* 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
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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
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Fix text-only VLM CPT packing truncation
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Handle streaming vision datasets in packing
* Harden multimodal packing detection
* Preserve safe packing boundaries
* Scope stream packing checks to VLMs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Narrow VLM packing detection
* Align packing mode and eval safety
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add qwen3_5/qwen3_next to PADDING_FREE_BLOCKLIST to avoid packed-sequence contamination
* Detect hybrid linear-attention models structurally instead of by name for packing guard
* Add experimental varlen packing for hybrid linear-attention models
Feed seq_idx to the causal conv and cu_seqlens to the gated-delta scan so
sample packing / padding-free reset state at sequence boundaries for hybrid
linear-attention models (Qwen3.5, Qwen3-Next). Gated behind
UNSLOTH_EXPERIMENTAL_HYBRID_PACKING and fail-closed: when the flag is off or
the accelerated kernels (causal_conv1d + fla) are unavailable, the guard keeps
these models on the padded path.
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* Harden hybrid linear-attention varlen packing shim
Make patch_hybrid_linear_attention_varlen robust across transformers 4.57.6
through 5.x and TRL 0.22.2 through 1.x, following the import_fixes.py style:
- Read UNSLOTH_EXPERIMENTAL_HYBRID_PACKING at call time so the flag takes effect
when set after importing unsloth.
- Idempotent: repeat calls on a patched model return True without re-validating
the wrappers or double-wrapping; signatures are checked on captured originals.
- Prefer the authoritative packed_seq_lengths (via get_packed_info_from_kwargs)
over position_ids resets, handling pad_to_multiple_of trailing tokens.
- Suppress injection for cached forwards (use_cache / past_key_values) so
generation and eval are left on the untouched decode path.
- Validate every gated-delta module before mutating any (transactional).
- Bind position_ids / use_cache from both positional and keyword args.
- Verify dispatch at runtime (Unsloth wraps each module forward, so the mixer
source is not statically inspectable) and warn once if the shim is never hit.
- Emit one deduped diagnostic on each fail-closed path.
Add CPU unit tests covering the hybrid guard detection, the boundary builders,
and the shim (fail-closed, active, idempotent, cached no-op, runtime handshake).
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* Abort hybrid packing when the varlen shim is not fully dispatched
The runtime handshake used a single per-module hit flag written by both the conv
and scan wrappers, so a partial dispatch (only one kernel routed through
self.<kernel>) passed the any() check and trained on contaminated data, and a
missing dispatch only logged a warning. Track conv and scan dispatch separately,
require both on every gated-delta module on the first packed forward, and raise
before loss/backward when either is missing (the batch is already flattened, so
there is no padded recovery at that point). Also skip an empty packed_seq_lengths
before it reaches max(), and document the position_ids fallback's left-pad
assumption.
Add tests for no-dispatch and partial (conv-only / scan-only) abort, the
packed_seq_lengths preference over a competing position_ids, MRoPE 3D position
ids, and the pad_to_multiple_of trailing-segment path through the metadata builder.
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* Import the hybrid packing patch from its submodule to satisfy the import-hoist lint
* Fail closed for hybrid packing on encoder-decoder, chunked-loss, and string-name models
The varlen shim only helps decoder-only hybrid models that run their mixer
through self.<kernel> on a live nn.Module forward. Three cases slipped past
the guard:
- Encoder-decoder configs (is_encoder_decoder) reached the packing path even
though flattening a cross-attention batch is unsound. Block them explicitly.
- TRL's chunked_nll loss (the 1.x default) calls the backbone directly and
bypasses model.forward, so the per-instance forward wrapper that refreshes
the varlen stash never runs. Detect that path and keep the model padded.
- A string model_name reaches the trainer before the module exists, so the
instance shim has nothing to patch. Resolve the config up front and keep
string hybrids on the padded path.
Adds encoder-decoder / decoder-only / chunked-loss / string-model tests.
* Harden the SFT source-injection replacements and forward auth args for string models
The wrapped-packing injection rewrote the sourced unsloth_zoo sft_prepare_dataset
with str.replace anchored on the exact 'All Unsloth Zoo code licensed under
LGPLv3' comment. str.replace never raises on a missing anchor, so a supported
newer unsloth_zoo (the dependency is only lower-bounded) that moved that header
would silently drop the setup while the truncation and pack_dataset edits still
referenced _unsloth_wrapped_packing / _inspect, raising NameError on every SFT
dataset preparation.
- Install the setup at the sft_prepare_dataset signature via re.subn (a structural
anchor that always exists) and raise if even that is missing.
- Route the remaining edits through a _require_replace helper that fails loudly on a
missing required anchor (or warns once for an optional one), formalizing the
verify-then-replace idiom the DPO patchers in this file already use.
- Reuse the guarded _unsloth_pack_has_strategy at the pack_dataset call instead of
re-calling inspect.signature(pack_dataset) unguarded, so a non-introspectable
pack_dataset cannot crash there after the setup already handled it.
- _resolve_string_model_config now forwards token / use_auth_token / cache_dir /
code_revision, so a private hybrid resolves its config instead of falling through
as non-hybrid and enabling packing without the varlen shim.
Adds regression tests for the drift-resistant injection, the helper, and the
string-model auth forwarding.
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* Honor top-level SFTConfig.trust_remote_code when resolving a string model
TRL merges the top-level args.trust_remote_code into the load via
model_init_kwargs.setdefault("trust_remote_code", args.trust_remote_code) before
create_model_from_path, so a remote-code hybrid is commonly set with
SFTConfig(trust_remote_code=True) rather than inside model_init_kwargs. The config
probe only read model_init_kwargs, so AutoConfig could fail for such a model, leave
model_config None, and let the guard treat it as non-hybrid, enabling packing
without the varlen shim. Mirror TRL's setdefault (model_init_kwargs wins).
* Tighten hybrid-packing comments for concision
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---------
Co-authored-by: alkinun <alkinunl@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Etherl <61019402+Etherll@users.noreply.github.com>
* Fix text-only VLM CPT packing truncation
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Handle streaming vision datasets in packing
* Harden multimodal packing detection
* Preserve safe packing boundaries
* Scope stream packing checks to VLMs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Narrow VLM packing detection
* Align packing mode and eval safety
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add qwen3_5/qwen3_next to PADDING_FREE_BLOCKLIST to avoid packed-sequence contamination
* Detect hybrid linear-attention models structurally instead of by name for packing guard
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Install wrapped-packing setup at the signature, not the Zoo license comment
The _unsloth_wrapped_packing / _inspect setup block was injected by matching the
exact 'All Unsloth Zoo code licensed under LGPLv3' comment line in the sourced
sft_prepare_dataset. The unsloth_zoo dependency is only lower-bounded, so a newer
Zoo that moves or drops that header made the setup a silent no-op while the
truncation and pack_dataset rewrites still emitted references to those names,
raising NameError on every SFT dataset preparation.
Anchor the setup on the function signature instead (a structural location that
always exists) and fail loudly if it cannot be found, so the helper variables are
always defined before they are referenced across Zoo versions.
Adds a regression test that patches in a Zoo source without the license header.
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Co-authored-by: Etherl <61019402+Etherll@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Keep native RoPE scaling when extending context; carry rope_theta for linear
When max_seq_length exceeds a model's native window, the loader overwrote the
model's rope_scaling with linear scaling. For models that already ship a scaled
RoPE (llama3/yarn/longrope) that is far worse for long context, and on
transformers v5 the linear dict omitted rope_theta (v5 keeps it under
rope_parameters), so the rotary base fell back to 10000 and broke past ~8K tokens.
Keep the native scaling and just widen the window; only synthesize linear for
plain-RoPE models, and carry rope_theta so v5 keeps the real base.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Only preserve native llama3 when extending context; keep linear fallback otherwise
The patched attention constructor (patch_llama_rope_scaling) rebuilds only linear,
llama3 and longrope and its longrope branch reads a top-level
original_max_position_embeddings, so preserving yarn or a nested-only longrope config
would raise during construction on transformers <= 4.47.1. Keep only llama3 native;
yarn/longrope/other types fall back to the linear override, still carrying rope_theta.
* Correct long-context extension comment to match llama3-only preservation
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Guard RoPE scaling against the transformers v5 buffer blank; honor extended factor
Add a family-agnostic guard that builds each rotary from a scaled config,
blanks its non-persistent buffers (what transformers v5 does on load), runs
loader._fix_rope_inv_freq, and asserts every buffer is restored to its scaled
value (llama3 and longrope). This catches the whole bug class, not just the
one call site, and is validated to fail on the pre-fix repair.
Also make LlamaExtendedRotaryEmbedding read the llama3 factor from the config
instead of hardcoding 8 (wrong for Llama-3.2, factor 32), falling back to the
Llama-3.1 defaults when built without a config.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Pass config into extended rotary codegen; skip v5 round-trip on transformers 4.x
- patch_llama_rope_scaling now builds the llama3 extended rotary with
config=self.config so it reads the real factor (32 for Llama-3.2) instead
of falling back to 8; the template already references self.config.
- test_v5_blank_repair_roundtrip now skips when loader._NEEDS_ROPE_FIX is
False, since _fix_rope_inv_freq is a no-op on transformers 4.x and cannot
restore the blanked buffers there.
* Raise stream deadlock-guard timeouts from 0.2s to 5.0s in passthrough tests
These asyncio.wait_for guards bound test setup and cross-task event
signaling that complete near-instantly on success; the 0.2s budget is a
latency assertion in disguise and times out under CI scheduling load
(seen on the 3.11 matrix leg while 3.10/3.12/3.13 pass the same commit).
5.0s matches the timeout used elsewhere in the suite and still fails fast
on a real hang. No test relies on the guard expiring.
* Extended rotary reads rope_parameters as well as rope_scaling
transformers v5 stores llama3 scaling under config.rope_parameters and
exposes rope_scaling only as a back-compat property. Reading that property
works on 5.0-5.13 (verified: factor resolves to 32 for Llama-3.2), but a
future release may drop the shim, after which the subclass path would fall
back to factor 8. Read either field so the factor survives the rename.
Adds test_extended_rotary_reads_rope_parameters_v5 (fails on the old
single-field read: rope_parameters-only config resolves to 8, not 32).
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---------
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* Fix llama3 RoPE scaling dropped on transformers v5
transformers v5 loads on meta then blanks non-persistent buffers, so
_fix_rope_inv_freq rebuilds inv_freq after load. It recomputed a vanilla
inv_freq and applied _apply_inv_freq_scaling, a no-op on the base
LlamaRotaryEmbedding used by the config/llama3 path, so inv_freq ended up
divided by 1 instead of the config factor (8 for Llama 3.1, 32 for Llama
3.2). This corrupts long-range positions and inflates long-context loss
about 3-5x. transformers 4.x was unaffected.
Route __init__ and the v5 repair through one _unsloth_recompute_inv_freq
so they cannot diverge, and stash the config on the rotary module so the
repair can rebuild the same scaled value.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add test for llama3 RoPE scaling under the transformers v5 repair
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update RoPE drift guard for the recompute refactor and guard the v5 repair
The drift guard's AST tripwire asserted the config-scaling call lived in the
if config is not None branch of LlamaRotaryEmbedding.__init__. The fix moved
that into _unsloth_recompute_inv_freq, so follow it there (with a fallback to
the old inline branch) and add a guard that loader._fix_rope_inv_freq rebuilds
inv_freq through the same helper. Also add a CPU functional check of the helper
and drop the redundant standalone test.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix FlashAttention fp32 crash with DoRA (use_dora=True)
DoRA upcasts lora_magnitude_vector to fp32 for the optimizer, which promotes
the q/k/v_proj output to fp32. FlashAttention only accepts fp16/bf16, so the
fp32 q/k/v raised 'FlashAttention only support fp16 and bf16 data type'.
Downcast q/k/v to the compute dtype before the flash kernels.
Fixes#1013
* Apply kwarg-spacing format hook to DoRA dtype test (pre-commit)
* DoRA+FA2: downcast any fp32 among Q/K/V and clamp to a flash-supported dtype
* Tighten code comments (no logic change)
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.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.
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---------
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* Handle rope_type 'default' on transformers 5 to stop false RoPE warning
transformers 5 reports rope_type="default" for every plain (unscaled) config
and dropped "default" from ROPE_INIT_FUNCTIONS. _compute_config_rope_inv_freq
then did ROPE_INIT_FUNCTIONS["default"], hit KeyError, returned None and logged
"Could not apply RoPE scaling 'default'; long-context generation may degrade"
on every model load. The inv_freq was still correct (the constructor recomputes
vanilla on None), but the warning is a false alarm for unscaled models.
Compute the unscaled inv_freq directly for rope_type "default"/None instead of
going through ROPE_INIT_FUNCTIONS, so plain configs return the right value with
no warning. Scaled types (llama3/linear/yarn/...) are unchanged.
Also skip test_object_style_rope_scaling_on_config_delegates_correctly when
transformers strict-validates rope_scaling (5.x): it rejects a non-dict object
on config.rope_scaling, so the object-style delegation path cannot be set up
there. The test still runs and asserts on transformers <5.
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* Fix config.rope_scaling being dropped by the replaced rotary embedding (#2405)
On modern transformers, LlamaModel builds its rotary embedding from config
using unsloth's replacement LlamaRotaryEmbedding class, whose config path
computed vanilla inv_freq and ignored config.rope_scaling entirely. The
llama3/linear/longrope dispatch in patch_llama_rope_scaling rewrites
LlamaAttention.__init__, which no longer constructs rotary embeddings, so it
never fires; the model-level rotary is then copied onto every attention
layer. Result: Llama-3.1/3.2/3.3 ran with unscaled RoPE on the
FastLanguageModel path and collapsed into repetition loops past roughly 29K
tokens (PASS at 28867, FAIL at 31767 in needle retrieval). FastModel was
unaffected because vision.py keeps transformers' own rotary. qwen2, qwen3,
qwen3_moe, mistral and cohere assign the same base class, so any rope-scaled
config of those families was equally exposed.
The fix makes the base class config path compute inv_freq and
attention_scaling via transformers' ROPE_INIT_FUNCTIONS (covers llama3,
linear, dynamic, yarn, longrope), with an inline llama3 fallback reading
factors from config for older transformers, degrading to prior behavior on
any failure. attention_scaling is applied in _set_cos_sin_cache (1.0 default,
exact no-op for unscaled paths) and persists across extend_rope_embedding.
A type(self) guard prevents double-scaling via the legacy scaled subclasses.
Adds tests/utils/test_rope_scaling_drift.py (AST tripwire + behavioral
inv_freq/cos-cache/extension checks, validated to fail 4 of 5 on the unfixed
code) and wires it into the existing consolidated CI HARD GATE step.
Verified on GPU: 48K-token needle retrieval flips FAIL to PASS for
FastLanguageModel in bf16 and 4bit, 20K stays PASS, scaled inv_freq matches
transformers exactly, and the left-padded batch generation guard still gets
exact solo-vs-batched token matches.
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* Address review: normalize object-style rope_scaling, vectorize llama3 fallback
config.rope_scaling can be a config object rather than a dict on newer
transformers; _rope_scaling_as_dict normalizes it (to_dict/dict/vars
fallbacks) before any .get() access, with a regression test using a
dataclass stand-in. The inline llama3 fallback now uses torch.where instead
of a per-frequency Python loop; verified bit-for-bit equal to transformers
ROPE_INIT_FUNCTIONS for factor 8 (Llama-3.1) and factor 32 (Llama-3.2).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address review: CPU-safe rope guard tests, normalized config for delegation
The rotary constructor builds per-device CUDA caches, so the behavioral tests
that instantiate it cannot run on GPU-less CI. Restructured into three layers:
the AST tripwire now also asserts the constructor stays wired to
_compute_config_rope_inv_freq; the CPU layer tests that pure helper directly
(llama3 dict, llama3 object, linear object, default type) with no
instantiation; the instantiation and cache tests are gated behind a real CUDA
probe (actual tensor allocation, so import-time CUDA spoofs cannot fool the
gate). Verified: 9 passed with GPU; 5 passed 4 skipped with CUDA hidden; 5
failed 4 skipped on the unfixed code in CPU mode.
Delegation to ROPE_INIT_FUNCTIONS now retries with a shallow config copy
carrying the normalized rope_scaling dict when the original was an object the
installed transformers cannot read; covered by a linear-object test, which has
no inline fallback and passes only through that retry path.
* Tighten comments in rope scaling fix and guard test
Comment and docstring reduction only; verified code-identical with
scripts/comment_tools.py check --strip-docstrings (AST signature match on
both Python files). All guard tests unchanged: 20 passed with GPU, 5 passed
4 skipped with CUDA hidden.
* Apply repo kwarg-spacing format
---------
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* Add regression guard for batched left-padded generation (#1066, #3699)
Three layers of tests plus a path-filtered CI workflow so the left-padding
position_ids / attention-mask bug class cannot silently return:
- tests/utils/test_prepare_inputs_ast_guard.py: import-free AST checks on
_fast_prepare_inputs_for_generation (cumsum-from-mask branch present,
cache_position only as fallback, no mask truncation, model families wired)
- tests/utils/test_prepare_inputs_leftpad.py: CPU behavioral unit test with
synthetic left-padded masks and fake caches; exact expected position_ids
for prefill and cached decode
- tests/utils/test_batched_leftpad_generation_gpu.py: optional GPU e2e,
solo vs batched prefix match, skipped without CUDA
- .github/workflows/batch-inference-guard.yml: ubuntu-latest CPU job running
the two deterministic layers on PRs touching unsloth/models/**
Validated: all pass on main; both CPU layers fail at 6d0f8643~1 (pre #4100)
and at 332eabf3~1 (pre #2216), reproducing the historical bug signatures.
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* Cite staging proof in batch-inference-guard header (staging-2 PRs 170/171)
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* Fold left-padding guard into consolidated Core CI; merge AST + behavioral tests
No new workflow and no new CI job: the guard now runs as one HARD GATE step
inside consolidated-tests-ci.yml, right after the callback signature drift
detector, where the CPU torch stack is already installed. The AST structural
checks and the behavioral unit tests live in a single file
(tests/utils/test_prepare_inputs_leftpad.py); the AST layer stays stdlib-only
with unsloth imported lazily inside the behavioral tests, so import breakage
cannot mask the structural checks.
Revalidated after the merge: 11 assertions pass on main, 8 fail at
6d0f8643~1 (pre #4100).
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* Update staging proof reference for consolidated gate (PRs 170/172)
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Trim and tighten code comments and docstrings across the repository. Comment-only: every changed file verified code-identical to main via AST/token comparison.
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.
Adds studio/backend/utils/datasets/dataset_none_detect.py, a standalone scanner that reports None/empty content turns in alpaca, chatml, sharegpt, and gptoss datasets without modifying data, plus generator and runner scripts under tests/utils. Depends only on the datasets library and is not wired into the package init, so it stays import-light.
* feat: Add cactus QAT scheme support
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* test(qat): add tests for cactus QAT scheme and fix missing import
* Fix cactus QAT scheme: correct MappingType import, tighten PerGroup filter
- Drop the broken `from torchao.dtypes import MappingType` import. `MappingType`
lives in `torchao.quantization` (and `torchao.quantization.quant_primitives`);
it is not exported from `torchao.dtypes` in any supported torchao release
(verified on 0.14, 0.16, 0.17). The previous code raised `ImportError` on
every cactus call and was masked as a misleading 'torchao not found' error.
- Since `IntxWeightOnlyConfig` already defaults `mapping_type` to
`MappingType.SYMMETRIC`, drop the explicit kwarg entirely and remove the
import. Behavior is unchanged.
- Introduce a named `group_size = 32` constant (matches the int4 / fp8-int4
pattern in the surrounding branches) and add a `% group_size == 0`
divisibility guard to the filter. `PerGroup(32)` requires
`in_features % 32 == 0` at `quantize_()` time, otherwise torchao raises
`ValueError: in_features (N) % group_size (32) must be == 0`. The old
`in_features >= 32` filter would admit non-aligned widths (e.g. 33, 48, 65,
127) and crash `_prepare_model_for_qat` for those shapes.
* Warn when cactus QAT skips non-divisible Linear layers
Multiple reviewers flagged that the divisibility guard added in the
previous commit can silently leave Linear layers in full precision when
their in_features is not a multiple of 32. For currently supported
Unsloth models (Qwen, Llama, Gemma, Mistral, Phi) every Linear width is
already a multiple of 32/64/128 so this never triggers, but surfacing
the coverage gap is cheap and avoids users assuming 100% QAT coverage
when they bring a custom model with unusual shapes.
Emit a UserWarning listing up to the first 8 skipped layers whenever
the cactus filter excludes any Linear due to the modulo guard. This
keeps the lenient silent-skip behavior (consistent with int4 /
fp8-int4), but stops making it silent.
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* feat: Implement Q-GaLore optimizer and custom embedding learning rate in the Unsloth trainer.
* feat: Implement QGaLoreAdamW8bit optimizer with 8-bit states, GaLore low-rank gradient projection, and optional INT8 weight quantization, along with supporting projector and tests.
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* feat: Introduce Q-GaLore AdamW optimizer with low-rank quantized gradient projection and integrate into the trainer, along with dedicated tests.
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* feat: Implement Q-GaLore AdamW optimizer with gradient projection and quantization, including trainer integration and corresponding tests.
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* Fix 3 bugs in Q-GaLore optimizer and add weight_quant forward hooks
1. Fix use-after-delete crash: move `del p._saved_data` after the
weight decay block so decoupled weight decay can reference the
current weights correctly (p.data).
2. Fix substring matching in make_q_galore_param_groups: split
parameter names on "." and check exact component matches to
prevent false positives (e.g. "not_q_proj" matching "q_proj").
3. Implement forward pre-hooks for weight_quant: after the optimizer
quantizes weights to INT8, replace p.data with a 1-element
placeholder to free float memory. A register_forward_pre_hook
dequantizes back to float before each forward pass. The trainer
calls install_weight_quant_hooks() when weight_quant is enabled.
4. Update test_weight_decay_uses_saved_data to match the fixed code
path (decoupled decay uses p.data, expected value 2.7). Add
test_weight_quant_hook_restores_float to verify the INT8-to-float
hook round-trip.
All 24/24 Q-GaLore tests pass. Benchmarked on Llama-3.2-1B-Instruct
FFT: Q-GaLore saves 32% VRAM (10.63 -> 7.24 GB) with better loss
convergence (1.3 vs 2.0 at step 100). No regressions in 31-notebook
sweep across Llama, Qwen, Mistral, Phi, Gemma, vision, and GRPO.
* Default weight_quant to False in QGaloreConfig
Benchmarks show weight_quant=True adds ~1 GB on Llama-3.2-1B due to
INT8 copy/scale overhead exceeding savings from the placeholder trick.
Users can still opt in explicitly. The optimizer logic is unchanged.
* Optimize Q-GaLore projector and optimizer step performance
Projector (q_galore_projector.py):
- Use torch.svd_lowrank with oversampling p=10 (Halko et al. 2009) instead
of full SVD for large matrices. Falls back to full SVD when min(m,n) <= 2*rank.
SVD steps are 6-8x faster on Llama-3.2-1B (22s -> 3s for first step).
- Cache the dequantized ortho matrix between project() and project_back() to
avoid redundant dequantization when quant=True.
- Replace F.cosine_similarity with torch.dot for 1-D unit vectors in the
adaptive schedule. Remove unused torch.nn.functional import.
- Use collections.deque(maxlen=queue_size) instead of list with manual pop(0).
Optimizer (q_galore_adamw.py):
- Remove redundant .clone() on dequantized weights (line 151) and on float
data before re-quantization (line 211). _dequantize already returns a fresh
tensor and _quantize/_quantize_stochastic only reads its input.
- Consolidate per-group torch.cuda.synchronize() into a single call after
all param groups complete.
- Use torch.empty instead of torch.zeros for the scalar placeholder tensor
that is never read.
Verified: 24/24 unit tests pass. Llama-3.2-1B 61-step training produces
losses within 0.24% relative diff (correlation >0.9999) of the original.
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* Refactor Ollama template wiring and harden packing helpers
Signed-off-by: Mohammad Miadh Angkad <MAngkad.BSDSBA2027@aim.edu>
* Fix Qwen3 and Gemma3n template bindings and tidy packing test helper
* Fix gptoss Ollama comment and tinyllama stop parameter
- Fix wrong comment referencing gemma3n for gptoss_ollama in chat_templates.py
- Add missing stop keyword to tinyllama PARAMETER in ollama_template_mappers.py
* Fix _DummyTrainer compatibility across TRL versions
The try/except only handled the removal of return_position_ids
(TRL v0.24+) but not the absence of padding_free (TRL v0.18.2).
Gracefully degrade through all optional collator flags so the
test works from trl>=0.18.2 through v0.27+.
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* add int8 weight-only QAT scheme, add test, fix tests for current torchao version
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* change quantization to PerAxis
* lambda =/
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* add torchao messages, remove group_size from int8
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* raise exception on missing torchao
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* touch up the torchao imports
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* vllm sampling params fix
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* do not patch base_trainer
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* seperate vllm fixes
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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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This reverts commit de3daaf429f81aceb6632932b0cb1af5149652a8.
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**Summary:** The existing QAT + LoRA path only applied fake
quantization to the original slow path, but the default is the
fast path that calls unsloth's fast LoRA primitives. This commit
integrates fake quantization into these fast primitives as well,
and add unit tests to assert that fake quantization is actually
taking place.
**Test Plan:**
Unit tests:
```
pytest tests/utils/test_qat.py
```
End-to-end test: https://gist.github.com/andrewor14/6360dd69b5784c71c46e80c14f53e6b6
Full fine-tuning Llama3.1-8B with and without QAT + LoRA on yahma/alpaca-cleaned for 1 epoch:
- Batch size = 8 (no grad accum)
- Learning rate = 2e-4
- Quantization scheme = int4 weight only (with bf16 activations)
Wikitext perplexity:
- Baseline = int4 quantized model finetuned without QAT
- QAT int4 quantized model (with this PR) achieved 33% lower perplexity than the int4 baseline
- QAT int4 quantized model without this PR was worse than the int4 baseline
```
==> unsloth_model_lora_baseline_output/lm_eval_float.log <==
| | |none | 0|word_perplexity|↓ |7.5551|± | N/A|
==> unsloth_model_lora_baseline_output/lm_eval_quantized.log <==
| | |none | 0|word_perplexity|↓ |8.7655|± | N/A|
==> unsloth_model_lora_qat_int4_output/lm_eval_quantized.log <==
| | |none | 0|word_perplexity|↓ |8.3548|± | N/A|
```
Leo Borcherding