* upgrade to torchao 0.17.0
* chore: lint
* refactor attention handling
* replace legacy attention boolean flags with capability properties
Replace checks with capability-based properties derived from attn_implementation
This separates three concerns that were conflated under flash_attention:
1. Backend selection -> attn_implementation enum
2. Packing capability -> attn_supports_packing property
3. Flash-attn library dependency -> attn_uses_flash_lib property
* compute attn capability flags in normalizer instead of properties
* make attn_implementation the single source of truth
* move attention-dependent validators to mode=after
* migrate remaining consumers to canonical attn_implementation
* expand attention tests + rewrite docs
* migrate example configs to canonical attn_implementation
* update doc snippets + reject gemma4-hybrid with non-FA2 backend
* remove dead gemma4 branch in _set_attention_config
* fix duplicate attn_implementation in gpt-oss yamls and flaky caplog tests
* drop "Phase 2" naming from attn-implementation tests
* regroup attn_implementation tests by feature concern
* clean up verbose comments and remove MD
Signed-off-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>
* fix(collator): pass return_dict=True at apply_chat_template top level for transformers 5.x
In transformers 5.x, ProcessorMixin.apply_chat_template gained its own
`return_dict` parameter (defaulting to False). When return_dict=False
and tokenize=True the method returns out["input_ids"] directly — a 2-D
tensor — rather than the full BatchFeature dict.
The old code placed `return_dict=True` inside processor_kwargs. In
transformers 5.x those kwargs are forwarded to the underlying processor
call self(...) where _merge_kwargs silently ignores any key not present
in MllamaProcessorKwargs (emitting a warning). The outer return_dict
therefore stayed False, apply_chat_template returned the raw input_ids
tensor, and the subsequent `batch["input_ids"]` attempted to index a
2-D tensor with the 9-character string "input_ids", producing:
IndexError: too many indices for tensor of dimension 2
The fix is to pass return_dict=True as a top-level keyword argument to
apply_chat_template (where it is actually consumed) and remove it from
processor_kwargs (where it was silently dropped). No version guard is
needed: transformers is pinned to ==5.5.4 in pyproject.toml.
Adds a unit-level regression test (tests/test_mm_chat_collator.py) that
mocks the processor to return a raw tensor when apply_chat_template is
called without top-level return_dict=True, verifying the four invariants:
process_rows returns a dict, input_ids is 2-D, labels is 2-D, and
apply_chat_template receives return_dict=True as a top-level kwarg.
Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_multimodal_dataset
Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_text_only_dataset
Signed-off-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>
* fix(collator): process_rows returns dict (BatchFeature) shape
Two related changes for the multimodal chat collator under transformers 5.x:
1. Wrap apply_chat_template result in dict(...) so process_rows returns
a plain dict rather than a BatchFeature instance. BatchFeature is a
Mapping but not a dict; downstream code that did
batch["labels"] = self.processing_strategy.process_labels(batch["input_ids"])
would index on a tensor when the result wasn't dict-shaped, raising
IndexError: too many indices for tensor of dimension 2
2. Soften the regression test's contract from `dict` to `Mapping` so it
exercises the actual semantic guarantee (key/value access) rather
than the implementation detail (dict vs BatchFeature). Test guards
against the original transformers 5.x breakage where apply_chat_template's
return_dict default went from True to False.
Includes regression test under tests/test_mm_chat_collator.py.
Bug surfaced via swarm dispatch task_01KQHPNAYD8XARSNSDJVW1GPF6 against
attn-implementation-refactor; squash-merged from agent commits 4de886fd
+ dc9fcf4f.
Signed-off-by: Wing Lian <wing@axolotl.ai>
---------
Signed-off-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>
* make pad_to_sequence_len default to the same value as sample_packing
* remove duplicate validation
* fix test
* update description meta
Co-authored-by: NanoCode012 <nano@axolotl.ai>
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* checkpoint model on first step callback
* remove debug
* add test cases; update existing tests not to save on first step
* move test out of solo
* delete
* default to False
* typo
* fix: update chat_template
* fix: handle gemma3 showing a lot of no content for turn 0
* fix: remove unknown config from examples
* fix: test
* fix: temporary disable gemma2 test
* fix: stop overwriting config.text_config unnecessarily
* fix: handling of set cache to the text_config section
* feat: add liger gemma support and bump liger to 0.5.5
* fix: add double use_cache setting
* fix: add support for final_logit_softcap in CCE for gemma2/3
* fix: set use_cache before model load
* feat: add missing layernorm override
* fix: handle gemma3 rmsnorm
* fix: use wrapper to pass dim as hidden_size
* fix: change dim to positional
* fix: patch with wrong mlp
* chore: refactor use_cache handling
* fix import issues
* fix tests.e2e.utils import
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* Add example YAML file for training Mistral using DPO
* added deduplication code
* Add exact deduplication feature and update examples
* Improve deduplication for train/eval overlap
Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements.
* Improve deduplication for train/eval overlap
Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements.
* Apply suggestions from code review
To handle the original case where we do not do deduplication
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Improve false collision detection to ensure dataset integrity
- Added test cases to simulate and verify handling of forced hash collisions between datasets.
- Ensured that datasets with identical hashes but different content are correctly identified, preventing incorrect deduplication.
- Updated unit tests to include scenarios where collisions occur across both training and evaluation datasets, as well as within a single dataset.
* Moved the constants file to the tests folder
- Relocated `constants.py` to the `tests` folder to improve modularity and maintain a clear separation between source and test files.
- Renamed `cicd/tests.py` to `cicd/cicd_tests.py` to resolve a conflict with `tests/__init__.py`, which caused Mypy to fail due to duplicate module names.
- Updated all references to `cicd.tests` in the codebase to `cicd.cicd_tests` to reflect the renaming and ensure compatibility.
- These changes ensure Mypy passes the pre-commit hook and maintain alignment with the project's structure.
* revert some changes from previous commit and fix relative import
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
