[FP8] Add opt-in ParallelLMHead dispatch to Fp8Config

[webcodes-cz]

Tracking issue: #40999

Purpose

Add legacy FP8 support for ParallelLMHead so that checkpoints with a
block-FP8-quantized lm_head (companion lm_head.weight_scale_inv,
DeepSeek-V3-style 128×128 blocks) can be served by stock vLLM. The
opt-in is driven from quantization_config.lm_head: true in the
checkpoint config, mirroring the existing pattern in awq_marlin,
gptq_marlin, cpu_wna16, and inc.

What this PR does

• Gap 1 — Fp8Config.get_quant_method() opts into ParallelLMHead
  when lm_head_quantized=True. Skipped ParallelLMHead returns
  UnquantizedEmbeddingMethod; non-skipped returns Fp8LinearMethod.
• Gap 2 — qwen3_5.py passes quant_config into the
  ParallelLMHead(...) constructor (the motivating model class). Other
  model classes are unchanged in this PR; see Follow-ups below.
• Gap 3 — companion-parameter loading. Fp8LinearMethod.create_weights,
  when called for a ParallelLMHead layer, installs a per-parameter
  scale loader (_make_lm_head_block_scale_loader) at parameter
  construction time. This avoids tripping
  VocabParallelEmbedding.weight_loader's
  loaded_weight.shape[output_dim] == self.org_vocab_size assertion on
  FP8 companion params like weight_scale_inv (shape
  [ceil(vocab/128), ceil(hidden/128)]).

The design selected for Gap 3 is the second option from the original
draft request-for-feedback (the more surgical
Fp8LinearMethod.create_weights path), not the broader generic
VocabParallelEmbedding.weight_loader extension.

End-to-end validation

Public reproducer:
🤗 https://huggingface.co/inferRouter/Qwen3.6-27B-FP8-lmhead-fp8

This is Qwen/Qwen3.6-27B-FP8 with one tensor changed: lm_head.weight
re-quantized BF16 → block-FP8 (e4m3fn) and lm_head.weight_scale_inv
added. All other shards are byte-identical to upstream; config.json
sets quantization_config.lm_head: true and removes lm_head from
modules_to_not_convert.

RTX 6000 Pro 96 GB — token emission verified

Run with --gpu-memory-utilization 0.85 --max-model-len 8192 --max-num-seqs 4 --kv-cache-dtype fp8_e4m3 --language-model-only:

• Fp8Config.get_quant_method dispatches ParallelLMHead →
  Fp8LinearMethod ✓
• CutlassFP8ScaledMMLinearKernel selected for the lm_head
  sampler matmul ✓
• _make_lm_head_block_scale_loader consumes
  weight_scale_inv (BF16, shape [1940, 40] for this checkpoint)
  without AssertionError ✓
• Model loading: 27.32 GiB (vs 27.64 GiB on the BF16-lm_head
  baseline; delta 0.32 GiB on this stack) ✓
• Application startup complete ✓
• Short deterministic prompt (Reply with exactly: GAP3 OK) →
  exact-match output, deterministic, finish_reason=stop ✓
• Longer Czech generation (~165 completion tokens, FP8 quantization
  topic) → grammatically clean, factually correct, no degenerate
  repetition ✓

RTX 5090 32 GB — fit and sanity verified

The PR's loader plumbing was additionally validated against the same
checkpoint on RTX 5090 32 GB by stacking it with the hybrid TurboQuant
runtime from #39931 on top of vllm/vllm-openai:v0.20.0. The lm_head
FP8 saving on this stack reads as ~1.18 GiB at "Model loading took"
(BF16-lm_head: 27.66–27.69 GiB → FP8-lm_head: 26.5 GiB), which
matches the physical [248320, 5120] BF16→FP8 delta. The earlier
0.32 GiB number on RTX 6000 Pro was specific to that stack's autotune /
profiling allocation; the full delta surfaces on the 0.20 + TurboQuant
runtime where the autotune scratch is paid out of a different bucket.

End-to-end: short deterministic + Czech sanity prompts pass on the
RTX 5090 stack. The reproducibility envelope for the 5090 path lives in
the HF model card.

Reproducing the failure on stock vLLM (without this PR)

huggingface-cli download inferrouter/Qwen3.6-27B-FP8-lmhead-fp8 \
  --local-dir ./qwen36-27b-lmhead-fp8

vllm serve ./qwen36-27b-lmhead-fp8 \
  --gpu-memory-utilization 0.92 \
  --max-model-len 4096 \
  --max-num-seqs 4 \
  --enforce-eager \
  --kv-cache-dtype fp8_e4m3

ValueError: There is no module or parameter named 'lm_head.weight_scale_inv'
in Qwen3_5ForCausalLM. The available parameters belonging to lm_head
(ParallelLMHead) are: {'lm_head.weight'}

With this PR applied, the same command loads cleanly and the engine
reaches Application startup complete.

Tests

Mechanically and functionally validated end-to-end on a real
Qwen3.6-27B-FP8-lmhead-fp8 checkpoint, on both RTX 6000 Pro 96 GB
(token emission, deterministic + Czech) and RTX 5090 32 GB (fit + math

• Czech sanity, via the [Feature] TurboQuant: support hybrid models and uniform quantization #39931 overlay).

Automated unit/integration test coverage is not added in this PR
because a usable in-tree FP8 lm_head checkpoint is needed and the
public reproducer above is the only existing one. I'm happy to add
tests once a maintainer-preferred test fixture / fixture path is
agreed.

Suggested coverage for a follow-up tests PR:

• untied lm_head and tied embeddings with
  quantization_config.lm_head=True
• TP=1 and TP=2 (vocab-parallel sharding × FP8 block scales)
• memory-drop assertion on a small synthetic FP8 model

Follow-ups (out of scope for this PR)

• 32 other vllm/model_executor/models/*.py files still construct
  ParallelLMHead(...) without quant_config=. This PR only updates
  qwen3_5.py (the motivating case). A mechanical follow-up PR will
  cover the rest once this dispatcher pattern is approved.
• Docs update for docs/features/quantization/fp8.md.

Related work

Related to #35696. This PR follows the general direction the maintainer
review on that PR pointed at: a generic config-driven opt-in rather
than an environment variable or model-specific dtype cast.

Checklist

• DCO Signed-off-by present
• No new dependencies
• Gap 1 (Fp8Config dispatcher)
• Gap 2 (qwen3_5 quant_config plumbing)
• Gap 3 (companion-param loader for ParallelLMHead)
• Tests (deferred — see Tests section)
• 32-model quant_config follow-up (separate PR)
• Docs update (docs/features/quantization/fp8.md)

---

Refs: #40999, #35696

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[gemini-code-assist]

Code Review

This pull request introduces FP8 quantization support for the ParallelLMHead in Qwen3.5 models. The changes include updating the Fp8Config to handle lm_head quantization and passing the quantization configuration to the model's head. Feedback points out that the current implementation incorrectly applies linear quantization methods to an embedding-sharded layer, which lacks the necessary interface and weight loader compatibility. Additionally, moving top-level imports into the method is recommended to prevent circular dependency issues.

[gemini-code-assist]

The current implementation of get_quant_method returns Fp8LinearMethod (or Fp8OnlineLinearMethod) for ParallelLMHead. However, Fp8LinearMethod is designed for LinearBase modules and does not implement the embedding method required by VocabParallelEmbedding (the base class of ParallelLMHead). While ParallelLMHead overrides forward to raise a RuntimeError, any code path that might attempt to use it as a standard embedding layer (e.g., if weights are tied and accessed via the embedding interface) will fail with a NotImplementedError.

Furthermore, as noted in the PR description, VocabParallelEmbedding.weight_loader does not currently handle the companion parameters (like weight_scale) created by Fp8LinearMethod. Returning a linear method for an embedding-sharded layer without ensuring the loader and interface compatibility is a high-risk change.

[gemini-code-assist]

Importing ParallelLMHead and UnquantizedEmbeddingMethod at the top level of fp8.py from vllm.model_executor.layers.vocab_parallel_embedding may lead to circular import issues in the future, as quantization configs are often imported by the layers they configure. It is generally safer to perform these imports inside get_quant_method or use TYPE_CHECKING for type hints and importlib for runtime checks if necessary.

[webcodes-cz]

Code Review

This pull request introduces FP8 quantization support for the ParallelLMHead in Qwen3.5 models. The changes include updating the Fp8Config to handle lm_head quantization and passing the quantization configuration to the model's head. Feedback points out that the current implementation incorrectly applies linear quantization methods to an embedding-sharded layer, which lacks the necessary interface and weight loader compatibility. Additionally, moving top-level imports into the method is recommended to prevent circular dependency issues.

Thanks for the review.

On the top-level ParallelLMHead import (comment on lines 41-44): the existing precedent in this directory is to import ParallelLMHead at module top level — awq_marlin.py, inc.py, cpu_wna16.py, gguf.py, and compressed_tensors/compressed_tensors.py all do exactly that for the same lm_head_quantized opt-in pattern. No circular import exists today (vocab_parallel_embedding does not import fp8). Keeping it consistent with the rest of the directory.

On the missing embedding() method (comment on lines 183-186): ParallelLMHead.forward() already raises RuntimeError to prevent its use as an embedding lookup, and none of the peer quant methods listed above (which all return their LinearMethodBase subclass for ParallelLMHead) define embedding() either. The layer-level guard is the established pattern. The loader-compatibility concern in the same comment is exactly what this PR's Gap 3 fix (now pushed in 3520a71) resolves: a custom per-parameter weight_loader is selected in create_weights for the FP8-block scale companion tensors, so VocabParallelEmbedding.weight_loader is not on the path for those params.

End-to-end validation (against Qwen3.6-27B-FP8 with lm_head.weight pre-quantized to float8_e4m3fn + lm_head.weight_scale_inv in bf16):

• Fp8Config.get_quant_method dispatches ParallelLMHead → Fp8LinearMethod ✓
• CutlassFP8ScaledMMLinearKernel selected for the lm_head ✓
• All weights load (no AssertionError on weight_scale_inv shape) ✓
• Model loading took 27.32 GiB vs 27.64 GiB BF16-lm_head baseline ✓
• KV cache reserves, server reaches Application startup complete ✓

First inference still OOMs on this specific deploy (RTX 5090 32 GB) inside the unrelated GDN/Mamba solve_tril Triton autotuner, which is the original sizing constraint that motivated the PR — orthogonal to the loader plumbing this PR addresses.

[webcodes-cz]

Status update — no action requested, just a measurement worth recording on the PR.

The PR's loader plumbing has now been additionally validated on RTX 5090 32 GB by stacking the same Qwen3.6-27B-FP8-lmhead-fp8 checkpoint with the hybrid TurboQuant runtime from #39931 on top of vllm/vllm-openai:v0.20.0. End-to-end startup + short deterministic + Czech-sanity prompts pass.

Memory delta on that stack:

• BF16 lm_head: model loading 27.66–27.69 GiB
• FP8 lm_head (this PR + checkpoint): model loading 26.5 GiB
• Real saving: ~1.18 GiB, which matches the physical [248320, 5120] BF16→FP8 delta exactly

This is materially larger than the 0.32 GiB delta originally reported on the RTX 6000 Pro 96 GB stack. That earlier number was specific to that stack's profiling / autotune budget (most of the weight-side saving was being absorbed by Triton autotune scratch). On the vLLM 0.20 + #39931 runtime the autotune scratch is paid out of a different bucket and the full FP8 delta surfaces at the Model loading took line.

The HF model card has been updated to reflect these numbers and explicitly marks "loadable today only via the C4 overlay; once #41000 merges and lands in a release image, vLLM will load it as-is": https://huggingface.co/inferRouter/Qwen3.6-27B-FP8-lmhead-fp8

The PR description is also refreshed (Gap 3 was actually committed in 3520a71; the original draft language is no longer accurate).

[claude]

Claude Code Review

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