Source code for lightning_fabric.plugins.collectives.torch_collective
import datetime
import os
from typing import Any, List, Optional, Union
import torch
import torch.distributed as dist
from torch import Tensor
from typing_extensions import Self
from lightning_fabric.plugins.collectives.collective import Collective
from lightning_fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_13
from lightning_fabric.utilities.types import CollectibleGroup, RedOpType, ReduceOp
if dist.is_available():
from torch.distributed.constants import default_pg_timeout
else:
default_pg_timeout = datetime.timedelta(seconds=1800)
[docs]class TorchCollective(Collective):
manages_default_group = False
def __init__(self) -> None:
if not dist.is_available():
raise RuntimeError("Torch distributed is not available.")
super().__init__()
@property
def group(self) -> CollectibleGroup:
if self._group is None:
self._group = dist.GroupMember.WORLD
return super().group
@property
def rank(self) -> int:
# local rank
return dist.get_rank(self.group) # type: ignore[arg-type]
@property
def world_size(self) -> int:
return dist.get_world_size(self.group) # type: ignore[arg-type]
def broadcast(self, tensor: Tensor, src: int) -> Tensor:
dist.broadcast(tensor, src, group=self.group)
return tensor
def all_reduce(self, tensor: Tensor, op: Union[str, ReduceOp, RedOpType] = "sum") -> Tensor:
op = self._convert_to_native_op(op)
dist.all_reduce(tensor, op=op, group=self.group)
return tensor
def reduce(self, tensor: Tensor, dst: int, op: Union[str, ReduceOp, RedOpType] = "sum") -> Tensor:
op = self._convert_to_native_op(op)
dist.reduce(tensor, dst, op=op, group=self.group)
return tensor
def all_gather(self, tensor_list: List[Tensor], tensor: Tensor) -> List[Tensor]:
dist.all_gather(tensor_list, tensor, group=self.group)
return tensor_list
def gather(self, tensor: Tensor, gather_list: List[Tensor], dst: int = 0) -> List[Tensor]:
dist.gather(tensor, gather_list, dst, group=self.group)
return gather_list
def scatter(self, tensor: Tensor, scatter_list: List[Tensor], src: int = 0) -> Tensor:
dist.scatter(tensor, scatter_list, src, group=self.group)
return tensor
def reduce_scatter(
self, output: Tensor, input_list: List[Tensor], op: Union[str, ReduceOp, RedOpType] = "sum"
) -> Tensor:
op = self._convert_to_native_op(op)
dist.reduce_scatter(output, input_list, op=op, group=self.group)
return output
def all_to_all(self, output_tensor_list: List[Tensor], input_tensor_list: List[Tensor]) -> List[Tensor]:
dist.all_to_all(output_tensor_list, input_tensor_list, group=self.group)
return output_tensor_list
def send(self, tensor: Tensor, dst: int, tag: int = 0) -> None:
dist.send(tensor, dst, tag=tag, group=self.group) # type: ignore[arg-type]
def recv(self, tensor: Tensor, src: Optional[int] = None, tag: int = 0) -> Tensor:
dist.recv(tensor, src, tag=tag, group=self.group) # type: ignore[arg-type]
return tensor
def all_gather_object(self, object_list: List[Any], obj: Any) -> List[Any]:
dist.all_gather_object(object_list, obj, group=self.group)
return object_list
def broadcast_object_list(
self, object_list: List[Any], src: int, device: Optional[torch.device] = None
) -> List[Any]:
dist.broadcast_object_list(object_list, src, group=self.group, device=device)
return object_list
def gather_object(self, obj: Any, object_gather_list: List[Any], dst: int = 0) -> List[Any]:
dist.gather_object(obj, object_gather_list, dst, group=self.group)
return object_gather_list
def scatter_object_list(
self, scatter_object_output_list: List[Any], scatter_object_input_list: List[Any], src: int = 0
) -> List[Any]:
dist.scatter_object_list(scatter_object_output_list, scatter_object_input_list, src, group=self.group)
return scatter_object_output_list
def barrier(self, device_ids: Optional[List[int]] = None) -> None:
if self.group == dist.GroupMember.NON_GROUP_MEMBER:
return
dist.barrier(group=self.group, device_ids=device_ids)
def monitored_barrier(self, timeout: Optional[datetime.timedelta] = None, wait_all_ranks: bool = False) -> None:
dist.monitored_barrier(group=self.group, timeout=timeout, wait_all_ranks=wait_all_ranks)
def setup(self, main_address: Optional[str] = None, main_port: Optional[str] = None, **kwargs: Any) -> Self:
if self.is_initialized():
return self
# maybe set addr
set_addr = False
addr_key = "MASTER_ADDR"
if main_address is not None and addr_key not in os.environ:
os.environ[addr_key] = main_address
set_addr = True
# maybe set port
set_port = False
port_key = "MASTER_PORT"
if main_port is not None and port_key not in os.environ:
os.environ[port_key] = str(main_port)
set_port = True
# this will `init_group`
super().setup(**kwargs)
# set as a class attribute so any instance can know whether we initialized the default process group
TorchCollective.manages_default_group = True
# cleanup
if set_addr:
os.environ.pop("MASTER_ADDR", None)
if set_port:
os.environ.pop("MASTER_PORT", None)
return self
def teardown(self) -> Self:
group_member = self.group != dist.GroupMember.NON_GROUP_MEMBER
super().teardown() # will destroy its own group
# try to destroy the default group. this should only be done by a group member to avoid race conditions,
# and only if the class is managing it
if (
group_member
and TorchCollective.manages_default_group
and dist.GroupMember.WORLD is not None # not destroyed already
and len(dist.distributed_c10d._pg_map) == 1 # only the default group is left
):
self.destroy_group(dist.GroupMember.WORLD)
TorchCollective.manages_default_group = False
elif TorchCollective.manages_default_group and dist.GroupMember.WORLD is None:
TorchCollective.manages_default_group = False
return self
@classmethod
def is_available(cls) -> bool:
return dist.is_available()
@classmethod
def is_initialized(cls) -> bool:
return dist.is_initialized()
@classmethod
def init_group(cls, **kwargs: Any) -> None:
dist.init_process_group(**kwargs)
@classmethod
def new_group(cls, **kwargs: Any) -> CollectibleGroup:
return dist.new_group(**kwargs)
@classmethod
def destroy_group(cls, group: CollectibleGroup) -> None:
# can be called by all processes in the default group, group will be `object()` if they are not part of the
# current group
if group in dist.distributed_c10d._pg_map:
dist.destroy_process_group(group) # type: ignore[arg-type]
@classmethod
def _convert_to_native_op(cls, op: Union[str, ReduceOp, RedOpType]) -> Union[ReduceOp, RedOpType]:
# in 1.13, `ReduceOp` has become an empty shell for `RedOpType`, the latter being the actually returned class.
# for example, `ReduceOp.SUM` returns a `RedOpType.SUM`. the only exception is `RedOpType.PREMUL_SUM` where
# `ReduceOp` is still the desired class, but it's created via a special `_make_nccl_premul_sum` function
if isinstance(op, ReduceOp) or _TORCH_GREATER_EQUAL_1_13 and isinstance(op, RedOpType):
return op
if not isinstance(op, str):
raise ValueError(f"Unsupported op {op!r} of type {type(op).__name__}")
op = op.upper()
# `ReduceOp` should contain `RedOpType`'s members
value = getattr(ReduceOp, op, None)
if value is None:
raise ValueError(f"op {op!r} is not a member of `ReduceOp`")
return value
