* 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).
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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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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|
```
Daniel Han