public override int Run(string[] remainingArguments)
{
this.CheckRequiredArguments();
if (remainingArguments.Length < 1)
{
throw new ArgumentNullException("dataset");
}
string modelPath = CommonCommandOptions.ExpandModelNameToPathOrExit(this.ModelName);
string checkpoint = Gpt2Checkpoints.ProcessCheckpointConfig(
modelPath: modelPath,
checkpoint: this.Checkpoint,
runName: this.RunName);
var encoder = Gpt2Encoder.LoadEncoder(modelPath);
string searchPattern = this.Include ?? "*";
string datasetName = remainingArguments[0];
var dataset = searchPattern.EndsWith("*.csv")
? LoadCsv(encoder, root: datasetName, field: this.ColumnName ?? throw new ArgumentException("column must be specified for training on .csv files"))
: Gpt2Dataset.LoadDataset(encoder, path: datasetName, pattern: searchPattern);
if (dataset.Count == 0)
{
Console.Error.WriteLine("The dataset is empty!");
return(-1);
}
var hParams = Gpt2Model.LoadHParams(modelPath);
var random = this.Seed is null ? new Random() : new Random(this.Seed.Value);
tf.random.set_seed(this.Seed);
var stop = new CancellationTokenSource();
Console.CancelKeyPress += delegate { stop.Cancel(); };
dynamic config = config_pb2.ConfigProto.CreateInstance();
config.gpu_options.allow_growth = true;
var trainer = new Gpt2TunerLegacy(dataset, encoder, hParams, this.BatchSize, this.SampleLength, random)
{
SaveEvery = this.SaveEvery,
SampleNum = this.SampleNum,
SampleEvery = this.SampleEvery,
};
string checkpointOutputDirectory = Path.Combine(modelPath, Gpt2Checkpoints.CheckpointDir);
trainer.FineTune(
checkpointsDir: checkpointOutputDirectory, checkpoint: checkpoint,
run: this.RunName,
counter: checkpoint == "fresh" ? 1 : (int?)null,
sessionConfig: config,
cancellation: stop.Token);
return(0);
}
public override int Run(string[] remainingArguments)
{
string modelPath = CommonCommandOptions.ExpandModelNameToPathOrExit(this.ModelName);
string checkpoint = Gpt2Checkpoints.ProcessCheckpointConfig(
modelPath: modelPath,
checkpoint: this.Checkpoint,
runName: this.RunName);
return(Run(
modelName: this.ModelName,
checkpoint: checkpoint,
seed: this.Seed,
sampleCount: this.SampleCount,
batchSize: this.BatchSize,
length: this.Length,
temperature: this.Temperature,
topK: this.TopK));
}