U JºcTã@sŠddlZddlmZmZddlmZmZmZddlZddl m Z ddl m m mmZddgZGdd„deƒZGdd„deƒZdS)éN)ÚABCÚabstractmethod)ÚUnionÚIterableÚDictÚ ModelAveragerÚPeriodicModelAveragerc@s&eZdZdZddd„Zedd„ƒZdS)raHBase class for all model averagers. Args: process_group: The process group to be used for all-reduce. If ``None``, the default process group, which is created by :func:`torch.distributed.init_process_group`, will be used. (default: ``None``) NcCs |dk r |ntjj|_d|_dS)Nr)ÚdistÚgroupZWORLDÚ process_groupÚstep)Úselfr ©rúZ/tmp/pip-unpacked-wheel-gikjz4vx/torch/distributed/algorithms/model_averaging/averagers.pyÚ__init__sÿzModelAverager.__init__cCst‚dS)N)ÚNotImplementedError©r ÚparamsrrrÚaverage_parameterssz ModelAverager.average_parameters)N)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrrrrrrr s csPeZdZdZd ‡fdd„ Zeeejj ee e ejj ffdœdd„Z ‡Z S) rak Averages parameters periodically after the warm-up stage. This can be used for running `post-local SGD `_, by running :class:`~torch.nn.DistributedDataParallel` (DDP) using the subgroups created by :meth:`~torch.distributed.new_subgroups`. Args: period (int): The number of steps per model averaging. Usually the period should be greater than ``1`` to reduce the communication cost. Otherwise, only DDP needs to be used. warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. process_group: The process group to be used for all-reduce. If ``None``, the default process group, which is created by :func:`torch.distributed.init_process_group`, will be used. (default: ``None``) Example:: >>> # xdoctest: +SKIP("undefined variables") >>> import torch >>> import torch.distributed as dist >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD >>> import torch.distributed.algorithms.model_averaging.averagers as averagers >>> import torch.nn as nn >>> >>> dist.init_process_group("nccl", rank=rank, world_size=16) >>> torch.cuda.set_device(rank) >>> module = nn.Linear(1, 1, bias=False).cuda() >>> model = nn.parallel.DistributedDataParallel( >>> module, device_ids=[rank], output_device=rank >>> ) >>> # Register a post-localSGD communication hook. >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) >>> model.register_comm_hook(state, post_localSGD_hook) >>> >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. >>> # After 100 steps, run model averaging every 4 steps. >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) >>> for step in range(0, 200): >>> optimizer.zero_grad() >>> loss = loss_fn(output, labels) >>> loss.backward() >>> optimizer.step() >>> # Will average model parameters globally every 4 steps. Thus, >>> # inter-node communication only occurs every 4 iterations after >>> # the initial ``warmup_steps`` period. >>> averager.average_parameters(model.parameters()) rNcsPtƒ |¡|dkrtdƒ‚||_|dkr4tdƒ‚n|dkrFt d¡||_dS)Nrz3Arg ``warmup_steps`` must be a non-negative number.éz(Arg ``period`` must be a positive value.aWhen period is 1, no need to use model averaging because the communication cost of all-reducing parameters will be no less than the cost of all-reducing gradients by DistributedDataParallel in the backward pass. Therefore, only DistributedDataParallel should be used for this case.)ÚsuperrÚ ValueErrorÚ warmup_stepsÚwarningsÚwarnÚperiod)r rrr ©Ú __class__rrrTs  ÿzPeriodicModelAverager.__init__)rcCsB|j|jkr0|j|j|jdkr0t ||j¡|jd7_dS)aV Averages parameters or parameter groups of an optimizer if ``step`` is no less than ``warmup_steps`` and it can be divided by ``period``, where ``step`` is increased by 1 at each iteration in the training loop. Args: params: The parameters of a model or parameter groups of an optimizer. rrN)r rrÚutilsZ&average_parameters_or_parameter_groupsr rrrrris ÿþz(PeriodicModelAverager.average_parameters)rN)rrrrrrrÚtorchÚnnÚ ParameterrÚstrrÚ __classcell__rrr rrs 7ü)rÚabcrrÚtypingrrrr#Ztorch.distributedZ distributedr Z2torch.distributed.algorithms.model_averaging.utilsZ algorithmsZmodel_averagingr"Ú__all__rrrrrrÚs