Candle-T5-Generation-Wasm / T5ModelEncoderWorker.js
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//load Candle Bert Module wasm module
let init, ModelEncoder;
async function fetchArrayBuffer(url) {
const cacheName = "t5-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.match(url);
if (cachedResponse) {
const data = await cachedResponse.arrayBuffer();
return new Uint8Array(data);
}
const res = await fetch(url, { cache: "force-cache" });
cache.put(url, res.clone());
return new Uint8Array(await res.arrayBuffer());
}
class Encoder {
static instance = {};
static async getInstance(weightsURL, tokenizerURL, configURL, modelID) {
if (modelID.includes("quantized")) {
({ default: init, ModelEncoder } = await import(
"./build/m-quantized.js"
));
} else {
({ default: init, ModelEncoder } = await import("./build/m.js"));
}
if (!this.instance[modelID]) {
await init();
self.postMessage({ status: "loading", message: "Loading Model" });
const [weightsArrayU8, tokenizerArrayU8, configArrayU8] =
await Promise.all([
fetchArrayBuffer(weightsURL),
fetchArrayBuffer(tokenizerURL),
fetchArrayBuffer(configURL),
]);
this.instance[modelID] = new ModelEncoder(
weightsArrayU8,
tokenizerArrayU8,
configArrayU8
);
} else {
self.postMessage({ status: "ready", message: "Model Already Loaded" });
}
return this.instance[modelID];
}
}
self.addEventListener("message", async (event) => {
const {
weightsURL,
tokenizerURL,
configURL,
modelID,
sentences,
normalize_embeddings,
} = event.data;
try {
self.postMessage({ status: "ready", message: "Starting T5 Encoder" });
const model = await Encoder.getInstance(
weightsURL,
tokenizerURL,
configURL,
modelID
);
self.postMessage({
status: "encoding",
message: "Encoding Sentences",
});
const output = model.decode({
sentences: sentences,
normalize_embeddings: normalize_embeddings || true,
});
self.postMessage({
status: "complete",
message: "complete",
output: output,
});
} catch (e) {
self.postMessage({ error: e });
}
});