396ce4a9ddd683a237496cfd8b5ef9b8c1017b24
2 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Wing Lian
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e4032fc90f |
Refactor separate attention flags with attn_implementation and capability/concerns feature flags (#3602)
* 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> |
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NanoCode012
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16e32232fb |
feat(docs): comprehensive improvement (#3564)
* docs: comprehensive documentation improvements for humans and agents New human docs: - grpo.qmd: GRPO deep dive (async, rewards, IS correction, scaling) - ebft.qmd: EBFT guide (structured/strided modes, feature extraction) - choosing_method.qmd: decision tree for SFT vs LoRA vs DPO vs GRPO - vllm_serving.qmd: vLLM setup for GRPO (server/colocate, LoRA sync) - training_stability.qmd: monitoring, NaN debugging, OOM, healthy metrics New agent docs: - AGENTS_SFT.md: agent reference for supervised fine-tuning - AGENTS_DPO.md: agent reference for preference learning (DPO/KTO/ORPO) Updated existing docs: - rlhf.qmd: cross-references to new GRPO/EBFT/choosing-method guides - getting-started.qmd: reorganized Next Steps with links to new guides - debugging.qmd: link to training stability guide - _quarto.yml: added new pages to sidebar navigation Removed: - bak.agents.md: stale backup that confused agents * docs: trim duplicated generic config from AGENTS_DPO.md Remove boilerplate training params (optimizer, gradient_checkpointing, flash_attention, etc.) from each method template. These are not preference-learning-specific and are already covered in AGENTS_SFT.md. Config templates now show only method-specific fields with a reference to AGENTS_SFT.md for the rest. * docs: deduplicate across new doc pages - grpo.qmd: collapse vLLM setup section to brief config + link to vllm_serving.qmd; collapse IS correction to essentials + link; replace full monitoring tables with summary + link to training_stability.qmd - vllm_serving.qmd: remove duplicated async/IS config reference tables (already in grpo.qmd config reference); replace full example config with link to grpo.qmd quick start - ebft.qmd: trim generic training params in quick start config * fix: train scripts * feat: split files into cleaner parts * fix: cleanup pretraining docs --------- Co-authored-by: Wing Lian <wing.lian@gmail.com> |