[Distributed][MoonEP] BF16 integration of MoonEP balanced EP backend - #52101
[Distributed][MoonEP] BF16 integration of MoonEP balanced EP backend#52101kaijunli-infr wants to merge 4 commits into
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Adds a correctness-first proof-of-concept integration of MoonEP (https://github.com/MoonshotAI/MoonEP), the balanced expert-parallelism communication library used by Kimi K3, as a vLLM all2all backend. Part of the Kimi K3 tracking issue (vllm-project#50001); roadmap RFC: vllm-project#52095. Included: - "moonep" All2AllBackend registration and has_moonep() probe - MoonEPAll2AllManager owning the cached moonep.Buffer - MoonEPPrepareAndFinalize (pad -> dispatch -> prefetch_weight -> stash plan; combine in finalize), conforming to the modular P/F interface - Replicated [E+B, ...] BF16 weight-layout helper and a reference segment-loop expert runner over cu_seqlens - torchrun validation script comparing the full dispatch/prefetch/ compute/combine path against a dense per-token reference MoE Validated on 4x GB300 (EP=4, NVLink): all ranks pass at default config, heavy router skew (--skew 8), and larger shapes (S=512 H=2048 top-8 of 64 experts); rel_err within BF16 tolerance. PoC limitations (follow-ups tracked in vllm-project#52095): BF16/unquantized only, eager only, reference segment loop instead of a grouped GEMM over cu_seqlens, weights replicated per rank instead of sharded symmetric-memory expert ownership, and no FusedMoE layer/engine wiring yet. Signed-off-by: kevinli <306243285+kaijunli-infr@users.noreply.github.com>
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tlrmchlsmth
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Generally the changes look good + straightforward so far, butI think we should hook up the experts in this PR and ensure it works e2e before landing. From codex:
When --all2all-backend moonep is selected, this branch installs a prepare/finalize implementation that emits [S*K, H] route rows but no dispatched top-k IDs. The generic modular wrapper consequently reuses the original [T, K] IDs, causing standard experts to fail their row-count assertion or consume incompatible routing; weight_layout=None also skips redundant-weight prefetching.
We also need to handle the MoonEP build but I'm OK doing that in a separate PR
| def has_moonep() -> bool: | ||
| """Whether the optional `moonep` package is available.""" | ||
| return _has_module("moonep") |
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This PR probably needs to add a helper to ensure that moonep is compatible with the current system as well
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Good call. I'll add a has_moonep()-adjacent compatibility check along the lines of has_deep_ep_v2()'s NCCL probe: verify the moonep package imports, and that the CUDA driver/device support the VMM + multicast (fabric) path MoonEP's symmetric-memory buffers require, so a misconfigured host fails at backend selection with a clear message rather than inside Buffer() construction.
| quant_config: FusedMoEQuantConfig, | ||
| defer_input_quant: bool = False, | ||
| ) -> mk.PrepareResultType: | ||
| if a1.dtype != torch.bfloat16: |
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Can you add an assert that self._plan is None here to catch if prepare is called again before finalize?
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Will do — I'll assert self._plan is None at the top of prepare so a prepare→prepare without an intervening finalize fails loudly.
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| def run_moonep_bf16_reference_experts( |
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Can this be moved to the test file?
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Yes. Once the real experts implementation is in (see the e2e scope above), the reference segment loop is test-only — I'll move it into the new test file alongside the pure-PyTorch reference MoE.
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| # SPDX-License-Identifier: Apache-2.0 | |||
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Can you turn this into a pytest test? e.g. add a new test in tests/kernels/moe/test_moonep_bf16_proc.py
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Will do — converting it to tests/kernels/moe/test_moonep_bf16_poc.py using parallel_launch from parallel_utils.py, following test_deepep_v2_moe.py, gated on has_moonep() and @multi_gpu_test. The standalone script goes away.
| apply_router_weight_on_input: bool, | ||
| weight_and_reduce_impl: mk.TopKWeightAndReduce, | ||
| ) -> None: | ||
| # Route weights were applied by the expert runner; combine only sums |
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You could assert isinstance(weight_and_reduce_impl, TopKWeightAndReduceNoOP) to make sure that MoonEP isn't mixed with the wrong kind of experts.
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Agreed — I'll assert isinstance(weight_and_reduce_impl, TopKWeightAndReduceNoOP) in finalize. MoonEP's combine does the K-sum itself and route weights are applied in the expert compute, so any experts implementation that expects to apply weights/reduce in finalize is the wrong pairing and should fail fast.
Thanks for the review, and agreed on both points. The diagnosis is right: today the backend is registered but the engine path would break — I'll fold the e2e work into this PR rather than a follow-up:
Also agreed on handling the MoonEP build (pinned commit in |
- Add check_moonep_system_support() (NVSwitch multicast probe) and call it from MoonEPAll2AllManager so unsupported hosts fail at backend selection - Assert prepare() is not re-entered before finalize() - Assert finalize() receives TopKWeightAndReduceNoOP (MoonEP applies route weights in expert compute and K-sums in combine) - Document that prefetch slots need no re-zeroing between calls - Move the reference segment runner into the test and convert the standalone validation script into tests/kernels/moe/test_moonep_bf16_poc.py (parallel_launch, torch_experts reference, skewed router) Signed-off-by: kevinli <306243285+kaijunli-infr@users.noreply.github.com>
Adds a FusedMoEExpertsModular implementation for MoonEP's expert-grouped [NvS, H] activation layout: three torch._grouped_mm calls (gate, up, down) over the cu_seqlens[E+B] segments with route weights applied before the down projection, so finalize is a pure MoonEP combine (TopKWeightAndReduceNoOP). Empty segments, including unused prefetch slots, are skipped by the grouped GEMM. Gate and up are separate contiguous [E+B, I, H] tensors because MoonEP's prefetch_weight asserts contiguity per projection; the up tensor is supplied via set_up_weight(). MoonEPPrepareAndFinalize gains set_weight_layout() (for wiring after weight loading) and num_dispatched_slots (NvS); prefetch_weight is now mandatory rather than silently skipped when no layout is set. The MoonEP test is parametrized over use_modular_kernel to also exercise MoonEPPrepareAndFinalize + MoonEPExperts through FusedMoEKernel. Signed-off-by: kevinli <306243285+kaijunli-infr@users.noreply.github.com>
Registers MoonEP as an unquantized MoE backend and connects the weight layout so --all2all-backend moonep works end to end: - UnquantizedMoeBackend.MOONEP, selected whenever moe_parallel_config.use_moonep_kernels (MoonEP owns the expert layout so it is not interchangeable with token-major experts backends) - convert_to_unquantized_kernel_format builds the [E+B] layout: all-gather each rank's local experts into global expert order (PoC bridge until sharded symmetric-memory ownership, RFC vllm-project#52095), then split into contiguous gate / up / down tensors as MoonEP's prefetch requires. The layer's w13_weight becomes the gate tensor and w2_weight the down tensor. - _setup_kernel hands the layout to MoonEPPrepareAndFinalize (set_weight_layout) and MoonEPExperts (set_up_weight) after the kernel is built - MoonEP prefetch tile constraint (H, I multiples of 128) validated at layout construction; MoonEPExperts ignores expert_map (global rows) Validated with vllm serve allenai/OLMoE-1B-7B-0924 --data-parallel-size 4 --enable-expert-parallel --all2all-backend moonep --enforce-eager on 4x GB300: greedy generations over 16 prompts are token-identical to the allgather_reducescatter baseline on 10/16 and match for 16-30 tokens before diverging on the rest, with mean |dlogprob| 0.011 on matched tokens (bf16 accumulation-order differences between grouped_mm and Triton). Signed-off-by: kevinli <306243285+kaijunli-infr@users.noreply.github.com>
kaijunli-infr
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Purpose
First item of the MoonEP integration roadmap RFC #52095: MoonEP
(https://github.com/MoonshotAI/MoonEP) as a vLLM all2all backend, BF16 and
eager, working end to end with
--all2all-backend moonep.MoonEP keeps every EP rank at exactly
S × Ktokens regardless of routerskew by planning a few dynamically redundant experts online and prefetching
their weights before expert compute. Its contract differs from DeepEP-style
backends:
dispatchreturns tokens already grouped per expert row(
[NvS, H]+cu_seqlens[E+B]+ an opaque plan), rows[E, E+B)areprefetch slots, and there are no per-token top-k ids after dispatch — so the
expert compute is a grouped GEMM over
cu_seqlenssegments.Included:
"moonep"All2AllBackend,has_moonep(), andcheck_moonep_system_support()(NVSwitch multicast probe run at backendselection so unsupported hosts fail with a clear message)
MoonEPAll2AllManagerowning the cachedmoonep.BufferMoonEPPrepareAndFinalizeon the modular prepare/finalize interface: padto static capacity →
dispatch→prefetch_weightfor the plannedredundant experts (plan stashed between prepare and finalize, the same
pattern DeepEP-HT uses for its handle) →
combinein finalize(
TopKWeightAndReduceNoOP)MoonEPExperts(FusedMoEExpertsModular): threetorch._grouped_mmcalls (gate, up, down) over the
cu_seqlens[E+B]segments, route weightsapplied before the down projection; empty segments and unused prefetch
slots are skipped natively
FusedMoEwiring:UnquantizedMoeBackend.MOONEP(auto-selected when thebackend is
moonep);convert_to_unquantized_kernel_formatbuilds the[E+B]layout — all-gather each rank's local experts into global order,split into contiguous gate / up / down tensors as MoonEP's prefetch
requires — and
_setup_kernelhands it to the P/F and expertstests/kernels/moe/test_moonep_bf16_poc.py: multi-GPU test of the fulldispatch/prefetch/compute/combine path against the shared
torch_expertsreference, parametrized over a reference segment-loop runner and the full
FusedMoEKernel(P/F +MoonEPExperts) composition, with a skewed routerto exercise the redundant-expert planner
Known limitations, tracked as follow-up items in #52095: BF16/unquantized
only; eager only (no CUDA graphs yet);
torch._grouped_mmrather than atuned grouped GEMM; expert weights replicated per rank via a one-time
all-gather at load, rather than MoonEP's sharded symmetric-memory ownership
(the memory-viability requirement for Kimi-K3 scale). The MoonEP build /
commit pin in
tools/ep_kernelsis deferred to a separate PR.Test Plan
On an NVSwitch multicast capable node with the
mooneppackage installed:Test Result
Kernel test —
test_moonep_bf16_moe: 16 passed on GB300 (EP=2):4 shapes (m=1, 37, 100, 512; K up to 2048; top-4 of 32 experts) × 2 router
skew levels (1.0 and 8.0, the latter forcing the planner to fill the
redundant-expert prefetch slots) × {reference runner,
FusedMoEKernel+MoonEPExperts}, each withinatol=rtol=6e-2oftorch_experts(thetolerance the DeepEP tests use).
End to end —
vllm serve allenai/OLMoE-1B-7B-0924(top-8 of 64experts, H=2048), DP=4 / EP=4, eager, on 4× GB300. Log confirms the path:
Using MoonEPAll2AllManager→Using MOONEP Unquantized MoE backend→Using MoonEPPrepareAndFinalize/Using MoonEPExperts. Greedy generationsover 16 prompts vs. the
allgather_reducescatterbaseline (same model,same flags):
The divergences are greedy tie-break flips on near-tie tokens (e.g. "made
up of gases and not solid matter" vs. "made up of gases. It is the fifth
planet"), consistent with bf16 accumulation-order differences between
torch._grouped_mmand the Triton kernel; all continuations are fluent andfactually correct (17×3=51, correct recursive Fibonacci, Einstein/1915,
Guido van Rossum, 100 °C).
The e2e model is OLMoE rather than Qwen3-30B-A3B because the test host's
GPUs are shared and the replicated-weights layout of this PR needs ~58 GB
per rank for Qwen3-30B-A3B; the run will be repeated on Qwen3-30B-A3B when
capacity allows. All pre-commit hooks pass on the changed files, including
mypy 3.12.
Notes
backend (searched "MoonEP" across PRs/issues; no implementation PRs
exist).