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); }