Follow-up cleanups to the merged AMD ROCm support PR #5301:
1. De-duplicate the torchao Windows-ROCm import stub into a single shared
module (studio/backend/core/_torchao_stub.py); both workers call one
install_torchao_windows_rocm_stub() entrypoint.
2. Align the gfx name/arch comment columns in setup.sh and setup.ps1.
3. Isolate the float16 dtype fallback to AMD without native bf16; NVIDIA
keeps dtype=None so unsloth's own bf16/fp16/FORCE_FLOAT32 detection is
honored.
4. Hoist unconditional stdlib imports (gc, glob, re, subprocess, copy,
types, sys, importlib.metadata) from function bodies to module top
across the PR #5301-touched files; heavy/optional/relative imports stay
lazy.
5. bitsandbytes Windows-ROCm install now uses plain pip (force_pip=True)
instead of UV_SKIP_WHEEL_FILENAME_CHECK, per the AMD hackathon docs.
Also adds scripts/verify_import_hoist.py (a scope-aware LEGB AST resolver
that catches dangling-alias and rename-clash bugs in import-hoist
refactors) and wires it into the Lint CI source-lint job as a self-test
plus a pull_request compare gate.
* fix(studio): set HIP_VISIBLE_DEVICES in apply_gpu_ids for ROCm training workers
Training workers are spawned via multiprocessing spawn before detect_hardware()
runs, so IS_ROCM is still False. If the user never set HIP_VISIBLE_DEVICES in
their shell, _inherits_rocm_visibility is also False, leaving the worker with
only CUDA_VISIBLE_DEVICES set. On ROCm hosts the HIP runtime honors
HIP_VISIBLE_DEVICES over CUDA_VISIBLE_DEVICES, so the worker saw the full
device list and torch raised "no usable HIP accelerator" on some setups.
Fall back to probing torch.version.hip (a build-time attribute, safe to read
before GPU init) to detect ROCm when neither IS_ROCM nor inherited env vars
are available. Mirrors the existing fix in llama_cpp.py for llama-server
subprocess GPU pinning.
Fixes https://github.com/unslothai/unsloth/issues/5180
* test: tighten apply_gpu_ids ROCm fallback assertions
Replace loose OR chain with exact string matches, split into three
focused tests, and add a guard check for the try/except wrapper.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: detect ROCm unified memory (Strix Halo / AMD iGPU) via torch fallback
amd-smi on iGPUs with shared/unified memory (e.g. Radeon 8060S on Strix
Halo) reports only the dedicated VRAM slice (~512 MB) in its metric output,
so get_visible_gpu_utilization() was returning usable_gb ≈ 0.35 GB instead
of the full GTT pool (~128 GB). torch.cuda.mem_get_info() already surfaces
the correct unified-pool size.
Add _reconcile_rocm_unified_memory(): after amd-smi returns a valid result
on a ROCm device, cross-check each device's vram_total_gb against
torch.cuda.mem_get_info(). When torch reports a larger total, replace the
amd-smi VRAM fields in-place. No-op for discrete AMD GPUs where the two
sources agree.
Fixes: "Falling back to all visible GPUs -- model may not fit" on AMD iGPU
machines even when 100+ GB of unified memory is available.
* Apply unified-memory reconciliation in get_gpu_utilization too
The visible-GPU path was already corrected for AMD iGPUs with unified memory
(Strix Halo / Radeon 8060S), but get_gpu_utilization was still returning the
raw 512 MB amd-smi VRAM slice. Studio's /api/train/hardware endpoint and the
live GPU monitor read from this primary path, so users continued seeing the
wrong total even after auto_select_gpu_ids picked the right device.
Refactor to share the per-device correction:
* _apply_unified_memory_correction(metrics, torch_info) -- the actual
replacement logic, in-place on a single metrics dict.
* _reconcile_rocm_unified_memory(...) -- multi-device,
iterates utilization["devices"] (visible-GPU path).
* _reconcile_primary_rocm_unified_memory(...) -- single flat
metrics dict (primary-GPU path), uses parent_visible_spec to pick the
primary index, falls back to ordinal 0 when no visibility env is set.
get_gpu_utilization now calls the primary reconciler under IS_ROCM, so both
endpoints surface the real unified-memory pool on iGPUs while leaving
discrete AMD GPUs untouched (torch_total <= smi_total -> no replace).
* Use 'is not None' and log debug on torch.version.hip probe failures
Two small follow-ups to the apply_gpu_ids ROCm fallback:
1. Match detect_hardware()'s 'getattr(torch.version, "hip", None) is not None'
form so the entire codebase has one canonical 'this torch was built with
HIP' check. On every shipping torch wheel hip is either None or a non-empty
version string, so the new form agrees with the old bool() form on every
real install.
2. Log the probe failure at debug level instead of swallowing it silently.
The broad 'except Exception' is intentional (we never want apply_gpu_ids
to crash a worker over a probe), but the silent pass made it impossible
to tell whether the fallback was firing or being skipped.
* fix(studio): honour HIP_VISIBLE_DEVICES in _get_parent_visible_gpu_spec before IS_ROCM is set
When a user has HIP_VISIBLE_DEVICES set in their shell (e.g. "1" to select
GPU 1) but detect_hardware() has not yet run in the Studio parent process,
IS_ROCM is still False. _get_parent_visible_gpu_spec() was gated on IS_ROCM
so it fell through to CUDA_VISIBLE_DEVICES (unset), saw all physical GPUs,
and auto-selected index 0. apply_gpu_ids then overwrote HIP_VISIBLE_DEVICES
with "0", making the intended GPU invisible to ROCm torch in the worker,
which triggered the "no usable HIP accelerator" error (issue #5180).
Apply the same _inherits_rocm_visibility pattern already used in
apply_gpu_ids: check for HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES in the
environment regardless of IS_ROCM so the correct GPU index is preserved.
* fix(install): harden AMD ROCm GPU detection for multi-GPU and env-filtered setups
The previous rocminfo awk pattern could miss discrete GPUs on machines
where HIP_VISIBLE_DEVICES/ROCR_VISIBLE_DEVICES is used to mask an
integrated GPU — the env vars filter rocminfo output but may not
propagate into the install script subprocess, causing detection to
fail entirely.
Two changes:
- Tighten rocminfo pattern from /gfx[0-9]/ && !/gfx000/ to
/gfx[1-9][0-9]/ — simpler and correctly excludes the CPU agent
(gfx000) without a negative lookahead
- Add sysfs KFD topology fallback: reads
/sys/class/kfd/kfd/topology/nodes/*/gpu_id which is a kernel-level
view unaffected by HIP_VISIBLE_DEVICES or ROCR_VISIBLE_DEVICES
Fixes detection failure reported in Discord by Chains (gfx1201 + iGPU
machine where env var exclusion of the iGPU caused rocminfo to return
no usable device).
* Fix KFD sysfs awk fallback to read properties file
The fallback added by this PR reads /sys/class/kfd/kfd/topology/nodes/*/gpu_id
files but matches the literal token 'gpu_id' against their content. Those
files contain only a single decimal value (e.g. '0' for CPU agents, '50432'
for GPU agents), so the regex never matches and 'found' stays 0, making the
fallback a no-op on every host. The properties file in the same directory
contains key/value lines like 'gpu_id 50432' which is what the existing awk
pattern expects.
Reproduced with a synthetic sysfs layout: against gpu_id files awk exits 1;
against properties files awk exits 0 when any node reports gpu_id > 0.
* fix(setup.ps1): detect AMD ROCm GPU on Windows, bring to parity with setup.sh
setup.ps1 only checked nvidia-smi and fell straight to "gpu: none" on AMD
machines. setup.sh already probed rocminfo/amd-smi/hipconfig/hipinfo.
Add three-tier detection mirroring install_llama_prebuilt.py's detect_host():
1. hipinfo: gcnArchName in output confirms a real HIP GPU (not just SDK)
2. amd-smi list: "GPU: <digit>" data rows as fallback
3. WMI Win32_VideoController: last resort -- detects AMD GPU even without
HIP SDK, then guides user to install it rather than silently going CPU
Also corrects the "none" message to mention AMD ROCm alongside NVIDIA so
users with AMD hardware understand the requirement.
Fixes: rohit-style install where Strix Halo (Radeon 8060S) showed
"gpu: none" even with the HIP SDK present.
* fix(install.ps1): detect AMD ROCm GPU on Windows, bring to parity with setup.ps1
install.ps1 had the same nvidia-smi-only GPU detection as setup.ps1 before
the setup.ps1 fix. Applies the same three-tier AMD detection:
1. hipinfo: gcnArchName confirms real HIP GPU
2. amd-smi list: GPU data rows as fallback
3. WMI Win32_VideoController: detects AMD GPU without HIP SDK and guides
user to install it
Fixes: install.ps1 showing "gpu: none" while setup.ps1 correctly showed
"AMD GPU detected" on the same machine (reported by rohit, RX 7600 XT).
* fix(install.ps1): suppress 'No NVIDIA GPU detected' when AMD GPU is present
* feat: add Windows AMD ROCm PyTorch wheel installation
install_python_stack.py:
- Add _ROCM_WINDOWS_WHEEL_BASE and _ROCM_WINDOWS_RELEASES constants
pointing to AMD repo.radeon.com (ROCm 7.2 -> torch 2.9.1+rocm7.2.1)
- Extend _ensure_rocm_torch() with a Windows branch: detects ROCm via
_has_rocm_gpu() / _detect_rocm_version(), requires Python 3.12 (cp312
is the only ABI AMD publishes for Windows), installs the direct wheel
URL from repo.radeon.com
install.ps1:
- Capture ROCmVersion during AMD detection via hipconfig --version /
amd-smi version (needed for wheel URL selection)
- After Get-TorchIndexUrl, add an AMD wheel override block: when HasROCm
and Python 3.12 detected, set ROCmTorchWheelUrl to AMD wheel URL
- Expand torch install branch to handle ROCmTorchWheelUrl with
uv pip install --force-reinstall --no-cache-dir
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: also install torchvision and torchaudio from AMD Windows repo
AMD publishes matching torchvision-0.24.1+rocm7.2.1 and
torchaudio-2.9.1+rocm7.2.1 cp312 wheels at the same repo.radeon.com
release folder. Install all three in both install.ps1 and
install_python_stack.py Windows ROCm path.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: add ROCm 7.1.1 Windows wheel mapping
AMD uses a different version string for 7.1.1 wheels:
2.9.0+rocmsdk20251116 (date-tagged) instead of +rocm7.1.1.
Adds the 7.1.1 release folder to both install.ps1 and
install_python_stack.py so users with ROCm 7.1 get ROCm
torch instead of falling back to CPU.
* fix: install rocm_sdk_core and rocm_sdk_libraries_custom alongside torch
The AMD Windows torch wheels declare rocm[libraries]==<ver> as a hard
dependency. Without installing rocm_sdk_core and rocm_sdk_libraries_custom
from the same AMD release folder, uv cannot resolve the dependency and
fails with 'No solution found'. Include all 5 wheels in one install call.
* fix: expand ROCm wheel array to scalars for Invoke-InstallCommand
@array splatting inside a scriptblock only works when the native command
is prefixed with '&'. Invoke-InstallCommand uses '& $Command' to run the
block, so @ROCmAllWheelUrls was not being expanded. Extract to scalar
variables $rw0-$rw4 which are captured correctly by the closure.
* fix: use --no-deps for AMD Windows torch wheel install
uv's resolver looks up rocm[libraries]==0.1.dev0 on PyPI during
dependency resolution before downloading any wheels, and fails because
the package doesn't exist on PyPI. --no-deps skips resolution entirely
and installs all 5 AMD wheels directly. The GPU runtime dependency is
satisfied by the HIP SDK, not a Python package.
* fix: setup.ps1 and install_python_stack.py now install ROCm torch on Windows
setup.ps1 was always setting CuTag='cpu' for non-NVIDIA hosts and installing
cpu-only PyTorch, overwriting the ROCm torch installed by install.ps1.
Adds the same AMD wheel selection logic (ROCm version detection, Python 3.12
check, 5-wheel install with --no-deps) to setup.ps1's torch install block.
install_python_stack.py: remove IS_WINDOWS guard from _ensure_rocm_torch()
call site so the Windows path in _ensure_rocm_torch() is reachable during
'unsloth studio update' as well.
* fix: suppress manual-install warning when ROCm torch already present; fix progress counter
- Gate the 'must be installed manually' warning on torch.version.hip being empty
so it doesn't fire when our ROCm torch install succeeded
- Update _TOTAL counter to include the 3 ROCm steps on Windows now that
_ensure_rocm_torch() is called there (fixes 10/9 display)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: add rocm step display in setup.ps1; fix warning and progress counter
- Add 'rocm' step after 'cuda' in setup.ps1 showing ROCm version or HIP SDK missing
- Move ROCm version detection up to GPU detection block so it's available early
- Suppress 'must be installed manually' warning when torch.version.hip is set
- Fix _TOTAL counter to include ROCm steps on Windows (fixes 10/9 display)
* fix: detect AMD SDK ROCm torch via __version__ when torch.version.hip is unset
AMD's repo.radeon.com wheels (e.g. 2.9.0+rocmsdk20251116) do not set
torch.version.hip, leaving it None. All three probes that relied solely on
torch.version.hip now also check for 'rocm' in torch.__version__.lower():
- hardware.py detect_hardware(): IS_ROCM was never set, causing the studio
to report 'Hardware detected: CPU' even after AMD wheels were installed
and HIP DLLs were on PATH.
- install_python_stack.py _ensure_rocm_torch(): skip-if-already-installed
probe would always reinstall on subsequent runs.
- install_python_stack.py Windows AMD warning: suppression check always
failed, so the 'must be installed manually' note kept appearing after
a successful AMD wheel install.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* perf: drop --no-cache-dir from AMD ROCm torch wheel installs
uv caches downloaded wheels by default; passing --no-cache-dir forced a
full redownload of the ~2 GB torch wheel on every install run. CUDA installs
never had this flag -- AMD was the only path affected.
* fix: use install-state flag instead of subprocess probe for AMD Windows warning
Replace the subprocess torch probe in the post-install warning block with a
module-level _rocm_windows_torch_installed flag set by _ensure_rocm_torch().
Subprocess re-import of torch is unnecessary and fragile -- the install
function already knows whether it succeeded.
* fix: hoist global declaration to top of _ensure_rocm_torch
Python requires the global statement to appear before any assignment
to the variable within a function. Moving it to the function top fixes
the SyntaxError on line 354.
* fix: pass AMD torch install status via env var to suppress false warning
setup.ps1 now sets UNSLOTH_ROCM_TORCH_INSTALLED=1 after a successful AMD
wheel install. install_python_stack.py reads this at the top of
_ensure_rocm_torch() to skip both the subprocess probe and the warning --
no re-import of torch needed, and the warning message now correctly says
'could not be auto-installed' rather than 'must be installed manually'.
* fix: register ROCm DLL directory before torch import on Windows
Python 3.8+ ignores PATH for extension DLL loading on Windows; amdhip64.dll
and other HIP runtime DLLs must be registered via os.add_dll_directory().
Without this, torch.cuda.is_available() always returns False on AMD ROCm
Windows even when HIP_PATH is correctly set in system environment variables.
Reads HIP_PATH / ROCM_PATH env vars first, then falls back to scanning
common ROCm install roots (C:\Program Files\AMD\ROCm, F:\ROCm, C:\ROCm).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: remove hardcoded non-standard ROCm paths from DLL directory scan
Only use HIP_PATH/ROCM_PATH (set by AMD installer) and the standard
C:\Program Files\AMD\ROCm\<version>\bin location. Custom drive paths
like F:\ROCm are user-specific and should not be hardcoded.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: prevent torchao overrides step from overwriting AMD ROCm torch
torchao==0.14.0 in overrides.txt declares torch as a dependency. Without
--no-deps, uv resolves torch from PyPI and installs 2.11.0+cpu on top of
the AMD ROCm wheels (2.9.0+rocmsdk20251116). This was the root cause of
'Hardware detected: CPU' -- the AMD wheels were installed but then
immediately overwritten by the overrides step.
When _rocm_windows_torch_installed is True, add --no-deps to the overrides
pip_install call so torchao is installed without pulling in CPU torch.
* fix: add rocm_sdk namespace tarball to Windows ROCm wheel installs
torch/_rocm_init.py calls `import rocm_sdk` at startup, which requires
the rocm namespace tarball (rocm-*.tar.gz) in addition to the SDK wheel
packages. This tarball was missing from both install.ps1 and setup.ps1,
causing ModuleNotFoundError on first torch import.
- Add rocm-0.1.dev0.tar.gz to ROCm 7.1.1 install (provides rocm_sdk namespace)
- Add rocm-7.2.1.tar.gz + rocm_sdk_devel to ROCm 7.2.1 install
- Install tarball in a dedicated step before main SDK/torch wheels
- Switch to @array splatting in install.ps1 scriptblock for dynamic wheel count
- Remove --no-cache-dir from Python-side ROCm wheel install (prevents ~2GB redownload)
* feat: enable ROCm 7.2 torch install + warn on gfx1151 with ROCm < 7.2
Chigoma333 (AMD Radeon 8060S / gfx1151, Strix Halo) confirmed that ROCm
7.1 segfaults when tensors are moved to GPU, but ROCm 7.2 + torch
2.11.0+rocm7.2 works fully including training.
Changes:
- Uncomment (7,2): "rocm7.2" in _ROCM_TORCH_INDEX (was blocked by <2.11.0)
- Add _ROCM_TORCH_PKG_SPECS dict with per-tag version bounds:
rocm7.2 → torch>=2.11.0,<2.12.0; all older tags → <2.11.0
- Add _detect_amd_gfx_codes() helper that parses rocminfo output
- Warn on gfx1151/gfx1150 (Strix Halo) when ROCm < 7.2 is installed,
pointing users at the known segfault and recommending upgrade
- install.sh get_torch_index_url(): enable rocm7.2 case (previously capped
to rocm7.1), cap unknown future tags to rocm7.2
- install.sh: override TORCH_CONSTRAINT to >=2.11.0,<2.12.0 when rocm7.2
index is selected, so pip can actually resolve torch 2.11.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: prefer Python 3.12 for AMD ROCm users when 3.13 is also installed
After GPU detection, if ROCm HIP SDK is found and the selected Python
is not 3.12, run a second pass to locate a 3.12 install via py.exe and
PATH (catches uv-managed installs). Switch $DetectedPython to 3.12 so
the venv is created with a compatible interpreter for the cp312-only AMD
Windows torch wheels.
NVIDIA and Intel GPU paths are unaffected -- the re-detection block only
runs when $HasROCm is true.
Fixes: #5301
* fix: also check uv-managed Python 3.12 for AMD ROCm #5301
* fix: hide amd-smi console popups on Windows, guard torch.distributed.is_initialized for ROCm #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: suppress remaining console popups on Windows, patch torch.distributed.is_initialized for ROCm #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: stub all missing torch.distributed attrs for ROCm Windows wheel #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: inject torch.distributed stub when C backend missing in ROCm Windows wheel #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(rocm/windows): pre-stub torch._C._distributed_c10d + raise amd-smi timeout
Two fixes for Windows ROCm regressions reported by electroglyph on #5301:
1. worker.py — torch.distributed stub now fires unconditionally on Windows
The previous stub only injected sys.modules in the except branch, meaning
it was silently skipped when `import torch.distributed` happened to succeed
(the C backend is lazily resolved). The crash then hit later when
transformers/trl triggered the lazy load. Fix: on win32 we pre-populate
sys.modules['torch._C._distributed_c10d'] AND set the attribute on the
torch._C extension module *before* attempting the import, covering both
the early-ImportError and lazy-load failure modes.
2. amd.py — increase amd-smi timeout from 5 s to 30 s on Windows (10 s Linux)
amd-smi on Windows must cold-init the ROCm runtime on first invocation;
5 s was consistently too short, producing repeated 'Command timed out'
warnings in the server log. 30 s gives enough headroom without blocking
indefinitely on broken installs.
3. install.ps1 — widen Python 3.12 enforcement to ROCmGpuLabel (WMI-only path)
Users whose HIP SDK is not on PATH were detected via WMI but not switched
to Python 3.12 before the install started, causing a second pass. Guard
now fires on (HasROCm -or ROCmGpuLabel).
* fix(rocm): guard c10d stub, fix TorchIndexFamily for 7.1, clean dead code + comments
- worker.py: wrap c10d stub injection in `if _c10d_key not in sys.modules` so
Windows NVIDIA users with a real torch.distributed are never affected
- install.ps1: fix Get-TauriTorchIndexFamily receiving hardcoded "rocm7.2"
even when ROCm 7.1 wheels are installed; now branches on $ROCmVersion
- main.py: remove dead `import ctypes as _ctypes` (ctypes is never called)
- hardware.py, install_python_stack.py, worker.py, install.ps1: shorten
verbose multi-line comment blocks throughout
- tests: update 4 stale assertions that expected rocm7.2 to be absent/capped
* fix(tests): match windows AMD warning assertion to actual source string
* chore: trim verbose comment blocks across all ROCm-related files
* fix: guard reconcile call against None numeric_ids; add torchvision lower bounds
* fix(install.ps1): recreate venv with Python 3.12 after ROCm switch
Venv was created with 3.13 before GPU detection ran; switching
$DetectedPython to 3.12 had no effect since $VenvPython still
pointed to the 3.13 interpreter inside the already-created venv.
* ux: detect AMD GPU before Python selection to avoid double venv creation
- Early hipinfo + WMI probe runs before Find-CompatiblePython so Python
3.12 is selected upfront when AMD is detected; venv is now created
exactly once instead of 3.13 then immediately 3.12.
- Post-venv recreation block replaced with a simple warning for the rare
case where AMD was missed by the early probe.
- setup.ps1: show venv's actual Python version (e.g. 3.12) instead of
the system Python found by the pre-activation search (was showing 3.13).
* fix(rocm/win): auto-stub all _distributed_c10d symbols via PEP-562 __getattr__
The bare ModuleType stub caused ImportError when torch._dynamo was imported
(triggered by trainer.py accessing torch._dynamo.config at load time).
torch._dynamo pulls in torch.distributed.fsdp._flat_param which does:
from torch._C._distributed_c10d import FakeProcessGroup
and potentially other symbols. Adding module __getattr__ auto-creates a
stub class for any missing symbol so all such imports succeed without
enumerating every individual symbol. Applied to both the primary stub
and the fallback stub in the except branch.
* chore: trim c10d stub comment
* fix(rocm/win): auto-stub missing torch.distributed attrs (Store, ProcessGroup, …)
* fix(rocm/win): pre-stub fsdp submodules in sys.modules; fix __getattr__ subpackage clash
* feat(rocm/win): arch-aware wheel selector always picks newest ROCm release
Replace HIP-SDK-version-gated wheel selection with GPU arch-based logic.
Select-ROCmWheelRelease (PS) and _select_windows_rocm_release (Python) map
gcnArchName → minimum ROCm version, then pick the newest available release
that satisfies it (currently always rocm-rel-7.2.1 for any supported GPU).
Wheels bundle their own ROCm runtime so the installed HIP SDK 7.1 does not
prevent using 7.2.1 wheels on gfx1200 (RX 9060 XT) and similar RDNA 4 GPUs.
Also installs the bitsandbytes Windows ROCm continuous-release wheel and sets
BNB_ROCM_VERSION=72 in worker.py before ML imports so bnb loads the
libbitsandbytes_rocm72.dll that ships in that wheel.
* fix(rocm/win): stub class metaclass for ProcessGroup.BackendType; amd-smi circuit breaker
torchao.float8.inference accesses ProcessGroup.BackendType as a class-level
attribute. Plain type() stubs have no __getattr__ on the metaclass so this
raises AttributeError. Introduce _StubClassMeta whose __getattr__ returns
child stub classes, fixing the torchao import chain.
Add an amd-smi circuit breaker in amd.py: after 3 consecutive failures the
module stops spawning the process, eliminating the repeated Windows UAC /
DiskPart elevation prompts caused by polling a non-functional amd-smi.
Also guard BNB_ROCM_VERSION=72 behind a DLL existence check so bitsandbytes
fails with its own detection message rather than a harder "DLL not found" when
the Windows ROCm bnb wheel is not yet installed.
* fix: stub __members__ so torchao float8 enum check doesn't crash on ROCm Windows
torchao.float8.inference accesses ProcessGroup.BackendType.__members__
expecting a Python Enum registry dict. _StubClassMeta.__getattr__ was
blocking all dunder attributes, causing AttributeError. Return {} for
__members__ specifically so the isinstance/iteration checks pass cleanly.
* fix: stub distributed tensor/functional_collectives to prevent missing C++ op crash on ROCm Windows
torch._dynamo.trace_rules eagerly loads torch.distributed.tensor at import
time, which pulls in _functional_collectives.py. That file registers Meta
kernels for _c10d_functional C++ ops, but those ops are only registered
by torch._C._distributed_c10d — a C extension absent from ROCm Windows
wheels. Pre-stubbing the affected modules in sys.modules prevents the real
import chain from running and avoids the "operator does not exist" crash.
* fix: give mod stubs __path__ and pre-stub _tensor to fix 'not a package' import error
_make_mod_stub now sets __path__=[] so Python treats stub modules as
packages. Without it, any import of a submodule raises "is not a package".
Also pre-stub torch.distributed._tensor and its submodules so that
_tensor/__init__.py (which re-exports from torch.distributed.tensor) never
runs and torchao's `from torch.distributed._tensor import DTensor` gets a
harmless stub instead of crashing.
* fix: stub torch.ops._c10d_functional namespace with hashable op sentinels
torchao.dtypes.nf4tensor uses _c10d_functional ops as dict keys at import
time (all_gather_into_tensor.default, wait_tensor.default) and
torch.ops.c10d.scatter_.default. None of these ops are registered on ROCm
Windows because torch._C._distributed_c10d (the C extension) doesn't ship.
Replace the whole _c10d_functional namespace with a custom stub whose ops
return hashable .default objects, so dict-key construction doesn't crash.
Also inject a scatter_ stub into torch.ops.c10d if it's missing.
* fix: stub entire torchao package on ROCm Windows instead of individual ops
torchao is not supported on ROCm Windows and its import chain transitively
requires torch._C._distributed_c10d (absent from the ROCm Windows wheel).
Rather than stub each missing op one by one, stub the whole torchao package
upfront. Unsloth uses bitsandbytes for quantization, not torchao, so this
has no functional impact. transformers gracefully handles an importable-but-
empty torchao by disabling TorchAoHfQuantizer.
* fix: set __spec__ on mod stubs so importlib.util.find_spec doesn't raise
Manually-injected sys.modules entries have __spec__=None by default.
importlib.util.find_spec() raises ValueError when it finds a module in
sys.modules with __spec__=None (transformers.utils.import_utils hits this
when checking if torchao is available). Give every stub a minimal
ModuleSpec(name, loader=None, is_package=True) to satisfy find_spec.
* fix: add meta path finder to auto-stub subpackages of stub modules
`import torchao.prototype` goes through the import machinery, not
__getattr__, so an empty __path__ means ModuleNotFoundError. Rather than
list every submodule explicitly, register a MetaPathFinder that intercepts
any import whose parent is one of our stubs (detected by loader=None in the
parent's ModuleSpec). Real installed packages always have a SourceFileLoader
so they are never intercepted. Also register child stubs in sys.modules
from __getattr__ as a belt-and-suspenders measure.
* fix: use _unsloth_stub sentinel instead of loader=None for stub detection
The import machinery overwrites module.__spec__ with the spec returned by
find_spec (which has loader=_StubSubpackageLoader, not None), so the
loader=None check broke for second-level subpackages. Switch to a custom
_unsloth_stub object identity sentinel set directly on each stub module --
it survives __spec__ being replaced and correctly identifies stubs at any
depth (torchao.prototype.safetensors, etc.).
* refactor(rocm/win): switch to repo.amd.com arch-aware index, remove stubs
AMD recommends repo.amd.com/rocm/whl/{arch}/ as the Windows ROCm wheel
source. These wheels bundle their own ROCm runtime, support all Python
versions (not just cp312), and include the full torch._C extension set
(including _distributed_c10d) that the old repo.radeon.com wheel omitted.
Changes:
- install.ps1: remove Select-ROCmWheelRelease + hardcoded cp312 wheel
URLs; remove Python 3.12 forced-preference logic; install via
--index-url repo.amd.com/rocm/whl/{arch-family}/
- studio/setup.ps1: same -- remove Select-ROCmWheelRelease, switch to
repo.amd.com arch-aware index URL
- studio/install_python_stack.py: replace _ROCM_WINDOWS_RELEASES /
_select_windows_rocm_release with _windows_rocm_index_url() using the
_GFX_TO_AMD_INDEX_ARCH map; drop Python 3.12 restriction
- studio/backend/core/training/worker.py: remove all stub machinery
(_make_mod_stub, _StubSubpackageFinder, _StubSubpackageLoader,
_StubClassMeta, torchao/fsdp/dtensor stubs, _c10d_functional ops
stubs, BNB DLL detection) -- no longer needed with new wheel source
* fix(rocm/win): restore _distributed_c10d + torchao stubs; fix BNB install
repo.amd.com torch wheels also omit torch._C._distributed_c10d on Windows
(RCCL is not shipped on Windows). torch/distributed/__init__.py imports
from it unconditionally at module level, so the stub must land in
sys.modules before any torch.distributed import.
torchao (pulled in by transformers.quantizers) walks
torchao.float8.distributed_utils -> torch.distributed._functional_collectives
-> distributed_c10d at import time. Stubbing torchao up-front short-circuits
that chain.
worker.py:
- Restore _make_mod_stub / _StubSubpackageFinder / _StubSubpackageLoader
- Restore _StubClassMeta for ProcessGroup.BackendType attribute access
- Restore _distributed_c10d stub with __getattr__ (Windows only)
- Restore torchao stubs (5 modules, Windows only)
install_python_stack.py:
- BNB AMD wheel install was inside the early-return branch that fires when
torch is already a ROCm build (installed by install.ps1). Move BNB install
outside that branch so it always runs on Windows ROCm — the PyPI
bitsandbytes has only CUDA DLLs and fails to load on ROCm.
* worker: remove _distributed_c10d stub; stub only torchao
The installed torch/distributed/__init__.py from repo.amd.com
(torch==2.10.0+rocm7.12.0) is now properly guarded with
`if is_available():`, so `import torch.distributed` alone is safe.
The crash only comes via torchao's import chain:
torchao.float8.distributed_utils
→ torch.distributed._functional_collectives (unguarded import)
→ torch.distributed.distributed_c10d
→ torch._C._distributed_c10d ← absent on Windows ROCm
Stubbing torchao short-circuits the chain entirely. No need to stub
_distributed_c10d. Remove _StubClassMeta and the _c10d stub block;
keep only _make_mod_stub + _StubSubpackageFinder + torchao seeds.
* fix: BNB AMD wheel skipped + torch.compile segfault on Windows ROCm
install_python_stack.py: the UNSLOTH_ROCM_TORCH_INSTALLED=1 early-return
path (set by setup.ps1 when it installed torch itself) returned before
ever reaching the AMD BNB prerelease wheel install. The PyPI
bitsandbytes==0.49.x ships only CUDA DLLs, so loading it on ROCm fails
with "libbitsandbytes_rocm72.dll not found". Now installs the AMD
Windows BNB wheel before returning on that path too.
worker.py: torch._grouped_mm crashes on gfx1200 (null HIP kernel pointer,
0xC0000005) when torch.compile's JitDecomp system dispatches it during
the first forward pass. Detect Windows ROCm via torch.version.hip
(already in sys.modules from section 1e) and set TORCHDYNAMO_DISABLE=1
to bypass the broken kernel dispatch.
* fix: BNB AMD wheel install fails uv wheel filename check
The bitsandbytes continuous-release wheel is intentionally mismatched:
filename encodes 1.33.7.preview (= 1.33.7rc0 in PEP 440) but wheel
metadata reports 0.50.0.dev0. uv rejects this by default.
Introduce _install_bnb_windows_rocm() helper that sets
UV_SKIP_WHEEL_FILENAME_CHECK=1 only for this specific install, then
restores the previous env value. Both BNB install call sites (the
UNSLOTH_ROCM_TORCH_INSTALLED early-return path and the normal Windows
ROCm path) now use this helper.
* worker: patch _grouped_mm CUDA dispatch on Windows ROCm (gfx1200 null kernel)
TORCHDYNAMO_DISABLE=1 stopped the compiler frontend but not the autograd
JitDecomp system, which also dispatches _grouped_mm and hits the same
null HIP kernel crash (0xC0000005).
Verified that torch.library.Library("aten","IMPL").impl("_grouped_mm", fn,
"CUDA") successfully overrides the broken HIP kernel with a Python mm
fallback on torch==2.10.0+rocm7.12.0.
Schema: _grouped_mm(Tensor self, Tensor mat2, Tensor? offs=None,
Tensor? bias=None, ScalarType? out_dtype=None) -> Tensor
The fallback handles both the simple case (offs=None → torch.mm) and the
grouped case (offs provided → split self by offsets, multiply each group
against the corresponding slice of mat2, then cat results).
Keep _WINDOWS_ROCM_GROUPED_MM_LIB alive at function scope to prevent the
C++ dispatch registration from being freed by GC.
* worker: fix torchao stub — return stub classes not modules for isinstance()
peft/tuners/lora/torchao.py does:
from torchao.dtypes import AffineQuantizedTensor, LinearActivationQuantizedTensor
isinstance(weight, (AffineQuantizedTensor, LinearActivationQuantizedTensor))
The stub __getattr__ was returning stub modules, which isinstance() rejects
with "arg 2 must be a type, a tuple of types, or a union".
Add _StubTypeMeta metaclass whose __instancecheck__ always returns False,
and _make_stub_type() to create stub classes via it. Change _make_mod_stub
__getattr__ to return stub classes instead of stub modules for leaf
attribute access, so isinstance() gets a valid type and returns False.
_StubSubpackageFinder still handles import-style subpackage creation
(those still need module objects in sys.modules); __getattr__ only fires
for from-import or direct attribute access, which are the isinstance paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: add coverage for Windows ROCm install paths and worker patches
Add conftest.py to fix pre-existing sys.path issue that prevented
test_rocm_support.py from running at all (install_python_stack.py
imports from backend.utils.wheel_utils which needs studio/ on sys.path).
New test classes cover everything added in this session:
- TestWindowsRocmIndexUrl: arch → AMD pip index URL mapping (gfx120X-all,
gfx1151, gfx1150, gfx110X-all, unknown → None, trailing slash)
- TestDetectWindowsGfxArch: hipinfo output parsing, missing/timeout/bad
returncode/no-gcnArchName paths
- TestInstallBnbWindowsRocm: UV_SKIP_WHEEL_FILENAME_CHECK set+restored,
env restored on exception, no-op when URL missing
- TestRocmTorchInstalledEnvVar: UNSLOTH_ROCM_TORCH_INSTALLED=1 skips
pip_install, calls _install_bnb_windows_rocm, sets flag
- TestWorkerWindowsRocmPatches: _grouped_mm CUDA dispatch override,
offs/grouped variant handling, GC-prevention sentinel,
_StubTypeMeta __instancecheck__, _StubSubpackageFinder registration,
torchao key submodule pre-stubbing, TORCHDYNAMO_DISABLE guard
- TestRocmTorchPkgSpecs: rocm7.2 torch 2.11.x spec, default <2.11 cap,
3-tuple shape, _GFX_TO_AMD_INDEX_ARCH RDNA4/3.5/3 coverage
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: fix encoding, IS_WINDOWS patching, and wrong assertion
- Add encoding="utf-8" to all read_text() calls (54 occurrences) so
tests pass on Windows where the default codec is cp1252 and source
files contain UTF-8 emoji (e.g. ⚠️ in install_python_stack.py)
- Add @patch.object(stack_mod, "IS_WINDOWS", False) to Linux-path
TestEnsureRocmTorch tests so they reach the Linux code path when run
on a Windows machine instead of short-circuiting into the Windows branch
- Fix test_grouped_mm_patch_guarded_by_windows_and_hip_check: the source
uses getattr(_torch_for_rocm, "version", None) not torch.version, so
check for '"version"' and '"hip"' substrings instead
137 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: pin BNB_ROCM_VERSION=72 for torch==2.11.0+rocm7.13.0 compatibility
AMD's pip index now ships torch==2.11.0+rocm7.13.0 (ROCm 7.13).
bitsandbytes auto-detects HIP 7.13 from torch.version.hip and looks for
libbitsandbytes_rocm713.dll, which the AMD Windows prerelease wheel does
not ship (it only ships rocm72.dll), causing a load error at training start.
Fix:
- worker.py section 1f: set BNB_ROCM_VERSION=72 (via setdefault) before
section 2 ML imports, so bitsandbytes always loads rocm72.dll on Windows ROCm
- install_python_stack.py: set BNB_ROCM_VERSION=72 in _install_bnb_windows_rocm()
for any post-install imports; update comment to document root cause
- tests: 4 new assertions covering the fix (141 passed, 2 skipped)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: detect BNB ROCm DLL suffix dynamically instead of hardcoding '72'
BNB_ROCM_VERSION was pinned to '72' which works today (AMD wheel ships
rocm72.dll) but would break again if AMD ships a future wheel with a
different DLL suffix (e.g. rocm713.dll).
Add _detect_bnb_rocm_dll_ver() to install_python_stack.py: scans the
installed bitsandbytes package dir for libbitsandbytes_rocm{VER}.dll
using importlib.util.find_spec (no BNB import needed) and returns the
suffix. '72' remains the fallback when detection fails.
Apply the same detection inline in worker.py section 1f. Both paths
still respect a pre-set BNB_ROCM_VERSION (caller override wins).
Tests: +8 cases covering detection logic and fallback (147 passed, 2 skipped).
* fix: patch torch.distributed stubs in server process for Windows ROCm
On Windows ROCm, torch.distributed ships without process-group helpers
(is_initialized, is_available, get_rank, get_world_size). The worker
subprocess already patches these in section 1e, but the main server
process calls _determine_attention_impl_for_gpu_estimate() which calls
unsloth's resolve_attention_implementation() → is_initialized(), causing:
"Could not resolve attention implementation for '...':
module 'torch.distributed' has no attribute 'is_initialized'"
Fix: patch the missing attrs onto torch.distributed at the top of
_determine_attention_impl_for_gpu_estimate, matching the same stubs
already applied in worker.py section 1e. No-ops on Linux/CUDA where
torch.distributed is fully populated.
* fix: gate _grouped_mm dispatch patch on HIP < 7.13
AMD fixed the gfx1200 null HIP kernel in ROCm 7.13 (torch 2.11+).
Users on the new wheel now get the real GPU _grouped_mm kernel for
MoE workloads instead of the Python mm fallback.
Changes:
- worker.py: add _hip_ver_at_least() helper; wrap full _grouped_mm
patch in `if not _hip_ver_at_least(7, 13):` with else branch that
logs the skip reason; update section-1f comment to document the fix
- test_rocm_support.py: add 5 tests covering the helper definition,
the (7, 13) gate expression, the else branch, the skip log message,
and the AMD-format version string parsing (.split(".")[:2])
Verified: torch==2.11.0+rocm7.13.0 — 3D batch and grouped (offs)
variants both succeed; null crash only present on rocm7.12 and earlier.
* fix: stub is_torchelastic_launched on torch.distributed for Windows ROCm
resolve_attention_implementation calls is_torchelastic_launched() which
does not exist in the incomplete torch.distributed shipped with the
Windows ROCm wheel, causing a warning on every model config load in the
server process. Add it to the stub table alongside the four helpers
already patched in _determine_attention_impl_for_gpu_estimate.
Also adds two tests: one confirming the new stub and one confirming all
five core distributed helpers are covered.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: explicit warnings on AMD ROCm arch/version fallbacks + Fast-Install arg order
setup.ps1:
- Fix Fast-Install argument order: packages before flags, consistent with
all other Fast-Install calls in the file
(was: Fast-Install --force-reinstall --index-url $url torch ...)
(now: Fast-Install torch torchvision torchaudio --force-reinstall --index-url $url)
- Add explicit [WARN] substep when $HasROCm is true but arch mapping fails:
- GPU arch detected but not in supported wheel list → names the arch and
lists supported families so user knows exactly what to report
- HIP SDK present (amd-smi path) but gcnArchName unreadable → instructs
user to re-install the HIP SDK; previously fell back silently to CPU
install.sh:
- Add [WARN] to stderr before silent CPU fallback when AMD GPU is confirmed
(rocminfo/amd-smi) but ROCm version cannot be read from any source
(amd-smi, /opt/rocm/.info/version, hipconfig, dpkg, rpm)
- Add [WARN] to stderr when ROCm version is too old (< 6.0) with upgrade link
install.ps1 and setup.sh: no changes needed (already handle these paths correctly)
* fix: robust gfx arch detection for Strix Halo / HIP-runtime-only installs
Covers users who have the HIP runtime (amd-smi available) but not the
full HIP SDK (no hipinfo), which is common on Strix Halo iGPU systems.
Without this, $ROCmGfxArch stays null and the installer silently falls
back to CPU-only PyTorch despite a working GPU.
Detection waterfall (setup.ps1 + install.ps1):
1. hipinfo gcnArchName -- full HIP SDK (existing, unchanged)
2. amd-smi list gfx pattern -- newer amd-smi versions embed arch
3. amd-smi static --asic -- ROCm 6+ ASIC details with GFX target
4. UNSLOTH_ROCM_GFX_ARCH env -- manual override escape hatch
5. GPU name → arch table -- best-effort from marketing name:
890M / Strix Halo → gfx1151 (RDNA 3.5 iGPU, Strix Halo)
880M / Strix Point → gfx1150 (RDNA 3.5 iGPU, Strix Point)
780M / Phoenix → gfx1103 (RDNA 3 iGPU)
RX 7900/7800/7700 → gfx1100 (RDNA 3 desktop)
RX 9070 XT / 9080 → gfx1201 (RDNA 4)
RX 9070 / 9060 XT → gfx1200 (RDNA 4)
When arch is inferred from name, a Cyan substep tells the user to set
UNSLOTH_ROCM_GFX_ARCH to skip inference on future installs.
WMI block intentionally does not set $HasROCm (no runtime confirmation).
Tests: 11 new tests in TestStrixHaloGfxArchDetection covering all five
detection levels, WMI safety, and gfx regex in both ps1 files.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: resolve hipinfo/hipconfig via HIP_PATH/ROCM_PATH when not on PATH
AMD HIP SDK sets HIP_PATH on Windows but does not always add the bin
directory to PATH. Get-Command hipinfo therefore silently fails and
detection falls through to WMI, which cannot provide a gfx arch, leaving
the user with a CPU-only PyTorch install and no warning.
Changes:
- setup.ps1 / install.ps1: before falling through to amd-smi, attempt to
locate hipinfo.exe and hipconfig.exe under $env:HIP_PATH\bin (then
$env:ROCM_PATH\bin) when Get-Command returns nothing
- Emit a [WARN] with the resolved path and a one-liner to permanently fix
PATH via SetEnvironmentVariable
- Emit a [WARN] when HIP_PATH/ROCM_PATH is set but the exe is still not
found (incomplete SDK install)
- Emit a [WARN] with the first hipinfo output line when hipinfo runs but
returns a non-zero exit code (e.g. "no ROCm-capable device detected")
- 18 new tests in TestHipSdkEnvPathResolution; total 183 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: print HIP SDK path and full hipconfig version in terminal on AMD detection
Both install.ps1 and setup.ps1 now emit substeps under the gpu step when
AMD ROCm is detected:
gpu AMD ROCm (gfx1200)
HIP SDK: C:\Program Files\AMD\ROCm\7.1
hipconfig: 7.1.51803-d3a86bd04
Previously only the gpu label (e.g. "AMD ROCm (gfx1200)") was shown with
no indication of where the SDK was found or which exact build was active.
The full hipconfig build string (e.g. 7.1.51803-d3a86bd04 instead of just
7.1) is now stored in ROCmVersionFull and also used in setup.ps1's
'rocm' step label.
9 new tests in TestHipSdkDetectedSubstep; total 192 passed, 2 skipped
* fix: Strix rocm7.1 segfault bypass + Ubuntu 24.04 HIP gcc-install-dir
Issue 1 (install.sh): gfx1151/gfx1150 + ROCm 7.1 causes a segfault in
torch._grouped_mm (moe_utils.py:167). The Radeon repo now ships cp313
wheels for rocm-rel-7.1, so _amd_gpu_radeon=true silently lands on the
broken combo. When Strix Halo/Point is detected and TORCH_INDEX_URL is
rocm7.1, override to rocm7.2 PyTorch index, update TORCH_CONSTRAINT, and
set _amd_gpu_radeon=false to bypass the Radeon repo entirely. Emits a
clear [WARN] explaining the segfault and linking to the ROCm upgrade docs.
Issue 2 (setup.sh): ROCm 7.x ships clang-20 which on Ubuntu 24.04+ picks
/usr/lib/gcc/x86_64-linux-gnu/14/ (runtime dir, no C++ headers), causing
'cstdlib file not found' and a failed llama.cpp HIP build. Iterate gcc
versions 14→11 to find the first install dir that has both runtime and
/usr/include/c++/<ver> headers, then pass --gcc-install-dir to clang via
CMAKE_HIP_FLAGS. Fix confirmed by h34v3nzc0dex (llama.cpp 417/417 clean).
11 new tests across TestStrixRocm71Override and TestSetupShGccInstallDir;
total 203 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: BNB_ROCM_VERSION in server process + torch._C._distributed_c10d stubs
Two errors visible in training logs on Windows ROCm:
1. Server process bitsandbytes crash:
"Configured ROCm binary not found at libbitsandbytes_rocm713.dll"
The installed BNB wheel ships rocm72.dll (not rocm713.dll). The
training worker already sets BNB_ROCM_VERSION=72 via DLL detection
but the server process (main.py) imported bitsandbytes before that
ran. Fix: add the same DLL-scan + BNB_ROCM_VERSION assignment to
main.py inside the existing win32 guard, before any downstream
import can pull in bitsandbytes.
2. torch.distributed import failure:
"No module named 'torch._C._distributed_c10d'; torch._C is not a package"
torch._C is a C extension on Windows ROCm — Python cannot do
submodule imports from it, so torch.distributed fails to import
before our attribute stubs could ever run. Fix: inject empty
ModuleType stubs for _distributed_c10d, _distributed_autograd and
_distributed_rpc into sys.modules inside the win32 guard in
hardware.py BEFORE importing torch.distributed, so the import
succeeds and our attribute stubs take effect.
9 new tests in TestServerStartupRocmFixes; total 212 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(win32): populate distributed c10d stub with dummy symbols
torch.distributed tries to `from torch._C._distributed_c10d import
FakeProcessGroup` (and ProcessGroup, Work, Store, etc.). The previous
empty ModuleType stub caused an AttributeError on those names.
Populate every stub with a _Dummy class for each known symbol so the
import chain completes silently on Windows ROCm where torch._C is a
compiled extension and its _distributed_c10d submodule doesn't exist.
Adds four new tests in TestServerStartupRocmFixes covering FakeProcessGroup,
ProcessGroup, setattr population, and all three _distributed_* siblings.
* fix(win32): distinguish HIP SDK installed vs GPU not ROCm-accessible
Previously, when hipinfo was found but exited non-zero (e.g. "no
ROCm-capable device detected"), both install.ps1 and setup.ps1 fell
through to the WMI-label-only branch and printed "AMD GPU detected --
HIP SDK not found" -- factually wrong since the SDK binary is present.
Add $HipSdkInstalled flag (set true when hipinfo binary is found,
regardless of exit code). When HipSdkInstalled && !HasROCm:
- Show "AMD GPU detected -- not ROCm-accessible (HIP <ver>)" instead
- Explain this is a driver issue, not an SDK issue, with a link
- Still run hipconfig version capture so version shows in output
- CPU-only hint now says "GPU not ROCm-accessible" not "require HIP SDK"
Also applies to setup.ps1 (same detection block, same branches).
Adds TestHipSdkInstalledButDeviceInaccessible (11 tests).
* fix(win32): scope ROCm workarounds to AMD hosts only
Three Codex-flagged issues where Windows ROCm workarounds incorrectly
applied to Windows CUDA (NVIDIA) machines:
main.py (P1): BNB_ROCM_VERSION was set unconditionally on all win32
hosts. On NVIDIA, bitsandbytes sees BNB_ROCM_VERSION and looks for a
ROCm DLL that doesn't exist, breaking bitsandbytes initialisation.
Fix: gate the block on HIP_PATH/ROCM_PATH being present (ROCm hosts only).
worker.py (P2): torchao stubs were seeded for all win32 runs, shadowing
real torchao on Windows CUDA and silently disabling torchao quantization
for NVIDIA users. Fix: gate on HIP_PATH/ROCM_PATH (win32 ROCm only).
install_python_stack.py (P1): _detect_windows_gfx_arch() only checked
shutil.which("hipinfo"), skipping the HIP_PATH/ROCM_PATH fallback that
the PowerShell installers use. On installs where the HIP SDK bin dir is
not on PATH, _ensure_rocm_torch() returned early without installing
ROCm wheels or bitsandbytes. Fix: mirror the env-var fallback.
* fix(linux): route Strix + ROCm 7.1 to AMD arch-specific index
Instead of falling back to pytorch.org/rocm7.2, the Strix override now
routes to repo.amd.com/rocm/whl/gfx1151/ (or gfx1150/) which serves
torch 2.11.0+rocm7.13.0 -- AMD's build containing the actual _grouped_mm
kernel fix, verified on real gfx1151 hardware by h34v3nzc0dex.
This exercises the real GPU kernel path rather than the rocm7.2 workaround.
UNSLOTH_AMD_ROCM_MIRROR can override the base URL for air-gapped installs.
Also teaches _tauri_torch_index_family to recognise AMD arch-specific URLs
(repo.amd.com/rocm/whl/gfx*) and return the rocm7.13 family label so
_tauri_gpu_branch correctly classifies these installs as rocm.
Suggested by h34v3nzc0dex based on hardware-verified probe results.
* fix(studio/rocm): gate ROCm-only side-effects on active torch runtime
Address five edge cases flagged during PR review:
1. studio/backend/main.py: BNB_ROCM_VERSION was set whenever HIP_PATH or
ROCM_PATH was present in the environment. A Windows CUDA user who once
installed the HIP SDK and reverted to a CUDA torch wheel still has those
env vars set, so bitsandbytes would try to load libbitsandbytes_rocm72.dll
against a CUDA torch and crash. Now probe torch.version.hip inside the
env-var guard (worker.py already does this).
2. studio/backend/main.py: os.add_dll_directory returned handles were
discarded. Per CPython docs, the directory leaves the DLL search list when
the handle is garbage collected. Retain handles in module-level
_ROCM_DLL_HANDLES list so they survive process lifetime.
3. studio/install_python_stack.py: _install_bnb_windows_rocm() returned None
regardless of pip_install_try outcome, and the caller flipped
_rocm_windows_torch_installed to True unconditionally. On a failed BNB
install the post-install "manual install may be required" warning was
suppressed and the user was misled. Helper now returns bool; caller gates
on it.
4. studio/install_python_stack.py: _detect_windows_gfx_arch returned the raw
capture group, so mixed-case hipinfo output ("Gfx1151") missed the
lowercase keys in _GFX_TO_AMD_INDEX_ARCH and silently fell back to CPU
torch. Lowercase the token.
5. studio/install_python_stack.py: UNSLOTH_ROCM_TORCH_INSTALLED=1 early-
return trusted the env var even when the venv was wiped between runs.
Subprocess-probe torch importability first; fall through to the full
install path if the probe fails.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py
(adds one new test for case 5 fall-through).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/rocm): worker.py parity + don't roll back ROCm torch on bnb failure
Addresses findings from a 10x reviewer pass on the prior fix commit:
1. studio/backend/core/training/worker.py (parity with main.py):
- Gate the torchao stub block on torch.version.hip / 'rocm' in
torch.__version__ instead of HIP_PATH / ROCM_PATH env-var presence.
Same root cause as main.py: HIP SDK env vars stick around on CUDA hosts.
- Add module-level Windows ROCm DLL registration block. Worker subprocesses
inherit env vars but not the parent's add_dll_directory handles, so the
first `import torch` in the worker could fail to find amdhip64.dll when
HIP_PATH\bin is not on PATH. Mirrors main.py setup. Handles retained at
module scope via _ROCM_DLL_HANDLES.
- Promote _WINDOWS_ROCM_GROUPED_MM_LIB to module scope with `global` in
run_training_process so the torch.library.Library registration survives
past function return / mid-run garbage collection.
- Harden _torch_has_hip() to also accept 'rocm' in torch.__version__
(AMD SDK / Radeon wheels may not set torch.version.hip).
2. studio/install_python_stack.py:
- Don't roll back ROCm torch when bitsandbytes install fails. The prior
commit gated _rocm_windows_torch_installed on _install_bnb_windows_rocm()
returning True; if torch installed successfully but bnb failed, the flag
stayed False and later install steps could overwrite ROCm torch with the
generic CPU torch wheel. Set the flag after torch install; surface bnb
failure as a separate warning instead.
- _detect_windows_gfx_arch now probes in three tiers: UNSLOTH_ROCM_GFX_ARCH
env-var override (matches the PowerShell installer), then hipinfo (PATH
or HIP_PATH\bin), then amd-smi (`static --asic`, `list`). Without the
amd-smi fallback, runtime-only Radeon installs without hipinfo on PATH
made `studio update` return early and leave the venv on CPU torch.
- Linux torch-already-rocm probe in _ensure_rocm_torch now matches the
Windows probe shape: accepts torch.version.hip OR 'rocm' in
torch.__version__ to cover AMD SDK / Radeon Linux wheels.
3. studio/backend/utils/hardware/hardware.py:
- apply_gpu_ids() final-fallback torch probe accepts 'rocm' in
torch.__version__ in addition to torch.version.hip, matching
detect_hardware(). AMD SDK wheels could otherwise leak through with
CUDA-only visibility masks on a spawned ROCm worker.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py
(no test changes needed; the probe shape that prints the hip version (or
'rocm' sentinel) preserves the existing non-empty-string contract).
Not addressed in this commit (deferred or out of scope):
- Tag drift / lemonade checksum (PR 5303 surface, not this PR).
- install.sh rocm7.2.1 URL: small fix, separate.
- install.ps1 / setup.ps1 'Radeon 8060S' marketing-name fallback table.
- Strix Halo + ROCm 7.1 routing asymmetry in Python update path.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/rocm): robustness pass - rocm tag normalisation, Strix routing parity, hardened detection
Robustness pass on top of 76137b2d. Four targeted fixes:
1. install.sh ROCm-tag routing normalisation.
`rocm7.2.1` would route to https://download.pytorch.org/whl/rocm7.2.1
which does not exist (PyTorch publishes major.minor URLs only). Same
for any future patch-level tag. Normalise every rocm{maj.min}* pattern
to the bare {maj.min} index URL.
2. install.ps1 + studio/setup.ps1 marketing-name fallback.
The gfx1151 row matched 890M / Strix Halo / HX 37x / HX 38x / AI 9 HX
but not the actual retail name 'AMD Radeon 8060S Graphics' shipped by
OEMs (Ryzen AI MAX+ 395). Add '8060S' to the regex.
3. install_python_stack.py Strix + ROCm 7.1 routing parity with install.sh.
The shell installer reroutes Strix Halo / Point + ROCm 7.1 to
repo.amd.com/rocm/whl/{gfx}/ (which serves torch 2.11.0+rocm7.13.0
with the upstream _grouped_mm fix). The Python `studio update` path
only warned and still installed the broken generic rocm7.1 wheel.
Mirror the override: detect gfx1151/gfx1150 on ROCm 7.1, route to
the AMD per-gfx index, honour UNSLOTH_AMD_ROCM_MIRROR override.
4. _detect_windows_gfx_arch amd-smi parsing tightened.
The amd-smi fallback added in the prior commit used a bare
`\bgfx[1-9][0-9a-z]{2,3}\b` match against the lowercased stdout,
which could pick up stray gfx references in warnings / device-name
strings. Anchor on labelled lines first (Target_Graphics_Version,
ASIC, Arch, gfx) and fall back to the bare match only when no
labelled line is present.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py;
sim_5301 23 cases pass (6 new sims for the Strix override + amd-smi parsing).
* fix(studio/rocm): multi-GPU selection, Strix sibling handling, defensive cleanups
Round 4 robustness pass based on 5 parallel Opus reviewers of head 21773215.
Seven items from across regression / edge-case / error-paths / architecture
reviews:
1. studio/backend/main.py BNB gate: aligned with the broad ROCm check used
everywhere else in this PR (torch.version.hip OR 'rocm' in __version__).
AMD SDK / Radeon Linux wheels do not always populate torch.version.hip;
without this, main.py would silently skip BNB_ROCM_VERSION while worker.py
set it.
2. studio/install_python_stack.py _install_bnb_windows_rocm: init _ok = False
before the try block. Without this, if pip_install_try itself raises
(e.g. OSError on uv binary missing), the finally block restored env vars
correctly but the subsequent `if not _ok:` raised UnboundLocalError,
masking the original exception.
3. studio/install_python_stack.py _detect_windows_gfx_arch:
- Rewrote to use re.findall (not re.search) on both hipinfo and amd-smi
output, dedup tokens preserving order, and select via new
_pick_visible_index() helper.
- HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES (first comma entry, integer)
now picks the right GPU on multi-AMD-GPU hosts. Out-of-range or non-int
values fall back to the first GPU (matches detect_host behaviour in
install_llama_prebuilt.py).
4. studio/install_python_stack.py Strix override now consults the runtime
target before flipping:
- Previous behaviour intersected gfx_codes with {gfx1151, gfx1150} and
picked the first Strix arch, ignoring whether HIP_VISIBLE_DEVICES
selected a non-Strix sibling (e.g. discrete RX 7900 in a mixed APU+dGPU
box). Could install Strix-specific wheels onto a gfx1100 dGPU.
- Now resolves the runtime gfx via _pick_visible_index() and only
overrides when that runtime target is in the Strix set.
5. studio/backend/main.py + studio/backend/core/training/worker.py: ROCm
version dir scan no longer sorts lexically. Previous sort placed "10.0"
before "7.0" alphabetically, which would mis-prioritise ROCm 10.x bin
dirs once AMD ships them. New _ver_key() splits on "." and sorts
numerically with a string fallback.
6. install.sh Strix override URL: replaced ${var%/} (strips one trailing
slash) with a while-loop that strips all trailing slashes, matching
Python's .rstrip("/"). A user setting UNSLOTH_AMD_ROCM_MIRROR with
"http://corp/whl///" no longer ends up with "http://corp/whl///gfx1151/"
which strict pip proxies (artifactory, sonatype) 404 on.
7. studio/install_python_stack.py: bumped torch import probe timeout from
30s to 90s. PyTorch's lazy .so loading can take 60-90s on cold NFS or
USB-backed venvs. The shorter timeout was producing a false "torch
missing" classification and reinstalling a working ROCm torch.
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass (added 7 new sims for
multi-GPU detection, Strix sibling handling, and _ok-init regression).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/rocm): worker BNB/grouped_mm broad gate, install.sh Strix visibility, runtime-only ROCm detection
Round-5 robustness pass based on 20 parallel reviewers of head 96b9e465.
1. studio/backend/core/training/worker.py - BNB version pin / dynamo disable
/ _grouped_mm fallback block was still gated on torch.version.hip alone
despite the torchao stub block above already using the broad check. AMD
SDK / Radeon Windows wheels (torch.__version__ contains "rocm" but
torch.version.hip is None) silently skipped the Windows ROCm runtime
patches. Aligned to the same broad check (8/20 reviewers).
2. studio/backend/core/training/worker.py - _hip_ver_at_least() now also
parses the ROCm version out of torch.__version__ (e.g. "2.11.0+rocm7.13.0")
when torch.version.hip is missing, so the kernel-fix gate is correct for
SDK / Radeon wheels too.
3. studio/backend/core/training/worker.py - _grouped_mm_safe_impl with
offs=None now picks torch.bmm/matmul for 3-D inputs instead of always
calling torch.mm. The real _grouped_mm accepts 3-D batched matmul; the
prior fallback raised "self must be a matrix" on MoE workloads (2/20).
4. studio/backend/main.py - dropped the HIP_PATH / ROCM_PATH env-var gate
from the BNB block; probe torch directly. Runtime-only Radeon / AMD SDK
Windows installs do not set those SDK env vars but still ship ROCm torch
(5/20 reviewers).
5. install.sh - Strix override now collects every gfx token from
rocminfo / amd-smi (in enumeration order), then indexes by
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES so a mixed Strix iGPU + non-
Strix dGPU host where the user selected the dGPU does NOT get rerouted
to the Strix per-gfx index. Mirrors the Python update path (5/20 reviewers).
6. install.sh - Strix detection chain now also probes `amd-smi static --asic`,
matching the PowerShell installer (1/20). Closes the gap on runtime-only
Strix hosts where `amd-smi list` does not surface a gfx token.
7. studio/install_python_stack.py - _has_rocm_gpu() now has the sysfs KFD
topology fallback (/sys/class/kfd/kfd/topology/nodes/*/gpu_id), matching
install.sh. On minimal package-managed installs without rocminfo /
amd-smi GUI tools, `studio update` can now detect the GPU and repair the
venv instead of returning early (2/20).
8. studio/install_python_stack.py - _detect_amd_gfx_codes() now falls back
to `amd-smi list` and `amd-smi static --asic` when rocminfo is missing
(2/20). Strix routing on runtime-only Radeon hosts now matches what
install.sh has done for a while.
9. studio/install_python_stack.py - Strix override now applies even when
has_hip_torch is True. The whole point of the override is to repair an
existing broken torch.version.hip == "7.1" install; skipping the
reinstall left users on the known _grouped_mm segfaulting stack (3/20).
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass. sim_cross 12 pass.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/rocm): code review hardening pass
- main.py: numeric DLL sort (string sort picked rocm72 over rocm713);
add basename() to regex; log warning on detection failure; log info
when BNB_ROCM_VERSION is set (mirrors worker.py)
- worker.py: explicit len-guard in _hip_ver_at_least() with warning
logs instead of silent IndexError/ValueError swallow
- hardware.py: isinstance(result, dict) guard before result.get() in
_smi_query() to prevent AttributeError on non-dict backend returns
- amd.py: round() before int() on parsed GPU IDs; log warning when
truncation occurs (defensive against malformed amd-smi output)
- setup.sh: quote --gcc-install-dir value in CMAKE_HIP_FLAGS so paths
with spaces do not break the CMake argument
- install.ps1, setup.ps1: apply colon-split + ToLower() to hipinfo
gcnArchName match (consistent with each other and with setup.sh)
- install.sh: tighten ROCm tag case patterns to explicit
rocmX.Y|rocmX.Y.* to avoid unintended prefix matches
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/training): GPU OOM guard to prevent system freeze on VRAM exhaustion
On RDNA 4 (gfx1200/gfx1201) and other ROCm GPUs, exhausting VRAM can
cause a HIP driver hang that freezes the entire system rather than
raising a recoverable Python exception.
Two-part fix:
- set_per_process_memory_fraction(0.90) caps the HIP/CUDA allocator at
90% of VRAM so PyTorch raises OutOfMemoryError before hitting the
hardware limit, keeping the driver alive and the system responsive
- top-level exception handler detects OOM errors by type and message
and surfaces a clear actionable message to the UI (reduce
max_seq_length, enable gradient_checkpointing, lower batch size)
instead of the raw CUDA/HIP error string
* fix(studio/rocm): OOM guard ROCm-only + unified memory, multi-GPU arch selection
OOM guard (worker.py):
- Scope to _hw.IS_ROCM only -- NVIDIA CUDA has a graceful OOM path and
does not need the allocator cap
- Detect unified memory by comparing torch VRAM against psutil system RAM;
use 0.80 on unified-memory APUs (gfx1151 Strix Halo) where the GPU pool
is carved from host RAM, 0.90 on discrete cards
Multi-GPU arch selection:
- install.ps1 / setup.ps1: replace -match (first hit only) with
[regex]::Matches() to collect all gcnArchName entries, then index by
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES
- install_python_stack.py: index into full token list before dedup so
HIP_VISIBLE_DEVICES=2 on [gfx1100, gfx1100, gfx1151] resolves gfx1151
- install.sh: remove awk dedup from gfx token collection for same reason
GCC multiarch (setup.sh):
- Only append -linux-gnu when gcc -print-multiarch does not already return
the full triple, fixing double-suffix on Ubuntu 24.04
* fix(tests): update ROCm version cap expectations from rocm7.1 to rocm7.2
Daniel's normalisation commit updated the cap from rocm7.1 to rocm7.2
since PyTorch now publishes that index and rocm7.2 ships torch 2.11.0.
Test expectations were stale.
* fix(tests): correct MLX smoke test losses_per_step assertion
logging_steps=1 with max_steps=30 produces 30 loss entries, not 7.
The assertion was stale from a previous config.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio/worker): detect unified-memory APU by GPU name not VRAM/RAM ratio
The previous heuristic (VRAM > 50 % of system RAM) false-positived on discrete
cards in low-RAM systems — e.g. RX 9060 XT 16 GB on a 16 GB or 24 GB machine
would trip the unified-memory path and log "unified memory host" when it should
say "discrete".
AMD iGPUs (gfx1150/gfx1151 Strix Halo, Strix Point, etc.) expose names with a
digit+M suffix ("AMD Radeon 890M"), while discrete cards use "RX NNNN [XT|XTX]"
naming. Matching that suffix is reliable across all current ROCm-capable AMD
consumer GPUs and does not require psutil.
Also includes the device name in the log line to ease future debugging.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(install/setup.ps1): force array on hipinfo gcnArchName parse to fix single-GPU arch truncation
When [regex]::Matches() finds exactly one match, PowerShell's pipeline
unwraps the result to a scalar string. Indexing a scalar string with [0]
returns the first *character*, so a one-GPU system would parse
gcnArchName "gfx1200" as "g", which is not in the supported arch map
and triggers the CPU-only fallback.
Wrapping with @() forces the result to remain an array regardless of
match count. On a single-GPU machine the arch is now correctly read as
"gfx1200" (or whatever the full name is) so the ROCm wheel index is
selected.
Reproducer: hipinfo exits 0 and outputs exactly one gcnArchName line.
Without @(), $_hipAllArches = "gfx1200" (String); $_hipAllArches[0] = 'g'.
With @(), $_hipAllArches = @("gfx1200") (Object[]); $_hipAllArches[0] = "gfx1200".
* fix(studio/rocm): classify unified-memory APU via VRAM/RAM ratio, not arch list
Replace the gcnArchName allowlist {gfx1150, gfx1151} with a
psutil-based heuristic: unified APUs expose the entire system RAM
as the HIP pool (ratio ≥ 0.90), discrete cards are well below that.
No arch name required — future APUs classify correctly without code changes.
Also removes the stale import re / \d[Mm]\b device-name regex that
5d84704 left behind, and logs vram/sys GiB for easier on-hardware
verification.
Addresses h34v3nzc0dex review: Radeon 8060S (gfx1151, 128 GiB
unified) now correctly gets 0.80 cap instead of 0.90.
* fix(studio/rocm): revert to gcnArchName for unified-memory APU classification
VRAM/RAM ratio >= 0.90 false-positives on machines where discrete VRAM
equals system RAM (e.g. RX 9060 XT 16 GB + 16 GB system RAM → ratio 1.0,
incorrectly classified as unified → wrong 0.80 cap applied).
gcnArchName is the correct signal: naming-independent, stable within a
product family, and already parsed throughout this PR. Unified set is
{gfx1150, gfx1151} (Strix Point + Strix Halo).
* fix(studio/llama-prebuilt): resolve hipinfo via HIP_PATH/ROCM_PATH on Windows
shutil.which("hipinfo") returns None when the HIP SDK bin dir is not on
PATH -- the HIP SDK installer sets HIP_PATH/ROCM_PATH but does not always
add the bin dir to PATH. This caused has_rocm=False in the prebuilt asset
selector, so AMD ROCm machines got the CPU llama.cpp zip instead of the
HIP one, silently running all chat inference on CPU.
Add _resolve_exe() that falls back to %HIP_PATH%\bin and %ROCM_PATH%\bin
when shutil.which() finds nothing, mirroring the same fallback already
present in setup.ps1.
* fix(studio/llama-prebuilt): pass --has-rocm from setup.ps1 to skip re-detection
The Python prebuilt installer re-detects ROCm independently via
shutil.which("hipinfo"), which fails when hipinfo is not on PATH
(HIP SDK sets HIP_PATH but doesn't always add the bin dir to PATH).
This caused has_rocm=False and downloaded the CPU llama.cpp zip even
on confirmed AMD ROCm machines.
setup.ps1 already performs reliable ROCm detection with its own
HIP_PATH/ROCM_PATH fallback. Add --has-rocm flag to
install_llama_prebuilt.py so setup.ps1 can forward its result directly,
and pass it whenever $HasROCm is true. The Python script then overrides
has_rocm=True in the HostInfo without re-probing.
* fix(studio/llama-prebuilt): add HIP asset to simple-policy Windows path
direct_upstream_release_plan (used by --simple-policy, which setup.ps1
always passes) only checked has_usable_nvidia on Windows and fell
straight to CPU for AMD ROCm machines, ignoring has_rocm entirely.
The --has-rocm override had no effect because the simple-policy code
path never reached resolve_asset_choice where has_rocm was checked.
Add an elif branch for has_rocm that tries the upstream HIP asset
(llama-TAG-bin-win-hip-radeon-x64.zip) before falling through to the
CPU fallback, consistent with the non-simple-policy path.
* fix(studio/setup.ps1): auto-remove mismatched llama.cpp install kind
When an existing llama.cpp install is the wrong kind for the current
GPU (e.g. windows-cpu on an AMD ROCm machine that should have
windows-hip), the prebuilt installer skips on tag match and never
upgrades. Read install_kind from UNSLOTH_PREBUILT_INFO.json before
invoking the installer and remove the directory if the kind doesn't
match, forcing a fresh download of the correct variant.
* fix(studio/setup.ps1): show live PyTorch install output in verbose mode for ROCm
The ROCm torch reinstall (setup.ps1 phase) always silently captured
output, so in --verbose mode the torch downgrade mid-install
(2.11.0+rocm → 2.10.0 → 2.11.0+rocm) looked like the final state was
2.10.0. Match the CPU/CUDA blocks which show live uv output when
$script:UnslothVerbose is set.
* fix(rocm/windows): set ROCBLAS_TENSILE_LIBPATH for bundled rocblas.dll
The llama.cpp ROCm prebuilt bundles rocblas.dll next to the binary but
not the Tensile kernel library files it depends on at runtime
(rocblas/library/TensileLibrary*.dat + *.hsaco). The bundled DLL
searches for these files relative to its own location by default, i.e.
<binary_dir>/rocblas/library/, which does not exist in the prebuilt
install tree. This causes a silent crash on the very first GEMM
(prefill) with no output from llama-server, seen by the caller as
WinError 10054 / 10061. Model load and the single-token warmup pass
because they use simpler code paths that do not trigger rocBLAS GEMM.
Fix: set ROCBLAS_TENSILE_LIBPATH in the subprocess env to
<HIP_PATH>/bin/rocblas/library so the bundled DLL finds the kernel
files from the system ROCm installation. Uses setdefault so a user-
supplied env var is never overwritten. No-ops on CUDA and CPU (no
HIP_PATH) and on Linux (win32 branch only).
Reproducer log:
rocBLAS error: Cannot read .../Release/rocblas/library/TensileLibrary.dat
rocBLAS error: Could not initialize Tensile host:
directory_iterator: The system cannot find the path specified.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(install.sh): restore gfx token dedup in Strix multi-GPU awk indexer
536a54df removed the per-source `| awk '!seen[$0]++'` dedup from the
_gfx_all collection step but left the indexer awk as bare NF, so on a
mixed-arch host (e.g. dGPU gfx1100 + Strix iGPU gfx1151) where
rocminfo emits each gfx token twice (Name: field + ISA triple),
HIP_VISIBLE_DEVICES=1 indexed vals[1] = the second gfx1100 occurrence
instead of gfx1151, triggering the Strix routing on the wrong GPU.
Add !seen[$0]++ to the indexer awk so duplicate tokens from the same
GPU collapse to one entry before the HIP_VISIBLE_DEVICES index is
applied -- matching exactly what the Python side does with dict.fromkeys()
in _detect_amd_gfx_codes(). The comment above the block ("skip
duplicates") already documented this as the intended behaviour.
* fix(studio/install): correct _TOTAL progress count on Windows
base_total += 3 fired for all non-macOS platforms including Windows,
but flash-attn (line 1620) and ROCm torch final (line 1705) are both
guarded by 'not IS_WINDOWS and not IS_MACOS', so on Windows with torch
enabled _TOTAL was 13 while only 11 _progress() calls actually execute.
Split into +1 for the ROCm torch check (all non-macOS) and +2 for the
two Linux-only steps, so Windows gets _TOTAL=11 and Linux gets 14.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(install.ps1): enforce torch>=2.11.0 for gfx120X and Strix on Windows
The AMD arch-specific index (repo.amd.com/rocm/whl/gfx120X-all/ and
gfx1151/) publishes torch wheels from 2.7.1 through 2.11.0. Without a
version floor pip can resolve to torch 2.10.0+rocm7.12 on RDNA 4
(gfx120X) or torch 2.10.0+rocm7.1 on Strix (gfx1151/gfx1150), both of
which have a null-pointer crash in torch._C._grouped_mm (TheRock
issues #5284 / #3284). torch 2.11.0+rocm7.13 contains the fix.
Add $ROCmTorchFloor alongside $ROCmIndexUrl: set to torch>=2.11.0 for
the two affected arch families, null for all others. Wire it into the
uv pip install call so the broken wheels are never selected.
* fix(rocm/windows): address Codex nits - deterministic DLL suffix, CUDA llama.cpp kind, HIP_VISIBLE_DEVICES arch indexing
- install_python_stack.py / worker.py: _detect_bnb_rocm_dll_ver() and the
inline worker probe now collect ALL libbitsandbytes_rocm*.dll suffixes and
return max() by numeric value instead of stopping at the first glob hit.
Filesystem glob order is not guaranteed; this ensures '713' always wins
over '72' when both variants are present in the wheel.
- setup.ps1 (expectedKind): add 'windows-cuda' branch so NVIDIA hosts are
not treated as 'windows-cpu'. Previously an existing windows-cuda prebuilt
was always considered a mismatch on non-ROCm machines, forcing an
unnecessary re-download on every update.
- setup.ps1 (amd-smi gfx arch): collect ALL gfx tokens from amd-smi list
output in GPU order and honour HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES
when selecting which arch to use. On mixed-arch AMD systems where the
visible GPU is not the first enumerated one, this prevents installing an
incompatible wheel index. Falls back to index 0 (same as before) when the
visibility var is unset or is a comma-separated list.
- test_rocm_support.py: add test_picks_highest_suffix_when_multiple_dlls to
cover the multi-DLL case that was previously untested.
* fix(rocm): misleading amd-smi log, BNB spec consistency, torch ceiling for AMD index
amd.py: split 'returncode != 0 or not stdout' into two separate branches.
Previously, exit-0 with empty output logged 'amd-smi returned code 0' (which
reads as success, not a warning) and incorrectly incremented the circuit-breaker
counter. Now: non-zero exit logs the code and counts toward the limit as before;
empty stdout on exit 0 logs at DEBUG level and does not penalise the counter
(amd-smi --json always emits at least [] on exit 0, so this branch is rare and
is not a tool failure).
main.py: replace spec.origin / os.path.dirname() with
spec.submodule_search_locations to match install_python_stack.py and worker.py.
For normal wheel installs both approaches reach the same directory, but using
submodule_search_locations is the canonical way and handles editable bitsandbytes
installs correctly. Also use max() by numeric suffix (same as the other two sites)
instead of a sort-then-break loop.
install.ps1: add <2.12.0 ceiling to the torch constraint for gfx120X (RDNA 4)
and gfx1151/gfx1150 (Strix). AMD actively publishes new versions on their
per-arch index; without a ceiling, a future 2.12.0+rocmX.Y wheel would be
pulled in automatically before being validated on these architectures. The
ceiling matches the existing Linux install_python_stack.py constraint for the
same arches. Bump both when 2.12.x is confirmed working.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(rocm): torch floor in setup.ps1, torchvision pin for Strix, rocmsdk in _hip_ver_at_least
setup.ps1: add \ (mirrors install.ps1) and derive \
from it. Previously the AMD index install called 'Fast-Install torch torchvision
torchaudio --force-reinstall --index-url \' with no version
constraint, so pip could resolve torch 2.10.0+rocm7.12 for gfx1151/gfx1200 --
the exact broken wheel the PR is meant to avoid. Now gfx120X and Strix enforce
'torch>=2.11.0,<2.12.0', matching install.ps1 and the Linux constraint.
install_python_stack.py: pin torchvision and torchaudio in _strix_override_pkgs.
The Strix Linux override uses --index-url (exclusive, no PyPI fallback); bare
unversioned 'torchvision' and 'torchaudio' could resolve a build from AMD's
index targeting a different torch major, causing ABI/version mismatches at
runtime. Now pinned to '>=0.26.0,<0.27.0' and '>=2.11.0,<2.12.0' respectively,
matching _ROCM_TORCH_CONSTRAINT['rocm7.2'].
worker.py: extend _hip_ver_at_least to handle AMD SDK wheel version strings.
The fallback regex r'rocm(\d+)\.(\d+)' cannot match '2.9.0+rocmsdk20251116'
(no rocmX.Y component), so the function always returned False on SDK/Radeon
wheels -- installing the Python _grouped_mm workaround on wheels that already
have the working HIP kernel. Added a second check: if the version string
contains '+rocmsdk', assume >= 7.13 (the rocmsdk format post-dates the
gfx120X null-kernel fix) and skip the fallback.
* fix(rocm): warn on OOB HIP_VISIBLE_DEVICES, bail on empty numeric_ids mask
- setup.ps1: when HIP/ROCR_VISIBLE_DEVICES names an index beyond the
detected GPU count, emit a yellow warning and fall back to GPU 0
instead of silently reading allGfxArches[-1] (wrong arch)
- hardware.py _reconcile_primary_rocm_unified_memory: distinguish
numeric_ids=None (no env var, use torch ordinal 0) from numeric_ids=[]
(empty mask / HIP_VISIBLE_DEVICES=-1, no GPU visible); bail out early
in the empty case to avoid querying torch.device(0) incorrectly
* fix(rocm): gate StubSubpackageFinder on win32 ROCm, add gcnArchName fallbacks
- worker.py _StubSubpackageFinder: the meta_path append was running on
every platform on every call to run_training_process; moved it inside
the if _is_win32_rocm: block since stubs are only seeded there and the
finder is a pure accumulation on Linux/Windows CUDA
- worker.py OOM guard: AMD SDK / Radeon wheels may not populate
gcnArchName, causing Strix Halo to be misclassified as discrete and
get the 0.90 cap (12.8 GB OS headroom) instead of 0.80 (25.6 GB);
now tries gcn_arch_name / arch_name / gfx_arch_name variants first,
then falls back to device-name matching (890M -> Strix Halo,
880M -> Strix Point) with a debug log when the fallback fires
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(rocm): pin torchvision/torchaudio in setup.ps1, remove -Unique from arch array
- setup.ps1 ROCm torch install: torchvision and torchaudio were passed
bare alongside pinned torch>=2.11.0,<2.12.0 for gfx1151/gfx1200 arches.
AMD publishes packages independently so a future torchvision 0.27 (for
torch 2.12) on the same arch index would cause pip ResolutionImpossible
or an ABI-incompatible install. Added torchvisionFloorMap and
torchaudioFloorMap mirroring install_python_stack.py's strix override
(torchvision>=0.26.0,<0.27.0, torchaudio>=2.11.0,<2.12.0) and derived
ROCmVisionSpec/ROCmAudioSpec used in all three Fast-Install call sites.
- setup.ps1 amd-smi arch detection: Select-Object -Unique was collapsing
same-arch multi-GPU arrays (e.g. two gfx1151 APUs -> 1-element array)
causing HIP_VISIBLE_DEVICES=1 to trigger a false out-of-range warning
and fall back to GPU 0 even though the correct GPU would have been at
index 1. Removed -Unique; added comment noting the positional-index
assumption and its non-contiguous-GPU limitation.
* fix(rocm): add 8060s/8050s to OOM guard device-name fallback, extract classifier helper
Path 3 of the OOM guard device-name fallback only checked for 890m/880m
(gfx1150 Strix Point SKU names). Strix Halo (gfx1151) ships as Radeon 8060S
(Ryzen AI MAX+ 395) and Radeon 8050S (cut-down SKU) -- neither matches, so
the fallback returned is_unified=False and applied the 0.90 fraction instead
of 0.80, leaving ~12.8 GiB OS headroom on a 128 GiB pool instead of ~25.6 GiB.
Fix: add 8060s and 8050s to the name-match set. Also correct the comment that
mislabelled 890M as a Strix Halo name (it is Strix Point).
Refactor: extract the three-path classifier into _rocm_classify_unified_memory()
so it can be unit-tested directly. Add 31 test cases in test_rocm_oom_guard.py
covering all three paths and the regression case (Radeon 8060S Graphics).
Reported-by: h34v3nzc0dex
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(rocm): pass explicit dtype on bf16-unsupported hardware (RDNA2)
dtype=None lets unsloth auto-detect the model dtype. On RDNA2 (gfx103x,
e.g. RX 6600) is_bfloat16_supported() incorrectly returns True, so unsloth
picks bf16 and the first bf16 kernel dispatch triggers:
LLVM ERROR: Cannot select: intrinsic %llvm.amdgcn.fdot2.bf16.bf16
Replace every dtype=None in load_model() with _auto_dtype which resolves
to None when bf16 is supported (all modern NVIDIA + RDNA3+) and
torch.float16 otherwise. This gives RDNA2 users a working float16
training path without touching NVIDIA behaviour at all.
Fixes: https://github.com/unslothai/unsloth/issues/5337
* fix: reduce log noise for expected non-issues on Windows ROCm
Three log lines fired at warning/error level for conditions that are
completely expected on a Windows HIP SDK-only setup:
amd.py
- amd-smi WinError 2 (FileNotFoundError): downgrade warning -> debug.
amd-smi ships with Adrenalin, not the HIP SDK; absence is normal.
- 'disabling' message: downgrade warning -> info with clearer text
'not available (not installed; expected on HIP SDK-only systems);
GPU VRAM polling disabled'
hardware.py
- torch.distributed.Store missing: downgrade warning -> debug.
The distributed stub added in this PR intentionally omits Store; the
attention-impl fallback to eager is expected and non-actionable.
worker.py
- causal-conv1d: add early Windows exit (info) in both
_ensure_causal_conv1d_fast_path and _causal_conv1d_install hook;
no cp313/win_amd64 wheel exists, so the install always fails.
- FLA: add early Windows exit (info) in
_ensure_flash_linear_attention_unconditional; triton dependency has
no cp313/win_amd64 wheel.
- Defense-in-depth: _install_package_wheel_first non-HIP PyPI failure
logs info+debug on Windows instead of error; FLA failure logs
info+debug on Windows instead of warning.
* [AMD] FIx installation of bitsandbytes when it's from .dev and skip rebuilding llama.cpp if we build it manually.
* fix: use force_pip for Windows ROCm bitsandbytes prebuilt wheel install
uv rejects the bnb continuous-release wheel due to filename/metadata
version mismatch (1.33.7.preview vs 0.50.0.dev0). Switch to force_pip=True
(pip bypass) instead of the UV_SKIP_WHEEL_FILENAME_CHECK env var workaround
-- cleaner and consistent with how the Linux path handles it.
BNB_ROCM_VERSION is still set post-install to the detected DLL suffix so
the worker subprocess loads the correct libbitsandbytes_rocm{VER}.dll even
when torch.version.hip reports a newer HIP version than the wheel ships.
* fix: three small correctness fixes found in PR review
- _install_bnb_windows_rocm: use UV_SKIP_WHEEL_FILENAME_CHECK=1 with
try/finally instead of force_pip=True so the env var is always
restored and the failing CI test passes
- _determine_attention_impl_for_gpu_estimate: gate torch._C distributed
stubs on IS_ROCM so Windows CUDA users keep the real extension
- install.ps1 amd-smi fallback: collect all gfx tokens and index by
HIP_VISIBLE_DEVICES, matching the hipinfo path on multi-GPU hosts
* fix: stub torchao in export subprocess on Windows ROCm
On Windows, the ROCm build of PyTorch ships without the distributed
C extension (torch._C._distributed_c10d). torchao, which is pulled in
transitively by transformers.quantizers at import time, walks into
torch.distributed._functional_collectives -> distributed_c10d and
crashes with:
No module named 'torch._C._distributed_c10d'; 'torch._C' is not a package
This only affected the export subprocess because the training subprocess
already applied an identical torchao stub (introduced separately to fix
the same root cause). The export subprocess had no such guard and died
during 'Importing Unsloth...' before any model loading could happen.
Fix: apply the same _StubSubpackageFinder / torchao stub pattern to the
export subprocess entry point, gated on Windows ROCm detection, before
any import of transformers or unsloth_zoo.
Root cause tracked in ROCm/TheRock#3284 (libuv / torch.distributed
missing on Windows ROCm builds).
Ref: https://github.com/ROCm/TheRock/issues/3284
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* install.sh, setup.sh: add GPU arch step logging to match PS1 scripts
Both shell scripts were missing the step "gpu" terminal log block that
install.ps1 and setup.ps1 emit. This adds equivalent output: GPU label
with gfx arch (e.g. "AMD ROCm (gfx1151)"), ROCm root path, hipconfig
version, and marketing name substep. Includes the same gfx arch detection
chain (rocminfo → amd-smi list → amd-smi static --asic), UNSLOTH_ROCM_GFX_ARCH
env override, and name-based arch inference table (Strix Halo/Point, RDNA 3/4)
as the PS1 versions. install.sh also replaces bare echo blocks for the AMD
ROCm and CPU-only cases with formatted substep output.
* Fix BNB_ROCM_VERSION gate, ROCm GPU mask preference, APU unified memory and Release build for PR #5301
- main.py: gate BNB_ROCM_VERSION on the rocm bnb DLL or HIP_PATH/ROCM_PATH instead of importing torch on every Windows host
- hardware.py: prefer HIP/ROCR visible-device masks only on ROCm hosts so a stale mask cannot override CUDA_VISIBLE_DEVICES on NVIDIA
- llama_cpp.py: set GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 only for unified-memory APUs (gfx1150/gfx1151)
- setup.sh: pass -DCMAKE_BUILD_TYPE=Release for the HIP source build
- add test_amd_apu_unified_memory.py
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: guard recompile_limit + fix AMD VRAM monitor fallback
trainer.py: torch._dynamo.config.recompile_limit does not exist in
some ROCm torch builds (e.g. pytorch.org/whl/rocm6.2 wheels). Guard
the assignment so training doesn't crash on RDNA2/RDNA3.
hardware.py: when amd-smi/nvidia-smi is unavailable or returns no
usable data (HIP SDK-only Windows, Docker, unexpected JSON format),
the existing fallback used torch.cuda.memory_allocated() which is
process-specific and reads near-zero even with a fully loaded model.
Switch to torch.cuda.mem_get_info() via _torch_get_per_device_info()
which reports system-wide VRAM occupancy so the GPU monitor shows
real usage on all AMD systems without requiring amd-smi.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: Windows VRAM monitor via Performance Counter API
When amd-smi/nvidia-smi is unavailable on Windows, query dedicated GPU
VRAM via Windows Performance Counters (same source as Task Manager).
This gives system-wide cross-process usage, fixing the near-zero reading
caused by torch.cuda.mem_get_info only seeing the Studio server process.
Linux fallback path unchanged (mem_get_info is system-wide on ROCm).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: rename to _rocm_windows_perf_counter_vram_gb, scope to IS_ROCM
Function is AMD ROCm specific — amd-smi absent on Windows when only the
HIP SDK is installed. Scoped to IS_ROCM so NVIDIA Windows path is
untouched (nvidia-smi handles that case).
* fix: AMD VRAM monitor — Linux DRM sysfs + Windows perf counter
Linux: read /sys/class/drm/card*/device/mem_info_vram_used|total for
system-wide GPU memory across all processes. No tools required, always
present on Linux AMD systems.
Windows: Windows Performance Counter API (already added).
Both paths are gated on IS_ROCM and only fire when amd-smi is absent.
torch mem_get_info remains as last resort (process-local).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: AMD GPU monitor — utilization, temperature, and power for Windows and Linux fallback paths
- Windows: GPU utilization via \GPU Engine(*engtype_3D*)\Utilization Percentage perf counter
- Windows: temperature and power via ADL (atiadlxx.dll, ships with Adrenalin)
- Linux: GPU utilization via DRM sysfs gpu_busy_percent
- Linux: temperature via hwmon temp1_input (millidegrees C)
- Linux: power via hwmon power1_average / power1_input (microwatts)
All paths are no-op fallbacks (None) when the source is unavailable.
Mirrors what nvidia-smi provides on the CUDA path.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: remove ADL ctypes — does not support AMD iGPU (Strix Halo)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Erland366 <erland.pg366@gmail.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* Studio setup.sh: cope with fresh CUDA toolkits like 13.3
CUDA 13.3 shipped today. Three loose ends in studio/setup.sh surfaced
during the llama.cpp build path:
1. setup.ps1 already aborts cleanly when the CUDA toolkit is below
llama.cpp's minimum (12.4) via #4517, but setup.sh still hit the
generic cmake failure described in #4437. Added a min-version check
that downgrades to a CPU build for nvcc < 12.4 with a clear message
pointing to the toolkit archive.
2. The first day a new CUDA toolkit ships, its host-compiler whitelist
lags whatever gcc/clang the distro is on, so nvcc rejects the host
compiler with a wall of "#error -- unsupported GNU version" before
any real compile runs. NVCC_PREPEND_FLAGS now carries
-allow-unsupported-compiler so the build moves on instead.
3. The Linux CUDA/ROCm configure failure path had no symmetry with the
macOS Metal fallback: a single nvcc failure left BUILD_OK=false and
no llama.cpp at all. Generalised the existing Metal -> CPU fallback
to cover any GPU_BACKEND, so a CUDA configure or build failure now
transparently retries with the CPU args and the user still ends up
with a working llama-server.
Pulled the version probe out into _nvcc_meets_llama_minimum so it can
be unit-tested. Added tests/sh/test_nvcc_meets_llama_minimum.sh and two
extra cases in tests/sh/test_get_torch_index_url.sh covering the legacy
"CUDA Version: 13.3" header (driver-reported) and the future 13.7
case. Wired the new test into tests/run_all.sh and the studio-backend
CI workflow.
* tests: relax pr4562 regression to allow generic GPU fallback label
* studio tests: assert setup.sh exports NVCC_PREPEND_FLAGS=-allow-unsupported-compiler
The -allow-unsupported-compiler flag is the core of the fresh-CUDA-toolkit fix
(it lets nvcc accept a host gcc/clang newer than its release-time whitelist, so
CUDA 13.3 day-one builds do not abort on '#error -- unsupported GNU version'),
but it had no automated coverage. Add a source-pattern test asserting the flag
is present, delivered via NVCC_PREPEND_FLAGS so it also covers cmake's CUDA
compiler-id probe, and kept out of CMAKE_ARGS for bash word-splitting safety.
* studio/setup.ps1: allow unsupported host compiler for CUDA build (Windows parity)
Mirror the Linux setup.sh headline fix from this PR on Windows. A freshly
released CUDA toolkit ships with a host-compiler whitelist that lags the
installed toolchain, so nvcc can reject the host with
"#error -- unsupported Microsoft Visual Studio version!" before any real
compile runs (the MSVC analogue of the gcc wall the Linux side hit on
CUDA 13.3). Set NVCC_PREPEND_FLAGS=-allow-unsupported-compiler in the CUDA
build branch so both cmake's configure-time CUDA compiler-id probe and the
cmake --build step proceed. The flag disables the host version check only and
is a no-op when the compiler is already supported.
Set via the process environment (not the $CmakeArgs array), after the
Refresh-Environment calls that re-sanitize CUDA env vars, and appended
idempotently to any value the user already set.
Validated with PowerShell 7.6.2: full setup.ps1 AST parse is clean and the
snippet is idempotent (empty -> set, existing -> append once, no duplicate).
Needs real Windows + CUDA CI to exercise the actual nvcc/MSVC build.
Adds test_setup_ps1_exports_allow_unsupported_compiler asserting the flag is
present, env-delivered, kept out of $CmakeArgs, and scoped to the CUDA-on branch.
* studio: tighten code comments added in this PR
Shorten the verbose multi-line comments and test docstrings introduced by
this PR (setup.sh, setup.ps1, and the shell/python tests) to be succinct
while preserving the rationale. No code or test-assertion changes.
Run the Linux llama.cpp prebuilt dependency preflight before reusing an existing install, so cached trees missing newly required per-tool shared libraries (libllama-server-impl.so / libllama-quantize-impl.so introduced upstream between b9279 and b9283) trigger a repair instead of silently skipping reinstall and failing at runtime. Adds a regression test for the new lib*-impl.so overlay layout.
* ci: broaden Linux llama.cpp runtime pattern to lib*.so*
#5741 patched the explicit Linux pattern list to add
``libllama-*-impl.so*`` after ggml-org/llama.cpp#23462 (between
b9279 and b9283) split each binary's entry code into a paired
``lib<binary>-impl.so`` shared library. Same class of upstream
repackaging will hit us again whenever a new shared lib is added.
Mirror what macOS already does and replace the per-lib list with a
single ``lib*.so*`` glob. ``copy_globs`` (line 3614) unions
patterns, so the per-variant ``libggml-cuda.so*`` / ``libggml-hip.so*``
entries were never filtering anything; the spec lives in
``runtime_payload_health_groups`` (line 5209) which keeps the
explicit minimum-required list per variant.
Dry-run against b9296-bin-ubuntu-x64.tar.gz: 40 files copied (all
ggml, llama, mtmd, impl variants + the two binaries we ship), 22
skipped (other CLIs, rpc-server, LICENSE). Functionally equal to
the post-#5741 set.
* cleanup: trim #5741 comments on the pydantic split
Comments added in #5741 explained the original bug in full each
time. They are mostly redundant with the commit message and the PR.
Trim them to one short paragraph per site.
No behavior change.
* ci: narrow Windows runtime pattern to llama-server.exe + llama-quantize.exe
Studio only invokes llama-server and llama-quantize. Mac and Linux
already filter to those two binaries; Windows was the odd one out
with ``*.exe`` copying every CLI upstream ships (llama-cli,
llama-bench, llama-mtmd-cli, ...).
Dry-run on b9296 (win cpu-x64, cpu-arm64, cuda-13.1, hip-radeon):
20 unused EXEs skipped per variant, all DLLs (incl. the new
llama-*-impl.dll family) still copied via ``*.dll``.
``existing_install_matches_choice`` already checks llama-server.exe
exists explicitly (line 5297), so the health gate is unchanged.
Bundles three independent CI regressions hitting the maintainer PR
backlog. Each one is verified end-to-end on a staging fork against
real Ubuntu / macOS / Windows GitHub-hosted runners before this
lands.
1. Windows --no-torch install: pydantic + pydantic-core drift to
incompatible versions under `uv pip install --no-deps -r
no-torch-runtime.txt` because pip resolves each independently
from latest. pydantic.VERSION 2.13.4 pins pydantic-core==2.46.4
but pydantic-core 2.47.0 was the freshest published wheel, so
`import pydantic` raised
`SystemError: pydantic-core 2.47.0 is incompatible with the
current pydantic version`. Resolve pydantic WITH deps in a
focused pip call (install.sh, install.ps1,
install_python_stack.py) before the --no-deps no-torch-runtime
pass so pip pins pydantic-core to the version pydantic declares.
pydantic's transitive deps (annotated-types, pydantic-core,
typing-extensions, typing-inspection) are torch-free. Drop the
redundant `Patch Studio venv with full typer / pydantic dep
trees` workaround from the four Windows smoke YAMLs.
Supersedes #5733 + #5734.
2. Linux Studio Update CI: upstream llama.cpp b9261+ split each
binary's entry code into a paired `libllama-<binary>-impl.so`
shared library. `llama-server` and `llama-quantize` NEEDED-link
against `libllama-server-impl.so` / `libllama-quantize-impl.so`
with RUNPATH `$ORIGIN`, so the prebuilt overlay must copy those
alongside the binaries. Without that, ldd reports them missing,
preflight rejects, the installer falls back to source build, and
studio-update-smoke annotates `setup.sh idempotency regressed`.
Add `libllama-*-impl.so*` to the Linux runtime patterns and lock
the pattern in test_rocm_support.TestRuntimePatterns.
3. Mac Studio UI Chat: change-password submit clicked while
disabled. The disable gate only checked new + confirm password
length, but Playwright's first click landed before the
current-password field's React state had committed, so the form
was simultaneously logically-invalid (current_password empty) and
the button was disabled. Tighten the gate to require
`currentPassword.length >= 8` and mirror the same check in the
submit handler so Enter / autofill cannot bypass.
Supersedes #5738.
* allow validation of custom releases to pass
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* require exact source provenance for branch direct linux releases
Mirror validated_checksums_for_bundle so incomplete checksum metadata
on a branch/pull/commit release fails closed with a clear error instead
of silently degrading to the legacy master-as-tag source hydration path
that this PR is meant to eliminate. Guard fires when source_commit,
the exact source archive hash, or a derivable source repo URL is
missing from the approved metadata.
Also set plan.llama_tag to approved_checksums.upstream_tag so the
ensure_converter_scripts fallback and the install fingerprint target
the concrete upstream tag (e.g. b9174) rather than the moving branch
label inferred from asset names (master). Legacy b#### releases are
unaffected: synthetic checksums already set upstream_tag to
bundle.upstream_tag, so the swap is a no-op on that path.
Add parametrized negative regression coverage for the three ways
exact provenance can be incomplete (missing source_commit, missing
exact source archive entry, missing source_repo) and update the
existing branch happy-path test to expect b9174.
* revert llama_tag swap to preserve install identity
Keep plan.llama_tag as bundle.upstream_tag (the branch label inferred
from asset names, e.g. master) rather than overriding it with the
approved metadata upstream tag (b9174). The override broke install
identity in two ways:
1. expected_install_fingerprint hashes upstream_tag = llama_tag, so
the same release would produce a different fingerprint depending on
which version of this code resolved it, causing spurious reinstalls
when users upgrade or roll back.
2. UNSLOTH_PREBUILT_INFO.json reports the value as the user-visible
record of which release was installed; tools and logs should see
the requested branch label, not the compatibility tag.
The approved metadata still records upstream_tag = b9174 internally
for source archive lookup; the new assertion makes that explicit.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
CI surfaced a flaky failure on Linux 'Repo tests (CPU)':
TestPwshPrForcePromotion.test_baked_in_pr_force_promotes ->
subprocess.TimeoutExpired after 10s on /usr/bin/pwsh startup.
The scripts under test run in well under a second; the 10s budget
only covered pwsh / bash launch time, which spikes on heavily-
loaded GitHub-hosted runners. Raise the default helper timeout to
60s for both run_bash and run_pwsh. Real bugs in the script logic
will still surface as wrong output or non-zero exit; this just
absorbs runner-side launch jitter.
* Studio: download paired cudart bundle on Windows CUDA installs
Upstream ggml-org/llama.cpp publishes Windows CUDA in two archives
that the release notes explicitly say are both required:
llama-<tag>-bin-win-cuda-X.Y-x64.zip (binaries + ggml DLLs)
cudart-llama-bin-win-cuda-X.Y-x64.zip (cudart64, cublas64, cublasLt64)
Studio's installer was downloading only the first one. The
``runtime_name`` / ``runtime_url`` fields on AssetChoice existed but
were never populated, and ``install_from_archives`` only handled
``choice.url``. With the cudart DLLs missing from
``install_dir/build/bin/Release``, the prebuilt binary's LoadLibrary
calls only resolved at runtime when the user happened to have a
version-matched system CUDA toolkit on PATH. That is the underlying
cause for the Windows reports in #5106 ("GPU detected but model
loaded entirely on RAM"): the prebuilt's CUDA backend silently fails
to load and llama-server falls back to CPU regardless of ``-ngl`` or
``--fit on``.
Wires the pairing through end to end:
* ``windows_cuda_attempts`` and ``published_windows_cuda_attempts``
look up the matching ``cudart-llama-bin-win-cuda-X.Y-x64.zip``
asset URL alongside the main archive and store it as
``runtime_url`` / ``runtime_name`` on the AssetChoice. We only
pair when the selected main archive is the binary archive
(``llama-...zip``) so the legacy cudart-only naming path is
unaffected.
* ``apply_approved_hashes`` resolves the runtime archive's hash from
the approved manifest. If the manifest does not list the runtime
archive, the pairing is dropped rather than installing without
checksum coverage. Preserves the supply-chain guarantee for
published bundles; upstream installs with no manifest are
unaffected (same risk surface as the existing main-archive
download).
* ``install_from_archives`` now downloads the runtime archive into a
separate temp dir and runs ``copy_globs`` against both source dirs.
Separate dirs avoid the "ambiguous archive layout" guard tripping
on shared filenames like LICENSE.txt, while the second
``copy_globs`` overlay drops the cudart DLLs into the same
``install_dir/build/bin/Release`` directory as the main binary.
Adds a ``runtime_sha256`` field on AssetChoice to carry the
verified hash through to the download step, alongside the existing
``runtime_name`` / ``runtime_url`` slots.
Tests: 5 new cases in tests/studio/install/test_selection_logic.py:
* upstream pairing populates runtime_url / runtime_name
* graceful degrade when cudart asset is absent in the release
* legacy cudart-only naming path does not self-pair
* apply_approved_hashes threads runtime_sha256 when the manifest
lists it
* apply_approved_hashes drops the pair when the runtime hash is
missing rather than installing without verification
130 install tests pass (125 baseline + 5 new). No regressions.
Refs #5106
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim comments to be more succinct
* Studio: refresh installs that pre-date the paired cudart bundle
expected_install_fingerprint did not hash the new runtime_name /
runtime_sha256 fields, and runtime_payload_health_groups for windows-
cuda only checked llama.dll / ggml-cuda.dll. The combination meant that
an install made before this PR -- the exact installs reporting #5106 --
would still match the post-PR choice: same main asset name + sha, same
llama.dll, same ggml-cuda.dll, missing cudart64_*.dll, but
existing_install_matches_choice returned True and the cudart download
path in install_from_archives never ran. Fresh installs got the fix;
existing affected installs did not.
This commit:
* Adds runtime_asset and runtime_sha256 to the fingerprint payload so
any change to (or first introduction of) the cudart pair invalidates
pre-existing installs.
* Refactors write_prebuilt_metadata to call expected_install_fingerprint
so the recorded fingerprint cannot drift from the expected one when
new keys are added.
* Extends runtime_payload_health_groups for windows-cuda to require
cudart64_*.dll and cublas64_*.dll *only when the choice carries a
paired runtime archive*. Gating on choice.runtime_name keeps the
no-pair fallback path (manifest missing cudart hash, upstream
without paired bundle) from looping on reinstall.
New tests:
* test_existing_install_matches_plan_windows_cuda_paired_requires_cudart
-- paired choice rejects installs missing cudart / cublas.
* test_existing_install_matches_plan_windows_cuda_unpaired_skips_cudart_check
-- unpaired choice still accepts legacy cudart-less installs.
* test_existing_install_fingerprint_changes_when_cudart_pair_added
-- direct fingerprint mismatch between the legacy and paired choice.
Refs #5106
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: tighten paired Windows CUDA install gates
Three follow-ups from a 12-reviewer batch over 526894a4 (PR #5322):
1. (12/12) Health check required cudart64_*.dll and cublas64_*.dll but
not cublasLt64_*.dll. The upstream cudart-llama-bin-win-cuda-X.Y-x64
bundle ships all three (verified against b9103 cuda-12.4 and
cuda-13.1: 3 DLLs, no executables), and a Windows install missing
any one of them still fails CUDA initialisation. Adding
cublasLt64_*.dll to runtime_payload_health_groups so a partial
install or a deletion of the third DLL triggers reinstall instead
of silently staying broken.
2. The runtime overlay copy used the same broad runtime_patterns_for_choice
set as the main archive (windows-cuda returns *.exe and *.dll). A
malformed runtime zip that contained a llama-server.exe alongside
the real cudart DLLs would have overwritten the main archive's
server binary. Introduced paired_runtime_dll_patterns() that
returns the cudart bundle's three specific filename patterns and
nothing else, and use that for the second copy_globs pass.
New end-to-end regression test packs a fake runtime zip with an
extra llama-server.exe and asserts the main binary survives.
3. (7/12) python_runtime_dirs in install_llama_prebuilt.py and
_windows_pip_nvidia_dll_dirs in llama_cpp.py walked different path
sets. The installer side missed nvidia/<pkg>/Library/bin (conda
layout) and nvidia/<pkg>/bin/x86_64 (current CUDA 13 unsuffixed
wheel layout), so preflight CUDA detection could fail even when
usable DLLs were present. Mirrored the same six-path set the
backend resolver uses, including arch subdirs.
New tests:
- test_paired_runtime_dll_patterns_excludes_executables
- test_runtime_overlay_cannot_overwrite_main_archive_payload (end-to-end)
- test_python_runtime_dirs_covers_cu13_and_library_bin
- extended test_existing_install_matches_plan_windows_cuda_paired_requires_cudart
with a cublasLt-missing case
Upstream cudart bundle contents verified empirically by downloading
the b9103 release artifacts directly: each cuda-X.Y bundle contains
exactly cudart64_X.dll + cublas64_X.dll + cublasLt64_X.dll, no exes.
Refs #5106
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* tests/studio/install: parallel UNSLOTH_STUDIO_HOME smoke test
Adds tests/studio/install/smoke_test_parallel_studio_home.py to lock in
the install-time and runtime isolation guarantees added by #5190.
The runner spawns N concurrent install.sh --local --no-torch jobs, each
with its own UNSLOTH_STUDIO_HOME and a redirected HOME, then launches N
backends on dynamically allocated ports and cross-checks every install
against its running process. Asserts:
install-time
- all N installs exit 0
- per-install bin / share / llama.cpp / unsloth_studio venv tree
- shim symlink resolves into its own venv, no cross-resolution
- share/studio_install_id is unique across the N installs
- share/studio.conf exports UNSLOTH_EXE / UNSLOTH_STUDIO_HOME /
UNSLOTH_LLAMA_CPP_PATH all pointing inside the install
- share/launch-studio.sh has @@DATA_DIR@@ substituted to its own
share/ at install time
- the redirected HOME stays clean: no rc-file append, no
.desktop file, no Studio.app stub, no shared marker
runtime
- /api/health returns 200 with status healthy and chat_only true
- /api/health.studio_root_id matches share/studio_install_id
(runtime resolver agrees with install-time write)
- studio_root_id values are pairwise distinct
- GET / and GET /api/chat return 200 on each backend
- /proc/PID/exe is the install's own venv python
Standalone smoke runner, not pytest collected. Default --n 4 finishes
in about 60 seconds on a warm uv cache; artifacts are removed on PASS
unless --keep is passed and kept on FAIL or ERROR for inspection.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests/studio/install: portability + log fd cleanup in parallel smoke
Two cleanups on the parallel UNSLOTH_STUDIO_HOME smoke runner:
- Skip the /proc/PID/exe runtime cross-resolution check on platforms
without /proc (macOS, BSD, Windows). install.sh supports macOS, so
the smoke should not hard-error there. The install-time symlink,
studio.conf and launch-studio.sh assertions already pin the venv
python target statically; the proc check stays as a Linux-only
redundant cross-resolution catch and now returns None cleanly on
other platforms instead of raising.
- Wrap the per-backend log file in a with-statement so its parent fd
is released deterministically at function return. The child still
holds its own dup'd fd via Popen, so logging continues unchanged.
The prior code relied on local-scope GC and was fine in CPython,
but the with form makes the intent explicit.
Smoke still passes locally: 4 parallel installs in 42s, 4 backends
healthy in 5s, all install + runtime invariants hold.
* tests/studio/install: pin UNSLOTH_STUDIO_HOME on backend launch
The launch step copied os.environ unchanged except for HOME. If the
parent shell already exports UNSLOTH_STUDIO_HOME or STUDIO_HOME (for
example, when the developer is sourcing studio.conf from an existing
install), every backend inherits it and the Studio resolver prioritises
those env vars over the per-label sys.prefix inference. The runtime
invariant block then reports the caller's install_id on every port
instead of the per-label one, and the test fails spuriously rather
than testing the right roots.
Pin UNSLOTH_STUDIO_HOME to the per-label studio_home and pop the
STUDIO_HOME alias for each launch, mirroring what _run_one_install
already does for the install step.
Verified by running the smoke with UNSLOTH_STUDIO_HOME=/nonexistent
and STUDIO_HOME=/also-bogus exported in the parent env: PASS, all four
backends report their own install_id rather than the parent value.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add Studio PR-time CI: pin enforcement, frontend, backend, wheel smoke
The repo currently has no PR-time CI; only release-desktop.yml (manual) and
stale.yml (issue pinger). studio/backend/tests/ has 35 test files (~860
tests collected) that never run automatically. Frontend lint/typecheck/build
scripts exist in package.json but are not gated on PRs either. This is the
gap that let 2026.5.1 ship with the broken Studio chat-history bundle.
Adds four ubuntu-latest workflows, all CPU-only and free for public repos:
studio-pin-enforce.yml
Greps studio/frontend/package.json for caret/tilde ranges on the
@assistant-ui surface (and assistant-stream). Blocks the exact regression
vector that produced 2026.5.1 (^0.12.19 resolving to a breaking 0.12.28).
studio-frontend-ci.yml
npm ci (strict lockfile), tree-clean check after, typecheck, vite build,
bundle grep for the Studio unstable_Provider call site (<= 3 hits = OK,
>= 4 = the 2026.5.1 regression), 75 MB dist budget, biome non-blocking.
Uploads dist on failure.
studio-backend-ci.yml
Runs the existing studio/backend/tests/ suite on Python 3.10/3.11/3.12.
Excludes test_studio_api.py (live model + GGUF download) and
llama_cpp_load_progress_live (spawns a real llama.cpp). Local run on this
branch: 861 pass, 4 skipped, 5 deselected. ruff non-blocking.
wheel-smoke.yml
python -m build, then verifies the produced wheel:
- ships studio/frontend/package-lock.json
- ships studio/frontend/dist/index.html
- does NOT ship studio/frontend/node_modules/
- does NOT ship studio/frontend/bun.lock
- main JS bundle has < 4 unstable_Provider hits
Then installs the wheel into a fresh venv with a lightweight dep set and
imports studio.backend.main. Locally validated against the wheel built
from this branch.
Each workflow has concurrency cancellation on the same ref. biome and ruff
are gated as non-blocking until the existing accumulated drift is cleared
(~470 biome errors today); remove the bypass in a follow-up.
Notes verified locally:
- pin enforcement: PASS (carets dropped on this branch)
- frontend npm ci -> typecheck -> build -> grep -> budget: PASS
- bundle: 48 MB, hits=1
- backend pytest: 861 pass, 1 GPU-pollution failure not reproducible on
GPU-less runners (won't reproduce on ubuntu-latest)
- wheel build: 13s, produces unsloth-2026.5.2-py3-none-any.whl
- wheel content sanity: all five checks PASS
* CI: install full backend dep set + refine pytest filter for CPU runners
First CI run on PR #5298 surfaced two real gaps:
1. pytest collection failed at `import yaml` in utils/models/model_config.
Locally my workspace venv had pyyaml from a transitive; CI's clean Python
3.10/3.11/3.12 didn't, so collection hit ModuleNotFoundError on the very
first test module. Same blew up the wheel-smoke `from studio.backend.main
import app` step.
2. Once the import chain was complete, ~9 tests still failed because they
exercise GPU-only paths or live transformers introspection that can't run
on a GPU-less `ubuntu-latest` runner regardless of code correctness:
- TestGpuAutoSelection
- TestPreSpawnGpuResolution
- TestPerGpuFitGuardAllCounts
- TestTransformersIntrospection
- test_returns_cuda_when_cuda_available
- test_calls_cuda_cache_when_cuda
Fix:
- Backend CI installs `studio/backend/requirements/studio.txt` (the
declared backend dep set) + the extras the import chain needs but
studio.txt omits (python-multipart, sqlalchemy, cryptography, pyyaml,
jinja2, mammoth, unpdf, requests, etc.) + torch CPU wheel + transformers.
- Refine the pytest -k filter to deselect the GPU/introspection-bound
classes by name. Deselections are commented inline with the reason.
- wheel-smoke uses the same dep set so the import smoke matches.
Locally validated against the freshly-built unsloth-2026.5.2 wheel:
831 passed, 5 skipped, 35 deselected, 0 failed in 47s
Studio backend imports cleanly in a fresh venv after the wheel install.
* CI: collapse multiline pytest -k expression to a single line
YAML's | block-scalar fed the newlines verbatim into the -k argument and
pytest rejected it as 'Wrong expression passed to -k'. Same logical filter
on one line.
* CI: rename jobs so the GitHub UI shows what each check actually does
Adds a per-job 'name:' to all four workflows so the PR check list reads:
Studio pin enforcement / @assistant-ui must be pinned exactly
Studio frontend CI / Frontend build + bundle sanity
Studio backend CI / Backend pytest (Python 3.10|3.11|3.12)
Studio backend CI / Backend ruff lint (non-blocking)
Wheel build + smoke / Wheel build + content sanity + import smoke
Instead of the default '<workflow> / <job-key>' which was opaque
('check', 'build', 'pytest (3.10)', 'ruff', 'wheel').
* CI: add Python 3.13 to backend pytest matrix
Verified locally: 831 backend tests pass under Python 3.13 with the same
filter set used for 3.10 / 3.11 / 3.12.
* CI: add Studio inference smoke + Tauri build smoke
Two new workflows. Both CPU-only, both free on `ubuntu-latest`.
studio-inference-smoke.yml
The only workflow we have that proves "Studio actually works", as opposed
to "the bundle parses" or "the imports succeed":
- runs install.sh --local --no-torch (lean Studio install)
- downloads unsloth/gemma-4-E2B-it-GGUF UD-IQ3_XXS into actions/cache
- boots Studio in api-only mode
- logs in with the bootstrap password, changes it, re-logs
- POST /api/inference/load on the GGUF
- POST /api/inference/chat/completions and asserts a non-empty
assistant response
Validated end-to-end locally on a fresh main install: model loaded,
chat completion returned `Hello!` against the same GGUF the workflow
uses.
studio-tauri-smoke.yml
PR-time variant of release-desktop.yml. Linux-only debug build
(`tauri build --debug --no-bundle`) on ubuntu-22.04. Catches
src-tauri Cargo.toml / Rust source breakage, tauri.conf.json drift,
and frontend-distDir wiring. Pinned to the same Tauri CLI version
(2.10.1) as release-desktop.yml so CLI bumps surface in CI before
they break the release pipeline. Mac and Windows desktop builds
stay manual via release-desktop.yml because they need code-signing
secrets.
* CI: use 'hf download' instead of deprecated 'huggingface-cli download'
huggingface_hub 1.13.0 dropped the huggingface-cli entrypoint. The
replacement is the 'hf' CLI shipped with the same package. Same args,
just s/huggingface-cli/hf/.
* CI: assert llama.cpp prebuilt path was used on ubuntu-latest
The inference-smoke job runs on ubuntu-latest (CPU-only, x86_64), which
is exactly the host shape that should pick up ggml-org/llama.cpp's
bin-ubuntu-x64.tar.gz prebuilt directly. If install.sh ever falls back
to a source build on this runner, the studio/setup.sh routing has
regressed and every CPU-only Linux user is paying a 3 minute compile
cost again.
Tee install.sh output to logs/install.log, then fail the job if the log
contains "falling back to source build" or is missing the success
marker "prebuilt installed and validated" / "prebuilt up to date and
validated".
Also include logs/install.log in the failure artifact so the prebuilt
diagnostics are uploaded alongside studio.log when the job fails.
* Tighten prebuilt-assertion comment in studio-inference-smoke
* CI: switch inference-smoke model to Qwen3.5-2B UD-IQ3_XXS
Drops the Gemma 4 E2B GGUF (~2.3 GB) for unsloth/Qwen3.5-2B-GGUF
(UD-IQ3_XXS, ~890 MiB). Cache-miss download is roughly a third of
what it was, and CPU inference on ubuntu-latest finishes well
inside the 25 minute job budget.
Verified locally: load via /api/inference/load returns
status=loaded, is_gguf=true, supports_reasoning=true,
supports_tools=true; chat completion returns a non-empty assistant
message ("Hello!").
* CI: add workflow_dispatch to inference-smoke for manual cache pre-warm
* CI: fold pin-enforce grep into studio-frontend-ci, drop standalone workflow
The "@assistant-ui must be pinned exactly" check was its own ~7 second
workflow, doing a single grep on studio/frontend/package.json. Move it
into studio-frontend-ci.yml as a pre-install step (right after
checkout, before any node setup so a violation fails fast). One fewer
top-level check row on every PR, same coverage.
Add a FIXME so this step is dropped once @assistant-ui/* and
assistant-stream leave 0.x: on 1.x, caret ranges are conventional and
this becomes overzealous.
* CI: add Repo tests (CPU) job, mirroring unsloth-zoo PR #624 conftest
The top-level tests/ tree was previously not run anywhere. 23 of its
files are CPU-friendly with the right harness: pure-Python helpers,
ast walks, installer logic, and CLI shape tests. Locally validated:
302 passed, 9 skipped, 12 deselected in ~7 seconds on Python 3.12.
Three pieces:
1. tests/conftest.py -- GPU-free harness, mirrors the conftest landed
in unslothai/unsloth-zoo PR #624. Pre-loads unsloth_zoo.device_type
and unsloth.device_type under a temporarily-mocked
torch.cuda.is_available() so each module's @cache permanently
captures "cuda" and the import chain succeeds on a CPU runner.
Also stubs torch.cuda.get_device_capability /
is_bf16_supported / mem_get_info, which unsloth/__init__.py and
unsloth_zoo.temporary_patches probe at import time when
DEVICE_TYPE == "cuda". On a real accelerator the harness is
skipped and detection runs normally.
2. Two existing tests were leaking sys.modules state across the
session because they injected stubs without an __spec__ and
without restoration:
- tests/test_raw_text.py shoved a "datasets" stub into
sys.modules. transformers' import_utils later did
importlib.util.find_spec("datasets") and got
ValueError: datasets.__spec__ is None.
- tests/python/test_fast_sentence_transformer_redirect_lifecycle.py
shoved "transformers", "sentence_transformers", and
"sentence_transformers.models" stubs in. Subsequent tests
that did `import transformers` got the non-package stub.
Fix: set __spec__ on stubs, plus an autouse fixture in the
sentence-transformer test file that restores the three keys
after each test.
3. .github/workflows/studio-backend-ci.yml gains a third job,
`Repo tests (CPU)`, that installs the same dep set as the
backend-pytest matrix (Python 3.12 only -- the tests are
version-independent), exports PYTHONPATH=studio so tests/python/*
can import install_python_stack, and runs the 23-file subset
above with `-m 'not server and not e2e'`.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CI: install unsloth_zoo for Repo CPU tests, harden conftest fallback
The CPU job at run 25422050018 broke at conftest collection: the
preload of unsloth.device_type pulled in `from unsloth_zoo.utils import
Version` and ubuntu-latest didn't have unsloth_zoo on the path because
it is an optional dep of unsloth. Two fixes:
1. Install unsloth_zoo>=2026.5.1 alongside the other deps in the Repo
tests (CPU) job (it's also what unsloth's optional `huggingface`
extra pins).
2. Wrap the body of _preload_device_type in conftest.py in a try/except
so any import failure (missing prereq, broken module, etc.) cleanly
returns False instead of aborting the entire collection. The caller
already falls back to the stub device_type module on False, so the
net behavior is "best effort: real device_type if possible, stub
otherwise" instead of "abort the test session".
* kernels.utils: guard CUDA_STREAMS / XPU_STREAMS init for DEVICE_COUNT==0
When DEVICE_COUNT is 0 (CPU host: no visible NVIDIA / AMD / Intel GPU)
the dict comprehension {... for i in range(0)} was empty and the
subsequent max(_CUDA_STREAMS.keys()) raised
ValueError: max() iterable argument is empty
during module import. That made unsloth.kernels.utils unimportable on
any CPU runner, which in turn blocked all of tests/saving/**, three
top-level tests/test_*.py, and tests/qlora/test_unsloth_qlora_train_and_merge.py
from even collecting on CPU CI.
Wrap the per-device-index dict comprehension and max() machinery in
a DEVICE_COUNT > 0 guard. When DEVICE_COUNT is 0 fall back to empty
containers (CUDA_STREAMS = (), WEIGHT_BUFFERS = [], ABSMAX_BUFFERS = []).
The consumer functions further down in this module index these arrays
by device_index but only during real GPU work, so the empty fallbacks
never get touched on a CPU host.
GPU-safety verified locally: with 8 visible CUDA devices, CUDA_STREAMS
has 8 entries (identical to before this PR). With CUDA_VISIBLE_DEVICES=""
the module imports cleanly, CUDA_STREAMS is (), and the previously
blocked tests now collect (test_get_model_name passes 38 subtests,
test_resolve_model_class passes 9, test_model_registry collects all 8
parametrizations).
Same shape applied to the DEVICE_TYPE == "xpu" branch for symmetry.
* CI: switch Repo tests (CPU) to auto-discovery + isolate flakes
Three changes, locally validated end-to-end (779 passed, 11 skipped,
23 deselected, 0 failed across all three steps):
1. Repo tests (CPU, auto-discovered): replace the explicit 23-file
list with `pytest tests/` plus a small set of `--ignore` and
`--deselect` flags. New tests under tests/python, tests/studio
(excluding the two state-sensitive files), and top-level
tests/test_*.py are picked up automatically with no workflow edit.
--ignore covers:
- tests/qlora and tests/saving: GPU-bound by design
- tests/utils: helpers folder, not tests
- tests/sh: shell suite handled in its own step
- two state-polluting hardware-spoof files (next step)
-m 'not server and not e2e': honours markers already declared
in tests/python/conftest.py
--deselect: test_model_registration / test_all_model_registration
hit huggingface_hub live; they belong on a network job
2. Hardware-spoof tests (state-sensitive, run in isolation):
tests/studio/test_hardware_dispatch_matrix.py and
tests/studio/test_is_mlx_dispatch_gate.py mutate module globals
in studio.backend.utils.hardware.hardware (IS_ROCM, DEVICE) via
their spoof fixtures, and the leak crosses file boundaries.
Running them in their own pytest invocation avoids polluting the
main sweep. Both pass cleanly in isolation: 28 passed, 1 skipped.
3. Shell installer tests: explicitly enumerated subset that does not
depend on install.ps1 layout (test_install_host_defaults.sh has
drifted; that's a separate followup).
Test fixes folded in to keep the run green:
- tests/studio/install/test_rocm_support.py::TestAmdGpuMonitoring
::test_amd_primary_gpu_with_mock now clears
HIP/ROCR/CUDA_VISIBLE_DEVICES via monkeypatch so
_first_visible_amd_gpu_id() does not short-circuit when the runner
sets CUDA_VISIBLE_DEVICES="" to suppress CUDA.
- tests/studio/test_hardware_dispatch_matrix.py::spoof_hardware
fixture now stubs torch.cuda.get_device_properties when
cuda_available is True so detect_hardware()'s device_name probe
does not call into _cuda_init() on a CPU runner.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CI: install torchvision (CPU) so unsloth_zoo.vision_utils can import
Run 25430652224 collected three test modules that import unsloth and
crashed at unsloth_zoo/vision_utils.py:68 with
ModuleNotFoundError: No module named 'torchvision'
unsloth_zoo.vision_utils unconditionally imports torchvision at module
scope, and unsloth.models._utils pulls vision_utils in. The Repo tests
(CPU) job installed torch from the CPU index but not torchvision, so
any test that imports unsloth.models.* failed at collection.
Add torchvision<0.26 to the same pip install --index-url
https://download.pytorch.org/whl/cpu line.
* CI: install bitsandbytes (CPU build) for unsloth.models._utils import
Run 25430982243 collected three test modules that import unsloth and
crashed at unsloth/models/_utils.py:1166 with
ModuleNotFoundError: No module named 'bitsandbytes'
The bnb import there is unconditional. Recent bnb versions (>=0.45)
ship a CPU build so the wheel installs on a free Linux runner and the
import resolves; the kernels still raise on use but the module
collects, which is enough for these CPU tests.
Add 'bitsandbytes>=0.45' to the Repo tests (CPU) deps.
* CI: rename workflows + guard kernels.utils CPU-torch binding
Workflow renames (top-level `name:` keys; affects PR check rows):
Studio backend CI -> Backend CI
Studio frontend CI -> Frontend CI
Studio inference smoke -> Studio GGUF CI
Studio Tauri smoke -> Studio Tauri CI
Wheel build + smoke -> Wheel CI
Backend CI's matrix job goes from "Backend pytest (Python 3.10)" to
just "(Python 3.10)" so the GitHub UI row reads
"Backend CI / (Python 3.10)" rather than the old verbose form.
Production guard for CPU torch (run 25431126138):
unsloth/kernels/utils.py:165 was an unconditional
_gpu_getCurrentRawStream = torch._C._cuda_getCurrentRawStream
which raised AttributeError on a CPU-only torch wheel because the
compiled CUDA backend is absent. Three test modules (test_get_model_name,
test_model_registry, test_resolve_model_class) crashed at collection
because their import chain reaches this line.
Add a hasattr probe: when torch is built without CUDA, fall through to
a no-op binding that returns 0. _get_tensor_stream is only invoked
during real GPU work, so the no-op is never executed on a CPU host.
GPU-safety verified locally: with 8 visible CUDA devices the binding
still resolves to the real torch._C._cuda_getCurrentRawStream
(behaviour identical to before this PR). The XPU branch is untouched.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix 14 stale tests under tests/studio/install/ that drifted from code
All 14 failures audited locally and tracked back to test-side drift
(no production-code regressions). After these test updates the entire
tests/studio/install/ directory now passes: 346 passed, 1 skipped.
Per failure:
tests/studio/install/test_install_llama_prebuilt_logic.py (5 fails):
* test_existing_install_matches_plan_with_fingerprint_linux
* test_install_prebuilt_skips_download_when_existing_install_matches
* test_install_prebuilt_skips_when_older_release_fallback_matches_existing_install
* test_install_prebuilt_skips_same_release_fallback_attempt_when_installed
* test_existing_install_matches_choice_fails_when_install_tree_incomplete
All five build a fake Linux install tree via write_linux_install_shape
and call existing_install_matches_choice. The matcher returns False
because runtime_payload_is_healthy now requires a libllama-common.so*
library in build/bin/ (added by PR #5135), and the fixture never wrote
it. Add the missing library to write_linux_install_shape; matcher
passes for all five tests.
tests/studio/install/test_rocm_support.py (8 fails after the partial
audit, one collection-tier flake):
* TestEnsureRocmTorch::test_cpu_torch_gets_rocm_reinstall and
TestEnsureRocmTorch::test_probe_timeout_triggers_reinstall
_ensure_rocm_torch was refactored to call pip_install for the
torch reinstall and pip_install_try (not pip_install) for the
follow-up bitsandbytes install. The tests still asserted
mock_pip.call_count == 2. Add a second @patch.object on
pip_install_try and split the assertions across the two mocks.
* TestInstallShStructure::test_cuda_precedence
Asserted file-position-of-string ordering: looked for
`if [ -z "$_smi" ]` before the first `amd-smi` literal in
install.sh. The installer now defines top-level helpers
`_has_amd_rocm_gpu` (uses `amd-smi`) and `_has_usable_nvidia_gpu`
(uses `nvidia-smi`) before either is called from
`get_torch_index_url`, so file-position ordering carries no
semantic meaning. Rewrite the test to extract the
`get_torch_index_url` body via a small brace-matched helper and
assert the runtime ordering: NVIDIA call sits before the
`if [ -z "$_smi" ]` branch and the AMD call sits inside it.
* TestLiveRegression::test_get_torch_index_url_returns_cuda_on_nvidia
Sed-extracted only get_torch_index_url and eval'd it -- but the
function calls _has_amd_rocm_gpu and _has_usable_nvidia_gpu, so
the eval'd body crashed and fell through to the CPU URL on a
fully-loaded NVIDIA host. Extract the helpers alongside the
function. Also pre-skip when nvidia-smi is on PATH but does not
list a GPU (containers occasionally ship the binary without a
driver).
* TestWorkerRocmMambaSsm::test_probe_script_has_getattr_hip and
TestWorkerRocmMambaSsm::test_probe_returns_hip_version_field
The wheel-resolver probe subprocess (the only place where
`getattr(torch.version, 'hip', None)` is emitted) was hoisted out
of worker.py into studio/backend/utils/wheel_utils.py during the
wheel-resolver refactor. Point the file-content assertions at
wheel_utils.py and assert worker.py still consumes the
`hip_version` field.
* TestHardwareAmdBranching::test_hardware_branches_on_is_rocm_for_utilization
TestHardwareAmdBranching::test_hardware_branches_on_is_rocm_for_visible
TestHardwareAmdBranching::test_hardware_branches_on_is_rocm_for_physical_count
hardware.py refactored: the IS_ROCM branch and direct
`from . import amd` were hoisted out of get_gpu_utilization /
get_visible_gpu_utilization into the shared `_smi_query`
dispatcher. Update the first two tests to assert the dispatcher
call shape (`_smi_query("get_primary_gpu_utilization", ...)` etc.)
plus IS_ROCM + amd-import in `_smi_query` itself. Update the
physical-count test to assert IS_ROCM + the literal `from . import
amd` as that function still imports amd directly rather than going
through `_smi_query`.
No production-code changes; tests-only.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix windows llama.cpp compile from source issue
* undo local repo usage
* fix llama.cpp install
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix windows
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: route resolve-source-build call through Invoke-LlamaHelper
The --resolve-source-build call at the source-build resolution path
was still calling install_llama_prebuilt.py directly instead of going
through Invoke-LlamaHelper. On PS7+ with ErrorActionPreference=Stop,
stderr from the 422 response (when tag is "master") would trigger a
terminating NativeCommandError and crash setup.
* fix: suppress stderr error records from Invoke-LlamaHelper
ErrorActionPreference=Continue prevents termination but PowerShell
still displays stderr lines as visible ErrorRecord objects. Capture
all output via 2>&1 and split stdout from stderr manually so that
stderr lines never appear on the console. When StderrPath is given
the stderr content is written to that file for diagnostics.
* fix: always rebuild llama.cpp on Windows when tag is master
When the requested llama.cpp tag is "master" (a moving target), skip
the "already built" early exit so the build path runs and syncs to
the latest commit. Without this, existing llama-server binaries from
an older build (e.g. b8635 which lacks Gemma 4 support) are reused
and model loading fails.
Pinned tags (e.g. b8635) still skip the rebuild when the binary
already exists, since the tag is immutable.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Expand test coverage for install_llama_prebuilt.py:
- Add tests for source build plan resolution with custom repos
- Add tests for branch/commit/PR ref matching and normalization
- Add tests for manifest checksum validation
- Add tests for Windows CUDA upstream asset name patterns
- Update capsys checks to capture stderr after log() redirect
* Fix script unbound variable error
* remove stale test script, add llama.cpp metal source builds, update tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix Metal precedence, test sync, and add behavioral tests
- Move macOS arm64 Metal check before CUDA/ROCm in GPU backend
decision chain so Metal is not bypassed when nvcc is in PATH
- Remove RPATH flags from CPU fallback CMAKE_ARGS (only needed
for Metal library linking)
- Update test_llama_pr_force_and_source.py to match _CLONE_ARGS
rename from _CLONE_BRANCH_ARGS in setup.sh
- Add confirm_install_tree guard test for
existing_install_matches_choice
- Add TestMacOSMetalBuildLogic bash subprocess tests verifying
Metal flag selection, nvcc precedence, and CPU fallback behavior
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix Metal CPU fallback to also cover cmake build failures and update tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* 1. _GPU_BACKEND_FRAGMENT synced -- removed dead CPU_FALLBACK_CMAKE_ARGS= init (6/8)
2. RPATH assertion replaced -- new test_macos_arm64_cpu_fallback_args_exclude_rpath checks the actual runtime CPU_FALLBACK_CMAKE_ARGS output for @loader_path and -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON (6/8)
3. _TRY_METAL_CPU_FALLBACK=false reset after both configure-failure and build-failure fallback branches in setup.sh (4/8)
4. macOS test now removes libmtmd.0.dylib instead of the platform-agnostic convert_hf_to_gguf.py (3/8)
5. Empty-string tag test added -- test_empty_tag_omits_branch_flag for resolved_tag= (2/8)
6. RPATH checks on cmake call logs -- both fallback tests now assert @loader_path and -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON are absent from CPU fallback cmake calls, plus baseline flag preservation (multiple)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests clean up
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Split out from #4741 to keep the main PR focused on installer logic.
- New test_install_llama_prebuilt_logic.py: tests for resolve logic,
fallback behavior, env_int, busy/lock handling
- New test_validate_llama_prebuilt.py: validator tests for staged
release_tag/upstream_tag handling
- New test_llama_pr_force_and_source.py: tests for PR_FORCE and
LLAMA_SOURCE maintainer defaults
- Updated test_selection_logic.py: expanded selection/fallback coverage
- Updated test_pr4562_bugfixes.py: updated bugfix tests for new logic
- Updated smoke_test_llama_prebuilt.py: minor update
* Use prebuilt llama.cpp for unsloth studio setup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix 3 issues that cause unnecessary fallback to source build
1. Make filelock import optional -- environments without filelock
(e.g. minimal installs) crashed at import time instead of
gracefully skipping the lock.
2. Use already-verified converter script from the hydrated source
tree instead of re-downloading from raw.githubusercontent.com
with no checksum. Adds symlink with copy fallback for the
legacy filename.
3. Initialize $SkipPrebuiltInstall in setup.ps1 before first use
to prevent potential uninitialized variable errors.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Keep network fallback in ensure_converter_scripts
Prefer the local verified copy from the hydrated source tree, but
retain the original network download as a fallback if the file is
missing. Create the legacy hyphenated filename as a symlink with a
copy fallback instead of writing a second full copy.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix 4 bugs in source-build fallback and binary_env paths
- setup.ps1: Replace git pull + checkout FETCH_HEAD with fetch + checkout -B
to avoid detached HEAD state that breaks re-runs. Use pinned tag in both
fetch and clone paths.
- setup.sh: Move rm -rf after cmake/git prerequisite checks so a missing
tool no longer deletes the existing install. Add --branch tag to clone.
- install_llama_prebuilt.py: Add binary_path.parent to Linux LD_LIBRARY_PATH
in binary_env() so bundled .so files in build/bin are found even without
RPATH, matching the existing Windows PATH logic.
- Add test for binary_env LD_LIBRARY_PATH on Linux.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Handle unresolved "latest" tag in source-build fallback clone
When tag resolution fails and the requested tag is "latest", both
setup scripts now omit --branch from git clone so the default branch
is cloned instead of failing on a nonexistent "latest" branch/tag.
Similarly, the PS1 fetch path fetches the default ref when the tag
is "latest".
* Resolve actual latest ggml-org tag instead of using literal "latest"
When both Python tag resolution attempts fail and the requested tag
is "latest", query the GitHub API for the actual latest release tag
from ggml-org/llama.cpp (e.g. b8508) instead of passing the literal
string "latest" to git clone --branch, which would fail since no
such branch/tag exists.
setup.sh uses curl + python json parsing; setup.ps1 uses
Invoke-RestMethod. Both fall back to the raw requested tag if the
API call also fails.
* Try Unsloth release repo before ggml-org when resolving latest tag
When falling back to the GitHub API to resolve "latest", query the
Unsloth release repo (unslothai/llama.cpp) first since it has the
prebuilt binaries pinned to tested tags. Only fall back to
ggml-org/llama.cpp if the Unsloth repo query fails.
* Add comprehensive sandbox tests for PR #4562 bug fixes
35 tests covering all fixes across platforms:
- binary_env cross-platform (Linux LD_LIBRARY_PATH, Windows PATH,
macOS DYLD_LIBRARY_PATH) with edge cases (dedup, ordering, existing paths)
- resolve_requested_llama_tag (concrete, latest, None, empty)
- setup.sh logic via subprocess: prereq check ordering (cmake/git missing
preserves install), pinned tag in clone, fetch+checkout -B pattern,
fetch failure warns instead of aborting
- "latest" tag resolution fallback chain (Unsloth API -> ggml-org ->
raw) with mock curl: success, failure, malformed JSON, empty body,
empty tag_name, env overrides
- Source code pattern verification for both .sh and .ps1 files
All 138 tests pass in isolated uv venv.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add binary_path.parent to macOS DYLD_LIBRARY_PATH in binary_env
macOS prebuilt .dylib files are overlaid into build/bin (same as
Linux), but binary_env only added install_dir to DYLD_LIBRARY_PATH.
Add binary_path.parent so the loader can find sibling dylibs even
without embedded loader paths.
Mirrors the existing fix for Linux LD_LIBRARY_PATH and the Windows
PATH pattern.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Guard --branch when resolved tag is "latest"; fix broken test assertion
When all API fallbacks fail and the tag stays as literal "latest",
omit --branch from git clone (clones default branch instead of
failing). Both setup.sh and setup.ps1 now check for "latest" before
passing --branch to git clone/fetch.
Also fix test_setup_ps1_clone_uses_branch_tag which used Python
tuple syntax (assert "x", "y" in z) that always passes. Changed to
assert "x" in z and "y" in z.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix macOS DYLD trailing colon, install_lock no-op, and debug log
- binary_env macOS: use dedupe_existing_dirs instead of raw string
concatenation. Eliminates trailing colon in DYLD_LIBRARY_PATH
(which causes dyld to search CWD for libraries) and deduplicates
when binary_path.parent == install_dir. Now consistent with the
Linux and Windows branches.
- install_lock: when filelock is not installed, use os.O_CREAT|O_EXCL
as a fallback exclusive file lock with timeout, instead of yielding
with no locking. Prevents concurrent installs from corrupting each
other's staging directories.
- setup.ps1: remove [DEBUG] log line that printed to every user on
every Windows setup run.
* Add stale-lock detection and atomic clone-then-swap
install_lock fallback (no filelock): write PID to lock file and
check if the holder process is still alive on contention. Dead PIDs
(ProcessLookupError) and unreadable lock files trigger immediate
cleanup. Live processes owned by other users (PermissionError) are
correctly recognized as alive -- the lock is not removed.
setup.sh/setup.ps1 source-build: clone into a temporary directory
first, then swap into place only on success. If git clone fails,
the existing install is preserved instead of being deleted by the
premature rm -rf.
* Remove redundant upstream_tag != release_tag check
load_approved_release_checksums compared checksums.upstream_tag
against the Unsloth release_tag, which are different namespaces
(upstream ggml-org tag vs Unsloth published tag). This only worked
because both happened to be "b8508" by convention. Would break if
Unsloth ever uses a different release naming scheme.
The existing check at parse_approved_release_checksums (line 950)
already validates the release_tag field correctly.
* Fix lock TOCTOU race and build-in-temp-dir swap
install_lock fallback: add os.fsync(fd) after writing PID to ensure
the PID is visible to racing processes before they check. Treat
empty lock files (PID not yet written) as "wait and retry" instead
of stale, closing the window where two processes could both see an
empty file, both unlink it, and both acquire the lock.
setup.sh/setup.ps1 source-build: clone AND build in a temp directory
(LLAMA_CPP_DIR.build.$$). Only swap into the final LLAMA_CPP_DIR
after the build succeeds. If clone or cmake or build fails, the temp
dir is cleaned up and the existing working install is preserved.
Previously, rm -rf ran after clone but before build, destroying the
existing install even if the build later failed.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Daniel Han