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568 lines
25 KiB
568 lines
25 KiB
# Copyright (c) Meta Platforms, Inc. and affiliates
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import logging
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import math
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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 ..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_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:
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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.parent_to_child_mapping: Dict[DeviceMesh, Dict[str, DeviceMesh]] = {}
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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, device_mesh: "DeviceMesh", mesh_dim: int, mesh_dim_name: str
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) -> "DeviceMesh":
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# Directly return the child mesh if it is already created.
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child_mesh_mappings = self.parent_to_child_mapping.get(device_mesh)
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if child_mesh_mappings:
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sub_mesh = child_mesh_mappings.get(mesh_dim_name)
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if sub_mesh:
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return sub_mesh
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# swap the current dim to the last dim then reshape to flatten out other
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# dims, so we can just extract the list of ranks which contains cur_rank.
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cur_rank = device_mesh.get_rank()
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pg_ranks_by_dim = device_mesh.mesh.swapdims(-1, mesh_dim).reshape(
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-1, device_mesh.mesh.size(mesh_dim)
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)
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for mesh_1d in pg_ranks_by_dim:
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sub_mesh = DeviceMesh(
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device_mesh.device_type,
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mesh_1d,
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mesh_dim_names=(mesh_dim_name,),
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_init_backend=False,
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)
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if cur_rank in mesh_1d:
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res_sub_mesh = sub_mesh
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res_sub_mesh._dim_group_infos = [device_mesh._dim_group_infos[mesh_dim]] # type: ignore[possibly-undefined]
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# Assign the current DeviceMesh as the parent of the child DeviceMesh.
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self.child_to_parent_mapping[res_sub_mesh] = device_mesh
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self.parent_to_child_mapping.setdefault(device_mesh, {})[
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mesh_dim_name
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] = res_sub_mesh
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return res_sub_mesh
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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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_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().cpu()
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if isinstance(mesh, torch.Tensor)
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else torch.tensor(mesh, dtype=torch.int)
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)
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self.mesh_dim_names = mesh_dim_names
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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._hash = hash((self._flatten_mesh_list, self.mesh.shape, id(self)))
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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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# 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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# 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(ranks=subgroup_ranks)
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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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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.mesh) == id(other.mesh):
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return True
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return (
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self.mesh.shape == other.mesh.shape
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and self._flatten_mesh_list == other._flatten_mesh_list
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)
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def __getitem__(self, mesh_dim_name: str) -> "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 (str): the name of the mesh dimension of the parent DeviceMesh
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to create a 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 self.mesh.ndim == 1:
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if self.mesh_dim_names and mesh_dim_name == self.mesh_dim_names[0]:
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return self
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else:
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raise RuntimeError(
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f"Invalid mesh_dim_name {mesh_dim_name} specified."
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)
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mesh_dim = _mesh_resources.get_mesh_dim_by_name(self, mesh_dim_name)
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submesh = _mesh_resources.create_child_mesh(self, mesh_dim, mesh_dim_name)
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return submesh
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def get_group(
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self, mesh_dim: Optional[Union[int, str]] = None
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) -> Union[ProcessGroup, List[ProcessGroup]]:
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"""
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Returns a list of ProcessGroups corresponding to the mesh dimensions, or
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returns a single ProcessGroup if mesh_dim is specified or the given mesh has
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only one mesh dimension.
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Args:
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mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
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of the mesh dimension. Default is None.
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Returns:
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A list of :class:`ProcessGroup` object when `mesh_dim` is not specified for
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a DeviceMesh with more than 1 dimension; otherwise, returns a single
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:class:`ProcessGroup` object.
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"""
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if not hasattr(self, "_dim_group_infos"):
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raise RuntimeError("DeviceMesh process groups not initialized!")
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if self.mesh.ndim == 1:
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return not_none(
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_find_pg_by_ranks_and_tag(*self._dim_group_infos[0][:2])
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)
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if mesh_dim is not None:
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if isinstance(mesh_dim, str):
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mesh_dim = _mesh_resources.get_mesh_dim_by_name(self, mesh_dim)
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return not_none(
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_find_pg_by_ranks_and_tag(*self._dim_group_infos[mesh_dim][:2])
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)
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else:
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dim_groups = []
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for ith_dim in range(self.mesh.ndim):
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dim_groups.append(
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not_none(
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_find_pg_by_ranks_and_tag(
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*self._dim_group_infos[ith_dim][:2]
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)
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)
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)
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return dim_groups
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def size(self, mesh_dim: Optional[int] = None) -> int:
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return self.mesh.numel() if mesh_dim is None else self.mesh.size(mesh_dim)
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@property
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def ndim(self) -> int:
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return self.mesh.ndim
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@property
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def shape(self) -> Tuple[int, ...]:
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return tuple(self.mesh.shape)
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def get_rank(self) -> int:
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"""
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Returns the current global rank.
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"""
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return get_rank()
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def get_local_rank(self, mesh_dim: Optional[Union[int, str]] = None) -> int:
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"""
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Returns the local rank of the given mesh_dim of the DeviceMesh.
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Args:
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mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
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of the mesh dimension. Default is None.
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Returns:
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An integer denotes the local rank.
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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_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".
|
|
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)}.",
|
|
)
|
|
|
|
mesh = torch.arange(math.prod(mesh_shape)).view(mesh_shape)
|
|
device_mesh = DeviceMesh(
|
|
device_type=device_type,
|
|
mesh=mesh,
|
|
mesh_dim_names=mesh_dim_names,
|
|
)
|
|
|
|
return device_mesh
|