[EPLB] Add the MoE Load Balancer policy: pluggable expert placement and replica routing

[yunyang1999]

Purpose

Add an optional expert-parallel load-balancing backend to EPLB: --eplb-config '{"policy": "mlb", ...}' delegates expert placement (the EPLB rearrangement) and, optionally, per-token replica routing to the MoE Load Balancer (moe_load_balancer), a framework-neutral library of load-balancing policies (DeepSeek EPLB hierarchical/global, LPLB, Waterfill for the shared expert, and UltraEP's online placement/quota refresh). vLLM keeps the mechanism, the library keeps the policies.

What the PR adds, all inactive unless policy: "mlb" is selected (the package is imported lazily; vLLM starts and runs unchanged without it):

• vllm/distributed/eplb/policy/mlb.py: the mlb EPLB policy (L1 placement) behind the existing AbstractEplbPolicy interface.
• vllm/distributed/eplb/mlb_runtime.py: the routing runtime (L2). A policy can answer per token with replica shares or with resolved physical ids; both go through the existing fused EPLB map-and-record Triton kernel in router/base_router.py, so load recording, the record gate and the padding mask are untouched. UltraEP's online refresh re-solves placement and per-replica quotas every N representative batches and moves the affected expert weights with a pairwise send/recv mover over the EPLB communicator (a symmetric-memory mover is available for single-host / multi-node-NVLink deployments).
• --eplb-config keys l2_algorithm, init_placement_path, save_placement_path: routing expression, and fit-once / serve-with-held-placement (the equivalent of SGLang's --init-expert-location).
• VLLM_FUSE_SHARED_EXPERTS / shared_expert_fusion.py: dispatch the shared expert through the EP all-to-all as one slot per rank instead of a replicated MLP, so a policy (Waterfill) can place it on the least-loaded rank. Off unless requested by the env var or by a policy that needs it.
• A DP-synced uniform_decode_across_dp flag on the forward context (one more row in the existing DP coordination all-reduce): a policy that issues a collective inside the forward must reach the same verdict on every rank, and the per-rank uniform_decode is exactly the wrong signal when a rank runs a dummy decode while its peers prefill. The dummy-run path replays the policy's per-step collective for the same reason (upstream already replays eplb_step there).
• rank_imbalance (per-layer peak-rank / mean-rank load) next to the existing balancedness in the EPLB log, since the latter reduces over layers rather than ranks.
• Two DeepEP knobs that came out of running EP16 across nodes: VLLM_DEEPEP_RDMA_BUFFER_SIZE_MB (size the RDMA buffer separately; DeepEP caps the NVL buffer when RDMA bytes are non-zero) and VLLM_DEEPEP_HT_USE_MNNVL (+ VLLM_DEEPEP_V2_*_GBS) for multi-node NVLink fabrics. Separable if preferred.
• Docs: a "MoE Load Balancer policy" section in docs/serving/expert_parallel_deployment.md.

Intrusiveness: the touched core paths (fused_moe/layer.py, router/base_router.py, eplb_state.py, gpu_model_runner.py, dp_utils.py, forward_context.py, deepseek_v2.py weight loading) are additive and guarded; with the default policy the fused router kernel compiles the same variant as before (the new constexpr flags are false), the DP all-reduce carries one extra int per rank, and the model runner's replay is skipped when no MLB routing is active.

Tuning knobs are read through vllm.envs (VLLM_MLB_L1_ALGORITHM, VLLM_MLB_L2_ALGORITHM, MLB_ULTRAEP_*); we intend to fold the MLB_ULTRAEP_* ones into --eplb-config fields in a follow-up and would welcome guidance on the preferred shape.

Test Plan

• Unit tests (skip cleanly without moe_load_balancer): tests/distributed/test_eplb_mlb_policy.py, tests/distributed/test_eplb_mlb_routing.py, tests/distributed/test_eplb_replica_shares_kernel.py (fused router kernel with replica shares / resolved ids, no MLB needed), tests/kernels/moe/test_routing.py (extended), plus the existing test_eplb_algo.py, test_eplb_utils.py; wired into the EPLB Algorithm CI step.
• pre-commit run --files <changed files>: ruff, ruff-format, typos, SPDX, torch.cuda-call check, markdownlint, test tethering, mypy pass.
• End-to-end, DeepSeek-V3 FP8, DeepEP high-throughput, DeepGEMM, eager, 1000 x 1024-token prompts per round at concurrency 256, max_tokens=1: (a) 8 x Hopper, TP1 x DP8, EP8, red16; (b) 2 x 8 Hopper, TP1 x DP16, EP16, red16/red32, placement recorded on one dataset and served on another (gsm8k -> mmlu -> gpqa -> gsm8k rotation).
• Greedy-decode consistency probe (48 prompts x 32 tokens, temperature=0) against plain vLLM, with a placement-only (held static placement) reference for the numeric floor.

Test Result

Static checks. pre-commit run --files <changed files>: every hook passes (ruff, ruff-format, typos, SPDX, torch.cuda-call check, markdownlint, Buildkite test tethering, mypy 3.10).

Unit tests (8 x Hopper, moe_load_balancer installed): tests/distributed/test_eplb_mlb_policy.py test_eplb_mlb_routing.py test_eplb_replica_shares_kernel.py tests/kernels/moe/test_routing.py test_eplb_algo.py test_eplb_utils.py -> 581 passed, 1 skipped.

Single node, DeepSeek-V3 FP8, TP1 x DP8, EP8, 16 redundant experts, gsm8k prompts, placement fitted on gsm8k and held (this branch):

Arm	tok/s	rank imbalance
plain vLLM (--enable-eplb off), three launches	19.44k / 19.47k / 19.51k	-
Static EPLB on the fitted placement (earlier report, same protocol)	20.51k	1.213
policy: mlb, UltraEP L1+L2, refresh every 8 batches, direct mover, synchronous apply	22.13k	1.051

Greedy-decode consistency (48 prompts x 32 tokens, temperature=0, identical texts): plain vLLM vs plain vLLM (two launches) 48/48; plain vs Static EPLB on the held placement 21/48; plain vs UltraEP 20/48; Static vs UltraEP 25/48. Any change of expert placement moves the agreement to about 20/48 (FP8 grouped-GEMM accumulation order); UltraEP sits at that floor and every divergence inspected is a plausible alternative continuation at a near-tie token.

Two nodes, DeepSeek-V3 FP8, TP1 x DP16, EP16, DeepEP high-throughput, placement recorded on one prompt set and served on another (gsm8k -> mmlu -> gpqa -> gsm8k), 84 arms (this branch's code minus the final polish):

red	serving set	Static EPLB, stale placement	Static EPLB, own placement	best UltraEP arm from the stale placement
16	mmlu	34.87k / 1.370	38.81k / 1.093	UltraEP+Waterfill i=8: 38.04k (98.0% of own)
16	gpqa	33.96k / 1.452	39.18k / 1.107	UltraEP+Waterfill i=1: 37.71k (96.3%)
16	gsm8k	31.93k / 1.616	39.15k / 1.112	UltraEP+Waterfill i=1: 38.00k (97.1%)
32	mmlu	34.79k / 1.391	39.11k / 1.088	UltraEP+Waterfill i=8: 38.92k (99.5%)
32	gpqa	34.46k / 1.406	38.59k / 1.099	UltraEP+Waterfill i=8: 38.59k (100.0%)
32	gsm8k	32.24k / 1.626	38.62k / 1.105	UltraEP+Waterfill i=8: 39.09k (101.2%)

A stale placement costs Static EPLB 10-18%; from the same stale placement UltraEP recovers to 96-101% of the no-drift reference. Arms whose code path this PR does not change (plain vLLM, identity layout, Static, Uniform) reproduce an earlier run of the same protocol within +-3%. MLB_ULTRAEP_MOVER=auto across the two hosts takes the collective fall-back to the direct mover on every rank and completes its first refresh without hanging.

Notes

• use_async must be false with policy: "mlb"; the config validation says so.
• The MLB package is not added to vLLM's requirements; it is an optional dependency installed by the operator (documented).

---

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Documentation preview: https://vllm--59168.org.readthedocs.build/en/59168/

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yunyang1999.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[abmfy]

Hi, https://github.com/xutizhou/moe_load_balancer 404 and seems private. Could you please make it public? Thanks!

[yunyang1999]

Hi, https://github.com/xutizhou/moe_load_balancer 404 and seems private. Could you please make it public? Thanks!

Hi, thanks for pointing this out. The open-source release is currently under process and will be delayed a bit, but we’ll let you know once the repo is ready to be made public.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yunyang1999.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yunyang1999.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork