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Distributed Optimizers


Distributed optimizer is not currently supported when using CUDA tensors

torch.distributed.optim exposes DistributedOptimizer, which takes a list of remote parameters (RRef) and runs the optimizer locally on the workers where the parameters live. The distributed optimizer can use any of the local optimizer Base class to apply the gradients on each worker.

class torch.distributed.optim.DistributedOptimizer(optimizer_class, params_rref, *args, **kwargs)[source]

DistributedOptimizer takes remote references to parameters scattered across workers and applies the given optimizer locally for each parameter.

This class uses get_gradients() in order to retrieve the gradients for specific parameters.

Concurrent calls to step(), either from the same or different clients, will be serialized on each worker – as each worker’s optimizer can only work on one set of gradients at a time. However, there is no guarantee that the full forward-backward-optimizer sequence will execute for one client at a time. This means that the gradients being applied may not correspond to the latest forward pass executed on a given worker. Also, there is no guaranteed ordering across workers.

DistributedOptimizer creates the local optimizer with TorchScript enabled by default, so that optimizer updates are not blocked by the Python Global Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed Model Parallel). This feature is currently enabled for most optimizers. You can also follow the recipe in PyTorch tutorials to enable TorchScript support for your own custom optimizers.

  • optimizer_class (optim.Optimizer) – the class of optimizer to instantiate on each worker.

  • params_rref (list[RRef]) – list of RRefs to local or remote parameters to optimize.

  • args – arguments to pass to the optimizer constructor on each worker.

  • kwargs – arguments to pass to the optimizer constructor on each worker.

>>> import torch.distributed.autograd as dist_autograd
>>> import torch.distributed.rpc as rpc
>>> from torch import optim
>>> from torch.distributed.optim import DistributedOptimizer
>>> with dist_autograd.context() as context_id:
>>>   # Forward pass.
>>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
>>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
>>>   loss = rref1.to_here() + rref2.to_here()
>>>   # Backward pass.
>>>   dist_autograd.backward(context_id, [loss.sum()])
>>>   # Optimizer.
>>>   dist_optim = DistributedOptimizer(
>>>      optim.SGD,
>>>      [rref1, rref2],
>>>      lr=0.05,
>>>   )
>>>   dist_optim.step(context_id)

Performs a single optimization step.

This will call torch.optim.Optimizer.step() on each worker containing parameters to be optimized, and will block until all workers return. The provided context_id will be used to retrieve the corresponding context that contains the gradients that should be applied to the parameters.


context_id – the autograd context id for which we should run the optimizer step.

class torch.distributed.optim.PostLocalSGDOptimizer(optim, averager)[source]

Wraps an arbitrary torch.optim.Optimizer and runs post-local SGD, This optimizer runs local optimizer at every step. After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.

  • optim (Optimizer) – The local optimizer.

  • averager (ModelAverager) – A model averager instance to run post-localSGD algorithm.


>>> import torch
>>> import torch.distributed as dist
>>> import torch.distributed.algorithms.model_averaging.averagers as averagers
>>> import torch.nn as nn
>>> from torch.distributed.optim import PostLocalSGDOptimizer
>>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
>>>   PostLocalSGDState,
>>>   post_localSGD_hook,
>>> )
>>> 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)
>>> # Create a post-localSGD optimizer that wraps a local optimizer.
>>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
>>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
>>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
>>> opt = PostLocalSGDOptimizer(
>>>     optim=local_optim,
>>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
>>> )
>>> # In the first 100 steps, DDP runs global gradient averaging at every step.
>>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
>>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
>>> for step in range(0, 200):
>>>    opt.zero_grad()
>>>    loss = loss_fn(output, labels)
>>>    loss.backward()
>>>    opt.step()

This is the same as torch.optim.Optimizer load_state_dict(), but also restores model averager’s step value to the one saved in the provided state_dict.

If there is no "step" entry in state_dict, it will raise a warning and initialize the model averager’s step to 0.


This is the same as torch.optim.Optimizer state_dict(), but adds an extra entry to record model averager’s step to the checkpoint to ensure reload does not cause unnecessary warm up again.


Performs a single optimization step (parameter update).

class torch.distributed.optim.ZeroRedundancyOptimizer(params, optimizer_class, process_group=None, parameters_as_bucket_view=False, overlap_with_ddp=False, **defaults)[source]

Wrap an arbitrary optim.Optimizer and shards its states across ranks in the group.

The sharing is done as described by ZeRO.

The local optimizer instance in each rank is only responsible for updating approximately 1 / world_size parameters and hence only needs to keep 1 / world_size optimizer states. After parameters are updated locally, each rank will broadcast its parameters to all other peers to keep all model replicas in the same state. ZeroRedundancyOptimizer can be used in conjunction with torch.nn.parallel.DistributedDataParallel to reduce per-rank peak memory consumption.

ZeroRedundancyOptimizer uses a sorted-greedy algorithm to pack a number of parameters at each rank. Each parameter belongs to a single rank and is not divided among ranks. The partition is arbitrary and might not match the the parameter registration or usage order.


params (Iterable) – an Iterable of torch.Tensor s or dict s giving all parameters, which will be sharded across ranks.

Keyword Arguments
  • optimizer_class (torch.nn.Optimizer) – the class of the local optimizer.

  • process_group (ProcessGroup, optional) – torch.distributed ProcessGroup (default: dist.group.WORLD initialized by torch.distributed.init_process_group()).

  • parameters_as_bucket_view (bool, optional) – if True, parameters are packed into buckets to speed up communication, and param.data fields point to bucket views at different offsets; if False, each individual parameter is communicated separately, and each params.data stays intact (default: False).

  • overlap_with_ddp (bool, optional) – if True, step() is overlapped with DistributedDataParallel ‘s gradient synchronization; this requires (1) either a functional optimizer for the optimizer_class argument or one with a functional equivalent and (2) registering a DDP communication hook constructed from one of the functions in ddp_zero_hook.py; parameters are packed into buckets matching those in DistributedDataParallel, meaning that the parameters_as_bucket_view argument is ignored. If False, step() runs disjointly after the backward pass (per normal). (default: False)

  • **defaults – any trailing arguments, which are forwarded to the local optimizer.


>>> import torch.nn as nn
>>> from torch.distributed.optim import ZeroRedundancyOptimizer
>>> from torch.nn.parallel import DistributedDataParallel as DDP
>>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
>>> ddp = DDP(model, device_ids=[rank])
>>> opt = ZeroRedundancyOptimizer(
>>>     ddp.parameters(),
>>>     optimizer_class=torch.optim.Adam,
>>>     lr=0.01
>>> )
>>> ddp(inputs).sum().backward()
>>> opt.step()


Currently, ZeroRedundancyOptimizer requires that all of the passed-in parameters are the same dense type.


If you pass overlap_with_ddp=True, be wary of the following: Given the way that overlapping DistributedDataParallel with ZeroRedundancyOptimizer is currently implemented, the first two or three training iterations do not perform parameter updates in the optimizer step, depending on if static_graph=False or static_graph=True, respectively. This is because it needs information about the gradient bucketing strategy used by DistributedDataParallel, which is not finalized until the second forward pass if static_graph=False or until the third forward pass if static_graph=True. To adjust for this, one option is to prepend dummy inputs.


ZeroRedundancyOptimizer is experimental and subject to change.


Add a parameter group to the Optimizer ‘s param_groups.

This can be useful when fine tuning a pre-trained network, as frozen layers can be made trainable and added to the Optimizer as training progresses.


param_group (dict) – specifies the parameters to be optimized and group-specific optimization options.


This method handles updating the shards on all partitions but needs to be called on all ranks. Calling this on a subset of the ranks will cause the training to hang because communication primitives are called depending on the managed parameters and expect all the ranks to participate on the same set of parameters.


Consolidate a list of state_dict s (one per rank) on the target rank.


to (int) – the rank that receives the optimizer states (default: 0).


RuntimeError – if overlap_with_ddp=True and this method is called before this ZeroRedundancyOptimizer instance has been fully initialized, which happens once DistributedDataParallel gradient buckets have been rebuilt.


This needs to be called on all ranks.

property join_device: device

Return default device.


Return the ZeRO join hook.

It enables training on uneven inputs by shadowing the collective communications in the optimizer step.

Gradients must be properly set before this hook is called.


kwargs (dict) – a dict containing any keyword arguments to modify the behavior of the join hook at run time; all Joinable instances sharing the same join context manager are forwarded the same value for kwargs.

This hook does not support any keyword arguments; i.e. kwargs is unused.

property join_process_group: Any

Return process group.


Load the state pertaining to the given rank from the input state_dict, updating the local optimizer as needed.


state_dict (dict) – optimizer state; should be an object returned from a call to state_dict().


RuntimeError – if overlap_with_ddp=True and this method is called before this ZeroRedundancyOptimizer instance has been fully initialized, which happens once DistributedDataParallel gradient buckets have been rebuilt.


Return the last global optimizer state known to this rank.


RuntimeError – if overlap_with_ddp=True and this method is called before this ZeroRedundancyOptimizer instance has been fully initialized, which happens once DistributedDataParallel gradient buckets have been rebuilt; or if this method is called without a preceding call to consolidate_state_dict().

Return type

Dict[str, Any]

step(closure=None, **kwargs)[source]

Perform a single optimizer step and syncs parameters across all ranks.


closure (Callable) – a closure that re-evaluates the model and returns the loss; optional for most optimizers.


Optional loss depending on the underlying local optimizer.

Return type



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