173 lines
6.9 KiB
Python
173 lines
6.9 KiB
Python
from typing import Dict, List, Optional, Union
|
|
|
|
import torch
|
|
from torch._C._distributed_rpc import _TensorPipeRpcBackendOptionsBase
|
|
from . import constants as rpc_contants
|
|
|
|
|
|
DeviceType = Union[int, str, torch.device]
|
|
|
|
__all__ = ["TensorPipeRpcBackendOptions"]
|
|
|
|
def _to_device(device: DeviceType) -> torch.device:
|
|
device = torch.device(device)
|
|
if device.type != "cuda":
|
|
raise ValueError(
|
|
"`set_devices` expect a list of CUDA devices, but got "
|
|
f"device type {device.type}."
|
|
)
|
|
return device
|
|
|
|
|
|
def _to_device_map(
|
|
device_map: Dict[DeviceType, DeviceType]
|
|
) -> Dict[torch.device, torch.device]:
|
|
full_device_map: Dict[torch.device, torch.device] = {}
|
|
reverse_map: Dict[torch.device, torch.device] = {}
|
|
for k, v in device_map.items():
|
|
k, v = torch.device(k), torch.device(v)
|
|
if v in reverse_map:
|
|
raise ValueError(
|
|
"`device_map` only supports 1-to-1 mapping, "
|
|
f"trying to map {k} and {reverse_map[v]} to {v}"
|
|
)
|
|
full_device_map[k] = v
|
|
reverse_map[v] = k
|
|
return full_device_map
|
|
|
|
|
|
def _to_device_list(devices: List[DeviceType]) -> List[torch.device]:
|
|
return list(map(_to_device, devices))
|
|
|
|
|
|
class TensorPipeRpcBackendOptions(_TensorPipeRpcBackendOptionsBase):
|
|
r"""
|
|
The backend options for
|
|
:class:`~torch.distributed.rpc.TensorPipeAgent`, derived from
|
|
:class:`~torch.distributed.rpc.RpcBackendOptions`.
|
|
|
|
Args:
|
|
num_worker_threads (int, optional): The number of threads in the
|
|
thread-pool used by
|
|
:class:`~torch.distributed.rpc.TensorPipeAgent` to execute
|
|
requests (default: 16).
|
|
rpc_timeout (float, optional): The default timeout, in seconds,
|
|
for RPC requests (default: 60 seconds). If the RPC has not
|
|
completed in this timeframe, an exception indicating so will
|
|
be raised. Callers can override this timeout for individual
|
|
RPCs in :meth:`~torch.distributed.rpc.rpc_sync` and
|
|
:meth:`~torch.distributed.rpc.rpc_async` if necessary.
|
|
init_method (str, optional): The URL to initialize the distributed
|
|
store used for rendezvous. It takes any value accepted for the
|
|
same argument of :meth:`~torch.distributed.init_process_group`
|
|
(default: ``env://``).
|
|
device_maps (Dict[str, Dict], optional): Device placement mappings from
|
|
this worker to the callee. Key is the callee worker name and value
|
|
the dictionary (``Dict`` of ``int``, ``str``, or ``torch.device``)
|
|
that maps this worker's devices to the callee worker's devices.
|
|
(default: ``None``)
|
|
devices (List[int, str, or ``torch.device``], optional): all local
|
|
CUDA devices used by RPC agent. By Default, it will be initialized
|
|
to all local devices from its own ``device_maps`` and corresponding
|
|
devices from its peers' ``device_maps``. When processing CUDA RPC
|
|
requests, the agent will properly synchronize CUDA streams for
|
|
all devices in this ``List``.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
num_worker_threads: int = rpc_contants.DEFAULT_NUM_WORKER_THREADS,
|
|
rpc_timeout: float = rpc_contants.DEFAULT_RPC_TIMEOUT_SEC,
|
|
init_method: str = rpc_contants.DEFAULT_INIT_METHOD,
|
|
device_maps: Optional[Dict[str, Dict[DeviceType, DeviceType]]] = None,
|
|
devices: Optional[List[DeviceType]] = None,
|
|
_transports: Optional[List] = None,
|
|
_channels: Optional[List] = None,
|
|
):
|
|
full_device_maps = (
|
|
{}
|
|
if device_maps is None
|
|
else {k: _to_device_map(v) for k, v in device_maps.items()}
|
|
)
|
|
full_device_list = [] if devices is None else _to_device_list(devices)
|
|
super().__init__(
|
|
num_worker_threads,
|
|
_transports,
|
|
_channels,
|
|
rpc_timeout,
|
|
init_method,
|
|
full_device_maps,
|
|
full_device_list,
|
|
)
|
|
|
|
def set_device_map(self, to: str, device_map: Dict[DeviceType, DeviceType]):
|
|
r"""
|
|
Set device mapping between each RPC caller and callee pair. This
|
|
function can be called multiple times to incrementally add
|
|
device placement configurations.
|
|
|
|
Args:
|
|
to (str): Callee name.
|
|
device_map (Dict of int, str, or torch.device): Device placement
|
|
mappings from this worker to the callee. This map must be
|
|
invertible.
|
|
|
|
Example:
|
|
>>> # xdoctest: +SKIP("distributed")
|
|
>>> # both workers
|
|
>>> def add(x, y):
|
|
>>> print(x) # tensor([1., 1.], device='cuda:1')
|
|
>>> return x + y, (x + y).to(2)
|
|
>>>
|
|
>>> # on worker 0
|
|
>>> options = TensorPipeRpcBackendOptions(
|
|
>>> num_worker_threads=8,
|
|
>>> device_maps={"worker1": {0: 1}}
|
|
>>> # maps worker0's cuda:0 to worker1's cuda:1
|
|
>>> )
|
|
>>> options.set_device_map("worker1", {1: 2})
|
|
>>> # maps worker0's cuda:1 to worker1's cuda:2
|
|
>>>
|
|
>>> rpc.init_rpc(
|
|
>>> "worker0",
|
|
>>> rank=0,
|
|
>>> world_size=2,
|
|
>>> backend=rpc.BackendType.TENSORPIPE,
|
|
>>> rpc_backend_options=options
|
|
>>> )
|
|
>>>
|
|
>>> x = torch.ones(2)
|
|
>>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
|
|
>>> # The first argument will be moved to cuda:1 on worker1. When
|
|
>>> # sending the return value back, it will follow the invert of
|
|
>>> # the device map, and hence will be moved back to cuda:0 and
|
|
>>> # cuda:1 on worker0
|
|
>>> print(rets[0]) # tensor([2., 2.], device='cuda:0')
|
|
>>> print(rets[1]) # tensor([2., 2.], device='cuda:1')
|
|
"""
|
|
full_device_map = _to_device_map(device_map)
|
|
curr_device_maps = super().device_maps
|
|
|
|
if to in curr_device_maps:
|
|
for k, v in full_device_map.items():
|
|
if k in curr_device_maps[to] and v != curr_device_maps[to][k]:
|
|
raise ValueError(
|
|
"`set_device_map` only supports 1-to-1 mapping, trying"
|
|
f" to map {k} to {v} and {curr_device_maps[to][k]}"
|
|
)
|
|
|
|
super()._set_device_map(to, full_device_map)
|
|
|
|
def set_devices(self, devices: List[DeviceType]):
|
|
r"""
|
|
Set local devices used by the TensorPipe RPC agent. When processing
|
|
CUDA RPC requests, the TensorPipe RPC agent will properly synchronize
|
|
CUDA streams for all devices in this ``List``.
|
|
|
|
Args:
|
|
devices (List of int, str, or torch.device): local devices used by
|
|
the TensorPipe RPC agent.
|
|
"""
|
|
self.devices = _to_device_list(devices)
|