[Bugfix][Core] Fix host memory leak from undrained new_block_ids

[Sunt-ing]

Purpose

Fixes #44175

Under sustained max_tokens=1 load, host RSS grows linearly with no plateau. The reporter saw ~6.0 → 6.57 GiB over a 12h run with Gemma-3-1b and prefix caching.

Who is affected: any V1 model with a full-attention or MLA KV-cache layer and no Mamba layers — i.e. essentially all standard attention models (Llama, Qwen, GPT/OPT, Gemma-2/3, DeepSeek-MLA). Mamba / hybrid-Mamba models are unaffected.

Root cause: #35219 added SingleTypeKVCacheManager.new_block_ids, appended on every full-attention/MLA block allocation but drained only when needs_kv_cache_zeroing (which equals has_mamba_layers):

• Recording is gated on the spec type (FullAttentionSpec/TQFullAttentionSpec/MLAAttentionSpec); draining is gated on having Mamba layers.
• So a non-Mamba model records on every block allocation but never drains → the list grows without bound (one int per allocated block per request).
• gc.freeze() at EngineCore startup hides the list from gc.get_objects()/tracemalloc, which is why it looks like allocator fragmentation.
• [BUGFIX][Mamba][Qwen3.5] Zero freed SSM cache blocks on GPU #35219 itself states "non-hybrid (pure attention) models are completely unaffected" — this fix makes that true.

Fix (consumer side, one file): drain take_new_block_ids() unconditionally every scheduler step, and only use the result when zeroing is enabled.

• The drained ids are consumed only by the zeroing path (take_new_block_ids() → SchedulerOutput.new_block_ids_to_zero → gpu_model_runner._zero_block_ids()), so unconditional draining is safe for all models.
• For Mamba models the drain already happened in that branch, so their behavior is byte-for-byte unchanged.
• I avoided gating the recording behind a new manager constructor flag: a default-False flag for a correctness feature (KV zeroing) would silently disable zeroing on any construction path that forgot to pass it.

Test Plan

Reproduced on current main and verified the fix on the same runs. Hardware: NVIDIA RTX PRO 6000 (Blackwell, sm120), CUDA 13.0, PyTorch 2.11, Python 3.12 — the leak is host-side and GPU-independent.

Common settings: V1, enable_prefix_caching=True, max_tokens=1, unique ~500-token prompts, MIG-like gpu_memory_utilization=0.10, enforce_eager, VLLM_USE_FLASHINFER_SAMPLER=0 (sm120). In-process runs set VLLM_ENABLE_V1_MULTIPROCESSING=0 to sample the EngineCore's VmRSS and new_block_ids length directly. This is the reporter's model on the same code path, not their exact MIG/container/12h run.

Reproduction script (in-process soak)

import os, random
os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"  # EngineCore in-process: /proc/self is the scheduler process
from vllm import LLM, SamplingParams

llm = LLM(model="google/gemma-3-1b-it", enforce_eager=True, gpu_memory_utilization=0.10,
          max_model_len=2048, enable_prefix_caching=True)  # or model="facebook/opt-125m"
sched = llm.llm_engine.engine_core.engine_core.scheduler            # in-process V1 EngineCore
mgrs = sched.kv_cache_manager.coordinator.single_type_managers      # only FullAttentionSpec managers record
new_block_ids = lambda: sum(len(m.new_block_ids) for m in mgrs)

def vmrss_mb():
    for line in open("/proc/self/status"):
        if line.startswith("VmRSS:"):
            return int(line.split()[1]) / 1024

sp = SamplingParams(max_tokens=1, temperature=0)
for i in range(100_000):
    llm.generate([f"r{i} " + " ".join(str(random.randint(0, 9999)) for _ in range(220))], sp, use_tqdm=False)
    if i % 20_000 == 0:
        print(f"req={i} VmRSS_MB={vmrss_mb():.1f} new_block_ids={new_block_ids()}")

Test Result

The leak reproduces on the reporter's exact model in the reporter's exact deployment (vllm serve), and the fix flattens the host RSS. Each row is the same run before/after the fix:

Model (KV-cache class)	Engine / setting	Reqs	Host-RSS growth: before → after fix
gemma-3-1b-it — reporter's model (hybrid SWA + global, no Mamba)	real vllm serve, 64-concurrent	400k	container RSS +163.8 MiB, no plateau → +1.5 MiB, flat
gemma-3-1b-it (same)	in-process	100k	EngineCore VmRSS +91.5 MiB → +1.2 MiB; list 6.8M → 0
opt-125m (pure full-attention)	in-process	300k	EngineCore VmRSS +167.4 MiB → +4.2 MiB; list 19.85M → 0
Qwen3-0.6B (pure full-attention)	in-process	20k	new_block_ids → 1.37M, linear (leaks)
Qwen3.5-4B — hybrid Mamba/SSM (needs_kv_cache_zeroing=True)	in-process	18k	new_block_ids stays 0 — no leak

• In-use leak, not fragmentation: every before-fix run's final gc.collect() frees 0 MB (malloc_trim does not return it).
• Magnitude matches the report: opt-125m converges to 8.84 B per leaked entry = a CPython list pointer ≈ the reporter's ~90 B/req (~0.57 GiB over ~6.5M reqs at 150 RPS × 12h).
• Negative control: the hybrid-Mamba family [BUGFIX][Mamba][Qwen3.5] Zero freed SSM cache blocks on GPU #35219 was built for (Qwen3.5-4B) does not leak — it drains every step. For needs_kv_cache_zeroing=True models the fix is a no-op, so it cannot affect the Mamba zeroing feature.
• MLA is covered by the same code, not a separate run: spec_manager_map[MLAAttentionSpec] is FullAttentionManager (the same class and recording line as the tested dense models) and MLA models have no Mamba layers.

Full VmRSS curves (before / after)

# REAL vllm serve (multiprocess), google/gemma-3-1b-it, container/tree RSS, 400k requests @ 363 rps
NO-FIX   done=400000 container_rss 4360.9 -> 4524.7 MiB  (+163.8, monotonic, no plateau)
WITHFIX  done=400000 container_rss 4271.5 -> 4273.0 MiB  (+1.5, flat)

# in-process engine, google/gemma-3-1b-it (reporter's model), 100k requests
NO-FIX   req=20064  dMB=2.3   list=1374362
NO-FIX   req=60192  dMB=23.2  list=4106748
NO-FIX   req=100000 dMB=91.5  list=6817315   gc_collect_freed=0.0MB
WITHFIX  req=40128  dMB=0.8   list=0
WITHFIX  req=100000 dMB=1.2   list=0          gc_collect_freed=0.0MB

# facebook/opt-125m, 300k requests (clean per-entry magnitude)
NO-FIX   req=100480 dMB=66.7  list=6656961   B/entry=10.50
NO-FIX   req=300032 dMB=167.4 list=19850356  B/entry=8.84   gc_collect_freed=0.0MB
WITHFIX  req=300032 dMB=4.2   list=0          gc_collect_freed=0.0MB

Tests / lint: behavior is verified by the before/after vllm serve and in-process soak runs above; the leak is a runtime accumulation a unit test can't faithfully express, so none is added. test_scheduler.py and test_single_type_kv_cache_manager.py pass unchanged on this branch. ruff / ruff-format / typos / SPDX-header all clean.

Not reproduced (out of scope here): the report also notes a CPU decline and a latency step-up at ~7h. These did not reproduce in this environment (no cgroup limits like the reporter's cpu: "2" container, and far more host RAM, so RSS never approaches a reclaim threshold) and are most likely downstream of the memory growth. @ashgold — could you confirm they clear once this fix is applied on your setup?

AI assistance was used to prepare this PR.

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[mergify]

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[ashgold]

@Sunt-ing Thank you!!
I will apply this PR into vLLM v0.22.0, test it again, and share the results by tomorrow.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Sunt-ing.

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[Sunt-ing]

Hi @ashgold How about the results?

[ashgold]

Hi @ashgold How about the results?

@Sunt-ing
The memory leak issue has been resolved. Thanks for your support!
However, the problem of increased latency as CPU usage decreased from an initial 1.3 to 1 has not been resolved. This seems to be a different issue from the memory leak.

[Sunt-ing]

Thanks for confirming the fix resolved the RSS growth.

On the latency step-up and the CPU decline: I tried to reproduce both and could not, on a faithful but substitute setup (not your exact MIG/H100 + container). Setup: gemma-3-1b-it, V1, prefix caching on, chunked prefill on, max_tokens=1, unique ~450-token prompts, on a single A800-80GB with the engine pinned to 2 cores (taskset, OMP_NUM_THREADS=1, to mirror your cpu: "2") and gpu_memory_utilization=0.10 to approximate the 1g.10gb KV pool. I drove it for 8h / 4,012,263 requests, past the ~7h / ~3.8M-request point where you saw the step-up.

Result: p50 latency stayed flat at ~232 ms and per-engine CPU stayed flat at ~1.6 cores for the entire run, with no discrete knee and no downward CPU drift. On this stock (pre-fix) build host RSS did grow linearly, but latency and CPU were unaffected by that growth, so the leak by itself does not produce the step-up here. Throughput was engine-bound at ~140 req/s (single-threaded EngineCore under the GIL), matching your ~150 rps, so this is the same load regime rather than a faster one.

One data point that may matter: the original report has RSS topping out around 6.57 GiB, well under the 16 GiB container limit, so the step-up does not line up with hitting that cap either. That points away from the V1 scheduling path and toward something specific to the deployment (the MIG 1g.10gb partition, the container cgroup, or kernel-level memory reclaim).

@ashgold when the latency steps up on your setup, could you grab a py-spy dump of the engine process plus gc.get_stats() at that moment? That would show whether the extra time is spent in Python/GC or blocked waiting, which is the part I could not trigger here.

repro (serve + load loop)

VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_ATTENTION_BACKEND=FLASH_ATTN OMP_NUM_THREADS=1 \
  taskset -c 0,1 vllm serve google/gemma-3-1b-it \
  --gpu-memory-utilization 0.10 --max-model-len 2048

# 32 concurrent clients, each request:
#   POST /v1/completions {"prompt": "<unique ~450-token text>", "max_tokens": 1, "temperature": 0}
# sampled every 15s: rolling per-request latency, engine process-tree CPU cores
#   (/proc/<pid>/stat jiffies), RSS, and /metrics (gpu_cache_usage, num_running).

[Sunt-ing]

ping @heheda12345, could you please take a look at this tiny pr? thanks~

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Sunt-ing.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[njhill]

Thanks @Sunt-ing. I added a change from @chaunceyjiang to avoid the overhead from unnecessarily adding/removing the extra blocks in non-mamba case.

[mergify]

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