[II] feat(kvarn): self-describing KVarN KV cache formats and scheduler sizing

[JMPSequeira]

What

KVarN (K-variance-normalized) KV cache formats: low-bit latent KV records with
per-group variance normalization (Sinkhorn-balanced), where the cache dtype
string is the sole, self-describing carrier of geometry — kvarn_mla_k5_g64
(MLA packed latent, 5-bit, group 64) and the standard variants
kvarn_k4v2_g128 / kvarn_k4v4_g128 / kvarn_k5v5_g64 (key/value bits +
group tile). No env override exists for the width; downstream geometry gates
fail closed on any width/group combination the kernels do not implement.

• vllm/model_executor/layers/quantization/kvarn/: config.py (dtype
  registry + parsing, KVarNConfig / KVarNMLAConfig geometry, shared
  precision-tail workspace envelope math, weight-size estimation),
  sinkhorn.py (NumPy reference normalization used by the tests).
• kv_cache_interface + single_type_kv_cache_manager: KVarNFullAttentionSpec
  / KVarNSlidingWindowSpec with group-locked tile sizes; MLAAttentionSpec
  carries cache_dtype_str so packed layouts stay self-describing end to end.
• kv_cache_utils: _get_kvarn_mla_workspace_config +
  _get_kvarn_mla_num_blocks charge the shared MLA dequantization workspace
  once for all local layers (it is shared across layers, unlike per-layer
  pages) and solve packed pages + workspace as one budget; incompatible shared
  geometries on one worker raise instead of guessing.
• platforms/interface.py: per-token page-size computation for the
  group-locked KVarN tiles (cannot use FullAttentionSpec's raw uint8 formula).
• torch_utils.get_kv_cache_torch_dtype: KVarN dtype strings → uint8 storage.

Foundation for the KVarN MLA attention backend (follow-up PR); this PR is
self-contained and fully CPU-testable.

Why

Serving JPsequeira/GLM-5.2-EXL3-TR3-3.40bpw-KVarN-K4V2 (public) requires the
KVarN cache format the checkpoint was quantized for. KVarN packs 5-bit
group-normalized latents into ~432-byte records, which is what makes 819K-token
contexts fit at ≥3.4 bpw on this class of hardware (evidence in the follow-up
backend PR).

Not duplicating an existing PR

• [GG] Support heterogeneous per-layer routed-expert widths (deepseek_v2 + EXL3) #249 (heterogeneous per-layer expert widths: deepseek_v2 + exl3.py) — no
  file or mechanism overlap; this PR touches no model code and no exl3 code.
• feat(exl3): extend rank-sliced bitrate to accept K2 #297 (exl3 k_values += 2) — exl3.py untouched here.
• qwen3_5: load rank-sliced EXL3 (Trellis) expert checkpoints #240 (qwen3_5 rank-sliced EXL3 loading) — untouched.
• Merged II EXL3 commits (fa51e84c5e, 75548a03c4, d9e617b311) and the
  DCP line (be8479c970 [GG] fix(dcp): build one-shard indexer query-split topology #245 et al.) — no KVarN code exists anywhere in
  dev/infernal-invocation today (verified: zero kvarn paths in the tree).

Tests

Existing coverage extended: tests/v1/core/test_kv_cache_utils.py +167 lines —
exact-fit/one-page-over workspace sizing, workspace charged once across local
layers, ngram override requires the combined budget (fails closed), shared
geometries must be compatible (raises), and non-KVarN/generic-KVarN page counts
do not reserve the MLA workspace.

Run on the ported tree (SM120 workstation, .venv torch 2.11.0+cu130):

pytest tests/v1/core/test_kv_cache_utils.py -q
→ 106 passed

Model-affecting? No — this PR adds cache-format plumbing that is inert unless a
KVarN dtype is selected; no default behavior changes. Kernel accuracy evidence
for the format itself is attached to the backend PR.

AI assistance

Ported and adapted to dev/infernal-invocation by an AI agent (Claude Opus 4.5)
under human direction from battle-tested fork commits; every line was written
and measured first in our serving stack. Commit trailers carry attribution.

### Diffstat

tests/v1/core/test_kv_cache_utils.py | 167 +++++
vllm/config/cache.py | 4 +
.../layers/quantization/kvarn/init.py | 13 +
.../layers/quantization/kvarn/config.py | 683 +++++++++++++++++
.../layers/quantization/kvarn/sinkhorn.py | 139 ++++
vllm/platforms/interface.py | 55 +-
vllm/utils/torch_utils.py | 6 +-
vllm/v1/core/kv_cache_utils.py | 199 +++++-
vllm/v1/core/single_type_kv_cache_manager.py | 13 +
vllm/v1/kv_cache_interface.py | 75 +++
10 files changed, 1335 insertions(+), 19 deletions(-)

---

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📥 Commits

Reviewing files that changed from the base of the PR and between d6e0bb7 and 264de6f.

📒 Files selected for processing (10)

• tests/v1/core/test_kv_cache_utils.py
• vllm/config/cache.py
• vllm/model_executor/layers/quantization/kvarn/__init__.py
• vllm/model_executor/layers/quantization/kvarn/config.py
• vllm/model_executor/layers/quantization/kvarn/sinkhorn.py
• vllm/platforms/interface.py
• vllm/utils/torch_utils.py
• vllm/v1/core/kv_cache_utils.py
• vllm/v1/core/single_type_kv_cache_manager.py
• vllm/v1/kv_cache_interface.py

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