*: bump deps

Author: tharvik

cli/package.json

@@ -17,7 +17,6 @@
   "dependencies": {
     "server": "*",
     "@epfml/discojs-node": "*",
-    "immutable": "4",
     "tslib": "2"
   },
   "devDependencies": {

cli/src/benchmark_gpt.ts

@@ -119,7 +119,8 @@ async function main(args: Required<CLIArguments>): Promise<void> {
     for (let i = 0; i < iterations; i++) {
       const timeStart = performance.now()
       for (let n = 0; n < maxNewTokens; n++) {
-        const next: number = (await model.predict(List.of(tokens))).first();
+				const next = (await model.predict(List.of(tokens))).first();
+				if (next === undefined) throw new Error("empty prediction");
         tokens = tokens.push(next)
       }
       inferenceTime += performance.now() - timeStart

cli/src/train_gpt.ts

@@ -36,9 +36,8 @@ async function main(): Promise<void> {
 
   const maxNewTokens = 14
   for (let n = 0; n < maxNewTokens; n++) {
-    const next: number = (await model.predict(
-      List.of(tokens), { seed })
-    ).first();
+		const next = (await model.predict(List.of(tokens), { seed })).first();
+		if (next === undefined) throw new Error("empty prediction");
     tokens = tokens.push(next)
   }
   const generation = tokenizer.decode(tokens.toArray(), { skip_special_tokens: true })

discojs/package.json

@@ -22,10 +22,9 @@
     "@epfml/isomorphic-wrtc": "1",
     "@jimp/core": "1",
     "@jimp/plugin-resize": "1",
-    "@msgpack/msgpack": "^3.0.0-beta2",
+    "@msgpack/msgpack": "<3.0.0-beta5",
     "@tensorflow/tfjs": "4",
     "@xenova/transformers": "2",
-    "immutable": "4",
     "isomorphic-ws": "5",
     "simple-peer": "9",
     "tslib": "2",

discojs/src/aggregator.spec.ts

@@ -55,7 +55,7 @@ AGGREGATORS.forEach(([name, Aggregator]) =>
               Map(
                 network
                   .entrySeq()
-                  .zip(Range(1312))
+                  .zip(Range(1312, Number.POSITIVE_INFINITY))
                   .map(([[id, agg], ws]) => [
                     id,
                     [agg, WeightsContainer.of([ws])],

discojs/src/client/decentralized/peer.ts

@@ -137,7 +137,8 @@ export class Peer {
 
     return Seq.Indexed([firstChunk])
       .concat(
-        Range(1 as ChunkID).zip(tail)
+        Range(1 as ChunkID, Number.POSITIVE_INFINITY)
+          .zip(tail)
           .map(([id, raw]) => {
             const chunk = Buffer.alloc(HEADER_SIZE + raw.length)
             chunk.writeUint16BE(messageID)

discojs/src/client/federated/federated_client.ts

@@ -129,6 +129,8 @@ export class FederatedClient extends Client {
    * @returns the new global weights sent by the server
    */
   override async onRoundEndCommunication(weights: WeightsContainer): Promise<WeightsContainer> {
+		if (this._ownId === undefined)
+			throw new Error("no received ID from server");
     if (this.aggregationResult === undefined) {
       throw new Error("local aggregation result was not set");
     }
@@ -138,7 +140,11 @@ export class FederatedClient extends Client {
     this.saveAndEmit("updating model")
     // Send our local contribution to the server
     // and receive the server global update for this round as an answer to our contribution
-    const payloadToServer: WeightsContainer = this.aggregator.makePayloads(weights).first()
+		const payloadToServer = this.aggregator
+			.makePayloads(weights)
+			.get(SERVER_NODE_ID);
+		if (payloadToServer === undefined)
+			throw new Error("aggregator didn't make a payload for the server");
     const msg: messages.SendPayload = {
       type: type.SendPayload,
       payload: await serialization.weights.encode(payloadToServer),

discojs/src/dataset/dataset.spec.ts

@@ -131,7 +131,7 @@ describe("dataset", () => {
   it("zips with non-async iterable", async () => {
     const dataset = new Dataset([1, 2, 3]);
 
-    const zipped = dataset.zip(Range());
+    const zipped = dataset.zip(Range(0, Number.POSITIVE_INFINITY));
 
     expect(await arrayFromAsync(zipped)).to.have.deep.ordered.members([
       [1, 0],

discojs/src/models/gpt/gpt.spec.ts

@@ -14,10 +14,14 @@ describe("gpt-tfjs", function () {
 
     const data = "Lorem ipsum dolor sit";
     const dataTokens = processing.tokenize(tokenizer, data);
+		const lastToken = dataTokens.last();
+		if (lastToken === undefined) throw new Error("no token generated");
     const seed = 42
-    const dataset = new Dataset<DataFormat.ModelEncoded["text"]>(
-      [[dataTokens.pop(), dataTokens.last()]]
-    ).repeat().batch(8);
+		const dataset = new Dataset<DataFormat.ModelEncoded["text"]>([
+			[dataTokens.pop(), lastToken],
+		])
+			.repeat()
+			.batch(8);
 
     const model = new GPT({
       modelType: "gpt-nano",
@@ -34,9 +38,10 @@ describe("gpt-tfjs", function () {
     const input = "Lorem ipsum dolor";
     const inputTokens = processing.tokenize(tokenizer, data);
 
-    const outputToken: number = (
-      await model.predict(List.of(inputTokens), { seed })
-    ).first();
+		const outputToken = (
+			await model.predict(List.of(inputTokens), { seed })
+		).first();
+		if (outputToken === undefined) throw new Error("empty prediction");
     const output = tokenizer.decode([outputToken]);
 
     expect(input + output).equal(data); // Assert that the model completes 'Lorem ipsum dolor' with 'sit'

discojs/src/models/tfjs.ts

@@ -44,8 +44,9 @@ export class TFJS<D extends "image" | "tabular"> extends Model<D> {
     validationDataset?: Dataset<Batched<DataFormat.ModelEncoded[D]>>,
   ): AsyncGenerator<BatchLogs, EpochLogs> {
     let batchesLogs = List<BatchLogs>();
-    for await (const [batch, batchNumber] of trainingDataset.zip(Range())) {
-
+		for await (const [batch, batchNumber] of trainingDataset.zip(
+			Range(0, Number.POSITIVE_INFINITY),
+		)) {
       const batchLogs = {
         batch: batchNumber,
         ...(await this.#runBatch(batch)),

docs/examples/wikitext.ts

@@ -45,7 +45,8 @@ async function main(): Promise<void> {
   // Predict a few tokens
   const numberOfTokens = 10;
   for (let i = 0; i < numberOfTokens; i++) {
-    const next: number = (await model.predict(List.of(tokens))).first();
+    const next = (await model.predict(List.of(tokens))).first();
+		if (next === undefined) throw new Error("no prediction");
     tokens = tokens.push(next)
   }
   console.log(tokenizer.decode(tokens.toArray()));