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CHANGELOG.md

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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[Next Version]

  • Implement reward-aware preference optimization.
  • Fix log probs mismatch issue between policy and reference policy in DPO & variants.

New features and optimizations

  • Critic and Reward Model server refactored. Now the reward model will have a flag called model.forward_micro_batch_size which determines the micro batch size that it runs inferences with. This can be higher than the training micro batch size since during inference we have less memory pressure.
  • In the critic and reward model server it is now possible to specify inference_micro_batch_size as a list, this allows us to give more information to PyTriton on the preferred batch sizes we want to run inference with.
  • It is no longer a requirement to specify num_rollout_samples to be a multiple of inference_micro_batch_size * dp size in PPO.

Breaking changes

  • inference.micro_batch_size is now renamed to inference.inference_micro_batch_size when running reward model inference in inference_rm.yaml this is to stay consistent with the naming scheme of the PPO critic.
  • It is no longer possible to specify add_EOS when running reward model or critic inference.

Bug Fixes

  • Make num_workers for dataloaders 0 by default. This prevents issues when using MPI (with TRT-LLM) or more sophisticated launchers.

[0.3.1] - 2024-05

  • SPIN: added rollout_micro_batch_size parameter which allows users to set the batch size for doing generation during SPIN training. previously the generation batch size was automatically set to the data parallel size (DP) of the model
  • SPIN: added wandb logging of average generation length and a small sample of generated responses (in plaintext) along with corresponding prompts

New features and optimizations

  • Add MoE Support for our reward models.
  • SFT/SteerLM: LoRA can now be enabled on all model layers
  • DPO: Enable LoRA on all model layers (In this case the actor will be reference model + LoRA weights, we can switch between actor/reference model by enabling/disabling LoRA)
  • PPO: Enable LoRA on all model layers (In this case the actor will be init policy + LoRA weights, we can switch between actor/init_policy model by enabling/disabling LoRA)
  • SteerLM 2.0: Add the SteerLM 2.0 model alignment method.
  • Added support for float values for val_check_interval for SFT
  • Added support for limit_train_batches as a float or int to DPO, SPIN, and SFT. This functionality mirrors the same parameter in PTL

Breaking changes

Bug Fixes

  • Fixed issue where random sampler keeps state when resetting for validation, leading to a different validation batch each validation step. Fixed by using a deterministic sampler
  • Fixed crash with float val check interval in DPOTrainer
  • Fixed crash with float val check interval when checking progress in DPOTrainer
  • Fixed potential crash in SPIN when prompts are longer than encoder_seq_len - generation.max_length
  • Fixed crash when calling the generate() method of an SFT model with pipeline parallelism greater than two
  • Fixed crash when calling the generate() method of an SFT model with compute_logprob=True and string inputs
  • Fixed crash when model.micro_batch_size > 1 in DPO
  • Fixed issue when model.encoder_seq_length is mismatched with model.data.train_ds.max_seq_length in SFT and SPIN.
  • Delete MegatronPretrainingRandomSampler from Aligner since it has been upstreamed into NeMo
  • Fixed SPIN not correctly using its val_check_interval parameter

[0.3.0] - 2024-05

New features and optimizations

[0.2.0] - 2024-02

New features and optimizations

  • Added public-facing official Dockerfile for NeMo-Aligner.
  • PPO: memory optimization to help avoid OOM in the actor when sending training data to the critic.
  • PPO: it is now possible to use a custom end string in sampling_params.end_strings that is different from <extra_id_1>.
  • SFT: added support for custom validation metrics based on model generations.
  • Added the ability to do multi-epoch (cfg.max_epochs > 1) training for reward models, DPO, PPO, and SFT
  • Added the SPIN (Self-Play Fine Tuning) algorithm (https://arxiv.org/abs/2401.01335) which allows SPIN SFT training using SFT-format dataset files
  • SFT/SteerLM: added LoRA tuning as an option besides full fine-tuning, only attention_qkv layer is supported

Breaking changes

  • We have changed the shuffle logic in the data sampler to support multi-epoch training, so training runs using identical parameters will not give the same results anymore because the shuffle logic has changed (specifically the seed value is modified slightly per epoch). If you run CI/regression type tests, then be warned that the test may break due to this shuffle change.

Bug Fixes

  • Fixed a potential issue when the base model's model.data.data_prefix config is a list and is about to be overridden with a dictionary from the training configuration.
  • exp_manager.max_time_per_run is now respected, the trainers will save and run validation before exiting if we've reached the time limit.
  • Fixed crash in PPO when using a separate reward model server (i.e., with combine_rm_and_critic_server=False).
  • Fixed crash when LR scheduler is not specified

[0.1.0] - 2023-12-04

Added

  • First open source release