Running models on WebGPU

WebGPU is a new web standard for accelerated graphics and compute. The API enables web developers to use the underlying system’s GPU to carry out high-performance computations directly in the browser. WebGPU is the successor to WebGL and provides significantly better performance, because it allows for more direct interaction with modern GPUs. Lastly, it supports general-purpose GPU computations, which makes it just perfect for machine learning!

As of October 2024, global WebGPU support is around 70% (according to caniuse.com), meaning some users may not be able to use the API.

If the following demos do not work in your browser, you may need to enable it using a feature flag:

Firefox: with the dom.webgpu.enabled flag (see here).
Safari: with the WebGPU feature flag (see here).
Older Chromium browsers (on Windows, macOS, Linux): with the enable-unsafe-webgpu flag (see here).

Usage in Transformers.js v3

Thanks to our collaboration with ONNX Runtime Web, enabling WebGPU acceleration is as simple as setting device: 'webgpu' when loading a model. Let’s see some examples!

Example: Compute text embeddings on WebGPU (demo)

import { pipeline } from "@huggingface/transformers";

// Create a feature-extraction pipeline
const extractor = await pipeline(
  "feature-extraction",
  "mixedbread-ai/mxbai-embed-xsmall-v1",
  { device: "webgpu" },
);

// Compute embeddings
const texts = ["Hello world!", "This is an example sentence."];
const embeddings = await extractor(texts, { pooling: "mean", normalize: true });
console.log(embeddings.tolist());
// [
//   [-0.016986183822155, 0.03228696808218956, -0.0013630966423079371, ... ],
//   [0.09050482511520386, 0.07207386940717697, 0.05762749910354614, ... ],
// ]

Example: Perform automatic speech recognition with OpenAI whisper on WebGPU (demo)

import { pipeline } from "@huggingface/transformers";

// Create automatic speech recognition pipeline
const transcriber = await pipeline(
  "automatic-speech-recognition",
  "onnx-community/whisper-tiny.en",
  { device: "webgpu" },
);

// Transcribe audio from a URL
const url = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav";
const output = await transcriber(url);
console.log(output);
// { text: ' And so my fellow Americans ask not what your country can do for you, ask what you can do for your country.' }

Example: Perform image classification with MobileNetV4 on WebGPU (demo)

import { pipeline } from "@huggingface/transformers";

// Create image classification pipeline
const classifier = await pipeline(
  "image-classification",
  "onnx-community/mobilenetv4_conv_small.e2400_r224_in1k",
  { device: "webgpu" },
);

// Classify an image from a URL
const url = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg";
const output = await classifier(url);
console.log(output);
// [
//   { label: 'tiger, Panthera tigris', score: 0.6149784922599792 },
//   { label: 'tiger cat', score: 0.30281734466552734 },
//   { label: 'tabby, tabby cat', score: 0.0019135422771796584 },
//   { label: 'lynx, catamount', score: 0.0012161266058683395 },
//   { label: 'Egyptian cat', score: 0.0011465961579233408 }
// ]

Reporting bugs and providing feedback

Due to the experimental nature of WebGPU, especially in non-Chromium browsers, you may experience issues when trying to run a model (even it it can run in WASM). If you do, please open an issue on GitHub and we’ll do our best to address it. Thanks!

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