Add opt-in FP8 vocab embedding support

[webcodes-cz]

Summary

• Adds opt-in FP8 runtime support for VocabParallelEmbedding / embed_tokens, gated on quantization_config.embed_tokens=true.
• Extends the FP8 ParallelLMHead opt-in landed in [FP8] Add opt-in ParallelLMHead dispatch to Fp8Config #41000 to the input vocabulary table.
• Loads FP8 embedding companion scale tensors through the vocab-parallel embedding loader.
• Threads quant_config / prefix into the Qwen3.5 embed_tokens constructor so serialized FP8 embeddings register their scale parameter.

Stacking and pin guidance for the builder

This PR is stacked on top of #41000. pull/41365.diff already contains the #41000 hunks plus the new embed_tokens hunks.

If you ship this through a patch --forward --batch style overlay pipeline:

• pin only #39931 (hybrid TurboQuant) and #41365
• do not also pin #41000 separately

Listing both #41000 and #41365 will fail-fast on the second patch invocation because the #41000 hunks are already present from the #41365 diff. That's the correct behaviour, not silent overwrite — but it means production builders should pin a list of two PR diffs, not three.

What this PR does

• engine — Fp8Config.get_quant_method() now opts into VocabParallelEmbedding when embed_tokens_quantized=True (mirrors the lm_head_quantized opt-in pattern from [FP8] Add opt-in ParallelLMHead dispatch to Fp8Config #41000).
• engine — Fp8LinearMethod.create_weights, when called for a VocabParallelEmbedding, installs a per-parameter scale loader so weight_scale_inv (shape [ceil(vocab/128), ceil(hidden/128)]) does not trip VocabParallelEmbedding.weight_loader's org_vocab_size assertion.
• engine — embedding rows are dequantized on demand at gather time (memory-first, not compute-fused). This proves load and runtime correctness; a fused FP8 embedding kernel is a separate follow-up.
• model — qwen3_5.py passes quant_config and prefix into the embed_tokens constructor (the dispatcher above is otherwise never reached).

End-to-end validation

Public reproducer: 🤗 inferRouter/Qwen3.6-27B-FP8-lmhead-embed-fp8 — Qwen3.6-27B with both lm_head.weight and model.language_model.embed_tokens.weight block-FP8 (e4m3fn, 128×128 blocks, BF16 weight_scale_inv companions). All other shards are byte-identical to the upstream Qwen/Qwen3.6-27B-FP8.

config.json carries the explicit opt-ins:

{
  "quantization_config": {
    "lm_head": true,
    "embed_tokens": true,
    "embeddings": true
  }
}

RTX 5090 32 GB — TP=1, validated 2026-04-30

Stack: vllm/vllm-openai:v0.20.0 + #39931 (hybrid TurboQuant) + this PR.

Smoke profile (initial fit verification):

--quantization fp8
--language-model-only
--kv-cache-dtype turboquant_k8v4
--gpu-memory-utilization 0.96
--max-model-len 4029
--max-num-seqs 4
--max-num-batched-tokens 10500
--max-cudagraph-capture-size 4
--enable-chunked-prefill
--reasoning-parser qwen3
--tool-call-parser qwen3_coder

• Model loading took 26.19 GiB (vs ~27.66 GiB on the BF16-embed_tokens baseline; delta ~1.18 GiB matches the physical [248320, 5120] BF16→FP8 saving)
• GPU KV cache size: 16,640 tokens
• Maximum concurrency for 4,029 tokens per request: 7.00×
• post-startup GPU free memory: ~2.59 GiB
• Application startup complete ✓
• short deterministic prompt: PASS (2+2 je 4.)
• 4 concurrent short Czech prompts: PASS, no OOM/crash

Production ceiling profile (the configuration the InferRouter builder pins for fleet deployment):

--gpu-memory-utilization 0.98
--max-model-len 4029
--max-num-seqs 8
--max-num-batched-tokens 12288
--max-cudagraph-capture-size 8
--kv-cache-dtype turboquant_k8v4
--language-model-only
--enable-chunked-prefill

(--enforce-eager is OFF; CUDA graphs are captured up to the cudagraph_capture_size ceiling above.)

Quality

Functional pass: load + first inference + concurrent decode under both profiles, no OOM.

Quality acceptance vs the upstream Qwen/Qwen3.6-27B-FP8 baseline is still pending. The eval gate — three-way side-by-side on the production probe set — has not been recorded yet. Embedding FP8 is a row-gather without matmul averaging, so per-block quantization error directly distorts the per-token vector; this is a structurally larger quality risk than lm_head FP8 alone and the eval result will gate whether this artefact graduates from lab.

Reproducing the failure on stock vLLM (without #41365)

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

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

Without the embedding opt-in, Fp8Config.get_quant_method() returns None for VocabParallelEmbedding, the embedding falls through to UnquantizedEmbeddingMethod, and embed_tokens.weight_scale_inv has nowhere to land at load time:

ValueError: There is no module or parameter named
'model.language_model.embed_tokens.weight_scale_inv' in Qwen3_5ForCausalLM.

With this PR applied (and #39931 for hybrid TurboQuant), the load completes and the engine reaches Application startup complete.

Tests

Mechanically validated end-to-end on the public reproducer above; not yet covered by automated tests because there is no upstream FP8-embed_tokens fixture. Suggested coverage for a follow-up tests PR:

• untied vs tied embeddings with quantization_config.embed_tokens=true
• TP=1 and TP=2 (vocab-parallel sharding × FP8 block scales on embed_tokens)
• gather correctness vs BF16 reference across the full vocab range
• memory-drop assertion on a small synthetic FP8 model

Follow-ups

• Fused FP8 embedding gather kernel (this PR's path is dequantize-on-gather, memory-first).
• Quality eval — three-way side-by-side: upstream FP8 vs lmhead-fp8 vs lmhead-embed-fp8. Gates whether the embed_tokens opt-in is recommended in production or kept as an optional headroom lever.

Related work

• [FP8] Add opt-in ParallelLMHead dispatch to Fp8Config #41000 — opt-in FP8 ParallelLMHead. This PR's diff is stacked on top.
• [Feature] TurboQuant: support hybrid models and uniform quantization #39931 — hybrid TurboQuant on Mamba/GDN models. Required to fit Qwen3.6-27B-FP8 on RTX 5090 32 GB at all.
• inferRouter/Qwen3.6-27B-FP8-lmhead-fp8 — the lm_head-only HF artefact, served via [Feature] TurboQuant: support hybrid models and uniform quantization #39931 + [FP8] Add opt-in ParallelLMHead dispatch to Fp8Config #41000.
• inferRouter/Qwen3.6-27B-FP8-lmhead-embed-fp8 — this PR's reproducer.

Checklist

• DCO Signed-off-by present
• No new dependencies
• Engine: Fp8Config dispatcher branch for VocabParallelEmbedding
• Engine: companion-param loader for FP8 embed_tokens.weight_scale_inv
• Model: qwen3_5.py passes quant_config / prefix into embed_tokens
• Tests (deferred — see Tests section)
• Quality eval acceptance
• Fused FP8 embedding gather kernel (separate PR)

---

Refs: #41000, #39931, #40999

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[gemini-code-assist]

Code Review

This pull request introduces FP8 quantization support for embeddings and the language model head. It adds the Fp8EmbeddingMethod class to handle VocabParallelEmbedding and updates Fp8Config to allow optional quantization of these components. The implementation includes specialized weight loaders for sharded scales and logic for dequantizing embedding lookups. Review feedback identified a missing kernel initialization in Fp8EmbeddingMethod that would cause crashes in models with tied weights, a lack of robustness in the scalar scale loader regarding tensor parallel degree changes, and a missing alignment assertion for block-quantized embedding shards.

[gemini-code-assist]

The Fp8EmbeddingMethod inherits from Fp8LinearMethod but its create_weights implementation fails to initialize self.fp8_linear. This is a critical issue for models with tied weights (tie_word_embeddings=True), such as some Qwen variants, because the lm_head will share the embed_tokens quantization method. When LogitsProcessor calls apply on the tied lm_head, it will crash with an AttributeError because self.fp8_linear is missing. Initializing the kernel ensures the method works correctly for both embedding lookups and linear projections.

            layer.register_parameter("weight_scale_inv", scale)

        self.fp8_linear = init_fp8_linear_kernel(
            activation_quant_key=self.activation_quant_key,
            weight_quant_key=self.weight_quant_key,
            weight_shape=layer.weight.shape,
            input_dtype=self.input_dtype,
            out_dtype=self.out_dtype,
            module_name=self.__class__.__name__,
        )
        self.use_marlin = isinstance(self.fp8_linear, MarlinFP8ScaledMMLinearKernel)

[webcodes-cz]

Implemented in e3547df: Fp8EmbeddingMethod.create_weights() now initializes self.fp8_linear with init_fp8_linear_kernel(...) and sets self.use_marlin, so tied embedding/lm_head paths can call apply() safely.

[gemini-code-assist]

This loader is not robust to changes in the tensor parallel (TP) degree. If a checkpoint was saved with a higher TP degree, loaded_weight may contain multiple per-tensor scales (one per shard). Attempting to reshape a multi-element tensor into a single-element param.data will raise a RuntimeError. It should handle this by taking the first element, which is consistent with how vLLM handles per-tensor scales during TP-degree-changing loads.

Suggested change

	def load(param, loaded_weight):
	param.data.copy_(loaded_weight.reshape(param.data.shape))
	def load(param, loaded_weight):
	if loaded_weight.numel() > param.data.numel():
	loaded_weight = loaded_weight.flatten()[0]
	param.data.copy_(loaded_weight.reshape(param.data.shape))

[webcodes-cz]

Implemented in e3547df: this loader now checks loaded_weight.numel() > param.data.numel(), takes loaded_weight.flatten()[0], then reshapes into param.data.shape.

[gemini-code-assist]

When using block quantization, it is essential to verify that the vocab-parallel shard start index is aligned with the quantization block size. If the shard is not aligned, the floor division (//) will result in an incorrect start_idx, causing the loader to fetch the wrong scales for the weights. This leads to silent accuracy degradation. An assertion should be added here, similar to the one in _make_lm_head_block_scale_loader.

Suggested change

	start_idx = layer.shard_indices.org_vocab_start_index // block_n
	start = layer.shard_indices.org_vocab_start_index
	assert start % block_n == 0, (
	f"FP8 embedding requires the vocab-parallel shard start "
	f"({start}) to be divisible by weight_block_size[0] "
	f"({block_n})"
	)
	start_idx = start // block_n

[webcodes-cz]

Implemented in e3547df: block-quantized embedding companion loading now asserts org_vocab_start_index % weight_block_size[0] == 0 before computing the scale shard start index.

[claude]

Claude Code Review

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