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Remove all2all backend envvar (#30363)

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Signed-off-by: Elizabeth Thomas <email2eliza@gmail.com>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Elizabeth Thomas
2025-12-18 13:46:28 -06:00
committed by GitHub
parent 97000a2be7
commit 41b6f9200f
12 changed files with 40 additions and 43 deletions
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49
vllm/config/parallel.py
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@@ -36,6 +36,14 @@ ExpertPlacementStrategy = Literal["linear", "round_robin"]
DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher"]
DataParallelBackend = Literal["ray", "mp"]
EPLBPolicyOption = Literal["default"]
All2AllBackend = Literal[
"naive",
"pplx",
"deepep_high_throughput",
"deepep_low_latency",
"allgather_reducescatter",
"flashinfer_all2allv",
]
@config
@@ -126,24 +134,14 @@ class ParallelConfig:
with 4 experts and 2 ranks, rank 0 will have experts [0, 2] and rank 1
will have experts [1, 3]. This strategy can help improve load balancing
for grouped expert models with no redundant experts."""
all2all_backend: (
Literal[
"naive",
"pplx",
"deepep_high_throughput",
"deepep_low_latency",
"allgather_reducescatter",
"flashinfer_all2allv",
]
| None
) = None
"""All2All backend for MoE expert parallel communication. If not set, uses
the value from VLLM_ALL2ALL_BACKEND environment variable. Available options:
- "naive": Naive all2all implementation using broadcasts
- "allgather_reducescatter": All2all based on allgather and reducescatter
- "pplx": Use pplx kernels
- "deepep_high_throughput": Use deepep high-throughput kernels
- "deepep_low_latency": Use deepep low-latency kernels
all2all_backend: All2AllBackend = "allgather_reducescatter"
"""All2All backend for MoE expert parallel communication. Available options:
- "naive": Naive all2all implementation using broadcasts\n
- "allgather_reducescatter": All2all based on allgather and reducescatter\n
- "pplx": Use pplx kernels\n
- "deepep_high_throughput": Use deepep high-throughput kernels\n
- "deepep_low_latency": Use deepep low-latency kernels\n
- "flashinfer_all2allv": Use flashinfer alltoallv kernels for mnnvl"""
max_parallel_loading_workers: int | None = None
@@ -495,20 +493,17 @@ class ParallelConfig:
from vllm.config.utils import get_hash_factors, hash_factors
factors = get_hash_factors(self, ignored_factors)
# Explicitly include backend affecting env factor as before
factors["VLLM_ALL2ALL_BACKEND"] = str(envs.VLLM_ALL2ALL_BACKEND)
return hash_factors(factors)
def __post_init__(self) -> None:
# Set all2all_backend from env var if not specified, with deprecation warning
if self.all2all_backend is None:
if envs.is_set("VLLM_ALL2ALL_BACKEND"):
logger.warning_once(
"VLLM_ALL2ALL_BACKEND environment variable is deprecated and "
"will be removed in v0.15.0. Please use the "
"--all2all-backend command-line argument instead."
)
self.all2all_backend = envs.VLLM_ALL2ALL_BACKEND
if envs.is_set("VLLM_ALL2ALL_BACKEND"):
logger.warning_once(
"VLLM_ALL2ALL_BACKEND environment variable is deprecated and "
"will be removed in a future release. Please use the "
"--all2all-backend command-line argument instead."
)
# Continue with the rest of the initialization
self.world_size = (