* 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: mention to install pytorch before axolotl
* feat(doc): include instruction to delinearize
* fix: update instruction for delinearize with adapter
* fixes for delinearization, and make qlora work with fsdp2
* Add back mistakenly removed lm_eval
* typo [skip ci]
* patch evals for torch.compile + fsdp2
* also check torch_compile w fsdp2
* lots of fixes for flex attn with llama4
* fix patch check and patch llama4 too
* attempt to make the patches stick
* use transformers 4.51.2
* update configs and README for llama4
* remove torch.compile for CI test
* cleanup any existing singletons
* set singleton cache to None instead of deleting
* use importlib reload with monkeypatch
* don't worry about transformers version, mark inputs with grads, fix regex
* make sure embeds aren't on cpu
* logging and mem improvements
* vllm version and add to docker, make sure to save processor on conversion
* fix ambiguous tensor bool check
* fix vllm to not use v1, upgrade hf transformers
* fix tests
* make flex_attn_compile_kwargs configurable, since this depends on model params
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
