FP16 Training with Model Parallelism - Amazon SageMaker

FP16 Training with Model Parallelism

For FP16 training, apply the following modifications to your training script and estimator.


This feature is available in the SageMaker model parallel library v1.10.0 and later.

Adapt your PyTorch training script

  1. Wrap your model using the smdistributed.modelparallel.torch.model_creation() context manager.

    # fp16_training_script.py import torch import smdistributed.modelparallel.torch as smp with smp.model_creation( dtype=torch.float16 if args.fp16 else torch.get_default_dtype() ): model = ...

    If you are using tensor parallelism, add tensor_parallelism=smp.tp_size() > 1 to the smp.model_creation context manager. Adding this line also helps automatically detect whether tensor parallelism is activated or not.

    with smp.model_creation( ... , tensor_parallelism=smp.tp_size() > 1 ): model = ...
  2. When you wrap the optimizer with smdistributed.modelparallel.torch.DistributedOptimizer, set either the static_loss_scaling or dynamic_loss_scaling argument. By default, static_loss_scaling is set to 1.0, and dynamic_loss_scaling is set to False. If you set dynamic_loss_scale=True, you can feed dynamic loss scaling options as a dictionary through the dynamic_loss_args argument. In most cases, we recommend you use dynamic loss scaling with the default options. For more information, options, and examples of the optimizer wrapper function, see the smdistributed.modelparallel.torch.DistributedOptimizer API.

    The following code is an example of wrapping an Adadelta optimizer object with dynamic loss scaling for FP16 training.

    optimizer = torch.optim.Adadelta(...) optimizer = smp.DistributedOptimizer( optimizer, static_loss_scale=None, dynamic_loss_scale=True, dynamic_loss_args={ "scale_window": 1000, "min_scale": 1, "delayed_shift": 2 } )

Configure a SageMaker PyTorch estimator

Add the FP16 parameter ("fp16") to the distribution configuration for model parallelism when creating a SageMaker PyTorch estimator object. For a complete list of the configuration parameters for model parallelism, see Parameters for smdistributed.

from sagemaker.pytorch import PyTorch smp_options = { "enabled": True, "parameters": { "microbatches": 4, "pipeline_parallel_degree": 2, "tensor_parallel_degree": 2, ..., "fp16": True } } fp16_estimator = PyTorch( entry_point="fp16_training_script.py", # Specify your train script ..., distribution={ "smdistributed": {"modelparallel": smp_options}, "mpi": {...} } ) fp16_estimator.fit(...)

When FP16 training starts, the model and the optimizer are wrapped by FP16_Module and FP16_Optimizer respectively, which are modified smdistributed versions of the Apex utils. FP16_Module converts the model to FP16 dtype and deals with the forward pass in FP16.


You can apply gradient clipping by calling clip_master_grads before optimizer.step.

optimizer.clip_master_grads(max_norm) # max_norm(float or int): max norm of the gradients

When using torch.optim.lr_scheduler and FP16 training, you need to pass optimizer.optimizer to the LR scheduler rather than the optimizer. See the following example code.

from torch.optim.lr_scheduler import StepLR scheduler = StepLR( optimizer.optimizer if smp.state.cfg.fp16 else optimizer, step_size=1, gamma=args.gamma )