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from mlx.core import array, Dtype, Device, Stream, scalar
from mlx.core.distributed import Group
from typing import Sequence, Optional, Union
class Group:
"""
An :class:`mlx.core.distributed.Group` represents a group of independent mlx
processes that can communicate.
"""
def rank(self) -> int:
"""Get the rank of this process"""
def size(self) -> int:
"""Get the size of the group"""
def split(self, color: int, key: int = -1) -> Group:
"""
Split the group to subgroups based on the provided color.
Processes that use the same color go to the same group. The ``key``
argument defines the rank in the new group. The smaller the key the
smaller the rank. If the key is negative then the rank in the
current group is used.
Args:
color (int): A value to group processes into subgroups.
key (int, optional): A key to optionally change the rank ordering
of the processes.
"""
def is_available(backend: str = 'any') -> bool:
"""
Check if a communication backend is available.
Note, this function returns whether MLX has the capability of
instantiating that distributed backend not whether it is possible to
create a communication group. For that purpose one should use
``init(strict=True)``.
Args:
backend (str, optional): The name of the backend to check for availability.
It takes the same values as :func:`init()`. Default: ``"any"``.
Returns:
bool: Whether the distributed backend is available.
"""
def init(strict: bool = False, backend: str = 'any') -> Group:
"""
Initialize the communication backend and create the global communication group.
Example:
.. code:: python
import mlx.core as mx
group = mx.distributed.init(backend="ring")
Args:
strict (bool, optional): If set to False it returns a singleton group
in case ``mx.distributed.is_available()`` returns False otherwise
it throws a runtime error. Default: ``False``
backend (str, optional): Which distributed backend to initialize.
Possible values ``mpi``, ``ring``, ``nccl``, ``jaccl``, ``any``. If
set to ``any`` all available backends are tried and the first one
that succeeds becomes the global group which will be returned in
subsequent calls. Default: ``any``
Returns:
Group: The group representing all the launched processes.
"""
def all_sum(x: array, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
All reduce sum.
Sum the ``x`` arrays from all processes in the group.
Args:
x (array): Input array.
group (Group): The group of processes that will participate in the
reduction. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The sum of all ``x`` arrays.
"""
def all_max(x: array, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
All reduce max.
Find the maximum of the ``x`` arrays from all processes in the group.
Args:
x (array): Input array.
group (Group): The group of processes that will participate in the
reduction. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The maximum of all ``x`` arrays.
"""
def all_min(x: array, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
All reduce min.
Find the minimum of the ``x`` arrays from all processes in the group.
Args:
x (array): Input array.
group (Group): The group of processes that will participate in the
reduction. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The minimum of all ``x`` arrays.
"""
def all_gather(x: array, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
Gather arrays from all processes.
Gather the ``x`` arrays from all processes in the group and concatenate
them along the first axis. The arrays should all have the same shape.
Args:
x (array): Input array.
group (Group): The group of processes that will participate in the
gather. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The concatenation of all ``x`` arrays.
"""
def send(x: array, dst: int, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
Send an array from the current process to the process that has rank
``dst`` in the group.
Args:
x (array): Input array.
dst (int): Rank of the destination process in the group.
group (Group): The group of processes that will participate in the
send. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: An array identical to ``x`` which when evaluated the send is performed.
"""
def recv(shape: Sequence[int], dtype: Dtype, src: int, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
Recv an array with shape ``shape`` and dtype ``dtype`` from process
with rank ``src``.
Args:
shape (Tuple[int]): The shape of the array we are receiving.
dtype (Dtype): The data type of the array we are receiving.
src (int): Rank of the source process in the group.
group (Group): The group of processes that will participate in the
recv. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The array that was received from ``src``.
"""
def recv_like(x: array, src: int, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
Recv an array with shape and type like ``x`` from process with rank
``src``.
It is equivalent to calling ``mx.distributed.recv(x.shape, x.dtype, src)``.
Args:
x (array): An array defining the shape and dtype of the array we are
receiving.
src (int): Rank of the source process in the group.
group (Group): The group of processes that will participate in the
recv. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The array that was received from ``src``.
"""
def sum_scatter(x: array, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array:
"""
Sum ``x`` across all processes in the group and shard the result along the first axis across ranks.
``x.shape[0]`` must be divisible by the group size.
The result is equivalent to ``all_sum(x)[rank*chunk_size:(rank+1)*chunk_size]``, where ``chunk_size = x.shape[0] // group.size()`` and ``rank`` is the rank of this process in the group.
Note: ``all_sum`` is mentioned only for illustration; the actual implementation does not perform ``all_sum`` and uses a single reduce-scatter collective instead.
Currently supported only for the NCCL backend.
Args:
x (array): Input array.
group (Group): The group of processes that will participate in the
sum scatter. If set to ``None`` the global group is used. Default:
``None``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The output array with shape ``[x.shape[0] // group.size(), *x.shape[1:]]``.
"""
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