public void VisualizeNeuralNetwork(string visNNFilePath)
{
(IEncoder encoder, IDecoder decoder, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding) = GetNetworksOnDeviceAt(-1);
// Build input sentence
List <List <string> > inputSeqs = ParallelCorpus.ConstructInputTokens(null);
int batchSize = inputSeqs.Count;
IComputeGraph g = CreateComputGraph(m_defaultDeviceId, needBack: false, visNetwork: true);
AttentionDecoder rnnDecoder = decoder as AttentionDecoder;
encoder.Reset(g.GetWeightFactory(), batchSize);
rnnDecoder.Reset(g.GetWeightFactory(), batchSize);
// Run encoder
IWeightTensor encodedWeightMatrix = Encode(g, inputSeqs, encoder, srcEmbedding, null, null);
// Prepare for attention over encoder-decoder
AttentionPreProcessResult attPreProcessResult = rnnDecoder.PreProcess(encodedWeightMatrix, batchSize, g);
// Run decoder
IWeightTensor x = g.PeekRow(tgtEmbedding, (int)SENTTAGS.START);
IWeightTensor eOutput = rnnDecoder.Decode(x, attPreProcessResult, batchSize, g);
IWeightTensor probs = g.Softmax(eOutput);
g.VisualizeNeuralNetToFile(visNNFilePath);
}
private float DecodeOutput(string[] OutputSentence, IComputeGraph g, float cost, SparseWeightMatrix sparseInput, List <WeightMatrix> encoded, AttentionDecoder decoder, WeightMatrix Whd, WeightMatrix bd, WeightMatrix Embedding)
{
int ix_input = (int)SENTTAGS.START;
for (int i = 0; i < OutputSentence.Length + 1; i++)
{
int ix_target = (int)SENTTAGS.UNK;
if (i == OutputSentence.Length)
{
ix_target = (int)SENTTAGS.END;
}
else
{
if (t_wordToIndex.ContainsKey(OutputSentence[i]))
{
ix_target = t_wordToIndex[OutputSentence[i]];
}
}
var x = g.PeekRow(Embedding, ix_input);
var eOutput = decoder.Decode(sparseInput, x, encoded, g);
if (UseDropout)
{
eOutput = g.Dropout(eOutput, 0.2f);
}
var o = g.muladd(eOutput, Whd, bd);
if (UseDropout)
{
o = g.Dropout(o, 0.2f);
}
var probs = g.SoftmaxWithCrossEntropy(o);
cost += (float)-Math.Log(probs.Weight[ix_target]);
o.Gradient = probs.Weight;
o.Gradient[ix_target] -= 1;
ix_input = ix_target;
}
return(cost);
}
/// <summary>
/// Given input sentence and generate output sentence by seq2seq model with beam search
/// </summary>
/// <param name="input"></param>
/// <param name="beamSearchSize"></param>
/// <param name="maxOutputLength"></param>
/// <returns></returns>
public List <List <string> > Predict(List <string> input, int beamSearchSize = 1, int maxOutputLength = 100)
{
(IEncoder encoder, IDecoder decoder, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding) = GetNetworksOnDeviceAt(-1);
List <List <string> > inputSeqs = ParallelCorpus.ConstructInputTokens(input);
int batchSize = 1; // For predict with beam search, we currently only supports one sentence per call
IComputeGraph g = CreateComputGraph(m_defaultDeviceId, needBack: false);
AttentionDecoder rnnDecoder = decoder as AttentionDecoder;
encoder.Reset(g.GetWeightFactory(), batchSize);
rnnDecoder.Reset(g.GetWeightFactory(), batchSize);
// Construct beam search status list
List <BeamSearchStatus> bssList = new List <BeamSearchStatus>();
BeamSearchStatus bss = new BeamSearchStatus();
bss.OutputIds.Add((int)SENTTAGS.START);
bss.CTs = rnnDecoder.GetCTs();
bss.HTs = rnnDecoder.GetHTs();
bssList.Add(bss);
IWeightTensor encodedWeightMatrix = Encode(g, inputSeqs, encoder, srcEmbedding, null, null);
AttentionPreProcessResult attPreProcessResult = rnnDecoder.PreProcess(encodedWeightMatrix, batchSize, g);
List <BeamSearchStatus> newBSSList = new List <BeamSearchStatus>();
bool finished = false;
int outputLength = 0;
while (finished == false && outputLength < maxOutputLength)
{
finished = true;
for (int i = 0; i < bssList.Count; i++)
{
bss = bssList[i];
if (bss.OutputIds[bss.OutputIds.Count - 1] == (int)SENTTAGS.END)
{
newBSSList.Add(bss);
}
else if (bss.OutputIds.Count > maxOutputLength)
{
newBSSList.Add(bss);
}
else
{
finished = false;
int ix_input = bss.OutputIds[bss.OutputIds.Count - 1];
rnnDecoder.SetCTs(bss.CTs);
rnnDecoder.SetHTs(bss.HTs);
IWeightTensor x = g.PeekRow(tgtEmbedding, ix_input);
IWeightTensor eOutput = rnnDecoder.Decode(x, attPreProcessResult, batchSize, g);
using (IWeightTensor probs = g.Softmax(eOutput))
{
List <int> preds = probs.GetTopNMaxWeightIdx(beamSearchSize);
for (int j = 0; j < preds.Count; j++)
{
BeamSearchStatus newBSS = new BeamSearchStatus();
newBSS.OutputIds.AddRange(bss.OutputIds);
newBSS.OutputIds.Add(preds[j]);
newBSS.CTs = rnnDecoder.GetCTs();
newBSS.HTs = rnnDecoder.GetHTs();
float score = probs.GetWeightAt(preds[j]);
newBSS.Score = bss.Score;
newBSS.Score += (float)(-Math.Log(score));
//var lengthPenalty = Math.Pow((5.0f + newBSS.OutputIds.Count) / 6, 0.6);
//newBSS.Score /= (float)lengthPenalty;
newBSSList.Add(newBSS);
}
}
}
}
bssList = BeamSearch.GetTopNBSS(newBSSList, beamSearchSize);
newBSSList.Clear();
outputLength++;
}
// Convert output target word ids to real string
List <List <string> > results = new List <List <string> >();
for (int i = 0; i < bssList.Count; i++)
{
results.Add(m_modelMetaData.Vocab.ConvertTargetIdsToString(bssList[i].OutputIds));
}
return(results);
}
/// <summary>
/// Decode output sentences in training
/// </summary>
/// <param name="outputSnts">In training mode, they are golden target sentences, otherwise, they are target sentences generated by the decoder</param>
/// <param name="g"></param>
/// <param name="encOutputs"></param>
/// <param name="decoder"></param>
/// <param name="decoderFFLayer"></param>
/// <param name="tgtEmbedding"></param>
/// <returns></returns>
private float DecodeAttentionLSTM(List <List <string> > outputSnts, IComputeGraph g, IWeightTensor encOutputs, AttentionDecoder decoder, IWeightTensor tgtEmbedding, int batchSize, bool isTraining = true)
{
float cost = 0.0f;
int[] ix_inputs = new int[batchSize];
for (int i = 0; i < ix_inputs.Length; i++)
{
ix_inputs[i] = m_modelMetaData.Vocab.GetTargetWordIndex(outputSnts[i][0]);
}
// Initialize variables accoridng to current mode
List <int> originalOutputLengths = isTraining ? ParallelCorpus.PadSentences(outputSnts) : null;
int seqLen = isTraining ? outputSnts[0].Count : 64;
float dropoutRatio = isTraining ? m_dropoutRatio : 0.0f;
HashSet <int> setEndSentId = isTraining ? null : new HashSet <int>();
// Pre-process for attention model
AttentionPreProcessResult attPreProcessResult = decoder.PreProcess(encOutputs, batchSize, g);
for (int i = 1; i < seqLen; i++)
{
//Get embedding for all sentence in the batch at position i
List <IWeightTensor> inputs = new List <IWeightTensor>();
for (int j = 0; j < batchSize; j++)
{
inputs.Add(g.PeekRow(tgtEmbedding, ix_inputs[j]));
}
IWeightTensor inputsM = g.ConcatRows(inputs);
//Decode output sentence at position i
IWeightTensor eOutput = decoder.Decode(inputsM, attPreProcessResult, batchSize, g);
//Softmax for output
using (IWeightTensor probs = g.Softmax(eOutput, runGradients: false, inPlace: true))
{
if (isTraining)
{
//Calculate loss for each word in the batch
for (int k = 0; k < batchSize; k++)
{
using (IWeightTensor probs_k = g.PeekRow(probs, k, runGradients: false))
{
int ix_targets_k = m_modelMetaData.Vocab.GetTargetWordIndex(outputSnts[k][i]);
float score_k = probs_k.GetWeightAt(ix_targets_k);
if (i < originalOutputLengths[k])
{
cost += (float)-Math.Log(score_k);
}
probs_k.SetWeightAt(score_k - 1, ix_targets_k);
ix_inputs[k] = ix_targets_k;
}
}
eOutput.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 j = 0; j < targetWords.Count; j++)
{
if (setEndSentId.Contains(j) == false)
{
outputSnts[j].Add(targetWords[j]);
if (targetWords[j] == ParallelCorpus.EOS)
{
setEndSentId.Add(j);
}
}
}
if (setEndSentId.Count == batchSize)
{
// All target sentences in current batch are finished, so we exit.
break;
}
ix_inputs = targetIdx;
}
}
}
return(cost);
}
/// <summary>
/// Decode output sentences in training
/// </summary>
/// <param name="outputSentences">In training mode, they are golden target sentences, otherwise, they are target sentences generated by the decoder</param>
/// <param name="g"></param>
/// <param name="encodedOutputs"></param>
/// <param name="decoder"></param>
/// <param name="decoderFFLayer"></param>
/// <param name="embedding"></param>
/// <returns></returns>
private float Decode(List <List <string> > outputSentences, IComputeGraph g, IWeightTensor encodedOutputs, AttentionDecoder decoder, IWeightTensor embedding,
int batchSize, bool isTraining = true)
{
float cost = 0.0f;
int[] ix_inputs = new int[batchSize];
for (int i = 0; i < ix_inputs.Length; i++)
{
ix_inputs[i] = (int)SENTTAGS.START;
}
// Initialize variables accoridng to current mode
List <int> originalOutputLengths = isTraining ? ParallelCorpus.PadSentences(outputSentences) : null;
int seqLen = isTraining ? outputSentences[0].Count : 64;
float dropoutRatio = isTraining ? m_dropoutRatio : 0.0f;
HashSet <int> setEndSentId = isTraining ? null : new HashSet <int>();
if (!isTraining)
{
if (outputSentences.Count != 0)
{
throw new ArgumentException($"The list for output sentences must be empty if current is not in training mode.");
}
for (int i = 0; i < batchSize; i++)
{
outputSentences.Add(new List <string>());
}
}
// Pre-process for attention model
AttentionPreProcessResult attPreProcessResult = decoder.PreProcess(encodedOutputs, batchSize, g);
for (int i = 0; i < seqLen; i++)
{
//Get embedding for all sentence in the batch at position i
List <IWeightTensor> inputs = new List <IWeightTensor>();
for (int j = 0; j < batchSize; j++)
{
inputs.Add(g.PeekRow(embedding, ix_inputs[j]));
}
IWeightTensor inputsM = g.ConcatRows(inputs);
//Decode output sentence at position i
IWeightTensor eOutput = decoder.Decode(inputsM, attPreProcessResult, batchSize, g);
//Softmax for output
using (IWeightTensor probs = g.Softmax(eOutput, runGradients: false, inPlace: true))
{
if (isTraining)
{
//Calculate loss for each word in the batch
for (int k = 0; k < batchSize; k++)
{
using (IWeightTensor probs_k = g.PeekRow(probs, k, runGradients: false))
{
int ix_targets_k = m_modelMetaData.Vocab.GetTargetWordIndex(outputSentences[k][i]);
float score_k = probs_k.GetWeightAt(ix_targets_k);
if (i < originalOutputLengths[k])
{
cost += (float)-Math.Log(score_k);
}
probs_k.SetWeightAt(score_k - 1, ix_targets_k);
ix_inputs[k] = ix_targets_k;
}
}
eOutput.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 j = 0; j < targetWords.Count; j++)
{
if (setEndSentId.Contains(j) == false)
{
outputSentences[j].Add(targetWords[j]);
if (targetWords[j] == ParallelCorpus.EOS)
{
setEndSentId.Add(j);
}
}
}
ix_inputs = targetIdx;
}
}
if (isTraining)
{
////Hacky: Run backward for last feed forward layer and dropout layer in order to save memory usage, since it's not time sequence dependency
g.RunTopBackward();
if (m_dropoutRatio > 0.0f)
{
g.RunTopBackward();
}
}
else
{
if (setEndSentId.Count == batchSize)
{
// All target sentences in current batch are finished, so we exit.
break;
}
}
}
return(cost);
}
/// <summary>
/// Decode output sentences in training
/// </summary>
/// <param name="outputSentences"></param>
/// <param name="g"></param>
/// <param name="encodedOutputs"></param>
/// <param name="decoder"></param>
/// <param name="Whd"></param>
/// <param name="bd"></param>
/// <param name="Embedding"></param>
/// <param name="predictSentence"></param>
/// <returns></returns>
private float Decode(List <List <string> > outputSentences, IComputeGraph g, IWeightMatrix encodedOutputs, AttentionDecoder decoder, FeedForwardLayer decoderFFLayer, IWeightMatrix Embedding, out List <List <string> > predictSentence)
{
predictSentence = null;
float cost = 0.0f;
var attPreProcessResult = decoder.PreProcess(encodedOutputs, g);
var originalOutputLengths = PadSentences(outputSentences);
int seqLen = outputSentences[0].Count;
int[] ix_inputs = new int[m_batchSize];
int[] ix_targets = new int[m_batchSize];
for (int i = 0; i < ix_inputs.Length; i++)
{
ix_inputs[i] = (int)SENTTAGS.START;
}
for (int i = 0; i < seqLen + 1; i++)
{
//Get embedding for all sentence in the batch at position i
List <IWeightMatrix> inputs = new List <IWeightMatrix>();
for (int j = 0; j < m_batchSize; j++)
{
List <string> OutputSentence = outputSentences[j];
ix_targets[j] = (int)SENTTAGS.UNK;
if (i >= seqLen)
{
ix_targets[j] = (int)SENTTAGS.END;
}
else
{
if (m_tgtWordToIndex.ContainsKey(OutputSentence[i]))
{
ix_targets[j] = m_tgtWordToIndex[OutputSentence[i]];
}
}
var x = g.PeekRow(Embedding, ix_inputs[j]);
inputs.Add(x);
}
var inputsM = g.ConcatRows(inputs);
//Decode output sentence at position i
var eOutput = decoder.Decode(inputsM, attPreProcessResult, g);
if (m_dropoutRatio > 0.0f)
{
eOutput = g.Dropout(eOutput, m_dropoutRatio);
}
var o = decoderFFLayer.Process(eOutput, g);
//Softmax for output
// var o = g.MulAdd(eOutput, Whd, bds);
var probs = g.Softmax(o, false);
o.ReleaseWeight();
//Calculate loss for each word in the batch
List <IWeightMatrix> probs_g = g.UnFolderRow(probs, m_batchSize, false);
for (int k = 0; k < m_batchSize; k++)
{
var probs_k = probs_g[k];
var score_k = probs_k.GetWeightAt(ix_targets[k]);
if (i < originalOutputLengths[k] + 1)
{
cost += (float)-Math.Log(score_k);
}
probs_k.SetWeightAt(score_k - 1, ix_targets[k]);
ix_inputs[k] = ix_targets[k];
probs_k.Dispose();
}
o.SetGradientByWeight(probs);
//Hacky: Run backward for last feed forward layer and dropout layer in order to save memory usage, since it's not time sequence dependency
g.RunTopBackward();
g.RunTopBackward();
if (m_dropoutRatio > 0.0f)
{
g.RunTopBackward();
}
}
return(cost);
}
public List <string> Predict(List <string> input)
{
reversEncoder.Reset();
encoder.Reset();
decoder.Reset();
List <string> result = new List <string>();
#if MKL
var G2 = new ComputeGraphMKL(false);
#else
var G2 = new ComputeGraph(false);
#endif
List <string> inputSeq = new List <string>();
// inputSeq.Add(m_START);
inputSeq.AddRange(input);
// inputSeq.Add(m_END);
List <string> revseq = inputSeq.ToList();
revseq.Reverse();
List <WeightMatrix> forwardEncoded = new List <WeightMatrix>();
List <WeightMatrix> backwardEncoded = new List <WeightMatrix>();
List <WeightMatrix> encoded = new List <WeightMatrix>();
SparseWeightMatrix sparseInput = new SparseWeightMatrix(1, s_Embedding.Columns);
Parallel.Invoke(
() =>
{
for (int i = 0; i < inputSeq.Count; i++)
{
int ix = (int)SENTTAGS.UNK;
if (s_wordToIndex.ContainsKey(inputSeq[i]) == false)
{
Logger.WriteLine($"Unknow input word: {inputSeq[i]}");
}
else
{
ix = s_wordToIndex[inputSeq[i]];
}
var x2 = G2.PeekRow(s_Embedding, ix);
var o = encoder.Encode(x2, G2);
forwardEncoded.Add(o);
sparseInput.AddWeight(0, ix, 1.0f);
}
},
() =>
{
for (int i = 0; i < inputSeq.Count; i++)
{
int ix = (int)SENTTAGS.UNK;
if (s_wordToIndex.ContainsKey(revseq[i]) == false)
{
Logger.WriteLine($"Unknow input word: {revseq[i]}");
}
else
{
ix = s_wordToIndex[revseq[i]];
}
var x2 = G2.PeekRow(s_Embedding, ix);
var o = reversEncoder.Encode(x2, G2);
backwardEncoded.Add(o);
}
});
backwardEncoded.Reverse();
for (int i = 0; i < inputSeq.Count; i++)
{
//encoded.Add(G2.concatColumns(forwardEncoded[i], backwardEncoded[i]));
encoded.Add(G2.add(forwardEncoded[i], backwardEncoded[i]));
}
//if (UseDropout)
//{
// for (int i = 0; i < encoded.Weight.Length; i++)
// {
// encoded.Weight[i] *= 0.2;
// }
//}
var ix_input = (int)SENTTAGS.START;
while (true)
{
var x = G2.PeekRow(t_Embedding, ix_input);
var eOutput = decoder.Decode(sparseInput, x, encoded, G2);
if (UseDropout)
{
for (int i = 0; i < eOutput.Weight.Length; i++)
{
eOutput.Weight[i] *= 0.2f;
}
}
var o = G2.muladd(eOutput, this.Whd, this.bd);
if (UseDropout)
{
for (int i = 0; i < o.Weight.Length; i++)
{
o.Weight[i] *= 0.2f;
}
}
var probs = G2.SoftmaxWithCrossEntropy(o);
var maxv = probs.Weight[0];
var maxi = 0;
for (int i = 1; i < probs.Weight.Length; i++)
{
if (probs.Weight[i] > maxv)
{
maxv = probs.Weight[i];
maxi = i;
}
}
var pred = maxi;
if (pred == (int)SENTTAGS.END)
{
break; // END token predicted, break out
}
if (result.Count > max_word)
{
break;
} // something is wrong
var letter2 = m_UNK;
if (t_indexToWord.ContainsKey(pred))
{
letter2 = t_indexToWord[pred];
}
result.Add(letter2);
ix_input = pred;
}
return(result);
}
