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https://github.com/zebrajr/pytorch.git
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4de85e3c30
We recently revisited the hash implementation and think `_parent_mesh` information should not be burned into DeviceMesh but rather be inferred from the MeshEnv which manages device meshes. As `mesh_dim_names` is considered in device mesh's hash. This should not affect the issue brought up in https://github.com/pytorch/pytorch/issues/121799 Pull Request resolved: https://github.com/pytorch/pytorch/pull/131636 Approved by: https://github.com/wanchaol
719 lines
31 KiB
Python
719 lines
31 KiB
Python
# mypy: allow-untyped-defs
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# Copyright (c) Meta Platforms, Inc. and affiliates
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import logging
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import math
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import threading
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from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
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import torch
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from torch.distributed import is_available
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from torch.utils._typing_utils import not_none
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__all__ = ["init_device_mesh", "DeviceMesh"]
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if not is_available():
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import sys
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# We need to create the stubs when distributed is not available.
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# Otherwise, we would fail the doc tests (```./.ci/pytorch/docs-test.sh```),
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# since it would try to import ``torch.distributed.device_mesh`` or
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# ``torch.distributed.init_device_mesh`` but cannot find them.
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class _DeviceMeshStub:
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pass
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def _init_device_mesh_stub():
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pass
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sys.modules["torch.distributed.device_mesh"].DeviceMesh = _DeviceMeshStub # type: ignore[attr-defined]
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sys.modules[
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"torch.distributed.device_mesh"
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].init_device_mesh = _init_device_mesh_stub # type: ignore[attr-defined]
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else:
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from torch.distributed.distributed_c10d import (
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_find_pg_by_ranks_and_tag,
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_get_default_group,
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_get_group_tag,
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get_backend,
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get_process_group_ranks,
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get_rank,
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get_world_size,
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init_process_group,
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is_initialized,
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new_group,
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ProcessGroup,
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)
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logger = logging.getLogger(__name__)
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# only import numpy typing when type checking
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if TYPE_CHECKING:
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try:
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from numpy.typing import ArrayLike
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except ImportError:
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logger.warning(
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"DeviceMesh requires numpy >= 1.21 to be installed for type checking"
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)
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class _MeshEnv(threading.local):
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def __init__(self) -> None:
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self.mesh_stack: List[DeviceMesh] = []
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self.child_to_parent_mapping: Dict[DeviceMesh, DeviceMesh] = {}
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self.mesh_dim_group_options: Dict[
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int, Tuple[str, Optional[ProcessGroup.Options]]
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] = {}
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def get_current_mesh(self) -> "DeviceMesh":
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if len(self.mesh_stack) == 0:
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raise RuntimeError("No device mesh is currently active!")
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return self.mesh_stack[-1]
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def create_child_mesh(
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self, parent_mesh: "DeviceMesh", submesh_dim_names: Tuple[str, ...]
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) -> "DeviceMesh":
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# submesh_dims are the mesh dimension of the submesh in the parent mesh.
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submesh_dims = [
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not_none(parent_mesh.mesh_dim_names).index(mesh_dim_name)
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for mesh_dim_name in submesh_dim_names
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]
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submesh_dim_sizes = [
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parent_mesh.mesh.size(mesh_dim) for mesh_dim in submesh_dims
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]
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mesh_dims_remained = list(range(parent_mesh.mesh.ndim))
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for submesh_dim in submesh_dims:
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mesh_dims_remained.remove(submesh_dim)
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# pg_ranks_by_dim is the size of [number of local ranks of the outermost submesh dimension, *sub_mesh_dims]
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# This means on each local rank of the outermost slice mesh dim, we have a tensor of submesh size with
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# the pg ranks of the submesh. From this, we can extract the submesh mesh tensor contains the current rank.
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pg_ranks_by_dim = parent_mesh.mesh.permute(
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*mesh_dims_remained, *submesh_dims
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).reshape(-1, *submesh_dim_sizes)
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cur_rank = parent_mesh.get_rank()
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for mesh_nd in pg_ranks_by_dim:
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submesh = DeviceMesh(
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parent_mesh.device_type,
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mesh_nd,
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mesh_dim_names=submesh_dim_names,
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_init_backend=False,
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)
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if cur_rank in mesh_nd:
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res_submesh = submesh
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res_submesh._dim_group_infos = [ # type: ignore[possibly-undefined]
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parent_mesh._dim_group_infos[mesh_dim] for mesh_dim in submesh_dims
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]
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self.child_to_parent_mapping[res_submesh] = parent_mesh
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return res_submesh
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def get_parent_mesh(self, device_mesh: "DeviceMesh") -> Optional["DeviceMesh"]:
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return self.child_to_parent_mapping.get(device_mesh, None)
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def get_parent_mesh_dim(self, device_mesh: "DeviceMesh") -> Optional[int]:
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"""
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Return the index of the mesh dim in the parent mesh.
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The device_mesh passed in needs to be sliced out from a parent mesh.
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"""
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parent_mesh = self.get_parent_mesh(device_mesh)
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child_mesh_dim_names = device_mesh.mesh_dim_names
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if parent_mesh and child_mesh_dim_names:
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assert (
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len(child_mesh_dim_names) == 1
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), "The child mesh can only be a 1D mesh."
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child_mesh_dim_name = child_mesh_dim_names[0]
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return self.get_mesh_dim_by_name(parent_mesh, child_mesh_dim_name)
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return None
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@staticmethod
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def num_devices_per_host(device_type: str) -> int:
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return _get_device_handle(device_type).device_count()
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@staticmethod
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def num_hosts(device_type: str) -> int:
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# ProcessGroup can't tell us this info so we have to infer it, assume
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# homogeneous hardware for now
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return get_world_size() // _MeshEnv.num_devices_per_host(device_type)
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def get_mesh_dim_by_name(
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self, device_mesh: "DeviceMesh", mesh_dim_name: str
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) -> int:
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if (
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device_mesh.mesh_dim_names is None
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or len(device_mesh.mesh_dim_names) == 0
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):
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raise KeyError(
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"No `mesh_dim_names` found.",
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)
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if mesh_dim_name not in device_mesh.mesh_dim_names:
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raise KeyError(
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f"Mesh dimension '{mesh_dim_name}' does not exist.",
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f"Available mesh dimensions are: mesh_dim_names={device_mesh.mesh_dim_names}",
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)
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return not_none(device_mesh.mesh_dim_names.index(mesh_dim_name))
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def _set_mesh_dim_group_options(
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self,
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dim: int,
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backend: str,
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pg_options: Optional[ProcessGroup.Options] = None,
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) -> None:
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self.mesh_dim_group_options[dim] = (backend, pg_options)
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_mesh_resources: _MeshEnv = _MeshEnv()
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def _get_device_handle(device_type: str = "cuda"):
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"""
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Get the module corresponding to the device_type which is cuda or cuda-like device.
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For example, when the device_type is cuda, the module `torch.cuda` is returned.
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Return None when there is no corresponding module for device_type, otherwise
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return the corresponding module.
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"""
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return getattr(torch, device_type, None)
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class DeviceMesh:
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"""
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DeviceMesh represents a mesh of devices, where layout of devices could be
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represented as a n-d dimension array, and each value of the n-d dimensional
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array is the global id of the default process group ranks.
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DeviceMesh could be used to describe the layout of devices across the cluster,
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and serves as a proxy for communication among the device lists within the cluster.
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DeviceMesh can be used as a context manager.
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.. note::
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DeviceMesh follows SPMD programming model, which means the same PyTorch Python program
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is running on all processes/ranks in the cluster. Therefore, users need to make sure the
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`mesh` array (which describes the layout of devices) should be identical across all ranks.
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Inconsistent `mesh` will lead to silent hang.
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Args:
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device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
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mesh (ndarray): A multi-dimensional array or an integer tensor describing the layout
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of devices, where the IDs are global IDs of the default process group.
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Returns:
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DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
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The following program runs on each process/rank in an SPMD manner. In this example, we have 2
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hosts with 4 GPUs each.
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A reduction over the first dimension of mesh will reduce across
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columns (0, 4), .. and (3, 7), a reduction over the second dimension
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of mesh reduces across rows (0, 1, 2, 3) and (4, 5, 6, 7).
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Example::
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>>> # xdoctest: +SKIP("no rank")
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>>> from torch.distributed.device_mesh import DeviceMesh
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>>>
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>>> # Initialize device mesh as (2, 4) to represent the topology
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>>> # of cross-host(dim 0), and within-host (dim 1).
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>>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
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"""
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device_type: str
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mesh: torch.Tensor
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mesh_dim_names: Optional[Tuple[str, ...]]
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def __init__(
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self,
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device_type: str,
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mesh: Union[torch.Tensor, "ArrayLike"],
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*,
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mesh_dim_names: Optional[Tuple[str, ...]] = None,
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_init_backend: bool = True,
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) -> None:
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self.device_type = device_type
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if isinstance(mesh, torch.Tensor) and mesh.device.type != "cpu":
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raise ValueError(f"`mesh` must be a CPU tensor, got {mesh}")
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self.mesh = (
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mesh.detach().to(dtype=torch.int)
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if isinstance(mesh, torch.Tensor)
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else torch.tensor(mesh, device="cpu", dtype=torch.int)
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)
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self.mesh_dim_names = tuple(mesh_dim_names) if mesh_dim_names else None
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# private field to pre-generate DeviceMesh's hash
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self._flatten_mesh_list = tuple(self.mesh.flatten().tolist())
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self._thread_id = None
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# Skip process group initialization if xla device or init backend is False
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# TODO(yeounoh) implement DeviceMesh backend and register XLA backend.
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if device_type != "xla":
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# always try to create default (world) pg, even if it is not initialized
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# already. The world pg is used for device mesh identity (rank) on each
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# process (we need to know if the current global rank is in the mesh or not).
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if _init_backend:
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self._get_or_create_default_group()
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self._init_process_groups()
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if is_initialized() and get_backend() == "threaded":
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self._thread_id = threading.get_ident()
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# calculate the coordinates of the current global rank on the mesh
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rank_coords = (self.mesh == get_rank()).nonzero()
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assert rank_coords.size(0) in (0, 1)
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self._coordinate_on_dim: Optional[List[int]] = (
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rank_coords[0].tolist() if rank_coords.size(0) > 0 else None
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)
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def _get_or_create_default_group(self):
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default_initialized = is_initialized()
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if not default_initialized:
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init_process_group()
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world_size = get_world_size()
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if self.mesh.numel() > world_size:
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raise RuntimeError(
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f"Mesh should not be bigger than default world size, but found {self.mesh.numel()} ranks!"
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)
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device_handle = _get_device_handle(self.device_type)
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# TODO: if user want to pass pg_options, offer a way to do it
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if not default_initialized and device_handle:
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# automatically set the current cuda/cuda-like device base on num of gpu devices available in each host
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# NOTE: This device selection would only work for homogeneous hardware.
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num_devices_per_host = device_handle.device_count()
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if (
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world_size > num_devices_per_host
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and world_size % num_devices_per_host != 0
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):
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raise RuntimeError(
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f"DeviceMesh only support homogeneous hardware, but found "
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f"{world_size} ranks and {num_devices_per_host} {self.device_type} devices!"
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)
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device_handle.set_device(get_rank() % num_devices_per_host)
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return _get_default_group()
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def _init_process_groups(self):
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# tag/ranks/group_name associated with each mesh dimension, each
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# mesh dimension should have one sub-group per rank
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#
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# TODO(yifu): remove tag and ranks once we fully migrate to native
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# functional collectives. See details in:
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# https://github.com/pytorch/pytorch/issues/93173#issuecomment-1907095208
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dim_group_infos: List[Tuple[str, List[int], str]] = []
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if self.mesh.ndim == 1 and self.mesh.numel() == get_world_size():
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# if the mesh is the same as world_pg, we just append the default
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# pg to the first dim groups, as new_group cannot have the exact
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# same ranks as world
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dim_group_infos.append(
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(
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_get_group_tag(_get_default_group()),
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list(range(get_world_size())),
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_get_default_group().group_name,
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)
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)
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else:
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# create sub pgs base on the mesh argument specified
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for dim in range(self.mesh.ndim):
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# swap the current dim to the last dim
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# then reshape to flatten out other dims
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pg_ranks_by_dim = self.mesh.swapdims(-1, dim).reshape(
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-1, self.mesh.size(dim)
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)
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# multi-dim mesh, create subgroups by looping over the pg_ranks
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# for each dim and append the groups
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for dim_mesh in pg_ranks_by_dim:
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subgroup_ranks = dim_mesh.tolist()
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# Respect dim group options specified via _MeshEnv.set_dim_group_options().
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# Inherit from the parent group if no options are specified for the group.
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if dim in _mesh_resources.mesh_dim_group_options:
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(
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backend,
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pg_options,
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) = _mesh_resources.mesh_dim_group_options[dim]
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else:
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backend, pg_options = None, None
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# We temporarily revert the re-use subgroup, since it breaks two internal tests.
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# Temporarily reverting to resolve test timeout while root-causing.
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# TODO: Add two tests to cover internal tests scenarios and re-enable reuse subgroup if exists.
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dim_group = new_group(
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ranks=subgroup_ranks,
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backend=backend,
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pg_options=pg_options,
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)
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# only add to dim_groups if the current rank in the subgroup
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if self.get_rank() in subgroup_ranks:
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if len(dim_group_infos) > dim:
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raise RuntimeError(
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f"Each device mesh dimension should get only one process group, but got {self.get_rank()} "
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f"in {subgroup_ranks}!"
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)
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dim_group_infos.append(
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(
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_get_group_tag(not_none(dim_group)),
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subgroup_ranks,
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dim_group.group_name,
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)
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)
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self._dim_group_infos = dim_group_infos
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def __enter__(self) -> "DeviceMesh":
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# set this mesh as the current mesh in mesh env
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_mesh_resources.mesh_stack.append(self)
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return self
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# pyre-fixme[2]: Parameter must be annotated.
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def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
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# pop this mesh from mesh env
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_mesh_resources.mesh_stack.pop()
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def __repr__(self) -> str:
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device_mesh_repr = (
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f"DeviceMesh({self.mesh.tolist()})"
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if not self.mesh_dim_names
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else f"DeviceMesh({self.mesh.tolist()}, mesh_dim_names={self.mesh_dim_names})"
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)
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return device_mesh_repr
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def __hash__(self):
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# lazily compute hash
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self._hash = getattr(self, "_hash", None)
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if not self._hash:
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self._hash = hash(
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(
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self._flatten_mesh_list,
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self.mesh.shape,
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self.device_type,
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self.mesh_dim_names,
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self._thread_id,
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)
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)
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return self._hash
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def __eq__(self, other: object) -> bool:
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if not isinstance(other, DeviceMesh):
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return False
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if id(self) == id(other):
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return True
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else:
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return (
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self._flatten_mesh_list == other._flatten_mesh_list
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and self.mesh.shape == other.mesh.shape
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and self.device_type == other.device_type
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and self.mesh_dim_names == other.mesh_dim_names
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and self._thread_id == other._thread_id
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)
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def __getitem__(
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self, mesh_dim_names: Union[str, Tuple[str, ...]]
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) -> "DeviceMesh":
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"""
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Slice the current DeviceMesh based on the mesh_dim_name given to create a child
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DeviceMesh.
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Args:
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mesh_dim_name (Union[str, Tuple[str]]): the name or the tuple of names of the
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mesh dimension of the parent DeviceMesh to create the child DeviceMesh for.
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Returns:
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A :class:`DeviceMesh` object
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The following program runs on each process/rank in an SPMD manner. In this example, we have 2
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hosts with 4 GPUs each.
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Calling mesh["tp"] on rank 0, 1, 2, 3 would return a 1D child DeviceMesh:([0, 1, 2, 3]).
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Calling mesh["tp"] on rank 4, 5, 6, 7 would return a 1D child DeviceMesh:([4, 5, 6, 7]).
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Calling mesh["dp"] on rank 0, 4 would return a 1D child DeviceMesh:([0, 4]).
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Calling mesh["dp"] on rank 1, 5 would return a 1D child DeviceMesh:([1, 5]).
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Calling mesh["dp"] on rank 2, 6 would return a 1D child DeviceMesh:([2, 6]).
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Calling mesh["dp"] on rank 3, 7 would return a 1D child DeviceMesh:([3, 7]).
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Example::
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>>> # xdoctest: +SKIP("no rank")
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>>> from torch.distributed.device_mesh import DeviceMesh
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>>>
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>>> # Initialize device mesh as (2, 4) to represent the topology
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>>> # of cross-host(dim 0), and within-host (dim 1).
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>>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
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"""
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if not self.mesh_dim_names:
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raise RuntimeError("Cannot slice a DeviceMesh without mesh_dim_names!")
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mesh_dim_names = (
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(mesh_dim_names,) if isinstance(mesh_dim_names, str) else mesh_dim_names
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)
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error_msg = (
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f"Invalid mesh_dim_name {mesh_dim_names} specified. "
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f"Valid mesh_dim_names should be a contiguous subsequence of {self.mesh_dim_names}."
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)
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if mesh_dim_names == self.mesh_dim_names:
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return self
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elif len(mesh_dim_names) > len(self.mesh_dim_names) or not all(
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mesh_dim_name in self.mesh_dim_names for mesh_dim_name in mesh_dim_names
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):
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raise KeyError(error_msg)
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# 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
|