feat(dcp): add selected-record sparse CKV decode

[FujitsuPolycom]

Summary

• add selected-record sparse CKV decode for B12X sparse MLA under DCP
• union and deduplicate MTP candidate top-K sets per request
• pool C1-C8 sparse workspaces and fall back before transport on overflow
• transfer only owner-resident native CKV records through the generic B12X
  CUDA-IPC/P2P exchange
• prefetch the three Shared layers following each Full/indexer layer
• preserve the native 432-byte NVFP4 + BF16-RoPE record without requantization
• close CUDA-IPC exchanges before model-parallel process groups are destroyed

The implementation is opt-in through
VLLM_B12X_MLA_SPARSE_DECODE_CKV_GATHER=1. It also exposes maximum-sequence
and pool-record controls. Unsupported topology, dtype, metadata, capacity, or
P2P conditions fall back before the selected-record transport begins.

Credit to @koush and @luke for the sparse-CKV gather, shared-layer lookahead,
and MTP deduplication design discussions that led to this port.

Dependencies

• stacked test base: PR1 + PR2 plus the patch-identical PR3 prefetch commit
• Sparkinfer transport dependency: FujitsuPolycom/sparkinfer#2
• this is a fork-only integration draft; no upstream PR has been opened

The stack is rebased on local-inference-lab/vllm@73e4a8cde and the renamed
local-inference-lab/sparkinfer@ec2cd4d package/API layout. The exact rebased
image passed the four-GPU direct/CE transport gate and the full model/API smoke.

Validation

• 46 focused lifecycle/policy tests passed; one hardware-only test skipped
• combined image tests: 96 passed, 18 skipped, plus three targeted transport
  tests passed
• Ruff format/check and git diff --check passed
• clean image built from fork commits without source overlays:
  ai01/glm52-fork-pr4:ef0b21f0b
• exact retrieval passed twice at 78,114 prompt tokens and twice at 120,315
  prompt tokens with sparse decode enabled
• TP4/DCP4/MTP3 C1/C2/C4/C8 policy, fallback, pooled-capacity, eager, CUDA
  graph, and exchange-cleanup paths are covered
• all 20 matched coding samples completed successfully with zero CJK
  corruption

Matched coding A/B

Same image, launch configuration, prompt, five runs per arm; only selected-
record sparse decode changed.

Coding peak	Sparse off	Sparse on	Gain
Median, model-default sampling	70.9	81.4 tok/s	+14.8%
Mean, model-default sampling	71.3	82.5 tok/s	+15.7%
Median, temperature 0	76.6	91.2 tok/s	+19.1%
Mean, temperature 0	76.3	91.3 tok/s	+19.7%

The coding probe is the benchmark harness's sequential CC1 Sieve prompt,
matching /mnt/test.py throughput semantics. A synthetic padding-stream decode
prompt produced much lower MTP acceptance and is not used as the primary
performance claim.

Capacity and prefill

• scheduler-visible GPU KV pool: 274,432 tokens off, 244,480 on
• current sparse pool cost: 29,952 logical tokens (10.9%)
• sparse-on cold prefill: 3,882 tok/s at 8K and 3,463 tok/s at 64K
• active C4 validation retained approximately 236-303 MiB free per GPU

Test host

ai01: ASUS Pro WS WRX90E-SAGE SE, AMD Ryzen Threadripper PRO 9965WX,
128 GiB RAM, and 4x NVIDIA RTX PRO 6000 Blackwell 96 GB GPUs at 400 W. Each GPU
uses a PCIe 5.0 x16-capable link; IOMMU is disabled. Serving topology was
TP4/DCP4/MTP3 with native nvfp4_ds_mla, BF16 RoPE, max model length 131,072,
max batch 4,096, max sequences 8, and graph size 32.

Remaining scope

• physical TP6/DCP6 and TP8/DCP8 validation is still required before claiming
  those deployments
• the current P2P exchange owner-compacts records before peer copy; a future
  direct-final-slot writer may reduce workspace and transport overhead further
• calibrated MLA outer scales from local-inference-lab/vllm#145 and the
  Sparkinfer compile-cache fix from local-inference-lab/sparkinfer#52 should
  be evaluated as a separate
  paired accuracy-baseline migration; the 432-byte transport ABI is unchanged

Design document

GLM-5.2 Sparse CKV under Decode Context Parallelism is the canonical architecture, configuration, validation, and upstream-decomposition reference for this PR stack.

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