* chore: update pre-commit hooks
* trigger linter when pre commit hooks are updated
* fix type checks from upgraded pre-commit
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Co-authored-by: djsaunde <1245942+djsaunde@users.noreply.github.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* fix: increase log level for root loggers and axolotl's
* fix: BasePlugin using wrong logger
* fix: update logger to take name from module
* feat: change logger class to AxolotlLogger to filter non-axolotl infos or below
* fix: change behavior to not disable existing loggers
* fix: update logging to respect correct env
* chore: fix comment
* fix: suppress accelerate log to LOG_LEVEL if not set
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Co-authored-by: salman <salman.mohammadi@outlook.com>
* feat: add num_proc and load from cache for rl mapping
* fix: refactor sft and rl trainer to set same base args
* feat: add report_to to set run name
* fix: consolidate handling of fp16, bf16, tf32 kwarg
* chore: consolidate eval_strat, loraplus, lr sched, max_length
* fix: deprecate old types
* fix: adding missing Any
* fix: max_steps incorrectly set
* fix: remove unnecessary datacollator kwarg insert and pop
* fix: update default max_steps
* fix: add missing weight_decay handling
* fix: ignore max_length for grpo
* feat: update CI on trainer_builder
* fix: comments
* improve handling of warmup/logging steps
* use transformers default for logging steps, not None
* fix: remove redundant override
* fix: lint
* feat: allow custom optim for rl methods
* fix: duplicate optim setting
* fix(test): set sequence_parallel_degree default in base cfg
* feat: add handling for seed and SP/ring-attn config
* chore: add back return typing from rebase
* fix(test): use RLType directly to skip needing to validate
* feat: split training builder into sub modules
* fix: remove deprecated clause
* chore: add missing config to doc
* fix: update quarto autodoc
* fix: import path for trainer builder and submodules
* fix: remove redundant configs from rebase mistake
* chore: simplify dynamo check
* fix: optimizer_cls_and_kwargs to be passed into trainer_kwargs
* fix: add missing rex from rebase
* fix: move pop optimizer_cls_and_kwargs
* fix: pop optimizer cls in rl too
* fix: leftover bug from rebase
* fix: update handling of trainer_cls in RL
* fix: address pr feedback
* feat: call hook_pre_create_trainer for rl
* chore: lint
* fix: return notimplemented for ppo
* feat: moved torch compile to base and refactor collator setting
* chore: remove unused importlib.util import
* fix: optimizer cls not being popped
* feat: move epoch setting to base
* fix: catch unhandled custom optimizer
* fix: remove duplicate lora plus setting
* chore: refactor if condition
* chore: refactor set_base_training_args into smaller modules
* fix: address TrainerBuilderBase class variables to instance var
* fix: add handling for beta3 and episilon2
* fix: change to pass dict via arg instead of updating dict
* chore: simplify if condition
* fix: force access to lr & weight decay in case not provided to early error
* fix: remove log sweep
* chore: refactor if condition
* fix: address renamed cfg
* fix: improve handling of cosine hyp
* fix: remove unused params
* chore: refactor
* chore: clarify doc safetensors
* fix: update import path to be unified following comments
* fix: duplicate kwargs passed
* feat: return separate trainer_kwargs
* chore: refactor
* chore: refactor based on comments
* chore: refactor based on comments
* fix: move gpustats callback to base
* chore: create trainer_cls_args first based on comments
* fix: ipo label smoothing passed incorrectly
* feat: add optimizer parity for RL methods with test
* feat: add parity for optimizer in RM/PRM and add test
* fix: remove redundant function override for orpo/cpo batch metrics
* fix: improve handling of dpo_label_smoothing and merge issue
* fix: test fixture returning wrong field
* fix: address avoid direct modify fixture
* chore: minor refactor
* Revert "chore: refactor"
This reverts commit 99c8859eb0.
* feat: rename trainer_builder to builders
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Co-authored-by: Wing Lian <wing@axolotl.ai>
* add two checks to handle legacy format interleaved ds
* fix: add warning about multiple image using legacy format
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Co-authored-by: NanoCode012 <nano@axolotl.ai>
* don't set peft_config on grpo to prevent double peft wrap
* remove overrides needed to support bug
* fix grpo tests
* require more CPU for multigpu to help with torch compile for vllm
* make setting `adam_beta3` and `adam_epsilon2` work correctly
* update config docs so users know args are specific to CAME optim
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Co-authored-by: Wing Lian <wing@axolotl.ai>
- Introduced DEFAULT_SEQUENCE_LEN_OVERFLOW_HANDLING constant in utils.py.
- Updated encode_packed_pretraining function to use this constant instead of a hardcoded value.
* offload activations to disk instead of CPU RAM
* add prefetch
* Disco :dance:
* include offload_disk in e2e test for AC
* document and make sure to cleanup
* fix annotation to match docs
* fix docs build
* address PR feedback
* lean mistral ft tests, remove e2e torch 2.4.1 test
* make sure to pass save_only_model for RL
* more tests to make ci leaner, add cleanup to modal ci
* fix module for import in e2e tests
* use mp spawn to prevent deadlocks with packing
* make sure cleanup shell script is executable when cloned out
- Simplified the truncation process for chosen and rejected responses to ensure they fit within the specified sequence length while preserving the prompt.
- Improved readability by restructuring the code and removing redundant checks.
- Ensured that the function returns the sample correctly after processing, maintaining compatibility with existing handling options.
- Added `excess_token_handling` option to the configuration, allowing users to choose between "drop" and "truncate" for handling tokens exceeding the maximum sequence length.
- Introduced `truncate_or_drop_long_seq` function to manage both single and batched samples based on the selected handling method.
- Updated relevant dataset processing functions to utilize the new handling option, ensuring backward compatibility with existing "drop" behavior.
- Enhanced logging to reflect truncation actions in dataset processing.
This change improves flexibility in managing sequence lengths during training and evaluation.
