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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]

New Features and Optimizations

  • Sequence packing is now supported when running DPO.
  • Added support for Knowledge Distillation with SFT. See the tutorial for details.
  • Added support for Megatron Core’s distributed optimizer, which can be configured using ++model.optim.name=mcore_distributed_optim.
  • Introduced ScopedTimer as a successor to SyncedTimer. SyncedTimer is marked for deprecation and will be removed in the next version. 
    from nemo_aligner.utils.distributed import ScopedTimer
timer = ScopedTimer()

# All durations are logged in the timer
with timer("step_time"):
    with timer("fwd"):
        model.fwd()
    with timer("bwd"):
        model.bwd()

# Consume all durations and reset internal store
durations = timer.consume_durations()
  • Add code and instructions for replicating Reward Modeling training in HelpSteer2 and HelpSteer2-Preference
  • Implement REINFORCE algorithm.

Breaking Changes

  • Upgrade TRTLLM dependency from v0.10.0 to v0.12.0 and migrate from GPTSession cpp runtime to ModelRunner python runtime. Please use the latest Dockerfile.
  • Using latest TransformerEngine versions may require ++model.dist_ckpt_load_strictness=log_all when loading from a older pre-existing checkpoint to not error out.
  • NeMo-Aligner now requires Megatron-LM==0.9.0 for the APIs to calculate the microbatch sizes (API introduced megatron.core.num_microbatches_calculator.reconfigure_num_microbatch_calculator).
  • NeMo-Aligner now requires a version of NeMo with this change to how the MoE spec is handled: NVIDIA/NeMo#9035 .

Bug Fixes

  • It is now required, for stability, to add export NCCL_ALGO=... to scripts launching PPO training loop. Please see the RLHF docs for information.

Deprecation Notices

  • SyncedTimer is marked for deprecation and will be removed in 0.7.0. Please switch to ScopedTimer
  • broadcast_2d_tensor and broadcast_2d_tensor_within_pp is marked for deprecation and will be removed in 0.7.0. Please switch to broadcast_tensor and broadcast_tensor_within_pp.

NVIDIA NeMo-Aligner 0.5.0

New Features and Optimizations

  • Implement Kahneman-Tversky Optimization (KTO).
  • Sequence packing is now supported when running SFT with prompt-response datasets.

Breaking Changes

Bug Fixes

  • Change log_prob_forward_micro_batch_size in DPO to mean the same as the micro_batch_size, which is how many samples(chosen and rejected included) that we process at once.
  • PPO TensorRT-LLM acceleration now no longer errors if using a tokenizer without a pad_id. Examples being llama3 and llama3.1 tokenizers from huggingface.

NVIDIA NeMo-Aligner 0.4.0

  • Implement reward-aware preference optimization.
  • Fix log probs mismatch issue between policy and reference policy in DPO & variants.
  • Added TRT-LLM support in PPO. This can be enabled by trainer.ppo.trt_llm.enable=True. There is also a reshard option to reshard out pipeline parallelism during inference (i.e running tensor and data parallel only) for further speedup via trainer.ppo.trt_llm.reshard=True.
  • PPO algorithm will now double check that generated samples ended with one of the stop words from sampling_params.end_strings, and zero out their gradients if this is not the case (which happens when reaching the maximum generation length)
  • Added critic warmup to the PPO with the flag trainer.ppo.critic_warmup_steps.
  • PPO log probs are now computed with higher_stability=True. This can change results for some models, but should result in overall greater stability.

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 on which it runs inferences. 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 provide more information to PyTriton regarding the preferred batch sizes for inference.
  • It is no longer a requirement to specify num_rollout_samples to be a multiple of inference_micro_batch_size * dp size in PPO.
  • Sequence packing is now supported when running SFT with prompt-response datasets.
  • Add online rejection sampling algorithm.

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.
  • NeMo-Aligner now requires Megatron-LM==0.8.0 for the APIs to calculate the microbatch sizes (API introduced megatron.core.num_microbatches_calculator.reconfigure_microbatch_calculator).

Bug Fixes

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

NVIDIA NeMo-Aligner 0.3.1

  • 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 their 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 a reference model plus LoRA weights. We can switch between the actor/reference model by enabling or disabling LoRA.
  • PPO: Enable LoRA on all model layers. In this case, the actor will be the init policy plus LoRA weights. We can switch between the actor/init_policy model by enabling or disabling LoRA.
  • SteerLM 2.0: Add the SteerLM 2.0 model alignment method.
  • val_check_interval in SFT now supports float values.
  • 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 the random sampler keeps its state during validation resets, resulting in varying validation batches at each step. This was addressed by switching to 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 NeMo-Aligner since it has been upstreamed into NeMo.
  • Fixed SPIN not correctly using its val_check_interval parameter.

NVIDIA NeMo-Aligner 0.3.0

New Features and Optimizations

NVIDIA NeMo-Aligner 0.2.0

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. It will no longer give the same results because the shuffle logic has changed (specifically the seed value is modified slightly per epoch). If you run CI/regression type tests, 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 the validation before exiting if the time limit has been reached.
  • 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.

NVIDIA NeMo-Aligner 0.1.0

Added

  • First open source release.