* 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>
* feat: move to uv first
* fix: update doc to uv first
* fix: merge dev/tests into uv pyproject
* fix: update docker docs to match current config
* fix: migrate examples to readme
* fix: add llmcompressor to conflict
* feat: rec uv sync with lockfile for dev/ci
* fix: update docker docs to clarify how to use uv images
* chore: docs
* fix: use system python, no venv
* fix: set backend cpu
* fix: only set for installing pytorch step
* fix: remove unsloth kernel and installs
* fix: remove U in tests
* fix: set backend in deps too
* chore: test
* chore: comments
* fix: attempt to lock torch
* fix: workaround torch cuda and not upgraded
* fix: forgot to push
* fix: missed source
* fix: nightly upstream loralinear config
* fix: nightly phi3 long rope not work
* fix: forgot commit
* fix: test phi3 template change
* fix: no more requirements
* fix: carry over changes from new requirements to pyproject
* chore: remove lockfile per discussion
* fix: set match-runtime
* fix: remove unneeded hf hub buildtime
* fix: duplicate cache delete on nightly
* fix: torchvision being overridden
* fix: migrate to uv images
* fix: leftover from merge
* fix: simplify base readme
* fix: update assertion message to be clearer
* chore: docs
* fix: change fallback for cicd script
* fix: match against main exactly
* fix: peft 0.19.1 change
* fix: e2e test
* fix: ci
* fix: e2e test
* build examples readmes with quarto
* chore: formatting
* feat: dynamic build docs
* feat: add more model guides
* chore: format
* fix: collapse sidebar completely to have space for model guides
* fix: security protection for generated qmd
* fix: adjust collapse level, add new models, update links
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* fix: force train split for json,csv,txt for test_datasets
* feat(doc): add info on mixing datasets for VLM
* feat(doc): max memory
* fix(doc): clarify lr groups
* fix: add info on vision not being dropped
* feat: add qwen3-vl to multimodal docs
* fix: add moe blocks to arch list
* feat(doc): improve mistral docs
* chore: add helpful link [skip-e2e]
* fix: add vram usage for mistral small
* Update link in docs/faq.qmd
Co-authored-by: salman <salman.mohammadi@outlook.com>
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: salman <salman.mohammadi@outlook.com>
* feat: add hunyuan cce support
* feat: update cce docs
* feat: add multipack support for granite and hunyuan
* feat: add hunyuan docs and example config
* feat: update readme instructions to include CCE installation
* fix: chat template log appearing despite tokenizer already having template
* feat: add vram usage
* fix: remove duplicate cce install
* fix: use latest commit of PR in case rebased/pushed
* Revert "fix: use latest commit of PR in case rebased/pushed"
This reverts commit 8b60aa00de.
* feat: update doc as upstream merged
* 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: do not add training and training_detail block by default
* fixed: magistral docs
* fix: address pad adding new fields and use built-in from_openai
* feat: try enable multiprocessing
* fix: check for keys before deleting attn_mask
* feat: add mistral pad test
* feat: add tool calling test
* feat: add devstral tokenizer tests
* fix: comma format
* chore: remove unused support_preprocessing as tokenizer is pickable now
* chore: update magistral doc
* feat: add devstral readme and example
* chore: refactor error handling
* feat: add fsdp config for magistral
* fix: add mllama self attention handling for lora kernels
* fix: no eval if val_set_size 0 despite having test_datasets
* fix: add note for cce for vlm in newer model
