@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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HTML
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<html>
<head>
<title>TensorFlow.js Model Benchmark</title>
<link href="https://fonts.googleapis.com/css?family=Roboto" rel="stylesheet">
<link href="./main.css" rel="stylesheet">
<script src="https://cdnjs.cloudflare.com/ajax/libs/dat-gui/0.7.2/dat.gui.min.js"></script>
</head>
<body>
<h2>TensorFlow.js Model Benchmark</h2>
<div id="modal-msg"></div>
<div id="container">
<div id="stats">
<div class="box">
<pre id="env"></pre>
</div>
<table class="table" id="timings">
<thead>
<tr>
<th>Type</th>
<th>Value</th>
</tr>
</thead>
<tbody>
</tbody>
</table>
<div class="box" id="perf-trendline-container">
<div class="label">Inference times</div>
<div class="trendline">
<div class="yMax"></div>
<div class="yMin"></div>
<svg>
<path></path>
</svg>
</div>
</div>
</div>
<table class="table" id="kernels">
<thead id="kernels-thead">
</thead>
<tbody></tbody>
</table>
</div>
<script src="https://unpkg.com/@tensorflow/tfjs-core@latest/dist/tf-core.js"></script>
<script src="https://unpkg.com/@tensorflow/tfjs-layers@latest/dist/tf-layers.js"></script>
<script src="https://unpkg.com/@tensorflow/tfjs-converter@latest/dist/tf-converter.js"></script>
<script src="https://unpkg.com/@tensorflow/tfjs-backend-wasm@latest/dist/tf-backend-wasm.js"></script>
<script src="https://unpkg.com/@tensorflow/tfjs-automl@latest/dist/tf-automl.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/universal-sentence-encoder"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/posenet@2"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/body-pix@2"></script>
<script src="./modelConfig.js"></script>
<script src="./util.js"></script>
<script>;
const state = {
numRuns: 50,
benchmark: 'mobilenet_v2',
run: (v) => {
runBenchmark();
},
backend: 'wasm',
kernelTiming: 'aggregate',
};
const modalDiv = document.getElementById('modal-msg');
const timeTable = document.querySelector('#timings tbody');
const envDiv = document.getElementById('env');
const kernelsTableHead = document.getElementById('kernels-thead');
const kernelTable = document.querySelector('#kernels tbody');
let model, predict, chartWidth;
async function showMsg(message) {
if (message != null) {
modalDiv.innerHTML = message + '...';
modalDiv.style.display = 'block';
} else {
modalDiv.style.display = 'none';
}
await tf.nextFrame();
await tf.nextFrame();
}
function showVersions() {
envDiv.innerHTML = JSON.stringify({
core: tf.version_core,
layers: tf.version_layers,
converter: tf.version_converter
}, null, 2);
}
async function showEnvironment() {
await tf.time(() => tf.add(tf.tensor1d([1]), tf.tensor1d([1])).data());
envDiv.innerHTML += `<br/>${JSON.stringify(tf.env().features, null, 2)
} `;
}
async function setupTable() {
kernelsTableHead.innerText = '';
kernelTable.innerHTML = '';
await tf.nextFrame();
const rows = ['<b>Kernel</b>', '<b>Time(ms)</b>'];
if (state.kernelTiming === 'individual') {
rows.push('<b>Inputs</b>', '<b>Output</b>');
if (state.backend === 'webgl') {
rows.push('<b>GPUPrograms</b>');
}
}
appendRow(kernelsTableHead, ...rows);
await tf.nextFrame();
}
function appendRow(tbody, ...cells) {
const tr = document.createElement('tr');
cells.forEach(c => {
const td = document.createElement('td');
if (c instanceof HTMLElement) {
td.appendChild(c);
} else {
td.innerHTML = c;
}
tr.appendChild(td);
});
tbody.appendChild(tr);
}
async function warmUpAndRecordTime() {
await showMsg('Warming up');
const start = performance.now();
let res = predict(model);
if (res instanceof Promise) {
res = await res;
}
if (res instanceof tf.Tensor) {
const tmp = res;
res = await res.data();
tmp.dispose();
}
const elapsed = performance.now() - start;
await showMsg(null);
appendRow(timeTable, '1st inference', printTime(elapsed));
}
async function loadAndRecordTime(benchmark) {
await showMsg('Loading the model');
const start = performance.now();
if (benchmark.model == null) {
model = await benchmark.load();
benchmark.model = model;
} else {
model = benchmark.model;
}
predict = benchmark.predictFunc();
const elapsed = performance.now() - start;
await showMsg(null);
appendRow(timeTable, `<b> Benchmark:</b> ${state.benchmark} `,
`<b> Runs:</b> ${state.numRuns} `);
appendRow(timeTable, 'Model load', printTime(elapsed));
}
const chartHeight = 150;
function populateTrendline(node, data, forceYMinToZero = false, yFormatter = d => d) {
node.querySelector("svg").setAttribute("width", chartWidth);
node.querySelector("svg").setAttribute("height", chartHeight);
const yMax = Math.max(...data);
let yMin = forceYMinToZero ? 0 : Math.min(...data);
if (yMin === yMax) {
yMin = 0;
}
node.querySelector(".yMin").textContent = yFormatter(yMin);
node.querySelector(".yMax").textContent = yFormatter(yMax);
const xIncrement = chartWidth / (data.length - 1);
node.querySelector("path")
.setAttribute("d", `M${data.map((d, i) => `${i * xIncrement},${chartHeight - ((d - yMin) / (yMax - yMin)) * chartHeight}`).join('L')} `);
}
async function measureAveragePredictTime() {
await showMsg(`Running predict ${state.numRuns} times`);
chartWidth = document.querySelector("#perf-trendline-container").getBoundingClientRect().width;
const times = [];
const numLeakedTensors = [];
for (let i = 0; i < state.numRuns; i++) {
const start = performance.now();
const tensorsBefore = tf.memory().numTensors;
let res = predict(model);
if (res instanceof Promise) {
res = await res;
}
if (res instanceof tf.Tensor) {
const tmp = res;
res = await res.data();
tmp.dispose();
}
times.push(performance.now() - start);
const memInfo = tf.memory();
const leakedTensors = memInfo.numTensors - tensorsBefore;
numLeakedTensors.push(leakedTensors);
}
const forceInferenceTrendYMinToZero = true;
populateTrendline(document.querySelector("#perf-trendline-container"), times, forceInferenceTrendYMinToZero, printTime);
await showMsg(null);
const average = times.reduce((acc, curr) => acc + curr, 0) / times.length;
const min = Math.min(...times);
appendRow(timeTable, `Subsequent average(${state.numRuns} runs)`, printTime(average));
appendRow(timeTable, 'Best time', printTime(min));
appendRow(timeTable, 'Leaked tensors', numLeakedTensors[0]);
}
async function profileMemory() {
await showMsg('Profile memory');
const start = performance.now();
let res;
const data = await tf.profile(() => res = predict(model));
if (res instanceof Promise) {
res = await res;
}
if (res instanceof tf.Tensor) {
const tmp = res;
res = await res.data();
tmp.dispose();
}
const elapsed = performance.now() - start;
await showMsg(null);
appendRow(timeTable, 'Peak memory', printMemory(data.peakBytes));
appendRow(timeTable, '2nd inference', printTime(elapsed));
}
function showKernelTime(kernels) {
const tbody = document.querySelector('#kernels tbody');
if (state.kernelTiming === 'individual') {
kernels.forEach(k => {
const nameSpan = document.createElement('span');
nameSpan.setAttribute('title', k.scopes.slice(0, -1).join(' --> '));
nameSpan.textContent = k.scopes[k.scopes.length - 1];
appendRow(tbody, nameSpan, k.time.toFixed(2), k.inputs, k.output, k.gpuProgramsInfo);
});
} else {
const kernelTotalTime = {};
kernels.forEach(k => {
const kernelName = k.scopes[0];
if (kernelTotalTime[kernelName] == null) {
kernelTotalTime[kernelName] = 0;
}
kernelTotalTime[kernelName] += k.time;
});
const result = Object.keys(kernelTotalTime)
.map(k => [k, kernelTotalTime[k]])
.sort((a, b) => b[1] - a[1]);
result.forEach(r => {
const nameSpan = document.createElement('span');
nameSpan.setAttribute('title', r[0]);
nameSpan.textContent = r[0];
appendRow(tbody, nameSpan, r[1].toFixed(2));
});
}
}
async function profileKernelTime() {
await showMsg('Profiling kernels');
_tfengine.ENV.set('DEBUG', true);
const oldLog = console.log;
let kernels = [];
console.log = msg => {
let parts = [];
if (typeof msg === 'string') {
parts = msg.split('\t').map(x => x.slice(2));
}
if (parts.length > 2) {
// heuristic for determining whether we've caught a profiler
// log statement as opposed to a regular console.log
// TODO(https://github.com/tensorflow/tfjs/issues/563): return timing information as part of tf.profile
const scopes = parts[0].trim()
.split('||')
.filter(s => s !== 'unnamed scope');
kernels.push({
scopes: scopes,
time: Number.parseFloat(parts[1]),
output: parts[2].trim(),
inputs: parts[4],
gpuProgramsInfo: parts[5]
});
} else {
oldLog.call(oldLog, msg);
}
}
let res = predict(model);
if (res instanceof Promise) {
res = await res;
}
if (res instanceof tf.Tensor) {
const tmp = res;
res = await res.data();
tmp.dispose();
}
await showMsg(null);
await sleep(10);
kernels = kernels.sort((a, b) => b.time - a.time);
appendRow(timeTable, 'Number of kernels', kernels.length);
// Add an empty row at the end of a benchmark run
appendRow(timeTable, '', '');
showKernelTime(kernels);
_tfengine.ENV.set('DEBUG', false);
// Switch back to the old log;
console.log = oldLog;
}
async function runBenchmark() {
const benchmark = benchmarks[state.benchmark];
await setupTable();
await loadAndRecordTime(benchmark);
await warmUpAndRecordTime();
await showMsg('Waiting for GC');
await sleep(1000);
await profileMemory();
await sleep(200);
await measureAveragePredictTime();
await sleep(200);
if (state.backend != 'webgl' || queryTimerIsEnabled()) {
await profileKernelTime();
} else {
showMsg('Skipping kernel times since query timer extension is not ' +
'available. <br/> Use Chrome 70+.');
};
}
async function onPageLoad() {
var gui = new dat.gui.GUI();
await tf.setBackend(state.backend);
gui.add(state, 'numRuns');
gui.add(state, 'benchmark', Object.keys(benchmarks));
gui.add(state, 'backend', ['wasm', 'webgl', 'cpu']).onChange(backend => {
tf.setBackend(backend);
});
gui.add(state, 'kernelTiming', ['aggregate', 'individual']);
gui.add(state, 'run');
showVersions();
await showEnvironment();
}
onPageLoad();
</script>
</body>
</html>