/// <summary>
/// Evaluate the quality of model on valid corpus.
/// </summary>
/// <param name="validCorpus">valid corpus to measure the quality of model</param>
/// <param name="RunNetwork">The network to run on specific device</param>
/// <param name="metrics">A set of metrics. The first one is the primary metric</param>
/// <param name="outputToFile">It indicates if valid corpus and results should be dumped to files</param>
/// <returns>true if we get a better result on primary metric, otherwise, false</returns>
internal bool RunValid(ParallelCorpus validCorpus, Func <IComputeGraph, List <List <string> >, List <List <string> >, int, bool, float> RunNetwork, List <IMetric> metrics, bool outputToFile = false)
{
List <string> srcSents = null;
List <string> refSents = null;
List <string> hypSents = null;
int batchSplitFactor = 1;
bool runNetwordSuccssed = false;
while (runNetwordSuccssed == false)
{
try
{
if (batchSplitFactor == 1)
{
Logger.WriteLine(Logger.Level.info, ConsoleColor.Gray, $"Start to evaluate model...");
}
else
{
Logger.WriteLine(Logger.Level.info, ConsoleColor.Gray, $"Retry to evaluate model. Batch split factor = '{batchSplitFactor}'");
}
srcSents = new List <string>();
refSents = new List <string>();
hypSents = new List <string>();
// Clear inner status of each metrics
foreach (IMetric metric in metrics)
{
metric.ClearStatus();
}
List <SntPairBatch> sntPairBatchs = new List <SntPairBatch>();
foreach (SntPairBatch item in validCorpus)
{
sntPairBatchs.Add(item);
if (sntPairBatchs.Count == DeviceIds.Length)
{
// Run forward on all available processors
Parallel.For(0, m_deviceIds.Length, i =>
{
SntPairBatch sntPairBatch = sntPairBatchs[i];
int batchSegSize = sntPairBatch.BatchSize / batchSplitFactor;
for (int k = 0; k < batchSplitFactor; k++)
{
// Construct sentences for encoding and decoding
List <List <string> > srcTkns = new List <List <string> >();
List <List <string> > refTkns = new List <List <string> >();
List <List <string> > hypTkns = new List <List <string> >();
for (int j = k * batchSegSize; j < (k + 1) * batchSegSize; j++)
{
srcTkns.Add(sntPairBatch.SntPairs[j].SrcSnt.ToList());
refTkns.Add(sntPairBatch.SntPairs[j].TgtSnt.ToList());
hypTkns.Add(new List <string>()
{
ParallelCorpus.BOS
});
}
// Create a new computing graph instance
using (IComputeGraph computeGraph = CreateComputGraph(DeviceIds[i], needBack: false))
{
// Run forward part
RunNetwork(computeGraph, srcTkns, hypTkns, DeviceIds[i], false);
}
lock (locker)
{
for (int j = 0; j < hypTkns.Count; j++)
{
foreach (IMetric metric in metrics)
{
metric.Evaluate(new List <List <string> >()
{
refTkns[j]
}, hypTkns[j]);
}
}
if (outputToFile)
{
for (int j = 0; j < srcTkns.Count; j++)
{
srcSents.Add(string.Join(" ", srcTkns[j]));
refSents.Add(string.Join(" ", refTkns[j]));
hypSents.Add(string.Join(" ", hypTkns[j]));
}
}
}
}
});
sntPairBatchs.Clear();
}
}
runNetwordSuccssed = true;
}
catch (Exception err)
{
batchSplitFactor *= 2;
if (batchSplitFactor >= 512)
{
Logger.WriteLine($"Batch split factor is larger than batch size, give it up.");
throw err;
}
}
}
Logger.WriteLine($"Metrics result:");
foreach (IMetric metric in metrics)
{
Logger.WriteLine(Logger.Level.info, ConsoleColor.DarkGreen, $"{metric.Name} = {metric.GetScoreStr()}");
}
if (outputToFile)
{
File.WriteAllLines("valid_src.txt", srcSents);
File.WriteAllLines("valid_ref.txt", refSents);
File.WriteAllLines("valid_hyp.txt", hypSents);
}
if (metrics.Count > 0)
{
if (metrics[0].GetPrimaryScore() > m_bestPrimaryScore)
{
Logger.WriteLine(Logger.Level.info, ConsoleColor.Green, $"We got a better score '{metrics[0].GetPrimaryScore().ToString("F")}' on primary metric '{metrics[0].Name}'. The previous score is '{m_bestPrimaryScore.ToString("F")}'");
//We have a better primary score on valid set
m_bestPrimaryScore = metrics[0].GetPrimaryScore();
return(true);
}
}
return(false);
}
internal void TrainOneEpoch(int ep, ParallelCorpus trainCorpus, ParallelCorpus validCorpus, ILearningRate learningRate, AdamOptimizer solver, List <IMetric> metrics, IModelMetaData modelMetaData,
Func <IComputeGraph, List <List <string> >, List <List <string> >, int, bool, float> ForwardOnSingleDevice)
{
int processedLineInTotal = 0;
DateTime startDateTime = DateTime.Now;
DateTime lastCheckPointDateTime = DateTime.Now;
double costInTotal = 0.0;
long srcWordCnts = 0;
long tgtWordCnts = 0;
double avgCostPerWordInTotal = 0.0;
TensorAllocator.FreeMemoryAllDevices();
Logger.WriteLine($"Start to process training corpus.");
List <SntPairBatch> sntPairBatchs = new List <SntPairBatch>();
foreach (SntPairBatch sntPairBatch in trainCorpus)
{
sntPairBatchs.Add(sntPairBatch);
if (sntPairBatchs.Count == m_deviceIds.Length)
{
float cost = 0.0f;
int tlen = 0;
int processedLine = 0;
// Copy weights from weights kept in default device to all other devices
CopyWeightsFromDefaultDeviceToAllOtherDevices();
// Run forward and backward on all available processors
Parallel.For(0, m_deviceIds.Length, i =>
{
SntPairBatch sntPairBatch_i = sntPairBatchs[i];
// Construct sentences for encoding and decoding
List <List <string> > srcTkns = new List <List <string> >();
List <List <string> > tgtTkns = new List <List <string> >();
int sLenInBatch = 0;
int tLenInBatch = 0;
for (int j = 0; j < sntPairBatch_i.BatchSize; j++)
{
srcTkns.Add(sntPairBatch_i.SntPairs[j].SrcSnt.ToList());
sLenInBatch += sntPairBatch_i.SntPairs[j].SrcSnt.Length;
tgtTkns.Add(sntPairBatch_i.SntPairs[j].TgtSnt.ToList());
tLenInBatch += sntPairBatch_i.SntPairs[j].TgtSnt.Length;
}
float lcost = 0.0f;
// Create a new computing graph instance
using (IComputeGraph computeGraph_i = CreateComputGraph(i))
{
// Run forward part
lcost = ForwardOnSingleDevice(computeGraph_i, srcTkns, tgtTkns, i, true);
// Run backward part and compute gradients
computeGraph_i.Backward();
}
lock (locker)
{
cost += lcost;
srcWordCnts += sLenInBatch;
tgtWordCnts += tLenInBatch;
tlen += tLenInBatch;
processedLineInTotal += sntPairBatch_i.BatchSize;
processedLine += sntPairBatch_i.BatchSize;
}
});
//Sum up gradients in all devices, and kept it in default device for parameters optmization
SumGradientsToTensorsInDefaultDevice();
//Optmize parameters
float lr = learningRate.GetCurrentLearningRate();
List <IWeightTensor> models = GetParametersFromDefaultDevice();
solver.UpdateWeights(models, processedLine, lr, m_regc, m_weightsUpdateCount + 1);
//Clear gradient over all devices
ZeroGradientOnAllDevices();
costInTotal += cost;
avgCostPerWordInTotal = costInTotal / tgtWordCnts;
m_weightsUpdateCount++;
if (IterationDone != null && m_weightsUpdateCount % 100 == 0)
{
IterationDone(this, new CostEventArg()
{
LearningRate = lr,
CostPerWord = cost / tlen,
AvgCostInTotal = avgCostPerWordInTotal,
Epoch = ep,
Update = m_weightsUpdateCount,
ProcessedSentencesInTotal = processedLineInTotal,
ProcessedWordsInTotal = srcWordCnts + tgtWordCnts,
StartDateTime = startDateTime
});
}
// Evaluate model every hour and save it if we could get a better one.
TimeSpan ts = DateTime.Now - lastCheckPointDateTime;
if (ts.TotalHours > 1.0)
{
CreateCheckPoint(validCorpus, metrics, modelMetaData, ForwardOnSingleDevice, avgCostPerWordInTotal);
lastCheckPointDateTime = DateTime.Now;
}
sntPairBatchs.Clear();
}
}
Logger.WriteLine(Logger.Level.info, ConsoleColor.Green, $"Epoch '{ep}' took '{DateTime.Now - startDateTime}' time to finish. AvgCost = {avgCostPerWordInTotal.ToString("F6")}, AvgCostInLastEpoch = {m_avgCostPerWordInTotalInLastEpoch.ToString("F6")}");
CreateCheckPoint(validCorpus, metrics, modelMetaData, ForwardOnSingleDevice, avgCostPerWordInTotal);
m_avgCostPerWordInTotalInLastEpoch = avgCostPerWordInTotal;
}
internal void TrainOneEpoch(int ep, ParallelCorpus trainCorpus, ParallelCorpus validCorpus, ILearningRate learningRate, AdamOptimizer solver, List <IMetric> metrics, IModelMetaData modelMetaData,
Func <IComputeGraph, List <List <string> >, List <List <string> >, int, bool, float> ForwardOnSingleDevice)
{
int processedLineInTotal = 0;
DateTime startDateTime = DateTime.Now;
DateTime lastCheckPointDateTime = DateTime.Now;
double costInTotal = 0.0;
long srcWordCntsInTotal = 0;
long tgtWordCntsInTotal = 0;
double avgCostPerWordInTotal = 0.0;
TensorAllocator.FreeMemoryAllDevices();
Logger.WriteLine($"Start to process training corpus.");
List <SntPairBatch> sntPairBatchs = new List <SntPairBatch>();
foreach (SntPairBatch sntPairBatch in trainCorpus)
{
sntPairBatchs.Add(sntPairBatch);
if (sntPairBatchs.Count == m_deviceIds.Length)
{
// Copy weights from weights kept in default device to all other devices
CopyWeightsFromDefaultDeviceToAllOtherDevices();
int batchSplitFactor = 1;
bool runNetwordSuccssed = false;
while (runNetwordSuccssed == false)
{
try
{
(float cost, int sWordCnt, int tWordCnt, int processedLine) = RunNetwork(ForwardOnSingleDevice, sntPairBatchs, batchSplitFactor);
processedLineInTotal += processedLine;
srcWordCntsInTotal += sWordCnt;
tgtWordCntsInTotal += tWordCnt;
//Sum up gradients in all devices, and kept it in default device for parameters optmization
SumGradientsToTensorsInDefaultDevice();
//Optmize parameters
float lr = learningRate.GetCurrentLearningRate();
List <IWeightTensor> models = GetParametersFromDefaultDevice();
solver.UpdateWeights(models, processedLine, lr, m_regc, m_weightsUpdateCount + 1);
costInTotal += cost;
avgCostPerWordInTotal = costInTotal / tgtWordCntsInTotal;
m_weightsUpdateCount++;
if (IterationDone != null && m_weightsUpdateCount % 100 == 0)
{
IterationDone(this, new CostEventArg()
{
LearningRate = lr,
CostPerWord = cost / tWordCnt,
AvgCostInTotal = avgCostPerWordInTotal,
Epoch = ep,
Update = m_weightsUpdateCount,
ProcessedSentencesInTotal = processedLineInTotal,
ProcessedWordsInTotal = srcWordCntsInTotal + tgtWordCntsInTotal,
StartDateTime = startDateTime
});
}
runNetwordSuccssed = true;
}
catch (Exception err)
{
batchSplitFactor *= 2;
Logger.WriteLine($"Increase batch split factor to {batchSplitFactor}, and retry it.");
if (batchSplitFactor >= sntPairBatchs[0].BatchSize)
{
Logger.WriteLine($"Batch split factor is larger than batch size, give it up.");
throw err;
}
}
}
// Evaluate model every hour and save it if we could get a better one.
TimeSpan ts = DateTime.Now - lastCheckPointDateTime;
if (ts.TotalHours > 1.0)
{
CreateCheckPoint(validCorpus, metrics, modelMetaData, ForwardOnSingleDevice, avgCostPerWordInTotal);
lastCheckPointDateTime = DateTime.Now;
}
sntPairBatchs.Clear();
}
}
Logger.WriteLine(Logger.Level.info, ConsoleColor.Green, $"Epoch '{ep}' took '{DateTime.Now - startDateTime}' time to finish. AvgCost = {avgCostPerWordInTotal.ToString("F6")}, AvgCostInLastEpoch = {m_avgCostPerWordInTotalInLastEpoch.ToString("F6")}");
CreateCheckPoint(validCorpus, metrics, modelMetaData, ForwardOnSingleDevice, avgCostPerWordInTotal);
m_avgCostPerWordInTotalInLastEpoch = avgCostPerWordInTotal;
}
/// <summary>
/// Evaluate the quality of model on valid corpus.
/// </summary>
/// <param name="validCorpus">valid corpus to measure the quality of model</param>
/// <param name="RunNetwork">The network to run on specific device</param>
/// <param name="metrics">A set of metrics. The first one is the primary metric</param>
/// <param name="outputToFile">It indicates if valid corpus and results should be dumped to files</param>
/// <returns>true if we get a better result on primary metric, otherwise, false</returns>
internal bool RunValid(ParallelCorpus validCorpus, Func <IComputeGraph, List <List <string> >, List <List <string> >, int, bool, float> RunNetwork, List <IMetric> metrics, bool outputToFile = false)
{
Logger.WriteLine(Logger.Level.info, ConsoleColor.Gray, $"Start to Evaluate model...");
List <string> srcSents = new List <string>();
List <string> refSents = new List <string>();
List <string> hypSents = new List <string>();
// Clear inner status of each metrics
foreach (var metric in metrics)
{
metric.ClearStatus();
}
foreach (var sntPairBatch in validCorpus)
{
// Construct sentences for encoding and decoding
List <List <string> > srcTkns = new List <List <string> >();
List <List <string> > refTkns = new List <List <string> >();
for (int j = 0; j < sntPairBatch.BatchSize; j++)
{
srcTkns.Add(sntPairBatch.SntPairs[j].SrcSnt.ToList());
refTkns.Add(sntPairBatch.SntPairs[j].TgtSnt.ToList());
}
List <List <string> > hypTkns = new List <List <string> >();
// Create a new computing graph instance
using (IComputeGraph computeGraph = CreateComputGraph(DeviceIds[0], needBack: false))
{
// Run forward part
RunNetwork(computeGraph, srcTkns, hypTkns, DeviceIds[0], false);
}
for (int i = 0; i < hypTkns.Count; i++)
{
foreach (var metric in metrics)
{
metric.Evaluate(new List <List <string> >()
{
refTkns[i]
}, hypTkns[i]);
}
}
if (outputToFile)
{
for (int j = 0; j < sntPairBatch.BatchSize; j++)
{
srcSents.Add(String.Join(" ", srcTkns[j]));
refSents.Add(String.Join(" ", refTkns[j]));
hypSents.Add(String.Join(" ", hypTkns[j]));
}
}
}
Logger.WriteLine($"Metrics result:");
foreach (IMetric metric in metrics)
{
Logger.WriteLine(Logger.Level.info, ConsoleColor.DarkGreen, $"{metric.Name} = {metric.GetScoreStr()}");
}
if (outputToFile)
{
File.WriteAllLines("valid_src.txt", srcSents);
File.WriteAllLines("valid_ref.txt", refSents);
File.WriteAllLines("valid_hyp.txt", hypSents);
}
if (metrics.Count > 0)
{
if (metrics[0].GetPrimaryScore() > m_bestPrimaryScore)
{
Logger.WriteLine(Logger.Level.info, ConsoleColor.Green, $"We got a better score '{metrics[0].GetPrimaryScore().ToString("F")}' on primary metric '{metrics[0].Name}'. The previous score is '{m_bestPrimaryScore.ToString("F")}'");
//We have a better primary score on valid set
m_bestPrimaryScore = metrics[0].GetPrimaryScore();
return(true);
}
}
return(false);
}