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);
}
/// <summary>
/// Interactively run the model
/// </summary>
/// <param name="modelName">Which model to use</param>
/// <param name="checkpoint">Which checkpoint to load</param>
/// <param name="seed">Seed for random number generators, fix seed to reproduce results</param>
/// <param name="sampleCount">Number of samples to return total</param>
/// <param name="batchSize">Number of batches (only affects speed/memory). Must divide sampleCount.</param>
/// <param name="length">Number of tokens in generated text, if null (default), is
/// determined by model hyperparameters</param>
/// <param name="temperature">randomness in boltzmann distribution.
/// Lower temperature results in less random completions. As the
/// temperature approaches zero, the model will become deterministic and
/// repetitive. Higher temperature results in more random completions.</param>
/// <param name="topK">Controls diversity. 1 means only 1 word is
/// considered for each step (token), resulting in deterministic completions,
/// while 40 means 40 words are considered at each step. 0 (default) is a
/// special setting meaning no restrictions. 40 generally is a good value.
/// </param>
public static int Run(string modelName = "117M", string?checkpoint = null, int?seed = null,
int sampleCount = 1,
int batchSize = 1, int?length = null, float temperature = 1, int topK = 0)
{
if (sampleCount % batchSize != 0)
{
throw new ArgumentException();
}
string modelPath = CommonCommandOptions.ExpandModelNameToPathOrExit(modelName);
var encoder = Gpt2Encoder.LoadEncoder(modelPath);
var hParams = Gpt2Model.LoadHParams(modelPath);
int nCtx = hParams.ContextTokens;
if (length is null)
{
length = nCtx;
}
else if (length > nCtx)
{
throw new ArgumentException("Can't get samples longer than window size: " + nCtx);
}
foreach (var gpu in tf.config.list_physical_devices("gpu"))
{
tf.config.experimental.set_memory_growth(gpu, true);
}
var sess = new Session(graph: new Graph());
using var sessionContext = sess.StartUsing();
Tensor context = v1.placeholder(tf.int32, new TensorShape(batchSize, null));
tf.random.set_seed(seed);
Tensor output = Gpt2Sampler.SampleSequence(
hParams: hParams,
length: length.Value,
context: context,
batchSize: batchSize,
temperature: temperature,
topK: topK);
var saver = new Saver();
checkpoint ??= tf.train.latest_checkpoint(modelPath);
saver.restore(sess, checkpoint);
bool interrupted = false;
Console.CancelKeyPress += (object sender, ConsoleCancelEventArgs args) =>
Volatile.Write(ref interrupted, args.Cancel = true);
while (!interrupted)
{
string text;
do
{
Console.Write("Model prompt >>> ");
text = Console.ReadLine();
if (Volatile.Read(ref interrupted))
{
break;
}
if (string.IsNullOrEmpty(text))
{
Console.WriteLine("Prompt should not be empty");
}
} while (!Volatile.Read(ref interrupted) && string.IsNullOrEmpty(text));
if (Volatile.Read(ref interrupted))
{
break;
}
var contextTokens = encoder.Encode(text);
if (!tf.test.is_gpu_available() && contextTokens.Count >= length.Value)
{
Console.Error.WriteLine();
Console.Error.WriteLine("Prompt is too long.");
Console.Error.WriteLine();
continue;
}
int generated = 0;
foreach (int _ in Enumerable.Range(0, sampleCount / batchSize))
{
ndarray <int> @out;
try {
@out = sess.run(output, feed_dict: new Dictionary <object, object> {
[context] = Enumerable.Repeat(contextTokens, batchSize).ToArray(),
})[.., contextTokens.Count..];
} catch (InvalidArgumentError ex) {
throw new ArgumentOutOfRangeException(
"Unable to generate sequence of desired length. "
+ "Try lowering length by passing -l (-sample-length) parameter. "
+ "Current length: " + length.Value,
innerException: ex);
}
foreach (int i in Enumerable.Range(0, batchSize))
{
generated++;
var part = @out[i].AsArray();
text = encoder.Decode(part);
Console.WriteLine($"{Delimiter} SAMPLE {generated} {Delimiter}");
Console.WriteLine(text);
}
}
Console.Write(Delimiter);
Console.WriteLine(Delimiter);
}
return(0);
}