๐ŸŽ‚ Low Precision GroupNorm#

[How to Use] - [Suggested Hyperparameters] - [Technical Details] - [Attribution]

Natural Language Processing, Math Equivalent

Low Precision GroupNorm forces torch.nn.GroupNorm modules to run in float16 or bfloat16 precision, improving utilization. This should not affect final model quality, but in rare cases may cause loss spikes.

How to Use#

Functional Interface#

# Apply surgery on the model to swap-in the Low Precision GroupNorm using the Composer functional API

import composer.functional as cf

def training_loop(model, train_loader):
    cf.apply_low_precision_groupnorm(model, precision='amp')

    opt = torch.optim.Adam(model.parameters())
    loss_fn = F.cross_entropy

    for X, y in train_loader:
        y_hat = model(X)
        loss = loss_fn(y_hat, y)

Composer Trainer#

from composer.trainer import Trainer
from composer.algorithms import LowPrecisionGroupNorm

trainer = Trainer(model=model,


Implementation Details#

Low Precision GroupNorm is implemented by performing model surgery, which looks for instances of torch.nn.GroupNorm and replaces them with composer.algorithms.LPGroupNorm. This class is a thin wrapper around torch.nn.GroupNorm that manually turns autocast off and sets the input dtype to lower precision.

Suggested Hyperparameters#

Low Precision GroupNorm uses the existing parameters from the original model. The functional version of Low Precision GroupNorm allows you to specify the precision mode, which should be set to the Composer precision format of your model. When using the algorithm through the Composer trainer, Low Precision GroupNorm will use the trainerโ€™s precision mode automatically.

Technical Details#

Low Precision GroupNorm wraps torch.nn.GroupNorm, forcing the module to run in a lower precision if you have autocast enabled. This depends on the precision argument passed to Trainer, with precision='amp_fp16' corresponding to torch.float16 and precision='amp_bf16' corresponding to torch.bfloat16.

This algorithm will have no effect if you are running in fp32 or fp16 mode.

โœ… Low Precision GroupNorm Improves Training Speed

In our experiments, Low Preicision GroupNorm improves the attainable tradeoffs between training speed and the final quality of the trained model. We recommend using Low Precision GroupNorm.


The Composer implementation of this method and the accompanying documentation were produced by MosaicML.

API Reference#

Algorithm class: composer.algorithms.LowPrecisionGroupNorm

Functional: composer.functional.apply_low_precision_groupnorm()