ZeRO: Optimizer state and gradient sharding¶
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models is a technique developed by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He in this paper. When training models with billions of parameters, GPU memory becomes a bottleneck. ZeRO can offer 4x to 8x memory reductions in memory thus allowing to fit larger models in memory.
How ZeRO works?¶
Memory requirement of a model can be broken down roughly into:
parameters momentum buffers (optimizer state)
ZeRO shards the optimizer state and the parameter gradients onto different devices and reduces the memory needed per device.
How to use ZeRO in VISSL?¶
VISSL uses FAIRScale library which implements ZeRO in PyTorch. Using VISSL in ZeRO involves no code changes and can simply be done by setting some configuration options in the yaml files.
In order to use ZeRO, user needs to set
OPTIMIZER.name=zero and nest the desired optimizer (for example SGD) settings in
An example for using ZeRO with LARC and SGD optimization:
OPTIMIZER: name: zero base_optimizer: name: sgd use_larc: False larc_config: clip: False trust_coefficient: 0.001 eps: 0.00000001 weight_decay: 0.000001 momentum: 0.9 nesterov: False
ZeRO works seamlessly with LARC and mixed precision training. Using ZeRO with activation checkpointing is not yet enabled primarily due to manual gradient reduction need for activation checkpointing.