We’re happy to announce that TensorFlow.js now provides a library to accelerate the operations. ” section in our README for more information.
JAVASCRIPT
<!-- Import @tensorflow/tfjs or @tensorflow/tfjs-core -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<!-- Adds the WASM backend to the global backend registry -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.js"></script>
<script>
tf.setBackend('wasm').then(() => main());
</script>tf.setBackend(‘wasm’)`.Demo
Check out the face detection demo (using the MediaPipe BlazeFace model) that runs on the WASM backend. For more details about the model, see this , and is than JavaScript can be parsed. JavaScript is dynamically typed and garbage collected, which can cause significant non-deterministic slowdowns at runtime. Additionally, modern JavaScript libraries (such as TensorFlow.js) use compilation tools like TypeScript and ES6 transpilers that generate ES5 code (for wide browser support) that is slower to execute than vanilla ES6 JavaScript.Versus WebGL: For most models, the WebGL backend will still outperform the WASM backend, however WASM can be faster for ultra-lite models (less than 3MB and 60M multiply-adds). In this scenario, the benefits of GPU parallelization are outweighed by the fixed overhead costs of executing WebGL shaders. Below we provide guidelines for finding this line. However, there is a instructions, allowing multiple floating point operations to be vectorized and executed in parallel. Preliminary tests show that enabling these extensions brings 2-3x speedup over WASM today. Keep an eye out for this to land in browsers! It will automatically be turned on for TensorFlow.js.
Portability and Stability
When it comes to machine learning, numerical precision matters. WASM natively supports floating point arithmetic, whereas the WebGL backend requires the OES_texture_float extension. Not all devices support this extension, which means a GPU-accelerated TensorFlow.js isn’t supported on some devices (e.g. older mobile devices where WASM is supported).Moreover, GPU drivers can be hardware-specific and different devices can have precision problems. On iOS, 32 bit floats aren’t supported on the GPU so we fall back to 16 bit floats, causing precision problems. In WASM, computation will always happen in 32 bit floats and thus have precision parity across all devices.
When should I use WASM?
In general, WASM is a good choice when models are smaller, if you care about wide device support, or if your project is sensitive to numerical stability. WASM, however, doesn’t have parity with our WebGL backend. If you are using the WASM backend and need an op to be implemented, feel free to file an issue on across the WebGL, WASM, and plain JS (CPU) backends.and and proposal to add SIMD instructions. Today, Chrome has partial support for SIMD under an experimental flag, Firefox and Edge status is for experimental QFMA SIMD instructions that should further improve performance of kernels. Benchmarks on popular ML models show QFMA SIMD giving an additional 26-50% speedup over regular SIMD.
The TF.js WASM backend will take advantage of SIMD through the for WASM SIMD. When SIMD lands, this will be invisible to the TensorFlow.js user.
Multithreading
The WASM spec recently got a for WASM threads enabled by default.When the threading proposal lands, we will be ready to take advantage of threads through the XNNPACK library with no changes to TensorFlow.js user code.
More information
- If you are interested in learning more, you can read our WebAssembly of resources by the Mozilla Developer Network.
- We’d appreciate your feedback and contributions via on GitHub!
Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
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