[Example]: Calibration-free FP8/NVFP4 PTQ for speculative-decoding drafters

[h-guo18]

What does this PR do?

Type of change: new example

Adds examples/speculative_decoding/scripts/quantize_drafter.py, a CLI that quantizes an exported speculative-decoding drafter to FP8 or NVFP4 — weight-only or weight+activation — with no calibration data.

It needs no modeling code either. Exported drafters such as nvidia/MiniMax-M3-DSpark have no importable model class, so each 2-D weight is wrapped in a throwaway nn.Linear under its checkpoint key and ModelOpt's usual quantizer_name patterns select over those names. Works for any drafter layout (DSpark / DFlash / EAGLE3 / Medusa).

Formats: w4a16_nvfp4, nvfp4, fp8, fp8_pc_pt — the ModelOpt formats vLLM's backend can actually serve. AWQ is deliberately not offered, since awq_lite silently degrades to plain RTN without a forward_loop.

Static activation scales without calibration. fp8 and nvfp4 normally need an activation amax measured on calibration data; a fixed input_scale of 1.0 is applied instead. That works because acceptance length is governed almost entirely by clipping, not resolution:

Sweeping the fixed scale over three decades (same setup as the Testing section below; bf16 baseline 3.1423):

input_scale	amax	FP8 AL	vs bf16	NVFP4 AL	vs bf16
0.003	1.3	2.2204	-29.34%	2.2076	-29.75%
0.01	4.5	2.6719	-14.97%	2.6641	-15.22%
0.03	13.4	2.9751	-5.32%	2.9259	-6.89%
0.1	44.8	3.1013	-1.31%	3.0206	-3.88%
0.2	89.6	3.1178	-0.78%	3.0015	-4.48%
0.3	134.4	3.1370	-0.17%	3.0222	-3.82%
0.5	224.0	3.1268	-0.50%	3.0360	-3.38%
1.0 (default)	448.0	3.1457	+0.11%	3.0193	-3.91%
2.0	896.0	3.1354	-0.22%	3.0172	-3.98%
4.0	1792.0	3.1245	-0.57%	3.0034	-4.42%

Both formats fall off a cliff below ~0.03, where the declared range sits far under the activations' true magnitude and most of the tensor is clipped. Both then sit on a flat plateau from ~0.3 to 4.0 with no drop-off at the top, so the scale only has to be big enough. 1.0 is the middle of that plateau, which is why it is hardcoded rather than exposed. NVFP4 trails FP8 by a roughly constant 3.5% across the plateau — that gap is the 4-bit resolution cost, and no choice of scale recovers it.

Deriving the amax from the weights instead was tried and does not work: max|W| averages 0.79 while a RMSNorm'd activation is O(1) with outlier channels in the tens, so the range lands 1–2 orders of magnitude low and clips, measuring -31% to -46% AL.

Where calibration would go. All of this sits behind resolve_activation_scales(), the single place deciding where a static amax comes from. Real calibration slots in ahead of the fixed fallback with no change to the CLI or the call site, and composes because set_static_activation_amax() skips quantizers that already have an amax:

if calib_forward_loop is not None:
    mtq.calibrate(root, quant_cfg["algorithm"], forward_loop=calib_forward_loop)
set_static_activation_amax(root)   # fills in what calibration did not reach

Serving a quantized drafter. Four things had to be written into the exported checkpoint before vLLM would load one:

• emit quant_method (modelopt_fp4 / modelopt) — vLLM reads that key, ModelOpt writes only quant_algo
• emit the exclusion list under ignore too — that is the key read from the flat quantization_config; exclude_modules alone yields an empty exclusion set
• add *<name> wildcards so exclusions match a runtime's nested module prefix (model.fc) rather than the checkpoint key (fc)
• add *qkv_proj / *gate_up_proj aliases for layers a runtime fuses, whose names appear in no checkpoint key

Nothing is then needed on the caller side. This closes the open question left in the previous revision of this PR: vLLM does read quantization_config off the draft checkpoint. ModelConfig._verify_quantization fills quantization in from quant_method when it is unset, so once the export declares that key — the first fix above — detection works on its own. Verified on Nemotron-3.5-Lightning passing nothing: Detected ModelOpt NVFP4 checkpoint (quant_algo=NVFP4) → FlashInferCuteDslNvFp4LinearKernel, AL 4.278 against 4.203 measured earlier.

specdec_bench also gains a DSPARK algorithm, which it did not have: an exported Qwen3DSparkModel would otherwise have to go through DFLASH and be built with vLLM method="dflash". The branch sets method="dspark" and leaves draft_sample_method on vLLM's own default of greedy. A target whose fused-collective workspace (sized at CUDA-graph capture) overflows at large speculative batches can disable graphs with --runtime_params '{"engine_args": {"enforce_eager": true}}'.

For DFlash-family drafters, qwen3_dflash.py builds its fused context-KV projection by reading qkv_proj.weight raw and calling F.linear, which cannot consume a packed weight. Keep those layers in bf16 with --exclude '*q_proj*' '*k_proj*' '*v_proj*' '*qkv_proj*'; o_proj and the MLP — the bulk of the drafter — still quantize. That exclusion is mandatory, not a tuning choice.

fc (the projection from the target's captured layers into the draft) is the one real knob, and it is a genuine trade rather than a free win — see the Testing section for both models' numbers. The examples quantize it; add '*fc*' to the exclude list to keep it in bf16.

embed_tokens, markov_head and confidence_head are excluded by default: they are 2-D so the flat view treats them as GEMMs, but they are embeddings or a single-output projection. lm_head is excluded by the preset itself — unlike on a base model it is 37% of this drafter's parameters, so --quantize_lm_head is a real lever (~1.9 GiB), but measure AL first. The flag re-enables both of lm_head's quantizers; re-enabling only the weight one would ship a W+A checkpoint whose lm_head has no input_scale while the config still advertises it as quantized.

Usage

# weight+activation FP8, calibration-free, lossless on both models measured below
python scripts/quantize_drafter.py \
    --drafter_path deepseek-ai/dspark_qwen3_8b_block7 \
    --qformat fp8 \
    --export_path ./dspark-qwen3-8b-fp8 \
    --exclude '*q_proj*' '*k_proj*' '*v_proj*' '*qkv_proj*'

# smallest: weight-only NVFP4
python scripts/quantize_drafter.py \
    --drafter_path nvidia/MiniMax-M3-DSpark \
    --qformat w4a16_nvfp4 \
    --export_path ./MiniMax-M3-DSpark-W4A16

Or end to end on Slurm — quantize, then measure AL — via the launcher examples added here, one per target:

uv run launch.py --yaml examples/Qwen/Qwen3-8B/hf_dspark_ptq_nvfp4.yaml --yes
uv run launch.py --yaml examples/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/hf_dspark_ptq_nvfp4.yaml --yes

Serving one, if you are not going through specdec_bench:

speculative_config = {
    "method": "dspark",
    "model": "./dspark-qwen3-8b-fp8",   # quantization is read from its config.json
    "num_speculative_tokens": 7,
}

Testing

Two targets with different architectures, so the conclusions are not one model's quirk:

• Qwen3-8B (dense transformer) + deepseek-ai/dspark_qwen3_8b_block7, block_size 7, TP1.
• Nemotron-3.5-Lightning-30B-A3B (hybrid Mamba-MoE) + nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16-DSpark, block_size 8, TP8, with the mamba engine settings the model card pins (mamba_backend=flashinfer, mamba_ssm_cache_dtype=float16, stochastic SSM-cache rounding).

Both: MT-Bench 80 questions, greedy, one vLLM instance per point.

recipe	activations	Qwen3-8B AL	vs bf16	Nemotron-3.5 AL	vs bf16
bf16 baseline	—	3.1423	—	4.3296	—
fp8	static, input_scale 1.0	3.1457	+0.11%	4.3289	-0.02%
fp8_pc_pt	dynamic per-token	3.1228	-0.62%	4.3411	+0.26%
w4a16_nvfp4, fc in bf16	bf16 (weight-only)	3.0392	-3.28%	4.2899	-0.92%
w4a16_nvfp4, fc quantized	bf16 (weight-only)	3.0186	-3.94%	4.2334	-2.22%
nvfp4	static, input_scale 1.0	3.0193	-3.91%	4.2030	-2.92%

FP8 weight+activation at the fixed input_scale of 1.0 is lossless on both. +0.11% and -0.02% are both inside run-to-run noise — the Nemotron baseline was measured twice under identical settings and the two runs differ by 0.94% (4.3093 / 4.3499), which sets the resolution of that column. On the same reading, fp8 and fp8_pc_pt are indistinguishable on Nemotron; the dynamic variant only pulls ahead on Qwen3. NVFP4 costs 3-4% on Qwen3 and 2-3% on Nemotron, i.e. the 4-bit weight resolution is the real price and it is model-dependent but bounded.

Whether to quantize fc is a per-model call rather than a general recommendation — it buys a few percent of size for an AL cost that differs by ~2x between these two drafters:

fc bf16 → quantized	Qwen3-8B	Nemotron-3.5
checkpoint size	3.293 → 3.181 GiB (-3.4%)	1.316 → 1.258 GiB (-4.4%)
AL	3.0392 → 3.0186 (-0.68%)	4.2899 → 4.2334 (-1.32%)

fc itself is only 3.5% (Qwen3) / 4.5% (Nemotron) of drafter parameters; embed_tokens is the bulk (26% / 36%) and is excluded by default.

The Qwen3 w4a16_nvfp4 rows were measured in a later session than the rest of that column; the fc-in-bf16 run reproduced the original number to four decimals (3.0392), so the column is internally comparable.

Also validated on nvidia/MiniMax-M3-DSpark: w4a16_nvfp4 runs in 67 s on CPU, 9.98 GiB (fp32) -> 3.51 GiB; all 43 quantized tensors round-trip within 0.0952 relative error; the 29 untouched tensors are bit-identical to bf16(source).

Before your PR is "Ready for review"

• Is this change backward compatible?: ✅ (example-only)
• 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?: ❌ — validated manually as above. Can add a tests/examples/speculative_decoding/ test over a small synthetic drafter if wanted before merge.
• Did you update Changelog?: N/A (example-only)
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Additional Information

The measurements above are one drafter on one target with one benchmark; the plateau's location and the ~3.5% NVFP4 gap should be re-measured before assuming they carry to a different drafter.

Note when reading an exported checkpoint: input_scale is amax/448 for FP8 but amax/(6*448) for NVFP4, so the one fixed amax records as 1.0 in an FP8 checkpoint and 0.1667 in an NVFP4 one. Both mean the same activation range.

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📝 Walkthrough

Walkthrough

This change adds calibration-free drafter quantization, launcher pipelines for Qwen3-8B and Nemotron, and DSPARK draft quantization handling in the speculative-decoding benchmark.

Changes

Drafter quantization workflow

Layer / File(s)	Summary
Quantizer inputs and configuration examples/speculative_decoding/scripts/quantize_drafter.py	The script parses quantization options, resolves checkpoints, discovers custom modules, builds linear views, and creates ModelOpt quantization settings.
Quantization and export examples/speculative_decoding/scripts/quantize_drafter.py	The script assigns activation scales, exports quantized weights and scales, writes runtime-compatible metadata, copies model assets, and reports storage sizes.
Launcher and benchmark pipeline tools/launcher/common/specdec/quantize_drafter.sh, tools/launcher/examples/Qwen/Qwen3-8B/hf_dspark_ptq_nvfp4.yaml, tools/launcher/examples/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/hf_dspark_ptq_nvfp4.yaml, tools/launcher/examples/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/engine_args.json	The launcher resolves the drafter checkpoint and invokes quantization. The Qwen3-8B and Nemotron pipelines benchmark the quantized drafter with DSPARK and MT-Bench. Nemotron uses Mamba runtime settings.
Benchmark quantization selection examples/specdec_bench/run.py, examples/specdec_bench/specdec_bench/models/vllm.py	The benchmark accepts DSPARK and --draft_quantization. The model uses the override or reads quant_method from the draft config.json, then applies DSPARK sampling, eager-execution, and Mamba parameter settings.

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: 🟠 High · up to 2e209

The quantization CLI trusts module paths from downloaded configuration files, allowing a malicious configuration to copy files outside the drafter source tree into an exported artifact; custom module exports may also fail because paths and dependencies are not preserved. These concrete security and correctness risks make the PR not merge-ready until fixed.

Sequence Diagram(s)

sequenceDiagram
  participant Launcher
  participant Quantizer
  participant DrafterCheckpoint
  participant QuantizedDrafter
  participant VLLMBenchmark
  Launcher->>Quantizer: invoke with resolved drafter path
  Quantizer->>DrafterCheckpoint: load safetensor shards
  Quantizer->>QuantizedDrafter: export quantized weights and metadata
  VLLMBenchmark->>QuantizedDrafter: load quantized draft model
  VLLMBenchmark->>VLLMBenchmark: resolve draft quantization and DSPARK settings

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Suggested reviewers: chenhanyu, aanoosheh

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@@            Coverage Diff             @@
##             main    #2027      +/-   ##
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+ Coverage   79.01%   79.04%   +0.02%     
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  Files         523      523              
  Lines       60695    60695              
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+ Misses      12735    12718      -17     

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examples-llm_qat	17.55% <ø> (-0.01%)	⬇️
examples-llm_sparsity	15.88% <ø> (ø)
examples-specdec_bench	12.98% <ø> (ø)
examples-speculative_decoding	17.49% <ø> (-0.07%)	⬇️
regression	14.89% <ø> (+0.07%)	⬆️
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Inline comments:
In `@examples/speculative_decoding/scripts/quantize_drafter.py`:
- Around line 124-126: Update the snapshot_download call in the quantization
setup to pass allow_patterns covering the safetensors shard files, config.json,
and all tokenizer files subsequently read or copied by the script, while
preserving the existing local-path behavior.
- Around line 136-155: Update the quantization flow to require CUDA before
loading checkpoints, pass device="cuda" when calling load_file(), construct each
nn.Linear in build_linear_view on weight.device, and create the static amax
tensor on module.weight.device so quantization and packing run on CUDA.

In `@tools/launcher/common/specdec/quantize_drafter.sh`:
- Around line 41-43: Update the quantize_drafter.py invocation to quote the
DRAFTER variable and preserve positional arguments by using "$DRAFTER" and "$@";
do not alter the surrounding command or argument order.
- Around line 30-38: Update the DRAFTER checkpoint selection logic around
DRAFTER_CKPT so exported-checkpoint auto-detection runs only when DRAFTER_CKPT
refers to an existing local directory. Preserve non-local model identifiers,
including Hugging Face repository IDs, unchanged for quantize_drafter.py instead
of searching them or exiting when no local checkpoint is found.
- Line 33: Update the DRAFTER checkpoint discovery command to sort checkpoint
basenames rather than full paths, using version-aware sorting with sort -V so
parent-directory hyphens cannot affect selection; preserve choosing the
final/latest checkpoint.

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📒 Files selected for processing (5)

• examples/specdec_bench/run.py
• examples/specdec_bench/specdec_bench/models/vllm.py
• examples/speculative_decoding/scripts/quantize_drafter.py
• tools/launcher/common/specdec/quantize_drafter.sh
• tools/launcher/examples/Qwen/Qwen3-8B/hf_dspark_ptq_nvfp4.yaml

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Claude review — findings

Scope: full review (the trigger comment carried no scoping instructions). All 5 changed files (+464/-0) reviewed: examples/speculative_decoding/scripts/quantize_drafter.py (new, 323 lines), examples/specdec_bench/{run.py,specdec_bench/models/vllm.py}, and the two tools/launcher/ files. Nothing deprioritized.

Findings: CRITICAL 1 · IMPORTANT 2 · SUGGESTION 3

Most impactful

1. --quantize_lm_head produces a checkpoint inconsistent with its own config (CRITICAL Export). The preset disables *lm_head* — every quantizer, not just the weight one — so re-enabling *lm_head*weight_quantizer restores half of it. With --qformat fp8, get_quantization_format classifies the layer as FP8 off num_bits alone without consulting input_quantizer, so lm_head is advertised as fully quantized and kept out of exclude_modules, yet no lm_head.input_scale is emitted. With --qformat nvfp4 it is worse: lm_head resolves to W4A16_NVFP4 against NVFP4 everywhere else, process_layer_quant_config takes the two-format branch, and the result is quant_algo: "MIXED_PRECISION" → quant_method: "modelopt" written for an NVFP4 checkpoint, exclude_modules silently dropped to [], and quantized_layers leaking into config.json. Appending {"quantizer_name": "*lm_head*", "enable": True} fixes both; a quant_algo != "MIXED_PRECISION" assertion would catch the general case, since every config key this script writes assumes the single-format path.

2. Sidecar copy is narrower than the config it ships (IMPORTANT Export). config.json is copied verbatim, keeping auto_map / processor_class, but only tokenizer.json, tokenizer_config.json, and generation_config.json come with it. Remote-code *.py, special_tokens_map.json, vocab.json/merges.txt, and chat_template.jinja are dropped — so for precisely the "exported drafter with no importable model class" case this script targets, a trust_remote_code load of the export hits a missing module. copy_non_safetensor_files_from_ckpt (already in-tree, already the hf_ptq baseline) covers this and correctly skips weights and the weight index.

3. build_linear_view pays for a full random init it discards (IMPORTANT Performance). Each nn.Linear(...) allocates its weight and runs kaiming_uniform_ over out × in, then the next line overwrites it. That is one model's worth of RNG before any quantization work — likely most of the reported 67 s on the 9.98 GiB MiniMax case — plus needless peak host memory. device="meta" plus a nn.Parameter assignment removes both.

Three SUGGESTIONs are inline: the 0-dim-scale skip guard fires only for per-tensor FP8 and misdiagnoses why (the condition it describes is unreachable), the amax downcast to the weight dtype undercuts the documented calibration extension point, and fp8_pb_wo is the one offered format with no canonical quant_algo mapping and no entry in the validation table.

Verified as correct

Worth recording, since the approach is unusual and several of its load-bearing assumptions do hold:

• Calibration-free weight scales are sound. max_calibrate runs weight_only_quantize unconditionally before the forward_loop is not None check (model_calib.py:340-345), so every weight quantizer gets its _amax from the weight tensor itself. mtq.quantize with no forward_loop is legitimate here, not a silent no-op.
• The flat nn.Linear view works with the presets. Preset quant_cfg is a list, so .append is valid; quantizer_name patterns are fnmatched against the dotted module FQNs the ModuleDict nesting reproduces. DEFAULT_EXCLUDE correctly compensates for the preset's parent_class: nn.Embedding rule, which the flat view cannot express — the code comment on that is accurate. All five SUPPORTED_QFORMATS resolve (fp8_pb_wo / fp8_pc_pt via QFORMAT_ALIASES).
• NVFP4 scale derivation matches the real export. get_weight_scaling_factor routes NVFP4 through NVFP4QTensor.get_weights_scaling_factor_from_quantizer exactly as unified_export_hf does, and to_quantized_weight is then handed the same scale pair, so packing is self-consistent. The input_scale arithmetic in the docstring checks out: amax/448 = 1.0 for FP8, amax/(6·448) = 0.1667 for NVFP4.
• Fused-sibling weight_scale_2 is not a hazard. SHARED_PATTERNS fullmatches q_proj|k_proj|v_proj and gate_proj|up_proj against FQNs, which the flat view satisfies, so the shared global amax is applied. And although that state ties buffers across members — which safetensors rejects — get_weights_scaling_factor_2_from_quantizer returns a freshly divided tensor per call, so the manually-built export_sd has no shared storage and save_file is safe. This path was not exercised by the PR's testing (--exclude '*q_proj*' '*k_proj*' '*v_proj*'), so it is worth noting that it holds by construction rather than by luck.
• specdec_bench wiring is clean. Every engine class reads its options via kwargs.get(...), so threading draft_quantization through the shared constructor call is inert for TRT-LLM / SGLang / auto_deploy. The quant_method key vllm.py reads is the one quantize_drafter.py writes. One nit not worth an inline: the --draft_quantization help text says the value is "read from the draft's config.json when omitted", which is true only for the vLLM backend — the other engines ignore it entirely.
• tools/launcher/common/specdec/quantize_drafter.sh follows the established unquoted-${@} convention used by the other launcher wrappers; the YAML pipeline is consistent with its sibling examples.

Risk

Moderate, and well contained. This is example-and-tooling-only — no modelopt/ source, no mode registration, no modelopt_state schema, so there is no backward-compatibility or restore-fidelity surface. The default paths (w4a16_nvfp4, and fp8 / nvfp4 without --quantize_lm_head) are the ones the PR measured and they hold up under reading. The CRITICAL is confined to the one opt-in flag, which the PR description itself flags as needing an AL measurement first — but as written it fails at checkpoint load rather than merely costing accuracy, and for NVFP4 it corrupts quant_method and exclude_modules rather than erroring loudly, so it should be fixed before the flag is advertised.

The documented decision not to add a test is reasonable for an example, though the offer in the checklist of a tests/examples/speculative_decoding/ case over a small synthetic drafter would be worth taking up: a round-trip over a 3-layer drafter would have caught both the --quantize_lm_head mixed-precision fallout and the missing input_scale cheaply, and would pin the config.json keys that four separate comments in this script exist to explain.

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Actionable comments posted: 2

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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
In `@examples/speculative_decoding/scripts/quantize_drafter.py`:
- Around line 129-133: Update the snapshot_download call in the quantization
setup to include the Python modules required by auto_map, while retaining the
existing safetensors, config.json, and sidecar downloads. Ensure the resulting
export_dir contains the custom .py files later copied by the export flow so
custom-model loading references remain valid.
- Around line 335-339: Update the export logic around the auto_map handling to
flatten list-valued entries, skip None values, and extract modules only from
valid string references before copying their Python files. Also update the
snapshot_download allow patterns so referenced *.py files are included, keeping
exported auto_map references resolvable.

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📒 Files selected for processing (6)

• examples/specdec_bench/run.py
• examples/specdec_bench/specdec_bench/models/vllm.py
• examples/speculative_decoding/scripts/quantize_drafter.py
• tools/launcher/common/specdec/quantize_drafter.sh
• tools/launcher/examples/Qwen/Qwen3-8B/hf_dspark_ptq_nvfp4.yaml
• tools/launcher/examples/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/hf_dspark_ptq_nvfp4.yaml

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• examples/specdec_bench/run.py
• tools/launcher/common/specdec/quantize_drafter.sh

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Actionable comments posted: 2

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Inline comments:
In `@examples/speculative_decoding/scripts/quantize_drafter.py`:
- Around line 123-135: Harden auto_map_modules and the subsequent module copy
flow against untrusted auto_map values by rejecting absolute paths and any
traversal components, then resolving each candidate and requiring it to remain
under source_dir before reading or copying. Preserve repository-prefix handling
while ensuring every resulting module path is validated against source_dir
before constructing the exported artifact path.
- Around line 354-357: Update the module export logic around auto_map_modules so
each module’s relative path under source_dir is preserved in export_dir,
creating parent directories before copying. Validate resolved module paths
remain within source_dir and reject traversal outside it, then recursively copy
any relative-import dependencies while retaining their relative paths.

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[talorabr]

LGTM

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