private bool CreateTrainableParameters(IModelMetaData mmd)
{
Logger.WriteLine($"Creating encoders and decoders...");
Seq2SeqModelMetaData modelMetaData = mmd as Seq2SeqModelMetaData;
RoundArray <int> raDeviceIds = new RoundArray <int>(DeviceIds);
if (modelMetaData.EncoderType == EncoderTypeEnums.BiLSTM)
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new BiEncoder("BiLSTMEncoder", modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, raDeviceIds.GetNextItem()), DeviceIds);
m_decoderFFLayer = new MultiProcessorNetworkWrapper <FeedForwardLayer>(new FeedForwardLayer("FeedForward", modelMetaData.HiddenDim * 2, modelMetaData.Vocab.TargetWordSize, dropoutRatio: 0.0f, deviceId: raDeviceIds.GetNextItem()), DeviceIds);
}
else
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new TransformerEncoder("TransformerEncoder", modelMetaData.MultiHeadNum, modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, m_dropoutRatio, raDeviceIds.GetNextItem()), DeviceIds);
m_decoderFFLayer = new MultiProcessorNetworkWrapper <FeedForwardLayer>(new FeedForwardLayer("FeedForward", modelMetaData.HiddenDim, modelMetaData.Vocab.TargetWordSize, dropoutRatio: 0.0f, deviceId: raDeviceIds.GetNextItem()), DeviceIds);
}
m_srcEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
modelMetaData.Vocab.SourceWordSize, modelMetaData.EmbeddingDim
}, raDeviceIds.GetNextItem(), normal: true, name: "SrcEmbeddings", isTrainable: true), DeviceIds);
// m_crfDecoder = new CRFDecoder(modelMetaData.Vocab.TargetWordSize);
return(true);
}
private bool CreateTrainableParameters(IModelMetaData mmd)
{
Logger.WriteLine($"Creating encoders and decoders...");
var modelMetaData = mmd as SeqLabelModelMetaData;
var raDeviceIds = new RoundArray <int>(this.DeviceIds);
if (modelMetaData.EncoderType == EncoderTypeEnums.BiLSTM)
{
this.m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new BiEncoder("BiLSTMEncoder", modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, raDeviceIds.GetNextItem(), true), this.DeviceIds);
this.m_decoderFFLayer = new MultiProcessorNetworkWrapper <FeedForwardLayer>(new FeedForwardLayer("FeedForward", modelMetaData.HiddenDim * 2, modelMetaData.Vocab.TargetWordSize, 0.0f, raDeviceIds.GetNextItem(), true), this.DeviceIds);
}
else
{
this.m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new TransformerEncoder("TransformerEncoder", modelMetaData.MultiHeadNum, modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, this.m_dropoutRatio, raDeviceIds.GetNextItem(), true), this.DeviceIds);
this.m_decoderFFLayer = new MultiProcessorNetworkWrapper <FeedForwardLayer>(new FeedForwardLayer("FeedForward", modelMetaData.HiddenDim, modelMetaData.Vocab.TargetWordSize, 0.0f, raDeviceIds.GetNextItem(), true), this.DeviceIds);
}
this.m_srcEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
modelMetaData.Vocab.SourceWordSize, modelMetaData.EmbeddingDim
}, raDeviceIds.GetNextItem(), normal: NormType.Normal, name: "SrcEmbeddings", isTrainable: true), this.DeviceIds);
// m_crfDecoder = new CRFDecoder(modelMetaData.Vocab.TargetWordSize);
this.m_posEmbedding = modelMetaData.EncoderType == EncoderTypeEnums.Transformer ? new MultiProcessorNetworkWrapper <IWeightTensor>(this.BuildPositionWeightTensor(Math.Max(this.m_maxSntSize, this.m_maxSntSize) + 2, modelMetaData.EmbeddingDim, raDeviceIds.GetNextItem(), "PosEmbedding", false), this.DeviceIds, true) : null;
return(true);
}
private bool CreateTrainableParameters(IModelMetaData mmd)
{
Logger.WriteLine($"Creating encoders and decoders...");
Seq2SeqModelMetaData modelMetaData = mmd as Seq2SeqModelMetaData;
RoundArray <int> raDeviceIds = new RoundArray <int>(DeviceIds);
if (modelMetaData.EncoderType == EncoderTypeEnums.BiLSTM)
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new BiEncoder("BiLSTMEncoder", modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, raDeviceIds.GetNextItem(), isTrainable: m_isEncoderTrainable), DeviceIds);
m_decoder = new MultiProcessorNetworkWrapper <AttentionDecoder>(
new AttentionDecoder("AttnLSTMDecoder", modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.HiddenDim * 2,
modelMetaData.Vocab.TargetWordSize, m_dropoutRatio, modelMetaData.DecoderLayerDepth, raDeviceIds.GetNextItem(), modelMetaData.EnableCoverageModel, isTrainable: m_isDecoderTrainable), DeviceIds);
}
else
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new TransformerEncoder("TransformerEncoder", modelMetaData.MultiHeadNum, modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, m_dropoutRatio, raDeviceIds.GetNextItem(),
isTrainable: m_isEncoderTrainable), DeviceIds);
m_decoder = new MultiProcessorNetworkWrapper <AttentionDecoder>(
new AttentionDecoder("AttnLSTMDecoder", modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.HiddenDim,
modelMetaData.Vocab.TargetWordSize, m_dropoutRatio, modelMetaData.DecoderLayerDepth, raDeviceIds.GetNextItem(), modelMetaData.EnableCoverageModel, isTrainable: m_isDecoderTrainable), DeviceIds);
}
m_srcEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
modelMetaData.Vocab.SourceWordSize, modelMetaData.EmbeddingDim
}, raDeviceIds.GetNextItem(), normal: true, name: "SrcEmbeddings", isTrainable: m_isSrcEmbTrainable), DeviceIds);
m_tgtEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
modelMetaData.Vocab.TargetWordSize, modelMetaData.EmbeddingDim
}, raDeviceIds.GetNextItem(), normal: true, name: "TgtEmbeddings", isTrainable: m_isTgtEmbTrainable), DeviceIds);
return(true);
}
private bool CreateTrainableParameters(IModel model)
{
Logger.WriteLine($"Creating encoders and decoders...");
var raDeviceIds = new RoundArray <int>(DeviceIds);
int contextDim;
(m_encoder, contextDim) = Encoder.CreateEncoders(model, m_options, raDeviceIds);
m_ffLayer = new MultiProcessorNetworkWrapper <FeedForwardLayer>(new FeedForwardLayer("FeedForward", contextDim, model.ClsVocab.Count, dropoutRatio: 0.0f, deviceId: raDeviceIds.GetNextItem(), isTrainable: true), DeviceIds);
m_srcEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
model.SrcVocab.Count, model.EncoderEmbeddingDim
}, raDeviceIds.GetNextItem(), normType: NormType.Uniform, name: "SrcEmbeddings", isTrainable: true), DeviceIds);
if (model.EncoderType == EncoderTypeEnums.Transformer)
{
m_posEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(PositionEmbedding.BuildPositionWeightTensor(m_options.MaxTestSentLength + 2, model.EncoderEmbeddingDim, raDeviceIds.GetNextItem(), "PosEmbedding", false), DeviceIds, true);
}
else
{
m_posEmbedding = null;
}
return(true);
}
private bool CreateTrainableParameters(IModel model)
{
Logger.WriteLine($"Creating encoders and decoders...");
var raDeviceIds = new RoundArray <int>(DeviceIds);
int contextDim;
(m_encoder, contextDim) = Encoder.CreateEncoders(model, m_options, raDeviceIds);
m_decoder = Decoder.CreateDecoders(model, m_options, raDeviceIds, contextDim);
m_decoderFFLayer = new MultiProcessorNetworkWrapper <IFeedForwardLayer>(new FeedForwardLayer("FeedForward_Decoder_0", model.HiddenDim, model.TgtVocab.Count, dropoutRatio: 0.0f, deviceId: raDeviceIds.GetNextItem(),
isTrainable: true, learningRateFactor: m_options.DecoderStartLearningRateFactor), DeviceIds);
(m_posEmbedding, m_segmentEmbedding) = Misc.CreateAuxEmbeddings(raDeviceIds, contextDim, Math.Max(Math.Max(m_options.MaxTrainSrcSentLength, m_options.MaxTestSrcSentLength), Math.Max(m_options.MaxTrainTgtSentLength, m_options.MaxTestTgtSentLength)), model);
(m_srcEmbedding, m_tgtEmbedding) = CreateSrcTgtEmbeddings(model, raDeviceIds, m_options.IsSrcEmbeddingTrainable, m_options.IsTgtEmbeddingTrainable, m_options.EncoderStartLearningRateFactor, m_options.DecoderStartLearningRateFactor);
if (model.PointerGenerator)
{
if (model.SharedEmbeddings == false)
{
throw new ArgumentException($"Shared embeddings is required to true for pointer generator.");
}
Logger.WriteLine($"Create pointer generator weights...");
m_pointerGenerator = new MultiProcessorNetworkWrapper <IFeedForwardLayer>(new FeedForwardLayer("PointerGenerator_0", model.HiddenDim, 1, dropoutRatio: 0.0f, deviceId: raDeviceIds.GetNextItem(),
isTrainable: true, learningRateFactor: m_options.DecoderStartLearningRateFactor), DeviceIds);
}
else
{
m_pointerGenerator = null;
}
return(true);
}
private bool CreateTrainableParameters(IModelMetaData mmd)
{
Logger.WriteLine($"Creating encoders and decoders...");
Seq2SeqModelMetaData modelMetaData = mmd as Seq2SeqModelMetaData;
RoundArray <int> raDeviceIds = new RoundArray <int>(DeviceIds);
int contextDim = 0;
if (modelMetaData.EncoderType == EncoderTypeEnums.BiLSTM)
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new BiEncoder("BiLSTMEncoder", modelMetaData.HiddenDim, modelMetaData.SrcEmbeddingDim, modelMetaData.EncoderLayerDepth, raDeviceIds.GetNextItem(), isTrainable: m_isEncoderTrainable), DeviceIds);
contextDim = modelMetaData.HiddenDim * 2;
}
else
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new TransformerEncoder("TransformerEncoder", modelMetaData.MultiHeadNum, modelMetaData.HiddenDim, modelMetaData.SrcEmbeddingDim, modelMetaData.EncoderLayerDepth, m_dropoutRatio, raDeviceIds.GetNextItem(),
isTrainable: m_isEncoderTrainable), DeviceIds);
contextDim = modelMetaData.HiddenDim;
}
if (modelMetaData.DecoderType == DecoderTypeEnums.AttentionLSTM)
{
m_decoder = new MultiProcessorNetworkWrapper <IDecoder>(
new AttentionDecoder("AttnLSTMDecoder", modelMetaData.HiddenDim, modelMetaData.TgtEmbeddingDim, contextDim,
modelMetaData.Vocab.TargetWordSize, m_dropoutRatio, modelMetaData.DecoderLayerDepth, raDeviceIds.GetNextItem(), modelMetaData.EnableCoverageModel, isTrainable: m_isDecoderTrainable), DeviceIds);
}
else
{
m_decoder = new MultiProcessorNetworkWrapper <IDecoder>(
new TransformerDecoder("TransformerDecoder", modelMetaData.MultiHeadNum, modelMetaData.HiddenDim, modelMetaData.TgtEmbeddingDim, modelMetaData.Vocab.TargetWordSize, modelMetaData.EncoderLayerDepth, m_dropoutRatio, raDeviceIds.GetNextItem(),
isTrainable: m_isDecoderTrainable), DeviceIds);
}
if (modelMetaData.EncoderType == EncoderTypeEnums.Transformer || modelMetaData.DecoderType == DecoderTypeEnums.Transformer)
{
m_posEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(BuildPositionWeightTensor(Math.Max(m_maxSrcSntSize, m_maxTgtSntSize) + 2, contextDim, raDeviceIds.GetNextItem(), "PosEmbedding", false), DeviceIds, true);
}
else
{
m_posEmbedding = null;
}
Logger.WriteLine($"Creating embeddings for source side. Shape = '({modelMetaData.Vocab.SourceWordSize} ,{modelMetaData.SrcEmbeddingDim})'");
m_srcEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
modelMetaData.Vocab.SourceWordSize, modelMetaData.SrcEmbeddingDim
}, raDeviceIds.GetNextItem(), normType: NormType.Uniform, fanOut: true, name: "SrcEmbeddings", isTrainable: m_isSrcEmbTrainable), DeviceIds);
Logger.WriteLine($"Creating embeddings for target side. Shape = '({modelMetaData.Vocab.TargetWordSize} ,{modelMetaData.TgtEmbeddingDim})'");
m_tgtEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
modelMetaData.Vocab.TargetWordSize, modelMetaData.TgtEmbeddingDim
}, raDeviceIds.GetNextItem(), normType: NormType.Uniform, fanOut: true, name: "TgtEmbeddings", isTrainable: m_isTgtEmbTrainable), DeviceIds);
return(true);
}
