/// <summary>
/// Run forward part on given single device
/// </summary>
/// <param name="computeGraph">The computing graph for current device. It gets created and passed by the framework</param>
/// <param name="srcSnts">A batch of input tokenized sentences in source side</param>
/// <param name="tgtSnts">A batch of output tokenized sentences in target side</param>
/// <param name="deviceIdIdx">The index of current device</param>
/// <returns>The cost of forward part</returns>
private float RunForwardOnSingleDevice(IComputeGraph computeGraph, List <List <string> > srcSnts, List <List <string> > tgtSnts, int deviceIdIdx, bool isTraining)
{
(IEncoder encoder, IDecoder decoder, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding, IWeightTensor posEmbedding) = GetNetworksOnDeviceAt(deviceIdIdx);
// Reset networks
encoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
decoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
List <int> originalSrcLengths = ParallelCorpus.PadSentences(srcSnts);
int srcSeqPaddedLen = srcSnts[0].Count;
int batchSize = srcSnts.Count;
IWeightTensor srcSelfMask = m_shuffleType == ShuffleEnums.NoPaddingInSrc ? null : MaskUtils.BuildPadSelfMask(computeGraph, srcSeqPaddedLen, originalSrcLengths, DeviceIds[deviceIdIdx]); // The length of source sentences are same in a single mini-batch, so we don't have source mask.
// Encoding input source sentences
IWeightTensor encOutput = Encode(computeGraph, srcSnts, encoder, srcEmbedding, srcSelfMask, posEmbedding, originalSrcLengths);
if (srcSelfMask != null)
{
srcSelfMask.Dispose();
}
// Generate output decoder sentences
if (decoder is AttentionDecoder)
{
return(DecodeAttentionLSTM(tgtSnts, computeGraph, encOutput, decoder as AttentionDecoder, tgtEmbedding, srcSnts.Count, isTraining));
}
else
{
if (isTraining)
{
return(DecodeTransformer(tgtSnts, computeGraph, encOutput, decoder as TransformerDecoder, tgtEmbedding, posEmbedding, batchSize, DeviceIds[deviceIdIdx], originalSrcLengths, isTraining));
}
else
{
for (int i = 0; i < m_maxTgtSntSize; i++)
{
using (var g = computeGraph.CreateSubGraph($"TransformerDecoder_Step_{i}"))
{
DecodeTransformer(tgtSnts, g, encOutput, decoder as TransformerDecoder, tgtEmbedding, posEmbedding, batchSize, DeviceIds[deviceIdIdx], originalSrcLengths, isTraining);
bool allSntsEnd = true;
for (int j = 0; j < tgtSnts.Count; j++)
{
if (tgtSnts[j][tgtSnts[j].Count - 1] != ParallelCorpus.EOS)
{
allSntsEnd = false;
break;
}
}
if (allSntsEnd)
{
break;
}
}
}
RemoveDuplicatedEOS(tgtSnts);
return(0.0f);
}
}
}
/// <summary>
/// Run forward part on given single device
/// </summary>
/// <param name="computeGraph">The computing graph for current device. It gets created and passed by the framework</param>
/// <param name="srcSnts">A batch of input tokenized sentences in source side</param>
/// <param name="tgtSnts">A batch of output tokenized sentences in target side</param>
/// <param name="deviceIdIdx">The index of current device</param>
/// <returns>The cost of forward part</returns>
private float RunForwardOnSingleDevice(IComputeGraph computeGraph, List <List <string> > srcSnts, List <List <string> > tgtSnts, int deviceIdIdx, bool isTraining)
{
(IEncoder encoder, IDecoder decoder, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding) = GetNetworksOnDeviceAt(deviceIdIdx);
// Reset networks
encoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
decoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
List <int> originalSrcLengths = ParallelCorpus.PadSentences(srcSnts);
int srcSeqPaddedLen = srcSnts[0].Count;
int batchSize = srcSnts.Count;
IWeightTensor encSelfMask = MaskUtils.BuildPadSelfMask(computeGraph, srcSeqPaddedLen, originalSrcLengths, deviceIdIdx);
IWeightTensor encDimMask = MaskUtils.BuildPadDimMask(computeGraph, srcSeqPaddedLen, originalSrcLengths, m_modelMetaData.HiddenDim, deviceIdIdx);
// Encoding input source sentences
IWeightTensor encOutput = Encode(computeGraph, srcSnts, encoder, srcEmbedding, encSelfMask, encDimMask);
// Generate output decoder sentences
if (decoder is AttentionDecoder)
{
return(DecodeAttentionLSTM(tgtSnts, computeGraph, encOutput, decoder as AttentionDecoder, tgtEmbedding, srcSnts.Count, isTraining));
}
else
{
if (isTraining)
{
List <int> originalTgtLengths = ParallelCorpus.PadSentences(tgtSnts);
int tgtSeqPaddedLen = tgtSnts[0].Count;
IWeightTensor encDecMask = MaskUtils.BuildSrcTgtMask(computeGraph, srcSeqPaddedLen, tgtSeqPaddedLen, originalSrcLengths, originalTgtLengths, deviceIdIdx);
return(DecodeTransformer(tgtSnts, computeGraph, encOutput, encDecMask, decoder as TransformerDecoder, tgtEmbedding, batchSize, deviceIdIdx, isTraining));
}
else
{
for (int i = 0; i < m_maxTgtSntSize; i++)
{
using (var g = computeGraph.CreateSubGraph($"TransformerDecoder_Step_{i}"))
{
List <int> originalTgtLengths = ParallelCorpus.PadSentences(tgtSnts);
int tgtSeqPaddedLen = tgtSnts[0].Count;
IWeightTensor encDecMask = MaskUtils.BuildSrcTgtMask(g, srcSeqPaddedLen, tgtSeqPaddedLen, originalSrcLengths, originalTgtLengths, deviceIdIdx);
DecodeTransformer(tgtSnts, g, encOutput, encDecMask, decoder as TransformerDecoder, tgtEmbedding, batchSize, deviceIdIdx, isTraining);
bool allSntsEnd = true;
for (int j = 0; j < tgtSnts.Count; j++)
{
if (tgtSnts[j][tgtSnts[j].Count - 1] != ParallelCorpus.EOS)
{
allSntsEnd = false;
break;
}
}
if (allSntsEnd)
{
break;
}
}
}
return(0.0f);
}
}
}
/// <summary>
/// Run forward part on given single device
/// </summary>
/// <param name="computeGraph">The computing graph for current device. It gets created and passed by the framework</param>
/// <param name="srcSnts">A batch of input tokenized sentences in source side</param>
/// <param name="tgtSnts">A batch of output tokenized sentences in target side</param>
/// <param name="deviceIdIdx">The index of current device</param>
/// <returns>The cost of forward part</returns>
private float RunForwardOnSingleDevice(IComputeGraph computeGraph, List <List <string> > srcSnts, List <List <string> > tgtSnts, int deviceIdIdx, bool isTraining)
{
var(encoder, decoder, srcEmbedding, tgtEmbedding, posEmbedding) = this.GetNetworksOnDeviceAt(deviceIdIdx);
// Reset networks
encoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
decoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
var originalSrcLengths = ParallelCorpus.PadSentences(srcSnts);
var srcSeqPaddedLen = srcSnts[0].Count;
var batchSize = srcSnts.Count;
var srcSelfMask = this.m_shuffleType == ShuffleEnums.NoPaddingInSrc ? null : MaskUtils.BuildPadSelfMask(computeGraph, srcSeqPaddedLen, originalSrcLengths, this.DeviceIds[deviceIdIdx]); // The length of source sentences are same in a single mini-batch, so we don't have source mask.
// Encoding input source sentences
var encOutput = this.Encode(computeGraph, srcSnts, encoder, srcEmbedding, srcSelfMask, posEmbedding, originalSrcLengths);
srcSelfMask?.Dispose();
// Generate output decoder sentences
if (decoder is AttentionDecoder)
{
return(this.DecodeAttentionLSTM(tgtSnts, computeGraph, encOutput, decoder as AttentionDecoder, tgtEmbedding, srcSnts.Count, isTraining));
}
else
{
if (isTraining)
{
return(this.DecodeTransformer(tgtSnts, computeGraph, encOutput, decoder as TransformerDecoder, tgtEmbedding, posEmbedding, batchSize, this.DeviceIds[deviceIdIdx], originalSrcLengths, isTraining));
}
else
{
for (var i = 0; i < this.m_maxTgtSntSize; i++)
{
using (var g = computeGraph.CreateSubGraph($"TransformerDecoder_Step_{i}"))
{
this.DecodeTransformer(tgtSnts, g, encOutput, decoder as TransformerDecoder, tgtEmbedding, posEmbedding, batchSize, this.DeviceIds[deviceIdIdx], originalSrcLengths, isTraining);
var allSntsEnd = true;
foreach (var t in tgtSnts)
{
if (t[^ 1] != ParallelCorpus.EOS)