_scale, float value to be multiplied with the learning rate when updating the parameters
  • wd_scale, float value to be multiplied with the weight decay when updating the parameters

There are some other fields that are specific to initialization methods.

Initialization methods

Users can set the type of init use the following built-in initialization methods,

  • kConst, set all parameters of the Param object to a constant value

    type: kConst
    value: float  # default is 1
  • kGaussian, initialize the parameters following a Gaussian distribution.

    type: kGaussian
    mean: float # mean of the Gaussian distribution, default is 0
    std: float # standard variance, default is 1
    value: float # default 0
  • kUniform, initialize the parameters following an uniform distribution

    type: kUniform
    low: float # lower boundary, default is -1
    high: float # upper boundary, default is 1
    value: float # default 0
  • kGaussianSqrtFanIn, initialize Param objects with two dimensions (i.e., matrix) using kGaussian and then multiple each parameter with 1/sqrt(fan_in), wherefan_in is the number of columns of the matrix.

  • kUniformSqrtFanIn, the same as kGaussianSqrtFanIn except that the distribution is an uniform distribution.

  • kUniformFanInOut, initialize matrix Param objects using kUniform and then multiple each parameter with sqrt(6/(fan_in + fan_out)), wherefan_in + fan_out sums up the number of columns and rows of the matrix.

For all above initialization methods except kConst, if their value is not 1, every parameter will be multiplied with value. Users can also implement their own initialization method following the Advanced user guide.

Advanced user guide

This sections describes the details on implementing new parameter initialization methods.

Base ParamGenerator

All initialization methods are implemented as subclasses of the base ParamGenerator class.

class ParamGenerator {
  virtual void Init(const ParamGenProto&);
  void Fill(Param*);

  ParamGenProto proto_;

Configurations of the initialization method is in ParamGenProto. The Fill function fills the Param object (passed in as an argument).

New ParamGenerator subclass

Similar to implement a new Layer subclass, users can define a configuration protocol message,

# in user.proto
message FooParamProto {
  optional int32 x = 1;
extend ParamGenProto {
  optional FooParamProto fooparam_conf =101;

The configuration of Param would be

param {
  init {
    user_type: 'FooParam" # must use user_type for user defined methods
    [fooparam_conf] { # must use brackets for configuring user defined messages
      x: 10

The subclass could be declared as,

class FooParamGen : public ParamGenerator {
  void Fill(Param*) override;

Users can access the configuration fields in Fill by

int x = proto_.GetExtension(fooparam_conf).x();

To use the new initialization method, users need to register it in the main function.

driver.RegisterParamGenerator<FooParamGen>("FooParam")  # must be consistent with the user_type in configuration

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Base Param class


int local_version_;
int slice_start_;
vector<int> slice_offset_, slice_size_;

shared_ptr<Blob<float>> data_;
Blob<float> grad_;
ParamProto proto_;

Each Param object has a local version and a global version (inside the data Blob). These two versions are used for synchronization. If multiple Param objects share the same values, they would have the same data_ field. Consequently, their global version is the same. The global version is updated by the stub thread. The local version is updated in Worker::Update function which assigns the global version to the local version. The Worker::Collect function is blocked until the global version is larger than the local version, i.e., when data_ is updated. In this way, we synchronize workers sharing parameters.

In Deep learning models, some Param objects are 100 times larger than others. To ensure the load-balance among servers, SINGA slices large Param objects. The slicing information is recorded by slice_*. Each slice is assigned a unique ID starting from 0. slice_start_ is the ID of the first slice of this Param object. slice_offset_[i] is the offset of the i-th slice in this Param object. slice_size_[i] is the size of the i-th slice. These slice information is used to create messages for transferring parameter values or gradients to different servers.

Each Param object has a grad_ field for gradients. Param objects do not share this Blob although they may share data_. Because each layer containing a Param object would contribute gradients. E.g., in RNN, the recurrent layers share parameters values, and the gradients used for updating are averaged from all recurrent these recurrent layers. In SINGA, the stub thread will aggregate local gradients for the same Param object. The server will do a global aggregation of gradients for the same Param object.

The proto_ field has some meta information, e.g., name and ID. It also has a field called owner which is the ID of the Param object that shares parameter values with others.


The base Param class implements two sets of functions,

virtual void InitValues(int version = 0);  // initialize values according to `init_method`
void ShareFrom(const Param& other);  // share `data_` from `other` Param
virtual Msg* GenGetMsg(bool copy, int slice_idx);
virtual Msg* GenPutMsg(bool copy, int slice_idx);
... // other message related functions.

Besides the functions for processing the parameter values, there is a set of functions for generating and parsing messages. These messages are for transferring parameter values or gradients between workers and servers. Each message corresponds to one Param slice. If copy is false, it means the receiver of this message is in the same process as the sender. In such case, only pointers to the memory of parameter value (or gradient) are wrapped in the message; otherwise, the parameter values (or gradients) should be copied into the message.

Implementing Param subclass

Users can extend the base Param class to implement their own parameter initialization methods and message transferring protocols. Similar to implementing a new Layer subclasses, users can create google protocol buffer messages for configuring the Param subclass. The subclass, denoted as FooParam should be registered in,

dirver.RegisterParam<FooParam>(kFooParam);  // kFooParam should be different to 0, which is for the base Param type
  • type, an integer representing the Param type. Currently SINGA provides one Param implementation with type 0 (the default type). If users want to use their own Param implementation, they should extend the base Param class and configure this field with kUserParam

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