Source code for torch.distributed.rpc.backend_registry
import collections
import enum
from typing import Dict, List, Set, Tuple
import torch
import torch.distributed as dist
from . import api
from . import constants as rpc_constants
BackendValue = collections.namedtuple(
"BackendValue", ["construct_rpc_backend_options_handler", "init_backend_handler"]
)
def _backend_type_repr(self):
return "BackendType." + self.name
_backend_type_doc = """
An enum class of available backends.
PyTorch ships with a builtin ``BackendType.TENSORPIPE`` backend.
Additional ones can be registered using the
:func:`~torch.distributed.rpc.backend_registry.register_backend` function.
"""
# Create an enum type, `BackendType`, with empty members.
# Can't handle Function Enum API (mypy bug #9079)
BackendType = enum.Enum(value="BackendType", names=dict()) # type: ignore[misc]
# Unable to assign a function a method (mypy bug #2427)
BackendType.__repr__ = _backend_type_repr # type: ignore[assignment]
BackendType.__doc__ = _backend_type_doc
def backend_registered(backend_name):
"""
Checks if backend_name is registered as an RPC backend.
Args:
backend_name (str): string to identify the RPC backend.
Returns:
True if the backend has been registered with ``register_backend``, else
False.
"""
return backend_name in BackendType.__members__.keys()
def register_backend(
backend_name, construct_rpc_backend_options_handler, init_backend_handler
):
"""Registers a new RPC backend.
Args:
backend_name (str): backend string to identify the handler.
construct_rpc_backend_options_handler (function):
Handler that is invoked when
rpc_backend.construct_rpc_backend_options(**dict) is called.
init_backend_handler (function): Handler that is invoked when the
`_init_rpc_backend()` function is called with a backend.
This returns the agent.
"""
global BackendType
if backend_registered(backend_name):
raise RuntimeError("RPC backend {}: already registered".format(backend_name))
# Create a new enum type, `BackendType`, with extended members.
existing_enum_dict = {member.name: member.value for member in BackendType}
extended_enum_dict = dict(
{
backend_name: BackendValue(
construct_rpc_backend_options_handler=construct_rpc_backend_options_handler,
init_backend_handler=init_backend_handler,
)
},
**existing_enum_dict
)
# Can't handle Function Enum API (mypy bug #9079)
BackendType = enum.Enum(value="BackendType", names=extended_enum_dict) # type: ignore[misc]
# Unable to assign a function a method (mypy bug #2427)
BackendType.__repr__ = _backend_type_repr # type: ignore[assignment]
BackendType.__doc__ = _backend_type_doc
return BackendType[backend_name]
def construct_rpc_backend_options(
backend,
rpc_timeout=rpc_constants.DEFAULT_RPC_TIMEOUT_SEC,
init_method=rpc_constants.DEFAULT_INIT_METHOD,
**kwargs
):
return backend.value.construct_rpc_backend_options_handler(
rpc_timeout, init_method, **kwargs
)
def init_backend(backend, *args, **kwargs):
return backend.value.init_backend_handler(*args, **kwargs)
def _init_process_group(store, rank, world_size):
# Initialize ProcessGroup.
process_group_timeout = rpc_constants.DEFAULT_PROCESS_GROUP_TIMEOUT
# We're using a bunch of private APIs here since `new_group` requires the
# default group to be initialized.
group = dist.ProcessGroupGloo(store, rank, world_size, process_group_timeout)
assert group is not None, "Failed to initialize default ProcessGroup."
if (rank != -1) and (rank != group.rank()):
raise RuntimeError(
"rank argument {} doesn't match pg rank {}".format(rank, group.rank())
)
if (world_size != -1) and (world_size != group.size()):
raise RuntimeError(
"world_size argument {} doesn't match pg size {}".format(
world_size, group.size()
)
)
return group
def _tensorpipe_construct_rpc_backend_options_handler(
rpc_timeout,
init_method,
num_worker_threads=rpc_constants.DEFAULT_NUM_WORKER_THREADS,
_transports=None,
_channels=None,
**kwargs
):
from . import TensorPipeRpcBackendOptions
return TensorPipeRpcBackendOptions(
rpc_timeout=rpc_timeout,
init_method=init_method,
num_worker_threads=num_worker_threads,
_transports=_transports,
_channels=_channels,
)
def _tensorpipe_validate_devices(devices, device_count):
return all(
d.type == "cpu" or (d.type == "cuda" and 0 <= d.index < device_count)
for d in devices
)
# detect if any worker has invalid device_map configurations, and return
# reverse device maps
def _tensorpipe_exchange_and_check_all_device_maps(
my_name, my_device_count, my_device_maps, my_devices, group
):
gathered: List[Tuple[
str, int, Dict[str, Dict[torch.device, torch.device]], List[torch.device]
]] = [("", 0, {}, []) for _ in range(group.size())]
dist.all_gather_object(
gathered, (my_name, my_device_count, my_device_maps, my_devices), group
)
all_names = [name for name, _, _, _ in gathered]
all_device_counts = {name: count for name, count, _, _ in gathered}
all_device_maps = {name: map_ for name, _, map_, _ in gathered}
all_devices = {name: devices for name, _, _, devices in gathered}
for node in all_names:
devices = all_devices[node]
if len(set(devices)) != len(devices):
raise ValueError(
f"Node {node} has duplicated devices\n"
f"devices = {devices}"
)
if not _tensorpipe_validate_devices(devices, all_device_counts[node]):
raise ValueError(
f"Node {node} has devices with invalid indices\n"
f"devices = {devices}\n"
f"device count = {all_device_counts[node]}"
)
for source_node in all_names:
if not set(all_device_maps[source_node].keys()).issubset(all_names):
raise ValueError(
f"Node {source_node} has invalid target node names in its device maps\n"
f"device maps = {all_device_maps[source_node].keys()}\n"
f"node names = {all_names}"
)
for target_node, map_ in all_device_maps[source_node].items():
if len(set(map_.values())) != len(map_):
raise ValueError(
f"Node {source_node} has duplicated target devices "
f"in its device map for {target_node}\n"
f"device map = {map_}"
)
if all_devices[source_node]:
if not set(map_.keys()).issubset(all_devices[source_node]):
raise ValueError(
f"Node {source_node} has unexpected source devices "
f"in its device map for {target_node}\n"
f"device map = {map_}\n"
f"devices = {all_devices[source_node]}"
)
elif not _tensorpipe_validate_devices(
map_.keys(), all_device_counts[source_node]
):
raise ValueError(
f"Node {source_node} has source devices with invalid indices "
f"in its device map for {target_node}\n"
f"device map = {map_}\n"
f"device count = {all_device_counts[source_node]}"
)
if all_devices[target_node]:
if not set(map_.values()).issubset(all_devices[target_node]):
raise ValueError(
f"Node {source_node} has unexpected target devices "
f"in its device map for {target_node}\n"
f"device map = {map_}\n"
f"devices = {all_devices[target_node]}"
)
elif not _tensorpipe_validate_devices(
map_.values(), all_device_counts[target_node]
):
raise ValueError(
f"Node {source_node} has target devices with invalid indices "
f"in its device map for {target_node}\n"
f"device map = {map_}\n"
f"device count = {all_device_counts[target_node]}"
)
# passed all checked, construct reverse mapping for return values
reverse_device_maps: Dict[str, Dict[torch.device, torch.device]] = {}
for node in all_names:
if my_name in all_device_maps[node]:
reverse_device_maps[node] = {
v: k for k, v in all_device_maps[node][my_name].items()
}
if not my_devices:
devices_set: Set[torch.device] = set()
for _, map_ in my_device_maps.items():
devices_set.update(map_.keys())
for _, map_ in reverse_device_maps.items():
devices_set.update(map_.keys())
devices_set.discard(torch.device("cpu"))
my_devices = list(devices_set)
my_devices = sorted(my_devices, key=lambda d: d.index)
return reverse_device_maps, my_devices
def _tensorpipe_init_backend_handler(store, name, rank, world_size, rpc_backend_options):
from . import TensorPipeRpcBackendOptions
from . import TensorPipeAgent
if not isinstance(store, dist.Store):
raise TypeError("`store` must be a c10d::Store. {}".format(store))
if not isinstance(
rpc_backend_options, TensorPipeRpcBackendOptions
):
raise TypeError(
"`rpc_backend_options` must be a `TensorPipeRpcBackendOptions`. {}".format(
rpc_backend_options
)
)
# The agent's join method is required to behave like a barrier and perform
# collective operations, for which it relies on a process group, instead of
# re-implementing this on top of RPCs.
group = _init_process_group(store, rank, world_size)
if torch.cuda.is_available():
# It's necessary to initialize PyTorch CUDA states here (e.g.,
# CUDACachingAllocator). If this is missing, we could hit errors like
# "allocator not initialized", because other processes might send
# CUDA-related RPC request to this process before user code in this
# process initializes its PyTorch CUDA states.
torch.cuda.init()
device_count = torch.cuda.device_count()
else:
device_count = 0
reverse_device_maps, devices = _tensorpipe_exchange_and_check_all_device_maps(
name,
device_count,
rpc_backend_options.device_maps,
rpc_backend_options.devices,
group,
)
# TODO: add try-except and destroy _agent in all processes if any fails.
agent = TensorPipeAgent(
store,
name,
rank,
world_size,
rpc_backend_options,
reverse_device_maps,
devices,
)
api._init_rpc_states(agent)
# Run one dummy round of RPC to initialize channels/transports. Without
# this, it's easy to hit timeout in rpc.shutdown() if there is no other RPC
# on that process before rpc.shutdown(), as the agent initialization can
# take longer than 5s.
api._all_gather(None, timeout=rpc_backend_options.rpc_timeout)
# Need a barrier here to make sure no peers leave before the rank0 finishes
# _all_gather
group.barrier().wait()
return agent
register_backend(
"TENSORPIPE",
_tensorpipe_construct_rpc_backend_options_handler,
_tensorpipe_init_backend_handler,
)
