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);
}
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);
}
