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cbb6a01 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | import {
pipeline,
TextStreamer,
DynamicCache,
InterruptableStoppingCriteria,
} from "@huggingface/transformers";
const MODEL_IDS = {
"1.7b": "onnx-community/Bonsai-1.7B-ONNX",
};
async function check() {
try {
const adapter = await navigator.gpu?.requestAdapter();
if (!adapter) throw new Error("WebGPU is not supported (no adapter found)");
} catch (e) {
self.postMessage({ status: "error", data: e.toString() });
}
}
class TextGenerationPipeline {
static instances = new Map();
static getInstance(modelKey, progress_callback = null) {
const modelId = MODEL_IDS[modelKey];
if (!modelId) throw new Error(`Unknown model: ${modelKey}`);
if (!this.instances.has(modelKey)) {
this.instances.set(
modelKey,
pipeline("text-generation", modelId, {
device: "webgpu",
dtype: "q1",
progress_callback,
}),
);
}
return this.instances.get(modelKey);
}
}
const stopping_criteria = new InterruptableStoppingCriteria();
let past_key_values_cache = null;
let current_model_key = null;
function disposePastKeyValues() {
past_key_values_cache?.dispose?.();
past_key_values_cache = null;
}
async function load(modelKey) {
if (current_model_key && current_model_key !== modelKey) {
disposePastKeyValues();
}
current_model_key = modelKey;
self.postMessage({ status: "loading", data: "Loading model..." });
const generator = await TextGenerationPipeline.getInstance(
modelKey,
(info) => {
if (info.status === "progress_total") {
self.postMessage({
status: "progress_total",
progress: Number(info.progress ?? 0),
loaded: Number(info.loaded ?? 0),
total: Number(info.total ?? 0),
});
}
},
);
self.postMessage({
status: "loading",
data: "Optimizing model for 1-bit execution",
});
const inputs = generator.tokenizer("a");
await generator.model.generate({ ...inputs, max_new_tokens: 1 });
self.postMessage({ status: "ready" });
}
async function generate(messages) {
const generator = await TextGenerationPipeline.getInstance(current_model_key);
let startTime;
let numTokens = 0;
let tps;
const streamer = new TextStreamer(generator.tokenizer, {
skip_prompt: true,
skip_special_tokens: true,
callback_function: (output) => {
self.postMessage({ status: "update", output, tps, numTokens });
},
token_callback_function: () => {
startTime ??= performance.now();
if (numTokens++ > 0) {
tps = (numTokens / (performance.now() - startTime)) * 1000;
}
},
});
self.postMessage({ status: "start" });
past_key_values_cache ??= new DynamicCache();
try {
const output = await generator(messages, {
max_new_tokens: 1024,
do_sample: false,
streamer,
stopping_criteria,
past_key_values: past_key_values_cache,
});
self.postMessage({
status: "complete",
output: output[0].generated_text.at(-1).content,
});
} catch (e) {
self.postMessage({ status: "error", data: e.toString() });
}
}
self.addEventListener("message", async (e) => {
const { type, data } = e.data;
switch (type) {
case "check":
check();
break;
case "load":
load(data);
break;
case "generate":
stopping_criteria.reset();
generate(data);
break;
case "interrupt":
stopping_criteria.interrupt();
break;
case "reset":
disposePastKeyValues();
stopping_criteria.reset();
break;
}
});
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