public IWeightTensor Perform(IWeightTensor state, AttentionPreProcessResult attenPreProcessResult, int batchSize, IComputeGraph graph)
{
int srcSeqLen = attenPreProcessResult.inputsBatchFirst.Rows / batchSize;
using (IComputeGraph g = graph.CreateSubGraph(m_name))
{
// Affine decoder state
IWeightTensor wc = g.Affine(state, m_Wa, m_bWa);
// Expand dims from [batchSize x decoder_dim] to [batchSize x srcSeqLen x decoder_dim]
IWeightTensor wc1 = g.View(wc, batchSize, 1, wc.Columns);
IWeightTensor wcExp = g.Expand(wc1, batchSize, srcSeqLen, wc.Columns);
IWeightTensor ggs = null;
if (m_enableCoverageModel)
{
// Get coverage model status at {t-1}
IWeightTensor wCoverage = g.Affine(m_coverage.Hidden, m_Wc, m_bWc);
IWeightTensor wCoverage1 = g.View(wCoverage, batchSize, srcSeqLen, -1);
ggs = g.AddTanh(attenPreProcessResult.uhs, wcExp, wCoverage1);
}
else
{
ggs = g.AddTanh(attenPreProcessResult.uhs, wcExp);
}
IWeightTensor ggss = g.View(ggs, batchSize * srcSeqLen, -1);
IWeightTensor atten = g.Mul(ggss, m_V);
IWeightTensor attenT = g.Transpose(atten);
IWeightTensor attenT2 = g.View(attenT, batchSize, srcSeqLen);
IWeightTensor attenSoftmax1 = g.Softmax(attenT2, inPlace: true);
IWeightTensor attenSoftmax = g.View(attenSoftmax1, batchSize, 1, srcSeqLen);
IWeightTensor inputs2 = g.View(attenPreProcessResult.inputsBatchFirst, batchSize, srcSeqLen, attenPreProcessResult.inputsBatchFirst.Columns);
IWeightTensor contexts = graph.MulBatch(attenSoftmax, inputs2, batchSize);
if (m_enableCoverageModel)
{
// Concatenate tensor as input for coverage model
IWeightTensor aCoverage = g.View(attenSoftmax1, attenPreProcessResult.inputsBatchFirst.Rows, 1);
IWeightTensor state2 = g.View(state, batchSize, 1, state.Columns);
IWeightTensor state3 = g.Expand(state2, batchSize, srcSeqLen, state.Columns);
IWeightTensor state4 = g.View(state3, batchSize * srcSeqLen, -1);
IWeightTensor concate = g.ConcatColumns(aCoverage, attenPreProcessResult.inputsBatchFirst, state4);
m_coverage.Step(concate, graph);
}
return(contexts);
}
}
public IWeightMatrix Perform(IWeightMatrix state, AttentionPreProcessResult attenPreProcessResult, IComputeGraph g)
{
var bWas = g.RepeatRows(bWa, state.Rows);
var wc = g.MulAdd(state, Wa, bWas);
var wcs = g.RepeatRows(wc, attenPreProcessResult.inputsUnfolder[0].Rows);
var ggs = g.AddTanh(attenPreProcessResult.uhs, wcs);
var atten = g.Mul(ggs, V);
List <IWeightMatrix> attens = g.UnFolderRow(atten, m_batchSize);
List <IWeightMatrix> contexts = new List <IWeightMatrix>();
List <IWeightMatrix> attensT = new List <IWeightMatrix>();
for (int i = 0; i < m_batchSize; i++)
{
attensT.Add(g.Transpose2(attens[i]));
}
var attenT = g.ConcatRows(attensT);
var attenSoftmax = g.SoftmaxM(attenT);
for (int i = 0; i < m_batchSize; i++)
{
IWeightMatrix context = g.Mul(g.PeekRow(attenSoftmax, i), attenPreProcessResult.inputsUnfolder[i]);
contexts.Add(context);
}
return(g.ConcatRows(contexts));
}
public IWeightMatrix Perform(IWeightMatrix state, AttentionPreProcessResult attenPreProcessResult, IComputeGraph g)
{
var bWas = g.RepeatRows(bWa, state.Rows);
var wc = g.MulAdd(state, Wa, bWas);
var wcs = g.RepeatRows(wc, attenPreProcessResult.inputs.Rows / m_batchSize);
var ggs = g.AddTanh(attenPreProcessResult.uhs, wcs);
var atten = g.Mul(ggs, V);
var atten2 = g.PermuteBatch(atten, m_batchSize);
var attenT = g.Transpose2(atten2);
var attenT2 = g.View(attenT, m_batchSize, attenPreProcessResult.inputs.Rows / m_batchSize);
var attenSoftmax = g.Softmax(attenT2);
IWeightMatrix contexts = g.MulBatch(attenSoftmax, attenPreProcessResult.inputs, m_batchSize);
return(contexts);
}
public IWeightTensor Perform(IWeightTensor state, AttentionPreProcessResult attenPreProcessResult, int batchSize, IComputeGraph graph)
{
IComputeGraph g = graph.CreateSubGraph(m_name);
var wc = g.Affine(state, m_Wa, m_bWa);
var wcs = g.RepeatRows(wc, attenPreProcessResult.inputs.Rows / batchSize);
var ggs = g.AddTanh(attenPreProcessResult.uhs, wcs);
var atten = g.Mul(ggs, m_V);
var atten2 = g.TransposeBatch(atten, batchSize);
var attenT = g.Transpose(atten2);
var attenT2 = g.View(attenT, batchSize, attenPreProcessResult.inputs.Rows / batchSize);
var attenSoftmax1 = g.Softmax(attenT2, inPlace: true);
var attenSoftmax = g.View(attenSoftmax1, batchSize, attenSoftmax1.Rows / batchSize, attenSoftmax1.Columns);
var inputs2 = g.View(attenPreProcessResult.inputs, batchSize, attenPreProcessResult.inputs.Rows / batchSize, attenPreProcessResult.inputs.Columns);
IWeightTensor contexts = g.MulBatch(attenSoftmax, inputs2, batchSize);
return(contexts);
}