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| author | Radamés Ajna <radamajna@gmail.com> | 2023-09-22 07:31:10 -0700 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-09-22 15:31:10 +0100 |
| commit | 19e52e5007e10816eefb2e1a1968be760c5d11a4 (patch) | |
| tree | f0d9cad35d261c3a28d2c4fa5a8ff1af84a4631f /candle-wasm-examples/t5/T5ModelEncoderWorker.js | |
| parent | 8601537e31af610c0bbd32ee8c8ee17ed802427c (diff) | |
| download | candle-19e52e5007e10816eefb2e1a1968be760c5d11a4.tar.gz candle-19e52e5007e10816eefb2e1a1968be760c5d11a4.tar.bz2 candle-19e52e5007e10816eefb2e1a1968be760c5d11a4.zip | |
T5 Wasm (#918)
* init t5 wasm model
* split workers for each model
* clean up
* add some ui
* readme
* index
* typo
* remove cache param, clear_kv_cache
* add max_length as param
* add model tasks option to ui
* add method to load quantized gguf from buffer
* Add quantized wasm module
* add quantized models to UI, dynamic import wasms
* link to quantized
* fix copy
* fix ModelEncoder
* fix README.md
Diffstat (limited to 'candle-wasm-examples/t5/T5ModelEncoderWorker.js')
| -rw-r--r-- | candle-wasm-examples/t5/T5ModelEncoderWorker.js | 83 |
1 files changed, 83 insertions, 0 deletions
diff --git a/candle-wasm-examples/t5/T5ModelEncoderWorker.js b/candle-wasm-examples/t5/T5ModelEncoderWorker.js new file mode 100644 index 00000000..a83b0ee0 --- /dev/null +++ b/candle-wasm-examples/t5/T5ModelEncoderWorker.js @@ -0,0 +1,83 @@ +//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 }); + } +}); |