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pytorch/torch/distributed/distributed_c10d.py
Mingzhe Li 66f9b1de1b [NCCL] enable p2p tests (#47797) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47797

NCCL p2p tests had hang issues before, the reason is that there were some unexpected context switches. For example, process 1 which is supposed to only use GPU1 could use GPU0 as a result of missing explicitly setting device.
ghstack-source-id: 116461969

Test Plan: waitforsandcastle

Reviewed By: jiayisuse

Differential Revision: D24863808

fbshipit-source-id: 92bd3a4874be8334210c7c8ee6363648893c963e
2020-11-12 10:44:50 -08:00

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import pickle
import torch
import warnings
import contextlib
from torch._six import string_classes
from datetime import timedelta
# This module is wildcard imported from torch.distributed.
# TODO: specify __all__
from .constants import default_pg_timeout
from .rendezvous import rendezvous, register_rendezvous_handler # noqa: F401
from torch._C._distributed_c10d import (
AllreduceOptions,
AllreduceCoalescedOptions,
AllToAllOptions,
BroadcastOptions,
GatherOptions,
ReduceOptions,
ReduceScatterOptions,
ScatterOptions,
ReduceOp,
PrefixStore,
)
_MPI_AVAILABLE = True
_NCCL_AVAILABLE = True
_GLOO_AVAILABLE = True
try:
from torch._C._distributed_c10d import ProcessGroupMPI
except ImportError:
_MPI_AVAILABLE = False
try:
from torch._C._distributed_c10d import ProcessGroupNCCL
except ImportError:
_NCCL_AVAILABLE = False
try:
from torch._C._distributed_c10d import ProcessGroupGloo
except ImportError:
_GLOO_AVAILABLE = False
# Some reduce ops are not supported by complex numbers and will result in an error.
# We currently provide complex support to the distributed API by viewing
# complex tensors as real (torch.view_as_real), meaning that calling
# these unsupported ops will return garbage values rather than error out.
# (e.g. max(2+3i, 3+2i) = 3+3i)
# We'd like calls to unsupported ops to error out accordingly,
# rather than returning garbage values.
def supports_complex(reduceOp: ReduceOp) -> bool:
denyList = [ReduceOp.MAX, ReduceOp.MIN, ReduceOp.PRODUCT,
ReduceOp.BAND, ReduceOp.BOR, ReduceOp.BXOR]
return reduceOp not in denyList
class Backend(object):
"""
An enum-like class of available backends: GLOO, NCCL, MPI, and other registered
backends.
The values of this class are lowercase strings, e.g., ``"gloo"``. They can
be accessed as attributes, e.g., ``Backend.NCCL``.
This class can be directly called to parse the string, e.g.,
``Backend(backend_str)`` will check if ``backend_str`` is valid, and
return the parsed lowercase string if so. It also accepts uppercase strings,
e.g., ``Backend("GLOO")`` returns ``"gloo"``.
.. note:: The entry ``Backend.UNDEFINED`` is present but only used as
initial value of some fields. Users should neither use it directly
nor assume its existence.
"""
UNDEFINED = "undefined"
GLOO = "gloo"
NCCL = "nccl"
MPI = "mpi"
TCP = "tcp"
def __new__(cls, name):
if not isinstance(name, string_classes):
raise ValueError("Backend name must be a string, but got: {}".format(name))
value = getattr(Backend, name.upper(), Backend.UNDEFINED)
if value == Backend.TCP:
raise ValueError("TCP backend has been deprecated. Please use "
"Gloo or MPI backend for collective operations "
"on CPU tensors.")
elif value == Backend.UNDEFINED:
raise ValueError("Invalid backend: '{}'".format(name))
elif value != Backend.GLOO and value != Backend.NCCL and value != Backend.MPI:
value = name
return value
@classmethod
def register_backend(cls, name, func):
"""
Registers a new backend.
This class method is used by 3rd party cpp extension to register new backend.
Arguments:
name (str): Backend name matching with the one in `init_process_group()`.
func (function): Function handler that instantiates the backend.
The function should be implemented in the backend cpp extension
and takes four arguments, including prefix_store, rank,
world_size, and timeout.
.. note:: This support of 3rd party backend is experimental and subject to change.
"""
setattr(Backend, name.upper(), func)
# `_backend`, `dist_backend`, and `reduce_op` are here to maintain backward
# compatibility with pre-c10d distributed package.
# TODO: remove them when users are ready to take a hard dependency on PyTorch 1.
_backend = Backend.UNDEFINED
dist_backend = Backend
class reduce_op(object):
r"""
Deprecated enum-like class for reduction operations: ``SUM``, ``PRODUCT``,
``MIN``, and ``MAX``.
:class:`~torch.distributed.ReduceOp` is recommended to use instead.
"""
def __init__(self):
# __members__ is a dict storing key-value pairs for enum classes
for k, v in ReduceOp.__members__.items():
setattr(self, k, v)
self.__members__ = ReduceOp.__members__
def __getattribute__(self, key):
warnings.warn("torch.distributed.reduce_op is deprecated, please use "
"torch.distributed.ReduceOp instead")
return object.__getattribute__(self, key)
reduce_op = reduce_op()
class group(object):
WORLD = object()
class GroupMember(object):
# Alias to group.WORLD for backward compatibility
WORLD = group.WORLD
NON_GROUP_MEMBER = object()
# Cached process groups
# For NCCL and GLOO pg, it is a map from ProcessGroup to (Backend, Store)
# For MPI pg, it is a map from ProcessGroup to (Backend, None)
_pg_map = {}
# Process group's names, map from ProcessGroup to str
_pg_names = {}
# Process group's global rank to local rank mapping
_pg_group_ranks = {}
# Default process group state
_default_pg = None
_default_pg_init_method = None
# Process group count for default naming
_group_count = 0
def _rank_not_in_group(group):
"""
Helper that checks if the current process's rank is not in a given group.
"""
if group == GroupMember.WORLD:
return False
return group == GroupMember.NON_GROUP_MEMBER
def _get_group_rank(group, rank):
"""
Helper that gets a given group's local rank in the group from a given global
rank.
"""
if group is GroupMember.WORLD:
raise RuntimeError("group.WORLD does not have local rank to global "
"rank mapping")
if group not in _pg_group_ranks:
raise RuntimeError("The given group does not exist")
try:
group_rank = _pg_group_ranks[group][rank]
except KeyError:
raise RuntimeError(f"The global rank {rank} is not part of the group {group}") from None
return group_rank
def _get_global_rank(group, group_rank):
"""
Helper that gets a given group's global rank from a given local rank in the
group.
"""
if group is GroupMember.WORLD:
raise RuntimeError("group.WORLD does not have local rank to global "
"rank mapping")
group_rank_map = _pg_group_ranks[group]
for rank, grp_rank in group_rank_map.items():
if grp_rank == group_rank:
return rank
raise RuntimeError("The group rank is not part of the group")
def _check_default_pg():
"""
Helper that checks if the default ProcessGroup has been initialized, with
assertion.
"""
assert _default_pg is not None, \
"Default process group is not initialized"
def _get_group_size(group):
"""
Helper that gets a given group's world size.
"""
if group is GroupMember.WORLD:
_check_default_pg()
return _default_pg.size()
if group not in _pg_group_ranks:
raise RuntimeError("The given group does not exist")
return len(_pg_group_ranks[group])
def _check_single_tensor(param, param_name):
"""
Helper to check that the parameter ``param_name`` is a single tensor.
"""
if not isinstance(param, torch.Tensor):
raise RuntimeError("Invalid function argument. Expected parameter `{}` "
"to be of type torch.Tensor.".format(param_name))
def _check_tensor_list(param, param_name):
"""
Helper to check that the parameter ``param_name`` is a list of tensors.
"""
if not isinstance(param, list) or \
not all(isinstance(p, torch.Tensor) for p in param):
raise RuntimeError("Invalid function argument. Expected parameter `{}` "
"to be of type List[torch.Tensor].".format(param_name))
def _check_op(op):
"""
Helper to check that the ``op`` is either isend or irecv.
"""
if op not in [isend, irecv]:
raise RuntimeError("Invalid ``op``. Expected ``op`` "
"to be of type ``torch.distributed.isend`` or "
"``torch.distributed.irecv``.")
def _check_p2p_op_list(p2p_op_list):
"""
Helper to check that the ``p2p_op_list`` is a list of P2POp instances and
all ops use the same backend.
"""
if not isinstance(p2p_op_list, list) or \
not all(isinstance(p2p_op, P2POp) for p2p_op in p2p_op_list):
raise RuntimeError("Invalid ``p2p_op_list``. Each op is expected to "
"to be of type ``torch.distributed.P2POp``.")
backend = get_backend(p2p_op_list[0].group)
if not all(backend == get_backend(p2p_op.group) for p2p_op in p2p_op_list):
raise RuntimeError("All groups need to use the same backend.")
def is_mpi_available():
"""
Checks if the MPI backend is available.
"""
return _MPI_AVAILABLE
def is_nccl_available():
"""
Checks if the NCCL backend is available.
"""
return _NCCL_AVAILABLE
def is_gloo_available():
"""
Checks if the Gloo backend is available.
"""
return _GLOO_AVAILABLE
def is_initialized():
"""
Checking if the default process group has been initialized
"""
return _default_pg is not None
def _get_default_group():
"""
Getting the default process group created by init_process_group
"""
if not is_initialized():
raise RuntimeError("Default process group has not been initialized, "
"please make sure to call init_process_group.")
return _default_pg
def _get_default_store():
"""
Getting the default store created by init_process_group
"""
if not is_initialized():
raise RuntimeError("Default process group has not been initialized, "
"please make sure to call init_process_group.")
_, default_store = _pg_map[_default_pg]
return default_store
def get_backend(group=group.WORLD):
"""
Returns the backend of the given process group.
Arguments:
group (ProcessGroup, optional): The process group to work on. The
default is the general main process group. If another specific group
is specified, the calling process must be part of :attr:`group`.
Returns:
The backend of the given process group as a lower case string.
"""
_check_default_pg()
if group == GroupMember.WORLD:
pg = _default_pg
else:
pg = group
if _rank_not_in_group(pg):
raise RuntimeError("Invalid process group specified")
return _pg_map.get(pg, None)[0]
def init_process_group(backend,
init_method=None,
timeout=default_pg_timeout,
world_size=-1,
rank=-1,
store=None,
group_name=''):
"""
Initializes the default distributed process group, and this will also
initialize the distributed package.
There are 2 main ways to initialize a process group:
1. Specify ``store``, ``rank``, and ``world_size`` explicitly.
2. Specify ``init_method`` (a URL string) which indicates where/how
to discover peers. Optionally specify ``rank`` and ``world_size``,
or encode all required parameters in the URL and omit them.
If neither is specified, ``init_method`` is assumed to be "env://".
Arguments:
backend (str or Backend): The backend to use. Depending on
build-time configurations, valid values include ``mpi``, ``gloo``,
and ``nccl``. This field should be given as a lowercase string
(e.g., ``"gloo"``), which can also be accessed via
:class:`Backend` attributes (e.g., ``Backend.GLOO``). If using
multiple processes per machine with ``nccl`` backend, each process
must have exclusive access to every GPU it uses, as sharing GPUs
between processes can result in deadlocks.
init_method (str, optional): URL specifying how to initialize the
process group. Default is "env://" if no
``init_method`` or ``store`` is specified.
Mutually exclusive with ``store``.
world_size (int, optional): Number of processes participating in
the job. Required if ``store`` is specified.
rank (int, optional): Rank of the current process.
Required if ``store`` is specified.
store(Store, optional): Key/value store accessible to all workers, used
to exchange connection/address information.
Mutually exclusive with ``init_method``.
timeout (timedelta, optional): Timeout for operations executed against
the process group. Default value equals 30 minutes.
This is applicable for the ``gloo`` backend. For ``nccl``, this is
applicable only if the environment variable ``NCCL_BLOCKING_WAIT``
or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When
``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the
process will block and wait for collectives to complete before
throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set,
this is the duration after which collectives will be aborted
asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT``
will provide errors to the user which can be caught and handled,
but due to its blocking nature, it has a performance overhead. On
the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little
performance overhead, but crashes the process on errors. This is
done since CUDA execution is async and it is no longer safe to
continue executing user code since failed async NCCL operations
might result in subsequent CUDA operations running on corrupted
data. Only one of these two environment variables should be set.
group_name (str, optional, deprecated): Group name.
To enable ``backend == Backend.MPI``, PyTorch needs to be built from source
on a system that supports MPI.
"""
global _pg_group_ranks
global _backend
global _default_pg
global _default_pg_init_method
if not isinstance(timeout, timedelta):
raise RuntimeError("Expected timeout argument to be of type"
"datetime.timedelta")
if _default_pg is not None:
raise RuntimeError("trying to initialize the default process group "
"twice!")
assert (store is None) or (init_method is None), \
"Cannot specify both init_method and store."
if store is not None:
assert world_size > 0, 'world_size must be positive if using store'
assert rank >= 0, 'rank must be non-negative if using store'
elif init_method is None:
init_method = "env://"
backend = Backend(backend)
if backend == Backend.MPI:
if world_size != -1 or rank != -1:
warnings.warn(
"For MPI backend, world_size ({}) and rank ({}) "
"are ignored since they are assigned by the "
"MPI runtime.".format(world_size, rank))
_default_pg = _new_process_group_helper(
-1,
-1,
[],
Backend.MPI,
None,
group_name=group_name,
timeout=timeout)
else:
# backward compatible API
if store is None:
rendezvous_iterator = rendezvous(
init_method, rank, world_size, timeout=timeout
)
store, rank, world_size = next(rendezvous_iterator)
store.set_timeout(timeout)
_default_pg = _new_process_group_helper(
world_size,
rank,
[],
backend,
store,
group_name=group_name,
timeout=timeout)
_pg_group_ranks[_default_pg] = {i: i for i in range(_default_pg.size())}
_backend = _pg_map[_default_pg][0]
_default_pg_init_method = init_method
# barrier at the end to ensure that once we return from this method, all
# process groups including global variables are updated correctly on all
# ranks.
barrier()
def _new_process_group_helper(world_size,
rank,
group_ranks,
backend,
store,
group_name=None,
timeout=default_pg_timeout):
"""
Create a new distributed process group.
This function must be called by ALL processes in the global group, even if
the calling process is not part of the newly created group. In that case,
this function returns GroupMember.NON_GROUP_MEMBER.
This function is called with ``group_ranks == []`` for the default group.
"""
global _pg_map
global _group_count
global _pg_names
if not group_name:
group_name = str(_group_count)
_group_count += 1
if group_name in _pg_names.values():
raise RuntimeError("The specified group name has already been "
"created, please use a different group name")
if not isinstance(timeout, timedelta):
raise RuntimeError("Expected timeout argument to be of type"
"datetime.timedelta")
# The list of group ranks is empty if we're creating the default group.
is_default_group = (len(group_ranks) == 0)
backend = Backend(backend)
if backend == Backend.MPI:
if not is_mpi_available():
raise RuntimeError(
"Distributed package doesn't have MPI built in."
" MPI is only included if you build PyTorch from"
" source on a host that has MPI installed.")
pg = ProcessGroupMPI.create(group_ranks)
if not pg:
return GroupMember.NON_GROUP_MEMBER
_pg_map[pg] = (Backend.MPI, None)
_pg_names[pg] = group_name
else:
# If this is a subgroup (which means group_ranks is specified),
# we check if the current process is a member of the new group.
if not is_default_group:
global_rank = _default_pg.rank()
if global_rank not in group_ranks:
return GroupMember.NON_GROUP_MEMBER
# Use the group name as prefix in the default store, such that
# a single store can be reused by multiple groups.
prefix_store = PrefixStore(group_name, store)
if backend == Backend.GLOO:
pg = ProcessGroupGloo(
prefix_store,
rank,
world_size,
timeout=timeout)
_pg_map[pg] = (Backend.GLOO, store)
_pg_names[pg] = group_name
elif backend == Backend.NCCL:
if not is_nccl_available():
raise RuntimeError("Distributed package doesn't have NCCL "
"built in")
pg = ProcessGroupNCCL(
prefix_store,
rank,
world_size,
timeout)
_pg_map[pg] = (Backend.NCCL, store)
_pg_names[pg] = group_name
else:
pg = getattr(Backend, backend.upper())(
prefix_store,
rank,
world_size,
timeout)
_pg_map[pg] = (backend, store)
_pg_names[pg] = group_name
return pg
def destroy_process_group(group=group.WORLD):
"""
Destroy a given process group, and deinitialize the distributed package
Arguments:
group (ProcessGroup, optional): The process group to be destroyed, if
group.WORLD is given, all process
groups including the default one will
be destroyed.
"""
global _pg_map
global _pg_names
global _pg_group_ranks
global _default_pg
global _default_pg_init_method
global _group_count
if group == GroupMember.NON_GROUP_MEMBER:
return
if group == GroupMember.WORLD:
pg = _default_pg
else:
pg = group
if _pg_map.get(pg, None) is None:
raise RuntimeError("Invalid process group specified")
if group == GroupMember.WORLD:
_default_pg = None
_default_pg_init_method = None
_pg_map.clear()
_pg_names.clear()
_pg_group_ranks.clear()
# when process group doesn't have an explicit name (only WORLD (default)
# process group can have an explicit name), we use global _group_counter
# to generate the name. We need to reset the counter on destruction to
# allow consistent value to be generated when we re-create process
# groups after some trainers recover from failure
#
# We only reset this when WORLD is being destroyed because if this
# process group is in good state, we aren't dealing with failures.
_group_count = 0
else:
del _pg_map[pg]
del _pg_names[pg]
del _pg_group_ranks[pg]
def get_rank(group=group.WORLD):
"""
Returns the rank of current process group
Rank is a unique identifier assigned to each process within a distributed
process group. They are always consecutive integers ranging from 0 to
``world_size``.
Arguments:
group (ProcessGroup, optional): The process group to work on
Returns:
The rank of the process group
-1, if not part of the group
"""
if _rank_not_in_group(group):
return -1
_check_default_pg()
if group == GroupMember.WORLD:
return _default_pg.rank()
return _get_group_rank(group, _default_pg.rank())
def get_world_size(group=group.WORLD):
"""
Returns the number of processes in the current process group
Arguments:
group (ProcessGroup, optional): The process group to work on
Returns:
The world size of the process group
-1, if not part of the group
"""
if _rank_not_in_group(group):
return -1
return _get_group_size(group)
def isend(tensor,
dst,
group=group.WORLD,
tag=0):
"""
Sends a tensor asynchronously.
Arguments:
tensor (Tensor): Tensor to send.
dst (int): Destination rank.
group (ProcessGroup, optional): The process group to work on
tag (int, optional): Tag to match send with remote recv
Returns:
A distributed request object.
None, if not part of the group
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return
if group == GroupMember.WORLD:
_check_default_pg()
return _default_pg.send([tensor], dst, tag)
else:
group_dst_rank = _get_group_rank(group, dst)
return group.send([tensor], group_dst_rank, tag)
def irecv(tensor,
src,
group=group.WORLD,
tag=0):
"""
Receives a tensor asynchronously.
Arguments:
tensor (Tensor): Tensor to fill with received data.
src (int): Source rank.
group (ProcessGroup, optional): The process group to work on
tag (int, optional): Tag to match recv with remote send
Returns:
A distributed request object.
None, if not part of the group
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return
if group == GroupMember.WORLD:
_check_default_pg()
return _default_pg.recv([tensor], src, tag)
else:
group_src_rank = _get_group_rank(group, src)
return group.recv([tensor], group_src_rank, tag)
def send(tensor,
dst,
group=group.WORLD,
tag=0):
"""
Sends a tensor synchronously.
Arguments:
tensor (Tensor): Tensor to send.
dst (int): Destination rank.
group (ProcessGroup, optional): The process group to work on
tag (int, optional): Tag to match send with remote recv
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return
if group == GroupMember.WORLD:
_check_default_pg()
_default_pg.send([tensor], dst, tag).wait()
else:
group_dst_rank = _get_group_rank(group, dst)
group.send([tensor], group_dst_rank, tag).wait()
def recv(tensor,
src=None,
group=group.WORLD,
tag=0):
"""
Receives a tensor synchronously.
Arguments:
tensor (Tensor): Tensor to fill with received data.
src (int, optional): Source rank. Will receive from any
process if unspecified.
group (ProcessGroup, optional): The process group to work on
tag (int, optional): Tag to match recv with remote send
Returns:
Sender rank
-1, if not part of the group
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return -1
if group == GroupMember.WORLD:
_check_default_pg()
pg = _default_pg
else:
pg = group
if src is None:
work = pg.recv_anysource([tensor], tag)
work.wait()
src_rank = work._source_rank()
if group == GroupMember.WORLD:
return src_rank
else:
return _get_global_rank(pg, src_rank)
else:
if group == GroupMember.WORLD:
pg.recv([tensor], src, tag).wait()
else:
group_src_rank = _get_group_rank(pg, src)
pg.recv([tensor], group_src_rank, tag).wait()
return src
class P2POp(object):
"""
A class to build point-to-point operations for ``batch_isend_irecv``.
This class builds the type of P2P operation, communication buffer, peer rank,
Process Group group, and tag. Instances of this class will be passed to
``batch_isend_irecv`` for point-to-point communications.
Arguments:
op (callable): A function to send data to or receive data from a peer process.
The type of ``op`` is either ``torch.distributed.isend`` or
``torch.distributed.irecv``.
tensor (Tensor): Tensor to send or receive.
peer (int): Destination or source rank.
group (ProcessGroup, optional): The process group to work on.
tag (int, optional): Tag to match send with recv.
"""
def __init__(self, op, tensor, peer, group=group.WORLD, tag=0):
self.op = op
self.tensor = tensor
self.peer = peer
self.group = group
self.tag = tag
def __new__(cls, op, tensor, peer, group=group.WORLD, tag=0):
_check_op(op)
_check_single_tensor(tensor, "tensor")
return object.__new__(cls)
@contextlib.contextmanager
def _batch_p2p_manager(backend):
if backend == Backend.NCCL:
ProcessGroupNCCL._group_start()
try:
yield
finally:
if backend == Backend.NCCL:
ProcessGroupNCCL._group_end()
def batch_isend_irecv(p2p_op_list):
"""
Send or Receive a batch of tensors asynchronously and return a list of requests.
Process each of the operations in p2p_op_list and return the corresponding
requests. NCCL and Gloo backend are currently supported.
Arguments:
p2p_op_list: A list of point-to-point operations(type of each operator is
``torch.distributed.P2POp``). The order of the isend/irecv in the list
matters and it needs to match with corresponding isend/irecv on the
remote end.
Returns:
A list of distributed request objects returned by calling the corresponding
op in the op_list.
Examples:
>>> send_tensor = torch.arange(2) + 2 * rank
>>> recv_tensor = torch.randn(2)
>>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1)%world_size)
>>> recv_op = dist.P2POp(dist.irecv, recv_tensor, (rank + 1)%world_size)
>>> reqs = batch_isend_irecv([send_op, recv_op])
>>> for req in reqs:
>>> req.wait()
>>> recv_tensor
tensor([2, 3]) # Rank 0
tensor([0, 1]) # Rank 1
.. note:: Note that when this API is used with the NCCL PG backend, users must set
the current GPU device with `torch.cuda.set_device`, otherwise it will
lead to unexpected hang issues.
"""
_check_p2p_op_list(p2p_op_list)
backend = get_backend(p2p_op_list[0].group)
reqs = []
with _batch_p2p_manager(backend):
for p2p_op in p2p_op_list:
op = p2p_op.op
tensor = p2p_op.tensor
peer = p2p_op.peer
curr_group = p2p_op.group
tag = p2p_op.tag
ret = op(tensor, peer, curr_group, tag)
if ret is not None:
reqs.append(ret)
return reqs
def broadcast_multigpu(tensor_list,
src,
group=group.WORLD,
async_op=False,
src_tensor=0):
"""
Broadcasts the tensor to the whole group with multiple GPU tensors
per node.
``tensor`` must have the same number of elements in all the GPUs from
all processes participating in the collective. each tensor in the list must
be on a different GPU
Only nccl and gloo backend are currently supported
tensors should only be GPU tensors
Arguments:
tensor_list (List[Tensor]): Tensors that participate in the collective
operation. If ``src`` is the rank, then the specified ``src_tensor``
element of ``tensor_list`` (``tensor_list[src_tensor]``) will be
broadcast to all other tensors (on different GPUs) in the src process
and all tensors in ``tensor_list`` of other non-src processes.
You also need to make sure that ``len(tensor_list)`` is the same
for all the distributed processes calling this function.
src (int): Source rank.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
src_tensor (int, optional): Source tensor rank within ``tensor_list``
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
if _rank_not_in_group(group):
return
opts = BroadcastOptions()
opts.rootRank = src
opts.rootTensor = src_tensor
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.broadcast(tensor_list, opts)
else:
group_src_rank = _get_group_rank(group, src)
opts.rootRank = group_src_rank
work = group.broadcast(tensor_list, opts)
if async_op:
return work
else:
work.wait()
def broadcast(tensor,
src,
group=group.WORLD,
async_op=False):
"""
Broadcasts the tensor to the whole group.
``tensor`` must have the same number of elements in all processes
participating in the collective.
Arguments:
tensor (Tensor): Data to be sent if ``src`` is the rank of current
process, and tensor to be used to save received data otherwise.
src (int): Source rank.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return
opts = BroadcastOptions()
opts.rootRank = src
opts.rootTensor = 0
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.broadcast([tensor], opts)
else:
group_src_rank = _get_group_rank(group, src)
opts.rootRank = group_src_rank
work = group.broadcast([tensor], opts)
if async_op:
return work
else:
work.wait()
def all_reduce_multigpu(tensor_list,
op=ReduceOp.SUM,
group=group.WORLD,
async_op=False):
r"""
Reduces the tensor data across all machines in such a way that all get
the final result. This function reduces a number of tensors on every node,
while each tensor resides on different GPUs.
Therefore, the input tensor in the tensor list needs to be GPU tensors.
Also, each tensor in the tensor list needs to reside on a different GPU.
After the call, all ``tensor`` in ``tensor_list`` is going to be bitwise
identical in all processes.
Complex tensors are supported.
Only nccl and gloo backend is currently supported
tensors should only be GPU tensors
Arguments:
tensor list (List[Tensor]): List of input and output tensors of
the collective. The function operates in-place and requires that
each tensor to be a GPU tensor on different GPUs.
You also need to make sure that ``len(tensor_list)`` is the same for
all the distributed processes calling this function.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
if _rank_not_in_group(group):
return
tensor_list = [t if not t.is_complex() else torch.view_as_real(t) for t in tensor_list]
opts = AllreduceOptions()
opts.reduceOp = op
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.allreduce(tensor_list, opts)
else:
work = group.allreduce(tensor_list, opts)
if async_op:
return work
else:
work.wait()
def all_reduce(tensor,
op=ReduceOp.SUM,
group=group.WORLD,
async_op=False):
"""
Reduces the tensor data across all machines in such a way that all get
the final result.
After the call ``tensor`` is going to be bitwise identical in all processes.
Complex tensors are supported.
Arguments:
tensor (Tensor): Input and output of the collective. The function
operates in-place.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
Examples:
>>> # All tensors below are of torch.int64 type.
>>> # We have 2 process groups, 2 ranks.
>>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
>>> tensor
tensor([1, 2]) # Rank 0
tensor([3, 4]) # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4, 6]) # Rank 0
tensor([4, 6]) # Rank 1
>>> # All tensors below are of torch.cfloat type.
>>> # We have 2 process groups, 2 ranks.
>>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j)
>>> tensor
tensor([1.+1.j, 2.+2.j]) # Rank 0
tensor([3.+3.j, 4.+4.j]) # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4.+4.j, 6.+6.j]) # Rank 0
tensor([4.+4.j, 6.+6.j]) # Rank 1
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return
if tensor.is_complex():
if not supports_complex(op):
raise RuntimeError(f"all_reduce does not support {op} on complex tensors")
tensor = torch.view_as_real(tensor)
opts = AllreduceOptions()
opts.reduceOp = op
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.allreduce([tensor], opts)
else:
work = group.allreduce([tensor], opts)
if async_op:
return work
else:
work.wait()
def all_reduce_coalesced(tensors,
op=ReduceOp.SUM,
group=group.WORLD,
async_op=False):
"""
WARNING: at this time individual shape checking is not implemented across nodes.
For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the allreduce
operation will proceed without complaint and return erroneous outputs. This lack
of shape checking results in significant performance improvements but users of this
function should take extra care to ensure that each node passes in tensors whose
shapes match across nodes.
Reduces each tensor in tensors (residing on the same device) across all machines
in such a way that all get the final result.
After the call each tensor in tensors is going to bitwise identical
in all processes.
Complex tensors are supported.
Arguments:
tensors (List[Tensor]): Input and output of the collective. The function
operates in-place.
op (Optional[ReduceOp]): One of the values from
``torch.distributed.ReduceOp`` enum. Specifies an operation used for
element-wise reductions.
group (Optional[ProcessGroup]): The process group to work on.
async_op (Optional[bool]): Whether this op should be an async op.
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group.
"""
_check_tensor_list(tensors, "tensor")
if _rank_not_in_group(group):
return
if any([t.is_complex() for t in tensors]) and not supports_complex(op):
raise RuntimeError(f"all_reduce does not support {op} on complex tensors")
tensors = [t if not t.is_complex() else torch.view_as_real(t) for t in tensors]
opts = AllreduceCoalescedOptions()
opts.reduceOp = op
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.allreduce_coalesced(tensors, opts)
else:
work = group.allreduce_coalesced(tensors, opts)
if async_op:
return work
else:
work.wait()
def reduce_multigpu(tensor_list,
dst,
op=ReduceOp.SUM,
group=group.WORLD,
async_op=False,
dst_tensor=0):
"""
Reduces the tensor data on multiple GPUs across all machines. Each tensor
in ``tensor_list`` should reside on a separate GPU
Only the GPU of ``tensor_list[dst_tensor]`` on the process with rank ``dst``
is going to receive the final result.
Only nccl backend is currently supported
tensors should only be GPU tensors
Arguments:
tensor_list (List[Tensor]): Input and output GPU tensors of the
collective. The function operates in-place.
You also need to make sure that ``len(tensor_list)`` is the same for
all the distributed processes calling this function.
dst (int): Destination rank
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
dst_tensor (int, optional): Destination tensor rank within
``tensor_list``
Returns:
Async work handle, if async_op is set to True.
None, otherwise
"""
if _rank_not_in_group(group):
return
opts = ReduceOptions()
opts.reduceOp = op
opts.rootRank = dst
opts.rootTensor = dst_tensor
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.reduce(tensor_list, opts)
else:
group_dst_rank = _get_group_rank(group, dst)
opts.rootRank = group_dst_rank
work = group.reduce(tensor_list, opts)
if async_op:
return work
else:
work.wait()
def reduce(tensor,
dst,
op=ReduceOp.SUM,
group=group.WORLD,
async_op=False):
"""
Reduces the tensor data across all machines.
Only the process with rank ``dst`` is going to receive the final result.
Arguments:
tensor (Tensor): Input and output of the collective. The function
operates in-place.
dst (int): Destination rank
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return
opts = ReduceOptions()
opts.reduceOp = op
opts.rootRank = dst
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.reduce([tensor], opts)
else:
group_dst_rank = _get_group_rank(group, dst)
opts.rootRank = group_dst_rank
work = group.reduce([tensor], opts)
if async_op:
return work
else:
work.wait()
def all_gather_multigpu(output_tensor_lists,
input_tensor_list,
group=group.WORLD,
async_op=False):
"""
Gathers tensors from the whole group in a list.
Each tensor in ``tensor_list`` should reside on a separate GPU
Only nccl backend is currently supported
tensors should only be GPU tensors
Complex tensors are supported.
Arguments:
output_tensor_lists (List[List[Tensor]]): Output lists. It should
contain correctly-sized tensors on each GPU to be used for output
of the collective, e.g. ``output_tensor_lists[i]`` contains the
all_gather result that resides on the GPU of
``input_tensor_list[i]``.
Note that each element of ``output_tensor_lists`` has the size of
``world_size * len(input_tensor_list)``, since the function all
gathers the result from every single GPU in the group. To interpret
each element of ``output_tensor_lists[i]``, note that
``input_tensor_list[j]`` of rank k will be appear in
``output_tensor_lists[i][k * world_size + j]``
Also note that ``len(output_tensor_lists)``, and the size of each
element in ``output_tensor_lists`` (each element is a list,
therefore ``len(output_tensor_lists[i])``) need to be the same
for all the distributed processes calling this function.
input_tensor_list (List[Tensor]): List of tensors(on different GPUs) to
be broadcast from current process.
Note that ``len(input_tensor_list)`` needs to be the same for
all the distributed processes calling this function.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
if _rank_not_in_group(group):
return
output_tensor_lists = [[t if not t.is_complex() else torch.view_as_real(t) for t in l] for l in output_tensor_lists]
input_tensor_list = [t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list]
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.allgather(output_tensor_lists, input_tensor_list)
else:
work = group.allgather(output_tensor_lists, input_tensor_list)
if async_op:
return work
else:
work.wait()
def _object_to_tensor(obj):
buffer = pickle.dumps(obj)
byte_storage = torch.ByteStorage.from_buffer(buffer)
byte_tensor = torch.ByteTensor(byte_storage)
local_size = torch.LongTensor([byte_tensor.numel()])
return byte_tensor, local_size
def _tensor_to_object(tensor, tensor_size):
buf = tensor.numpy().tobytes()[:tensor_size]
out = pickle.loads(buf)
return out
def all_gather_object(object_list, obj, group=group.WORLD):
"""
Gathers picklable objects from the whole group into a list. Similar to
:func:`all_gather`, but Python objects can be passed in. Note that the object
must be picklable in order to be gathered.
Arguments:
object_list (list[Any]): Output list. It should be correctly sized as the
size of the group for this collective and will contain the output.
object (Any): Pickable Python object to be broadcast from current process.
group (ProcessGroup, optional): The process group to work on. If None,
the default process group will be used.
Returns:
None. If the calling rank is part of this group, the output of the
collective will be populated into the input ``object_list``. If the
calling rank is not part of the group, the passed in ``object_list`` will
be unmodified.
.. note:: Note that this API differs slightly from the :func:`all_gather`
collective since it does not provide an ``async_op`` handle and thus
will be a blocking call.
.. note:: For NCCL-based processed groups, internal tensor representations
of objects must be moved to the GPU device before communication takes
place. In this case, the device used is given by
``torch.cuda.current_device()`` and it is the user's responsiblity to
ensure that this is set so that each rank has an individual GPU, via
``torch.cuda.set_device()``.
.. warning::
:func:`all_gather_object` uses ``pickle`` module implicitly, which is
known to be insecure. It is possible to construct malicious pickle data
which will execute arbitrary code during unpickling. Only call this
function with data you trust.
"""
if _rank_not_in_group(group):
return
input_tensor, local_size = _object_to_tensor(obj)
group_backend = get_backend(group)
is_nccl_backend = group_backend == Backend.NCCL
current_device = torch.device("cpu")
if is_nccl_backend:
# See note about using torch.cuda.current_device() here in docstring.
# We cannot simply use my_rank since rank == device is not necessarily
# true.
current_device = torch.cuda.current_device()
input_tensor = input_tensor.to(current_device)
local_size = local_size.to(current_device)
# Gather all local sizes. This is so that we can find the max size, and index
# until the correct size when deserializing the tensors.
group_size = get_world_size(group=group)
object_sizes_tensor = torch.zeros(group_size, dtype=int, device=current_device)
object_size_list = [
object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
]
# Allgather tensor sizes
all_gather(object_size_list, local_size, group=group)
max_object_size = max(object_size_list)
# Resize tensor to max size across all ranks.
input_tensor.resize_(max_object_size)
coalesced_output_tensor = torch.empty(
max_object_size * group_size, dtype=torch.uint8, device=current_device
)
# Output tensors are nonoverlapping views of coalesced_output_tensor
output_tensors = [
coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
for i in range(group_size)
]
all_gather(output_tensors, input_tensor, group=group)
# Deserialize outputs back to object.
for i, tensor in enumerate(output_tensors):
tensor = tensor.type(torch.ByteTensor)
tensor_size = object_size_list[i]
object_list[i] = _tensor_to_object(tensor, tensor_size)
def gather_object(obj, object_gather_list=None, dst=0, group=group.WORLD):
"""
Gathers picklable objects from the whole group in a single process.
Similar to :func:`gather`, but Python objects can be passed in. Note that the
object must be picklable in order to be gathered.
Arguments:
obj (Any): Input object. Must be picklable.
object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
should be correctly sized as the size of the group for this
collective and will contain the output. Must be ``None`` on non-dst
ranks. (default is ``None``)
dst (int, optional): Destination rank. (default is 0)
group: (ProcessGroup, optional): The process group to work on. If None,
the default process group will be used.
Returns:
None. On the ``dst`` rank, ``object_gather_list`` will contain the
output of the collective.
.. note:: Note that this API differs slightly from the gather collective
since it does not provide an async_op handle and thus will be a blocking
call.
.. note:: Note that this API is not supported when using the NCCL backend.
.. warning::
:func:`gather_object` uses ``pickle`` module implicitly, which is
known to be insecure. It is possible to construct malicious pickle data
which will execute arbitrary code during unpickling. Only call this
function with data you trust.
"""
if _rank_not_in_group(group):
return
# Ensure object_gather_list is specified appopriately.
my_rank = get_rank()
_validate_output_list_for_rank(my_rank, dst, object_gather_list)
input_tensor, local_size = _object_to_tensor(obj)
group_backend = get_backend(group)
current_device = torch.device("cpu")
is_nccl_backend = group_backend == Backend.NCCL
if is_nccl_backend:
current_device = torch.cuda.current_device()
input_tensor = input_tensor.to(current_device)
local_size = local_size.to(current_device)
# Gather all local sizes. This is so that we can find the max size, and index
# until the correct size when deserializing the tensors.
group_size = get_world_size(group=group)
object_sizes_tensor = torch.zeros(group_size, dtype=int, device=current_device)
object_size_list = [
object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
]
# Allgather tensor sizes. An all-gather is needed here despite this being a
# gather, since each rank needs to broadcast a tensor of the same (maximal)
# size.
all_gather(object_size_list, local_size, group=group)
max_object_size = max(object_size_list)
# Resize tensor to max size across all ranks.
input_tensor.resize_(max_object_size)
# Avoid populating output tensors if the result won't be gathered on this rank.
if my_rank == dst:
coalesced_output_tensor = torch.empty(
max_object_size * group_size, dtype=torch.uint8, device=current_device
)
# Output tensors are nonoverlapping views of coalesced_output_tensor
output_tensors = [
coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
for i in range(group_size)
]
# All ranks call gather with equal-sized tensors.
gather(
input_tensor,
gather_list=output_tensors if my_rank == dst else None,
dst=dst,
group=group,
)
if my_rank != dst:
return
for i, tensor in enumerate(output_tensors):
tensor = tensor.type(torch.ByteTensor)
tensor_size = object_size_list[i]
object_gather_list[i] = _tensor_to_object(tensor, tensor_size)
def broadcast_object_list(object_list, src, group=group.WORLD):
"""
Broadcasts picklable objects in ``object_list`` to the whole group. Similar
to :func:`broadcast`, but Python objects can be passed in.
Note that all objects in ``object_list`` must be picklable in order to be
broadcasted.
Arguments:
object_list (List[Any]): List of input objects to broadcast.
Each object must be picklable. Only objects on the ``src`` rank will
be broadcast, but each rank must provide lists of equal sizes.
src (int): Source rank from which to broadcast ``object_list``.
group: (ProcessGroup, optional): The process group to work on. If None,
the default process group will be used.
Returns:
``None``. If rank is part of the group, ``object_list`` will contain the
broadcasted objects from ``src`` rank.
.. note:: For NCCL-based processed groups, internal tensor representations
of objects must be moved to the GPU device before communication takes
place. In this case, the device used is given by
``torch.cuda.current_device()`` and it is the user's responsiblity to
ensure that this is set so that each rank has an individual GPU, via
``torch.cuda.set_device()``.
.. note:: Note that this API differs slightly from the :func:`all_gather`
collective since it does not provide an ``async_op`` handle and thus
will be a blocking call.
.. warning::
:func:`broadcast_object_list` uses ``pickle`` module implicitly, which
is known to be insecure. It is possible to construct malicious pickle
data which will execute arbitrary code during unpickling. Only call this
function with data you trust.
"""
if _rank_not_in_group(group):
return
my_rank = get_rank()
# Serialize object_list elements to tensors on src rank.
if my_rank == src:
tensor_list, size_list = zip(*[_object_to_tensor(obj) for obj in object_list])
object_sizes_tensor = torch.cat(size_list)
else:
object_sizes_tensor = torch.LongTensor(len(object_list))
group_backend = get_backend(group)
is_nccl_backend = group_backend == Backend.NCCL
current_device = torch.device("cpu")
if is_nccl_backend:
# See note about using torch.cuda.current_device() here in docstring.
# We cannot simply use my_rank since rank == device is not necessarily
# true.
current_device = torch.cuda.current_device()
object_sizes_tensor = object_sizes_tensor.to(current_device)
object_sizes_tensor = object_sizes_tensor.to(current_device)
# Broadcast object sizes
broadcast(object_sizes_tensor, src=src, group=group)
# Concatenate and broadcast serialized object tensors
if my_rank == src:
object_tensor = torch.cat(tensor_list)
else:
object_tensor = torch.ByteTensor(torch.sum(object_sizes_tensor).item())
if is_nccl_backend:
object_tensor = object_tensor.to(current_device)
broadcast(object_tensor, src=src, group=group)
# Deserialize objects using their stored sizes.
offset = 0
if my_rank != src:
for i, obj_size in enumerate(object_sizes_tensor):
obj_view = object_tensor[offset : offset + obj_size]
obj_view = obj_view.type(torch.ByteTensor)
offset += obj_size
object_list[i] = _tensor_to_object(obj_view, obj_size)
def all_gather(tensor_list,
tensor,
group=group.WORLD,
async_op=False):
"""
Gathers tensors from the whole group in a list.
Complex tensors are supported.
Arguments:
tensor_list (list[Tensor]): Output list. It should contain
correctly-sized tensors to be used for output of the collective.
tensor (Tensor): Tensor to be broadcast from current process.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
Examples:
>>> # All tensors below are of torch.int64 dtype.
>>> # We have 2 process groups, 2 ranks.
>>> tensor_list = [torch.zero(2, dtype=torch.int64) for _ in range(2)]
>>> tensor_list
[tensor([0, 0]), tensor([0, 0])] # Rank 0 and 1
>>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
>>> tensor
tensor([1, 2]) # Rank 0
tensor([3, 4]) # Rank 1
>>> dist.all_gather(tensor_list, tensor)
>>> tensor_list
[tensor([1, 2]), tensor([3, 4])] # Rank 0
[tensor([1, 2]), tensor([3, 4])] # Rank 1
>>> # All tensors below are of torch.cfloat dtype.
>>> # We have 2 process groups, 2 ranks.
>>> tensor_list = [torch.zero(2, dtype=torch.cfloat) for _ in range(2)]
>>> tensor_list
[tensor([0.+0.j, 0.+0.j]), tensor([0.+0.j, 0.+0.j])] # Rank 0 and 1
>>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j)
>>> tensor
tensor([1.+1.j, 2.+2.j]) # Rank 0
tensor([3.+3.j, 4.+4.j]) # Rank 1
>>> dist.all_gather(tensor_list, tensor)
>>> tensor_list
[tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # Rank 0
[tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # Rank 1
"""
_check_tensor_list(tensor_list, "tensor_list")
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
return
tensor_list = [t if not t.is_complex() else torch.view_as_real(t) for t in tensor_list]
tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.allgather([tensor_list], [tensor])
else:
work = group.allgather([tensor_list], [tensor])
if async_op:
return work
else:
work.wait()
def all_gather_coalesced(output_tensor_lists,
input_tensor_list,
group=group.WORLD,
async_op=False):
"""
Gathers input tensors from the whole group in a list in a coalesced manner.
Complex tensors are supported.
Arguments:
output_tensor_lists (list[list[Tensor]]): Output list. It should contain
correctly-sized tensors to be used for output of the collective.
input_tensor_list (list[Tensor]): Tensors to be broadcast from
current process. At least one tensor has to be non empty.
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op.
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
Example:
we have 2 process groups, 2 ranks.
rank 0 passes:
input_tensor_list = [[[1, 1], [1, 1]], [2], [3, 3]]
output_tensor_lists =
[[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
[[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
rank 1 passes:
input_tensor_list = [[[3, 3], [3, 3]], [5], [1, 1]]
output_tensor_lists =
[[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
[[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
both rank 0 and 1 get:
output_tensor_lists =
[[[1, 1], [1, 1]], [2], [3, 3]],
[[3, 3], [3, 3]], [5], [1, 1]]].
WARNING: at this time individual shape checking is not implemented across nodes.
For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the
all_gather_coalesced operation will proceed without complaint and return
erroneous outputs. This lack of shape checking results in significant
performance improvements but users of this function should take extra care
to ensure that each node passes in tensors whose shapes match across nodes.
"""
# We only check basic compatibility with C++ params here, C++ code will
# do shape and type checking.
if _rank_not_in_group(group):
return
_check_tensor_list(input_tensor_list, "tensor_list")
if not isinstance(output_tensor_lists, list):
raise RuntimeError("Invalid function argument: "
"output_tensor_lists should be a list")
for output_tensor_list in output_tensor_lists:
_check_tensor_list(output_tensor_list, "output_tensor_lists")
output_tensor_lists = [[t if not t.is_complex() else torch.view_as_real(t) for t in l] for l in output_tensor_lists]
input_tensor_list = [t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list]
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.allgather_coalesced(
output_tensor_lists, input_tensor_list)
else:
work = group.allgather_coalesced(output_tensor_lists, input_tensor_list)
if async_op:
return work
else:
work.wait()
def _validate_output_list_for_rank(my_rank, dst, gather_list):
if dst == my_rank:
if not gather_list:
raise ValueError(
"Argument ``gather_list`` must be specified on destination rank."
)
elif gather_list:
raise ValueError(
"Argument ``gather_list`` must NOT be specified "
"on non-destination ranks."
)
def gather(tensor,
gather_list=None,
dst=0,
group=group.WORLD,
async_op=False):
"""
Gathers a list of tensors in a single process.
Arguments:
tensor (Tensor): Input tensor.
gather_list (list[Tensor], optional): List of appropriately-sized
tensors to use for gathered data (default is None, must be specified
on the destination rank)
dst (int, optional): Destination rank (default is 0)
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
_check_single_tensor(tensor, "tensor")
# Parameter ``gather_list`` may be left unspecified on non-dst ranks.
if gather_list:
_check_tensor_list(gather_list, "gather_list")
else:
gather_list = []
if _rank_not_in_group(group):
return
my_rank = get_rank()
_validate_output_list_for_rank(my_rank, dst, gather_list)
output_tensors = [gather_list] if dst == my_rank else []
input_tensors = [tensor]
opts = GatherOptions()
opts.rootRank = dst
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.gather(output_tensors, input_tensors, opts)
else:
group_dst_rank = _get_group_rank(group, dst)
opts.rootRank = group_dst_rank
work = group.gather(output_tensors, input_tensors, opts)
if async_op:
return work
else:
work.wait()
def scatter(tensor,
scatter_list=None,
src=0,
group=group.WORLD,
async_op=False):
"""
Scatters a list of tensors to all processes in a group.
Each process will receive exactly one tensor and store its data in the
``tensor`` argument.
Arguments:
tensor (Tensor): Output tensor.
scatter_list (list[Tensor]): List of tensors to scatter (default is
None, must be specified on the source rank)
src (int): Source rank (default is 0)
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
_check_single_tensor(tensor, "tensor")
# Parameter ``scatter_list`` may be left unspecified on non-src ranks.
if scatter_list:
_check_tensor_list(scatter_list, "scatter_list")
else:
scatter_list = []
if _rank_not_in_group(group):
return
my_rank = get_rank()
if src == my_rank:
if not scatter_list:
raise ValueError("Argument ``scatter_list`` must be specified "
"on source rank.")
input_tensors = [scatter_list]
output_tensors = [tensor]
else:
if scatter_list:
raise ValueError("Argument ``scatter_list`` must NOT be specified "
"on non-source ranks.")
input_tensors = []
output_tensors = [tensor]
opts = ScatterOptions()
opts.rootRank = src
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.scatter(output_tensors, input_tensors, opts)
else:
group_src_rank = _get_group_rank(group, src)
opts.rootRank = group_src_rank
work = group.scatter(output_tensors, input_tensors, opts)
if async_op:
return work
else:
work.wait()
def reduce_scatter_multigpu(output_tensor_list,
input_tensor_lists,
op=ReduceOp.SUM,
group=group.WORLD,
async_op=False):
"""
Reduce and scatter a list of tensors to the whole group. Only nccl backend
is currently supported.
Each tensor in ``output_tensor_list`` should reside on a separate GPU, as
should each list of tensors in ``input_tensor_lists``.
Arguments:
output_tensor_list (List[Tensor]): Output tensors (on different GPUs)
to receive the result of the operation.
Note that ``len(output_tensor_list)`` needs to be the same for all
the distributed processes calling this function.
input_tensor_lists (List[List[Tensor]]): Input lists. It should
contain correctly-sized tensors on each GPU to be used for input of
the collective, e.g. ``input_tensor_lists[i]`` contains the
reduce_scatter input that resides on the GPU of
``output_tensor_list[i]``.
Note that each element of ``input_tensor_lists`` has the size of
``world_size * len(output_tensor_list)``, since the function
scatters the result from every single GPU in the group. To
interpret each element of ``input_tensor_lists[i]``, note that
``output_tensor_list[j]`` of rank k receives the reduce-scattered
result from ``input_tensor_lists[i][k * world_size + j]``
Also note that ``len(input_tensor_lists)``, and the size of each
element in ``input_tensor_lists`` (each element is a list,
therefore ``len(input_tensor_lists[i])``) need to be the same for
all the distributed processes calling this function.
group (ProcessGroup, optional): The process group to work on.
async_op (bool, optional): Whether this op should be an async op.
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group.
"""
if _rank_not_in_group(group):
return
opts = ReduceScatterOptions()
opts.reduceOp = op
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.reduce_scatter(
output_tensor_list,
input_tensor_lists,
opts
)
else:
work = group.reduce_scatter(
output_tensor_list,
input_tensor_lists,
opts
)
if async_op:
return work
else:
work.wait()
def reduce_scatter(output,
input_list,
op=ReduceOp.SUM,
group=group.WORLD,
async_op=False):
"""
Reduces, then scatters a list of tensors to all processes in a group.
Arguments:
output (Tensor): Output tensor.
input_list (list[Tensor]): List of tensors to reduce and scatter.
group (ProcessGroup, optional): The process group to work on.
async_op (bool, optional): Whether this op should be an async op.
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group.
"""
_check_single_tensor(output, "output")
_check_tensor_list(input_list, "input_list")
if _rank_not_in_group(group):
return
opts = ReduceScatterOptions()
opts.reduceOp = op
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.reduce_scatter([output], [input_list], opts)
else:
work = group.reduce_scatter([output], [input_list], opts)
if async_op:
return work
else:
work.wait()
def all_to_all_single(output,
input,
output_split_sizes=None,
input_split_sizes=None,
group=group.WORLD,
async_op=False):
"""
Each process splits input tensor and then scatters the split list
to all processes in a group. Then concatenate the received tensors from all
the processes in the group and return single output tensor.
Arguments:
output (Tensor): Gathered cancatenated output tensor.
input (Tensor): Input tensor to scatter.
output_split_sizes: (list[Int], optional): Output split sizes for dim 0
if specified None or empty, dim 0 of ``output`` tensor must divide
equally by ``world_size``.
input_split_sizes: (list[Int], optional): Input split sizes for dim 0
if specified None or empty, dim 0 of ``input`` tensor must divide
equally by ``world_size``.
group (ProcessGroup, optional): The process group to work on.
async_op (bool, optional): Whether this op should be an async op.
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group.
.. warning::
`all_to_all_single` is experimental and subject to change.
Examples:
>>> input = torch.arange(4) + rank * 4
>>> input
tensor([0, 1, 2, 3]) # Rank 0
tensor([4, 5, 6, 7]) # Rank 1
tensor([8, 9, 10, 11]) # Rank 2
tensor([12, 13, 14, 15]) # Rank 3
>>> output = torch.empty([4], dtype=torch.int64)
>>> dist.all_to_all_single(output, input)
>>> output
tensor([0, 4, 8, 12]) # Rank 0
tensor([1, 5, 9, 13]) # Rank 1
tensor([2, 6, 10, 14]) # Rank 2
tensor([3, 7, 11, 15]) # Rank 3
>>> # Essentially, it is similar to following operation:
>>> scatter_list = list(input.chunk(world_size))
>>> gather_list = list(output.chunk(world_size))
>>> for i in range(world_size):
>>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
>>> # Another example with uneven split
>>> input
tensor([0, 1, 2, 3, 4, 5]) # Rank 0
tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1
tensor([20, 21, 22, 23, 24]) # Rank 2
tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3
>>> input_splits
[2, 2, 1, 1] # Rank 0
[3, 2, 2, 2] # Rank 1
[2, 1, 1, 1] # Rank 2
[2, 2, 2, 1] # Rank 3
>>> output_splits
[2, 3, 2, 2] # Rank 0
[2, 2, 1, 2] # Rank 1
[1, 2, 1, 2] # Rank 2
[1, 2, 1, 1] # Rank 3
>>> output = ...
>>> dist.all_to_all_single(output, input, output_splits, input_splits)
>>> output
tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0
tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1
tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2
tensor([ 5, 17, 18, 24, 36]) # Rank 3
"""
if _rank_not_in_group(group):
return
opts = AllToAllOptions()
_check_single_tensor(output, "output")
_check_single_tensor(input, "input")
output_split_sizes = [] if output_split_sizes is None else output_split_sizes
input_split_sizes = [] if input_split_sizes is None else input_split_sizes
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.alltoall_base(output, input, output_split_sizes, input_split_sizes, opts)
else:
work = group.alltoall_base(output, input, output_split_sizes, input_split_sizes, opts)
if async_op:
return work
else:
work.wait()
def all_to_all(output_tensor_list,
input_tensor_list,
group=group.WORLD,
async_op=False):
"""
Each process scatters list of input tensors to all processes in a group and
return gathered list of tensors in output list.
Arguments:
output_tensor_list (list[Tensor]): List of tensors to be gathered one
per rank.
input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
group (ProcessGroup, optional): The process group to work on.
async_op (bool, optional): Whether this op should be an async op.
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group.
.. warning::
`all_to_all` is experimental and subject to change.
Examples:
>>> input = torch.arange(4) + rank * 4
>>> input = list(input.chunk(4))
>>> input
[tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0
[tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1
[tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2
[tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
>>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
>>> dist.all_to_all(output, input)
>>> output
[tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0
[tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1
[tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2
[tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3
>>> # Essentially, it is similar to following operation:
>>> scatter_list = input
>>> gather_list = output
>>> for i in range(world_size):
>>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
>>> input
tensor([0, 1, 2, 3, 4, 5]) # Rank 0
tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1
tensor([20, 21, 22, 23, 24]) # Rank 2
tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3
>>> input_splits
[2, 2, 1, 1] # Rank 0
[3, 2, 2, 2] # Rank 1
[2, 1, 1, 1] # Rank 2
[2, 2, 2, 1] # Rank 3
>>> output_splits
[2, 3, 2, 2] # Rank 0
[2, 2, 1, 2] # Rank 1
[1, 2, 1, 2] # Rank 2
[1, 2, 1, 1] # Rank 3
>>> input = list(input.split(input_splits))
>>> input
[tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0
[tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
[tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2
[tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3
>>> output = ...
>>> dist.all_to_all(output, input)
>>> output
[tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0
[tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1
[tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2
[tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3
"""
if _rank_not_in_group(group):
return
opts = AllToAllOptions()
_check_tensor_list(output_tensor_list, "output_tensor_list")
_check_tensor_list(input_tensor_list, "input_tensor_list")
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.alltoall(output_tensor_list, input_tensor_list, opts)
else:
work = group.alltoall(output_tensor_list, input_tensor_list, opts)
if async_op:
return work
else:
work.wait()
def barrier(group=group.WORLD,
async_op=False):
"""
Synchronizes all processes.
This collective blocks processes until the whole group enters this function,
if async_op is False, or if async work handle is called on wait().
Arguments:
group (ProcessGroup, optional): The process group to work on
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
"""
if _rank_not_in_group(group):
return
if group == GroupMember.WORLD:
_check_default_pg()
work = _default_pg.barrier()
else:
work = group.barrier()
if async_op:
return work
else:
work.wait()
def new_group(ranks=None, timeout=default_pg_timeout, backend=None):
"""
Creates a new distributed group.
This function requires that all processes in the main group (i.e. all
processes that are part of the distributed job) enter this function, even
if they are not going to be members of the group. Additionally, groups
should be created in the same order in all processes.
Arguments:
ranks (list[int]): List of ranks of group members. If ``None``, will be
set to all ranks. Default is ``None``.
timeout (timedelta, optional): Timeout for operations executed against
the process group. Default value equals 30 minutes.
This is only applicable for the ``gloo`` backend.
backend (str or Backend, optional): The backend to use. Depending on
build-time configurations, valid values are ``gloo`` and ``nccl``.
By default uses the same backend as the global group. This field
should be given as a lowercase string (e.g., ``"gloo"``), which can
also be accessed via :class:`Backend` attributes (e.g.,
``Backend.GLOO``).
Returns:
A handle of distributed group that can be given to collective calls.
"""
_check_default_pg()
global _pg_group_ranks
default_backend, default_store = _pg_map[_default_pg]
global_rank = _default_pg.rank()
global_world_size = _default_pg.size()
# Default to the same backend as the global process group
# if the backend is not specified.
if not backend:
backend = default_backend
# checks the input ranks
if ranks is not None:
ranks = sorted(ranks)
group_world_size = len(ranks)
if group_world_size > global_world_size:
raise RuntimeError("the new group's world size should be less or "
"equal to the world size set by "
"init_process_group")
# check ranks' sanity
for rank in ranks:
if rank < 0 or rank >= global_world_size:
raise RuntimeError("The new group's rank should be within the "
"the world_size set by init_process_group")
if global_rank in ranks:
group_rank = ranks.index(global_rank)
else:
group_rank = None
else:
ranks = list(range(global_world_size))
group_world_size = global_world_size
group_rank = global_rank
backend = Backend(backend)
pg = _new_process_group_helper(group_world_size,
group_rank,
ranks,
backend,
default_store,
timeout=timeout)
# Create the global rank to group rank mapping
_pg_group_ranks[pg] = {
global_rank: group_rank
for group_rank, global_rank in enumerate(ranks)
}
# barrier at the end to ensure that once we return from this method, all
# process groups including global variables are updated correctly on all
# ranks.
barrier()
return pg
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