enable split_group for pytorch process group creation#42471
enable split_group for pytorch process group creation#42471tushar00jain wants to merge 2 commits into
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Code Review
This pull request introduces a new communication path using torch.distributed.split_group for creating device and CPU subgroups, replacing the legacy new_group method. This change requires eager initialization of the default process group with mixed backends and explicit device-id binding. The review feedback identifies that the VLLM_USE_SPLIT_GROUP environment variable should default to "1" to align with the PR's objectives. Additionally, the reviewer points out that hardcoded "cuda" and "nccl" strings in the initialization logic and error messages should be replaced with dynamic platform detection to maintain support for non-CUDA hardware like XPU and ROCm.
| # and ``init_distributed_environment`` initializes the default PG with | ||
| # mixed ``cpu:gloo,cuda:nccl`` backend + eager ``device_id`` binding. | ||
| "VLLM_USE_SPLIT_GROUP": lambda: bool( | ||
| int(os.getenv("VLLM_USE_SPLIT_GROUP", "0")) |
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The pull request description indicates that split_group should be enabled by default. However, the environment variable getter currently defaults to "0" (False). This should be changed to "1" to align with the intended behavior and the PR's objective.
| int(os.getenv("VLLM_USE_SPLIT_GROUP", "0")) | |
| int(os.getenv("VLLM_USE_SPLIT_GROUP", "1")) |
| if local_rank == -1: | ||
| # local rank not set, this usually happens in single-node | ||
| # setting, where we can use rank as local rank | ||
| local_rank = ( | ||
| envs.LOCAL_RANK if distributed_init_method == "env://" else rank | ||
| ) | ||
|
|
||
| if envs.VLLM_USE_SPLIT_GROUP: | ||
| # Use mixed backend so the default PG has both CPU (gloo) and | ||
| # CUDA (nccl) backends. Pass device_id to eagerly initialize | ||
| # the NCCL communicator, enabling split_group for subgroups. | ||
| # On CPU-only systems, fall back to gloo. | ||
| if torch.accelerator.is_available() and backend != "gloo": | ||
| init_backend = "cpu:gloo,cuda:nccl" | ||
| device_id = torch.device(f"cuda:{local_rank}") | ||
| else: | ||
| init_backend = "gloo" | ||
| device_id = None | ||
| torch.distributed.init_process_group( | ||
| backend=init_backend, | ||
| init_method=distributed_init_method, | ||
| world_size=world_size, | ||
| rank=rank, | ||
| timeout=timeout, | ||
| device_id=device_id, | ||
| ) |
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Hardcoding the device type to cuda and the backend to nccl breaks support for other platforms supported by vLLM (e.g., XPU, ROCm). Use current_platform to determine the correct device type and backend string dynamically. Additionally, ensure local_rank is correctly initialized before it is used for the device_id binding.
if local_rank == -1:
local_rank = (
envs.LOCAL_RANK if distributed_init_method == "env://" else rank
)
if envs.VLLM_USE_SPLIT_GROUP:
from vllm.platforms import current_platform
if current_platform.is_cuda_alike():
device_type = "cuda"
elif current_platform.is_xpu():
device_type = "xpu"
elif current_platform.is_out_of_tree():
device_type = current_platform.device_name
else:
device_type = "cpu"
# Use mixed backend so the default PG has both CPU (gloo) and
# device backends. Pass device_id to eagerly initialize
# the device communicator, enabling split_group for subgroups.
# On CPU-only systems, fall back to gloo.
if torch.accelerator.is_available() and backend != "gloo":
init_backend = f"cpu:gloo,{device_type}:{backend}"
device_id = torch.device(f"{device_type}:{local_rank}")
else:
init_backend = "gloo"
device_id = None
torch.distributed.init_process_group(
backend=init_backend,
init_method=distributed_init_method,
world_size=world_size,
rank=rank,
timeout=timeout,
device_id=device_id,
)| if ( | ||
| envs.VLLM_USE_SPLIT_GROUP | ||
| and torch.accelerator.is_available() | ||
| ): | ||
| # When an external launcher (e.g. torchrun) initialized the default | ||
| # PG, require both ``device_id`` and a CPU backend. ``GroupCoordinator`` | ||
| # builds two ``split_group`` subgroups (device-only and CPU+device), | ||
| # so the parent must already have both backends — ``split_group`` only | ||
| # selects subsets, it doesn't add new backends. | ||
| default_pg = torch.distributed.distributed_c10d._get_default_group() | ||
| assert default_pg.bound_device_id is not None, ( | ||
| "External launcher initialized the default process group " | ||
| "without device_id. vLLM requires the default PG to be device-" | ||
| "bound for split_group. Pass device_id=torch.device(f'cuda:" | ||
| "{local_rank}') to torch.distributed.init_process_group()." | ||
| ) | ||
| try: | ||
| default_pg._get_backend(torch.device("cpu")) | ||
| except RuntimeError as e: | ||
| raise RuntimeError( | ||
| "External launcher initialized the default process group " | ||
| "without a CPU (gloo) backend. vLLM requires both CPU and " | ||
| "device backends. Pass backend='cpu:gloo,cuda:nccl' to " | ||
| "torch.distributed.init_process_group()." | ||
| ) from e |
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The error messages hardcode cuda and nccl, which is misleading on non-CUDA platforms. These should be updated to use dynamic platform information to provide accurate guidance to users on other hardware.
if (
envs.VLLM_USE_SPLIT_GROUP
and torch.accelerator.is_available()
):
from vllm.platforms import current_platform
if current_platform.is_cuda_alike():
device_type = "cuda"
elif current_platform.is_xpu():
device_type = "xpu"
elif current_platform.is_out_of_tree():
device_type = current_platform.device_name
else:
device_type = "cpu"
if local_rank == -1:
local_rank = envs.LOCAL_RANK if distributed_init_method == "env://" else rank
# When an external launcher (e.g. torchrun) initialized the default
# PG, require both ``device_id`` and a CPU backend. ``GroupCoordinator``
# builds two ``split_group`` subgroups (device-only and CPU+device),
# so the parent must already have both backends — ``split_group`` only
# selects subsets, it doesn't add new backends.
default_pg = torch.distributed.distributed_c10d._get_default_group()
assert default_pg.bound_device_id is not None, (
"External launcher initialized the default process group "
"without device_id. vLLM requires the default PG to be device-"
f"bound for split_group. Pass device_id=torch.device(f'{device_type}:"
f"{local_rank}') to torch.distributed.init_process_group()."
)
try:
default_pg._get_backend(torch.device("cpu"))
except RuntimeError as e:
raise RuntimeError(
"External launcher initialized the default process group "
"without a CPU (gloo) backend. vLLM requires both CPU and "
f"device backends. Pass backend='cpu:gloo,{device_type}:{backend}' to "
"torch.distributed.init_process_group()."
) from e
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Summary: # Use `torch.distributed.split_group` for process-group creation ## Summary This PR replaces `torch.distributed.new_group` with `torch.distributed.split_group` for cpu/device subgroup creation in `GroupCoordinator`. `split_group` is required by both the deprecation of lazy NCCL initialization and the planned migration to torchcomms. --- ## Motivation ### 1. Lazy init is going away — eager init is now the recommended path PyTorch is removing the lazy-init path for NCCL process groups in favor of eager initialization (PG creation eagerly constructs the NCCL communicator). `ncclCommSplit` operates on an *existing* parent communicator. If the parent was never eagerly initialized, there is nothing to split. Eager init is the prerequisite for moving away from `new_group`. ### 2. `split_group` is more efficient than `new_group` `new_group` runs a **full bootstrap** for every subgroup created that makes `new_group` startup dominate end-to-end init time at scale. `split_group` uses `ncclCommSplit` to **reuse the parent communicator's bootstrap state** — the child communicator is produced in a single collective splitting step on the existing connections. More details in [c10d split_group RFC](https://dev-discuss.pytorch.org/t/rfc-c10d-a-new-pytorch-api-split-group-to-create-a-process-group-through-ncclcommsplit/2233). ### 3. Forward-compatibility with torchcomms We plan to migrate vLLM's collective backend to [**torchcomms**](https://meta-pytorch.org/torchcomms/main/index.html) in a follow-up PR. torchcomms is the modern PyTorch communications library designed to replace the legacy `ProcessGroup` + `Backend` abstraction. Critically, **torchcomms only supports `split_group`** for subgroup creation; there is no `new_group` equivalent. Adopting `split_group` here makes the eventual torchcomms swap possible with just an environment variable change. --- ## What changed `GroupCoordinator.__init__` now creates two backend-specific subgroups via `split_group(backend=...)`: ```python # Device subgroup (e.g. cuda:nccl) — narrow filter self_device_group = torch.distributed.split_group( split_ranks=[ranks], group_desc=f"{group_name}:device", backend=f"{device_type}:{torch_distributed_backend}", ) # CPU subgroup — must include the parent's default device type to # satisfy split_group's filter constraint, but only the gloo backend is # used for CPU collectives. self_cpu_group = torch.distributed.split_group( split_ranks=[ranks], group_desc=f"{group_name}:cpu", backend=f"cpu:gloo,{device_backend_str}", ) ``` Scripts that initialize the default process group themselves (e.g. `torchrun`-launched code) must now pass: ```python torch.distributed.init_process_group( backend="cpu:gloo,cuda:nccl", device_id=torch.device(f"cuda:{local_rank}"), ) ``` If a caller passes a parent PG missing either, vLLM raises a descriptive error pointing at the exact init call to update. The example torchrun tests in this PR demonstrate the recommended pattern. --- ## Tests All tests run on a 4 × H100 (Hopper) host against the PyTorch nightly: `torch==2.13.0.dev20260510+cu130` ### 1. End-to-end inference correctness (vs PyTorch default) For `Qwen/Qwen3.5-35B-A3B` and `openai/gpt-oss-20b` models, exercised **7 backend configurations** (`default`, `custom_ar_only`, `symm_mem_only`, `nccl_symm_mem`, `pynccl_only`, `flashinfer`, `torch_dist`) with first-10-token exact-match correctness checks and throughput measurements. ### 2. Buildkite distributed-area CI (Hopper / 4 × H100) 8 of the 14 steps in `.buildkite/test_areas/distributed.yaml` | # | Step | |---|---| | 1 | Distributed Comm Ops (2 GPUs) | 2 | Distributed DP Tests (2 GPUs) | 3 | Distributed Compile + RPC (2 GPUs) | 4 | Distributed Torchrun + Shutdown (2 GPUs) | 5 | Distributed Torchrun + Examples (4 GPUs) | 6 | Distributed DP Tests (4 GPUs) | 7 | Distributed Compile + Comm (4 GPUs) | 8 | Pipeline + Context Parallelism (4 GPUs) --- ## Rollback Plan Added a environment variable `VLLM_USE_SPLIT_GROUP` that gates the changes introduced - `split_group` is disabled by default in this PR, will have a follow up PR to enable it --- Signed-off-by: Tushar Jain <tushar00jain@users.noreply.github.com>
Summary: A follow up on vllm-project#41980 to enable the usage of `split_group` API by default --- Signed-off-by: Tushar Jain <tushar00jain@users.noreply.github.com>
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Summary:
A follow up on #41980 to enable the usage of
split_groupAPI by defaultSigned-off-by: Tushar Jain tushar00jain@users.noreply.github.com
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