fix(mrv2): reuse profiled CUDA graph pool

[yatesdr]

Hardware status — 2026-07-22: The exact patched proof boot changed
MRV2 accounting as intended but failed with the identical illegal access.
This falsifies pool reuse as the root-cause fix for that boot crash. Keep
this PR draft; review it only as an MRV2 allocator-consistency/efficiency
change until it has an independent justification.

Summary

Reuse the production CUDA graph pool while MRV2 profiles graph memory.

MRV2 currently captures into a fresh private pool, destroys the profiling
graphs, subtracts memory retained by that pool from the future graph-memory
reservation, and then captures production graphs into a different global
pool. Capacity retained by the profiling pool is therefore counted as
resident non-Torch memory but cannot be shared with production capture. The
patch makes MRV2's gross - retained calculation and later pool selection
internally consistent.

The hardware result below proves this inconsistency was real and the patch
changed its accounting, but also proves it was not the cause of the
production-capture illegal access being investigated.

The behavioral change is one line:

- profiling_pool = current_platform.graph_pool_handle()
+ profiling_pool = current_platform.get_global_graph_pool()

No graph, MTP, DCP, capture-size, quantization, or memory-profiling feature is
disabled. Profiling graphs are still synchronized, destroyed, and have their
external graph channels rolled back before production capture.

Root-cause proof from the code

An independent reviewer can validate the reasoning in
vllm/v1/worker/gpu/model_runner.py:

1. profile_cudagraph_memory() assigns every target/speculator manager and
   every eager/lazy wrapper to profiling_pool.
2. It calls both self.cudagraph_manager.capture(...) and
   self.speculator.capture() before computing the gross measurement.
3. _cleanup_cudagraph_memory_profile() begins and ends with accelerator
   synchronization, clears target/speculator/wrapper graphs, and frees the
   minimal profiling KV state.
4. Only after that cleanup does MRV2 compute
   retained_pool_size = start_free - free_after_cleanup and return
   gross_cuda_graph_size - retained_pool_size.
5. Before this patch, profiling_pool came from a fresh
   graph_pool_handle(). Production managers and wrappers are constructed
   with get_global_graph_pool() in cudagraph_utils.py, cuda_graph.py, and
   breakable_cudagraph.py.

Therefore the subtraction is a stronger future-headroom guarantee if the
future capture can reuse the pool whose retained capacity was subtracted. The
patched hardware result shows that this correction alone is insufficient to
prevent the separate production-capture failure.

The regression test fails if profiling calls graph_pool_handle() and checks
that the target manager, existing wrapper, lazily-created wrapper, cleanup,
and restored state all use the production pool.

Review byte pins on the current dev/gilded-gnosis target:

File	Base SHA-256	Patched SHA-256
vllm/v1/worker/gpu/model_runner.py	cbe6e868b8ea901b9d53f90d2548f97eea53be00f028ea2b90cb9103805d7543	2eab8362e2ce3e1004941988347b9921072053d52198b6f88be2d98d03cdd779
tests/v1/cudagraph/test_breakable_cudagraph.py	e7f5b222dc9f70de392e2e0a18b42c953c6eb74217934f2cb2b3dd0a41829d94	7af4d27f0aaf9426cfa40b21aacef7c5608a41fb572825f70673ccc33dd4261b

Operator evidence

Source: CN3 operator report, 2026-07-22. The image under test was:

ghcr.io/yatesdr/glm52-serve@sha256:7e51a7cf...
gilded-gnosis-v20-vllm3e731bc-si1a88b38-int8-nvme-mtpfix

It contained vLLM 3e731bc, SparkInfer 1a88b38, INT8 wire PR #69,
filesystem-tier PR #165, and indexer padding PR #166. The MRV2 pool fix in
this PR was not present.

The size-64 MRV2 profiling pass completed and emitted:

Estimated MRV2 CUDA graph memory: 0.36 GiB additional
(1.08 GiB captured, 0.72 GiB retained and counted as non-torch)

Because this log is emitted after both target and speculator capture and after
the synchronized cleanup, it proves the complete profiled graph set finished;
the later failure is not evidence that the size-64 graph set is intrinsically
uncapturable.

Three clean-start boots were recorded:

Boot	Configuration	Available KV	KV pool	Result
1	GMU .970, MNS 16, graph 64	3.25 GiB	~435,968 estimated	Clean max-length fit failure: 480k required 3.57 GiB
2	GMU .980, MNS 16, graph 64	4.14 GiB	544,000	Illegal memory access during production speculative graph capture
3	GMU .978, MNS 8, graph 32	4.41 GiB	592,640	Identical illegal memory access at the identical site
4	GMU .980, MNS 16, graph 64, pool-reuse patch	4.14 GiB	555,520	Identical illegal memory access at the identical site

Boots 2 and 3 failed on all four workers through:

spec_decode/autoregressive/speculator.py
  -> decode_cudagraph_manager.capture()
  -> cudagraph_utils.py: prepare_inputs_to_capture()
  -> input_batch.py: make_dummy()
  -> CUDA error: an illegal memory access was encountered

make_dummy() performs a host-to-device copy and is a synchronization point;
it reports a preceding asynchronous CUDA failure rather than necessarily being
the faulting operation.

The cap-32 differential motivated this patch. Reducing graph demand increased
available KV from 4.14 to 4.41 GiB and the allocator expanded KV from 544,000
to 592,640 tokens. Production capture gained no usable post-KV headroom and
failed in the same place. That was consistent with the two-pool hypothesis,
but Boot 4 is the decisive falsifier for that hypothesis as the crash cause.

At stand-down CN3 had zero containers and CUDA processes, 107.3 GB free in
/dev/shm, and 829 GB free on the NVMe filesystem. This rules out a stale
worker, shared-memory exhaustion, or filesystem-capacity failure for these
boots.

Patched hardware result and falsification

The exact Boot 2 profile was rerun with image-local patch commit 982cda45
(the same source bytes as this forward port):

TP4 / DCP4 / MTP3
max_num_seqs=16
max CUDA graph capture size=64
max_model_len=480000
gpu_memory_utilization=0.980

The patch was byte-verified in-image. It changed the MRV2 tuple from
1.08/0.72/0.36 GiB captured/retained/additional to
1.08/0.66/0.42 GiB, and the KV pool changed from 544,000 to 555,520 tokens.
All four ranks agreed. This proves the new pool selection was active and
affected allocator state.

Production speculator capture then failed two seconds after starting with the
same illegal-address stack as Boots 2 and 3. Profiling speculator capture had
succeeded five minutes earlier. The shortfall hypothesis is therefore
falsified: same pool, changed accounting, three memory budgets, and two graph
caps all produce the invariant fault.

The remaining fault is an asynchronous illegal access from work preceding the
reported InputBatch.make_dummy() synchronization point. Production differs
from profiling chiefly in using the full KV/block-table state and in running
kernel warmup between the two passes.

Validation

• git diff --check
• Python bytecode compilation of both changed files
• regression test added for pool identity and cleanup ordering
• CN3 differential evidence above obtained without this patch
• patched CN3 exact-profile runtime gate: FAILED; patch is not the crash fix

This PR remains draft and must not be promoted using the boot-crash
justification.

AI assistance disclosure

The root-cause analysis, patch, regression-test update, and PR write-up were
prepared with assistance from OpenAI Codex and reviewed by the operator/author.

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📒 Files selected for processing (2)

• tests/v1/cudagraph/test_breakable_cudagraph.py
• vllm/v1/worker/gpu/model_runner.py

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[yatesdr]

Independent validation update: the patched A/B changed the MRV2 tuple from 1.08/0.72/0.36 GiB captured/retained/additional to 1.08/0.66/0.42 GiB and increased the 480k KV pool from 544,000 to 555,520 tokens, proving that production reused the pool selected during profiling. Later candidates retained this patch and completed profiling plus production capture, served normally, and ran the 10-cell throughput suite with zero errors. This is not the fix for the unrelated query-BMM crash; its demonstrated benefit is allocator-accounting consistency and 11,520 additional KV tokens in the controlled A/B. That independent justification is now complete.