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import datetime
import pathlib
from typing import Optional
import torch
from diffusers.utils import is_accelerate_available
from ..logging import get_logger
from ..utils import get_device_info
from .base import BaseParallelBackend
from .utils import apply_ddp_accelerate
if not is_accelerate_available():
raise ImportError(
"Please install the accelerate package using `pip install accelerate` to use the AccelerateParallelBackend."
)
from accelerate import Accelerator
from accelerate.data_loader import DataLoader
from accelerate.utils import (
DataLoaderConfiguration,
DistributedDataParallelKwargs,
InitProcessGroupKwargs,
ProjectConfiguration,
)
logger = get_logger()
_device_type, _device_module = get_device_info()
class AccelerateParallelBackend(BaseParallelBackend):
def __init__(
self,
world_size: int,
pp_degree: int = 1,
dp_degree: int = 1,
dp_shards: int = -1,
cp_degree: int = 1,
tp_degree: int = 1,
backend: str = "nccl",
timeout: int = 180,
logging_dir: Optional[str] = None,
output_dir: Optional[str] = None,
gradient_accumulation_steps: Optional[int] = None,
) -> None:
super().__init__()
self._world_size = world_size
self._pp_degree = pp_degree
self._dp_degree = dp_degree
self._dp_shards = dp_shards
self._cp_degree = cp_degree
self._tp_degree = tp_degree
self._output_dir = pathlib.Path(output_dir) if output_dir is not None else None
self._logging_dir = (
self._output_dir / logging_dir if output_dir is not None and logging_dir is not None else None
)
self._backend = backend
self._timeout = timeout
self._gradient_accumulation_steps = gradient_accumulation_steps
if pp_degree > 1 or dp_shards > 1 or cp_degree > 1 or tp_degree > 1:
raise ValueError(
"AccelerateParallelBackend does not support anything but Distributed Data Parallelism at the moment."
)
if dp_degree != world_size:
raise ValueError("Data parallel degree must be equal to world size.")
self._accelerator: Accelerator = None
self._mesh: torch.distributed.DeviceMesh = None
def apply_ddp(self, model: torch.nn.Module, *args, **kwargs) -> torch.nn.Module:
project_config = None
ddp_kwargs = None
init_process_group_kwargs = None
if self._accelerator is None:
project_config = ProjectConfiguration(project_dir=self._output_dir, logging_dir=self._logging_dir)
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=False)
dataloader_config = DataLoaderConfiguration(
split_batches=False, dispatch_batches=False, use_stateful_dataloader=True
)
init_process_group_kwargs = InitProcessGroupKwargs(
backend=self._backend, timeout=datetime.timedelta(seconds=self._timeout)
)
self._accelerator, model = apply_ddp_accelerate(
model,
project_config,
ddp_kwargs,
init_process_group_kwargs,
dataloader_config,
self._gradient_accumulation_steps,
accelerator=self._accelerator,
)
logger.debug("Applied AccelerateParallel::apply_ddp to model.")
return model
def prepare_dataset(self, dataset: torch.utils.data.IterableDataset) -> torch.utils.data.IterableDataset:
logger.debug("AccelerateParallelBackend::prepare_dataset completed!")
return dataset
def prepare_dataloader(
self,
dataset: torch.utils.data.IterableDataset,
batch_size: int = 1,
num_workers: int = 0,
pin_memory: bool = False,
) -> DataLoader:
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory
)
dataloader = self._accelerator.prepare_data_loader(dataloader)
logger.debug("AccelerateParallelBackend::prepare_dataloader completed!")
return dataloader
def prepare_optimizer(self, optimizer, lr_scheduler):
optimizer = self._accelerator.prepare_optimizer(optimizer)
lr_scheduler = self._accelerator.prepare_scheduler(lr_scheduler)
return optimizer, lr_scheduler
def get_mesh(self, name: Optional[str] = None) -> torch.distributed.DeviceMesh:
def _get_mesh():
if name is None:
return self._mesh
try:
return self._mesh[name]
except (KeyError, RuntimeError):
return self._mesh
if self._mesh is not None:
return _get_mesh()
mesh_list = [("dp_replicate", self._dp_degree), ("dp_shard", self._dp_shards)]
mesh_list = [(name, degree) for name, degree in mesh_list if degree > 1]
names = [x[0] for x in mesh_list]
degrees = [x[1] for x in mesh_list]
mesh = torch.distributed.device_mesh.init_device_mesh(_device_type, mesh_shape=degrees, mesh_dim_names=names)
dp_mesh_names, dp_cp_mesh_names, dp_shard_cp_mesh_names = [], [], []
if self.data_replication_enabled:
dp_mesh_names.append("dp_replicate")
dp_cp_mesh_names.append("dp_replicate")
if self.data_sharding_enabled:
dp_mesh_names.append("dp_shard")
dp_cp_mesh_names.append("dp_shard")
dp_shard_cp_mesh_names.append("dp_shard")
if self.context_parallel_enabled:
dp_cp_mesh_names.append("cp")
dp_shard_cp_mesh_names.append("cp")
if len(dp_mesh_names) > 0:
mesh[tuple(dp_mesh_names)]._flatten(mesh_dim_name="dp")
if len(dp_cp_mesh_names) > 0:
mesh[tuple(dp_cp_mesh_names)]._flatten(mesh_dim_name="dp_cp")
if len(dp_shard_cp_mesh_names) > 0:
mesh[tuple(dp_shard_cp_mesh_names)]._flatten(mesh_dim_name="dp_shard_cp")
logger.debug(f"Device mesh: {mesh}")
self._mesh = mesh
return _get_mesh()
@property
def world_size(self):
return self._accelerator.num_processes
@property
def rank(self):
return self._accelerator.process_index
@property
def local_rank(self):
return self._accelerator.local_process_index
@property
def is_main_process(self):
r"""Returns `True` if the current process is the main process on the master node."""
return self._accelerator.is_main_process
@property
def is_local_main_process(self):
r"""Returns `True` if the current process is the main process on local node."""
return self._accelerator.is_local_main_process
@property
def device(self):
return self._accelerator.device
def wait_for_everyone(self):
self._accelerator.wait_for_everyone()
def destroy(self):
self._accelerator.end_training()
@property
def pipeline_parallel_enabled(self):
return self._pp_degree > 1
@property
def data_parallel_enabled(self):
return self._dp_degree > 1 or self._dp_shards > 1
@property
def data_replication_enabled(self):
return self._dp_degree > 1
@property
def data_sharding_enabled(self):
return self._dp_shards > 1
@property
def context_parallel_enabled(self):
return self._cp_degree > 1
@property
def tensor_parallel_enabled(self):
return self._tp_degree > 1
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