public override int Run(string[] remainingArguments)
{
this.CheckRequiredArguments();
if (remainingArguments.Length < 1)
{
throw new ArgumentNullException("dataset");
}
string datasetName = remainingArguments[0];
string checkpoint = Gpt2Trainer.ProcessCheckpointConfig(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)
: Gpt2Trainer.LoadDataset(encoder, path: datasetName, pattern: searchPattern);
var hParams = Gpt2Model.LoadHParams(this.ModelName);
var random = this.Seed == null ? new Random() : new Random(this.Seed.Value);
var stop = new CancellationTokenSource();
Console.CancelKeyPress += delegate { stop.Cancel(); };
new Gpt2Trainer(dataset, encoder, hParams, this.BatchSize, this.SampleLength, random)
.Train(checkpoint, this.RunName, stop.Token);
return(0);
}
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 == null ? new Random() : new Random(this.Seed.Value);
var stop = new CancellationTokenSource();
Console.CancelKeyPress += delegate { stop.Cancel(); };
dynamic config = config_pb2.ConfigProto();
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);
while (true)
{
string text;
do
{
Console.Write("Model prompt >>> ");
text = Console.ReadLine();
if (string.IsNullOrEmpty(text))
{
Console.WriteLine("Prompt should not be empty");
}
} while (string.IsNullOrEmpty(text));
var contextTokens = encoder.Encode(text);
int generated = 0;
foreach (var _ in Enumerable.Range(0, sampleCount / batchSize))
{
var @out = sess.run(output, feed_dict: new PythonDict <object, object> {
[context] = Enumerable.Repeat(contextTokens, batchSize),
})[Range.All, Range.StartAt(contextTokens.Count)];
foreach (int i in Enumerable.Range(0, batchSize))
{
generated++;
ndarray part = @out[i];
text = encoder.Decode(part);
Console.WriteLine($"{Delimiter} SAMPLE {generated} {Delimiter}");
Console.WriteLine(text);
}
}
Console.Write(Delimiter);
Console.WriteLine(Delimiter);
}
});
}
public void Train(string checkpoint, string run, int?counter, dynamic sessionConfig = null, CancellationToken cancellation = default)
{
Session sess = sessionConfig == null
? Session.NewDyn(config : sessionConfig)
: new Session();
sess.UseSelf(session => {
var context = tf.placeholder(tf.int32, new TensorShape(this.batchSize, null));
var output = Gpt2Model.Model(this.hParams, input: context);
Tensor labels = context[Range.All, Range.StartAt(1)];
Tensor logits = output["logits"][Range.All, Range.EndAt(new Index(1, fromEnd: true))];
var loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits_dyn(
labels: labels,
logits: logits));
var sample = Gpt2Sampler.SampleSequence(
this.hParams,
length: this.sampleLength,
context: context,
batchSize: this.batchSize,
temperature: 1.0f,
topK: 40);
var trainVars = tf.trainable_variables().Where((dynamic var) => var.name.Contains("model"));
var optimizer = new AdamOptimizer(learning_rate: 0.0002).minimize(loss, var_list: trainVars);
var saver = new Saver(
var_list: trainVars,
max_to_keep: 5,
keep_checkpoint_every_n_hours: 1);
session.run(tf.global_variables_initializer());
Console.WriteLine("Loading checkpoint " + checkpoint);
saver.restore(session, checkpoint);
Console.WriteLine("Loading dataset...");
var sampler = new TrainingSampler(this.dataset, this.random);
Console.WriteLine($"Dataset has {sampler.TokenCount} tokens");
string counterFile = Path.Combine(Gpt2Checkpoints.CheckpointDir, run, "counter");
if (counter == null && File.Exists(counterFile))
{
counter = int.Parse(File.ReadAllText(counterFile), CultureInfo.InvariantCulture) + 1;
}
counter = counter ?? 1;
string runCheckpointDir = Path.Combine(Gpt2Checkpoints.CheckpointDir, run);
string runSampleDir = Path.Combine(SampleDir, run);
void Save()
{
Directory.CreateDirectory(runCheckpointDir);
Console.WriteLine("Saving " + Path.Combine(runCheckpointDir, Invariant($"model-{counter}")));
saver.save(session,
Path.Combine(runCheckpointDir, "model"),
global_step: counter.Value);
File.WriteAllText(path: counterFile, contents: Invariant($"{counter}"));
}
void GenerateSamples()
{
var contextTokens = np.array(new[] { this.encoder.EncodedEndOfText });
var allText = new List <string>();
int index = 0;
string text = null;
while (index < this.SampleNum)
{
var @out = session.run(sample, feed_dict: new PythonDict <object, object> {
[context] = Enumerable.Repeat(contextTokens, this.batchSize),
});
foreach (int i in Enumerable.Range(0, Math.Min(this.SampleNum - index, this.batchSize)))
{
text = this.encoder.Decode(@out[i]);
text = Invariant($"======== SAMPLE {index + 1} ========\n{text}\n");
allText.Add(text);
index++;
}
}
Console.WriteLine(text);
Directory.CreateDirectory(runSampleDir);
File.WriteAllLines(
path: Path.Combine(runSampleDir, Invariant($"samples-{counter}")),
contents: allText);
}
var avgLoss = (0.0, 0.0);
var startTime = DateTime.Now;
while (!cancellation.IsCancellationRequested)
{
if (counter % this.SaveEvery == 0)
{
Save();
}
if (counter % this.SampleEvery == 0)
{
GenerateSamples();
}
var batch = Enumerable.Range(0, this.batchSize)
.Select(_ => sampler.Sample(1024))
.ToArray();
var placeholderValues = new PythonDict <object, object> {
[context] = batch,
};
var tuple = session.run_dyn((optimizer, loss), feed_dict: placeholderValues);
var lv = tuple.Item2;
avgLoss = (avgLoss.Item1 * 0.99 + lv, avgLoss.Item2 * 0.99 + 1);
Console.WriteLine($"[{counter} | {DateTime.Now-startTime}] loss={lv} avg={avgLoss.Item1/avgLoss.Item2}");
counter++;
}
Console.WriteLine("Interrupted");
Save();
});
}
public static Tensor SampleSequence(HParams hParams, int length,
string startToken = null, int?batchSize = null, dynamic context = null,
float temperature = 1, int topK = 0)
{
if (((startToken == null) ^ (context == null)) == false)
{
throw new ArgumentException($"Exactly one of {nameof(startToken)} or {nameof(context)} has to be specified");
}
SortedDictionary <string, dynamic> Step(HParams @params, Tensor tokens, dynamic past = null)
{
var lmOutput = Gpt2Model.Model(hParams: @params, input: tokens, past: past, reuse: _ReuseMode.AUTO_REUSE);
var logits = lmOutput["logits"][Range.All, Range.All, Range.EndAt((int)@params.get("n_vocab"))];
Tensor presents = lmOutput["present"];
int?[] pastShape = Gpt2Model.PastShape(hParams: @params, batchSize: batchSize);
presents.set_shape_(pastShape.Cast <object>());
return(new SortedDictionary <string, object>
{
["logits"] = logits,
["presents"] = presents,
});
}
Tensor result = null;
new name_scope("sample_sequence").Use(_ =>
{
// Don't feed the last context token -- leave that to the loop below
// TODO: Would be slightly faster if we called step on the entire context,
// rather than leaving the last token transformer calculation to the while loop.
var contextOutput = Step(hParams, context[Range.All, Range.EndAt(new Index(1, fromEnd: true))]);
Tensor[] Body(object past, dynamic prev, object output)
{
var nextOutputs = Step(hParams, prev[Range.All, tf.newaxis], past: past);
Tensor logits = nextOutputs["logits"][Range.All, -1, Range.All] / tf.to_float(temperature);
logits = TopLogits(logits, topK: topK);
var samples = tf.multinomial_dyn(logits, num_samples: 1, output_dtype: tf.int32);
return(new Tensor[]
{
tf.concat(new [] { past, nextOutputs["presents"] }, axis: -2),
tf.squeeze(samples, axis: new[] { 1 }),
tf.concat(new [] { output, samples }, axis: 1),
});
}
bool True(object _a, object _b, object _c) => true;
dynamic[] loopVars = new[] {
contextOutput["presents"],
context[Range.All, -1],
context,
};
TensorShape[] shapeInvariants = new[] {
new TensorShape(Gpt2Model.PastShape(hParams: hParams, batchSize: batchSize)),
new TensorShape(batchSize),
new TensorShape((int?)batchSize, (int?)null),
};
result = tf.while_loop(
cond: PythonFunctionContainer.Of <object, object, object, bool>(True),
body: PythonFunctionContainer.Of(new Func <object, object, object, Tensor[]>(Body)),
parallel_iterations: 10,
swap_memory: false,
name: null,
maximum_iterations: tf.constant(length),
loop_vars: loopVars,
shape_invariants: shapeInvariants,
back_prop: false)
[2];
});
return(result);
}
