Source code for torch.distributed.device_mesh
# mypy: allow-untyped-defs
# Copyright (c) Meta Platforms, Inc. and affiliates
import logging
import math
import threading
from functools import reduce
from itertools import chain
from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
import torch
from torch.distributed import is_available
from torch.utils._typing_utils import not_none
__all__ = ["init_device_mesh", "DeviceMesh"]
if not is_available():
import sys
# We need to create the stubs when distributed is not available.
# Otherwise, we would fail the doc tests (```./.ci/pytorch/docs-test.sh```),
# since it would try to import ``torch.distributed.device_mesh`` or
# ``torch.distributed.init_device_mesh`` but cannot find them.
class _DeviceMeshStub:
pass
def _init_device_mesh_stub():
pass
sys.modules["torch.distributed.device_mesh"].DeviceMesh = _DeviceMeshStub # type: ignore[attr-defined]
sys.modules[
"torch.distributed.device_mesh"
].init_device_mesh = _init_device_mesh_stub # type: ignore[attr-defined]
else:
from torch.distributed.distributed_c10d import (
_find_pg_by_ranks_and_tag,
_get_default_group,
_get_group_tag,
get_backend,
get_process_group_ranks,
get_rank,
get_world_size,
init_process_group,
is_initialized,
new_group,
ProcessGroup,
)
logger = logging.getLogger(__name__)
# only import numpy typing when type checking
if TYPE_CHECKING:
try:
from numpy.typing import ArrayLike
except ImportError:
logger.warning(
"DeviceMesh requires numpy >= 1.21 to be installed for type checking"
)
class _MeshEnv(threading.local):
def __init__(self) -> None:
self.mesh_stack: List[DeviceMesh] = []
self.child_to_root_mapping: Dict[DeviceMesh, DeviceMesh] = {}
self.mesh_dim_group_options: Dict[
int, Tuple[str, Optional[ProcessGroup.Options]]
] = {}
self.root_to_flatten_mapping: Dict[DeviceMesh, Dict[str, DeviceMesh]] = {}
# Record flatten mesh name to its mesh dim index in root mesh.
self.flatten_name_to_root_dims: Dict[
DeviceMesh, Dict[str, Tuple[int, ...]]
] = {}
def get_current_mesh(self) -> "DeviceMesh":
if len(self.mesh_stack) == 0:
raise RuntimeError("No device mesh is currently active!")
return self.mesh_stack[-1]
def create_sub_mesh(
self,
device_mesh: "DeviceMesh",
submesh_dim_names: Tuple[str, ...],
submesh_dims: List[Tuple[int, ...]],
) -> "DeviceMesh":
# Get the submesh dim size from the submesh_dims.
# For example, if we have a 3D mesh with mesh_shape (2, 2, 2) mesh_dim_names ("dp", "cp", "tp") and we want
# to slice out mesh["dp_cp"], then submesh_dims = [(0, 1), (2,)] and submesh_dim_size = [2 * 2, 2] = [4, 2].
# If we want to slice out mesh["dp", "cp"], then submesh_dims = [(0,), (1,)] and submesh_dim_size = [2, 2].
slice_dim_size = [
reduce(
lambda x, y: device_mesh.mesh.size(x) * device_mesh.mesh.size(y),
mesh_dim,
)
if len(mesh_dim) > 1
else device_mesh.mesh.size(mesh_dim[0])
for mesh_dim in submesh_dims
]
mesh_tensor = device_mesh.mesh
# slice_dim_idx could be differnt from submesh_dims, as we may need to flatten out some dims.
slice_dim_idx = []
slice_dim_group_info = []
# keep track of the number of dims that have been flattened so we can get the correct slice_dim_idx in the
# flattened mesh tensor.
num_dims_flatten = 0
for mesh_dim_indices, mesh_dim_name in zip(submesh_dims, submesh_dim_names):
# Currently, this only allows slicing out a contiguous flattened dim.
# TODO: we need to handle reconstructing a non-contiguous flattened dim.
if len(mesh_dim_indices) > 1:
# We need to move the start_dim and end_dim to the left if some dims are already flattened.
mesh_tensor = mesh_tensor.flatten(
start_dim=mesh_dim_indices[0] - num_dims_flatten,
end_dim=mesh_dim_indices[-1] - num_dims_flatten,
)
# If some dims are already flattened, we need to adjust the slice_dim_idx accordingly.
# For example, if the submesh_dims = [(0, 1), (2,), (3, 4)] with 0-1 flattened and 3-4 flattened,
# then the final slice_dim_idx should be [0, 1, 2].
slice_dim_idx.append(mesh_dim_indices[0] - num_dims_flatten)
num_dims_flatten += len(mesh_dim_indices) - 1
slice_dim_group_info.append(
self.root_to_flatten_mapping[device_mesh][
mesh_dim_name
]._dim_group_infos[0]
)
else:
slice_dim_idx.append(mesh_dim_indices[0] - num_dims_flatten)
slice_dim_group_info.append(
device_mesh._dim_group_infos[mesh_dim_indices[0]]
)
# mesh_tensor has already been flattened if needed. So mesh_tensor.ndim <= device_mesh.mesh.ndim now.
mesh_dims_remained_idx = list(range(mesh_tensor.ndim))
for idx in slice_dim_idx:
mesh_dims_remained_idx.remove(idx)
# pg_ranks_by_dim is the size of [number of local ranks of the outermost submesh dimension, *slice_dim_idx]
# This means on each local rank of the outermost slice mesh dim, we have a tensor of submesh size with
# the pg ranks of the submesh. From this, we can extract the submesh mesh tensor contains the current rank.
pg_ranks_by_dim = mesh_tensor.permute(
*mesh_dims_remained_idx, *slice_dim_idx
).reshape(-1, *slice_dim_size)
cur_rank = device_mesh.get_rank()
for mesh_nd in pg_ranks_by_dim:
submesh = DeviceMesh(
device_mesh.device_type,
mesh_nd,
mesh_dim_names=submesh_dim_names,
_init_backend=False,
)
if cur_rank in mesh_nd:
res_submesh = submesh
res_submesh._dim_group_infos = slice_dim_group_info # type: ignore[possibly-undefined]
self.child_to_root_mapping[res_submesh] = device_mesh
return res_submesh
def create_flatten_mesh(
self, device_mesh: "DeviceMesh", mesh_dim_name: Optional[str] = None
) -> "DeviceMesh":
root_mesh = _mesh_resources.get_root_mesh(device_mesh)
flatten_dims_in_root = [
not_none(root_mesh.mesh_dim_names).index(flattened_mesh_dim_name)
for flattened_mesh_dim_name in not_none(device_mesh.mesh_dim_names)
]
if not mesh_dim_name:
mesh_dim_name = "_".join(
[
not_none(root_mesh.mesh_dim_names)[dim]
for dim in flatten_dims_in_root
]
)
# Check whether the mesh_dim_name for flattened mesh is valid.
self.flatten_name_to_root_dims.setdefault(root_mesh, {})
invalid_dim_names = chain(
*list(not_none(root_mesh.mesh_dim_names)),
*self.flatten_name_to_root_dims[root_mesh].keys(),
)
if mesh_dim_name in invalid_dim_names:
raise RuntimeError(
f"{mesh_dim_name} already exists for submesh of the {root_mesh}. ",
f"The mesh_dim_names of submesh and flattened mesh are {invalid_dim_names}. "
f"Please specify another valid mesh_dim_name.",
)
# Quick return if the flatten mesh has been created before.
# TODO: If we decide to restrict flatten initialization once, we should remove
# this check and throw an error if the flatten mesh is already created before.
if (
root_mesh in self.root_to_flatten_mapping
and mesh_dim_name in self.root_to_flatten_mapping[root_mesh]
):
return self.root_to_flatten_mapping[root_mesh][mesh_dim_name]
flattened_mesh_dim_size = math.prod(device_mesh.mesh.size())
remained_dims_in_root = list(range(root_mesh.mesh.ndim))
for flatten_dim_in_root in flatten_dims_in_root:
remained_dims_in_root.remove(flatten_dim_in_root)
pg_ranks_by_dim = root_mesh.mesh.permute(
*remained_dims_in_root, *flatten_dims_in_root
).reshape(-1, flattened_mesh_dim_size)
cur_rank = root_mesh.get_rank()
for mesh_nd in pg_ranks_by_dim:
# need to init backend here since the flattened pg doesn't exist in root mesh.
flattened_mesh = DeviceMesh(
root_mesh.device_type,
mesh_nd,
mesh_dim_names=(mesh_dim_name,),
)
if cur_rank in mesh_nd:
res_flattened_mesh = flattened_mesh
self.child_to_root_mapping[res_flattened_mesh] = root_mesh # type: ignore[possibly-undefined]
self.root_to_flatten_mapping.setdefault(root_mesh, {})[mesh_dim_name] = res_flattened_mesh # type: ignore[possibly-undefined]
self.flatten_name_to_root_dims[root_mesh][mesh_dim_name] = tuple(flatten_dims_in_root) # type: ignore[possibly-undefined]
return res_flattened_mesh
def get_root_mesh(self, device_mesh: "DeviceMesh") -> "DeviceMesh":
# If a mesh could not be found in the child_to_root_mapping, it is a root mesh itself.
# A root mesh is not created through slicing.
# We considers the root mesh of a root mesh is itself.
root_mesh = self.child_to_root_mapping.get(device_mesh, None)
return device_mesh if not root_mesh else root_mesh
def get_root_mesh_dim(self, device_mesh: "DeviceMesh") -> Optional[int]:
"""
Returns the index of the mesh dim in the root mesh.
The device_mesh passed in needs to be sliced out from the root mesh
or submesh of the root mesh.
"""
root_mesh = self.get_root_mesh(device_mesh)
child_mesh_dim_names = device_mesh.mesh_dim_names
if root_mesh and child_mesh_dim_names:
assert (
len(child_mesh_dim_names) == 1
), "The submesh can only be a 1D mesh."
child_mesh_dim_name = child_mesh_dim_names[0]
return self.get_mesh_dim_by_name(root_mesh, child_mesh_dim_name)
return None
@staticmethod
def num_devices_per_host(device_type: str) -> int:
return _get_device_handle(device_type).device_count()
@staticmethod
def num_hosts(device_type: str) -> int:
# ProcessGroup can't tell us this info so we have to infer it, assume
# homogeneous hardware for now
return get_world_size() // _MeshEnv.num_devices_per_host(device_type)
def get_mesh_dim_by_name(
self, device_mesh: "DeviceMesh", mesh_dim_name: str
) -> int:
if (
device_mesh.mesh_dim_names is None
or len(device_mesh.mesh_dim_names) == 0
):
raise KeyError(
"No `mesh_dim_names` found.",
)
if mesh_dim_name not in device_mesh.mesh_dim_names:
raise KeyError(
f"Mesh dimension '{mesh_dim_name}' does not exist.",
f"Available mesh dimensions are: mesh_dim_names={device_mesh.mesh_dim_names}",
)
return not_none(device_mesh.mesh_dim_names.index(mesh_dim_name))
def _set_mesh_dim_group_options(
self,
dim: int,
backend: str,
pg_options: Optional[ProcessGroup.Options] = None,
) -> None:
self.mesh_dim_group_options[dim] = (backend, pg_options)
def _get_slice_mesh_dims(
self, device_mesh, mesh_dim_names
) -> List[Tuple[int, ...]]:
"""
Validate whether the mesh_dim_names is valid for slicing the given device_mesh.
If valid, return dim indexes of the slice mesh in the device mesh.
"""
if device_mesh != self.get_root_mesh(device_mesh):
raise RuntimeError("Cannot create a submesh from a submesh.")
# The slice mesh_dim_names should consist either the device_mesh's mesh_dim_names
# or its flattened mesh's mesh_dim_names.
self.flatten_name_to_root_dims.setdefault(device_mesh, {})
flatten_name_to_root_dims = self.flatten_name_to_root_dims[device_mesh]
valid_mesh_dim_names = [
*device_mesh.mesh_dim_names,
*flatten_name_to_root_dims,
]
if not all(
mesh_dim_name in valid_mesh_dim_names
for mesh_dim_name in mesh_dim_names
):
raise KeyError(
f"Invalid mesh_dim_names {mesh_dim_names} specified. "
f"Valid mesh_dim_names are {valid_mesh_dim_names}."
)
# Validate the order of the slice mesh dim indices.
# This needs to be in ascending order.
curr_idx = -1
slice_mesh_dims = []
for mesh_dim_name in mesh_dim_names:
if mesh_dim_name in flatten_name_to_root_dims:
mesh_indices = flatten_name_to_root_dims[mesh_dim_name]
# TODO: this doesn't allow non-contiguous slicing with flatten dim yet. next_idx
# should be mesh_indices[0] once we support non-contiguous slicing with flatten dim.
next_idx = mesh_indices[-1]
slice_mesh_dims.append(mesh_indices)
else:
next_idx = device_mesh.mesh_dim_names.index(mesh_dim_name)
slice_mesh_dims.append((next_idx,))
if next_idx <= curr_idx:
raise KeyError(
f"Invalid mesh_dim_names {mesh_dim_names} specified. ",
f"Found mesh dim indices to slice: {slice_mesh_dims}. ",
"Mesh dim indices should be in ascending order.",
)
curr_idx = next_idx
return slice_mesh_dims
def _get_all_submeshes(
self, device_mesh: "DeviceMesh", mesh_dim_name: str
) -> List["DeviceMesh"]:
"""
Return all the submeshes of a given mesh dimension of the device mesh.
"""
mesh_dim = self.get_mesh_dim_by_name(device_mesh, mesh_dim_name)
pg_ranks_by_dim = device_mesh.mesh.swapdims(-1, mesh_dim).reshape(
-1, device_mesh.mesh.size(mesh_dim)
)
cur_rank = device_mesh.get_rank()
res_submeshes = []
for mesh_1d in pg_ranks_by_dim:
submesh = DeviceMesh(
device_mesh.device_type,
mesh_1d,
mesh_dim_names=(mesh_dim_name,),
_init_backend=False,
)
submesh._dim_group_infos = (
[device_mesh._dim_group_infos[mesh_dim]]
if cur_rank in mesh_1d
else []
)
res_submeshes.append(submesh)
return res_submeshes
_mesh_resources: _MeshEnv = _MeshEnv()
def _get_device_handle(device_type: str = "cuda"):
"""
Get the module corresponding to the device_type which is cuda or cuda-like device.
For example, when the device_type is cuda, the module `torch.cuda` is returned.
Return None when there is no corresponding module for device_type, otherwise
return the corresponding module.
"""
return getattr(torch, device_type, None)
[docs] class DeviceMesh:
"""
DeviceMesh represents a mesh of devices, where layout of devices could be
represented as a n-d dimension array, and each value of the n-d dimensional
array is the global id of the default process group ranks.
DeviceMesh could be used to describe the layout of devices across the cluster,
and serves as a proxy for communication among the device lists within the cluster.
DeviceMesh can be used as a context manager.
.. note::
DeviceMesh follows SPMD programming model, which means the same PyTorch Python program
is running on all processes/ranks in the cluster. Therefore, users need to make sure the
`mesh` array (which describes the layout of devices) should be identical across all ranks.
Inconsistent `mesh` will lead to silent hang.
Args:
device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
mesh (ndarray): A multi-dimensional array or an integer tensor describing the layout
of devices, where the IDs are global IDs of the default process group.
Returns:
DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
The following program runs on each process/rank in an SPMD manner. In this example, we have 2
hosts with 4 GPUs each.
A reduction over the first dimension of mesh will reduce across
columns (0, 4), .. and (3, 7), a reduction over the second dimension
of mesh reduces across rows (0, 1, 2, 3) and (4, 5, 6, 7).
Example::
>>> # xdoctest: +SKIP("no rank")
>>> from torch.distributed.device_mesh import DeviceMesh
>>>
>>> # Initialize device mesh as (2, 4) to represent the topology
>>> # of cross-host(dim 0), and within-host (dim 1).
>>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
"""
device_type: str
mesh: torch.Tensor
mesh_dim_names: Optional[Tuple[str, ...]]
def __init__(
self,
device_type: str,
mesh: Union[torch.Tensor, "ArrayLike"],
*,
mesh_dim_names: Optional[Tuple[str, ...]] = None,
_init_backend: bool = True,
) -> None:
self.device_type = device_type
if isinstance(mesh, torch.Tensor) and mesh.device.type != "cpu":
raise ValueError(f"`mesh` must be a CPU tensor, got {mesh}")
self.mesh = (
mesh.detach().to(dtype=torch.int)
if isinstance(mesh, torch.Tensor)
else torch.tensor(mesh, device="cpu", dtype=torch.int)
)
self.mesh_dim_names = tuple(mesh_dim_names) if mesh_dim_names else None
# private field to pre-generate DeviceMesh's hash
self._flatten_mesh_list = tuple(self.mesh.flatten().tolist())
self._thread_id = None
# Skip process group initialization if xla device or init backend is False
# TODO(yeounoh) implement DeviceMesh backend and register XLA backend.
if device_type != "xla":
# always try to create default (world) pg, even if it is not initialized
# already. The world pg is used for device mesh identity (rank) on each
# process (we need to know if the current global rank is in the mesh or not).
if _init_backend:
self._get_or_create_default_group()
self._init_process_groups()
if is_initialized() and get_backend() == "threaded":
self._thread_id = threading.get_ident()
# calculate the coordinates of the current global rank on the mesh
rank_coords = (self.mesh == get_rank()).nonzero()
assert rank_coords.size(0) in (0, 1)
self._coordinate_on_dim: Optional[List[int]] = (
rank_coords[0].tolist() if rank_coords.size(0) > 0 else None
)
def _get_or_create_default_group(self):
default_initialized = is_initialized()
if not default_initialized:
init_process_group()
world_size = get_world_size()
if self.mesh.numel() > world_size:
raise RuntimeError(
f"Mesh should not be bigger than default world size {world_size}, but found {self.mesh.numel()} ranks!"
)
device_handle = _get_device_handle(self.device_type)
# TODO: if user want to pass pg_options, offer a way to do it
if not default_initialized and device_handle:
# automatically set the current cuda/cuda-like device base on num of gpu devices available in each host
# NOTE: This device selection would only work for homogeneous hardware.
num_devices_per_host = device_handle.device_count()
if (
world_size > num_devices_per_host
and world_size % num_devices_per_host != 0
):
raise RuntimeError(
f"DeviceMesh only support homogeneous hardware, but found "
f"{world_size} ranks and {num_devices_per_host} {self.device_type} devices!"
)
device_handle.set_device(get_rank() % num_devices_per_host)
return _get_default_group()
def _init_process_groups(self):
# tag/ranks/group_name associated with each mesh dimension, each
# mesh dimension should have one sub-group per rank
#
# TODO(yifu): remove tag and ranks once we fully migrate to native
# functional collectives. See details in:
# https://github.com/pytorch/pytorch/issues/93173#issuecomment-1907095208
dim_group_infos: List[Tuple[str, List[int], str]] = []
if self.mesh.ndim == 1 and self.mesh.numel() == get_world_size():
# Append the default pg to the first dim groups only if the default pg is compatible with `self.device_type`.
# Otherwise, create new pg.
default_group = _get_default_group()
ranks = list(range(get_world_size()))
dim_group = (
new_group(backend="cpu:gloo,cuda:nccl", ranks=ranks)
if torch.cuda.is_available()
and get_backend(default_group) == "gloo"
else default_group
)
dim_group_infos.append(
(
_get_group_tag(dim_group),
ranks,
dim_group.group_name,
)
)
else:
# create sub pgs base on the mesh argument specified
for dim in range(self.mesh.ndim):
# swap the current dim to the last dim
# then reshape to flatten out other dims
pg_ranks_by_dim = self.mesh.swapdims(-1, dim).reshape(
-1, self.mesh.size(dim)
)
# multi-dim mesh, create subgroups by looping over the pg_ranks
# for each dim and append the groups
for dim_mesh in pg_ranks_by_dim:
subgroup_ranks = dim_mesh.tolist()
# Respect dim group options specified via _MeshEnv.set_dim_group_options().
# Inherit from the parent group if no options are specified for the group.
if dim in _mesh_resources.mesh_dim_group_options:
(
backend,
pg_options,
) = _mesh_resources.mesh_dim_group_options[dim]
else:
backend, pg_options = None, None
# We temporarily revert the re-use subgroup, since it breaks two internal tests.
# Temporarily reverting to resolve test timeout while root-causing.
# TODO: Add two tests to cover internal tests scenarios and re-enable reuse subgroup if exists.
dim_group = new_group(
ranks=subgroup_ranks,
backend=backend,
pg_options=pg_options,
)
# only add to dim_groups if the current rank in the subgroup
if self.get_rank() in subgroup_ranks:
if len(dim_group_infos) > dim:
raise RuntimeError(
f"Each device mesh dimension should get only one process group, but got {self.get_rank()} "
f"in {subgroup_ranks}!"
)
dim_group_infos.append(
(
_get_group_tag(not_none(dim_group)),
subgroup_ranks,
dim_group.group_name,
)
)
self._dim_group_infos = dim_group_infos
def __enter__(self) -> "DeviceMesh":
# set this mesh as the current mesh in mesh env
_mesh_resources.mesh_stack.append(self)
return self
# pyre-fixme[2]: Parameter must be annotated.
def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
# pop this mesh from mesh env
_mesh_resources.mesh_stack.pop()
def __repr__(self) -> str:
device_mesh_repr = (
f"DeviceMesh('{self.device_type}', {self.mesh.tolist()})"
if not self.mesh_dim_names
else f"DeviceMesh('{self.device_type}', {self.mesh.tolist()}, mesh_dim_names={self.mesh_dim_names})"
)
return device_mesh_repr
def __hash__(self):
# lazily compute hash
self._hash = getattr(self, "_hash", None)
if not self._hash:
self._hash = hash(
(
self._flatten_mesh_list,
self.mesh.shape,
self.device_type,
self.mesh_dim_names,
self._thread_id,
)
)
return self._hash
def __eq__(self, other: object) -> bool:
if not isinstance(other, DeviceMesh):
return False
if id(self) == id(other):
return True
else:
return (
self._flatten_mesh_list == other._flatten_mesh_list
and self.mesh.shape == other.mesh.shape
and self.device_type == other.device_type
and self.mesh_dim_names == other.mesh_dim_names
and self._thread_id == other._thread_id
)
def __getitem__(
self, mesh_dim_names: Union[str, Tuple[str, ...]]
) -> "DeviceMesh":
"""
Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
The submesh created consists of the dimensions and the communicators indicated by
``mesh_dim_names``
Args:
mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
mesh dimension of the DeviceMesh to create the submesh for.
Returns:
A :class:`DeviceMesh` object
The following program runs on each process/rank in an SPMD manner in a world size of 8.
In the first example:
Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]).
Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]).
Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]).
Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]).
Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]).
In the second example:
Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
Example::
>>> # xdoctest: +SKIP("no rank")
>>> from torch.distributed.device_mesh import DeviceMesh
>>>
>>> # Initialize a 2D device mesh as (2, 4) to represent the topology
>>> # of cross-host(dim 0), and within-host (dim 1).
>>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
>>> tp_mesh = mesh_2d["tp"]
>>> dp_mesh = mesh_2d["dp"]
>>>
>>> # Initialize a 3D mesh.
>>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
>>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
>>> dp_cp_mesh = mesh_3d["dp", "cp"]
>>> cp_dp_mesh = mesh_3d["cp", "dp"]
"""
if not self.mesh_dim_names:
raise RuntimeError("Cannot slice a DeviceMesh without mesh_dim_names!")
mesh_dim_names = (
(mesh_dim_names,) if isinstance(mesh_dim_names, str) else mesh_dim_names
)
if mesh_dim_names == self.mesh_dim_names:
return self
else:
slice_mesh_dims = _mesh_resources._get_slice_mesh_dims(
self, mesh_dim_names
)
submesh = _mesh_resources.create_sub_mesh(
self, mesh_dim_names, slice_mesh_dims
)
return submesh
def get_group(self, mesh_dim: Optional[Union[int, str]] = None) -> ProcessGroup:
"""
Returns the single ProcessGroup specified by mesh_dim, or, if mesh_dim is not specified and the
DeviceMesh is 1-dimensional, returns the only ProcessGroup in the mesh.
Args:
mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
of the mesh dimension. Default is None.
Returns:
A :class:`ProcessGroup` object.
"""
if not hasattr(self, "_dim_group_infos"):
raise RuntimeError("DeviceMesh process groups not initialized!")
if self.mesh.ndim > 1 and mesh_dim is None:
raise RuntimeError(
f"Found the DeviceMesh have {self.mesh.ndim} dimensions",
"Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
"If you want to get the list of all the ProcessGroups in the DeviceMesh,"
"please use `get_all_groups()` instead.",
)
# Quick return if the current device_mesh is a 1D mesh.
if self.mesh.ndim == 1 and mesh_dim is None:
return not_none(
_find_pg_by_ranks_and_tag(*self._dim_group_infos[0][:2]) # type: ignore[index]
)
root_mesh = _mesh_resources.get_root_mesh(self)
root_to_flatten_mapping = _mesh_resources.root_to_flatten_mapping.get(
root_mesh, None
)
if root_to_flatten_mapping and mesh_dim in root_to_flatten_mapping.keys():
dim_group_infos = root_to_flatten_mapping[mesh_dim]._dim_group_infos[0][:2] # type: ignore[index]
return not_none(_find_pg_by_ranks_and_tag(*dim_group_infos))
else:
mesh_dim = (
_mesh_resources.get_mesh_dim_by_name(self, mesh_dim)
if isinstance(mesh_dim, str)
else mesh_dim
)
return not_none(
_find_pg_by_ranks_and_tag(*self._dim_group_infos[mesh_dim][:2]) # type: ignore[index]
)
def get_all_groups(self) -> List[ProcessGroup]:
"""
Returns a list of ProcessGroups for all mesh dimensions.
Returns:
A list of :class:`ProcessGroup` object.
"""
return [self.get_group(i) for i in range(self.mesh.ndim)]
@staticmethod
def from_group(
group: Union[ProcessGroup, List[ProcessGroup]],
device_type: str,
mesh: Optional[Union[torch.Tensor, "ArrayLike"]] = None,
*,
mesh_dim_names: Optional[Tuple[str, ...]] = None,
) -> "DeviceMesh":
"""
Constructs a :class:`DeviceMesh` with ``device_type`` from an
existing :class:`ProcessGroup`.
The constructed device mesh has number of dimensions equal to the
number of groups passed. If more than one group is passed, then the
``mesh`` argument is required.
"""
if isinstance(group, ProcessGroup):
group_ranks = get_process_group_ranks(group)
if (
isinstance(mesh, torch.Tensor) and mesh.tolist() != group_ranks
) or (mesh is not None and mesh != group_ranks):
raise ValueError(
f"Invalid mesh {str(mesh)} for ProcessGroup with ranks {group_ranks}"
)
mesh = torch.tensor(group_ranks, device="cpu", dtype=torch.int)
device_mesh = DeviceMesh(
device_type,
mesh,
mesh_dim_names=mesh_dim_names,
_init_backend=False,
)
device_mesh._dim_group_infos = [
(_get_group_tag(group), group_ranks, group.group_name)
]
return device_mesh
groups = list(group)
if len(groups) == 0:
raise ValueError("Expects at least one ProcessGroup to be passed")
if mesh is None:
raise ValueError("Must pass mesh if passing multiple ProcessGroups")
mesh = (
mesh.detach().to(dtype=torch.int, device="cpu")
if isinstance(mesh, torch.Tensor)
else torch.tensor(mesh, device="cpu", dtype=torch.int)
)
if mesh.ndim != len(groups):
raise ValueError(
"Expects mesh with ndim equal to number of ProcessGroups but got "
f"mesh {mesh.tolist()} and {len(groups)} ProcessGroups"
)
device_mesh = DeviceMesh(
device_type, mesh, mesh_dim_names=mesh_dim_names, _init_backend=False
)
device_mesh._dim_group_infos = [
(
_get_group_tag(group),
get_process_group_ranks(group),
group.group_name,
)
for group in groups
]
return device_mesh
def size(self, mesh_dim: Optional[int] = None) -> int:
return self.mesh.numel() if mesh_dim is None else self.mesh.size(mesh_dim)
@property
def ndim(self) -> int:
return self.mesh.ndim
@property
def shape(self) -> Tuple[int, ...]:
return tuple(self.mesh.shape)
def get_rank(self) -> int:
"""
Returns the current global rank.
"""
return get_rank()
def get_local_rank(self, mesh_dim: Optional[Union[int, str]] = None) -> int:
"""
Returns the local rank of the given mesh_dim of the DeviceMesh.
Args:
mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
of the mesh dimension. Default is None.
Returns:
An integer denotes the local rank.
The following program runs on each process/rank in an SPMD manner. In this example, we have 2
hosts with 4 GPUs each.
Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 0, 1, 2, 3 would return 0.
Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 4, 5, 6, 7 would return 1.
Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 0, 4 would return 0.
Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 1, 5 would return 1.
Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 2, 6 would return 2.
Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 3, 7 would return 3.
Example::
>>> # xdoctest: +SKIP("no rank")
>>> from torch.distributed.device_mesh import DeviceMesh
>>>
>>> # Initialize device mesh as (2, 4) to represent the topology
>>> # of cross-host(dim 0), and within-host (dim 1).
>>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
"""
if self.ndim > 1 and mesh_dim is None:
raise RuntimeError(
f"Found the DeviceMesh have {self.mesh.ndim} dimensions",
"Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
)
elif mesh_dim is None:
mesh_dim = 0
mesh_dim_group = not_none(self.get_group(mesh_dim))
assert isinstance(
mesh_dim_group, ProcessGroup
), "We expect ProcessGroup before calling `get_rank`!"
return not_none(get_rank(mesh_dim_group))
def get_coordinate(self) -> Optional[List[int]]:
"""
Return the relative indices of this rank relative to all
dimensions of the mesh. If this rank is not part of the mesh, return None.
"""
return self._coordinate_on_dim if self._coordinate_on_dim else None
def _flatten(self, mesh_dim_name: Optional[str] = None) -> "DeviceMesh":
"""
Returns a 1D DeviceMesh by flattening the current DeviceMesh.
If no mesh_dim_name is provided, the default is a string concatentaing the mesh_dim_names of the
given submesh with each mesh_dim_name separated by "_". For example, if we have a 3D mesh
DeviceMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mesh_dim_names=("dp", "cp", "tp")), calling
mesh_3d["dp", "cp"]._flatten() will create a 1D submesh DeviceMesh([0, 1, 2, 3], mesh_dim_names=("dp_cp",))
on rank 0, 1, 2, 3 and a 1D submesh DeviceMesh([4, 5, 6, 7], mesh_dim_names=("dp_cp",)) on rank 4, 5, 6, 7.
After the flattened dimension is created, to access the flattened dimesnion in mesh_3d, one can use the
existing slicing method to obtain the flattened mesh through calling mesh_3d["dp_cp"].
"""
if not self.mesh_dim_names:
raise RuntimeError(
"Cannot flatten a DeviceMesh without mesh_dim_names!"
)
return _mesh_resources.create_flatten_mesh(self, mesh_dim_name)
[docs] def init_device_mesh(
device_type: str,
mesh_shape: Tuple[int, ...],
*,
mesh_dim_names: Optional[Tuple[str, ...]] = None,
) -> DeviceMesh:
"""
Initializes a `DeviceMesh` based on `device_type`, `mesh_shape`, and `mesh_dim_names` parameters.
This creates a DeviceMesh with an n-dimensional array layout, where `n` is the length of `mesh_shape`.
If `mesh_dim_names` is provided, each dimension is labeled as `mesh_dim_names[i]`.
.. note::
`init_device_mesh` follows SPMD programming model, meaning the same PyTorch Python program
runs on all processes/ranks in the cluster. Ensure `mesh_shape` (the dimensions of the nD array
describing device layout) is identical across all ranks. Inconsistent `mesh_shape` may lead to hanging.
.. note::
If no process group is found, init_device_mesh will initialize distributed process group/groups
required for distributed communications behind the scene.
Args:
device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
Passing in a device type with a GPU index, such as "cuda:0", is not allowed.
mesh_shape (Tuple[int]): A tuple defining the dimensions of the multi-dimensional array
describing the layout of devices.
mesh_dim_names (Tuple[str], optional): A tuple of mesh dimension names to assign to each dimension
of the multi-dimensional array describing the layout of devices. Its length must match the length
of `mesh_shape`. Each string in `mesh_dim_names` must be unique.
Returns:
DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
Example::
>>> # xdoctest: +SKIP("no rank")
>>> from torch.distributed.device_mesh import init_device_mesh
>>>
>>> mesh_1d = init_device_mesh("cuda", mesh_shape=(8,))
>>> mesh_2d = init_device_mesh("cuda", mesh_shape=(2, 8), mesh_dim_names=("dp", "tp"))
"""
if mesh_dim_names is not None:
if len(set(mesh_dim_names)) != len(mesh_dim_names):
raise RuntimeError(
"Each mesh_dim_name must be unique.",
f"Found repeated mesh_dim_name in mesh_dim_names {mesh_dim_names}",
)
if len(mesh_shape) != len(mesh_dim_names):
raise RuntimeError(
"mesh_shape and mesh_dim_names should have same length!",
f"Found len(mesh_dim_names): {len(mesh_dim_names)} and len(mesh_shape):{len(mesh_shape)}.",
)
# assume valid device types are all letters
if device_type and not device_type.isalpha():
raise RuntimeError(
f"Device type with GPU index is not supported but got {device_type}. ",
"If you maintained a 'torch.device' object, it's recommended to pass in 'device.type'.",
)
# Always initialize the mesh's tensor on CPU, regardless of what the
# external device type has been set to be (e.g. meta)
with torch.device("cpu"):
mesh = torch.arange(math.prod(mesh_shape), dtype=torch.int).view(mesh_shape)
device_mesh = DeviceMesh(
device_type=device_type,
mesh=mesh,
mesh_dim_names=mesh_dim_names,
)
return device_mesh
