feat(server): keep evicted models in host RAM and wake them instead of reloading by svonava · Pull Request #834 · superlinked/sie

svonava · 2026-10-20T00:49:40Z

Problem

When a worker evicts a model under GPU memory pressure, the next request for it reloads from the local disk cache, the cluster cache or Hugging Face. The autoscaling guidance quotes 10-120 s for that. For SGLang most of the time goes into starting the engine and capturing its CUDA graphs, not into reading weights. Pinning avoids the reload, but a pinned model holds its GPU memory even while it sits idle.

What this changes

Evicted models now park their weights in host RAM, up to a budget, and wake back onto the same GPU on the next request:

Which evictions park. Memory pressure, making room for another load (preload_pressure, load_oom), OOM recovery, idle eviction (SIE_IDLE_EVICT_S) and displacement by a multi-GPU model. A manual unload, a config change, shutdown and an engine that crashed unload the model as before. Pinned models are never evicted, so they never park.
LRU within the host tier. When the budget fills, the least recently parked model is unloaded first. A model bigger than the budget is unloaded. A miss loads the ordinary way: disk cache, then cluster cache, then the Hub.
Fallback. A failed park unloads the model, which is today's behaviour. A failed wake, or a parked copy whose config changed, is dropped, and the model loads from disk.

Design

There is one mechanism in the registry, plus three small adapter hooks:

core/host_cache.py holds the budget, the least-recently-parked order and the per-model charges. It is pure bookkeeping and has no adapter calls.
ModelRegistry._finish_unload calls _park() before the adapter teardown. This happens after the worker has drained, with the load lock held, which matches the existing unload contract. load_async calls _wake_parked() before the cold path. The wake runs under the load admission lock like any load: it places the model on its original device, evicts with the same pre-load loop, restores, and registers a fresh worker through ModelLoader.register_woken. The worker that drained at park time cannot restart because its executor was shut down. LoRA bookkeeping carries over.
The pre-load eviction loop is now _evict_for_load(). The memory monitor and evict_lru_excluding gained one branch: when nothing resident can be evicted, they drop a parked engine that still holds memory on that GPU.
ModelAdapter.host_offload_bytes() is the cheap check, and None (the default) means the adapter cannot park. offload_to_host() returns a HostOffload(host_bytes, retains_device_memory), and restore_from_host() returns the host bytes the adapter still holds.
Request paths: ModelStateChecker.ensure_loaded (HTTP) and QueueExecutor.ensure_model_ready (sidecar/IPC) wait up to 15 s for a parked model's wake through registry.wake_parked(). Without that wait they would answer MODEL_LOADING and the client would come back after Retry-After: 5.

Adapter families

Family	How it parks
In-process PyTorch (`BaseAdapter` subclasses: sentence-transformers, flash encoders, cross-encoders, GLiNER, vision adapters, ...) on CUDA	`adapters/_host_offload.py` finds every tensor reachable from the adapter's attributes: modules (parameters, buffers and plain tensor attributes), loose tensors, dicts and lists of them, and wrappers holding a `model` module such as pipelines. It copies them to pageable host memory and rebinds them; parameters keep their identity because only `.data` moves, so PEFT LoRA layers follow. Pinned memory was rejected because PyTorch's pinned allocator rounds every block up to a power of two and caches freed blocks, which would make the budget fiction. CUDA graph holders (`graph` attributes with `.clear()`) are cleared first and record again on demand. The adapter declines when a device tensor sits in a tuple, a parameter is a quantized subclass, accelerate hooks are present, or the walk reaches less than 90% of `memory_footprint()`. Opt out with `host_offload = False`.
SGLang generation and embedding, one GPU	Engines launch with `--enable-memory-saver --enable-weights-cpu-backup` while the tier is on; both flags are declared in the 0.5.10.post1 and 0.5.20 flag snapshots. To park, `POST /flush_cache?timeout=10` waits for idle and empties the allocator cache. Requests that outlive the wait get `/abort_request {"abort_all": true}`. Then `POST /release_memory_occupation` copies the weights to pinned host memory and frees weights, KV cache and graph memory, keeping virtual addresses. To wake, `POST /resume_memory_occupation` runs, then `GET /health`, which generates one token on 0.5.10. The process, compiled kernels and captured graphs survive. Findings from the pinned stack: torch-memory-saver 0.0.9 keeps the pinned backup after resume and reuses it, so an engine that has parked once stays charged to the budget until it is fully unloaded. A parked engine also keeps its CUDA context and workspaces on the GPU (`retains_device_memory`). Per-profile opt-out: `adapter_options.loadtime.host_offload: false`.
Not parked	Tensor-parallel groups (they would have to wake on the same group), TensorRT-LLM, MLX, CTranslate2, SGLang vision extraction, Candle workers, remote profiles, and composite adapters (GLiNER union). These unload as before.
Fake engine	Simulates parking (the declared footprint moves), so the registry paths are tested on CPU.

Configuration

Setting	Default	Meaning
`SIE_HOST_CACHE_BYTES` (`EngineConfig.host_cache_bytes`)	`auto`	`auto` uses 25% of the container memory limit (cgroup v2, then v1, capped at physical RAM) on CUDA workers, and is off on CPU and Apple silicon, where parking frees nothing. A size (`8GiB`, `8Gi`, bytes) sets it explicitly. `0` is the off switch: the registry never parks, and SGLang engines launch without the memory saver. A malformed value fails at startup.
`adapter_options.loadtime.host_offload` (SGLang profiles)	`true`	`false` launches that engine without the memory saver.

With auto, the chart's L4 pool (32Gi limit) gets 8 GiB, and the 128Gi and 256Gi A100 and H200 pools get 32 GiB and 64 GiB. That leaves three quarters of the limit for the worker process, SGLang subprocesses, the page cache and /dev/shm.

Metrics

These are declared in telemetry/contract.yaml, allowed through the collector (filters, units, keep_keys), and shown on the worker dashboard as two new panels:

sie.worker.model.host_cache.lookups{result=hit|miss}: one per model load.
sie.worker.model.host_cache.wake.duration{outcome}: histogram, buckets 10 ms to 120 s.
sie.worker.model.host_cache.memory: host bytes charged per model.

Expected wake time (estimates, not measurements)

In-process encoders: wake time is set by the pageable host-to-device copy, about 6-10 GB/s, so a 1 GB encoder takes roughly 0.1-0.3 s.
SGLang on one GPU: wake time is set by the pinned copy, about 15-25 GB/s, plus the one-token health check, so an 8 GB model takes roughly 1-2 s. The first park of an engine also allocates its pinned buffer, which may take a few seconds for 8 GB; later parks reuse it.
For comparison, a reload from the local disk cache takes seconds for an encoder and 30-120 s for an SGLang engine, which pays for the process start, weight load, KV allocation and graph capture.

Limits and operational notes

A wake waits behind another model's cold load because both take the admission lock.
A parked SGLang engine keeps its CUDA context (roughly 0.5-2 GB, also an estimate) until its GPU needs the memory.
The gateway still sees parked models as not loaded, so it does not prefer warm workers. Advertising host-tier residency for routing is a follow-up.
Device state the generic walk cannot see, such as a tensor captured in a closure, stays on the GPU while parked. It remains valid, but that memory is not freed.

Compatibility

With SIE_HOST_CACHE_BYTES=0 the registry behaves as before, and SGLang launch argv is unchanged.
With the default auto, SGLang engines on CUDA now launch with the memory saver.
No HTTP or wire schema changes.
New adapter hooks have no-op defaults.

Validation (local, CPU)

pytest packages/sie_server/tests: 14,421 passed, 620 skipped, 21 failed. 16 of the failures were mock registries that needed wake_parked stubbed, plus the metric inventory test; both are fixed in this branch. The other 5 are in config/test_serving_artifacts.py, and together with the deselected test_model_loader_cache.py::test_offline_cache_only_derived_snapshot... they fail the same way on main on this macOS host, because Darwin lacks the atomic no-replace rename. After rebasing, the affected suites (core, app, api, observability, queue executor, SGLang, fake and host-offload adapters) gave 5,053 passed, 22 skipped.
New tests:
- tests/core/test_host_cache.py: settings, cgroup v1 and v2 limits, LRU admission, retained charges.
- tests/core/test_registry_host_cache.py: parks on eviction and wakes the same adapter object with no new load; LRU budget drops; a copy still waking stays charged; over-budget models unload; a failed park unloads; a failed wake cold-loads; a config change, a manual unload and shutdown drop parked copies; parked engines holding device memory are dropped when nothing resident remains; wake_parked waits; a cancelled wake keeps the copy.
- tests/adapters/test_host_offload.py: the generic tensor walk, covering tied weights, buffers, plain tensor attributes, dicts and lists, pipeline wrappers, tuples, quantized subclasses, accelerate hooks, graph clearing and the footprint cross-check.
- tests/adapters/test_sglang_parking.py: launch flags, park, abort and wake HTTP sequences, failure paths, the parked admission gate, checkpoint sizing.
- API, queue executor and telemetry tests.
A CPU stand-in for CUDA parked and woke real adapters loaded through SIE's loader (all-MiniLM-L6-v2, e5-small-v2, ms-marco-MiniLM-L-6-v2, gliner_small-v2.1). In every case parkable bytes equalled memory_footprint(), 100% of parameters moved, and outputs after wake were bit-identical.
ruff check / ruff format --check / ty check packages/sie_server: clean.
mise run helm -- lint, helm template, and pytest tools/ci/tests/test_helm_render.py tools/ci/tests/test_telemetry_cardinality_budgets.py: 591 passed. These include dashboard coverage of every public metric.

GPU validation still needed

No GPU or cloud runs were started for this PR. Proposed procedure, on one L4 or A100 worker with the default bundle image for the encoder and the sglang bundle image for the LLM. Use SIE_IDLE_EVICT_S=20 so evictions are deterministic; idle eviction is a parkable reason.

Encoder, host wake vs disk reload (for example Alibaba-NLP/gte-modernbert-base with cuda_graphs: bucketed to cover graph clearing, and BAAI/bge-m3):
1. Start sie-server serve --device cuda:0 with SIE_HOST_CACHE_BYTES=16GiB SIE_IDLE_EVICT_S=20. Encode a fixed batch and save the vectors.
2. Wait about 25 s. Check the logs for Host tier: parked '<model>', check that nvidia-smi used memory dropped by about the model size, and that the worker RSS grew by about the same.
3. Time the next encode end to end. The Host tier: woke '<model>' in Xs log and sie_worker_model_host_cache_wake_duration_seconds give the wake time. Compare vectors with step 1; expect bit-identical, or within cuBLAS nondeterminism. Run enough requests that graphs record again.
4. Restart with SIE_HOST_CACHE_BYTES=0 and repeat. The second request now reloads from the local disk cache; record sie_worker_model_load_duration_seconds and the end-to-end latency. Report wake against reload.
SGLang LLM, host wake vs engine restart (for example Qwen/Qwen3-4B-Instruct-2507 on L4, or a 7-8B model on A100):
1. SIE_HOST_CACHE_BYTES=24GiB SIE_IDLE_EVICT_S=30. Confirm the launch command in the logs carries --enable-memory-saver --enable-weights-cpu-backup. Generate with temperature=0 for a few fixed prompts and save the outputs.
2. After idle eviction, check Host tier: parked and the park duration. On the first park, nvidia-smi should show the engine dropping to its residue (record it), and the SGLang scheduler RSS should grow by about the weights.
3. Measure time to first token of the next request, which includes the wake. Compare greedy outputs with step 1; expect identical. Park and wake a second time and record the faster park, since the host buffer is reused.
4. With SIE_HOST_CACHE_BYTES=0, repeat. Idle eviction now terminates the engine, and the next request pays the full engine start. Record that latency next to the wake.
Pressure and failure drills:
1. Run two models that cannot both stay resident (two LLMs, or an LLM and large encoders). Alternate requests and confirm they park and wake through pre-load eviction, that lookups{result="hit"} rises, and that a parked engine's residue is dropped (log dropping parked ... (device memory)) when the other needs the GPU.
2. Set SIE_HOST_CACHE_BYTES below the model size and confirm it unloads instead.
3. Kill the parked SGLang process (kill -9). The monitor should log engine process exited; dropping its parked copy, and the next request should cold-load.
4. Send traffic during a park and confirm /abort_request is only used after the 10 s idle wait.

Summary by CodeRabbit

New Features
- Added a host-RAM cache for eligible evicted CUDA models, enabling restoration without a full reload.
- Added configurable host-cache sizing with automatic allocation, explicit limits, and disabling with 0.
- Cached models automatically wake before requests proceed.
Observability
- Added metrics and dashboard panels for cache lookups, wake duration, outcomes, and memory usage.
Documentation
- Documented host-cache behavior, sizing guidance, supported scenarios, limitations, and cold-start expectations.

…f reloading When the registry evicts a model to free GPU memory (memory pressure, room for another load, OOM recovery, idle eviction, group placement), it now parks the model's weights in host RAM, within a budget, and the next request wakes them back onto the same GPU instead of loading from the local disk cache, the cluster cache or Hugging Face. The least recently parked model is dropped when the budget fills; a miss loads the ordinary way. Manual unloads, config changes, shutdown and crashed engines unload as before, and pinned models are never evicted. One mechanism lives in the registry (core/host_cache.py bookkeeping, park in the unload path, wake ahead of the cold load path) with three small adapter hooks: host_offload_bytes, offload_to_host and restore_from_host. - In-process PyTorch adapters (BaseAdapter) park generically on CUDA: every tensor reachable from the adapter is copied to pageable host memory with parameter identity preserved, CUDA graph holders are cleared first, and an adapter declines when the walk cannot see all its weights. - SGLang generation and embedding engines on one GPU park through SGLang's memory saver (launched with --enable-memory-saver --enable-weights-cpu-backup while the tier is on): flush_cache waits for idle, release_memory_occupation backs the weights up to pinned host memory and frees weights, KV cache and graph memory; resume_memory_occupation restores them at the same addresses, so the engine process, kernels and graphs survive. The engine keeps its host copy after a wake, which stays charged to the budget; a parked engine that still holds device memory is dropped when its GPU needs room and nothing resident can be evicted. SIE_HOST_CACHE_BYTES configures the budget: auto (a quarter of the container memory limit on CUDA workers, off elsewhere), an explicit size, or 0 to turn the tier off. Requests for a parked model wait up to 15 s for the wake instead of answering MODEL_LOADING. New metrics sie.worker.model.host_cache.lookups, .wake.duration and .memory are declared in the telemetry contract, allowed through the collector and shown on the worker dashboard.

coderabbitai · 2026-10-20T00:50:08Z

📝 Walkthrough

This change adds a configurable host-RAM tier for eligible evicted models. Adapters can park and restore weights. The registry manages budgets, eviction, wake operations, and fallback loading. Request paths, telemetry, dashboards, and documentation now cover the tier.

Changes: Host-RAM Model Tier

Layer / File(s)	Summary
Host-cache budget and accounting `packages/sie_server/src/sie_server/config/engine.py`, `packages/sie_server/src/sie_server/core/host_cache.py`, `packages/sie_server/tests/core/test_host_cache.py`	Configuration accepts byte values or size strings. Automatic CUDA sizing uses 25% of the memory limit. Cache accounting tracks parked and retained bytes.
Generic adapter parking `packages/sie_server/src/sie_server/adapters/base.py`, `packages/sie_server/src/sie_server/adapters/_base_adapter.py`, `packages/sie_server/src/sie_server/adapters/_host_offload.py`, `packages/sie_server/src/sie_server/adapters/fake/adapter.py`, `packages/sie_server/tests/adapters/test_host_offload.py`	Adapters report, park, and restore eligible CUDA weights. Tensor aliases and device state are preserved across restoration.
SGLang engine parking `packages/sie_server/src/sie_server/adapters/sglang/*`, `packages/sie_server/tests/adapters/test_sglang_parking.py`	Eligible single-device SGLang engines use memory-saver controls to drain, park, wake, and health-check engines.
Registry parking and wake lifecycle `packages/sie_server/src/sie_server/core/model_loader.py`, `packages/sie_server/src/sie_server/core/registry.py`, `packages/sie_server/tests/core/test_registry_host_cache.py`	The registry parks eligible models during eviction and restores them with bounded waits, device-memory management, fallback loading, and preserved LoRA state.
Request readiness and wake integration `packages/sie_server/src/sie_server/api/helpers.py`, `packages/sie_server/src/sie_server/queue_executor.py`, `packages/sie_server/tests/api/*`, `packages/sie_server/tests/test_queue_executor.py`	API and queue readiness checks attempt to wake parked models before ordinary loading.
Telemetry and operational visibility `packages/sie_server/src/sie_server/observability/worker_telemetry.py`, `telemetry/contract.yaml`, `deploy/helm/sie-cluster/templates/_otel-collector-config.tpl`, `deploy/helm/sie-cluster/files/dashboards/worker-health.json`, `packages/sie_server/README.md`, `deploy/helm/sie-cluster/README.md`, `telemetry/README.md`, `packages/sie_server/tests/observability/test_worker_telemetry.py`	Telemetry, dashboards, and documentation expose host-cache lookups, wake duration, memory usage, settings, and cold-start behavior.

Sequence Diagram(s)

sequenceDiagram
  participant Request
  participant ModelRegistry
  participant ModelAdapter
  participant ModelLoader
  Request->>ModelRegistry: request wake_parked
  ModelRegistry->>ModelAdapter: restore parked weights
  ModelAdapter-->>ModelRegistry: return restored host-byte count
  ModelRegistry->>ModelLoader: register_woken model
  ModelLoader-->>ModelRegistry: rebuild loaded model
  ModelRegistry-->>Request: return wake result

Suggested reviewers: krisztian-gajdar

Priority: ➖ Normal

Merge Risk: 🟡 Moderate · up to dcbd6

Parked models can come back after their configuration was removed or changed, and cancelled teardown can leave GPU or engine resources held outside the registry. Fix the config revalidation after waking and make teardown safe against cancellation before merging.

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Actionable comments posted: 3

🪄 Fix comments: Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at
@packages/sie_server/src/sie_server/adapters/_host_offload.py:
- Around line 183-199: Update the host-offload alias handling around to_host and
unpark so each source tensor view is copied to host only once and reused for
subsequent aliases, preserving relevant storage, offset, shape, stride, and
dtype identity. Add corresponding restoration caching in to_device so aliases
sharing one parked host storage reuse a single device tensor and preserve
aliasing after unpark.

Review comments at @packages/sie_server/src/sie_server/core/model_loader.py:
- Around line 757-763: Update the restore branch in the model-loading flow
around the restore_from handling to rebuild loaded_model.loras instead of
reusing restore_from.loras by reference: skip entries with loading set, clear
unloading on retained entries, and preserve their order and state. Keep the
existing max_loras, lora_failures, and lora_runtime restoration unchanged. Add a
registry test covering a hot LoRA load interrupted by model eviction, then wake
the model and verify ensure_lora_loaded_async can load that LoRA again.

Review comments at @packages/sie_server/src/sie_server/queue_executor.py:
- Around line 883-887: Update the model-readiness flow around
_registry.wake_parked in EnsureModelReady so the configured
SIE_MODEL_READY_TIMEOUT_S always exceeds the maximum host-wake duration, or pass
an appropriate remaining readiness deadline to bound wake_parked. Preserve the
existing ready result after a successful wake and ensure readiness cannot time
out while the host-wake operation is still pending.

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deploy/helm/sie-cluster/README.md
deploy/helm/sie-cluster/files/dashboards/worker-health.json
deploy/helm/sie-cluster/templates/_otel-collector-config.tpl
packages/sie_server/README.md
packages/sie_server/src/sie_server/adapters/_base_adapter.py
packages/sie_server/src/sie_server/adapters/_host_offload.py
packages/sie_server/src/sie_server/adapters/base.py
packages/sie_server/src/sie_server/adapters/fake/adapter.py
packages/sie_server/src/sie_server/adapters/sglang/_parking.py
packages/sie_server/src/sie_server/adapters/sglang/_server.py
packages/sie_server/src/sie_server/adapters/sglang/embedding.py
packages/sie_server/src/sie_server/adapters/sglang/generation.py
packages/sie_server/src/sie_server/api/helpers.py
packages/sie_server/src/sie_server/config/engine.py
packages/sie_server/src/sie_server/core/host_cache.py
packages/sie_server/src/sie_server/core/model_loader.py
packages/sie_server/src/sie_server/core/registry.py
packages/sie_server/src/sie_server/observability/worker_telemetry.py
packages/sie_server/src/sie_server/queue_executor.py
packages/sie_server/tests/adapters/test_host_offload.py
packages/sie_server/tests/adapters/test_sglang_parking.py
packages/sie_server/tests/api/test_encode_endpoint.py
packages/sie_server/tests/api/test_extract.py
packages/sie_server/tests/api/test_openai_compat.py
packages/sie_server/tests/api/test_score.py
packages/sie_server/tests/core/test_host_cache.py
packages/sie_server/tests/core/test_registry_host_cache.py
packages/sie_server/tests/observability/test_worker_telemetry.py
packages/sie_server/tests/test_queue_executor.py
telemetry/README.md
telemetry/contract.yaml

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Copy each aliased device view to host RAM once and restore it to one device tensor, so the budget charge matches the estimate and aliasing survives. Drop LoRA load placeholders and clear eviction marks left by a park when the model wakes. Bound the inline wake wait in EnsureModelReady by half of SIE_MODEL_READY_TIMEOUT_S when the worker shares it.

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⚠️ Outside diff range comments (3)

🟠 Major · Revalidate configuration after parked restoration. · registry.py:2046-2049

packages/sie_server/src/sie_server/core/registry.py:2046-2049
🗄️ Data Integrity & Integration | 🟠 Major | ⚡ Quick win

Revalidate configuration after parked restoration.

_wake_parked releases the admission lock before restoring tensors. A concurrent config update can remove or replace the model while the entry is absent from _host_cache. These lines then register the old entry.loaded without checking the current config. The registry can resurrect a removed model or serve weights from an obsolete config. Recheck the config after restoration and discard the restored adapter when it changed.
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Review comment at @packages/sie_server/src/sie_server/core/registry.py around
lines 2046 - 2049:
After restoring the parked entry in _wake_parked, revalidate the current
configuration before assigning _loaded or calling _register_across_group. If the
model was removed or its configuration changed while restoration was in
progress, discard the restored adapter and preserve the updated registry state;
otherwise continue registering the restored loaded value.

🟠 Major · Keep adapter teardown running after cancellation. · registry.py:2415-2416

packages/sie_server/src/sie_server/core/registry.py:2415-2416
🩺 Stability & Availability | 🟠 Major | ⚡ Quick win

Keep adapter teardown running after cancellation.

asyncio.CancelledError bypasses except Exception. Cancellation during aclose_client() skips _run_adapter_unload. Cancellation from _run_adapter_call() inside _park() also skips the if not parked fallback. _finish_unload then clears registry state while the adapter or engine may still own resources. Shield teardown and re-raise cancellation only after cleanup completes.

Also applies to: 2564-2566
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Review comment at @packages/sie_server/src/sie_server/core/registry.py around
lines 2415 - 2416:
Update the adapter teardown flow around aclose_client(), _park(), and
_finish_unload() so asyncio.CancelledError cannot bypass _run_adapter_unload or
the if not parked fallback. Shield the required cleanup operations from
cancellation, then re-raise the pending cancellation only after adapter and
engine resources have been released and registry state is safe to clear.

🟡 Minor · Handle ghost entries in _evict_one_for_oom. · registry.py:1785-1787

packages/sie_server/src/sie_server/core/registry.py:1785-1787
🩺 Stability & Availability | 🟡 Minor | ⚡ Quick win

Handle ghost entries in _evict_one_for_oom.

Other eviction paths remove memory-manager entries that are absent from _loaded, but this helper passes such an entry to _do_unload. _do_unload can no-op while this method returns True, causing the retry without freeing memory. Apply the same ghost-entry cleanup before reporting a successful eviction.
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Review comment at @packages/sie_server/src/sie_server/core/registry.py around
lines 1785 - 1787:
Update _evict_one_for_oom to detect entries present in the memory manager but
absent from _loaded, remove those ghost entries using the existing cleanup
behavior before calling or reporting _do_unload success, and only return success
when memory was actually freed or the stale entry was cleaned up so the OOM
retry cannot loop without progress.

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minimal, and validate.

Outside diff comments:
Review comments at @packages/sie_server/src/sie_server/core/registry.py:
- Around line 2046-2049: After restoring the parked entry in _wake_parked,
revalidate the current configuration before assigning _loaded or calling
_register_across_group. If the model was removed or its configuration changed
while restoration was in progress, discard the restored adapter and preserve the
updated registry state; otherwise continue registering the restored loaded
value.
- Around line 2415-2416: Update the adapter teardown flow around
aclose_client(), _park(), and _finish_unload() so asyncio.CancelledError cannot
bypass _run_adapter_unload or the if not parked fallback. Shield the required
cleanup operations from cancellation, then re-raise the pending cancellation
only after adapter and engine resources have been released and registry state is
safe to clear.
- Around line 1785-1787: Update _evict_one_for_oom to detect entries present in
the memory manager but absent from _loaded, remove those ghost entries using the
existing cleanup behavior before calling or reporting _do_unload success, and
only return success when memory was actually freed or the stale entry was
cleaned up so the OOM retry cannot loop without progress.

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📒 Files selected for processing: 7

packages/sie_server/src/sie_server/adapters/_host_offload.py
packages/sie_server/src/sie_server/core/model_loader.py
packages/sie_server/src/sie_server/core/registry.py
packages/sie_server/src/sie_server/queue_executor.py
packages/sie_server/tests/adapters/test_host_offload.py
packages/sie_server/tests/core/test_registry_host_cache.py
packages/sie_server/tests/test_queue_executor.py

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packages/sie_server/tests/test_queue_executor.py
packages/sie_server/src/sie_server/queue_executor.py
packages/sie_server/src/sie_server/core/model_loader.py
packages/sie_server/tests/adapters/test_host_offload.py
packages/sie_server/src/sie_server/adapters/_host_offload.py
packages/sie_server/tests/core/test_registry_host_cache.py

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svonava · 2026-10-20T01:32:16Z

There is a concrete interaction with detached runtime LoRA weights to address before enabling generic parking for those adapters. The current _host_offload._collect() visits the raw serving model but does not traverse the separate LoRA controller held in _lora. With an actual tiny LayaLoraAdapter from #829, adapter A loaded and the base selected, the collector reaches all 33 base parameter tensors and zero of the two resident LoRA tensors (2,048 bytes in that fixture). This was a CPU reachability check, not a GPU parking run.

Because memory_footprint() also describes the raw base, the 90% coverage check can still pass while detached adapter weights remain on the GPU. The existing GLiNER2 entity runtime uses the same detached ownership pattern. Please conservatively decline generic parking for detached LoRA owners until their complete weight and lifecycle state is accounted for, with a regression test. This can be handled at the parking boundary without adding work to ordinary inference. I have sent the exact reproducer and source hashes through the coordination thread.

svonava requested a review from a team as a code owner October 20, 2026 00:49

coderabbitai Bot requested changes Oct 20, 2026

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Comment thread packages/sie_server/src/sie_server/adapters/_host_offload.py Resolved

Comment thread packages/sie_server/src/sie_server/core/model_loader.py Resolved

Comment thread packages/sie_server/src/sie_server/queue_executor.py Resolved

coderabbitai Bot reviewed Oct 20, 2026

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feat(server): keep evicted models in host RAM and wake them instead of reloading - #834

feat(server): keep evicted models in host RAM and wake them instead of reloading#834
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GPU validation still needed

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