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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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")

    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,
    _bagua_reduce_ops = {}

log = logging.getLogger(__name__)

class LightningBaguaModule(_LightningModuleWrapperBase):
    def __init__(
        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
        # 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:
                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
                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 <>`_ library, with advanced distributed training algorithms and system optimizations. This strategy requires the `bagua` package to be installed. See `installation guide <>`_ 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 <>`_. """ 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() "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 ````. """ 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 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 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

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