fix(kimi_k3): load dense EXL3 DSpark drafts

[myshytf]

Summary

Enable Kimi-K3 DSpark draft layers to consume dense EXL3 checkpoints without changing the unquantized BF16 path.

The DSpark model names its runtime attention/MLP layers after an offset reserved for the target-side layer namespace. A five-layer draft therefore constructs quantized linears under model.layers.5..9, while its checkpoint metadata is keyed by layers.0..4. The model also reparsed a fresh draft quantization config inside each decoder layer, discarding the packed-module mapping applied by the model loader. As a result, valid EXL3 tensors were not selected and the loader constructed ordinary dense weights instead.

This change:

• reuses the already configured vllm_config.quant_config for every draft layer;
• adds idempotent metadata aliases from checkpoint layer indices to the runtime offset without changing stored tensor names;
• declares the packed source mappings for fused q/kv-a and gate/up projections;
• derives streamed context scratch placement from any projection parameter and uses the current activation dtype, so packed linears do not require a dense .weight parameter.

If no draft quantization config is present, all paths continue to receive None and retain the existing BF16 behavior.

Compatibility and invariants

• Metadata aliases preserve the original checkpoint keys; weight loading still consumes the original stored tensor names.
• Packed gate/up projections are quantized only when both source projections are present in the quantization metadata.
• Mixed q-a/kv-a projections remain unquantized unless both sources use the same quantized format.
• Quantized context projections allocate BF16 activation scratch on the packed parameter's device.
• This PR does not add or change an EXL3 CUDA kernel or checkpoint format.

Validation

Unit and static checks

python -m pytest tests/models/test_dspark_mla.py -q
31 passed

ruff check vllm/models/kimi_k3/nvidia/dspark_mla.py tests/models/test_dspark_mla.py
All checks passed!

ruff format --check vllm/models/kimi_k3/nvidia/dspark_mla.py tests/models/test_dspark_mla.py
2 files already formatted

python -m py_compile vllm/models/kimi_k3/nvidia/dspark_mla.py tests/models/test_dspark_mla.py
git diff --check

The new tests cover configured-config reuse, runtime layer aliases, packed-module declarations, idempotence, and a quantized context projection with no dense .weight attribute.

Full model qualification

Single-stream qualification used a five-layer Inferact Kimi-K3 DSpark draft with an EXL3 MCG K2 checkpoint on an RTX 3090 (SM86), while the Kimi-K3 target remained live:

• full checkpoint closure: 26 quantized matrices, 104 packed tensors, all MCG markers and source hashes verified;
• full model load and post-load processing completed;
• dedicated draft KV smoke passed with 279,600-token capacity;
• CUDA graphs captured for B1K1, B1K2, B1K3, B2K1, B2K2, and B2K3;
• eager fallbacks: 0;
• draft allocation: 6.998 GiB;
• container restarts/OOMs: 0.

Deterministic direct-target A/B, three 800-token runs per draft, same prompt/seed, temperature=0, ignore_eos=true:

Draft	Decode average	Range	Acceptance length
BF16	44.77 tok/s	44.66-44.91	2.569
EXL3 MCG K2	46.49 tok/s	46.45-46.54	2.530

The final concatenated reasoning/content output was byte-identical between BF16 and EXL3 runs (3,693 characters, SHA-256 9e112899297062dd615f8472ee1279beab37120fb769bd3d6c456c5a0032e6e0). Mean draft GPU query time fell from 8.85 ms to 6.12 ms, and draft allocation fell from 10.56 GiB to 7.00 GiB.

Known concurrency limitation

A subsequent live two-request test exposed a separate remote-draft concurrency failure: the first request continued generating, the second was deferred, and the target eventually logged a 30-second ZeroMQ receive timeout (zmq.error.Again) from the draft RPC. The deployment was immediately reverted to the BF16 draft; both requests then resumed. The measurements above therefore qualify single-stream EXL3 loading/numerics/performance only and must not be interpreted as production-readiness for the current remote B2 path. Reproduction and repair of that concurrency path are follow-up work; this loader PR does not claim to fix it.

Duplicate-work check

Searches for open K3 DSpark EXL3 and quantized DSpark PRs found no implementation of this loader fix. PR #473 concerns DFlash auxiliary projection memory, and PR #465 adds remote DSpark transport; neither handles dense EXL3 metadata/configuration in K3DSparkModel.

AI assistance

Hermes Agent assisted with source analysis, test construction, and validation. The submitted diff and all reported tests/results were reviewed and executed by the submitter.

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• vllm/models/kimi_k3/nvidia/dspark_mla.py

📝 Walkthrough

Walkthrough

Changes

DSpark quantization integration

Layer / File(s)	Summary
Quantization configuration preparation vllm/models/kimi_k3/nvidia/dspark_mla.py, tests/models/test_dspark_mla.py	The model prepares the draft quantization configuration once. Layer-indexed tensor storage entries receive offset aliases. Tests verify reuse and idempotence.
Configuration propagation and context buffers vllm/models/kimi_k3/nvidia/dspark_mla.py, tests/models/test_dspark_mla.py	The prepared configuration is passed to decoder projections. Streamed context buffers use the default dtype and the first projection parameter's device. Tests cover quantized projections without a dense weight.
Packed module mapping vllm/models/kimi_k3/nvidia/dspark_mla.py, tests/models/test_dspark_mla.py	K3DSparkForCausalLM declares mappings for fused QKV and gate/up projections. Tests verify both mappings.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: ⚪ Minimal · up to 7227a

The change enables dense EXL3 DSpark draft loading while preserving BF16 behavior; the only outstanding item is a localized documentation improvement with no runtime or user impact, so no actionable merge-blocking risk remains.

Sequence Diagram(s)

sequenceDiagram
  participant K3DSparkModel
  participant QuantConfigPreparation
  participant K3DSparkDecoderLayer
  participant MultiHeadLatentAttention
  participant KimiMLP
  K3DSparkModel->>QuantConfigPreparation: prepare quant_config with start_layer_id
  QuantConfigPreparation-->>K3DSparkModel: return aliased quant_config
  K3DSparkModel->>K3DSparkDecoderLayer: pass quant_config
  K3DSparkDecoderLayer->>MultiHeadLatentAttention: pass quant_config
  K3DSparkDecoderLayer->>KimiMLP: pass quant_config

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Suggested reviewers: lukealonso, voipmonitor, zjy0516

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 5.26% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 19 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely describes the main change: fixing dense EXL3 DSpark draft loading for Kimi-K3.

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Actionable comments posted: 1

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Inline comments:
In `@vllm/models/kimi_k3/nvidia/dspark_mla.py`:
- Around line 127-130: Add a Google-style docstring to the module-level function
_prepare_dspark_quant_config describing configuration reuse and layer-offset
alias behavior, with explicit Args: and Returns: sections documenting its
parameters and QuantizationConfig-or-None result.

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• tests/models/test_dspark_mla.py
• vllm/models/kimi_k3/nvidia/dspark_mla.py

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[myshytf]

Review follow-up (3be2772): _prepare_dspark_quant_config documents where the draft quantization config comes from and how per-tensor storage entries gain runtime layer-name aliases (Args/Returns). tests/models/test_dspark_mla.py: 31 passed.