Surfaces a class of GRPO config errors at axolotl-train startup instead
of letting them bubble out of GRPOTrainer.__init__ after the model loads.
Three checks under RLValidationMixin.check_grpo_batch_size_divisibility:
- effective generation_batch_size (or mb*GA fallback) must be divisible
by trl.num_generations, with a hint pointing at the smallest GA bump
that fixes the violation
- num_generations >= 2 (group-relative advantage needs variance; with
num_gen=1 the policy never updates)
- When world_size > 1, effective gbs >= num_generations * world_size
11 unit tests cover the table: divisible/non-divisible, explicit and
implicit gbs, multi-rank constraint, GRPO-disabled passthrough, and
unset num_generations.
* fix: DPO tool role KeyError, dataset hash output_dir, config validators [skip-e2e]
- Add 'tool' to default role_map_inv in dpo/chat_template.py default() and
argilla_chat() so datasets with tool-call messages no longer raise
KeyError: 'tool' (closes#3217)
- Fix generate_dataset_hash_from_config to use canonical tokenizer config +
overrides content instead of tokenizer.name_or_path when added_tokens_overrides
is set, preventing cache busting when only output_dir changes (closes#3303)
- Add three Pydantic config validators to AxolotlConfigWCapabilities:
* save_strategy: 'best' requires metric_for_best_model
* streaming=True is incompatible with val_set_size > 0
* lora_target_modules list entries must be valid Python regex patterns
- Tests for all three changes
* review: condense comment in shared.py, swap Mistral model for SmolLM2-135M in test_hash
* chore: lint
* move the validators out of the w/ capabilities schema
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Co-authored-by: Wing Lian <wing@axolotl.ai>
