* fix: add XPU device support and update hardcoded CUDA selections
* fix: add XPU device support for pytest CUDA skipped tests
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* Fix device handling for PR #7401
- perplexity_eval.py: use DEVICE_TYPE_TORCH, not DEVICE_TYPE. The latter can
be "hip" or "mlx", which .to() rejects, so this regressed ROCm.
- test_batched_leftpad_generation_gpu.py: XPU diverges here today, so mark it
non-strict xfail on XPU instead of reverting to a CUDA-only guard. Keeps the
real XPU gap visible and turns green once it is fixed.
- Guard torch.xpu.is_available() with hasattr, matching device_type.py.
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* Re-enable the flash varlen attention test in CI for PR #7401
attention_dispatch.py now predefines flash_attn_func / flash_attn_varlen_func
as None, so test_run_attention_flash_varlen_receives_window_and_softcap no
longer needs flash_attn importable to be monkeypatched. Verified on a runner
shaped like the CPU-only one: the test fails against main's attention_dispatch
and passes at this head, so the deselect is now dead weight.
* Tighten comments for PR #7401
Drop the hasattr rationale: torch.xpu has existed since torch 2.3 and the
dependency floor is 2.4, so no supported build predates the namespace. The
guard stays as cheap defence, but the comment claimed something untrue.
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* fix: keep offline GGUF export off the Hub for VLM tokenizers (#7481)
Resolve cached snapshot directories before loading PreTrainedTokenizerFast
during VLM processor fallback so transformers does not call is_base_mistral()
-> model_info() when HF_HUB_OFFLINE is set. Also probe the local cache in
_has_tokenizer_model instead of model_info when offline.
Fixesunslothai/unsloth#7481
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* test: add real-cache offline GGUF integration checks for #7481
Download unsloth/gemma-3-270m-it-bnb-4bit (~430MB) and verify offline
snapshot resolution and tokenizer load with network blocked. Full unsloth
import tests remain GPU-gated.
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* fix: address Codex review on offline GGUF tokenizer paths (#7481)
- Only rewrite Hub repo ids to cached snapshot dirs when offline
- Copy tokenizer.model from cache offline in preserve_sentencepiece
- Do not cache negative offline tokenizer.model probe results
- Add regression tests for all three review items
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* fix: probe HF cache before model_info for local-only GGUF saves (#7481)
Always resolve tokenizer.model from the local Hub cache before calling
model_info, and skip Hub metadata when the tokenizer was loaded with
local_files_only or offline env vars. Fixes Codex review on PR #7482.
* Fix lint blocker, false-green tests and offline defaults for PR #7482
Drop the two unused _env_says_offline imports that fail the Source lint
import-hoist check.
test_has_tokenizer_model_offline_skips_model_info and its local_files_only
twin set model_info.side_effect = AssertionError, but _has_tokenizer_model
wraps that call in "except Exception: return False", so the AssertionError
was swallowed and both passed on the merge base with the fix absent. Assert
model_info.call_count == 0 instead; both now fail on the base with
assert 1 == 0.
The real-cache integration tests called hf_hub_download and
PreTrainedTokenizerFast directly, so they exercised plain huggingface_hub and
passed identically on both trees. Route them through the resolver this PR
adds, and gate the file at module level since importing unsloth needs a GPU
host either way.
_resolve_hub_repo_local_dir and _resolve_hub_repo_cached_file defaulted to
local_files_only = False, so a helper named "resolve local dir" would
download with backoff retries when called without the flag. Every caller
already passes it explicitly, so default it closed.
Use tempfile.gettempdir() rather than a hardcoded /tmp, which silently
skipped both files on Windows, the platform in the bug report. Patch
socket.socket connect rather than replacing the class, which broke
isinstance checks.
Wire the unit tests into the Bucket-A CI list; Repo tests (CPU) ignores
tests/saving, so none of these ran anywhere.
* docs: note transformers 4.57.2-5.5.4 window for local tokenizer resolve
Name the version range where from_pretrained still probes model_info under
local_files_only, and point at the 5.6.0 upstream fix so the helper can be
removed once the supported floor moves past it.
* fix: enable real-cache suite in offline GGUF integration runner
Pass UNSLOTH_INTEGRATION_IMPORT=1 into the pytest subprocess so the
documented runner actually executes the real-cache tests instead of
reporting success after only the fake-cache unit file runs.
* docs: note integration runner enables UNSLOTH_INTEGRATION_IMPORT
Document that the runner sets the gate itself and still needs a host
that can import unsloth.
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* Keep an explicit local_files_only load local-only at save time
transformers takes local_files_only as an explicit from_pretrained parameter,
so it never lands in tokenizer.init_kwargs, and _offline_aware_load restores
HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE as soon as the load window closes. A VLM
loaded with local_files_only = True but no offline env var therefore came back
with the Hub repo id in name_or_path and nothing recording the request, so
_tokenizer_wants_local_only returned False on the later save and
_has_tokenizer_model fell through to HfApi.model_info - and then
_preserve_sentencepiece_tokenizer_assets fetched tokenizer.model from the Hub
with local_files_only = False. On a disconnected host that is a network wait
before the export gives up.
Stamp the load's local-only mode onto the returned processor and its tokenizer
inside the forced-offline window, and honour that stamp in
_tokenizer_wants_local_only, so the save path inherits the load's contract.
Verified against a real hub-cache layout whose snapshot has tokenizer metadata
but no tokenizer.model: before, one model_info call plus an hf_hub_download with
local_files_only = False; after, zero model_info calls and cache probes only.
Two tests added to tests/saving/test_offline_gguf_vlm_tokenizer_7481.py; both
fail with the loader_utils hunk reverted and pass with it in place.
* Carry the load's cache_dir through to saving for PR #7482
The local-only stamp added in e7b7400de preserved only the boolean. Saving
still derived its cache from HF_HUB_CACHE or HF_HOME, which does not see a
caller-supplied cache_dir, and FastBaseModel.from_pretrained threads one all
the way down. So a local_files_only load against a custom cache missed on the
probe, and the stamp then stopped the Hub fallback that used to cover it, and
tokenizer.model was silently left out of the GGUF staging directory.
Stamp the cache_dir alongside the local-only marker and prefer it at both
sites in save.py that derive one from the environment. Reverting save.py
alone, with the helper still present, fails the new test on behaviour.
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* Merge main and drop an unused import for PR #7482
Brings the branch up to date with main, which clears the stale Source lint
blocker inherited from #7476 by taking studio/backend/utils/hardware/__init__.py
out of this PR's changed-file set.
pytest was imported in the new test file and never used, which the
import-hoist check flags in its own right.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* tests: read checked-in files as UTF-8 instead of the platform default
Path.read_text() with no encoding uses locale.getpreferredencoding(), which
is UTF-8 on the Linux runners and cp1252 on a stock Windows install. Nine
module-level reads of checked-in source files were relying on that default.
studio/backend/routes/inference.py carries the DeepSeek tool-call token
regexes, so it holds U+FF5C and U+2581. Under cp1252 that read raised
UnicodeDecodeError on byte 0x81 at position 97806, and because the reads run
at import time it took test_cancel_atomicity.py and test_cancel_id_wiring.py
out at collection, not as failures. Green on CI, permanently broken for a
Windows contributor running the suite locally.
Adds a guard: at module scope there is no tmp_path fixture, so a bare
read_text()/write_text()/open() there is always touching a checked-in file.
That makes the rule mechanical enough to enforce with no allowlist, while
staying quiet about temp-dir I/O inside test bodies where the platform
default is harmless.
The repo already spells this correctly in 464 other places; this only stops
the stragglers coming back.
* tests: cover import-time helper reads and keep the guard py3.9-safe
Follows up on the Codex review:
- add `from __future__ import annotations`, since `str | None` in
`_offender` is evaluated at import on Python 3.9 and pyproject declares
requires-python ">=3.9,<3.15".
- widen the guard from module scope to import time. Class bodies and the
bodies of module-level helpers called from an executing statement run
during collection too, so `CODE = _extract_mixed_precision_code()` was
the same hazard as an inline read. `if __name__ == "__main__":` blocks
are skipped: pytest never executes them.
- scan studio/backend/tests/ as well as tests/. Both trees are collected
on Windows by separate CI jobs, and the offender that started this,
test_tool_xml_strip.py reading routes/inference.py, lives there.
Widening it surfaced seven more import-time reads of checked-in sources;
all now name utf-8.
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* Harden the import-time encoding guard for PR #7438
Close the detector gaps raised in review, all of which I reproduced against
the actual AST before changing anything.
False negatives (the guard let a real hazard through):
- _is_main_guard ignored the comparison operator, so if __name__ != "__main__"
counted as script-only even though its body runs at import.
- The else arm of a main guard was discarded with the rest of the If node.
- Decorators and argument defaults on a module-level def were skipped with the
body, though both are evaluated when the def executes.
- Path.open() in text mode was invisible; only builtin open() was matched.
- encoding = None and encoding = "locale" both re-select the platform default,
but the keyword merely being present counted as pinned.
False positives (the guard would have blocked a compliant contributor):
- A non-literal mode fell through to the "r" default, so open(p, mode) was
flagged even when mode is "rb", where adding encoding= is a ValueError and
there is no edit that satisfies the rule.
- Same for open(*args) and a **kwargs splat, which hide the mode and can hide
an encoding.
- Lambda bodies and comprehension elements were walked even though neither runs
at definition.
Verified: still reports the same 22 offenders on unpatched main, green on this
branch and on the tree merged with latest main (557 files), and an adversarial
corpus of 33 cases now scores zero false positives and zero false negatives.
Also corrected two docstring claims: neither collecting job runs on Windows,
and the read is governed by locale.getencoding().
* Walk eager comprehensions and treat io.open as the builtin
Two regressions from the previous commit, both reproduced against the AST
before changing anything.
Lumping list, set and dict comprehensions in with generator expressions was
wrong. Only a genexp is lazy; the other three run their element expression,
their filters and their nested iterators immediately, so
CONTENTS = [p.read_text() for p in PATHS] at module scope is an import-time
read the guard was silently missing. Comprehensions are now walked in full and
only the genexp keeps the outermost-iterable-only treatment.
io was also in the not-a-path-opener list, but io.open is the builtin, with the
same mode position and the same platform default. io.open(CHECKED_IN_FILE) is
exactly the hazard this guard exists for, so it is matched now, with binary
modes and a pinned encoding still exempt. tarfile.open and fitz.open stay
exempt since neither has an encoding to name.
Verified: 13 targeted cases covering all five eager comprehension forms and
io.open in text, binary and pinned shapes all classify correctly; still 22
offenders on unpatched main; green on this branch and on the tree merged with
latest main.
* Close three more walker gaps in the import-time guard
All three reproduced against the AST first.
A generator expression handed straight to a call is consumed there, so
DATA = "".join(p.read_text() for p in paths) runs its element at import. Only
an unconsumed genexp bound to a name stays lazy, so the walker now follows the
consumed ones in full and keeps the outermost-iterable-only treatment for the
rest.
if "__main__" == __name__ is an equivalent and accepted spelling of the main
guard, but requiring __name__ on the left meant its body was treated as
import-time code. That is a false positive on a block pytest never runs, so
both operand orders are recognised now.
The helper table was built from module-level defs only, so a def in a class
body invoked while the class is constructed was never followed, contradicting
the walker's stated coverage of class bodies. Helpers are now collected from
the module body and from class bodies at any nesting.
Verified: 15 targeted cases including all three fixes and the earlier ones
still classify correctly; still 22 offenders on unpatched main; green on this
branch and on the tree merged with latest main.
* Handle positional read_text encodings, lazy generators and nested helpers
* Guard reads reached from test bodies, unbound Path calls and __file__ paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Follow derived paths, skip lazy generator helpers, cover compressed openers
* Guard the CLI tests, helper parameters and unbound Path arguments
* Discover test roots and follow literal, in-place and tuple-derived paths
* Identify module openers by import, unwrap starred paths, pin subprocess snippets
* Resolve import origins, seed helper locals, follow named generators and parametrize
* Scope imports lexically, list tracked test files, bind unpacked names
* Resolve aliased openers, keyword-only params, destructured targets, next()
* Pin the encoding on subprocess snippets, workflow lint and CLI output for PR #7438
* Harden the CLI encoding guard against detached streams for PR #7438
* Tighten the encoding guard's path and scope analysis for PR #7438
* Resolve path provenance more precisely and keep POSIX stream encodings for PR #7438
* Resolve qualified path classes and scope conditional imports for PR #7438
* Scope CLI stream setup to the entry point and align two encoding pairs for PR #7438
---------
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Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Replace standalone Studio wording with Unsloth
Replace the single word Studio with Unsloth wherever it is used as
shorthand for Unsloth Studio in docs, CLI output, UI strings, i18n
locales, workflow display names, comments and docstrings.
Kept unchanged: the full name Unsloth Studio, third party product
names (LM Studio, Visual Studio, Mac Studio), feature names
(Recipe Studio, Fine-tuning Studio and its translations), and all
identifiers such as env vars, commands, paths and filenames.
* Address review feedback on the Studio wording rename
Use "an" before Unsloth where the rename left the article as "a".
Restore the split brand where Unsloth and Studio render as two halves
of the full product name: the onboarding sidebar subtitle and the
IPv6 localhost warning. Scope two messages to the full name Unsloth
Studio where plain Unsloth was misleading: the AMD README bullet and
the CLI studio setup error.
* fix(tokenizer): check for tokenizer.model after saving it, not before
`fix_sentencepiece_tokenizer` creates its temporary directory, then returns
early unless that directory already contains a tokenizer.model:
if not os.path.exists(temporary_location):
os.makedirs(temporary_location) # fresh, empty
if not os.path.isfile(f"{temporary_location}/tokenizer.model"):
return new_tokenizer # always true
old_tokenizer.save_pretrained(temporary_location) # writes that file
The file only appears on the line after the check, so the guard is always
true and the body never runs. Nothing else writes that path either --
`convert_to_fast_tokenizer` saves into a per-name subdirectory, not
`{temporary_location}/tokenizer.model`.
Both call sites are in `get_chat_template` and are commented "Must fix the
sentence piece tokenizer since there's no tokenizer.model file!" -- the
guard defeats the exact intent the caller states. The effect is silent: the
caller still gets a working `new_tokenizer`, but the sentencepiece piece
rename is skipped, so the mapped token (e.g. the eos token remapped to
`<|im_end|>`) is missing from tokenizer.model and GGUF/llama.cpp exports
carry the old piece.
`check_if_sentencepiece_model` in save.py does the same probe in the right
order -- makedirs, save_pretrained, then isfile. Match it.
Tests are added under tests/saving/ next to the existing sentencepiece
coverage, and to the two Bucket-A lists in consolidated-tests-ci.yml, since
Repo tests (CPU) --ignores tests/saving and these need protobuf.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Clear stale tokenizer.model before the sentencepiece guard
The guard now runs after old_tokenizer.save_pretrained, but the default
temporary_location is a fixed reusable directory. A fast-only tokenizer writes
no tokenizer.model, so a stale file from an earlier sentencepiece call could
pass the guard and patch the wrong model (e.g. mixing models in one process,
like a long-running server). Remove any existing tokenizer.model first, and add
a regression test.
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* Empty the reusable sentencepiece scratch directory each call
The final AutoTokenizer.from_pretrained reloads the whole temporary_location, so
removing only a stale tokenizer.model still let other artifacts from a previous
tokenizer (added_tokens.json, chat template, etc.) leak into the reload when the
default reusable directory is used across models in one process. Recreate the
directory instead, and add a regression test for the leaked-artifact case.
* Clear only top-level scratch files, keep subdirectories
Recreating the whole reusable directory deleted the {name} subtree that
convert_to_fast_tokenizer stores a converted tokenizer's source vocab in, so
old_tokenizer.save_pretrained could not copy tokenizer.model and the guard
returned the tokenizer unpatched for those legacy converted tokenizers. Remove
only stale top-level files (all the final reload reads) and leave subdirectories
intact. Add a regression test for the converted-source subdirectory.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Keep the current tokenizer's own source vocab when clearing
On a repeated get_chat_template(map_eos_token=True) call, the returned tokenizer's
vocab_file points back at the top-level tokenizer.model, and the cleanup deleted
that source before old_tokenizer.save_pretrained could re-emit it, so the guard
returned the tokenizer unpatched. Skip removing the old tokenizer's own source
vocab while still clearing stale files from a different tokenizer, and add a
regression test.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use a per-call temporary directory for the sentencepiece fix
The scratch directory defaulted to a single shared path, so concurrent or repeated
get_chat_template(map_eos_token=True) calls could delete or overwrite each other's
tokenizer.model between save and reload (tripping the piece assertion or reloading
the wrong model), and stale files from an earlier tokenizer could leak into the
reload. Work in a unique per-call subdirectory instead: this isolates every call
without deleting anything the caller owns, and replaces the earlier per-file cleanup.
Tests updated to read the patched model from the reloaded directory and to cover
isolation and source-vocab preservation.
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* Pass only the applied token mappings into the sentencepiece fix
get_chat_template mirrors token remaps into tokenizer.model via
fix_sentencepiece_tokenizer, but two caller paths passed a mapping that did not
match what they wrote to the fast tokenizer JSON, so once the sentencepiece patch
runs the model and JSON disagree:
- the mapped-token path skipped entries whose target already existed but still
passed the full mapping, renaming a piece the JSON never changed;
- the EOS-swap path swapped both tokens in the JSON but passed only one direction,
leaving two stop_word pieces and no old EOS piece.
Pass the applied mapping (and both swap directions) instead. Add regression tests.
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* Tighten sentencepiece guard comments
* Add SPDX license identifier to sentencepiece guard test
* Reclaim the per-call sentencepiece scratch directory
The per-call tempfile.mkdtemp fixed the shared-directory race but never cleaned
up, so a long-running process leaked one scratch dir per call. The dir cannot be
deleted eagerly for sentencepiece tokenizers because the returned tokenizer's
vocab_file points into it (a later save_pretrained copies the patched
tokenizer.model from there). Reclaim it correctly instead: remove the dir right
away on the fast-only path (the returned tokenizer never references it), and
attach a weakref.finalize so the sentencepiece dir is removed once its tokenizer
is garbage collected. Add regression tests for both.
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* Tighten the scratch-dir reclaim comment
---------
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Co-authored-by: danielhanchen <unslothshared@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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* fix(save): unsloth_push_to_hub_gguf(save_method="lora") raises NameError
unsloth_push_to_hub_gguf reads is_main_process at save.py:3181 but never
declares it. Its twin unsloth_save_pretrained_gguf declares it (2783) and
uses it the same way (2839) -- the LoRA branch was copied between the twins,
the parameter it depends on was not. There is no module-level global, so the
name resolves as a global load and the branch raises NameError 100% of the
time.
save_pretrained_gguf(save_method="lora", push_to_hub=True) raises a
ValueError that tells users to "use .push_to_hub_gguf(save_method='lora')
instead" -- the documented escape hatch is the broken call.
Add is_main_process to the signature, positioned as in the twin, and forward
it to unsloth_save_pretrained_gguf on the merged path so the parameter is not
silently ignored there. Default stays True, so nothing changes for existing
callers.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(save): preserve GGUF push compatibility
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
* Single-pass GGUF export for direct outtypes + parallel multi-quant
save_to_gguf defaulted first_conversion to model_dtype before the block
that picks the optimal base conversion, leaving that block dead since it
landed (#3356). Every default export (fast_quantized -> q8_0) therefore
ran two passes: convert HF -> 16-bit GGUF, then llama-quantize -> q8_0,
writing a 2x-size intermediate that the cleanup step deletes again.
- Route single-output exports whose type convert_hf_to_gguf.py emits
directly (f32/f16/bf16/q8_0) through one conversion pass with no
16-bit intermediate. Measured on Qwen2.5-0.5B-Instruct (8-core CPU):
bytes written 1525 MB -> 531 MB (2.9x less), peak extra disk 994 MB
-> 0, wall time neutral on local NVMe (14.8s vs 15.4s). The dequantized
q8_0 tensors are bit-identical to the two-pass output (max diff 0 over
all 290 tensors, same quant-type table). On disk-capped runtimes
(Kaggle 20 GB, Colab) the removed intermediate is the difference
between an export that fits and one that dies - see the Kaggle error
text this file already carries. imatrix runs keep the two-pass route
since only llama-quantize can apply one; explicit first_conversion is
still honored.
- Run independent llama-quantize passes two at a time when several
quant methods are requested (thread budget split between workers,
outputs byte-identical, order preserved). Measured 1.38x wall-clock
on q4_k_m+q5_k_m+q6_k. Sequential under UNSLOTH_ENABLE_LOGGING=1 to
keep subprocess logs readable; kill switch
UNSLOTH_PARALLEL_GGUF_QUANTS=0. Duplicate methods now quantize once.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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* Guard parallel GGUF quant on Kaggle and make multi-quant failures atomic
Each llama-quantize pass loads the whole model into RAM, so running two at once on Kaggle can OOM a host that succeeded sequentially; skip the parallel path there. On a failed multi-quant export, stop launching queued passes and remove orphaned quant outputs so a failure leaves no partial GGUFs behind, keeping the 16-bit base for retry. Also accept 0/false/no/off/empty for UNSLOTH_PARALLEL_GGUF_QUANTS so a well-meant 'false' actually disables parallelism, and add tests/saving/test_gguf_single_pass_export.py to the CI saving bucket so the new tests run.
* Preserve pre-existing outputs for canceled quant passes on failure
The parallel cleanup unlinked every requested output name, so a failed rerun could delete a valid model.<METHOD>.gguf left by an earlier successful export for a method whose pass was canceled and never ran this session. Skip canceled futures and only remove outputs from passes that actually executed.
* Gate parallel GGUF quant on available memory and preserve prior outputs
Skip the two-worker path when RAM cannot hold two full-model quantizations at once (and on Colab as well as Kaggle), so a multi-quant export that fit sequentially no longer OOMs. On failure, remove only outputs this run newly created, tracked against a pre-launch snapshot, so a rerun into an existing _gguf directory never deletes a valid artifact from an earlier export.
---------
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Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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* Fix repeated base model downloads across checkpoint exports (#6890)
Pre-warm the HF hub cache with the 16bit base weights before
merge_and_overwrite_lora runs. The merge fetches shards with
hf_hub_download(local_dir=...), which never populates the hub cache, so
temporary merge directories (GGUF checkpoint exports) forced a full
re-download of the base model for every checkpoint. The first export now
downloads once into the cache and later exports copy from it.
Skips itself when already cached, offline, on Kaggle/Colab, for local or
nf4/fp4 bases, non-downloading save methods, or low disk. Opt out with
UNSLOTH_PREWARM_HUB_CACHE=0.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Show MB for small base models in the pre-warm download message
* Harden pre-warm: getattr for model config, abspath for relative HF_HUB_CACHE
- Read config._name_or_path via getattr so a model without a config skips
cleanly instead of taking the outer error path.
- abspath the cache probe so a relative HF_HUB_CACHE walks up to a real root
rather than "", which would zero the free-space check and skip pre-warm.
Both from PR review; each covered by a test that fails without the fix.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Pre-warm the live-env HF cache so runtime redirects still hit (#6890)
Resolve the hub cache the same way the merge does (unsloth_zoo _active_caches,
live env) instead of huggingface_hub's import-time-frozen constants.HF_HUB_CACHE,
and pass it as cache_dir to the cached probe, disk check and snapshot_download.
Without this, a runtime HF_HOME/HF_HUB_CACHE redirect (unsloth_zoo
redirect_hf_cache_if_readonly on a read-only default cache, or Studio) makes the
pre-warm populate a different directory than the one the merge reads, so the
cache-copy fast path misses and the base re-downloads on every export anyway.
Adds 3 regression tests covering the cache_dir threading and the redirect case.
* Apply ruff-format kwarg spacing to the pre-warm cache-dir changes
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Pre-warm the 16bit sibling for FP8 bases so their merged_16bit exports reuse the cache too
For a merged_16bit export of an FP8 base with an existing 16bit sibling, the merge
swaps to the sibling and downloads that (unsloth_zoo _resolve_fp8_16bit_sibling), so
pre-warming the FP8 repo missed the cache and re-downloaded the sibling every export.
Mirror the swap and pre-warm the sibling. No sibling still caches the FP8 repo for the
in-place dequant path. Adds 2 regression tests.
* Filter pre-warm shards through the safetensors index like the merge does
Repos that ship a leftover shard set the index does not reference (e.g. granite-3.2)
made the disk gate over-count and snapshot_download fetch shards the merge never reads.
Mirror the merge: on the download path, keep only index-referenced shards. Runs after
the already-cached check so the cached fast path stays network-free. Adds 2 tests.
* Tighten pre-warm comments
---------
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* Fix export-time trust_remote_code bypass in FP8/INT8/GGUF-LoRA export
The torchao, compressed-tensors, and LoRA GGUF export paths re-read the merged
checkpoint and used to set trust_remote_code from the checkpoint config's static
auto_map (the torchao path also scanned the staged tokenizer/processor configs).
A model that loads with built-in Transformers classes can carry an auto_map entry,
which skips the load-time remote-code consent scan (that only runs when the load
already requested trust_remote_code) yet flips trust_remote_code on at export,
running unvetted custom code.
Derive the reload trust_remote_code from the approved load decision instead: a new
_loaded_via_remote_code() checks whether the in-memory model / tokenizer was itself
loaded from custom code (its class lives in the transformers_modules package),
walking PEFT / wrapper layers. Built-in-loaded models no longer gain trust from
config metadata; genuine custom-code models (loaded with consent) still reload
correctly. Add CPU-only regression tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden _loaded_via_remote_code against a None/missing __module__
Read type(node).__module__ via getattr and require a string before startswith,
so a dynamically created or C-extension class with a None module does not raise
during export. Add a regression test.
* Split model and tokenizer trust for the compressed subprocess, walk processor components
The compressed-tensors export collapsed model and tokenizer trust into
one --trust-remote-code flag, so an approved custom tokenizer would have
let an unapproved model's custom code run inside the quantization
subprocess. The subprocess now takes --trust-remote-code-tokenizer for
the processor load and keeps --trust-remote-code for the model loads,
matching the torchao path's separate model_trust / tok_trust.
_loaded_via_remote_code now also walks processor components (tokenizer,
image_processor, feature_extractor, video_processor), so an approved
custom tokenizer held inside a built-in ProcessorMixin keeps its trust
on the export reload instead of failing with trust_remote_code=False.
The walk is a bounded BFS with a seen set so wrapper cycles terminate.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Pin llm-compressor auto-install to a vetted version range
install_llm_compressor() auto-installs llm-compressor on first use of an FP8/FP4
compressed export when it is not already present. The install command used the bare
package name, so pip resolved to whatever the configured index served; a compromised,
dependency-confused, or inflated-version ("999.0.0") release could then run under the
Unsloth process at install and import time.
Bound the automatic install to a vetted range
(_LLM_COMPRESSOR_SPEC = "llmcompressor>=0.8.0,<0.13"), which the oneshot /
QuantizationModifier API this uses supports, so pip can no longer jump to an arbitrary
future or inflated version. An already-installed newer llm-compressor is still used
as-is (the import short-circuits), so this only constrains the auto-install, never a
user's own install.
Add UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL=1 to forbid the automatic install
entirely and require a manual, vetted install, for locked-down or air-gapped
environments. Update the manual-install hints to the pinned spec.
Add tests/saving/test_llm_compressor_install_pin.py: static (ast) guards that the spec
stays a bounded pin, that the install command never passes an unpinned llmcompressor
literal, and that the opt-out env gate is evaluated before any install runs.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Loosen llm-compressor auto-install ceiling to <1.0 so new models still export
The earlier <0.13 ceiling was too tight: brand-new architectures (for example
Qwen3_5ForConditionalGeneration / qwen3_5, gemma-4 MoE) can require a newer
llm-compressor, and _unsloth_save_compressed_tensors already fails with
"requires a newer llm-compressor" when a scheme is unavailable. Capping the
auto-install at 0.12 would block getting that newer release and break compressed
export for new models.
Widen to llmcompressor>=0.8.0,<1.0. pip still auto-installs the latest 0.x
(where new-architecture support lands), while the <1.0 ceiling continues to block
a jump to an inflated-version ("999.0.0") or 1.0+ dependency-confusion release.
An already-installed newer llm-compressor is still used as-is (the import
short-circuits), and UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL still forbids the
automatic install entirely for locked-down environments.
* Lower llm-compressor floor to 0.6.0 so supported old torch still resolves
The >=0.8.0 floor conflicts with the torch this install pins in its constraints
file. Unsloth supports torch>=2.4, but llm-compressor 0.7.0+ require torch>=2.7
(0.10+ need >=2.9, 0.12+ need >=2.10). On a supported torch 2.4-2.6 box pip then
has no candidate in [0.8.0, 1.0) and FP8/FP4 export fails before quantization.
Lower the floor to 0.6.0 (its metadata only needs torch>=1.7), which never
conflicts with any supported torch. pip still prefers the newest compatible
release, so modern torch continues to get the latest 0.x (0.12.0). The <1.0
ceiling that blocks an inflated-version supply-chain jump is unchanged.
Add a regression test asserting the floor stays <= 0.6.0.
* Cap llm-compressor auto-install ceiling to a vetted minor (<0.13)
A bare <1.0 ceiling still admits any 0.x, so an inflated "0.999.0" served by a
compromised or misconfigured index would win pip's highest-version selection --
the same dependency-confusion this pin is meant to block. Cap the ceiling to the
current vetted minor (<0.13) so that jump is blocked; bump it deliberately, after
vetting, when a newer llm-compressor is needed (e.g. for a brand-new architecture
scheme). Current new models are unaffected: 0.12.0 is < 0.13 and supports them.
The 0.6.0 floor (torch>=1.7 compatible) is unchanged, so resolution still works
across Unsloth's whole supported torch range (2.4 -> 0.6.0 ... 2.12 -> 0.12.0).
Add a regression test asserting the ceiling admits the current vetted release but
blocks an inflated 0.x and the next major.
* Trim comments in the llm-compressor pin (comment-only, no code change)
* Cap llm-compressor auto-install to the exact vetted patch (<=0.12.0)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Condense the verbose comments and docstrings added by the recent
chat template, GPT-OSS detection, PEFT tensor-parallel, and Studio
inference proxy fixes. Comments and whitespace only; no code changes.
* Add FP8/FP4 compressed export to save_pretrained_merged
Adds compressed-tensors export (for vLLM) to save_pretrained_merged /
push_to_hub_merged via llm-compressor, alongside the existing lora /
merged_16bit / merged_4bit / gguf / torchao paths:
model.save_pretrained_merged("model", tokenizer, save_method="fp8")
Supported save_method values: fp8 (FP8_DYNAMIC), mxfp4, nvfp4 (W4A4) and
mxfp8. The LoRA is merged to 16bit at save_directory, then a quantized
checkpoint is written to save_directory + "-<fmt>". nvfp4 needs a small
calibration set (defaults to ultrachat, overridable via calibration_dataset).
Notes:
- llm-compressor is installed lazily on first use, pinning the current torch
and transformers via a constraints file so they are not upgraded (a plain
install pulls transformers>=5 and breaks Unsloth).
- Quantization runs in a separate process (unsloth/_compressed_quantize.py,
launched by file path) so Unsloth's transformers attention patches do not
interfere with the forward llm-compressor runs during calibration, mirroring
how GGUF export shells out to llama.cpp.
- mxfp8 needs a newer llm-compressor (transformers>=5); it is recognised and
raises a clear error until that stack is available.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: main-process guard, calibration subsampling, tokenizer + dtype handling
- Route the 16bit merge through unsloth_generic_save for both LoRA and full
finetuned models, so non-PEFT models are written in 16bit consistently
instead of saving the original (possibly quantized) weights directly.
- Honor is_main_process: only the main process quantizes and writes the
compressed output, so distributed ranks do not race on the same dirs.
- Subsample an in-memory calibration Dataset before save_to_disk so large
training sets are not fully copied to a temp dir.
- Tolerate a missing tokenizer in the converter (data-free exports); still
require one for calibration based schemes.
- Open config.json via a context manager in both files.
- Drop the redundant nvfp4 entry from the unsupported-name check (fp4 covers it).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add direct LoRA to GGUF export and harden FP8/FP4 compressed export
- Run llm-compressor install and scheme check before the 16bit merge so
unsupported schemes (e.g. mxfp8) fail fast without writing a checkpoint
- Only the main process installs, merges, quantizes and uploads; isolate
hub pushes to a temp dir and clean all temp dirs in a finally
- Forward standard save kwargs (state_dict, max_shard_size, ...) to the merge
- Fall back to the first dataset split for Hub calibration ids
- Export LoRA adapters to GGUF via convert_lora_to_gguf.py: modernize
save_pretrained_ggml/push_to_hub_ggml and add save_method="lora" to
save_pretrained_gguf/push_to_hub_gguf; resolve base from the adapter config
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix LoRA GGUF shell-injection test and compressed export trailing-slash path
- Update tests/saving/test_save_shell_injection.py for the new delegation: the
LoRA to GGUF conversion now lives in _unsloth_save_lora_gguf, so assert it
passes argv as a list with no shell=True and that the legacy ggml wrappers
delegate to it instead of calling subprocess.Popen directly
- Normalize the local save_directory before building the "<dir>-<fmt>" sibling
so a trailing slash no longer nests the compressed output inside the 16bit dir
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Polish FP8/FP4 and LoRA GGUF export after review
- Warn (not silently downgrade) when an explicit quantization_method is not a
valid LoRA GGUF outtype; default stays f16
- Correct the inference hardware note: MXFP8 is 8-bit (cc >= 8.9), only FP4
needs Blackwell for full activation quantization
- Document that a local fp8/fp4 save keeps the 16bit merge at save_directory
and writes the quantized checkpoint to save_directory + "-<fmt>"
* Use sequential calibration pipeline and validate Hub access early
- nvfp4 calibration no longer forces the memory-hungry "basic" pipeline. The
quantization runs in a clean subprocess, so llm-compressor's default
sequential pipeline (layer-by-layer onloading) works and lets large models
that do not fit at once still calibrate; fall back to "basic" only if tracing
fails
- For push_to_hub compressed exports, create/validate the repo up front so a bad
token or denied repo fails before the merge and quantization instead of after
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden compressed export: explicit sequential pipeline, base-tokenizer calibration, GPU memory
- nvfp4 calibration now passes pipeline="sequential" explicitly (layer-by-layer
onloading) instead of relying on the inferred default, with a "basic" fallback
- Calibration datasets with a messages column no longer require a chat template:
base / non-chat tokenizers fall back to concatenating message contents
- Free the in-memory model's CUDA memory before the quantize subprocess loads its
own copy from disk (best-effort, single-device non-quantized only; restored
afterward), so a single GPU need not hold two copies at once
- Create the calibration temp dir in the system temp location instead of next to
the save directory, avoiding stray dirs in the workspace
* Free the failed calibration model before the basic-pipeline retry
In the sequential -> basic NVFP4 fallback, release the partially-processed model
and clear the CUDA cache before loading a fresh copy, so the retry does not
transiently hold two model copies on the GPU.
* Harden calibration data handling and compressed-export edge cases
- Calibration messages without a chat template now handle multimodal (list)
content, None content, and null message rows instead of crashing on join
- Raise a clear error when the calibration dataset is empty after subsampling
- Reset llm-compressor's global session before freeing the model in the
sequential -> basic NVFP4 fallback, so the old model is actually released
- LoRA GGUF export accepts a single-element list quantization_method
- Attach datasets metadata to the pushed repo on compressed hub exports
- Warn (instead of silently) if the model cannot be restored to its device
- Raise a clear error if the LoRA base model id cannot be determined
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Handle DatasetDict calibration, MoE routers, and MTP models in compressed export
- Reduce an in-memory DatasetDict calibration set to a single split before row
subsampling, so save_to_disk does not copy every split to the temp dir
- For MoE models, keep the router/gate unquantized and pass
moe_calibrate_all_experts so every expert is calibrated
- Warn when a model carries MTP / speculative-decoding tensors that the
compressed export does not include
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Support many more compressed-tensors schemes and address review
- Expand save_method to cover the full set of compressed-tensors preset schemes:
FP8 (dynamic/static/block), INT8, W8A8, W8A16, W4A16(+asym), W4A8, W4AFP8,
MXFP4(+A16), NVFP4(+A16), plus the gated MXFP8; calibration is used only for
the static-activation schemes (FP8 static, NVFP4)
- Broaden the near-miss save_method error to cover int/w-prefixed names
- MoE: also keep the Qwen shared-expert gate unquantized
- Strip non-model-input columns from already-tokenized calibration data so the
collator does not choke on a leftover messages column
- Forward the Hub token to the LoRA converter and the quantize subprocess so
gated/private base models and calibration datasets work without a global login
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Collapse compressed-tensors export help line so ruff-format converges
The print line in print_quantization_methods needed two ruff-format passes to
reach a fixpoint (merge implicit string concat, then collapse the single-arg
print). pre-commit.ci applies one pass per run, so it kept reformatting. Land
the converged single-line form directly.
* Add CPU-only regression tests for the export API
Cover all export paths without a GPU, for slow CPU-only CI:
- pure-function checks of the compressed-tensors scheme registry and save_method
normalization (aliases, calibration flags, near-miss errors)
- AST checks that every merged saver dispatches compressed export, the GGUF savers
expose the lora branch, torchao routes PTQ/QAT, the public methods stay attached,
and the export subprocesses remain shell-safe (argv list, sys.executable, no shell)
- monkeypatched dispatch checks that fp8/nvfp4/merged_16bit, the LoRA-GGUF outtype
resolution, and torchao PTQ/QAT reach the right helper with the right arguments
* Run the CPU-only export tests in consolidated CI
tests/saving is --ignored by the Repo tests (CPU) job, so the new GPU-free export
tests are added by path to consolidated-tests-ci.yml (collection sanity + Bucket-A run),
alongside the existing CPU saving tests, so they actually execute on CPU CI.
* Add GPU GGUF export + llama-cli inference smoke test
tests/saving/test_gguf_export_and_inference.py: skipif no CUDA. Trains a tiny
phrase-imprinting LoRA, exports a full-model q8_0 GGUF (merge -> convert_hf_to_gguf
-> llama-quantize), asserts a valid GGUF (magic + size), and - when a llama-cli
binary is available - runs one bounded generation (byte cap + watchdog kill) and
asserts the trained phrase round-trips through HF -> GGUF -> quantize -> inference.
The llama-cli step skips gracefully since the export only builds llama-quantize.
* Fix variant mismatch in compressed (FP8/FP4) export
save_pretrained_merged(..., save_method=fp8/nvfp4, variant=...) forwarded
the variant into the intermediate 16bit merge, so Transformers wrote
variant-named shards (model.<variant>.safetensors). The converter
subprocess then reloaded that directory with the default weight filenames,
so the compressed export failed after doing the merge.
Pop the variant out of the intermediate merge (internal staging that the
subprocess reloads with default names) and forward it via --variant so it
is applied to the final compressed checkpoint instead. Add a CPU AST guard
for the contract.
* Harden export paths from review
- install_llm_compressor: fall back to uv pip when this interpreter has no
pip seeded (uv-created/relocatable venvs), instead of failing with
No module named pip.
- LoRA GGUF export: if convert_lora_to_gguf.py is missing (a prebuilt or
reused CWD llama.cpp install carries binaries but not the converter
script), force a dedicated source checkout that ships it.
- push_to_hub_gguf(save_method=lora): return on non-main ranks, matching the
local save_pretrained_gguf lora branch, so only rank 0 converts/uploads.
- compressed export VLM detection: require a vision_config or a
ForVisionText2Text architecture; a bare *ForConditionalGeneration also
matches text seq2seq models (T5/BART/Whisper) and is no longer treated as
a VLM on its own.
- GGUF GPU smoke test: drop SFTConfig(max_length=1024), which raises under
newer TRL padding-free training; length enforcement is not needed here.
* Add imatrix option to GGUF export, enabling IQ low-bit quants
save_pretrained_gguf / push_to_hub_gguf gain imatrix_file:
None -> no imatrix (unchanged)
'/path' -> pass to llama-quantize --imatrix (a *.gguf_file is renamed to *.gguf)
True -> download the upstream unsloth/<base>-GGUF imatrix (imatrix_unsloth.dat or
.gguf_file), raising a clear error if none exists
An importance matrix unlocks the IQ low-bit quants (iq2_xxs, iq4_xs, ...), which were hard
disabled before. They are gated: requesting one without an imatrix raises a clear error.
- _resolve_imatrix_file resolves path/True (PEFT base first, normalized via get_model_name,
derives unsloth/<base>-GGUF, copies out of the HF cache before renaming *.gguf_file).
- IMATRIX_QUANTS registry replaces the old commented-out IQ entries; save_to_gguf accepts a
resolved imatrix and threads it into the quantize calls.
- The --imatrix flag is emitted by unsloth_zoo's quantize_gguf (companion change). save.py
fails fast with an upgrade hint if the installed unsloth_zoo lacks the imatrix kwarg.
Tests: tests/saving/test_imatrix_export.py (CPU: resolution, repo derivation, IQ gate,
--imatrix wiring) wired into CI; tests/saving/test_gguf_export_and_inference.py extended with
GPU iq2_xxs/iq4_xs export + inference. Verified end to end on Llama-3.2-1B: imatrix
auto-downloaded, iq2_xxs/iq4_xs exported and run via llama.cpp.
Note: requires the companion unsloth_zoo quantize_gguf imatrix change.
* Address imatrix/compressed review feedback: unsloth org GGUF repo, fail-fast, calibration split
- imatrix auto-resolve (imatrix_file=True): derive the upstream repo as unsloth/<base>-GGUF
instead of <org>/<base>-GGUF, so official bases (e.g. meta-llama/Llama-3.1-8B-Instruct) find
the matching Unsloth GGUF imatrix repo rather than failing on a nonexistent meta-llama/...-GGUF.
- Resolve/validate the imatrix before the 16-bit merge in save_pretrained_gguf, so a bad path or
an unavailable upstream imatrix fails fast instead of after a long, multi-GB merge.
- Compressed calibration: when a Hub dataset has no "train" split, resolve the first split name
and slice it, instead of materializing the whole dataset just to take num_samples rows. Keeps
the original materialize-then-subselect path as a last resort.
Tests: add unsloth/<base>-GGUF mapping for an official base id, and create the imatrix file in the
quantize_gguf flag test (quantize_gguf now validates the imatrix exists).
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Windows installer: fix DiskPart UAC, drive-root cache, spurious rename warning, CPU-base messaging
amd-smi gate (DiskPart UAC mid-install): the AMD torch wheel ships hipInfo.exe
inside the venv, and the bitsandbytes fix prepends that venv Scripts dir to PATH.
shutil.which("hipinfo") then found it and flipped _amd_smi_allowed() to True, so
the post-install AMD probe fell through to `amd-smi list` (the venv hipInfo failed
to report gcnArchName, which is why the arch came from the GPU-name table) and
amd-smi elevated, popping the DiskPart UAC. Fix: a hipinfo resolved inside the
active venv (sys.prefix) is the torch-wheel binary, not a HIP SDK, and must not
open the gate. Mirrored in install_python_stack.py, install_llama_prebuilt.py, and
backend utils/hardware/amd.py (the runtime VRAM poller had the same latent prompt).
TORCHINDUCTOR_CACHE_DIR: move from C:\tc to <StudioHome>\TORCHINDUCTOR_CACHE_DIR so
the inductor/Triton cache lives under the user's Studio home, not the system drive
root. Long paths are already enabled above so deep inductor paths still fit.
unsloth.exe rename: skip the rename (and its "pip may fail with WinError 32"
warning) when SKIP_STUDIO_BASE=1. In the install.ps1 flow base packages are not
reinstalled, so unsloth.exe is never rewritten; the self-rename only failed because
setup runs via unsloth.exe (the running launcher holds its own file). The
'studio update' flow still attempts it.
CPU PyTorch messaging: clarify that the CPU base is temporary and setup replaces it
with GPU ROCm wheels, and print an explicit "GPU ROCm PyTorch installed" line after
the AMD wheels land, so the log makes clear the final install is GPU-accelerated.
Adds two regression tests covering the venv-internal vs external hipInfo gate.
Verified end-to-end on a Strix Halo box (Radeon 8060S / gfx1151): install.ps1
--local from this branch completed exit 0 with no DiskPart prompt, no rename
warning, the cache under the Studio home, and "GPU ROCm PyTorch installed
(gfx1151)"; Studio then booted and detected "ROCm (HIP 7.13.99004) -- AMD Radeon
8060S Graphics".
* Windows installer: drop the unreliable unsloth.exe rename and its WinError 32 warning
setup.ps1 used to rename the running unsloth.exe out of the way before the
base-package upgrade so pip could replace it. That rename never actually
worked: setup runs *via* unsloth.exe, so renaming our own running
uv-trampoline launcher failed with a sharing violation (WinError 32) and only
printed a scary 'could not rename unsloth.exe; pip may fail with WinError 32'
warning on every Windows install and update.
It also was not needed. pip tolerates a running/locked console-script .exe: it
moves the old one aside and writes the new one. The base upgrade routes through
pip on Windows, so the upgrade succeeds (or, in the install.ps1 flow with
SKIP_STUDIO_BASE=1, the base is not touched at all) and unsloth.exe is left
intact either way.
Removing the rename block and its failed-install restore block removes the
false warning for all Windows devices in both the install and update flows.
* Windows installer: gate venv-internal hipInfo.exe in PowerShell amd-smi probe; harden venv path checks
Follow-up to PR #6296.
- install.ps1 and setup.ps1: ignore the AMD torch wheel hipInfo.exe that lives
inside the Studio venv when probing for a HIP SDK, so amd-smi no longer reopens
the DiskPart UAC during install/update. Mirrors _path_inside_venv in the Python
installers, which already do this.
- amd.py, install_llama_prebuilt.py, install_python_stack.py: normcase the venv
containment check (Windows paths are case-insensitive) and run the
HIP_PATH/ROCM_PATH candidate through it too.
- setup.ps1: fall back to a short TORCHINDUCTOR cache dir when long paths are
unavailable, and create the dir wildcard-safely.
- tests: isolate sys.prefix in the gate helper, add HIP_PATH/ROCM_PATH cases, and
assert the PowerShell venv exclusion.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Windows installer: install ROCm PyTorch directly for a known AMD arch
When the GPU arch is known (name-inferred from the GPU-name table) but ROCm
could not be probe-verified (no HIP SDK, no amd-smi), the bootstrap installed
a CPU PyTorch base that setup.ps1 then force-reinstalled as ROCm. The
repo.amd.com wheels bundle their own runtime (no HIP SDK required), which
setup.ps1 already relies on, so the CPU base was a pure wasted download/install.
- Gate the ROCm index on a known arch, not only on probe-verified ROCm, so a
mapped arch installs ROCm torch directly. Unmapped arches and no-GPU hosts
still get CPU (unchanged).
- Fall back to a CPU base if the ROCm-index install fails, so a transient
repo.amd.com outage does not abort the install (setup.ps1 retries ROCm).
- Correct the stale comment that claimed ROCm wheels need a confirmed HIP SDK.
- Add a regression test for the arch-based gate.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Windows installer: correct the unsloth.exe rename-removal comment
The comment claimed the base upgrade 'routes through pip on Windows' and that
pip 'moves the old unsloth.exe aside, then writes the new one'. That is not what
the code does. install_python_stack tries uv first; on a locked launcher uv
aborts and falls back to pip, but the pip fallback strips --upgrade-package and
base.txt lists only bare unsloth/unsloth-zoo, so pip finds them already
satisfied and no-ops. The running unsloth.exe is left intact at its current
version either way. Reword the comment to describe the real uv-first /
pip-fallback-no-op behavior. No functional change.
* Windows installer: close two gaps in the venv-internal hipinfo exclusion
Review follow-up. The amd-smi/DiskPart gate could still reopen in two cases:
- setup.ps1 ran the HIP probe long before $VenvDir is assigned, so without
VIRTUAL_ENV (the `unsloth studio update` path) $venvRoots was empty and the
venv-internal hipInfo.exe was not recognized. Seed the venv root from
UNSLOTH_SETUP_PYTHON and the default Studio home too (both installers).
- The HIP_PATH/ROCM_PATH candidate was accepted without the venv filter, so an
env var pointing into the venv (AMD wheel) still set $HipSdkInstalled. Run
Test-HipinfoIsVenvInternal on the candidate as well (both installers).
Extend the PS gate test to assert both. Both .ps1 parse clean; install tests
pass (the venv-internal / HIP probe coverage at 359 passed).
* Windows installer: correct the CPU-base message for arches with no ROCm wheels
After gating the ROCm index on a known arch, a mapped arch sets $ROCmIndexUrl
and installs ROCm directly, so it no longer reaches the "temporary CPU base"
branch. That branch is now reached only by a name-inferred arch with no ROCm
wheels (e.g. RDNA2 gfx103X), where setup.ps1 does NOT install ROCm. The old
text ("setup replaces it with GPU ROCm wheels ... the final install IS
GPU-accelerated") was therefore always wrong there. Say plainly that PyTorch
stays on CPU for this GPU.
* Windows installer: seed the venv-internal hipInfo check from a custom Studio home
Test-HipinfoIsVenvInternal seeded the venv root from VIRTUAL_ENV, VenvDir, the
setup python, and the default %USERPROFILE% path only. A standalone
`unsloth studio update` with a custom UNSLOTH_STUDIO_HOME (or STUDIO_HOME alias)
and none of those set would not recognize the venv hipInfo on PATH, reopening the
amd-smi/DiskPart gate. Seed the custom home too, in both installers, and assert
it in the gate test.
* Studio installer: resolve venv aliases and expand ~ in the hipInfo venv filter
Two review points on the amd-smi/DiskPart UAC gate:
1. _path_inside_venv compared os.path.abspath of sys.prefix and the hipInfo
path, which does not resolve symlinks, junctions, or 8.3 short names. A venv
reached through an aliased path then fails the check, so its bundled
hipInfo.exe is mistaken for an external HIP SDK and amd-smi runs (the
DiskPart prompt this fix exists to suppress). Switch to os.path.realpath in
all three copies (amd.py, install_llama_prebuilt.py, install_python_stack.py).
2. setup.ps1's early venv-internal hipInfo probe seeded the venv root from a
custom Studio home (UNSLOTH_STUDIO_HOME / STUDIO_HOME) without expanding a
leading ~, while the canonical resolver does. With a tilde form,
[IO.Path]::GetFullPath kept the literal ~ relative to cwd, so the custom-home
hipInfo escaped the filter and reopened the gate. Expand ~ in the probe the
same way as the resolver.
tests/studio/install/test_pr5940_followups.py: 30 passed (adds a symlink
realpath case and a setup.ps1 tilde-expansion guard).
* Studio installer: mirror the hipInfo venv filter and ROCm wheel pins into install.ps1
Follow-up review on the same install.ps1 paths:
1. install.ps1's venv-internal hipInfo probe (Test-HipinfoIsVenvInternal)
seeded the venv root from a custom Studio home without expanding a leading
~, unlike the canonical resolver and setup.ps1. A tilde form left
[IO.Path]::GetFullPath with the literal ~ (relative to cwd), so the
custom-home hipInfo escaped the filter and reopened the amd-smi/DiskPart
gate. Expand ~ in the probe, matching the setup.ps1 fix.
2. The AMD ROCm path installed torchvision/torchaudio bare while pinning torch
to below 2.12. AMD's per-arch index publishes the companions independently
and may ship torchvision 0.27 (for torch 2.12) before removing 0.26, so a
bare resolve can pick an ABI-incompatible set and fall back to CPU. Add
torchvision/torchaudio floor maps and pass the pinned specs, mirroring
setup.ps1 and install_python_stack.py.
3. The ROCm-to-CPU fallback torch install used Invoke-InstallCommand (no
retry), the only torch step in the file without it. Switch to
Invoke-InstallCommandRetry so the recovery path survives a transient index
failure.
tests/studio/install/test_pr5940_followups.py: 33 passed (parametrized tilde
check over both installers, a torch/companion floor-map parity test, and a
CPU-fallback retry guard).
* Studio installer: scan all PATH hipinfo so the venv copy can't shadow a real HIP SDK
The amd-smi HIP-SDK probe used shutil.which("hipinfo") / Get-Command hipinfo,
which return only the first hit on PATH. The AMD torch wheel ships hipInfo.exe
inside the venv and the bnb fix (plus the Studio backend) prepend the venv
Scripts dir to PATH, so that venv-internal copy lands first. When a real HIP SDK
hipinfo sits later on PATH with HIP_PATH/ROCM_PATH unset, the first-hit probe
stopped at the venv copy, treated it as "not a HIP SDK", and closed the amd-smi
gate -- AMD users in that PATH-only SDK setup lost amd-smi telemetry and could
fall back to CPU. Scan every PATH entry and keep the first hipinfo that is not
venv-internal; only the venv copy is ignored, so the UAC/DiskPart suppression is
unchanged.
Applied to all three Python copies (install_llama_prebuilt.py,
install_python_stack.py, backend/utils/hardware/amd.py) via a new
_external_hipinfo_on_path helper, and both PowerShell callers (install.ps1,
setup.ps1) now use Get-Command hipinfo -All filtered by Test-HipinfoIsVenvInternal.
tests/studio/install/test_pr5940_followups.py: 36 passed (real-PATH scan tests, a
shadow-regression test for the exact venv-first ordering, and a parity check that
every Python copy uses the scanning helper).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio uninstallers: fix leftovers (false "removed", shared icon, llama lock)
Auditing a dual native+WSL uninstall on a real device surfaced three leftovers:
1. uninstall.ps1 removed the data dir (which holds unsloth.ico) before the
shortcuts that reference that icon, so Explorer's icon cache briefly held it
open. Remove-Item -Recurse reported success yet left the locked file, and the
dir was never re-attempted, so it orphaned with a false "removed" log.
_RemovePath now verifies the path is actually gone (retrying transient locks)
and reports honestly, and the data dir is re-swept after the shortcuts go.
2. install.sh writes a shared unsloth.ico to %LOCALAPPDATA%\Unsloth Studio for
the WSL shortcut, but uninstall.sh never removed it, orphaning the icon (and
dir) after a WSL uninstall. uninstall.sh now drops that icon and the dir when
empty, in both the powershell.exe and drvfs-fallback paths.
3. ~/.unsloth/.llama.cpp.install.lock was never removed, so the rmdir of
~/.unsloth failed and the dir lingered. Both uninstallers now remove the lock.
Verified by running both uninstallers on a real dual install: device fully clean
(no install dirs, shortcuts, PATH/registry entries, shared icon, or lock left).
* install.sh: auto-route Strix Halo WSL to an existing Ubuntu 24.04
ROCm-on-WSL is the GPU runtime for Strix Halo and only targets Ubuntu
24.04. When the installer runs in a newer default distro (e.g. 26.04) it
cannot enable the GPU and silently falls back to CPU. If a 24.04 distro
already exists, re-run the install there and stop in the current one so the
GPU path is taken without the user having to know about the distro
requirement.
Runs before venv creation so the wrong distro is left untouched, guards
against re-route loops via UNSLOTH_WSL_REROUTED, leaves a working ROCm
distro alone (librocdxg present), and skips the GGUF-only / opt-out /
non-Strix cases. When no 24.04 distro exists we keep today's behaviour:
continue to CPU and print the `wsl --install Ubuntu-24.04` guidance, never
auto-downloading a distro.
Adds tests/sh/test_strixhalo_wsl_reroute.sh (hermetic: extracts the
function, rewrites its paths to fixtures, mocks wsl.exe) covering the full
decision matrix, wired into tests/run_all.sh.
* uninstall.ps1: keep shared unsloth.ico for a surviving WSL shortcut
A dual native+WSL install shares %LOCALAPPDATA%\Unsloth Studio\unsloth.ico:
install.sh points the WSL shortcut's icon there while the native install owns the
dir. The native uninstaller removed the whole dir unconditionally, so uninstalling
native while keeping WSL left the WSL shortcut with a blank icon. The old code only
avoided this when Explorer happened to hold the icon open, which is unreliable; on a
real dual install the dir was deleted and the WSL shortcut went blank.
_RemoveDataDirKeepingWslIcon now scans the Start Menu + Desktop for a surviving
"Unsloth Studio (WSL ...).lnk" and, if found, removes everything in the data dir
except unsloth.ico (keeping the dir) instead of deleting it; with no WSL shortcut it
removes the dir as before. uninstall.sh still drops the icon and the empty dir when
WSL itself is uninstalled, so every uninstall order ends clean.
Adds tests/studio/test_uninstall_dual_install_icon.ps1 (AST-extracts the helper and
runs it against a temp dir with controlled shortcut dirs) covering the dual,
native-only, empty, and missing-dir cases, wired into the windows-inference smoke
workflow. Verified on a real dual install: native uninstall now keeps unsloth.ico
and the WSL shortcut's icon stays intact.
* installer: condense AMD/ROCm code comments (no behavior change)
Tighten the comments added for the Strix Halo native+WSL installer work so
they are shorter and clearer without losing intent: the venv-internal hipInfo
amd-smi gate, the ROCm torch/companion floor maps, the WSL 24.04 reroute, and
the dual-install uninstall icon handling. Comment-only; code paths unchanged.
107 insertions, 166 deletions across 11 files.
* install.sh: run the Strix Halo WSL reroute before any STUDIO_HOME write
The reroute fired after mkdir -p "$STUDIO_HOME" and the legacy-venv migration,
so rerouting 26.04 -> 24.04 left an empty ~/.unsloth/studio stub in the origin
distro (and ran venv migration in the distro about to be abandoned). Move the
reroute ahead of the venv section so the origin distro is left untouched, matching
the function's own comment. Behavior is identical on every non-reroute path.
* installer: fix ROCm CPU-fallback, hipinfo gate edge cases, uninstall icon, WSL 22.04
- install.ps1: clear $ROCmIndexUrl/$ROCmTorchFloor after the CPU fallback so the
flavor-repair block does not retry the failed ROCm index and abort the install;
pin the ROCm companion specs ($visionSpec/$audioSpec) in the repair path too.
- install.ps1 + setup.ps1: skip a bare drive root in Test-HipinfoIsVenvInternal so a
non-venv UNSLOTH_SETUP_PYTHON does not match the whole drive; iterate
HIP_PATH/HIP_PATH_57/ROCM_PATH and take the first non-venv hipinfo.
- amd.py, install_llama_prebuilt.py, install_python_stack.py: strip surrounding
quotes from PATH entries before probing for hipinfo.
- install.sh: pipefail the WSL reroute curl|sh; do not reroute supported Ubuntu 22.04.
- uninstall.sh: keep the shared unsloth.ico while any Unsloth shortcut (native or
another WSL distro) still references it, in both the powershell and drvfs paths.
- tests: regression coverage for all of the above.
* installer: forward reroute options, guard ROCm bootstrap, harden hipinfo gate
- install.sh: forward the caller's --package/--python/--verbose/--tauri and a custom
UNSLOTH_STUDIO_HOME into the WSL reroute (was a bare default install); bail on
--local; run the reroute BEFORE dependency/uv install so the origin distro is left
untouched; set UNSLOTH_SKIP_ROCM_WSL_SETUP after a failed reroute so the later
ROCm-on-WSL bootstrap does not install into the unsupported origin distro.
- install.ps1 + setup.ps1: Get-Command hipinfo -CommandType Application so only real
executables match (not an alias/function named hipinfo).
- uninstall.ps1: guard $env:APPDATA when building the default shortcut search dirs.
- tests: cover option forwarding, --local bail, the bootstrap guard, and the gate change.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* installer: guard origin ROCm bootstrap on every CPU-only fallback; harden ~ expansion
WSL reroute: the no-wsl.exe, no-24.04-target and --local fallbacks all tell the
user the install continues CPU-only, but only the failed-reroute branch set
UNSLOTH_SKIP_ROCM_WSL_SETUP=1. The later _maybe_bootstrap_rocm_wsl gate keys off
that flag, so the other three branches could still install ROCm into the
unsupported origin distro (e.g. 26.04). Set the skip guard on all of them.
Forward UNSLOTH_ROCM_WSL_AUTO into the reroute so a Tauri/consented GPU bootstrap
carries through to the rerouted 24.04 child instead of dropping to the prompt path.
install.ps1/setup.ps1: guard the venv-probe ~ expansion on a non-empty
$env:USERPROFILE so Join-Path does not throw on a profile-less service account.
Tests: add no-wsl.exe and UNSLOTH_ROCM_WSL_AUTO reroute cases, the USERPROFILE
guard assertion, and route shell-test fixtures through a single trap-cleaned root.
* installer: pin + soften Windows ROCm Python repair, reroute to 22.04, harden gates
install_python_stack.py: the Windows AMD ROCm repair in _ensure_rocm_torch()
installed bare torch/torchvision/torchaudio via the fatal pip_install -- the same
asymmetry already fixed on the PowerShell side. A transient repo.amd.com failure
could abort the whole install even after install.ps1/setup.ps1 fell back to CPU.
Pin companions per-arch (gfx120X/Strix -> the rocm7.2 trio, mirroring the PS floor
maps) and make the retry nonfatal: keep the existing build and let the user re-run
update to retry ROCm, so the chain install.ps1 -> setup.ps1 -> stack stays CPU-safe.
install.sh: reroute now targets an installed Ubuntu 24.04 OR 22.04 (24.04 preferred);
both are AMD-supported for ROCm-on-WSL, matching the leave-alone set, so a box with
only 22.04 reaches the GPU instead of staying CPU-only.
install.ps1/setup.ps1: a bare ~ for UNSLOTH_STUDIO_HOME left an empty Join-Path child
(PS 5.1 throws); fall back to USERPROFILE directly and only join a real remainder.
_path_inside_venv (amd.py + both installers): guard a root-dir sys.prefix so commonpath
can't classify every path on the drive as venv-internal (defensive; venv never at root).
uninstall.sh: guard an empty LOCALAPPDATA in the PS-interop icon cleanup (mirror APPDATA).
Tests: add 22.04-target reroute cases, Windows ROCm pin+nonfatal coverage (text +
behavioral), root-dir guard coverage, and bare-~/LOCALAPPDATA guard assertions.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* install.sh: match WSL reroute target by exact distro name, not substring
The 24.04/22.04 reroute target was chosen with grep -F (substring), so a custom
distro such as 'Ubuntu-24.04-test' (with no exact Ubuntu-24.04) was picked as the
target; the later 'wsl -d Ubuntu-24.04' then fails and the Strix Halo install stays
CPU-only. Match whole lines (grep -ixF) and reuse the matched name so only a real
Ubuntu-24.04/22.04 is targeted. Adds substring-rejection + exact-vs-custom tests.
* install.sh: keep the WSL reroute target to Ubuntu 24.04 (helper-supported only)
The ROCm-on-WSL bootstrap (scripts/install_rocm_wsl_strixhalo.sh) dies on any
VERSION_ID other than 24.04 and pins the noble repo, so treating 22.04 as
GPU-supported let the parent report a successful reroute while the child fell
back to CPU. Drop 22.04 from the supported set and the reroute target list;
24.04 stays the sole target (keeping the exact whole-line distro match). An
already-working ROCm on any other version is still left alone by the librocdxg
check above.
tests: reroute 22.04 cases updated to the 24.04-only behavior; make the
"no wsl.exe" case hermetic so a real host wsl.exe can't leak in on dev boxes;
stop the tauri exit-order check from mis-flagging the reroute helper's
[ "$TAURI_MODE" = true ] && ... --tauri one-liner.
* installer: tighten comment wording across the Strix Halo install/uninstall paths
Condense the verbose multi-line comment blocks (amd-smi hipinfo gate, ROCm
torch install + CPU fallback, WSL reroute, uninstall icon-keep) into fewer,
clearer lines. Comments and a few docstrings only; no code, logic, or
behavior change. Verified with bash -n, the PowerShell parser, and ast.parse,
and the installer test suite still passes.
* add AGPL-3.0 SPDX headers to the .sh/.ps1 scripts missing them
Every shell and PowerShell script under the Studio/installer surface now
carries the standard SPDX-License-Identifier: AGPL-3.0-only + copyright
header (after the shebang where present): the installer (install.sh,
install.ps1), build.sh, the .github and src-tauri scripts, the installer
test suite, and the moe kernel test. Header-only, line endings preserved;
bash -n, the PowerShell parser, and the installer tests all pass.
* installer: drop the duplicate AGPL header from install.sh and install.ps1
Both already carry an SPDX-License-Identifier: AGPL-3.0-only header below
their usage comment block; the prior header pass added a second one at the
top because it only scanned the first few lines. Remove the duplicate so each
file keeps a single original header.
* installer: force-reinstall CPU fallback torch; propagate Tauri NEED_SUDO from reroute
install.ps1/setup.ps1: when the AMD ROCm wheel install fails and we fall back to a
CPU base, force-reinstall the torch/vision/audio triplet. A failed ROCm install can
leave an unpinned ROCm torch (e.g. 2.10.0+rocm on gfx110X/gfx90a) that still
satisfies the CPU torch>=2.4,<2.11.0 range, so without --force-reinstall uv keeps the
ROCm build and only swaps the companions -- a mismatched venv the flavor-repair block
won't fix. setup.ps1 scopes the forced reinstall to the ROCm-fallback path
() so the genuine CPU-only install stays fast.
install.sh: the Strix Halo WSL reroute treated every nonzero child exit as a reroute
failure and fell back to CPU. In --tauri mode the child uses exit 2 ([TAURI:NEED_SUDO])
to ask the desktop app to elevate for the target distro; capture the child's exit code
and propagate exit 2 in Tauri mode (the child already printed the NEED_SUDO line)
instead of masking it. CLI mode still falls back to CPU on a generic failure.
Tests: reroute Tauri exit-2 propagation (and non-Tauri CPU-fallback) cases;
run_func now preserves the child exit code; force-reinstall assertions for both
PowerShell installers.
Note: codex's _rr_q apostrophe finding is a false positive -- the helper already
emits POSIX-correct 'O'\''Brien' and round-trips under both sh and bash.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* setup.ps1: fix $cpuForce array collapse in the ROCm->CPU torch fallback
An if-expression assignment ($cpuForce = if ($ROCmCpuFallback) { @("--force-reinstall") })
collapses the single-element array to a scalar string, so @cpuForce splatting enumerated
it character-by-character into broken single-letter args (- - f o r c e ...), which made
uv/pip reject the install and aborted the whole Studio setup on the AMD ROCm->CPU fallback
path. Build $cpuForce as a real array assigned outside the if-expression so the splat passes
a single --force-reinstall arg. Genuine CPU-only installs stay fast (empty array, no flag).
Test now asserts the array-build form and rejects the if-expression form.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* uninstall: remove the isolated Node.js runtime (~/.unsloth/node)
The isolated Node.js runtime (install_node_prebuilt.py, added with the managed-Node
change) installs to ~/.unsloth/node in default mode -- a sibling of studio, so deleting
<studio> leaves it behind (~200MB orphaned after uninstall). Both uninstallers already
remove the other default-mode siblings (llama.cpp/.cache/.staging); add node alongside
them. uninstall.ps1 also adds it to the handle-lock sweep so a held node.exe can't block
the delete. Env/custom mode nests node under the custom root, removed with that root.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix save crash for legacy list-form _tied_weights_keys (NemotronH)
transformers >= 5 save_pretrained reads module._tied_weights_keys.keys(),
which raises 'list' object has no attribute 'keys' for modules that still
declare the attribute as a list (e.g. NemotronH backbone.layers.N.mixer.*_proj),
crashing GGUF export and merged saves part-way through.
Coerce any legacy list/tuple _tied_weights_keys into the dict form transformers
5.x expects, mapping each key to itself. Only the keys are read (as dedup
patterns) so behaviour is preserved, and older transformers that iterate the
attribute directly see the same keys. The helper is idempotent and best-effort
so a save never fails over it. Called from unsloth_save_model,
unsloth_save_pretrained_gguf and unsloth_generic_save after tokenizer patching.
Adds version-independent unit tests covering list/tuple coercion, dict and
None/empty pass-through, idempotency and odd-object tolerance.
* Coerce empty/set _tied_weights_keys too
transformers only skips _tied_weights_keys when it is None, so an empty list,
tuple or set still reaches .keys() and raises the same AttributeError. Coerce
every non-dict container (including the empty case and sets) to a dict, and add
tests for empty/set inputs.
* Tighten comments in tied-weights save fix
* Scope tied-weights-keys coercion to the save call
Coercing legacy list-form _tied_weights_keys to {k: k} fixed the transformers
5 save crash, but persisted a self-mapping on the live model. transformers 5
re-ties from the dict's values, so a later resize/re-tie would no-op the tie
instead of pointing the output weights back at the input embeddings.
Replace the in-place mutation with a decorator that coerces before the save and
restores the originals afterwards (including on exception), so the save sees the
dict form transformers needs while the model keeps its original tie metadata.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim comments to be more succinct
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Reduce and tighten comments and docstrings in tests
Shorten verbose comments and docstrings across the test suite without
changing any test logic. Remove narration that restates the next line,
collapse long module and test docstrings to a single line, and drop banner
separators. Keep regression context (issue and PR references, run ids),
skip reasons, mocking and timing rationale, license headers, lint and type
directives, and commented-out code.
Comments and docstrings only: an AST signature check confirms no code,
assertions, or string literals changed, and the suite byte-compiles cleanly.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix UnicodeDecodeError on Windows reading subprocess output in save path
On Windows the default text encoding is the locale code page (cp1252), not
UTF-8. The text-mode subprocess calls in save.py (text=True /
universal_newlines=True) set no explicit encoding, so they decode
llama.cpp / Ollama output with cp1252. When a child process emits a byte
undefined in cp1252 -- e.g. 0x9d, which appears inside the UTF-8 encoding
of common punctuation / box-drawing glyphs and in non-ASCII file paths --
the read raises UnicodeDecodeError and aborts GGUF export.
Add encoding="utf-8", errors="replace" to all 8 text-mode subprocess calls.
errors="replace" also avoids silent mojibake for inputs whose bytes happen
to be valid-but-wrong in cp1252.
Add tests/saving/test_save_subprocess_utf8_encoding.py:
- an AST drift detector asserting every text-mode subprocess call in
save.py pins encoding="utf-8" (runs without importing torch/unsloth_zoo)
- a behavioural test reproducing the cp1252 failure and the utf-8 fix
Relates-to: #2660
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Trim and tighten code comments and docstrings across the repository. 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.
Preserves the source tokenizer's eos_token in tokenizer_config.json after merged saves so runtimes such as vLLM read the correct stop token. Centralized inside the patched tokenizer save_pretrained so all save paths (merged_16bit, GGUF, torchao, push_to_hub) benefit, with filename_prefix support.
Fixes#5386
* chore: fix typos in studio/backend/routes/models.py
* chore: fix typos in tests/saving/non_peft/test_mistral_non_peft.py
* chore: fix typos in tests/saving/non_peft/test_whisper_non_peft.py
* chore: fix typos in tests/saving/vision_models/test_index_file_sharded_model.py
* chore: fix typos in tests/saving/vision_models/test_push_to_hub_merged.py
* chore: fix typos in tests/saving/vision_models/test_save_merge_qwen2.5vl32B_model_ocr_benchmark.py
* chore: fix typos in tests/saving/vision_models/test_save_merge_vision_model_ocr_benchmark.py
* chore: fix typos in unsloth/import_fixes.py
* Split: keep only 6 file(s)
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* fix: patch CONTROL type for special tokens in sentencepiece GGUF export (fixes#5070)
When converting a Gemma 3 fine-tune to GGUF via save_pretrained_gguf,
tokens like <start_of_turn> (id=105) and <end_of_turn> (id=106) are
already present in the sentencepiece model but are typed as NORMAL (1)
instead of CONTROL (3). llama.cpp only recognises CONTROL tokens when
parse_special=True is active, so these tokens get BPE-split during
chat inference and the model produces garbage output.
fix_sentencepiece_gguf now reads tokenizer.json's added_tokens list and,
for any token with "special": true whose ID falls within the existing
sentencepiece vocabulary, updates its type from NORMAL to CONTROL before
writing the patched tokenizer.model to disk. The same CONTROL type is
also applied when new tokens are appended for the out-of-range case, so
both code paths are consistent.
* Wire fix_sentencepiece_gguf into tokenizer save path and guard np.diff
- save.py: call fix_sentencepiece_gguf inside unsloth_tokenizer_save_pretrained
after _preserve_sentencepiece_tokenizer_assets. The helper was previously
unreferenced in the repo, so the PR's CONTROL-type patch never actually ran
during save_pretrained_gguf.
- tokenizer_utils.py: add an early-return guard for len(added_tokens_ids) < 2
before the existing np.diff contiguity check. np.diff on a single-element
array returns [] and .min() raises ValueError, which would discard the new
in-vocab CONTROL patch; the guard flushes tokenizer.model first. Guard is
inserted before the existing lines (diff = np.diff(...) and the min/max
check) so their blame is unchanged.
Dropped the separate refactor to fold the four duplicated "if patched > 0:
write tokenizer.model" blocks into a helper because doing so re-indents
lines whose blame is "Formatting & bug fixes"; the duplication
remains the author's pattern.
* Fix review findings: negative token_id guard and np.diff single-element
- tokenizer_utils.py:481: add 0 <= lower bound to the special_token_ids
bounds check. Previously a negative token_id from tokenizer.json passed
'token_id < sentence_piece_size' and Python's negative indexing wrapped
tokenizer_file.pieces[-1] to silently corrupt the last piece to CONTROL.
- tokenizer_utils.py:513: replace the loop-1 'if len < 2: return' guard
(which was too broad: it silently skipped vocab extension for single-entry
added_tokens.json) with a pre-pass that substitutes a trivially-contiguous
2-element sentinel for the contiguity check, then restores the original
array before the append loop. Lines 519 ('diff = np.diff(added_tokens_ids)')
and 520-529 (min/max/boundary checks and early-return write blocks) are
left literally unchanged so blame remains intact.
* Restore real added_tokens_ids before min boundary check
Move the '_real_added_tokens_ids' restore above the
'added_tokens_ids.min() != sentence_piece_size' check. With the previous
order the sentinel [sentence_piece_size, sentence_piece_size + 1] was
still in scope when the min check ran, so any single-entry added_tokens
.json with an out-of-range start id (e.g. 99 when sentence_piece_size=2)
bypassed the boundary check and fell through to the append loop.
* Scope fix_sentencepiece_gguf to GGUF export path only
Previously wired fix_sentencepiece_gguf into unsloth_tokenizer_save_pretrained,
which is the generic monkey-patch replacement for every tokenizer.save_pretrained
call. That caused the GGUF-specific mutation (and the unconditional protobuf
import in fix_sentencepiece_gguf) to run on every LoRA / merged 16-bit /
push_to_hub / torchao save, where it has no purpose and can abort the entire
save if the protobuf runtime is unavailable.
- save.py: remove fix_sentencepiece_gguf call from unsloth_tokenizer_save_pretrained.
- save.py: add the call inside unsloth_save_pretrained_gguf immediately before
save_to_gguf, wrapped in try/except so a protobuf import failure logs a
warning and lets GGUF conversion proceed rather than aborting the save.
* Broaden special-token retag to USER_DEFINED and narrow save.py except
- tokenizer_utils.py:483: the in-vocab retag previously only promoted NORMAL
pieces to CONTROL, but the real Gemma tokenizer (e.g. unsloth/functiongemma
-270m-it) stores <start_of_turn>/<end_of_turn> as USER_DEFINED (type 4).
Extend the predicate to cover both NORMAL and USER_DEFINED so tokens marked
"special": true in tokenizer.json are promoted regardless of their current
sentencepiece type. Only tokens explicitly flagged special are touched, so
non-special USER_DEFINED pieces are unchanged; already-CONTROL pieces stay
unchanged. The warning message is generalised accordingly.
- save.py:2294: narrow the except clause from Exception to ImportError. The
loop-3 try/except was added to tolerate a missing protobuf runtime; leaving
it broad also swallows OSError/PermissionError mid-write, which would ship
a corrupted tokenizer.model to save_to_gguf. ImportError still covers the
protobuf case while letting I/O errors propagate to the outer save handler.
* Harden fix_sentencepiece_gguf: widen except, protobuf fallback, revert USER_DEFINED widen, guard entry id
- save.py:2294: widen except from ImportError back to Exception. The loop-4
narrowing let JSONDecodeError / KeyError / OSError / PermissionError from
fix_sentencepiece_gguf abort the entire GGUF export, a regression vs
pre-PR behavior. The outer save_to_gguf try/except still covers GGUF-side
failures; any fix-side failure now logs a typed warning and lets
conversion proceed.
- tokenizer_utils.py:445: the direct 'from transformers.utils import
sentencepiece_model_pb2' raises TypeError ("Descriptors cannot be created
directly") on modern protobuf runtimes. Prepend a sys.modules.setdefault
pre-population using transformers.convert_slow_tokenizer.import_protobuf()
so the subsequent from-import finds a compatible module via the module
cache. The original import line is left verbatim at its place as the
final resolver.
- tokenizer_utils.py:483: revert loop-4 widening; retag only NORMAL pieces
to CONTROL. Retagging USER_DEFINED pieces caused a concrete tokenization
regression where an intentionally-USER_DEFINED in-vocab special token had
its sentencepiece encoding broken ('<user> hello' changed from [11, 3, 8]
to [11, 0, 12, 21, 0, 8]). The PR's stated scope is the NORMAL->CONTROL
Gemma case; USER_DEFINED handling is deferred.
- tokenizer_utils.py:475: defensive guard around entry["id"]. A malformed
added_tokens entry missing the "id" field or with a non-int id is now
skipped rather than raising KeyError / inserting garbage.
* Add review tests for sentencepiece GGUF fix
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Co-authored-by: octo-patch <octo-patch@github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* [WIP] Fast inference for qwen3.5
* fix tokenizer not saving properly
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* gate tokenizer.model saving
* fix for gated/private models
* Fix tokenizer save review findings
- save.py:261 restore dict-based _TOKENIZER_MODEL_CACHE so negative
results are cached; the set() in 0129fb5e regressed non-SentencePiece
tokenizer saves to a fresh HfApi.model_info call on every checkpoint.
Don't cache on exception so gated/private repos can retry later with a
valid token.
- save.py:282 guard `repo_info.siblings` with `or []`; huggingface_hub
types this Optional and returns None for empty or new repos, which
made any() raise TypeError out of save_pretrained.
- save.py:3487 split push_to_hub into local save + _preserve + push so
uploaded tokenizer_config.json/tokenizer.model include the fix rather
than the unfixed copies written before the upload.
- save.py:3352 call patch_saving_functions on tokenizers passed to
unsloth_save_pretrained_torchao to match the other three save
entrypoints; previously torchao saves skipped the preservation patch.
* Fix push_to_hub repo_id conflict and torchao token forwarding
- save.py:3493-3496 pop `repo_id` from kwargs (defaulting to
`save_directory`) before calling `self.push_to_hub(repo_id, **kwargs)`.
The previous `self.push_to_hub(save_directory, **kwargs)` passed
`save_directory` as the first positional `repo_id` while also
forwarding a user-supplied `repo_id` through kwargs, raising
`TypeError: got multiple values for argument 'repo_id'` on the
standard `save_pretrained(local_path, push_to_hub=True, repo_id=...)`
call shape. This regression was introduced by the earlier iteration
that split push_to_hub into an explicit second step.
- save.py:3314 forward `token=token` on the torchao non-PEFT
`tokenizer.save_pretrained(torchao_save_directory)` call so the
patched wrapper can reach gated repos when HF_TOKEN is not in the
environment. Left the sibling `unsloth_generic_save` call at 3063
untouched (blame points at an earlier full-finetuned
save_pretrained_merged fix and the token gap there is lower risk).
* Fix torchao tokenizer reload and push_to_hub repo_id default
- save.py:3283 after `auto_processor.from_pretrained(save_directory)`
re-runs `patch_saving_functions(tokenizer)` on the freshly loaded
tokenizer. The rebind at 3283 was overwriting the patched tokenizer
passed into `unsloth_save_pretrained_torchao`, so the subsequent
`tokenizer.push_to_hub` (3309) and `tokenizer.save_pretrained`
(3314) bypassed `_preserve_sentencepiece_tokenizer_assets` and left
`{save_directory}-torchao` without `tokenizer.model` / restored
`added_tokens_decoder`.
- save.py:3497 fall back to `os.path.basename(save_directory)` for
`repo_id` instead of the raw `save_directory`. The round-2 fallback
diverged from `transformers.PreTrainedTokenizerBase.save_pretrained`,
which defaults `repo_id = save_directory.split(os.path.sep)[-1]`;
nested local paths like `./out/my-repo` now resolve to `my-repo`
(the Hub id) instead of the full filesystem path.
* Revert tokenizer save_pretrained repo_id basename fallback
- save.py:3497 default `repo_id` back to `save_directory` as-is rather
than `os.path.basename(save_directory)`. The basename fallback (added
last iteration to match upstream transformers) stripped the user
namespace from the Unsloth convention `tokenizer.save_pretrained(
"user/repo", push_to_hub=True)`, redirecting the upload to
`{current_user}/repo`. save.py itself treats `save_directory` as the
repo id at 572, 593, 1723, 1779, 1836, 1844, 1858, and 3025, so the
wrapper should follow the same convention. Users who pass a nested
filesystem path with `push_to_hub=True` can supply explicit
`repo_id=...`.
* Guard processor.tokenizer recursion against None
save.py:3511 change `elif hasattr(model, "tokenizer")` to
`elif getattr(model, "tokenizer", None) is not None`. The previous
guard only checked attribute existence; a ProcessorMixin that sets
`tokenizer = None` (audio-only or manually constructed) would enter
the branch and crash inside the recursive patch_saving_functions on
`model.push_to_hub.__name__`.
* Add review tests for tokenizer save
* Consolidate review tests
Drop redundant assertion in test_patch_saving_functions_still_patches_non_none_tokenizer.
The hasattr check already proves the patch applied; the or-chained
repeat assertion added no signal.
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* vllm sampling params fix
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* do not patch base_trainer
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* Apply suggestion from @danielhanchen
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* Revert "[pre-commit.ci] auto fixes from pre-commit.com hooks"
This reverts commit 58b483dc0d1790f99580665801d3fa0d7267c533.
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Summary:
Previously the test was not ran correctly and the save to local path is not tested
this PR added support for that and tries to test properly
Note: `python tests/saving/test_unsloth_save.py` doesn't run test
Test Plan:
pytest tests/saving/test_unsloth_save.py -k test_save_torchao
Reviewers:
Subscribers:
Tasks:
Tags:
Summary:
Allow users merge the LoRA weights and then do a post training quantization with torchao
Usage:
```
from torchao.quantization import Int8DynamicActivationInt8WeightConfig
torchao_config = Int8DynamicActivationInt8WeightConfig()
model.save_pretrained_torchao(
save_path,
tokenizer=tokenizer,
torchao_config=torchao_config,
)
```
Test Plan:
python tests/saving/test_unsloth_save.py
Reviewers:
Subscribers:
Tasks:
Tags:
JoshuaL3000