Source code for pytorch_lightning.utilities.distributed
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"""Utilities that can be used with distributed training."""
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import Tensor
from torch.nn.parallel.distributed import DistributedDataParallel
from lightning_fabric.utilities.distributed import _all_gather_ddp_if_available as new_all_gather_ddp_if_available
from lightning_fabric.utilities.distributed import _distributed_available as new_distributed_available
from lightning_fabric.utilities.distributed import _gather_all_tensors as new_gather_all_tensors
from lightning_fabric.utilities.distributed import (
_get_default_process_group_backend_for_device as new_get_default_process_group_backend_for_device,
)
from lightning_fabric.utilities.distributed import _init_dist_connection as new_init_dist_connection
from lightning_fabric.utilities.distributed import _sync_ddp as new_sync_ddp
from lightning_fabric.utilities.distributed import _sync_ddp_if_available as new_sync_ddp_if_available
from pytorch_lightning.utilities.rank_zero import rank_zero_debug, rank_zero_deprecation, rank_zero_info
[docs]def register_ddp_comm_hook(
model: DistributedDataParallel,
ddp_comm_state: Optional[object] = None,
ddp_comm_hook: Optional[Callable] = None,
ddp_comm_wrapper: Optional[Callable] = None,
) -> None:
"""Function to register communication hook for DDP model https://pytorch.org/docs/master/ddp_comm_hooks.html.
Args:
model:
DDP model
ddp_comm_state:
state is passed to the hook and can be used to maintain
and update any state information that users would like to
maintain as part of the training process. Examples: error
feedback in gradient compression, peers to communicate with
next in GossipGrad etc.
ddp_comm_hook:
hook(state: object, bucket: dist._GradBucket) -> torch.futures.Future
This callable function is called once the bucket is ready. The
hook can perform whatever processing is needed and return
a Future indicating completion of any async work (ex: allreduce).
If the hook doesn't perform any communication, it can also
just return a completed Future. The Future should hold the
new value of grad bucket's tensors. Once a bucket is ready,
c10d reducer would call this hook and use the tensors returned
by the Future and copy grads to individual parameters.
ddp_comm_wrapper:
communication hook wrapper to support a communication hook such
as FP16 compression as wrapper, which could be combined with
ddp_comm_hook
Examples:
>>> from torch.distributed.algorithms.ddp_comm_hooks import ( # doctest: +SKIP
... default_hooks as default,
... powerSGD_hook as powerSGD,
... post_localSGD_hook as post_localSGD,
... )
>>>
>>> # fp16_compress_hook for compress gradients
>>> ddp_model = ...
>>> register_ddp_comm_hook( # doctest: +SKIP
... model=ddp_model,
... ddp_comm_hook=default.fp16_compress_hook,
... )
>>>
>>> # powerSGD_hook
>>> ddp_model = ...
>>> register_ddp_comm_hook( # doctest: +SKIP
... model=ddp_model,
... ddp_comm_state=powerSGD.PowerSGDState(
... process_group=None,
... matrix_approximation_rank=1,
... start_powerSGD_iter=5000,
... ),
... ddp_comm_hook=powerSGD.powerSGD_hook,
... )
>>>
>>> # post_localSGD_hook
>>> subgroup, _ = torch.distributed.new_subgroups() # doctest: +SKIP
>>> ddp_model = ...
>>> register_ddp_comm_hook( # doctest: +SKIP
... model=ddp_model,
... state=post_localSGD.PostLocalSGDState(
... process_group=None,
... subgroup=subgroup,
... start_localSGD_iter=1_000,
... ),
... ddp_comm_hook=post_localSGD.post_localSGD_hook,
... )
>>>
>>> # fp16_compress_wrapper combined with other communication hook
>>> ddp_model = ...
>>> register_ddp_comm_hook( # doctest: +SKIP
... model=ddp_model,
... ddp_comm_state=powerSGD.PowerSGDState(
... process_group=None,
... matrix_approximation_rank=1,
... start_powerSGD_iter=5000,
... ),
... ddp_comm_hook=powerSGD.powerSGD_hook,
... ddp_comm_wrapper=default.fp16_compress_wrapper,
... )
"""
if ddp_comm_hook is None:
return
# inform mypy that ddp_comm_hook is callable
ddp_comm_hook: Callable = ddp_comm_hook
if ddp_comm_wrapper is not None:
rank_zero_info(
f"DDP comm wrapper is provided, apply {ddp_comm_wrapper.__qualname__}({ddp_comm_hook.__qualname__})."
)
ddp_comm_hook = ddp_comm_wrapper(ddp_comm_hook)
rank_zero_debug(f"Registering DDP comm hook: {ddp_comm_hook.__qualname__}.")
model.register_comm_hook(state=ddp_comm_state, hook=ddp_comm_hook) # type: ignore[operator]
def _broadcast_object_list(obj: Any, rank: int) -> Any:
objects = [obj if torch.distributed.get_rank() == rank else None]
torch.distributed.broadcast_object_list(objects, src=rank)
return objects[0]
# TODO: Refactor with the Strategy Collectives once finalized.
def _collect_states_on_rank_zero(state: Dict[str, Any]) -> Dict[int, Any]:
"""This distributed utility collects dictionary state across all processes.
Args:
state: Dictionary containing the state of the current process
Returns:
states: On global rank 0, a dictionary where the primary keys are
the process rank and the values their associated states. Otherwise, returns None.
"""
if not new_distributed_available():
return {0: state}
return {rank: _broadcast_object_list(state, rank) for rank in range(torch.distributed.get_world_size())}
def all_gather_ddp_if_available(*args: Any, **kwargs: Any) -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.all_gather_ddp_if_available` has been deprecated in v1.8.0 and will"
" be removed in v2.0.0. This function is internal but you can copy over its implementation."
)
return new_all_gather_ddp_if_available(*args, **kwargs)
def distributed_available() -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.distributed_available` has been deprecated in v1.8.0 and will"
" be removed in v2.0.0. This function is internal but you can copy over its implementation."
)
return new_distributed_available()
def gather_all_tensors(*args: Any, **kwargs: Any) -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.gather_all_tensors` has been deprecated in v1.8.0 and will"
" be removed in v2.0.0. This function is internal but you can copy over its implementation."
)
return new_gather_all_tensors(*args, **kwargs)
[docs]class AllGatherGrad(torch.autograd.Function):
"""Gathers tensors from the whole group and stacks them.
This implementation is copied from PyTorch.
.. deprecated:: v1.8.0
This function has been deprecated in v1.8.0 in favor of :func:`torch.distributed.nn.functional.all_gather` and
will be removed in v2.0.0.
"""
[docs] @staticmethod
def forward( # type: ignore[override]
ctx: Any,
tensor: Tensor,
group: Optional["torch.distributed.ProcessGroup"] = None,
) -> Tensor:
rank_zero_deprecation(
"`AllGatherGrad` has been deprecated in v1.8.0 and will be removed in v2.0.0."
" Use `torch.distributed.nn.functional.all_gather` instead.",
stacklevel=6,
)
ctx.group = group
gathered_tensor = [torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())]
torch.distributed.all_gather(gathered_tensor, tensor, group=group)
gathered_tensor = torch.stack(gathered_tensor, dim=0)
return gathered_tensor
[docs] @staticmethod
def backward(ctx: Any, *grad_output: Tensor) -> Tuple[Tensor, None]:
grad_output = torch.cat(grad_output)
torch.distributed.all_reduce(grad_output, op=torch.distributed.ReduceOp.SUM, async_op=False, group=ctx.group)
return grad_output[torch.distributed.get_rank()], None
def get_default_process_group_backend_for_device(*args: Any, **kwargs: Any) -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.get_default_process_group_backend_for_device` has been deprecated"
" in v1.8.0 and will be removed in v2.0.0. This function is internal but you can copy over its implementation."
" `lightning_fabric.utilities.distributed.get_default_process_group_backend_for_device` instead."
)
return new_get_default_process_group_backend_for_device(*args, **kwargs)
def init_dist_connection(*args: Any, **kwargs: Any) -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.init_dist_connection` has been deprecated in v1.8.0 and will"
" be removed in v2.0.0. This function is internal but you can copy over its implementation."
)
return new_init_dist_connection(*args, **kwargs)
def sync_ddp(*args: Any, **kwargs: Any) -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.sync_ddp` has been deprecated in v1.8.0 and will"
" be removed in v2.0.0. This function is internal but you can copy over its implementation."
)
return new_sync_ddp(*args, **kwargs)
def sync_ddp_if_available(*args: Any, **kwargs: Any) -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.sync_ddp_if_available` has been deprecated in v1.8.0 and will"
" be removed in v2.0.0. This function is internal but you can copy over its implementation."
)
return new_sync_ddp_if_available(*args, **kwargs)
def tpu_distributed() -> bool:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.tpu_distributed` has been deprecated in v1.8.0 and will"
" be removed in v2.0.0. This function is internal but you can copy over its implementation."
)
from lightning_fabric.accelerators.tpu import _tpu_distributed
return _tpu_distributed()
def rank_zero_only(*args: Any, **kwargs: Any) -> Any:
rank_zero_deprecation(
"`pytorch_lightning.utilities.distributed.rank_zero_only` has been deprecated in v1.8.1 and will"
" be removed in v2.0.0. You can import it from `pytorch_lightning.utilities` instead."
)
from pytorch_lightning.utilities.rank_zero import rank_zero_only as new_rank_zero_only
return new_rank_zero_only(*args, **kwargs)
