Without a trace the Settings GPU label and /health keep reporting CUDA
while generation runs on CPU, so the user has no way to confirm the
override engaged.
The override is documented in gpu-acceleration.mdx and listed as step 1 of
the get_torch_device() precedence in tts-generation.mdx, but grepping the
tree for VOICEBOX_FORCE_CPU matched only those two doc files - nothing read
it. Users whose GPU has no compiled kernels in the bundled PyTorch had no
way to fall back to CPU short of renaming the installed CUDA backend
directory.
Resolve it before torch is imported, so it still works when the installed
build is itself the reason CPU is wanted.
* feat(windows): add native ROCm support for AMD GPUs
Implements native ROCm architecture for Windows.
- Adds backend build pipeline for voicebox-server-rocm.exe
- Detects AMD GPUs dynamically and routes PyTorch allocations
- Adds automatic download and update logic for ROCm dependencies
- Refactors UI in GpuPage.tsx and GpuAcceleration.tsx to add AMD flows
- Fixes 'Switch to CPU' lock on Windows via Tauri backend_override state
- Resolves PyInstaller/rocm_sdk UnboundLocalError silent crashes
- Resolves Numba/NumPy 2.x incompatibilities during Qwen3-TTS load
- Resolves HF_HUB_OFFLINE Catch-22 for CustomVoice processor caching
* fix(rocm): host libs archive under the app release tag, drop offline-load regression
Align the ROCm libs download with the CUDA pattern: both the server core and
the libs archive are published under the app-version release tag, with the libs
content version encoded in the filename only. The previous code fetched libs
from a separate rocm7.2-v1 tag, which disagreed with the download test.
Also revert the unrelated Qwen CustomVoice changes that wrapped model loading in
force_offline_if_cached (not imported — a NameError on load for every platform)
and re-added a Base-model cache gate. The inference-path offline guard was
deliberately removed previously.
* feat(rocm): gate download on AMD detection and persist the backend variant
The ROCm download section now only shows when the backend reports an AMD GPU on
Windows (new supports_rocm health field, backed by the memoized
is_amd_gpu_windows detection that was previously unused), or when ROCm is already
downloaded/active.
Make the backend override honor a pinned variant: set_backend_override persists
the choice to disk so it survives an app restart, start_server reads it back,
and a cuda/rocm pin now actually selects that variant instead of always
preferring ROCm. A stale pin to a deleted backend self-heals to the default
order rather than forcing CPU. Add the web no-op stub for the new method.
* chore(rocm): drop incomplete vitest harness for the unused GpuAcceleration component
GpuAcceleration.tsx is not routed anywhere (GpuPage is the live settings view),
and the added vitest setup referenced testing-library/vitest deps that were not
in the lockfile, breaking the web typecheck. Remove the dead component's test
and its scaffolding to keep this PR scoped to the ROCm feature.
* ci(rocm): add ROCm release-artifact pipeline
Mirror the CUDA packaging path for ROCm so the runtime download has artifacts to
fetch. scripts/package_rocm.py splits the PyInstaller --rocm onedir into
voicebox-server-rocm.tar.gz (core) + rocm-libs-rocm7.2-v1.tar.gz (AMD runtime:
HIP DLLs, rocBLAS Tensile data, MIOpen kernel DBs) + rocm-libs.json, matching
the names services/rocm.py expects, both under the app-version release tag.
The new build-rocm-windows job in release.yml builds on windows-latest/cp312 and
lets build_binary.py --rocm pull the official AMD Radeon wheels.
The file classifier can't be validated against a real AMD build on CI, so it has
unit coverage (test_package_rocm.py) against a synthetic onedir layout. The
prefixes/dir markers may need a tweak after the first real build on AMD
hardware — the packager hard-fails loudly if it classifies zero ROCm files.
---------
Co-authored-by: Jamie Pine <[email protected]>
Applies the compatibility-checker portion of #367. Adds a
check_cuda_compatibility() helper that compares the current device's
compute capability against torch.cuda._get_arch_list() and returns a
human-readable warning if the PyTorch build doesn't support it.
Wired into three places:
• HealthResponse gains a gpu_compatibility_warning field so clients
can surface the issue in the UI
• Startup logs the warning as WARN level
• _get_gpu_status() appends "[UNSUPPORTED - see logs]" to the GPU
label shown in settings
Skipped #367's other half — the switch from stable to nightly cu128
wheels across release.yml, build_binary.py, and justfile. That's
redundant with #401's TORCH_CUDA_ARCH_LIST=...12.0+PTX approach and
would introduce non-deterministic builds from shifting nightly
releases.
Co-Authored-By: nyzxor <[email protected]>
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
Auto-detect Intel Arc GPUs during Windows setup and install PyTorch
with XPU support + intel-extension-for-pytorch. Enable allow_xpu=True
on all TTS backends (Chatterbox, Chatterbox Turbo, Hume TADA, LuxTTS)
that previously only supported CUDA. Add shared empty_device_cache()
and manual_seed() helpers in base.py to handle XPU memory management
and reproducible seeding alongside CUDA.
