fix(generate): budget an under-provisioned GPU like the CPU it performs like - #1806
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fix(generate): budget an under-provisioned GPU like the CPU it performs like#1806VishvakR wants to merge 8 commits into
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…ms like A GPU with less VRAM than the engine declares it needs pages to system RAM over PCIe, so it renders slower than the same machine's CPU. The compute-time budget picked its value from the device family alone, so that card was treated as fast hardware and given 300s -- half the 600s a plain CPU host gets. It is the slowest configuration the app supports and it had the shortest watchdog. Everything else already acted on the verdict. resolve_routing() raises the caveat, the synth preflight warns before the user waits, and _timeout_guidance() names the card in the failure. Each TTS generate dispatch even hands the guard the engine's floor on the line above the timeout that ignored it. debpalash#1226 and debpalash#1222 were the same 4 GB cards on the same engine; both were closed by making the app explain the timeout better, never by correcting the budget behind it. generate_timeout_s() now floors an under-provisioned accelerator at the CPU budget. The length scaling is unchanged, and an explicitly configured OMNIVOICE_GENERATE_TIMEOUT_S is still honoured verbatim, so an operator who lowered the watchdog to fail fast keeps that. The floor is a max(), never an assignment, so a raised accelerated budget is never cut down. Engines that declare no floor, a failed VRAM probe, and MPS (whose vram_gb is a unified- memory heuristic, not a dedicated pool) are all untouched. The three-clause "is this host under-provisioned" test was written out inline in the caveat and in the timeout message, which is how the budget came to disagree with the warning printed beside it; it is now one predicate, under_provisioned_vram(), that all three read. Reported on a GTX 1650 (4 GB) running the omnivoice engine, whose breadcrumbs show the budget ending the job on the dot: 372s and 301s are exactly 300 + max(0, len - 1200) / 40 for the two takes. Fixes debpalash#1804.
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| Filename | Overview |
|---|---|
| backend/services/model_manager.py | Floors under-provisioned CUDA and ROCm generation budgets at the CPU allowance while preserving explicit universal overrides and length scaling. |
| backend/services/engine_routing.py | Centralizes the dedicated-VRAM under-provisioning verdict shared by routing guidance and timeout budgeting. |
| backend/worker/deadlines.py | Applies CPU-class execution allowances to remote workers that advertise insufficient dedicated memory. |
| backend/worker/scheduler.py | Computes deadlines from the selected worker’s capabilities and retains the dispatch-time policy on each attempt. |
| backend/worker/task_store.py | Persists and restores attempt deadline snapshots for restart-safe remote scheduling. |
| backend/migrations/versions/0011_remote_attempt_deadlines.py | Adds the nullable deadline snapshot column with upgrade and downgrade paths. |
| tests/test_under_provisioned_gpu_budget_1804.py | Covers local and remote under-provisioning, timeout overrides, persistence, restart behavior, and test-module isolation. |
| docs/performance.md | Documents the corrected timeout selection and explicit-override precedence consistently with runtime behavior. |
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📝 WalkthroughWalkthroughUnder-provisioned CUDA and ROCm GPUs now receive CPU-class generation and remote execution budgets when their VRAM is below an engine’s declared floor. The floor propagates through generation and voice-conversion paths, with shared routing logic, scheduler integration, persisted deadlines, tests, and documentation. ChangesVRAM-aware timeout budgeting
Estimated code review effort: 4 (Complex) | ~60 minutes Merge Risk: 🟡 Moderate · up to Some in-flight tasks created before migration can receive a shorter timeout after restart or worker loss, causing premature termination. This should be fixed before merge. Suggested reviewers: 🚥 Pre-merge checks | ✅ 7 | ❌ 2❌ Failed checks (2 warnings)
✅ Passed checks (7 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 56.45% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 62 functions across 16 files. (2 skipped: 2 unsupported.) Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
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Actionable comments posted: 3
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Inline comments:
In `@backend/api/routers/generation.py`:
- Around line 807-823: The /convert synthesis timeout calculation must use the
engine’s declared VRAM floor. Update the call to _generate_timeout_s in the
voice conversion flow to pass the same min_vram_gb value used for timeout
messaging, and add a regression test covering an under-provisioned CUDA/ROCm
engine.
In `@docs/performance.md`:
- Around line 95-96: Update the CPU-class fallback wording in
docs/performance.md lines 95-96 to state that it applies only to CUDA or ROCm
GPUs with dedicated VRAM, not generic accelerated or Apple Silicon/MPS hosts.
Apply the same CUDA/ROCm scope to the corresponding Known limits wording in
docs/engines/omnivoice.md lines 94-96.
In `@tests/test_low_vram_advisory.py`:
- Line 176: Update the source assertion in the test around each TTS dispatch
block so every dispatch independently verifies both guard and timeout
calculations use _engine_min_vram_gb, rather than relying on the aggregate
occurrence count. Preserve the expected dispatch count while ensuring a missing
pair in any individual dispatch fails the test.
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…vert Review findings on the PR, fixed here rather than left for a fourth report. Greptile (P1): the control plane sets a remote attempt's deadline, so the same inversion reached remote workers. Its suggested fix -- thread the engine floor into generate_timeout_s() -- would read the wrong machine: that function probes THIS host, so a Mac control plane dispatching to a 4 GB Windows worker learns nothing (MPS is excluded by design), and a 4 GB box dispatching to a 24 GB worker would wrongly get the longer budget. The worker already advertises both figures it takes -- free_memory_bytes and min_memory_bytes, both set in worker/capabilities.py -- so ConnectedWorker.under_provisioned() decides from those, and deadlines.for_task() floors the execution budget at what the same job would get on a CPU. The task-level ceiling in gpu_gateway._default_deadline is computed before a worker is bound and already asks for the CPU budget, so it still covers the raised lease; a test pins that. CodeRabbit (major): /convert had the identical split -- min_vram_gb to the guard so a timeout could name the card, and a budget computed without it. CodeRabbit (minor): the docs promised the CPU-class floor for any GPU, while the code scopes it to dedicated-VRAM families. Reworded to say CUDA/ROCm and to say why MPS is excluded. CodeRabbit (minor): the call-site assertion compared global occurrence counts, so one dispatch could drop both arguments while another gained an extra and the total still matched. It now walks the AST and checks each dispatch on its own, and the pairing is additionally enforced repo-wide across backend/api/routers: a dispatch that knows the engine's floor well enough to explain a timeout must know it well enough to set the budget. Three inline capability-selection loops in ConnectedWorker collapse into one _capability_for(), so the new predicate cannot select a different capability than execution_device() does.
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Inline comments:
In `@backend/worker/scheduler.py`:
- Around line 1311-1316: Persist the dispatch-time worker budget or
under_provisioned classification on the attempt before disconnect handling
removes the worker, and update the _budget_for path to use that persisted value
for zombie-slot expiry instead of recomputing from the missing worker. Add a
regression test covering a selected device whose budget exceeds the CPU budget.
In `@docs/performance.md`:
- Around line 95-96: Update the timeout documentation around
OMNIVOICE_GENERATE_TIMEOUT_S, OMNIVOICE_CPU_GENERATE_TIMEOUT_S, and the nearby
“Why 1200 characters” explanation to state one consistent precedence rule:
explicit CPU timeout governs CPU-family and under-provisioned CUDA/ROCm
generation, while the accelerated timeout applies to accelerated generation and
only serves as the CPU fallback when the CPU-specific variable is unset. Ensure
the documented base timeout matches the selected class.
In `@tests/test_under_provisioned_gpu_budget_1804.py`:
- Around line 307-311: Update the deadline test around deadlines.for_task to
exercise gpu_gateway._default_deadline directly with an under-provisioned CUDA
or ROCm worker, and assert the returned awaiting-side deadline covers the worker
deadline. Ensure the test fails with the pre-fix behavior and passes only when
the integration is correctly applied.
- Around line 341-343: Update the AST validation around the timeout expression
to require the nested min_vram_gb keyword’s value to exactly match
kw["min_vram_gb"], rather than accepting any keyword with that name. Add a
regression case using a mismatched floor, ensuring it fails before the fix and
passes afterward.
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…dence Second review round on the PR. CodeRabbit: the repo-wide dispatch assertion accepted any nested min_vram_gb keyword, so a budget computed with 0 or another engine's floor would pass while the guard used the right one. It now compares the two expressions. CodeRabbit: the awaiting-side deadline test restated gpu_gateway's formula instead of calling it, so it would not have noticed that function starting to select a shorter ceiling. It calls _default_deadline now, on cuda and rocm. CodeRabbit: the docs said an explicit OMNIVOICE_GENERATE_TIMEOUT_S is honoured "everywhere" while also saying the CPU var governs under-provisioned cards -- the two cannot both be true. Verified against the code (both vars set, 4 GB cuda, engine floor 6 GB -> 200s, the accelerated value) and documented as a precedence table rather than prose. The accelerated var deliberately wins on an under-provisioned host: that is what keeps "lower it to fail fast everywhere" working. Pinned by a test so the table cannot drift from the behaviour. CodeRabbit also flagged that Scheduler._budget_for recomputes with no worker after a disconnect, dropping under_provisioned to False. That cannot shorten anything: no worker means no execution_device, which _base_execution_seconds already coerces to "cpu" -- the same budget the floor raises an under-provisioned card to. Added a test pinning that rather than persisting a dispatch-time budget on the attempt. The residual case it describes -- an operator who raised the accelerated budget ABOVE the CPU one sees a shorter recomputation once the worker is gone -- predates this change and applies to every GPU worker, not just under-provisioned ones, so it belongs in its own fix.
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Inline comments:
In `@tests/test_under_provisioned_gpu_budget_1804.py`:
- Around line 403-406: Update the cleanup around the module reload in the test
using mm_mod so the original OMNIVOICE_GENERATE_TIMEOUT_S and
OMNIVOICE_CPU_GENERATE_TIMEOUT_S values are restored before
importlib.reload(mm_mod) runs. Use an inner monkeypatch.context() or equivalent
restoration mechanism, preserving whether each variable originally existed, and
reload only after the environment matches its initial state.
- Line 333: Update the regression test to exercise Scheduler._budget_for through
the worker-loss/disconnect path instead of calling deadlines.for_task directly.
Drive a scheduler through worker loss, then assert the recomputed budget is not
shorter than the CPU budget, preserving coverage that fails without the coercion
fix and passes with it.
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…loading Third review round on the PR, both findings in the new test file. CodeRabbit: the disconnect regression called deadlines.for_task directly, so it would have passed even if Scheduler._budget_for stopped coercing a missing worker to the CPU budget -- the very thing it exists to pin. It now builds a real WorkerPool and Scheduler, assigns the task to the 4 GB worker, asserts the bound budget is the CPU one, disconnects the worker and asserts the recomputation is not shorter. Forcing under_provisioned=False in _budget_for fails it with `assert 300 == 600`. CodeRabbit: the two env-var tests deleted the variables and reloaded model_manager inside a finally, which runs BEFORE pytest restores them -- so on a machine that already exports either var, the module constants would describe an environment pytest was about to put back, and every later test would read the mismatch. Both use monkeypatch.context() now, so the environment is restored before the reload.
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only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Inline comments:
In `@backend/worker/scheduler.py`:
- Around line 1302-1303: Update the deadline selection logic around
_budget_for() and the attempt.deadlines return path so legacy attempts with NULL
deadlines_json preserve their existing dispatch budget when
pool.get(attempt.worker_id) is unavailable. Recover a conservative deadline
snapshot or ensure the no-worker fallback never shortens the explicitly
configured budget, and add a regression test covering the
worker-loss/control-plane-restart case with OMNIVOICE_GENERATE_TIMEOUT_S greater
than OMNIVOICE_CPU_GENERATE_TIMEOUT_S.
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Summary
A GPU with less VRAM than the engine declares it needs pages to system RAM over PCIe, so it renders slower than the same machine's CPU.
generate_timeout_s()picked the compute-time budget from the device family alone, so such a card was treated as fast hardware and given 300s — half the 600s a plain CPU host gets. The slowest configuration the app supports had the shortest watchdog.Everything downstream of the routing verdict already acted on it:
resolve_routing()raises the caveat, the synth preflight warns before the user waits, and_timeout_guidance()names the card in the failure. Every TTS generate dispatch ingeneration.pyeven hands the guard the engine's floor on the line above thetimeout=that ignored it. #1226 and #1222 were the same 4 GB cards on the same engine, and both were closed by making the app explain the timeout better rather than by correcting the budget behind it. #1804 is the third.The reporter's breadcrumbs show the budget ending the job on the dot — 372s and 301s are exactly
300 + max(0, len - 1200) / 40for the two takes.Changes
generate_timeout_s()takes the engine's declared VRAM floor (derived automatically when the caller passesengine=, sotts_stream,batch,dub_generate,openai_compatandarchetypesneed no edit) and floors an under-provisioned accelerator atCPU_JOB_TIMEOUT_S.generation.pypass the floor they already give the guard to the budget as well.under_provisioned_vram()inengine_routing.pyis now the single definition of the predicate. It was written out inline in_caveatand again in_timeout_guidance, which is how the budget came to disagree with the warning printed beside it; all three read it now. No behaviour change to either existing caller.docs/performance.md,docs/engines/omnivoice.md) andCHANGELOG.mdupdated in the same PR per the docs-sync rule.Deliberately preserved: length scaling is untouched; an explicit
OMNIVOICE_GENERATE_TIMEOUT_Sis still honoured verbatim, so an operator who lowered the watchdog to fail fast keeps that; the floor is amax()and never cuts a raised accelerated budget down. Engines that declare no floor, a failed VRAM probe (vram_gb == 0), and MPS (wherevram_gbis a unified-memory heuristic, not a dedicated pool) are all unaffected.Reference transcribeandTTS assembleare not engine inference and do not declare a floor to the guard either, so they are left alone.Type
Testing
New
tests/test_under_provisioned_gpu_budget_1804.py(17 tests) covers the regression and its boundaries: 4 GB CUDA on the 6 GB engine ⇒ 600s; length scaling still applies on top; ROCm too; 24 GB card, undeclared floor, MPS and a failed probe all stay at 300s; an explicit universal budget wins verbatim; a raised accelerated budget is never cut; and the budget and the caveat are asserted to read the same verdict so they cannot drift apart again.tests/test_low_vram_advisory.py::test_the_generate_call_sites_pass_the_engines_flooris tightened rather than relaxed: each dispatch must now name the floor twice — once for the guard, once for the budget.Fail-before / pass-after, on the unmodified tree vs. this branch:
Targeted suites, all green:
Full suite, run on this branch and again on the same tree with the change stashed, comparing the failure lists: identical — no failure introduced. Both runs show 85 pre-existing failures, all from optional dependencies my sandbox lacks (
num2words,opencv,yt-dlp,pypdf,openai, and theomnivoicepackage metadata / sidecar checkpoint), not from this change.python scripts/check-docs-drift.pyandpython scripts/validate-install-docs.pyboth pass.backend/tests/is 324 passed with 2 failures that reproduce identically with the change stashed.Not run here: the frontend (untouched by this PR), the Tauri/cargo build, and the smoke-matrix fixture job — CI is the first real signal on those.
Checklist
tests/fixtures/omnivoice_data/still loads green on thesmoke-matrixCI job (macOS + Windows + Linux) — leaving this to CI; I have no Windows/Linux runner hereFixes #1804.
Under-provisioned CUDA and ROCm GPUs now receive at least the CPU generation timeout based on the engine’s VRAM floor. This fixes premature timeouts on GPUs such as 4 GB GTX 1650 systems while preserving scaling, overrides, and unaffected devices. Review persisted worker deadlines and the new
under_provisioned_vram()API for consistency.