unsloth/studio/setup.sh
Leo Borcherding b6d5636cc0
fix/strix halo and windows AMD ROCm support (#5301)
* 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.

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* 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

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* 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.

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* 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)

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* 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.

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* 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).

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* 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.

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* 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

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* 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

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* fix: suppress remaining console popups on Windows, patch torch.distributed.is_initialized for ROCm #5301

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* fix: stub all missing torch.distributed attrs for ROCm Windows wheel #5301

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* fix: inject torch.distributed stub when C backend missing in ROCm Windows wheel #5301

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* 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.

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* 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

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* 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

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* 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)

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* 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.

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* 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.

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* 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

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* 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

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* 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

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* 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).

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* 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.

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* 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).

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* 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.

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* 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

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* 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.

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* 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.

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* 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.

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* 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.

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* 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.

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* 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>
2026-05-29 22:29:56 -07:00

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#!/usr/bin/env bash
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
RULE=$(printf '\342\224\200%.0s' {1..52})
# ── Parse flags ──
# --local: install from the local repo checkout (overlays unsloth as editable
# and unsloth-zoo from git main). Mirrors install.sh --local for the Colab
# path that runs setup.sh directly without going through install.sh.
if [ "$#" -gt 0 ]; then
for _arg in "$@"; do
case "$_arg" in
--local)
export STUDIO_LOCAL_INSTALL=1
export STUDIO_LOCAL_REPO="$REPO_ROOT"
;;
esac
done
fi
# ── Maintainer-editable defaults ──────────────────────────────────────────
# Change these in the GitHub-hosted script so all users get updated defaults.
# User environment variables always override these baked-in values.
#
# _DEFAULT_LLAMA_PR_FORCE : PR number to build by default ("" = normal path)
# _DEFAULT_LLAMA_SOURCE : git clone URL for source builds
# _DEFAULT_LLAMA_TAG : llama.cpp ref to build ("latest" = newest release,
# "master" = bleeding-edge, "bNNNN" = specific tag)
# Prefer "latest" over "master" -- "master" bypasses
# the prebuilt resolver (no matching GitHub release),
# forces a source build, and causes HTTP 422 errors.
# Only use "master" temporarily when the latest release
# is missing support for a new model architecture.
# ──────────────────────────────────────────────────────────────────────────
_DEFAULT_LLAMA_PR_FORCE=""
_DEFAULT_LLAMA_SOURCE="https://github.com/ggml-org/llama.cpp"
_DEFAULT_LLAMA_TAG="latest"
_DEFAULT_LLAMA_FORCE_COMPILE_REF="master"
# ── Colors (same palette as startup_banner / install_python_stack) ──
if [ -n "${NO_COLOR:-}" ]; then
C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
elif [ -t 1 ] || [ -n "${FORCE_COLOR:-}" ]; then
C_TITLE=$'\033[38;5;150m'
C_DIM=$'\033[38;5;245m'
C_OK=$'\033[38;5;108m'
C_WARN=$'\033[38;5;136m'
C_ERR=$'\033[91m'
C_RST=$'\033[0m'
else
C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
fi
# ── Output helpers ──
# Consistent column layout: 2-space indent, 15-char label (fits llama-quantize), then value.
# Usage: step <label> <message> [color] (color defaults to C_OK)
step() { printf " ${C_DIM}%-15.15s${C_RST}${3:-$C_OK}%s${C_RST}\n" "$1" "$2"; }
substep() { printf " ${C_DIM}%-15s%s${C_RST}\n" "" "$1"; }
_is_verbose() {
[ "${UNSLOTH_VERBOSE:-0}" = "1" ]
}
verbose_substep() {
if _is_verbose; then
substep "$1"
fi
return 0
}
run_maybe_quiet() {
if _is_verbose; then
"$@"
else
"$@" > /dev/null 2>&1
fi
}
# ── Helper: run command quietly, show output only on failure ──
_run_quiet() {
local on_fail=$1
local label=$2
shift 2
if _is_verbose; then
local exit_code
"$@" && return 0
exit_code=$?
step "error" "$label failed (exit code $exit_code)" "$C_ERR" >&2
if [ "$on_fail" = "exit" ]; then
exit "$exit_code"
else
return "$exit_code"
fi
fi
local tmplog
tmplog=$(mktemp) || {
step "error" "Failed to create temporary file" "$C_ERR" >&2
[ "$on_fail" = "exit" ] && exit 1 || return 1
}
if "$@" >"$tmplog" 2>&1; then
rm -f "$tmplog"
return 0
else
local exit_code=$?
step "error" "$label failed (exit code $exit_code)" "$C_ERR" >&2
cat "$tmplog" >&2
rm -f "$tmplog"
if [ "$on_fail" = "exit" ]; then
exit "$exit_code"
else
return "$exit_code"
fi
fi
}
run_quiet() {
_run_quiet exit "$@"
}
run_quiet_no_exit() {
_run_quiet return "$@"
}
_nvcc_meets_llama_minimum() {
# Echo "ok|too_old|unknown" then the parsed "X.Y" version, one per line.
# llama.cpp needs CUDA toolkit >= 12.4 (#4437; setup.ps1 aborts via #4517).
_nvcc_bin=$1
[ -n "$_nvcc_bin" ] || { echo "unknown"; echo ""; return 0; }
_raw=$("$_nvcc_bin" --version 2>/dev/null \
| sed -n 's/.*release \([0-9][0-9]*\.[0-9][0-9]*\).*/\1/p' \
| head -1)
if [ -z "$_raw" ]; then
echo "unknown"; echo ""; return 0
fi
_maj=${_raw%%.*}
_min_raw=${_raw#*.}
_min=${_min_raw%%.*}
if [ "$_maj" -lt 12 ] 2>/dev/null; then
echo "too_old"
elif [ "$_maj" -eq 12 ] && [ "$_min" -lt 4 ] 2>/dev/null; then
echo "too_old"
else
echo "ok"
fi
echo "$_raw"
}
print_llama_error_log() {
local log_file=$1
[ -s "$log_file" ] || return 0
substep "llama.cpp diagnostics (last 120 lines):"
tail -n 120 "$log_file" | sed 's/^/ | /' >&2
}
installed_llama_prebuilt_release() {
local install_dir=${1:-}
local metadata_path="$install_dir/UNSLOTH_PREBUILT_INFO.json"
[ -f "$metadata_path" ] || return 0
python - "$metadata_path" <<'PY' 2>/dev/null || true
import json
import sys
from pathlib import Path
try:
payload = json.loads(Path(sys.argv[1]).read_text(encoding="utf-8"))
except Exception:
raise SystemExit(0)
if not isinstance(payload, dict):
raise SystemExit(0)
repo = str(payload.get("published_repo") or "").strip()
release_tag = str(payload.get("release_tag") or "").strip()
llama_tag = str(payload.get("tag") or "").strip()
if not repo or not release_tag:
raise SystemExit(0)
message = f"installed release: {repo}@{release_tag}"
if llama_tag and llama_tag != release_tag:
message += f" (tag {llama_tag})"
print(message)
PY
}
print_installed_llama_prebuilt_release() {
local install_dir=${1:-}
local installed_release
installed_release="$(installed_llama_prebuilt_release "$install_dir")"
if [ -n "$installed_release" ]; then
substep "$installed_release"
fi
}
# ── Banner ──
echo ""
printf " ${C_TITLE}%s${C_RST}\n" "🦥 Unsloth Studio Setup"
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
verbose_substep "verbose diagnostics enabled"
_LLAMA_ONLY="${UNSLOTH_STUDIO_LLAMA_ONLY:-0}"
if [ "$_LLAMA_ONLY" = "1" ]; then
substep "llama.cpp only mode"
fi
if [ "${STUDIO_LOCAL_INSTALL:-0}" = "1" ]; then
substep "local mode: overlaying $REPO_ROOT (editable) + unsloth-zoo from git main"
fi
# ── Clean up stale caches ──
rm -rf "$REPO_ROOT/unsloth_compiled_cache"
rm -rf "$SCRIPT_DIR/backend/unsloth_compiled_cache"
rm -rf "$SCRIPT_DIR/tmp/unsloth_compiled_cache"
# ── Detect Colab ──
IS_COLAB=false
keynames=$'\n'$(printenv | cut -d= -f1)
if [[ "$keynames" == *$'\nCOLAB_'* ]]; then
IS_COLAB=true
fi
if [ "$_LLAMA_ONLY" != "1" ]; then
# ── Detect whether frontend needs building ──
# Skip if SKIP_STUDIO_FRONTEND=1 (Tauri desktop app bundles its own frontend),
# or if dist/ exists AND no tracked input is newer than dist/.
if [ "${SKIP_STUDIO_FRONTEND:-0}" = "1" ]; then
_NEED_FRONTEND_BUILD=false
step "frontend" "bundled (Tauri)"
else
_NEED_FRONTEND_BUILD=true
if [ -d "$SCRIPT_DIR/frontend/dist" ]; then
_changed=$(find "$SCRIPT_DIR/frontend" -maxdepth 1 -type f \
! -name 'bun.lock' \
-newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null)
if [ -z "$_changed" ]; then
_changed=$(find "$SCRIPT_DIR/frontend/src" "$SCRIPT_DIR/frontend/public" \
-type f -newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null) || true
fi
[ -z "$_changed" ] && _NEED_FRONTEND_BUILD=false
fi
fi # end SKIP_STUDIO_FRONTEND guard
if [ "$_NEED_FRONTEND_BUILD" = false ]; then
step "frontend" "up to date"
verbose_substep "frontend dist is newer than source inputs"
else
# ── Node ──
NEED_NODE=true
if command -v node &>/dev/null && command -v npm &>/dev/null; then
NODE_MAJOR=$(node -v | sed 's/v//' | cut -d. -f1)
NODE_MINOR=$(node -v | sed 's/v//' | cut -d. -f2)
NPM_MAJOR=$(npm -v | cut -d. -f1)
# Vite 8 requires Node ^20.19.0 || >=22.12.0
NODE_OK=false
if [ "$NODE_MAJOR" -eq 20 ] && [ "$NODE_MINOR" -ge 19 ]; then NODE_OK=true; fi
if [ "$NODE_MAJOR" -eq 22 ] && [ "$NODE_MINOR" -ge 12 ]; then NODE_OK=true; fi
if [ "$NODE_MAJOR" -ge 23 ]; then NODE_OK=true; fi
if [ "$NODE_OK" = true ] && [ "$NPM_MAJOR" -ge 11 ]; then
NEED_NODE=false
else
if [ "$IS_COLAB" = true ] && [ "$NODE_OK" = true ]; then
# In Colab, just upgrade npm directly - nvm doesn't work well
if [ "$NPM_MAJOR" -lt 11 ]; then
substep "upgrading npm..."
run_maybe_quiet npm install -g npm@latest
fi
NEED_NODE=false
fi
fi
fi
if [ "$NEED_NODE" = true ]; then
substep "installing nvm..."
export NODE_OPTIONS=--dns-result-order=ipv4first
if _is_verbose; then
curl -so- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
else
curl -so- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash > /dev/null 2>&1
fi
export NVM_DIR="$HOME/.nvm"
set +u
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"
if [ -f "$HOME/.npmrc" ]; then
if grep -qE '^\s*(prefix|globalconfig)\s*=' "$HOME/.npmrc"; then
sed -i.bak '/^\s*\(prefix\|globalconfig\)\s*=/d' "$HOME/.npmrc"
fi
fi
substep "installing Node LTS..."
run_quiet "nvm install" nvm install --lts
if _is_verbose; then
nvm use --lts
else
nvm use --lts > /dev/null 2>&1
fi
set -u
NODE_MAJOR=$(node -v | sed 's/v//' | cut -d. -f1)
NPM_MAJOR=$(npm -v | cut -d. -f1)
if [ "$NODE_MAJOR" -lt 20 ]; then
step "node" "FAILED -- version must be >= 20 (got $(node -v))" "$C_ERR"
exit 1
fi
if [ "$NPM_MAJOR" -lt 11 ]; then
substep "upgrading npm..."
run_quiet "npm update" npm install -g npm@latest
fi
fi
step "node" "$(node -v) | npm $(npm -v)"
verbose_substep "node check: NEED_NODE=$NEED_NODE NODE_OK=${NODE_OK:-unknown} NPM_MAJOR=${NPM_MAJOR:-unknown}"
# ── Install bun (optional, faster package installs) ──
# Uses npm to install bun globally -- Node is already guaranteed above,
# avoids platform-specific installers, PATH issues, and admin requirements.
if ! command -v bun &>/dev/null; then
substep "installing bun..."
if run_maybe_quiet npm install -g bun && command -v bun &>/dev/null; then
substep "bun installed ($(bun --version))"
else
substep "bun install skipped (npm will be used instead)"
fi
else
substep "bun already installed ($(bun --version))"
fi
# ── Build frontend ──
substep "building frontend..."
cd "$SCRIPT_DIR/frontend"
_HIDDEN_GITIGNORES=()
_dir="$(pwd)"
while [ "$_dir" != "/" ]; do
_dir="$(dirname "$_dir")"
if [ -f "$_dir/.gitignore" ] && grep -qx '\*' "$_dir/.gitignore" 2>/dev/null; then
mv "$_dir/.gitignore" "$_dir/.gitignore._twbuild"
_HIDDEN_GITIGNORES+=("$_dir/.gitignore")
fi
done
_restore_gitignores() {
for _gi in "${_HIDDEN_GITIGNORES[@]+"${_HIDDEN_GITIGNORES[@]}"}"; do
mv "${_gi}._twbuild" "$_gi" 2>/dev/null || true
done
}
trap _restore_gitignores EXIT
# Use bun for install if available (faster), fall back to npm.
# Build always uses npm (Node runtime -- avoids bun runtime issues on some platforms).
# NOTE: We intentionally avoid run_quiet for the bun install attempt because
# run_quiet calls exit on failure, which would kill the script before the npm
# fallback can run. Instead we capture output manually and only show it on failure.
#
# IMPORTANT: bun's package cache can become corrupt -- packages get stored
# with only metadata (package.json, README) but no actual content (bin/,
# lib/). When this happens bun install exits 0 but leaves binaries missing.
# We verify critical binaries after install. If missing, we clear the cache
# and retry once before falling back to npm.
_try_bun_install() {
local _log _exit_code=0
_log=$(mktemp)
bun install >"$_log" 2>&1 || _exit_code=$?
# bun may create .exe shims on Windows (Git Bash / MSYS2) instead of plain scripts
if [ "$_exit_code" -eq 0 ] \
&& { [ -x node_modules/.bin/tsc ] || [ -f node_modules/.bin/tsc.exe ] || [ -f node_modules/.bin/tsc.bunx ]; } \
&& { [ -x node_modules/.bin/vite ] || [ -f node_modules/.bin/vite.exe ] || [ -f node_modules/.bin/vite.bunx ]; }; then
rm -f "$_log"
return 0
fi
# Either bun install failed or it exited 0 but left packages missing
if [ "$_exit_code" -ne 0 ]; then
echo " bun install failed (exit code $_exit_code):"
else
echo " bun install exited 0 but critical binaries are missing:"
fi
sed 's/^/ | /' "$_log" >&2
rm -f "$_log"
rm -rf node_modules
return 1
}
_bun_install_ok=false
if command -v bun &>/dev/null; then
substep "using bun for package install (faster)"
if _try_bun_install; then
_bun_install_ok=true
else
# First attempt failed, likely due to corrupt cache entries.
# Clear the cache and retry once.
echo " Clearing bun cache and retrying..."
run_maybe_quiet bun pm cache rm || true
if _try_bun_install; then
_bun_install_ok=true
fi
fi
fi
if [ "$_bun_install_ok" = false ]; then
run_quiet_no_exit "npm install" npm install --no-fund --no-audit --loglevel=error
_npm_install_rc=$?
if [ "$_npm_install_rc" -ne 0 ]; then
exit "$_npm_install_rc"
fi
fi
run_quiet "npm run build" npm run build
_restore_gitignores
trap - EXIT
_MAX_CSS=$(find "$SCRIPT_DIR/frontend/dist/assets" -name '*.css' -exec wc -c {} + 2>/dev/null | sort -n | tail -1 | awk '{print $1}')
if [ -z "$_MAX_CSS" ]; then
step "frontend" "built (warning: no CSS emitted)" "$C_WARN"
elif [ "$_MAX_CSS" -lt 100000 ]; then
step "frontend" "built (warning: CSS may be truncated)" "$C_WARN"
else
step "frontend" "built"
fi
cd "$SCRIPT_DIR"
fi # end frontend build check
# ── oxc-validator runtime ──
if [ -d "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator" ] && command -v npm &>/dev/null; then
cd "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator"
run_quiet_no_exit "npm install (oxc validator runtime)" npm install --no-fund --no-audit --loglevel=error
_oxc_install_rc=$?
if [ "$_oxc_install_rc" -ne 0 ]; then
exit "$_oxc_install_rc"
fi
cd "$SCRIPT_DIR"
fi
# ── Python venv + deps ──
# UNSLOTH_STUDIO_HOME (or STUDIO_HOME alias) overrides the install root
# (mirrors install.sh). UNSLOTH_STUDIO_HOME wins when both are set.
_studio_override_var=""
_studio_override="${UNSLOTH_STUDIO_HOME:-}"
if [ -n "$_studio_override" ]; then
_studio_override_var="UNSLOTH_STUDIO_HOME"
else
_studio_override="${STUDIO_HOME:-}"
[ -n "$_studio_override" ] && _studio_override_var="STUDIO_HOME"
fi
# Strip whitespace so " " is treated as unset (matches Python .strip()).
_studio_override=$(printf '%s' "$_studio_override" | sed -e 's/^[[:space:]]*//' -e 's/[[:space:]]*$//')
case "$_studio_override" in
"~") _studio_override="$HOME" ;;
"~/"*) _studio_override="$HOME/${_studio_override#'~/'}" ;;
esac
if [ -n "$_studio_override" ]; then
# setup.sh runs against an existing install (via 'unsloth studio update');
# a typo in the override must fail fast instead of materializing an
# empty workspace dir. Mirrors setup.ps1 behavior.
if [ ! -d "$_studio_override" ]; then
echo "ERROR: $_studio_override_var=$_studio_override does not exist." >&2
echo " Run install.sh to create the install root before 'unsloth studio update'." >&2
exit 1
fi
[ -w "$_studio_override" ] || { echo "ERROR: $_studio_override_var=$_studio_override is not writable." >&2; exit 1; }
STUDIO_HOME="$(CDPATH= cd -P -- "$_studio_override" && pwd -P)" || exit 1
else
STUDIO_HOME="$HOME/.unsloth/studio"
fi
VENV_DIR="$STUDIO_HOME/unsloth_studio"
VENV_T5_530_DIR="$STUDIO_HOME/.venv_t5_530"
VENV_T5_550_DIR="$STUDIO_HOME/.venv_t5_550"
[ -d "$REPO_ROOT/.venv" ] && rm -rf "$REPO_ROOT/.venv"
[ -d "$REPO_ROOT/.venv_overlay" ] && rm -rf "$REPO_ROOT/.venv_overlay"
[ -d "$REPO_ROOT/.venv_t5" ] && rm -rf "$REPO_ROOT/.venv_t5"
[ -d "$REPO_ROOT/.venv_t5_530" ] && rm -rf "$REPO_ROOT/.venv_t5_530"
[ -d "$REPO_ROOT/.venv_t5_550" ] && rm -rf "$REPO_ROOT/.venv_t5_550"
# Note: do NOT delete $STUDIO_HOME/.venv here — install.sh handles migration
_COLAB_NO_VENV=false
if [ ! -x "$VENV_DIR/bin/python" ]; then
if [ "$IS_COLAB" = true ]; then
# On Colab there is no Studio venv -- install backend deps into system Python.
# Strip all version constraints so pip keeps Colab's pre-installed
# packages (huggingface-hub, datasets, transformers) and only pulls
# in genuinely missing ones (structlog, fastapi, etc.).
substep "Colab detected, installing Studio backend dependencies..."
_COLAB_REQS_TMP="$(mktemp)"
sed 's/[><=!~;].*//' "$SCRIPT_DIR/backend/requirements/studio.txt" \
| grep -v '^#' | grep -v '^$' > "$_COLAB_REQS_TMP"
if [ -s "$_COLAB_REQS_TMP" ]; then
if ! run_quiet_no_exit "install Colab backend deps" pip install -q -r "$_COLAB_REQS_TMP"; then
rm -f "$_COLAB_REQS_TMP"
step "python" "Colab backend dependency install failed" "$C_ERR"
exit 1
fi
else
step "python" "no Colab backend dependencies resolved from requirements file" "$C_WARN"
fi
rm -f "$_COLAB_REQS_TMP"
_COLAB_NO_VENV=true
else
step "python" "venv not found at $VENV_DIR" "$C_ERR"
substep "Run install.sh first to create the environment:"
substep "curl -fsSL https://unsloth.ai/install.sh | sh"
exit 1
fi
else
source "$VENV_DIR/bin/activate"
fi
install_python_stack() {
python "$SCRIPT_DIR/install_python_stack.py"
}
USE_UV=false
if command -v uv &>/dev/null; then
USE_UV=true
elif {
if _is_verbose; then
curl -LsSf https://astral.sh/uv/install.sh | sh
else
curl -LsSf https://astral.sh/uv/install.sh | sh > /dev/null 2>&1
fi
}; then
export PATH="$HOME/.local/bin:$PATH"
command -v uv &>/dev/null && USE_UV=true
fi
fast_install() {
if [ "$USE_UV" = true ]; then
uv pip install --python "$(command -v python)" "$@" && return 0
fi
python -m pip install "$@"
}
cd "$SCRIPT_DIR"
# On Colab without a venv, skip venv-dependent Python deps sections but
# continue to llama.cpp install so GGUF inference is available.
if [ "$_COLAB_NO_VENV" = true ]; then
step "python" "backend deps installed into system Python"
substep "continuing to llama.cpp install for GGUF inference support"
fi
# ── Check if Python deps need updating ──
# Compare installed package version against PyPI latest.
# Skip all Python dependency work if versions match (fast update path).
# On Colab (no venv), skip this version check (it needs $VENV_DIR/bin/python)
# but still run install_python_stack below (it uses sys.executable).
_SKIP_PYTHON_DEPS=false
_SKIP_VERSION_CHECK=false
if [ "$_COLAB_NO_VENV" = true ]; then
_SKIP_VERSION_CHECK=true
fi
_PKG_NAME="${STUDIO_PACKAGE_NAME:-unsloth}"
if [ "$_SKIP_VERSION_CHECK" != true ] && [ "${SKIP_STUDIO_BASE:-0}" != "1" ] && [ "${STUDIO_LOCAL_INSTALL:-0}" != "1" ]; then
# Only check when NOT called from install.sh (which just installed the package)
INSTALLED_VER=$("$VENV_DIR/bin/python" -c "
import sys; from importlib.metadata import version
print(version(sys.argv[1]))
" "$_PKG_NAME" 2>/dev/null || echo "")
LATEST_VER=$(curl -fsSL --max-time 5 "https://pypi.org/pypi/$_PKG_NAME/json" 2>/dev/null \
| "$VENV_DIR/bin/python" -c "import sys,json; print(json.load(sys.stdin)['info']['version'])" 2>/dev/null \
|| echo "")
if [ -n "$INSTALLED_VER" ] && [ -n "$LATEST_VER" ] && [ "$INSTALLED_VER" = "$LATEST_VER" ]; then
step "python" "$_PKG_NAME $INSTALLED_VER is up to date"
_SKIP_PYTHON_DEPS=true
elif [ -n "$INSTALLED_VER" ] && [ -n "$LATEST_VER" ]; then
substep "$_PKG_NAME $INSTALLED_VER -> $LATEST_VER available, updating..."
elif [ -z "$LATEST_VER" ]; then
substep "could not reach PyPI, updating to be safe..."
fi
fi
if [ "$_SKIP_PYTHON_DEPS" = false ]; then
install_python_stack
else
step "python" "dependencies up to date"
verbose_substep "python deps check: installed=$_PKG_NAME@${INSTALLED_VER:-unknown} latest=${LATEST_VER:-unknown}"
fi
# ── 6b. Pre-install transformers 5.x into .venv_t5_530/ and .venv_t5_550/ ──
# Models like GLM-4.7-Flash, Qwen3 MoE need transformers>=5.3.0.
# Gemma 4 models need transformers>=5.5.0.
# Pre-install into separate directories to avoid runtime pip overhead.
# The training subprocess prepends the appropriate dir to sys.path.
#
# Runs outside the _SKIP_PYTHON_DEPS gate so that upgrades from legacy
# single .venv_t5 are always migrated to the tiered layout.
# why: in env-override mode $STUDIO_HOME is user-chosen; require the
# ownership marker before rm -rf so unrelated dirs survive. Gated on the
# canonical comparison so an override pointing at the legacy default still
# behaves like a default install.
_STUDIO_OWNED_MARKER=".unsloth-studio-owned"
_LEGACY_STUDIO_HOME="$HOME/.unsloth/studio"
_studio_home_canon="$STUDIO_HOME"
if [ -d "$_studio_home_canon" ]; then
_studio_home_canon=$(CDPATH= cd -P -- "$_studio_home_canon" 2>/dev/null && pwd -P) \
|| _studio_home_canon="$STUDIO_HOME"
fi
if [ -d "$_LEGACY_STUDIO_HOME" ]; then
_LEGACY_STUDIO_HOME=$(CDPATH= cd -P -- "$_LEGACY_STUDIO_HOME" 2>/dev/null && pwd -P) \
|| _LEGACY_STUDIO_HOME="$HOME/.unsloth/studio"
fi
_STUDIO_HOME_IS_CUSTOM=false
if [ "$_studio_home_canon" != "$_LEGACY_STUDIO_HOME" ]; then
_STUDIO_HOME_IS_CUSTOM=true
fi
_assert_studio_owned_or_absent() {
_aso_dir="$1"
_aso_label="$2"
[ -d "$_aso_dir" ] || return 0
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ] && [ ! -f "$_aso_dir/$_STUDIO_OWNED_MARKER" ]; then
echo "ERROR: $_aso_dir already exists and is not marked as a Studio-owned $_aso_label." >&2
echo " Move it aside or choose an empty UNSLOTH_STUDIO_HOME before re-running." >&2
exit 1
fi
}
_NEED_T5_INSTALL=false
if [ -d "$STUDIO_HOME/.venv_t5" ]; then
# Legacy layout — migrate
_assert_studio_owned_or_absent "$STUDIO_HOME/.venv_t5" "legacy transformers sidecar venv"
rm -rf "$STUDIO_HOME/.venv_t5"
_NEED_T5_INSTALL=true
fi
[ ! -d "$VENV_T5_530_DIR" ] && _NEED_T5_INSTALL=true
[ ! -d "$VENV_T5_550_DIR" ] && _NEED_T5_INSTALL=true
# Also reinstall when python deps were updated (packages may need rebuild)
[ "$_SKIP_PYTHON_DEPS" = false ] && _NEED_T5_INSTALL=true
if [ "$_NEED_T5_INSTALL" = true ]; then
_assert_studio_owned_or_absent "$VENV_T5_530_DIR" "transformers 5.3 sidecar venv"
[ -d "$VENV_T5_530_DIR" ] && rm -rf "$VENV_T5_530_DIR"
mkdir -p "$VENV_T5_530_DIR"
: > "$VENV_T5_530_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
run_quiet "install transformers 5.3.0" fast_install --target "$VENV_T5_530_DIR" --no-deps "transformers==5.3.0"
run_quiet "install huggingface_hub for t5_530" fast_install --target "$VENV_T5_530_DIR" --no-deps "huggingface_hub==1.8.0"
run_quiet "install hf_xet for t5_530" fast_install --target "$VENV_T5_530_DIR" --no-deps "hf_xet==1.4.2"
run_quiet "install tiktoken for t5_530" fast_install --target "$VENV_T5_530_DIR" "tiktoken"
step "transformers" "5.3.0 pre-installed"
_assert_studio_owned_or_absent "$VENV_T5_550_DIR" "transformers 5.5 sidecar venv"
[ -d "$VENV_T5_550_DIR" ] && rm -rf "$VENV_T5_550_DIR"
mkdir -p "$VENV_T5_550_DIR"
: > "$VENV_T5_550_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
run_quiet "install transformers 5.5.0" fast_install --target "$VENV_T5_550_DIR" --no-deps "transformers==5.5.0"
run_quiet "install huggingface_hub for t5_550" fast_install --target "$VENV_T5_550_DIR" --no-deps "huggingface_hub==1.8.0"
run_quiet "install hf_xet for t5_550" fast_install --target "$VENV_T5_550_DIR" --no-deps "hf_xet==1.4.2"
run_quiet "install tiktoken for t5_550" fast_install --target "$VENV_T5_550_DIR" "tiktoken"
step "transformers" "5.5.0 pre-installed"
fi
fi
# ── GPU detection summary (mirrors setup.ps1 step "gpu" block) ──
_setup_amd_detected=false
_setup_gfx_all=""
_setup_mkt=""
if command -v rocminfo >/dev/null 2>&1 && \
rocminfo 2>/dev/null | awk '/Name:[[:space:]]*gfx[1-9][0-9]/{found=1} END{exit !found}'; then
_setup_amd_detected=true
_setup_gfx_all=$(rocminfo 2>/dev/null | grep -oE 'gfx[1-9][0-9a-z]{2,3}' || true)
_setup_mkt=$(rocminfo 2>/dev/null | awk -F': ' \
'/Marketing Name:/{gsub(/^[[:space:]]+|[[:space:]]+$/,"", $2); if($2){print $2; exit}}' || true)
elif command -v amd-smi >/dev/null 2>&1 && \
amd-smi list 2>/dev/null | awk '/^GPU[[:space:]]*[:\[][[:space:]]*[0-9]/{ found=1 } END{ exit !found }'; then
_setup_amd_detected=true
_setup_gfx_all=$(amd-smi list 2>/dev/null | grep -oE 'gfx[1-9][0-9a-z]{2,3}' || true)
[ -z "$_setup_gfx_all" ] && \
_setup_gfx_all=$(amd-smi static --asic 2>/dev/null | grep -oE 'gfx[1-9][0-9a-z]{2,3}' || true)
_setup_mkt=$(amd-smi static --asic 2>/dev/null | awk -F'[:|]' \
'/[Mm]arket.?[Nn]ame/{gsub(/^[[:space:]]+|[[:space:]]+$/,"", $2); if($2){print $2; exit}}' || true)
fi
if command -v nvidia-smi >/dev/null 2>&1 && \
nvidia-smi -L 2>/dev/null | awk '/^GPU[[:space:]]+[0-9]+:/{found=1} END{exit !found}'; then
step "gpu" "NVIDIA GPU detected"
elif [ "$_setup_amd_detected" = true ]; then
_setup_vis="${HIP_VISIBLE_DEVICES:-${ROCR_VISIBLE_DEVICES:-}}"
_setup_vis_idx=0
if [ -n "$_setup_vis" ] && [ "$_setup_vis" != "-1" ]; then
_setup_first="${_setup_vis%%,*}"
case "$_setup_first" in ''|*[!0-9]*) ;; *) _setup_vis_idx=$_setup_first ;; esac
fi
_setup_gfx=$(printf '%s\n' "$_setup_gfx_all" | awk -v idx="$_setup_vis_idx" \
'NF && !seen[$0]++ { a[n++]=$0 } END { if(idx>=n) idx=0; if(n>0) print a[idx] }')
# UNSLOTH_ROCM_GFX_ARCH env override (mirrors setup.ps1)
if [ -n "${UNSLOTH_ROCM_GFX_ARCH:-}" ]; then
_setup_gfx="${UNSLOTH_ROCM_GFX_ARCH}"
substep "gfx arch from UNSLOTH_ROCM_GFX_ARCH env override: $_setup_gfx"
# Name-based arch inference when tools don't report gfx (mirrors setup.ps1 nameArchTable)
elif [ -z "$_setup_gfx" ] && [ -n "$_setup_mkt" ]; then
case "$_setup_mkt" in
*"9070 XT"*|*9080*) _setup_gfx="gfx1201" ;; # RDNA 4
*9070*|*9060*) _setup_gfx="gfx1200" ;; # RDNA 4
*"8060S"*|*"890M"*|*"Strix Halo"*|*"HX 37"*|*"HX 38"*|*"AI 9 HX"*) _setup_gfx="gfx1151" ;; # RDNA 3.5 iGPU
*"880M"*|*"Strix Point"*|*"AI 9 36"*|*"AI 7 35"*|*"AI 5 34"*) _setup_gfx="gfx1150" ;; # RDNA 3.5 iGPU
*"RX 7900"*|*"RX 7800"*|*"RX 7700"*) _setup_gfx="gfx1100" ;; # RDNA 3 desktop
*"RX 7600"*) _setup_gfx="gfx1102" ;; # RDNA 3
*"780M"*|*"760M"*|*"740M"*|*"Phoenix"*) _setup_gfx="gfx1103" ;; # RDNA 3 iGPU
esac
if [ -n "$_setup_gfx" ]; then
substep "gfx arch inferred from GPU name: $_setup_gfx"
substep "Tip: set UNSLOTH_ROCM_GFX_ARCH=$_setup_gfx to skip inference next time"
fi
fi
# ROCm version via hipconfig, then amd-smi
_setup_rocm_ver=""
if command -v hipconfig >/dev/null 2>&1; then
_setup_rocm_ver=$(hipconfig --version 2>/dev/null | awk 'NR==1 && /^[0-9]/{print; exit}' || true)
fi
if [ -z "$_setup_rocm_ver" ] && command -v amd-smi >/dev/null 2>&1; then
_setup_rocm_ver=$(amd-smi version 2>/dev/null | awk -F'ROCm version: ' \
'NF>1{gsub(/[[:space:]]/,"", $2); print $2; exit}' || true)
fi
if [ -n "$_setup_gfx" ]; then
step "gpu" "AMD ROCm ($_setup_gfx)"
else
step "gpu" "AMD ROCm"
fi
_setup_rocm_root="${ROCM_PATH:-${HIP_PATH:-/opt/rocm}}"
substep "ROCm: $_setup_rocm_root"
[ -n "$_setup_rocm_ver" ] && substep "hipconfig: $_setup_rocm_ver"
[ -n "$_setup_mkt" ] && [ -n "$_setup_gfx" ] && substep "GPU: $_setup_mkt"
else
step "gpu" "none (chat-only / GGUF)" "$C_WARN"
substep "Training and GPU inference require an NVIDIA or AMD ROCm GPU."
fi
# ── 7. Prefer prebuilt llama.cpp bundles before any source build path ──
# Nest llama.cpp under $STUDIO_HOME only for real env-overrides; legacy
# default keeps ~/.unsloth/llama.cpp so pre-PR builds are still discovered.
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
UNSLOTH_HOME="$STUDIO_HOME"
else
UNSLOTH_HOME="$HOME/.unsloth"
fi
mkdir -p "$UNSLOTH_HOME"
LLAMA_CPP_DIR="$UNSLOTH_HOME/llama.cpp"
LLAMA_SERVER_BIN="$LLAMA_CPP_DIR/build/bin/llama-server"
_NEED_LLAMA_SOURCE_BUILD=false
_LLAMA_CPP_DEGRADED=false
_LLAMA_FORCE_COMPILE="${UNSLOTH_LLAMA_FORCE_COMPILE:-0}"
_REQUESTED_LLAMA_TAG="${UNSLOTH_LLAMA_TAG:-${_DEFAULT_LLAMA_TAG}}"
_HOST_SYSTEM="$(uname -s 2>/dev/null || true)"
_HOST_MACHINE="$(uname -m 2>/dev/null || true)"
# Pick the release repo install_llama_prebuilt.py plans against.
# unslothai/llama.cpp ships only Linux CUDA bundles, so CPU-only Linux
# x86_64 routes to ggml-org for bin-ubuntu-x64.tar.gz. Anything with a
# GPU tool installed stays on unslothai (CUDA bundle / ROCm source build).
_LINUX_HAS_GPU=false
for _GPU_TOOL in nvidia-smi rocminfo amd-smi hipconfig hipinfo; do
if command -v "$_GPU_TOOL" >/dev/null 2>&1; then
_LINUX_HAS_GPU=true
break
fi
done
if [ "$_HOST_SYSTEM" = "Darwin" ]; then
_HELPER_RELEASE_REPO="ggml-org/llama.cpp"
elif [ "$_HOST_SYSTEM" = "Linux" ] \
&& [ "$_HOST_MACHINE" = "x86_64" ] \
&& [ "$_LINUX_HAS_GPU" = false ]; then
_HELPER_RELEASE_REPO="ggml-org/llama.cpp"
elif [ "$_HOST_SYSTEM" = "Linux" ] \
&& { [ "$_HOST_MACHINE" = "aarch64" ] || [ "$_HOST_MACHINE" = "arm64" ]; } \
&& [ "$_LINUX_HAS_GPU" = false ]; then
# Linux ARM64 (Ampere Altra, Raspberry Pi 5, GitHub `ubuntu-24.04-arm`,
# CPU-only Jetson rescue mode, ...). unslothai/llama.cpp only ships
# the Linux CUDA bundles, so without this branch the prebuilt
# resolver returns 0 attempts on every release and the installer
# falls all the way back to a source build. Upstream ggml-org ships
# llama-bNNNN-bin-ubuntu-arm64.tar.gz from at least b9072 onward.
_HELPER_RELEASE_REPO="ggml-org/llama.cpp"
else
_HELPER_RELEASE_REPO="unslothai/llama.cpp"
fi
unset _GPU_TOOL
_LLAMA_PR="${UNSLOTH_LLAMA_PR:-}"
_SKIP_PREBUILT_INSTALL=false
_LLAMA_PR_FORCE="${UNSLOTH_LLAMA_PR_FORCE:-${_DEFAULT_LLAMA_PR_FORCE}}"
_LLAMA_SOURCE="${_DEFAULT_LLAMA_SOURCE}"
_LLAMA_SOURCE="${_LLAMA_SOURCE%.git}" # normalize: strip trailing .git
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
_RESOLVED_SOURCE_REF_KIND="tag"
_RESOLVED_LLAMA_TAG="$_REQUESTED_LLAMA_TAG"
if [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
_NEED_LLAMA_SOURCE_BUILD=true
_SKIP_PREBUILT_INSTALL=true
fi
# Baked-in PR_FORCE promotes to _LLAMA_PR when user hasn't set one.
if [ -z "$_LLAMA_PR" ] && [ -n "$_LLAMA_PR_FORCE" ] && \
[[ "$_LLAMA_PR_FORCE" =~ ^[0-9]+$ ]] && [ "$_LLAMA_PR_FORCE" -gt 0 ]; then
_LLAMA_PR="$_LLAMA_PR_FORCE"
step "llama.cpp" "baked-in PR_FORCE=$_LLAMA_PR_FORCE" "$C_WARN"
fi
if [ -n "$_LLAMA_PR" ]; then
if ! [[ "$_LLAMA_PR" =~ ^[0-9]+$ ]] || [ "$_LLAMA_PR" -le 0 ]; then
step "llama.cpp" "UNSLOTH_LLAMA_PR=$_LLAMA_PR is not a valid PR number" "$C_ERR"
exit 1
fi
step "llama.cpp" "UNSLOTH_LLAMA_PR=$_LLAMA_PR -- will build from PR head" "$C_WARN"
_RESOLVED_LLAMA_TAG="pr-$_LLAMA_PR"
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
_RESOLVED_SOURCE_REF="pr-$_LLAMA_PR"
_RESOLVED_SOURCE_REF_KIND="pull"
_NEED_LLAMA_SOURCE_BUILD=true
_SKIP_PREBUILT_INSTALL=true
fi
verbose_substep "requested llama.cpp tag: $_REQUESTED_LLAMA_TAG (repo: $_HELPER_RELEASE_REPO)"
if [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
step "llama.cpp" "UNSLOTH_LLAMA_FORCE_COMPILE=1 -- skipping prebuilt" "$C_WARN"
_NEED_LLAMA_SOURCE_BUILD=true
elif [ "${_SKIP_PREBUILT_INSTALL:-false}" = true ]; then
substep "prebuilt install skipped -- falling back to source build"
else
substep "installing prebuilt llama.cpp..."
if [ -d "$LLAMA_CPP_DIR" ]; then
substep "existing install detected -- validating update"
fi
# why: install_llama_prebuilt.py uses os.replace(), which would displace
# an unrelated $UNSLOTH_STUDIO_HOME/llama.cpp before the source-build
# ownership check below ever runs.
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
_assert_studio_owned_or_absent "$LLAMA_CPP_DIR" "llama.cpp install"
fi
_PREBUILT_CMD=(
python "$SCRIPT_DIR/install_llama_prebuilt.py"
--install-dir "$LLAMA_CPP_DIR"
--llama-tag "$_REQUESTED_LLAMA_TAG"
--published-repo "$_HELPER_RELEASE_REPO"
--simple-policy
)
if [ -n "${UNSLOTH_LLAMA_RELEASE_TAG:-}" ]; then
_PREBUILT_CMD+=(--published-release-tag "$UNSLOTH_LLAMA_RELEASE_TAG")
fi
_PREBUILT_LOG="$(mktemp)"
set +e
if _is_verbose; then
"${_PREBUILT_CMD[@]}" 2>&1 | tee "$_PREBUILT_LOG"
_PREBUILT_STATUS=${PIPESTATUS[0]}
else
"${_PREBUILT_CMD[@]}" >"$_PREBUILT_LOG" 2>&1
_PREBUILT_STATUS=$?
fi
set -e
if [ "$_PREBUILT_STATUS" -eq 0 ]; then
if grep -Fq "already matches" "$_PREBUILT_LOG"; then
step "llama.cpp" "prebuilt up to date and validated"
else
step "llama.cpp" "prebuilt installed and validated"
fi
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ] && [ -d "$LLAMA_CPP_DIR" ]; then
: > "$LLAMA_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
fi
print_installed_llama_prebuilt_release "$LLAMA_CPP_DIR"
verbose_substep "llama.cpp install dir: $LLAMA_CPP_DIR"
rm -f "$_PREBUILT_LOG"
elif [ "$_PREBUILT_STATUS" -eq 3 ]; then
step "llama.cpp" "install blocked by active llama.cpp process" "$C_WARN"
print_llama_error_log "$_PREBUILT_LOG"
rm -f "$_PREBUILT_LOG"
if [ -d "$LLAMA_CPP_DIR" ]; then
substep "existing install was restored"
fi
substep "close Studio or other llama.cpp users and retry"
exit 3
else
step "llama.cpp" "prebuilt install failed (continuing)" "$C_WARN"
print_llama_error_log "$_PREBUILT_LOG"
rm -f "$_PREBUILT_LOG"
if [ -d "$LLAMA_CPP_DIR" ]; then
substep "prebuilt update failed; existing install restored"
fi
substep "falling back to source build"
_NEED_LLAMA_SOURCE_BUILD=true
fi
fi
# Source-built llama.cpp installs do not have the prebuilt metadata used above
# for exact release matching. Reuse a complete local source build unless the
# caller explicitly requested a rebuild or a PR-specific llama.cpp checkout.
if [ "$_NEED_LLAMA_SOURCE_BUILD" = true ] && \
[ "$_LLAMA_FORCE_COMPILE" != "1" ] && \
[ -z "$_LLAMA_PR" ] && \
[ -x "$LLAMA_CPP_DIR/build/bin/llama-server" ] && \
[ -x "$LLAMA_CPP_DIR/build/bin/llama-quantize" ]; then
step "llama.cpp" "existing source build found; skipping rebuild"
ln -sf build/bin/llama-quantize "$LLAMA_CPP_DIR/llama-quantize"
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
: > "$LLAMA_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
fi
_NEED_LLAMA_SOURCE_BUILD=false
fi
# ── 8. WSL: pre-install GGUF build dependencies for fallback source builds ──
# On WSL, sudo requires a password and can't be entered during GGUF export
# (runs in a non-interactive subprocess). Install build deps here instead.
if [ "$_NEED_LLAMA_SOURCE_BUILD" = true ] && grep -qi microsoft /proc/version 2>/dev/null; then
_GGUF_DEPS="pciutils build-essential cmake curl git libcurl4-openssl-dev"
apt-get update -y >/dev/null 2>&1 || true
apt-get install -y $_GGUF_DEPS >/dev/null 2>&1 || true
_STILL_MISSING=""
for _pkg in $_GGUF_DEPS; do
case "$_pkg" in
build-essential) command -v gcc >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
pciutils) command -v lspci >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
libcurl4-openssl-dev) command -v curl-config >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
*) command -v "$_pkg" >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
esac
done
_STILL_MISSING=$(echo "$_STILL_MISSING" | sed 's/^ *//')
if [ -z "$_STILL_MISSING" ]; then
step "gguf deps" "installed"
elif command -v sudo >/dev/null 2>&1; then
step "gguf deps" "sudo required for: $_STILL_MISSING" "$C_WARN"
printf " %-15s" ""
printf "accept? [Y/n] "
if [ -r /dev/tty ]; then
read -r REPLY </dev/tty || REPLY="y"
else
REPLY="y"
fi
case "$REPLY" in
[nN]*)
substep "skipped -- run manually:"
substep "sudo apt-get install -y $_STILL_MISSING"
_SKIP_GGUF_BUILD=true
;;
*)
sudo apt-get update -y
sudo apt-get install -y $_STILL_MISSING
step "gguf deps" "installed"
;;
esac
else
step "gguf deps" "missing (no sudo) -- install manually:" "$C_WARN"
substep "apt-get install -y $_STILL_MISSING"
_SKIP_GGUF_BUILD=true
fi
fi
# ── 9. Build llama.cpp binaries for GGUF inference + export when prebuilt install fails ──
# Builds at ~/.unsloth/llama.cpp — a single shared location under the user's
# home directory. This is used by both the inference server and the GGUF
# export pipeline (unsloth-zoo).
# - llama-server: for GGUF model inference
# - llama-quantize: for GGUF export quantization (symlinked to root for check_llama_cpp())
if [ "$_NEED_LLAMA_SOURCE_BUILD" = false ]; then
:
elif [ "${_SKIP_GGUF_BUILD:-}" = true ]; then
step "llama.cpp" "skipped (missing build deps)" "$C_WARN"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
else
{
if ! command -v cmake &>/dev/null; then
step "llama.cpp" "skipped (cmake not found)" "$C_WARN"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
elif ! command -v git &>/dev/null; then
step "llama.cpp" "skipped (git not found)" "$C_WARN"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
else
if [ -z "$_LLAMA_PR" ]; then
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
if [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
if [ "$_REQUESTED_LLAMA_TAG" = "latest" ]; then
_RESOLVED_SOURCE_REF="${UNSLOTH_LLAMA_FORCE_COMPILE_REF:-${_DEFAULT_LLAMA_FORCE_COMPILE_REF}}"
_RESOLVED_SOURCE_REF_KIND="branch"
else
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
_RESOLVED_SOURCE_REF_KIND="tag"
fi
elif [ "$_REQUESTED_LLAMA_TAG" = "latest" ]; then
_RESOLVE_TAG_ARGS=(--resolve-llama-tag latest --published-repo "ggml-org/llama.cpp" --output-format json)
set +e
_RESOLVE_TAG_JSON="$(python "$SCRIPT_DIR/install_llama_prebuilt.py" "${_RESOLVE_TAG_ARGS[@]}" 2>/dev/null)"
_RESOLVE_TAG_STATUS=$?
set -e
if [ "$_RESOLVE_TAG_STATUS" -eq 0 ] && [ -n "${_RESOLVE_TAG_JSON:-}" ]; then
_RESOLVED_SOURCE_REF="$(
printf '%s' "$_RESOLVE_TAG_JSON" | python -c 'import json,sys; print(json.load(sys.stdin).get("llama_tag",""))' 2>/dev/null || true
)"
else
_RESOLVED_SOURCE_REF=""
fi
if [ -z "$_RESOLVED_SOURCE_REF" ]; then
_RESOLVED_SOURCE_REF="latest"
fi
_RESOLVED_SOURCE_REF_KIND="tag"
else
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
_RESOLVED_SOURCE_REF_KIND="tag"
fi
if [ -z "$_RESOLVED_SOURCE_URL" ]; then
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
fi
if [ -z "$_RESOLVED_SOURCE_REF" ]; then
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
fi
fi
verbose_substep "source build repo: $_RESOLVED_SOURCE_URL"
verbose_substep "source build ref: ${_RESOLVED_SOURCE_REF:-latest} (${_RESOLVED_SOURCE_REF_KIND})"
BUILD_OK=true
mkdir -p "$(dirname "$LLAMA_CPP_DIR")"
_BUILD_TMP="${LLAMA_CPP_DIR}.build.$$"
rm -rf "$_BUILD_TMP"
if [ -n "$_LLAMA_PR" ]; then
run_quiet_no_exit "clone llama.cpp" \
git clone --depth 1 "${_LLAMA_SOURCE}.git" "$_BUILD_TMP" || BUILD_OK=false
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "fetch PR #$_LLAMA_PR" \
git -C "$_BUILD_TMP" fetch --depth 1 origin "pull/$_LLAMA_PR/head:pr-$_LLAMA_PR" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "checkout PR #$_LLAMA_PR" \
git -C "$_BUILD_TMP" checkout "pr-$_LLAMA_PR" || BUILD_OK=false
fi
elif [ "$_RESOLVED_SOURCE_REF_KIND" = "pull" ] && [ -n "$_RESOLVED_SOURCE_REF" ]; then
run_quiet_no_exit "clone llama.cpp" \
git clone --depth 1 "${_RESOLVED_SOURCE_URL}.git" "$_BUILD_TMP" || BUILD_OK=false
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "fetch source PR ref" \
git -C "$_BUILD_TMP" fetch --depth 1 origin "$_RESOLVED_SOURCE_REF" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "checkout source PR ref" \
git -C "$_BUILD_TMP" checkout -B unsloth-llama-build FETCH_HEAD || BUILD_OK=false
fi
elif [ "$_RESOLVED_SOURCE_REF_KIND" = "commit" ] && [ -n "$_RESOLVED_SOURCE_REF" ]; then
run_quiet_no_exit "clone llama.cpp" \
git clone --depth 1 "${_RESOLVED_SOURCE_URL}.git" "$_BUILD_TMP" || BUILD_OK=false
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "fetch source commit" \
git -C "$_BUILD_TMP" fetch --depth 1 origin "$_RESOLVED_SOURCE_REF" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "checkout source commit" \
git -C "$_BUILD_TMP" checkout -B unsloth-llama-build FETCH_HEAD || BUILD_OK=false
fi
else
_CLONE_ARGS=(git clone --depth 1)
if [ "$_RESOLVED_SOURCE_REF" != "latest" ] && [ -n "$_RESOLVED_SOURCE_REF" ]; then
_CLONE_ARGS+=(--branch "$_RESOLVED_SOURCE_REF")
fi
_CLONE_ARGS+=("${_RESOLVED_SOURCE_URL}.git" "$_BUILD_TMP")
run_quiet_no_exit "clone llama.cpp" \
"${_CLONE_ARGS[@]}" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
# Set Release explicitly (llama.cpp only defaults to it on non-MSVC/Xcode).
CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_NATIVE=ON"
_TRY_METAL_CPU_FALLBACK=false
_HOST_SYSTEM="$(uname -s 2>/dev/null || true)"
_HOST_MACHINE="$(uname -m 2>/dev/null || true)"
_IS_MACOS_ARM64=false
if [ "$_HOST_SYSTEM" = "Darwin" ] && { [ "$_HOST_MACHINE" = "arm64" ] || [ "$_HOST_MACHINE" = "aarch64" ]; }; then
_IS_MACOS_ARM64=true
fi
if command -v ccache &>/dev/null; then
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
fi
CPU_FALLBACK_CMAKE_ARGS="$CMAKE_ARGS"
GPU_BACKEND=""
NVCC_PATH=""
if command -v nvcc &>/dev/null; then
NVCC_PATH="$(command -v nvcc)"
GPU_BACKEND="cuda"
elif [ -x /usr/local/cuda/bin/nvcc ]; then
NVCC_PATH="/usr/local/cuda/bin/nvcc"
export PATH="/usr/local/cuda/bin:$PATH"
GPU_BACKEND="cuda"
elif ls /usr/local/cuda-*/bin/nvcc &>/dev/null 2>&1; then
# Pick the newest cuda-XX.X directory
NVCC_PATH="$(ls -d /usr/local/cuda-*/bin/nvcc 2>/dev/null | sort -V | tail -1)"
export PATH="$(dirname "$NVCC_PATH"):$PATH"
GPU_BACKEND="cuda"
fi
# Check for ROCm (AMD) only if CUDA was not already selected
ROCM_HIPCC=""
if [ -z "$GPU_BACKEND" ]; then
if command -v hipcc &>/dev/null; then
ROCM_HIPCC="$(command -v hipcc)"
GPU_BACKEND="rocm"
elif [ -x /opt/rocm/bin/hipcc ]; then
ROCM_HIPCC="/opt/rocm/bin/hipcc"
export PATH="/opt/rocm/bin:$PATH"
GPU_BACKEND="rocm"
elif ls /opt/rocm-*/bin/hipcc &>/dev/null 2>&1; then
ROCM_HIPCC="$(ls -d /opt/rocm-*/bin/hipcc 2>/dev/null | sort -V | tail -1)"
export PATH="$(dirname "$ROCM_HIPCC"):$PATH"
GPU_BACKEND="rocm"
fi
fi
_BUILD_DESC="building"
if [ "$_IS_MACOS_ARM64" = true ]; then
# Metal takes precedence on Apple Silicon (CUDA/ROCm not functional on macOS)
_BUILD_DESC="building (Metal)"
CMAKE_ARGS="$CMAKE_ARGS -DGGML_METAL=ON -DGGML_METAL_EMBED_LIBRARY=ON -DGGML_METAL_USE_BF16=ON -DCMAKE_INSTALL_RPATH=@loader_path -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON"
CPU_FALLBACK_CMAKE_ARGS="$CPU_FALLBACK_CMAKE_ARGS -DGGML_METAL=OFF"
_TRY_METAL_CPU_FALLBACK=true
elif [ -n "$NVCC_PATH" ]; then
# Returns "ok|too_old|unknown\nX.Y" on stdout.
_NVCC_CHECK="$(_nvcc_meets_llama_minimum "$NVCC_PATH")"
_NVCC_STATUS="$(printf '%s\n' "$_NVCC_CHECK" | sed -n '1p')"
_NVCC_VER="$(printf '%s\n' "$_NVCC_CHECK" | sed -n '2p')"
if [ "$_NVCC_STATUS" = "too_old" ]; then
substep "CUDA toolkit $_NVCC_VER is below llama.cpp minimum (12.4)." "$C_ERR"
substep "install a newer CUDA toolkit: https://developer.nvidia.com/cuda-toolkit-archive" "$C_WARN"
substep "falling back to CPU llama.cpp build for this run." "$C_WARN"
NVCC_PATH=""
GPU_BACKEND=""
_BUILD_DESC="building (CPU, CUDA toolkit < 12.4)"
else
CMAKE_ARGS="$CMAKE_ARGS -DGGML_CUDA=ON"
CUDA_ARCHS=""
if command -v nvidia-smi &>/dev/null; then
_raw_caps=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null || true)
while IFS= read -r _cap; do
_cap=$(echo "$_cap" | tr -d '[:space:]')
if [[ "$_cap" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
_arch="${BASH_REMATCH[1]}${BASH_REMATCH[2]}"
# Append if not already present
case ";$CUDA_ARCHS;" in
*";$_arch;"*) ;;
*) CUDA_ARCHS="${CUDA_ARCHS:+$CUDA_ARCHS;}$_arch" ;;
esac
fi
done <<< "$_raw_caps"
fi
if [ -n "$CUDA_ARCHS" ]; then
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHS}"
_BUILD_DESC="building (CUDA, sm_${CUDA_ARCHS//;/+sm_})"
else
_BUILD_DESC="building (CUDA)"
fi
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_FLAGS=--threads=0"
# Accept a host gcc/clang newer than nvcc's whitelist; a fresh
# toolkit (e.g. CUDA 13.3) otherwise aborts with "#error --
# unsupported GNU version". Via env, not CMAKE_ARGS, to avoid
# word-splitting.
export NVCC_PREPEND_FLAGS="${NVCC_PREPEND_FLAGS:+$NVCC_PREPEND_FLAGS }-allow-unsupported-compiler"
fi
elif [ "$GPU_BACKEND" = "rocm" ]; then
# Resolve hipcc symlinks to find the real ROCm root
_HIPCC_REAL="$(readlink -f "$ROCM_HIPCC" 2>/dev/null || printf '%s' "$ROCM_HIPCC")"
ROCM_ROOT=""
if command -v hipconfig &>/dev/null; then
ROCM_ROOT="$(hipconfig -R 2>/dev/null || true)"
fi
if [ -z "$ROCM_ROOT" ]; then
ROCM_ROOT="$(cd "$(dirname "$_HIPCC_REAL")/.." 2>/dev/null && pwd)"
fi
_BUILD_DESC="building (ROCm)"
CMAKE_ARGS="$CMAKE_ARGS -DGGML_HIP=ON"
# ROCm 7.x ships clang-20 which on Ubuntu 24.04+ defaults to the
# highest-numbered gcc lib dir (/usr/lib/gcc/x86_64-linux-gnu/14/)
# which contains runtime objects but NOT C++ headers, causing:
# fatal error: 'cstdlib' file not found
# Find the newest gcc install dir that actually has both the
# runtime dir AND /usr/include/c++/<ver> headers, then pass it
# to clang via --gcc-install-dir so HIP builds succeed.
_GCC_INSTALL_DIR=""
_gcc_pm="$(gcc -print-multiarch 2>/dev/null)"
case "$_gcc_pm" in
*-linux-gnu*) _GCC_MULTIARCH="$_gcc_pm" ;;
*) _GCC_MULTIARCH="$(uname -m)-linux-gnu" ;;
esac
for _gcc_ver in 14 13 12 11; do
if [ -d "/usr/lib/gcc/$_GCC_MULTIARCH/$_gcc_ver/include" ] && \
[ -d "/usr/include/c++/$_gcc_ver" ]; then
_GCC_INSTALL_DIR="/usr/lib/gcc/$_GCC_MULTIARCH/$_gcc_ver"
break
fi
done
if [ -n "$_GCC_INSTALL_DIR" ]; then
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_HIP_FLAGS=--gcc-install-dir=\"$_GCC_INSTALL_DIR\""
substep "ROCm HIP gcc install dir: $_GCC_INSTALL_DIR"
fi
export ROCM_PATH="$ROCM_ROOT"
export HIP_PATH="$ROCM_ROOT"
# Use upstream-recommended HIP compiler (not legacy hipcc-as-CXX)
if command -v hipconfig &>/dev/null; then
_HIP_CLANG_DIR="$(hipconfig -l 2>/dev/null || true)"
[ -n "$_HIP_CLANG_DIR" ] && export HIPCXX="$_HIP_CLANG_DIR/clang"
fi
# Detect AMD GPU architecture (gfx target)
GPU_TARGETS=""
if command -v rocminfo &>/dev/null; then
_gfx_list=$(rocminfo 2>/dev/null | grep -oE 'gfx[0-9]{2,4}[a-z]?' | sort -u || true)
_valid_gfx=""
for _gfx in $_gfx_list; do
if [[ "$_gfx" =~ ^gfx[0-9]{2,4}[a-z]?$ ]]; then
# Drop bare family-level targets (gfx10, gfx11, gfx12, ...)
# when a specific sibling is present in the same list.
# rocminfo on ROCm 6.1+ emits both the specific GPU and
# the LLVM generic family line (e.g. gfx1100 alongside
# gfx11-generic), and the outer grep above captures the
# bare family prefix from the generic line. Passing that
# bare prefix to -DGPU_TARGETS breaks the HIP/llama.cpp
# build because clang only accepts specific gfxNNN ids.
# No real AMD GPU has a 2-digit gfx id, so this filter
# can only ever drop family prefixes, never real targets.
if [[ "$_gfx" =~ ^gfx[0-9]{2}$ ]] \
&& echo "$_gfx_list" | grep -qE "^${_gfx}[0-9][0-9a-z]?$"; then
continue
fi
_valid_gfx="${_valid_gfx}${_valid_gfx:+;}$_gfx"
fi
done
[ -n "$_valid_gfx" ] && GPU_TARGETS="$_valid_gfx"
fi
if [ -n "$GPU_TARGETS" ]; then
CMAKE_ARGS="$CMAKE_ARGS -DGPU_TARGETS=${GPU_TARGETS}"
_BUILD_DESC="building (ROCm, ${GPU_TARGETS//;/+})"
fi
elif [ -d /usr/local/cuda ] || nvidia-smi &>/dev/null; then
_BUILD_DESC="building (CPU, CUDA driver found but nvcc missing)"
elif [ -d /opt/rocm ] || command -v rocm-smi &>/dev/null; then
_BUILD_DESC="building (CPU, ROCm driver found but hipcc missing)"
else
_BUILD_DESC="building (CPU)"
fi
substep "$_BUILD_DESC..."
NCPU=$(nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
CMAKE_GENERATOR_ARGS=""
if command -v ninja &>/dev/null; then
CMAKE_GENERATOR_ARGS="-G Ninja"
fi
# GPU label for the CPU-fallback message: Metal, else GPU_BACKEND
# (cuda/rocm). Empty on a bare CPU build (nothing to fall back from).
_gpu_fallback_label() {
if [ "$_TRY_METAL_CPU_FALLBACK" = true ]; then
echo "Metal"
elif [ -n "$GPU_BACKEND" ]; then
printf '%s' "$GPU_BACKEND" | tr '[:lower:]' '[:upper:]'
fi
}
if ! run_quiet_no_exit "cmake llama.cpp" cmake $CMAKE_GENERATOR_ARGS -S "$_BUILD_TMP" -B "$_BUILD_TMP/build" $CMAKE_ARGS; then
_FB_LABEL="$(_gpu_fallback_label)"
if [ -n "$_FB_LABEL" ]; then
_TRY_METAL_CPU_FALLBACK=false
substep "$_FB_LABEL configure failed; retrying CPU build..." "$C_WARN"
rm -rf "$_BUILD_TMP/build"
if run_quiet_no_exit "cmake llama.cpp (cpu fallback)" cmake $CMAKE_GENERATOR_ARGS -S "$_BUILD_TMP" -B "$_BUILD_TMP/build" $CPU_FALLBACK_CMAKE_ARGS; then
_BUILD_DESC="building (CPU fallback after $_FB_LABEL configure failed)"
# Now configured for CPU; clear GPU_BACKEND so a later
# build-step failure won't re-enter fallback on this config.
GPU_BACKEND=""
else
BUILD_OK=false
fi
else
BUILD_OK=false
fi
fi
fi
if [ "$BUILD_OK" = true ]; then
if ! run_quiet_no_exit "build llama-server" cmake --build "$_BUILD_TMP/build" --config Release --target llama-server -j"$NCPU"; then
_FB_LABEL="$(_gpu_fallback_label)"
if [ -n "$_FB_LABEL" ]; then
_TRY_METAL_CPU_FALLBACK=false
substep "$_FB_LABEL build failed; retrying CPU build..." "$C_WARN"
rm -rf "$_BUILD_TMP/build"
if run_quiet_no_exit "cmake llama.cpp (cpu fallback)" cmake $CMAKE_GENERATOR_ARGS -S "$_BUILD_TMP" -B "$_BUILD_TMP/build" $CPU_FALLBACK_CMAKE_ARGS; then
_BUILD_DESC="building (CPU fallback after $_FB_LABEL build failed)"
GPU_BACKEND=""
run_quiet_no_exit "build llama-server (cpu fallback)" cmake --build "$_BUILD_TMP/build" --config Release --target llama-server -j"$NCPU" || BUILD_OK=false
else
BUILD_OK=false
fi
else
BUILD_OK=false
fi
fi
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "build llama-quantize" cmake --build "$_BUILD_TMP/build" --config Release --target llama-quantize -j"$NCPU" || true
fi
# Swap only after build succeeds -- preserves existing install on failure
if [ "$BUILD_OK" = true ]; then
_assert_studio_owned_or_absent "$LLAMA_CPP_DIR" "llama.cpp install"
rm -rf "$LLAMA_CPP_DIR"
mv "$_BUILD_TMP" "$LLAMA_CPP_DIR"
: > "$LLAMA_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
# Symlink to llama.cpp root -- check_llama_cpp() looks for the binary there
QUANTIZE_BIN="$LLAMA_CPP_DIR/build/bin/llama-quantize"
if [ -f "$QUANTIZE_BIN" ]; then
ln -sf build/bin/llama-quantize "$LLAMA_CPP_DIR/llama-quantize"
fi
else
rm -rf "$_BUILD_TMP"
fi
if [ "$BUILD_OK" = true ] && [ -f "$LLAMA_SERVER_BIN" ]; then
step "llama.cpp" "built"
[ -f "$LLAMA_CPP_DIR/llama-quantize" ] && step "llama-quantize" "built"
elif [ "$BUILD_OK" = true ]; then
step "llama.cpp" "binary not found after build" "$C_WARN"
_LLAMA_CPP_DEGRADED=true
else
step "llama.cpp" "build failed" "$C_ERR"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
fi
fi
}
fi # end _SKIP_GGUF_BUILD check
# ── Footer ──
if [ "$_LLAMA_ONLY" = "1" ]; then
echo ""
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_WARN}%s${C_RST}\n" "llama.cpp update finished (limited: llama.cpp unavailable)"
else
printf " ${C_TITLE}%s${C_RST}\n" "llama.cpp update finished"
fi
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
elif [ "$IS_COLAB" = true ]; then
echo ""
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_WARN}%s${C_RST}\n" "Unsloth Studio Setup Complete (limited: llama.cpp unavailable)"
else
printf " ${C_TITLE}%s${C_RST}\n" "Unsloth Studio Setup Complete"
fi
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
substep "from colab import start"
substep "start()"
else
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_WARN}%s${C_RST}\n" "Unsloth Studio Installed (limited: llama.cpp unavailable)"
else
printf " ${C_TITLE}%s${C_RST}\n" "Unsloth Studio Installed"
fi
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_DIM}%-15s${C_WARN}%s${C_RST}\n" "launch" "unsloth studio -p 8888"
else
printf " ${C_DIM}%-15s${C_OK}%s${C_RST}\n" "launch" "unsloth studio -p 8888"
fi
printf " ${C_DIM}%-15s%s${C_RST}\n" "" "(add -H 0.0.0.0 to allow network / cloud access)"
fi
echo ""
# When called from install.sh (SKIP_STUDIO_BASE=1), exit non-zero so the
# installer can report the GGUF failure after finishing PATH/shortcut setup.
# When called directly via 'unsloth studio update', keep the install
# successful -- the footer above already reports the limitation and Studio
# is still usable for non-GGUF workflows.
if [ "$_LLAMA_CPP_DEGRADED" = true ] && [ "${SKIP_STUDIO_BASE:-0}" = "1" ]; then
exit 1
fi