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import datetime
import os
import pathlib
from typing import Optional
import datasets.distributed
import torch
from ..data import DPDataLoader
from ..logging import get_logger
from ..utils import get_device_info
from .base import BaseParallelBackend
from .utils import apply_ddp_ptd
_device_type, _device_module = get_device_info()
logger = get_logger()
class PytorchDTensorParallelBackend(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
for degree in [pp_degree, dp_degree, dp_shards, cp_degree, tp_degree]:
if degree < 1:
raise ValueError(f"Parallel degree must be at least 1, got {degree}.")
if dp_shards * pp_degree * dp_degree * cp_degree * tp_degree != world_size:
raise ValueError(
f"World size {world_size} must be divisible by the product of all parallel degrees and data parallel shards."
)
torch.distributed.init_process_group(backend=self._backend, timeout=datetime.timedelta(seconds=self._timeout))
_device_module.set_device(self.local_rank)
logger.info(
f"Initialized parallel state with:\n"
f" - World size: {world_size}\n"
f" - Pipeline parallel degree: {pp_degree}\n"
f" - Data parallel degree: {dp_degree}\n"
f" - Context parallel degree: {cp_degree}\n"
f" - Tensor parallel degree: {tp_degree}\n"
f" - Data parallel shards: {dp_shards}\n"
)
self._mesh: torch.distributed.DeviceMesh = None
def apply_ddp(
self, model: torch.nn.Module, device_mesh: Optional[torch.distributed.DeviceMesh] = None
) -> torch.nn.Module:
if device_mesh is None:
device_mesh = self.get_mesh()
apply_ddp_ptd(model, device_mesh)
logger.debug("Applied PytorchDTensorParallel::apply_ddp to model.")
return model
def prepare_dataset(self, dataset: torch.utils.data.IterableDataset) -> torch.utils.data.IterableDataset:
dp_mesh = self.get_mesh("dp_replicate")
if dp_mesh is None:
dp_mesh = self.get_mesh()
if self.world_size > 1:
dp_local_rank, dp_world_size = dp_mesh.get_local_rank(), dp_mesh.size()
else:
dp_local_rank, dp_world_size = 0, 1
dataset._data = datasets.distributed.split_dataset_by_node(dataset._data, dp_local_rank, dp_world_size)
logger.debug("PytorchDTensorParallelBackend::prepare_dataset completed!")
return dataset
def prepare_dataloader(
self, dataset: torch.utils.data.IterableDataset, batch_size: int, num_workers: int, pin_memory: bool
) -> DPDataLoader:
dp_mesh = self.get_mesh("dp_replicate")
if dp_mesh is None:
dp_mesh = self.get_mesh()
if self.world_size > 1:
dp_local_rank = dp_mesh.get_local_rank()
else:
dp_local_rank = 0
dataloader = DPDataLoader(dp_local_rank, dataset, batch_size=batch_size, num_workers=num_workers)
logger.debug("PytorchDTensorParallelBackend::prepare_dataloader completed!")
return dataloader
def prepare_optimizer(self, optimizer, lr_scheduler):
logger.debug("PytorchDTensorParallelBackend::prepare_optimizer completed!")
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):
if self._mesh.ndim == 0:
return None
return self._mesh
if self._mesh is not None:
return _get_mesh()
mesh_list = [
("pp", self._pp_degree),
("dp_replicate", self._dp_degree),
("dp_shard", self._dp_shards),
("cp", self._cp_degree),
("tp", self._tp_degree),
]
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 torch.distributed.get_world_size()
@property
def rank(self):
return torch.distributed.get_rank()
@property
def local_rank(self):
return int(os.environ.get("LOCAL_RANK", 0))
@property
def is_main_process(self):
r"""Returns `True` if the current process is the main process on the master node."""
return self.rank == 0
@property
def is_local_main_process(self):
r"""Returns `True` if the current process is the main process on local node."""
return self.local_rank == 0
@property
def device(self):
return torch.device(_device_type, self.local_rank)
def wait_for_everyone(self):
return torch.distributed.barrier()
# @contextmanager
# def main_process_first(self):
# if self.is_main_process:
# yield
# self.wait_for_everyone()
# else:
# self.wait_for_everyone()
# yield
def destroy(self):
if self.is_main_process:
self.tracker.finish()
return torch.distributed.destroy_process_group()
@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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