Mixed-precision training in AI: Everything you need to know

What drives machine learning model training?

Today's machine learning models require more extensive training than ever, demanding massive data sets and powerful computing resources.

More data

Models learn by identifying patterns in data. Real-world data often includes complex relationships and subtle nuances that cannot be encapsulated succinctly, requiring vast amounts of data for models to train on.

For example, teaching a model to recognize cars involves accounting for hundreds of different makes, models and colors, viewed from a wide range of distances, angles and lighting conditions. The more diverse and extensive the training data, the better the model learns to capture these nuances, leading to better accuracy and reliability once deployed.

More compute

Modern computing hardware has evolved to meet the demands of large-scale model training. Advances in storage and processors, such as CPUs, GPUs and tensor cores, coupled with major increases in global computing capacity, provide the infrastructure needed to handle enormous volumes of data. And public cloud providers make virtually unlimited computing resources available for training on demand.

More sophisticated algorithms

Innovations like deep learning and neural networks have improved models' abilities to recognize more complex patterns, generalize to new information, avoid overfitting and handle edge cases. These situations, however, demand more extensive data and training time.

Mixed-precision training

Training machine learning and AI models requires time and money. To meet business demands, developers need to balance training time and expenses with model performance and accuracy. This is where mixed-precision training can offer significant benefits.

Mixed precision's role in deep learning

Deep neural networks excel at solving difficult problems that involve making complex data associations and accurate inferences. For example, neural networks can analyze medical images in healthcare, spot signs of financial fraud in banking transactions or optimize logistics in fleet management and equipment maintenance.

These activities require extensive training because the model layers that constitute the neural network must be capable of detecting subtle nuances in data patterns. That training, in turn, means extensive computational time and associated costs.

Mixed-precision training -- using FP16 rather than FP32 for some computations -- might seem counterintuitive, given the precision usually required in deep learning. However, when combined with loss scaling, a technique that adjusts loss values during FP16-to-FP32 conversions, mixed-precision training can achieve accuracy comparable to full FP32 training, while reducing computation time and memory requirements.

Mixed-precision training considerations

Mixed-precision training's benefits are not automatic or guaranteed. Faster processing requires suitable computational hardware optimized for FP16, such as tensor cores, which accelerate math-intensive operations in linear and convolutional layers.

Another key consideration is understanding which operations can safely use FP16 and which require FP32. Data scientists and machine learning engineers must carefully manage loss scaling factors, as incorrect scaling can cause deviations in accuracy. And teams must closely monitor outcomes to ensure model accuracy meets performance goals.

Pros and cons of mixed-precision training

Mixed-precision training offers several benefits over single-precision training:

• Supports larger neural networks. Training cycles require less memory, enabling development of larger neural networks.
• Lower computational overhead. Computational processes require less memory bandwidth, enabling less costly calculations.
• Faster outcomes. FP16 operations execute much faster on compatible hardware, reducing training time.

Ultimately, mixed-precision training is most useful when maximum training speed is a top priority, overall accuracy is unaffected and managing system memory use is critical.

However, there are some limitations to consider:

• Narrow hardware compatibility. Performance improvements necessitate hardware optimized for FP16, such as Nvidia GPUs with tensor cores, Google Cloud Tensor Processing Units or newer Intel CPUs.
• Need for loss scaling. Loss scaling is essential to prevent numerical instability encountered with lower-precision training.
• Increased monitoring. Model output monitoring is critical to ensure no significant loss in accuracy compared with single-precision training.

Getting started with mixed-precision training

There is no universal approach for using mixed-precision training with machine learning models, but there are some tactics that can help developers get started.

Consider tool options

Review deep learning frameworks, such as Microsoft Cognitive Toolkit, NVCaffe, PyTorch and TensorFlow. Many of these tools now support half-precision training on hardware with tensor core functionality, such as Nvidia Volta and Turing architectures. However, not all frameworks can use every processor architecture. For example, PyTorch can use the latest hardware architecture but does not inherently utilize tensor cores.

Use code examples

Make use of mixed-precision libraries and APIs, such as PyTorch's torch.amp package or TensorFlow's mixed-precision utilities. These resources can provide helpful code examples and tools for developers looking to configure and optimize mixed-precision workflows.

Consider model compatibility

Not all projects are compatible with mixed-precision training. Developers should analyze the model's structure and, if necessary, update or optimize the model to support FP16 computations where possible.

Use weights and loss scaling

Data quality is critical in the mixed-precision training process, so preserve single-precision copies of weights to ensure accurate gradient accumulation after each optimization step. Further, use appropriate loss scaling to maintain small gradient values and prevent significant deviations between FP16 and FP32 outcomes.

Enable compatible hardware

Consider the computing hardware loadout and enable hardware in an appropriate manner. For example, dedicate one GPU to storing memory for the training session, and enable features like CUDA, a parallel computing technology and programming model for GPU acceleration.

Automate where possible

Simplify the mixed-precision training process with automation tools. For example, the autocast context manager in PyTorch's torch.amp package can be used to automatically apply mixed-precision settings during training.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 20 years of technical writing experience in the PC and technology industry.