gradient-accumulator 0.4.2

Package for gradient accumulation in TensorFlow

GradientAccumulator

Seemless gradient accumulation for TensorFlow 2

GradientAccumulator was developed by SINTEF Health due to the lack of an easy-to-use method for gradient accumulation in TensorFlow 2.

The package is available on PyPI and is compatible with and have been tested against TensorFlow 2.2-2.12 and Python 3.6-3.12, and works cross-platform (Ubuntu, Windows, macOS).

Continuous integration

Build Type	Status
Code coverage
Documentations
Unit tests

Install

Stable release from PyPI:

pip install gradient-accumulator

Or from source:

pip install git+https://github.com/andreped/GradientAccumulator

Getting started

A simple example to add gradient accumulation to an existing model is by:

from gradient_accumulator import GradientAccumulateModel
from tensorflow.keras.models import Model

model = Model(...)
model = GradientAccumulateModel(accum_steps=4, inputs=model.input, outputs=model.output)

Then simply use the model as you normally would!

In practice, using gradient accumulation with a custom pipeline might require some extra overhead and tricks to get working.

For more information, see documentations which are hosted at gradientaccumulator.readthedocs.io

What?

Gradient accumulation (GA) enables reduced GPU memory consumption through dividing a batch into smaller reduced batches, and performing gradient computation either in a distributing setting across multiple GPUs or sequentially on the same GPU. When the full batch is processed, the gradients are the accumulated to produce the full batch gradient.

Why?

In TensorFlow 2, there did not exist a plug-and-play method to use gradient accumulation with any custom pipeline. Hence, we have implemented two generic TF2-compatible approaches:

Method	Usage
GradientAccumulateModel	model = GradientAccumulateModel(accum_steps=4, inputs=model.input, outputs=model.output)
GradientAccumulateOptimizer	opt = GradientAccumulateOptimizer(accum_steps=4, optimizer=tf.keras.optimizers.SGD(1e-2))

Both approaches control how frequently the weigths are updated, but in their own way. Approach (1) is for single-GPU only, whereas (2) supports both single-GPU and distributed training (multi-GPU). However, note that (2) is not yet working as intended. Hence, use (1) for most applications.

Our implementations enable theoretically infinitely large batch size, with identical memory consumption as for a regular mini batch. If a single GPU is used, this comes at the cost of increased training runtime. Multiple GPUs could be used to improve runtime performance.

Technique	Usage
Batch Normalization	layer = AccumBatchNormalization(accum_steps=4)
Adaptive Gradient Clipping	model = GradientAccumulateModel(accum_steps=4, agc=True, inputs=model.input, outputs=model.output)
Mixed precision	model = GradientAccumulateModel(accum_steps=4, mixed_precision=True, inputs=model.input, outputs=model.output)

• As batch normalization (BN) is not natively compatible with GA, we have implemented a custom BN layer which can be used as a drop-in replacement.
• Support for adaptive gradient clipping has been added as an alternative to BN.
• Mixed precision can also be utilized on both GPUs and TPUs.

For more information on usage, supported techniques, and examples, refer to the documentations.

Acknowledgements

The gradient accumulator model wrapper is based on the implementation presented in this thread on stack overflow. The adaptive gradient clipping method is based on the implementation by @sayakpaul. The optimizer wrapper is derived from the implementation by @fsx950223 and @stefan-falk.

The documentations hosted here was made possible by the incredible ReadTheDocs team which offer free documentation hosting!

How to cite?

If you used this package or found the project relevant in your research, please, include the following citation:

@software{andre_pedersen_2023_7890319,
  author       = {André Pedersen and Javier Pérez de Frutos and David Bouget},
  title        = {andreped/GradientAccumulator: v0.4.2},
  month        = may,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v0.4.2},
  doi          = {10.5281/zenodo.7890319},
  url          = {https://doi.org/10.5281/zenodo.7890319}
}