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…s can use The dubbing and generation recovery paths open-coded the CUDA/MPS pair when flushing the device cache. Engines pick their device through torch.accelerator, so on an Ascend NPU or Intel XPU host those paths flushed nothing at all: the allocator kept the blocks the offload had just freed, and the next allocation failed with that memory still counted as in use. Extract the narrow primitive free_vram() already used (no gc.collect, no cuBLAS clear, covers CUDA/MPS/XPU/NPU, never raises) as release_device_cache() and call it from the five recovery paths. free_vram() keeps its gc + cuBLAS behaviour. Verified on an Ascend 910B4 (torch 2.15.0.dev + torch_npu 2.15.0.dev): reserved npu memory stayed at 134.0 MiB with the old pair and dropped to 0.0 MiB with the shared helper; the new tests fail 9/9 on the pre-change tree.
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Included review availability: Your plan provides up to 10 included reviews per hour; 8 remain after this review. 📝 WalkthroughWalkthroughThe model manager adds a shared accelerator cache-release helper. Dubbing, transcription, translation, and generation cleanup paths use it. Tests cover backend selection and cleanup behavior. The changelog and performance documentation are updated. ChangesAccelerator cache release
Call-feature changelog
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix · Severity of issue fixed: Medium Suggested reviewers: Merge Risk: ⚪ Minimal · up to No concrete issue remains that should delay merging after normal checks. 🚥 Pre-merge checks | ✅ 8 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (8 passed)
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The PR appears safe to merge with no outstanding findings. SummaryThe latest revision completes the NLLB MPS out-of-memory fallback by releasing the old MPS allocator cache after moving the model and inputs to CPU.
Reviews (4) · Last reviewed commit: "fix(dub): flush MPS allocator after NLLB..." |
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Two review findings on the cache-release helper: 1. On a hybrid host (CUDA probes as available, inference runs elsewhere) the elif chain flushed CUDA and never reached the active allocator. Ask the same question the engine sidecars ask -- torch.accelerator.current_accelerator( check_available=True) -- and flush that backend, falling back to the CUDA/MPS/XPU/NPU probe chain only when the build cannot answer. 2. free_vram() propagated empty_cache() failures before, and model_lifecycle unload callers report a failed flush to the user, so the helper takes raise_on_failure and free_vram() passes True. The direct recovery calls stay best-effort. Tests pin both, plus the cpu-answer and pre-2.6 fallbacks.
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Both Greptile findings are right — fixed in 1. Hybrid hosts: flush the accelerator the engines actually use. The 2. New tests pin both behaviours, plus a Re-verified after the change: |
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Inline comments:
In `@backend/api/routers/dub_core.py`:
- Line 2150: Run release_device_cache through the existing GPU executor and
await its completion before emitting the SSE final event, keeping the
synchronous cache flush off the event loop.
In `@backend/services/model_manager.py`:
- Line 3364: Update the device-flush branch that checks `name` so it uses the
caller’s selected device rather than relying only on `current_accelerator()`.
Pass the relevant device into the flush path, including `_nllb_device` for NLLB,
so recovery flushes the cache for the device the engine actually used.
- Line 3382: Remove exc_info=True from the DEBUG log in the cache-release
exception handler, keeping the fixed message and preventing traceback details
from being logged.
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CHANGELOG.mdbackend/api/routers/dub_core.pybackend/api/routers/dub_generate.pybackend/api/routers/dub_translate.pybackend/api/routers/generation.pybackend/services/model_manager.pydocs/performance.mdtests/test_device_cache_release.py
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Inline comments:
In `@backend/api/routers/dub_translate.py`:
- Line 361: Update the NLLB unload path around release_device_cache to retain
the last accelerator used before a CPU fallback and flush that accelerator’s
cache after unload, even when _nllb_device is "cpu".
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Summary
The dubbing and generation recovery paths flush the accelerator cache by hand, with the CUDA/MPS pair open-coded at every call site:
Engines pick their device through
torch.accelerator(moss_tts_v15,confucius4,dots_tts), so on an Ascend NPU host the model runs onnpuand on an Intel Arc host onxpu— and those paths flush nothing there. The allocator keeps the blocks the offload just freed, which is the exact situation the flush exists for: the next allocation fails with that memory still counted as in use. Refs #2194.Changes
backend/services/model_manager.py— extract the narrow primitivefree_vram()already used asrelease_device_cache(): nogc.collect(), no cuBLAS clear, covers CUDA/MPS/XPU/NPU. It asks the same question the engine sidecars ask —torch.accelerator.current_accelerator(check_available=True)— and flushes that backend, falling back to the CUDA/MPS/XPU/NPU probe chain when the build cannot answer (pre-2.6, or a driver that raises while probing). Best-effort by default;raise_on_failure=Truekeepsfree_vram()'s original contract (it propagatedempty_cache()failures, andmodel_lifecycleunload callers report a failed flush to the user).api/routers/generation.py::_oom_friendly_reraiseapi/routers/dub_generate.py::_prepare_oom_retryand the throttled per-segment release indub_generateapi/routers/dub_translate.py::_unload_nllband the per-row retry in_generate_rowsapi/routers/dub_core.py— both transcription paths, which only flushed MPS beforetests/test_device_cache_release.py— pins the primitive to all four backends, plus "never raises", "no vendor backend shipped by this torch build", and a source rule that these modules never open-code a backend-specific flush again.CHANGELOG.md,docs/performance.md.Type
Testing
Real machine: Ascend 910B4 (8×),
torch 2.15.0.dev20260917+cpu+torch_npu 2.15.0.dev20260917;torch.cuda.is_available() == False,torch.npu.is_available() == True, andtorch.accelerator.current_accelerator(check_available=True).type == "npu"— the same resolution the engines use, so the helper takes the NPU branch directly. Reserved NPU memory read withtorch.npu.memory_reserved()around a 64 MiBnpu:0allocation that is then dropped (del+gc.collect()):So the old pair is a silent no-op on NPU (fail-before), and the helper really hands the blocks back (pass-after).
free_vram()and a real NPU matmul after the flush both run fine.Tests on that machine:
pytest tests/test_device_cache_release.py→ 13 passedpytest tests/test_device_cache_release.py tests/test_changelog_style.py tests/test_npu_memory.py→ 36 passedtests/test_dub_remote_safety.py's monkeypatch mechanism (it patchestorch.cuda.is_available/torch.cuda.empty_cache, and the helper reads that same patched module) → its assertions still hold, because the CUDA path is unchanged.Not tested: the full backend suite — the NPU container has no web deps (fastapi/pydantic/soundfile/librosa absent), so
api/routers/*cannot be imported there; no CUDA/MPS/XPU hardware was available, so those branches are covered by the unit tests only; multi-GPU/HCCL paths are untouched.Checklist
tests/fixtures/omnivoice_data/on thesmoke-matrixCI job — not run here (no web deps on the NPU host); the behaviour change is confined to cache flushes.This change adds
release_device_cache()to flush the accelerator selected by the engines, including CUDA, MPS, XPU, and NPU, and uses it in generation, dubbing, translation, and transcription recovery paths.free_vram()uses the helper while preserving flush-error propagation; recovery calls remain best-effort. Test execution status is not provided, so verify the new tests before merging.