nfm_on_angel_en.md

NFM

1. Introduction of Algorithm

NFM (Neural Factorization Machines) algorithm is based on Embedding, multiply each pairs of corresponding elements of Embedding results, and then add to obtain a vector which has the same dimension as Embedding, and then input into DNN to further extract the high-order crossed feature. It is worth noting that NFM does not abandon the first-order feature, and finally the first-order feature are combined with higher-order features for prediction, and the framework is as follows:

1.1 Description of the BiInnerCross layer

In implementation, it is stored by Embedding, After calling Embedding's 'calOutput', computing and output result together. So the Embedding output of a sample is

The calculation formula for BiInteractionCross is as follows:

Implemented as Scala code as:

  val sum1Vector = VFactory.denseDoubleVector(outputDim)
  val sum2Vector = VFactory.denseDoubleVector(outputDim)
  (0 until batchSize).foreach { row =>
      mat.getRow(row).getPartitions.foreach { vectorOuter =>
      sum1Vector.iadd(vectorOuter)
      sum2Vector.iadd(vectorOuter.mul(vectorOuter))
    }

    blasMat.setRow(row, sum1Vector.imul(sum1Vector).isub(sum2Vector).imul(0.5))
    sum1Vector.clear()
    sum2Vector.clear()
  }

1.2 Description of other layers

• SparseInputLayer: Sparse data input layer, specially optimized for sparse high-dimensional data, essentially a FClayer
• Embedding: Implicit embedding layer, if the feature is not one-hot, multiply the feature value
• FCLayer: The most common layer in DNN, linear transformation followed by transfer function
• SumPooling: Adding multiple input data as element-wise, requiring inputs have the same shape
• SimpleLossLayer: Loss layer, you can specify different loss functions

1.3 Building Network

  override def buildNetwork(): Unit = {
    val wide = new SparseInputLayer("input", 1, new Identity(),
      JsonUtils.getOptimizerByLayerType(jsonAst, "SparseInputLayer"))

    val embeddingParams = JsonUtils.getLayerParamsByLayerType(jsonAst, "Embedding")
      .asInstanceOf[EmbeddingParams]
    val embedding = new Embedding("embedding", embeddingParams.outputDim, embeddingParams.numFactors,
      embeddingParams.optimizer.build()
    )

    val interactionCross = new BiInteractionCross("BiInteractionCross", embeddingParams.numFactors, embedding)
    val hiddenLayer = JsonUtils.getFCLayer(jsonAst, interactionCross)

    val join = new SumPooling("sumPooling", 1, Array[Layer](wide, hiddenLayer))

    new SimpleLossLayer("simpleLossLayer", join, lossFunc)
  }

2. Running

2.1 Explanation of Json configuration File

There are many parameters of NFM, which need to be specified by Json configuration file (for a complete description of Json configuration file, please refer toJson explanation), A typical example is:(see data)

{
  "data": {
    "format": "dummy",
    "indexrange": 148,
    "numfield": 13,
    "validateratio": 0.1
  },
  "model": {
    "modeltype": "T_FLOAT_SPARSE_LONGKEY",
    "modelsize": 148
  },
  "train": {
    "epoch": 10,
    "numupdateperepoch": 10,
    "lr": 0.01,
    "decay": 0.1
  },
  "default_optimizer": "Momentum",
  "layers": [
    {
      "name": "wide",
      "type": "sparseinputlayer",
      "outputdim": 1,
      "transfunc": "identity"
    },
    {
      "name": "embedding",
      "type": "embedding",
      "numfactors": 8,
      "outputdim": 104,
      "optimizer": {
        "type": "momentum",
        "momentum": 0.9,
        "reg2": 0.01
      }
    },
    {
      "name": "biinteractioncross",
      "type": "BiInteractionCross",
      "outputdim": 8,
      "inputlayer": "embedding"
    },
    {
      "name": "fclayer",
      "type": "FCLayer",
      "outputdims": [
        50,
        50,
        1
      ],
      "transfuncs": [
        "relu",
        "relu",
        "identity"
      ],
      "inputlayer": "biinteractioncross"
    },
    {
      "name": "sumPooling",
      "type": "SumPooling",
      "outputdim": 1,
      "inputlayers": [
        "wide",
        "fclayer"
      ]
    },
    {
      "name": "simplelosslayer",
      "type": "simplelosslayer",
      "lossfunc": "logloss",
      "inputlayer": "sumPooling"
    }
  ]
}

2.2 Explanation of submit scripts

runner="com.tencent.angel.ml.core.graphsubmit.GraphRunner"
modelClass="com.tencent.angel.ml.core.graphsubmit.AngelModel"

$ANGEL_HOME/bin/angel-submit \
    --angel.job.name NFM \
    --action.type train \
    --angel.app.submit.class $runner \
    --ml.model.class.name $modelClass \
    --angel.train.data.path $input_path \
    --angel.save.model.path $model_path \
    --angel.log.path $log_path \
    --angel.workergroup.number $workerNumber \
    --angel.worker.memory.gb $workerMemory  \
    --angel.worker.task.number $taskNumber \
    --angel.ps.number $PSNumber \
    --angel.ps.memory.gb $PSMemory \
    --angel.output.path.deleteonexist true \
    --angel.task.data.storage.level $storageLevel \
    --angel.task.memorystorage.max.gb $taskMemory \
    --angel.worker.env "LD_PRELOAD=./libopenblas.so" \
    --angel.ml.conf $nfm_json_path \
    --ml.optimizer.json.provider com.tencent.angel.ml.core.PSOptimizerProvider

For the deep learning model, its data, training and network configuration should be specified with the Json file first. Resources such as: worker,ps depend on detail dataset.