Source code for pytorch_lightning.strategies.horovod
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import ExitStack
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.optim import Optimizer
import pytorch_lightning as pl
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.parallel import ParallelStrategy
from pytorch_lightning.utilities.distributed import distributed_available
from pytorch_lightning.utilities.distributed import group as dist_group
from pytorch_lightning.utilities.distributed import ReduceOp
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _HOROVOD_AVAILABLE
from pytorch_lightning.utilities.rank_zero import rank_zero_only
if _HOROVOD_AVAILABLE:
import horovod.torch as hvd
[docs]class HorovodStrategy(ParallelStrategy):
"""Plugin for Horovod distributed training integration."""
strategy_name = "horovod"
def __init__(
self,
accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
):
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=None,
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
)
rank_zero_only.rank = self.global_rank
self._exit_stack: Optional[ExitStack] = None
@property
def global_rank(self) -> int:
return hvd.rank()
@property
def local_rank(self) -> int:
return hvd.local_rank()
@property
def world_size(self) -> int:
return hvd.size()
@property
def root_device(self):
return self.parallel_devices[self.local_rank]
@property
def distributed_sampler_kwargs(self):
distributed_sampler_kwargs = dict(num_replicas=self.world_size, rank=self.global_rank)
return distributed_sampler_kwargs
@property
def handles_gradient_accumulation(self) -> bool:
"""Whether the plugin handles gradient accumulation internally."""
return True
[docs] def setup(self, trainer: "pl.Trainer") -> None:
self.model_to_device()
super().setup(trainer)
self._exit_stack = ExitStack()
self._exit_stack.__enter__()
if not trainer.training:
# no need to setup optimizers
return
def _unpack_lightning_optimizer(opt):
return opt._optimizer if isinstance(opt, LightningOptimizer) else opt
optimizers = self.optimizers
optimizers = [_unpack_lightning_optimizer(opt) for opt in optimizers]
# Horovod: scale the learning rate by the number of workers to account for
# increased total batch size
for optimizer in optimizers:
for param_group in optimizer.param_groups:
param_group["lr"] *= self.world_size
# Horovod: adjust base LR used by schedulers to match scaled optimizer initial LR
lr_scheduler_configs = self.lr_scheduler_configs
for config in lr_scheduler_configs:
scheduler = config.scheduler
scheduler.base_lrs = [lr * self.world_size for lr in scheduler.base_lrs]
# Horovod: broadcast parameters & optimizer state to ensure consistent initialization
hvd.broadcast_parameters(self.lightning_module.state_dict(), root_rank=0)
for optimizer in optimizers:
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
accumulation_scheduler = trainer.accumulation_scheduler
if accumulation_scheduler.epochs != [0]:
raise MisconfigurationException(
"Horovod currently does not support different `accumulate_grad_batches` at different epochs."
)
self.optimizers = self._wrap_optimizers(optimizers, trainer.accumulate_grad_batches)
for optimizer in self.optimizers:
# Synchronization will be performed explicitly following backward()
self._exit_stack.enter_context(optimizer.skip_synchronize())
[docs] def broadcast(self, obj: object, src: int = 0) -> object:
obj = hvd.broadcast_object(obj, src)
return obj
[docs] def model_to_device(self):
if self.root_device.type == "cuda":
# this can potentially be removed after #8312. Not done due to lack of horovod testing
torch.cuda.set_device(self.root_device)
self.model.to(self.root_device)
def join(self):
if self.root_device.type == "cuda":
hvd.join(self.local_rank)
else:
hvd.join()
[docs] def reduce(self, tensor, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = "mean"):
"""Reduces a tensor from several distributed processes to one aggregated tensor.
Args:
tensor: the tensor to sync and reduce
group: the process group to gather results from. Defaults to all processes (world)
reduce_op: the reduction operation. Defaults to 'mean'/'avg'.
Can also be a string 'sum' to calculate the sum during reduction.
Return:
reduced value, except when the input was not a tensor the output remains is unchanged
"""
if group is not None:
raise ValueError("Horovod does not support allreduce using a subcommunicator at this time. Unset `group`.")
if reduce_op in (None, "avg", "mean"):
reduce_op = hvd.Average
elif reduce_op in ("sum", ReduceOp.SUM):
reduce_op = hvd.Sum
else:
raise ValueError(f"unrecognized `reduce_op`: {reduce_op}")
# sync all processes before reduction
self.join()
return hvd.allreduce(tensor, op=reduce_op)
[docs] def all_gather(
self, result: torch.Tensor, group: Optional[Any] = dist_group.WORLD, sync_grads: bool = False
) -> torch.Tensor:
if group is not None and group != dist_group.WORLD:
raise ValueError("Horovod does not support allgather using a subcommunicator at this time. Unset `group`.")
if len(result.shape) == 0:
# Convert scalars to single dimension tensors
result = result.reshape(1)
# sync and gather all
self.join()
return hvd.allgather(result)
[docs] def post_backward(self, closure_loss: torch.Tensor) -> None:
# synchronize all horovod optimizers.
for optimizer in self.optimizers:
optimizer.synchronize()
def _wrap_optimizers(
self, optimizers: List[Optimizer], accumulate_grad_batches: int
) -> List["hvd.DistributedOptimizer"]:
"""Wraps optimizers to perform gradient aggregation via allreduce."""
return [
hvd.DistributedOptimizer(
opt,
backward_passes_per_step=accumulate_grad_batches,
named_parameters=self._filter_named_parameters(self.lightning_module, opt),
)
if "horovod" not in str(opt.__class__)
else opt
for opt in optimizers
]
@staticmethod
def _filter_named_parameters(model: nn.Module, optimizer: Optimizer) -> List[Tuple[str, nn.Parameter]]:
opt_params = {p for group in optimizer.param_groups for p in group.get("params", [])}
return [(name, p) for name, p in model.named_parameters() if p in opt_params]
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
strategy_registry.register(
cls.strategy_name,
cls,
description=f"{cls.__class__.__name__}",
)
[docs] def teardown(self) -> None:
super().teardown()
# teardown may be called before `_exit_stack` is set
if self._exit_stack:
self._exit_stack.__exit__(None, None, None)
self._exit_stack = None
# Make sure all workers have finished training before returning to the user
self.join()
if self.root_device.type == "cuda":
# GPU teardown
self.lightning_module.cpu()
# clean up memory
torch.cuda.empty_cache()
