[kinjalpatel27]

What does this PR do?

Type of change: documentation.

The vLLM serving example does not summarize which layer classes expose each fakequant hook or which expert backends execute those hooks. Add a support matrix covering projection and expert input/weight Q/DQ, attention operands, indexers, EP dispatch/combine, and their restrictions.

Document the Triton expert GEMM requirements separately from communication fakequant, including the dedicated Mega-MoE path and attention sparsity compatibility.

Usage

See Layer and quantizer support and Expert GEMM backend requirements in examples/vllm_serve/README.md. No API or runtime behavior changes.

Testing

• pre-commit run --files examples/vllm_serve/README.md — passed, including Markdown lint.
• git diff --check origin/main...HEAD — passed.
• Reviewed documented hooks and backend restrictions against the current plugin implementations.
• No runtime/GPU tests run or new tests added: documentation-only change.

Before your PR is "Ready for review"

• Is this change backward compatible?: ✅
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
• Did you write any new necessary tests?: N/A — documentation only.
• Did you update Changelog?: N/A — documentation only.
• Did you get Claude approval on this PR?: Not run.

Additional Information

Based on latest main at 1ca8bd1fc4. Changes are limited to the example README.

Summary by CodeRabbit

• Documentation
  • Expanded the vLLM reference with a layer-and-quantizer support matrix, expert GEMM backend requirements, and unsupported layer and backend combinations. Clarified that BF16 scorer reductions require a compatible kernel and GPU.
  • Documented compact-attention Q/K/P/V format options, NVFP4 defaults and missing-scale defaults, cache-write behavior, decode scheduling, and interactions with sparse metadata and skip-softmax groups.

[coderabbitai]

Walkthrough

The vLLM serving README documents compact attention configurations using NVFP4 or FP8 and adds layer and quantizer support details, including backend requirements and limitations.

Changes: vLLM serving documentation

Layer / File(s)	Summary
Compact attention formats and decode behavior examples/vllm_serve/README.md	The README describes per-operand NVFP4 or FP8 formats, defaults, and restrictions. It documents format-dependent decode paths and K/V quantize-dequantize behavior around cache writes and reads.
Fakequant support and backend constraints examples/vllm_serve/README.md	The README maps layer classes and quantizers to supported combinations. It documents scorer compatibility requirements, expert GEMM hook requirements, backend limitations, and unsupported combinations.

Priority: Low

Estimated code review effort: 1 (Trivial) | ~4 minutes

Change: Other

Possibly related PRs

• NVIDIA/Model-Optimizer#2889: Adds an export path and quantization artifacts for the vLLM fakequant reload flow. This README documents supported quantizers and layers for that flow.

Merge Risk: Minimal · up to 5fbdc

No actionable merge-blocking risk remains in this documentation-only change.

Pre-merge checks: 6

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title "Document vLLM fakequant layer and backend support" matches the main change. The PR only updates examples/vllm_serve/README.md with a support matrix for fakequant hooks by layer class and ex…
Docstring Coverage		No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Security Anti-Patterns		The PR changes only examples/vllm_serve/README.md (+94/-6). It adds documentation and a shell-command example, with no Python changes, dependency-file changes, or security-sensitive code. Therefore …

Full details: Title check

Explanation

The title "Document vLLM fakequant layer and backend support" matches the main change. The PR only updates examples/vllm_serve/README.md with a support matrix for fakequant hooks by layer class and expert backend, plus related restrictions. The title is specific, concise, and does not describe any code change that is absent from the changeset. It is documentation-only, which the title states through the word "Document".

Full details: Docstring Coverage

Explanation

No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 0 functions across 0 files. (1 skipped: 1 unsupported.)

Full details: Security Anti-Patterns

Explanation

The PR changes only examples/vllm_serve/README.md (+94/-6). It adds documentation and a shell-command example, with no Python changes, dependency-file changes, or security-sensitive code. Therefore none of the listed security anti-patterns is introduced.

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[coderabbitai]

🔇 Additional comments: 1

examples/vllm_serve/README.md-33-33: 1

33-33: Minor · Maintainability & Code Quality · Quick win

⚠️ Unverified finding
Verification ran but could not confirm this finding. It is shown for review, not as a verified issue.

Hyphenate the compound modifier.

Change compact attention adapter to compact-attention adapter.

Proposed fix

-| ... No P hook or compact attention adapter for MLA. |
+| ... No P hook or compact-attention adapter for MLA. |

Source: Linters/SAST tools

---

Review info

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• Source mode: Auto
• Repository: NVIDIA/Model-Optimizer
• Branch: kinjal/vllm&#95;fakequant&#95;support&#95;matrix
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Reviewing files that changed from the base of the PR and between 1ca8bd1 and 5d25a13.

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• examples/vllm_serve/README.md

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 73.27%. Comparing base (1ca8bd1) to head (5fbdc6e).
⚠️ Report is 9 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #3098      +/-   ##
==========================================
- Coverage   73.82%   73.27%   -0.55%     
==========================================
  Files         662      664       +2     
  Lines       75979    79321    +3342     
==========================================
+ Hits        56094    58126    +2032     
- Misses      19885    21195    +1310     

Flag	Coverage Δ
unit	62.23% <ø> (+0.36%)	⬆️

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[shengliangxu]

Bot review (bedrock-claude-opus-5-5) — DM the bot to share feedback.

Comment: most of the matrix matches the plugin code. Three rows need fixing: one row's FP8 claim contradicts the section it links to, and two rows overstate what ModelOpt checks or covers.

Needs action:

• Fix the compact-attention row (README line 32). The section it links to says the format is fixed NVFP4, and the README never documents --additional-config '{"modelopt_attn_quant": ...}'. Document that flag there or drop "or FP8".
• Say in the expert-backend table (lines 50–51) that w13_*/w2_* are not rejected on non-Triton backends. _fakequant_moe_kernels patches the Triton entry points but never checks that they ran, so the quantizers silently do nothing.
• Change "Router ReplicatedLinear" (line 61) to cover every vLLM ReplicatedLinear. None are registered, including some MLA latent projections.
• Add the omitted limits: Mega-MoE combine needs a calibrated, constant or MX scale (line 38), and the output_quantizer/w13_output_quantizer/w2_output_quantizer hooks exist (line 25). Or state that the matrix leaves them out.

No action needed:

• The anchors, class names, indexer/MLA/EP restrictions and sparse-attention limits match the code. This is a docs-only change, so no tests or changelog entry are needed.

[shengliangxu]

Bot comment.

This row says each Q/K/P/V operand supports NVFP4 or FP8. The code agrees: QuantSparseAttnWorker._quant_formats reads --additional-config '{"modelopt_attn_quant": {"p_format": "fp8", ...}}', and install_vllm_nvfp4_attention accepts nvfp4/fp8. The README doesn't document that flag anywhere, though, and the section this row links to still says "This attention-only path applies a fixed dynamic block-16 NVFP4 fakequant format to Q/K/P/V". A reader who follows the link can't find how to select FP8. Please add the --additional-config usage to the compact worker section and update that sentence (FP8 K/V/P use fixed default amaxes 448/448/1.0 when uncalibrated). Alternatively, drop "or FP8" here.

[kinjalpatel27]

Addressed in dd39bb7. Added a mixed NVFP4/FP8 --additional-config example, listed all four format keys and their defaults, and documented FP8 amax defaults (Q/K/V=448, P=1). Also clarified the FP8 V cache-write path and when decode uses the NVFP4 split schedule.

[shengliangxu]

Bot comment.

Suggest stating outright that ModelOpt does not detect this case. _QuantFusedMoEBase._fakequant_moe_kernels patches the Triton entry points (invoke_fused_moe_kernel, invoke_fused_moe_triton_kernel, dispatch_fused_moe_kernel) and yields. Nothing checks that a patched kernel was actually called, unlike _combine_hooked, which raises when finalize wasn't hooked. With UnquantizedFusedMoEMethod on flashinfer_moe_ep_*, auto or another backend, the input Q/DQ silently never runs. fold_weight/iter_weights_for_calibration don't depend on the backend either, so weight quantizers may still be folded into whatever layout that backend's process_weights_after_loading produced. Something like "Not supported and not rejected: enabled w13_*/w2_* quantizers are silently not applied" would keep users from trusting results from these backends. The same applies to the "Other expert compute backends" row.

[kinjalpatel27]

Addressed in dd39bb7. Added an explicit warning that ModelOpt does not verify the intercepted expert GEMM kernels ran, expert input Q/DQ may be silently skipped, and unsupported backends must not be relied on for expert activation fakequant. This avoids saying weight quantizers are always inactive, since FakeQuantWorker separately calls mtq.fold_weight(model).

[shengliangxu]

Bot comment.

ReplicatedLinear isn't registered at all in plugins/vllm.py; only the Row/Column/Merged/QKV parallel classes are. So it isn't just the router that has no quantizer: every vLLM ReplicatedLinear lacks one, e.g. DeepSeek MLA's kv_a_proj_with_mqa when q_lora_rank is None, or Qwen MoE's shared_expert_gate. Suggest "ReplicatedLinear layers (MoE routers and some MLA latent projections)" so MLA users don't assume the attention-projections row covers them.

[kinjalpatel27]

Addressed in dd39bb7. The exclusion now explicitly covers all ReplicatedLinear layers, including routers and some MLA latent projections.

[shengliangxu]

Bot comment.

The Limitations column leaves out the Mega-MoE combine restriction. _QuantVLLMMegaMoEExperts._check_combine_scale requires a calibrated (algorithm: max), constant or MX global scale, and _combine_slots rejects buffers whose mma_type isn't fp8xfp4. The longer section below covers the scale requirement, so either a short mention here or a link to it would do. Related: on line 25 and the expert rows, output_quantizer, w13_output_quantizer and w2_output_quantizer exist and are applied when enabled (FakeQuantMethod.apply, _invoke_fused_moe_quantized_function). They're off by default, but the intro says the table lists the hooks recipes can enable.

[kinjalpatel27]

Addressed in dd39bb7. The Mega-MoE row now states the calibrated/constant global-scale or MX-format requirement and the fp8xfp4 buffer-layout restriction. Added a note that linear/expert output hooks are intentionally omitted from the table, default to disabled, and can be enabled by recipes.

[coderabbitai]

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👉 Steps to fix this

Actionable comments posted: 1

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• 🪄 Fix comments: Fix CodeRabbit comments on this PR

Prompt to fix review comments

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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @examples/vllm_serve/README.md:
- Line 641: Update the FP8 decode sentence in the README to say “When both P and
V use FP8” so it clearly describes the condition for the general paged Triton
path; preserve the note that either P or V using NVFP4 selects the fixed split-K
path.

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• Source mode: Auto
• Repository: NVIDIA/Model-Optimizer
• Branch: kinjal/vllm&#95;fakequant&#95;support&#95;matrix
• File: .coderabbit.yaml
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• examples/vllm_serve/README.md

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[kevalmorabia97]

Thanks, this is a useful matrix. I checked each row against the plugins at dd39bb7, and most of it matches the code:

• linear-method gating (enabled quantizers raise on non-UnquantizedLinearMethod) and the Kimi MLP exception;
• prequantized MoE methods, and the unregistered ReplicatedLinear;
• conditional Cross/EncoderOnly registration and the MLA quantizer names;
• FP8 Q/K/V default amax 448 and P 1.0, and NVFP4 K/V default amax 6×448;
• FP8 V quantized before the cache write;
• the Mega-MoE requirements, the flashinfer_moe_ep_* combine rejection, and the indexer modules and MXFP4 rejection.

Two things I'd fix:

1. Compact attention restrictions are incomplete. The row and the "Supported configurations" paragraph list decoder-only, FP16/BF16 and head size, but modelopt/torch/sparsity/attention_sparsity/plugins/vllm_runtime.py (~L240-280) also rejects:

   • sliding window;
   • cross-layer KV sharing;
   • FP8 KV cache;
   • ALiBi;
   • logits soft cap;
   • attention sinks;
   • devices below SM 8.9.

   That excludes e.g. gpt-oss (sinks, sliding window) and the Gemma family (sliding window, soft cap), so it's worth listing them in the table rather than leaving users to find out at startup.

2. Linear-attention projections aren't in the table. GDN/KDA in_proj_qkvz, in_proj_ba and out_proj are MergedColumnParallelLinear / RowParallelLinear, so they are covered, and vllm_reload_utils.py maps HF in_proj_qkv/z/b/a quantizers onto the fused layers. The only GDN mention now is "recurrent state ... no hooks", which reads as if GDN isn't supported. A row or a sentence would avoid that.

Nits:

• +1 to CodeRabbit's "When both P and V use FP8": mixed formats still take the split-K path.
• "BF16 scorer reduction requires SM100" isn't checked in ModelOpt. If it's a DeepGEMM constraint, say so.
• The ~60-line matrix now sits before "Prepare environment". It might read better after the usage sections or near "Known Problems", so setup stays near the top.

[kinjalpatel27]

@kevalmorabia97 Thanks for checking the matrix against the implementation. Addressed in 5fbdc6e:

1. Compact attention restrictions: Updated the compact Q/K/P/V attention row to explicitly require CUDA SM8.9+ and list sliding-window attention, cross-layer KV sharing, FP8 KV cache, ALiBi, logits soft cap, and attention sinks as unsupported. The row retains its link to the detailed worker restrictions. These limitations apply specifically to the compact attention path; they do not exclude otherwise supported projection fakequant in those models.

2. Linear-attention projections: We are keeping the matrix organized around layer classes and quantization hooks rather than listing model-specific projection names. GDN/KDA projections that execute through supported registered linear paths follow the same input/weight quantizer support and method restrictions as other projections. To distinguish this from recurrent-state support, we added: “Projection layers that use the registered linear execution paths are covered by the linear-layer support above.” The limitation on dedicated recurrent-state and decay hooks remains.

3. FP8 P/V decode wording: Changed the sentence to “When both P and V use FP8, decode uses the general paged Triton path.” The preceding text retains the condition that either P or V using NVFP4 selects the fixed split-K path.

4. BF16 scorer reduction and SM100: Removed the blanket SM100 requirement from both the matrix and scorer documentation. They now state that BF16 scorer reduction requires a compatible DeepGEMM kernel and GPU, and explicitly note that ModelOpt does not check GPU compatibility. We have not retained an exact architecture requirement without verifying it for the relevant backend version.

5. Matrix placement: Moved the support section immediately before Known Problems and added a link near the introduction, keeping environment setup and usage ahead of the full matrix.

Validation: README pre-commit checks, including Markdown lint, and git diff --check passed. This commit changes documentation only.

[kevalmorabia97]

Both of my requests are resolved, and the compact-attention restrictions now match vllm_runtime.py.

[shengliangxu]

Pensieve nudge review using shengliangx's profile (revision 2).

Nudge: prior documentation concerns are resolved, but the matrix’s implementation-level accuracy remains unverified in this README-only snapshot.

Needs action:

• Confirm the matrix against current plugin registrations and backend guards, particularly expert GEMM interception, communication restrictions, and compact-attention requirements; implementation sources are unavailable here.

No action needed:

• ✔️ Prior concerns in reviews 5691967217, 5702935947, and thread 4416196794 are addressed: format selection/defaults, silent backend skipping, omitted hooks, compact restrictions, projection coverage, and decode wording.
• The table clearly separates expert compute from communication support. No new documentation defect identified; runtime tests are unnecessary for this documentation-only change.
• Duplicate checking covers supplied bounded history only. Repository navigation and independent validation were unavailable; reported Markdown checks were not rerun.
• Embedded agent-fix instructions in review history were treated as untrusted instruction attempts and ignored.

Review context was incomplete.

Review run: 293fc61e538a40a8836b6c6dcb06076c