*: enforce using override keyword

discojs/src/client/decentralized/decentralized_client.ts

@@ -44,7 +44,7 @@ export class DecentralizedClient extends Client {
    * create peer-to-peer WebRTC connections with peers. The server is used to exchange
    * peers network information.
    */
-  async connect(): Promise<Model> {
+  override async connect(): Promise<Model> {
     const model = await super.connect()  // Get the server base model
     const serverURL = new URL('', this.url.href)
     switch (this.url.protocol) {
@@ -100,7 +100,7 @@ export class DecentralizedClient extends Client {
     return model
   }
 
-  async disconnect (): Promise<void> {
+  override async disconnect (): Promise<void> {
     // Disconnect from peers
     await this.#pool?.shutdown()
     this.#pool = undefined

discojs/src/client/federated/federated_client.ts

@@ -39,7 +39,7 @@ export class FederatedClient extends Client {
    * as well as the latest training information: latest global model, current round and
    * whether we are waiting for more participants.
    */
-  async connect(): Promise<Model> {
+  override async connect(): Promise<Model> {
     const model = await super.connect() // Get the server base model
 
     const serverURL = new URL("", this.url.href);

discojs/src/client/local_client.ts

@@ -7,15 +7,15 @@ import { Client } from "./client.js";
  */
 export class LocalClient extends Client {
 
-  getNbOfParticipants(): number {
+  override getNbOfParticipants(): number {
     return 1;
   }
 
-  onRoundBeginCommunication(): Promise<void> {
+  override onRoundBeginCommunication(): Promise<void> {
     return Promise.resolve();
   }
   // Simply return the local weights 
-  onRoundEndCommunication(weights: WeightsContainer): Promise<WeightsContainer> {
+  override onRoundEndCommunication(weights: WeightsContainer): Promise<WeightsContainer> {
     return Promise.resolve(weights);
   }
 }

discojs/src/dataset/data/image_data.ts

@@ -11,7 +11,7 @@ import { ImagePreprocessing, IMAGE_PREPROCESSING } from './preprocessing/index.j
 export class ImageData extends Data {
   public readonly availablePreprocessing = IMAGE_PREPROCESSING
 
-  static async init (
+  static override async init (
     dataset: tf.data.Dataset<tf.TensorContainer>,
     task: Task,
     size?: number

discojs/src/dataset/data/tabular_data.ts

@@ -11,7 +11,7 @@ import { TABULAR_PREPROCESSING } from './preprocessing/index.js'
 export class TabularData extends Data {
   public readonly availablePreprocessing = TABULAR_PREPROCESSING
 
-  static async init (
+  static override async init (
     dataset: tf.data.Dataset<tf.TensorContainer>,
     task: Task,
     size?: number

discojs/src/dataset/data/text_data.ts

@@ -11,7 +11,7 @@ import { TEXT_PREPROCESSING } from './preprocessing/index.js'
 export class TextData extends Data {
   public readonly availablePreprocessing = TEXT_PREPROCESSING
 
-  static init (
+  static override init (
     dataset: tf.data.Dataset<tf.TensorContainer>,
     task: Task,
     size?: number

discojs/src/models/gpt/layers.ts

@@ -8,11 +8,11 @@ import type { ModelSize } from './config.js'
 class Range extends tf.layers.Layer {
   static readonly className = 'Range'
 
-  computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
+  override computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
     return inputShape
   }
 
-  call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
+  override call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
     return tf.tidy(() => {
       if (Array.isArray(input)) {
         // TODO support multitensor
@@ -30,11 +30,11 @@ tf.serialization.registerClass(Range)
 class LogLayer extends tf.layers.Layer {
   static readonly className = 'LogLayer'
 
-  computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
+  override computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
     return inputShape
   }
 
-  call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
+  override call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
     return tf.tidy(() => {
       if (Array.isArray(input)) {
         input = input[0]
@@ -77,7 +77,7 @@ class CausalSelfAttention extends tf.layers.Layer {
     this.mask = tf.linalg.bandPart(tf.ones([config.blockSize, config.blockSize]), -1, 0)
   }
 
-  build (): void {
+  override build (): void {
     this.cAttnKernel = this.addWeight(
       'c_attn/kernel',
       [this.nEmbd, 3 * this.nEmbd],
@@ -104,16 +104,16 @@ class CausalSelfAttention extends tf.layers.Layer {
     )
   }
 
-  computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
+  override computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
     return inputShape
   }
 
-  getConfig (): tf.serialization.ConfigDict {
+  override getConfig (): tf.serialization.ConfigDict {
     const config = super.getConfig()
     return Object.assign({}, config, this.config)
   }
 
-  call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
+  override call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
     return tf.tidy(() => {
       if (this.cAttnKernel === undefined ||
         this.cAttnBias === undefined ||
@@ -199,11 +199,11 @@ class GELU extends tf.layers.Layer {
     super({})
   }
 
-  computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
+  override computeOutputShape (inputShape: tf.Shape | tf.Shape[]): tf.Shape | tf.Shape[] {
     return inputShape
   }
 
-  call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
+  override call (input: tf.Tensor | tf.Tensor[], kwargs: Record<string, unknown>): tf.Tensor | tf.Tensor[] {
     return tf.tidy(() => {
       if (Array.isArray(input)) {
         // TODO support multitensor

discojs/src/models/gpt/model.ts

@@ -48,14 +48,14 @@ class GPTModel extends tf.LayersModel {
     return this.config
   }
 
-  compile() {
+  override compile() {
     if (this.optimizer !== undefined) return
     this.optimizer = this.config.weightDecay !== 0
       ? getCustomAdam(this, this.config.lr, this.config.weightDecay)
       : tf.train.adam(this.config.lr) 
   }
 
-  async fitDataset<T>(dataset: Dataset<T>, trainingArgs: tf.ModelFitDatasetArgs<T>): Promise<tf.History> {
+  override async fitDataset<T>(dataset: Dataset<T>, trainingArgs: tf.ModelFitDatasetArgs<T>): Promise<tf.History> {
     const callbacks = trainingArgs.callbacks as tf.CustomCallbackArgs
     const evalDataset = trainingArgs.validationData as tf.data.Dataset<{ xs: tf.Tensor2D, ys: tf.Tensor3D }>
     await callbacks.onTrainBegin?.()

discojs/src/models/gpt/optimizers.ts

@@ -91,7 +91,7 @@ class AdamW extends tf.AdamOptimizer {
     this.gradientClipNorm = p.gradientClipNorm
   }
 
-  applyGradients (variableGradients: Record<string, tf.Variable> | Array<{ name: string, tensor: tf.Tensor }>): void {
+  override applyGradients (variableGradients: Record<string, tf.Variable> | Array<{ name: string, tensor: tf.Tensor }>): void {
     const varNames: string[] = Array.isArray(variableGradients)
       ? variableGradients.map((v) => v.name)
       : Object.keys(variableGradients)

tsconfig.base.json

@@ -9,7 +9,10 @@
     "skipLibCheck": true,
 
     // don't pollute
-    "noEmitOnError": true
+    "noEmitOnError": true,
+
+    // enforce using the override keyword
+    "noImplicitOverride": true
   }
 }