cli: add train_gpt script

cli/package.json

@@ -7,6 +7,7 @@
     "watch": "nodemon --ext ts --ignore dist --watch ../discojs-node/dist --watch ../server/dist --watch . --exec npm run",
     "start": "npm run build && node dist/cli.js",
     "benchmark_gpt": "npm run build && node dist/benchmark_gpt.js",
+    "train_gpt": "npm run build && node dist/train_gpt.js",
     "build": "tsc",
     "lint": "npx eslint .",
     "test": ": nothing"

cli/src/train_gpt.ts

@@ -0,0 +1,40 @@
+import * as tf from "@tensorflow/tfjs-node"
+import { AutoTokenizer } from "@xenova/transformers";
+import { models, processing } from "@epfml/discojs";
+
+async function main(): Promise<void> { 
+  const data = "Lorem ipsum dolor sit amet, consectetur adipis"
+  const datasetSource = new tf.data.FileDataSource(Buffer.from(data))
+  const textDataset = new tf.data.TextLineDataset(datasetSource)
+
+  const config: models.GPTConfig = {
+    modelType: 'gpt-nano',
+    lr: 0.01,
+    maxIter: 50,
+    evaluateEvery:50,
+    maxEvalBatches: 10,
+    blockSize: 16,
+    vocabSize: 50257,
+    debug: false
+  }
+
+  const tokenizer = await AutoTokenizer.from_pretrained('Xenova/gpt2')
+  const tokenDataset = textDataset.map((text: string) => {
+    const tokens = processing.tokenizeAndLeftPad(text, tokenizer, config.blockSize + 1)
+    const ys = tf.oneHot(tokens.slice(1), tokenizer.model.vocab.length)
+    const xs = tf.tensor(tokens.slice(0, config.blockSize), undefined, 'int32')
+    return {xs, ys}
+  }).repeat().batch(16) as tf.data.Dataset<{ xs: tf.Tensor2D, ys: tf.Tensor3D }>
+
+  const model = new models.GPT(config)
+
+  for await (const logs of model.train(tokenDataset, undefined)) {
+    console.log(logs)
+  }
+
+  const generation = await model.generate("Lorem", tokenizer, { maxNewTokens: 10, doSample: false, topk: 5, temperature:0.1 })
+  console.log(generation)
+}
+
+// You can run this example with "npm run run_gpt" from this folder
+main().catch(console.error)