/// <summary>
/// Run forward part on given single device
/// </summary>
/// <param name="g">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. In training mode, it inputs target tokens, otherwise, it outputs target tokens generated by decoder</param>
/// <param name="deviceIdIdx">The index of current device</param>
/// <returns>The cost of forward part</returns>
public override List <NetworkResult> RunForwardOnSingleDevice(IComputeGraph g, ISntPairBatch sntPairBatch, int deviceIdIdx, bool isTraining, DecodingOptions decodingOptions)
{
List <NetworkResult> nrs = new List <NetworkResult>();
var srcSnts = sntPairBatch.GetSrcTokens(0);
var tgtSnts = sntPairBatch.GetTgtTokens(0);
(IEncoder encoder, IWeightTensor srcEmbedding, IWeightTensor posEmbedding, FeedForwardLayer decoderFFLayer) = GetNetworksOnDeviceAt(deviceIdIdx);
// Reset networks
encoder.Reset(g.GetWeightFactory(), srcSnts.Count);
var originalSrcLengths = BuildInTokens.PadSentences(srcSnts);
var srcTokensList = m_modelMetaData.SrcVocab.GetWordIndex(srcSnts);
BuildInTokens.PadSentences(tgtSnts);
var tgtTokensLists = m_modelMetaData.ClsVocab.GetWordIndex(tgtSnts);
int seqLen = srcSnts[0].Count;
int batchSize = srcSnts.Count;
// Encoding input source sentences
IWeightTensor encOutput = Encoder.Run(g, sntPairBatch, encoder, m_modelMetaData, m_shuffleType, srcEmbedding, posEmbedding, null, srcTokensList, originalSrcLengths);
IWeightTensor ffLayer = decoderFFLayer.Process(encOutput, batchSize, g);
float cost = 0.0f;
IWeightTensor probs = g.Softmax(ffLayer, inPlace: true);
if (isTraining)
{
var tgtTokensTensor = g.CreateTokensTensor(tgtTokensLists);
cost = g.CrossEntropyLoss(probs, tgtTokensTensor);
}
else
{
// Output "i"th target word
using var targetIdxTensor = g.Argmax(probs, 1);
float[] targetIdx = targetIdxTensor.ToWeightArray();
List <string> targetWords = m_modelMetaData.ClsVocab.ConvertIdsToString(targetIdx.ToList());
for (int k = 0; k < batchSize; k++)
{
tgtSnts[k] = targetWords.GetRange(k * seqLen, seqLen);
}
}
NetworkResult nr = new NetworkResult
{
Cost = cost,
Output = new List <List <List <string> > >()
};
nr.Output.Add(tgtSnts);
nrs.Add(nr);
return(nrs);
}
public static IWeightTensor BuildTensorForSourceTokenGroupAt(IComputeGraph computeGraph, ISntPairBatch sntPairBatch, ShuffleEnums shuffleType, IEncoder encoder, IModel modelMetaData, IWeightTensor srcEmbedding, IWeightTensor posEmbedding, IWeightTensor segmentEmbedding, int groupId)
{
var contextTokens = InsertCLSToken(sntPairBatch.GetSrcTokens(groupId));
var originalSrcContextLength = BuildInTokens.PadSentences(contextTokens);
var contextTokenIds = modelMetaData.SrcVocab.GetWordIndex(contextTokens);
IWeightTensor encContextOutput = InnerRunner(computeGraph, contextTokenIds, originalSrcContextLength, shuffleType, encoder, modelMetaData, srcEmbedding, posEmbedding, segmentEmbedding);
int contextPaddedLen = contextTokens[0].Count;
float[] contextCLSIdxs = new float[sntPairBatch.BatchSize];
for (int j = 0; j < sntPairBatch.BatchSize; j++)
{
contextCLSIdxs[j] = j * contextPaddedLen;
}
IWeightTensor contextCLSOutput = computeGraph.IndexSelect(encContextOutput, contextCLSIdxs);
return(contextCLSOutput);
}
/// <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>
public override List <NetworkResult> RunForwardOnSingleDevice(IComputeGraph computeGraph, ISntPairBatch sntPairBatch, int deviceIdIdx, bool isTraining, DecodingOptions decodingOptions)
{
(var encoder, var decoder, var decoderFFLayer, var srcEmbedding, var tgtEmbedding, var posEmbedding, var segmentEmbedding, var pointerGenerator) = GetNetworksOnDeviceAt(deviceIdIdx);
var srcSnts = sntPairBatch.GetSrcTokens(0);
var originalSrcLengths = BuildInTokens.PadSentences(srcSnts);
var srcTokensList = m_modelMetaData.SrcVocab.GetWordIndex(srcSnts);
if (isTraining && srcSnts[0].Count > m_options.MaxTrainSrcSentLength + 2)
{
throw new InvalidDataException($"The source sentence is too long. Its length = '{srcSnts[0].Count}', but MaxTrainSrcSentLength is '{m_options.MaxTrainSrcSentLength}'. The sentence is '{string.Join(" ", srcSnts[0])}'");
}
IWeightTensor encOutput;
if (!isTraining && (m_options.ProcessorType == ProcessorTypeEnums.CPU))
{
// Try to get src tensor from cache
string cacheKey = GenerateCacheKey(srcSnts);
if (!m_memoryCache.TryGetValue(cacheKey, out encOutput))
{
encOutput = Encoder.Run(computeGraph, sntPairBatch, encoder, m_modelMetaData, m_shuffleType, srcEmbedding, posEmbedding, segmentEmbedding, srcTokensList, originalSrcLengths);
var cacheEntryOptions = new MemoryCacheEntryOptions().SetSize(1);
m_memoryCache.Set(cacheKey, encOutput.CopyWeightsRef($"cache_{encOutput.Name}", false), cacheEntryOptions);
}
}
else
{
// Compute src tensor
encOutput = Encoder.Run(computeGraph, sntPairBatch, encoder, m_modelMetaData, m_shuffleType, srcEmbedding, posEmbedding, segmentEmbedding, srcTokensList, originalSrcLengths);
}
List <NetworkResult> nrs = new List <NetworkResult>();
// Generate output decoder sentences
int batchSize = srcSnts.Count;
var tgtSnts = sntPairBatch.GetTgtTokens(0);
var tgtTokensList = m_modelMetaData.TgtVocab.GetWordIndex(tgtSnts);
NetworkResult nr = new NetworkResult();
decoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
if (decoder is AttentionDecoder)
{
nr.Cost = Decoder.DecodeAttentionLSTM(tgtTokensList, computeGraph, encOutput, decoder as AttentionDecoder, decoderFFLayer, tgtEmbedding, m_modelMetaData.TgtVocab, srcSnts.Count, isTraining);
nr.Output = new List <List <List <string> > >
{
m_modelMetaData.TgtVocab.ConvertIdsToString(tgtTokensList)
};
}
else
{
if (isTraining)
{
(var c, _) = Decoder.DecodeTransformer(tgtTokensList, computeGraph, encOutput, decoder as TransformerDecoder, decoderFFLayer, tgtEmbedding, posEmbedding, originalSrcLengths, m_modelMetaData.TgtVocab, m_shuffleType,
m_options.DropoutRatio, null, isTraining, pointerGenerator: pointerGenerator, srcSeqs: srcTokensList);
nr.Cost = c;
nr.Output = null;
}
else
{
Dictionary <string, IWeightTensor> cachedTensors = new Dictionary <string, IWeightTensor>();
List <List <BeamSearchStatus> > beam2batchStatus = Decoder.InitBeamSearchStatusListList(batchSize, tgtTokensList);
for (int i = tgtTokensList[0].Count; i < decodingOptions.MaxTgtSentLength; i++)
{
List <List <BeamSearchStatus> > batch2beam2seq = null; //(batch_size, beam_search_size)
try
{
foreach (var batchStatus in beam2batchStatus)
{
var batch2tgtTokens = Decoder.ExtractBatchTokens(batchStatus);
using var g = computeGraph.CreateSubGraph($"TransformerDecoder_Step_{i}");
(var cost2, var bssSeqList) = Decoder.DecodeTransformer(batch2tgtTokens, g, encOutput, decoder as TransformerDecoder, decoderFFLayer, tgtEmbedding, posEmbedding,
originalSrcLengths, m_modelMetaData.TgtVocab, m_shuffleType, 0.0f, decodingOptions, isTraining,
outputSentScore: decodingOptions.BeamSearchSize > 1, previousBeamSearchResults: batchStatus,
pointerGenerator: pointerGenerator, srcSeqs: srcTokensList,
cachedTensors: cachedTensors);
bssSeqList = Decoder.SwapBeamAndBatch(bssSeqList); // Swap shape: (beam_search_size, batch_size) -> (batch_size, beam_search_size)
batch2beam2seq = Decoder.CombineBeamSearchResults(batch2beam2seq, bssSeqList);
}
}
catch (OutOfMemoryException)
{
GC.Collect();
Logger.WriteLine(Logger.Level.warn, $"We have out of memory while generating '{i}th' tokens, so terminate decoding for current sequences.");
break;
}
if (decodingOptions.BeamSearchSize > 1)
{
// Keep top N result and drop all others
for (int k = 0; k < batchSize; k++)
{
batch2beam2seq[k] = BeamSearch.GetTopNBSS(batch2beam2seq[k], decodingOptions.BeamSearchSize);
}
}
beam2batchStatus = Decoder.SwapBeamAndBatch(batch2beam2seq);
if (Decoder.AreAllSentsCompleted(beam2batchStatus))
{
break;
}
}
nr.Cost = 0.0f;
nr.Output = m_modelMetaData.TgtVocab.ExtractTokens(beam2batchStatus);
if (cachedTensors != null)
{
foreach (var pair in cachedTensors)
{
pair.Value.Dispose();
}
}
}
}
nr.RemoveDuplicatedEOS();
nrs.Add(nr);
return(nrs);
}
/// <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>
public override List <NetworkResult> RunForwardOnSingleDevice(IComputeGraph computeGraph, ISntPairBatch sntPairBatch, int deviceIdIdx, bool isTraining, DecodingOptions decodingOptions)
{
List <NetworkResult> nrs = new List <NetworkResult>();
(IEncoder encoder, IWeightTensor srcEmbedding, List <IFeedForwardLayer> encoderFFLayer, IWeightTensor posEmbedding, IWeightTensor segmentEmbedding) = GetNetworksOnDeviceAt(deviceIdIdx);
var srcSnts = sntPairBatch.GetSrcTokens(0);
var originalSrcLengths = BuildInTokens.PadSentences(srcSnts);
var srcTokensList = m_modelMetaData.SrcVocab.GetWordIndex(srcSnts);
IWeightTensor encOutput = Encoder.Run(computeGraph, sntPairBatch, encoder, m_modelMetaData, m_shuffleType, srcEmbedding, posEmbedding, segmentEmbedding, srcTokensList, originalSrcLengths);
int srcSeqPaddedLen = srcSnts[0].Count;
int batchSize = srcSnts.Count;
float[] clsIdxs = new float[batchSize];
for (int i = 0; i < batchSize; i++)
{
for (int j = 0; j < srcSnts[i].Count; j++)
{
if (srcSnts[i][j] == BuildInTokens.CLS)
{
clsIdxs[i] = i * srcSeqPaddedLen + j;
break;
}
}
}
IWeightTensor clsWeightTensor = computeGraph.IndexSelect(encOutput, clsIdxs);
for (int i = 0; i < m_encoderFFLayer.Length; i++)
{
float cost = 0.0f;
NetworkResult nr = new NetworkResult
{
Output = new List <List <List <string> > >()
};
IWeightTensor ffLayer = encoderFFLayer[i].Process(clsWeightTensor, batchSize, computeGraph);
using (IWeightTensor probs = computeGraph.Softmax(ffLayer, runGradients: false, inPlace: true))
{
if (isTraining)
{
var tgtSnts = sntPairBatch.GetTgtTokens(i);
for (int k = 0; k < batchSize; k++)
{
int ix_targets_k_j = m_modelMetaData.ClsVocabs[i].GetWordIndex(tgtSnts[k][0]);
float score_k = probs.GetWeightAt(new long[] { k, ix_targets_k_j });
cost += (float)-Math.Log(score_k);
probs.SetWeightAt(score_k - 1, new long[] { k, ix_targets_k_j });
}
ffLayer.CopyWeightsToGradients(probs);
nr.Cost = cost / batchSize;
}
else
{
// Output "i"th target word
using var targetIdxTensor = computeGraph.Argmax(probs, 1);
float[] targetIdx = targetIdxTensor.ToWeightArray();
List <string> targetWords = m_modelMetaData.ClsVocabs[i].ConvertIdsToString(targetIdx.ToList());
nr.Output.Add(new List <List <string> >());
for (int k = 0; k < batchSize; k++)
{
nr.Output[0].Add(new List <string>());
nr.Output[0][k].Add(targetWords[k]);
}
}
}
nrs.Add(nr);
}
return(nrs);
}
/// <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>
public override List <NetworkResult> RunForwardOnSingleDevice(IComputeGraph computeGraph, ISntPairBatch sntPairBatch, int deviceIdIdx, bool isTraining, DecodingOptions decodingOptions)
{
(IEncoder encoder, IDecoder decoder, IFeedForwardLayer encoderFFLayer, IFeedForwardLayer decoderFFLayer, IWeightTensor srcEmbedding, IWeightTensor tgtEmbedding, IWeightTensor posEmbedding, IWeightTensor segmentEmbedding) = GetNetworksOnDeviceAt(deviceIdIdx);
var srcSnts = sntPairBatch.GetSrcTokens(0);
var originalSrcLengths = BuildInTokens.PadSentences(srcSnts);
var srcTokensList = m_modelMetaData.SrcVocab.GetWordIndex(srcSnts);
IWeightTensor encOutput = Encoder.Run(computeGraph, sntPairBatch, encoder, m_modelMetaData, m_shuffleType, srcEmbedding, posEmbedding, segmentEmbedding, srcTokensList, originalSrcLengths);
List <NetworkResult> nrs = new List <NetworkResult>();
int srcSeqPaddedLen = srcSnts[0].Count;
int batchSize = srcSnts.Count;
float[] clsIdxs = new float[batchSize];
for (int i = 0; i < batchSize; i++)
{
for (int j = 0; j < srcSnts[i].Count; j++)
{
if (srcSnts[i][j] == BuildInTokens.CLS)
{
clsIdxs[i] = i * srcSeqPaddedLen + j;
break;
}
}
}
IWeightTensor clsWeightTensor = computeGraph.IndexSelect(encOutput, clsIdxs);
float cost = 0.0f;
NetworkResult nrCLS = new NetworkResult
{
Output = new List <List <List <string> > >()
};
IWeightTensor ffLayer = encoderFFLayer.Process(clsWeightTensor, batchSize, computeGraph);
using (IWeightTensor probs = computeGraph.Softmax(ffLayer, runGradients: false, inPlace: true))
{
if (isTraining)
{
var clsSnts = sntPairBatch.GetTgtTokens(0);
for (int k = 0; k < batchSize; k++)
{
int ix_targets_k_j = m_modelMetaData.ClsVocab.GetWordIndex(clsSnts[k][0]);
float score_k = probs.GetWeightAt(new long[] { k, ix_targets_k_j });
cost += (float)-Math.Log(score_k);
probs.SetWeightAt(score_k - 1, new long[] { k, ix_targets_k_j });
}
ffLayer.CopyWeightsToGradients(probs);
nrCLS.Cost = cost / batchSize;
}
else
{
// Output "i"th target word
using var targetIdxTensor = computeGraph.Argmax(probs, 1);
float[] targetIdx = targetIdxTensor.ToWeightArray();
List <string> targetWords = m_modelMetaData.ClsVocab.ConvertIdsToString(targetIdx.ToList());
nrCLS.Output.Add(new List <List <string> >());
for (int k = 0; k < batchSize; k++)
{
nrCLS.Output[0].Add(new List <string>());
nrCLS.Output[0][k].Add(targetWords[k]);
}
}
}
// Reset networks
decoder.Reset(computeGraph.GetWeightFactory(), srcSnts.Count);
// Generate output decoder sentences
var tgtSnts = sntPairBatch.GetTgtTokens(1);
var tgtTokensList = m_modelMetaData.TgtVocab.GetWordIndex(tgtSnts);
NetworkResult nr = new NetworkResult();
if (decoder is AttentionDecoder)
{
nr.Cost = Decoder.DecodeAttentionLSTM(tgtTokensList, computeGraph, encOutput, decoder as AttentionDecoder, decoderFFLayer, tgtEmbedding, m_modelMetaData.TgtVocab, srcSnts.Count, isTraining);
nr.Output = new List <List <List <string> > >
{
m_modelMetaData.TgtVocab.ConvertIdsToString(tgtTokensList)
};
}
else
{
if (isTraining)
{
(var c, _) = Decoder.DecodeTransformer(tgtTokensList, computeGraph, encOutput, decoder as TransformerDecoder, decoderFFLayer, tgtEmbedding, posEmbedding, originalSrcLengths, m_modelMetaData.TgtVocab, m_shuffleType, m_options.DropoutRatio, null, isTraining);
nr.Cost = c;
nr.Output = null;
}
else
{
List <List <BeamSearchStatus> > beam2batchStatus = Decoder.InitBeamSearchStatusListList(batchSize, tgtTokensList);
for (int i = 0; i < decodingOptions.MaxTgtSentLength; i++)
{
List <List <BeamSearchStatus> > batch2beam2seq = null; //(batch_size, beam_search_size)
try
{
foreach (var batchStatus in beam2batchStatus)
{
var batch2tgtTokens = Decoder.ExtractBatchTokens(batchStatus);
using var g = computeGraph.CreateSubGraph($"TransformerDecoder_Step_{i}");
(var cost2, var bssSeqList) = Decoder.DecodeTransformer(batch2tgtTokens, g, encOutput, decoder as TransformerDecoder, decoderFFLayer, tgtEmbedding, posEmbedding,
originalSrcLengths, m_modelMetaData.TgtVocab, m_shuffleType, 0.0f, decodingOptions, isTraining,
outputSentScore: decodingOptions.BeamSearchSize > 1, previousBeamSearchResults: batchStatus);
bssSeqList = Decoder.SwapBeamAndBatch(bssSeqList);
batch2beam2seq = Decoder.CombineBeamSearchResults(batch2beam2seq, bssSeqList);
}
}
catch (OutOfMemoryException)
{
GC.Collect();
Logger.WriteLine(Logger.Level.warn, $"We have out of memory while generating '{i}th' tokens, so terminate decoding for current sequences.");
break;
}
if (decodingOptions.BeamSearchSize > 1)
{
// Keep top N result and drop all others
for (int k = 0; k < batchSize; k++)
{
batch2beam2seq[k] = BeamSearch.GetTopNBSS(batch2beam2seq[k], decodingOptions.BeamSearchSize);
}
}
beam2batchStatus = Decoder.SwapBeamAndBatch(batch2beam2seq);
if (Decoder.AreAllSentsCompleted(beam2batchStatus))
{
break;
}
}
nr.Cost = 0.0f;
nr.Output = m_modelMetaData.TgtVocab.ExtractTokens(beam2batchStatus);
}
}
nr.RemoveDuplicatedEOS();
nrs.Add(nrCLS);
nrs.Add(nr);
return(nrs);
}
