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. To use Zero, you can either use Sharded Data Parallel (SDP), inspired by ZeRO-2 or Fully Sharded Data Parallel, which was inspired by ZeRO-3. The main difference between the two, is that SDP shards the gradients and optimizer state, whereas FSDP additionally shards the model parameters. This decreases memory at the expense of communication. For more information see this FAIRscale doc.
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" use_zero: True 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.
To use Sharded Data Parallel (SDP), inspired by Zero-2, merely set:
MODEL: SHARDED_DDP_SETUP: # set this to true if you want to use SDP instead of DDP. # VISSL will automatically set optimizer = zero and # configure the settings required to run SDP successfully. USE_SDP: True reduce_buffer_size: -1
To use Fully Sharded Data Parallel (FSDP), inspired by Zero-3, merely set:
MODEL: FSDP_CONFIG: # set this option to True to enable FSDP and automatically determine the config # for FSDP based on AMP true/false. AUTO_SETUP_FSDP: True # Set this option to a positive number to automatically wrap "big" layers with # a dedicated FSDP wrapping: the number provided here is the number of # parameters that serves as threshold to decide if a layer is "big" AUTO_WRAP_THRESHOLD: 0 AMP_TYPE: "01" # Parameters of fairscale FSDP flatten_parameters: True mixed_precision: True fp32_reduce_scatter: False # Only makes sense to be True when mixed_precision is True. compute_dtype: float32 # Choose "float32" or "float16" bucket_cap_mb: 0 clear_autocast_cache: True verbose: True
Warning: This has only been fully tested with SwAV + Regnet models.