Source code for pytorch_lightning.strategies.bagua
# 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.
import logging
import os
from typing import Any, Dict, List, Optional, Union
import torch
from lightning_utilities.core.imports import module_available
from torch import Tensor
from torch.nn import Module
import pytorch_lightning as pl
from lightning_lite.plugins import CheckpointIO, ClusterEnvironment
from lightning_lite.utilities.optimizer import _optimizers_to_device
from lightning_lite.utilities.seed import reset_seed
from lightning_lite.utilities.types import ReduceOp
from pytorch_lightning.overrides.base import _LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.ddp import DDPStrategy
from pytorch_lightning.strategies.strategy import TBroadcast
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.exceptions import MisconfigurationException
_BAGUA_AVAILABLE = module_available("bagua.torch_api")
if _BAGUA_AVAILABLE:
import bagua.torch_api as bagua
from bagua.torch_api.algorithms import Algorithm
from bagua.torch_api.algorithms.q_adam import QAdamOptimizer
from bagua.torch_api.communication import allreduce_inplace, barrier, broadcast_object, is_initialized
from bagua.torch_api.communication import ReduceOp as BaguaReduceOp
from bagua.torch_api.data_parallel.distributed import DistributedDataParallel_V1_9_0 as BaguaDistributedDataParallel
# Convert a reduce op to its equivalent `bagua.torch_api.ReduceOp`
_bagua_reduce_ops = {
ReduceOp.SUM: BaguaReduceOp.SUM,
ReduceOp.PRODUCT: BaguaReduceOp.PRODUCT,
ReduceOp.MIN: BaguaReduceOp.MIN,
ReduceOp.MAX: BaguaReduceOp.MAX,
ReduceOp.BAND: BaguaReduceOp.BAND,
ReduceOp.BOR: BaguaReduceOp.BOR,
ReduceOp.BXOR: BaguaReduceOp.BXOR,
"avg": BaguaReduceOp.AVG,
"mean": BaguaReduceOp.AVG,
"sum": BaguaReduceOp.SUM,
}
else:
_bagua_reduce_ops = {}
log = logging.getLogger(__name__)
class LightningBaguaModule(_LightningModuleWrapperBase):
def __init__(
self,
forward_module: Optional[Union["pl.LightningModule", _LightningPrecisionModuleWrapperBase]] = None,
pl_module: Optional[Union["pl.LightningModule", _LightningPrecisionModuleWrapperBase]] = None,
) -> None:
self._validate_init_arguments(pl_module, forward_module)
forward_module = pl_module or forward_module
super().__init__(forward_module=forward_module)
# Bagua use `bagua_module_name` to distinguish different modules
self._bagua_module_name = f"{forward_module.__class__.__name__}{id(forward_module)}"
def forward(self, *inputs: Any, **kwargs: Any) -> Any:
pl_module = self.lightning_module
trainer = pl_module._trainer
if trainer is not None:
if trainer.training:
output = self._forward_module.training_step(*inputs, **kwargs)
# In manual_optimization, we need to prevent DDP reducer as
# it is done manually in `LightningModule.manual_backward`
# `require_backward_grad_sync` will be reset in the
# ddp_strategy `post_training_step` hook
if not pl_module.automatic_optimization:
# Using bagua strategy, the model is redefined in model.inner
# and cannot be accessed directly. We need this to make manual
# backward work.
trainer.model.inner.require_backward_grad_sync = False # type: ignore[union-attr]
return output
else:
return super().forward(*inputs, **kwargs)
return self._forward_module(*inputs, **kwargs)
[docs]class BaguaStrategy(DDPStrategy):
strategy_name = "bagua"
def __init__(
self,
algorithm: str = "gradient_allreduce",
flatten: bool = True,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
**bagua_kwargs: Union[Any, Dict[str, Any]],
):
"""Strategy for training using the `Bagua <https://github.com/BaguaSys/bagua>`_ library, with advanced
distributed training algorithms and system optimizations.
This strategy requires the `bagua` package to be installed. See
`installation guide <https://tutorials.baguasys.com/installation>`_ for more information.
The :class:`BaguaStrategy` is only supported on GPU and on Linux systems.
Arguments:
algorithm: Distributed algorithm used to do the actual communication and update. Built-in algorithms
include "gradient_allreduce", "bytegrad", "decentralized", "low_precision_decentralized", "qadam" and
"async".
flatten: Whether to flatten the Bagua communication buckets. The flatten operation will reset data
pointer of bucket tensors so that they can use faster code paths.
bagua_kwargs: Additional keyword arguments that will be passed to initialize the Bagua algorithm. More
details on keyword arguments accepted for each algorithm can be found in the
`documentation <https://bagua.readthedocs.io/en/latest/autoapi/bagua/torch_api/algorithms/index.html>`_.
"""
if not _BAGUA_AVAILABLE:
raise MisconfigurationException(
"To use the `BaguaStrategy`, you must have `Bagua` installed. Use `pip install bagua` to install it."
)
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=cluster_environment,
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
)
self._bagua_algorithm = algorithm
self._bagua_flatten = flatten
self._bagua_kwargs = bagua_kwargs
def setup_distributed(self) -> None:
reset_seed()
# determine which process we are and world size
self.set_world_ranks()
self._init_bagua_distributed()
def _init_bagua_distributed(self) -> None:
self._set_node_environment_variables()
log.info(
"Initializing Bagua Distributed: "
f"GLOBAL_RANK: {self.global_rank}, "
f"MEMBER: {self.global_rank + 1}/{self.world_size}"
)
# need to set device first before initialize Bagua distributed environment
# Note: setup_environment calls super().setup_distributed after calling init_distributed()
torch.cuda.set_device(self.local_rank)
if not is_initialized():
bagua.init_process_group()
def _set_node_environment_variables(self) -> None:
"""Set the environment variables as required by the :func:`bagua.init_process_group` call.
This enables the use of other cluster environments which don't set these exact variables, e.g., Bagua can be
launched with ``torch.distributed.run``.
"""
os.environ["MASTER_ADDR"] = self.cluster_environment.main_address # type: ignore[union-attr]
os.environ["MASTER_PORT"] = str(self.cluster_environment.main_port) # type: ignore[union-attr]
os.environ["RANK"] = str(self.global_rank)
os.environ["NODE_RANK"] = str(self.node_rank)
os.environ["WORLD_SIZE"] = str(self.world_size)
os.environ["LOCAL_RANK"] = str(self.local_rank)
[docs] def setup(self, trainer: "pl.Trainer") -> None:
self._rank_0_will_call_children_scripts = self.broadcast(self._rank_0_will_call_children_scripts)
if self._should_run_deadlock_detection():
self._share_information_to_prevent_deadlock()
assert self.accelerator is not None
self.accelerator.setup(trainer)
# move the model to the correct device
self.model_to_device()
trainer_fn = trainer.state.fn
if trainer_fn == TrainerFn.FITTING:
if self._layer_sync and self.model:
self.model = self._layer_sync.apply(self.model)
self.setup_precision_plugin()
if trainer_fn == TrainerFn.FITTING:
# set up optimizers after the module has been moved to the device
# but before the module has been wrapped
self.setup_optimizers(trainer)
_optimizers_to_device(self.optimizers, self.root_device)
# skip wrapping the model if we are not fitting as no gradients need to be exchanged
self._configure_bagua_model(trainer)
def _check_qadam_optimizer(self) -> None:
has_qadam_optimizer = any([isinstance(opt, QAdamOptimizer) for opt in self.optimizers])
if not has_qadam_optimizer or len(self.optimizers) > 1 or len(self.lr_scheduler_configs) > 1:
raise MisconfigurationException("Bagua QAdam can only accept one QAdamOptimizer and one LR Scheduler.")
self._bagua_kwargs["q_adam_optimizer"] = self.optimizers[0]
def _configure_bagua_model(self, trainer: "pl.Trainer") -> None:
model = LightningBaguaModule(self.model) # type: ignore[arg-type]
self.model = self._setup_model(model)
# start the background communication for async algorithm
if trainer.training and self._bagua_algorithm == "async":
self.model.bagua_algorithm.resume(self.model) # type: ignore
def _setup_model(self, model: Module) -> "BaguaDistributedDataParallel":
"""Wraps the model into a Bagua distributed module."""
if self._bagua_algorithm == "qadam":
self._check_qadam_optimizer()
algorithm = Algorithm.init(self._bagua_algorithm, **self._bagua_kwargs)
return BaguaDistributedDataParallel(
module=model,
optimizers=self.optimizers,
algorithm=algorithm,
gradient_as_bucket_view=self._bagua_flatten,
)
@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:
# abort the background communication for async algorithm
assert self.lightning_module is not None
if self.lightning_module.trainer.training and self._bagua_algorithm == "async":
self.model.bagua_algorithm.abort(self.model) # type: ignore
if isinstance(self.model, BaguaDistributedDataParallel):
self.model = self.lightning_module
super().teardown()
[docs] def barrier(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]
if is_initialized():
barrier()
[docs] def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
return broadcast_object(obj, src)
def post_training_step(self) -> None:
assert self.lightning_module is not None
# Using bagua strategy, the model is redefined in model.inner
# and cannot be accessed directly. We need to redefine the
# post_training_step function to make manual backward work.
if not self.lightning_module.automatic_optimization:
self.model.inner.require_backward_grad_sync = True # type: ignore[union-attr]
[docs] def reduce(
self, tensor: Tensor, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = "mean"
) -> Tensor:
"""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.
Can also be a string 'sum' or ReduceOp.
Return:
The reduced value, except when the input was not a tensor the output remains is unchanged.
"""
if not isinstance(tensor, Tensor):
return tensor
if group is not None:
raise ValueError("`Bagua` does not support allreduce using a subcommunicator at this time. Unset `group`.")
if reduce_op is None:
op = BaguaReduceOp.AVG
else:
op = _bagua_reduce_ops.get(reduce_op, None)
if op is None:
raise ValueError(f"Unrecognized `reduce_op` for `BaguaStrategy`: {reduce_op}")
allreduce_inplace(tensor, op=op)
return tensor
