unsloth/tests/studio/install/test_install_whisper_prebuilt_logic.py
Michael Han d5cf96d628
Studio: add local speech-to-text dictation engine (#7095)
* Studio: add Voice settings tab (dictation, dictionary, read aloud)

New Voice tab in Settings, placed just before About:

- Dictation: microphone picker, browser STT engine, recognition language,
  and an inline mic test with a live transcript
- Dictation dictionary: entries rewrite matching speech to their exact
  spelling and casing, applied in both dictation paths
- Recent dictations: last 20 final transcripts with copy and clear, so
  text can be recovered if it lands in the wrong place
- Read aloud: optional button on assistant responses with two engines,
  curated system voices (novelty and legacy voices filtered, quality
  ranked, capped at 20) or the TTS audio model loaded in Unsloth via
  /audio/generate (e.g. Orpheus), plus speed, pitch, volume and preview

Settings persist in localStorage (unsloth_voice_settings) and are read
at call time so changes apply without reloading the runtime. Adds en
keys plus the tab label for ja, zh-CN and pt-BR.

* Studio: drop the single option STT engine select, rename TTS option

The STT engine dropdown only had one entry, so it added noise without
giving a real choice. The engine row can come back once local STT
models land. Also renames the TTS engine option Unsloth TTS model to
Load TTS model to make the action clearer.

* Studio: harden Voice settings against edge cases found in simulation

Simulated the feature across Chromium, Firefox and WebKit plus node
level unit runs and backend contract checks. Fixes from the findings:

- Dictionary rewrite used a replacement string, so entries containing
  dollar patterns corrupted transcripts (A$$AP became A$AP, $& injected
  the match). Switched to the callback form of String.replace
- Persisted voice settings now validate types on hydration: non string
  micDeviceId, dictationLanguage and ttsVoiceURI, and non boolean
  ttsEnabled fall back to defaults instead of flowing into the UI
- Dictionary entries are trimmed, capped at 120 chars and re-sanitized
  on hydration
- The Test dictation panel now falls back to the default microphone
  when the saved device is unplugged, matching the composer adapter

Test coverage: 46 unit assertions (dictionary regex edge cases across
unicode, word boundaries and injection, voice curation for simulated
macOS, Windows and Linux voice inventories, corrupt storage merge),
13 backend contract checks against /audio/generate on an isolated
instance, and 60 browser assertions across the three engines covering
rendering, degradation without SpeechRecognition, curation in a real
DOM, dictionary persistence with unicode and dollar entries, the
no-model preview error path and corrupt localStorage recovery.

* Studio: address Voice settings review feedback

Verified each review comment before acting. Confirmed and fixed:

- Editing a dictionary entry was broken in two ways: the store trimmed
  on every keystroke so spaces could not be typed, and clearing the
  field deleted the entry and unmounted the input mid edit. Updates now
  keep the raw value and a blur commit trims or removes the entry
- The unplugged mic fallback checked instanceof DOMException, but a
  cross browser probe showed Firefox and WebKit throw
  OverconstrainedError objects that are not DOMExceptions, so the
  fallback never fired there. Matching on the error name now
- When the browser ended a dictation test on its own (silence timeout),
  the mic stream stayed open. All recognition end paths now stop the
  tracks and save the transcript through a single finalize path
- The studio TTS audio element now releases its WAV data URL as soon as
  playback ends, fails or is cancelled
- Allow microphone now reports insecure contexts (no mediaDevices)
  accurately instead of claiming access was blocked
- Voice tab copy moved into i18n keys per src/i18n/AGENTS.md, so locale
  overlays can translate it; en is the baseline and parity passes
- unsloth_voice_settings added to the Reset all local preferences key
  list so voice preferences obey the reset
- Non default microphones note that the system default is used when the
  browser speech engine cannot bind a specific device, since browsers
  without the start(track) overload ignore the argument silently

Re-ran the full simulation set after the changes: 46 unit assertions,
13 backend contract checks and 60 browser assertions across Chromium,
Firefox and WebKit all pass, plus a dedicated browser probe for the
dictionary editing behavior.

* Studio: use the chat mic icon in Voice settings for consistency

The Voice tab and its buttons used the hugeicons Mic02 glyph while the
chat composer uses a custom filled mic. Extract that composer icon into
a shared lib/mic-icon component, drop the duplicate inline copies in
thread.tsx and shared-composer.tsx, and use it for the Voice tab icon
and the tab's mic buttons so the microphone looks the same everywhere.

* Studio: address second round of Voice settings review feedback

Verified each new comment against the current code first. One item was
already fixed in the previous round (recording transcripts when the
browser ends a dictation test on its own). Confirmed and fixed:

- The microphone row showed a picker with generic names when browsers
  enumerate unlabeled devices before permission, leaving no way to
  grant access from the row. It now branches on whether labels are
  visible and shows Allow microphone otherwise
- Compare chat dictation ignored the selected microphone. It now opens
  the chosen device with the same fallback rules as the main adapter,
  passes the track to recognition where supported and releases the
  stream when recognition ends
- Closing the Voice tab cancelled the shared speechSynthesis even when
  read aloud was playing a chat message. Cleanup now only cancels when
  the tab owns an active preview
- Double clicking Start test could race two recognizers and leak the
  first stream. A starting flag set before the getUserMedia await makes
  start reentrancy safe
- Turning off the read aloud setting mid playback removed the only stop
  control. The stop button now renders whenever a message is speaking
- When an engine lacks the start(track) overload, both dictation paths
  now release the selected device stream before retrying with the
  default microphone instead of holding it open
- Read aloud support no longer requires Web Speech synthesis: the
  Unsloth TTS engine only needs audio playback, so it stays available
  in WebViews without speechSynthesis, with a clear error if the system
  engine is chosen there

Not addressed here: cancelling in flight backend TTS generation on
stop. The route runs generation in a worker thread without a
cancellation path, which is shared pre existing behavior with audio
chat generation and belongs in a backend change.

All suites re-run green: 46 unit, 13 backend contract and 60 browser
matrix assertions across Chromium, Firefox and WebKit, plus probes for
the unlabeled device branch and the double click race.

* Studio: drop empty and duplicate voiceURIs so the Voice tab never renders a crashing Select item

* Studio: guard dictation mic lifecycle in Voice test and Compare composer

Release a microphone opened after the component unmounts, and stop Compare
dictation on a permission or security failure instead of silently recording
from the default device, matching the main chat adapter.

* Studio: fix dictation and read-aloud lifecycle edge cases in Voice settings

- Join final dictation chunks with a space so recorded transcripts do not merge words
- Ignore a stale recognizer onend so a quick stop then restart is not torn down
- Use previewingRef so a double click on TTS preview does not orphan the first request
- Keep the read-aloud stop control visible when a new run starts while a message is spoken
- Stop the dictionary remove button from deleting an adjacent entry on a blur then click race

* Studio: trim redundant Voice settings comments

* Studio: fix Voice preview and Compare dictation edge cases

- Only cancel the shared speechSynthesis for a system-voice preview, so stopping
  a Studio preview no longer stops an unrelated chat read-aloud
- Release the Studio preview audio and its WAV data URL on normal completion
- Iterate every finalized result in Compare dictation so batched phrases are kept
- Cap persisted recent dictations to the last 20 on hydration

* Studio: use clipboard fallback for recents and release failed preview audio

- Copy recent dictations via the copyToClipboard helper so the execCommand
  fallback works in Safari and insecure http LAN contexts
- Release the Studio preview audio when play() rejects, not just on ended/error

* Studio: add local speech-to-text dictation engine

Add an offline dictation engine that transcribes with a local faster-whisper
model, alongside the existing browser (Web Speech) engine. The browser engine
streams audio to Apple or Google speech services and needs internet; the new
engine runs on the server, works offline, and drives any chat model without
evicting it (it loads in the backend process, separate from the model
subprocess). It also gives Firefox dictation, which has no Web Speech support.

Backend: a lazily-loaded, kept-warm faster-whisper sidecar and three routes
under /api/inference/audio (stt/status, stt/load, transcribe). faster-whisper
is torch-free, so this does not disturb the existing model stack.

Frontend: a Dictation engine setting (browser or local model), a curated model
picker with sizes, and MediaRecorder capture posted to the transcribe route.
The model warms automatically when the engine is selected, with live status.

* Studio: stream local STT transcription as you speak

Local dictation showed nothing until you stopped, because the whole clip was
transcribed once on stop. Now the growing recording is re-transcribed on a
fast pass every second and emitted as live interim text, with an accurate
final pass on stop. Partial recordings decode fine, and the model refines
earlier words as more audio arrives.

Adds an interim flag to the transcribe route (beam 1, no VAD) for the fast
preview pass; the final stop uses the accurate path.

* Studio: make local dictation stop instant and reliable

Stopping local dictation waited for a final network transcription before the
session ended, so the stop button did not flip and a second click ended the
session early and dropped the text. Now stop commits the live transcript
immediately, releases the mic at once, and ignores a second stop while
finalizing. Previews run more often so the committed text is current.

* Studio: record local dictation in short clips for reliable streaming

Re-transcribing a growing buffer every second got slower as it grew, flooded
the backend, showed stale words, and could leave the stop button stuck waiting
on a backlog. Record short independent clips instead and transcribe each once,
appending the text as you speak. Work per clip is bounded, so stopping is
prompt (with a hard timeout as a safety net) and long dictations stay smooth.

* Studio: dictate then transcribe once on stop, ChatGPT style

Local STT dictation streamed by re-transcribing the growing clip, which
was quadratic and saturated the backend (multi-second lag), and stop only
halted the recorder without releasing the mic, so it kept recording. Record
the microphone continuously, release it the instant the user stops, and
transcribe the whole clip once. Stopping is immediate and the transcript
lands in about a second. Also add the tiny model for the fastest option.

* Studio: surface dictation and read-aloud failures instead of failing silently

- Compare dictation reports microphone and speech-recognition errors via toast,
  reusing the main chat adapter's describeMediaError and describeSpeechError
- Read-aloud toasts genuine model or synthesis failures while ignoring cancellations

* Studio: ChatGPT-style recording bar for dictation

Clicking the mic now drops the composer into a dedicated recording bar
with a live waveform, a discard (X) and a confirm (tick), instead of a
plain stop button. The tick stops recording and transcribes the clip;
the X throws the recording away and keeps whatever text was already in
the composer. The model adapter taps the mic with an analyser to drive
the waveform, and the router tracks the live session so the X can cancel
it without transcribing.

* Studio: transcribe dictation while speaking, ChatGPT layout

Match ChatGPT's recording layout: the bar now renders in place of the
input with the left plus button kept, the waveform in the middle, and
the discard and confirm buttons together on the right.

Cut the post-confirm delay by transcribing in the background as the user
talks. The audio is split at natural pauses (voice-activity detection off
the same analyser that drives the waveform) and each clip is transcribed
as it is cut, so confirming only has to finish the short final tail. The
model is also warmed when recording starts so the first run never pays a
cold load.

* Studio: ChatGPT waveform, hide tools while dictating, faster STT

Make the recording UI read like ChatGPT: the waveform is now a dense row
of round dots that rise into thin centered bars, and while dictating only
the plus button shows, with the mode badge and tool toggles hidden so the
bar is just the waveform and controls.

Speed up transcription: decode greedily (beam_size=1), which is several
times faster on CPU with negligible accuracy loss on short dictation
clips, and cap background segments at 6s so the final tail after confirm
stays short.

* Studio: finish ChatGPT voice bar and low-latency STT

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* Studio: full-width waveform with a timer that freezes on stop

Use the full-width waveform for the recording bar: brighter, bigger bars
that advance on a fixed cadence (keeping peaks between advances) so they
glide instead of racing by, inset from the composer edges. Keep a visible
timer and the green confirm button, matching the ChatGPT reference, and
freeze the timer and waveform the moment the user confirms.

* Studio: fix multilingual local dictation

* Studio: speed up dictation and release local STT

* Studio: harden dictation finalization and STT decoding

* Studio: restore Firefox dictation fallback

* Studio: add dictation history manager

* Studio: manage speech model downloads

* Studio: remove em dash from voice model label

* Studio: move dictation history into Voice

* Studio: source local STT from Unsloth Whisper models

Point the dictation STT sidecar and its Model Hub download entries at
Unsloth's Hugging Face Whisper repos (small, large-v3-turbo, large-v3)
and run them through Transformers, so Studio only ever downloads
Unsloth-uploaded weights. Drop faster-whisper and the Systran/mobiuslabs
repos; keep the Model Hub as the only download path via local_files_only,
and keep PyAV for audio decoding.

Device selection uses float16 on CUDA and float32 on MPS and CPU, since
Whisper's decoder is unstable in float16 on MPS and repeats tokens.

Shorten the model picker labels to name plus download size and update the
STT tests for the new backend.

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* Studio: smooth dictation waveform and keep pill height

* Studio: align STT model dropdown width and tidy voice copy

* Studio: guide to local engine when browser dictation is offline

* Studio: clarify voice section and STT model copy

* Studio: keep STT warm with training-aware eviction

* Harden STT lifecycle and browser compatibility

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* Fix model discovery test lint

* Harden cross-browser microphone errors

* Harden cross-browser microphone errors

* Surface voice test recognition errors and fall back to Studio TTS

- Voice test now toasts non-abort speech-recognition failures instead of
  ending silently, matching the main and Compare dictation paths.
- Read-aloud routes to the backend model when the runtime lacks Web Speech
  synthesis (audio-only WebView), so it no longer errors immediately.

* Fix reviewed STT lifecycle races

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* Fix read-aloud fallback controls

* Guard read-aloud stop when deleting a non-speaking message

aui.message().stopSpeaking() throws unless this message is the one being
read aloud, so calling it unconditionally rejected the delete handler before
the message was removed. Only stop speech when this message is speaking.

* Cap recent dictation transcript length before persisting

Recent dictations only limited entry count, so a long transcript stored the
full text in the persisted voice settings and a few could exceed the
localStorage quota, throwing synchronously from the uncaught dictation cleanup
path. Truncate each entry on save and on hydration, matching the dictionary cap.

* Studio: keep dictation mic clickable and guide to local model

Register the dictation adapter unconditionally so the mic stays enabled
for any engine and starts working right after switching to the local
model on an already-open thread.

When the browser engine cannot run (Firefox, Brave, non-secure origins),
clicking the mic shows a toast that points to the local speech-to-text
model instead of leaving a disabled button. The toast stacks its action
below the text with a fully rounded button.

* Studio: add bottom padding below the dictation guidance toast button

* Studio: increase bottom padding under the dictation toast button

* Studio: add bottom padding inside the dictation toast button

* Studio: add five Whisper defaults and custom model search

Add private UnslothAI Tiny and Base mirrors to the curated local STT choices while keeping Small as the default. Let users search or paste a Transformers-compatible Whisper repository and validate it end to end.

Keep short dictations in one clip to avoid repeated padded encoder work, then split longer recordings near Whisper's 30-second boundary.

Update hidden model filters and tests, including the CPU-only CI runtime stub for PyAV.

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* Studio: use public Unsloth Whisper repositories

Point the Tiny and Base dictation defaults to the public unsloth repositories and remove the private mirror references from model filtering and tests.

* Studio: update Whisper download sizes

Reflect the cleaned public Tiny and Base repositories in the curated model labels.

* Studio: right-align STT model size, fix dropdown wheel scroll, refresh sizes

- Show the download size on the right of each model row so long names
  like Whisper Large v3 Turbo no longer hide it
- Update curated Whisper sizes to the safetensors weights actually
  downloaded: Tiny 151 MB, Base 290 MB, Small 967 MB
- Drive the model list scroll from a wheel handler so the mouse wheel
  scrolls it inside the Settings dialog, not just the scrollbar
- Add a search icon and shorten the placeholder to Search model

* Studio: do not search when a dictation model is picked, shrink repo label

- Treat the filled-in model text as a selection, not a query, so choosing
  a model no longer kicks off a Hugging Face search
- Make the repository line under each model name smaller

* Studio: tighten dictation model and local engine descriptions

* Studio: keep model display on pick instead of the query, shrink row text

- Guard the combobox input so selecting a model shows its name and does
  not echo the typed query back or start a search
- Map the item label to the friendly display so picks fill the field
- Reduce the model name and size text in each row

* Studio: show only the model name in the dictation field, shrink size label

- Drop the download size from the search field; the name alone is shown
  once a model is selected, with sizes kept in the dropdown list
- Reduce the size label text in each row

* Studio: clarify the dictation model description

* Studio: drop Hugging Face from the dictation model description

* Studio: move the dictation dictionary to its own Manage subpage

- Replace the inline entry list with a Manage row, matching Dictation
  history, so a long dictionary no longer crowds Voice settings
- Add a DictationDictionaryView subpage that holds the entry editor

* Studio: match STT field font, use best voice for System default

- Bump the dictation model field text to text-sm so it matches the
  engine dropdown next to it
- Resolve the System default read-aloud voice to the top curated voice
  instead of the browser default, which is a robotic legacy voice on macOS

* Studio: rerank read-aloud voices and drop duplicate voice entries

- Rank by vendor quality, then the user's locale, then a preferred list of
  natural voices, so the best voice leads instead of the first alphabetically
- Collapse voices that macOS reports twice under one name and language

* Studio: fold dictionary and recents into the dictation section

- Drop the separate Dictation dictionary and Recent dictations headings;
  their Manage rows now sit under Dictation, split by the row divider
- Shorten the custom spellings description

* Studio: add search and sort to dictation history

- Filter saved dictations by text with a search field
- Sort by newest, oldest, or A to Z; show a no-matches message
- Keep Clear all available regardless of the current filter

* Studio: settle cancelled STT loads before training and fix dictation review items

Wait for a cancelled STT load to exit and release its memory before
reporting it freed for training, so the loader cannot still be inside
from_pretrained()/.to(device) holding VRAM when the training subprocess
starts. A load that finishes before observing the cancel now gets
unloaded so the memory is actually reclaimed.

Clear the accelerator cache before the CPU fallback in load() so a failed
CUDA/MPS load does not strand reserved VRAM once the sidecar is marked
CPU-resident.

Send the saved Hugging Face token when polling STT download progress so a
gated or private repo resolves and shows the correct Load/Downloaded
state instead of reporting missing.

Mark the composer Dictate button as type="button" so clicking it does not
also submit the draft when the composer already has text or attachments.

* Studio: pin dictation settings per session and close STT startup races

Capture the STT model and language when a dictation session starts and
pass them to every queued segment and the warm-up load, so changing the
model or language mid-recording no longer transcribes the same clip with
the wrong model or a model that is not downloaded.

Check the local runtime at the top of transcribe(), before the model
cache lookup and the bounded audio decode, so a server missing PyTorch or
Transformers returns 501 up front instead of decoding a long clip first.

Treat the training startup window as active for STT device selection.
start_training frees VRAM in before_spawn but only assigns _proc later, so
a concurrent STT load could take the GPU that was just cleared. A startup
flag now reports training active from the free until the process is live,
forcing those loads to CPU; a finally clears it on every exit.

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* Studio: stub the STT runtime check in transcribe orchestration tests

transcribe() now verifies the local runtime up front, so the unit tests
that exercise transcription orchestration must treat the runtime as
present to keep passing where PyTorch, Transformers, and PyAV are not
installed. Stub ensure_stt_available in the shared fixture and restore
the real check in the availability and load-rejection tests.

* Harden custom Whisper dictation models

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* Add whisper.cpp dictation engine with per-engine downloads and history rework

Engines
- New GGML STT sidecar that runs a managed whisper-server subprocess with
  idle unload, plus a pinned static build script (scripts/build_whisper_cpp.sh)
- Dictation engine picker now offers Browser, Local transcription
  (whisper.cpp), and Local transcription (Transformers)
- Both local engines serve the same five curated Whisper models and download
  them directly with byte-level progress reported by /audio/stt/status
- Models auto load on selection and when their download finishes
- Unload and training admission account for both engines

Benchmarks (Apple Silicon, greedy, warm, same checkpoints)
- whisper.cpp transcribes 2.4x to 5x faster than Transformers and loads in
  about 0.45s vs 0.86s for Whisper Small
- whisper.cpp GGUF path is unchanged by the Transformers addition
  (load 0.445s -> 0.444s, short clip 0.391s -> 0.347s, long 1.197s -> 1.129s)

Voice settings UI
- Plain curated model select replaces the searchable combobox
- Single download progress bar with transfer rate for both engines
- Dictation history now stores every dictation with Show more pagination,
  a top Clear history action, and links back to the chat it was spoken into
- Archived chats dialog gets the same pagination
- Delete dialog offers deleting a dictation together with its chat

Tests: 88 backend STT tests pass, including new snapshot download coverage.
Frontend typecheck, lint, i18n parity, and production build pass.

* Merge local engines into one option and source GGML models from unslothai

Engine selection
- The dictation engine dropdown is back to two choices: Browser and Local
  transcription. The selected model decides the backend: curated ids run
  GGML checkpoints through whisper.cpp, searched Hugging Face repositories
  run safetensors through Transformers
- Model picker lists the curated models and searches Hugging Face for other
  Whisper repositories, validating them before selection. The trigger is a
  plain button so the selection never renders inside a text input
- /audio/stt/status accepts a model query param so downloaded state works
  for custom repositories; the engine param on load, transcribe, and
  download routes is derived from the model everywhere

Model source
- Curated GGML checkpoints now download from the Unsloth-hosted
  unslothai/whisper-*-GGUF repositories (one repo per model) instead of
  ggerganov/whisper.cpp; cache lookups, progress totals, and in-flight blob
  tracking are per-model

Fixes
- Voice settings and dictation history were not persisting: the quota-safe
  localStorage wrapper was declared after the store that uses it, so the
  persist storage factory failed silently. Every settings write also threw
  mid-click, which kept the model picker popover from closing on selection
- is_model_downloaded now verifies config, preprocessor config, and real
  weight files instead of trusting an offline snapshot lookup, so a partial
  download left by an aborted fetch shows the Download button instead of
  failing to load
- Removed whisper.cpp mentions from user-facing text: the ready status
  shows Loaded instead of the runtime name, picker rows show the source
  repository, and runtime error messages say local transcription runtime

Verified with automated browser sessions and live API checks: selection
closes the picker with no page errors, persisted settings hydrate on
reload, a stale partial snapshot triggers download then loads on MPS and
transcribes, and curated models download from the unslothai repos. 88
backend STT tests, typecheck, lint, i18n parity, and build pass.

* Skip the duplicate source line for custom models in the STT picker

A custom repository's display name is its id, so search results and the
appended current selection rendered the same string twice. The source
line now only renders when it differs from the name; curated rows keep
their name, unslothai source repository, and download size.

* Verify every shard of a sharded checkpoint in the downloaded check

A snapshot holding one of N shards (or a corrupt shard index) passed the
downloaded check and then failed at load. When model.safetensors.index.json
exists, every shard in its weight map must now be present. Found by
simulation; covered by a regression test.

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* Rename stale _starting references in the pump resilience tests

The startup flag on TrainingBackend was renamed to _spawn_in_progress but
two tests added alongside it still asserted on the old name, failing the
Python 3.11 to 3.13 CI jobs.

* Make the selected model row clearly highlighted in the STT picker

The current selection was a faint background tint. It now uses the accent
background with a medium weight name. Two line rows use a small corner
radius; single line custom repo rows keep the pill shape.

* Address review feedback on STT snapshot checks, VRAM release, and dictation UX

Verify snapshot completeness in the load preflight so a partial download
fails before the audio is decoded, for curated and custom repos alike.
Drop the failed accelerator traceback before the CPU retry so the cache
clear can actually release that memory. Keep unloading the GGUF sidecar
after cancelling an in-flight Transformers load; both engines can hold
memory at once. Allow Auto language with English-only .en checkpoints,
matching the backend which sends no forced language. Keep the discard
button usable while a transcription is pending so a slow or hung request
cannot trap the composer in dictation mode. Stop linking Compare and
settings test dictations to the unrelated active single chat thread.

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* Move the CPU retry out of the exception handler

On Python 3.10 the interpreter exception state keeps its own reference
to the traceback, so dropping it from the caught exception was not
enough to release the failed accelerator load during the retry. Leaving
the handler before clearing the cache works on every supported version.

* Address review feedback on session handoff, chat pinning, and server lifetime

Starting a dictation from a second entry point now cancels the session
it replaces, so the old recording cannot keep the microphone open or
save a transcript with no discard button pointing at it. The linked
chat is pinned when recording starts, so switching threads while a
transcription finalizes cannot relink the transcript to the newly
opened chat. whisper-server is now bound to Studio's lifetime like the
other long-lived children: PDEATHSIG on Linux, the parent job object on
Windows, and pid adoption so the shutdown sweep reaps it; before this
it survived a Ctrl+C exit as an orphan still holding the model.

* Remove the dictation mic test from Voice settings

The composer dictate button covers the same check, so the test row, its
transcript panel, the unsupported fallback row, and their strings and
search entry are gone.

* Studio STT: gate GGUF whisper-server on training and fix dictation retry and dictionary edits

GGUF (whisper.cpp) sidecar:
- Launch whisper-server with --no-gpu while training is active, mirroring the Transformers sidecar's CPU device choice, so a mid-training dictation cannot reclaim the VRAM training just freed.
- Report is_loading() during whisper-server startup so training VRAM admission accounts for the accelerator memory it is about to bind.
- Require PyAV in is_available() so /audio/stt/status reports the engine unavailable when uploads cannot be decoded, instead of loading fine and then 501ing at transcription.
- Reject a missing model before decoding audio, matching the Transformers download preflight.

Voice settings:
- The download Retry button now restarts the download; the sidecar error is sticky until a new start(), so re-polling alone never cleared it.

Dictation dictionary:
- Tabbing from an emptied entry to its remove button no longer commit-splices the row first, which shifted indices and deleted the wrong entry.

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* Studio STT: fix curated GGUF whisper filenames to match hosted repos

The unslothai/whisper-*-GGUF repos host the checkpoint as whisper-<id>.bin,
not ggml-<id>.bin, so every curated dictation download and cached-path
lookup 404'd and the whisper.cpp engine could never load a model. Point
GGML_STT_MODELS at the real filenames and guard the naming with a test.

* Studio STT: validate a custom dictation repo before downloading it

The Transformers STT engine accepts an arbitrary owner/model repo, but the
download route handed it straight to snapshot_download, pulling a possibly large
non-Whisper repository into the shared HF cache. Confirm the repo is a Whisper
checkpoint first with the existing metadata-only validate_remote_model (no
weights); curated ids short-circuit and the GGUF engine (curated-only) is
unaffected. A non-Whisper repo now 422s before any download.

* Studio STT: preempt a still-loading GGUF server for training admission

A whisper-server still in its startup window binds accelerator memory but has no
loaded_model yet, so training admission could miss it and launch into an OOM.
Make the GGUF startup cancellable (cancel_pending_load signals an abort event and
terminates the starting process without the load lock; _wait_for_server observes
it and raises SttLoadCancelledError; wait_for_load_to_settle blocks on the lock
until the killed server is reaped), and always fold the GGUF sidecar into the
resident-STT summary so a resident Transformers model cannot mask a loading GGUF
server. free_stt_model_for_training now cancels an in-flight load and waits for it
to settle before training claims the memory.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio STT: fall back to Transformers when whisper-server is absent

A curated dictation model (including the default small) hard-pinned the GGUF
engine, but standard installs do not ship whisper-server, so every recording
501'd instead of using the Transformers engine that serves the same checkpoint
-- the GGUF sidecar's own documented contract. Add _resolve_serving_stt_engine:
a GGUF request for a curated id (the only ids GGUF accepts, all Transformers-
servable) downgrades to Transformers when whisper-server is unavailable, applied
consistently to download, load and transcribe (not unload, which targets a
specific engine). The Voice tab likewise falls back to the Transformers status so
the model is not shown unavailable and download is not blocked.

* Studio STT: hide custom Whisper caches from the legacy model pickers

The legacy /cached-models (and /cached-gguf) routes called is_hidden_model with
only the owner/model id, which cannot reach the config-based Whisper check, so a
downloaded custom (non-curated) Whisper checkpoint was still offered as a chat
model. Pass the cached snapshot path so _path_is_whisper_model inspects the repo
config and hides it, matching the discovery route.

* Studio STT: hide GGUF dictation repos, lock-free status, unload fallback, split training eviction

- Hide the curated GGUF dictation repos (unslothai/whisper-*-GGUF) from the chat
  model inventory and pickers, backend and frontend. Only their Transformers
  safetensors companions were hidden; the GGUF repos use a different org and a
  -GGUF suffix and carry a raw .bin with no whisper config.json, so they leaked
  into chat pickers.
- Make the GGUF sidecar loaded_model/device accessors lock-free, mirroring the
  Transformers sidecar. transcribe() holds self._lock across the whole inference
  call, so /audio/stt status polls and training admission previously blocked
  behind an in-flight transcription.
- stt_unload resolves through the serving resolver: a "gguf" pick on a host
  without whisper-server is served by the Transformers fallback, so unload must
  target that engine or the resident model is never freed. Unload also attempts
  every engine even if one raises, so a failure freeing one backend no longer
  skips the other.
- free_stt_model_for_training frees the Transformers and GGUF sidecars under
  independent exception boundaries so a failure unloading one no longer skips
  the other before training claims the memory.

Adds tests/test_stt_review_fixes.py covering all four.

* Studio STT: resolve Auto dictation language for the model engine + snapshot process liveness

- The model dictation adapter sent the raw setting (the literal "auto") to the
  backend, while the browser engine resolves Auto via resolveDictationLanguage.
  A batch of non-English voice notes came back mostly English on Auto. Add
  resolveModelDictationLanguage: only the literal "auto" is resolved to a
  concrete locale, gated so it becomes a language the model AND Whisper can
  honor (mirroring the backend's known-whisper-languages set); an explicit
  language, or a locale Whisper cannot honor, stays unchanged/auto-detect. Wire
  it into both adapter call sites.
- GgmlSttSidecar._process_alive() read self._process twice; a concurrent
  unload() nulls it under the lock while loaded_model/device read lock-free, so
  a null between the two reads called None.poll(). Snapshot once. Adds a
  deterministic regression test.

* studio: tighten comments and docstrings in the dictation modules

* studio: harden dictation model downloads, GGML readiness, and recording paths

Address review findings on the STT dictation feature:

- build_whisper_cpp.sh refuses to delete a whisper.cpp tree under a custom
  Studio home unless it carries the Studio ownership marker, matching the
  setup.sh policy, and marks trees it creates
- _snapshot_is_complete validates every shard of a sharded PyTorch
  (pytorch_model.bin.index.json) checkpoint like the safetensors path, and
  requires tokenizer assets (tokenizer.json or vocab.json + merges.txt)
- custom-repo downloads pin the revision resolved at validation time and
  restrict snapshot_download to the model/tokenizer/config/preprocessor file
  classes Studio loads
- the GGML sidecar holds its port reservation until just before spawning
  whisper-server and only accepts readiness from a responder that both looks
  like whisper.cpp's server and belongs to the still-running managed child,
  probing twice, so mic audio cannot be posted to a foreign local process
- the recording adapter transcribes every non-empty segment; the RMS meter
  only shapes segment boundaries and can no longer discard quiet speech
- Compare-pane dictation can cancel a pending transcription on second click,
  with the button relabeled while finalizing
- localStorage quota recovery halves the dictation history until the save
  fits, so small histories shrink too
- the System default TTS voice resolves to the platform default voice
- new dictation UI imports go through the chat and hub feature barrels

Regression tests cover the build-script gate, sharded PyTorch and tokenizer
completeness, revision pinning and allow patterns, and the whisper-server
readiness probe.

* Fix STT download and voice picker follow-ups

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Add dictation button regression coverage

* Studio: prebuilt whisper.cpp via the shared llama.cpp install core, slim bundles paired to the llama prebuilt (#7294)

* Studio STT: add prebuilt whisper.cpp (whisper-server) installer

New install_whisper_prebuilt.py downloads a per-platform whisper-server
bundle published by the unslothai/whisper.cpp prebuilt CI into the managed
whisper.cpp dir (build/bin/whisper-server) so local dictation needs no
compiler. Mirrors install_node_prebuilt.py / install_llama_prebuilt.py:
host + backend detection, sha256 pins (whisper_prebuilt_pins.json) as the
trust anchor, staging + install lock + atomic swap, traversal-safe extract,
co-located shared libs (RUNPATH=$ORIGIN), an UNSLOTH_WHISPER_PREBUILT_INFO.json
marker with idempotent "already matches", and exit codes 0/1/2/3. Not wired
into setup yet; the pins ship empty so every asset fails closed until the
first fork release is published and its digests are reviewed in.

* Studio STT: install prebuilt whisper.cpp during setup and update

Add a fail-open whisper.cpp block to setup.sh after the llama.cpp section so
`unsloth studio update` (and a fresh install) fetch the prebuilt whisper-server
into the managed whisper.cpp dir the sidecar discovers. It skips a user-set
WHISPER_SERVER_PATH/UNSLOTH_WHISPER_CPP_PATH, honors UNSLOTH_SKIP_WHISPER_INSTALL,
forwards the resolved ROCm gfx, and never aborts setup: a busy install keeps the
existing runtime, and an unavailable prebuilt stays quiet (source build is opt-in
via UNSLOTH_WHISPER_FORCE_COMPILE) since Transformers STT and browser dictation
remain. Register UNSLOTH_WHISPER_PREBUILT_INFO.json as Studio-owned evidence.

* Studio STT: harden whisper-server child env + WSL ROCm detection

- Sidecar spawns whisper-server with a scrubbed child env that prepends the
  binary dir (co-located GPU libs) to the loader path, and on WSL2 ROCm loads
  the system HIP first (HSA_ENABLE_DXG_DETECTION=1) so a bundle's bare-metal HIP
  does not segfault on /dev/dxg. Secret-bearing vars are dropped from the child.
- find_whisper_server_binary now requires an executable, not just a file.
- Installer rocm probe passes HSA_ENABLE_DXG_DETECTION and falls back to
  /opt/rocm/bin/rocminfo so a WSL ROCm host is not misdetected as CPU-only;
  gfx parsing skips the gfx000 CPU agent and generic ISA lines.
- Tests for the child env (secret scrub, lib dir, WSL HIP precedence), the
  executable check, and the WSL rocm detection.

* Studio STT: in-app whisper.cpp prebuilt update stack + ship pins in the wheel

Mirror the llama.cpp update stack for the whisper.cpp prebuilt so Studio can
detect and install a newer whisper-server release from inside the app:
- backend/utils/whisper_cpp_freshness.py: read UNSLOTH_WHISPER_PREBUILT_INFO.json
  and compare the installed release against the newest unslothai/whisper.cpp
  release. Whisper tags are v<upstream>-unsloth.<N>, so is_behind compares a
  (major, minor, patch, serial) key with a strict downgrade guard; 24h cache;
  fail-open.
- backend/utils/whisper_cpp_update.py: run install_whisper_prebuilt.py to fetch
  and atomically swap the newest bundle, unloading the warm GGUF sidecar first.
- backend/routes/whisper.py mounted at /api/whisper (update-status + update).
- pyproject: add whisper_prebuilt_pins.json to studio package-data so the
  installer's trust anchor ships in the wheel (it is a data file, not a .py
  module, so package discovery alone does not include it; node_prebuilt_pins.json
  is listed for the same reason). Without this a pip-installed wheel had no pins
  and the prebuilt install aborted to Transformers STT.
Adds test_whisper_cpp_freshness.py (version parser, is_behind matrix + downgrade
guard, marker layouts, stale decision, fail-open).

* Studio STT: verify whisper prebuilts via the release checksum index, like llama.cpp

Re-align the whisper.cpp prebuilt installer to install_llama_prebuilt.py's trust
model: instead of a committed whisper_prebuilt_pins.json, verify every download
against the release's own whisper-prebuilt-sha256.json checksum index, fetched
from the same GitHub release.

- parse_release_checksums / fetch_release_checksums / expected_sha256_for replace
  the pins layer. The index is validated for schema/component and that its
  release_tag matches the resolved release; an asset absent from it, a release
  that does not publish it, or a manifest sha256 that disagrees with it all fail
  closed to a source build.
- resolve_release_tag now resolves the newest published release at runtime (or an
  explicit --published-release-tag), matching llama and the freshness check;
  removed the pinned-default and the UNSLOTH_WHISPER_ALLOW_UNVERIFIED opt-in.
- Delete studio/whisper_prebuilt_pins.json and drop its pyproject package-data
  entry (nothing to ship now, same as llama which has no committed pins).
- Adds test_install_whisper_prebuilt_checksums.py (index parser, fail-closed on
  uncovered asset, tampered-manifest guard, newest-release resolution).

This is a same-origin checksum (integrity, not authenticity), identical to the
llama.cpp installer; pair releases with GitHub artifact attestations for provenance.

* Resolve whisper prebuilt release via the download host (no GitHub API)

Mirror install_llama_prebuilt.py's fast path: resolve the release tag from
the releases/latest redirect and fetch the manifest + checksum index from
constructed releases/download URLs, so the common install path makes zero
api.github.com calls (unauthenticated api.github.com is capped at 60 req/hour
per IP; the download host is not). Fall back to the GitHub API only on a 404,
malformed asset, or tag mismatch.

* Studio STT: coverage-aware whisper prebuilt selection via a shared core

whisper's select_artifact returned the first os/arch/backend manifest match and
ignored the SM-coverage fields the release manifest already carries, so a
Blackwell B200 (sm_100) was served cuda12-legacy (sms 50-61) -- runnable only via
forward PTX JIT. install_llama_prebuilt.py on the same host correctly picks
cuda13-newer.

Extract the coverage-aware selection into a shared, component-agnostic core under
studio/backend/utils/prebuilt/ (selection + GPU host-capability detection), lifted
from llama's linux_cuda_choice_from_release / _artifact_covers_sms / _sm_range and
generalised over a normalised artifact. whisper's HostInfo now records the GPU
compute caps + driver CUDA version (honoring CUDA_VISIBLE_DEVICES), and
select_artifact routes CUDA/ROCm through the shared selector: every visible SM
must be covered, the tightest-covering profile wins (Blackwell-aware runtime-line
ordering), ROCm matches the gfx target exactly, and an uncovered GPU falls back to
the CPU bundle. CPU/Metal/Vulkan keep first-match. The resolver JSON, exit codes,
and "already matches" contract are unchanged.

On the B200 the installer now resolves cuda13-newer, matching llama.

* Studio STT: gate whisper CUDA selection on the on-disk runtime, like llama

The prebuilt CUDA bundles are dynamically linked and intentionally do NOT ship
libcudart/libcublas -- they load the same runtime the host already has. So the
driver's advertised CUDA version is only an upper bound: a cuda13 bundle still
needs cuda13 runtime libraries present on disk. Port llama's on-disk runtime
scan (detected_linux_runtime_lines / detected_windows_runtime_lines) into the
shared core and intersect it with the driver-compatible lines in
select_cuda_attempts. A host with a cuda13 driver but only cuda12 runtime (e.g.
torch-cuda12) now correctly gets a cuda12 bundle instead of an unloadable cuda13
one; a host with no CUDA runtime at all falls back to CPU.

Fixes a glob bug in the port (any(Path(d).glob(p) for d in dirs) tests generator
truthiness, not a match) that made every major report present; add a real
filesystem test that exercises the scan.

* studio: harden shared prebuilt core to full llama parity

Apply the review findings on the shared coverage-aware prebuilt-consumer
core so whisper.cpp selection is exactly equivalent to the llama.cpp path.

hosts.py: port llama's CUDA_VISIBLE_DEVICES handling. A GPU hidden by an
index/UUID selector now reports has_usable_nvidia False instead of staying
usable, via supports_explicit_visible_device_matching plus the physical /
explicit-match branches, and _select_visible_rows now matches rows the way
llama does (index or UUID, gpu- prefix optional) and skips unmatched tokens
rather than keeping all rows. Adds the Linux /proc/driver/nvidia/gpus
fallback and has_physical_nvidia. Adds parse_macos_version.

runtime_libs.py: the Linux on-disk scan now requires the exact libcudart /
libcublas SONAME (libcudart.so.13), not a libcudart.so.13* glob, so a bare
versioned file without the SONAME symlink no longer counts as loadable.
Hardens the ldconfig parse against an empty left-hand side.

selection.py: fix the Blackwell/torch reordering so it keys on the covering
runtime lines (falls through to the torch preference when the covering lines
were filtered out), matching linux_cuda_choice_from_release. Corrects the
compatible_runtime_lines_for_driver docstring: the bundles do not ship the
CUDA runtime, so the driver version is only an upper bound and the caller
must intersect with the on-disk scan.

install_whisper_prebuilt.py: enforce a macOS artifact's min_os (new
HostInfo.macos_version) so a bundle that cannot load on the host OS version
is dropped. Keep resolver stdout to only the JSON line by leaving logs on
stderr in --resolve-prebuilt mode, and map an unexpected probe failure to
prebuilt_available False instead of a traceback.

Tests: new host-probe suite for the visible-device logic, exact-SONAME
runtime-scan cases, macOS min_os filtering, resolver stdout-only-JSON,
exit-code mapping, and the repo key.

* studio: fix whisper prebuilt selection + launch parity gaps from review

A parallel review surfaced integration defects where the whisper path could
select or launch a bundle that cannot run on a concrete host. Each is fixed to
match install_llama_prebuilt.py.

macOS min_os: the manifest labels macOS requirements as macos-<version>
(e.g. macos-14.0), which the version parser could not read, so the guard was a
no-op and a macOS-13 host would install the macos-14 Metal bundle. Strip the
platform prefix before parsing.

ROCm gfx detection: _detect_rocm_gfx returned the first gfx token and ignored
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES / CUDA_VISIBLE_DEVICES. Since exact
ROCm matching treats that token as the active GPU, a mixed APU + dGPU host
(gfx1151 + gfx1100) with HIP_VISIBLE_DEVICES=1 installed the wrong archive. Route
through a shared pick_rocm_gfx_target (lifted from llama) that parses per-GPU
sections and honors the visibility vars (empty / -1 -> no AMD GPU).

--rocm-gfx override: recording the arch without setting has_rocm left the host on
its CUDA/CPU path so the ROCm bundle was never picked. --rocm-gfx now implies
has_rocm and clears NVIDIA state, like llama's _apply_host_overrides.

CUDA launch env: a CUDA bundle ships the ggml CUDA backend but not
libcudart/libcublas, and the sidecar launch env exposed only the bundle dir, so
on a host whose CUDA runtime lives only in the PyTorch wheels the selection would
gate cuda usable but the server could not load it. Add the CUDA-from-PyTorch
runtime dirs to the child loader path for CUDA bundles (bundle dir still first),
mirroring binary_env.

Also normalize a manifest artifact's supported_sms defensively (parity with
llama's parser) and document that blackwell_min_toolkit_for_caps is retained for
the Phase B llama Windows path.

Not changed (verified parity, not defects): Linux/Windows min_os is enforced
nowhere in llama (macOS only); the resolver is optimistic about the checksum
index and the install path verifies.

* studio: tighten prebuilt-core code comments

* studio: lift shared prebuilt installer core out of the whisper installer

* studio: reuse the llama.cpp prebuilt installer machinery for whisper

* studio: unify llama and whisper prebuilt installers on a shared descriptor core

* studio: consolidate prebuilt installer tests into the shared core suite

Grow tests/studio/install/test_prebuilt_core.py from 62 to 164 tests so every
component-agnostic behavior runs against both descriptors: the full seven
profile CUDA release matrix (multi-GPU, on-disk runtime gating, shuffle
stability, missing SM metadata, dotted SM normalization, no-driver fallback
policy), the ROCm gfx family matrix, macOS min_os gating and its helper,
backend resolution incl. cpu-fallback precedence and Intel-mac auto detect,
checksum-index non-object and plain-lookup cases, the tar symlink/hardlink
extraction guards moved from the llama suite, and the compute-cap, visible
device, runtime-line and Blackwell helper value tables moved verbatim from
the llama characterization suites.

Delete only tests whose exact behavior the master now asserts for the same
component: 40 pure-alias helper cases in test_selection_logic.py (replaced by
value-identical master tables plus an alias-identity pin), 6 extraction moves
and the master-absorbed zip-symlink case in the llama logic suite, 3 routing
twins in test_rocm_support.py already pinned byte-for-byte in
test_selection_logic.py, the 2 Blackwell helper tables in the backend resolve
suite, 28 whisper logic tests and 10 whisper checksum tests re-asserted by
the master whisper parameterization. Wrapper wiring pins, the llama release
plan dialect, fingerprints and every llama-only behavior stay untouched.

* studio: dedupe sidecar and update helpers into the backend prebuilt package

* studio: chain whisper.cpp prebuilt updates onto the llama.cpp update flow

* studio: consume paired slim whisper prebuilts via the llama ggml runtime

* studio: serve every whisper backend from slim prebuilts

* studio: drop the whisper fat per-accelerator selection chain

unslothai/whisper.cpp releases are slim-only from v1.9.1-unsloth.2: one
ggml-less bundle per os/arch, paired to the llama.cpp prebuilt that provides
every ggml backend. Delete the whisper-side fat CUDA/ROCm/metal/vulkan
selection glue; keep slim selection + pairing, link_ggml_runtime, and one
legacy shape, the published fat CPU bundle of an explicitly pinned pre-slim
release. Exit 2 now reads as prebuilt unavailable (whisper never source
builds); setup already treats it that way.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Wire libomp runtime DLL alongside ggml in slim whisper installs

llama's clang-built windows-arm64 ggml-base.dll imports
libomp140.aarch64.dll, shipped in the llama bundle but not a system DLL.
Without it next to whisper-server.exe the loader fails with
STATUS_DLL_NOT_FOUND before main. MSVC x64 links vcomp140.dll from
System32 and Linux ggml uses system libgomp.so.1, so only windows-arm64
was affected. The empty-runtime guard still requires a real ggml
library; libomp alone is not a pairing.

* studio: drop whisper-side fat-selection support structure

Slim whisper bundles are selected per os/arch only; all accelerator
capability comes from the installed llama.cpp prebuilt, whose installer
already did the coverage-aware selection. Remove the machinery that only
existed to pick among fat per-accelerator whisper bundles:

- prebuilt_core: delete the generic CUDA/ROCm coverage selection
  (select_cuda_artifact, select_rocm_artifact, ArtifactView adapters,
  detected_cuda_runtime_lines, the exact-SONAME linux probe) that no
  shipped component routes through; llama keeps its own selection chain
  and whisper shadows select_artifact with the slim-only version.
  select_artifact is now a plain os/arch/backend first-match.
- install_whisper_prebuilt: drop the HostInfo CUDA fields
  (compute_caps, driver_cuda_version, torch_runtime_line) and the torch
  runtime probe that populated them; nothing reachable reads them, and
  the resolver payload sources runtime_line from the artifact.
- whisper_cpp_update: delete the standalone start_update job worker;
  whisper applies only run as the chained phase of the combined
  llama+whisper update. The status payload keeps its job field (idle).
- routes/whisper: drop the progress logger that could never fire.
- tests: remove tests of the deleted paths and tests duplicating the
  descriptor-parameterized core suite or the llama freshness suite.

Contracts unchanged: resolver JSON keys, exit codes, marker fields,
pairing logs, and the pinned pre-slim fat CPU escape hatch.

* Address review feedback on the whisper prebuilt update and install paths

- Pin the chained whisper phase to the release the freshness check
  offered, so the download-host latest pointer cannot reinstall an
  older build in a loop
- Wire the whisper prebuilt install into setup.ps1 (Windows setup
  previously skipped it entirely)
- Treat a non-executable server or missing wired ggml libraries as a
  broken install instead of reporting already matches
- Keep whisper sidecar reloads out of the job-level reload flag and
  resync chat state after a partial chained update that unloaded llama
- Repoint home and profile vars for the whisper-server subprocess at a
  managed scratch dir and drop credential-store pointers
- Clear the prebuilt marker before the opt-in source build overwrite
- Write the prebuilt marker with explicit utf-8 encoding

* Tighten comments in the whisper prebuilt consumer

* Harden the Windows whisper setup phase and the chained update edges

- setup.ps1: honor WHISPER_SERVER_PATH / UNSLOTH_WHISPER_CPP_PATH /
  UNSLOTH_SKIP_WHISPER_INSTALL, run the custom-home ownership guard
  before the atomic install, and forward the release-tag pin and ROCm
  hints like setup.sh
- sidecar: a cpu-selected install launches whisper-server with --no-gpu
  (slim wiring links every llama backend, so the flag is what keeps a
  deliberate CPU choice off the GPU)
- chained update: leave whisper unpinned on macOS (the llama phase can
  walk back there, and a newest-tag pin could be an impossible pairing
  on every retry) and treat installer exit 2 as kept-existing-runtime
  instead of failing the combined job
- job.to_tag now comes only from the llama phase, so a whisper-only
  round cannot report a llama update that never ran

* Fix slim whisper runtime follow-ups

* Address remaining whisper update reviews

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Address remaining prebuilt update reviews

* Fix remaining chained update reviews

* Fix remaining whisper runtime review edges

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: danielhanchen <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>

---------

Co-authored-by: danielhanchen <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
2026-07-23 01:39:03 -07:00

1357 lines
51 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Logic tests for studio/install_whisper_prebuilt.py -- the prebuilt whisper-server installer.
# No network/GPU: release resolution is injected and archives are built on disk in a tmp dir.
import importlib.util
import io
import json
import sys
import tarfile
import zipfile
from pathlib import Path
import pytest
PACKAGE_ROOT = Path(__file__).resolve().parents[3]
MODULE_PATH = PACKAGE_ROOT / "studio" / "install_whisper_prebuilt.py"
# The installer imports install_llama_prebuilt (same directory); make studio/
# importable so that resolves under spec-based loading.
_STUDIO_DIR = str(MODULE_PATH.parent)
if _STUDIO_DIR not in sys.path:
sys.path.insert(0, _STUDIO_DIR)
SPEC = importlib.util.spec_from_file_location("studio_install_whisper_prebuilt", MODULE_PATH)
assert SPEC is not None and SPEC.loader is not None
M = importlib.util.module_from_spec(SPEC)
sys.modules[SPEC.name] = M
SPEC.loader.exec_module(M)
HostInfo = M.HostInfo
PrebuiltFallback = M.PrebuiltFallback
ReleaseCompatibilityError = M.ReleaseCompatibilityError
BusyInstallConflict = M.BusyInstallConflict
RELEASE_TAG = "v1.9.1-unsloth.1"
UPSTREAM_TAG = "v1.9.1"
SOURCE_COMMIT = "0" * 40
STUDIO_PROTOCOL = "inference/multipart-v1"
def _host(
whisper_os: str,
whisper_arch: str,
*,
has_usable_nvidia: bool = False,
has_rocm: bool = False,
rocm_gfx: str | None = None,
macos_version: tuple[int, int] | None = None,
) -> HostInfo:
ext = ".zip" if whisper_os == "windows" else ".tar.gz"
return HostInfo(
system = {"linux": "Linux", "macos": "Darwin", "windows": "Windows"}[whisper_os],
machine = whisper_arch,
whisper_os = whisper_os,
whisper_arch = whisper_arch,
archive_ext = ext,
is_windows = whisper_os == "windows",
is_macos = whisper_os == "macos",
is_apple_silicon = whisper_os == "macos" and whisper_arch == "arm64",
has_usable_nvidia = has_usable_nvidia,
has_rocm = has_rocm,
rocm_gfx = rocm_gfx,
macos_version = macos_version,
)
def _artifact(os_: str, arch: str, backend: str, asset: str, sha256: str, **extra) -> dict:
art = {
"os": os_,
"arch": arch,
"backend": backend,
"asset": asset,
"sha256": sha256,
"runtime_line": extra.get("runtime_line"),
}
art.update(extra)
return art
def _manifest(
artifacts: list[dict],
*,
component: str = "whisper.cpp",
schema_version: int = 1,
) -> dict:
return {
"schema_version": schema_version,
"component": component,
"studio_protocol": STUDIO_PROTOCOL,
"upstream_tag": UPSTREAM_TAG,
"source_commit": SOURCE_COMMIT,
"artifacts": artifacts,
}
# ── Host detection (probes come from install_llama_prebuilt.detect_host) ──
def _llama_host(
system: str,
machine: str,
*,
has_usable_nvidia: bool = False,
compute_caps: list[str] | None = None,
driver_cuda_version: tuple[int, int] | None = None,
has_rocm: bool = False,
rocm_gfx: str | None = None,
macos_version: tuple[int, int] | None = None,
):
"""A fake install_llama_prebuilt HostInfo, as llama_detect_host would return."""
lowered = machine.lower()
return M.llama.HostInfo(
system = system,
machine = machine,
is_windows = system == "Windows",
is_linux = system == "Linux",
is_macos = system == "Darwin",
is_x86_64 = lowered in {"x86_64", "amd64"},
is_arm64 = lowered in {"arm64", "aarch64"},
nvidia_smi = None,
driver_cuda_version = driver_cuda_version,
compute_caps = list(compute_caps or []),
visible_cuda_devices = None,
has_physical_nvidia = has_usable_nvidia,
has_usable_nvidia = has_usable_nvidia,
has_rocm = has_rocm,
rocm_gfx_target = rocm_gfx,
macos_version = macos_version,
)
@pytest.mark.parametrize(
"system,machine,exp_os,exp_arch,exp_ext",
[
("Linux", "x86_64", "linux", "x64", ".tar.gz"),
("Linux", "aarch64", "linux", "arm64", ".tar.gz"),
("Darwin", "x86_64", "macos", "x64", ".tar.gz"),
("Darwin", "arm64", "macos", "arm64", ".tar.gz"),
("Windows", "AMD64", "windows", "x64", ".zip"),
("Windows", "ARM64", "windows", "arm64", ".zip"),
],
)
def test_detect_host(monkeypatch, system, machine, exp_os, exp_arch, exp_ext):
monkeypatch.setattr(M, "llama_detect_host", lambda: _llama_host(system, machine))
host = M.detect_host()
assert (host.whisper_os, host.whisper_arch, host.archive_ext) == (exp_os, exp_arch, exp_ext)
assert host.is_windows == (exp_os == "windows")
assert host.is_apple_silicon == (exp_os == "macos" and exp_arch == "arm64")
def test_detect_host_maps_rocm_fields(monkeypatch):
monkeypatch.setattr(
M,
"llama_detect_host",
lambda: _llama_host("Linux", "x86_64", has_rocm = True, rocm_gfx = "gfx1100"),
)
host = M.detect_host()
assert host.has_rocm is True
assert host.rocm_gfx == "gfx1100"
assert host.has_usable_nvidia is False
@pytest.mark.parametrize(
"system,machine",
[("Plan9", "x86_64"), ("Linux", "sparc64"), ("Linux", "armv7l")],
)
def test_detect_host_unsupported(monkeypatch, system, machine):
monkeypatch.setattr(M, "llama_detect_host", lambda: _llama_host(system, machine))
with pytest.raises(PrebuiltFallback):
M.detect_host()
# ── Asset naming (pure) ──
def test_whisper_asset_name():
assert (
M.whisper_asset_name(RELEASE_TAG, _host("linux", "x64"), "cpu")
== "whisper-v1.9.1-unsloth.1-linux-x64-cpu.tar.gz"
)
assert (
M.whisper_asset_name(RELEASE_TAG, _host("macos", "arm64"), "metal")
== "whisper-v1.9.1-unsloth.1-macos-arm64-metal.tar.gz"
)
assert (
M.whisper_asset_name(RELEASE_TAG, _host("windows", "x64"), "cuda12")
== "whisper-v1.9.1-unsloth.1-windows-x64-cuda12.zip"
)
# ── Install layout ──
def test_whisper_server_path_layout():
nix = _host("linux", "x64")
win = _host("windows", "x64")
assert M.whisper_server_path(Path("/w"), nix) == Path("/w/build/bin/whisper-server")
assert M.whisper_server_path(Path("/w"), win) == Path("/w/build/bin/Release/whisper-server.exe")
# ── Manifest parse + basic selection (wiring pin; the rejection matrix,
# extraction guards, resolver payload shape and macOS min_os gating are
# asserted against the real whisper descriptor in
# tests/studio/install/test_prebuilt_core.py) ──
def test_parse_manifest_ok_and_basic_selection():
cpu_asset = "whisper-v1.9.1-unsloth.1-linux-x64-cpu.tar.gz"
manifest = M.parse_manifest(_manifest([_artifact("linux", "x64", "cpu", cpu_asset, "a" * 64)]))
assert manifest["component"] == "whisper.cpp"
assert manifest["studio_protocol"] == STUDIO_PROTOCOL
host = _host("linux", "x64")
# The pinned pre-slim escape hatch: only the fat CPU bundle ever matches.
assert M.select_artifact(manifest, host, "cpu")["asset"] == cpu_asset
assert M.select_artifact(manifest, host, "metal") is None
def test_select_artifact_never_picks_fat_gpu_bundles():
# A pinned pre-slim release's fat GPU bundles are dead shapes: a cuda/rocm
# backend selects nothing (the core then retries with cpu), never the fat
# per-accelerator artifact.
manifest = M.parse_manifest(
_manifest(
[
_artifact("linux", "x64", "cpu", "whisper-linux-x64-cpu.tar.gz", "a" * 64),
_artifact("linux", "x64", "cuda", "whisper-linux-x64-cuda13.tar.gz", "b" * 64),
_artifact("linux", "x64", "rocm", "whisper-linux-x64-rocm.tar.gz", "c" * 64),
]
)
)
cuda_host = _host("linux", "x64", has_usable_nvidia = True)
assert M.select_artifact(manifest, cuda_host, "cuda") is None
rocm_host = _host("linux", "x64", has_rocm = True, rocm_gfx = "gfx1100")
assert M.select_artifact(manifest, rocm_host, "rocm") is None
artifact, backend, used_fallback = M.select_artifact_with_fallback(manifest, cuda_host, "cuda")
assert artifact["asset"] == "whisper-linux-x64-cpu.tar.gz"
assert backend == "cpu" and used_fallback is True
# ── Traversal-safe extraction ──
def _add_file(
tar: tarfile.TarFile,
name: str,
data: bytes,
mode: int = 0o644,
):
info = tarfile.TarInfo(name)
info.size = len(data)
info.mode = mode
tar.addfile(info, io.BytesIO(data))
# ── Fixture archive + full staging/activate install ──
def _build_cpu_bundle(tmp_path: Path, host: HostInfo) -> tuple[Path, str, str]:
"""Build a fake CPU bundle archive with a dummy server + lib; return (path, name, sha256)."""
asset = M.whisper_asset_name(RELEASE_TAG, host, "cpu")
archive = tmp_path / asset
server_name = M.server_binary_name(host)
with tarfile.open(archive, "w:gz") as tar:
_add_file(tar, server_name, b"#!/bin/sh\necho whisper\n", mode = 0o755)
_add_file(tar, "libwhisper.so", b"dummy-libwhisper")
_add_file(tar, "libggml-base.so", b"dummy-libggml")
return archive, asset, M.sha256_file(archive)
def _install_env(
monkeypatch,
tmp_path,
host,
*,
asset,
sha256,
backend_in_manifest = "cpu",
):
"""Wire monkeypatches so install_prebuilt runs fully offline against a local
archive, injecting the release bundle + checksum index (the checksum trust
model) via the single fetch_release_for_install seam."""
manifest = M.parse_manifest(
_manifest(
[_artifact(host.whisper_os, host.whisper_arch, backend_in_manifest, asset, sha256)]
)
)
bundle = M.ReleaseBundle(
repo = "unslothai/whisper.cpp",
release_tag = RELEASE_TAG,
manifest = manifest,
asset_urls = {asset: f"https://example.invalid/{asset}"},
)
checksums = {asset: sha256}
monkeypatch.setattr(
M,
"fetch_release_for_install",
lambda repo, *, published_release_tag = None: (bundle, checksums),
)
return bundle
def test_install_produces_colocated_layout_and_marker(tmp_path, monkeypatch):
host = _host("linux", "x64")
archive, asset, sha256 = _build_cpu_bundle(tmp_path, host)
def fake_download(url, destination):
destination.parent.mkdir(parents = True, exist_ok = True)
destination.write_bytes(archive.read_bytes())
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(M, "download_file", fake_download)
_install_env(monkeypatch, tmp_path, host, asset = asset, sha256 = sha256)
install_dir = tmp_path / "whisper.cpp"
rc = M.install_prebuilt(install_dir, backend = "cpu")
assert rc == M.EXIT_SUCCESS
server = install_dir / "build" / "bin" / "whisper-server"
assert server.is_file()
assert server.stat().st_mode & 0o111 # +x set on unix
# every shared lib from the archive is co-located next to the server
assert (install_dir / "build" / "bin" / "libwhisper.so").is_file()
assert (install_dir / "build" / "bin" / "libggml-base.so").is_file()
# marker written at the component root
marker = json.loads((install_dir / M.METADATA_FILENAME).read_text())
assert marker["component"] == "whisper.cpp"
assert marker["release_tag"] == RELEASE_TAG
assert marker["backend"] == "cpu"
assert marker["studio_protocol"] == STUDIO_PROTOCOL
assert marker["install_fingerprint"]
def test_install_is_idempotent(tmp_path, monkeypatch):
host = _host("linux", "x64")
archive, asset, sha256 = _build_cpu_bundle(tmp_path, host)
calls = {"n": 0}
def fake_download(url, destination):
calls["n"] += 1
destination.parent.mkdir(parents = True, exist_ok = True)
destination.write_bytes(archive.read_bytes())
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(M, "download_file", fake_download)
_install_env(monkeypatch, tmp_path, host, asset = asset, sha256 = sha256)
install_dir = tmp_path / "whisper.cpp"
assert M.install_prebuilt(install_dir, backend = "cpu") == M.EXIT_SUCCESS
assert calls["n"] == 1
# Second run: marker + binary already match -> "already matches", no download.
assert M.install_prebuilt(install_dir, backend = "cpu") == M.EXIT_SUCCESS
assert calls["n"] == 1
def test_install_sha_mismatch_then_retry_fails_closed(tmp_path, monkeypatch):
host = _host("linux", "x64")
archive, asset, _real_sha = _build_cpu_bundle(tmp_path, host)
wrong_sha = "f" * 64 # the checksum index claims a different sha than the archive
def fake_download(url, destination):
destination.parent.mkdir(parents = True, exist_ok = True)
destination.write_bytes(archive.read_bytes())
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(M, "download_file", fake_download)
_install_env(monkeypatch, tmp_path, host, asset = asset, sha256 = wrong_sha)
install_dir = tmp_path / "whisper.cpp"
with pytest.raises(PrebuiltFallback):
M.install_prebuilt(install_dir, backend = "cpu")
# A failed verify never activates a binary.
assert not (install_dir / "build" / "bin" / "whisper-server").exists()
# ── Busy lock -> exit 3 ──
def test_busy_lock_maps_to_exit_busy(tmp_path, monkeypatch):
host = _host("linux", "x64")
archive, asset, sha256 = _build_cpu_bundle(tmp_path, host)
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(M, "download_file", lambda u, d: None)
_install_env(monkeypatch, tmp_path, host, asset = asset, sha256 = sha256)
from contextlib import contextmanager
@contextmanager
def busy_lock(_path):
raise BusyInstallConflict("held by another process")
yield # pragma: no cover
monkeypatch.setattr(M, "install_lock", busy_lock)
rc = M.main(["--install-dir", str(tmp_path / "whisper.cpp"), "--backend", "cpu"])
assert rc == M.EXIT_BUSY
# ── Resolver JSON shape ──
def test_resolve_mode_keeps_stdout_json_only(tmp_path, monkeypatch, capsys):
# Even when the slim pairing emits diagnostics, --resolve-prebuilt must keep
# stdout to exactly the JSON line (setup.sh / whisper_cpp_update.py parse
# it); the slim_selection log noise belongs on stderr.
host = _cuda_host()
bin_dir = _fake_llama_bin(tmp_path)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer")
)
manifest = _slim_manifest()
bundle = M.ReleaseBundle(
repo = "unslothai/whisper.cpp",
release_tag = RELEASE_TAG,
manifest = manifest,
asset_urls = {
a["asset"]: f"https://example.invalid/{a['asset']}" for a in manifest["artifacts"]
},
)
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(
M, "fetch_release_for_install", lambda repo, *, published_release_tag = None: (bundle, {})
)
# A prior install test may have left the module flag True; the resolver must
# force it back to stderr regardless.
monkeypatch.setattr(M, "_LOG_TO_STDOUT", True, raising = False)
rc = M.main(["--resolve-prebuilt", "--output-format", "json"])
assert rc == M.EXIT_SUCCESS
captured = capsys.readouterr()
payload = json.loads(captured.out.strip()) # exactly one JSON line, parseable
assert payload["asset"] == SLIM_ASSET
assert "[whisper-prebuilt]" not in captured.out # no log noise on stdout
assert "slim_selection:" in captured.err # diagnostics routed to stderr
def test_main_maps_prebuilt_fallback_to_exit_error(tmp_path, monkeypatch):
def boom(*a, **kw):
raise PrebuiltFallback("no prebuilt")
monkeypatch.setattr(M, "install_prebuilt", boom)
rc = M.main(["--install-dir", str(tmp_path / "whisper.cpp"), "--backend", "cpu"])
assert rc == M.EXIT_ERROR
def test_main_forwards_requested_whisper_tags(tmp_path, monkeypatch):
seen = {}
def install(*args, **kwargs):
seen.update(kwargs)
return M.EXIT_SUCCESS
monkeypatch.setattr(M, "install_prebuilt", install)
rc = M.main(["--install-dir", str(tmp_path / "whisper.cpp"), "--whisper-tag", "v1.9.0"])
assert rc == M.EXIT_SUCCESS
assert seen["whisper_tag"] == "v1.9.0"
monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
monkeypatch.setattr(
M,
"resolve_prebuilt",
lambda host, **kwargs: seen.update(kwargs)
or {"prebuilt_available": False, "repo": "unslothai/whisper.cpp"},
)
assert M.main(["--resolve-prebuilt", "v1.8.0", "--output-format", "json"]) == 0
assert seen["whisper_tag"] == "v1.8.0"
def test_main_reserves_exit_2_for_release_incompatibility(tmp_path, monkeypatch):
def boom(*a, **kw):
raise ReleaseCompatibilityError("slim bundle requires llama.cpp b2; installed b1")
monkeypatch.setattr(M, "install_prebuilt", boom)
rc = M.main(["--install-dir", str(tmp_path / "whisper.cpp"), "--backend", "cpu"])
assert rc == M.EXIT_INCOMPATIBLE
def test_main_maps_unexpected_error_to_exit_error(tmp_path, monkeypatch):
def boom(*a, **kw):
raise RuntimeError("kaboom")
monkeypatch.setattr(M, "install_prebuilt", boom)
rc = M.main(["--install-dir", str(tmp_path / "whisper.cpp"), "--backend", "cpu"])
assert rc == M.EXIT_ERROR
def test_resolve_mode_unexpected_error_reports_unavailable(monkeypatch, capsys):
# An unexpected failure inside the probe maps to prebuilt_available=False, not
# a traceback, so the caller falls back cleanly.
host = _host("linux", "x64")
monkeypatch.setattr(M, "detect_host", lambda: host)
def boom(repo, *, published_release_tag = None):
raise RuntimeError("network down")
monkeypatch.setattr(M, "fetch_release_for_install", boom)
rc = M.main(["--resolve-prebuilt", "--output-format", "json"])
assert rc == M.EXIT_SUCCESS
payload = json.loads(capsys.readouterr().out.strip())
assert payload == {"prebuilt_available": False, "repo": "unslothai/whisper.cpp"}
# ── --rocm-gfx / --has-rocm overrides (llama parity) ──
def test_rocm_gfx_override_implies_has_rocm():
# --rocm-gfx alone (no --has-rocm) must enable ROCm and clear NVIDIA, else the
# host stays on its CUDA/CPU path and never picks the ROCm bundle.
base = _host("linux", "x64", has_usable_nvidia = True)
out = M.apply_host_overrides(base, rocm_gfx = "gfx1100")
assert out.has_rocm is True
assert out.rocm_gfx == "gfx1100"
assert out.has_usable_nvidia is False
assert M.auto_detect_backend(out) == "rocm"
# ── Slim bundles paired with the installed llama.cpp ggml runtime ──
SLIM_LLAMA_TAG = "b10069-mix-fb3d4ca"
SLIM_ASSET = "whisper-v1.9.1-unsloth.1-linux-x64-slim.tar.gz"
CPU_ASSET = "whisper-v1.9.1-unsloth.1-linux-x64-cpu.tar.gz"
def _fake_llama_bin(
tmp_path: Path,
*,
backend_module: str | None = "libggml-cuda.so",
sonames: tuple[str, ...] = ("libggml.so.0", "libggml-base.so.0"),
) -> Path:
bin_dir = tmp_path / "llama.cpp" / "build" / "bin"
bin_dir.mkdir(parents = True, exist_ok = True)
for name in sonames:
(bin_dir / name).write_bytes(b"ggml-old-" + name.encode())
if backend_module:
(bin_dir / backend_module).write_bytes(b"ggml-backend-old")
(bin_dir / "libggml-cpu-x64.so").write_bytes(b"ggml-cpu-old")
(bin_dir / "libllama.so").write_bytes(b"not-ggml") # must never be linked
return bin_dir
def _slim_artifact(**extra) -> dict:
art = {
"os": "linux",
"arch": "x64",
"backend": "slim",
"asset": SLIM_ASSET,
"sha256": "c" * 64,
"install_kind": "slim",
"requires_llama_tag": SLIM_LLAMA_TAG,
"requires_ggml_version": "0.17.0",
"requires_ggml_sonames": ["libggml.so.0", "libggml-base.so.0"],
"min_os": None,
}
art.update(extra)
return art
def _slim_manifest(slim_extra: dict | None = None) -> dict:
"""The transition-release shape: a slim bundle beside the published fat CPU
bundle (the pinned pre-slim escape hatch)."""
artifacts = [
_artifact("linux", "x64", "cpu", CPU_ASSET, "a" * 64),
_slim_artifact(**(slim_extra or {})),
]
return M.parse_manifest(_manifest(artifacts))
def _cuda_host() -> HostInfo:
return _host("linux", "x64", has_usable_nvidia = True)
def test_installed_llama_runtime_reads_marker(tmp_path):
root = tmp_path / "llama.cpp"
bin_dir = root / "build" / "bin"
bin_dir.mkdir(parents = True)
(root / "UNSLOTH_PREBUILT_INFO.json").write_text(
json.dumps({"release_tag": SLIM_LLAMA_TAG, "bundle_profile": "cuda13-newer"})
)
assert M.llama.installed_llama_runtime(root) == (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer")
@pytest.mark.parametrize(
"prepare",
[
lambda root: None, # no marker at all
lambda root: (root / "UNSLOTH_PREBUILT_INFO.json").write_text("{}"), # no release_tag
lambda root: (root / "UNSLOTH_PREBUILT_INFO.json").write_text("not json"),
],
)
def test_installed_llama_runtime_rejects_incomplete_installs(tmp_path, prepare):
root = tmp_path / "llama.cpp"
(root / "build" / "bin").mkdir(parents = True)
prepare(root)
assert M.llama.installed_llama_runtime(root) is None
def test_installed_llama_runtime_requires_bin_dir(tmp_path):
root = tmp_path / "llama.cpp"
root.mkdir()
(root / "UNSLOTH_PREBUILT_INFO.json").write_text(json.dumps({"release_tag": SLIM_LLAMA_TAG}))
assert M.llama.installed_llama_runtime(root) is None # marker but no build/bin
def test_slim_selected_when_all_pairing_checks_pass(tmp_path, monkeypatch):
bin_dir = _fake_llama_bin(tmp_path)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer")
)
artifact, backend, used_fallback = M.select_artifact_with_fallback(
_slim_manifest(), _cuda_host(), "cuda"
)
assert artifact["asset"] == SLIM_ASSET
assert backend == "cuda" and used_fallback is False
@pytest.mark.parametrize(
"runtime,slim_extra",
[
# No llama install at all.
(lambda bin_dir: None, None),
# Installed llama tag does not match requires_llama_tag.
(lambda bin_dir: (bin_dir, "b99999-mix-0000000", "cuda13-newer"), None),
# A required soname is missing from the llama bin dir.
(
lambda bin_dir: (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer"),
{"requires_ggml_sonames": ["libggml.so.0", "libggml-base.so.0", "libggml-extra.so.9"]},
),
# Manifest omits the soname contract entirely.
(lambda bin_dir: (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer"), {"requires_ggml_sonames": []}),
],
)
def test_slim_pairing_failure_falls_back_to_pinned_cpu(tmp_path, monkeypatch, runtime, slim_extra):
# With the fat per-accelerator chain gone, a failed pairing degrades to the
# one legacy shape: the release's published fat CPU bundle.
bin_dir = _fake_llama_bin(tmp_path)
monkeypatch.setattr(M, "installed_llama_runtime", lambda: runtime(bin_dir))
artifact, backend, used_fallback = M.select_artifact_with_fallback(
_slim_manifest(slim_extra), _cuda_host(), "cuda"
)
assert artifact["asset"] == CPU_ASSET
assert backend == "cpu" and used_fallback is True
def test_slim_missing_accel_module_rides_the_cpu_module(tmp_path, monkeypatch):
# Sonames present but no libggml-cuda.so in the llama bin dir: the cuda
# pairing fails, and the cpu retry still serves the same slim bundle via
# the llama cpu modules.
bin_dir = _fake_llama_bin(tmp_path, backend_module = None)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer")
)
artifact, backend, used_fallback = M.select_artifact_with_fallback(
_slim_manifest(), _cuda_host(), "cuda"
)
assert artifact["asset"] == SLIM_ASSET
assert backend == "cpu" and used_fallback is True
def test_slim_selected_for_cpu_backend_on_linux(tmp_path, monkeypatch):
# Slim-only releases must serve cpu too: with the llama cpu modules present
# the cpu backend rides the same slim bundle as the GPUs.
bin_dir = _fake_llama_bin(tmp_path)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer")
)
artifact, backend, _fb = M.select_artifact_with_fallback(
_slim_manifest(), _host("linux", "x64"), "cpu"
)
assert artifact["asset"] == SLIM_ASSET and backend == "cpu"
def test_slim_cpu_requires_a_cpu_module(tmp_path, monkeypatch):
# Sonames present but no libggml-cpu* variant in the llama bin dir -> fat cpu.
bin_dir = tmp_path / "llama.cpp" / "build" / "bin"
bin_dir.mkdir(parents = True)
for name in ("libggml.so.0", "libggml-base.so.0"):
(bin_dir / name).write_bytes(b"ggml")
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "linux-cpu")
)
artifact, backend, _fb = M.select_artifact_with_fallback(
_slim_manifest(), _host("linux", "x64"), "cpu"
)
assert artifact["asset"] == CPU_ASSET and backend == "cpu"
WIN_SLIM_ASSET = "whisper-v1.9.1-unsloth.1-windows-x64-slim.zip"
def test_slim_selected_for_cpu_backend_on_windows(tmp_path, monkeypatch):
bin_dir = tmp_path / "llama.cpp" / "build" / "bin" / "Release"
bin_dir.mkdir(parents = True)
for name in ("ggml.dll", "ggml-base.dll", "ggml-cpu-haswell.dll"):
(bin_dir / name).write_bytes(b"ggml")
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "windows-cpu")
)
manifest = M.parse_manifest(
_manifest(
[
_slim_artifact(
os = "windows",
arch = "x64",
asset = WIN_SLIM_ASSET,
requires_ggml_sonames = ["ggml.dll", "ggml-base.dll"],
)
]
)
)
artifact, backend, _fb = M.select_artifact_with_fallback(
manifest, _host("windows", "x64"), "cpu"
)
assert artifact["asset"] == WIN_SLIM_ASSET and backend == "cpu"
MAC_SLIM_ASSET = "whisper-v1.9.1-unsloth.1-macos-arm64-slim.tar.gz"
def _fake_llama_bin_macos(tmp_path: Path) -> Path:
"""The dylib names the live llama macos bundle ships (core + cpu + metal)."""
bin_dir = tmp_path / "llama.cpp" / "build" / "bin"
bin_dir.mkdir(parents = True, exist_ok = True)
for name in (
"libggml.dylib",
"libggml-base.dylib",
"libggml-cpu.dylib",
"libggml-metal.dylib",
"libggml-blas.dylib",
):
(bin_dir / name).write_bytes(b"ggml-" + name.encode())
(bin_dir / "libllama.dylib").write_bytes(b"not-ggml")
return bin_dir
def _macos_slim_manifest(arch: str = "arm64") -> dict:
return M.parse_manifest(
_manifest(
[
_slim_artifact(
os = "macos",
arch = arch,
asset = MAC_SLIM_ASSET,
requires_ggml_sonames = ["libggml.dylib", "libggml-base.dylib"],
)
]
)
)
@pytest.mark.parametrize("backend", ["metal", "cpu"])
def test_slim_selected_for_metal_and_cpu_on_macos(tmp_path, monkeypatch, backend):
bin_dir = _fake_llama_bin_macos(tmp_path)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "macos-metal-arm64")
)
host = _host("macos", "arm64", macos_version = (14, 0))
artifact, effective, _fb = M.select_artifact_with_fallback(
_macos_slim_manifest(), host, backend
)
assert artifact["asset"] == MAC_SLIM_ASSET
assert effective == backend # the accel identity, never "slim"
def test_macos_auto_backends_route_to_slim(tmp_path, monkeypatch):
# auto -> metal on Apple Silicon, cpu on Intel macs; both must pick slim.
bin_dir = _fake_llama_bin_macos(tmp_path)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "macos-metal-arm64")
)
silicon = _host("macos", "arm64", macos_version = (14, 0))
assert M.resolve_backend(silicon, "auto", cpu_fallback = False) == "metal"
artifact, backend, _fb = M.select_artifact_with_fallback(
_macos_slim_manifest(), silicon, "metal"
)
assert artifact["asset"] == MAC_SLIM_ASSET and backend == "metal"
intel = _host("macos", "x64", macos_version = (14, 0))
assert M.resolve_backend(intel, "auto", cpu_fallback = False) == "cpu"
artifact, backend, _fb = M.select_artifact_with_fallback(
_macos_slim_manifest(arch = "x64"), intel, "cpu"
)
assert artifact["asset"] == MAC_SLIM_ASSET and backend == "cpu"
def test_slim_metal_requires_the_metal_module(tmp_path, monkeypatch):
# A macos llama runtime without libggml-metal*.dylib cannot back metal; the
# cpu fallback still rides the same slim bundle via the cpu module.
bin_dir = tmp_path / "llama.cpp" / "build" / "bin"
bin_dir.mkdir(parents = True)
for name in ("libggml.dylib", "libggml-base.dylib", "libggml-cpu.dylib"):
(bin_dir / name).write_bytes(b"ggml")
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "macos-cpu-x64")
)
host = _host("macos", "arm64", macos_version = (14, 0))
artifact, backend, used_fallback = M.select_artifact_with_fallback(
_macos_slim_manifest(), host, "metal"
)
assert artifact["asset"] == MAC_SLIM_ASSET
assert backend == "cpu" and used_fallback is True
def test_slim_only_release_pairing_failure_is_actionable(tmp_path, monkeypatch):
# A slim-only release with no llama install is an operational failure, not
# the narrowly handled installed-version skew.
lines: list[str] = []
monkeypatch.setattr(M, "log", lines.append)
monkeypatch.setattr(M, "installed_llama_runtime", lambda: None)
manifest = M.parse_manifest(_manifest([_slim_artifact()]))
with pytest.raises(PrebuiltFallback):
M.select_artifact_with_fallback(manifest, _cuda_host(), "cuda")
assert any(
f"slim bundle requires llama.cpp {SLIM_LLAMA_TAG}; "
"install or update llama.cpp first" in line
for line in lines
)
def test_slim_release_tag_skew_has_distinct_compatibility_error(tmp_path, monkeypatch):
bin_dir = _fake_llama_bin(tmp_path)
monkeypatch.setattr(
M,
"installed_llama_runtime",
lambda: (bin_dir, "b10068-mix-old", "cuda13-newer"),
)
manifest = M.parse_manifest(_manifest([_slim_artifact()]))
with pytest.raises(ReleaseCompatibilityError, match = SLIM_LLAMA_TAG):
M.select_artifact_with_fallback(manifest, _cuda_host(), "cuda")
def test_link_ggml_runtime_hardlinks_every_ggml_library(tmp_path):
bin_dir = _fake_llama_bin(tmp_path)
whisper_bin = tmp_path / "whisper.cpp" / "build" / "bin"
linked = M.link_ggml_runtime(bin_dir, whisper_bin)
assert linked == [
"libggml-base.so.0",
"libggml-cpu-x64.so",
"libggml-cuda.so",
"libggml.so.0",
]
for name in ("libggml.so.0", "libggml-base.so.0", "libggml-cuda.so", "libggml-cpu-x64.so"):
source, dest = bin_dir / name, whisper_bin / name
assert dest.is_file() and not dest.is_symlink()
assert dest.stat().st_ino == source.stat().st_ino # a true hardlink
assert not (whisper_bin / "libllama.so").exists() # only ggml libraries wire over
def test_link_ggml_runtime_copy_fallback(tmp_path, monkeypatch):
def no_link(src, dst, **kwargs):
raise OSError("cross-device link")
monkeypatch.setattr(M.os, "link", no_link)
bin_dir = _fake_llama_bin(tmp_path)
whisper_bin = tmp_path / "whisper.cpp" / "build" / "bin"
assert len(M.link_ggml_runtime(bin_dir, whisper_bin)) == 4
dest = whisper_bin / "libggml.so.0"
assert dest.read_bytes() == (bin_dir / "libggml.so.0").read_bytes()
assert dest.stat().st_nlink == 1 # a copy, not a link
def test_link_ggml_runtime_hardlinks_dylibs(tmp_path):
# macOS wiring: the libggml* glob must take the .dylib names too.
bin_dir = _fake_llama_bin_macos(tmp_path)
whisper_bin = tmp_path / "whisper.cpp" / "build" / "bin"
linked = M.link_ggml_runtime(bin_dir, whisper_bin)
assert linked == [
"libggml-base.dylib",
"libggml-blas.dylib",
"libggml-cpu.dylib",
"libggml-metal.dylib",
"libggml.dylib",
]
for name in linked:
source, dest = bin_dir / name, whisper_bin / name
assert dest.is_file() and not dest.is_symlink()
assert dest.stat().st_ino == source.stat().st_ino # a true hardlink
assert not (whisper_bin / "libllama.dylib").exists()
def test_link_ggml_runtime_fails_closed_on_empty_runtime(tmp_path):
empty = tmp_path / "empty"
empty.mkdir()
with pytest.raises(PrebuiltFallback):
M.link_ggml_runtime(empty, tmp_path / "whisper_bin")
def test_link_ggml_runtime_wires_windows_libomp(tmp_path):
# llama's clang-built windows-arm64 ggml-base.dll imports
# libomp140.aarch64.dll (bundled, not a system DLL): the wiring must place
# it next to whisper-server.exe or the loader dies with DLL_NOT_FOUND.
bin_dir = tmp_path / "llama_bin"
bin_dir.mkdir()
for name in ("ggml.dll", "ggml-base.dll", "ggml-cpu.dll", "libomp140.aarch64.dll"):
(bin_dir / name).write_bytes(b"x")
(bin_dir / "llama.dll").write_bytes(b"x") # never wired
whisper_bin = tmp_path / "whisper_bin"
linked = M.link_ggml_runtime(bin_dir, whisper_bin)
assert linked == ["ggml-base.dll", "ggml-cpu.dll", "ggml.dll", "libomp140.aarch64.dll"]
assert not (whisper_bin / "llama.dll").exists()
def test_rocm_runtime_wires_complete_windows_dll_overlay(tmp_path):
bin_dir = tmp_path / "llama_bin"
bin_dir.mkdir()
dlls = {
"ggml.dll",
"ggml-base.dll",
"ggml-hip.dll",
"amdhip64.dll",
"rocblas.dll",
"hipblaslt.dll",
"hsa-runtime64.dll",
# The llama Windows ROCm archive can carry transitive DLLs whose names
# do not contain hip/roc/amd. Its installer overlays every DLL.
"runtime-support.dll",
}
for name in dlls:
(bin_dir / name).write_bytes(name.encode())
(bin_dir / "not-a-runtime.txt").write_text("ignored")
whisper_bin = tmp_path / "whisper_bin"
linked = M.link_ggml_runtime(bin_dir, whisper_bin, backend = "rocm")
linked_dirs = M.link_runtime_directories(
bin_dir,
whisper_bin,
backend = "rocm",
host = _host("windows", "x64"),
)
assert set(linked) == dlls
assert linked_dirs == []
assert all((whisper_bin / name).is_file() for name in dlls)
assert not (whisper_bin / "not-a-runtime.txt").exists()
def test_assemble_does_not_copy_packaging_marker_beside_server(tmp_path):
host = _host("linux", "x64")
bundle = tmp_path / "bundle"
bundle.mkdir()
(bundle / "whisper-server").write_text("server")
(bundle / M.METADATA_FILENAME).write_text('{"backend":"slim"}')
staged = tmp_path / "staged"
M.assemble_install_tree(bundle, staged, host)
assert not (staged / "build" / "bin" / M.METADATA_FILENAME).exists()
def test_rocm_runtime_wires_packaged_dependency_closure_and_catalogs(tmp_path, monkeypatch):
llama_bin = _fake_llama_bin(tmp_path, backend_module = "libggml-hip.so")
for name in ("libamdhip64.so.6", "libhipblas.so.2", "libcustomrocm.so.1"):
(llama_bin / name).write_bytes(name.encode())
for directory in M.SLIM_ROCM_RUNTIME_DIRS:
catalog = llama_bin / directory / "library" / "gfx1100"
catalog.mkdir(parents = True)
(catalog / "kernel.dat").write_bytes(directory.encode())
dependencies = {
"libggml-hip.so": {"libamdhip64.so.6", "libcustomrocm.so.1"},
"libamdhip64.so.6": {"libhipblas.so.2"},
}
monkeypatch.setattr(M, "_elf_needed", lambda path: dependencies.get(path.name, set()))
whisper_bin = tmp_path / "whisper-bin"
linked = M.link_ggml_runtime(llama_bin, whisper_bin, backend = "rocm")
linked_dirs = M.link_runtime_directories(
llama_bin,
whisper_bin,
backend = "rocm",
host = _host("linux", "x64"),
)
assert "libcustomrocm.so.1" in linked
assert "libhipblas.so.2" in linked
assert linked_dirs == ["hipblaslt", "rocblas"]
for directory in linked_dirs:
linked_catalog = whisper_bin / directory / "library" / "gfx1100" / "kernel.dat"
source_catalog = llama_bin / directory / "library" / "gfx1100" / "kernel.dat"
assert linked_catalog.stat().st_ino == source_catalog.stat().st_ino
def test_rocm_runtime_requires_both_kernel_catalogs(tmp_path):
llama_bin = _fake_llama_bin(tmp_path, backend_module = "libggml-hip.so")
(llama_bin / "hipblaslt").mkdir()
(llama_bin / "hipblaslt" / "kernel.dat").write_bytes(b"kernel")
with pytest.raises(PrebuiltFallback, match = "rocblas"):
M.link_runtime_directories(
llama_bin,
tmp_path / "whisper-bin",
backend = "rocm",
host = _host("linux", "x64"),
)
def test_rocm_runtime_catalog_copy_fallback(tmp_path, monkeypatch):
llama_bin = _fake_llama_bin(tmp_path, backend_module = "libggml-hip.so")
for directory in M.SLIM_ROCM_RUNTIME_DIRS:
catalog = llama_bin / directory
catalog.mkdir()
(catalog / "kernel.dat").write_bytes(directory.encode())
monkeypatch.setattr(M.os, "link", lambda *args: (_ for _ in ()).throw(OSError("xdev")))
whisper_bin = tmp_path / "whisper-bin"
M.link_runtime_directories(
llama_bin,
whisper_bin,
backend = "rocm",
host = _host("linux", "x64"),
)
for directory in M.SLIM_ROCM_RUNTIME_DIRS:
assert (whisper_bin / directory / "kernel.dat").read_bytes() == directory.encode()
def test_existing_install_requires_executable_server(tmp_path, monkeypatch):
# A marker-matching install with a non-executable server must reinstall:
# the sidecar refuses it via os.access(X_OK), so "already matches" would
# otherwise leave dictation permanently broken.
host = _host("linux", "x64")
selection = object()
monkeypatch.setattr(M.core, "existing_install_matches", lambda *a: True)
server = tmp_path / "build" / "bin" / "whisper-server"
server.parent.mkdir(parents = True)
server.write_text("bin")
monkeypatch.setattr(M, "installed_server_path", lambda d, h: server)
monkeypatch.setattr(M, "load_prebuilt_metadata", lambda d: {})
server.chmod(0o644)
assert M.existing_install_matches(tmp_path, host, selection) is False
server.chmod(0o755)
assert M.existing_install_matches(tmp_path, host, selection) is True
def test_existing_slim_install_requires_wired_libraries(tmp_path, monkeypatch):
# A slim install whose hardlinked ggml files vanished (llama dir deleted)
# must reinstall so update re-wires instead of reporting up to date.
host = _host("linux", "x64")
monkeypatch.setattr(M.core, "existing_install_matches", lambda *a: True)
server = tmp_path / "build" / "bin" / "whisper-server"
server.parent.mkdir(parents = True)
server.write_text("bin")
server.chmod(0o755)
monkeypatch.setattr(M, "installed_server_path", lambda d, h: server)
marker = {
"install_kind": "slim",
"runtime_wiring_version": M.SLIM_RUNTIME_WIRING_VERSION,
"linked_libraries": ["libggml.so.0"],
}
monkeypatch.setattr(M, "load_prebuilt_metadata", lambda d: marker)
marker.pop("runtime_wiring_version")
assert M.existing_install_matches(tmp_path, host, object()) is False
marker["runtime_wiring_version"] = M.SLIM_RUNTIME_WIRING_VERSION
assert M.existing_install_matches(tmp_path, host, object()) is False
(server.parent / "libggml.so.0").write_text("lib")
assert M.existing_install_matches(tmp_path, host, object()) is True
marker.update(backend = "rocm", linked_runtime_directories = [])
assert M.existing_install_matches(tmp_path, _host("windows", "x64"), object()) is True
def test_link_ggml_runtime_libomp_alone_is_not_a_pairing(tmp_path):
# A libomp without any ggml library is not a usable llama runtime.
bin_dir = tmp_path / "llama_bin"
bin_dir.mkdir()
(bin_dir / "libomp140.aarch64.dll").write_bytes(b"x")
with pytest.raises(PrebuiltFallback):
M.link_ggml_runtime(bin_dir, tmp_path / "whisper_bin")
def _build_slim_bundle(tmp_path: Path, host: HostInfo) -> tuple[Path, str]:
"""Slim archive per the CI contract: whisper-server + libwhisper only."""
archive = tmp_path / SLIM_ASSET
with tarfile.open(archive, "w:gz") as tar:
_add_file(tar, M.server_binary_name(host), b"#!/bin/sh\necho whisper\n", mode = 0o755)
_add_file(tar, "libwhisper.so.1", b"dummy-libwhisper")
return archive, M.sha256_file(archive)
def _slim_install_env(monkeypatch, tmp_path, host, *, sha256: str) -> Path:
llama_bin = _fake_llama_bin(tmp_path)
monkeypatch.setattr(
M,
"installed_llama_runtime",
lambda install_dir = None: (llama_bin, SLIM_LLAMA_TAG, "cuda13-newer"),
)
manifest = M.parse_manifest(
_manifest(
[
_artifact("linux", "x64", "cpu", CPU_ASSET, "a" * 64),
_slim_artifact(sha256 = sha256),
]
)
)
bundle = M.ReleaseBundle(
repo = "unslothai/whisper.cpp",
release_tag = RELEASE_TAG,
manifest = manifest,
asset_urls = {SLIM_ASSET: f"https://example.invalid/{SLIM_ASSET}"},
)
checksums = {SLIM_ASSET: sha256, CPU_ASSET: "a" * 64}
monkeypatch.setattr(
M,
"fetch_release_for_install",
lambda repo, *, published_release_tag = None: (bundle, checksums),
)
return llama_bin
def test_slim_install_wires_links_and_marker(tmp_path, monkeypatch):
host = _cuda_host()
archive, sha256 = _build_slim_bundle(tmp_path, host)
def fake_download(url, destination):
destination.parent.mkdir(parents = True, exist_ok = True)
destination.write_bytes(archive.read_bytes())
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(M, "download_file", fake_download)
llama_bin = _slim_install_env(monkeypatch, tmp_path, host, sha256 = sha256)
install_dir = tmp_path / "whisper.cpp"
assert M.install_prebuilt(install_dir, backend = "cuda") == M.EXIT_SUCCESS
whisper_bin = install_dir / "build" / "bin"
assert (whisper_bin / "whisper-server").is_file()
assert (whisper_bin / "libwhisper.so.1").is_file()
for name in ("libggml.so.0", "libggml-base.so.0", "libggml-cuda.so", "libggml-cpu-x64.so"):
dest = whisper_bin / name
assert dest.is_file() and not dest.is_symlink()
assert dest.stat().st_ino == (llama_bin / name).stat().st_ino
marker = json.loads((install_dir / M.METADATA_FILENAME).read_text())
assert marker["backend"] == "cuda" # the accel identity, not "slim"
assert marker["asset"] == SLIM_ASSET
assert marker["install_kind"] == "slim"
assert marker["paired_llama_tag"] == SLIM_LLAMA_TAG
assert marker["linked_from"] == str(llama_bin)
# The wired filenames land in the marker; the sidecar launch guard
# verifies exactly these names instead of hardcoded per-OS globs.
assert marker["linked_libraries"] == [
"libggml-base.so.0",
"libggml-cpu-x64.so",
"libggml-cuda.so",
"libggml.so.0",
]
assert marker["runtime_wiring_version"] == M.SLIM_RUNTIME_WIRING_VERSION
assert marker["linked_runtime_directories"] == []
def test_slim_links_survive_a_llama_dir_swap(tmp_path, monkeypatch):
# The whole point of hardlinks: replace the llama dir contents after wiring
# and whisper's links must still hold the OLD inodes/content.
host = _cuda_host()
archive, sha256 = _build_slim_bundle(tmp_path, host)
def fake_download(url, destination):
destination.parent.mkdir(parents = True, exist_ok = True)
destination.write_bytes(archive.read_bytes())
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(M, "download_file", fake_download)
llama_bin = _slim_install_env(monkeypatch, tmp_path, host, sha256 = sha256)
install_dir = tmp_path / "whisper.cpp"
assert M.install_prebuilt(install_dir, backend = "cuda") == M.EXIT_SUCCESS
whisper_lib = install_dir / "build" / "bin" / "libggml.so.0"
old_inode = whisper_lib.stat().st_ino
old_content = whisper_lib.read_bytes()
# Simulate the llama updater swapping in a new release's libraries.
for path in llama_bin.iterdir():
path.unlink()
(llama_bin / "libggml.so.0").write_bytes(b"ggml-NEW")
assert whisper_lib.stat().st_ino == old_inode # old inode survives the swap
assert whisper_lib.read_bytes() == old_content
assert whisper_lib.stat().st_nlink == 1 # the llama side is gone; ours remains
# The exact resolver key set shipped before slim existed; install_kind is the
# one additive field and must stay the only difference.
_LEGACY_RESOLVER_KEYS = {
"prebuilt_available",
"repo",
"release_tag",
"upstream_tag",
"backend",
"requested_backend",
"cpu_fallback",
"asset",
"os",
"arch",
"runtime_line",
}
def _resolver_payload(monkeypatch, capsys, manifest, host) -> dict:
bundle = M.ReleaseBundle(
repo = "unslothai/whisper.cpp",
release_tag = RELEASE_TAG,
manifest = manifest,
asset_urls = {},
)
monkeypatch.setattr(M, "detect_host", lambda: host)
monkeypatch.setattr(
M, "fetch_release_for_install", lambda repo, *, published_release_tag = None: (bundle, {})
)
assert M.main(["--resolve-prebuilt", "--output-format", "json"]) == M.EXIT_SUCCESS
return json.loads(capsys.readouterr().out.strip())
def test_resolver_reports_install_kind_fat_additively(monkeypatch, capsys):
# The pinned pre-slim escape hatch resolves the published fat CPU bundle.
manifest = M.parse_manifest(_manifest([_artifact("linux", "x64", "cpu", CPU_ASSET, "a" * 64)]))
payload = _resolver_payload(monkeypatch, capsys, manifest, _host("linux", "x64"))
assert set(payload) == _LEGACY_RESOLVER_KEYS | {"install_kind"}
assert payload["install_kind"] == "fat"
assert payload["asset"] == CPU_ASSET
def _release_for_selection(release_tag: str, upstream_tag: str, artifact: dict) -> M.ReleaseBundle:
payload = _manifest([artifact])
payload["upstream_tag"] = upstream_tag
return M.ReleaseBundle(
repo = "unslothai/whisper.cpp",
release_tag = release_tag,
manifest = M.parse_manifest(payload),
asset_urls = {},
)
def test_whisper_tag_selects_matching_published_upstream(monkeypatch):
latest_asset = "latest-linux-x64-cpu.tar.gz"
pinned_asset = "pinned-linux-x64-cpu.tar.gz"
latest = _release_for_selection(
"v1.9.2-unsloth.1",
"v1.9.2",
_artifact("linux", "x64", "cpu", latest_asset, "a" * 64),
)
pinned = _release_for_selection(
"v1.9.0-unsloth.3",
"v1.9.0",
_artifact("linux", "x64", "cpu", pinned_asset, "b" * 64),
)
monkeypatch.setattr(
M,
"fetch_release_for_install",
lambda *args, **kwargs: (_ for _ in ()).throw(
AssertionError("an upstream pin consulted the unrelated newest release")
),
)
monkeypatch.setattr(
M,
"_published_release_tags",
lambda repo: [latest.release_tag, pinned.release_tag],
)
monkeypatch.setattr(
M,
"_fetch_release_candidate",
lambda repo, tag: latest if tag == latest.release_tag else pinned,
)
monkeypatch.setattr(M, "fetch_release_checksums", lambda bundle: {pinned_asset: "b" * 64})
payload = M.resolve_prebuilt(
_host("linux", "x64"),
published_repo = "unslothai/whisper.cpp",
published_release_tag = None,
whisper_tag = "v1.9.0",
backend = "cpu",
cpu_fallback = False,
)
assert payload["prebuilt_available"] is True
assert payload["release_tag"] == pinned.release_tag
assert payload["upstream_tag"] == "v1.9.0"
def test_macos_walks_back_to_newest_compatible_release(monkeypatch):
latest_asset = "latest-macos-arm64-cpu.tar.gz"
compatible_asset = "compatible-macos-arm64-cpu.tar.gz"
latest = _release_for_selection(
"v1.9.2-unsloth.1",
"v1.9.2",
_artifact(
"macos",
"arm64",
"cpu",
latest_asset,
"a" * 64,
min_os = "macos-15.0",
),
)
compatible = _release_for_selection(
"v1.9.1-unsloth.1",
"v1.9.1",
_artifact(
"macos",
"arm64",
"cpu",
compatible_asset,
"b" * 64,
min_os = "macos-13.0",
),
)
monkeypatch.setattr(
M,
"fetch_release_for_install",
lambda repo, *, published_release_tag = None: (latest, {latest_asset: "a" * 64}),
)
monkeypatch.setattr(
M,
"_published_release_tags",
lambda repo: [latest.release_tag, compatible.release_tag],
)
monkeypatch.setattr(M, "_fetch_release_candidate", lambda repo, tag: compatible)
monkeypatch.setattr(
M,
"fetch_release_checksums",
lambda bundle: {compatible_asset: "b" * 64},
)
payload = M.resolve_prebuilt(
_host("macos", "arm64", macos_version = (14, 7)),
published_repo = "unslothai/whisper.cpp",
published_release_tag = None,
backend = "cpu",
cpu_fallback = False,
)
assert payload["prebuilt_available"] is True
assert payload["release_tag"] == compatible.release_tag
def test_macos_walkback_never_masks_checksum_failure(monkeypatch):
asset = "latest-macos-arm64-cpu.tar.gz"
latest = _release_for_selection(
"v1.9.2-unsloth.1",
"v1.9.2",
_artifact("macos", "arm64", "cpu", asset, "a" * 64, min_os = "macos-13.0"),
)
monkeypatch.setattr(
M,
"fetch_release_for_install",
lambda repo, *, published_release_tag = None: (latest, {asset: "b" * 64}),
)
monkeypatch.setattr(
M,
"_published_release_tags",
lambda repo: (_ for _ in ()).throw(AssertionError("integrity failure walked back")),
)
with pytest.raises(PrebuiltFallback, match = "disagrees"):
M._release_plan_for_host(
_host("macos", "arm64", macos_version = (14, 7)),
published_repo = "unslothai/whisper.cpp",
published_release_tag = None,
whisper_tag = "latest",
requested_backend = "cpu",
)
def test_resolver_reports_install_kind_slim_when_paired(tmp_path, monkeypatch, capsys):
bin_dir = _fake_llama_bin(tmp_path)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "cuda13-newer")
)
payload = _resolver_payload(monkeypatch, capsys, _slim_manifest(), _cuda_host())
assert set(payload) == _LEGACY_RESOLVER_KEYS | {"install_kind"}
assert payload["install_kind"] == "slim"
assert payload["asset"] == SLIM_ASSET
assert payload["backend"] == "cuda"
def test_resolver_reports_metal_slim_on_macos(tmp_path, monkeypatch, capsys):
# Same contract shape on macs: backend stays the accel (metal), the one
# additive field says the asset installs slim.
bin_dir = _fake_llama_bin_macos(tmp_path)
monkeypatch.setattr(
M, "installed_llama_runtime", lambda: (bin_dir, SLIM_LLAMA_TAG, "macos-metal-arm64")
)
host = _host("macos", "arm64", macos_version = (14, 0))
payload = _resolver_payload(monkeypatch, capsys, _macos_slim_manifest(), host)
assert set(payload) == _LEGACY_RESOLVER_KEYS | {"install_kind"}
assert payload["install_kind"] == "slim"
assert payload["asset"] == MAC_SLIM_ASSET
assert payload["backend"] == "metal"
assert payload["requested_backend"] == "metal"