Save memory with mixed precision¶

What is Mixed Precision?¶

Like most deep learning frameworks, PyTorch trains on 32-bit floating-point (FP32) arithmetic by default. However, many deep learning models do not require this to reach complete accuracy. Mixed precision training delivers significant computational speedup by conducting operations in half-precision while keeping minimum information in single-precision to maintain as much information as possible in crucial areas of the network. Switching to mixed precision has resulted in considerable training speedups since the introduction of Tensor Cores in the Volta and Turing architectures. It combines FP32 and lower-bit floating points (such as FP16) to reduce memory footprint and increase performance during model training and evaluation. It accomplishes this by recognizing the steps that require complete accuracy and employing a 32-bit floating point for those steps only while using a 16-bit floating point for the rest. Compared to complete precision training, mixed precision training delivers all these benefits while ensuring no task-specific accuracy is lost [1].

This is how you select the precision in Fabric:

from lightning.fabric import Fabric

# This is the default
fabric = Fabric(precision=32)

# FP16 mixed precision
fabric = Fabric(precision=16)

# Precision values can also be set as a string
fabric = Fabric(precision="16")

# BFloat16 precision (Volta GPUs and later)
fabric = Fabric(precision="bf16")

# Double precision
fabric = Fabric(precision=64)

The same values can also be set through the command line interface:

lightning run model train.py --precision=bf16

Note

In some cases, it is essential to remain in FP32 for numerical stability, so keep this in mind when using mixed precision. For example, when running scatter operations during the forward (such as torchpoint3d), the computation must remain in FP32.

FP16 Mixed Precision¶

In most cases, mixed precision uses FP16. Supported PyTorch operations automatically run in FP16, saving memory and improving throughput on the supported accelerators. Since computation happens in FP16, there is a chance of numerical instability during training. This is handled internally by a dynamic grad scaler which skips invalid steps and adjusts the scaler to ensure subsequent steps fall within a finite range. For more information see the autocast docs.

This is how you enable FP16 in Fabric:

# Select FP16 mixed precision
fabric = Fabric(precision=16)

# Or as a string
fabric = Fabric(precision="16")

Note

When using TPUs, setting precision=16 will enable bfloat16, the only supported half-precision type on TPUs.

BFloat16 Mixed Precision¶

BFloat16 Mixed precision is similar to FP16 mixed precision. However, it maintains more of the “dynamic range” that FP32 offers. This means it can improve numerical stability than FP16 mixed precision. For more information, see this TPU performance blog post.

# Select BF16 precision
fabric = Fabric(precision="bf16")

Under the hood, we use torch.autocast with the dtype set to bfloat16, with no gradient scaling. It is also possible to use BFloat16 mixed precision on the CPU, relying on MKLDNN.

Note

BFloat16 is also experimental and may not provide significant speedups or memory improvements, offering better numerical stability. For GPUs, the most significant benefits require Ampere based GPUs, such as A100s or 3090s.

Control where precision gets applied¶

Fabric automatically casts the data type and operations in the forward of your model:

fabric = Fabric(precision="bf16")

model = ...
optimizer = ...

# Here, Fabric sets up the `model.forward` for precision auto-casting
model, optimizer = fabric.setup(model, optimizer)

# Precision casting gets handled in your forward, no code changes required
output = model.forward(input)

# Precision does NOT get applied here (only in forward)
loss = loss_function(output, target)

If you want to enable operations in lower bit-precision outside your model’s forward(), you can use the autocast() context manager:

# Precision now gets also handled in this part of the code:
with fabric.autocast():
    loss = loss_function(output, target)