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