File size: 3,984 Bytes
afed82d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
// Copyright 2024 The MediaPipe Authors.

// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at

//      http://www.apache.org/licenses/LICENSE-2.0

// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// ---------------------------------------------------------------------------------------- //

import {FilesetResolver, LlmInference} from 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai';

const input = document.getElementById('input');
const output = document.getElementById('output');
const submit = document.getElementById('submit');
const status = document.getElementById('status');

const modelFileName = 'gemma-2b-it-gpu-int4.bin'; /* Update the file name */
let startTime;

/**
 * Display newly generated partial results to the output text box.
 */
function displayPartialResults(partialResults, complete) {
  output.textContent += partialResults;

  if (complete) {
    if (!output.textContent) {
      output.textContent = 'Result is empty';
    }
    submit.disabled = false;

    const wordCount = output.textContent.split(' ').length;
    const seconds = Math.round((performance.now() - startTime) / 1000, 2);
    const wordCountPerSecond = Math.round(wordCount / seconds, 2);
    status.innerHTML = `${wordCount} words in ${seconds} seconds, ${wordCountPerSecond} words per second`;
  }
}

// Get model via Origin Private File System
async function getModelOPFS(name, url, updateModel) {
  const root = await navigator.storage.getDirectory();
  let fileHandle;

  async function updateFile() {
    const response = await fetch(url);
    const buffer = await readResponse(response);
    fileHandle = await root.getFileHandle(name, {create: true});
    const writable = await fileHandle.createWritable();
    await writable.write(buffer);
    await writable.close();
    return buffer;
  }

  if (updateModel) {
    return await updateFile();
  }

  try {
    fileHandle = await root.getFileHandle(name);
    const blob = await fileHandle.getFile();
    return await blob.arrayBuffer();
  } catch (e) {
    return await updateFile();
  }
}

async function readResponse(response) {
  const contentLength = response.headers.get('Content-Length');
  let total = parseInt(contentLength ?? '0');
  let buffer = new Uint8Array(total);
  let loaded = 0;

  const reader = response.body.getReader();
  async function read() {
    const {done, value} = await reader.read();
    if (done) return;

    let newLoaded = loaded + value.length;
    if (newLoaded > total) {
      total = newLoaded;
      let newBuffer = new Uint8Array(total);
      newBuffer.set(buffer);
      buffer = newBuffer;
    }
    buffer.set(value, loaded);
    loaded = newLoaded;
    return read();
  }

  await read();
  return buffer;
}

/**
 * Main function to run LLM Inference.
 */
async function runDemo() {
  const genaiFileset = await FilesetResolver.forGenAiTasks(
      'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai/wasm');
  let llmInference;
  const modelBuffer = new Int8Array(await getModelOPFS(modelFileName, modelFileName, false));

  submit.onclick = () => {
    startTime = performance.now();
    output.textContent = '';
    status.innerHTML = '';
    submit.disabled = true;
    llmInference.generateResponse(input.value, displayPartialResults);
  };

  submit.value = 'Loading the model...'
  LlmInference
      .createFromModelBuffer(genaiFileset, modelBuffer)
      .then(llm => {
        llmInference = llm;
        submit.disabled = false;
        submit.value = 'Get Response'
      }).catch(() =>{
        alert('Failed to initialize the task.');
      });
}

runDemo();