GitHub macOS runners (and any headless or virtualized Mac) have no usable
Metal, so the macos-arm64 prebuilt loads the validation model on CPU. The new
offload check rejected it and forced a source build that also runs on CPU and
then failed to launch, breaking 'Install + load (macos-*)' and 'Studio API &
Auth Tests'.
A CPU-only Metal load is an unfixable environment limitation, not a fixable
binary fault like a missing cudart/cublas DLL or a PTX-only CUDA build, so a
rebuild gives the same result. Split the GPU kinds into _GPU_INSTALL_KINDS
(launched with --n-gpu-layers, so a broken libggml-metal.dylib still surfaces)
and _GPU_OFFLOAD_REQUIRED_KINDS (CUDA/ROCm/HIP only, where a CPU-only load is
rejected). validate_server, the existing-install re-validation, the fast-path
reuse gate, the smoke-test require-signal, and the success message all key on
the offload-required set, so macOS accepts its prebuilt while CUDA/ROCm
rejection is unchanged.
run_smoke_spoof drives the rejection contract with a CUDA kind on every runner
(install_kind is explicit, so it is OS-independent) and adds a macOS-only case
asserting a CPU-only Metal load is accepted. New unit tests cover the split.
- direct_upstream_release_plan: Linux x86_64 and arm64 CPU branches now also
require not host.has_rocm, so a ROCm host whose HIP prebuilt is missing or
rejected never silently takes a CPU upstream tarball as a 'success'.
- setup.ps1: a Windows ROCm host with no usable HIP prebuilt and no HIP source
build path is now marked LlamaCppDegraded so the CPU last-resort installs a
clearly labelled CPU build instead of a 'built' install that runs on CPU.
- validate_server gains require_gpu_signal; smoke_test_server_binary sets it for
GPU install kinds so the '0 = offload confirmed' CLI contract treats a
no-GPU-signal log as inconclusive (EXIT_ERROR) rather than a silent pass.
- run_smoke_spoof + unit test cover the new no-signal-is-inconclusive contract.
A metadata match used to short-circuit straight to reuse, so a previously
installed CPU-only 'GPU' binary survived every rerun and restart -- the exact
'picks CPU forever' report. install_prebuilt now smoke-tests a matching GPU
install and reinstalls if it loads on CPU; setup.sh/setup.ps1 smoke-test a reused
source build on a GPU host and rebuild if it ran on CPU. Non-GPU installs keep
the fast path.
Reviewers found a real CPU-only log shape that passed: an 'offloading 0
repeating layers to GPU' planning line, or a GPU KV/compute buffer, could read
as GPU before the definitive 'offloaded 0/33' was seen. Check the explicit
counted offload first (any N>0 wins, all zero is CPU-only), restrict the
buffer-size signal to GPU model buffers (KV/compute on GPU with weights on CPU
is still CPU inference), and add HIP/MUSA/CANN to the model-buffer markers so an
older log naming those backends is not misread as CPU. Same in both classifiers.
Exclude host-pinned buffers (CUDA_Host etc.) from the GPU buffer-size signal so a
binary that pins host memory but loads weights on CPU is not misread as GPU
offload. Scan every 'offloaded N/M layers to GPU' line and accept if any N>0 so a
speculative draft model logging 0/k before the main model's 33/33 is not flagged
CPU-only. Same fix in the installer and runtime classifiers.
Adds a GPU-less smoke that drives install_llama_prebuilt.py --smoke-test against
a fake llama-server emitting CPU-only vs GPU device_info logs, asserting a
CPU-only binary tagged as a GPU install is rejected (exit 2) and a GPU one is
accepted (exit 0), plus the selection and classifier unit tests, on all three
OSes. This is the coverage gap that let the silent CPU-only path ship.
setup.sh/setup.ps1 now run install_llama_prebuilt.py --smoke-test on a freshly
source-built GPU binary and retry a CPU build if it loaded on CPU only. When a
GPU host's source build produces no binary, both scripts fall back to the CPU
prebuilt (--cpu-fallback) as a labelled last resort instead of leaving the host
without llama.cpp. setup.ps1 also guards an empty CUDA arch (no PTX-only binary,
#5854). The POSIX smoke-test exit code is captured set -e safe. Adds a fake
llama-server and an end-to-end spoof test that runs the real validate_server
against it with no GPU.
A llama-server whose GPU backend fails to initialize still serves HTTP 200 from
CPU, so install validation accepted it and Studio ran 'GPU' inference on CPU
(#5807/#5106/#5830). Add a log classifier (buffer-size, offloaded-layers, and
device_info signals) and reject a GPU-intended binary that loaded on CPU, so the
resolver advances. Crucially, a GPU host no longer gets a CPU prebuilt appended
to its simple-policy attempts, so when no GPU bundle offloads the installer falls
through to a source build for the native arch instead of silently installing CPU.
Add a --smoke-test CLI for setup scripts and align the runtime classifier with
the same signals so its CPU-only warning fires on current llama.cpp.
* Studio: cover B300 (sm_103) with the Linux prebuilt bundles
sm_103 (B300 / GB300 Blackwell Ultra) was in no bundle's supported_sms,
so those hosts fell through to a slow source compile. The newer and
portable bundles already ship base compute_100 PTX, which the driver
JIT-compiles forward to sm_103, so list sm_103 alongside sm_100 in those
bundles and let B300 install the prebuilt.
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setup.ps1 already forwards --has-rocm whenever AMD is detected regardless
of whether gfx resolved. setup.sh only forwarded --rocm-gfx, so a Linux
host where AMD was detected but gfx resolution failed would forward nothing,
leaving the installer with has_rocm=False and falling back to a source build.
Add the elif branch to forward --has-rocm when _setup_amd_detected is true
but _setup_gfx is empty, matching setup.ps1 parity. Add a source-level test
to verify both flags are present in setup.sh.
setup.sh and setup.ps1 resolve the AMD gfx target (rocminfo/hipinfo/amd-smi,
name inference, or UNSLOTH_ROCM_GFX_ARCH) but never passed it to
install_llama_prebuilt.py. The installer re-probed on its own and, on hosts where
hipinfo/amd-smi cannot report the arch (amd-smi-only Linux, HIP-runtime-only
Strix Halo, name-inferred GPUs), left rocm_gfx_target unset. The lemonade HIP
prebuilt selection needs that arch, so those hosts got no GPU prebuilt and fell
back to a source build.
Add a --rocm-gfx argument (defaulting to UNSLOTH_ROCM_GFX_ARCH) and fold it into
the host profile via _apply_host_overrides: a forwarded gfx is authoritative
(setup already applied visible-device selection) and implies has_rocm. setup.sh
and setup.ps1 now forward their resolved arch.
Add unit tests for _normalize_forwarded_gfx and _apply_host_overrides, plus
source checks that both setup scripts forward --rocm-gfx.
* Studio: fall back to source build for arm64 Linux GPU hosts
setup.sh routes a Linux host with any GPU tool to the unslothai/llama.cpp fork,
which publishes only linux-x64 bundles. On an arm64 host with a GPU (GH200,
GB200, DGX Spark) the resolver then selected an x64 CUDA bundle, which cannot run
on aarch64. Routing those hosts to ggml-org instead would install a CPU-only
arm64 build, silently losing the GPU.
Guard resolve_simple_install_release_plans so an arm64 Linux host on the fork
raises PrebuiltFallback before any release is fetched, letting setup.sh do a
source build that actually targets the GPU. x86_64 hosts and arm64 CPU hosts
(which route to ggml-org) are unaffected.
Add tests covering the arm64 fork raise, the x86_64 pass-through, and the arm64
CPU ggml-org path.
* Studio: install ggml-org arm64 CPU prebuilt when the arm64 GPU source build fails
Per review of #5924: arm64 Linux GPU hosts have no CUDA prebuilt anywhere (the
unslothai fork is x64 only, ggml-org ships no Linux CUDA build), so they source
build for the GPU. If that build produces no binary, the host was left without
llama.cpp.
Add a --cpu-fallback flag to install_llama_prebuilt.py that drops the host GPU
attributes so the CPU prebuilt for the host arch is selected (a GPU host cannot
otherwise pick the CPU bundle). setup.sh calls it against ggml-org as a last
resort for arm64 Linux when the source build degraded, installing the
ubuntu-arm64 CPU build instead of leaving the host with no llama.cpp.
Add tests: force_cpu drops GPU attrs before planning, a CPU-forced arm64 host
selects the ggml-org ubuntu-arm64 bundle, and setup.sh wires the fallback.
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The pinned b9360 cuda-13.1 Windows fallback for Blackwell (sm_120) only fired
when the driver advertised CUDA >= 13.1. The mainstream Blackwell branch ships
the r580 driver that reports CUDA 13.0, so those hosts missed the pin, were
gated off the in-release 13.3 build, and dropped to the CPU-only cuda-12.4
build.
b9360's binary is native sm_120a SASS (no PTX, so no JIT) and its bundled
runtime is cuda-13.1 cudart; both run on a 13.0 r580+ driver under CUDA
minor-version compatibility. Lower _PINNED_BLACKWELL_DRIVER_FLOOR to (13, 0) so
the pin covers the 13.0 branch too. The generic published-runtime gate stays
conservative (an unverified in-release 13.1 build is still gated off 13.0); only
the specific, hash-pinned, SASS-verified b9360 build is extended.
Refs #5887.
* Studio: match the Linux llama.cpp prebuilt to the runtime cudart major
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* Studio: defer the Windows CUDA Toolkit check so prebuilt users are not blocked
setup.ps1 section 1e validated the installed CUDA Toolkit against the driver in
Phase 1 and hard-exited when the toolkit was newer than the driver supports.
That ran before the prebuilt llama.cpp install (Phase 3.4), which is
self-contained and needs no local toolkit. A Blackwell host (sm_120) on a driver
that advertises CUDA 13.2 with CUDA Toolkit 13.3 installed was blocked entirely,
even though the prebuilt selector would have given it a working GPU build.
The toolkit (nvcc) is only consumed by the Phase 4 source build. Move section 1e
into a Resolve-CudaToolkit function and call it lazily at the source-compile
branch, gated on $HasNvidiaSmi. The prebuilt path no longer touches the toolkit;
forced/source builds keep the identical requirement, winget auto-install, and
error text via -RequireOrExit.
Add a pwsh unit test that extracts the real function and runs it against a
spoofed Blackwell sm_120 / driver 13.2 / toolkit 13.3 host: the prebuilt path
defers, the forced build still fails fast.
Refs #5879.
* Studio: correct a stale Phase-1 prerequisites comment (CUDA Toolkit is now resolved lazily)
* Studio: retrigger Windows GGUF CI (artifact upload flake)
Extracts the real guard block from install.sh and asserts: clean arm64 venv
untouched, x86_64 venv rebuilt as arm64, the x86_64-then-3.13.8 corner case,
arm64 3.13.8 downgrade preserved, --python skip, and Intel/Rosetta no-op.
Co-authored-by: Ramakrishna Bachu <ramankrishna10@gmail.com>
* Studio: pin the last pre-macOS-26 llama.cpp prebuilt instead of walking back
ggml-org moved their macOS build runner to macOS 26 (Tahoe) at b9428, so
b9428 and every newer upstream prebuilt is stamped minos 26 and fails to
dyld-load on macOS 14 / 15. #5883 handled this by walking back release by
release at install time.
Replace that with a deterministic pin: a host below macOS 26 selects b9415
directly (the last upstream build stamped below 26: arm64 minos 14, x64
minos 13.3), so it loads on macOS 13.3 / 14 / 15 / 26. Hosts on macOS 26+
and unknown-version hosts keep latest selection unchanged.
Only the ggml-org upstream path is pinned; the unslothai/llama.cpp fork
ships its own minos-13.3 prebuilts (#5893), so the pin is a no-op there and
goes dormant once macOS routes to the fork. The Mach-O minos preflight from
#5883 stays as a post-download backstop.
Refs #5883, #5893.
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* docs: fork ships arm64 minos 14 / x64 minos 13.3, not uniform 13.3
The per-slice fork producer pins arm64 to 14.0 and x64 to 13.3; update the
pinned_macos_release_tag docstring to match. No behavior change.
---------
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Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Make the NVIDIA prebuilt selection version-dynamic and driver-gated on both the
Windows (ggml-org) and Linux (unslothai/llama.cpp) paths, so CUDA version bumps
are handled with no code change while staying safe across driver versions.
- Derive candidate CUDA runtime lines from the driver major; pick the highest
upstream-published minor the driver can actually run, so a sub-13.3 driver is
never handed an unguaranteed 13.3 build.
- Pin b9360's cuda-13.1 build (immutable, hash-verified) as a GPU fallback for a
Blackwell host on a 13.1/13.2 driver that the in-release 13.3 build gates off,
on both the simple and published install paths. Dormant for every other host
and self-disabling once upstream ships a driver-runnable build again.
- Seed the published-path ordering from the release's real published minors so a
future CUDA major is selectable with no code change.
Refs #5861, #5817, #5807, #5858, #5854, #5826, #5887.
Make macOS llama.cpp prebuilt selection host-OS-version aware: skip a prebuilt whose minimum-OS exceeds the host and walk back to the newest release that loads (macOS 26 keeps latest; 14/15 land on a compatible older release). Source-build fallback pins CMAKE_OSX_DEPLOYMENT_TARGET=13.3. CI: binary-load assertion plus a macos-14/15/26 install matrix. No change to Linux/Windows or CUDA selection.
Follow-up cleanups to the merged AMD ROCm support PR #5301:
1. De-duplicate the torchao Windows-ROCm import stub into a single shared
module (studio/backend/core/_torchao_stub.py); both workers call one
install_torchao_windows_rocm_stub() entrypoint.
2. Align the gfx name/arch comment columns in setup.sh and setup.ps1.
3. Isolate the float16 dtype fallback to AMD without native bf16; NVIDIA
keeps dtype=None so unsloth's own bf16/fp16/FORCE_FLOAT32 detection is
honored.
4. Hoist unconditional stdlib imports (gc, glob, re, subprocess, copy,
types, sys, importlib.metadata) from function bodies to module top
across the PR #5301-touched files; heavy/optional/relative imports stay
lazy.
5. bitsandbytes Windows-ROCm install now uses plain pip (force_pip=True)
instead of UV_SKIP_WHEEL_FILENAME_CHECK, per the AMD hackathon docs.
Also adds scripts/verify_import_hoist.py (a scope-aware LEGB AST resolver
that catches dangling-alias and rename-clash bugs in import-hoist
refactors) and wires it into the Lint CI source-lint job as a self-test
plus a pull_request compare gate.
* fix(studio): set HIP_VISIBLE_DEVICES in apply_gpu_ids for ROCm training workers
Training workers are spawned via multiprocessing spawn before detect_hardware()
runs, so IS_ROCM is still False. If the user never set HIP_VISIBLE_DEVICES in
their shell, _inherits_rocm_visibility is also False, leaving the worker with
only CUDA_VISIBLE_DEVICES set. On ROCm hosts the HIP runtime honors
HIP_VISIBLE_DEVICES over CUDA_VISIBLE_DEVICES, so the worker saw the full
device list and torch raised "no usable HIP accelerator" on some setups.
Fall back to probing torch.version.hip (a build-time attribute, safe to read
before GPU init) to detect ROCm when neither IS_ROCM nor inherited env vars
are available. Mirrors the existing fix in llama_cpp.py for llama-server
subprocess GPU pinning.
Fixes https://github.com/unslothai/unsloth/issues/5180
* test: tighten apply_gpu_ids ROCm fallback assertions
Replace loose OR chain with exact string matches, split into three
focused tests, and add a guard check for the try/except wrapper.
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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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* perf: drop --no-cache-dir from AMD ROCm torch wheel installs
uv caches downloaded wheels by default; passing --no-cache-dir forced a
full redownload of the ~2 GB torch wheel on every install run. CUDA installs
never had this flag -- AMD was the only path affected.
* fix: use install-state flag instead of subprocess probe for AMD Windows warning
Replace the subprocess torch probe in the post-install warning block with a
module-level _rocm_windows_torch_installed flag set by _ensure_rocm_torch().
Subprocess re-import of torch is unnecessary and fragile -- the install
function already knows whether it succeeded.
* fix: hoist global declaration to top of _ensure_rocm_torch
Python requires the global statement to appear before any assignment
to the variable within a function. Moving it to the function top fixes
the SyntaxError on line 354.
* fix: pass AMD torch install status via env var to suppress false warning
setup.ps1 now sets UNSLOTH_ROCM_TORCH_INSTALLED=1 after a successful AMD
wheel install. install_python_stack.py reads this at the top of
_ensure_rocm_torch() to skip both the subprocess probe and the warning --
no re-import of torch needed, and the warning message now correctly says
'could not be auto-installed' rather than 'must be installed manually'.
* fix: register ROCm DLL directory before torch import on Windows
Python 3.8+ ignores PATH for extension DLL loading on Windows; amdhip64.dll
and other HIP runtime DLLs must be registered via os.add_dll_directory().
Without this, torch.cuda.is_available() always returns False on AMD ROCm
Windows even when HIP_PATH is correctly set in system environment variables.
Reads HIP_PATH / ROCM_PATH env vars first, then falls back to scanning
common ROCm install roots (C:\Program Files\AMD\ROCm, F:\ROCm, C:\ROCm).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: remove hardcoded non-standard ROCm paths from DLL directory scan
Only use HIP_PATH/ROCM_PATH (set by AMD installer) and the standard
C:\Program Files\AMD\ROCm\<version>\bin location. Custom drive paths
like F:\ROCm are user-specific and should not be hardcoded.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: prevent torchao overrides step from overwriting AMD ROCm torch
torchao==0.14.0 in overrides.txt declares torch as a dependency. Without
--no-deps, uv resolves torch from PyPI and installs 2.11.0+cpu on top of
the AMD ROCm wheels (2.9.0+rocmsdk20251116). This was the root cause of
'Hardware detected: CPU' -- the AMD wheels were installed but then
immediately overwritten by the overrides step.
When _rocm_windows_torch_installed is True, add --no-deps to the overrides
pip_install call so torchao is installed without pulling in CPU torch.
* fix: add rocm_sdk namespace tarball to Windows ROCm wheel installs
torch/_rocm_init.py calls `import rocm_sdk` at startup, which requires
the rocm namespace tarball (rocm-*.tar.gz) in addition to the SDK wheel
packages. This tarball was missing from both install.ps1 and setup.ps1,
causing ModuleNotFoundError on first torch import.
- Add rocm-0.1.dev0.tar.gz to ROCm 7.1.1 install (provides rocm_sdk namespace)
- Add rocm-7.2.1.tar.gz + rocm_sdk_devel to ROCm 7.2.1 install
- Install tarball in a dedicated step before main SDK/torch wheels
- Switch to @array splatting in install.ps1 scriptblock for dynamic wheel count
- Remove --no-cache-dir from Python-side ROCm wheel install (prevents ~2GB redownload)
* feat: enable ROCm 7.2 torch install + warn on gfx1151 with ROCm < 7.2
Chigoma333 (AMD Radeon 8060S / gfx1151, Strix Halo) confirmed that ROCm
7.1 segfaults when tensors are moved to GPU, but ROCm 7.2 + torch
2.11.0+rocm7.2 works fully including training.
Changes:
- Uncomment (7,2): "rocm7.2" in _ROCM_TORCH_INDEX (was blocked by <2.11.0)
- Add _ROCM_TORCH_PKG_SPECS dict with per-tag version bounds:
rocm7.2 → torch>=2.11.0,<2.12.0; all older tags → <2.11.0
- Add _detect_amd_gfx_codes() helper that parses rocminfo output
- Warn on gfx1151/gfx1150 (Strix Halo) when ROCm < 7.2 is installed,
pointing users at the known segfault and recommending upgrade
- install.sh get_torch_index_url(): enable rocm7.2 case (previously capped
to rocm7.1), cap unknown future tags to rocm7.2
- install.sh: override TORCH_CONSTRAINT to >=2.11.0,<2.12.0 when rocm7.2
index is selected, so pip can actually resolve torch 2.11.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: suppress remaining console popups on Windows, patch torch.distributed.is_initialized for ROCm #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: stub all missing torch.distributed attrs for ROCm Windows wheel #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: inject torch.distributed stub when C backend missing in ROCm Windows wheel #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: add coverage for Windows ROCm install paths and worker patches
Add conftest.py to fix pre-existing sys.path issue that prevented
test_rocm_support.py from running at all (install_python_stack.py
imports from backend.utils.wheel_utils which needs studio/ on sys.path).
New test classes cover everything added in this session:
- TestWindowsRocmIndexUrl: arch → AMD pip index URL mapping (gfx120X-all,
gfx1151, gfx1150, gfx110X-all, unknown → None, trailing slash)
- TestDetectWindowsGfxArch: hipinfo output parsing, missing/timeout/bad
returncode/no-gcnArchName paths
- TestInstallBnbWindowsRocm: UV_SKIP_WHEEL_FILENAME_CHECK set+restored,
env restored on exception, no-op when URL missing
- TestRocmTorchInstalledEnvVar: UNSLOTH_ROCM_TORCH_INSTALLED=1 skips
pip_install, calls _install_bnb_windows_rocm, sets flag
- TestWorkerWindowsRocmPatches: _grouped_mm CUDA dispatch override,
offs/grouped variant handling, GC-prevention sentinel,
_StubTypeMeta __instancecheck__, _StubSubpackageFinder registration,
torchao key submodule pre-stubbing, TORCHDYNAMO_DISABLE guard
- TestRocmTorchPkgSpecs: rocm7.2 torch 2.11.x spec, default <2.11 cap,
3-tuple shape, _GFX_TO_AMD_INDEX_ARCH RDNA4/3.5/3 coverage
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: fix encoding, IS_WINDOWS patching, and wrong assertion
- Add encoding="utf-8" to all read_text() calls (54 occurrences) so
tests pass on Windows where the default codec is cp1252 and source
files contain UTF-8 emoji (e.g. ⚠️ in install_python_stack.py)
- Add @patch.object(stack_mod, "IS_WINDOWS", False) to Linux-path
TestEnsureRocmTorch tests so they reach the Linux code path when run
on a Windows machine instead of short-circuiting into the Windows branch
- Fix test_grouped_mm_patch_guarded_by_windows_and_hip_check: the source
uses getattr(_torch_for_rocm, "version", None) not torch.version, so
check for '"version"' and '"hip"' substrings instead
137 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: detect BNB ROCm DLL suffix dynamically instead of hardcoding '72'
BNB_ROCM_VERSION was pinned to '72' which works today (AMD wheel ships
rocm72.dll) but would break again if AMD ships a future wheel with a
different DLL suffix (e.g. rocm713.dll).
Add _detect_bnb_rocm_dll_ver() to install_python_stack.py: scans the
installed bitsandbytes package dir for libbitsandbytes_rocm{VER}.dll
using importlib.util.find_spec (no BNB import needed) and returns the
suffix. '72' remains the fallback when detection fails.
Apply the same detection inline in worker.py section 1f. Both paths
still respect a pre-set BNB_ROCM_VERSION (caller override wins).
Tests: +8 cases covering detection logic and fallback (147 passed, 2 skipped).
* fix: patch torch.distributed stubs in server process for Windows ROCm
On Windows ROCm, torch.distributed ships without process-group helpers
(is_initialized, is_available, get_rank, get_world_size). The worker
subprocess already patches these in section 1e, but the main server
process calls _determine_attention_impl_for_gpu_estimate() which calls
unsloth's resolve_attention_implementation() → is_initialized(), causing:
"Could not resolve attention implementation for '...':
module 'torch.distributed' has no attribute 'is_initialized'"
Fix: patch the missing attrs onto torch.distributed at the top of
_determine_attention_impl_for_gpu_estimate, matching the same stubs
already applied in worker.py section 1e. No-ops on Linux/CUDA where
torch.distributed is fully populated.
* fix: gate _grouped_mm dispatch patch on HIP < 7.13
AMD fixed the gfx1200 null HIP kernel in ROCm 7.13 (torch 2.11+).
Users on the new wheel now get the real GPU _grouped_mm kernel for
MoE workloads instead of the Python mm fallback.
Changes:
- worker.py: add _hip_ver_at_least() helper; wrap full _grouped_mm
patch in `if not _hip_ver_at_least(7, 13):` with else branch that
logs the skip reason; update section-1f comment to document the fix
- test_rocm_support.py: add 5 tests covering the helper definition,
the (7, 13) gate expression, the else branch, the skip log message,
and the AMD-format version string parsing (.split(".")[:2])
Verified: torch==2.11.0+rocm7.13.0 — 3D batch and grouped (offs)
variants both succeed; null crash only present on rocm7.12 and earlier.
* fix: stub is_torchelastic_launched on torch.distributed for Windows ROCm
resolve_attention_implementation calls is_torchelastic_launched() which
does not exist in the incomplete torch.distributed shipped with the
Windows ROCm wheel, causing a warning on every model config load in the
server process. Add it to the stub table alongside the four helpers
already patched in _determine_attention_impl_for_gpu_estimate.
Also adds two tests: one confirming the new stub and one confirming all
five core distributed helpers are covered.
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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
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* 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
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* 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
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* 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
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* 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.
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* 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).
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* 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).
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* 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.
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* fix: remove ADL ctypes — does not support AMD iGPU (Strix Halo)
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Erland366 <erland.pg366@gmail.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* Studio setup.sh: cope with fresh CUDA toolkits like 13.3
CUDA 13.3 shipped today. Three loose ends in studio/setup.sh surfaced
during the llama.cpp build path:
1. setup.ps1 already aborts cleanly when the CUDA toolkit is below
llama.cpp's minimum (12.4) via #4517, but setup.sh still hit the
generic cmake failure described in #4437. Added a min-version check
that downgrades to a CPU build for nvcc < 12.4 with a clear message
pointing to the toolkit archive.
2. The first day a new CUDA toolkit ships, its host-compiler whitelist
lags whatever gcc/clang the distro is on, so nvcc rejects the host
compiler with a wall of "#error -- unsupported GNU version" before
any real compile runs. NVCC_PREPEND_FLAGS now carries
-allow-unsupported-compiler so the build moves on instead.
3. The Linux CUDA/ROCm configure failure path had no symmetry with the
macOS Metal fallback: a single nvcc failure left BUILD_OK=false and
no llama.cpp at all. Generalised the existing Metal -> CPU fallback
to cover any GPU_BACKEND, so a CUDA configure or build failure now
transparently retries with the CPU args and the user still ends up
with a working llama-server.
Pulled the version probe out into _nvcc_meets_llama_minimum so it can
be unit-tested. Added tests/sh/test_nvcc_meets_llama_minimum.sh and two
extra cases in tests/sh/test_get_torch_index_url.sh covering the legacy
"CUDA Version: 13.3" header (driver-reported) and the future 13.7
case. Wired the new test into tests/run_all.sh and the studio-backend
CI workflow.
* tests: relax pr4562 regression to allow generic GPU fallback label
* studio tests: assert setup.sh exports NVCC_PREPEND_FLAGS=-allow-unsupported-compiler
The -allow-unsupported-compiler flag is the core of the fresh-CUDA-toolkit fix
(it lets nvcc accept a host gcc/clang newer than its release-time whitelist, so
CUDA 13.3 day-one builds do not abort on '#error -- unsupported GNU version'),
but it had no automated coverage. Add a source-pattern test asserting the flag
is present, delivered via NVCC_PREPEND_FLAGS so it also covers cmake's CUDA
compiler-id probe, and kept out of CMAKE_ARGS for bash word-splitting safety.
* studio/setup.ps1: allow unsupported host compiler for CUDA build (Windows parity)
Mirror the Linux setup.sh headline fix from this PR on Windows. A freshly
released CUDA toolkit ships with a host-compiler whitelist that lags the
installed toolchain, so nvcc can reject the host with
"#error -- unsupported Microsoft Visual Studio version!" before any real
compile runs (the MSVC analogue of the gcc wall the Linux side hit on
CUDA 13.3). Set NVCC_PREPEND_FLAGS=-allow-unsupported-compiler in the CUDA
build branch so both cmake's configure-time CUDA compiler-id probe and the
cmake --build step proceed. The flag disables the host version check only and
is a no-op when the compiler is already supported.
Set via the process environment (not the $CmakeArgs array), after the
Refresh-Environment calls that re-sanitize CUDA env vars, and appended
idempotently to any value the user already set.
Validated with PowerShell 7.6.2: full setup.ps1 AST parse is clean and the
snippet is idempotent (empty -> set, existing -> append once, no duplicate).
Needs real Windows + CUDA CI to exercise the actual nvcc/MSVC build.
Adds test_setup_ps1_exports_allow_unsupported_compiler asserting the flag is
present, env-delivered, kept out of $CmakeArgs, and scoped to the CUDA-on branch.
* studio: tighten code comments added in this PR
Shorten the verbose multi-line comments and test docstrings introduced by
this PR (setup.sh, setup.ps1, and the shell/python tests) to be succinct
while preserving the rationale. No code or test-assertion changes.
* Detect CUDA UMD Version from newer nvidia-smi output (#5812)
Newer NVIDIA drivers (e.g. 610.x on Windows) print the driver's max
CUDA capability as "CUDA UMD Version: X.Y" instead of the legacy
"CUDA Version: X.Y" header. The installers and Studio setup scripts
were only matching the legacy spelling, so on a fresh RTX 5090
laptop with a 13.x driver they failed to detect any CUDA version
and fell through to the cu126 wheel default.
Accept both spellings everywhere we parse nvidia-smi output:
- install.ps1: Get-TorchIndexUrl regex now allows " UMD"
- install.sh: two-expression sed (POSIX BRE has no "?"); the two
patterns are mutually exclusive per line, head -1 picks the match
- studio/setup.ps1: Get-PytorchCudaTag and the $DriverMaxCuda
detector both relaxed
- studio/install_llama_prebuilt.py: substring scan replaced with a
regex search using the same pattern
- tests/sh/test_get_torch_index_url.sh: new make_mock_smi_umd helper
plus three UMD cases (13.3 -> cu130, 12.8 -> cu128, 11.8 -> cu118);
all 30 tests pass locally
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Run the Linux llama.cpp prebuilt dependency preflight before reusing an existing install, so cached trees missing newly required per-tool shared libraries (libllama-server-impl.so / libllama-quantize-impl.so introduced upstream between b9279 and b9283) trigger a repair instead of silently skipping reinstall and failing at runtime. Adds a regression test for the new lib*-impl.so overlay layout.
* Studio: unblock cross-platform install on Linux ARM64 + Windows ARM64
Three independent bugs that together prevent `install.sh` /
`install.ps1` from completing on the ARM machines GitHub Actions now
ships (`ubuntu-24.04-arm`, `windows-11-arm`) and on equivalent real
hosts (Ampere Altra, Raspberry Pi 5, Snapdragon X Elite, ...).
Validated on the staging-2 cross-OS smoke suite -- five per-OS
workflows pinned to `ubuntu-latest`, `ubuntu-24.04-arm`, `macos-14`,
`macos-15-intel`, `windows-11-arm`. Before this change Windows ARM
exits 1 in the winget gate and Linux ARM source-builds llama.cpp
because the prebuilt selector returns 0 attempts; with it both reach
healthy /api/health.
1. studio/install_llama_prebuilt.py -- resolve_simple_install_release_plans
had explicit branches for windows+x86_64, macos+arm64, macos+x86_64
and linux+x86_64 only. Upstream ggml-org/llama.cpp ships
`llama-bNNNN-bin-ubuntu-arm64.tar.gz` and
`llama-bNNNN-bin-win-cpu-arm64.zip` (visible in the b9334 release
manifest), so the missing elif branches force every Linux ARM64 and
Windows ARM64 host into a source build even when a perfectly good
upstream prebuilt is one HTTP GET away. Two new branches mirror the
existing CPU variants; runtime_patterns_for_choice and
runtime_payload_health_groups gain `linux-arm64` (.so layout) and
`windows-arm64` (.dll layout) so the health-check pass-through
matches the asset shape.
2. studio/setup.sh -- the helper-release-repo selector routed any
non-x86_64 Linux to `unslothai/llama.cpp`, which only publishes the
Linux CUDA bundle set. The result on Linux ARM64 was a guaranteed
`direct_linux_release_plan` raise of "no compatible Linux prebuilt
asset was found" on every release in the scan, then a source-build
fallback. Pin Linux ARM64 (CPU-only) to `ggml-org/llama.cpp` so the
new branch in (1) can see the upstream asset. setup.ps1 already
hardcodes `ggml-org/llama.cpp`, so Windows ARM64 picks up (1)
without an additional change.
3. install.ps1 -- the winget pre-check hard-failed before Python or uv
detection. `windows-11-arm` runners (and many corporate Windows
hosts without the Microsoft Store) ship without winget but already
have a usable Python plus the Astral uv PowerShell installer
reachable. Demote the winget check to a soft warning, defer the
hard failure to the Python install branch (which is the only path
that genuinely needs winget), and let the uv install fall through
to `https://astral.sh/uv/install.ps1` when winget is absent. The
uv PowerShell installer was already the existing fallback for the
"winget present but uv install failed" case; this just makes it
the primary path on hosts without winget.
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* Studio: filter torchcodec on platforms without wheels
torchcodec 0.10.0 ships wheels for manylinux_2_28_x86_64,
macosx_12_0_arm64, and win_amd64 only -- visible on its PyPI page and
in the resolver error reported by #4446. install_python_stack.py
pulls torchcodec via extras-no-deps.txt, which is now installed
unconditionally during `unsloth studio update --local` (the update
command has no --no-torch flag). Result on Linux aarch64 /
Windows ARM64 / Intel Mac (when invoked outside the install.sh
auto-skip-torch path):
ERROR: Could not find a version that satisfies the requirement
torchcodec==0.10.0 (from versions: 0.0.0.dev0, ...)
ERROR: No matching distribution found for torchcodec==0.10.0
error Installing extras (no-deps) (pip) failed (exit code 1)
`NO_TORCH_SKIP_PACKAGES` already lists torchcodec but only fires
when NO_TORCH is true -- the update path inherits no NO_TORCH from
the original install and inferrence falls back to IS_MAC_INTEL only,
so Linux aarch64 / Windows ARM64 sail past the guard. Adds a
platform predicate PLATFORM_LACKS_TORCHCODEC_WHEEL and applies the
torchcodec filter unconditionally there, independent of NO_TORCH.
Surfaced by the staging-2 cross-OS smoke `unsloth studio update`
step on ubuntu-24.04-arm; verified the same step is green with this
patch overlaid.
* Studio: skip librosa on no-torch hosts (unblocks Intel Mac install)
Closes the last cross-platform install gap surfaced by the staging-2
cross-OS smoke (see unslothai/unsloth#5046 for the original report):
`install.sh --local` on macos-15-intel fails at
× Failed to build `llvmlite==0.47.0`
error: failed-wheel-build-for-install
╰─> llvmlite
error studio setup failed (exit code 1)
Root cause: upstream llvmlite dropped the macosx_x86_64 wheel between
0.42.0 and 0.46.0 (https://pypi.org/project/llvmlite/0.47.0/#files --
only macosx_arm64 / manylinux / win_amd64 remain). pip falls back to
a from-source build of llvmlite's FFI, which needs LLVM 14/15 dev
headers and matching llvm-config -- not present in Xcode Command
Line Tools' libclang and not installed by install.sh's MAC_INTEL
deps branch.
llvmlite enters Studio's tree via librosa -> numba -> llvmlite in
extras.txt. openai-whisper (extras.txt:28) would also pull numba but
is already filtered on no-torch hosts. Adding librosa to the same
NO_TORCH_SKIP_PACKAGES set makes the install go through cleanly on
Intel Mac (auto-detected NO_TORCH=true via the MAC_INTEL branch) and
on any user-passed --no-torch host where torch-dependent audio
pipelines would not run anyway.
Tracked / verified on the danielhanchen/unsloth-staging-2#154 smoke
matrix (macos-15-intel).
* Studio UI tests: retry evaluate_fetch on transport-level failure (PR #5790)
Mac Studio UI CI on this PR (run 26496820814, job 78026959359) failed
with /api/models/list status=0 error='TypeError: Failed to fetch'.
The artifact studio.log shows the server answered the two preceding
/api/models/list calls from the React mount (both 200) but never
received the third call from the test script: the browser reused a
kept-alive HTTP/1.1 socket that uvicorn (5s keep_alive_timeout) had
closed ~130ms earlier. Chromium under --single-process on macos-14
free runners is most prone to this; the post /api/auth/change-password
session churn accelerates it. A rerun on the same SHA passed, which is
the classic flake signature.
evaluate_fetch in tests/studio/_playwright_robust.py already returns a
structured {status: 0, body: None, error: "..."} on JS-side throws, but
every caller treats status=0 as fatal. Add a bounded retry inside the
helper so the one class of failure recovers transparently:
status != 0 -> real HTTP response (incl. 4xx/5xx); propagate.
error has "AbortError" -> caller's AbortSignal deadline; propagate.
else (status==0) -> stale-keepalive or other transport failure;
retry after 250ms / 500ms backoff so the pool
evicts the dead socket before the next attempt.
Defaults transport_retries=2, transport_backoff_ms=250 (max added
latency on the happy path is zero; on a transport failure: up to
750ms of sleep). Callers keep the existing {status, body, error} shape;
no call-site changes needed.
Verified: tests/studio/_playwright_robust.py compiles; signature
gains two kwonly args (transport_retries, transport_backoff_ms);
8 evaluate_fetch call sites in playwright_chat_ui.py +
playwright_extra_ui.py pick up the retry without change.
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* tool mask support
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* Handle tool masks with older zoo builds
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* Keep tool mask implementation in zoo
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* tests: unblock three stale assertions broken on main
MLX CI on Mac M1 + Backend CI (both Repo tests CPU and Python 3.10/11/12/13)
have been red on every push to main for days. None of the underlying code
is wrong; three test files have stale anchors / assertions left behind by
PR #5537 (max_steps bump) and PR #5775 (composer + provision-desktop-auth).
1. tests/studio/run_real_mlx_smoke.py:393
PR #5537 bumped max_steps from 7 to 30 for seed-robust convergence but
left `assert len(losses_per_step) == 7`. With logging_steps=1 the
callback fires once per step; 30 entries, not 7. Track config.max_steps
so the gate auto-follows future bumps.
2. tests/studio/test_composer_rtl_bidi_attribute.py:29
PR #5775 changed the composer aria-label from the literal
`aria-label="Message input"` to a JSX ternary
`aria-label={overlay ? "Image edit instructions" : "Message input"}`.
Anchor on the inner string literal `"Message input"` instead.
3. studio/backend/tests/test_desktop_auth.py:487
The guarded_import in test_provision_desktop_auth_writes_secret_and_creates_db_without_backend_deps
blocks any import whose name == "utils", including the relative
`from .utils import echo` inside typer._click.decorators (typer 0.25+).
Gate the block on level == 0 so only absolute imports of `utils` /
`auth` / `fastapi` / `structlog` are rejected; relative imports
inside third-party packages pass through.
All three tests pass locally; the MLX one is a mechanical 7->config.max_steps
swap and will be exercised by MLX CI on this PR.
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* Studio: expose --parallel / -np on `unsloth studio run`
The CLI was hardcoding `llama_parallel_slots=4` in `run_kwargs` at
`unsloth_cli/commands/studio.py`, leaving users unable to tune the
concurrent decode slot count even though the engine, KV-cache math,
and `studio.backend.run.run_server(llama_parallel_slots=...)`
plumbing all already accepted any N. This change adds a `--parallel`
/ `--n-parallel` / `-np` typer option (default 4 -- matches the
previous hardcoded value), forwards it into `run_kwargs`, and pins
the new surface with 4 unit tests.
Per-request state in `routes/inference.py` is already isolated
(`cancel_event` and `prev_text` are per-request locals in every
streaming handler; the `_lock` / `_serial_load_lock` only wrap
load/unload, not chat completions), so no concurrency refactor is
needed alongside this -- the engine layer already handles N
concurrent requests on one loaded model when llama-server is told
to.
Range guards: 1 <= N <= 64. With higher N each slot gets ctx/N KV
cache; users tuning this should be aware that per-call context
shrinks proportionally.
`unsloth studio` (the bare default command, no subcommand) still
defaults to llama_parallel_slots=1 via `run_server`'s own default;
this PR does not change that path -- it only exposes the knob on the
one-liner `studio run` command that already silently used 4.
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* Forward --parallel through venv re-exec and drop colliding short aliases
`unsloth studio run` re-execs into the Studio venv when invoked from
outside it (the common path). The arg-builder forwards every typer
option but the new --parallel, so the child re-execs at the default 4
and any user value is silently dropped. Worse: pre-PR users who
already pass `-np N` as a pass-through extra (where llama.cpp's
last-wins parsing made it stick) silently lose N after this PR lands.
Forward --parallel explicitly in the re-exec arg list.
While auditing the re-exec path, also drop the colliding 1-char
short aliases -m (--model) and -f (--frontend) plus the redundant
-hfr. Click's short-option clustering had been silently mis-parsing
~11 llama-server short flags via the pass-through path: -fa as
`-f a`, -mg 0 as `-m g` + stray 0, -fitt 1024 as `-f itt` + stray
1024, -hff path as `-f f` + stray `-h path`, -cmoe / -cram / -sm /
-ncmoe etc. The docstring promise ("any flag this command does not
recognize is forwarded verbatim") was silently violated.
-hf (2-char) is kept because Click treats multi-char shorts atomically
(no clustering of -hff / -hfv / -hffv / -hft) and -hf is documented
in basics/api/README.md. --model / --hf-repo / --frontend long forms
all unchanged. studio_default keeps -f because it has no pass-through.
Tests:
- test_studio_run_parallel_flag.py: 8 new re-exec coverage cases
(all 3 aliases, 3 platforms via sys.platform mock, pre-PR `-np`
regression, mixed with pass-through extras).
- test_studio_run_short_alias_clashes.py (new): surface checks that
the removed shorts cannot reappear, plus 11 parametrized cases
proving each previously-broken llama-server short flag now passes
through verbatim, plus a happy-path test that documented -hf still
works for `org/repo:variant` syntax.
All 27 tests pass. Negative test (revert either fix) shows the new
tests catch the regression.
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* Fix stale studio run docstring describing rejected llama-server flags
The pre-PR docstring listed --port, -c / --ctx-size, --api-key, -ngl,
--jinja, --flash-attn, --no-context-shift as "rejected with HTTP 400",
but only --port and --api-key (plus other networking / auth / model
identity / single-model UI flags) are actually in
studio/backend/core/inference/llama_server_args.py's denylist. -c /
-ngl / --jinja / --flash-attn / --no-context-shift are pass-through
and last-wins-override Studio's auto-set value.
Rewrite the docstring to match the real denylist groups and point at
the canonical source. Also add --parallel to one of the examples now
that it is a first-class flag.
* ci: broaden Linux + narrow Windows llama.cpp runtime patterns + trim #5741 comments (#5746)
* ci: broaden Linux llama.cpp runtime pattern to lib*.so*
#5741 patched the explicit Linux pattern list to add
``libllama-*-impl.so*`` after ggml-org/llama.cpp#23462 (between
b9279 and b9283) split each binary's entry code into a paired
``lib<binary>-impl.so`` shared library. Same class of upstream
repackaging will hit us again whenever a new shared lib is added.
Mirror what macOS already does and replace the per-lib list with a
single ``lib*.so*`` glob. ``copy_globs`` (line 3614) unions
patterns, so the per-variant ``libggml-cuda.so*`` / ``libggml-hip.so*``
entries were never filtering anything; the spec lives in
``runtime_payload_health_groups`` (line 5209) which keeps the
explicit minimum-required list per variant.
Dry-run against b9296-bin-ubuntu-x64.tar.gz: 40 files copied (all
ggml, llama, mtmd, impl variants + the two binaries we ship), 22
skipped (other CLIs, rpc-server, LICENSE). Functionally equal to
the post-#5741 set.
* cleanup: trim #5741 comments on the pydantic split
Comments added in #5741 explained the original bug in full each
time. They are mostly redundant with the commit message and the PR.
Trim them to one short paragraph per site.
No behavior change.
* ci: narrow Windows runtime pattern to llama-server.exe + llama-quantize.exe
Studio only invokes llama-server and llama-quantize. Mac and Linux
already filter to those two binaries; Windows was the odd one out
with ``*.exe`` copying every CLI upstream ships (llama-cli,
llama-bench, llama-mtmd-cli, ...).
Dry-run on b9296 (win cpu-x64, cpu-arm64, cuda-13.1, hip-radeon):
20 unused EXEs skipped per variant, all DLLs (incl. the new
llama-*-impl.dll family) still copied via ``*.dll``.
``existing_install_matches_choice`` already checks llama-server.exe
exists explicitly (line 5297), so the health gate is unchanged.
* Lower default weight_decay in RL config from 0.01 to 0.001 (#5747)
In full FT, AdamW weight decay shrinks the parameter directly so the
implicit prior is W -> 0. In LoRA the trained parameters are A and B
while the effective weight is W = W_init + (alpha/r) * B @ A; decaying
A and B separately drives BA -> 0, hence W -> W_init rather than 0.
The previous default of 0.01 inherited from full-FT recipes adds a
measurable pull on the merged adapter back toward the base model over
a few thousand steps. 0.001 keeps a small Frobenius-norm prior on
||A||^2 + ||B||^2 for numerical stability without meaningfully biasing
the merged weight toward init, and aligns with the value used across
the unsloth notebook templates.
* Studio: strip orphan tool_call XML leaking into visible content (#5735)
* Studio: strip orphan tool_call XML from streamed visible content
The speculative-buffer state machine in
`studio/backend/core/inference/llama_cpp.py` can slice a tool_call XML
block between the silent DRAINING path and the user-visible
content_accum, depending on when in the model's emission the BUFFERING
-> STREAMING -> DRAINING transitions fire. Three leak shapes were
observed in a 2026-05-22 sweep of 900 Qwen3.5 / Qwen3.6 GGUF runs:
Pre-fix XML leak rate: 20/900 (2.22%), concentrated 6.7% on the
larger Q8 / MTP configs:
Qwen3.6-35B-A3B Q8_0 4/60 (6.7%)
Qwen3.6-35B-A3B-MTP Q4 4/60 (6.7%)
Qwen3.5-35B-A3B Q8_0 3/60 (5.0%)
Qwen3.6-27B Q8_0 3/60 (5.0%)
The existing `_TOOL_XML_RE` only matched well-formed
`<tool_call>...</tool_call>` and `<function=...></function>` pairs, so
unterminated openings (close was DRAINED) and orphan closes (opening
was DRAINED) survived the strip and reached the user.
Fix relaxes the regex to also strip:
1. Orphan opening up to end-of-string: `(?:</tool_call>|\Z)`
2. Orphan closing tag: bare `</tool_call>` / `</function>`
Verified on the full sweep: 20/900 -> 0/900 (100% of detected leaks
eliminated). 16 unit tests in `test_tool_xml_strip.py` pin all three
leak shapes plus the well-formed cases, plus parametrised checks on
the 5 actual real-world leak samples from the sweep data.
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* Studio: strip tail-only </parameter> orphan + tighten regex
The 2026-05-22 gdpval sweep surfaced a 4th XML-leak shape not caught
by the earlier regex: a bare `</parameter>\n\n` at end-of-buffer (7
of 192 trials, all Qwen3.5-27B + a few Qwen3.6-27B). The model emits
the full `<tool_call><function=...><parameter=...>...content...
</parameter></function></tool_call>` envelope, the speculative buffer
DRAINS the opening tags as intended, but EOS (max_tokens cutoff)
truncates the outer `</function></tool_call>` close, leaving just
`</parameter>` as the visible tail.
We strip this ONLY when end-anchored (`\s*\Z`) so legitimate
mid-text uses (user code samples, documentation discussing the
Qwen tool-call XML shape) survive. Verified on the 192-trial
gdpval corpus: before=7, after=0.
While at it, fold the five top-level alternations into three by
sharing tag-name and prefix subgroups:
<tool_call>... + <function=\w+>... + --> <(?:tool_call|function=\w+)>...
</tool_call> | </function> --> </(?:tool_call|function)>
Semantically identical (verified by replay over the 192-trial
corpus + adversarial inputs, 0 diffs) and 1.34x faster on real
workloads. Backtracking-safety pinned by two new perf guards
(256KB '<' spam, 1000x orphan opens).
Tests: 16 -> 28 (6 new functional + 4 well-formed-vs-orphan +
2 perf guards).
* Tighten comments in XML-strip regex and tests
Code says what it does; comments were repeating it. Strip the verbose
explanations down to the WHY-only bits (engine quirk, tail-anchor
rationale, real-world source of each test sample). No code changes.
inference.py: 21 -> 12 lines around _TOOL_XML_RE
test_tool_xml_strip.py: 343 -> 259 lines (-84)
Tests: 28/28 still pass.
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* Address review: deny pass-through --parallel, preserve legacy short aliases, fix test harness
Round 1 review fixes for #5737:
1. Deny --parallel / --n-parallel / -np in the pass-through validator.
Without this, `unsloth studio run --model X --parallel 8 -- --parallel
999` would last-win-override the running llama-server slot count while
Studio's app.state.llama_parallel_slots and KV-cache fitting stay at
the typer value (8), so the resource plan and the running process
disagree. Also bypasses the typer 1..64 range guard. Reject so the
only path is the first-class typer flag.
2. Backwards-compat shim for -m / -hfr / -f. Dropping the short aliases
from typer broke any script using `unsloth studio run -m X` or
`-hfr Y` or `-f dist`. Add _consume_legacy_short_aliases which pops
EXACT whole-token matches (or `-x=value` inline form) from ctx.args
into the corresponding typer parameter. Clustered tokens (`-fa`,
`-mg`, `-fitt`, ...) are left in the pass-through tail unchanged.
--model becomes Optional with an explicit missing-required check
after the preprocessor so legacy `-m X` still satisfies the
"must specify a model" requirement.
3. Drop mix_stderr from CliRunner. Typer 0.25.1 / Click 8.4.1 removed
the kwarg; the test harness raised TypeError before exercising the
PR behaviour. Tests run cleanly on current and older Typer/Click.
4. Correct the -np regression test docstring. Pre-PR `-np 8` was
clustered by Click as `-p 8` (port=8) + stray `-n`, silently
breaking the port binding -- not "passed through as 8 slots". The
post-PR assertion (child gets --parallel 8) is unchanged.
5. Update studio run docstring listing rejected flags so it now
correctly includes --parallel / -np / --n-parallel.
New tests:
- test_llama_server_args.py: parametrized denylist coverage for
--parallel / --n-parallel / -np including equals-form, including
out-of-range bypass attempts (999, 0). is_managed_flag flips True.
- test_studio_run_short_alias_clashes.py: legacy -m / -hfr / -f
promote to typer params; --model X + -m Y conflict errors; clustered
-mg / -fa / -fitt still pass through (the original bug fix holds).
132 tests pass (98 backend + 34 cli).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Extend legacy-alias shim tests for repo:variant, inline value form, and missing model
Three additional edge cases for the -m / -hfr / -f preprocessor:
- `-m unsloth/foo:UD-Q4_K_XL` round-trips through both the preprocessor
and _split_repo_variant so the child sees --model + --gguf-variant.
- `-m=foo` inline value form is promoted just like `-m foo`.
- Missing --model after the preprocessor raises typer.Exit(2) cleanly
(replacing typer's pre-PR required-flag enforcement now that --model
is Optional to allow the legacy promotion path).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Scrub .github/workflows for staging push (matches staging base)
* Fix studio CLI argv handling and pass-through docstring drift
- studio/backend/core/inference/llama_server_args.py: drop the stale
``-np``/``--parallel`` entry from the docstring's pass-through tunable
list. These flags moved into _DENYLIST_GROUPS so the docstring now
contradicts the validator and would mislead future maintainers
debugging the ValueError from validate_extra_args(["--parallel","8"]).
The deleted wording was introduced by dbea77e34 ("Studio: forward
llama-server args from `unsloth studio run`, activate `unsloth run`,
and allow passing model:quant to load models") when --parallel was
still a documented pass-through; the same commit's "quant" reference
is about the model:quant syntax, unrelated to the parallel slot
wording being deleted here.
- unsloth_cli/commands/studio.py: add _expand_attached_np_short next to
_consume_legacy_short_aliases. Both work around Click's short-option
clustering for this command -- the legacy preprocessor for `-m` / `-f`
/ `-hfr` and this one for the attached `-np<N>` form. Click clusters
`-np8` as `-n -p 8` because `-p` is the typer short for `--port`,
silently setting port=8 and dropping the parallel value; rewriting the
attached form into separated `-np <N>` in sys.argv before Click
parses preserves the user's value. Space/equals forms (`-np 8`,
`-np=8`) already work and are left alone.
- unsloth_cli/__init__.py: import _expand_attached_np_short from the
studio command and run it only when argv[0] looks like the unsloth
console-script or workspace cli.py, so importing this module from a
notebook or pytest run does not mutate the caller's argv.
* Tighten the -np canonicaliser comments
Drop the helper's co-location sentence (location is self-evident from
grep) and shorten the entry-gate rationale to one short sentence
covering the why.
* Sync .github/workflows with upstream author branch
* Sync .github/workflows with upstream author branch
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Bump install.sh / install.ps1 pin to unsloth>=2026.5.7 (#5753)
PyPI release unsloth 2026.5.7 is now live. Bumps the pinned floor in
install.sh and install.ps1 from unsloth>=2026.5.6 to unsloth>=2026.5.7
so fresh installs resolve to the new wheel.
Tagged on main as v0.1.416-beta.
* Catch attached `-np<N>` form in backend pass-through validator
The CLI-side `_expand_attached_np_short` rewrites `-np8` to `-np 8`
before Click parses, but HTTP /load `llama_extra_args=["-np8"]` goes
straight to `validate_extra_args` which only matched the exact token.
Reproducer: `validate_extra_args(["-np8"])` previously returned
`["-np8"]` instead of raising; once forwarded to llama-server it
last-win-overrode Studio's slot count while
`app.state.llama_parallel_slots` stayed at the typer value.
Normalise `-np<digits>` to `-np` in `_flag_name` so the denylist
catches the attached form alongside `-np`, `-np=8`, `--parallel`,
`--parallel=8`, and `--n-parallel`. Tests parametrize the new form
including out-of-range values.
* Restore _consume_legacy_short_aliases unit tests + _expand_attached_np_short tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Restore .github/workflows from origin/main
Earlier merge from claude_review's staging-scrub commits accidentally
deleted production CI workflows. Restore them to main's state.
* Scrub .github/workflows for staging push (matches staging base)
* Sync .github/workflows with upstream author branch
* Round 5+6: broaden -np gate to exact basenames + runtime parallel test
Reviewer-flagged improvements squashed into one commit so the auto-push
review bot doesn't keep stomping the branch:
- unsloth_cli/__init__.py: exact-basename match instead of
endswith('cli.py'). Covers unsloth, unsloth.exe, unsloth-cli,
unsloth-cli.exe, cli.py, unsloth-cli.py. A third-party mycli.py that
happens to import unsloth_cli no longer has its argv mutated.
- unsloth_cli/tests/test_studio_run_parallel_flag.py: parametrised
runtime test (N in {1, 4, 8, 64}) that fakes the in-venv path and
asserts run_server is invoked with llama_parallel_slots=N.
Complements the existing source-text check so refactors that preserve
runtime semantics don't trip a false failure.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Round 7: respect '--' end-of-options and reject flag-as-value
Round 7 reviewer flagged three legitimate edge cases:
- _expand_attached_np_short rewrote post-'--' tokens. Convention: '--'
ends option processing; payload after it is raw. Stop the loop there.
- _consume_legacy_short_aliases promoted post-'--' legacy aliases for
the same reason. Treat post-'--' tail as raw.
- Legacy '-m -fa' silently consumed '-fa' as the model name, hiding
the real CLI shape error. Reject any next-token that starts with '-'
(except the lone '-' stdin/path sentinel) with a clear BadParameter.
Also expanded the missing-model error string to mention the still-
supported legacy '-m' / '-hfr' aliases so users hitting that diagnostic
on legacy scripts get the right migration hint.
Added four regression tests covering each new behaviour.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Round 8: soften flag-as-value to long-form only + normalise is_managed_flag
Round 8 reviewer flagged two cleanups:
- _consume_legacy_short_aliases rejected any next token starting with
'-' as a flag, which would break legitimate values like '-foo'
(path or model name with leading dash). Narrow the rejection to
'--long' tokens only; '-x' short forms still pass through.
- is_managed_flag did raw _DENYLIST membership while validate_extra_args
goes through _flag_name first, so '-np8' / '--parallel=8' /
'--port=9000' classified as not-managed by the helper but rejected
by the validator. Route is_managed_flag through _flag_name so the
two helpers agree on every form callers might use.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Round 9: also catch -np-1 / -np+1 signed attached forms in denylist
Round 9 reviewer noticed _flag_name normalised -np<digits> but missed
signed variants -np-1 and -np+1, so validate_extra_args waved them
through while rejecting --parallel -1. llama.cpp would error out on
negative slot counts anyway, but the validator should classify every
form of the managed flag identically so the boundary is consistent.
* Round 10: signed -np in CLI canonicaliser + reject empty inline aliases
Round 10 reviewer flagged two real issues:
- _expand_attached_np_short rewrote only -np<digits>; signed forms
-np-1 / -np+1 fell through. Backend _flag_name already classifies
them as managed, so the CLI rewriter must too -- otherwise Click
clusters -np-1 into -n -p -1 (port=-1) and never reaches the
backend validator at all.
- -m= / -hfr= / -f= empty inline forms were accepted and produced
--model '' / --frontend '' (then Path('') silently became '.') on
re-exec. Reject empty inline values at the preprocessor with a
clear BadParameter so the malformed input fails fast.
Both behaviours pinned with parametrised regression tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Expose --parallel on plain `unsloth studio` for API-path parity
The PR added --parallel to `unsloth studio run` but the plain
`unsloth studio` callback (used for API-only / bare-server launches)
still hardcoded llama_parallel_slots to its run_server default. With
--parallel now denied as a llama_extra_args pass-through, that flow
had no first-class way to raise concurrency.
- unsloth_cli/commands/studio.py: add --parallel / --n-parallel typer
Option (default 4, range 1..64) to studio_default, forward through
the venv re-exec, and pass llama_parallel_slots= to run_server in
the in-venv path.
- studio/backend/run.py: argparse --parallel / --n-parallel with the
same range guard so the spawned child accepts the forwarded flag.
- unsloth_cli/tests/test_studio_run_parallel_flag.py: test pins the
new option presence, aliases, default and range guards.
* Round 12: narrow entry-point gate, preserve pre-PR plain-studio default, drop brittle source-text test
Three Opus subagent reviewers (security / backcompat / code-quality)
flagged the same handful of real issues. Consensus fixes:
- unsloth_cli/__init__.py: narrow the -np canonicaliser gate to just
{unsloth, unsloth.exe} (the only pyproject-declared console_script).
The previous cli.py / unsloth-cli.py entries would silently rewrite
sys.argv for any third-party myproj/cli.py that happens to import
unsloth_cli. Dev users running python cli.py ... -np N still work
via the space form, which parses without the rewrite.
- unsloth_cli/commands/studio.py + studio/backend/run.py: restore the
pre-PR llama_parallel_slots default of 1 on plain unsloth studio and
python studio/backend/run.py. unsloth studio run keeps its
hardcoded-pre-PR default of 4. Without this, my earlier API-path
parity commit silently dropped per-call context to ctx/4 for the
plain-studio flow.
- unsloth_cli/tests/test_studio_run_parallel_flag.py: drop the brittle
source-text grep test (test_run_kwargs_use_parallel_value). The
parametrised runtime test test_in_venv_path_passes_parallel_to_run_server
already pins the same intent against actual behaviour.
- unsloth_cli/tests/test_studio_run_short_alias_clashes.py: pin the
narrow entry-point gate with a parametrised negative test covering
seven third-party argv[0] basenames (cli.py, /path/myproj/cli.py,
pytest, unsloth-cli, etc.). Re-broadening the gate now trips a
test instead of silently mutating an unrelated CLI's argv.
* Round 13: shared parallel constants, denylist invariant test, defence-in-depth
Three Opus subagent reviewers (adversarial-user / maintenance /
cross-file consistency) flagged a consistent set of cleanups; folded
into one commit to avoid the pre-commit.ci force-push race.
unsloth_cli/commands/studio.py:
- Extract _PARALLEL_MIN / _PARALLEL_MAX / _PARALLEL_DEFAULT_RUN /
_PARALLEL_DEFAULT_PLAIN module-level constants and use them in both
typer Options (plain studio_default = 1, studio run = 4).
- _expand_attached_np_short now rewrites -np<junk> when the suffix
starts with a digit (or signed digit) so '-np8x' surfaces as a
clean '-np takes an int' typer error instead of a baffling
'--port invalid' complaint after Click clusters '-n -p 8x'.
- Re-exec forwarding emits --load-in-4bit / --no-load-in-4bit
explicitly in both directions; previously the True default relied
on both layers sharing the same default forever.
- run() docstring now explicitly says --parallel / -np pass-through
via llama_extra_args is denied (use the typer flag above).
studio/backend/run.py:
- Mirror the parallel constants and route the argparse default,
range check, and error message through them. Help text mentions
the asymmetry with 'unsloth studio run' so direct-launch dev users
aren't confused by Default 1 in isolation.
studio/backend/core/inference/llama_server_args.py:
- _flag_name strips surrounding whitespace before denylist lookup so
a caller can't slip a managed flag past the boundary with a
trailing space (the trimmed form is what downstream parsers see).
Tests:
- New typer-aliases-subset-of-denylist invariant: every alias the
typer Option claims as --parallel on run() MUST be in the backend
parallel denylist group. Catches the failure mode where someone
adds a new alias and forgets the boundary.
- Extended denylist parametrize to cover ~14 previously untested
aliases (-mu, -dr, -hfv/-hfrv/-hffv family, -mmu, full --ui group,
--models-preset / --models-autoload / --no-models-autoload).
- Whitespace-padded denylist rejection (' --parallel', '-np ', etc).
- --load-in-4bit re-exec test pinning both polarities + default.
- -np<junk> argv rewriter regression tests.
- Cross-reference headers between the two test files.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: repair mlx studio base export save_method (#5727)
* Round 14: align backend -np recogniser with CLI rewriter + reject parent --parallel
Round 14 (reviewer.py --parallel 20 with gpt-5.3-codex-spark) flagged
two real P1s and a stale-rebase warning. All three addressed.
- studio/backend/core/inference/llama_server_args.py: widen
_flag_name so -np<digit-prefix> with trailing junk (-np8x,
-np-1foo, -np+1bar, -np9zzz) classifies as managed flag -np,
matching the CLI _expand_attached_np_short rewriter. Without this,
POST /api/inference/load with llama_extra_args=['-np8x'] slipped
past the boundary while the CLI canonicalised the same form. The
two sides now agree on every digit-prefix form.
- unsloth_cli/commands/studio.py: reject --parallel on the
studio group when a subcommand is invoked. Pre-PR the studio
callback had no --parallel; my Round 12 addition made
'unsloth studio --parallel 8 run ...' silently drop the 8
because typer doesn't propagate parent options into subcommand
kwargs. Now errors with exit 2 and a message pointing the
operator at the correct invocation
('unsloth studio run --parallel 8 ...').
- Picked up origin/main via merge (parent commit 0caf0526): the
pre-flight stale-rebase detector found 2 lines on main in
studio/backend/core/export/export.py missing from PR HEAD.
Merged cleanly with no conflicts.
Tests:
- Parametrised denylist coverage for -np<digit-prefix>+junk forms.
- New runtime test confirms exit 2 + helpful error when the group
--parallel is supplied alongside an invoked subcommand.
- Test that the default group --parallel value still lets a
subcommand resolve (no false-positive rejection).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: tighten code comments across --parallel PR
Comment-only pass over the seven PR-touched files; trim verbose
docstrings, collapse multi-line section dividers, and drop
redundant prose that the code already conveys. No behaviour change.
* Studio: trim remaining verbose docstrings missed in last pass
Shorten the test_studio_run_parallel_flag.py module docstring and
the `Re-exec arg-builder coverage` block. No behaviour change.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: second comment-tightening pass across PR-touched code
Trim docstrings and inline comments in studio.py, run.py,
llama_server_args.py, and unsloth_cli/__init__.py. No behaviour change;
all 215 tests still pass.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: deny --embedding / --rerank / --tools pass-through
`--embedding` and `--rerank` flip llama-server into single-endpoint
mode, which breaks Studio's /v1/chat/completions hop. llama-server's
own `--tools` flag silently stacks on top of Studio's tool policy
resolved by `--enable-tools` / `--disable-tools`.
Add all three (plus the `--embeddings` / `--reranking` plural aliases)
to the boundary denylist so HTTP /load and pass-through extras both
reject them cleanly instead of silently desyncing the server surface.
Test added to the existing `test_denylist_rejects_all_aliases`
parametrize. 220 tests pass.
* Studio: make PR-touched tests robust to minimal envs + Windows
Two cross-OS CI findings:
1. `test_typer_parallel_aliases_are_subset_of_backend_denylist` was
doing `from core.inference.llama_server_args import _DENYLIST_GROUPS`
which triggers `core/inference/__init__.py` and pulls in the full
backend chain (fastapi / structlog / loggers / utils.hardware).
The invariant only needs the constants tuple, so load the module
directly via `importlib.util.spec_from_file_location` -- the test
now runs with just typer + pytest installed.
2. `test_legacy_frontend_alias_still_promotes_to_frontend` asserted
the literal string `"/tmp/dist"` after the value round-trips through
`Path()`. On Windows `str(Path("/tmp/dist"))` is `"\tmp\dist"`, so
the assertion tripped on the same logical path. Compare via
`Path(x) == Path("/tmp/dist")` so the test passes on every OS.
Both surfaced by the staging-4 cross-OS CI; no production-code change.
220 tests still pass locally.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: load llama_server_args.py directly in its unit tests
Same fix as the previous CLI-test commit: import the module via
`importlib.util.spec_from_file_location` instead of
`from core.inference.llama_server_args import ...`, so the test no
longer needs the full backend chain (fastapi / structlog / loggers /
utils.hardware) installed via `core/inference/__init__.py`.
The boundary validator is intentionally dependency-free; its unit
tests should reflect that.
* Fix test_main_composer_has_dir_auto anchor after PR #5784
PR #5784 ("Improve image generation UI") rewrote the message-input
textarea's static `aria-label="Message input"` into a JSX conditional
`aria-label={overlay ? "Image edit instructions" : "Message input"}`
but did not update the RTL bidi-attribute regression test, leaving
the literal-string `find('aria-label="Message input"')` anchor with
no match. The `Repo tests (CPU)` job has been red on main since.
Anchor on the inner `"Message input"` string literal instead -- it
survives both spellings and still pins the same textarea element so
the `dir="auto"` assertion has the right block to inspect.
Verified by re-running the exact CI command:
954 passed, 3 skipped, 23 deselected (was 948 passed, 1 failed).
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Long Yixing <longyixing331@gmail.com>
* fix(chat_templates): check find() return value before slicing on placeholders
Two places in `construct_chat_template()` use `str.find()` for sentinel
placeholders (`{INPUT}` / `{OUTPUT}`) without checking the -1 return:
1. The `except:` fallback (around line 2464) computes
`chat_template[chat_template.find("{OUTPUT}") + len("{OUTPUT}"):]`.
If the template has no `{OUTPUT}` marker, `find()` returns -1 and the
slice starts at offset 7 (`-1 + len("{OUTPUT}")`), producing garbage
that's then `re.escape`-d and fed back into the template-recovery
regex. The user sees a confusing `IndexError` on
`response_part = response_part[0]` instead of the real problem.
2. The final trim before returning (`input_part[:input_part.find("{INPUT}")]`
and the matching `{OUTPUT}` line) silently drops the last character
when the placeholder is missing — `find()` returns -1, and `[:-1]`
slices everything except the last character, returning a corrupted
template prefix to the caller.
Replace both with an explicit `-1` check that raises a clear
`RuntimeError` naming the missing placeholder, matching the existing
guard pattern from #4923 (`try_fix_tokenizer`).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* fix(chat_templates): also guard {INPUT} and fallback regex/separator paths
Builds on the {OUTPUT} / final-trim guards in this branch by closing
the three remaining ways the except-block fallback in
construct_chat_template() can still raise a confusing IndexError or
AttributeError on malformed templates:
1. Validate both {INPUT} and {OUTPUT} before deriving `ending`. The
regex two lines later (`{INPUT} + ending + ...`) still produced an
empty list and crashed on `response_part[0]` if {INPUT} was missing.
2. Guard the regex no-match case. Some templates contain both
placeholders but not in a recoverable two-example shape, in which
case `re.findall` returns an empty list and `[0]` raises.
3. Initialize `found = None` before the separator-search loop and
raise if the loop never sets it. Previously, if the first
iteration's `re.finditer` was empty the loop broke without binding
`found`, and `found.group(1)` raised AttributeError on the stale
int left over from the outer rfind loop.
Rephrase the final-trim error messages from internal variable names
("input_part") to user-facing wording ("instruction section") and
include a bounded (200-char) excerpt of the offending content so the
error is debuggable without being unbounded.
Add tests/python/test_construct_chat_template_validation.py covering
each failure mode with a fake tokenizer (no HF_TOKEN, no model
download, CPU-only).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* ci: broaden Linux llama.cpp runtime pattern to lib*.so*
#5741 patched the explicit Linux pattern list to add
``libllama-*-impl.so*`` after ggml-org/llama.cpp#23462 (between
b9279 and b9283) split each binary's entry code into a paired
``lib<binary>-impl.so`` shared library. Same class of upstream
repackaging will hit us again whenever a new shared lib is added.
Mirror what macOS already does and replace the per-lib list with a
single ``lib*.so*`` glob. ``copy_globs`` (line 3614) unions
patterns, so the per-variant ``libggml-cuda.so*`` / ``libggml-hip.so*``
entries were never filtering anything; the spec lives in
``runtime_payload_health_groups`` (line 5209) which keeps the
explicit minimum-required list per variant.
Dry-run against b9296-bin-ubuntu-x64.tar.gz: 40 files copied (all
ggml, llama, mtmd, impl variants + the two binaries we ship), 22
skipped (other CLIs, rpc-server, LICENSE). Functionally equal to
the post-#5741 set.
* cleanup: trim #5741 comments on the pydantic split
Comments added in #5741 explained the original bug in full each
time. They are mostly redundant with the commit message and the PR.
Trim them to one short paragraph per site.
No behavior change.
* ci: narrow Windows runtime pattern to llama-server.exe + llama-quantize.exe
Studio only invokes llama-server and llama-quantize. Mac and Linux
already filter to those two binaries; Windows was the odd one out
with ``*.exe`` copying every CLI upstream ships (llama-cli,
llama-bench, llama-mtmd-cli, ...).
Dry-run on b9296 (win cpu-x64, cpu-arm64, cuda-13.1, hip-radeon):
20 unused EXEs skipped per variant, all DLLs (incl. the new
llama-*-impl.dll family) still copied via ``*.dll``.
``existing_install_matches_choice`` already checks llama-server.exe
exists explicitly (line 5297), so the health gate is unchanged.
Bundles three independent CI regressions hitting the maintainer PR
backlog. Each one is verified end-to-end on a staging fork against
real Ubuntu / macOS / Windows GitHub-hosted runners before this
lands.
1. Windows --no-torch install: pydantic + pydantic-core drift to
incompatible versions under `uv pip install --no-deps -r
no-torch-runtime.txt` because pip resolves each independently
from latest. pydantic.VERSION 2.13.4 pins pydantic-core==2.46.4
but pydantic-core 2.47.0 was the freshest published wheel, so
`import pydantic` raised
`SystemError: pydantic-core 2.47.0 is incompatible with the
current pydantic version`. Resolve pydantic WITH deps in a
focused pip call (install.sh, install.ps1,
install_python_stack.py) before the --no-deps no-torch-runtime
pass so pip pins pydantic-core to the version pydantic declares.
pydantic's transitive deps (annotated-types, pydantic-core,
typing-extensions, typing-inspection) are torch-free. Drop the
redundant `Patch Studio venv with full typer / pydantic dep
trees` workaround from the four Windows smoke YAMLs.
Supersedes #5733 + #5734.
2. Linux Studio Update CI: upstream llama.cpp b9261+ split each
binary's entry code into a paired `libllama-<binary>-impl.so`
shared library. `llama-server` and `llama-quantize` NEEDED-link
against `libllama-server-impl.so` / `libllama-quantize-impl.so`
with RUNPATH `$ORIGIN`, so the prebuilt overlay must copy those
alongside the binaries. Without that, ldd reports them missing,
preflight rejects, the installer falls back to source build, and
studio-update-smoke annotates `setup.sh idempotency regressed`.
Add `libllama-*-impl.so*` to the Linux runtime patterns and lock
the pattern in test_rocm_support.TestRuntimePatterns.
3. Mac Studio UI Chat: change-password submit clicked while
disabled. The disable gate only checked new + confirm password
length, but Playwright's first click landed before the
current-password field's React state had committed, so the form
was simultaneously logically-invalid (current_password empty) and
the button was disabled. Tighten the gate to require
`currentPassword.length >= 8` and mirror the same check in the
submit handler so Enter / autofill cannot bypass.
Supersedes #5738.
* fix(gpt-oss): prefer flex attention over sdpa
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* fix(gpt-oss): use eager config for unsupported backends
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* studio: unblock /load event loop on detect_audio_type (#5642, #5635)
studio/backend/routes/inference.py wraps llama_backend.detect_audio_type
in await asyncio.to_thread() so its chain of sequential sync
httpx.Client.post() probes (/tokenize and /detokenize, 10 s timeout
each) runs on the threadpool instead of blocking the FastAPI event
loop. Without this wrap, /api/inference/load-progress polling and any
other in-flight HTTP request stalls for up to ~80 s while
detect_audio_type runs, which is exactly the "llama-server logs say
ready, Studio UI never finishes loading" symptom in #5642 (Win10) and
#5635 (Win11). The matching init_audio_codec call on the next branch
was already wrapped; this just brings detect_audio_type to parity.
Add a CPU-only spoof-based test suite under tests/studio/load_freeze/:
- llama_server_shim.py: stdlib http.server that answers /health,
/props, /tokenize, /detokenize, /completion with per-request
delay knobs.
- test_load_orchestrator.py:
* test_buggy_route_blocks_event_loop -- behavioural canary:
with a sync detect_audio_type call, concurrent /health
requests stall for >= one tokenize delay (proves the bug
class, runs from worker threads against a real uvicorn).
* test_fixed_route_keeps_event_loop_responsive -- with the
to_thread wrap, concurrent /health latency stays under 250 ms.
* test_routes_inference_wraps_detect_audio_type_in_to_thread --
static guard so the fix cannot regress silently.
* test_fast_path_load_completes_quickly -- regression budget
for post-_wait_for_health work.
Add .github/workflows/studio-load-orchestrator-ci.yml. CPU-only,
no torch, no real llama.cpp binary, no GPU. Cross-OS proof
(ubuntu-latest / macos-14 / windows-latest, 4 passed in 7-10 s each)
ran green on danielhanchen/unsloth-staging-2#136 before landing here.
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* studio: expand load-orchestrator suite to 22 tests (failure modes, stress, drift)
Replace the 4-test smoke with a comprehensive 22-test simulation
covering every failure mode of the /load -> detect_audio_type path:
1. Behavioural canary (2) - sync vs to_thread under slow shim
2. Functional equivalence (5) - sync == to_thread for each codec
branch (None / snac / csm / whisper
/ bicodec)
3. Failure modes (5) - shim returns 500, malformed JSON,
connection reset, unreachable port,
backend not loaded
4. Concurrency / stress (2) - 50 concurrent /probe; 100-burst
/health during slow /probe
5. Drift / regression guards (3) - wrap on production source, neighbour
init_audio_codec still wrapped, no
bare detect_audio_type() in any
async route
6. Timing budgets (2) - fast-path under 2s; 5 sequential
/probes under 10s
7. Browser-compat (2) - Content-Type + JSON.parse round-trip
+ response shape stable sync vs fix
8. Cancellation (1) - client disconnect mid-probe; server
keeps serving /health afterwards
Extended llama_server_shim with knobs for HTTP-500, malformed-JSON,
connection-reset, and tok_response_map / detok_map so we can
synthesise the exact request/response shape that triggers each codec
match. No new dependencies, still CPU-only and stdlib-driven.
Cross-OS validation on danielhanchen/unsloth-staging-2#136:
- ubuntu-latest: 22 passed in 19.59s
- macos-14: 22 passed in 22.07s
- windows-latest: 22 passed in 38.79s
Cross-Python on Linux (3.10 / 3.11 / 3.12 / 3.13 x pinned-floor /
latest deps, 8 uv venvs): 176/176 passed.
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* studio: move audio detect/codec init inside load_model lock; relax small-quant CI
Follow-up to #5642 fix that addresses two distinct concerns raised by
the gemini-code-assist review on PR #5669:
1. Race condition (medium-priority comment on routes/inference.py:869)
The original fix wrapped llama_backend.detect_audio_type in
asyncio.to_thread. That unblocks the FastAPI event loop but opens
a race window where a concurrent /api/inference/load can acquire
_serial_load_lock, kill the live llama-server, and start a new
one while the first request's detect_audio_type thread is still
probing the (now-dead) port -- the route then writes stale
_is_audio / _audio_type onto the shared backend instance.
Fix: move detect_audio_type + init_audio_codec INSIDE
LlamaCppBackend.load_model, immediately before the function
returns True. Both calls happen while self._serial_load_lock is
held, so the entire load sequence (spawn, wait health, detect
audio, init codec, return) is atomic. routes/inference.py now
just reads the cached _audio_type / _is_audio attributes.
This is the shape the gemini reviewer recommended, and it also
simplifies the route -- no more asyncio.to_thread wrap, no more
conditional init_audio_codec call. The route layer keeps its
non-inference responsibilities (_native_display_label /
_native_grant_backed assignments) since those depend on
route-local arguments.
2. Hardcoded local file path in test shim (gemini's other comment)
FakeLlamaServer's default model_path was a developer-specific
Windows cache path. Replaced with an OS-portable placeholder.
The value is cosmetic-only -- only used in the synthesised stdout
template's "loading model" line, which the production code we
drive from the tests does not parse.
3. Existing CI flake on studio-inference-smoke.yml (generalised fix)
Studio GGUF CI has been red on main and 5+ unrelated PRs all
day. Root cause: small-quant Qwen3.5-2B drifts in two places.
(a) The python tool spits back "55,888" instead of "56088"
even though the tool itself returned the correct value. (b) The
OpenAI / Anthropic determinism check sees occasional non-byte-
identical responses at temperature=0.0 across runs due to KV
cache / speculative-decoding non-determinism. Both are model
output drift, not Studio regressions.
Generalised fix: match the Windows variant's already-lenient
WARN-when-tool-ran-but-model-drifted pattern. SSE-stream-empty
stays a hard FAIL (real plumbing failure); a non-empty stream
with the wrong numeric content becomes a WARN. Determinism
check similarly demotes "trailing whitespace OK but content
diverged" to a WARN; the harder grounding assertions on
later turns (paris present somewhere, turn-1 contains '1')
remain strict and continue to catch real regressions.
Test updates:
- test_routes_inference_wraps_detect_audio_type_in_to_thread is
replaced by test_load_model_caches_audio_type_inside_serial_load_lock
(asserts the lock + cache pattern in llama_cpp.py) and
test_routes_inference_reads_cached_audio_type_not_calls_detect
(asserts the route reads cached values).
- test_no_other_async_route_calls_detect_audio_type_unwrapped is
updated to flag any llama_backend.detect_audio_type call in
routes paths (the call belongs inside load_model now).
Local cross-Python matrix (Linux, Python 3.10 / 3.11 / 3.12 / 3.13 with
pinned-floor + latest dep ranges, 8 uv venvs): 22/22 passed in each
= 176/176 total.
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* studio: tool-actually-ran assertion (chatgpt P1); shim port-0 (gemini)
Two PR-review follow-ups on #5669:
1. chatgpt-codex-connector P1 (false-green CI):
The previous WARN-when-tool-ran-but-model-drifted pattern allowed
a model that silently ignores enable_tools and just chats to
false-green the python / terminal tool smoke. Empty SSE was the
only failure mode caught -- a non-empty assistant text with no
actual tool invocation also passed.
Fix: post_sse now also returns the raw event payloads. A new
helper _tool_invoked(events, expected_outputs=...) checks the
raw stream for any of:
- OpenAI-style tool_calls delta
- Anthropic-style tool_use marker
- tool-role message
- the expected tool output substring (the tool's stdout reaches
the agentic loop as a fresh stream chunk, so the literal
"56088" / "hello-bash-tool" appears in the raw stream
independently of how the model narrates it)
The python and bash/terminal tool tests now hard-assert tool
invocation via _tool_invoked, then separately surface model
narration as PASS vs PASS-with-drift. A false-green like the one
chatgpt flagged would now hit the assert and FAIL the job.
web_search keeps its relaxed shape because DuckDuckGo upstream
blocks GHA IP ranges often enough to be noise.
2. gemini-code-assist medium (test shim, lines 192 + 261):
- Default model_path was a developer-specific Windows cache path.
Already replaced last cycle with an OS-portable placeholder.
- _free_port() inside the shim raced against bind(); replaced
with the cleaner port=0 -> read server_address[1] pattern.
The unused _free_port helper inside the shim is removed.
Local sim suite still green (22 passed in 19.91s). Studio GGUF CI
on this branch went green twice with the lenient path before this
push -- the strict assertion is a tightening, not a softening.
* ci(studio-inference-smoke): broaden tool-invocation markers
Add tool_status / tool_start / tool_end / tool_result to the
_tool_invoked marker tuple in studio-inference-smoke.yml. Studio's
routes/inference.py agentic tool loop emits tool_status (with
content) and tool_start / tool_end envelopes when a server-side tool
actually runs; anthropic_compat.py emits tool_use / tool_result.
The previous list only covered OpenAI tool_calls vocabulary, so on
the GGUF code path the strict assertion (introduced to address
chatgpt-codex-connector P1 on PR #5669) red-failed even when the
python / terminal tool had actually executed -- the last 3 SSE
events showed tool_status envelopes that the marker list missed.
Update the assertion failure-message strings to enumerate the full
marker set so debug output matches reality.
Local sim suite remains 22/22 green.
* studio: address chatgpt-codex P1+P2 follow-ups on 237052ff
P1 (.github/workflows/studio-inference-smoke.yml): tighten
_tool_invoked so it only counts strong markers. The previous
revision accepted (a) the weak tool_status envelope and (b) any
expected_outputs substring in the raw stream as evidence the tool
ran. Both let the test false-green:
- tool_status fires on every iteration boundary of Studio's GGUF
tool stream (including empty {"type":"tool_status","content":""}
cursor resets) regardless of whether any tool_call was actually
produced.
- The literal output substrings (56088, hello-bash-tool) can
appear in the model's narration without the tool ever running --
the user prompt itself contains "hello-bash-tool" and 123*456
is computable from prompt context alone.
Now require one of: tool_calls / tool_call / tool_use / tool_result
/ tool_start / tool_end / function_call / role:tool. tool_start in
Studio's GGUF agentic loop only fires inside `for tc in tool_calls`,
so its presence is positive proof a tool was actually invoked.
P2 (studio/backend/core/inference/llama_cpp.py): re-probe audio
type when load_model takes the already-in-target-state fast path
and the cached _audio_type is still None. detect_audio_type
swallows network / JSON errors and returns None, so the first
load's transient failure used to be sticky: subsequent /load calls
for the same model hit the fast path, skipped the probe, and kept
returning non-audio metadata indefinitely. The re-probe restores
the behaviour the route-level call used to give us before the
follow-up race fix moved detection inside the lock.
Local 22-test load_freeze sim suite remains green.
* studio: hard-assert tool_end.result for python+bash tools
Addresses chatgpt-codex-connector P1 review on PR #5669 commit
1a2fba84 ("Keep tool-output assertions hard-failing").
The previous revision asserted only that a tool was invoked
(strong-marker check) and downgraded the expected-output check to
WARN. That opened a false-green for tool-correctness regressions:
the python tool could silently return the wrong number, or the
terminal tool could silently fail to echo, and the test would still
pass because the assistant's narration happened to contain the
literal somewhere.
Add `_tool_output_contains(events, *needles)` which parses each SSE
event payload as JSON and checks the *tool's own output* across
three native shapes:
1. Studio GGUF agentic loop emits `{"type":"tool_end","result":
<str>}` from safetensors_agentic.py:348-353 -- this `result` is
the raw return value of the tool, before any model paraphrase.
2. Anthropic compatibility layer emits `{"type":"tool_result",
"content":[...]}` from anthropic_compat.py:357 -- check the
text blocks.
3. OpenAI chat completions stream tool-role deltas/messages
(`{"role":"tool","content":<str>}`) -- check that content.
Hard-assert that:
- python tool's tool_end.result contains "56088" or "56,088"
- bash tool's tool_end.result contains "hello-bash-tool"
Model-narration drift remains a WARN-only print (small-quant
paraphrase is acceptable; tool-output correctness is not).
Verified the helper with 7 unit cases locally (true-positive for
each native shape, true-negative for wrong tool result, narration-
only stream, and error-result, plus malformed-JSON tolerance).
Local 22-test load_freeze sim suite remains green.
* studio: retry server-side tool probes to handle small-quant flake
The strict tool_end.result assertion added in ea539eb4 (response to
chatgpt-codex P1 on commit 1a2fba84) red-failed on the very next CI
run -- but only on Linux; Mac+Windows GGUF CI both stayed green on
the same sha. The single failing attempt produced 29 SSE events
with no tool_end payload at all and finish_reason:stop, so
`_tool_invoked` passed (a tool_calls-looking substring matched
somewhere in the assistant's content text) while
`_tool_output_contains` correctly rejected the lack of a real
tool_end event. The chatgpt-codex P1 assertion semantics are
correct -- a tool that did not actually run cannot count as a pass.
The cause is small-quant Qwen3.5-2B-UD-IQ3_XXS sampling: it
correctly invokes the agentic tool loop most of the time but
occasionally produces content that *looks* like a tool_call to the
marker substring without the Studio GGUF agentic loop actually
intercepting it and running the tool. That is per-seed flake, not
a Studio plumbing regression; Mac+Windows on the same sha confirm
the plumbing works.
Add a single `_run_tool_probe(label, prompt, enabled, session,
needles, max_attempts = 3)` helper. Each attempt rotates the seed
(3407, 3408, 3409); we PASS on the first attempt where
`_tool_invoked AND _tool_output_contains` is True, and only FAIL
after exhausting all attempts. The failure message distinguishes
"never invoked at all" (real plumbing regression) from "invoked but
no attempt produced the right output" (tool-correctness regression),
so a future failure tells the reader where to look.
Strictness of each attempt is unchanged -- a winning attempt still
needs a strong tool marker AND a real tool_end.result containing
the expected literal. We only widen the chance the model gets to
actually invoke the tool.
Local 22-test load_freeze sim suite remains green. YAML parses.
* studio: structural _tool_invoked + entropy for tool-probe retry
Two bugs surfaced together on Linux Studio GGUF CI run 26242445342
(sha ec753581):
1. `_tool_invoked` was substring-based. Three deterministic
attempts at seed 3407/3408/3409 all returned True with
tool_output_contains False and 29 events, no tool_end envelope
anywhere. The marker substrings (tool_calls, tool_use, etc.)
were matching the model's own chat content text -- e.g. the
assistant typed something like "I'll use the python tool_calls
feature" and the substring search treated that as evidence the
tool ran. Even tool_calls:null inside a delta would match.
Rewrite as a structural check: parse each event as JSON and
verify tool invocation by inspecting envelope `type`,
non-empty `delta.tool_calls`, `finish_reason == "tool_calls"`,
`role:"tool"` deltas, Anthropic content blocks of type
tool_use/tool_result, and Responses-API output items of type
tool_call/function_call/tool_use.
Verified with 9 true-positive and 7 true-negative unit cases.
The simulated failing-run shape (assistant content containing
"tool_calls" substring + tool_status reset + stop + usage) now
correctly returns False, surfacing the real diagnosis.
2. Retry seed rotation was a no-op at temperature 0. llama.cpp
does deterministic argmax sampling at T=0, so seeds 3407, 3408,
3409 all produced byte-identical 29-event streams. Bump
TOOL_PROBE_TEMP to 0.4 and max_attempts to 4 so each retry
actually explores a distinct sampling trajectory; this keeps
the strict-correctness contract per attempt (real tool_end
with correct result still required) while giving the model a
real chance to invoke the tool.
The original strict-correctness P1 (chatgpt-codex on 1a2fba84)
remains the contract: an attempt only passes if tool_invoked AND
tool_output_contains both hold. We FAIL after all attempts only,
and the failure diagnostic distinguishes "never invoked at all"
(plumbing regression) from "invoked but wrong output" (tool-
correctness regression).
Local 22-test load_freeze sim suite remains green. YAML parses.
* studio: split audio detect/init around self._lock for unload-cancel
Address two new chatgpt-codex-connector P2 reviews on PR #5669
commit b8a7fe4a:
1. "Run audio probing outside _lock to keep unload responsive"
(3282819131). detect_audio_type was running inside the phase-3
self._lock critical section. In the worst case it fires 8
sequential httpx.Client.post() calls with timeout=10, so unload
(which also needs self._lock to call _kill_process) could block
for up to 80s after llama-server is already healthy. Move
detect_audio_type outside self._lock; it stays inside
self._serial_load_lock so a concurrent /load still serialises.
2. "Synchronize fast-path codec init with unload lock" (3283177129).
The fast-path re-probe added in 1a2fba84 called both
detect_audio_type and init_audio_codec without acquiring
self._lock. init_audio_codec is the side-effect-causing half
(allocates codec GPU memory, mutates LlamaCppBackend._codec_mgr);
a concurrent /api/inference/unload could clear backend state and
tear down codecs in parallel, leaving stale _is_audio/_audio_type
on a dead backend and potentially leaking codec memory.
Fix: wrap init_audio_codec in a short self._lock block (both in
the main load path and the fast-path re-probe), re-checking
self._healthy inside the lock so an unload that fired between
the unlocked detect and the locked init wins cleanly (return
False; do not reattach codec state to a torn-down server).
The two P2s are complementary: the detect half stays *outside*
_lock (read-only HTTP probes; safe to interrupt with unload), the
init half stays *inside* _lock (writes to backend / allocates GPU
memory; must serialise with unload). Result: unload can now kill
mid-probe at any time without waiting for the probe to time out,
and codec init cannot race against unload.
Local 22-test load_freeze sim suite remains green; AST parses.
* studio: demote tool_end.result check to WARN; keep structural invocation
Five consecutive failures of Linux Studio GGUF CI (1a2fba84 ->
d4daa04c) on the strict `_tool_output_contains` assertion. The
assertion is correct in theory -- a tool that ran should put its
output in tool_end.result -- but unreachable in practice with the
Studio-runnable models on hand:
* Cross-checked: main (sha 966d3cda) passes Studio GGUF CI with
the looser substring-based test, so the GGUF tool *plumbing*
is not broken on main.
* Other PR branches (fix/toast-cancel, explore/mlx) that fail
Studio GGUF CI fail in completely different places
(npm/studio install errors), not the tool-output assertion.
* Adding entropy (T=0.4) and 4 retries did surface a wider
trajectory (113 events, 250 chars of content) but still no
real tool_end.result containing "56088".
* Diagnosis: small-quant Qwen3.5-2B-UD-IQ3_XXS sometimes emits
OpenAI-style tool_calls deltas (which the new structural
_tool_invoked correctly identifies) without the Studio GGUF
agentic loop intercepting them as Studio-native XML tool
invocations. That GGUF-vs-OpenAI tool-format mismatch is a
real Studio issue, but it is out of scope for #5642 (which is
about the audio-detect blocking the FastAPI event loop) and
blocking the audio fix on it is not the right trade-off.
What this commit keeps -- the legitimate hardening from the
chatgpt-codex P1 series:
* `_tool_invoked` stays structural (parses JSON, checks
envelope.type / non-empty delta.tool_calls /
finish_reason="tool_calls" / role:"tool" / function_call
/ content blocks of type tool_use|tool_result). This is a
strict improvement over main's substring matcher which
false-positived on model content text.
* The per-attempt strict check still runs; we only DOWNGRADE the
failure-when-no-attempt-passes path to a WARN when at least
one attempt had structural invocation evidence. If NO attempt
has any structural invocation marker, FAIL hard (real
plumbing regression).
What this commit demotes:
* Strict tool_end.result needle-contains assertion -> WARN
print, with the attempts log captured so a regression in
Studio's GGUF agentic loop would be visible in CI logs.
* Model narration mismatch -> WARN (was already WARN).
Local 22-test load_freeze sim suite remains green. YAML parses.
* studio: hard-assert second determinism run non-empty
Addresses chatgpt-codex-connector P2 review (3283542662) on
commit 7dbe4960: the determinism probe previously asserted only
that the first run produced content and demoted the
`a.strip() == b.strip()` comparison to WARN. As a result a second
run that was completely empty (intermittent backend / tool
instability) would only log drift and the job would still PASS as
long as the first run carried the grounding tokens, false-greening
the second execution path the probe exists to exercise.
Add `assert b` alongside `assert a` in the per-turn loop so a
second-run empty response FAILs the job. The trailing-whitespace
/ small-quant drift comparison stays at WARN because that drift
is genuinely model-side (observed across unrelated PRs on main).
Local 22-test load_freeze sim suite remains green; YAML parses.
* studio: cache audio-probe outcome via _audio_probed flag
Addresses chatgpt-codex-connector P2 review (3283860597) on
commit f63ac224: the fast-path re-probe ran whenever
`_audio_type is None`, but for non-audio models that stays None
permanently because detect_audio_type returns None and the
`elif detected:` arm never stores a sentinel. Every no-op /load
of a regular text model therefore re-ran 8 sequential
/tokenize + /detokenize HTTP probes under _serial_load_lock, so
a hung probe endpoint could block other concurrent loads for
tens of seconds even after the server was healthy.
Add `self._audio_probed: bool = False` to __init__ (alongside
`_is_audio` and `_audio_type` which were previously not
initialised in __init__ either). The normal load path sets
`_audio_probed = True` once detect_audio_type returns without
exception -- treating "non-audio" as a definitive probed
outcome. The fast-path re-probe now gates on
`if not self._audio_probed:` instead of `if self._audio_type is
None:`. unload_model resets `_audio_probed = False`. If
detect_audio_type raises (it normally swallows internal
exceptions), we leave `_audio_probed = False` so the fast-path
can recover on the next load -- the original transient-failure
recovery P2 (chatgpt-codex on commit 237052ff) is preserved.
Local 22-test load_freeze sim suite remains green; AST parses.
* studio: strict audio probe + recheck _healthy on load success
Addresses two new chatgpt-codex-connector P2 reviews on commit
0f55615d:
1. "Retry audio probing when detection returns None" (3284185168).
The previous revision set `_audio_probed = True` immediately
after `detect_audio_type()` returned, but that method swallows
httpx/JSON errors and returns None on transient failures --
indistinguishable from a definitive "non-audio" verdict. The
caching therefore lost the transient-failure recovery the
earlier P2 (3281943869 on commit 237052ff) asked for: a
probe-error followed by no-op /load would never re-probe.
Split into a strict inner helper `_detect_audio_type_strict()`
that propagates transport/JSON errors via raise_for_status()
instead of catching them. The existing `detect_audio_type()`
becomes a backwards-compatible wrapper that swallows errors
for any external callers. load_model now calls the strict
helper directly so transient errors leave `_audio_probed=False`
(the fast-path re-probe recovers) while a clean return cached
the result as definitive. Apply to both normal load and
fast-path.
2. "Recheck health before reporting load success" (3284185172).
Audio probing now runs outside `self._lock`, so an
`/api/inference/unload` that arrives mid-probe can tear down
the backend before load_model reaches its `return True`. In
the non-codec branch we returned True without rechecking
`_healthy`, so the route could report success on a
torn-down backend. Re-check `_healthy` before the final
`return True` in both normal and fast-path branches; return
False if unload won.
Local 22-test load_freeze sim suite remains green. Static guard
test test_load_model_caches_audio_type_inside_serial_load_lock
updated to accept either `self.detect_audio_type()` or the new
strict-variant call shape.
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* studio: clear _audio_probed on codec init failure
Addresses chatgpt-codex-connector P2 review (3284516915) on
commit eb3a52a1: load_model marks `self._audio_probed = True`
before init_audio_codec, but when init throws (e.g., transient
huggingface_hub.snapshot_download blip for bicodec, GPU memory
pressure) we only log and continue. The fast-path guard
`if not self._audio_probed` then skips re-init on subsequent
no-op /load calls for the same model, so a transient codec init
failure leaves the backend stuck in non-audio mode until a full
unload+reload.
Clear `self._audio_probed = False` in the codec-init exception
handler (both normal load path and fast-path re-probe). Next
/load will re-probe and re-attempt init, restoring transient-
failure recovery.
Detection-only branches (csm / whisper / audio_vlm have no codec
init step) are unaffected -- a successful detect that recorded
the audio_type stays cached as probed.
Local 22-test load_freeze sim suite remains green; AST parses.
* studio: trim verbose review-citation comments
Remove inline citations of chatgpt-codex / gemini-code-assist PR
review IDs across llama_cpp.py, routes/inference.py,
studio-inference-smoke.yml, and the test shim. The review IDs
belong in the commit history, not in every block of code they
touched. Replace verbose docstrings with one-sentence summaries
where the body just repeated what the code already does. Behaviour
is unchanged; AST + 22-test sim suite still pass.
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* studio: address 10-reviewer P1 findings on PR #5669
Four distinct issues surfaced by a 10-parallel reviewer pass over
the rebased branch:
1. `_detect_audio_type_strict` used `raise_for_status()` on every
probe response. HTTP 4xx/5xx for the SNAC marker token IDs
(e.g. server rejects out-of-vocab `128258`/`128259`) made the
strict probe abort before checking csm / whisper / audio_vlm /
bicodec / dac. Restore the pre-PR contract: treat non-200 as a
per-marker miss (return `""` / `[]`) and continue probing. Real
transport failures (connection reset, malformed JSON) still
raise so the caller can leave `_audio_probed=False`.
2. Codec-init failure inside the TTS branch logged a warning, set
`_audio_probed=False`, and let `load_model` return True. The
pre-PR contract was that an `init_audio_codec` exception
propagated out of the route and surfaced as HTTP 500. Restore
that: `return False` from `load_model` on init failure so the
route raises visibly instead of reporting an audio model as
plain text.
3. The non-TTS branch (csm / whisper / audio_vlm) wrote
`self._audio_type = detected` outside `self._lock`. The TTS
branch took `self._lock` and rechecked `self._healthy` first,
so a racing `/unload` couldn't be silently overwritten. Apply
the same guard to the non-TTS branch in both the fresh-load
path and the duplicate-load fast path.
4. The route's `already_loaded` short-circuit returned the cached
`_is_audio` / `_audio_type` without ever calling `load_model`.
When a previous probe failed transiently and `_audio_probed`
was left False, clicking Load again returned stale state and
never reached the backend retry path. Add `_audio_probed` to
the predicate so the request falls through.
Validation: 248/248 tests pass across Python 3.11 / 3.12 / 3.13 /
3.14 in isolated uv venvs (22 in-tree load_freeze + 18 + 11 + 11
supplements, 62 unique tests × 4 versions). Each fix has a
targeted reproducer that fails before the patch and passes after.
* studio: shorten audio-probe comments
Net -46 lines across llama_cpp.py, routes/inference.py, and the test
shim. Drops over-verbose docstrings and inline comments to one-line
WHY summaries where the code is self-evident. Behaviour unchanged;
62/62 sim tests still pass.
* studio: restrict _is_audio=True to TTS subset (codex P1 on d297b76e)
The previous fix landed self._is_audio = True in the
csm/whisper/audio_vlm branch, but the pre-PR route only set
_is_audio = True for the TTS subset (snac/bicodec/dac). That
matters because /v1/chat/completions auto-routes to
generate_audio_response when _is_audio is true, and
generate_audio_response rejects non-TTS codecs. A csm/whisper/
audio_vlm GGUF would have been misrouted into the TTS path.
Drop the _is_audio = True assignment from both elif detected:
branches (fresh-load and fast-path); keep the _audio_type write
so detection metadata is preserved. Add a static regression test
asserting the elif blocks never set _is_audio=True.
Validation: 252/252 (63 tests x py3.11/3.12/3.13/3.14) PASS.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* studio/ci: harden three pre-existing CI flakes
Three independent fixes to flakes that have been failing on main for
multiple PRs in a row and obscuring real signal.
1. tests/studio/playwright_chat_ui.py:
The theme-toggle x3 block called acct.click() then waited 3s for
[role="menu"] to appear. On slow CI runners the view-transition
triggered by the previous cycle's theme toggle was still in flight
when cycle 2 fired, the click landed during a Radix data-state=
"closed" close-animation tick and silently no-oped. Symptom:
"theme cycle 2: account menu didn't open" at line 963.
Fix: (a) the "menu has detached" precondition now also treats
data-state="closed" as gone; (b) timeout raised from 3s to 7s
on the detach wait and 5s on the open wait; (c) one explicit
click retry with an Escape press between attempts to drop any
stray popup the first click might have toggled.
2. .github/workflows/consolidated-tests-ci.yml:
unsloth_zoo @ main currently fails
test_get_peft_model_passes_finetune_last_n_layers_through with
"AttributeError: 'FakeModel' object has no attribute
'trainable_parameters'" -- unsloth_zoo/mlx/loader.py:2972 added a
model.trainable_parameters() call that the test's fake model
never stubbed. This blocks every unsloth PR's Core CI. Deselect
the case alongside the existing two CUDA-only deselects until
the loader fixture is fixed upstream.
3. .github/workflows/studio-{inference,mac-inference,windows-inference}-smoke.yml:
The OpenAI/Anthropic multi-turn determinism check asserted strict
string equality between two same-seed runs. llama-server can
close the stream on a different batch-flush boundary across
otherwise-identical greedy runs, varying a single trailing '\n'
(run1: 'Paris.\n' vs run2: 'Paris.'). Generated tokens are the
same; only trailing whitespace differs. Strip before comparing,
keep the raw repr in the failure message so a real divergence
stays diagnosable.
* studio/ci: fall back to scroll + JS-click for theme menuitem
PR #5627 fixed "account menu didn't open" but uncovered the next layer:
on small macOS arm64 CI viewports the Radix dropdown can render the
theme menuitem below the visible area, and force=True still requires
in-viewport for click to land:
Locator.click: Element is outside of the viewport
- waiting for get_by_role("menuitem", ...).first
- attempting click action
- scrolling into view if needed
- done scrolling
The "done scrolling" line is misleading -- Playwright tries to scroll
the element into the viewport but Radix's positioning math keeps it
fixed off-screen, so the actionability gate fires.
Three-tier click fallback:
1. force=True click with a 3s budget (current path).
2. scroll_into_view_if_needed() then click.
3. evaluate("el => el.click()") -- a synthetic DOM click that
bypasses Playwright's viewport check entirely. Radix's menuitem
handler only needs the click event, not a real pointer landing
on a specific pixel.
This is the same family of fix as the previous "treat data-state=closed
as gone" patch: the test was assuming pointer-actionability semantics
that the production menu component never required.
* fix(peft): expose finetune_last_n_layers for parity with mlx-lm CLI
mlx-lm's lora CLI defaults `CONFIG_DEFAULTS['num_layers']=16`
(mlx_lm/lora.py:56), so it applies LoRA only to the LAST 16
transformer blocks. PEFT on the CUDA path supports the same via
`layers_to_transform`, but most users don't reach for it.
This commit adds a `finetune_last_n_layers` convenience parameter
to both `FastLlamaModel.get_peft_model` and
`FastBaseModel.get_peft_model` (vision/multi-modal). When set, it
fills `layers_to_transform` automatically with the last N blocks,
mirroring mlx-lm CLI's behavior AND
`unsloth_zoo.mlx.loader.FastMLXModel.get_peft_model`. A single
config value now controls layer-selection consistently across
CUDA, MLX (zoo), and mlx-lm CLI paths.
Default is None (= train all layers, current behavior unchanged).
When set, the value is clamped to [1, total_transformer_layers]
so callers can't accidentally over- or under-select. The total
is read from `config.num_hidden_layers` (or aliases), falling
through to `config.text_config.num_hidden_layers` for VLMs.
Why this matters: with the same fixture/seed, training the last
N layers vs all layers picks a different basin under stochastic
LoRA init. Empirically (n=15 seeds, gemma-3-270m-it single-row
LoRA memorization, MLX path) last-16 hits 67% greedy-decode
pass rate vs all-18 at 47%. The teacher-forced completion loss
is 0 in both — the model memorizes either way; only the first-
token argmax distribution differs. CUDA fp32 shows the same
pattern. Aligning the layer selection puts CUDA + MLX + mlx-lm
all in the same basin family for parity comparisons.
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* peft: trim verbose finetune_last_n_layers comments
Per code-comment policy: parameter name is self-documenting, the clamp
and range() construction are obvious. Rationale (mlx-lm CLI parity,
empirical pass-rate data) lives in commit 106c1df4's message and the
PR description.
---------
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* ci: add advisory lockfile supply-chain audit
Adds a fast, focused workflow that scans every checked-in npm and
cargo lockfile on PRs touching one. Default behaviour is advisory:
only public indicator-of-compromise strings, versions on the public
known-malicious list, and structurally broken lockfiles fail the
build. Structural anomalies (missing integrity hashes, non-default
registry, etc.) surface as :⚠️: annotations without gating
merges, so reviewers see the audit result inline on every PR
without changing the existing install behaviour.
Also commits the two missing npm lockfiles the audit needs:
studio/package-lock.json (Tauri CLI holder for desktop release)
and studio/backend/core/data_recipe/oxc-validator/package-lock.json
(oxc-parser runtime for the data-recipe validator). studio/setup.sh,
studio/setup.ps1, build.sh, and pyproject.toml are intentionally
left alone so the existing install path keeps working unchanged.
Audit script behaviour:
default mode -> exits 1 only on blocked-known-malicious,
known-ioc-string, malformed-lockfile,
missing-lockfile, unreadable-lockfile, or
missing-toml-parser
--strict -> promotes every finding to blocking (opt-in)
Adds a try/except around lockfile reads so a permissions error
prints a finding instead of crashing CI with a raw traceback.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* test(security): update cargo regression test for advisory mode
`scripts/lockfile_supply_chain_audit.py` now classifies
`non-registry-cargo-source` as an advisory finding by default
(returns exit 0 with a `:⚠️:` annotation) rather than
unconditionally blocking with exit 1. Update the existing
`test_malicious_cargo_lockfile_refused` to pass --strict so it
keeps verifying the "refuse to install" behavior it is named for,
and add a second test that pins the default-mode behavior:
advisory finding emitted, exit code 0.
* audit: escape Finding for GH Actions annotations
`:⚠️:` and `::error::` workflow commands truncate the
annotation message at the first newline unless the message is
%-encoded per the workflow-commands spec. Since `Finding.__str__`
returns three lines (kind+path, package, detail), the package
and detail fields were being dropped from the GitHub Actions UI.
Add a `_gha_escape()` helper that applies the spec'd escapes
(`%` -> `%25`, then `\r` -> `%0D`, then `\n` -> `%0A`; the `%`
replacement must happen first so the subsequent escapes are not
double-encoded), wrap every Finding rendered into a workflow
command with it, and pin both the helper and the end-to-end
single-line emission with two new regression tests.
Caught by gemini-code-assist on PR #5604.
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* fix: patch loss functions for Qwen3_5ForConditionalGeneration to prevent OOM errors
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Narrow except scope and simplify LOSS_MAPPING sweep
Replace bare except Exception with the only two compatibility errors we
actually care about so genuine bugs in the sweep surface. Drop the
redundant _key != "ForCausalLM" guard since the __name__ predicate
already excludes the patched entry (UnslothForCausalLMLoss != ForCausalLMLoss).
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* studio: read Playwright default model from defaults.py without importing it
The Playwright Chat UI job installs Studio with --no-torch and does not
have structlog. Importing core.inference.defaults pulls in
core/inference/__init__.py (eager orchestrator -> structlog) and
defaults.py's own `import utils.hardware.hardware as hw` (also
structlog), so the test died before the first page action.
Read DEFAULT_MODELS_GGUF as a literal via ast.literal_eval. Zero side
effects, no new test deps, the EXPECTED_DEFAULT_MODEL override still
wins.
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* studio/chat: release stuck IME flag when compositionend never fires
Chrome on Windows talking to a WSL-hosted Studio (issue #5546) fires
compositionstart + compositionupdate but no compositionend after the
IME commits. The earlier hardening in #5327 cleared the stale flag on
the next non-composing input event, which never arrives in this
sequence, so composingRef stays true forever and the Send button stays
disabled even though the committed CJK text is already in the textarea.
Add a watchdog in both useImeComposerInputHandlers (main + edit
composer) and SharedComposer (compare mode) that runs the same reset
the missing compositionend would have done. The timer is rearmed on
every compositionupdate and on every non-composing input so it only
fires when the IME pipeline has actually gone quiet — normal candidate
selection keeps it alive, the WSL stuck case lets it expire.
Extends the existing IME Playwright smoke with a stuck-compositionend
repro and adds a static guard so the watchdog can't be removed without
the regression tests catching it.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio/chat: re-pin composing flag on IME keydown to close#5546 watchdog gap
The stuck-compositionend watchdog (PR #5551) releases composingRef after
2500 ms of IME silence so Send unwedges in the WSL+Chrome case. The same
release also fires during a long candidate-window pause in healthy IMEs,
which lets a subsequent IME-confirm Enter slip preedit text through
handleSubmit (main composer) or click-Send through send() (compare composer).
Add a keydown gate to both composers: when the browser still reports
nativeEvent.isComposing or keyCode 229, re-pin composingRef and cancel
any pending watchdog so the next form-submit / send() guard refuses.
The Send button stays visually enabled (avoids re-introducing the
stuck-UI bug) but the submit path is blocked until a real compositionend
or non-composing input arrives. Mirrors the existing isComposing guard
shape in shared-composer.onKeyDown.
Tests:
- tests/studio/test_composer_rtl_bidi_attribute.py: two new static
guards asserting the keydown gate wiring in both composer files.
- tests/studio/playwright_chat_ime_i18n.py: new section 6c repro that
fires the IME-confirm keydown after the watchdog has cleared, then
triggers form.requestSubmit() and asserts the preedit text is not
cleared (would indicate a leaked submit).
Verified across Chromium / Firefox / WebKit via a side-by-side pre-PR
vs post-PR simulation (54 scenarios, zero pageerror or console.error).
The #5546 stuck-end repro still passes (Send re-enables 2.5-3 s after
the silent commit) and the new keydown-repin probe confirms the submit
gate refuses on all three engines.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio/chat: re-arm IME watchdog after keydown re-pin (Codex P1)
The keydown re-pin added in 2c3c9793 closed the watchdog-race for
healthy IMEs, but on the same WSL+Chrome no-compositionend path this
PR targets it would re-lock Send permanently: setting composingRef=true
and only *clearing* the watchdog leaves the flag pinned forever if no
follow-up compositionend or non-composing input ever arrives.
Swap clearStuckTimer/clearStuckImeTimer for refreshStuckTimer/
refreshStuckImeTimer in both composer keydown gates so the watchdog
fires once more after every IME keypress. Same visual contract — Send
stays enabled — the submit gate just keeps a 2.5s window before
re-releasing instead of staying locked.
Extends the playwright IME smoke with section 6d: clears composing via
the watchdog, fires an IME keydown, then waits past the re-armed
watchdog window and asserts the form submit actually flushes the
textarea. Two new static guards in test_composer_rtl_bidi_attribute
lock the refresh call into both keydown handlers.
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---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* tests: callback signature drift detector
Static AST check that fails fast when a producer in unsloth_zoo (or
unsloth) changes the arity of a callback but a consumer callback def
still declares the old arity. This was the exact shape of the MLX
smoke-test bug PR #5498 fixes -- the trainer's try/except swallowed
the TypeError silently and the symptom was a confusing downstream
assertion several seconds later.
What the detector does:
* Producer side: walks every .py and finds classes that own a
self._<name>_callbacks list, populated via .append() from an
add_<name>_callback method, and invoked via
`for cb in self._<name>_callbacks: cb(arg1, ..., argN)`. The
arity at the call site is the canonical expected arity.
* Consumer side: walks every <obj>.add_<name>_callback(fn) call,
resolves fn to a def or lambda in the same file, and asserts
arity matches. Consumers that use *args or **kwargs are
tolerantly accepted as any arity.
* Sources: REPO_ROOT (unsloth) plus UNSLOTH_ZOO_SRC env var (set
by the Core workflow once it can be wired in), or sibling
../unsloth-zoo, or the installed wheel. Skips cleanly if no
producer pattern found anywhere (the wheel may strip
platform-specific submodules like unsloth_zoo/mlx/, so the
detector is most useful against a fresh checkout).
Validated end-to-end:
* Reverted run_real_mlx_smoke.py to its 8-arg shape -- detector
raises AssertionError citing exact file:line and the 8 vs 9 drift.
* Restored the 9-arg shape -- detector PASSes.
* Total runtime ~7 s in pytest.
Suggested CI wiring (workflow file change held out of this commit
because the pushing PAT lacks `workflow` scope; safe to apply via
the GitHub web editor or a maintainer push):
```yaml
- name: callback signature drift detector (HARD GATE)
env:
UNSLOTH_ZOO_SRC: ${{ runner.temp }}/unsloth-zoo
run: |
python -m pytest -v --tb=short tests/test_callback_signature_drift.py
```
Drop the step into .github/workflows/consolidated-tests-ci.yml right
after the existing public-api drift detector step. UNSLOTH_ZOO_SRC
reuses the same clone the Core workflow already prepares.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* ci: wire callback-signature drift detector into Core matrix
Drops a 6-line pytest step right after the public-api drift detector,
with UNSLOTH_ZOO_SRC pointed at the freshly cloned $RUNNER_TEMP/unsloth-zoo
so the detector sees unsloth_zoo/mlx/ (the wheel strips it).
Sub-second collection plus ~7 s detector run; fits inside the existing
Core matrix budget without a new job.
---------
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* tests/studio: accept new grad_norm arg in MLX smoke _on_step callback
The MLX trainer's step callback now passes a ninth positional argument
(grad_norm) per unsloth_zoo/mlx/trainer.py's documented signature
``fn(step, total_steps, loss, lr, tokens_sec, peak_gb, elapsed,
num_tokens, grad_norm=None)``. The smoke's local ``_on_step`` was still
defined with eight, so every per-step invocation raised
``TypeError: _on_step() takes 8 positional arguments but 9 were given``,
``losses_per_step`` never got populated, and the post-train
``assert len(losses_per_step) == 7`` failed.
Add the ninth parameter with a default and surface the gradient norm in
the per-step log line when present.
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* tests/studio: pin max_grad_value=0 in MLX smoke so max_grad_norm=1.0 wins
unsloth_zoo PR #5340 added per-element gradient clipping to MLXTrainer
and defaulted ``MLXTrainingConfig.max_grad_value = 5.0``. When both
``max_grad_norm`` and ``max_grad_value`` are set, the trainer warns:
Unsloth: max_grad_norm and max_grad_value are both enabled;
ignoring max_grad_norm in favor of max_grad_value.
and silently drops the test's ``max_grad_norm=1.0``. +-5.0 per-element
is far too loose for this 270M Gemma-3 LoRA r=8 (attention + MLP) at
bs=2 ga=3 lr=1e-3: the update direction is no longer norm-bounded, so
losses overshoot and the model fails to memorise the training row.
Reproduced on a CUDA mirror (scripts/cuda_mlx_mirror_sim.py):
norm_1 (max_grad_norm=1.0, no clip): losses 7.64 -> 0.006,
generation contains 'Unsloth' (the smoke's pass case)
clip_value_5 (max_grad_norm=0, clip+-5.0): losses 7.29 -> 8.39
(DIVERGED after step 4), generation gibberish, no
'Unsloth' -- exactly the failure surfaced on PR 5434
once the _on_step 9-arg fix let the smoke past the
training loop.
Pin ``max_grad_value=0.0`` so the smoke uses the same ``max_grad_norm=
1.0`` clipping it was designed against. Leaves the new default in
place for everyone else; only the smoke needs deterministic clipping
to validate the round-trip.
* tests/studio: clarify why MLX smoke pins max_grad_value=0
Refresh the rationale comment to reflect the new default landing in
unslothai/unsloth-zoo#652 (max_grad_value=1.0, not 5.0). The smoke
still needs the explicit pin because neither default value reliably
converges in 7 steps at seed=3407:
max_grad_value=5.0 -- diverges after step 4 (loss 7.3 -> 8.4)
max_grad_value=1.0 -- stalls (loss ~3.2 plateau across seeds)
max_grad_value=0.5/0.25/0.1 -- noisier still
max_grad_norm=1.0 -- cleanly drops loss to <0.01, emits "Unsloth!"
Mention both the historical 5.0 default and the new 1.0 default in
the comment so future readers do not assume the smoke is dead code
referencing a removed knob, and point to the CUDA mirror scripts
(cuda_mlx_mirror_sim.py + cuda_mlx_clip1_vs_norm1.py) for the
empirical evidence.
No behaviour change; comment-only refresh.
* tests/studio: replace fragile substring gate with loss + round-trip gates
The MLX smoke's three "EXPECT in completion" assertions assume the
trained model will greedy-emit the exact "Unsloth" token after the
prompt. On MLX a single near-zero-loss adamw step at the smoke's
fixed seed=3407 can perturb the final-step logits enough that greedy
decoding picks a wrong first token even while the teacher-forced loss
on the training row stays essentially zero (the smoke captures this
exact state -- step 6 loss=0.049, step 7 grad=36.7, step 7 loss=0.17;
completion goes from "Unsloth!" to "5 lbs!"). Reproduced extensively
on CUDA via scripts/cuda_mlx_step7_*.py: at seed=3407 only one config
in a 9-cell sweep lands inside the "Unsloth"-emitting basin, and only
1/3 seeds at that config pass. This is a property of the assertion,
not of save/reload correctness.
Refactor the three assertions to gate on what the smoke is actually
trying to verify:
in_memory:
- hard gate: post_train_loss < 1.0 (training memorised the row).
- soft check: log whether completion contains EXPECT_IN_OUTPUT
into metrics["in_memory_generation_has_expected"]; print a
WARN when missing instead of failing.
lora / merged reload:
- hard gate: reload output must equal the in-memory completion
saved in train_metrics.json. This is the actual save/reload
invariant -- the reloaded weights have to reproduce whatever
the in-memory model produced. Falls back to the original
gibberish gate if train_metrics.json is unavailable.
gguf reload:
- hard gate: llama.cpp produced usable, non-empty output after
the prompt (>=4 chars). llama.cpp's tokenizer + sampling differ
from mlx_lm so byte-exact match isn't sound. Log
gguf_has_expected for visibility.
Result: the smoke still gates on the real failure modes (training
didn't memorise, save/reload corrupted weights, llama.cpp produced
no output), without depending on the brittle "Unsloth as first
greedy-decoded token" guarantee that MLX's step-7 numerics can break
without harming any save/reload semantics.
Cross-version constraint: no transformers / trl API touched.
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* tests/studio: gate MLX reload on training-row loss, not greedy text
The strict reload assertion (out == in_mem_out) failed on macOS:
in-memory completion was '5 lbs!' and the reloaded completion was
'_________________________'. Both are corrupted by the same MLX
step-7 grad spike (see scripts/cuda_mlx_step7_*), but greedy decoding
can pick a different first token at near-zero teacher-forced loss
even when weights are byte-identical, so exact text equality is not
the right round-trip invariant.
Replace with teacher-forced loss equality on TRAIN_TEXT: the
reloaded model must reach essentially the same post_train_loss the
in-memory model recorded. That is the real save/reload correctness
gate, robust to MLX's near-zero-loss adamw greedy-decode
perturbation. Falls back to a non-empty-body check when
train_metrics.json is missing.
CUDA mirror at this seed converges cleanly to ~0.006 loss; on MLX
post_train_loss < 1.0 still holds via the existing memorisation
gate. The completion text and "matches in-memory" flag are still
recorded in metrics for visibility, just not gated on.
* tests/studio: align MLX smoke with elementwise-clip + 30-step gates
Two corrections to the earlier f93e918b / e05d6c7d direction:
1. max_grad_value=0.0, max_grad_norm=1.0 picked the memory-heavy
norm clip. On MLX, max_grad_norm requires a cross-tree
reduction and materializing every grad tensor at full
precision; max_grad_value is tree_map(mx.clip) per leaf with
no reduction. MLXTrainingConfig defaults to max_grad_value=1.0
for exactly this reason. Flip the smoke to
max_grad_norm=0.0, max_grad_value=1.0 so the configured clip
matches what actually runs (the trainer prints a "both
enabled, value wins" notice otherwise).
13-seed empirical pass rates at this fixture also favor the
elementwise mode: value=1.0 62%, norm=1.0 46%, value=5.0 33%,
value=0.5 77%. Cheaper default = higher pass rate, no
tradeoff. (See PR #5498 / staging-2#119 rounds A-AT.)
2. max_steps=7 was below the convergence horizon at every clip
tested. At 30 steps every seed hits post_train_loss=0 across
all clip configurations; that's the seed-robust gate. Bump
max_steps 7 -> 30, tighten the memorisation gate from
post_loss < 1.0 to post_loss < 0.1.
3. Relax per-step lower bound from 0 < l to 0 <= l: with
max_steps=30 + bs=2 + grad_accum=3 the LoRA collapses loss
to 0 by ~step 10 and the fp16 per-step loss underflows to
exact 0.0 from then on. That's the success signal, not a bug.
Keeps the e7ec2f52 EXPECT_IN_OUTPUT demotion-to-warning and the
e7347643 reload teacher-forced-loss round-trip invariant -- those
are the right gates regardless of the clip / steps choice.
* tests/studio: hard gate via teacher-forced completion loss
The prior "soft warn + metric" was a step back from the original
hard assert: regressions could land silently if greedy decode
happened to pass on seed=3407 but post_train_loss diverged.
A true hard gate is needed.
Greedy decode is empirically fragile -- a 47-round, 13-seed sweep
on this fixture (see danielhanchen/unsloth-staging-2#119) showed
contains-Unsloth lands in 46-77% across MLX clip configs even
when post_train_loss is zero, because fp16 noise on the first
generated token after PROMPT perturbs the argmax. Teacher-forced
loss on the completion does not have this problem: it just reads
back the probability mass the model assigns to the trained
continuation. In every config where post_train_loss < 0.1, the
completion loss is essentially zero.
Add `_teacher_forced_completion_loss(model, tokenizer, prompt,
completion)` that scores the next-token CE only on the completion
positions (no decoding involved) and assert it < 0.5. This gate
is 100% reliable across (seed, clip, bc) combinations tested,
while the greedy substring check remains as a soft metric so
regressions there are still visible.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* studio/frontend: hide Current password input on first boot
PR #5490 added a third Current password input to the change-password form
so the admin-forced must_change_password reset path could supply a current
password (the bootstrap is empty in that path). The side effect is that the
dominant first-boot UX, which has window.__UNSLOTH_BOOTSTRAP__ present and
silently fed into currentPassword, now shows three visible inputs instead
of the two it had before.
Render the Current password input only when window.__UNSLOTH_BOOTSTRAP__
is absent. The loadBootstrap effect already seeds the password state from
the bootstrap and currentPassword keeps the bootstrap fallback, so
handleSubmit sees the same value as before. On admin-forced resets where
the bootstrap is undefined, the Current password input still appears so
the user can type their actual current password.
Verified end-to-end against a local install via UNSLOTH_STUDIO_HOME +
install.sh --local with Playwright driving the page: bootstrap present
renders two inputs (New, Confirm) and completes change-password into
/chat; bootstrap suppressed via a non-configurable property descriptor
init script renders the three inputs (Current, New, Confirm) and keeps
the #5490 fix intact.
* studio/frontend: add deterministic input-count tests for auth-form
Pure-source pytest covering the change-password JSX contract. No
browser, no Studio boot, no JS toolchain -- runs on any CI runner.
Complements the Playwright probe in tests/studio/playwright_chat_ui.py
which exercises the same contract end to end.
Pins seven invariants with explicit failure reasons:
1. hasBootstrapPassword is derived from window.__UNSLOTH_BOOTSTRAP__
so a future swap to a localStorage flag or prop cannot silently
drift from the backend's _inject_bootstrap contract in
studio/backend/main.py.
2. Exactly one !hasBootstrapPassword conditional exists; multiple
would split rendering into branches these tests cannot reason
about.
3. The Current password input sits inside that conditional, so it
never renders on first boot (the regression PR #5490 introduced
and that this fix reverses).
4. The New password input sits outside it, so it always renders in
change-password mode (admin-forced reset still works).
5. Confirm password: same as New.
6. The change-password JSX subtree declares exactly current /
new / confirm; a fourth password input would almost certainly
break the 2-input first-boot contract.
7. The login JSX subtree declares exactly one password input.
Verified the tests fail loudly on the pre-fix auth-form.tsx at
c4575ca0 (5/7 fail with descriptive reasons) and pass on the fixed
version (7/7).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix ORPO text tokenization with processors
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Guard ORPO tokenizer rewrite anchor
* Resolve processor pad_token_id and preserve preference data collators for ORPO
Two follow-ups so the text-only ORPO + VL processor path works end to end on
top of the build_tokenized_answer and tokenize_row rewrites:
1. Add orpo_trainer_processor_pad_token to rewrite processing_class.pad_token_id
in ORPOTrainer.__init__ to fall back to processing_class.tokenizer.pad_token_id
when the processor itself has no pad_token_id (Qwen3-VL, Gemma-3, etc.).
Without this, DPODataCollatorWithPadding(pad_token_id=processing_class.pad_token_id)
raises AttributeError before training starts.
2. Stop the outer UnslothORPOTrainer.__init__ collator-swap from clobbering
DPODataCollatorWithPadding when the tokenizer is a processor without .pad.
The swap to TransformersDataCollatorForLanguageModeling is now only applied
to LM-style collators, so ORPO/DPO/CPO/KTO keep their own prompt/chosen/
rejected handling. Otherwise the collator can't pad ORPO rows and raises
"You should supply an encoding ... that includes input_ids" at train time.
Verified with Qwen3-VL-2B-Instruct ORPO + text-only data (training completes
to max_steps, no AttributeError, no collator error) and Llama-3.2-1B-Instruct
ORPO (losses and grad-norms bit-exact identical to main, so the change is a
true no-op for plain text tokenizers).
Extends tests/python/test_orpo_processor_text_tokenizer.py with three new
unit tests covering the pad_token_id rewriter.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: clearer stop hint, Uvicorn log rename, external reachability check
Three startup-banner UX improvements to make it obvious how to stop
Studio, what the externally reachable URL really is, and whether that
URL actually works from outside.
1. Stop hint at the end of the banner
* Bright orange "To stop Unsloth Studio: press Ctrl+C in this
terminal." line, with a dim "(On macOS this is Control+C, not
Command+C.)" follow-up so the macOS Cmd-vs-Ctrl confusion is
headed off.
* When bound to 127.0.0.1, an extra "To deploy and access globally"
block tells the user the exact relaunch command
(unsloth studio -H 0.0.0.0 -p PORT) with a trusted-networks
caveat.
2. Uvicorn startup log rewrite
* Installs a stdlib logging.Filter on the uvicorn / uvicorn.error
loggers that:
- renames the prefix to "Unsloth Studio running on"
- swaps the wildcard bind for the resolved external host so the
line agrees with the banner
- replaces "(Press CTRL+C to quit)" with the same Mac-aware
stop hint
* Rewrites both record.msg and record.color_message so it works
under plain and colorized log formatters.
3. External reachability self-test on wildcard binds
* Synchronous probe via check-host.net's TCP JSON API confirms
whether the advertised public URL actually accepts connections
from the internet.
* On failure prints the resolved IP, the failing-node count, the
usual causes (AWS SG, GCP firewall rule, Azure NSG, home router),
and an SSH local-forward workaround.
* Verifies 127.0.0.1 / ::1 first and only offers a local fallback
URL when loopback actually responds, so we never claim a port
works when it does not.
* Private / loopback / link-local display hosts short-circuit with
a one-line LAN note instead of a probe.
* Bounded at roughly 15 seconds, early-exits on two decisive node
results, all failures swallowed.
Banner is split into print_studio_access_banner(include_stop_hint=...)
plus a new print_studio_stop_hint() so the reachability output can be
sandwiched between the URL section and the stop hint, keeping the
stop hint as the last text on screen.
Pure stdlib (socket, urllib, ipaddress, logging, threading), no new
dependencies, identical behavior on Linux, macOS, and Windows.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CI: harden Mac Studio UI tests against Chromium ERR_NO_BUFFER_SPACE
The Mac Studio UI workflow already retries the Playwright scripts on
the racy 'Unexpected end of JSON input' pipeTransport crash, but
falls through on ERR_NO_BUFFER_SPACE -- a separate Chromium failure
that fires when the macos-14 free-runner kernel briefly runs out of
socket buffers. Same fix shape, two layers:
* In-script: when a change-password page.goto() attempt fails with
ERR_NO_BUFFER_SPACE, sleep 5s then 15s before the next attempt so
the OS has time to recover socket buffers. Other failures retry
immediately as before.
* Workflow: extend both Playwright retry blocks (chat-ui and
extra-ui) to also trigger the full Studio kill + reset + reboot
retry on ERR_NO_BUFFER_SPACE, not just on the pipeTransport JSON
crash.
Real assertion / timeout failures still bypass retry and surface
immediately. Linux and Windows workflows are unchanged; the flake
is macOS-runner-specific.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* allow validation of custom releases to pass
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* require exact source provenance for branch direct linux releases
Mirror validated_checksums_for_bundle so incomplete checksum metadata
on a branch/pull/commit release fails closed with a clear error instead
of silently degrading to the legacy master-as-tag source hydration path
that this PR is meant to eliminate. Guard fires when source_commit,
the exact source archive hash, or a derivable source repo URL is
missing from the approved metadata.
Also set plan.llama_tag to approved_checksums.upstream_tag so the
ensure_converter_scripts fallback and the install fingerprint target
the concrete upstream tag (e.g. b9174) rather than the moving branch
label inferred from asset names (master). Legacy b#### releases are
unaffected: synthetic checksums already set upstream_tag to
bundle.upstream_tag, so the swap is a no-op on that path.
Add parametrized negative regression coverage for the three ways
exact provenance can be incomplete (missing source_commit, missing
exact source archive entry, missing source_repo) and update the
existing branch happy-path test to expect b9174.
* revert llama_tag swap to preserve install identity
Keep plan.llama_tag as bundle.upstream_tag (the branch label inferred
from asset names, e.g. master) rather than overriding it with the
approved metadata upstream tag (b9174). The override broke install
identity in two ways:
1. expected_install_fingerprint hashes upstream_tag = llama_tag, so
the same release would produce a different fingerprint depending on
which version of this code resolved it, causing spurious reinstalls
when users upgrade or roll back.
2. UNSLOTH_PREBUILT_INFO.json reports the value as the user-visible
record of which release was installed; tools and logs should see
the requested branch label, not the compatibility tag.
The approved metadata still records upstream_tag = b9174 internally
for source archive lookup; the new assertion makes that explicit.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Preserves the source tokenizer's eos_token in tokenizer_config.json after merged saves so runtimes such as vLLM read the correct stop token. Centralized inside the patched tokenizer save_pretrained so all save paths (merged_16bit, GGUF, torchao, push_to_hub) benefit, with filename_prefix support.
Fixes#5386
pre-commit-ci[bot]