* feat(studio): add S3 dataset configuration foundation (#4539)
Add foundational types and configuration for S3 bucket dataset loading:
- Add S3Config type to frontend training types
- Add S3Config Pydantic model to backend training models
- Add "s3" as a DatasetSource option
- Add s3Config state and setS3Config action to training config store
- Add i18n translations for S3 configuration (English and Chinese)
This provides the type definitions and UI text for S3 integration.
Full implementation requires boto3 dependency and data loading logic.
Refs: #4539
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* Wire S3 config into training pipeline and prevent secrets persistence
- Pass s3_config from request into training_kwargs so it flows to training subprocess
- Add s3Config to NON_PERSISTED_STATE_KEYS to prevent AWS secrets from being
saved to localStorage
Addresses code review feedback on PR #5951.
* Exclude S3 config from database persistence to protect secrets
Filter out s3_config (which contains secret_access_key) from the
config_json stored in training_runs table, preventing AWS credentials
from being persisted to disk.
Addresses P1 security feedback on PR #5951.
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* Re-raise HTTPException in start_training and defer s3 DatasetSource widening for PR #5951
* Redact s3_config from W&B run config and accept camelCase S3 credential aliases for PR #5951
* feat(studio): implement S3 dataset loading end-to-end
Builds the actual S3 loader on top of the hardened #5951 foundation,
turning the 501-gated scaffold into a working dataset source.
Backend:
- Add core/training/s3_dataset.py: lists and downloads supported dataset
files (parquet/json/jsonl/csv) from an S3 bucket to a temp dir, using
IAM-role or access-key credentials. boto3 is imported lazily (optional dep).
- Wire s3_config into UnslothTrainer.load_and_format_dataset (downloads then
reuses the existing local-file path) and thread it through worker.py.
- Replace the 501 "not implemented" gate with a boto3-availability guard so
S3 works when boto3 is present and fails clearly when it is not.
- Add boto3 to studio.txt requirements.
- Add tests/test_s3_dataset.py (8 tests) covering download/filtering,
collisions, missing-boto3, and S3Config camelCase/IAM validation.
Frontend:
- Widen DatasetSource to include "s3"; add s3_config to the training payload
type and mapper; add an S3 validation branch and selectS3Source store action.
- Add s3-config-form.tsx (bucket/region/prefix/keys/IAM toggle) reusing the
existing studio.dataset.s3.* i18n strings.
- Add a Hugging Face / Local / Amazon S3 source toggle in dataset-section;
the S3 config card replaces the dataset combobox when S3 is selected.
- Fix DatasetPreviewDialog to accept the widened DatasetSource type.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix S3 dataset loader for PR #6222
* Fix S3 dataset edge cases for PR #6222
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix S3 IAM payload handling for PR #6222
* Block multimodal S3 datasets for PR #6222
---------
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: Ash <ash@MacBook-Pro.local>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Fix step count mismatch when sequence packing is enabled
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Emit a single step-0 progress event and guard applyStatus totalSteps
Merge the two consecutive _update_progress calls before train() so the
step-0 gate in _on_progress fires once instead of twice, avoiding a
duplicate startup event and a null-metric step-0 row in training_metrics.
Apply the same positive-number guard to applyStatus that applyProgress
uses, so a stale or startup status poll can no longer overwrite the
packed step count with 0 or replace it with a stale total.
* Log debug message when train_dataset length is unavailable
The TypeError fallback for length-less datasets (e.g. streaming
IterableDataset) was silent, leaving no trace that the step estimate
came from the raw dataset rather than the packed one.
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Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
* Studio: training survives a non-writable HF datasets cache
A shared HF datasets cache can contain subtrees owned by another user
(for example populated by an earlier root-run job). datasets then dies
with "[Errno 13] Permission denied: ..._builder.lock" while locking
the cached builder and the training run fails. load_dataset in the
training worker and trainer now goes through a wrapper that catches the
EACCES and rebuilds the dataset in a Studio-owned cache under
cache_root()/hf-datasets, logging the fallback.
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* Scope the HF_DATASETS_CACHE override to the fallback load
* Route non-streaming dataset preview loads through the cache-safe wrapper
---------
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Trim and tighten code comments and docstrings across the repository. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Trim and tighten code comments and docstrings across studio/ Python. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Raise ruff line-length to 100 and extend the local pre-commit format pipeline (def-signature magic-comma normalization, short multi-line assert collapse, kwarg '=' spacing, blank-line-after-short-import removal, adjacent string-literal / f-string+plain merge, redundant-pass pruning). Every transform re-checks the file AST and is dropped if it would differ; the whole-repo reformat is verified AST-identical per file and idempotent.
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
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* perf: drop --no-cache-dir from AMD ROCm torch wheel installs
uv caches downloaded wheels by default; passing --no-cache-dir forced a
full redownload of the ~2 GB torch wheel on every install run. CUDA installs
never had this flag -- AMD was the only path affected.
* fix: use install-state flag instead of subprocess probe for AMD Windows warning
Replace the subprocess torch probe in the post-install warning block with a
module-level _rocm_windows_torch_installed flag set by _ensure_rocm_torch().
Subprocess re-import of torch is unnecessary and fragile -- the install
function already knows whether it succeeded.
* fix: hoist global declaration to top of _ensure_rocm_torch
Python requires the global statement to appear before any assignment
to the variable within a function. Moving it to the function top fixes
the SyntaxError on line 354.
* fix: pass AMD torch install status via env var to suppress false warning
setup.ps1 now sets UNSLOTH_ROCM_TORCH_INSTALLED=1 after a successful AMD
wheel install. install_python_stack.py reads this at the top of
_ensure_rocm_torch() to skip both the subprocess probe and the warning --
no re-import of torch needed, and the warning message now correctly says
'could not be auto-installed' rather than 'must be installed manually'.
* fix: register ROCm DLL directory before torch import on Windows
Python 3.8+ ignores PATH for extension DLL loading on Windows; amdhip64.dll
and other HIP runtime DLLs must be registered via os.add_dll_directory().
Without this, torch.cuda.is_available() always returns False on AMD ROCm
Windows even when HIP_PATH is correctly set in system environment variables.
Reads HIP_PATH / ROCM_PATH env vars first, then falls back to scanning
common ROCm install roots (C:\Program Files\AMD\ROCm, F:\ROCm, C:\ROCm).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: remove hardcoded non-standard ROCm paths from DLL directory scan
Only use HIP_PATH/ROCM_PATH (set by AMD installer) and the standard
C:\Program Files\AMD\ROCm\<version>\bin location. Custom drive paths
like F:\ROCm are user-specific and should not be hardcoded.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: prevent torchao overrides step from overwriting AMD ROCm torch
torchao==0.14.0 in overrides.txt declares torch as a dependency. Without
--no-deps, uv resolves torch from PyPI and installs 2.11.0+cpu on top of
the AMD ROCm wheels (2.9.0+rocmsdk20251116). This was the root cause of
'Hardware detected: CPU' -- the AMD wheels were installed but then
immediately overwritten by the overrides step.
When _rocm_windows_torch_installed is True, add --no-deps to the overrides
pip_install call so torchao is installed without pulling in CPU torch.
* fix: add rocm_sdk namespace tarball to Windows ROCm wheel installs
torch/_rocm_init.py calls `import rocm_sdk` at startup, which requires
the rocm namespace tarball (rocm-*.tar.gz) in addition to the SDK wheel
packages. This tarball was missing from both install.ps1 and setup.ps1,
causing ModuleNotFoundError on first torch import.
- Add rocm-0.1.dev0.tar.gz to ROCm 7.1.1 install (provides rocm_sdk namespace)
- Add rocm-7.2.1.tar.gz + rocm_sdk_devel to ROCm 7.2.1 install
- Install tarball in a dedicated step before main SDK/torch wheels
- Switch to @array splatting in install.ps1 scriptblock for dynamic wheel count
- Remove --no-cache-dir from Python-side ROCm wheel install (prevents ~2GB redownload)
* feat: enable ROCm 7.2 torch install + warn on gfx1151 with ROCm < 7.2
Chigoma333 (AMD Radeon 8060S / gfx1151, Strix Halo) confirmed that ROCm
7.1 segfaults when tensors are moved to GPU, but ROCm 7.2 + torch
2.11.0+rocm7.2 works fully including training.
Changes:
- Uncomment (7,2): "rocm7.2" in _ROCM_TORCH_INDEX (was blocked by <2.11.0)
- Add _ROCM_TORCH_PKG_SPECS dict with per-tag version bounds:
rocm7.2 → torch>=2.11.0,<2.12.0; all older tags → <2.11.0
- Add _detect_amd_gfx_codes() helper that parses rocminfo output
- Warn on gfx1151/gfx1150 (Strix Halo) when ROCm < 7.2 is installed,
pointing users at the known segfault and recommending upgrade
- install.sh get_torch_index_url(): enable rocm7.2 case (previously capped
to rocm7.1), cap unknown future tags to rocm7.2
- install.sh: override TORCH_CONSTRAINT to >=2.11.0,<2.12.0 when rocm7.2
index is selected, so pip can actually resolve torch 2.11.0
* [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
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* fix: suppress remaining console popups on Windows, patch torch.distributed.is_initialized for ROCm #5301
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* fix: stub all missing torch.distributed attrs for ROCm Windows wheel #5301
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* fix: inject torch.distributed stub when C backend missing in ROCm Windows wheel #5301
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* fix(rocm/windows): pre-stub torch._C._distributed_c10d + raise amd-smi timeout
Two fixes for Windows ROCm regressions reported by electroglyph on #5301:
1. worker.py — torch.distributed stub now fires unconditionally on Windows
The previous stub only injected sys.modules in the except branch, meaning
it was silently skipped when `import torch.distributed` happened to succeed
(the C backend is lazily resolved). The crash then hit later when
transformers/trl triggered the lazy load. Fix: on win32 we pre-populate
sys.modules['torch._C._distributed_c10d'] AND set the attribute on the
torch._C extension module *before* attempting the import, covering both
the early-ImportError and lazy-load failure modes.
2. amd.py — increase amd-smi timeout from 5 s to 30 s on Windows (10 s Linux)
amd-smi on Windows must cold-init the ROCm runtime on first invocation;
5 s was consistently too short, producing repeated 'Command timed out'
warnings in the server log. 30 s gives enough headroom without blocking
indefinitely on broken installs.
3. install.ps1 — widen Python 3.12 enforcement to ROCmGpuLabel (WMI-only path)
Users whose HIP SDK is not on PATH were detected via WMI but not switched
to Python 3.12 before the install started, causing a second pass. Guard
now fires on (HasROCm -or ROCmGpuLabel).
* fix(rocm): guard c10d stub, fix TorchIndexFamily for 7.1, clean dead code + comments
- worker.py: wrap c10d stub injection in `if _c10d_key not in sys.modules` so
Windows NVIDIA users with a real torch.distributed are never affected
- install.ps1: fix Get-TauriTorchIndexFamily receiving hardcoded "rocm7.2"
even when ROCm 7.1 wheels are installed; now branches on $ROCmVersion
- main.py: remove dead `import ctypes as _ctypes` (ctypes is never called)
- hardware.py, install_python_stack.py, worker.py, install.ps1: shorten
verbose multi-line comment blocks throughout
- tests: update 4 stale assertions that expected rocm7.2 to be absent/capped
* fix(tests): match windows AMD warning assertion to actual source string
* chore: trim verbose comment blocks across all ROCm-related files
* fix: guard reconcile call against None numeric_ids; add torchvision lower bounds
* fix(install.ps1): recreate venv with Python 3.12 after ROCm switch
Venv was created with 3.13 before GPU detection ran; switching
$DetectedPython to 3.12 had no effect since $VenvPython still
pointed to the 3.13 interpreter inside the already-created venv.
* ux: detect AMD GPU before Python selection to avoid double venv creation
- Early hipinfo + WMI probe runs before Find-CompatiblePython so Python
3.12 is selected upfront when AMD is detected; venv is now created
exactly once instead of 3.13 then immediately 3.12.
- Post-venv recreation block replaced with a simple warning for the rare
case where AMD was missed by the early probe.
- setup.ps1: show venv's actual Python version (e.g. 3.12) instead of
the system Python found by the pre-activation search (was showing 3.13).
* fix(rocm/win): auto-stub all _distributed_c10d symbols via PEP-562 __getattr__
The bare ModuleType stub caused ImportError when torch._dynamo was imported
(triggered by trainer.py accessing torch._dynamo.config at load time).
torch._dynamo pulls in torch.distributed.fsdp._flat_param which does:
from torch._C._distributed_c10d import FakeProcessGroup
and potentially other symbols. Adding module __getattr__ auto-creates a
stub class for any missing symbol so all such imports succeed without
enumerating every individual symbol. Applied to both the primary stub
and the fallback stub in the except branch.
* chore: trim c10d stub comment
* fix(rocm/win): auto-stub missing torch.distributed attrs (Store, ProcessGroup, …)
* fix(rocm/win): pre-stub fsdp submodules in sys.modules; fix __getattr__ subpackage clash
* feat(rocm/win): arch-aware wheel selector always picks newest ROCm release
Replace HIP-SDK-version-gated wheel selection with GPU arch-based logic.
Select-ROCmWheelRelease (PS) and _select_windows_rocm_release (Python) map
gcnArchName → minimum ROCm version, then pick the newest available release
that satisfies it (currently always rocm-rel-7.2.1 for any supported GPU).
Wheels bundle their own ROCm runtime so the installed HIP SDK 7.1 does not
prevent using 7.2.1 wheels on gfx1200 (RX 9060 XT) and similar RDNA 4 GPUs.
Also installs the bitsandbytes Windows ROCm continuous-release wheel and sets
BNB_ROCM_VERSION=72 in worker.py before ML imports so bnb loads the
libbitsandbytes_rocm72.dll that ships in that wheel.
* fix(rocm/win): stub class metaclass for ProcessGroup.BackendType; amd-smi circuit breaker
torchao.float8.inference accesses ProcessGroup.BackendType as a class-level
attribute. Plain type() stubs have no __getattr__ on the metaclass so this
raises AttributeError. Introduce _StubClassMeta whose __getattr__ returns
child stub classes, fixing the torchao import chain.
Add an amd-smi circuit breaker in amd.py: after 3 consecutive failures the
module stops spawning the process, eliminating the repeated Windows UAC /
DiskPart elevation prompts caused by polling a non-functional amd-smi.
Also guard BNB_ROCM_VERSION=72 behind a DLL existence check so bitsandbytes
fails with its own detection message rather than a harder "DLL not found" when
the Windows ROCm bnb wheel is not yet installed.
* fix: stub __members__ so torchao float8 enum check doesn't crash on ROCm Windows
torchao.float8.inference accesses ProcessGroup.BackendType.__members__
expecting a Python Enum registry dict. _StubClassMeta.__getattr__ was
blocking all dunder attributes, causing AttributeError. Return {} for
__members__ specifically so the isinstance/iteration checks pass cleanly.
* fix: stub distributed tensor/functional_collectives to prevent missing C++ op crash on ROCm Windows
torch._dynamo.trace_rules eagerly loads torch.distributed.tensor at import
time, which pulls in _functional_collectives.py. That file registers Meta
kernels for _c10d_functional C++ ops, but those ops are only registered
by torch._C._distributed_c10d — a C extension absent from ROCm Windows
wheels. Pre-stubbing the affected modules in sys.modules prevents the real
import chain from running and avoids the "operator does not exist" crash.
* fix: give mod stubs __path__ and pre-stub _tensor to fix 'not a package' import error
_make_mod_stub now sets __path__=[] so Python treats stub modules as
packages. Without it, any import of a submodule raises "is not a package".
Also pre-stub torch.distributed._tensor and its submodules so that
_tensor/__init__.py (which re-exports from torch.distributed.tensor) never
runs and torchao's `from torch.distributed._tensor import DTensor` gets a
harmless stub instead of crashing.
* fix: stub torch.ops._c10d_functional namespace with hashable op sentinels
torchao.dtypes.nf4tensor uses _c10d_functional ops as dict keys at import
time (all_gather_into_tensor.default, wait_tensor.default) and
torch.ops.c10d.scatter_.default. None of these ops are registered on ROCm
Windows because torch._C._distributed_c10d (the C extension) doesn't ship.
Replace the whole _c10d_functional namespace with a custom stub whose ops
return hashable .default objects, so dict-key construction doesn't crash.
Also inject a scatter_ stub into torch.ops.c10d if it's missing.
* fix: stub entire torchao package on ROCm Windows instead of individual ops
torchao is not supported on ROCm Windows and its import chain transitively
requires torch._C._distributed_c10d (absent from the ROCm Windows wheel).
Rather than stub each missing op one by one, stub the whole torchao package
upfront. Unsloth uses bitsandbytes for quantization, not torchao, so this
has no functional impact. transformers gracefully handles an importable-but-
empty torchao by disabling TorchAoHfQuantizer.
* fix: set __spec__ on mod stubs so importlib.util.find_spec doesn't raise
Manually-injected sys.modules entries have __spec__=None by default.
importlib.util.find_spec() raises ValueError when it finds a module in
sys.modules with __spec__=None (transformers.utils.import_utils hits this
when checking if torchao is available). Give every stub a minimal
ModuleSpec(name, loader=None, is_package=True) to satisfy find_spec.
* fix: add meta path finder to auto-stub subpackages of stub modules
`import torchao.prototype` goes through the import machinery, not
__getattr__, so an empty __path__ means ModuleNotFoundError. Rather than
list every submodule explicitly, register a MetaPathFinder that intercepts
any import whose parent is one of our stubs (detected by loader=None in the
parent's ModuleSpec). Real installed packages always have a SourceFileLoader
so they are never intercepted. Also register child stubs in sys.modules
from __getattr__ as a belt-and-suspenders measure.
* fix: use _unsloth_stub sentinel instead of loader=None for stub detection
The import machinery overwrites module.__spec__ with the spec returned by
find_spec (which has loader=_StubSubpackageLoader, not None), so the
loader=None check broke for second-level subpackages. Switch to a custom
_unsloth_stub object identity sentinel set directly on each stub module --
it survives __spec__ being replaced and correctly identifies stubs at any
depth (torchao.prototype.safetensors, etc.).
* refactor(rocm/win): switch to repo.amd.com arch-aware index, remove stubs
AMD recommends repo.amd.com/rocm/whl/{arch}/ as the Windows ROCm wheel
source. These wheels bundle their own ROCm runtime, support all Python
versions (not just cp312), and include the full torch._C extension set
(including _distributed_c10d) that the old repo.radeon.com wheel omitted.
Changes:
- install.ps1: remove Select-ROCmWheelRelease + hardcoded cp312 wheel
URLs; remove Python 3.12 forced-preference logic; install via
--index-url repo.amd.com/rocm/whl/{arch-family}/
- studio/setup.ps1: same -- remove Select-ROCmWheelRelease, switch to
repo.amd.com arch-aware index URL
- studio/install_python_stack.py: replace _ROCM_WINDOWS_RELEASES /
_select_windows_rocm_release with _windows_rocm_index_url() using the
_GFX_TO_AMD_INDEX_ARCH map; drop Python 3.12 restriction
- studio/backend/core/training/worker.py: remove all stub machinery
(_make_mod_stub, _StubSubpackageFinder, _StubSubpackageLoader,
_StubClassMeta, torchao/fsdp/dtensor stubs, _c10d_functional ops
stubs, BNB DLL detection) -- no longer needed with new wheel source
* fix(rocm/win): restore _distributed_c10d + torchao stubs; fix BNB install
repo.amd.com torch wheels also omit torch._C._distributed_c10d on Windows
(RCCL is not shipped on Windows). torch/distributed/__init__.py imports
from it unconditionally at module level, so the stub must land in
sys.modules before any torch.distributed import.
torchao (pulled in by transformers.quantizers) walks
torchao.float8.distributed_utils -> torch.distributed._functional_collectives
-> distributed_c10d at import time. Stubbing torchao up-front short-circuits
that chain.
worker.py:
- Restore _make_mod_stub / _StubSubpackageFinder / _StubSubpackageLoader
- Restore _StubClassMeta for ProcessGroup.BackendType attribute access
- Restore _distributed_c10d stub with __getattr__ (Windows only)
- Restore torchao stubs (5 modules, Windows only)
install_python_stack.py:
- BNB AMD wheel install was inside the early-return branch that fires when
torch is already a ROCm build (installed by install.ps1). Move BNB install
outside that branch so it always runs on Windows ROCm — the PyPI
bitsandbytes has only CUDA DLLs and fails to load on ROCm.
* worker: remove _distributed_c10d stub; stub only torchao
The installed torch/distributed/__init__.py from repo.amd.com
(torch==2.10.0+rocm7.12.0) is now properly guarded with
`if is_available():`, so `import torch.distributed` alone is safe.
The crash only comes via torchao's import chain:
torchao.float8.distributed_utils
→ torch.distributed._functional_collectives (unguarded import)
→ torch.distributed.distributed_c10d
→ torch._C._distributed_c10d ← absent on Windows ROCm
Stubbing torchao short-circuits the chain entirely. No need to stub
_distributed_c10d. Remove _StubClassMeta and the _c10d stub block;
keep only _make_mod_stub + _StubSubpackageFinder + torchao seeds.
* fix: BNB AMD wheel skipped + torch.compile segfault on Windows ROCm
install_python_stack.py: the UNSLOTH_ROCM_TORCH_INSTALLED=1 early-return
path (set by setup.ps1 when it installed torch itself) returned before
ever reaching the AMD BNB prerelease wheel install. The PyPI
bitsandbytes==0.49.x ships only CUDA DLLs, so loading it on ROCm fails
with "libbitsandbytes_rocm72.dll not found". Now installs the AMD
Windows BNB wheel before returning on that path too.
worker.py: torch._grouped_mm crashes on gfx1200 (null HIP kernel pointer,
0xC0000005) when torch.compile's JitDecomp system dispatches it during
the first forward pass. Detect Windows ROCm via torch.version.hip
(already in sys.modules from section 1e) and set TORCHDYNAMO_DISABLE=1
to bypass the broken kernel dispatch.
* fix: BNB AMD wheel install fails uv wheel filename check
The bitsandbytes continuous-release wheel is intentionally mismatched:
filename encodes 1.33.7.preview (= 1.33.7rc0 in PEP 440) but wheel
metadata reports 0.50.0.dev0. uv rejects this by default.
Introduce _install_bnb_windows_rocm() helper that sets
UV_SKIP_WHEEL_FILENAME_CHECK=1 only for this specific install, then
restores the previous env value. Both BNB install call sites (the
UNSLOTH_ROCM_TORCH_INSTALLED early-return path and the normal Windows
ROCm path) now use this helper.
* worker: patch _grouped_mm CUDA dispatch on Windows ROCm (gfx1200 null kernel)
TORCHDYNAMO_DISABLE=1 stopped the compiler frontend but not the autograd
JitDecomp system, which also dispatches _grouped_mm and hits the same
null HIP kernel crash (0xC0000005).
Verified that torch.library.Library("aten","IMPL").impl("_grouped_mm", fn,
"CUDA") successfully overrides the broken HIP kernel with a Python mm
fallback on torch==2.10.0+rocm7.12.0.
Schema: _grouped_mm(Tensor self, Tensor mat2, Tensor? offs=None,
Tensor? bias=None, ScalarType? out_dtype=None) -> Tensor
The fallback handles both the simple case (offs=None → torch.mm) and the
grouped case (offs provided → split self by offsets, multiply each group
against the corresponding slice of mat2, then cat results).
Keep _WINDOWS_ROCM_GROUPED_MM_LIB alive at function scope to prevent the
C++ dispatch registration from being freed by GC.
* worker: fix torchao stub — return stub classes not modules for isinstance()
peft/tuners/lora/torchao.py does:
from torchao.dtypes import AffineQuantizedTensor, LinearActivationQuantizedTensor
isinstance(weight, (AffineQuantizedTensor, LinearActivationQuantizedTensor))
The stub __getattr__ was returning stub modules, which isinstance() rejects
with "arg 2 must be a type, a tuple of types, or a union".
Add _StubTypeMeta metaclass whose __instancecheck__ always returns False,
and _make_stub_type() to create stub classes via it. Change _make_mod_stub
__getattr__ to return stub classes instead of stub modules for leaf
attribute access, so isinstance() gets a valid type and returns False.
_StubSubpackageFinder still handles import-style subpackage creation
(those still need module objects in sys.modules); __getattr__ only fires
for from-import or direct attribute access, which are the isinstance paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: add coverage for Windows ROCm install paths and worker patches
Add conftest.py to fix pre-existing sys.path issue that prevented
test_rocm_support.py from running at all (install_python_stack.py
imports from backend.utils.wheel_utils which needs studio/ on sys.path).
New test classes cover everything added in this session:
- TestWindowsRocmIndexUrl: arch → AMD pip index URL mapping (gfx120X-all,
gfx1151, gfx1150, gfx110X-all, unknown → None, trailing slash)
- TestDetectWindowsGfxArch: hipinfo output parsing, missing/timeout/bad
returncode/no-gcnArchName paths
- TestInstallBnbWindowsRocm: UV_SKIP_WHEEL_FILENAME_CHECK set+restored,
env restored on exception, no-op when URL missing
- TestRocmTorchInstalledEnvVar: UNSLOTH_ROCM_TORCH_INSTALLED=1 skips
pip_install, calls _install_bnb_windows_rocm, sets flag
- TestWorkerWindowsRocmPatches: _grouped_mm CUDA dispatch override,
offs/grouped variant handling, GC-prevention sentinel,
_StubTypeMeta __instancecheck__, _StubSubpackageFinder registration,
torchao key submodule pre-stubbing, TORCHDYNAMO_DISABLE guard
- TestRocmTorchPkgSpecs: rocm7.2 torch 2.11.x spec, default <2.11 cap,
3-tuple shape, _GFX_TO_AMD_INDEX_ARCH RDNA4/3.5/3 coverage
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: fix encoding, IS_WINDOWS patching, and wrong assertion
- Add encoding="utf-8" to all read_text() calls (54 occurrences) so
tests pass on Windows where the default codec is cp1252 and source
files contain UTF-8 emoji (e.g. ⚠️ in install_python_stack.py)
- Add @patch.object(stack_mod, "IS_WINDOWS", False) to Linux-path
TestEnsureRocmTorch tests so they reach the Linux code path when run
on a Windows machine instead of short-circuiting into the Windows branch
- Fix test_grouped_mm_patch_guarded_by_windows_and_hip_check: the source
uses getattr(_torch_for_rocm, "version", None) not torch.version, so
check for '"version"' and '"hip"' substrings instead
137 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: pin BNB_ROCM_VERSION=72 for torch==2.11.0+rocm7.13.0 compatibility
AMD's pip index now ships torch==2.11.0+rocm7.13.0 (ROCm 7.13).
bitsandbytes auto-detects HIP 7.13 from torch.version.hip and looks for
libbitsandbytes_rocm713.dll, which the AMD Windows prerelease wheel does
not ship (it only ships rocm72.dll), causing a load error at training start.
Fix:
- worker.py section 1f: set BNB_ROCM_VERSION=72 (via setdefault) before
section 2 ML imports, so bitsandbytes always loads rocm72.dll on Windows ROCm
- install_python_stack.py: set BNB_ROCM_VERSION=72 in _install_bnb_windows_rocm()
for any post-install imports; update comment to document root cause
- tests: 4 new assertions covering the fix (141 passed, 2 skipped)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: detect BNB ROCm DLL suffix dynamically instead of hardcoding '72'
BNB_ROCM_VERSION was pinned to '72' which works today (AMD wheel ships
rocm72.dll) but would break again if AMD ships a future wheel with a
different DLL suffix (e.g. rocm713.dll).
Add _detect_bnb_rocm_dll_ver() to install_python_stack.py: scans the
installed bitsandbytes package dir for libbitsandbytes_rocm{VER}.dll
using importlib.util.find_spec (no BNB import needed) and returns the
suffix. '72' remains the fallback when detection fails.
Apply the same detection inline in worker.py section 1f. Both paths
still respect a pre-set BNB_ROCM_VERSION (caller override wins).
Tests: +8 cases covering detection logic and fallback (147 passed, 2 skipped).
* fix: patch torch.distributed stubs in server process for Windows ROCm
On Windows ROCm, torch.distributed ships without process-group helpers
(is_initialized, is_available, get_rank, get_world_size). The worker
subprocess already patches these in section 1e, but the main server
process calls _determine_attention_impl_for_gpu_estimate() which calls
unsloth's resolve_attention_implementation() → is_initialized(), causing:
"Could not resolve attention implementation for '...':
module 'torch.distributed' has no attribute 'is_initialized'"
Fix: patch the missing attrs onto torch.distributed at the top of
_determine_attention_impl_for_gpu_estimate, matching the same stubs
already applied in worker.py section 1e. No-ops on Linux/CUDA where
torch.distributed is fully populated.
* fix: gate _grouped_mm dispatch patch on HIP < 7.13
AMD fixed the gfx1200 null HIP kernel in ROCm 7.13 (torch 2.11+).
Users on the new wheel now get the real GPU _grouped_mm kernel for
MoE workloads instead of the Python mm fallback.
Changes:
- worker.py: add _hip_ver_at_least() helper; wrap full _grouped_mm
patch in `if not _hip_ver_at_least(7, 13):` with else branch that
logs the skip reason; update section-1f comment to document the fix
- test_rocm_support.py: add 5 tests covering the helper definition,
the (7, 13) gate expression, the else branch, the skip log message,
and the AMD-format version string parsing (.split(".")[:2])
Verified: torch==2.11.0+rocm7.13.0 — 3D batch and grouped (offs)
variants both succeed; null crash only present on rocm7.12 and earlier.
* fix: stub is_torchelastic_launched on torch.distributed for Windows ROCm
resolve_attention_implementation calls is_torchelastic_launched() which
does not exist in the incomplete torch.distributed shipped with the
Windows ROCm wheel, causing a warning on every model config load in the
server process. Add it to the stub table alongside the four helpers
already patched in _determine_attention_impl_for_gpu_estimate.
Also adds two tests: one confirming the new stub and one confirming all
five core distributed helpers are covered.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: explicit warnings on AMD ROCm arch/version fallbacks + Fast-Install arg order
setup.ps1:
- Fix Fast-Install argument order: packages before flags, consistent with
all other Fast-Install calls in the file
(was: Fast-Install --force-reinstall --index-url $url torch ...)
(now: Fast-Install torch torchvision torchaudio --force-reinstall --index-url $url)
- Add explicit [WARN] substep when $HasROCm is true but arch mapping fails:
- GPU arch detected but not in supported wheel list → names the arch and
lists supported families so user knows exactly what to report
- HIP SDK present (amd-smi path) but gcnArchName unreadable → instructs
user to re-install the HIP SDK; previously fell back silently to CPU
install.sh:
- Add [WARN] to stderr before silent CPU fallback when AMD GPU is confirmed
(rocminfo/amd-smi) but ROCm version cannot be read from any source
(amd-smi, /opt/rocm/.info/version, hipconfig, dpkg, rpm)
- Add [WARN] to stderr when ROCm version is too old (< 6.0) with upgrade link
install.ps1 and setup.sh: no changes needed (already handle these paths correctly)
* fix: robust gfx arch detection for Strix Halo / HIP-runtime-only installs
Covers users who have the HIP runtime (amd-smi available) but not the
full HIP SDK (no hipinfo), which is common on Strix Halo iGPU systems.
Without this, $ROCmGfxArch stays null and the installer silently falls
back to CPU-only PyTorch despite a working GPU.
Detection waterfall (setup.ps1 + install.ps1):
1. hipinfo gcnArchName -- full HIP SDK (existing, unchanged)
2. amd-smi list gfx pattern -- newer amd-smi versions embed arch
3. amd-smi static --asic -- ROCm 6+ ASIC details with GFX target
4. UNSLOTH_ROCM_GFX_ARCH env -- manual override escape hatch
5. GPU name → arch table -- best-effort from marketing name:
890M / Strix Halo → gfx1151 (RDNA 3.5 iGPU, Strix Halo)
880M / Strix Point → gfx1150 (RDNA 3.5 iGPU, Strix Point)
780M / Phoenix → gfx1103 (RDNA 3 iGPU)
RX 7900/7800/7700 → gfx1100 (RDNA 3 desktop)
RX 9070 XT / 9080 → gfx1201 (RDNA 4)
RX 9070 / 9060 XT → gfx1200 (RDNA 4)
When arch is inferred from name, a Cyan substep tells the user to set
UNSLOTH_ROCM_GFX_ARCH to skip inference on future installs.
WMI block intentionally does not set $HasROCm (no runtime confirmation).
Tests: 11 new tests in TestStrixHaloGfxArchDetection covering all five
detection levels, WMI safety, and gfx regex in both ps1 files.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: resolve hipinfo/hipconfig via HIP_PATH/ROCM_PATH when not on PATH
AMD HIP SDK sets HIP_PATH on Windows but does not always add the bin
directory to PATH. Get-Command hipinfo therefore silently fails and
detection falls through to WMI, which cannot provide a gfx arch, leaving
the user with a CPU-only PyTorch install and no warning.
Changes:
- setup.ps1 / install.ps1: before falling through to amd-smi, attempt to
locate hipinfo.exe and hipconfig.exe under $env:HIP_PATH\bin (then
$env:ROCM_PATH\bin) when Get-Command returns nothing
- Emit a [WARN] with the resolved path and a one-liner to permanently fix
PATH via SetEnvironmentVariable
- Emit a [WARN] when HIP_PATH/ROCM_PATH is set but the exe is still not
found (incomplete SDK install)
- Emit a [WARN] with the first hipinfo output line when hipinfo runs but
returns a non-zero exit code (e.g. "no ROCm-capable device detected")
- 18 new tests in TestHipSdkEnvPathResolution; total 183 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: print HIP SDK path and full hipconfig version in terminal on AMD detection
Both install.ps1 and setup.ps1 now emit substeps under the gpu step when
AMD ROCm is detected:
gpu AMD ROCm (gfx1200)
HIP SDK: C:\Program Files\AMD\ROCm\7.1
hipconfig: 7.1.51803-d3a86bd04
Previously only the gpu label (e.g. "AMD ROCm (gfx1200)") was shown with
no indication of where the SDK was found or which exact build was active.
The full hipconfig build string (e.g. 7.1.51803-d3a86bd04 instead of just
7.1) is now stored in ROCmVersionFull and also used in setup.ps1's
'rocm' step label.
9 new tests in TestHipSdkDetectedSubstep; total 192 passed, 2 skipped
* fix: Strix rocm7.1 segfault bypass + Ubuntu 24.04 HIP gcc-install-dir
Issue 1 (install.sh): gfx1151/gfx1150 + ROCm 7.1 causes a segfault in
torch._grouped_mm (moe_utils.py:167). The Radeon repo now ships cp313
wheels for rocm-rel-7.1, so _amd_gpu_radeon=true silently lands on the
broken combo. When Strix Halo/Point is detected and TORCH_INDEX_URL is
rocm7.1, override to rocm7.2 PyTorch index, update TORCH_CONSTRAINT, and
set _amd_gpu_radeon=false to bypass the Radeon repo entirely. Emits a
clear [WARN] explaining the segfault and linking to the ROCm upgrade docs.
Issue 2 (setup.sh): ROCm 7.x ships clang-20 which on Ubuntu 24.04+ picks
/usr/lib/gcc/x86_64-linux-gnu/14/ (runtime dir, no C++ headers), causing
'cstdlib file not found' and a failed llama.cpp HIP build. Iterate gcc
versions 14→11 to find the first install dir that has both runtime and
/usr/include/c++/<ver> headers, then pass --gcc-install-dir to clang via
CMAKE_HIP_FLAGS. Fix confirmed by h34v3nzc0dex (llama.cpp 417/417 clean).
11 new tests across TestStrixRocm71Override and TestSetupShGccInstallDir;
total 203 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: BNB_ROCM_VERSION in server process + torch._C._distributed_c10d stubs
Two errors visible in training logs on Windows ROCm:
1. Server process bitsandbytes crash:
"Configured ROCm binary not found at libbitsandbytes_rocm713.dll"
The installed BNB wheel ships rocm72.dll (not rocm713.dll). The
training worker already sets BNB_ROCM_VERSION=72 via DLL detection
but the server process (main.py) imported bitsandbytes before that
ran. Fix: add the same DLL-scan + BNB_ROCM_VERSION assignment to
main.py inside the existing win32 guard, before any downstream
import can pull in bitsandbytes.
2. torch.distributed import failure:
"No module named 'torch._C._distributed_c10d'; torch._C is not a package"
torch._C is a C extension on Windows ROCm — Python cannot do
submodule imports from it, so torch.distributed fails to import
before our attribute stubs could ever run. Fix: inject empty
ModuleType stubs for _distributed_c10d, _distributed_autograd and
_distributed_rpc into sys.modules inside the win32 guard in
hardware.py BEFORE importing torch.distributed, so the import
succeeds and our attribute stubs take effect.
9 new tests in TestServerStartupRocmFixes; total 212 passed, 2 skipped
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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: add VLM image-size control for training
Studio vision fine-tuning had no explicit way to cap image resolution, so
users could not trade visual detail against context and memory use from the
training UI, YAML config, or API payload. :) Add a nullable `vision_image_size`
setting that keeps the current model default when unset and applies a
max-side resize when provided.
- Add `vision_image_size` to the training request model, route payload, backend
training config, and frontend API/types plumbing.
- Validate the value server-side as either null or an integer in the supported
256-2048 range.
- Surface an Image Size selector for vision LoRA training with Default plus
common preset sizes.
- Include the value in training start payloads only for image-dataset vision
models, and serialize it into vision-aware YAML configs.
- Map backend model defaults back into the training store and reset the value
when reapplying model defaults.
- Pass the resize through the Torch trainer via `UnslothVisionDataCollator`
using max-dimension semantics.
- Apply the same max-dimension resize in the MLX VLM path before mlx-vlm's
internal collation, preserving aspect ratio and avoiding upscaling.
- Add backend validation coverage and MLX resize-size tests for the new
behavior.
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* Studio: thread vision_image_size into DeepSeek OCR + writable MLX ndarray
- trainer.py: DeepSeek OCR collator now honors the new vision_image_size
setting as image_size. Falls back to 640 when null. base_size stays at
1024 and crop_mode stays True so the Gundam preset's dynamic cropping
of large documents keeps working.
- worker.py: _resize_mlx_vlm_image returns np.array(image, copy=True)
instead of np.asarray(image). The PIL view from np.asarray is not
writable, which makes HF VLM processors emit "The given NumPy array
is not writable, and PyTorch does not support non-writable tensors..."
when they call torch.from_numpy. copy=True keeps the same shape and
dtype but produces a writable buffer.
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* Studio: align YAML export gate with API mapper + extend Image Size dropdown
- training-section.tsx: handleSaveConfig now passes
isVisionModel && isDatasetImage === true to serializeConfigToYaml,
matching buildTrainingStartPayload. Stops vision_image_size from
leaking into exported YAML for text-only datasets where the API
would have sent null.
- params-section.tsx: add 256 to visionImageSizePresets so the
dropdown spans the validator's full [256, 2048] range. Also render
a synthetic SelectItem for the current value when it was loaded
from YAML or model defaults and is not in the preset list, so the
controlled Select always shows the active size.
* Studio: validate vision_image_size in YAML/model-default loader
mapBackendModelConfigToTrainingPatch now mirrors the backend validator
at studio/backend/models/training.py:169 by dropping any value that is
not an integer in [256, 2048]. Pre-fix, an imported YAML like
vision_image_size: 4096 or 640.5 would land in the store and the UI
would happily display it, only to fail when Start Training posted to
the backend. With this guard the store never holds a value the backend
would reject.
* Studio: precise error messages for invalid vision_image_size inputs
Switch the field_validator to mode="before" so True/False surface as
bool (not Pydantic's coerced 1/0) and give a precise
"must be an integer or null" message instead of the misleading
"must be in [256, 2048] (got 1)". Also explicitly accepts numpy
Integral and integral Real scalars so YAML or programmatic callers
using numpy ints keep working.
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* Studio: test that bool inputs yield the precise 'integer or null' error
Regression guard for the validator switch to mode="before". Pre-fix,
vision_image_size: True was rejected with "must be in [256, 2048]
(got 1)" because Pydantic coerced before our check ran. New test
asserts the message now reads "integer or null".
* Studio: tighten vision_image_size loader + YAML save + MLX rounding
Round 2 of follow-up review surfaced three usability issues:
- model-defaults.ts: switching to a model whose backend YAML omits
vision_image_size now explicitly resets the store value to null.
Pre-fix, a stale 2048 from a previous model would silently apply
to the new run because every checked-in model-default file omits
the key.
- training-section.tsx: handleSaveConfig now includes vision fields
unless isDatasetImage is definitively false. isDatasetImage is null
during dataset checks, after dataset edits, and on import; treating
unknown as "drop" would silently lose the user's selection in those
windows. Confirmed-text-only datasets still drop the value.
- worker.py: _mlx_vlm_max_resized_size now mirrors the Torch collator's
integer formula (w * size + size_func // 2) // size_func instead of
Python round(), which uses banker's rounding and disagreed by 1px on
half-pixel inputs like 333x1000 with target 500 (was 166, now 167).
Test_mlx_training_worker_config gains parity assertions.
* Studio: reset vision_image_size in the model-config error fallback path
mapBackendModelConfigToTrainingPatch resets stale image size on the
success path, but if the /api/models/config endpoint throws,
training-config-store.ts falls through to checkVisionModel and only
updates capability flags. Pre-fix that left a stale 2048 (or any
prior selection) in the store, so once dataset detection marked the
new dataset as image, the next training start would silently apply
the previous model's size. The error branch now also resets to the
DEFAULT_HYPERPARAMS.visionImageSize sentinel.
* Studio: revert DeepSeek OCR Image Size knob + move missing-key reset
Round 3 of the parallel-reviewer pass surfaced two issues that I had
introduced earlier in this PR's follow-ups.
- trainer.py: my prior change threaded vision_image_size into the
DeepSeek OCR collator's image_size argument. The collator's
(image_size, base_size, crop_mode) is a single preset
(Tiny / Small / Base / Large / Gundam); changing image_size in
isolation desynchronizes the per-crop pixel grid from num_queries
downstream and produces wrong token grids on documents larger than
the per-crop tile. The fix pins the collator back at the Gundam
preset and logs a clear "ignored for DeepSeek OCR" notice when the
user has selected a non-default Image Size.
- model-defaults.ts + training-config-store.ts: the round 4 fix that
reset visionImageSize when a model YAML omitted the key also fired
on same-model reloads (ensureModelDefaultsLoaded re-fires on page
refresh), wiping a value the user had just selected. The reset is
now in setSelectedModel, gated on selectedModel != previousModel,
so true model switches still clear stale values while reloads keep
the user's selection.
* Studio: extend DeepSeek OCR Image Size exclusion to MLX + frontend
Round 4 of the parallel-reviewer pass flagged that the Torch trainer
exclusion I added did not have a matching MLX guard, and that the UI
still offered the dropdown for DeepSeek OCR even though the backend
ignores it.
- worker.py: _run_mlx_training now mirrors the Torch exclusion. When
the model name matches DeepSeek OCR, vision_image_size is forced
back to None before _adapt_for_mlx_vlm sees it, so dataset images
pass through unchanged just like the Torch path. Emits a clear
status line when this happens.
- params-section.tsx: the Image Size Row is now gated on
showVisionImageSize (showVisionLora && !isDeepseekOcr) instead of
showVisionLora alone, so DeepSeek OCR users no longer see a control
that silently has no effect.
- mappers.ts: buildTrainingStartPayload sends null for vision_image_size
whenever the selected model is DeepSeek OCR, so the backend log line
about ignoring the value never fires from a UI-driven start.
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* Studio: tighten YAML import/save for vision_image_size
Two YAML-path asymmetries that could leak a stale image size into
training:
- parseYamlConfig now treats a missing training.vision_image_size as
null. Without this, importing a YAML saved before this feature (or
any config that omits the key) preserved whatever value the user had
previously set on a different model. The model-defaults reload path
still uses Object.hasOwn so same-model defaults reloads do not wipe
a manual selection; only file import normalises the missing key.
- handleSaveConfig now passes a DeepSeek-OCR-specific guard to
serializeConfigToYaml so saved YAML matches what the API mapper
actually sends. Previously a state with visionImageSize set could
emit the key even though Studio ignored it at training time for
DeepSeek OCR, and a later import for a non-DeepSeek vision model
would activate the stale value.
serializeConfigToYaml gains an optional third parameter
includeVisionImageSize defaulting to includeVisionFields, preserving
the existing 2-arg call signature for backwards compatibility.
* Studio: also reset vision_image_size when YAML lacks a training section
Round 9's parseYamlConfig normalization only fired when the YAML had a
training mapping that omitted vision_image_size. A lora-only or
logging-only YAML (or one with `training: null`) still left trainingObj
unset, the mapper saw no vision_image_size key, and the previously
selected store value persisted into the next training run.
Now an absent or null training section is synthesised as
{ vision_image_size: null } so model-defaults.ts always patches
visionImageSize back to Default on file import. Same-model defaults
reloads still preserve manual choices via the existing Object.hasOwn
gate in mapBackendModelConfigToTrainingPatch.
* Studio: unify parseYamlConfig non-object training handling
A fresh static review (Opus subagent) flagged P3-1: parseYamlConfig
only synthesised vision_image_size: null when raw.training was either
absent or a plain object missing the key. If raw.training is a scalar
or an array (malformed but still parseable), the value was passed
through unchanged, the mapper's Object.hasOwn returned false, and any
previously selected visionImageSize persisted - the same stale-state
leak the lora-only fallback was added to close.
Treat any non-plain-object raw.training (null, array, scalar) as a
malformed/missing section and reset to { vision_image_size: null }.
* Studio: tighten code comments for vision_image_size path
* Studio: tighten vision_image_size validator + restore lost comment context
Two issues surfaced by a fresh adversarial review of the validator:
1. v.strip().lstrip("+-").isdigit() let "++512" / "--256" / "+-+512"
slip past the gate, then int("++512") raised an uncaught ValueError
and Pydantic surfaced "invalid literal for int() with base 10: '++512'"
instead of the contracted "vision_image_size must be an integer or null".
2. str.isdigit() returns True for Unicode digit families (full-width '５１２',
Arabic-Indic '٥١٢', Devanagari '१०२४'), and int() coerces them, so the
value reaching the backend wasn't the ASCII the user typed.
Replaced the lstrip+isdigit pair with re.fullmatch(r'[+-]?[0-9]+', stripped),
which rejects both shapes with the precise error and accepts the documented
ones ('256', '+512', ' 1024 '). Added 8 regression test cases covering
multi-sign strings, lone sign, and the three Unicode digit families.
Also restored comment context lost in f9c39331:
- model-defaults.ts: name studio/backend/models/training.py:_check_vision_image_size
as the spec the [256, 2048] range mirrors, so a maintainer changing the
cap in one file can find the other.
- training-section.tsx: enumerate the three windows in which isDatasetImage
is null (before a check, after dataset edits, on import) so a future
maintainer doesn't simplify the gate to `isCheckingDataset`.
- worker.py: qualify the writable-ndarray comment with "when a resize is
requested" so it doesn't misadvertise the resize=None early-return.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* studio: extend offline DNS auto-detect to inference parent + training
#5505 fixed the GGUF/llama-server load path. Studio still has two
adjacent code paths that burn ~30-60s of soft-failed timeouts before
the worker subprocess starts when DNS to huggingface.co is dead and
the model is already in the local HF cache.
Inference parent process (routes/inference.py:load_model):
* ModelConfig.from_identifier now runs inside _hf_offline_if_dns_dead
so the LoRA-detect hf_model_info call and the urllib config probes
in utils/transformers_version.py short-circuit when DNS is dead.
* utils/models/model_config.py: extracted the inline HF_HUB_OFFLINE/
TRANSFORMERS_OFFLINE check used by list_gguf_variants and
detect_gguf_model_remote into a shared _env_offline() helper, then
reused it to gate the LoRA-detect hf_model_info call.
* utils/transformers_version.py: _check_tokenizer_config_needs_v5 and
_check_config_needs_550 now early-return False when offline instead
of issuing a 10s urllib.urlopen against huggingface.co/raw/main.
Training worker (core/training/worker.py:run_training_process):
* Add the same 2s DNS probe used by core/inference/worker.py at the
top of the training subprocess. On failure, set HF_HUB_OFFLINE,
TRANSFORMERS_OFFLINE, and HF_DATASETS_OFFLINE before the rest of
the subprocess imports torch/transformers/unsloth, so every
from_pretrained, snapshot_download, and load_dataset call below
resolves from cache. Scope is per-subprocess; the orchestrator
always spawns a fresh worker per training run.
Training trainer (core/training/trainer.py:load_model):
* Skip the proactive hf_model_info gated-repo probe when _env_offline()
is true. The API is unreachable anyway, and a gated model that is
already cached is exactly the scenario the user is trying to train
against. from_pretrained surfaces the real error if access is
actually denied.
Tests (tests/test_offline_inference_parent.py, 7 new cases):
* _env_offline truthy/falsy parsing across HF_HUB_OFFLINE and
TRANSFORMERS_OFFLINE.
* transformers_version urllib short-circuit when offline.
* LoRA detect hf_model_info skip when offline.
Existing tests/test_offline_gguf_cache_fallback.py still passes
(26 cases) because the inline env check was extracted, not changed.
* tests: prefer real httpx over stub in offline-test files
The studio test stub convention only included the 6 httpx exception
names that existed callers needed. Newer huggingface_hub (1.15+)
imports HTTPError, Response, Request, HTTPStatusError, AsyncClient,
and more at module import time. When httpx is truly absent the stub
chase becomes a treadmill.
Use the real package when installed (the CI install list already
includes httpx, so this is the production environment). Fall back to
the stub only when httpx is genuinely missing.
No code under test changes.
* studio: detect cached LoRA adapters offline; tighten test
Two follow-ups from the review pass on #5512:
* ModelConfig.from_identifier no longer skips the remote LoRA-detect
hf_model_info call when _env_offline() is true. huggingface_hub
short-circuits the call via OfflineModeIsEnabled in ~0ms when
HF_HUB_OFFLINE is set, so the original 25s concern was moot once
routes/inference.py wrapped the call in _hf_offline_if_dns_dead.
Skipping the API meant users with a cached LoRA adapter
(adapter_config.json on disk) got is_lora=False and the load
failed. After the API call (which raises fast offline) a new
cache-fallback walks the HF cache snapshot for adapter_config.json
via the existing _iter_hf_cache_snapshots helper.
* test_hf_model_info_not_called_when_offline replaced. The old test
raised AssertionError inside production code that catches Exception,
so it passed even if the call happened. New tests use MagicMock and
assert call_count >= 1, plus a fixture that stages a fake HF cache
with adapter_config.json to verify the offline cache detection.
Test count goes from 7 to 8 in test_offline_inference_parent.py.
Combined with test_offline_gguf_cache_fallback.py: 34 pass in 9.75s.
* Fix/adjust offline training DNS probe per PR #5505 review
Same fix as #5505's _probe_dns_dead refactor: run gethostbyname on a
daemon thread with join timeout so concurrent sockets in the parent
interpreter never inherit a process-wide socket.setdefaulttimeout
mutation. Adds a static-pin regression test that the inference parent
file does not regress on this.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim verbose code comments per review feedback
Shorten the longer explanatory comments added by this PR while keeping
the WHY of each non-obvious branch:
- trainer.py: collapse the 5-line proactive gated-check comment.
- training/worker.py: trim the offline auto-detect preamble and the
"logger isn't configured" note.
- routes/inference.py: shorten the DNS-probe wrap rationale.
- transformers_version.py: collapse the two urllib short-circuit notes.
- model_config.py: shorten the LoRA detect + cache-fallback notes.
- tests/test_offline_inference_parent.py: tighter module docstring,
trim class docstrings, drop multi-line explainer comments inside the
tests; behaviour and coverage unchanged (9/9 tests still pass).
---------
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* add eval batch size
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
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Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* feat(studio): add Continued Pretraining (CPT) support
Implements CPT as a first-class training method in Unsloth Studio,
resolving feature request #4565.
Changes:
- frontend/src/types/training.ts: add 'cpt' to TrainingMethod union
- frontend/src/lib/vram.ts: add 'cpt' to VramTrainingMethod (fp16 footprint)
- frontend/src/features/export/constants.ts: add CPT to METHOD_LABELS
- frontend/src/features/training/api/mappers.ts: map 'cpt' -> 'Continued Pretraining',
force packing=true and train_on_completions=false for CPT payloads
- frontend/src/features/studio/sections/model-section.tsx: add 'Continued Pretraining'
option (purple dot) to Method selector; update tooltip
- frontend/src/features/onboarding/.../model-selection-step.tsx: add CPT to
onboarding wizard method dropdown
- backend/models/training.py: update training_type field description
- backend/core/training/worker.py: detect is_cpt flag, force packing=True,
train_on_completions=False, pass is_cpt to _train_worker
- backend/core/training/trainer.py: _train_worker reads is_cpt kwarg, forces
packing on, skips train_on_responses_only for raw-text pretraining
CPT behaviour:
- Full model weights (no LoRA adapters), same as Full Finetuning
- Sequence packing always enabled for GPU efficiency
- Trains on every token (no chat-format masking)
- VRAM estimated at fp16 (2.0 bytes/param)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update mappers.ts
* Add CPT raw dataset support and UI fixes
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add missing training methods module
* Handle invalid raw-text rows and expose raw in onboarding
---------
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Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
Co-authored-by: Etherll <mrmrmidessam@gmail.com>
* feat(studio): add Tauri native GGUF intake
* feat(studio): polish native GGUF intake
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio): load backend helpers during local setup
* fix(studio): acquire native load lease before unload
* Studio: harden native path lease verification and Tauri intake
- Wrap path.resolve(strict=True) and Path.stat() in NativePathLeaseError so a deleted or unmounted GGUF returns 400 instead of leaking the full filesystem path through the generic load_model/validate_model handler.
- Re-apply _reject_network_or_device_path to the resolved canonical path for defense in depth after symlink resolution.
- Replace try/except ValueError pattern in the device-path guard with Path.is_relative_to; the previous shape silently swallowed NativePathLeaseError (which subclasses ValueError) so /dev,/proc,/sys were never actually rejected.
- Broaden the lease redaction regex and dict-key check (Python and Rust diagnostics) to cover both native_path_lease and nativePathLease so the camelCase form emitted by Tauri/frontend payloads is also redacted.
- Hoist the redact_native_paths import to module top in loggers/handlers; the recursive filter no longer pays a per-record import lookup.
- Persist activeNativePathToken in the chat runtime store so the rollback branch can mint a fresh lease and reload the previous native GGUF when a new load fails after unload; clear it in clearCheckpoint and overwrite it on each successful load.
- use-native-drop: read options through a ref so the Tauri onDragDropEvent listener is registered once and stays attached across option changes; reject ambiguous multi-file drops up front instead of silently registering only the first GGUF.
- pick_native_model: use an async pick_file with a tokio oneshot channel instead of blocking_pick_file so the Tokio worker is not held for the duration of the OS dialog.
- registerNativeModelPath: drop the duplicate sourceKind argument; the Rust command parameter is source_kind.
- install_python_stack: insert the script directory (studio/) on sys.path; the previous insert pointed at studio/backend/ which does not satisfy `from backend.utils.wheel_utils import ...`.
* install_python_stack: keep _BACKEND_DIR on sys.path
Restore the studio/backend insertion. Although the immediately following `from backend.utils.wheel_utils import (...)` is satisfied by studio/ already being on sys.path[0] when invoked as `python studio/install_python_stack.py`, wheel_utils itself runs `from utils.native_path_leases import ...`, which requires studio/backend/ to be importable. Without the backend insertion, the existing tests/python/test_install_python_stack.py collection fails with ModuleNotFoundError: No module named 'utils'.
* Studio: tighten native path lease lifecycle and Tauri intake IPC
- register_native_model_path now hardcodes NativePathSourceKind::Drop on the Rust side and the frontend stops sending source_kind. The previous JS payload (source_kind only) never reached the Rust deserializer because Tauri's default ArgumentCase::Camel maps the Rust parameter source_kind to the JS key sourceKind, so drag/drop registration silently failed. Hardcoding the source kind also keeps audit metadata trustworthy on this command.
- Add native_path_secret_removed_for_child_start context manager and wrap multiprocessing.Process.start() at the inference, export, training, and data-recipe job spawn sites. The previous wrapper-only scrub left UNSLOTH_STUDIO_NATIVE_PATH_LEASE_SECRET visible to spawn-platform import-time worker code. The wrapper run_without_native_path_secret stays as defense-in-depth inside the child.
- Stop passing exc_info=True from the native-grant load/validate error logs in routes/inference.py. The structlog filter_sensitive_data processor runs before the renderer, so ConsoleRenderer formatted tracebacks bypassed redaction; the redacted str(e) preserves the message text.
- Replace the os.path.normcase string equality on the resolved canonical path with Path.samefile (with a normcase fallback) so Windows leases that differ only in extended-length \\?\ prefix or short-name spelling are accepted.
- Wrap consumeNativePathToken in its own try/catch in the chat runtime rollback. If the previous native-model token has aged out of TOKEN_TTL we now surface a clear modelsError instead of silently swallowing the rollback inside the outer catch.
- Reject non-ASCII lease strings in _split_lease and convert UnicodeEncodeError / binascii.Error / ValueError raised by _b64decode into NativePathLeaseError so verify_native_path_lease never escapes raw exceptions to the route handler.
- Tighten dropStateForPaths to mark multi-file payloads invalid so the overlay matches the post-fix drop handler that rejects the same payload.
- Replace the one-shot fetch in useNativePathLeasesSupported with a delayed-retry loop so the picker/drop becomes available once the backend is up rather than staying disabled for the rest of the session after a transient failure.
- Drop the unused setActiveNativePathToken setter; the value is set via setState directly in use-chat-model-runtime.
- Add a toast on auto-load failure in use-native-drop so a collapsed model selector does not hide the error.
- Burn the lease nonce before _validate_current_stat so a stat-failed lease is single-use even if a later state change happens to match the original size/mtime.
* Studio: cache lease secret, harden native path stat checks, polish intake UX
- Cache the decoded UNSLOTH_STUDIO_NATIVE_PATH_LEASE_SECRET on first verify and validate that it is base64-decodable and at least 32 bytes. Subsequent _decode_secret calls return from the cache and never touch os.environ, so concurrent /api/inference/load and /api/health requests no longer race with native_path_secret_removed_for_child_start scrubbing the env. native_path_leases_supported now wraps _decode_secret so the health flag matches what verify_native_path_lease actually accepts.
- Replace path.is_file()/is_dir() + path.stat() with os.lstat() in _validate_current_stat and explicitly reject S_ISLNK; size and mtime checks now refer to the link itself, closing the same-size+same-mtime symlink-swap window that the prior follow-symlink stat() left open.
- Add an issued_at_ms < expires_at_ms sanity check in _validate_payload to reject internally inconsistent (HMAC-protected) lease payloads.
- Sort _NATIVE_PATH_REDACTIONS by length (descending) before iterating in redact_native_paths so a longer registered path is replaced before a shorter prefix path; otherwise logs containing /foo/X.gguf.bak after only /foo/X.gguf was registered would leak the .bak suffix.
- classify_existing_path now re-checks the canonical path with symlink_metadata after canonicalize, so a regular file that is replaced with a symlink in the small canonicalize window is rejected at registration.
- ModelSelector renders the local file picker as its own block (not in the eject ternary), so a user with an active model can still replace it via the picker rather than only via drag/drop.
- useNativePathLeasesSupported caps the readiness probe at MAX_READINESS_POLLS (60 = ~5 minutes) and aborts the in-flight fetch on unmount via AbortController, so a permanently-disabled backend stops generating sustained traffic and hot-reload no longer leaks open connections.
- useChooseNativeModel returns a stable useCallback closure and guards the OS dialog with a useRef so rapid double-clicks cannot open multiple dialogs and orphan Rust tokens.
- Branch the multi-file drop toast: if no GGUF was present we say "Only .gguf model files can be dropped here." and otherwise "Drop a single .gguf model file." so users dropping non-GGUF attachments get an accurate explanation.
* native_path_leases: lstat the signed canonical path before resolving
The earlier change to lstat inside _validate_current_stat operates on grant.canonical_path, which is the post-resolve target. If the user atomically replaces the originally-signed file with a symlink to a different file of identical size and mtime, path.resolve(strict=True) follows the symlink, samefile returns True (both ends share the new inode), and the lstat in _validate_current_stat sees the regular target file rather than the symlink, so the swap goes undetected.
Add an os.lstat on the signed canonical path before path.resolve(strict=True), and reject S_ISLNK there. The lstat in _validate_current_stat stays as defense-in-depth for swaps that occur strictly between resolve and stat.
* Studio: scrub native lease secret before mp.Queue spawn and tighten lease lifecycle
- Move _CTX.Queue / _CTX.Event / _CTX.Process construction inside native_path_secret_removed_for_child_start at the inference, export, training and data-recipe spawn sites. The first Queue creation lazily spawns Python's multiprocessing.resource_tracker child, so when it ran outside the scrub context the tracker process inherited the lease secret. Reproduced via the proc filesystem environ entry; the wrapped order keeps the tracker clean.
- native_path_secret_removed_for_child_start now refcounts entries: the env var is popped on the first entry and restored only when the last context exits. Concurrent training/inference/export starts no longer serialize on the env lock across the entire proc.start yield, while still guaranteeing the env stays empty for the duration of every overlapping spawn.
- run_without_native_path_secret now also nulls the module-level cached lease secret. With the existing spawn-only multiprocessing context the cache is irrelevant in practice, but a future fork caller would otherwise inherit the in-memory secret even though the env var was scrubbed.
- filter_sensitive_data now applies the native lease key check on the top-level event_dict, not only on nested dicts, so a logger call that includes a lease value as a top-level keyword field actually redacts it (the bare value does not match the prefix-anchored regex).
- chat-page loadNativeModelIntent now passes intent.id to clearModelIntent so a second drag-drop during an in-flight first auto-load is not wiped from the chip area when the first resolves.
- Bump useNativePathLeasesSupported's MAX_READINESS_POLLS from 60 to 720 so first-run installs that compile llama.cpp from source or download large CUDA wheels (well past 5 minutes) don't permanently disable the native picker.
* native_path_leases: serialize first-decode against scrub context
_decode_secret used a separate _SECRET_INIT_LOCK from the env scrub's _NATIVE_PATH_ENV_LOCK, so the very first decode (before the cache is populated) could race a concurrent native_path_secret_removed_for_child_start and read os.environ during the env-empty window, raising "Native path grants require the managed desktop backend." Subsequent calls hit the cache and were already safe.
Acquire _NATIVE_PATH_ENV_LOCK around the env read inside _SECRET_INIT_LOCK and fall back to _SCRUB_SAVED_SECRET when the scrub has temporarily popped the env var. Lock ordering (init then env) is consistent with no other caller, so no deadlock.
* Studio: surface native model load errors and harden native path label cache
- Native model load and validate now bubble up the actual exception (with
paths redacted) and apply the same friendly-error rewrite the non-native
path uses, so users see "CUDA OOM", "trust_remote_code required", etc.
instead of a generic "Failed to load native model: <label>".
- run_without_native_path_secret now also nulls _SCRUB_SAVED_SECRET so a
forked grandchild that imports native_path_leases cannot recover the
secret via the scrub-aware fallback in _decode_secret.
- _NATIVE_PATH_LABELS now has its own 10000-entry cap independent of the
100-entry redaction list, so display_label_for_native_path no longer
falls back to returning the raw canonical path after 101 native paths
in one session. Redaction list keeps the 100-entry cap for log-scan
performance.
- _validate_payload now also rejects null bytes in display_label, which
is echoed back in HTTP responses and log lines.
* Studio: harden native path lease validation and chained native rollback
- child_env_without_native_path_secret now copies os.environ under
_NATIVE_PATH_ENV_LOCK so a concurrent scrub-context env pop cannot
raise RuntimeError: dictionary changed size during iteration in a
background hardware scan or other env reader.
- _validate_payload and grant construction route every signed numeric
field (version, issued_at_ms, expires_at_ms, size_bytes, modified_ms)
through new _required_int / _optional_int helpers that wrap raw int()
ValueError into NativePathLeaseError. The single upstream catcher
produces 400 instead of 500 for malformed signed payloads.
- verify_native_path_lease now runs _validate_current_stat before
_consume_nonce, so a transient stat error on the canonical path no
longer permanently burns the nonce. Concurrent verifies still
serialize through _consume_nonce, so single-use is preserved.
- Chained native model rollback now restores activeNativePathToken in
the chat runtime store after a successful rollback loadModel. Without
this, a second consecutive failed switch could not re-roll-back
because the store token had been overwritten by the failed attempt.
- validate_model now applies the same not_supported_hints friendly
rewrite to native model errors that load_model already does, so a
native .gguf that fails validation with an upstream "is not supported"
message gets the same actionable wording as the non-native branch.
* Studio: harden native path log redaction, status disclosure, and chip lifecycle
- structlog processor chain now runs format_exc_info before
filter_sensitive_data so traceback strings are produced (and then
redacted) rather than passed through as untouched (type, value, tb)
tuples that the JSON or console renderer formats after the redaction
filter has already finished.
- native_path_secret_removed_for_child_start clears _CACHED_LEASE_SECRET
in addition to popping the env var, so a fork during the scrub window
cannot inherit the cached bytes via the parent's heap. Parent verify
calls during the window keep working through the existing scrub-aware
fallback in _decode_secret.
- load_model's except ValueError handler now redacts native paths and
uses the native model log label when native_grant_backed is true.
Previously a ValueError raised after lease verification (e.g. from
ModelConfig.from_identifier or downstream GGUF parsing) returned the
raw exception string in the HTTP response body.
- llama_cpp_backend now records the native display label at GGUF load
time, and /api/inference/status prefers it over the redaction store.
After a Python backend restart the redaction store is empty; the
attribute keeps the friendly label, and an absolute model_identifier
with no other label source falls back to the basename so the canonical
path no longer appears in active_model.
- reveal_path_token uses native "reveal and select" commands on macOS
(open -R) and Windows (explorer /select,) so the file is highlighted
in the file manager. Linux keeps the existing parent-directory open.
- Native model rollback that fails because the previous token cannot be
consumed now throws a rollback-specific Error, and the outer empty
catch was replaced with one that re-throws the rollback error. The
rollback-specific message now reaches the user instead of being
overwritten by the original load error message.
- NativeModelChip tracks the Rust token's expiresAtMs on a single
setTimeout, disables the Load button at expiry, and relabels it
"Select again" with an explanatory tooltip so users do not click into
a guaranteed-failure path after the 15-minute TTL elapses.
* Studio: tighten native artifact policy, mmproj sibling check, and intake UX
- is_open_safe_artifact no longer grants Open for directories. Reveal
already handles directory navigation, so the change closes the
attack surface where a macOS .app artifact could be launched via
open_path_token + open::that_detached.
- Display labels are sanitized in classify_existing_path. Control
characters in filenames (newlines, tabs, NUL et al.) are replaced
with spaces and the label is trimmed and capped, so a file named
with embedded newlines cannot inject forged log lines or scramble
the UI status panel.
- validate_entry_path skips the size_bytes/modified_ms equality check
when the operation is Reveal or Open. Cloud-sync agents (Dropbox,
iCloud Drive, OneDrive) routinely rewrite extended-attribute
metadata which bumps mtime, and the user expects Reveal/Open to
remain available for files in synced folders.
- llama_cpp_backend gains a _native_grant_backed flag at GGUF load
success. /api/inference/status only applies the absolute-path
basename fallback when that flag is true, so a non-native absolute
local GGUF still reports its canonical model_identifier and unload
by identifier keeps working.
- Native vision GGUFs now run through _validate_native_mmproj_companion
before llama-server starts: the companion mmproj must be a regular
file, not a symlink, and must live in the same resolved directory as
the granted GGUF. This stops a hostile sibling or symlinked mmproj
from being loaded under a single-file lease.
- Chained native rollback restructured: the rollback loadModel + state
+ refresh runs inside its own try/catch that swallows so the outer
throw error surfaces the ORIGINAL load failure. The native-token
consume-failure case still throws the rollback-specific message
early, before the inner block runs, so its actionable guidance is
preserved.
- Loading-model state and the duplicate-load guard in the chat runtime
hook now compare both the model id and the native path token. Two
drops or picks with the same basename in different folders no longer
silently dedup; the second token is honored.
- chat-page loadNativeModelIntent awaits selectModel before clearing
the pending intent. If selectModel returns early via dedup or
throws, the chip and its token stay so the user can retry instead
of losing the selection.
- NativeModelChip's Reveal button is disabled when the lease has
expired (Rust would reject it anyway), and the Load button label
reads "Expired" instead of "Select again" so the disabled element
no longer promises an action it cannot perform.
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* feat: add checkpoint resume for stopped training runs
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* fix:add resume checkpoint helpers
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: use checkpoint parent as resume output dir
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* fix: save optimizer and scheduler state on stop-and-save
Use Trainer._save_checkpoint instead of save_state so resume restores
optimizer momentum and LR-schedule position via the checkpoint-NNN/
subdir written by HF's official path.
* fix: clean up resume training history and startup progress
* fix: preserve resume output dirs
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* fix: tighten resume run lookup
* fix: remove stale output-dir lookup
* fix: preserve startup download progress
---------
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Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
* add unsloth studio desktop app
* Fix review findings
- studio/src-tauri/tauri.conf.json: retarget updater to staging repo
(danielhanchen/unsloth-staging-2); switch to unslothai/unsloth on upstream merge.
- studio/src-tauri/linux/postremove.sh: drop the interactive read loop and the
/home/* iteration. Package maintainer scripts must stay non-interactive and
must not touch other users' data.
- studio/frontend/src/app/auth-guards.ts: honor tauriAutoAuth() boolean. Failed
auto-auth now redirects to /login; requireGuest/requirePasswordChangeFlow
only redirect to /chat when auth succeeds. The new early-return on failed
auth is intentional so the login / change-password flows remain reachable
when desktop auth is not yet established.
- studio/frontend/src/config/env.ts: keep fetched=false on health failure so
later calls retry instead of caching the client-side platform guess.
- studio/src-tauri/src/install.rs: pick the available system package manager
(apt-get, dnf, zypper, pacman); AppImage bundles run on non-Debian distros.
- studio/frontend/src/lib/open-link.ts + markdown-text/sources callers: return
boolean from openLink so callers only preventDefault on handled URLs; relative
hrefs now navigate natively.
- studio/frontend/src/features/settings/tabs/about-tab.tsx: fetch(apiUrl(...))
so the version request targets the backend port in desktop mode. The bare
/api/health predates the Tauri webview (blame: the earlier onboarding commit,
which ran with same-origin frontend/backend); in desktop mode the webview
origin is tauri://localhost so the bare path fails.
- install.ps1: gate the install_python_stack.py hotfix on a sentinel comment
instead of a content regex; append the sentinel after applying so reruns
are unambiguous.
- unsloth_cli/commands/studio.py _write_auth_secret: use the atomic mkstemp +
os.replace path on Windows too; chmod calls are wrapped in try/except OSError.
- studio/src-tauri/src/preflight.rs probe_existing_backends: fan out the health
probes concurrently; desktop-auth status still runs sequentially per candidate.
reqwest::Client is internally Arc-wrapped so the in-loop .clone() is a
refcount bump, not a deep clone; annotated inline.
- studio/src-tauri/src/preflight.rs run_cli_probe: wait() after kill() to reap
the child, matching probe_cli_capability.
- studio/src-tauri/src/process.rs + main.rs: add stop_backend_detached and use
it from the tray quit handler so the 5s graceful-wait does not block the
Tauri main loop. RunEvent::Exit keeps the synchronous safety-net call.
- studio/backend/main.py: drop the permissive localhost CORS regex in
api-only mode; the explicit allow_origins list is sufficient.
- .github/workflows/release-desktop.yml: drop max-parallel: 1 so platform
builds run in parallel, and lift releaseBody to an env var so the three
tauri-action invocations share one source of truth.
* Fix review findings (loop 2)
- studio/backend/auth/storage.py update_password: clear_desktop_secret()
alongside clear_bootstrap_password() so rotating the admin password
also revokes any previously provisioned .desktop_secret. Without this,
an old local desktop credential keeps minting fresh admin tokens via
/api/auth/desktop-login after a password rotation.
- studio/src-tauri/src/desktop_auth.rs provision_desktop_auth: wrap
cmd.output().await in tokio::time::timeout(30s). DESKTOP_AUTH_LOCK is
held across the whole desktop_auth flow, and previously a hanging
`unsloth studio provision-desktop-auth` subprocess would pin the lock
indefinitely and freeze every subsequent desktop_auth call.
* Add review tests
* Consolidate review tests
Merge review-added tests into the existing studio/backend/tests/test_desktop_auth.py
(the PR's authoritative desktop-auth test file). Drops three scaffolding files under
tests/python/ in favor of five focused tests next to the tests they extend:
- test_update_password_clears_desktop_secret (runtime)
- test_update_password_on_unknown_user_leaves_desktop_secret_intact (runtime)
- test_cli_provisioning_delegates_to_storage_create_desktop_secret (source-level)
- test_cli_connect_auth_db_reads_storage_db_path (source-level)
- test_desktop_auth_provision_has_bounded_timeout (Rust source-level)
* Revert auth-guards.ts Tauri branches to unconditional form
The review loop on PR 5144 introduced a regression: the isTauri branch of
requireAuth redirected to /login when tauriAutoAuth() returned false, and
requireGuest / requirePasswordChangeFlow silently fell through on the same
condition. The Tauri desktop app authenticates via a local auto-generated
secret; it must never surface /login or /change-password to the user. A
failed auto-auth should let the startup layer retry, not expose a password
form.
Restore the three Tauri branches to the author's original unconditional
form (requireAuth: return; requireGuest / requirePasswordChangeFlow: throw
redirect({to: '/chat'})). Keep the rest of the review fixes -- the
apiUrl() fetch wrapping, authRedirect helper, and fetchAuthStatus refactor
are all legitimate improvements and are preserved.
* Revert release-desktop.yml to author's version
The review loop's workflow-file tweaks (drop max-parallel: 1, lift releaseBody
to an env var) are cosmetic. OAuth tokens cannot push workflow-file changes,
and fine-grained PATs cannot honor maintainerCanModify on a third-party fork.
Reverting the workflow file to wasimysaid's version lets the push go through
without needing a classic PAT with both repo and workflow scopes.
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---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Daniel Han <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add vision detection cache to is_vision_model() to avoid redundant subprocess spawns
is_vision_model() is called 4-5 times per training run for the same model
with zero caching. For transformers 5.x models, each call spawns a full
subprocess (~6s each). This adds a module-level _vision_detection_cache dict
following the same pattern as the existing _audio_detection_cache used by
detect_audio_type(). The function is refactored into a thin cache wrapper
around _is_vision_model_uncached(), saving ~12s per training run.
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* Include hf_token in vision cache key for gated model correctness
Cache key is now (model_name, hf_token) instead of just model_name.
This prevents stale False results when an unauthenticated probe for a
gated model is followed by an authenticated call.
* Remove test file from main PR - will be submitted separately
* Fix vision cache: normalize model names and skip caching transient failures
- Normalize model names in cache key using resolve_cached_repo_id_case()
to avoid duplicate entries for different casings of the same HF repo
(aligns with case normalization from #4822)
- Return None instead of False on transient failures (network errors,
subprocess timeouts, HF API issues) so the cache layer can distinguish
"definitely not a vision model" from "failed to check"
- Only cache definitive True/False results; transient failures are retried
on the next call instead of being permanently locked in as False
* Refine failure handling: cache deterministic failures, guard normalization
- Subprocess non-zero exit, JSON errors, and general exceptions return
False (deterministic, cached) instead of None (retryable). Only
subprocess.TimeoutExpired returns None since timeouts are transient.
- Wrap cache key normalization in try/except so resolve_cached_repo_id_case
or normalize_path failures fall back to raw model_name instead of
crashing callers.
* Harden vision detection cache: fix transient failure handling, thread safety, token security
- All subprocess failure paths now return None (transient) instead of False,
preventing permanent misclassification of VLMs after temporary HF/auth/network errors
- Use SHA256 fingerprint for hf_token in cache key instead of raw bearer token
- Add threading.Lock with double-checked locking to prevent thundering herd
of concurrent subprocess spawns for the same uncached model
- Distinguish permanent failures (RepositoryNotFoundError, GatedRepoError,
ValueError) from transient ones in _is_vision_model_uncached
- Pass resolved/normalized model name to detection (not just cache key)
- Log normalization fallback at debug level instead of silent swallow
- Thread hf_token through callers in routes/models.py and trainer.py
that previously omitted it
* Refine lock strategy and token fingerprint
- Move detection computation outside the lock to avoid serializing
long-running subprocess spawns (60s timeout) and HF API calls across
all concurrent model checks. Lock is now only held for cache writes.
- Use full SHA256 digest for token fingerprint instead of truncated
16-char prefix to eliminate collision risk.
* Fix huggingface_hub import fallback and use atomic cache read
- Add fallback import path for RepositoryNotFoundError/GatedRepoError
from huggingface_hub.utils (older hub versions) when .errors is
not available
- Use sentinel-based dict.get() for single atomic cache read instead
of two-step in/[] pattern (future-proof for no-GIL runtimes)
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* fix(studio): change default weight_decay from 0.01 to 0.001
The default weight decay across Studio was 0.01 but should be 0.001.
Updated the default in all backend fallbacks, the Pydantic model, the
frontend config, and every YAML preset/model-default config.
* fix(studio): auto-set learning rate based on training method
Default LR should be 2e-4 for LoRA/QLoRA and 2e-5 for full fine-tuning.
Frontend: track whether the user has manually edited the LR field via a
_learningRateManuallySet flag (same pattern as trainOnCompletions).
When switching training method and the user has not touched the LR,
auto-set it to the appropriate default. Reset the flag on model load.
Backend: change trainer.py start_training default from 5e-5 to 2e-4,
update default.yaml fallback from 5e-5 to 2e-4, and fix
full_finetune.yaml from 0.0002 (2e-4) to 2e-5.
* refactor(studio): centralize weight_decay and learning rate defaults
Create studio/backend/core/training/constants.py as the single source of
truth for DEFAULT_WEIGHT_DECAY (0.001), DEFAULT_LEARNING_RATE (2e-4),
DEFAULT_LEARNING_RATE_FULL (2e-5), and DEFAULT_LEARNING_RATE_STR ("2e-4").
All backend modules (trainer.py, training.py, worker.py, models/training.py)
now import from constants.py instead of hardcoding values.
On the frontend, add LR_DEFAULT_LORA and LR_DEFAULT_FULL to
config/training.ts and use them in the store instead of magic numbers.
A comment cross-references the backend constants file.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix model-specific LR override, persist migration, and flag resets
- Preserve model-specific learning rates from YAML configs when the
async autoSelectTrainingMethod callback fires (fixes Qwen2.5-1.5B
getting 2e-4 instead of its configured 1e-5, etc.)
- Bump zustand persist version to 9 with migration so existing users
with weightDecay=0.01 get updated to 0.001
- Clear _learningRateManuallySet in reset() and applyConfigPatch()
for consistency with trainOnCompletions flag behavior
- Add DEFAULT_LEARNING_RATE_FULL_STR to constants.py
* Refine applyConfigPatch to only clear LR flag when patch includes LR
Only reset _learningRateManuallySet when the applied config patch
actually provides a learningRate value. This prevents unrelated config
patches from silently disarming the manual-edit guard, which would
cause a subsequent setTrainingMethod call to overwrite the user's
custom LR.
* Preserve model-specific LR when switching between qlora and lora
Only auto-switch the learning rate when the training category changes
(adapter <-> full fine-tuning). Switching between qlora and lora keeps
the current LR since both methods share the same learning rate range.
This preserves curated per-model defaults (e.g. 1e-5 for
Qwen2.5-1.5B-Instruct) when the user toggles between adapter methods.
* Remove constants.py, use YAML configs as the source of truth
The YAML config files (model-specific + default.yaml) are the intended
config layer for training defaults. The Python backend fallbacks now use
inline values that match the YAML configs, rather than importing from a
separate constants module. This keeps the config architecture simple:
YAML files are the single source of truth, and the inline Python
fallbacks are just safety nets that mirror them.
* fix(studio): preserve model-specific LR when switching training method
Stash YAML-provided learning rate and use it to restore the correct
value when switching between adapter and full fine-tune modes.
- qlora <-> lora no longer overwrites the model's LR
- full -> adapter restores the YAML LR instead of a hardcoded constant
- selecting a model while on full fine-tune uses LR_DEFAULT_FULL
instead of applying the YAML adapter LR
---------
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Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
* [WIP] balanced device map for studio
* gpus as a request parameter
* API for multi GPU stuff
* return multi gpu util in new API
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* Use balanced_low0 instead of balanced
* Use balanced_low0 instead of balanced
* Fix device_map typo, UUID parsing crash, set() filter bug, and broken tests
- balanced_low0 -> balanced_low_0 (transformers/accelerate rejects the old string)
- get_parent_visible_gpu_ids() now handles UUID/MIG CUDA_VISIBLE_DEVICES
gracefully instead of crashing on int() parse
- _get_backend_visible_gpu_info() set() or None bug: empty set is falsy so
CUDA_VISIBLE_DEVICES=-1 would disable filtering and report all GPUs
- test_gpu_selection.py: add missing get_visible_gpu_utilization import and
add required job_id arg to start_training() calls
* Smart GPU determinism using estimates
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* disallow gpu selection for gguf for now
* cleanup
* Slightly larger baseline
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Treat empty list as auto
* Verbose logging/debug
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Cleanup and revert unnecessary deletions
* Cleanup excessive logs and guard against disk/cpu offload
* auth for visibility API. cleanup redundant imports. Adjust QLoRA estimate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* support for non cuda gpus
* Fix multi-GPU auto-selection memory accounting
The multi_gpu_factor was applied uniformly to all GPUs including the
first one, which unfairly penalizes single-GPU capacity when
transitioning to multi-GPU. This created a discontinuity where a model
that barely fits 1 GPU would suddenly require 2 GPUs because the first
GPU's free memory was discounted by 20%.
Now the first GPU keeps its full free memory, and only additional GPUs
have an overhead factor (0.85) applied to account for inter-GPU
communication and sharding overhead. This gives more accurate
auto-selection and avoids unnecessary multi-GPU for models that
comfortably fit on one device.
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* Add sandbox tests for multi-GPU selection logic
24 tests covering model size estimation, memory requirements, automatic
GPU selection, device map generation, GPU ID validation, and multi-GPU
overhead accounting. All tests use mocks so they run without GPUs on
Linux, macOS, and Windows.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix reviewer findings: 4bit inference estimate, fallback, GGUF gpu_ids, retry
1. 4-bit inference now uses reduced memory estimate (model_size/3 + buffer)
instead of the FP16 1.3x multiplier. This prevents over-sharding
quantized models across unnecessary GPUs.
2. When model size estimation fails, auto_select_gpu_ids now falls back to
all visible GPUs instead of returning None (which could default to
single-GPU loading for an unknown-size model).
3. GGUF inference route now treats gpu_ids=[] as auto-selection (same as
None) instead of rejecting it as an unsupported explicit request.
4. Training retry path for "could not get source code" now preserves the
gpu_ids parameter so the retry lands on the same GPUs.
5. Updated sandbox tests to cover the new 4-bit inference estimate branch.
* Remove accidentally added unsloth-zoo submodule
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix UUID/MIG visibility and update test expectations
1. nvidia.py: When CUDA_VISIBLE_DEVICES uses UUID/MIG tokens, the
visibility APIs now return "unresolved" with empty device lists instead
of exposing all physical GPUs. This prevents the UI from showing GPUs
that the backend process cannot actually use.
2. test_gpu_selection.py: Updated test expectations to match the new
multi-GPU overhead accounting (first GPU at full capacity, 0.85x for
additional GPUs) and 4-bit inference memory estimation formula.
All 60 tests now pass.
* Add CPU/disk offload guard to audio inference path
The audio model loading branch returned before the common
get_offloaded_device_map_entries() check, so audio models loaded with a
multi-GPU device_map that spilled layers to CPU/disk would be accepted
instead of rejected. Now audio loads also verify no modules are offloaded.
* Improve VRAM requirement estimates
* Replace balanced_low_0 with balanced
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refine calculations for slightly easier nums
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* adjust estimates
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use nums instead of obj to avoid seralisation error
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Harden nvidia-smi parsing and fix fallback GPU list
1. nvidia.py: Wrap int() casts for GPU index and memory in try/except
so MIG slices, N/A values, or unexpected nvidia-smi output skip the
unparseable row instead of aborting the entire GPU list.
2. nvidia.py: Handle GPU names containing commas by using the last
field as memory instead of a fixed positional index.
3. hardware.py: fallback_all now uses gpu_candidates (GPUs with verified
VRAM data) instead of raw devices list, which could include GPUs
with null VRAM that were excluded from the ranking.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* cleanup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* consolidate raise_if_offload
* Improve MoE support. Guard against nvidia-smi failures
* Improve MoE support. Guard against nvidia-smi failures
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix shared-expert LoRA undercount, torch VRAM fallback, and apply_gpu_ids edge case
1. vram_estimation.py: compute_lora_params now includes shared experts
(n_shared_experts) alongside routed experts when computing MoE LoRA
adapter parameters. Previously only n_experts were counted, causing
the estimator to undercount adapter, optimizer, and gradient memory
for DeepSeek/GLM-style models with shared experts.
2. hardware.py: _torch_get_per_device_info now uses mem_get_info (which
reports system-wide VRAM usage) instead of memory_allocated (which
only reports this process's PyTorch allocations). This prevents
auto-selection from treating a GPU as mostly free when another
process is consuming VRAM. Falls back to memory_allocated when
mem_get_info is unavailable.
3. hardware.py: apply_gpu_ids([]) now returns early instead of setting
CUDA_VISIBLE_DEVICES="" which would disable CUDA entirely. Empty
list inherits the parent visibility, same as None.
4. hardware.py: Upgraded fallback_all GPU selection log from debug to
warning so operators are notified when the model likely will not fit
in available VRAM.
* Guard nvidia-smi subprocess calls against OSError and TimeoutExpired
get_visible_gpu_utilization and get_backend_visible_gpu_info now catch
OSError (nvidia-smi not found) and TimeoutExpired internally instead
of relying on callers to wrap every invocation. Returns the standard
available=False sentinel on failure so the torch-based fallback in
hardware.py can take over.
* Guard get_primary_gpu_utilization and reset GPU caches between tests
1. nvidia.py: get_primary_gpu_utilization now catches OSError and
TimeoutExpired internally, matching the pattern already used in
get_visible_gpu_utilization and get_backend_visible_gpu_info. All
three nvidia-smi callers are now self-contained.
2. test_gpu_selection.py: Added _GpuCacheResetMixin that resets the
module-level _physical_gpu_count and _visible_gpu_count caches in
tearDown. Applied to all test classes that exercise GPU selection,
device map, or visibility functions. This prevents stale cache
values from leaking between tests and causing flaky results on
machines with real GPUs.
* Fix nvidia-smi fallback regression and physical GPU count validation
1. hardware.py: get_gpu_utilization, get_visible_gpu_utilization, and
get_backend_visible_gpu_info now check result.get("available") before
returning the nvidia-smi result. When nvidia-smi is unavailable or
returns no data (e.g., containers without nvidia-smi, UUID/MIG masks),
the functions fall through to the torch-based fallback instead of
returning an empty result. This fixes a regression where the internal
exception handling in nvidia.py prevented the caller's except block
from triggering the fallback.
2. hardware.py: resolve_requested_gpu_ids now separates negative-ID
validation from physical upper-bound validation. The physical count
check is only enforced when it is plausibly a true physical count
(i.e., higher than the largest parent-visible ID), since
torch.cuda.device_count() under CUDA_VISIBLE_DEVICES returns the
visible count, not the physical total. The parent-visible-set check
remains authoritative in all cases. This prevents valid physical IDs
like [2, 3] from being rejected as "out of range" when nvidia-smi is
unavailable and CUDA_VISIBLE_DEVICES="2,3" makes torch report only
2 devices.
* Fix UUID/MIG torch fallback to enumerate devices by ordinal
When CUDA_VISIBLE_DEVICES uses UUID or MIG identifiers,
get_parent_visible_gpu_ids() returns [] because the tokens are
non-numeric. The torch fallback in get_visible_gpu_utilization() and
get_backend_visible_gpu_info() previously passed that empty list to
_torch_get_per_device_info(), getting nothing back.
Now both functions detect the empty-list case and fall back to
enumerating torch-visible ordinals (0..device_count-1) with
index_kind="relative". This means the UI and auto-selection still
see real device data in Kubernetes, MIG, and Slurm-style UUID
environments where nvidia-smi output cannot be mapped to physical
indices.
Updated test_uuid_parent_visibility to verify the new torch fallback
path returns available=True with relative ordinals.
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* Add type hint for gpu_ids parameter in InferenceOrchestrator.load_model
---------
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* feat(db): add SQLite storage layer for training history
* feat(api): add training history endpoints and response models
* feat(training): integrate DB persistence into training event loop
* feat(ui): add training history views and card grid
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(studio): address review issues in training history persistence
- Strip hf_token/wandb_token from config before SQLite storage
- Add UUID suffix to job_id for collision resistance
- Use isfinite() for 0.0 metric handling throughout
- Respect _should_stop in error event finalization
- Run schema DDL once per process, not per connection
- Close connection on schema init failure
- Guard cleanup_orphaned_runs at startup
- Cap _metric_buffer at 500 entries
- Make FLUSH_THRESHOLD a class constant
- Map 'running' to 'training' phase in historical view
- Derive LR/GradNorm from history arrays in historical view
- Fix nested button with div[role=button] in history cards
- Guard String(value) against null/undefined in config popover
- Clear selectedHistoryRunId on auto tab switch
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* fix(studio): address round-2 review findings across training backend and frontend
Backend (training.py):
- Move state mutation after proc.start() so a failed spawn does not wedge
the backend with is_training=True
- Create DB run row eagerly after proc.start() so runs appear in history
during model loading, not after first metric event
- Rewrite _flush_metrics_to_db() with snapshot-before-insert pattern to
preserve metrics arriving during the write and retain buffer on failure
- Guard eval_loss with float() coercion and math.isfinite(), matching the
existing grad_norm guard
- Increase pump thread join timeout from 3s to 8s to cover SQLite's
default 5s lock timeout
Frontend (studio-page.tsx):
- Fix history navigation: check isTrainingRunning instead of
showTrainingView in onSelectRun so completed runs are not misrouted
- Replace activeTab state + auto-switch useEffect with derived tab to
eliminate react-hooks/set-state-in-effect lint violation
Frontend (historical-training-view.tsx):
- Add explicit "running" branch to message ternary so running runs no
longer fall through to "Training errored"
- Derive loading from detail/error state and move cleanup to effect
return to eliminate react-hooks/set-state-in-effect lint violation
Frontend (progress-section.tsx):
- Derive stopRequested from isTrainingRunning && stopRequestedLocal to
eliminate react-hooks/set-state-in-effect lint violation and remove
unused useEffect import
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* fix(studio): resolve 3 remaining bugs from round-2 review
1. Stuck on Current Run tab [12/20]: Only force "current-run" tab when
isTrainingRunning is true, not when stale completed-run data exists.
After training ends, users can freely navigate to Configure.
2. Incomplete metric sanitization [7/20]: Apply float() coercion and
isfinite() guards to loss and learning_rate, matching the existing
pattern used by grad_norm and eval_loss. Prevents TypeError from
string values and NaN leaks into history arrays.
3. Stop button state leak across runs [10/20]: Add key={runtime.jobId}
to ProgressSection so React remounts it when a new run starts,
resetting stopRequestedLocal state.
* fix(studio): deduplicate loss/lr sanitization in training event handler
Reuse _safe_loss/_safe_lr from the progress update block instead of
re-sanitizing the same raw event values for metric history.
* fix(studio): restore loss > 0 guard to prevent eval steps injecting 0.0 into metric histories
Round-2/3 fixes relaxed the history append guard from `loss > 0` to
`loss is not None`, which let eval-only log events (where loss defaults
to 0.0) append fake zeros into loss_history and lr_history. Restore the
`loss > 0` check to match the worker's own has_train_loss gate. The
float() coercion and isfinite() sanitization from round-3 remain intact.
* fix(studio): resolve training history bugs — nullable loss/lr, tab nav, sparkline
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* fix(studio): prevent ModuleNotFoundError in dataset.map() on Windows
On Windows, dataset.map() uses "spawn", which requires workers to
import compiled modules from disk. Previously, clear_unsloth_compiled_cache()
deleted the entire directory, causing workers to crash when looking for
UnslothSFTTrainer.py.
Changes:
1. Added `preserve_patterns` to cache cleanup to keep `Unsloth*Trainer.py`
on Windows while clearing model-specific files.
2. Added the cache directory to PYTHONPATH for spawn workers.
Linux/macOS behavior is unchanged.
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* Fix spawn-platform coverage, CWD path mismatch, and race condition for PR #4473
- Extend platform guard from win32-only to include macOS (also uses spawn
since Python 3.8, same ModuleNotFoundError would occur)
- Replace fragile CWD-based PYTHONPATH registration with centralized
register_compiled_cache_on_path() that uses the same __file__-relative
_CACHE_DIRS already used by cache_cleanup -- fixes path mismatch when
studio is launched from a directory other than the repo root
- Move PYTHONPATH registration to the top of _train_worker(), before any
dataset.map() call (previously it ran late in config assembly, after
dataset formatting which also calls dataset.map())
- Update inference.py model-unload to preserve trainer files on spawn
platforms, preventing a race where unloading a model via inference tab
would delete UnslothSFTTrainer.py while training workers are importing it
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* Fix cache-dir precedence reversal in register_compiled_cache_on_path()
Iterating _CACHE_DIRS in forward order while calling insert(0) each time
reverses the declared priority: later entries shadow earlier ones. When
multiple compiled-cache directories exist, spawned workers could import a
stale trainer from the wrong cache.
Fix: iterate in reverse so that the highest-priority entry (first in
_CACHE_DIRS) is inserted last and ends up at position 0 in sys.path and
PYTHONPATH.
* fix: harden worker-count helpers against cpu_count=None and desired<=0
- safe_num_proc: guard os.cpu_count() with `or 1`, clamp multi-GPU
path with max(1, min(4, desired)), clamp return with max(1, desired)
- safe_thread_num_proc: same os.cpu_count() guard and return clamp
- Add regression tests (31 L1 unit + 10 sandbox edge-case tests)
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* remove regression tests from PR
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* fix: handle Windows subprocess crash during dataset.map()
Windows uses spawn (not fork) for multiprocessing. Spawned workers
cannot resolve Unsloth's dynamically compiled cache modules from
unsloth_compiled_cache/, causing ModuleNotFoundError and RuntimeError
during dataset.map() tokenization.
Add two platform-guarded patches for sys.platform == "win32":
1. Force HF_DATASETS_MULTITHREADING_MAX_WORKERS=1 and set spawn method
2. Monkey-patch Dataset.map() to force num_proc=None
Fixes#4490
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* address review: extend spawn fix to macOS, add multiprocess fallback
- Change platform checks from sys.platform == "win32" to
sys.platform != "linux" so macOS (also spawn-based) is covered
- Wrap multiprocess import in try/except falling back to stdlib
multiprocessing when the multiprocess package isn't installed
- Rename _win32_safe_map to _spawn_safe_map to reflect broader scope
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* fix: replace global Dataset.map monkey-patch with targeted num_proc routing
The previous approach had issues: Patch 1 set HF_DATASETS_MULTITHREADING_MAX_WORKERS
and forced set_start_method (dead code on platforms already using spawn), and Patch 2
globally monkey-patched Dataset.map() (too broad, missed Dataset.filter()).
Replace with a two-layer fix:
1. Studio layer: Add dataset_map_num_proc() that returns None on spawn platforms
(Windows, macOS). Unlike num_proc=1 which still creates Pool(1) and spawns a
worker, num_proc=None runs Dataset.map()/filter() truly in-process.
Update all dataset.map() callsites to use it. ThreadPoolExecutor callers
(format_conversion.py) keep using safe_num_proc() since threads are unaffected.
2. Root-cause layer: Propagate UNSLOTH_COMPILE_LOCATION via PYTHONPATH on spawn
platforms so spawned workers can import compiled modules. Mirrors the .venv_t5
pattern in worker.py. Does not import unsloth_zoo.compiler (heavy torch/triton
imports). Completely skipped on Linux.
Also extend safe_num_proc() to return 1 on macOS (was only guarding Windows),
and narrow the transformers 5.x dataloader guard from != "linux" to explicit
("win32", "darwin").
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* fix: add safe_thread_num_proc() for ThreadPoolExecutor callsites
safe_num_proc() correctly caps to 1 on macOS/Windows for process-based
multiprocessing, but format_conversion.py reuses it for ThreadPoolExecutor
workers. Threads share address space and are unaffected by spawn, so
capping to 1 makes image URL downloads sequential -- a real regression.
Add safe_thread_num_proc() that skips the platform guard but keeps the
cpu_count heuristic, and switch both ThreadPoolExecutor callsites in
format_conversion.py to use it.
* fix: remove double-wrap in dataset_num_proc + fix num_proc=1 in datasets route
- trainer.py:3009: Replace safe_num_proc(max(1, os.cpu_count() // 4))
with max(1, (os.cpu_count() or 1) // 4) to avoid double-wrapping
inside dataset_map_num_proc which already calls safe_num_proc
- trainer.py:15-20: Clarify comment on PYTHONPATH propagation
- datasets.py:445: Change num_proc=1 to num_proc=None for 10-row
preview slice (avoids unnecessary multiprocessing overhead)
* fix: guard os.cpu_count() against None in worker-count helpers
os.cpu_count() can return None on some platforms. Use (os.cpu_count() or 1)
to prevent TypeError in safe_num_proc() and safe_thread_num_proc().
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* full finetuning studio
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* Update studio/backend/core/training/trainer.py
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## Summary
- Add web search tool calling for GGUF models (Search toggle, DuckDuckGo via ddgs)
- Add KV cache dtype dropdown (f16/bf16/q8_0/q5_1/q4_1) in Chat Settings
- Fix Qwen3/3.5 inference defaults per official docs (thinking on/off params)
- Enable reasoning by default for Qwen3.5 4B and 9B
- Replace "Generating" toast with inline spinner
- Fix stop button via asyncio.to_thread (event loop no longer blocked)
- Fix CUDA 12 compat lib paths for llama-server on CUDA 13 systems
- Fix auto-load model name not appearing in selector
- Training progress messages + dataset_num_proc fix
Integrated PRs:
- #4327 (imagineer99): BETA badge alignment (already in tree)
- #4340 (Manan Shah): prioritize training models in model selection
- #4344 (Roland Tannous): setup.sh macOS python version compatibility
- #4345 (Manan Shah): revamp model+dataset checking logic
* user can upload eval dataset, removed bugs
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* resolving merge conflicts
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* resolving gpt comments
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Instead of downloading the full dataset and then slicing, use
streaming mode to only fetch the rows needed (up to slice_end + 1)
when a manual dataset slice is configured.
ashzak