# mypy: allow-untyped-defs # Copyright (c) Meta Platforms, Inc. and affiliates import logging import math import threading from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union import torch from torch.distributed import is_available from ..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_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_parent_mapping: Dict[DeviceMesh, DeviceMesh] = {} self.mesh_dim_group_options: Dict[ int, Tuple[str, Optional[ProcessGroup.Options]] ] = {} 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_child_mesh( self, parent_mesh: "DeviceMesh", submesh_dim_names: Tuple[str, ...] ) -> "DeviceMesh": # submesh_dims are the mesh dimension of the submesh in the parent mesh. submesh_dims = [ not_none(parent_mesh.mesh_dim_names).index(mesh_dim_name) for mesh_dim_name in submesh_dim_names ] submesh_dim_sizes = [ parent_mesh.mesh.size(mesh_dim) for mesh_dim in submesh_dims ] mesh_dims_remained = list(range(parent_mesh.mesh.ndim)) for submesh_dim in submesh_dims: mesh_dims_remained.remove(submesh_dim) # pg_ranks_by_dim is the size of [number of local ranks of the outermost submesh dimension, *sub_mesh_dims] # 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 = parent_mesh.mesh.permute( *mesh_dims_remained, *submesh_dims ).reshape(-1, *submesh_dim_sizes) cur_rank = parent_mesh.get_rank() for mesh_nd in pg_ranks_by_dim: submesh = DeviceMesh( parent_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._parent_mesh = parent_mesh # type: ignore[possibly-undefined] res_submesh._dim_group_infos = [ parent_mesh._dim_group_infos[mesh_dim] for mesh_dim in submesh_dims # type: ignore[possibly-undefined] ] self.child_to_parent_mapping[res_submesh] = parent_mesh return res_submesh def get_parent_mesh(self, device_mesh: "DeviceMesh") -> Optional["DeviceMesh"]: return self.child_to_parent_mapping.get(device_mesh, None) def get_parent_mesh_dim(self, device_mesh: "DeviceMesh") -> Optional[int]: """ Return the index of the mesh dim in the parent mesh. The device_mesh passed in needs to be sliced out from a parent mesh. """ parent_mesh = self.get_parent_mesh(device_mesh) child_mesh_dim_names = device_mesh.mesh_dim_names if parent_mesh and child_mesh_dim_names: assert ( len(child_mesh_dim_names) == 1 ), "The child mesh can only be a 1D mesh." child_mesh_dim_name = child_mesh_dim_names[0] return self.get_mesh_dim_by_name(parent_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) _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) 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._parent_mesh: Optional[DeviceMesh] = None self._thread_id = threading.get_ident() # 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() # 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, 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(): # if the mesh is the same as world_pg, we just append the default # pg to the first dim groups, as new_group cannot have the exact # same ranks as world dim_group_infos.append( ( _get_group_tag(_get_default_group()), list(range(get_world_size())), _get_default_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.mesh.tolist()})" if not self.mesh_dim_names else f"DeviceMesh({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._parent_mesh, 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._parent_mesh == other._parent_mesh 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_name given to create a child DeviceMesh. Args: mesh_dim_name (Union[str, Tuple[str]]): the name or the tuple of names of the mesh dimension of the parent DeviceMesh to create the child DeviceMesh for. Returns: A :class:`DeviceMesh` object 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["tp"] on rank 0, 1, 2, 3 would return a 1D child DeviceMesh:([0, 1, 2, 3]). Calling mesh["tp"] on rank 4, 5, 6, 7 would return a 1D child DeviceMesh:([4, 5, 6, 7]). Calling mesh["dp"] on rank 0, 4 would return a 1D child DeviceMesh:([0, 4]). Calling mesh["dp"] on rank 1, 5 would return a 1D child DeviceMesh:([1, 5]). Calling mesh["dp"] on rank 2, 6 would return a 1D child DeviceMesh:([2, 6]). Calling mesh["dp"] on rank 3, 7 would return a 1D child DeviceMesh:([3, 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]]) """ 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 ) error_msg = ( f"Invalid mesh_dim_name {mesh_dim_names} specified. " f"Valid mesh_dim_names should be a contiguous subsequence of {self.mesh_dim_names}." ) if mesh_dim_names == self.mesh_dim_names: return self elif len(mesh_dim_names) > len(self.mesh_dim_names) or not all( mesh_dim_name in self.mesh_dim_names for mesh_dim_name in mesh_dim_names ): raise KeyError(error_msg) # Check if the user-provided slicing is a valid contiguous subsequence of the mesh_dim_names # of the current DeviceMesh. else: outermost_dim_name = mesh_dim_names[0] outermost_dim_idx = self.mesh_dim_names.index(outermost_dim_name) for i, j in zip( mesh_dim_names, self.mesh_dim_names[outermost_dim_idx : len(mesh_dim_names)], ): if i != j: raise KeyError(error_msg) submesh = _mesh_resources.create_child_mesh(self, mesh_dim_names) 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.", ) if self.mesh.ndim == 1 and mesh_dim is None: mesh_dim = 0 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": """ Contstructs 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 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