From de7c5990bb0b9baa042602ff766d085a85378b4f Mon Sep 17 00:00:00 2001
From: alikia2x <alikia2x@outlook.com>
Date: Sun, 13 Oct 2024 18:33:49 +0800
Subject: [PATCH] add: energy score into inferencing

---
 components/onesearch/onesearch.tsx | 16 ++++++++++------
 lib/nlp/energyScore.ts             | 13 +++++++++++++
 2 files changed, 23 insertions(+), 6 deletions(-)
 create mode 100644 lib/nlp/energyScore.ts

diff --git a/components/onesearch/onesearch.tsx b/components/onesearch/onesearch.tsx
index 14b09be..66e8b9a 100644
--- a/components/onesearch/onesearch.tsx
+++ b/components/onesearch/onesearch.tsx
@@ -21,6 +21,8 @@ import tokenize from "lib/nlp/tokenize/tokenizer";
 import { getEmbedding, getEmbeddingLayer } from "lib/nlp/getEmbedding";
 import { loadVocab } from "lib/nlp/tokenize/loadVocab";
 import BPETokenizer from "lib/nlp/tokenize/BPEtokenizer";
+import energyScore from "lib/nlp/energyScore";
+import bytesToUnicode from "lib/nlp/tokenize/bytesToUnicode";
 
 interface EmbeddingLayer {
 	[key: number]: Float32Array<ArrayBufferLike>;
@@ -130,16 +132,12 @@ export default function OneSearch() {
 	}
 
 	async function getNLUResult(query: string) {
-		const start = new Date().getTime();
 		if (embeddingLayer === null || NLUsession === null || tokenizer == null) return;
-		const tokenIds = await tokenize(query, tokenizer);
-		console.log(new Date().getTime() - start, "ms");
+		const tokenIds = await tokenize(bytesToUnicode(query), tokenizer);
 		const embeddings = getEmbedding(tokenIds, embeddingLayer, 64);
 		const inputTensor = new ort.Tensor("float32", embeddings, [1, 64, 96]);
 		const feeds = { input: inputTensor };
-		console.log(new Date().getTime() - start, "ms");
 		const results = await NLUsession.run(feeds);
-		console.log(new Date().getTime() - start, "ms");
 		return results;
 	}
 
@@ -171,7 +169,13 @@ export default function OneSearch() {
 
 		(async function () {
 			const result = await getNLUResult(query);
-			console.log(result);
+			if (result === undefined) return;
+			const rawData = result.output.data;
+			const data: number[] = [];
+			for (let i=0;i<rawData.length;i++){
+				data.push(rawData[i] as number);
+			}
+			console.log(data, energyScore(data));
 		})();
 	}, [query, engineName]);
 
diff --git a/lib/nlp/energyScore.ts b/lib/nlp/energyScore.ts
new file mode 100644
index 0000000..d10c254
--- /dev/null
+++ b/lib/nlp/energyScore.ts
@@ -0,0 +1,13 @@
+function logsumexp(arr: number[]): number {
+	const maxVal = Math.max(...arr);
+	const sumExp = arr.reduce((sum, val) => sum + Math.exp(val - maxVal), 0);
+	return Math.log(sumExp) + maxVal;
+}
+
+function minusEnergyScore(logits: number[]): number {
+	return logsumexp(logits);
+}
+
+const energyScore = minusEnergyScore;
+
+export default energyScore;