private float DecodeTransformer(List <List <string> > outInputSeqs, IComputeGraph g, IWeightTensor encOutputs, IWeightTensor encMask, TransformerDecoder decoder,
IWeightTensor tgtEmbedding, int batchSize, int deviceId, bool isTraining = true)
{
float cost = 0.0f;
var originalInputLengths = ParallelCorpus.PadSentences(outInputSeqs);
int tgtSeqLen = outInputSeqs[0].Count;
IWeightTensor tgtDimMask = MaskUtils.BuildPadDimMask(g, tgtSeqLen, originalInputLengths, m_modelMetaData.HiddenDim, deviceId);
using (IWeightTensor tgtSelfTriMask = MaskUtils.BuildPadSelfTriMask(g, tgtSeqLen, originalInputLengths, deviceId))
{
List <IWeightTensor> inputs = new List <IWeightTensor>();
for (int i = 0; i < batchSize; i++)
{
for (int j = 0; j < tgtSeqLen; j++)
{
int ix_targets_k = m_modelMetaData.Vocab.GetTargetWordIndex(outInputSeqs[i][j], logUnk: true);
inputs.Add(g.PeekRow(tgtEmbedding, ix_targets_k));
}
}
IWeightTensor tgtInputEmbeddings = inputs.Count > 1 ? g.ConcatRows(inputs) : inputs[0];
IWeightTensor decOutput = decoder.Decode(tgtInputEmbeddings, encOutputs, tgtSelfTriMask, encMask, tgtDimMask, batchSize, g);
decOutput = g.Mul(decOutput, g.Transpose(tgtEmbedding));
using (IWeightTensor probs = g.Softmax(decOutput, runGradients: false, inPlace: true))
{
if (isTraining)
{
var leftShiftInputSeqs = ParallelCorpus.LeftShiftSnts(outInputSeqs, ParallelCorpus.EOS);
var originalOutputLengths = ParallelCorpus.PadSentences(leftShiftInputSeqs, tgtSeqLen);
for (int i = 0; i < batchSize; i++)
{
for (int j = 0; j < tgtSeqLen; j++)
{
using (IWeightTensor probs_i_j = g.PeekRow(probs, i * tgtSeqLen + j, runGradients: false))
{
if (j < originalOutputLengths[i])
{
int ix_targets_i_j = m_modelMetaData.Vocab.GetTargetWordIndex(leftShiftInputSeqs[i][j], logUnk: true);
float score_i_j = probs_i_j.GetWeightAt(ix_targets_i_j);
if (j < originalOutputLengths[i])
{
cost += (float)-Math.Log(score_i_j);
}
probs_i_j.SetWeightAt(score_i_j - 1, ix_targets_i_j);
}
else
{
probs_i_j.CleanWeight();
}
}
}
}
decOutput.CopyWeightsToGradients(probs);
}
else
{
// Output "i"th target word
int[] targetIdx = g.Argmax(probs, 1);
List <string> targetWords = m_modelMetaData.Vocab.ConvertTargetIdsToString(targetIdx.ToList());
for (int i = 0; i < batchSize; i++)
{
outInputSeqs[i].Add(targetWords[i * tgtSeqLen + tgtSeqLen - 1]);
}
}
}
}
return(cost);
}
/// <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)
{
(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);
}
}
}
private float DecodeTransformer(List <List <string> > tgtSeqs, IComputeGraph g, IWeightTensor encOutputs, TransformerDecoder decoder,
IWeightTensor tgtEmbedding, IWeightTensor posEmbedding, int batchSize, int deviceId, List <int> srcOriginalLenghts, bool isTraining = true)
{
float cost = 0.0f;
var tgtOriginalLengths = ParallelCorpus.PadSentences(tgtSeqs);
int tgtSeqLen = tgtSeqs[0].Count;
int srcSeqLen = encOutputs.Rows / batchSize;
using (IWeightTensor srcTgtMask = MaskUtils.BuildSrcTgtMask(g, srcSeqLen, tgtSeqLen, tgtOriginalLengths, srcOriginalLenghts, deviceId))
{
using (IWeightTensor tgtSelfTriMask = MaskUtils.BuildPadSelfTriMask(g, tgtSeqLen, tgtOriginalLengths, deviceId))
{
List <IWeightTensor> inputs = new List <IWeightTensor>();
for (int i = 0; i < batchSize; i++)
{
for (int j = 0; j < tgtSeqLen; j++)
{
int ix_targets_k = m_modelMetaData.Vocab.GetTargetWordIndex(tgtSeqs[i][j], logUnk: true);
var emb = g.PeekRow(tgtEmbedding, ix_targets_k, runGradients: j < tgtOriginalLengths[i] ? true : false);
inputs.Add(emb);
}
}
IWeightTensor inputEmbs = inputs.Count > 1 ? g.ConcatRows(inputs) : inputs[0];
inputEmbs = AddPositionEmbedding(g, posEmbedding, batchSize, tgtSeqLen, inputEmbs);
IWeightTensor decOutput = decoder.Decode(inputEmbs, encOutputs, tgtSelfTriMask, srcTgtMask, batchSize, g);
using (IWeightTensor probs = g.Softmax(decOutput, runGradients: false, inPlace: true))
{
if (isTraining)
{
var leftShiftInputSeqs = ParallelCorpus.LeftShiftSnts(tgtSeqs, ParallelCorpus.EOS);
for (int i = 0; i < batchSize; i++)
{
for (int j = 0; j < tgtSeqLen; j++)
{
using (IWeightTensor probs_i_j = g.PeekRow(probs, i * tgtSeqLen + j, runGradients: false))
{
if (j < tgtOriginalLengths[i])
{
int ix_targets_i_j = m_modelMetaData.Vocab.GetTargetWordIndex(leftShiftInputSeqs[i][j], logUnk: true);
float score_i_j = probs_i_j.GetWeightAt(ix_targets_i_j);
cost += (float)-Math.Log(score_i_j);
probs_i_j.SetWeightAt(score_i_j - 1, ix_targets_i_j);
}
else
{
probs_i_j.CleanWeight();
}
}
}
}
decOutput.CopyWeightsToGradients(probs);
}
//if (isTraining)
//{
// var leftShiftInputSeqs = ParallelCorpus.LeftShiftSnts(tgtSeqs, ParallelCorpus.EOS);
// int[] targetIds = new int[batchSize * tgtSeqLen];
// int ids = 0;
// for (int i = 0; i < batchSize; i++)
// {
// for (int j = 0; j < tgtSeqLen; j++)
// {
// targetIds[ids] = j < tgtOriginalLengths[i] ? m_modelMetaData.Vocab.GetTargetWordIndex(leftShiftInputSeqs[i][j], logUnk: true) : -1;
// ids++;
// }
// }
// cost += g.UpdateCost(probs, targetIds);
// decOutput.CopyWeightsToGradients(probs);
//}
else
{
// Output "i"th target word
int[] targetIdx = g.Argmax(probs, 1);
List <string> targetWords = m_modelMetaData.Vocab.ConvertTargetIdsToString(targetIdx.ToList());
for (int i = 0; i < batchSize; i++)
{
tgtSeqs[i].Add(targetWords[i * tgtSeqLen + tgtSeqLen - 1]);
}
}
}
}
}
return(cost);
}
/// <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)