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Fix typos in distributed_device_mesh.rst #2792

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Mar 6, 2024
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Fix typos in distributed_device_mesh.rst #2792

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6 changes: 3 additions & 3 deletions recipes_source/distributed_device_mesh.rst
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Expand Up @@ -14,7 +14,7 @@ Prerequisites:


Setting up distributed communicators, i.e. NVIDIA Collective Communication Library (NCCL) communicators, for distributed training can pose a significant challenge. For workloads where users need to compose different parallelisms,
users would need to manually set up and manage NCCL communicators (for example, :class:`ProcessGroup`) for each parallelism solutions. This process could be complicated and susceptible to errors.
users would need to manually set up and manage NCCL communicators (for example, :class:`ProcessGroup`) for each parallelism solution. This process could be complicated and susceptible to errors.
:class:`DeviceMesh` can simplify this process, making it more manageable and less prone to errors.

What is DeviceMesh
Expand All @@ -30,7 +30,7 @@ Users can also easily manage the underlying process_groups/devices for multi-dim

Why DeviceMesh is Useful
------------------------
DeviceMesh is useful when working with multi-dimensional parallelism (i.e. 3-D parallel) where parallelism composability is requried. For example, when your parallelism solutions require both communication across hosts and within each host.
DeviceMesh is useful when working with multi-dimensional parallelism (i.e. 3-D parallel) where parallelism composability is required. For example, when your parallelism solutions require both communication across hosts and within each host.
The image above shows that we can create a 2D mesh that connects the devices within each host, and connects each device with its counterpart on the other hosts in a homogenous setup.

Without DeviceMesh, users would need to manually set up NCCL communicators, cuda devices on each process before applying any parallelism, which could be quite complicated.
Expand Down Expand Up @@ -95,7 +95,7 @@ access the underlying :class:`ProcessGroup` if needed.
from torch.distributed.device_mesh import init_device_mesh
mesh_2d = init_device_mesh("cuda", (2, 4), mesh_dim_names=("replicate", "shard"))

# Users can acess the undelying process group thru `get_group` API.
# Users can access the underlying process group thru `get_group` API.
replicate_group = mesh_2d.get_group(mesh_dim="replicate")
shard_group = mesh_2d.get_group(mesh_dim="shard")

Expand Down