* add int8 weight-only QAT scheme, add test, fix tests for current torchao version
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* change quantization to PerAxis
* lambda =/
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* add torchao messages, remove group_size from int8
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* raise exception on missing torchao
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* touch up the torchao imports
* [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>
ModulesToSaveWrapper was removed from peft.tuners.tuners_utils in PEFT
0.16.0. The class has been available in peft.utils.other since at least
PEFT 0.7.1, which is the minimum version Unsloth requires.
This fixes the ImportError when using PEFT >= 0.16.0.
- Fix Kaggle misclassification by prioritizing filesystem markers over env vars
- Preserve telemetry pings when statistics is explicitly provided
- Replace bare except with except Exception
- Minor cleanup based on automated review feedback
Fixed Codex regression: keep snapshot_download pings for explicit statistics values; detection only runs when statistics is None. Also replaced bare except.
Problem: Kaggle notebook environments can expose both KAGGLE_* and COLAB_* environment keys. _get_statistics currently checks COLAB_ before KAGGLE_, causing Kaggle sessions to be labeled colab/colabpro.
Prefer filesystem markers (e.g. /kaggle/working, /content + /opt/colab) before env-key heuristics, then fall back to the existing env-key checks. This avoids misclassification when providers leak overlapping env vars.
Kaggle test notebook: https://www.kaggle.com/code/hnxnq07/kaggle-stats-gathering-test
Changes:
- Change default compile_mode from "reduce-overhead" to "default" since CUDA
Graphs (used by reduce-overhead) is incompatible with PEFT/LoRA
- Add _estimate_compile_threshold() to calculate minimum steps needed for
torch.compile to be beneficial based on model parameter count
- Add _apply_torch_compile() helper with accelerate unwrap_model bug workaround
- Defer torch.compile application to trainer initialization time so we can
check max_steps against the breakeven threshold
- Patch SentenceTransformerTrainer to auto-apply compile when max_steps
exceeds the calculated threshold
Breakeven thresholds (with 1.2x safety margin):
- 22M params (MiniLM): ~1388 steps
- 110M params (mpnet): ~242 steps
- 335M params (snowflake): ~203 steps
This ensures torch.compile warmup cost is only paid when training is long
enough to benefit from the speedup.
- Fix test file: use return_tokenized instead of return_tensors
- Fix test file: use text_dataset instead of undefined dataset variable
- Move parameter validation to constructor (fail fast on invalid params)
- Add labels field in tokenized output for causal LM training
- Add empty file handling with clear error message
- Add tests for constructor validation and labels field
This PR fixes the "Arch conditional MMA instruction used without targeting
appropriate compute capability. Aborting." errors that occur when using
FBGEMM on Blackwell GPUs (B200/B100, SM100).
Changes:
- Add stderr filters in import_fixes.py for CUTLASS/FBGEMM MMA errors
- Add warning filters for various deprecation messages
- Update check_fbgemm_gpu_version() to disable FBGEMM instead of raising
an error when old versions are detected
- Update test_has_fbgemm() in fp8.py to catch broader CUTLASS/CUDA errors
and gracefully fall back to Triton kernels
- Update loader_utils.py to disable FBGEMM instead of raising ValueError
for old fbgemm_gpu versions
The key behavior change is that FBGEMM errors no longer crash the script.
Instead, FBGEMM is disabled and Triton kernels are used automatically.
This allows Unsloth to work on SM100 GPUs where CUTLASS SM90 kernels fail,
and also gracefully handles old FBGEMM versions.
