What is Mixed Precision? (ELI5)
Imagine you have two ways to measure something: one super-precise and another that’s a bit faster but not as exact. In computing, these two “measuring sticks” are called 32-bit precision (FP32) and 16-bit precision (FP16). Normally, computers use the super-precise 32-bit to handle all the calculations. But here’s the trick: for many tasks in AI, we don’t always need to be that precise, and we can get away with using the faster, less precise 16-bit version (FP16).
Using mixed precision means combining both FP16 and FP32 in a smart way: letting the faster FP16 do most of the work, but switching to FP32 for certain critical tasks to ensure accuracy. The result? You get much faster computation with less memory used, but without losing the quality of your AI models.
How Does It Work?
1. FP16 for Speed: Most of the simpler calculations in AI (like matrix multiplications) can run using FP16, which is faster and uses less memory. This is because smaller numbers can still give you good enough results.
2. FP32 for Stability: For operations where precision is super important (like when the model updates its weights), the system switches to FP32. This keeps the model from losing accuracy over time.
3. Loss Scaling: Since FP16 can sometimes result in numbers that are too small to be captured accurately (underflow), a process called loss scaling is used. This means temporarily making the numbers bigger during training so they don’t disappear, and then shrinking them back to normal afterward.
Why is it Important?
Using mixed precision is especially important in AI and machine learning because models are getting bigger and more complex, and they need tons of computational power. By using FP16 where it works and FP32 where it’s needed, mixed precision speeds up training, saves memory, and allows us to handle larger models.
What’s the Standard Today and Going into 2025?
As of today and moving toward 2025, mixed precision training has become the standard for AI/ML applications, especially in:
1. AI Models: Large models like GPT, Megatron, NeMo, and BERT rely heavily on mixed precision to optimize training time and memory usage, especially on high-end GPUs (like NVIDIA’s A100 or H100 series).
2. Molecular Dynamics: In fields like molecular dynamics (simulating interactions between atoms and molecules), precision is crucial, but even here, mixed precision is used in combination with tensor cores on NVIDIA GPUs. Mixed precision allows researchers to simulate more complex systems at faster speeds without losing significant accuracy.
3. Standardization: NVIDIA’s APEX and AMP (Automatic Mixed Precision) libraries, along with frameworks like TensorFlow and PyTorch, now offer out-of-the-box support for mixed precision. This means developers don’t need to manually switch between FP16 and FP32; it’s handled automatically.
By 2025, mixed precision will likely be even more optimized and widespread, as models continue to grow, and new hardware (GPUs) will be designed specifically to support these faster computations at scale.
In summary, mixed precision strikes the perfect balance between speed and accuracy in AI, ML, and scientific computing, making it the go-to technique as we push the boundaries of technology.