[Bugfix] Fix FP8 MoE double memory allocation for non-gated models

[littlecircle0730]

Purpose

Fix #44489

Fp8MoEMethod and Fp8OnlineMoEMethod always allocate 2 * intermediate_size_per_partition for w13 tensors regardless of is_act_and_mul.

For non-gated MoE models (is_act_and_mul=False), only the up projection exists, so the extra allocation is unnecessary and can lead to significant memory waste. For example, NemotronH wastes approximately 14 GiB across 23 MoE layers, resulting in OOM on a single 44 GiB GPU.

This change aligns the FP8 MoE allocation logic with UnquantizedFusedMoEMethod by allocating w13 tensors based on self.moe.is_act_and_mul.

Test Plan

Core: fp8 + non-gated MoE (use_gate=False) — directly exercises the fix

.venv/bin/python -m pytest tests/kernels/moe/test_moe_layer.py::test_moe_layer_no_parallel
-v -k "fp8 and not fp8_blocked and not modelopt_fp4" --no-header

NemotronH quantization test

.venv/bin/python -m pytest tests/model_executor/test_nemotron_h_quantization.py -v
Note: fp8_blocked tests are skipped — they require H100+ (fp8e4nv) and fail on A10 with a pre-existing architecture incompatibility unrelated to this fix.

Test Result

112 passed, 80 skipped in 82.77s # test_moe_layer_no_parallel (fp8, non-blocked)
1 passed # test_nemotron_h_quantization

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[littlecircle0730]

Hi maintainers,

This is my first contribution to vLLM.
Would it be possible to add the ready label so CI can run?

Thank you!

[justin-labry]

Reporter of #44489 here. I independently arrived at the same fix (honor self.moe.is_act_and_mul when sizing w13 in create_weights, mirroring UnquantizedFusedMoEMethod) and verified it end-to-end on real hardware, so I can confirm this PR works.

Setup: single NVIDIA L40S (~44.4 GiB usable), vLLM 0.22.0, bf16 NemotronH non-gated MoE (NemotronHForCausalLM, 128 routed experts, moe_intermediate_size=1856, hidden_size=2688, mlp_hidden_act="relu2"), online quantization via --quantization fp8.

Before the fix: OOM during weight processing — the per-layer w13 FP8 buffer is 128 × (2×1856) × 2688 = 1.19 GiB instead of 128 × 1856 × 2688 = 0.60 GiB; across 23 MoE layers the model inflates from ~32 GB to ~46 GB and exceeds the card:

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.19 GiB.
GPU 0 has a total capacity of 44.39 GiB of which 1.16 GiB is free.

After the fix: model loads at ~32 GB, Available KV cache memory: 11.2 GiB, Application startup complete. Greedy outputs are correct (verified EN / math / Korean), so the non-gated layout runs correctly, not just fits:

• "The capital of France is" → " Paris."
• 17 × 23 → 391
• "대한민국의 수도는" → " 서울입니다."

A couple of notes that may help reviewers:

• This PR patches both Fp8MoEMethod and Fp8OnlineMoEMethod. The online path (Fp8OnlineMoEMethod) is the one that actually OOMs with --quantization fp8 on a bf16 checkpoint, so covering it is the important part. (For comparison, fix: honor is_act_and_mul for FP8 MoE w13 weight allocation (#44489) #44491 only patches the base Fp8MoEMethod.)
• For the online path no further change is needed beyond create_weights: Fp8OnlineMoEMethod.process_weights_after_loading quantizes the whole w13 per expert with a single scale, so it is agnostic to the w13 first-dim size.

One environment note: on a box with the CUDA driver but no toolkit (nvcc), --quantization fp8 additionally needs VLLM_USE_FLASHINFER_SAMPLER=0 to avoid a FlashInfer JIT compile of the sampler — unrelated to this PR, just flagging for anyone reproducing.

(Disclosure: diagnosis and the patch were done with AI assistance; the reproduction and verification above were run by me on the hardware described.)

[justin-labry]

Reporter of #44489 here — thanks for picking this up, and for covering the Fp8OnlineMoEMethod path (that's the one that actually OOMs with --quantization fp8 on a bf16 checkpoint).

I'd applied the equivalent change locally (branching w13_up_dim on self.moe.is_act_and_mul in Fp8OnlineMoEMethod.create_weights) before this PR existed, and can confirm it resolves the OOM on real single-GPU hardware, which complements the A10 CI here (where fp8_blocked is skipped and the actual single-card fit isn't exercised).

Setup

• GPU: 1× NVIDIA L40S (46 GB; ~44.4 GiB usable)
• Model: NemotronH-family non-gated MoE (up_proj only, relu2), bf16 ≈ 63 GB, online FP8 via --quantization fp8
• Same architecture as the public repro checkpoint nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B

Memory (per MoE layer, w13 FP8 buffer)

Path	w13 / layer	× 23 layers	FP8 model total	Single L40S
before (2×)	1.19 GiB	27.4 GiB	~46 GB	❌ OOM
after (is_act_and_mul)	0.60 GiB	13.7 GiB	~32 GB	✅ fits, ~11 GiB free for KV

Run command (works after the fix)

vllm serve <bf16 NemotronH non-gated checkpoint> \
  --quantization fp8 --gpu-memory-utilization 0.95 \
  --max-model-len 8192 --max-num-seqs 8 --tensor-parallel-size 1 --trust-remote-code

Correctness: outputs verified across English, math (17×23 = 391), and Korean — no regression from the smaller w13 allocation, as expected since the online path quantizes the whole w13 per expert with a single scale and is agnostic to its first-dim size.

LGTM functionally. Happy to re-run anything specific on the L40S if useful.

(Unrelated to this PR, for anyone reproducing on a toolkit-less host: I also needed VLLM_USE_FLASHINFER_SAMPLER=0, since the FlashInfer sampler JIT-compiles a CUDA kernel and requires nvcc. Not relevant to the allocation fix itself.)

[littlecircle0730]

Hi @justin-labry,

Appreciate your detailed validation and feedback. It's especially helpful to have confirmation from real hardware and a production-like setup, since the issue is difficult to fully reproduce through unit tests alone.

Thanks as well for highlighting the distinction between Fp8MoEMethod and Fp8OnlineMoEMethod and for confirming that the online FP8 path behaves correctly after the fix. :)

[littlecircle0730]

Friendly ping. This PR fixes/reproduces issue #44489.
Would appreciate a review when someone has time. Thanks!

[littlecircle0730]

Hi maintainers,
I have 3 merged PR in vLLM now. I am still not able to run the CI without ready label.
Would it be possible to add the ready label so CI can run?
Thank you!

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @littlecircle0730.

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

[aoshen02]

Hi, thanks for the pr. I've solve the problem in #51125 and list you as an author. Hope it works for you, lmk if any other problems.