[chun-wan]

Hybrid Mamba/gated-delta-net (GDN) models (e.g. Qwen3-Next) keep a persisted conv + recurrent state cache. With sleep mode (RLHF reuse: sleep -> weight update -> wake_up), the state-cache tag is discarded on sleep and its device memory is re-created on wake. If a new sequence consumes that slot before it is reset, the gated-delta-rule kernel propagates the (non-zeroed) memory; when it holds NaN/inf the output becomes NaN and argmax collapses every token to id 0 (!), producing reward=0 / NaN log-probs in RL training.

Add a regression test that sleeps and wakes a small hybrid GDN model (tiny-random/qwen3-next-moe) and asserts post-wake generation is neither degenerate (single repeated token) nor NaN.

Purpose

On some amdgpu driver versions the physical VRAM backing a buffer is not scrubbed
on release, so pages remapped in create_and_map (e.g. on wake_up after a
sleep() that discarded a tag) can contain stale data from a previously freed
allocation, possibly from another process. Hybrid Mamba / gated-delta-net (GDN)
models keep a persisted conv+recurrent state cache; a new sequence that reads an
unzeroed slot propagates garbage/NaN and collapses generation to token id 0
("!"), giving reward=0 / NaN logprobs under the RLHF sleep/wake pattern.
This PR:

• zeroes the freshly (re)mapped region in create_and_map, scoped to the ROCm
  path only (#ifdef USE_ROCM); the CUDA path and shared cumem.py are unchanged;
• adds a regression test for hybrid GDN sleep/wake correctness.
  Root cause is the kernel-side scrub-on-release in amdgpu_bo_release_notify()
  being unreliable on older amdgpu (e.g. 6.10.x); newer drivers individualize the
  resv + add a DMA_RESV_USAGE_KERNEL fence so the wipe completes before reuse.
  This change makes vLLM robust regardless of driver behavior.

Test Plan

pytest tests/models/language/generation/test_gdn_sleep_wake.py
plus the real Qwen3.5-35B-A3B (TP=8) through repeated sleep/wake.

Test Result

ROCm 7.0.2 / gfx942 (MI300X), host amdgpu 6.10.5 (affected). Real 35B, 16 prompts/cycle ("bad" = NaN logprob / token-0 collapse):

build	warm	wake-1	wake-2	wake-3	wake-4	wake-5
baseline	0/16	15/16	0/16	0/16	0/16	0/16
with this fix	0/16	0/16	0/16	0/16	0/16	0/16
Driver confirmation (cross-process VMM test, write 0xAB + free in proc A, read in proc B): amdgpu 6.10.5 reads back 0xAB (stale); 6.14.14 reads zero. This fix zeroes the woken region regardless.

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[tjtanaa]

Overall looks good. I am approving this PR by prioritizing functionality over speed as this is necessary for integrating into training framework. Given the training environment highly likely to be dependent on its own set of environments and dependencies

[chun-wan]

Summary (fix + regression test)

Problem. Hybrid Mamba / gated-delta-net (GDN) models (e.g. Qwen3-Next) read a persisted conv+recurrent state cache. Under sleep mode (RLHF: sleep() -> weight update -> wake_up()), pages remapped in create_and_map can be backed by VRAM that the amdgpu driver did not scrub on release, so a new sequence reads stale/NaN state -> output collapses to token id 0 ("!") with reward=0 / NaN logprobs.

Layer isolation (cross-process VMM probe: write 0xAB + free in proc A, alloc + read in proc B):

• amdgpu 6.10.5 -> reads back 0xAB (stale); amdgpu 6.14.14 -> reads back zero.
• Same host driver 6.10.5 with two different ROCm userspaces (vLLM 0.18.1 and 0.19.1) -> both stale => it is the kernel driver, not CLR/ROCr.

Fix. Zero the freshly (re)mapped region in create_and_map, ROCm path only (#ifdef USE_ROCM); CUDA path and shared cumem.py unchanged. Failure sets error_code via CUDA_CHECK.

Verification — real Qwen3.5-35B-A3B, 16 prompts/cycle, 5 sleep/wake cycles (bad = NaN logprob / token-0 collapse):

build	warm	wake-1	wake-2	wake-3	wake-4	wake-5
baseline (no fix)	0/16	15/16	0/16	0/16	0/16	0/16
with this fix	0/16	0/16	0/16	0/16	0/16	0/16

Perf. One-time hipMemset at wake/alloc only; MI300X ~2.6 TB/s (0.41 ms/GiB). No steady-state impact.

[aoshen02]

Could you take a look at the CI, seems AMD: Language Models Tests (Hybrid) 2 (https://buildkite.com/vllm/ci/builds/73073#019ee055-5d02-4694-b9d0-0fc34312f4e9) is relevant?

[chun-wan]

The AMD: Language Models Tests (Hybrid) 2 failure is fixed in the latest commit (877aeed).

Root cause: the test used gpu_memory_utilization=0.6. On the ROCm/amdgpu driver, VRAM discarded by sleep() is not returned to the free pool before wake_up() re-creates it (the worker log showed only ~2 GiB reclaimed while ~152 GiB stayed resident), so wake_up() had to allocate a second ~150 GiB copy → cuMemCreate OOM at cumem_allocator.cpp:175. Lowering it to 0.4 keeps the ~2× peak within device memory; the tiny model still gets ample KV/state cache.

Hybrid 1 & 2 both pass now. The remaining red checks in the latest build are unrelated pre-existing flakes / CI-infra failures (e.g. amd-v1-spec-decode ci_base metadata hash mismatch, amd-v1-sample-plus-logits ngram logprob tolerance, amd-kernels-quantization-test-2 w8a8 engine-core crash, and several non-AMD pytorch-compilation / basic-models-extra-initialization jobs that a ROCm-only #ifdef USE_ROCM change can't affect). Could you help re-trigger CI when you get a chance? Thanks!

[aoshen02]

Hi, the ci is automattcally triggered, could you take a look again of the red CI, if ok, I will merge.

[chun-wan]

One more angle on the tech-debt question, separate from the driver versions.

The failure mode here is silent. There is no crash, no exception, no warning in the logs — generation just collapses to token id 0, logprobs go NaN, and an RL run quietly produces reward=0. It took a cross-process VMM probe to trace it back to memory that had not been scrubbed. As noted above, AMD still supports the affected driver lines with current ROCm userspace, and vLLM neither pins nor checks a driver version, so a user can land on the affected configuration with no indication that anything is wrong. That is the part I would weigh most heavily: the cost of being wrong is asymmetric.

The cost of the fix sits entirely outside the hot path — one memset per create_and_map, nothing per token. On a fully-utilised 192GB MI300X that is roughly 70-100 ms, against the seconds of weight reload that follow every wake, so well under 1% of wake latency and zero steady-state impact.

It is also worth saying that neither cuMemCreate nor hipMemCreate documents that the memory it returns is zeroed. So this removes a dependency on unspecified behaviour rather than patching around one specific driver. Scoping it to ROCm is a pragmatic choice to leave the CUDA path's performance untouched, not a claim that CUDA is contractually safe.

[chun-wan]

CI finished on b3c8377: 232 of 243 contexts green. arm-cpu-test now passes — thanks again @oops-oom, #49985 was indeed the fix. model-executor and the nixl PD accuracy test, both of which failed on the previous run, also went green this time.

Ten steps failed instead, and the set is completely disjoint from the previous run's — zero overlap between the two. Eight of them (elastic-ep-scaling-test, fault-tolerance-e2e-2xh100, fusion-and-compile-unit-tests-2xb200, fusion-e2e-config-sweep-h100, kimi-k3-unit-tests-b200, lm-eval-kv-offload-4xh100, pytorch-fullgraph, v1-core-plus-kv-plus-metrics) run on NVIDIA, where these +23 lines are inside #ifdef USE_ROCM and are not compiled at all.

The two AMD ones deserved a closer look, and they do not hold up either. create_and_map is only reachable via my_malloc/python_create_and_map, both of which require the sleep-mode CuMemAllocator; gpu_worker.py returns a nullcontext() unless enable_cumem_allocator is set, and that flag defaults to false and appears zero times anywhere under tests/kernels. Independently, #46844 fails exactly shards 1 and 3 of amd-kernels-moe-test-*-mi300-1 (2, 4 and 5 pass) while touching no csrc at all — the same two shards, with no allocator change in sight.

@tjtanaa @aoshen02 — could you retry just the 10 failed steps on build 80971? That preserves the 232 already-green contexts and keeps the head SHA stable, so auto-merge can fire as soon as the roll-up turns green. Pushing another main merge would re-roll ~200 steps and, judging by the last three runs, would most likely just produce a different random red set.

[chun-wan]

/ci retry

[github-actions]

✅ Queued 3 failed job(s) for retry in Buildkite CI #81354.

[chun-wan]

/ci retry

[github-actions]

✅ Queued 2 failed job(s) for retry in Buildkite CI #81354.

[chun-wan]

/ci retry

[github-actions]

✅ Queued 2 failed job(s) for retry in Buildkite CI #81354.

[chun-wan]

/ci retry

[github-actions]

✅ Queued 2 failed job(s) for retry in Buildkite CI #81354.

[chun-wan]

/ci retry

[github-actions]

✅ Queued 2 failed job(s) for retry in Buildkite CI #81354.

[chun-wan]

/ci retry

[github-actions]

✅ Queued 2 failed job(s) for retry in Buildkite CI #81856.