Mixedprecision

Mixed precision is a computational strategy that uses more than one floating-point precision within a single computation. In practice, it typically combines lower-precision representations, such as FP16 (half precision) or BF16, with higher-precision FP32 (single precision) to accelerate performance while maintaining acceptable numerical accuracy.

In many workloads, most arithmetic is performed in lower precision, while certain operations—such as accumulations of gradients during training or critical numerical paths—are kept in higher precision to limit error propagation. This approach can reduce memory usage and increase throughput on modern hardware that offers fast low-precision math and higher-precision fallback.

Key techniques include automatic mixed precision (AMP), which automatically casts eligible operations to lower precision and uses higher precision for sensitive steps; and loss scaling, which multiplies the loss before backpropagation to prevent small gradient values from underflowing in FP16. Some implementations use dynamic loss scaling that adjusts scaling factor during training.

Hardware and software support has grown: NVIDIA GPUs with tensor cores, BF16 support in newer accelerators, and software libraries in major frameworks such as PyTorch with torch.cuda.amp, TensorFlow's mixed precision API, and JAX dtype policies.

Challenges include numerical stability, limited compatibility for certain operators, and potential reproducibility differences due to reduced precision. Appropriate use requires tuning and sometimes selectively maintaining FP32 for critical layers, or using loss scaling and clipping.

Applications are common in deep learning training and inference for large models where memory and compute constraints are important. Mixed precision can enable faster training and lower energy use while preserving accuracy within acceptable bounds.

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