public void Train(string checkpoint, string run, int?counter, dynamic sessionConfig = null, CancellationToken cancellation = default)
{
Session session = sessionConfig is null
? Session.NewDyn(config : sessionConfig)
: new Session();
using (session.StartUsing()) {
var context = tf.placeholder(tf.int32, new TensorShape(this.batchSize, null));
var output = Gpt2Model.Model(this.hParams, input: context);
Tensor labels = context[.., 1..];
public override int Run(string[] remainingArguments)
{
this.CheckRequiredArguments();
if (remainingArguments.Length < 1)
{
throw new ArgumentNullException("dataset");
}
string datasetName = remainingArguments[0];
string checkpoint = Gpt2Checkpoints.ProcessCheckpointConfig(
gpt2Root: Environment.CurrentDirectory,
checkpoint: this.Checkpoint,
modelName: this.ModelName,
runName: this.RunName);
var encoder = Gpt2Encoder.LoadEncoder(this.ModelName);
string searchPattern = this.Include ?? "*";
var dataset = searchPattern.EndsWith("*.csv")
? LoadCsv(encoder, root: datasetName, field: this.ColumnName)
: Gpt2Dataset.LoadDataset(encoder, path: datasetName, pattern: searchPattern);
if (dataset.Count == 0)
{
Console.Error.WriteLine("The dataset is empty!");
return(-1);
}
var hParams = Gpt2Model.LoadHParams(this.ModelName);
var random = this.Seed is null ? new Random() : new Random(this.Seed.Value);
var stop = new CancellationTokenSource();
Console.CancelKeyPress += delegate { stop.Cancel(); };
dynamic config = config_pb2.ConfigProto.CreateInstance();
config.gpu_options.allow_growth = true;
new Gpt2Trainer(dataset, encoder, hParams, this.BatchSize, this.SampleLength, random)
{
SaveEvery = this.SaveEvery,
SampleNum = this.SampleNum,
SampleEvery = this.SampleEvery,
}
.Train(checkpoint, this.RunName,
sessionConfig: config,
counter: checkpoint == "fresh" ? 1 : (int?)null,
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 void 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();
}
var encoder = Gpt2Encoder.LoadEncoder(modelName);
var hParams = Gpt2Model.LoadHParams(modelName);
int nCtx = ((dynamic)hParams).n_ctx;
if (length is null)
{
length = nCtx;
}
else if (length > nCtx)
{
throw new ArgumentException("Can't get samples longer than window size: " + hParams.get("n_ctx"));
}
new Session(graph: new Graph()).UseSelf(sess => {
var context = tf.placeholder(tf.int32, new TensorShape(batchSize, null));
tf.set_random_seed(seed);
var output = Gpt2Sampler.SampleSequence(
hParams: hParams,
length: length.Value,
context: context,
batchSize: batchSize,
temperature: temperature,
topK: topK);
var saver = new Saver();
checkpoint = checkpoint ?? tf.train.latest_checkpoint(Path.Combine("models", modelName));
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 (var _ 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 = (ndarray <int>)@out[i];
text = encoder.Decode(part);
Console.WriteLine($"{Delimiter} SAMPLE {generated} {Delimiter}");
Console.WriteLine(text);
}
}
Console.Write(Delimiter);
Console.WriteLine(Delimiter);
}
});
}
