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