Add compact fixed-page CPU KV offload for packed heterogeneous caches

[coltonottley]

Summary

This adds an optional compact CPU KV offload layout for packed heterogeneous KV caches.

The implementation was developed and validated on two independent TP=2 hosts, each with 2x RTX PRO 6000 Blackwell GPUs. In the deployed DeepSeek V4 Flash geometry, compact per-group fixed-page storage reduced CPU payload amplification by about 19x versus storing full packed rows. Combined with separate TP2 shared-primary rank dedup work in vllm-project#48906, the measured end-to-end opportunity is about 38-40x. This PR contains the compact-layout part only; it does not duplicate vllm-project#48906.

Compact mode is disabled by default and activates only with:

kv_connector_extra_config:
  enable_compact_layout: true

What is included

The six commits are intentionally layered:

1. Foundation — transport-neutral compact accounting/address types, fixed-page scatter allocation, and variable-size LRU/ARC eviction.
2. Geometry transport — carries a two-field scheduler-safe signature and worker-side packed slice geometry through the existing normalized offloading config.
3. Transfer planner — maps actual GPU block IDs and packed slice offsets to compact CPU addresses, including fragmented physical spans and partial first chunks.
4. Manager storage — extends the existing CPUOffloadingManager with atomic batch allocation/eviction, pending/committed ownership, load refcounts, and exact counters.
5. Worker/spec wiring — uses the existing CPU spec, worker, and async descriptor lifecycle for both GPU-to-CPU stores and CPU-to-GPU loads.
6. Safe compact copy submission — keeps the existing stream/event/job lifecycle, but compact scatter selects ordered per-descriptor cudaMemcpyAsync; ordinary swaps retain the cuMemcpyBatchAsync default.

The storage allocator uses fixed 64 KiB pages, reduced by gcd(64 KiB, per-rank budget) when necessary. Logical payloads can span arbitrary physical pages, so long-history churn does not require a contiguous free extent.

Scope boundaries

This PR deliberately does not include:

• shared TP1 CPU ownership or rank deduplication — that belongs to [KV Offload] Deduplicate replicated MLA KV in the shared CPU region vllm-project/vllm#48906;
• canonical per-layer mapping ownership — see [KV Connector] Add per-layer canonical KV page mappings for parallelism-agnostic offload vllm-project/vllm#48408;
• multi-tier CPU/NVMe orchestration — see RFC [RFC]: Multi-tier KV offloading via the vLLM offloading connector vllm-project/vllm#38260;
• model-specific bounded-tail or early-eviction policy;
• ExtentAllocator, shared mmap ownership, or endpoint lifecycle changes.

Current compact support is explicit for PP=1. TP>1 with PP=1 is supported. Compact mode with PP>1 fails with ValueError rather than silently constructing incomplete geometry.

Correctness details

• Compact charge units are real bytes per native GPU block per worker. blocks_per_chunk remains separate and is applied exactly once when sizing an offload address.
• Packed slice offsets and real payload sizes are validated against the transported group signature.
• Store preparation produces complete physical spans before DMA.
• Pending keys cannot be loaded.
• Active loads own canonical BlockStatus references and cannot be evicted or reused until completion.
• Legacy CPU offloading remains inert and unchanged when compact geometry is absent.

Validation

Final source head: 2e75dbf24ef0bbc0415f6890777e4c4990098790
Base: 832775efd136968c0b241006a6113486e5ceedc5
Tree: 11cca2ccf51f482b1efb1a743acffb3e584e6187

Why source tests were not treated as sufficient

The previous exact image passed the source suite and still exposed two real integration defects on 2x RTX PRO 6000 Blackwell GPUs:

1. Startup failed because scheduler projection had collapsed rich heterogeneous worker geometry to a representative spec before physical slice derivation.
2. After that was repaired, the first request failed because scheduler-side aggregate accounting and worker-side physical slice ownership were split inconsistently.

The final series fixes the ownership boundary rather than weakening validation:

• scheduler manager construction consumes only transported aggregate per-group payload charges;
• rich worker KVCacheConfig owns physical packed-slice derivation before scheduler projection;
• scheduler projection explicitly clears worker-only physical slices;
• worker creation fails loud if required physical metadata is absent;
• the CPU manager accepts aggregate payload maps, while transfer handlers alone own physical offsets.

Source regression coverage

The final rebased source passed 223/223 tests across compact transport/accounting, allocator and eviction policy, transfer planning, manager lifecycle, real bidirectional compact transfer, worker/spec wiring, and legacy/shared-region behavior.

Also clean:

• Ruff check and format check;
• py_compile for changed Python files;
• git diff --check;
• DCO trailers on all six commits;
• exact six-commit ancestry on the current target base;
• clean worktree.

These tests are regression coverage. Hardware qualification below is the acceptance proof.

Exact-image hardware qualification

The exact rebased tree was built into immutable image:

• image ID: sha256:b2b79283a8aa48de32a0ac988ddc0b6c5497e2b0bbfd5d9f6d34c7273c70c59a
• PyTorch 2.11.0+cu130, CUDA 13.0, FlashInfer 0.6.14

Qualification used one independent TP=2 endpoint with 2x RTX PRO 6000 Blackwell GPUs, DeepSeek V4 Flash, MTP2, FP8 KV, 1,000,000-token model length, and a 64 GiB compact CPU tier. No source overlays or bind-mounted code were present.

Both exact-image startups reported:

• 11.71 GiB available GPU KV memory;
• 1,229,713 GPU KV-cache tokens;
• 1.23x maximum concurrency at a 1,000,000-token request;
• zero restarts.

Runtime gates passed:

• normal text completion;
• parsed tool call with correct arguments and finish_reason=tool_calls;
• exact 1,000,000-token admission;
• canonical decode: 190.95 output tok/s scored over runs 2-4;
• canonical unique cold ~100K prefill: 6,764 tok/s on scored run 2 (run 1: 6,879 tok/s).

The CPU-hop proof used a cold 18,877-token target, 30 seconds of quiescence, then fifteen byte-distinct 198,184-token GPU-only pressure requests before exact replay:

• cold store: 2,557,243,520 bytes;
• replay output: exact QUIESCENT_CPU_HIT_OK;
• external CPU hit: 18,688 tokens;
• local GPU hits: 0;
• recomputed tail: 189 tokens;
• CPU-to-GPU load: 174,897,280 bytes in 0.0103 s;
• all fifteen pressure requests completed;
• zero accepted-run tracebacks, assertions, 5xx responses, or process restarts.

This exact-image hardware run is the acceptance gate for the final series; the earlier source-only green run is not presented as sufficient evidence.

TP4 copy-submission defect and repair

A subsequent exact-image TP=4 qualification found a third hardware-only integration defect. A compact CPU-to-GPU replay with about 19,634 copy descriptors per rank left one rank spinning inside execute_model while the other ranks slept. The engine timed out after two minutes. Source comparison localized the difference to compact scatter using cuMemcpyBatchAsync; the accepted product path used ordered per-descriptor cudaMemcpyAsync because large/repeated batch submissions were unsafe.

The sixth commit adds one explicit use_batch_api argument through the existing stable native op. Generic callers keep the batch API default. Compact scatter alone selects use_batch_api=False; stream, event, buffer, and job ownership remain on the canonical lifecycle. There is no descriptor-count threshold.

Native CUDA tests on the rebuilt extension passed 5/5, covering ordinary batch mode, explicit per-copy mode on default and dedicated streams, and a 20,000-descriptor regression.

The repaired immutable image is:

• image ID: sha256:7407ea290680afcd8212c31238fda950be7637d340d17102884867f9c9b5b751
• source: 2e75dbf24ef0bbc0415f6890777e4c4990098790
• tree: 11cca2ccf51f482b1efb1a743acffb3e584e6187
• PyTorch 2.11.0+cu130, CUDA 13.0, FlashInfer 0.6.14

Final TP=4 qualification used 4x RTX PRO 6000 Blackwell GPUs, DeepSeek V4 Flash, MTP2, FP8 KV, full 1,000,000-token context, and a 64 GiB compact CPU tier. Startup reported 48.67 GiB GPU KV and 5,111,043 GPU-KV tokens. Text and parsed-tool smokes passed.

The strict semantic harness obtained one complete qualified cold-control-plus-CPU-hop chain for all six probes across a primary run and one disclosed stochastic supplement. Every accepted CPU hop had:

• zero local GPU-cache hits;
• 48,128 external CPU-KV tokens/hits;
• 803,200,000 CPU-to-GPU load bytes;
• exact identity with the probe's three stable cold answers.

The formerly fatal probe passed. The same TP=4 process survived both runs with zero restarts, zero SHM starvation/RPC timeout/fatal/Xid, zero queued requests, and all four ranks/GPUs returning to sleeping/P8 idle state. One duplicate supplemental probe produced a null model final despite successful transfer; it is preserved as stochastic output and was not used as an accepted chain.

Public hardware evidence and related ownership:

• shared-primary/rank-dedup evidence: [KV Offload] Deduplicate replicated MLA KV in the shared CPU region vllm-project/vllm#48906 (comment)
• TP4 capacity / multi-tier direction: [RFC]: Multi-tier KV offloading via the vLLM offloading connector vllm-project/vllm#38260 (comment)
• TP4 validation context: [Bugfix][KV Offload] Bound unaligned SWA loads by physical GPU blocks vllm-project/vllm#49052 (comment)

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[jasl]

@coltonottley Merged into our fork's codex/ds4-sm120-min-enable (tag sm120-pr-41834-stable-preview-20260721b). Thank you — this is careful, well-layered work, and the opt-in isolation holds: we verified the default path (compact off, PCP=1) is byte-identical and there's no interaction with our two offload deltas (vllm-project#48959 pending-bound, vllm-project#48911 reachable-tails).

We deep-reviewed the six commits and then ran the full tests/v1/kv_offload/ suite (not just the compact tests). Three things came out that are worth folding back / upstreaming — the first two are real bugs, the third a test that encoded a bug:

1. spec.py — fixed-page size can collapse → init OOM/hang (compact-ON). Deriving the page size from math.gcd(64*1024, per_rank_budget) means a budget with low 2-adic valuation collapses it to a few bytes. A round decimal cpu_bytes_to_use=100 GB gives per_rank = 50000000000 = 2^10 * 48828125, so gcd = 1024 and FixedPageAllocator eagerly builds a a ~48.8M-entry free-page list (~1.7 GB of ints); a 2*odd budget yields page size 2 and a multi-billion-entry list → effective OOM/hang at worker init. We fixed it by rounding the per-rank budget down to a whole number of fixed 64 KiB pages (your own stated "stable 64 KiB fixed-page target"), which keeps the page count bounded and wastes < 64 KiB/rank.

2. offloading/config.py — breaks the upstream "KV sharding ignores PCP" invariant. build_offloading_config now multiplies tokens_per_block by prefill_context_parallel_size (context_parallel_factor = dcp * pcp). Upstream's PCP PR vllm-project#46570 explicitly established that KV-cache sharding does not scale with PCP, and ships a test asserting it — test_offloading_spec_kv_sharding_ignores_prefill_context_parallel expects tokens_per_block == (16,) at prefill_context_parallel_size=2, and your change makes it (32,). It's a no-op at PCP=1 (so it passed your qualification), but it fails that upstream test and diverges for any PCP>1 offload user. We reverted to block_size * decode_context_parallel_size. If the compact accounting genuinely needs a PCP-aware size, it probably belongs in the compact-specific charge rather than the shared tokens_per_block.

3. test_spec_compact_page_size_non_divisible_budget asserted the gcd page size (== 64 for budget 123456), i.e. it locked in the behavior from (1); we rewrote it to validate the budget-rounding fix.

None of this affects our target (GB10 never enables CPU offload), and the feature is cleanly opt-in — we merged it as a community preview. The two real fixes are small and I'm happy to open them as a PR against your branch, or you can fold them in; either way they'd be worth carrying upstream since vllm-project#46570's invariant is upstream's.

For reference, our validation: tests/v1/kv_offload/ 538 passed, our offload-connector regression 162 passed, and the default serving path stayed clean (coherence c=1 2/2, GSM8K full-set 0.956, IMA-clean) with your csrc changes built in.